diff --git a/Dockerfile b/Dockerfile new file mode 100644 index 0000000..25e47ce --- /dev/null +++ b/Dockerfile @@ -0,0 +1,53 @@ +# syntax=docker/dockerfile:1.6 + +############################ +# Builder: install deps with uv +############################ +FROM python:3.11-slim AS builder + +# System deps (add more if you need: git, build-essential, etc.) +RUN apt-get update && apt-get install -y --no-install-recommends \ + ca-certificates \ + git \ + build-essential \ + && rm -rf /var/lib/apt/lists/* + + +WORKDIR /app + +# Install uv +# (uv is a single binary; pip install also works, but this is usually faster/cleaner) +RUN pip install --no-cache-dir uv + +# Copy only dependency metadata first for better layer caching +COPY pyproject.toml ./ +# If you have a lockfile, copy it too: +COPY uv.lock ./ + +# Create a virtualenv and sync deps from lockfile +# --frozen: fail if lockfile doesn't match pyproject +RUN uv venv /opt/venv && \ + . /opt/venv/bin/activate && \ + uv sync --active --frozen --no-dev + +############################ +# Runtime: copy venv + code +############################ +FROM python:3.11-slim AS runtime + +ENV VIRTUAL_ENV=/opt/venv +ENV PATH="/opt/venv/bin:$PATH" +ENV PYTHONUNBUFFERED=1 +WORKDIR /app + +# Copy venv from builder +COPY --from=builder /opt/venv /opt/venv + +# Copy repo contents +COPY . /app + +# Make sure imports work from repo root +ENV PYTHONPATH=/app + +# Default help +CMD ["python", "-c", "print('Container ready. Try: python scripts/measure_similarity.py')"] diff --git a/Dockerfile.gpu b/Dockerfile.gpu new file mode 100644 index 0000000..b08a864 --- /dev/null +++ b/Dockerfile.gpu @@ -0,0 +1,53 @@ +# syntax=docker/dockerfile:1.6 + +############################ +# Builder: install deps with uv (GPU-enabled) +############################ +FROM python:3.11-slim AS builder + +# System deps (add more if you need: git, build-essential, etc.) +RUN apt-get update && apt-get install -y --no-install-recommends \ + ca-certificates \ + git \ + build-essential \ + && rm -rf /var/lib/apt/lists/* + +WORKDIR /app + +# Install uv +RUN pip install --no-cache-dir uv + +# Copy only dependency metadata first for better layer caching +COPY pyproject.toml ./ +COPY uv.lock ./ + +# Create a virtualenv and sync deps from lockfile, including CUDA extra +# --frozen: fail if lockfile doesn't match pyproject +RUN uv venv /opt/venv && \ + . /opt/venv/bin/activate && \ + uv sync --active --frozen --no-dev --extra cuda12 + +############################ +# Runtime: copy venv + code +############################ +FROM python:3.11-slim AS runtime + +ENV VIRTUAL_ENV=/opt/venv +ENV PATH="/opt/venv/bin:$PATH" +ENV PYTHONUNBUFFERED=1 +ENV PYTHONPATH=/app + +# NVIDIA runtime hints (GPU is still assigned at `docker run --gpus ...`) +ENV NVIDIA_VISIBLE_DEVICES=all +ENV NVIDIA_DRIVER_CAPABILITIES=compute,utility + +WORKDIR /app + +# Copy venv from builder +COPY --from=builder /opt/venv /opt/venv + +# Copy repo contents +COPY . /app + +# Default help +CMD ["python", "-c", "print('GPU container ready. Try: python scripts/measure_similarity.py')"] diff --git a/README.md b/README.md index ec1aa52..0e02e6c 100644 --- a/README.md +++ b/README.md @@ -1,87 +1,357 @@ # Proteogram: an image embedding-based search approach to protein structure similarity -This repo has the source code for the `proteogram` project and paper. +## Introduction -## Abstract +Proteogram is a novel approach to protein structure similarity search that represents protein structures as image data, enabling the use of computer vision models for efficient and accurate similarity detection. This repository leverages the SCOPe 2.08 protein structure dataset and classification hierarchy (https://scop.berkeley.edu) both to train as well as evaluate models. -**Motivation:** Huge strides have been made in the last decade in the detection of protein similarity across evolutionarily remote distances using protein structure alignment for known structures (sequence-order dependent/independent, sequence-alignment dependent/independent). These methods accurately identify similar structures according to various measures such as TM-score, RMSD, and cosine-similarity. The main challenges remaining are computational efficiency, gains in accuracy, and new enhancements to the structural data representations, such as the incorporation of physicochemical properties and evolutionary information. We have developed Proteogram, a new approach to protein structure search that leverages computer vision models to create embedding vectors for a cosine-similarity-based search across a database of Proteograms, wherein each Proteogram represents a single protein monomer as image data. The Proteogram is a novel data type consisting of three categories of residue-level information: alpha-carbon backbone distances, hydrophobicity similarities, and charge similarities. These three dimensions of data are stacked to form an NxN 3-channel data structure (where N is the residue length), which can, consequently, be captured on disk as an RGB image and are inherently sequence-alignment independent. Proteograms not only represent spatial similarity through distograms (the pair-wise residue alpha-carbon distances), but also physicochemical properties of residues through the integration of hydrophobicity values and charge states. Thus, this work outlines a novel and efficient approach to the current challenges in protein structure similarity, alignment, and search. +### Proteogram v1: Distance, Hydrophobicity, and Charge Maps -**Results:** Two state-of-the-art methods in structure alignment and similarity search, USalign and GTalign, serve as points of comparison to the method introduced in our study. Two evaluation datasets are used to understand search performance, one from the GTalign study and a new one introduced in this paper. On the GTalign evaluation dataset, superfamily and family performance could not be discerned easily, however on a more representative dataset, the Proteogram approach scores highest in Precision@K for superfamily and family-level search results. Additionally, for the search step itself, the Proteogram approach has the highest throughput (according to pair-wise alignments per minute) of the three methods (1.63 times faster than GTalign and 89.4 times faster than USalign). This unique and promising technique shows the gains that can be achieved by accounting for physicochemical information in addition to spatial information with a data structure compatible with advanced computer vision models. +The original Proteogram approach creates an NxN 3-channel image representation (where N is the residue length) by stacking three categories of residue-level information: + +1. **Alpha-carbon backbone distances** - Pair-wise residue Cα distances (distogram) +2. **Hydrophobicity similarities** - Residue-residue hydrophobicity comparisons +3. **Charge similarities** - Residue-residue charge state comparisons + +This representation captures both spatial similarity through distograms and physicochemical properties through hydrophobicity and charge maps. The resulting RGB image is inherently sequence-alignment independent and can be processed by standard computer vision models to generate embedding vectors for cosine-similarity-based search. + +### Proteogram v2: Incorporating MD Simulations + +Proteogram v2 extends the original approach by incorporating molecular dynamics (MD) simulations to compute physics-based residue-residue interaction energies. Instead of using static distance and property maps, v2 runs a complete MD simulation pipeline using OpenMM with the AMBER ff19SB force field to calculate: + +- **Van der Waals energies** - Attractive and repulsive Lennard-Jones interactions +- **Electrostatic energies** - Attractive and repulsive Coulomb interactions + +The MD pipeline includes energy minimization, NPT and NVT equilibration, and production dynamics. The resulting 3-channel data (with 6 attributes in total) provides a richer representation of protein structure that accounts for dynamic conformational sampling and explicit solvent effects. + +For detailed information on the MD simulation methodology, see the [MD Simulation Methodology documentation](docs/md_simulation_methodology.md). ## Getting started -This repo uses Python 3.8+. +This repo uses Python 3.11+. ### Installing the package -This project uses [Python Poetry](https://python-poetry.org/) to manage packages. Using `poetry==1.8.3`, the following commands may be found useful. +This project uses [uv](https://docs.astral.sh/uv/) as the package manager. To install `uv`, follow the [installation instructions](https://docs.astral.sh/uv/getting-started/installation/). + +#### Create a virtual environment + +Create and activate a uv-managed virtual environment: +```bash +uv venv +source .venv/bin/activate # On Unix/macOS +# or +.venv\Scripts\activate # On Windows +``` + +#### CPU-only installation -To install all packages from the `pyproject.toml`: +For systems without a GPU or for development/testing on CPU: +```bash +uv sync ``` -poetry install + +This installs OpenMM with CPU-only support. + +#### GPU installation (CUDA 12) + +For systems with NVIDIA GPUs, install with CUDA 12 support for accelerated MD simulations: +```bash +uv sync --extra cuda12 ``` -### Setting up an environment +This uses the optional `cuda12` dependencies defined in `pyproject.toml` to install `openmm-cuda-12` and related CUDA packages. -A virtual envrionment is highly encouraged. One way to create a virtaul environment is with the built-in tool `venv` as in: +> **Note:** Ensure you have compatible NVIDIA drivers and CUDA 12 toolkit installed. See the [OpenMM documentation](http://docs.openmm.org/latest/userguide/application/01_getting_started.html#installing-openmm) for GPU requirements. +#### [Optional] Adding dependencies + +To add a package dependency: +```bash +uv add ``` -python3 -m venv env + +To add a development dependency: +```bash +uv add --dev ``` -To use the virtual environment, it needs to be activated which on Unix systems: +### Set up configuration + +1. Copy the example configuration file: + ```bash + cp scripts/config.example.yml scripts/config.yml + ``` + +2. Edit `scripts/config.yml` to configure: + - `scope_structures_dir`: Path to your input PDB structure files (here we used SCOPe 2.08 structures) + - `all_proteograms_dir`: Path where generated proteograms will be saved + - `limit_file`: (Optional) Path to a file listing specific structures to process + +### Creating Proteograms + +To create Proteograms for your protein structures, run the following from the `scripts` folder: +```bash +cd scripts +uv run python create_v2_proteograms.py ``` -source env/bin/activate + +Optional arguments: +- `--overwrite`: Recreate Proteograms even if they already exist +- `--verbose`: Enable verbose output and logging +- `--save_simulated_pdb`: Save the final MD simulation structure as a PDB file to a subfolder + +### Measure similarity of a single domain to a database of Proteograms + +To compare a new structure against an existing database of Proteograms and retrieve the top-k most similar proteins: + +1. Ensure you have a database of pre-computed Proteograms (supplied separately or generated using the step above) + +2. Run the similarity search from the `scripts` folder: + ```bash + cd scripts + uv run python measure_similarity_single_domain.py + ``` + + Modify the script to specify your query structure and the path to the proteogram database. + +### Running an MD simulation + +The `NonBondedForceModel` module provides a complete pipeline for running molecular dynamics simulations and calculating residue-residue interaction energies. Here's an example: + +```python +from proteogram.nonbonded_forces import NonBondedForceModel +import numpy as np + +model = NonBondedForceModel( + pdb_path='protein.pdb', + temperature=311.75, # Kelvin + pressure=1.0, # atmospheres + padding=1.0, # nanometers (water box padding around protein) + timestep=2.0, # femtoseconds + use_gpu=False, + output_dir='output' +) + +# Full MD pipeline. Returns 4 matrices (vdw/es attractive/repulsive). +vdw_attractive, vdw_repulsive, es_attractive, es_repulsive = model.run_full_pipeline( + npt_steps=50000, # steps (50,000 × 2 fs = 100 ps NPT equilibration) + nvt_steps=50000, # steps (50,000 × 2 fs = 100 ps NVT equilibration) + production_steps=500000, # steps (500,000 × 2 fs = 1 ns production run) + energy_calc_interval=10000, # steps between energy snapshots (10,000 × 2 fs = 20 ps; 50 frames total) + return_simulated_pdb=False, + subtract_solvent_energies=True, + debug=True +) + +print('VdW attractive matrix shape:', vdw_attractive.shape) +print('Electrostatic repulsive matrix shape:', es_repulsive.shape) + +model.cleanup() +``` + +For detailed information on the MD simulation methodology, force calculations, and energy validation, see the [MD Simulation Methodology documentation](docs/md_simulation_methodology.md). + +## Scripts Reference + +The following table provides an overview of all scripts in the `scripts/` folder, their purpose, and the configuration variables or command-line arguments they use. + +| Script | Purpose | Config Variables (`config.yml`) | Command-Line Arguments | +|--------|---------|--------------------------------|------------------------| +| `create_v2_proteograms.py` | Create proteograms using MD-based nonbonded energy calculations, distances, and hydrophobicity deltas (v2) | `limit_file`, `scope_structures_dir`, `all_proteograms_dir` | `--max_workers`, `--overwrite`, `--verbose`, `--save_simulated_pdb` | +| `create_proteograms.py` | Create proteograms using distances, hydrophobicity deltas, and charge maps (v1) | `scope_structures_dir`, `eval_proteograms_dir`, `limit_file` | None | +| `measure_similarity_single_domain.py` | Search a single structure against a proteogram database | `top_k`, `model_file`, `embed_file`, `embed_file_exists`, `proteogram_sim_results`, `proteograms_dir_single_search` | None | +| `measure_similarity.py` | Batch similarity search across all proteograms | `top_k`, `model_file`, `embed_file`, `proteogram_sim_results`, `proteograms_for_sim_dir`, `search_images_dir` | None | +| `train_multiple_models.py` | Train a from-scratch ConvNet or fine-tune ResNet18 for proteogram classification, with early stopping, class balancing, and per-class evaluation | `training_data_dir`, `num_epochs_cnn`, `learning_rate_cnn`, `batch_size_cnn`, `cnn_model_file_prefix`, `scope_level` | `--data_dir/-d` (overrides `training_data_dir`), `--epochs/-e`, `--batch_size/-b`, `--lr/-l`, `--model/-m` (`cnn`\|`resnet18`), `--level` (`class`\|`fold`\|`superfamily`\|`family`, default: `class`), `--tsv_file/-t`, `--patience`, `--val_size`, `--exclude_classes/-x`, `--overwrite/-o`, `--resize`, `--verbose/-v` | +| `evaluate_methods.py` | Evaluate proteogram approach vs GTalign and USalign | `gtalign_results_dir`, `usalign_results`, `save_bad_searches_dir`, `save_good_searches_dir` | None | +| `make_training_and_eval_data.py` | Create training/validation datasets with SCOPe annotations | `scope_eval_set`, `scope_structures_dir`, `scope_cla_file`, `scope_des_file`, `scope_hie_file`, `training_structures_dir`, `training_proteograms_dir`, `eval_structures_dir`, `eval_proteograms_dir`, `label_df_out` | None | +| `make_training_data_exclude_eval.py` | Create training data excluding evaluation set proteins | `scope_eval_set`, `scope_structures_dir`, `scope_cla_file`, `scope_des_file`, `scope_hie_file`, `training_structures_dir`, `training_proteograms_dir`, `eval_structures_dir`, `eval_proteograms_dir`, `label_df_out`, `scope_level` | None | +| `create_annotation_file.py` | Generate annotation lookup file from SCOPe/RCSB/PDBe | `limit_file`, `scope_structures_dir`, `annot_file`, `fasta_style_file`, `scope_cla_file`, `scope_des_file`, `scope_hie_file` | None | +| `find_structures_in_scope.py` | Find PDB structures in SCOPe 2.08 database | None (hardcoded paths in script) | None | +| `get_structures_scope20840_list.py` | Download and parse PDB structures by chain | None (hardcoded paths in script) | None | +| `copy_structures.py` | Copy structure files filtered by amino acid length | None (hardcoded paths in script) | None | + +> **Note:** Scripts with "None (hardcoded paths in script)" require editing the script directly to set file paths. See `config.example.yml` for descriptions of all configuration variables. + +## Workflow for paper where the Proteogram approach was compared to GTalign and USalign + +### Overview of v1 approach + +![](assets/Workflow-Structure-Compression.png) + +### Proteogram v1 generation + +![](assets/proteogram_generation.png) + +Example of resulting top 5 most similar Proteograms using the v1 approach: + +![](assets/AF-A0A3M6TU40-F1-model_v4_A_top_sims.jpg) + +## References + +1. **GTalign** - Margelevicius, M. (2024). GTalign: High-performance protein structure alignment, superposition, and search. *Nature Communications*, 15, 1261. https://doi.org/10.1038/s41467-024-45653-4 + +2. **US-align** - Zhang, C., Shine, M., Pyle, A.M., & Zhang, Y. (2022). US-align: universal structure alignments of proteins, nucleic acids, and macromolecular complexes. *Nature Methods*, 19, 1109–1115. https://doi.org/10.1038/s41592-022-01585-1 + +3. **SCOPe 2.08** - Chandonia, J.M., Fox, N.K., & Brenner, S.E. (2017). SCOPe: Manual curation and artifact removal in the Structural Classification of Proteins - extended database. *Journal of Molecular Biology*, 429(3), 348-355. https://doi.org/10.1016/j.jmb.2016.11.023 + +4. **OpenMM** - Eastman, P., Swails, J., Chodera, J.D., McGibbon, R.T., Zhao, Y., Beauchamp, K.A., Wang, L.P., Simmonett, A.C., Harrigan, M.P., Stern, C.D., Wiewiora, R.P., Brooks, B.R., & Pande, V.S. (2017). OpenMM 7: Rapid development of high performance algorithms for molecular dynamics. *PLOS Computational Biology*, 13(7), e1005659. https://doi.org/10.1371/journal.pcbi.1005659 + +5. **AMBER ff19SB** - Tian, C., Kasavajhala, K., Belfon, K.A.A., Raguette, L., Huang, H., Migues, A.N., Bickel, J., Wang, Y., Pincay, J., Wu, Q., & Simmerling, C. (2020). ff19SB: Amino-Acid-Specific Protein Backbone Parameters Trained against Quantum Mechanics Energy Surfaces in Solution. *Journal of Chemical Theory and Computation*, 16(1), 528-552. https://doi.org/10.1021/acs.jctc.9b00591 + +6. **ResNet** - He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep Residual Learning for Image Recognition. *Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)*, 770-778. https://doi.org/10.1109/CVPR.2016.90 + +7. **Foldseek** - van Kempen, M., Kim, S.S., Tumescheit, C., Mirdita, M., Lee, J., Gilchrist, C.L.M., Söding, J., & Steinegger, M. (2024). Fast and accurate protein structure search with Foldseek. *Nature Biotechnology*, 42, 243–246. https://doi.org/10.1038/s41587-023-01773-0 + + +# Docker Guide (uv based) + +This repo is dockerized to run scripts under `/scripts` (e.g. measure_similarity.py, create_proteograms.py etc.) + +## CPU vs GPU containers (important) + +Docker images do **not** automatically get GPU access at build time. +GPU access is assigned **when you run the container**. + +- Use a CPU image/container for CPU workflows. +- Use a GPU-capable image/container for GPU workflows. +- Start the GPU container with `--gpus ...` (or the equivalent in Compose/Kubernetes). + +Also note: `--platform` (for example `linux/amd64` or `linux/arm64`) controls CPU architecture, **not** whether GPU is attached. + +## Prerequisites +- Docker installed +- `uv.lock` present in repo root (recommended for reproducible builds) +- For GPU containers: NVIDIA driver + NVIDIA Container Toolkit installed on the host + +## `uv.lock` usage (important for Docker builds) + +Both Dockerfiles install dependencies with: + +```bash +uv sync --active --frozen ... ``` -To activate the virtual envrionment on Windows: +`--frozen` means the build will fail if `uv.lock` is missing or out of sync with +`pyproject.toml`. + +### When you change dependencies + +If you edit `pyproject.toml` (or dependency extras), regenerate and commit the lockfile: + +```bash +uv lock +uv sync --frozen +git add pyproject.toml uv.lock ``` -env\Scripts\activate + +### Common error: lockfile mismatch + +If Docker build fails around `uv sync --frozen`, run: + +```bash +uv lock ``` -See [Create and Use Virtual Environments](https://packaging.python.org/en/latest/guides/installing-using-pip-and-virtual-environments/#create-and-use-virtual-environments) for more on `venv`. +Then rebuild the image. -### Set up configuration +## Supported Docker platforms + +- **CPU image (`Dockerfile`)** + - Intended for standard Linux Docker platforms. + - Commonly works on: `linux/amd64`, `linux/arm64`. + +- **GPU image (`Dockerfile.gpu`)** + - Intended for Linux hosts with NVIDIA GPU runtime support. + - Primary supported platform: `linux/amd64`. -Fill out a `config.yml` (based on `config.example.yml`) file and place in the `scripts` folder where all scripts should be run. +### Notes on platform vs GPU -### Creating proteograms +- `--platform` selects CPU architecture (for example `linux/amd64`, `linux/arm64`). +- GPU access is assigned at runtime with `--gpus ...`. +- GPU use also depends on host setup (NVIDIA drivers + NVIDIA Container Toolkit). + +--- + +## Build the Docker image + +From the repo root (the folder that contains `Dockerfile`, `pyproject.toml`, `uv.lock`): -To create proteograms of your domains, run the following from the `scripts` folder: ``` -python create_proteograms.py +sudo docker build -t proteogram:dev . ``` -### Measure similarity of a single domain to DB of proteograms +For clarity, build/tag CPU and GPU images explicitly: -To utilize a new structure and compare to a DB (directory) of proteograms, run the following from the `scripts` folder (modify script values for your particular structure - see script): +```bash +sudo docker build -t proteogram:cpu . ``` -python measure_similarity_single_domain.py + +GPU image (uses `Dockerfile.gpu` and installs `cuda12` extra dependencies via uv): + +```bash +sudo docker build -f Dockerfile.gpu -t proteogram:gpu . ``` -Example resulting search image (scores and proteogram files are also output): +> `Dockerfile` = CPU image; `Dockerfile.gpu` = GPU-capable Python environment. +> GPU access is still granted only at runtime with `--gpus ...`. -![example set of 5 search hits](assets/AF-A0A3M6TU40-F1-model_v4_A_top_sims.jpg) +## Verify the image -### Additional notes +Verify Python and package import +``` +docker run --rm proteogram:dev python -c "import proteogram; print('import ok')" +``` -To add a package dependency to the environment (and add to the main dependencies section of the `pyproject.toml`): +Verify scripts inside the container ``` -poetry add +docker run --rm proteogram:dev python scripts/measure_similarity.py ``` -To add a package dependency to the environment (and add to the dev dependencies section of the `pyproject.toml`): +## Run CPU container + +Run normally (no GPU flags): + +```bash +docker run --rm -it proteogram:cpu bash ``` -poetry add --dev + +## Run GPU container + +Assign GPU at runtime with Docker's `--gpus` flag: + +```bash +docker run --rm --gpus all -it proteogram:gpu bash ``` -## Workflow for paper where the proteogram approach was compared to GTalign and USalign +Use a specific GPU device (example GPU 0 only): +```bash +docker run --rm --gpus '"device=0"' -it proteogram:gpu bash +``` -### Overview +Verify OpenMM can see CUDA platform in GPU container: -![](assets/Workflow-Structure-Compression.png) +```bash +docker run --rm --gpus all proteogram:gpu \ + python -c "from openmm import Platform; print([Platform.getPlatform(i).getName() for i in range(Platform.getNumPlatforms())])" +``` -### Proteogram generation +You should see `CUDA` in the printed platform list. -![](assets/proteogram_generation.png) +For CPU-only service, use `proteogram:cpu` and omit GPU device reservations. + +Interactively login to container and inspect the contents to see expected files. +``` +docker run --rm -it proteogram:dev bash +``` + +### Mount the datasets +Note: `-v` bind mounts are applied **only at container run time**. The data is +not stored in the image and will not be present unless you start the container +with the `-v` flag. +``` +sudo docker run --rm -it \ + -v "$(pwd)/scripts/data/pdbstyle-2.08:/app/scripts/data/pdbstyle-2.08" \ + proteogram:dev \ + bash +``` \ No newline at end of file diff --git a/docs/md_simulation_methodology.md b/docs/md_simulation_methodology.md new file mode 100644 index 0000000..d87b4b8 --- /dev/null +++ b/docs/md_simulation_methodology.md @@ -0,0 +1,621 @@ +# Molecular Dynamics Simulation Methodology for Residue-Residue Interaction Energies and Forces in Proteograms + +This document describes the complete pipeline for calculating pairwise residue-residue interaction energies and forces from molecular dynamics (MD) simulations using OpenMM. + +## Table of Contents + +1. [Overview](#overview) +2. [PDB Preprocessing](#pdb-preprocessing) +3. [System Setup](#system-setup) +4. [Simulation Pipeline](#simulation-pipeline) + - [Energy Minimization](#1-energy-minimization) + - [NPT Equilibration](#2-npt-equilibration) + - [NVT Equilibration](#3-nvt-equilibration) + - [Production MD](#4-production-md) +5. [Energy Calculations](#energy-calculations) + - [Van der Waals Interactions](#van-der-waals-lennard-jones-interactions) + - [Electrostatic Interactions](#electrostatic-coulomb-interactions) +6. [Output Matrices](#output-matrices) +7. [Usage Example](#usage-example) +8. [Appendix](#appendix) +9. [References](#references) + +--- + +## Overview + +The goal of this pipeline is to compute **pairwise residue-residue interaction energies and forces** for a protein structure. These interactions are separated into: + +- **Van der Waals (VdW)**: Attractive and repulsive components +- **Electrostatic (ES)**: Attractive and repulsive components + +The pipeline uses the **AMBER ff19SB** force field [4] with **TIP3P-FB** water model, implemented in OpenMM [3]. + +--- + +## PDB Preprocessing + +Before simulation, the PDB structure is "fixed" using PDBFixer: + +| Step | Description | +|------|-------------| +| 1 | Find missing residues | +| 2 | Find and replace non-standard residues | +| 3 | Remove heterogens (ligands, crystal waters) | +| 4 | Find and add missing atoms | +| 5 | Add hydrogens at pH 7.0 | + +--- + +## System Setup + +### Force Field +- **Protein**: AMBER ff19SB (`amber19-all.xml`) [2, 4] +- **Water**: TIP3P-FB (`amber19/tip3pfb.xml`) + +### Solvation +| Parameter | Value | +|-----------|-------| +| Water model | TIP3P | +| Box padding | 1.0 nm | +| Neutralization | Yes ($Na^+$/$Cl^-$ ions) | + +### Partial Charge Assignment + +When `forcefield.createSystem()` is called, OpenMM automatically assigns **partial charges** to all atoms using pre-computed values from the AMBER ff19SB force field. + + +#### RESP Charges (Restrained Electrostatic Potential) + +AMBER force fields use **RESP charges** [8], which are derived from: + +1. **Quantum mechanical (QM) calculations** at the HF/6-31G* level of theory +2. **Electrostatic potential (ESP) fitting** - charges are optimized to reproduce the QM electrostatic potential around the molecule +3. **Restraints** - equivalent atoms (e.g., methyl hydrogens) are constrained to have equal charges +4. **Multi-conformational fitting** - multiple conformations are averaged for robust charges + + +RESP charges are specifically parameterized to work synergistically with the other AMBER force field terms (VdW, bonds, angles, dihedrals), ensuring accurate reproduction of experimental properties like solvation free energies and protein folding thermodynamics. + +--- + +## Simulation Pipelines + +### Default Parameters + +| Parameter | Value | Description | +|-----------|-------|-------------| +| Temperature | 300 K | Simulation temperature | +| Pressure | 1 atm | Simulation pressure | +| Timestep | 2 fs | Integration timestep | +| Integrator | Langevin Middle | With 1 $ps^{-1}$ friction | + +> **Note: State Continuity During Equilibration** +> +> The equilibration stages preserve **atomic positions** between stages, but systems are recreated as needed: +> +> 1. **Energy Minimization** → Optimized positions are saved +> 2. **NPT Equilibration** → Starts from minimized positions; barostat is **added** to allow volume changes; uses alpha-carbon restraints +> 3. **NVT Equilibration** → A **new system is created** from NPT positions; no barostat, with alpha-carbon restraints and temperature ramping +> 4. **Production MD** → A **new system is created** from NVT positions (without constraints) for accurate force calculations +> +> This approach ensures each stage has the appropriate force configuration while preserving the structural progress from previous stages. + +--- + +### 1. Energy Minimization + +**Purpose**: Remove steric clashes and bad contacts in the initial structure. + +| Parameter | Value | +|-----------|-------| +| Algorithm | L-BFGS (OpenMM default) | +| Max iterations | 1000 | +| Constraints | Alpha-carbon (CA) position restraints (1000 kJ/mol/nm²) | + +The system's potential energy is minimized by adjusting atomic positions until forces are below a tolerance threshold. Alpha-carbon position restraints prevent large-scale structural changes during minimization while allowing local relaxation of side chains and removal of steric clashes. + +--- + +### 2. NPT Equilibration + +**Purpose**: Equilibrate the system at constant **N**umber of particles, **P**ressure, and **T**emperature to achieve proper density. + +| Parameter | Value | +|-----------|-------| +| Ensemble | NPT | +| Steps | 50,000 (100 ps) | +| Barostat | Monte Carlo Barostat | +| Pressure | 1 atm | +| Temperature | 300 K | +| Reporting interval | 5,000 steps (10 ps) | +| Constraints | Alpha-carbon (CA) position restraints (1000 kJ/mol/nm²) | + +The Monte Carlo Barostat adjusts the simulation box volume to maintain constant pressure. Alpha-carbon atoms are restrained to their initial positions using harmonic restraints with a force constant of **1000 kJ/mol/nm²** to prevent large-scale protein conformational changes during equilibration while allowing side chains and solvent to relax. + +--- + +### 3. NVT Equilibration + +**Purpose**: Equilibrate at constant **N**umber of particles, **V**olume, and **T**emperature after fixing the box size from NPT. + +| Parameter | Value | +|-----------|-------| +| Ensemble | NVT | +| Steps | 50,000 (100 ps) | +| Temperature ramp | 5 K → 300 K | +| Ramp schedule | +5 K every ~833 steps (60 increments) | +| Reporting interval | 5,000 steps (10 ps) | +| Constraints | Alpha-carbon (CA) position restraints (1000 kJ/mol/nm²) | + +**Temperature Ramping Protocol**: +- Initial velocities set to 5 K +- Temperature increased gradually over 60 intervals +- Final temperature: 300 K + +Alpha-carbon position restraints are maintained during temperature ramping to prevent protein unfolding during heating. This slow heating with backbone restraints helps prevent instabilities. + +--- + +### 4. Production MD + +**Purpose**: Generate an equilibrated trajectory for analysis. + +| Parameter | Value | +|-----------|-------| +| Ensemble | NVT | +| Steps | 500,000 (1 ns) | +| Energy/force calculation interval | 10,000 steps (20 ps) | +| Constraints | None (for accurate force calculation) | + +**Note**: HBonds constraints are removed during production to enable accurate force calculations on all atoms. + +--- + +## Energy Calculations + +### Van der Waals (Lennard-Jones) Interactions + +The Lennard-Jones potential [1] describes van der Waals interactions: + +#### Potential Energy + +$$U_{LJ}(r) = 4\epsilon_{ij} \left[ \left(\frac{\sigma_{ij}}{r}\right)^{12} - \left(\frac{\sigma_{ij}}{r}\right)^{6} \right]$$ + +Where: +- $r$ = distance between atoms +- $\epsilon_{ij}$ = well depth (combined) +- $\sigma_{ij}$ = collision diameter (combined) + +#### Combining Rules (Lorentz-Berthelot) [5, 6] + +$$\sigma_{ij} = \frac{\sigma_i + \sigma_j}{2}$$ + +$$\epsilon_{ij} = \sqrt{\epsilon_i \cdot \epsilon_j}$$ + +#### Separated Energy Terms + +| Component | Formula | Description | +|-----------|---------|-------------| +| **Repulsive** | $U_{rep} = 4\epsilon_{ij} \left(\frac{\sigma_{ij}}{r}\right)^{12}$ | Short-range Pauli repulsion | +| **Attractive** | $U_{att} = -4\epsilon_{ij} \left(\frac{\sigma_{ij}}{r}\right)^{6}$ | London dispersion | + +--- + +### Electrostatic (Coulomb) Interactions [7] + +#### Potential Energy + +$$U_{elec}(r) = \frac{k_e \cdot q_i \cdot q_j}{r}$$ + +Where: +- $k_e$ = Coulomb constant = 138.935456 kJ·nm/(mol·e²) in OpenMM units +- $q_i, q_j$ = partial charges in elementary charge units +- $r$ = distance between atoms + + +#### Classification + +| Condition | Energy | Type | +|-----------|--------|------| +| $q_i \cdot q_j > 0$ (like charges) | Positive | **Repulsive** | +| $q_i \cdot q_j < 0$ (opposite charges) | Negative | **Attractive** | + +--- + +## Pairwise Residue Calculation + +For each pair of protein residues $(i, j)$: + +1. **Iterate over all atom pairs** $(a_i, a_j)$ where $a_i \in \text{residue } i$ and $a_j \in \text{residue } j$ + +2. **Calculate distance**: + $$r = \|\vec{r}_{a_j} - \vec{r}_{a_i}\|$$ + +3. **Skip if too close**: $r < 0.1$ nm (prevents singularities) + +4. **Compute energies** using formulas above + +5. **Sum contributions** from all atom pairs + +6. **Normalize** by the number of atom pairs: + $$\bar{E}_{ij} = \frac{\sum_{a_i, a_j} E(a_i, a_j)}{n_{atoms,i} \times n_{atoms,j}}$$ + +7. **Store in upper triangle** of NxN matrix (symmetric) + +### Solvent Exclusion + +The pairwise residue-residue interaction energies are calculated **exclusively between protein residues**. Energy contributions from solvent molecules (water) and ions ($Na^+$/$Cl^-$) are explicitly subtracted from the residue-residue energies: + +- Protein-water, protein-ion, water-water, and ion-ion interactions are subtracted out and thus not included in the output matrices +- This ensures the resulting energy matrices represent **intrinsic protein residue-residue interactions** without solvent-mediated effects + +This approach isolates the direct non-bonded interactions within the protein structure, providing a cleaner signal for structural analysis and comparison. + +--- + +## Output Matrices + +The pipeline produces **8 NxN matrices** (where N = number of protein residues): + +### Energy Matrices (units: kJ/mol) + +| Matrix | Description | +|--------|-------------| +| `vdw_energy_attractive` | Attractive VdW energies ($r^{-6}$ term) | +| `vdw_energy_repulsive` | Repulsive VdW energies ($r^{-12}$ term) | +| `es_energy_attractive` | Attractive electrostatic (opposite charges) | +| `es_energy_repulsive` | Repulsive electrostatic (like charges) | + +### Matrix Properties + +- **Dimensions**: N × N (protein residues only, excludes water/ions) +- **Storage**: Upper triangle only (matrices are symmetric) +- **Averaging**: Values averaged over all frames during production +- **Normalization**: Per atom-pair average for each residue pair + +--- + +## Usage Example + +### Minimal API usage (in-memory) + +```python +from proteogram.nonbonded_forces import NonBondedForceModel +import numpy as np + +model = NonBondedForceModel( + pdb_path='protein.pdb', + temperature=311.75, # Kelvin + pressure=1.0, # atmospheres + padding=1.0, # nanometers (water box padding around protein) + timestep=2.0, # femtoseconds + use_gpu=False, + output_dir='output' +) + +# Full MD pipeline. Returns 4 matrices (vdw/es attractive/repulsive). +vdw_attractive, vdw_repulsive, es_attractive, es_repulsive = model.run_full_pipeline( + npt_steps=50000, # steps (50,000 × 2 fs = 100 ps NPT equilibration) + nvt_steps=50000, # steps (50,000 × 2 fs = 100 ps NVT equilibration) + production_steps=500000, # steps (500,000 × 2 fs = 1 ns production run) + energy_calc_interval=10000, # steps between energy snapshots (10,000 × 2 fs = 20 ps; 50 frames total) + return_simulated_pdb=False, + subtract_solvent_energies=True, + debug=True +) + +print('VdW attractive matrix shape:', vdw_attractive.shape) +print('Electrostatic repulsive matrix shape:', es_repulsive.shape) + +model.cleanup() +``` + +### Script form (CLI-friendly) + +```python +#!/usr/bin/env python +import argparse +from pathlib import Path +import numpy as np +from proteogram.nonbonded_forces import NonBondedForceModel + + +def main(): + parser = argparse.ArgumentParser(description='Run residue-residue nonbonded force pipeline') + parser.add_argument('pdb_path', type=Path, help='Input protein PDB file') + parser.add_argument('--outdir', type=Path, default=Path('output'), help='Output directory') + parser.add_argument('--steps', type=int, default=500000, help='Production steps') + parser.add_argument('--no-gpu', action='store_true', help='Force CPU execution') + args = parser.parse_args() + + args.outdir.mkdir(parents=True, exist_ok=True) + + model = NonBondedForceModel( + pdb_path=str(args.pdb_path), + temperature=310.15, + use_gpu=not args.no_gpu, + output_dir=str(args.outdir), + memory_efficient=True # optional, recommends low memory usage for large systems + ) + + vdw_att, vdw_rep, es_att, es_rep = model.run_full_pipeline( + npt_steps=50000, + nvt_steps=50000, + production_steps=args.steps, + energy_calc_interval=10000, + return_simulated_pdb=True, + subtract_solvent_energies=True, + debug=False + ) + + # Save final structure + pdb_out = args.outdir / f'{args.pdb_path.stem}_production.pdb' + with open(pdb_out, 'w') as f: + f.write(model.get_simulated_pdb_stream().read()) + + # Save energy matrices + np.save(args.outdir / 'vdw_attractive.npy', vdw_att) + np.save(args.outdir / 'vdw_repulsive.npy', vdw_rep) + np.save(args.outdir / 'es_attractive.npy', es_att) + np.save(args.outdir / 'es_repulsive.npy', es_rep) + + model.cleanup() + + +if __name__ == '__main__': + main() +``` + +--- + +## Appendix + +### Appendix A: Energy Monitoring and Validation + +#### General Expectations + +The "good" values for potential energy during MD simulations depend heavily on system size (number of atoms), but here is some general guidance: + +##### NPT Equilibration +- **Potential energy should decrease and stabilize** over time +- Typical range: **-50,000 to -500,000 kJ/mol** for solvated proteins (depends on system size) +- Energy should fluctuate around a stable mean once equilibrated +- **Density** should converge to ~1.0 g/cm³ for aqueous systems + +##### NVT Equilibration +- During temperature ramping (5K → 300K), energy will **increase gradually** with temperature (this is expected) +- Final energy should be similar to or slightly higher than NPT final energy +- Fluctuations should be smaller than NPT (fixed volume means less variability) + +##### Production MD +- Energy should fluctuate around a **stable mean** +- No systematic drift (increasing or decreasing trend) +- Fluctuations typically 1-5% of mean energy + +--- + +#### What to Watch For + +| Indicator | Good | Bad | +|-----------|------|-----| +| **Energy trend** | Decreasing during equilibration, then stable | Continuously increasing or erratic | +| **Fluctuations** | Small, consistent | Large spikes or sudden jumps | +| **Magnitude** | Negative (bound system) | Positive (system exploding) | +| **Per-atom energy** | -10 to -20 kJ/mol/atom | > 0 or < -50 kJ/mol/atom | + +#### Red Flags + +1. **Positive total energy** → System is breaking apart ("exploding") +2. **Energy drops dramatically** → Atoms overlapping (bad geometry) +3. **Large oscillations that don't dampen** → Unstable integration (timestep too large?) +4. **Sudden energy spikes** → Steric clashes, bad contacts, or numerical instability + +--- + +#### Rough Estimates by System Size + +| System | Atoms (approx) | Typical Energy (kJ/mol) | Per-atom (kJ/mol) | +|--------|---------------|-------------------------|-------------------| +| Small protein + water | 10,000–30,000 | -100,000 to -200,000 | -10 to -15 | +| Medium protein + water | 50,000–100,000 | -300,000 to -600,000 | -10 to -15 | +| Large protein + water | 200,000+ | -1,000,000+ | -10 to -15 | + +**Note**: These are rough estimates. Absolute values matter less than trends—track whether energy is stable, not whether it matches a specific number. + +--- + +#### Energy Validation in Code + +The pipeline includes automatic energy monitoring with warnings for: + +- Positive potential energy (system instability) +- Large energy increases (>10% change between checks) +- Very large energy jumps (>100,000 kJ/mol) +- Unusual per-atom energies (positive or very negative) +- Large fluctuations during production (>5% of mean) + +Example output: +``` +Running NPT equilibration for 50000 steps... + Initial potential energy: -150234.5 kJ/mol (-12.34 kJ/mol/atom) + Final potential energy: -152456.7 kJ/mol (-12.52 kJ/mol/atom) + Energy decreased by 2222.2 kJ/mol (good) +NPT equilibration complete. +``` + +--- + +### Appendix B: Alpha-Carbon Restraint Implementation + +During energy minimization, NPT equilibration, and NVT equilibration, alpha-carbon (CA) atoms are restrained to their initial positions using OpenMM's [3] `CustomExternalForce` with a harmonic restraint potential that is periodic boundary condition (PBC) aware: + +$$U_{restraint} = k \cdot d_{PBC}^2$$ + +Where: +- $k$ = 1000 kJ/mol/nm² (force constant) +- $d_{PBC}$ = minimum-image distance to reference position $(x_0, y_0, z_0)$ + +**Implementation**: +```python +# Get indices of CA atoms +ca_indices = [atom.index for atom in topology.atoms() if atom.name == 'CA'] + +# Custom external force to restrain CA atoms to their initial positions +restraint_force = CustomExternalForce("k*periodicdistance(x, y, z, x0, y0, z0)^2") + +# Add per-particle parameters for reference positions +restraint_force.addPerParticleParameter('x0') # reference x +restraint_force.addPerParticleParameter('y0') # reference y +restraint_force.addPerParticleParameter('z0') # reference z + +# Add global parameter for force constant +restraint_force.addGlobalParameter('k', 1000.0 * kilojoules_per_mole / nanometer**2) + +# Add CA atoms to the constraint force +for idx in ca_indices: + restraint_force.addParticle(idx, positions[idx].value_in_unit(nanometers)) +system.addForce(restraint_force) +``` + +### Appendix C: RESP Charges [8] + +#### RESP Charges Lookup Table Approach + +For standard amino acids, charges are **pre-computed and stored** in the force field XML files. OpenMM looks up these values based on residue name and atom name: + +| Atom (Alanine) | Partial Charge (e) | +|----------------|-------------------| +| N | -0.4157 | +| H | +0.2719 | +| CA | +0.0337 | +| HA | +0.0823 | +| C | +0.5973 | +| O | -0.5679 | + +#### RESP vs. Gasteiger + +| Property | RESP (AMBER) | Gasteiger | +|----------|--------------|-----------| +| **Basis** | Quantum mechanical ESP | Electronegativity equalization | +| **Accuracy** | High (validated for MD) | Lower for polar groups | +| **Consistency** | Matched to force field VdW parameters | Standalone method | +| **Use case** | MD simulations | Cheminformatics, docking | + +RESP charges are specifically parameterized to work synergistically with the other AMBER force field terms (VdW, bonds, angles, dihedrals), ensuring accurate reproduction of experimental properties like solvation free energies and protein folding thermodynamics [8, 9]. + +--- + +### Appendix D: Force and Energy Calculations - Detailed Derivations + +This appendix provides detailed derivations and unit analysis for the force and energy calculations used in the pipeline. + +#### Van der Waals (Lennard-Jones) Interactions [1] + +##### Potential Energy + +The Lennard-Jones potential is: + +$$U_{LJ}(r) = 4\epsilon_{ij} \left[ \left(\frac{\sigma_{ij}}{r}\right)^{12} - \left(\frac{\sigma_{ij}}{r}\right)^{6} \right]$$ + +This is properly separated into: +- **Repulsive**: $U_{rep} = 4\epsilon_{ij} \left(\frac{\sigma_{ij}}{r}\right)^{12}$ (always positive) +- **Attractive**: $U_{att} = -4\epsilon_{ij} \left(\frac{\sigma_{ij}}{r}\right)^{6}$ (always negative) + +##### Force Derivation + +The force is the negative gradient of potential energy: + +$$F = -\frac{dU}{dr}$$ + +Taking the derivative of $U_{LJ}$: + +$$F_{LJ}(r) = -\frac{d}{dr}\left[4\epsilon_{ij} \left( \frac{\sigma_{ij}^{12}}{r^{12}} - \frac{\sigma_{ij}^{6}}{r^{6}} \right)\right]$$ + +$$F_{LJ}(r) = -4\epsilon_{ij} \left( -12\frac{\sigma_{ij}^{12}}{r^{13}} + 6\frac{\sigma_{ij}^{6}}{r^{7}} \right)$$ + +$$F_{LJ}(r) = \frac{24\epsilon_{ij}}{r} \left[ 2\left(\frac{\sigma_{ij}}{r}\right)^{12} - \left(\frac{\sigma_{ij}}{r}\right)^{6} \right]$$ + +##### Separated Force Terms + +- **Repulsive**: $F_{rep} = \frac{48\epsilon_{ij}}{r} \left(\frac{\sigma_{ij}}{r}\right)^{12}$ (positive, pushes apart) +- **Attractive**: $F_{att} = -\frac{24\epsilon_{ij}}{r} \left(\frac{\sigma_{ij}}{r}\right)^{6}$ (negative, pulls together) + +##### Combining Rules (Lorentz-Berthelot) [5, 6] + +$$\sigma_{ij} = \frac{\sigma_i + \sigma_j}{2} \quad \text{(arithmetic mean)}$$ + +$$\epsilon_{ij} = \sqrt{\epsilon_i \cdot \epsilon_j} \quad \text{(geometric mean)}$$ + +--- + +#### Electrostatic (Coulomb) Interactions [7] + +##### Potential Energy + +$$U_{elec}(r) = \frac{k_e \cdot q_i \cdot q_j}{r}$$ + +Where $k_e = 138.935456$ kJ·nm/(mol·e²) is the Coulomb constant in OpenMM units [3]. + +##### Force Derivation + +$$F_{elec}(r) = -\frac{dU_{elec}}{dr} = -\frac{d}{dr}\left(\frac{k_e q_i q_j}{r}\right) = \frac{k_e q_i q_j}{r^2}$$ + +The sign convention: +- Like charges ($q_i \cdot q_j > 0$): Force is **positive** (repulsive) +- Opposite charges ($q_i \cdot q_j < 0$): Force is **negative** (attractive) + +--- + +#### Units Summary + +| Quantity | OpenMM Units | +|----------|--------------| +| Distance $r$ | nanometers (nm) | +| Charge $q$ | elementary charge (e) | +| $\sigma$ | nanometers (nm) | +| $\epsilon$ | kJ/mol | +| Coulomb constant $k_e$ | kJ·nm/(mol·e²) | +| **Energy** | **kJ/mol** | +| **Force** | **kJ/(mol·nm)** | + +##### Unit Verification for LJ Energy + +$$[\epsilon] \cdot \left[\frac{\sigma}{r}\right]^6 = \frac{\text{kJ}}{\text{mol}} \cdot \left(\frac{\text{nm}}{\text{nm}}\right)^6 = \frac{\text{kJ}}{\text{mol}} \quad \checkmark$$ + +##### Unit Verification for LJ Force + +$$\frac{[\epsilon]}{[r]} \cdot \left[\frac{\sigma}{r}\right]^{12} = \frac{\text{kJ/mol}}{\text{nm}} \cdot 1 = \frac{\text{kJ}}{\text{mol} \cdot \text{nm}} \quad \checkmark$$ + +##### Unit Verification for Coulomb Energy + +$$[k_e] \cdot [q]^2 / [r] = \frac{\text{kJ} \cdot \text{nm}}{\text{mol} \cdot \text{e}^2} \cdot \text{e}^2 / \text{nm} = \frac{\text{kJ}}{\text{mol}} \quad \checkmark$$ + +##### Unit Verification for Coulomb Force + +$$[k_e] \cdot [q]^2 / [r]^2 = \frac{\text{kJ} \cdot \text{nm}}{\text{mol} \cdot \text{e}^2} \cdot \text{e}^2 / \text{nm}^2 = \frac{\text{kJ}}{\text{mol} \cdot \text{nm}} \quad \checkmark$$ + +--- + +#### Summary Table + +| Component | Energy Formula | Force Formula | Sign | +|-----------|----------------|---------------|------| +| **VdW Repulsive** | $4\epsilon_{ij} \left(\frac{\sigma_{ij}}{r}\right)^{12}$ | $\frac{48\epsilon_{ij}}{r} \left(\frac{\sigma_{ij}}{r}\right)^{12}$ | + (pushes) | +| **VdW Attractive** | $-4\epsilon_{ij} \left(\frac{\sigma_{ij}}{r}\right)^{6}$ | $-\frac{24\epsilon_{ij}}{r} \left(\frac{\sigma_{ij}}{r}\right)^{6}$ | - (pulls) | +| **ES Repulsive** | $\frac{k_e q_i q_j}{r}$ when $q_iq_j > 0$ | $\frac{k_e q_i q_j}{r^2}$ when $q_iq_j > 0$ | + (pushes) | +| **ES Attractive** | $\frac{k_e q_i q_j}{r}$ when $q_iq_j < 0$ | $\frac{k_e q_i q_j}{r^2}$ when $q_iq_j < 0$ | - (pulls) | + +--- + +## References + +1. Lennard-Jones, J. E. (1931). "Cohesion". Proceedings of the Physical Society. 43 (5): 461–482. +2. Case, D. A., et al. (2020). "AMBER 2020 Reference Manual". +3. Eastman, P., et al. (2017). "OpenMM 7: Rapid development of high performance algorithms for molecular dynamics". PLOS Computational Biology. +4. Tian, C., et al. (2020). "ff19SB: Amino-Acid-Specific Protein Backbone Parameters Trained against Quantum Mechanics Energy Surfaces in Solution". Journal of Chemical Theory and Computation. 16 (1): 528–552. +5. Lorentz, H. A. (1881). "Ueber die Anwendung des Satzes vom Virial in der kinetischen Theorie der Gase". Annalen der Physik. 248 (1): 127–136. +6. Berthelot, D. (1898). "Sur le mélange des gaz". Comptes Rendus. 126: 1703–1706. +7. Coulomb, C. A. (1785). "Premier mémoire sur l'électricité et le magnétisme". Histoire de l'Académie Royale des Sciences. 569–577. +8. Bayly, C. I., et al. (1993). "A well-behaved electrostatic potential based method using charge restraints for deriving atomic charges: the RESP model". The Journal of Physical Chemistry. 97 (40): 10269–10280. +9. Cornell, W. D., et al. (1995). "A Second Generation Force Field for the Simulation of Proteins, Nucleic Acids, and Organic Molecules". Journal of the American Chemical Society. 117 (19): 5179–5197. diff --git a/proteogram/nonbonded_forces.py b/proteogram/nonbonded_forces.py new file mode 100644 index 0000000..b393935 --- /dev/null +++ b/proteogram/nonbonded_forces.py @@ -0,0 +1,2204 @@ +"""Modelling non-bonded forces in PDB file (Van der Waals and Coulombic) with OpenMM. + +This module provides functionality for: + - Preprocessing PDB structures (fixing, solvation, ionization) + - Running MD simulations (equilibration and production) + - Calculating residue-residue interaction energies (VdW and electrostatic) with solvent effects removed +""" +from openmm.app import ( + PDBFile, ForceField, Modeller, Simulation, PME, + HBonds, StateDataReporter, DCDReporter +) +from openmm import ( + LangevinMiddleIntegrator, MonteCarloBarostat, Platform, + NonbondedForce, CustomNonbondedForce, Context, System, + VerletIntegrator, CustomExternalForce +) +from openmm.unit import ( + kelvin, atmosphere, picosecond, picoseconds, femtoseconds, + nanometer, kilojoules_per_mole, elementary_charge, angstrom, + kilojoules_per_mole, nanometers +) +from pdbfixer import PDBFixer + +import gc +import io +import linecache +import numpy as np +import sys +import warnings +from typing import Optional +from pathlib import Path +import psutil + + +class NonBondedForceModel: + """Model for computing non-bonded forces between residues using MD simulation. + + This class provides a complete pipeline for: + 1. Preprocessing PDB structures (fixing, solvation, ionization) + 2. Running MD simulations (NPT and NVT equilibration, production) + 3. Calculating residue-residue interaction energies + + Attributes: + pdb_path (str): Path to the input PDB file. + temperature (float): Simulation temperature in Kelvin. + pressure (float): Simulation pressure in atmospheres. + padding (float): Padding around protein for water box in nanometers. + timestep (float): Integration timestep in femtoseconds. + forcefield (ForceField): OpenMM force field object. + topology: OpenMM topology of the system. + positions: Atomic positions. + system (System): OpenMM system object. + simulation (Simulation): OpenMM simulation object. + residue_atom_indices (dict): Mapping of residue index to atom indices. + """ + + # Default simulation parameters + DEFAULT_TEMPERATURE = 310.15 # Kelvin (37 C) + DEFAULT_PRESSURE = 1.0 # atmospheres + DEFAULT_FRICTION_COEFFICIENT = 1.0 # 1/ps + DEFAULT_PADDING = 1.0 # nanometers + DEFAULT_TIMESTEP = 2.0 # femtoseconds + DEFAULT_NPT_STEPS = 50000 # 100 ps with 2 fs timestep + DEFAULT_NVT_STEPS = 50000 # 100 ps with 2 fs timestep + DEFAULT_PRODUCTION_STEPS = 500000 # 1 ns with 2 fs timestep + DEFAULT_REPORTING_INTERVAL = 5000 # Report every 10 ps + + def __init__( + self, + pdb_path: str, + temperature: float = DEFAULT_TEMPERATURE, + pressure: float = DEFAULT_PRESSURE, + padding: float = DEFAULT_PADDING, + timestep: float = DEFAULT_TIMESTEP, + use_gpu: bool = False, + output_dir: Optional[str] = None, + memory_efficient: bool = False + ): + """Initialize the NonBondedForceModel. + + Args: + pdb_path (str): Path to the input PDB file. + temperature (float, optional): Simulation temperature in Kelvin. + Defaults to 300 K. + pressure (float, optional): Simulation pressure in atmospheres. + Defaults to 1 atm. + padding (float, optional): Padding around protein for water box + in nanometers. Defaults to 1.0 nm. + timestep (float, optional): Integration timestep in femtoseconds. + Defaults to 2 fs. + use_gpu (bool, optional): Whether to use GPU acceleration. + Defaults to False (CPU). + output_dir (str, optional): Directory for saving debug outputs + (e.g., energy plots). Defaults to the PDB file's parent directory. + """ + self.pdb_path = pdb_path + self.temperature = temperature * kelvin + self.pressure = pressure * atmosphere + self.padding = padding * nanometer + self.timestep = timestep * femtoseconds + self.use_gpu = use_gpu + self.output_dir = Path(output_dir) if output_dir else Path(pdb_path).parent + # Toggle for memory-efficient solvent subtraction path (slower, lower memory) + self.memory_efficient = memory_efficient + + # These will be set during setup + self.forcefield = None + self.topology = None + self.positions = None + self.modeller = None + self.system = None + self.simulation = None + self.residue_atom_indices = {} + self.protein_residue_indices = [] + self.debug = False + + # Store periodic box vectors for proper Context transitions + self._box_vectors = None + + # Cache for particle parameters (reduces memory allocation in energy calculations) + self._cached_particle_params = None + + # Energy logging for debug mode + # Each stage stores: {'time_ps': [], 'energy_kj': [], 'stage': str} + self.energy_log = { + 'initial': {'time_ps': [], 'energy_kj': [], 'stage': 'Initial'}, + 'minimization': {'time_ps': [], 'energy_kj': [], 'stage': 'Minimization'}, + 'npt': {'time_ps': [], 'energy_kj': [], 'stage': 'NPT Equilibration'}, + 'nvt': {'time_ps': [], 'energy_kj': [], 'stage': 'NVT Equilibration'}, + 'production': {'time_ps': [], 'energy_kj': [], 'stage': 'Production'} + } + + def __del__(self): + """Destructor to ensure cleanup when object is garbage collected.""" + try: + self.cleanup_all_resources() + except Exception: + pass # Ignore errors during destruction + + def __enter__(self): + """Context manager entry - allows 'with NonBondedForceModel(...) as model:'""" + return self + + def __exit__(self, exc_type, exc_val, exc_tb): + """Context manager exit - ensures cleanup on exit.""" + self.cleanup_all_resources() + return False + + @staticmethod + def fix_pdb_file(pdb_path: str) -> io.StringIO: + """Fix a PDB structure for input to MD simulation. + + Performs the following fixes: + - Replace non-standard residues with standard equivalents + - Remove heterogens including crystal waters + - Add missing atoms + - Add hydrogens (at pH 7.0) + + Args: + pdb_path (str): Path to the input PDB file. + + Returns: + io.StringIO: A PDB file in memory after fixing. + """ + fixer = PDBFixer(pdb_path) + + fixer.findMissingResidues() + fixer.findNonstandardResidues() + fixer.replaceNonstandardResidues() + fixer.removeHeterogens(keepWater=False) + fixer.findMissingAtoms() + fixer.addMissingAtoms() + fixer.addMissingHydrogens(pH=7.0) + + # Write PDB file info to an IO stream + pdb_file_in_mem = io.StringIO() + PDBFile.writeFile(fixer.topology, fixer.positions, pdb_file_in_mem) + pdb_file_in_mem.seek(0) + + return pdb_file_in_mem + + def setup_system(self) -> None: + """Set up the MD simulation system. + + This method: + 1. Fixes the PDB structure + 2. Loads the AMBER ff19SB force field with TIP3P water + 3. Solvates the protein in a water box + 4. Adds ions to neutralize the system + 5. Creates the OpenMM system with PME electrostatics + + Raises: + RuntimeError: If system setup fails. + """ + # Step 1: Fix the PDB structure + fixed_pdb = self.fix_pdb_file(self.pdb_path) + pdb = PDBFile(fixed_pdb) + fixed_pdb.close() # Close StringIO to free memory + + # Step 2: Load force field (AMBER ff19SB + TIP3P water) + self.forcefield = ForceField('amber19-all.xml', 'amber19/tip3pfb.xml') + + # Step 3: Create modeller and add solvent + self.modeller = Modeller(pdb.topology, pdb.positions) + + # Handle modified amino acids that OpenMM doesn't recognize + # Replace them with their standard equivalents before solvation + self._replace_modified_residues() + + # Store protein residue indices before adding water + self._identify_protein_residues() + + # Add water box with padding + self.modeller.addSolvent( + self.forcefield, + model='tip3p', + padding=self.padding, + neutralize=True, # Add ions to neutralize + positiveIon='Na+', + negativeIon='Cl-' + ) + + self.topology = self.modeller.topology + self.positions = self.modeller.positions + + # Store original positions as backup for position validation + self._original_positions = self.positions + + # Step 4: Create the system with PME for long-range electrostatics + self.system = self.forcefield.createSystem( + self.topology, + nonbondedMethod=PME, + nonbondedCutoff=1.0 * nanometer, + constraints=HBonds # Allows 2 fs timestep + ) + + # Build residue-to-atom mapping for energy calculations + self._build_residue_atom_mapping() + + def _replace_modified_residues(self) -> None: + """Replace modified amino acids with their standard equivalents. + + OpenMM's AMBER force field doesn't have parameters for modified AAs like MSE + (selenomethionine), so we convert them to standard equivalents before solvation. + + This preserves the 3D structure while allowing force field compatibility. + """ + # Mapping of modified residues to their standard equivalents + residue_replacements = { + 'MSE': 'MET', # Selenomethionine -> Methionine + 'HYP': 'PRO', # Hydroxyproline -> Proline + 'PTR': 'TYR', # Phosphotyrosine -> Tyrosine + 'SEP': 'SER', # Phosphoserine -> Serine + 'TPO': 'THR', # Phosphothreonine -> Threonine + } + + # Iterate through topology and replace residue names + for residue in self.modeller.topology.residues(): + if residue.name in residue_replacements: + standard_name = residue_replacements[residue.name] + print(f" INFO: Converting {residue.name} (residue {residue.index}) to {standard_name}") + residue.name = standard_name + + def _identify_protein_residues(self) -> None: + """Identify protein residue indices before solvation.""" + protein_resnames = { + 'ALA', 'ARG', 'ASN', 'ASP', 'CYS', 'GLN', 'GLU', 'GLY', 'HIS', + 'ILE', 'LEU', 'LYS', 'MET', 'PHE', 'PRO', 'SER', 'THR', 'TRP', + 'TYR', 'VAL', 'HIE', 'HID', 'HIP', 'CYX', + # Modified amino acids + 'MSE', # Selenomethionine (variant of MET) + 'PTR', 'SEP', 'TPO', # Phosphorylated variants + 'HYP', # Hydroxyproline (variant of PRO) + } + self.protein_residue_indices = [] + for i, residue in enumerate(self.modeller.topology.residues()): + if residue.name in protein_resnames: + self.protein_residue_indices.append(i) + + def _build_residue_atom_mapping(self) -> None: + """Build a mapping from residue index to atom indices.""" + self.residue_atom_indices = {} + for residue in self.topology.residues(): + res_idx = residue.index + atom_indices = [atom.index for atom in residue.atoms()] + self.residue_atom_indices[res_idx] = atom_indices + + # Also identify solvent and ion atoms for interaction group calculations + self._identify_solvent_atoms() + + def _identify_solvent_atoms(self) -> None: + """Identify solvent (water) and ion atom indices after solvation. + + This builds sets of atom indices for: + - protein_atom_indices: All atoms belonging to protein residues + - solvent_atom_indices: Water molecules (HOH, WAT, TIP3, etc.) + - ion_atom_indices: Ions (Na+, Cl-, K+, etc.) + """ + solvent_resnames = {'HOH', 'WAT', 'TIP3', 'TIP4', 'SPC', 'T3P', 'T4P', 'T5P'} + ion_resnames = {'NA', 'CL', 'K', 'MG', 'CA', 'ZN', 'NA+', 'CL-', 'K+', 'MG2+', 'CA2+', 'ZN2+'} + + self.protein_atom_indices = set() + self.solvent_atom_indices = set() + self.ion_atom_indices = set() + + for residue in self.topology.residues(): + atom_indices = [atom.index for atom in residue.atoms()] + resname = residue.name.upper() + + if residue.index in self.protein_residue_indices: + self.protein_atom_indices.update(atom_indices) + elif resname in solvent_resnames: + self.solvent_atom_indices.update(atom_indices) + elif resname in ion_resnames: + self.ion_atom_indices.update(atom_indices) + else: + # Unknown residue type - could be ligand, treat as solvent for now + self.solvent_atom_indices.update(atom_indices) + + def _get_platform(self) -> Platform: + """Get the appropriate compute platform. + + Returns: + Platform: OpenMM platform (CUDA if GPU, CPU otherwise). + """ + if self.use_gpu: + try: + return Platform.getPlatformByName('CUDA') + except Exception: + print("CUDA not available, falling back to CPU") + return Platform.getPlatformByName('CPU') + return Platform.getPlatformByName('CPU') + + + def _create_new_simulation(self, + hbonds_constraint: bool = False, + add_calpha_restraint: bool = False, + add_barostat: bool = False) -> None: + """Create an OpenMM simulation object. + + Args: + hbonds_constraint (bool): Whether to constrain hydrogen bonds. + add_barostat (bool): Whether to add a barostat for NPT simulation. + add_calpha_restraint (bool): Whether to add constraints to CA atoms. + """ + # Create a fresh system copy for modification + if hbonds_constraint: + constraints = HBonds + else: + constraints = None + + system = self.forcefield.createSystem( + self.topology, + nonbondedMethod=PME, + nonbondedCutoff=1.0 * nanometer, + constraints=constraints + ) + + if add_barostat: + barostat = MonteCarloBarostat(self.pressure, self.temperature) + system.addForce(barostat) # FIX: Use new 'system' not 'self.system' + + if add_calpha_restraint: + # Get indices of CA atoms + ca_indices = [atom.index for atom in self.topology.atoms() if atom.name == 'CA'] + + # Custom external force to restrain CA atoms to their initial positions + # restraint_force = CustomExternalForce("0.5*k*((x-x0)^2 + (y-y0)^2 + (z-z0)^2)") + restraint_force = CustomExternalForce("k*periodicdistance(x, y, z, x0, y0, z0)^2") + + # Add per-particle parameters for reference positions + restraint_force.addPerParticleParameter('x0') # reference x + restraint_force.addPerParticleParameter('y0') # reference y + restraint_force.addPerParticleParameter('z0') # reference z + + # Add global parameter for force constant + restraint_force.addGlobalParameter('k', 1000.0 * kilojoules_per_mole / nanometer**2) + + # Add CA atoms to the constraint force + for idx in ca_indices: + restraint_force.addParticle(idx, self.positions[idx].value_in_unit(nanometers)) + system.addForce(restraint_force) + + integrator = LangevinMiddleIntegrator( + self.temperature, + self.DEFAULT_FRICTION_COEFFICIENT / picosecond, + self.timestep + ) + + platform = self._get_platform() + + # Clean up old simulation to free memory (especially CUDA) + self._cleanup_old_simulation() + + # Don't store system here - Simulation manages its own system lifetime + # Create fresh system for this simulation + self.simulation = Simulation( + self.topology, + system, + integrator, + platform + ) + + # Restore periodic box vectors from previous Context if available + # This is critical for proper NPT→NVT transitions + if self._box_vectors is not None: + try: + self.simulation.context.setPeriodicBoxVectors(*self._box_vectors) + except Exception as e: + print(f"WARNING: Failed to set periodic box vectors: {e}") + + # Set positions after box vectors are set + self.simulation.context.setPositions(self.positions) + + + def minimize_energy(self, max_iterations: int = 1000) -> None: + """Perform energy minimization. + + Args: + max_iterations (int): Maximum number of minimization iterations. + """ + print("Performing energy minimization...") + + # Log initial energy before minimization (debug mode) + if self.debug: + initial_state = self.simulation.context.getState(getEnergy=True) + initial_energy = initial_state.getPotentialEnergy().value_in_unit(kilojoules_per_mole) + del initial_state + self._log_energy('initial', 0.0, initial_energy) + self._log_energy('minimization', 0.0, initial_energy) + + self.simulation.minimizeEnergy(maxIterations=max_iterations) + _st = self.simulation.context.getState(getPositions=True) + self.positions = _st.getPositions() + del _st + + # Log final energy after minimization (debug mode) + if self.debug: + final_state = self.simulation.context.getState(getEnergy=True) + final_energy = final_state.getPotentialEnergy().value_in_unit(kilojoules_per_mole) + del final_state + # Use a small time increment to show minimization as a step + self._log_energy('minimization', 0.1, final_energy) + print(f" [DEBUG] Minimization: {initial_energy:.1f} -> {final_energy:.1f} kJ/mol") + + self.cleanup_all_resources(final_run=False) + + print("Energy minimization complete.") + + + def get_simulated_pdb_stream(self) -> io.StringIO: + """Get the current structure as a PDB file stream. + + This method writes the current positions to an in-memory PDB file + that can be saved or further processed. Can be called after any + simulation step (minimization, equilibration, or production). + + Returns: + io.StringIO: An in-memory PDB file containing the current structure. + + Raises: + RuntimeError: If positions are not available (simulation not run). + """ + if self.positions is None: + raise RuntimeError( + "No positions available. Run minimize_energy() or a simulation first." + ) + if self.topology is None: + raise RuntimeError( + "No topology available. Run setup_system() first." + ) + + pdb_stream = io.StringIO() + PDBFile.writeFile(self.topology, self.positions, pdb_stream) + pdb_stream.seek(0) + return pdb_stream + + + def _log_energy( + self, + stage: str, + time_ps: float, + energy_kj: float + ) -> None: + """Log energy value for a given stage (only when debug is enabled). + + Args: + stage (str): One of 'initial', 'minimization', 'npt', 'nvt', 'production'. + time_ps (float): Time in picoseconds. + energy_kj (float): Potential energy in kJ/mol. + """ + if self.debug and stage in self.energy_log: + self.energy_log[stage]['time_ps'].append(time_ps) + self.energy_log[stage]['energy_kj'].append(energy_kj) + + + def _reset_energy_log(self) -> None: + """Reset all energy logs (useful when starting a new simulation).""" + for stage in self.energy_log: + self.energy_log[stage]['time_ps'] = [] + self.energy_log[stage]['energy_kj'] = [] + + + def cleanup_all_resources(self, + final_run: bool = False) -> None: + """Release all OpenMM resources and clear cached data. + + Call this method when done with the simulation to free memory. + This is especially important when processing multiple PDB files + in sequence, particularly when using GPU/CUDA. + + Note: + For CUDA, memory may not be immediately released to the OS + but will be available for reuse by subsequent OpenMM operations. + """ + # CRITICAL: Clear linecache FIRST before any exception handling + # linecache accumulates source code lines from exceptions + # Clearing it prevents memory accumulation across multiple proteins + try: + linecache.clearcache() + except: + pass + + # CRITICAL: Clear exception tracebacks that might hold Context references + # Exception tracebacks keep frames alive, which keeps all local variables alive + try: + # Try to clear exception info in Python 2/3 compatible way + if hasattr(sys, 'exc_clear'): + sys.exc_clear() # Python 2 + # Also clear sys.__traceback__ + sys.__traceback__ = None + except: + pass + + # CRITICAL: Delete reporters first, they may hold references to Context + if self.simulation is not None: + try: + # Remove all reporters - they can hold strong references + self.simulation.reporters.clear() + except Exception: + pass + + # Delete the simulation, which owns and will free the Context and Integrator. + # Note: accessing simulation.context and calling del on it is a no-op — + # the C++ Context is owned by the C++ Simulation and is only freed when + # the Simulation destructor runs (i.e., when self.simulation is deleted). + if self.simulation is not None: + sim = self.simulation + self.simulation = None # Clear our reference first to break any cycles + del sim + gc.collect() # Force garbage collection after deleting simulation + + # Clear the modeller which holds topology and positions copies + if self.modeller is not None: + del self.modeller + self.modeller = None + + if final_run: + # Note: Don't delete self.system - it's managed by Simulation + # Deleting it here could cause issues with active simulations + self.system = None + + # Clear forcefield (holds parsed XML data) + if self.forcefield is not None: + del self.forcefield + self.forcefield = None + + # Clear topology and positions + self.topology = None + self.positions = None + # Clear backup position references that might hold state objects + if hasattr(self, '_original_positions'): + self._original_positions = None + if hasattr(self, '_pre_npt_positions'): + self._pre_npt_positions = None + + # Clear cached data + self._cached_particle_params = None + + # Clear large data structures + self.residue_atom_indices = {} + self.protein_residue_indices = [] + if hasattr(self, 'protein_atom_indices'): + self.protein_atom_indices = set() + if hasattr(self, 'solvent_atom_indices'): + self.solvent_atom_indices = set() + if hasattr(self, 'ion_atom_indices'): + self.ion_atom_indices = set() + + # Force multiple aggressive garbage collection passes + # CUDA memory requires multiple gc passes to fully release + # With Context objects involved, we need at least 3-5 passes + for _ in range(5): + gc.collect() + + # Try to clear CUDA memory pools if available + if self.use_gpu: + self._clear_cuda_cache() + + # Final explicit linecache and traceback clearing + try: + linecache.clearcache() + except: + pass + self._clear_exception_traceback() + + # Final aggressive garbage collection + for _ in range(5): + gc.collect() + + # On Linux, glibc malloc retains freed C++ heap pages (from thousands of + # temporary OpenMM Contexts created during per-residue energy calculations) + # as a fragmented free list rather than returning them to the OS immediately. + # malloc_trim(0) forces glibc to release all free pages above the top of heap. + # This is a no-op on macOS/Windows and is safe to call at any time. + try: + import ctypes + ctypes.cdll.LoadLibrary("libc.so.6").malloc_trim(0) + except Exception: + pass # Not on Linux or libc not available — skip silently + + def cleanup(self) -> None: + """Public cleanup method - alias for cleanup_all_resources() for backwards compatibility. + + Call this when done with the simulation to free memory. + This is especially important when processing multiple PDB files in sequence. + """ + self.cleanup_all_resources() + + + def _clear_cuda_cache(self) -> None: + """Attempt to clear CUDA memory caches using available libraries. + + Tries PyTorch first (commonly installed), then cupy. + This helps release GPU memory that OpenMM's CUDA backend may hold. + """ + # Try PyTorch CUDA cache clear (most commonly available) + try: + import torch + if torch.cuda.is_available(): + torch.cuda.empty_cache() + torch.cuda.synchronize() + # Also clear torch memory caches + if hasattr(torch, 'cuda') and hasattr(torch.cuda, 'reset_peak_memory_stats'): + torch.cuda.reset_peak_memory_stats() + except ImportError: + pass + except Exception: + pass + + # Try cupy CUDA cache clear + try: + import cupy + mempool = cupy.get_default_memory_pool() + mempool.free_all_blocks() + pinned_mempool = cupy.get_default_pinned_memory_pool() + pinned_mempool.free_all_blocks() + except ImportError: + pass + except Exception: + pass + + # Force final garbage collection + gc.collect() + + def _clear_exception_traceback(self) -> None: + """Clear exception tracebacks and linecache to prevent holding Context references. + + CRITICAL FIX FOR 5 PERSISTENT CONTEXT OBJECTS: + + Python's linecache module accumulates source code lines from exception + tracebacks. Each Context creation followed by an exception means: + 1. Exception frame created with Context reference + 2. Traceback stored with source code lines + 3. linecache caches those source code lines indefinitely + 4. Context never released because linecache holds frame references + + Solution: Clear linecache + exception info immediately after any exception. + """ + try: + # CRITICAL: Clear linecache FIRST - this prevents accumulation + # linecache.clearcache() removes all cached source code lines + # which breaks the reference chain: linecache -> traceback -> frame -> Context + linecache.clearcache() + except: + pass + + try: + # Clear the current exception info + if hasattr(sys, 'exc_clear'): + sys.exc_clear() # Python 2 + except: + pass + + try: + # Clear sys.__traceback__ which may hold frame references + sys.__traceback__ = None + except: + pass + + try: + gc.collect() + except: + pass + + + def _get_positions_and_cleanup(self) -> None: + """Get the positions and box vectors from the current simulation + state and clean up the state.""" + if self.simulation is not None: + try: + state = self.simulation.context.getState(getPositions=True) + self.positions = state.getPositions() + try: + # Store the periodic box vectors from the state before deleting context + self._box_vectors = state.getPeriodicBoxVectors() + except Exception: + pass + del state + gc.collect() + except Exception as e: + print(f"WARNING: Failed to get positions and/or clean up state/context: {e}") + self._clear_exception_traceback() + self._box_vectors = None + + + def plot_energy_history( + self, + output_path: Optional[str] = None, + show_plot: bool = False + ) -> None: + """Plot energy vs. time for all simulation stages. + + Creates a multi-panel figure showing energy evolution during: + - Initial state (single point) + - Energy minimization + - NPT equilibration + - NVT equilibration + - Production run + + Args: + output_path (str, optional): Path to save the figure. If None, + saves to the same directory as the PDB file with suffix '_energy.png'. + show_plot (bool): Whether to display the plot interactively. + Defaults to False. + """ + try: + import matplotlib.pyplot as plt + import matplotlib.patches as mpatches + except ImportError: + warnings.warn( + "matplotlib not installed. Cannot generate energy plot. " + "Install with: pip install matplotlib" + ) + return + + # Collect all stages that have data + stages_with_data = [] + for stage_key, stage_data in self.energy_log.items(): + if stage_data['energy_kj']: + stages_with_data.append((stage_key, stage_data)) + + if not stages_with_data: + print("No energy data to plot.") + return + + # Color scheme for different stages + stage_colors = { + 'initial': '#2ecc71', # Green + 'minimization': '#3498db', # Blue + 'npt': '#9b59b6', # Purple + 'nvt': '#e67e22', # Orange + 'production': '#e74c3c' # Red + } + + # Create figure with multiple subplots + fig, axes = plt.subplots(2, 1, figsize=(12, 8), height_ratios=[1, 2]) + + # Top panel: Combined view with cumulative time + ax_combined = axes[0] + cumulative_time = 0.0 + stage_boundaries = [] # Track where each stage ends + + for stage_key, stage_data in stages_with_data: + times = np.array(stage_data['time_ps']) + energies = np.array(stage_data['energy_kj']) + color = stage_colors.get(stage_key, '#7f8c8d') + + if len(times) > 0: + # Adjust times to be cumulative + adjusted_times = times + cumulative_time + + if stage_key == 'initial': + # Plot as a single point + ax_combined.scatter(adjusted_times, energies, color=color, + s=100, zorder=5, label=stage_data['stage']) + else: + ax_combined.plot(adjusted_times, energies, color=color, + linewidth=1.5, label=stage_data['stage']) + + # Update cumulative time + if len(times) > 1: + cumulative_time = adjusted_times[-1] + stage_boundaries.append((cumulative_time, stage_data['stage'])) + + # Add vertical lines at stage boundaries + for boundary_time, stage_name in stage_boundaries[:-1]: # Skip last boundary + ax_combined.axvline(x=boundary_time, color='gray', linestyle='--', + alpha=0.5, linewidth=0.8) + + ax_combined.set_xlabel('Time (ps)', fontsize=11) + ax_combined.set_ylabel('Potential Energy (kJ/mol)', fontsize=11) + ax_combined.set_title('Energy Evolution Throughout Simulation', fontsize=12, fontweight='bold') + ax_combined.legend(loc='upper right', fontsize=9) + ax_combined.grid(True, alpha=0.3) + + # Bottom panel: Individual stage panels + ax_individual = axes[1] + + # Calculate how many stages have more than 1 data point + multi_point_stages = [(k, d) for k, d in stages_with_data + if len(d['energy_kj']) > 1] + + if multi_point_stages: + n_stages = len(multi_point_stages) + + # Create inset axes for each stage + for i, (stage_key, stage_data) in enumerate(multi_point_stages): + times = np.array(stage_data['time_ps']) + energies = np.array(stage_data['energy_kj']) + color = stage_colors.get(stage_key, '#7f8c8d') + + # Calculate subplot position + width = 0.8 / n_stages + left = 0.1 + i * (0.85 / n_stages) + + # Create inset axis + ax_inset = ax_individual.inset_axes([left, 0.15, width * 0.9, 0.75]) + ax_inset.plot(times, energies, color=color, linewidth=1.2) + ax_inset.set_title(stage_data['stage'], fontsize=10, fontweight='bold') + ax_inset.set_xlabel('Time (ps)', fontsize=8) + ax_inset.set_ylabel('Energy (kJ/mol)', fontsize=8) + ax_inset.tick_params(axis='both', labelsize=7) + ax_inset.grid(True, alpha=0.3) + + # Add energy change annotation + energy_change = energies[-1] - energies[0] + change_text = f'ΔE = {energy_change:+.1f} kJ/mol' + ax_inset.annotate(change_text, xy=(0.5, 0.02), xycoords='axes fraction', + ha='center', fontsize=8, + color='green' if energy_change < 0 else 'red') + + # Hide the main bottom axis (we're using insets) + ax_individual.set_visible(False) + + plt.tight_layout() + + if output_path is None: + # Save to output_dir (defaults to PDB file's parent directory) + debug_plots_dir = self.output_dir / "debug_plots" + debug_plots_dir.mkdir(parents=True, exist_ok=True) + + pdb_stem = Path(self.pdb_path).stem + output_path = debug_plots_dir / f"{pdb_stem}_energy.png" + + plt.savefig(output_path, dpi=150, bbox_inches='tight') + + if show_plot: + plt.show() + else: + plt.close() + + def _validate_energy( + self, + energy_kj: float, + stage: str, + prev_energy: float | None = None, + n_atoms: int | None = None + ) -> list[str]: + """Validate energy values and return warnings if issues detected. + + Args: + energy_kj (float): Current potential energy in kJ/mol. + stage (str): Simulation stage name (e.g., 'NPT', 'NVT', 'Production'). + prev_energy (float, optional): Previous energy value for trend checking. + n_atoms (int, optional): Number of atoms for per-atom normalization. + + Returns: + list[str]: List of warning messages (empty if no issues). + """ + warnings_list = [] + + # Check for positive energy (system exploding) + if energy_kj > 0: + warnings_list.append( + f"WARNING [{stage}]: Positive potential energy ({energy_kj:.1f} kJ/mol) - " + "system may be unstable or exploding!" + ) + + # Check for sudden large energy increase + if prev_energy is not None: + delta = energy_kj - prev_energy + # Warning if energy increases by more than 10% + if prev_energy < 0 and delta > abs(prev_energy) * 0.1: + warnings_list.append( + f"WARNING [{stage}]: Large energy increase detected " + f"({delta:.1f} kJ/mol, {100*delta/abs(prev_energy):.1f}% change)" + ) + # Warning for very large jumps + if abs(delta) > 100000: # 100,000 kJ/mol jump + warnings_list.append( + f"WARNING [{stage}]: Very large energy change ({delta:.1f} kJ/mol) - " + "check for instabilities" + ) + + # Check per-atom energy if n_atoms provided + if n_atoms is not None and n_atoms > 0: + per_atom = energy_kj / n_atoms + # Typical range is -10 to -20 kJ/mol per atom + if per_atom > 0: + warnings_list.append( + f"WARNING [{stage}]: Positive per-atom energy ({per_atom:.2f} kJ/mol/atom)" + ) + elif per_atom < -50: + warnings_list.append( + f"WARNING [{stage}]: Unusually low per-atom energy ({per_atom:.2f} kJ/mol/atom) - " + "possible atomic overlaps" + ) + + return warnings_list + + def _validate_positions(self) -> bool: + """Validate that positions are finite (not NaN or inf). + + Returns: + bool: True if positions are valid, False if they contain NaN/inf. + """ + try: + if self.positions is None: + return False + + positions_array = self.positions.value_in_unit(nanometers) + return np.all(np.isfinite(positions_array)) + except Exception: + # If anything goes wrong during validation, assume positions are valid + # to avoid breaking the pipeline + return True + + def _fix_positions_if_needed(self) -> None: + """Check positions for NaN/inf values and fix them if found. + + If positions are corrupted, reverts to the original positions from setup. + """ + try: + if not self._validate_positions(): + print("WARNING: Positions contain NaN/inf values, attempting to fix") + # Try to revert to original or pre-NPT positions + if hasattr(self, '_pre_npt_positions') and self._pre_npt_positions is not None: + self.positions = self._pre_npt_positions + print(" Reverted to positions before NPT equilibration") + elif hasattr(self, '_original_positions') and self._original_positions is not None: + self.positions = self._original_positions + print(" Reverted to original positions after system setup") + else: + print(" WARNING: No backup positions available, continuing with current positions") + # Don't raise - just continue and hope for the best + + # Final validation (don't raise, just warn) + if not self._validate_positions(): + print(" WARNING: Positions still appear invalid after attempted fix") + except Exception as e: + # Don't let position validation failures crash the pipeline + print(f" WARNING: Position validation/fixing encountered error: {e}") + print(" Continuing anyway...") + # CRITICAL: Clear the exception traceback to avoid Context reference holding + self._clear_exception_traceback() + gc.collect() + + def _get_n_atoms(self) -> int: + """Get the total number of atoms in the system.""" + return sum(1 for _ in self.topology.atoms()) + + def equilibrate_npt( + self, + steps: int = DEFAULT_NPT_STEPS, + report_interval: int = DEFAULT_REPORTING_INTERVAL + ) -> None: + """Run NPT equilibration (constant pressure and temperature). + This is run before NVT equilibration. + + Args: + steps (int): Number of simulation steps. + report_interval (int): Interval for reporting state data. + """ + print(f"Running NPT equilibration for {steps} steps...") + + # Store positions before NPT as backup + self._pre_npt_positions = self.positions + + # Create simulation with barostat + self._create_new_simulation( + add_calpha_restraint=True, + add_barostat=True) + + # Add reporter for monitoring + self.simulation.reporters.append( + StateDataReporter( + sys.stdout, + report_interval, + step=True, + potentialEnergy=True, + temperature=True, + density=True, + separator='\t' + ) + ) + + # Get initial energy for monitoring + n_atoms = self._get_n_atoms() + initial_state = self.simulation.context.getState(getEnergy=True) + initial_energy = initial_state.getPotentialEnergy().value_in_unit(kilojoules_per_mole) + del initial_state # CRITICAL: Delete state to avoid holding Context reference + print(f" Initial potential energy: {initial_energy:.1f} kJ/mol " + f"({initial_energy/n_atoms:.2f} kJ/mol/atom)") + + # Track energies for validation + energy_history = [initial_energy] + check_interval = max(steps // 5, report_interval) # Check 5 times during equilibration + + # Calculate timestep in ps for energy logging + timestep_ps = self.timestep.value_in_unit(picoseconds) + + # Log initial energy (debug mode) + if self.debug: + self._log_energy('npt', 0.0, initial_energy) + + # Run equilibration in chunks for monitoring + steps_run = 0 + while steps_run < steps: + chunk = min(check_interval, steps - steps_run) + self.simulation.step(chunk) + steps_run += chunk + + # Get current energy + state = self.simulation.context.getState(getEnergy=True) + current_energy = state.getPotentialEnergy().value_in_unit(kilojoules_per_mole) + energy_history.append(current_energy) + + # Log energy (debug mode) + if self.debug: + time_ps = steps_run * timestep_ps + self._log_energy('npt', time_ps, current_energy) + + # Validate energy + warnings_list = self._validate_energy( + current_energy, 'NPT', + prev_energy=energy_history[-2] if len(energy_history) > 1 else None, + n_atoms=n_atoms + ) + for w in warnings_list: + warnings.warn(w) + print(f" {w}") + + # Final energy check + final_state = self.simulation.context.getState(getEnergy=True) + final_energy = final_state.getPotentialEnergy().value_in_unit(kilojoules_per_mole) + del final_state # CRITICAL: Delete state to avoid holding Context reference + print(f" Final potential energy: {final_energy:.1f} kJ/mol " + f"({final_energy/n_atoms:.2f} kJ/mol/atom)") + + # Check for energy explosion (indicates coordinate corruption) + if final_energy / n_atoms > 100 or final_energy / n_atoms > 0: + print(f"WARNING: NPT final energy is unreasonably high ({final_energy/n_atoms:.1f} kJ/mol/atom)") + print(" This may indicate coordinate corruption in the system") + # This will be detected and fixed at the start of NVT + + # Check overall trend + if final_energy > initial_energy: + warnings.warn( + f"NPT equilibration: Energy increased from {initial_energy:.1f} to {final_energy:.1f} kJ/mol" + ) + print(f" WARNING: Energy increased during NPT equilibration") + else: + print(f" Energy decreased by {initial_energy - final_energy:.1f} kJ/mol (good)") + + self._get_positions_and_cleanup() + + # Validate positions after NPT equilibration + if not self._validate_positions(): + print("WARNING: NPT equilibration produced invalid positions") + # Try to fix by reverting to pre-NPT positions + if hasattr(self, '_pre_npt_positions') and self._pre_npt_positions is not None: + self.positions = self._pre_npt_positions + print(" Reverted to positions before NPT equilibration") + else: + raise RuntimeError("NPT equilibration corrupted positions and no backup available") + + self.cleanup_all_resources(final_run=False) + + print("NPT equilibration complete.") + + def equilibrate_nvt( + self, + steps: int = DEFAULT_NVT_STEPS, + report_interval: int = DEFAULT_REPORTING_INTERVAL + ) -> None: + """Run NVT equilibration (constant volume and temperature). + This is run after NPT equilibration. + + Args: + steps (int): Number of simulation steps. + report_interval (int): Interval for reporting state data. + """ + print(f"Running NVT equilibration for {steps} steps...") + + # Validate positions before proceeding (check for NaN from previous NPT) + self._fix_positions_if_needed() + + # Create simulation with barostat + self._create_new_simulation( + add_calpha_restraint=True, + add_barostat=False) + + # Add reporter for monitoring + self.simulation.reporters.append( + StateDataReporter( + sys.stdout, + report_interval, + step=True, + potentialEnergy=True, + temperature=True, + separator='\t' + ) + ) + + # Get initial energy for monitoring + n_atoms = self._get_n_atoms() + initial_state = self.simulation.context.getState(getEnergy=True) + initial_energy = initial_state.getPotentialEnergy().value_in_unit(kilojoules_per_mole) + del initial_state # CRITICAL: Delete state to avoid holding Context reference + print(f" Initial potential energy: {initial_energy:.1f} kJ/mol " + f"({initial_energy/n_atoms:.2f} kJ/mol/atom)") + + # Sanity check: if energy is ridiculously high, coordinates are corrupted + # Typical folded protein energy is -10 to -30 kJ/mol/atom + # If it's > 0 or < -100, something is very wrong + if initial_energy / n_atoms > 100 or initial_energy / n_atoms > 0: + print(f"ERROR: Initial NVT energy is unreasonably high ({initial_energy/n_atoms:.1f} kJ/mol/atom)") + print(" This indicates corrupted particle coordinates") + if hasattr(self, '_pre_npt_positions') and self._pre_npt_positions is not None: + print(" Attempting to use pre-NPT positions...") + self.positions = self._pre_npt_positions + # Properly clean up the corrupted simulation before creating new one + self.cleanup_all_resources(final_run=False) + self._create_new_simulation( + add_calpha_restraint=True, + add_barostat=False) + # Try energy calculation again + initial_state = self.simulation.context.getState(getEnergy=True) + initial_energy = initial_state.getPotentialEnergy().value_in_unit(kilojoules_per_mole) + del initial_state + print(f" Retrying with pre-NPT positions: {initial_energy:.1f} kJ/mol ({initial_energy/n_atoms:.2f} kJ/mol/atom)") + if initial_energy / n_atoms > 100 or initial_energy / n_atoms > 0: + raise RuntimeError(f"Even pre-NPT positions are corrupted (energy: {initial_energy/n_atoms:.1f} kJ/mol/atom)") + else: + raise RuntimeError(f"Initial NVT energy is corrupted ({initial_energy/n_atoms:.1f} kJ/mol/atom) and no backup positions available") + + # Track energies for validation + energy_history = [initial_energy] + check_interval = max(steps // 5, report_interval) # Check 5 times during equilibration + + # Calculate timestep in ps for energy logging + timestep_ps = self.timestep.value_in_unit(picoseconds) + + # Log initial energy (debug mode) + if self.debug: + self._log_energy('nvt', 0.0, initial_energy) + + # Run equilibration in chunks for monitoring + steps_run = 0 + while steps_run < steps: + chunk = min(check_interval, steps - steps_run) + self.simulation.step(chunk) + steps_run += chunk + + # Get current energy + state = self.simulation.context.getState(getEnergy=True) + current_energy = state.getPotentialEnergy().value_in_unit(kilojoules_per_mole) + energy_history.append(current_energy) + + # Log energy (debug mode) + if self.debug: + time_ps = steps_run * timestep_ps + self._log_energy('nvt', time_ps, current_energy) + + # Validate energy + warnings_list = self._validate_energy( + current_energy, 'NVT', + prev_energy=energy_history[-2] if len(energy_history) > 1 else None, + n_atoms=n_atoms + ) + for w in warnings_list: + warnings.warn(w) + print(f" {w}") + + # Final energy check + final_state = self.simulation.context.getState(getEnergy=True) + final_energy = final_state.getPotentialEnergy().value_in_unit(kilojoules_per_mole) + print(f" Final potential energy: {final_energy:.1f} kJ/mol " + f"({final_energy/n_atoms:.2f} kJ/mol/atom)") + + # Check overall trend + if final_energy > initial_energy: + warnings.warn( + f"NVT equilibration: Energy increased from {initial_energy:.1f} to {final_energy:.1f} kJ/mol" + ) + print(f" WARNING: Energy increased during NVT equilibration") + else: + print(f" Energy decreased by {initial_energy - final_energy:.1f} kJ/mol (good)") + + self._get_positions_and_cleanup() + + self.cleanup_all_resources(final_run=False) + + print("NVT equilibration complete.") + + def equilibrate_nvt_with_warming( + self, + steps: int = DEFAULT_NVT_STEPS, + report_interval: int = DEFAULT_REPORTING_INTERVAL + ) -> None: + """Run NVT equilibration (constant volume and temperature). + This is run after NPT equilibration. + + Args: + steps (int): Number of simulation steps. + report_interval (int): Interval for reporting state data. + """ + print(f"Running NVT equilibration for {steps} steps...") + + # Validate positions before proceeding (check for NaN from previous NPT) + self._fix_positions_if_needed() + + # Create simulation without barostat but with CA constraints + self._create_new_simulation( + add_calpha_restraint=True, + add_barostat=False) + + # Slowly warm up temperature - every 1000 steps raise + # the temperature by 5 K + try: + self.simulation.context.setVelocitiesToTemperature(5*kelvin) + except Exception as e: + # Be more lenient - try the full temperature, then continue anyway + try: + self.simulation.context.setVelocitiesToTemperature(self.temperature) + except Exception as e2: + # If velocity setting fails completely, log warning but continue + # (velocities might be auto-initialized by the integrator) + print(f"WARNING: Failed to set initial velocities: {e2}") + print(" Continuing anyway with integrator-provided velocities") + + # Add reporter for monitoring + self.simulation.reporters.append( + StateDataReporter( + sys.stdout, + report_interval, + step=True, + potentialEnergy=True, + temperature=True, + separator='\t' + ) + ) + + # Get initial energy for monitoring + n_atoms = self._get_n_atoms() + initial_state = self.simulation.context.getState(getEnergy=True) + initial_energy = initial_state.getPotentialEnergy().value_in_unit(kilojoules_per_mole) + del initial_state + print(f" Initial potential energy: {initial_energy:.1f} kJ/mol " + f"({initial_energy/n_atoms:.2f} kJ/mol/atom)") + + # Sanity check: if energy is ridiculously high, coordinates are corrupted + # Typical folded protein energy is -10 to -30 kJ/mol/atom + if initial_energy / n_atoms > 100 or initial_energy / n_atoms > 0: + print(f"ERROR: Initial NVT energy is unreasonably high ({initial_energy/n_atoms:.1f} kJ/mol/atom)") + print(" This indicates corrupted particle coordinates") + if hasattr(self, '_pre_npt_positions') and self._pre_npt_positions is not None: + print(" Attempting to use pre-NPT positions...") + self.positions = self._pre_npt_positions + # Properly clean up the corrupted simulation before creating new one + self.cleanup_all_resources(final_run=False) + self._create_new_simulation( + add_calpha_restraint=True, + add_barostat=False) + # Try energy calculation again + initial_state = self.simulation.context.getState(getEnergy=True) + initial_energy = initial_state.getPotentialEnergy().value_in_unit(kilojoules_per_mole) + del initial_state + print(f" Retrying with pre-NPT positions: {initial_energy:.1f} kJ/mol ({initial_energy/n_atoms:.2f} kJ/mol/atom)") + if initial_energy / n_atoms > 100 or initial_energy / n_atoms > 0: + raise RuntimeError(f"Even pre-NPT positions are corrupted (energy: {initial_energy/n_atoms:.1f} kJ/mol/atom)") + else: + raise RuntimeError(f"Initial NVT energy is corrupted ({initial_energy/n_atoms:.1f} kJ/mol/atom) and no backup positions available") + + # Track energies for validation + energy_history = [initial_energy] + + # Calculate timestep in ps for energy logging + timestep_ps = self.timestep.value_in_unit(picoseconds) + + # Log initial energy (debug mode) + if self.debug: + self._log_energy('nvt', 0.0, initial_energy) + + T = 5 + n = 1000 + n_intervals = steps // n + energy_check_interval = max(n_intervals // 10, 1) # Check ~10 times + + steps_run = 0 + for i in range(n_intervals): + self.simulation.step(n) + steps_run += n + temperature = (T+(i*T))*kelvin + self.simulation.integrator.setTemperature(temperature) + + # Periodic energy validation + if (i + 1) % energy_check_interval == 0: + state = self.simulation.context.getState(getEnergy=True) + current_energy = state.getPotentialEnergy().value_in_unit(kilojoules_per_mole) + energy_history.append(current_energy) + + # Log energy (debug mode) + if self.debug: + time_ps = steps_run * timestep_ps + self._log_energy('nvt', time_ps, current_energy) + + # During heating, energy is expected to increase, but watch for instabilities + warnings_list = self._validate_energy( + current_energy, 'NVT', + prev_energy=None, # Don't warn about increase during heating + n_atoms=n_atoms + ) + for w in warnings_list: + warnings.warn(w) + print(f" {w}") + + # Final energy check + final_state = self.simulation.context.getState(getEnergy=True) + final_energy = final_state.getPotentialEnergy().value_in_unit(kilojoules_per_mole) + del final_state + print(f" Final potential energy: {final_energy:.1f} kJ/mol " + f"({final_energy/n_atoms:.2f} kJ/mol/atom)") + + # Note: Energy increase during NVT heating is expected + if final_energy > initial_energy: + print(f" Energy increased by {final_energy - initial_energy:.1f} kJ/mol " + f"(expected during temperature ramping from 5K to 300K)") + + # Check for very large energy (possible instability) + if final_energy > 0: + warnings.warn("NVT equilibration ended with positive energy - system may be unstable") + print(f" WARNING: Positive final energy detected!") + + self._get_positions_and_cleanup() + + self.cleanup_all_resources(final_run=False) + + print("NVT equilibration complete.") + + def run_production( + self, + steps: int = DEFAULT_PRODUCTION_STEPS, + energy_calc_interval: int = 10000, + subtract_solvent: bool = True + ) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]: + """Run production MD and calculate residue-residue interaction energies and forces. + + This method runs an MD simulation and periodically calculates the + pairwise interaction energies and forces between all protein residues. + + Args: + steps (int): Number of production steps. + energy_calc_interval (int): Interval (in steps) for calculating + interaction energies. + subtract_solvent (bool): Whether to subtract solvent interaction energies. + + Returns: + tuple[np.ndarray, ...]: A tuple of four NxN matrices: + - vdw_attractive: Attractive VdW energies (kJ/mol) + - vdw_repulsive: Repulsive VdW energies (kJ/mol) + - es_attractive: Attractive electrostatic energies (kJ/mol) + - es_repulsive: Repulsive electrostatic energies (kJ/mol) + """ + print(f"Running production MD for {steps} steps...") + + # Create fresh simulation for production + self._create_new_simulation( + hbonds_constraint=False, + add_calpha_restraint=False, + add_barostat=False) + + n_residues = len(self.protein_residue_indices) + n_frames = steps // energy_calc_interval + n_atoms = self._get_n_atoms() + + report_interval = max(steps // 10, self.DEFAULT_REPORTING_INTERVAL) + # Add reporter for monitoring + self.simulation.reporters.append( + StateDataReporter( + sys.stdout, + report_interval, + step=True, + potentialEnergy=True, + temperature=True, + separator='\t' + ) + ) + + # Get initial energy + initial_state = self.simulation.context.getState(getEnergy=True) + initial_energy = initial_state.getPotentialEnergy().value_in_unit(kilojoules_per_mole) + del initial_state + print(f" Initial potential energy: {initial_energy:.1f} kJ/mol " + f"({initial_energy/n_atoms:.2f} kJ/mol/atom)") + + # Track energy statistics + energy_history = [initial_energy] + energy_min = initial_energy + energy_max = initial_energy + + # Calculate timestep in ps for energy logging + timestep_ps = self.timestep.value_in_unit(picoseconds) + + # Log initial energy (debug mode) + if self.debug: + self._log_energy('production', 0.0, initial_energy) + + # Accumulators for energies + vdw_energy_attractive_sum = np.zeros((n_residues, n_residues)) + vdw_energy_repulsive_sum = np.zeros((n_residues, n_residues)) + es_energy_attractive_sum = np.zeros((n_residues, n_residues)) + es_energy_repulsive_sum = np.zeros((n_residues, n_residues)) + + steps_run = 0 + frame_count = 0 + for _ in range(0, steps, energy_calc_interval): + # Run simulation chunk + self.simulation.step(energy_calc_interval) + steps_run += energy_calc_interval + + # Get current state with positions and energy + state = self.simulation.context.getState(getPositions=True, getEnergy=True) + positions = state.getPositions(asNumpy=True) + current_energy = state.getPotentialEnergy().value_in_unit(kilojoules_per_mole) + del state # Release Context reference before the expensive per-residue calculation + + # Track energy statistics + energy_history.append(current_energy) + energy_min = min(energy_min, current_energy) + energy_max = max(energy_max, current_energy) + + # Log energy (debug mode) + if self.debug: + time_ps = steps_run * timestep_ps + self._log_energy('production', time_ps, current_energy) + + # Validate energy periodically + if frame_count % 10 == 0: + warnings_list = self._validate_energy( + current_energy, 'Production', + prev_energy=energy_history[-2] if len(energy_history) > 1 else None, + n_atoms=n_atoms + ) + for w in warnings_list: + warnings.warn(w) + print(f" {w}") + + # Calculate pairwise energies and forces for this frame + (vdw_e_att, vdw_e_rep, es_e_att, es_e_rep) = self._calculate_pairwise_energies( + positions=positions, + subtract_solvent=subtract_solvent + ) + + # # Calculate pairwise energies and forces for this frame + # (vdw_e_att, vdw_e_rep, es_e_att, es_e_rep) = self._calculate_pairwise_energies_get_potential( + # positions + # ) + # vdw_f_att, vdw_f_rep, es_f_att, es_f_rep = 1,1,1,1 # Placeholder for forces - need to implement force calculation + + # Accumulate energies + vdw_energy_attractive_sum += vdw_e_att + vdw_energy_repulsive_sum += vdw_e_rep + es_energy_attractive_sum += es_e_att + es_energy_repulsive_sum += es_e_rep + + frame_count+=1 + + # Periodic garbage collection to free memory from temporary objects + if frame_count % 5 == 0: + gc.collect() + + # Final energy statistics + final_energy = energy_history[-1] + mean_energy = np.mean(energy_history) + std_energy = np.std(energy_history) + + print(f"\n Production MD Energy Statistics:") + print(f" Initial: {initial_energy:.1f} kJ/mol") + print(f" Final: {final_energy:.1f} kJ/mol") + print(f" Mean: {mean_energy:.1f} ± {std_energy:.1f} kJ/mol") + print(f" Range: [{energy_min:.1f}, {energy_max:.1f}] kJ/mol") + print(f" Per-atom mean: {mean_energy/n_atoms:.2f} kJ/mol/atom") + + # Warn if energy fluctuations are very large + if std_energy > abs(mean_energy) * 0.05: # >5% relative fluctuation + warnings.warn( + f"Large energy fluctuations during production: std={std_energy:.1f} kJ/mol " + f"({100*std_energy/abs(mean_energy):.1f}% of mean)" + ) + print(f" WARNING: Large energy fluctuations detected") + + # Average over frames + vdw_energy_attractive_avg = vdw_energy_attractive_sum / frame_count + vdw_energy_repulsive_avg = vdw_energy_repulsive_sum / frame_count + es_energy_attractive_avg = es_energy_attractive_sum / frame_count + es_energy_repulsive_avg = es_energy_repulsive_sum / frame_count + + print("Production MD complete.") + + return [vdw_energy_attractive_avg, vdw_energy_repulsive_avg, es_energy_attractive_avg, es_energy_repulsive_avg] + + + def _get_context(self) -> Context: + """ + Create the integrator and (re)set up the simulation Context. + """ + integrator = LangevinMiddleIntegrator(self.DEFAULT_TEMPERATURE, + self.DEFAULT_FRICTION_COEFFICIENT / picosecond, + self.DEFAULT_TIMESTEP) + context = Context(self.system, + integrator, + Platform.getPlatformByName(self._get_platform()), + self.platform_properties) + return context.setPositions(self.positions) + + + def _energy_calculation(self, + solute_coulomb_scale, + solute_lj_scale, + solvent_coulomb_scale, + solvent_lj_scale): + """ + Calculate the energy with a new Context. + """ + context = self._get_context() + context.setParameter("solute_coulomb_scale", solute_coulomb_scale) + context.setParameter("solute_lj_scale", solute_lj_scale) + context.setParameter("solvent_coulomb_scale", solvent_coulomb_scale) + context.setParameter("solvent_lj_scale", solvent_lj_scale) + energy = context.getState(getEnergy=True, groups={0}).getPotentialEnergy() + del context # Clean up Context to free memory + return energy + + + def _get_vdw_and_electrostatic_energy(self): + """ + Now we can evaluate the interaction energies by subtracting internal + energies from the total energy for each type, Coulomb and Lennard-Jones. + """ + total_coulomb = self._energy_calculation(1, 0, 1, 0) + solute_coulomb = self._energy_calculation(1, 0, 0, 0) + solvent_coulomb = self._energy_calculation(0, 0, 1, 0) + total_lj = self._energy_calculation(0, 1, 0, 1) + solute_lj = self._energy_calculation(0, 1, 0, 0) + solvent_lj = self._energy_calculation(0, 0, 0, 1) + vdw_interaction_energy = total_lj - solute_lj - solvent_lj + electrostatic_interaction_energy = total_coulomb - solute_coulomb -solvent_coulomb + return vdw_interaction_energy, electrostatic_interaction_energy + + def _calculate_residue_solvent_energies( + self, + positions: np.ndarray + ) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]: + """Calculate per-residue interaction energies with solvent using numpy. + + Computes the same LJ and Coulomb interactions as the previous + CustomNonbondedForce / NoCutoff / interaction-group approach but + entirely in vectorised numpy — no temporary OpenMM Contexts are + created, so there is no C++ heap fragmentation. + + On CPU (the typical path here) this is faster than the OpenMM approach + because OpenMM's per-Context setup overhead (kernel compilation, N-particle + array allocation) dominates the actual ~10-atom × ~29,000-solvent pair work. + + LJ: 4*eps*((sigma/r)^12 - (sigma/r)^6) + sigma = 0.5*(sigma_i + sigma_j), eps = sqrt(eps_i * eps_j) + Coulomb: 138.935456 * q_i * q_j / r (kJ·nm / (mol·e²)) + + Args: + positions: Atomic positions — plain numpy array (nm) or OpenMM Quantity. + + Returns: + tuple: Four N-length arrays (one value per protein residue): + - vdw_energy_solvent: VdW energy with solvent (kJ/mol) + - es_energy_solvent: Electrostatic energy with solvent (kJ/mol) + - vdw_force_solvent: |VdW energy| / n_atoms proxy (kJ/(mol·nm)) + - es_force_solvent: |ES energy| / n_atoms proxy (kJ/(mol·nm)) + """ + # Extract plain float64 array in nm (handles both numpy and OpenMM Quantity) + if hasattr(positions, 'value_in_unit'): + pos_nm = np.asarray(positions.value_in_unit(nanometer), dtype=np.float64) + else: + pos_nm = np.asarray(positions, dtype=np.float64) + + n_residues = len(self.protein_residue_indices) + vdw_energy_solvent = np.zeros(n_residues) + es_energy_solvent = np.zeros(n_residues) + vdw_force_solvent = np.zeros(n_residues) + es_force_solvent = np.zeros(n_residues) + + # Read per-atom force-field parameters once + nonbonded_force = None + for force in self.system.getForces(): + if isinstance(force, NonbondedForce): + nonbonded_force = force + break + if nonbonded_force is None: + raise RuntimeError("No NonbondedForce found in system") + + n_particles = nonbonded_force.getNumParticles() + sigma_all = np.empty(n_particles, dtype=np.float64) + epsilon_all = np.empty(n_particles, dtype=np.float64) + charge_all = np.empty(n_particles, dtype=np.float64) + for idx in range(n_particles): + q, s, e = nonbonded_force.getParticleParameters(idx) + sigma_all[idx] = s.value_in_unit(nanometer) + epsilon_all[idx] = e.value_in_unit(kilojoules_per_mole) + charge_all[idx] = q.value_in_unit(elementary_charge) + + # Solvent + ion indices as a sorted numpy array + solvent_indices = np.array( + sorted(self.solvent_atom_indices | self.ion_atom_indices), dtype=np.int64) + + # Pre-extract solvent data — reused for every residue + pos_solv = pos_nm[solvent_indices] # (n_solv, 3) + sigma_solv = sigma_all[solvent_indices] # (n_solv,) + epsilon_solv = epsilon_all[solvent_indices] # (n_solv,) + charge_solv = charge_all[solvent_indices] # (n_solv,) + + k_coulomb = 138.935456 # kJ·nm/(mol·e²) + r_min = 0.1 # nm — skip pairs < 1 Å to avoid singularity + + for i, res_i in enumerate(self.protein_residue_indices): + res_idx = np.array(list(self.residue_atom_indices[res_i]), dtype=np.int64) + n_atoms = len(res_idx) if len(res_idx) > 0 else 1 + + pos_res = pos_nm[res_idx] # (n_res, 3) + sigma_res = sigma_all[res_idx] # (n_res,) + epsilon_res = epsilon_all[res_idx] # (n_res,) + charge_res = charge_all[res_idx] # (n_res,) + + # Pairwise distance matrix (n_res, n_solv) — use einsum to avoid + # keeping the full 3-D difference array in memory + diff = pos_res[:, np.newaxis, :] - pos_solv[np.newaxis, :, :] + r2 = np.einsum('ijk,ijk->ij', diff, diff) + del diff + r = np.sqrt(r2); del r2 + valid = r > r_min + r_safe = np.where(valid, r, 1.0) # sentinel to avoid divide-by-zero + + # Lennard-Jones (Lorentz-Berthelot combining rules) + sigma_ij = 0.5 * (sigma_res[:, np.newaxis] + sigma_solv[np.newaxis, :]) + epsilon_ij = np.sqrt(np.abs(epsilon_res[:, np.newaxis] * epsilon_solv[np.newaxis, :])) + sr6 = (sigma_ij / r_safe) ** 6 + lj_energy = float(np.sum(np.where(valid, 4.0 * epsilon_ij * (sr6 * sr6 - sr6), 0.0))) + del sigma_ij, epsilon_ij, sr6 + + # Coulomb + q_ij = charge_res[:, np.newaxis] * charge_solv[np.newaxis, :] + coulomb_energy = float(np.sum(np.where(valid, k_coulomb * q_ij / r_safe, 0.0))) + del q_ij, valid, r, r_safe + + vdw_energy_solvent[i] = lj_energy / n_atoms + es_energy_solvent[i] = coulomb_energy / n_atoms + vdw_force_solvent[i] = abs(lj_energy) / n_atoms + es_force_solvent[i] = abs(coulomb_energy) / n_atoms + + return vdw_energy_solvent, es_energy_solvent, vdw_force_solvent, es_force_solvent + + def _calculate_residue_solvent_energy_for_residue(self, res_i: int, positions: np.ndarray) -> tuple[float, float]: + """Compute solvent interaction energies for a single residue using numpy. + + Returns (vdw_energy, es_energy) normalised by number of atoms in the residue. + """ + if hasattr(positions, 'value_in_unit'): + pos_nm = np.asarray(positions.value_in_unit(nanometer), dtype=np.float64) + else: + pos_nm = np.asarray(positions, dtype=np.float64) + + nonbonded_force = None + for force in self.system.getForces(): + if isinstance(force, NonbondedForce): + nonbonded_force = force + break + if nonbonded_force is None: + raise RuntimeError("No NonbondedForce found in system") + + n_particles = nonbonded_force.getNumParticles() + sigma_all = np.empty(n_particles, dtype=np.float64) + epsilon_all = np.empty(n_particles, dtype=np.float64) + charge_all = np.empty(n_particles, dtype=np.float64) + for idx in range(n_particles): + q, s, e = nonbonded_force.getParticleParameters(idx) + sigma_all[idx] = s.value_in_unit(nanometer) + epsilon_all[idx] = e.value_in_unit(kilojoules_per_mole) + charge_all[idx] = q.value_in_unit(elementary_charge) + + solvent_indices = np.array( + sorted(self.solvent_atom_indices | self.ion_atom_indices), dtype=np.int64) + res_idx = np.array(list(self.residue_atom_indices[res_i]), dtype=np.int64) + n_atoms = len(res_idx) if len(res_idx) > 0 else 1 + + pos_res = pos_nm[res_idx] + pos_solv = pos_nm[solvent_indices] + sigma_res = sigma_all[res_idx]; sigma_solv = sigma_all[solvent_indices] + epsilon_res = epsilon_all[res_idx]; epsilon_solv = epsilon_all[solvent_indices] + charge_res = charge_all[res_idx]; charge_solv = charge_all[solvent_indices] + + k_coulomb = 138.935456 + r_min = 0.1 + + diff = pos_res[:, np.newaxis, :] - pos_solv[np.newaxis, :, :] + r = np.sqrt(np.einsum('ijk,ijk->ij', diff, diff)); del diff + valid = r > r_min + r_safe = np.where(valid, r, 1.0) + + sigma_ij = 0.5 * (sigma_res[:, np.newaxis] + sigma_solv[np.newaxis, :]) + epsilon_ij = np.sqrt(np.abs(epsilon_res[:, np.newaxis] * epsilon_solv[np.newaxis, :])) + sr6 = (sigma_ij / r_safe) ** 6 + lj_energy = float(np.sum(np.where(valid, 4.0 * epsilon_ij * (sr6 * sr6 - sr6), 0.0))) + + q_ij = charge_res[:, np.newaxis] * charge_solv[np.newaxis, :] + coulomb_energy = float(np.sum(np.where(valid, k_coulomb * q_ij / r_safe, 0.0))) + + return lj_energy / n_atoms, coulomb_energy / n_atoms + + def _calculate_pairwise_energies( + self, + positions: np.ndarray, + subtract_solvent: bool = True + ) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]: + """Calculate pairwise residue-residue interaction energies and forces. + + Uses Lennard-Jones potential for VdW and Coulomb's law for electrostatics. + Separates attractive and repulsive contributions for both energies. + + If subtract_solvent=True, the residue-solvent interaction energies are + subtracted from the diagonal elements, providing a measure of the + desolvation penalty for each residue pair interaction. + + WARNING: subtract_solvent=True is computationally expensive as it + iterates over all solvent atoms for each protein residue. + + Args: + positions (np.ndarray): Current atomic positions in nanometers. + subtract_solvent (bool): If True, subtract residue-solvent energies + from the pairwise energies. This approximates the desolvation + penalty when two residues interact. Defaults to False. + + Returns: + tuple: Eight NxN matrices: + - vdw_energy_attractive: Attractive VdW energies (kJ/mol) + - vdw_energy_repulsive: Repulsive VdW energies (kJ/mol) + - es_energy_attractive: Attractive electrostatic energies (kJ/mol) + - es_energy_repulsive: Repulsive electrostatic energies (kJ/mol) + """ + n_residues = len(self.protein_residue_indices) + + # Calculate residue-solvent energies if needed for subtraction + if subtract_solvent: + if self.debug: + process = psutil.Process() + mem_before_solvent = process.memory_info().rss / 1024 / 1024 + (vdw_solv, es_solv, + vdw_force_solv, es_force_solv) = self._calculate_residue_solvent_energies(positions) + if self.debug: + mem_after_solvent = process.memory_info().rss / 1024 / 1024 + print(f"Solvent energy computation: {mem_before_solvent:.1f} -> {mem_after_solvent:.1f} MB (delta: {mem_after_solvent - mem_before_solvent:.1f} MB)") + + # Energy matrices + vdw_energy_attractive = np.zeros((n_residues, n_residues)) + vdw_energy_repulsive = np.zeros((n_residues, n_residues)) + es_energy_attractive = np.zeros((n_residues, n_residues)) + es_energy_repulsive = np.zeros((n_residues, n_residues)) + + # Get nonbonded force parameters from the system + nonbonded_force = None + for force in self.system.getForces(): + if isinstance(force, NonbondedForce): + nonbonded_force = force + break + + if nonbonded_force is None: + raise RuntimeError("No NonbondedForce found in system") + + # Cache particle parameters to avoid repeated OpenMM calls (major memory/speed optimization) + if not hasattr(self, '_cached_particle_params') or self._cached_particle_params is None: + self._cached_particle_params = {} + for atom_idx in range(nonbonded_force.getNumParticles()): + charge, sigma, epsilon = nonbonded_force.getParticleParameters(atom_idx) + self._cached_particle_params[atom_idx] = ( + charge.value_in_unit(elementary_charge), + sigma.value_in_unit(nanometer), + epsilon.value_in_unit(kilojoules_per_mole) + ) + + # Coulomb constant in OpenMM units (kJ·nm/(mol·e²)) + k_coulomb = 138.935456 + + # Calculate pairwise energies between protein residues + for i, res_i in enumerate(self.protein_residue_indices): + atoms_i = self.residue_atom_indices[res_i] + + for j, res_j in enumerate(self.protein_residue_indices): + if j <= i: + continue # Only upper triangle + + atoms_j = self.residue_atom_indices[res_j] + + # Energy accumulators + vdw_energy_att_ij = 0.0 + vdw_energy_rep_ij = 0.0 + es_energy_att_ij = 0.0 + es_energy_rep_ij = 0.0 + + for atom_i in atoms_i: + charge_i, sigma_i, epsilon_i = self._cached_particle_params[atom_i] + pos_i = positions[atom_i] + + for atom_j in atoms_j: + charge_j, sigma_j, epsilon_j = self._cached_particle_params[atom_j] + pos_j = positions[atom_j] + + # Calculate distance + r_vec = pos_j - pos_i + r = np.sqrt(np.sum(r_vec**2)) + + if r < 0.1: # Skip if too close (< 1 Angstrom) + continue + + # Lennard-Jones combining rules (Lorentz-Berthelot) + sigma_ij = (sigma_i + sigma_j) / 2 + epsilon_ij = np.sqrt(epsilon_i * epsilon_j) + + # LJ potential: 4*eps*[(sigma/r)^12 - (sigma/r)^6] + sigma_over_r = sigma_ij / r + sr6 = sigma_over_r ** 6 + sr12 = sr6 ** 2 + + # LJ Energy terms + # Repulsive term (r^-12) + vdw_energy_rep_ij += 4 * epsilon_ij * sr12 + # Attractive term (r^-6) + vdw_energy_att_ij += -4 * epsilon_ij * sr6 + + # Coulomb potential: k * q1 * q2 / r + es_energy = k_coulomb * charge_i * charge_j / r + + if es_energy > 0: + es_energy_rep_ij += es_energy + else: + es_energy_att_ij += es_energy + + # Normalize by number of atom pairs + norm_factor = len(atoms_i) * len(atoms_j) + vdw_energy_att_ij /= norm_factor + vdw_energy_rep_ij /= norm_factor + es_energy_att_ij /= norm_factor + es_energy_rep_ij /= norm_factor + + # Store in matrices (upper triangle) + vdw_energy_attractive[i, j] = vdw_energy_att_ij + vdw_energy_repulsive[i, j] = vdw_energy_rep_ij + es_energy_attractive[i, j] = es_energy_att_ij + es_energy_repulsive[i, j] = es_energy_rep_ij + + if subtract_solvent: + if getattr(self, 'memory_efficient', False): + if self.debug: + process = psutil.Process() + mem_before_efficient = process.memory_info().rss / 1024 / 1024 + # Memory-efficient on-demand per-residue solvent energy calculation + vdw_cache = {} + es_cache = {} + for i in range(n_residues): + for j in range(i + 1, n_residues): + if i not in vdw_cache: + res_idx_i = self.protein_residue_indices[i] + vdw_cache[i], es_cache[i] = self._calculate_residue_solvent_energy_for_residue(res_idx_i, positions) + if j not in vdw_cache: + res_idx_j = self.protein_residue_indices[j] + vdw_cache[j], es_cache[j] = self._calculate_residue_solvent_energy_for_residue(res_idx_j, positions) + + avg_vdw_solv = (vdw_cache[i] + vdw_cache[j]) / 2.0 + avg_es_solv = (es_cache[i] + es_cache[j]) / 2.0 + + vdw_energy_attractive[i, j] -= avg_vdw_solv + es_energy_attractive[i, j] -= avg_es_solv + + # Clear caches to free memory + del vdw_cache + del es_cache + gc.collect() + if self.debug: + mem_after_efficient = process.memory_info().rss / 1024 / 1024 + print(f"Memory-efficient solvent subtraction: {mem_before_efficient:.1f} -> {mem_after_efficient:.1f} MB (delta: {mem_after_efficient - mem_before_efficient:.1f} MB)") + else: + if self.debug: + process = psutil.Process() + mem_before_standard = process.memory_info().rss / 1024 / 1024 + for i in range(n_residues): + for j in range(i + 1, n_residues): + # Average the solvent energies of both residues involved + # This represents the "desolvation cost" of forming this contact + avg_vdw_solv = (vdw_solv[i] + vdw_solv[j]) / 2 + avg_es_solv = (es_solv[i] + es_solv[j]) / 2 + + # Subtract from attractive terms (solvent interaction is typically stabilizing) + vdw_energy_attractive[i, j] -= avg_vdw_solv + es_energy_attractive[i, j] -= avg_es_solv + if self.debug: + mem_after_standard = process.memory_info().rss / 1024 / 1024 + print(f"Standard solvent subtraction: {mem_before_standard:.1f} -> {mem_after_standard:.1f} MB (delta: {mem_after_standard - mem_before_standard:.1f} MB)") + + return (vdw_energy_attractive, vdw_energy_repulsive, es_energy_attractive, es_energy_repulsive) + + def _calculate_pairwise_energies_get_potential(self) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]: + """Calculate pairwise residue-residue interaction energies using OpenMM's getPotentialEnergy. + + This method uses OpenMM's parameter offset feature to compute interaction + energies directly, rather than manually calculating LJ and Coulomb potentials. + This approach follows the OpenMM Cookbook methodology for computing interaction + energies between groups of atoms. + + The interaction energy is computed as: + E_interaction = E_total(A+B) - E_internal(A) - E_internal(B) + + This method separates Coulomb and Lennard-Jones contributions using + separate parameter scales for each. + + Note: + This method creates a new system and context for each residue pair, + which is computationally expensive but accurate. For large proteins, + consider using the manual calculation method instead. + + Args: + positions (np.ndarray): Current atomic positions in nanometers. + + Returns: + tuple[np.ndarray, np.ndarray]: Two NxN matrices (only upper triangle filled): + - lj_interaction_energies: Lennard-Jones interaction energies (kJ/mol) + - coulomb_interaction_energies: Coulomb interaction energies (kJ/mol) + """ + n_residues = len(self.protein_residue_indices) + + # Initialize energy matrices + vdw_energy_attractive = np.zeros((n_residues, n_residues)) + vdw_energy_repulsive = np.zeros((n_residues, n_residues)) + es_energy_attractive = np.zeros((n_residues, n_residues)) + es_energy_repulsive = np.zeros((n_residues, n_residues)) + + # Get all protein atom indices for reference + all_protein_atoms = set() + for res_idx in self.protein_residue_indices: + all_protein_atoms.update(self.residue_atom_indices[res_idx]) + + print(f"Calculating pairwise energies for {n_residues} residues...") + total_pairs = n_residues * (n_residues - 1) // 2 + # pair_count = 0 + + # Calculate pairwise energies between protein residues + for i, res_i in enumerate(self.protein_residue_indices): + atoms_i = set(self.residue_atom_indices[res_i]) + + for j, res_j in enumerate(self.protein_residue_indices): + if j <= i: + continue # Only upper triangle + + atoms_j = set(self.residue_atom_indices[res_j]) + + # Create a fresh system for this residue pair calculation + system = self.forcefield.createSystem( + self.topology, + nonbondedMethod=PME, + nonbondedCutoff=1.0 * nanometer, + constraints=None + ) + + # Find and modify the NonbondedForce + for force in system.getForces(): + if isinstance(force, NonbondedForce): + force.setForceGroup(0) + force.setUseDispersionCorrection(False) + + # Add global parameters for scaling + force.addGlobalParameter("res_i_coulomb_scale", 1) + force.addGlobalParameter("res_i_lj_scale", 1) + force.addGlobalParameter("res_j_coulomb_scale", 1) + force.addGlobalParameter("res_j_lj_scale", 1) + + # Set up parameter offsets for each particle + for atom_idx in range(force.getNumParticles()): + charge, sigma, epsilon = force.getParticleParameters(atom_idx) + + # Zero out all parameters first + force.setParticleParameters(atom_idx, 0, 0, 0) + + # Add parameter offsets based on which residue the atom belongs to + if atom_idx in atoms_i: + force.addParticleParameterOffset( + "res_i_coulomb_scale", atom_idx, charge, 0*nanometer, 0*kilojoules_per_mole + ) + force.addParticleParameterOffset( + "res_i_lj_scale", atom_idx, 0*elementary_charge, sigma, epsilon + ) + elif atom_idx in atoms_j: + force.addParticleParameterOffset( + "res_j_coulomb_scale", atom_idx, charge, 0*nanometer, 0*kilojoules_per_mole + ) + force.addParticleParameterOffset( + "res_j_lj_scale", atom_idx, 0*elementary_charge, sigma, epsilon + ) + # Other atoms (water, ions, other residues) are zeroed out + + # Zero out all exceptions (intra-residue interactions) + for exc_idx in range(force.getNumExceptions()): + p1, p2, chargeProd, sigma, epsilon = force.getExceptionParameters(exc_idx) + force.setExceptionParameters(exc_idx, p1, p2, 0, 0, 0) + else: + # Put other forces in a different group so they're not evaluated + force.setForceGroup(2) + + # Create context for energy evaluation + integrator = VerletIntegrator(0.001 * picoseconds) + platform = self._get_platform() + context = Context(system, integrator, platform) + context.setPositions(self.positions) + + # Define energy calculation helper + def get_energy(res_i_coulomb, res_i_lj, res_j_coulomb, res_j_lj): + context.setParameter("res_i_coulomb_scale", res_i_coulomb) + context.setParameter("res_i_lj_scale", res_i_lj) + context.setParameter("res_j_coulomb_scale", res_j_coulomb) + context.setParameter("res_j_lj_scale", res_j_lj) + st = context.getState(getEnergy=True, groups={0}) + val = st.getPotentialEnergy() + del st + return val + + # Calculate Coulomb interaction energy + # E_interaction = E_total(i+j) - E_internal(i) - E_internal(j) + total_coulomb = get_energy(1, 0, 1, 0) + res_i_coulomb = get_energy(1, 0, 0, 0) + res_j_coulomb = get_energy(0, 0, 1, 0) + coulomb_interaction = total_coulomb - res_i_coulomb - res_j_coulomb + + # Calculate LJ interaction energy + total_lj = get_energy(0, 1, 0, 1) + res_i_lj = get_energy(0, 1, 0, 0) + res_j_lj = get_energy(0, 0, 0, 1) + lj_interaction = total_lj - res_i_lj - res_j_lj + + # Store results (convert to float, remove units) + lj_interaction_energy = lj_interaction.value_in_unit(kilojoules_per_mole) + coulomb_interaction_energy = coulomb_interaction.value_in_unit(kilojoules_per_mole) + + if lj_interaction_energy < 0: + vdw_energy_attractive[i, j] = lj_interaction_energy + else: + vdw_energy_repulsive[i, j] = lj_interaction_energy + + if coulomb_interaction_energy < 0: + es_energy_attractive[i, j] = coulomb_interaction_energy + else: + es_energy_repulsive[i, j] = coulomb_interaction_energy + + # pair_count += 1 + # if pair_count % 100 == 0: + # print(f" Processed {pair_count}/{total_pairs} residue pairs...") + + # Clean up context and system to free memory + del context + del integrator + del system + + # Periodic garbage collection for CUDA memory + if j % 20 == 0: + gc.collect() + + # Force garbage collection after all pairs processed + gc.collect() + + print(f"Completed pairwise energy calculations for {total_pairs} pairs.") + + return (vdw_energy_attractive, vdw_energy_repulsive, es_energy_attractive, es_energy_repulsive) + + def run_full_pipeline( + self, + npt_steps: int = DEFAULT_NPT_STEPS, + nvt_steps: int = DEFAULT_NVT_STEPS, + production_steps: int = DEFAULT_PRODUCTION_STEPS, + energy_calc_interval: int = 10000, + return_simulated_pdb: bool = False, + subtract_solvent_energies: bool = True, + debug: bool = False + ) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, + np.ndarray, np.ndarray, np.ndarray, np.ndarray] | \ + tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, + np.ndarray, np.ndarray, np.ndarray, np.ndarray, io.StringIO]: + """Run the complete MD simulation pipeline. + + This is a convenience method that runs all steps: + 1. System setup (fix PDB, solvate, ionize) + 2. Energy minimization + 3. NPT equilibration + 4. NVT equilibration + 5. Production MD with energy and force calculations + + Args: + npt_steps (int): Number of NPT equilibration steps. + nvt_steps (int): Number of NVT equilibration steps. + production_steps (int): Number of production MD steps. + energy_calc_interval (int): Interval for calculating energies/forces. + return_simulated_pdb (bool): If True, return the final production + structure as a PDB stream. Defaults to False. + debug (bool): If True, print additional information during the pipeline + such as energy statistics and graphs. + + Returns: + tuple[np.ndarray, ...]: Eight NxN matrices (or nine items if + return_simulated_pdb is True): + - vdw_attractive: Attractive VdW energies (kJ/mol) + - vdw_repulsive: Repulsive VdW energies (kJ/mol) + - es_attractive: Attractive electrostatic energies (kJ/mol) + - es_repulsive: Repulsive electrostatic energies (kJ/mol) + - production_pdb (io.StringIO): Final production structure as PDB + stream (only if return_simulated_pdb=True) + """ + print("=" * 60) + print("Starting full MD simulation pipeline") + print("=" * 60) + + # Set debug mode for energy logging and plotting + self.debug = debug + + # Step 1: Setup system + print("\n[Step 1/5] Setting up system...") + self.setup_system() + + # Step 2: Energy minimization of system + print("\n[Step 2/5] Energy minimization...") + self._create_new_simulation( + add_barostat=False, add_calpha_restraint=True) + self.minimize_energy() + + # Step 3: NPT equilibration with new system including barostat force + print("\n[Step 3/5] NPT equilibration...") + self.equilibrate_npt(steps=npt_steps) + + # Step 4: NVT equilibration using same system without barostat force + print("\n[Step 4/5] NVT equilibration...") + self.equilibrate_nvt_with_warming(steps=nvt_steps) + + # Step 5: Production MD using new system and simulation with energy calculations + print("\n[Step 5/5] Production MD...") + results = self.run_production( + steps=production_steps, + energy_calc_interval=energy_calc_interval, + subtract_solvent=subtract_solvent_energies) + + # Capture production structure if requested + production_pdb_stream = None + if return_simulated_pdb: + production_pdb_stream = self.get_simulated_pdb_stream() + + print("\n" + "=" * 60) + print("Pipeline complete!") + print("=" * 60) + + # Generate energy plots if debug mode is enabled + if self.debug: + print("\nGenerating energy plots...") + self.plot_energy_history() + print("Energy plots saved to debug_plots directory") + + # Clean up to free memory + self.cleanup_all_resources(final_run=True) + + if return_simulated_pdb: + results.append(production_pdb_stream) + return results + diff --git a/proteogram/proteogram_v2.py b/proteogram/proteogram_v2.py new file mode 100644 index 0000000..aa78e3f --- /dev/null +++ b/proteogram/proteogram_v2.py @@ -0,0 +1,302 @@ +"""Proteogram V2 module for protein structure analysis and visualization. + +This module defines the ProteogramV2 class, which provides methods for generating proteogram maps from PDB files. The proteogram maps include distance, hydrophobicity, +Van der Waals, and electrostatic interaction maps. The class also integrates with the NonBondedForceModel module to perform molecular dynamics simulations for calculating the non-bonded interaction energies.""" + +import numpy as np +import warnings +import gc + +from Bio.PDB.PDBParser import PDBParser, PDBConstructionWarning +from Bio.PDB.Polypeptide import PPBuilder + +from .constants import HYDROPHOBICITY_LIST, RESIDUE_LIST +from .nonbonded_forces import NonBondedForceModel + + +# Ignore PDB construction warnings +warnings.filterwarnings("ignore", category=PDBConstructionWarning) + +class ProteogramV2: + """Proteogram V2 class for generating protein structure maps. + + This class provides methods for calculating distance maps, hydrophobicity maps, + and other structural features from PDB files. + + Attributes: + pdb_path (str): Path to the PDB file. + structure: Parsed PDB structure. + model: First model from the PDB structure. + chain: Selected chain from the model. + allowed_amino_acids (dict): Mapping of residue names to single-letter codes. + sequence (str): Amino acid sequence of the chain. + atom_distance_cutoff (float): Distance cutoff for atom interactions in Angstroms. + hydrophobicity_delta_cutoff (float): Cutoff for hydrophobicity delta calculations. + sequence_len_lower_cutoff (int): Minimum sequence length for valid chains. + sequence_len_upper_cutoff (float): Maximum sequence length for valid chains. + """ + + def __init__(self, + pdb_path, + output_dir, + chain_id, + atom_distance_cutoff=10, + hydrophobicity_delta_cutoff=30, + sequence_len_lower_cutoff=20, + sequence_len_upper_cutoff=1e9, + use_gpu=False): + """Initialize the ProteogramV2 instance. + + Args: + pdb_path (str): Path to the PDB file. + chain_id (str): Chain identifier to extract from the PDB file. + atom_distance_cutoff (float, optional): Distance cutoff for atom + interactions in Angstroms. Defaults to 15. + hydrophobicity_delta_cutoff (float, optional): Cutoff for + hydrophobicity delta calculations. Defaults to 20. + sequence_len_lower_cutoff (int, optional): Minimum sequence length + for valid chains. Defaults to 20. + sequence_len_upper_cutoff (float, optional): Maximum sequence length + for valid chains. Defaults to 1e9. + use_gpu (bool, optional): Whether to use GPU acceleration. Defaults to False. + + Raises: + KeyError: If the specified chain_id is not found in the PDB file. + """ + self.pdb_path = pdb_path + self.output_dir = output_dir + parser = PDBParser() + self.structure = parser.get_structure("protein_id", self.pdb_path) + self.model = self.structure[0] + try: + self.chain = self.model[chain_id] + except KeyError: + raise KeyError(f"Chain ID {chain_id} not found in PDB file.") + self.allowed_amino_acids = {b: a for a, b in RESIDUE_LIST} + self.sequence = ''.join( + [self.allowed_amino_acids[res.resname] for res in self.chain]) + self.atom_distance_cutoff = atom_distance_cutoff + self.hydrophobicity_delta_cutoff = hydrophobicity_delta_cutoff + self.sequence_len_lower_cutoff = sequence_len_lower_cutoff + self.sequence_len_upper_cutoff = sequence_len_upper_cutoff + self.use_gpu = use_gpu + + def is_valid_chain(self): + """Check if the chain meets the sequence length criteria. + + Returns: + bool: True if the chain length is within the specified cutoffs, + False otherwise. + """ + seq_len = len(self.sequence) + return (self.sequence_len_lower_cutoff <= seq_len <= self.sequence_len_upper_cutoff) + + def calculate_proteogram(self, + return_simulated_pdb: bool = False, + debug: bool = False, + subtract_solvent_energies: bool = True, + memory_efficient: bool = False): + """Calculate the proteogram maps. + + Computes distance, hydrophobicity, Van der Waals, and electrostatic maps + for the protein structure. + + Args: + return_simulated_pdb (bool): If True, also return the final + production structure from the MD simulation as a PDB stream. + Defaults to False. + debug (bool): If True, print debug information during calculations. + Defaults to False. + subtract_solvent_energies (bool): If True, subtract solvent-only + energies from the protein+solvent energies to isolate the protein + contributions. Defaults to False. + + Returns: + tuple: A tuple containing: + - numpy.ndarray | None: The stacked proteogram array if + successful, None otherwise. + - dict | None: Error dictionary if any errors occurred, + None otherwise. + - io.StringIO | None: Production PDB structure stream + (only if return_simulated_pdb=True). + """ + disto_map = self.calc_dist_matrix() + hydro_map = self.calc_hydrophobicity_map(self.sequence, disto_map) + + # Initialize the model + model = NonBondedForceModel( + pdb_path=self.pdb_path, + output_dir=self.output_dir, + temperature=310.15, # Kelvin (37 C) + timestep=2.0, # Femtoseconds + use_gpu=self.use_gpu, # Set True for GPU acceleration + memory_efficient=memory_efficient + ) + + energy_calc_interval = 10000 # Default: Calculate energies every 20 ps + if memory_efficient: + energy_calc_interval = 50000 # Calculate energies every 100 ps + + # Run the full pipeline (recommended) + pipeline_result = model.run_full_pipeline( + npt_steps=50000, # 100 ps NPT equilibration + nvt_steps=50000, # 100 ps NVT equilibration + production_steps=500000, # 1 ns production + energy_calc_interval=energy_calc_interval, + return_simulated_pdb=return_simulated_pdb, + debug=debug, + subtract_solvent_energies=subtract_solvent_energies # Subtract solvent-only energies + ) + + # Explicit clean-up of OpenMM resources after pipeline completion + model.cleanup_all_resources() + model._clear_cuda_cache() + model._cleanup_old_simulation() # Ensure any old simulations are cleaned up + del model + + # Unpack results based on whether simulated PDB was requested + if return_simulated_pdb: + vdw_e_att, vdw_e_rep, es_e_att, es_e_rep, \ + simulated_pdb = pipeline_result + else: + vdw_e_att, vdw_e_rep, es_e_att, es_e_rep = pipeline_result + simulated_pdb = None + + # Normalize all maps to [0-255] + norm_disto_map, disto_err = self.normalize_map(disto_map) + norm_hydro_map, hydro_err = self.normalize_map(hydro_map) + norm_vdw_att_map, vdw_att_err = self.normalize_map(vdw_e_att) + norm_vdw_rep_map, vdw_rep_err = self.normalize_map(vdw_e_rep) + norm_es_att_map, es_att_err = self.normalize_map(es_e_att) + norm_es_rep_map, es_rep_err = self.normalize_map(es_e_rep) + + # Clear the original energy maps to save memory + del disto_map, hydro_map, vdw_e_att, vdw_e_rep, es_e_att, es_e_rep + del pipeline_result + gc.collect() # Force garbage collection after deleting large arrays + + # Check for normalization errors + errors = { + 'distance': disto_err, + 'hydrophobicity': hydro_err, + 'vdw_attractive': vdw_att_err, + 'electronic_repulsive': vdw_rep_err, + 'electrostatic_attractive': es_att_err, + 'electrostatic_repulsive': es_rep_err + } + # Filter to only include actual errors + errors = {k: v for k, v in errors.items() if v} + + # Create upper and lower triangle + try: + final_upper = np.dstack( + [norm_vdw_att_map, norm_vdw_rep_map, norm_disto_map]) + final_lower = np.rot90(np.dstack( + [norm_es_att_map, norm_es_rep_map, norm_hydro_map]), + 2 + ) + final_data = final_upper + final_lower + # Clear intermediate arrays + del norm_disto_map, norm_hydro_map, norm_vdw_att_map, norm_vdw_rep_map + del norm_es_att_map, norm_es_rep_map, final_upper, final_lower + gc.collect() # Force garbage collection after large array operations + if return_simulated_pdb: + return final_data, None, simulated_pdb + return final_data, None + except Exception as e: + gc.collect() # Force garbage collection even on error + if return_simulated_pdb: + return None, {'Error stacking maps': str(e)}, simulated_pdb + return None, {'Error stacking maps': str(e)} + + @staticmethod + def normalize_map(arr): + """Normalize any numpy array to [0-255] using Min-Max linear scaling. + + Args: + arr (numpy.ndarray): Input array to normalize. + + Returns: + tuple: A tuple containing: + - numpy.ndarray: Normalized array with values in range [0, 255]. + - str: Error message if normalization failed, empty string otherwise. + """ + err = '' + try: + arr = ((arr - arr.min()) * (1/(arr.max() - arr.min()) * 255)).astype('uint8') + except Exception as e: + err = f'Problem normalizing map: {e}' + return arr, err + + def calc_dist_matrix(self): + """Calculate the C-alpha distance matrix for the chain. + + Computes pairwise distances between all C-alpha atoms in the chain. + Only the upper triangle of the matrix is populated; the lower triangle + contains zeros. + + Returns: + numpy.ndarray: A symmetric matrix of shape (n_residues, n_residues) + containing C-alpha distances in Angstroms. + """ + ca_atoms = [res["CA"] for res in self.chain if "CA" in res] + n_residues = len(ca_atoms) + # Initialize a results matrix with zeros + distogram = np.zeros((n_residues, n_residues), dtype=np.float64) + + # Assign upper triangle the c-alpha distances (lower triangle remains all 0) + for i in range(n_residues): + for j in range(i + 1, n_residues): # Only iterate over unique pairs (upper triangle) + # Use the distance operator overload for Atom objects + distance = ca_atoms[i] - ca_atoms[j] + distogram[i, j] = distance + + return distogram + + def calc_hydrophobicity_map(self, sequence, disto_map): + """Calculate the hydrophobicity difference map. + + Computes the absolute difference in hydrophobicity values between + residue pairs that are within the atom distance cutoff. + + Args: + sequence (str): Amino acid sequence of the protein. + disto_map (numpy.ndarray): Distance matrix from calc_dist_matrix. + + Returns: + numpy.ndarray: A matrix of shape (len(sequence), len(sequence)) + containing hydrophobicity delta values for residue pairs + within the distance cutoff. + """ + hydro_map = np.zeros((len(sequence), len(sequence))) + + for row in range(len(sequence)): + for col in range(row+1, len(sequence)): + # If residues less than cutoff num of Angstroms + if disto_map[row,col] < self.atom_distance_cutoff: + try: + row_val = np.abs(HYDROPHOBICITY_LIST[sequence[row]]) + col_val = np.abs(HYDROPHOBICITY_LIST[sequence[col]]) + delta = np.abs(row_val - col_val) + # Throw exception when can't retrieve hydrophobicity + except: + delta = 0 + hydro_map[row,col] = delta + # If on the diag, set to 0 + if row == col: + hydro_map[row,col] = 0 + + return hydro_map + + def set_sequence(self): + """Set the protein sequence from the PDB structure. + + Uses Biopython's PPBuilder to extract the amino acid sequence + from the structure and stores it in self.sequence. + """ + ppb = PPBuilder() + seq = '' + for pp in ppb.build_peptides(self.structure): + seq += pp.get_sequence() + self.sequence = seq + \ No newline at end of file diff --git a/pyproject.toml b/pyproject.toml index 426a7d0..04420f5 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,29 +1,45 @@ -[tool.poetry] +[project] name = "proteogram" -version = "0.0.2" +version = "0.0.3" description = "Protein structure to image manipulation and analysis." -authors = ["Micheleen Harris"] -license = "LICENSE" -readme = "README.md" +requires-python = ">=3.11" +dependencies = [ + "biopython>=1.8", + "matplotlib>=3.6", + "MDAnalysis[analysis,extra_formats,parallel]>=2.10.0", + "numpy>=1.26", + "openmm>=8.4", + "pandas>=3.0", + "pdbfixer @ git+https://github.com/openmm/pdbfixer@94cfa4c", + "pillow>=12.1", + "torch>=2.2", + "torchvision>=0.17", + "torchsummary>=1.5", + "kmeans-pytorch>=0.3", + "tqdm>=4.67", + "psutil>=7.2.2", + "objgraph>=3.6.2", + "PyYAML>=6.0", + "pyrotein @ git+https://github.com/carbonscott/pyrotein.git@main", +] -[tool.poetry.dependencies] -python = "^3.11" -nglview = "^3.1.2" -biopython = "^1.8" -pandas = "^2.2.2" -matplotlib = "^3.6" -rcsbsearchapi = "1.5.1" -ase = "3.23.0" -numpy = "^1" -torch = "^2.2" -torchvision = "^0.17" -torchsummary = "^1.5" -kmeans-pytorch = "^0.3" -pyrotein = { git = "https://github.com/carbonscott/pyrotein.git", branch = "main" } +[project.optional-dependencies] +cuda12 = [ + "openmm==8.4.0", + "nvidia-cuda-nvcc-cu12==12.9.86", + "openmm-cuda-12==8.4.0.post2" +] +test = ["pytest", "pytest-cov"] +notebook = [ + "jupyterlab>=4.2.5", + "nglview>=3.1.4", + "rcsbsearchapi>=1.5.1" +] -[tool.poetry.dev-dependencies] -jupyterlab = "4.2.5" +[tool.setuptools.packages.find] +# Exclude patterns should match the full package name +exclude = ["assets", "docs", "paper", "notebooks", "scripts", "tmp"] [build-system] -requires = ["poetry-core"] -build-backend = "poetry.core.masonry.api" \ No newline at end of file +requires = ["setuptools>=61.0"] +build-backend = "setuptools.build_meta" diff --git a/pyproject_poetry.toml b/pyproject_poetry.toml new file mode 100644 index 0000000..7b99c1f --- /dev/null +++ b/pyproject_poetry.toml @@ -0,0 +1,29 @@ +[tool.poetry] +name = "proteogram" +version = "0.0.2" +description = "Protein structure to image manipulation and analysis." +authors = ["Micheleen Harris"] +license = "LICENSE" +readme = "README.md" + +[tool.poetry.dependencies] +python = "^3.11" +nglview = "^3.1.2" +biopython = "^1.8" +pandas = "^2.2.2" +matplotlib = "^3.6" +rcsbsearchapi = "1.5.1" +ase = "3.23.0" +numpy = "^1" +torch = "^2.2" +torchvision = "^0.17" +torchsummary = "^1.5" +kmeans-pytorch = "^0.3" +pyrotein = { git = "https://github.com/carbonscott/pyrotein.git", branch = "main" } + +[tool.poetry.dev-dependencies] +jupyterlab = "4.2.5" + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" diff --git a/scripts/config.example.yml b/scripts/config.example.yml index c287358..42553bf 100644 --- a/scripts/config.example.yml +++ b/scripts/config.example.yml @@ -1,43 +1,49 @@ -# for create_proteograms.py (and will be used in train_model.py) +# for create_proteograms.py and create_v2_proteograms.py limit_file: '' # optional, limit to these structures, .ent file base name one per line -structures_dir: '' # all .ent structures (multiple dirs ok) -proteograms_dir: '' # output dir +scope_structures_dir: '' # input structures, .ent file format +eval_proteograms_dir: '' # input proteogram dir for eval set +all_proteograms_dir: '' # input proteogram dir for all structures +eval_structures_dir: '' # input structures for eval set, .ent file format + +# for measure_similarity_single_domain.py +proteograms_dir_single_search: '' # dir of proteograms to search against + +# for create_annotation_file.py +annot_file: './/ProteogramData_SCOP_RCSB_PDBe_AnnotationsLookup.tsv' +fasta_style_file: './/structure_based_seqs.fa' # [optional script] for split_structures_into_chains.py pdb_download_dir: '' -# for make_training_data.py +# for make_training_data.py and make_training_data_exclude_eval.py scope_level: '' # family, superfamily, fold or class scope_eval_set: '' # file to indicate proteins to be excluded, .ent file base name one per line -scope_structures_dir: '' # input structures, .ent file format scope_cla_file: './data/dir.cla.scope.2.08-stable.txt' scope_des_file: './data/dir.des.scope.2.08-stable.txt' scope_hie_file: './data/dir.hie.scope.2.08-stable.txt' training_structures_dir: '' # copy structures used for proteograms for gtalign runs (for eval) training_proteograms_dir: '' # output dir -label_df_out: '//scope2.08_annotations_for_pdb.tsv' # annotations output file +label_df_out: './/scope2.08_annotations_for_pdb.tsv' # annotations output file -# for train_model.py +# for train_multiple_models.py - ResNet18 or CNN training training_data_dir: '' # input proteogram dir -num_epochs: 25 -learning_rate: 0.001 -batch_size: 8 -pretrained: False # True or False - +num_epochs: 25 # can be overridden by command line argument +learning_rate: 0.001 # can be overridden by command line argument +batch_size: 8 # can be overridden by command line argument +pretrained: True # True or False +model_file_prefix: 'scope_cnn_model' # parameters will be appended to this prefix + # for measure_similarity.py -model_file: '/resnet18.pt' proteograms_for_sim_dir: '' # input proteogram dir for similarity search +model_file: './/scope_cnn_model.pt' # model file for embedding proteograms for similarity search top_k: 5 embed_file_exists: 'False' # either "True" or "False" if you have "embeddings.pt" -embed_file: '/embeddings.pt' # name of pytorch tensors file representing embeddings of proteograms -search_images_dir: '/search_images_dir' # search results (top k protegram hits per query) -proteogram_sim_results: '/proteogram_similarity_results.tsv' # similarity results (top k) as table +embed_file: './/embeddings.pt' # name of pytorch tensors file representing embeddings of proteograms +search_images_dir: './/search_images_dir' # search results (top k protegram hits per query) +proteogram_sim_results: './/proteogram_similarity_results.tsv' # similarity results (top k) as table # for evaluate_methods.py gtalign_results_dir: '/gtalign_out' # dir name for gtalign output usalign_results: './/usalign_out.tsv' # tab-delimited ouptut from USalign -save_bad_searches_dir: '/bad_proteogram_searches' # dir for poor search result images -save_good_searches_dir: '/good_proteogram_searches' # dir for very good search result images - -# for measure_similarity_single_chain.py -proteograms_dir_single_search: '' # dir of proteograms to search against \ No newline at end of file +save_bad_searches_dir: './/bad_proteogram_searches' # dir for poor search result images +save_good_searches_dir: './/good_proteogram_searches' # dir for very good search result images \ No newline at end of file diff --git a/scripts/convert2fasta.sh b/scripts/convert2fasta.sh new file mode 100755 index 0000000..0c8ebfc --- /dev/null +++ b/scripts/convert2fasta.sh @@ -0,0 +1,15 @@ +#!/bin/bash + +# Define the directory you want to start from +start_dir="./data/pdbstyle-2.08" # Change this to your directory of PDB files + +echo "Starting recursive loop using find in $start_dir" + +# Use find to list files and loop through the output +# -type f ensures only regular files are returned +# -print0 and read -d '' safely handle filenames with spaces or special characters +find "$start_dir" -type f -print0 | while IFS= read -r -d '' file; do + echo "Processing file: $file" + pdb2fasta "$file" >> "pdbstyle-2.08.fasta" +done + diff --git a/scripts/create_annotation_file.py b/scripts/create_annotation_file.py new file mode 100644 index 0000000..c72d47c --- /dev/null +++ b/scripts/create_annotation_file.py @@ -0,0 +1,213 @@ +""" +Using a list of SCOPe-named structure files, look up key annotations and +create a tab-delimited file to hold those annotations for lookup. Uses +local SCOPe database files and RCSB PDB API as well as PDBe API calls. +""" +import warnings +import glob +import os +import requests +import json +import pandas as pd + +from Bio.PDB.PDBParser import PDBParser, PDBConstructionWarning +from Bio.PDB.Polypeptide import PPBuilder +from Bio.SCOP import Scop + +from proteogram.utils import read_yaml + + +# Ignore PDB construction warnings +warnings.filterwarnings("ignore", category=PDBConstructionWarning) + +def get_sequence(pdb_path): + """Get a protein sequence from a PDB file""" + seq = '' + try: + p = PDBParser(PERMISSIVE=0) + structure = p.get_structure('xyz', pdb_path) + ppb = PPBuilder() + for pp in ppb.build_peptides(structure): + seq += pp.get_sequence() + except Exception: + seq = '' + return seq + +if __name__ == '__main__': + + config = read_yaml('config.yml') + limit_file = config['limit_file'] + structures_dir = config['scope_structures_dir'] + annot_file = config['annot_file'] + fasta_style_file = config['fasta_style_file'] + scope_cla_handle = config['scope_cla_file'] + scope_des_handle = config['scope_des_file'] + scope_hie_handle = config['scope_hie_file'] + + limit_to_these_structs = [] + if limit_file: + with open(limit_file, 'r') as f: + for line in f: + limit_to_these_structs.append( + os.path.basename(line.strip()).replace('.ent','')) + + # Create a Scop object to get fold, superfamily and family + scop = Scop(cla_handle=open(scope_cla_handle, 'r'), + des_handle=open(scope_des_handle, 'r'), + hie_handle=open(scope_hie_handle, 'r')) + + pdb_files = glob.glob(os.path.join(structures_dir, '**', '*'), recursive=True) + + annot_data = [] + for_fasta = {} + for pdb_file in pdb_files: + if not os.path.isfile(pdb_file): + continue + + # Get the scope id from structure filename + bname = os.path.basename(pdb_file).split('.') + bname = '.'.join(bname[:-1]) + + # Check if we have a restricted list and if pdb file is in it + if limit_file: + if bname not in limit_to_these_structs: + continue + + # Sequence info + seq = get_sequence(pdb_file) + + # SCOPe info + try: + pdb_id = bname[1:5].upper() + chain = bname[5].upper() + pdb_id_chain = pdb_id + '_' + chain + prot_file = f'{pdb_id}_{chain}.jpg' + except: + print(f'Problem with filename {os.path.basename(pdb_file)}') + continue + try: + scop_entry = scop.getDomainBySid(bname) + # Parse out info for our dataframe + sccs = scop_entry.sccs + sccs_spl = sccs.split('.') + cls, fold, sfam, fam = sccs_spl[0], '.'.join(sccs_spl[:2]), '.'.join(sccs_spl[:3]), sccs + except: + cls, fold, sfam, fam = '', '', '', '' + + # Gene Ontology + try: + go_response = requests.get(f'https://www.ebi.ac.uk/pdbe/graph-api/mappings/go/{pdb_id}') + go_response = json.dumps(go_response.json()) + except: + go_response = '' + + # RCSB Data API annotations for entry + deposit_date = '' + experimental_method = '' + molecular_weight = '' + disulfide_bond_count = '' + entity_count = '' + try: + rcsb_response = requests.get(f'https://data.rcsb.org/rest/v1/core/entry/{pdb_id}') + rcsb_response = rcsb_response.json() + deposit_date = rcsb_response['rcsb_accession_info']['deposit_date'] + experimental_method = rcsb_response['rcsb_entry_info']['experimental_method'] + molecular_weight = rcsb_response['rcsb_entry_info']['molecular_weight'] + disulfide_bond_count = rcsb_response['rcsb_entry_info']['disulfide_bond_count'] + protein_entity_count = rcsb_response['rcsb_entry_info']['polymer_entity_count_protein'] + except: + deposit_date = '' + experimental_method = '' + molecular_weight = '' + disulfide_bond_count = '' + protein_entity_count = '' + + # RCSB Data API annotations for uniprot - get if is transmembrane + is_tm = False + tm_cnts = 0 + try: + rcsb_response = requests.get(f'https://data.rcsb.org/rest/v1/core/uniprot/{pdb_id}') + rcsb_response = rcsb_response.json() + # Get first entry and uniprot feature info (locations and sequence indices) + rcsb_uniprot_features = rcsb_response[0]['rcsb_uniprot_feature'] + except Exception as excp: + rcsb_uniprot_features = [] + + for entry in rcsb_uniprot_features: + # Get TM regions + if 'type' in entry and entry['type'] == 'TRANSMEMBRANE_REGION': + is_tm = True + tm_cnts+=1 + + row = [bname, + os.path.basename(pdb_file), + prot_file, + pdb_id, + chain, + pdb_id_chain, + cls, + fold, + sfam, + fam, + len(seq), + deposit_date, + experimental_method, + molecular_weight, + disulfide_bond_count, + protein_entity_count, + is_tm, + tm_cnts, + go_response, + seq] + + annot_data.append(row) + + fasta_style_id = f'>{pdb_id_chain}|{bname}|{fam}' + for_fasta[fasta_style_id] = seq + + annot_df = pd.DataFrame(annot_data) + annot_df.columns = ['SCOPeID', + 'PDBFileName', + 'ProteogramFileName', + 'PDBId', + 'ChainId', + 'PDBAndChainId', + 'SCOPeClass', + 'SCOPeFold', + 'SCOPeSuperfamily', + 'SCOPeFamily', + 'PDBSequenceLength', + 'PDBDepositDate', + 'PDBExperimentalMethod', + 'PDBMolecularWeight', + 'PDBDisulfideBond', + 'PDBProteinEntityCount', + 'PDBIsTransmembrane', + 'PDBTransmembraneRegionCounts', + 'PDBeGOAnnotation', + 'PDBSequence'] + + # Save the results + try: + annot_df.to_csv( + os.path.join(annot_file), + sep='\t', + index=False) + except Exception as e: + print(f'Problem saving to specific location: {e}, so saving in cwd.') + annot_df.to_csv( + os.path.join('.', os.path.basename(annot_file)), + sep='\t', + index=False) + + try: + f = open(fasta_style_file, 'w') + except Exception as e: + print(f'Problem saving to specific location: {e}, so saving in cwd.') + f = open(os.path.join('.', os.path.basename(fasta_style_file), 'w')) + + # Write a fasta-style file with the protein sequences from all structures processed + for entry in for_fasta: + f.write(entry + '\n' + for_fasta[entry] + '\n') + f.close() + diff --git a/scripts/create_balanced_scope_train_eval_lists.py b/scripts/create_balanced_scope_train_eval_lists.py new file mode 100644 index 0000000..2411735 --- /dev/null +++ b/scripts/create_balanced_scope_train_eval_lists.py @@ -0,0 +1,377 @@ +#!/usr/bin/env python3 +""" +Create balanced training and evaluation datasets from CD-HIT clustered sequences. + +This script reads a FASTA-like .lst file from CD-HIT results and a TSV lookup +table (created with the "create_scope_lookup_table.py" script) with SCOPe +annotations to create balanced training and evaluation datasets with N proteins from a specified SCOPe classification level. + +Usage: + python create_balanced_scope_eval_list.py \ + --lst-file path/to/cdhits_result.lst \ + --lookup-tsv path/to/annotations.tsv \ + --class-column SCOPeClass \ + --n-per-class 100 \ + --eval-fraction 0.2 \ + --train-output train_set.txt \ + --eval-output eval_set.txt \ + [--split-train 10] \ + [--seed 42] + +Class column options: + - SCOPeClass: Major structural class (e.g., a, b, c, d) + - SCOPeFold: Fold level classification + - SCOPeSuperfamily: Superfamily level classification + - SCOPeFamily: Family level classification +""" + +import argparse +import math +import pandas as pd +import random +from collections import defaultdict +from pathlib import Path + + +def parse_lst_file(lst_file: str) -> list[str]: + """ + Parse a FASTA-like .lst file and extract structure identifiers. + + Lines starting with ">" contain identifiers in the format: + >d3wsed_:D\t247 + + We extract the part before the ":" (e.g., "d3wsed_"). + + Args: + lst_file: Path to the .lst file + + Returns: + List of structure identifiers + """ + identifiers = [] + + with open(lst_file, 'r') as f: + for line in f: + line = line.strip() + if line.startswith('>'): + # Remove the '>' and split by ':' to get the identifier + # Format: >d3wsed_:D\t247 + header = line[1:] # Remove '>' + # Split by ':' and take the first part + identifier = header.split(':')[0] + identifiers.append(identifier) + + return identifiers + + +# Valid SCOPe classification columns +VALID_CLASS_COLUMNS = ['SCOPeClass', 'SCOPeFold', 'SCOPeSuperfamily', 'SCOPeFamily'] + + +def load_lookup_table(tsv_file: str, class_column: str) -> pd.DataFrame: + """ + Load the TSV lookup table with SCOPe annotations. + + Args: + tsv_file: Path to the TSV file with SCOPe annotations + class_column: Column name for classification level + + Returns: + DataFrame with lookup table + """ + df = pd.read_csv(tsv_file, sep='\t') + + if class_column not in df.columns: + raise ValueError(f"TSV file must contain '{class_column}' column. " + f"Found columns: {list(df.columns)}") + + return df + + +def create_balanced_dataset( + identifiers: list[str], + lookup_df: pd.DataFrame, + n_per_class: int, + id_column: str = 'SCOPeID', + class_column: str = 'SCOPeClass', + seed: int | None = None +) -> tuple[dict[str, list[str]], dict[str, int]]: + """ + Create a balanced dataset with N proteins from each SCOPe classification. + + Args: + identifiers: List of structure identifiers from the .lst file + lookup_df: DataFrame with SCOPe annotations + n_per_class: Number of proteins to sample per classification + id_column: Column name for the identifier in the lookup table + class_column: Column name for the classification level + seed: Random seed for reproducibility + + Returns: + Tuple of (dict mapping class to list of identifiers, dict of class counts) + """ + if seed is not None: + random.seed(seed) + + # Create a set for faster lookup + id_set = set(identifiers) + + # Group identifiers by SCOPe classification + class_to_ids = defaultdict(list) + + for _, row in lookup_df.iterrows(): + scope_id = row[id_column] + scope_class = row[class_column] + + if scope_id in id_set: + class_to_ids[scope_class].append(scope_id) + + # Sample N from each class + sampled_by_class = {} + class_counts = {} + + for scope_class in sorted(class_to_ids.keys()): + available_ids = class_to_ids[scope_class] + n_available = len(available_ids) + + if n_available >= n_per_class: + sampled = random.sample(available_ids, n_per_class) + else: + # Take all available if less than N + sampled = available_ids + print(f"Warning: Class '{scope_class}' has only {n_available} " + f"proteins (requested {n_per_class})") + + sampled_by_class[scope_class] = sampled + class_counts[scope_class] = len(sampled) + + return sampled_by_class, class_counts + + +def split_train_eval( + sampled_by_class: dict[str, list[str]], + eval_fraction: float, + seed: int | None = None +) -> tuple[list[str], list[str], dict[str, tuple[int, int]]]: + """ + Split the balanced dataset into training and evaluation sets. + + The split is performed per-class to maintain balance in both sets. + + Args: + sampled_by_class: Dict mapping class to list of identifiers + eval_fraction: Fraction of data to use for evaluation (0.0-1.0) + seed: Random seed for reproducibility + + Returns: + Tuple of (train_ids, eval_ids, dict of (train_count, eval_count) per class) + """ + if seed is not None: + random.seed(seed) + + train_ids = [] + eval_ids = [] + split_counts = {} + + for scope_class in sorted(sampled_by_class.keys()): + ids = sampled_by_class[scope_class].copy() + random.shuffle(ids) + + n_eval = max(1, int(len(ids) * eval_fraction)) + n_train = len(ids) - n_eval + + class_eval = ids[:n_eval] + class_train = ids[n_eval:] + + eval_ids.extend(class_eval) + train_ids.extend(class_train) + split_counts[scope_class] = (len(class_train), len(class_eval)) + + return train_ids, eval_ids, split_counts + + +def split_into_files( + ids: list[str], + output_path: Path, + n_splits: int +) -> list[Path]: + """ + Split a list of identifiers into N separate files. + + Args: + ids: List of identifiers to split + output_path: Base path for output files (e.g., train.txt -> train_part01.txt) + n_splits: Number of files to split into + + Returns: + List of paths to created files + """ + lines_per_file = math.ceil(len(ids) / n_splits) + created_files = [] + + base_name = output_path.stem + suffix = output_path.suffix + parent = output_path.parent + + for i in range(n_splits): + start = i * lines_per_file + end = min(start + lines_per_file, len(ids)) + chunk = ids[start:end] + + if not chunk: # Skip empty chunks + break + + out_file = parent / f"{base_name}_part{i+1:02d}{suffix}" + with open(out_file, 'w') as f: + for identifier in chunk: + f.write(f"{identifier}\n") + created_files.append(out_file) + + return created_files + + +def main(): + parser = argparse.ArgumentParser( + description='Create a balanced evaluation dataset from CD-HIT results', + formatter_class=argparse.RawDescriptionHelpFormatter, + epilog=__doc__ + ) + + parser.add_argument( + '--lst-file', '-l', + required=True, + help='Path to the CD-HIT .lst file (FASTA-like format)' + ) + + parser.add_argument( + '--lookup-tsv', '-t', + required=True, + help='Path to the TSV lookup table with SCOPe annotations' + ) + + parser.add_argument( + '--class-column', '-c', + choices=VALID_CLASS_COLUMNS, + default='SCOPeClass', + help='Classification column to use for balancing. ' + 'Options: SCOPeClass, SCOPeFold, SCOPeSuperfamily, SCOPeFamily ' + '(default: SCOPeClass)' + ) + + parser.add_argument( + '--n-per-class', '-n', + type=int, + default=100, + help='Number of proteins to sample per class (default: 100)' + ) + + parser.add_argument( + '--eval-fraction', '-e', + type=float, + default=0.2, + help='Fraction of data to use for evaluation (default: 0.2)' + ) + + parser.add_argument( + '--train-output', + required=True, + help='Path to output file for training set (one identifier per line)' + ) + + parser.add_argument( + '--eval-output', + required=True, + help='Path to output file for evaluation set (one identifier per line)' + ) + + parser.add_argument( + '--split-train', + type=int, + default=None, + help='Split training set into N separate files for parallel processing ' + '(e.g., --split-train 10 creates train_part01.txt through train_part10.txt)' + ) + + parser.add_argument( + '--id-column', '-i', + default='SCOPeID', + help='Column name for identifier in lookup table (default: SCOPeID)' + ) + + parser.add_argument( + '--seed', '-s', + type=int, + default=None, + help='Random seed for reproducibility' + ) + + args = parser.parse_args() + + # Parse the .lst file + print(f"Reading identifiers from: {args.lst_file}") + identifiers = parse_lst_file(args.lst_file) + print(f"Found {len(identifiers)} identifiers in .lst file") + + # Load lookup table + print(f"Loading lookup table from: {args.lookup_tsv}") + lookup_df = load_lookup_table(args.lookup_tsv, args.class_column) + print(f"Lookup table has {len(lookup_df)} entries") + + # Create balanced dataset + print(f"\nCreating balanced dataset with {args.n_per_class} proteins per {args.class_column}...") + sampled_by_class, class_counts = create_balanced_dataset( + identifiers=identifiers, + lookup_df=lookup_df, + n_per_class=args.n_per_class, + id_column=args.id_column, + class_column=args.class_column, + seed=args.seed + ) + + # Split into train and eval sets + print(f"\nSplitting into train ({1-args.eval_fraction:.0%}) and eval ({args.eval_fraction:.0%}) sets...") + train_ids, eval_ids, split_counts = split_train_eval( + sampled_by_class=sampled_by_class, + eval_fraction=args.eval_fraction, + seed=args.seed + ) + + # Print summary + print(f"\nClass distribution (total / train / eval):") + for scope_class in sorted(class_counts.keys()): + total = class_counts[scope_class] + train_n, eval_n = split_counts[scope_class] + print(f" {scope_class}: {total} / {train_n} / {eval_n}") + + print(f"\nTotal selected: {sum(class_counts.values())}") + print(f" Training set: {len(train_ids)}") + print(f" Evaluation set: {len(eval_ids)}") + + # Save training set + train_path = Path(args.train_output) + train_path.parent.mkdir(parents=True, exist_ok=True) + + if args.split_train and args.split_train > 1: + # Split training set into multiple files + created_files = split_into_files(train_ids, train_path, args.split_train) + print(f"\nSplit training set into {len(created_files)} files:") + for f in created_files: + print(f" {f}") + else: + # Save as single file + with open(train_path, 'w') as f: + for identifier in train_ids: + f.write(f"{identifier}\n") + print(f"\nSaved training set to: {args.train_output}") + + # Save evaluation set + eval_path = Path(args.eval_output) + eval_path.parent.mkdir(parents=True, exist_ok=True) + with open(eval_path, 'w') as f: + for identifier in eval_ids: + f.write(f"{identifier}\n") + print(f"Saved evaluation set to: {args.eval_output}") + + +if __name__ == '__main__': + main() diff --git a/scripts/create_v2_proteograms.py b/scripts/create_v2_proteograms.py new file mode 100644 index 0000000..8d0757e --- /dev/null +++ b/scripts/create_v2_proteograms.py @@ -0,0 +1,266 @@ +from proteogram.proteogram_v2 import ProteogramV2 +import gc +import glob +import os +from time import time +import matplotlib +matplotlib.use('agg') # Use non-interactive backend to avoid memory buildup +import matplotlib.pyplot as plt +import torch +import warnings +import argparse +from tqdm import tqdm +from Bio.PDB.PDBParser import PDBConstructionWarning +from proteogram.utils import read_yaml +import psutil +import tracemalloc +try: + import objgraph +except ImportError: + objgraph = None +# Ignore PDB construction warnings +warnings.filterwarnings("ignore", category=PDBConstructionWarning) + + +if __name__ == '__main__': + parser = argparse.ArgumentParser( + description="Create Proteograms (v2).") + parser.add_argument("--max_workers", "-w", + type=int, + default=None, + help="Max number of workers for multiprocessing \ + (default: 0, all available nodes).") + parser.add_argument('--overwrite', + action='store_true', + help="Recreate / overwrite Proteograms.") + parser.add_argument('--verbose', + action='store_true', + help="Verbose output.") + parser.add_argument('--debug', + action='store_true', + help="Enable debug mode with additional logging and plots.") + parser.add_argument('--memory-efficient', + action='store_true', + help="Use memory-efficient solvent subtraction (slower).") + parser.add_argument('--save_simulated_pdb', + action='store_true', + help="Save the final simulated PDB structure.") + args = parser.parse_args() + + start = time() + + # Tell glibc to route large allocations (>= 128 KB) through mmap rather than + # the sbrk heap. mmap-allocated pages are returned to the OS via munmap as + # soon as they are freed, bypassing the per-arena free-list fragmentation that + # otherwise causes RSS to grow by ~2 GB per protein. M_TRIM_THRESHOLD=0 + # additionally tells glibc to trim the heap break after every free that leaves + # space at the top of the main arena. + # These are no-ops on macOS/Windows; set them once before any OpenMM objects + # are created so all subsequent large C++ allocations take the mmap path. + try: + import ctypes as _ctypes + _libc = _ctypes.CDLL("libc.so.6") + _M_TRIM_THRESHOLD = -1 # mallopt param: trim heap after free + _M_MMAP_THRESHOLD = -3 # mallopt param: mmap allocations >= this size + _libc.mallopt(_M_TRIM_THRESHOLD, 0) # trim aggressively + _libc.mallopt(_M_MMAP_THRESHOLD, 128 * 1024) # mmap for allocations >= 128 KB + except Exception: + pass # not Linux or libc unavailable — skip silently + + if args.overwrite: + recreate = True + else: + recreate = False + config = read_yaml('config.yml') + limit_file = config['limit_file'] + structures_dir = config['scope_structures_dir'] + proteograms_output_dir = config['all_proteograms_dir'] + + # Only create proteograms for these structures in the input limit file + limit_to_these_structs = [] + if limit_file: + with open(limit_file, 'r') as f: + for line in f: + limit_to_these_structs.append(line.strip()) + + # If the output dir exists, don't recreate, otherwise make one + if os.path.exists(proteograms_output_dir): + print(f'Directory {proteograms_output_dir} exists, will use.') + else: + os.makedirs(proteograms_output_dir) + + # Create output directory for production PDB structures if requested + production_pdb_output_dir = None + if args.save_simulated_pdb: + production_pdb_output_dir = os.path.join( + proteograms_output_dir, 'production_structures') + if os.path.exists(production_pdb_output_dir): + print(f'Directory {production_pdb_output_dir} exists, will use.') + else: + os.makedirs(production_pdb_output_dir) + print(f'Created directory {production_pdb_output_dir} for production structures.') + + pdb_files = glob.glob(os.path.join(structures_dir, '**', '*'), + recursive=True) + existing_image_files = glob.glob( + os.path.join(proteograms_output_dir, '*')) + + # Make a list of (pdb-file-name, image-file-name) tuples as input + # to process pool + file_list = [] + exts = ['ent', 'mmcif', 'cif', 'pdb'] + for pdb_file in pdb_files: + bname = os.path.basename(pdb_file) + if len(bname.split('.')) == 1: + # If there is no extension, add .ent as default + bname += '.ent' + + # name without extensions + bname_noext = bname + if bname.split('.')[-1] in exts: + for x in exts: + bname_noext = bname_noext.replace(f'.{x}', '') + + # There are structure files we wish to limit to + # Compare using basename without extension since limit file may not include extensions + if limit_to_these_structs: + if bname_noext not in limit_to_these_structs and bname not in limit_to_these_structs: + continue + + chain_id = bname[1:5].upper()+':'+ bname[5].upper() + + image_file = os.path.join(proteograms_output_dir, + f'{bname_noext}.jpg') + if recreate == False: + if image_file not in existing_image_files: + file_list.append((pdb_file, image_file)) + else: # recreate + file_list.append((pdb_file, image_file)) + + print(f'Number of structure files = {len(file_list)}') + + # Check if CUDA-enabled GPU is available + use_gpu = torch.cuda.is_available() + + # Insert regular loop over files using ProteogramV2 here + problem_files = [] + problem_pdb_cnts = 0 + process = psutil.Process() + if args.debug: + # Record baseline OpenMM Context count BEFORE any protein is processed. + # OpenMM creates a small number of Contexts internally when benchmarking + # platforms (Platform.findPlatform). Any count seen after cleanup that + # equals this baseline is NOT a leak — it is expected class-level state. + try: + import openmm as _omm_baseline + _baseline_ctx_count = sum( + 1 for obj in gc.get_objects() if type(obj) is _omm_baseline.Context) + print(f"=== BASELINE OpenMM Context count (platform init): {_baseline_ctx_count} ===") + except Exception: + _baseline_ctx_count = 0 + if objgraph: + print("=== BASELINE OBJECT COUNTS ===") + objgraph.show_most_common_types(limit=15) + print("="*40) + for pdb_file, image_file in tqdm(file_list): + if args.debug: + mem_before = process.memory_info().rss / 1024 / 1024 + if torch.cuda.is_available(): + gpu_mem_before = torch.cuda.memory_allocated() / 1024 / 1024 + print(f"GPU memory before: {gpu_mem_before:.1f} MB") + try: + print(f'Processing {pdb_file}...') + bname = os.path.basename(pdb_file) + chain_id = bname[5].upper() + # Create ProteogramV2 instance + # Note: the cutoff values are in Angstroms for distance and + # and are chosen to balance capturing meaningful interactions + # while managing computational cost. These can be adjusted along + # with sequence length cutoffs based on the specific proteins + # being analyzed. + proteogram = ProteogramV2(pdb_file, + output_dir=proteograms_output_dir, + chain_id=chain_id, + atom_distance_cutoff=20, + hydrophobicity_delta_cutoff=30, + sequence_len_lower_cutoff=20, + sequence_len_upper_cutoff=1000, + use_gpu=use_gpu) + + # Skip chains that don't meet the sequence length cutoffs + if not proteogram.is_valid_chain(): + print(f'Skipping {pdb_file}: sequence length {len(proteogram.sequence)} outside [{proteogram.sequence_len_lower_cutoff}, {proteogram.sequence_len_upper_cutoff}]') + del proteogram + continue + + # Calculate Proteogram with optional simulated PDB output + if args.save_simulated_pdb: + final_data, err, simulated_pdb_stream = proteogram.calculate_proteogram( + return_simulated_pdb=True, + subtract_solvent_energies=True, + debug=args.debug, + memory_efficient=args.memory_efficient) + else: + final_data, err = proteogram.calculate_proteogram( + subtract_solvent_energies=True, + debug=args.debug, + memory_efficient=args.memory_efficient) + simulated_pdb_stream = None + + if err is not None and args.verbose: + print(f'Error calculating Proteogram for {pdb_file}: {err}') + + # If Proteogram data is not None, save the image + if final_data is not None: + # Save image + plt.imsave(image_file, final_data.astype('uint8')) + plt.close('all') # Clear matplotlib figures from memory + plt.clf() # Clear current figure + + # Save production simulation PDB structure if requested + if simulated_pdb_stream is not None and production_pdb_output_dir is not None: + # Extract base name without extension and add simulation suffix + bname_base = os.path.splitext(bname)[0] + production_pdb_file = os.path.join( + production_pdb_output_dir, + f'{bname_base}_production.pdb' + ) + with open(production_pdb_file, 'w') as f: + f.write(simulated_pdb_stream.read()) + if args.verbose: + print(f'Saved production structure to {production_pdb_file}') + + # Clean up to free memory between proteins + del proteogram + if final_data is not None: + del final_data + if simulated_pdb_stream is not None: + simulated_pdb_stream.close() + del simulated_pdb_stream + + # Force aggressive garbage collection + gc.collect() + + if torch.cuda.is_available(): + torch.cuda.empty_cache() + if args.debug: + mem_after = process.memory_info().rss / 1024 / 1024 + print(f"\nMemory after cleanup: {mem_after:.1f} MB (delta: {mem_after - mem_before:.1f} MB)") + if torch.cuda.is_available(): + gpu_mem_after = torch.cuda.memory_allocated() / 1024 / 1024 + print(f"GPU memory after: {gpu_mem_after:.1f} MB (delta: {gpu_mem_after - gpu_mem_before:.1f} MB)") + + except Exception as e: + problem_files.append(pdb_file) + problem_pdb_cnts += 1 + if args.verbose: + print(f'Problem with file {pdb_file}: {e}') + + if args.verbose: + # Save problem pdbs to file + with open(os.path.join(proteograms_output_dir, 'problem_structures.txt'), 'w') as f: + for problem_file in problem_files: + f.write(problem_file + '\n') + + print(f'Problems with {problem_pdb_cnts} structure files.') + print(f'Computation took {time()-start} seconds') \ No newline at end of file diff --git a/scripts/train_model.py b/scripts/train_model.py deleted file mode 100644 index 814a806..0000000 --- a/scripts/train_model.py +++ /dev/null @@ -1,202 +0,0 @@ -""" -Finetune model based on transfer learning in the following tutorial: -https://pytorch.org/tutorials/beginner/transfer_learning_tutorial.html -Watch out for emtpy folders in the DataLoader (remove any empty folders). -""" -import torch -import torch.nn as nn -import torch.nn.functional as F -import torch.optim as optim -from torch.optim import lr_scheduler -import torch.backends.cudnn as cudnn -from torch.utils.data import WeightedRandomSampler -import numpy as np -import torchvision -from torchvision import datasets, models, transforms -from torchvision.transforms import v2 -import matplotlib.pyplot as plt -import time -import os -from PIL import Image -from tempfile import TemporaryDirectory - -from proteogram.utils import read_yaml - - -config = read_yaml('config.yml') - -# Data augmentation and normalization for training -# Just normalization for validation -data_transforms = { - 'train': v2.Compose([ - v2.RandomAutocontrast(p=0.1), - v2.RandomApply( - [v2.ColorJitter(brightness=0,hue=0,saturation=0.3)], - p=0.1), - v2.RandomApply( - [v2.RandomAdjustSharpness(sharpness_factor=2)], - p=0.1), - v2.RandomApply( - [v2.RandomErasing(p=0.5)], - p=0.1), - v2.Resize(224), - v2.ToTensor(), - v2.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) - ]), - 'val': v2.Compose([ - v2.Resize(224), - v2.ToTensor(), - v2.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) - ]), -} - -data_dir = config['training_data_dir'] -model_file = config['model_file'] - -device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") - -# Create datasets -image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x), - data_transforms[x]) - for x in ['train', 'val']} - -# Get sample weights from dataset indices and labels/targets -y_train = [image_datasets['train'].targets[i] for i in range(len(image_datasets['train'].samples))] -class_sample_count = np.array( - [len(np.where(y_train == t)[0]) for t in np.unique(y_train)]) -weight = 1. / class_sample_count -sample_weights = np.array([weight[t] for t in y_train]) -sample_weights = torch.from_numpy(sample_weights) - -# Sampler to deal with class imbalance -sampler = WeightedRandomSampler(sample_weights.type('torch.DoubleTensor'), len(sample_weights)) - -# Create data loaders -dataloaders = {} -dataloaders['train'] = torch.utils.data.DataLoader(image_datasets['train'], - batch_size=config['batch_size'], - num_workers=8, - shuffle=True) -dataloaders['val'] = torch.utils.data.DataLoader(image_datasets['val'], - batch_size=config['batch_size'], - num_workers=8, - shuffle=True) -dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'val']} -class_names = image_datasets['train'].classes - -# Train model function -def train_model(model, criterion, optimizer, scheduler, num_epochs=10): - since = time.time() - - # Create a temporary directory to save training checkpoints - with TemporaryDirectory() as tempdir: - best_model_params_path = os.path.join(tempdir, 'best_model_params.pt') - - torch.save(model.state_dict(), best_model_params_path) - best_acc = 0.0 - - for epoch in range(num_epochs): - print(f'Epoch {epoch}/{num_epochs - 1}') - print('-' * 10) - - # Each epoch has a training and validation phase - for phase in ['train', 'val']: - if phase == 'train': - model.train() # Set model to training mode - else: - model.eval() # Set model to evaluate mode - - running_loss = 0.0 - running_corrects = 0 - - # Iterate over data. - for inputs, labels in dataloaders[phase]: - inputs = inputs.to(device) - labels = labels.to(device) - - # zero the parameter gradients - optimizer.zero_grad() - - # forward - # track history if only in train - with torch.set_grad_enabled(phase == 'train'): - outputs = model(inputs) - _, preds = torch.max(outputs, 1) - loss = criterion(outputs, labels) - - # backward + optimize only if in training phase - if phase == 'train': - loss.backward() - optimizer.step() - - # statistics - running_loss += loss.item() * inputs.size(0) - running_corrects += torch.sum(preds == labels.data) - - epoch_loss = running_loss / dataset_sizes[phase] - epoch_acc = running_corrects.double() / dataset_sizes[phase] - - if phase == 'val': - scheduler.step(epoch_loss) - - print(f'{phase} Loss: {epoch_loss:.4f} Acc: {epoch_acc:.4f}') - - # deep copy the model - if phase == 'val' and epoch_acc > best_acc: - best_acc = epoch_acc - torch.save(model.state_dict(), best_model_params_path) - - print() - - time_elapsed = time.time() - since - print(f'Training complete in {time_elapsed // 60:.0f}m {time_elapsed % 60:.0f}s') - print(f'Best val Acc: {best_acc:4f}') - - # load best model weights - model.load_state_dict(torch.load(best_model_params_path, weights_only=True)) - return model - -# Load pre-trained model -if config['pretrained'] == 'True': - model_ft = models.resnet18(weights='IMAGENET1K_V1') -else: - # No pretrained weights (no transfer learning) - model_ft = models.resnet18() -num_ftrs = model_ft.fc.in_features -print(num_ftrs) -## Here the size of each output sample is set to 2. -## Alternatively, it can be generalized to ``nn.Linear(num_ftrs, len(class_names))``. -model_ft.fc = nn.Linear(num_ftrs, len(class_names)) - -# VGG19 -#model_ft = models.vgg19(weights='IMAGENET1K_V1') -#num_ftrs = model_ft.classifier[0].in_features - -#input_size = model_ft.classifier[0].in_features -#model_ft.classifier = nn.Sequential( -# nn.Linear(input_size, 128), nn.ReLU(), -# nn.Linear(128, len(class_names))) - -model_ft = model_ft.to(device) - -criterion = nn.CrossEntropyLoss() - -# Observe that all parameters are being optimized -optimizer_ft = optim.SGD(model_ft.parameters(), - lr=config['learning_rate'], - momentum=0.9) - -# Decay LR by a factor of 0.1 every 7 epochs -#exp_lr_scheduler = lr_scheduler.StepLR(optimizer_ft, step_size=7, gamma=0.1) -exp_lr_scheduler = lr_scheduler.ReduceLROnPlateau(optimizer_ft, 'min', factor=0.1) - -# Train and evaluate (all done in train_model function) -model_ft = train_model(model_ft, criterion, optimizer_ft, exp_lr_scheduler, - num_epochs=config['num_epochs']) - -# Save model as file -try: - torch.save(model_ft, os.path.join(data_dir, f"resnet18_lr{config['learning_rate']}_bs{config['batch_size']}.pt")) -except: - torch.save(model_ft, os.path.join(data_dir, 'resnet18_new.pt')) - diff --git a/scripts/train_multiple_models.py b/scripts/train_multiple_models.py new file mode 100644 index 0000000..b6dd15d --- /dev/null +++ b/scripts/train_multiple_models.py @@ -0,0 +1,686 @@ +""" +This script trains a CNN on the Proteogram dataset, with options for architecture (from-scratch ConvNet or pretrained ResNet18), hyperparameters (epochs, batch size, learning rate), and logging. It includes early stopping based on validation loss, and saves the best model weights to disk. The training and validation loss curves are plotted and saved to a file. After training, the model is evaluated on a held-out test set, with per-class accuracies and a classification report printed to the console. + +Requires a GPU for reasonable training time, especially for ResNet18. For reproducibility, random seeds are fixed and a deterministic data split is used. + +Data location and other parameters can be configured via command-line arguments or a config.yml file. Command-line arguments take precedence over config.yml. The Proteogram dataset should be prepared in advance using the create_v2_proteograms.py script. The root directory ("training_data_dir" in config.yml) should contain the "train" and "eval" subdirectories with the respective, representative images. An annotation TSV file is also required, which should be specified via the --tsv_file argument or included in config.yml as "tsv_file". The model weights will be saved to the path specified by "cnn_model_file_prefix" in config.yml, with a suffix indicating the architecture and hyperparameters. + +Here is more information about the SCOPe dataset: https://scop.berkeley.edu + + Usage example: + python train_multiple_models.py --model resnet18 --epochs 50 --batch_size 32 --lr 1e-4 +""" +import copy +from sched import scheduler +import pandas as pd +import numpy as np +import glob +import os +import random +import matplotlib +import matplotlib.pyplot as plt +from PIL import Image +import argparse + +from sklearn.metrics import classification_report + +import torch +from torch.utils.data import random_split, Dataset, DataLoader +import torch.nn as nn +import torch.nn.functional as F +import torch.optim as optim +import torchvision.models as tv_models +import torchvision.transforms as transforms + +from proteogram.utils import read_yaml + + +matplotlib.use('agg') +# # For reproducibility, set seeds +torch.manual_seed(0) +random.seed(0) +np.random.seed(0) + +class ProteogramDataset(Dataset): + """SCOPe-based Proteograms dataset.""" + + def __init__(self, tsv_file, root_dir, pad=True, level='class', new_size=128, transform=None, + names_to_labels=None, min_class_size=0, min_image_size=0, exclude_classes=None): + """ + Arguments + --------- + tsv_file : string + Path to the tsv file with annotations. + root_dir : string + Directory with all the images. + level : string + The scope category level [class, fold, superfamily, family] + transform : callable, optional + Optional transform to be applied on a sample. + names_to_labels : dict, optional + Pre-built name→integer mapping from the train dataset. When supplied + (eval mode), samples whose class is absent from this mapping are + dropped rather than creating a new mapping. + min_class_size : int + Classes with fewer than this many samples are excluded (train mode + only, ignored when names_to_labels is supplied). Default: 0 (keep all). + min_image_size : int + Images whose width or height is below this threshold (in pixels) are + excluded. Since proteograms are NxN where N = residue count, this is + equivalent to filtering by sequence length. Default: 0 (keep all). + exclude_classes : list of str, optional + Class names to exclude entirely (train mode only; eval samples for + excluded classes are automatically dropped via the names_to_labels + mechanism). Default: None (keep all). + """ + self.annot_frame = pd.read_csv(tsv_file, sep='\t') + self.root_dir = root_dir + self.files = glob.glob(os.path.join(self.root_dir, '*.jpg')) + + if min_image_size > 0: + small = [] + valid_files = [] + for f in self.files: + w, h = Image.open(f).size + if w < min_image_size or h < min_image_size: + small.append(os.path.basename(f)) + else: + valid_files.append(f) + self.files = valid_files + if small: + print(f'WARNING: {len(small)} image(s) smaller than ' + f'{min_image_size}x{min_image_size} px excluded. ' + f'First few: {small[:5]}') + self.transform = transform + self.pad = pad + self.level = level # class, fold, superfamily or family + self.new_size = new_size + + level_col = {'class': 'SCOPeClass', 'fold': 'SCOPeFold', + 'superfamily': 'SCOPeSuperfamily', 'family': 'SCOPeFamily'} + col = level_col.get(self.level, 'SCOPeFamily') + + # Look up annotation label for each image file + self.label_names = [] + missing = [] + for file in self.files: + bname = os.path.basename(file).replace('.jpg', '') + row = self.annot_frame[self.annot_frame['SCOPeID'] == bname] + if len(row) == 0: + missing.append(bname) + self.label_names.append(None) + else: + self.label_names.append(row.iloc[0][col]) + + if missing: + print(f'WARNING: {len(missing)} image(s) not found in TSV annotations ' + f'and will be excluded. First few: {missing[:5]}') + + # Drop files with no annotation + paired = [(f, l) for f, l in zip(self.files, self.label_names) if l is not None] + if not paired: + raise RuntimeError( + f'No images matched any entry in the TSV SCOPeID column. ' + f'Check that image basenames (without .jpg) match the SCOPeID values.') + self.files, self.label_names = zip(*paired) + self.files, self.label_names = list(self.files), list(self.label_names) + + if names_to_labels is None: + # Train mode: optionally exclude named classes + if exclude_classes: + excluded_set = set(exclude_classes) + unknown = excluded_set - set(self.label_names) + if unknown: + print(f'WARNING: --exclude_classes named class(es) not found in data: ' + + ', '.join(sorted(unknown))) + paired = [(f, l) for f, l in zip(self.files, self.label_names) + if l not in excluded_set] + if not paired: + raise RuntimeError('All classes were excluded — check --exclude_classes.') + print(f'Excluding {len(excluded_set - unknown)} named class(es): ' + + ', '.join(sorted(excluded_set - unknown))) + self.files, self.label_names = zip(*paired) + self.files, self.label_names = list(self.files), list(self.label_names) + + # Train mode: optionally exclude classes below the minimum size threshold + if min_class_size > 0: + label_counts = {name: self.label_names.count(name) + for name in set(self.label_names)} + excluded = {n for n, c in label_counts.items() if c < min_class_size} + if excluded: + print(f'Excluding {len(excluded)} class(es) with < {min_class_size} samples: ' + + ', '.join(f'{n} ({label_counts[n]})' for n in sorted(excluded))) + paired = [(f, l) for f, l in zip(self.files, self.label_names) + if l not in excluded] + if not paired: + raise RuntimeError('All classes were excluded — lower min_class_size.') + self.files, self.label_names = zip(*paired) + self.files, self.label_names = list(self.files), list(self.label_names) + + self.label_names_unique = set(self.label_names) + self.names_to_labels = {name: i for i, name in enumerate(sorted(self.label_names_unique))} + self.labels_to_names = {i: name for name, i in self.names_to_labels.items()} + else: + # Eval mode: reuse the train mapping; drop samples for unseen classes + unknown = set(self.label_names) - set(names_to_labels.keys()) + if unknown: + print(f'Dropping {len(unknown)} eval class(es) not in training set ' + f'(excluded during training): {sorted(unknown)}') + paired = [(f, l) for f, l in zip(self.files, self.label_names) + if l in names_to_labels] + self.files, self.label_names = zip(*paired) + self.files, self.label_names = list(self.files), list(self.label_names) + self.label_names_unique = set(self.label_names) + self.names_to_labels = names_to_labels + self.labels_to_names = {v: k for k, v in names_to_labels.items()} + + self.labels = [self.names_to_labels[n] for n in self.label_names] + + def get_pad(self, curr_size: int, target_size: int): + d = target_size - curr_size + if d <= 0: return (0, 0) # no need to pad + p1 = d // 2 + p2 = d - p1 + return (p1, p2) + + def __len__(self): + return len(self.files) + + def __getitem__(self, idx): + if torch.is_tensor(idx): + idx = idx.tolist() + img_name = self.files[idx] + label = torch.tensor(self.labels[idx]) + if self.pad: + image = plt.imread(img_name) + H, W = image.shape[0], image.shape[1] + DH, DW = self.new_size, self.new_size # desired height / width + padding = (self.get_pad(H, DH), self.get_pad(W, DW), (0, 0)) + image = np.pad(image, padding, constant_values=128) # pad with gray + image = image[0:DH, 0:DW, :] # crop if needed + else: + image = Image.open(img_name).convert('RGB') + image = image.resize((self.new_size, self.new_size)) + image = np.array(image) + if self.transform: + image = self.transform(image) + return image, label + + +class ConvNet(nn.Module): + """From-scratch CNN: 4 conv blocks (3→64→128→256→256) + GAP + FC. + + Uses BatchNorm after every conv layer for stable small-dataset training. + Global Average Pooling makes the architecture input-size agnostic. + Dropout (p=0.5) is applied in the FC layers only. + """ + def __init__(self, num_classes): + super().__init__() + + def _block(in_ch, out_ch): + return nn.Sequential( + nn.Conv2d(in_ch, out_ch, kernel_size=3, padding=1), + nn.BatchNorm2d(out_ch), + nn.ReLU(inplace=True), + nn.MaxPool2d(2, 2), + ) + + self.block1 = _block(3, 64) + self.block2 = _block(64, 128) + self.block3 = _block(128, 256) + self.block4 = _block(256, 256) + self.gap = nn.AdaptiveAvgPool2d(1) # (batch, 256, 1, 1) + self.fc1 = nn.Linear(256, 128) + self.fc2 = nn.Linear(128, num_classes) + + def forward(self, x): + x = self.block1(x) + x = self.block2(x) + x = self.block3(x) + x = self.block4(x) + x = self.gap(x).view(x.size(0), -1) # flatten: (batch, 256) + x = F.dropout(F.relu(self.fc1(x)), p=0.5, training=self.training) + x = self.fc2(x) + return x + + +def build_resnet18(num_classes, freeze_layers=('layer1',)): + """Pretrained ResNet18 with the classification head replaced. + + Only the very first residual block (layer1) is frozen — proteograms encode + distance-matrix geometry that looks nothing like ImageNet, so the backbone + needs freedom to adapt. Regularisation comes from AdamW weight decay + rather than aggressive layer freezing. A Dropout is inserted before the + final linear layer for additional regularisation. + """ + model = tv_models.resnet18(weights=tv_models.ResNet18_Weights.IMAGENET1K_V1) + for name, param in model.named_parameters(): + if any(name.startswith(layer) for layer in freeze_layers): + param.requires_grad = False + in_features = model.fc.in_features + model.fc = nn.Sequential( + nn.Dropout(0.5), + nn.Linear(in_features, num_classes), + ) + return model + + +def train_model(model, train_loader, val_loader, optimizer, epochs, + patience=None, device=torch.device('cpu')): + """Train the ConvNet, tracking train and val loss each epoch. + + If `patience` is set, applies early stopping: training halts when val loss + has not improved for that many consecutive epochs, and the best weights are + restored. If `patience` is None, all epochs run and no weight restoration + is performed. + """ + model.to(device) + training_loss = [] + val_loss_history = [] + + best_val_loss = float('inf') + best_weights = None + best_epoch = -1 + epochs_no_improve = 0 + + # LR scheduler uses its own patience (independent of early stopping patience) + scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, patience=5, factor=0.5) + + loaders = {'train': train_loader, 'val': val_loader} + + for epoch in range(epochs): + epoch_losses = {} + for phase in ['train', 'val']: + model.train() if phase == 'train' else model.eval() + running_loss = 0.0 + n_batches = 0 + with torch.set_grad_enabled(phase == 'train'): + for data, target in loaders[phase]: + data, target = data.to(device), target.to(device) + optimizer.zero_grad() + output = model(data) + loss = loss_criteria(output, target) + if phase == 'train': + loss.backward() + optimizer.step() + running_loss += loss.item() + n_batches += 1 + epoch_losses[phase] = running_loss / n_batches + lr_before = optimizer.param_groups[0]['lr'] + scheduler.step(epoch_losses['val']) + lr_after = optimizer.param_groups[0]['lr'] + + + training_loss.append(epoch_losses['train']) + val_loss_history.append(epoch_losses['val']) + + improved = epoch_losses['val'] < best_val_loss + if improved: + best_val_loss = epoch_losses['val'] + if patience is not None: + best_weights = copy.deepcopy(model.state_dict()) + best_epoch = epoch + epochs_no_improve = 0 + else: + epochs_no_improve += 1 + + suffix = '' + if patience is not None: + suffix = ' | *' if improved else f' | (no improvement {epochs_no_improve}/{patience})' + if lr_after < lr_before: + suffix += f' | LR reduced: {lr_before:.2e} → {lr_after:.2e}' + print(f'Epoch {epoch:>4d}: train loss: {epoch_losses["train"]:.6f} ' + f'val loss: {epoch_losses["val"]:.6f}' + suffix) + + if patience is not None and epochs_no_improve >= patience: + print(f'Early stopping at epoch {epoch} — no val loss improvement for {patience} epochs.') + break + + if patience is not None and best_weights is not None: + print(f'Restoring best weights (val loss: {best_val_loss:.6f})') + model.load_state_dict(best_weights) + return model, training_loss, val_loss_history, best_epoch + 1 + +def split_train_test(full_dataset, generator): + """Split a PyTorch Dataset object into train and test sets""" + total_size = len(full_dataset) + train_size = int(total_size * 0.7) + test_size = total_size - train_size + train_dataset, test_dataset = random_split( + full_dataset, + [train_size, test_size], + generator=generator + ) + return train_dataset, test_dataset + +def get_accuracies(model, test_loader, class_names, labels_to_names, device=torch.device('cpu')): + """Accuracies per class""" + correct_pred = {classname: 0 for classname in class_names} + total_pred = {classname: 0 for classname in class_names} + total_correct = 0 + len_data = len(test_loader) + y_pred = [] + y_test = [] + model.eval() + model.to(device) + with torch.no_grad(): + for (data, targets) in test_loader: + data, targets = data.to(device), targets.to(device) + outputs = model(data) + _, predictions = torch.max(outputs, 1) + # collect the correct predictions for each class + for label, prediction in zip(targets, predictions): + if label == prediction: + total_correct += 1 + correct_pred[labels_to_names[int(label)]] += 1 + total_pred[labels_to_names[int(label)]] += 1 + y_pred.append(labels_to_names[int(prediction)]) + y_test.append(labels_to_names[int(label)]) + + # Print accuracy for each class + for classname, correct_count in correct_pred.items(): + try: + accuracy = 100 * float(correct_count) / total_pred[classname] + print(f'Accuracy for class: {classname:5s} is {accuracy:.1f} %') + except ZeroDivisionError: + print(f'No samples in test set for class: {classname}') + print(f'Overall accuracy: {(total_correct/len_data*100):.1f} %') + + print('\nAdditional Classification Report:') + print(classification_report(y_test, y_pred)) + + +def view_pred_set(model, test_loader, num_preds, labels_to_names, fig_path): + """Graph a set of predictions with labels and save plot.""" + predictions = [] + images = [] + labels = [] + with torch.no_grad(): + cnt = 1 + for (data, target) in test_loader: + images.append(data) + labels.append(target[0]) + outputs = model(data) + _, predicted = torch.max(outputs, 1) + predictions.append(predicted[0]) + + if cnt == num_preds: + break + cnt += 1 + + for i in range(num_preds): + plot_row = max(1, int(num_preds/2)) + plt.subplot(2, plot_row, i + 1) + img = images[i] + npimg = img.numpy() + npimg = np.squeeze(npimg, axis=0) + npimg = npimg / 2 + 0.5 + plt.imshow(np.transpose(npimg, (1, 2, 0))) + plt.axis('off') + + color = "green" + pred = int(predictions[i].numpy()) + label = int(labels[i].numpy()) + name = labels_to_names[label] + if label != pred: + color = "red" + plt.title(name, color=color) + + plt.suptitle('Objects Found by Model', size=20) + plt.savefig(fig_path) + +def load_model(model_path, classes, image_size): + """Load the ConvNet model from disk.""" + model = ConvNet(classes, image_size) + ConvNet.load_state_dict(torch.load(model_path)) + return model + +def plot_losses(training_loss, val_loss, fig_path): + """Plot training and validation loss curves on the same axes and save to file.""" + epochs = range(1, len(training_loss) + 1) + plt.figure() + plt.plot(epochs, training_loss, label='Train loss') + plt.plot(epochs, val_loss, label='Val loss') + plt.xlabel('Epoch') + plt.ylabel('Loss') + plt.title('Training and Validation Loss') + plt.legend() + plt.tight_layout() + plt.savefig(fig_path, dpi=150) + plt.close() + print(f'Loss curve saved to {fig_path}') + +def seed_worker(worker_id): + worker_seed = torch.initial_seed() % 2**32 + np.random.seed(worker_seed) + random.seed(worker_seed) + +if __name__ == '__main__': + + parser = argparse.ArgumentParser( + description="Train CNN on Proteograms.") + parser.add_argument('--data_dir', '-d', + type=str, + default=None, + help="Root directory containing 'train' and 'eval' subdirectories. " + "Overrides training_data_dir in config.yml.") + parser.add_argument("--epochs", "-e", + type=int, + help="Number of training epochs.") + parser.add_argument("--batch_size", "-b", + type=int, + help="Training batch size.") + parser.add_argument("--lr", "-l", + type=float, + help="Training learning rate.") + parser.add_argument('--model', '-m', + choices=['cnn', 'resnet18'], + default='cnn', + help="Model architecture: 'cnn' (from-scratch 4-block ConvNet) " + "or 'resnet18' (pretrained ResNet18 fine-tuning). Default: cnn.") + parser.add_argument('--overwrite', '-o', + action='store_true', + help="Recreate / overwrite model") + parser.add_argument('--resize', + action='store_true', + help="Resize images to new_size instead of padding. " + "Default is to pad with gray, which preserves the " + "1-pixel-per-residue-pair semantic of proteograms.") + parser.add_argument('--verbose', '-v', + action='store_true', + help="Verbose output and logging.") + parser.add_argument('--tsv_file', '-t', + type=str, + default=None, + help="Path to the TSV annotations file. Defaults to " + "ProteogramData_SCOP_RCSB_PDBe_AnnotationsLookup.tsv " + "in the proteograms directory.") + parser.add_argument('--patience', + type=int, + default=None, + help="Early stopping patience: stop after this many epochs " + "with no improvement in val loss. Omit to disable early stopping.") + parser.add_argument('--val_size', + type=float, + default=0.15, + help="Fraction of train images to hold out as validation " + "set for early stopping (default: 0.15).") + parser.add_argument('--exclude_classes', '-x', + type=str, + default=None, + help="Comma-separated list of SCOPe class names to exclude " + "from training and evaluation (e.g. 'j,h'). " + "Useful for removing very small or low-quality classes.") + parser.add_argument('--level', + choices=['class', 'fold', 'superfamily', 'family'], + default='class', + help="SCOPe hierarchy level to use as the classification target. " + "'class' is the highest (broadest) level; 'family' is the lowest " + "(finest). Default: class.") + args = parser.parse_args() + + config = read_yaml('config.yml') + root_dir = args.data_dir or config['training_data_dir'] + # Get level from command line or config, with command line taking precedence. Default to 'class' if neither is provided. + level = args.level or config.get('scope_level', 'class') + + if args.epochs: + epochs = args.epochs + elif 'num_epochs' in config: + epochs = config['num_epochs'] + else: + raise ValueError("Number of epochs must be specified via command line or config.yml") + if args.lr: + lr = args.lr + elif 'learning_rate' in config: + lr = config['learning_rate'] + else: + raise ValueError("Learning rate must be specified via command line or config.yml") + if args.batch_size: + batch_size = args.batch_size + elif 'batch_size' in config: + batch_size = config['batch_size'] + else: + raise ValueError("Batch size must be specified via command line or config.yml") + + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + + image_resize = 200 # pad to largest possible size; GAP makes the FC layer size-agnostic + + # ResNet18 was trained with ImageNet normalisation — apply the same preprocessing + # so the pretrained feature detectors remain valid. The from-scratch CNN does not + # need this (no pretrained assumptions), so we skip it there. + _imagenet_norm = transforms.Normalize(mean=[0.485, 0.456, 0.406], + std=[0.229, 0.224, 0.225]) + _augment = [ + transforms.RandomApply([transforms.ColorJitter(brightness=0.1, contrast=0.1)], p=0.2), + transforms.RandomApply([transforms.RandomAdjustSharpness(sharpness_factor=2)], p=0.2), + ] + + if args.model == 'resnet18': + transform_train = transforms.Compose([transforms.ToTensor()] + _augment + [_imagenet_norm]) + transform_eval = transforms.Compose([transforms.ToTensor(), _imagenet_norm]) + else: + transform_train = transforms.Compose([transforms.ToTensor()] + _augment + [_imagenet_norm]) + transform_eval = transforms.Compose([transforms.ToTensor(), _imagenet_norm]) + + tsv_file = args.tsv_file or os.path.join( + root_dir, 'ProteogramData_SCOP_RCSB_PDBe_AnnotationsLookup.tsv') + + exclude_classes = [c.strip() for c in args.exclude_classes.split(',')] \ + if args.exclude_classes else None + + train_dataset = ProteogramDataset( + tsv_file=tsv_file, + root_dir=os.path.join(root_dir, 'train'), + level=args.level, + new_size=image_resize, + pad=not args.resize, + transform=transform_train, + # exclude classes with certain number of samples to avoid extreme class imbalance and unreliable eval metrics; ignored if names_to_labels is supplied + min_class_size=20, + min_image_size=20, + exclude_classes=exclude_classes) + + eval_dataset = ProteogramDataset( + tsv_file=tsv_file, + root_dir=os.path.join(root_dir, 'eval'), + level=args.level, + new_size=image_resize, + pad=not args.resize, + transform=transform_eval, + names_to_labels=train_dataset.names_to_labels, + min_image_size=20) + + # This is for reproducibility - https://docs.pytorch.org/docs/stable/notes/randomness.html + g = torch.Generator() + g.manual_seed(0) + + # Carve a validation split from the train folder (never touches eval/) + n_total = len(train_dataset) + n_val = max(1, int(n_total * args.val_size)) + n_train = n_total - n_val + train_split, val_split = random_split(train_dataset, [n_train, n_val], generator=g) + print(f'Split: {n_train} train / {n_val} val / {len(eval_dataset)} test (held-out)') + + # WeightedRandomSampler: oversample minority classes so each epoch sees + # a balanced class distribution regardless of raw class frequencies. + train_labels = [train_dataset.labels[i] for i in train_split.indices] + label_counts = torch.bincount(torch.tensor(train_labels)) + class_weights = torch.where( + label_counts > 0, 1.0 / label_counts.float(), torch.zeros_like(label_counts.float())) + sample_weights = [class_weights[lbl].item() for lbl in train_labels] + sampler = torch.utils.data.WeightedRandomSampler( + weights=sample_weights, num_samples=len(sample_weights), replacement=True) + + class_names = train_dataset.label_names_unique + + train_loader = DataLoader(train_split, + batch_size=args.batch_size, + # Note, shuffle is mutually exclusive with sampler + shuffle=True, + # sampler=sampler, + worker_init_fn=seed_worker) + val_loader = DataLoader(val_split, + batch_size=args.batch_size, + shuffle=False, + worker_init_fn=seed_worker, + generator=g) + test_loader = DataLoader(eval_dataset, + batch_size=1, + shuffle=False, + worker_init_fn=seed_worker, + generator=g) + + num_classes = len(class_names) + + if args.model == 'resnet18': + model = build_resnet18(num_classes) + # Differential LR: lower rate for pretrained backbone, full rate for new head + backbone_params = [p for n, p in model.named_parameters() if 'fc' not in n and p.requires_grad] + head_params = list(model.fc.parameters()) + optimizer = optim.AdamW([ + {'params': backbone_params, 'lr': args.lr * 0.1}, + {'params': head_params, 'lr': args.lr}, + ], weight_decay=1e-3) + print(f'ResNet18: backbone LR={args.lr * 0.1:.2e}, head LR={args.lr:.2e}') + else: + model = ConvNet(num_classes) + optimizer = optim.Adam(model.parameters(), lr=args.lr) + print(f'ConvNet (from scratch): LR={args.lr:.2e}') + + loss_criteria = nn.CrossEntropyLoss(label_smoothing=0.1) + model, training_loss, val_loss, epochs_trained = train_model(model, + train_loader=train_loader, + val_loader=val_loader, + optimizer=optimizer, + epochs=args.epochs, + patience=args.patience, + device=device) + + plot_losses(training_loss, val_loss, + fig_path=os.path.join(root_dir, 'loss_curves.png')) + + model_file = config.get('model_file_prefix', 'scope_proteogram_model') \ + + f'_{args.model}_lr{lr}_bs{batch_size}_e{epochs_trained}.pt' + model_path = os.path.join(root_dir, model_file) + + # Save model + if os.path.exists(model_path) and not args.overwrite: + print(f'Model file {model_path} exists and overwrite not set, not saving model.') + else: + # Save only the model weights + torch.save(model.state_dict(), model_path) + print(f'Saved model to {model_path}') + + get_accuracies(model, + test_loader, + class_names, + train_dataset.labels_to_names) + + view_pred_set(model, + test_loader, + num_preds=10, + 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