A model for protein sequence prediction given backbone coordinates

Installation (Linux)

All Python dependencies are specified in environment.yml.

mamba env create -f environment.yaml

Inference

Basic Usage

CUDA_VISIBLE_DEVICES=0 python sample.py \
  --pdb_path <path_to_pdb> \
  --ckpt_path ./weights/mpnn_dataset/pdb_weights.pt \
  --num_predictions 8 \
  --output_root_dir ./scratch/

Command Line Options

Input Options

• --pdb_path: Path to a single input PDB file
• --pdb_dir: Path to directory containing multiple PDB files to process
• --json_path: Path to JSON file containing list of PDB file paths
• --ckpt_path: Path to model checkpoint file (default: ./weights/afdb_dataset/afdb_weights.pt)
• --device: Device to run on, e.g., 'cuda:0' or 'cpu' (default: cuda:0)

Output Options

• --output_root_dir: Root directory for output files (default: ./)
  • Creates subdirectories: backbones/ (PDB files) and seqs/ (FASTA files)
• --exclude_colon: Exclude ":" separator between chains in output sequences

Sampling Options

• --num_predictions: Number of predictions per input structure (default: 8)
• --temp: Temperature parameter for sampling (default: 0.1)
• --noise_std: Standard deviation of Gaussian noise to add to coordinates (default: 0.0)
• --half_half: Run half predictions with noise_std=0 and half with noise_std=0.2

Conditioning Options

• --chain_condition: Condition on specific chain (e.g., 'A', 'B')
• --res_condition: Space-separated list of residue indices to condition on (e.g., --res_condition 1 2 3 10 15)

Amino Acid Constraints

• --omit_AA: Space-separated list of amino acid one-letter codes to exclude from sampling (e.g., --omit_AA C M)

Advanced Sampling Options

• --tied_weights: Use tied weights during prediction
• --cfg: Run classifier-free guidance
• --sample_purity: Enable purity sampling
• --partial_flows: Run partial flow matching
• --t: Forward diffusion time for partial flows (default: 0.5)

Examples

Process a single PDB file:

python sample.py \
  --pdb_path ./examples/6zht.pdb \
  --ckpt_path ./weights/mpnn_dataset/pdb_weights.pt \
  --num_predictions 8 \
  --output_root_dir ./output/

Process directory of PDB files:

python sample.py \
  --pdb_dir ./examples/ \
  --ckpt_path ./weights/mpnn_dataset/pdb_weights.pt \
  --num_predictions 8 \
  --output_root_dir ./output/

Keep chain A fixed and redesign all other residues using a higher sampling temperature (0.3), excluding cysteines, adding backbone noise with a standard deviation of 0.2, and generating 8 sequence samples:

python sample.py \
  --pdb_path ./examples/6zht.pdb \
  --chain_condition A \
  --temp 0.3 \
  --omit_AA C \
  --noise_std 0.2 \
  --num_predictions 8 \
  --output_root_dir ./output/

Process multiple PDB files using JSON list (proteinmpnn style):

python sample.py \
  --json_path ./examples/files.json \
  --ckpt_path ./weights/mpnn_dataset/pdb_weights.pt \
  --num_predictions 8 \
  --output_root_dir ./output/

Generate Summary

Generate an interactive HTML summary of your predictions:

python generate_summary.py output/

This creates output/summary.html with:

• Interactive structure viewer using py2Dmol
• Multiple sequence generation visualization
• PSSM heatmap and sequence logo plots

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This repository is a modified version of OpenFold and incorporates components from MultiFlow, ProteinMPNN, Protenix, and py2Dmol

For any questions and concerns feel free to submit an issue