PETIMOT: Protein Motion Inference from Sparse Data

PETIMOT (Protein sEquence and sTructure-based Inference of MOTions) predicts protein conformational changes using SE(3)-equivariant graph neural networks and pre-trained protein language models.

Installation

# Create and activate conda environment
conda create -n petimot python=3.9
conda activate petimot

# Clone and install
git clone https://github.com/PhyloSofS-Team/PETIMOT.git
cd petimot
pip install -r requirements.txt

Usage

Reproduce paper results

1. Download resources from Figshare:

• Download default_2025-02-07_21-54-02_epoch_33.pt into the weights/ directory
• Download and extract ground_truth.zip into the ground_truth/ directory

2. Run inference and evaluation:

python -m petimot infer_and_evaluate \
    --model-path weights/default_2025-02-07_21-54-02_epoch_33.pt \
    --list-path eval_list.txt \
    --ground-truth-path ground_truth/ \
    --prediction-path predictions/ \
    --evaluation-path evaluation/

Compare with baseline methods

1. Download baseline predictions from Figshare :

• Download and extract baseline_predictions.zip into the baselines/ directory

2. Run evaluation:

python -m petimot evaluate \
    --prediction-path baselines/alphaflow_pdb_distilled/ \
    --ground-truth-path ground_truth/ \
    --output-path evaluation/

Available baseline predictions:

• AlphaFlow (distilled)
• ESMFlow (distilled)
• Normal Mode Analysis

Predict motions for your own PDB files

# Single PDB structure
python -m petimot infer \
    --model-path weights/default_2025-02-07_21-54-02_epoch_33.pt \
    --list-path protein.pdb \
    --output-path predictions/

# Multiple structures (provide paths in a text file)
python -m petimot infer \
    --model-path weights/default_2025-02-07_21-54-02_epoch_33.pt \
    --list-path protein_list.txt \
    --output-path predictions/