Predicting structural movements from AlphaFold distograms.
DistoMove trains a neural network to predict per-residue-pair structural mobility from AlphaFold2 distogram logits. Given a set of AlphaFold2 pickle outputs for a target protein, it learns to classify residue pairs into movement classes based on structural labels, enabling prediction of conformational flexibility without running MD simulations.
Input features are the 64-bin distogram logits from AlphaFold2 (optionally extended with the predicted aligned error, PAE, to 65 features). Two network architectures are supported:
- 2dconv (default): linear projection → 2D convolution over the L×L residue-pair map → linear classifier
- mlp: purely feedforward, operating on each residue pair independently
Labels are quantized into movement classes using distance thresholds [0, 1, 3, 10] Å and trained with binary cross-entropy loss. Validation metrics include AUROC, AUPR, and top-10 accuracy.
conda env create -f environment.yml
conda activate DistoMoveRequires a CUDA-capable GPU. The environment is based on PyTorch 2.1.0 + CUDA 11.8.
Training labels (multiclass_labels.pkl) are available on Zenodo. Place the file in the working directory before training.
AlphaFold2 pickle files (containing distogram.logits and optionally predicted_aligned_error) should be organized as:
<pkl_dir>/<target>/afsample2/result*.pkl
python train.py <target> [options]| Argument | Default | Description |
|---|---|---|
target | (required) | Target protein identifier (used for leave-one-out validation) |
--pkl-dir | (hardcoded path) | Directory containing AlphaFold2 pickle files |
--training-to-use | 10 | Number of AF2 samples per target to use (1, 5, or 10) |
--network-type | 2dconv | Network architecture: 2dconv or mlp |
--no-pae | False | Exclude PAE features (uses 64 input channels instead of 65) |
--no-half-precision | False | Disable float16 training |
--device | auto | cuda or cpu |
python train.py 1ABC --training-to-use 10 --network-type 2dconvTraining uses leave-one-out validation: the specified target is held out as the validation set and all other proteins in the label dictionary are used for training.
Training produces the following directories (named by run configuration):
checkpoints_<prefix>/— model checkpoints saved every 10 epochsplots_<prefix>/— per-epoch PR/ROC curves and residue-pair maps (PDF)pickles_<prefix>/— raw model outputs and labels per epochmetrics_<prefix>/— CSV files with per-epoch AUC, AUPR, and top-10 accuracy
python make_labels.pyGenerates multiclass_labels.pkl from structural data. See script for input format details.
Apache 2.0