PPIformer

PPIformer is a state-of-the-art predictor of the effects of mutations on protein-protein interactions (PPIs), as quantified by the binding energy changes (ddG). The model was pre-trained on the PPIRef dataset via a coarse-grained structural masked modeling and fine-tuned on SKEMPI v2.0 via log odds. PPIformer was shown to successfully identify known favorable mutations of the staphylokinase thrombolytic and a human antibody against the SARS-CoV-2 spike protein. Please see more details in our paper.

Please do not hesitate to contact us or create an issue/PR if you have any questions or suggestions. ✌️

Web on Hugging Face Spaces 🤗

The preview of PPIformer is available via an interactive user interface on Hugging Face Spaces:

Installation

To install PPIformer locally, clone this repository and install the environment (you may need to adjust the versions of the PyTorch-based packages in the script depending on your system):

git clone https://github.com/anton-bushuiev/PPIformer; cd PPIformer
. scripts/installation/install.sh

Inference

Quick ddG prediction

import torch
from ppiformer.tasks.node import DDGPPIformer
from ppiformer.utils.api import download_weights, predict_ddg
from ppiformer.definitions import PPIFORMER_WEIGHTS_DIR, PPIFORMER_TEST_DATA_DIR

# Download the weights
download_weights()

# Load the ensamble of fine-tuned models
device = 'cpu'
models = [DDGPPIformer.load_from_checkpoint(PPIFORMER_WEIGHTS_DIR / f'ddg_regression/{i}.ckpt', map_location=torch.device(device)).eval() for i in range(3)]

# Specify input
ppi_path = PPIFORMER_TEST_DATA_DIR / '1bui_A_C.pdb'  # PDB or PPIRef file (see https://github.com/anton-bushuiev/PPIRef?tab=readme-ov-file#extracting-ppis)
muts = ['SC16A', 'FC47A', 'SC16A,FC47A']  # List of single- or multi-point mutations

# Predict
ddg = predict_ddg(models, ppi_path, muts)
ddg
> tensor([-0.3708,  1.5188,  1.1482])

Multi-GPU ddG screening

TBD

Training

TBD

How it works

TBD

TODO

• Pre-training and fine-tuning examples with scripts/run.py
• Installation script examples for AMD GPUs and NVIDIA GPUs
• SSL-pretrained weights (without fine-tuning)

References

If you find this repository useful, please cite our paper:

@article{
  bushuiev2024learning,
  title={Learning to design protein-protein interactions with enhanced generalization},
  author={Anton Bushuiev and Roman Bushuiev and Petr Kouba and Anatolii Filkin and Marketa Gabrielova and Michal Gabriel and Jiri Sedlar and Tomas Pluskal and Jiri Damborsky and Stanislav Mazurenko and Josef Sivic},
  booktitle={The Twelfth International Conference on Learning Representations},
  year={2024}
}