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ThermoMPNN
ThermoMPNN is a graph neural network (GNN) trained using transfer learning to predict changes in stability for protein point mutants.
For details on ThermoMPNN training and methodology, please see the accompanying paper.
UPDATE (August 2024): A new ThermoMPNN model has been released for prediction of ddG for double mutant pairs at a new repo, ThermoMPNN-D. For details, see our new preprint!
UPDATE (September 2024): An experimental ThermoMPNN model has been added for indel (insertion/deletion) ddG prediction. This can be found at ThermoMPNN-I. We include a new Colab notebook for ThermoMPNN-I as well. Note that this model has only had limited in silico validation.
Citing this work
If you use the code, please cite:
@article{
doi:10.1073/pnas.2314853121,
author = {Henry Dieckhaus and Michael Brocidiacono and Nicholas Z. Randolph and Brian Kuhlman },
title = {Transfer learning to leverage larger datasets for improved prediction of protein stability changes},
journal = {Proceedings of the National Academy of Sciences},
volume = {121},
number = {6},
pages = {e2314853121},
year = {2024},
doi = {10.1073/pnas.2314853121},
URL = {https://www.pnas.org/doi/abs/10.1073/pnas.2314853121},
eprint = {https://www.pnas.org/doi/pdf/10.1073/pnas.2314853121},
}
Colab Implementation
For a user-friendly version of ThermoMPNN requiring no installation, use this Colab notebook.
Installation
To install ThermoMPNN, first clone this repository
git clone https://github.com/Kuhlman-Lab/ThermoMPNN.git
Then use the file environment.yaml install the necessary python dependencies (I recommend using mamba for convenience):
mamba env create -f environment.yaml
This will create a conda environment called thermoMPNN.
NOTE: installing from the .yaml file may cause pytorch to install incorrectly (CPU version instead of GPU version).
Alternate installation instructions:
# create and activate environment
mamba create -n thermoMPNN python=3.10
mamba activate thermoMPNN
# install pytorch related packages
mamba install pytorch torchvision torchaudio pytorch-cuda=11.8 pytorch-lightning -c nvidia -c pytorch -c conda-forge
# install all other packages
mamba install joblib omegaconf pandas numpy tqdm mmseqs2 wandb biopython -c bioconda -c conda-forge -c anaconda
# check for proper GPU recognition (should return True)
python -c 'import torch; print(torch.cuda.is_available())'
Note: if you are planning to do any model training or complicated inference (i.e., from a CSV), you will need to update the local.yaml file to reflect dataset locations on your local system so that ThermoMPNN can find the data it needs. This step can be skipped if only running custom_inference.py.
Inference
There are a few different ways to run inference with ThermoMPNN all located in the analysis directory.
From a PDB
The simplest way is to use the custom_inference.py script to pass a custom PDB to ThermoMPNN for site-saturation mutagenesis.
From a CSV and many PDBs
For larger batches of predictions, it is recommended to set up a CustomDataset object by inheriting from the ddgBenchDataset class in the datasets.py file, then add this dataset to the SSM.py script to get aggregated predictions for the whole dataset.
For benchmarking purposes
The thermompnn_benchmarking.py is set up to score different models on a CustomDataset object or one of the datasets used in this study. An example inference SLURM script is provided at examples/inference.sh.
Training
The main training script is train_thermompnn.py. To set up a training run, you must write a config.yaml file (example provided) to specify model hyperparameters. You also must provide a local.yaml file to tell ThermoMPNN where to find your data. These files serve as experiment logs as well.
Training ThermoMPNN requires the use of a GPU. On a small dataset (<5000 data points), training takes <30s per epoch, while on a mega-scale dataset (>200,000 data points), it takes 8-12min per epoch (on a single V100 GPU). An example training SLURM script is provided at examples/train.sh.
Splits and Model Weights
For the purpose of replication and future benchmarking, the dataset splits used in this study are included as .pkl files under the dataset_splits/ directory.
ThermoMPNN model weights can be found in the models/ directory. The following model weights are provided:
- thermoMPNN_default.pt (best ThermoMPNN model trained on Megascale training dataset)
Dataset Availability
The datasets used in this study can be found in the following locations:
Fireprot: https://doi.org/10.5281/zenodo.8169288 Megascale: https://doi.org/10.5281/zenodo.7401274 S669: https://doi.org/10.1093/bib/bbab555 SSYM, P53, Myoglobin, etc: https://protddg-bench.github.io