| # ProteinMPNN |
| To train/retrain ProteinMPNN clone this github repo and install Python>=3.0, PyTorch, Numpy. |
|
|
| The multi-chain training data (16.5 GB, PDB biounits, 2021 August 2) can be downloaded from here: `https://files.ipd.uw.edu/pub/training_sets/pdb_2021aug02.tar.gz`; The small subsample (47 MB) of this data for testing purposes can be downloaded from here: `https://files.ipd.uw.edu/pub/training_sets/pdb_2021aug02_sample.tar.gz` |
|
|
| ``` |
| Training set for ProteinMPNN curated by Ivan Anishchanko. |
| |
| Each PDB entry is represented as a collection of .pt files: |
| PDBID_CHAINID.pt - contains CHAINID chain from PDBID |
| PDBID.pt - metadata and information on biological assemblies |
| |
| PDBID_CHAINID.pt has the following fields: |
| seq - amino acid sequence (string) |
| xyz - atomic coordinates [L,14,3] |
| mask - boolean mask [L,14] |
| bfac - temperature factors [L,14] |
| occ - occupancy [L,14] (is 1 for most atoms, <1 if alternative conformations are present) |
| |
| PDBID.pt: |
| method - experimental method (str) |
| date - deposition date (str) |
| resolution - resolution (float) |
| chains - list of CHAINIDs (there is a corresponding PDBID_CHAINID.pt file for each of these) |
| tm - pairwise similarity between chains (TM-score,seq.id.,rmsd from TM-align) [num_chains,num_chains,3] |
| asmb_ids - biounit IDs as in the PDB (list of str) |
| asmb_details - how the assembly was identified: author, or software, or smth else (list of str) |
| asmb_method - PISA or smth else (list of str) |
| |
| asmb_chains - list of chains which each biounit is composed of (list of str, each str contains comma separated CHAINIDs) |
| asmb_xformIDX - (one per biounit) xforms to be applied to chains from asmb_chains[IDX], [n,4,4] |
| [n,:3,:3] - rotation matrices |
| [n,3,:3] - translation vectors |
| |
| list.csv: |
| CHAINID - chain label, PDBID_CHAINID |
| DEPOSITION - deposition date |
| RESOLUTION - structure resolution |
| HASH - unique 6-digit hash for the sequence |
| CLUSTER - sequence cluster the chain belongs to (clusters were generated at seqID=30%) |
| SEQUENCE - reference amino acid sequence |
| |
| valid_clusters.txt - clusters used for validation |
| |
| test_clusters.txt - clusters used for testing |
| ``` |
|
|
| Code organization: |
| * `training.py` - the main script to train the model |
| * `model_utils.py` - utility functions and classes for the model |
| * `utils.py` - utility functions and classes for data loading |
| * `exp_020/` - sample outputs |
| * `submit_exp_020.sh` - sample SLURM submit script |
| ----------------------------------------------------------------------------------------------------- |
| Input flags for `training.py`: |
| ``` |
| argparser.add_argument("--path_for_training_data", type=str, default="my_path/pdb_2021aug02", help="path for loading training data") |
| argparser.add_argument("--path_for_outputs", type=str, default="./test", help="path for logs and model weights") |
| argparser.add_argument("--previous_checkpoint", type=str, default="", help="path for previous model weights, e.g. file.pt") |
| argparser.add_argument("--num_epochs", type=int, default=200, help="number of epochs to train for") |
| argparser.add_argument("--save_model_every_n_epochs", type=int, default=10, help="save model weights every n epochs") |
| argparser.add_argument("--reload_data_every_n_epochs", type=int, default=2, help="reload training data every n epochs") |
| argparser.add_argument("--num_examples_per_epoch", type=int, default=1000000, help="number of training example to load for one epoch") |
| argparser.add_argument("--batch_size", type=int, default=10000, help="number of tokens for one batch") |
| argparser.add_argument("--max_protein_length", type=int, default=10000, help="maximum length of the protein complext") |
| argparser.add_argument("--hidden_dim", type=int, default=128, help="hidden model dimension") |
| argparser.add_argument("--num_encoder_layers", type=int, default=3, help="number of encoder layers") |
| argparser.add_argument("--num_decoder_layers", type=int, default=3, help="number of decoder layers") |
| argparser.add_argument("--num_neighbors", type=int, default=48, help="number of neighbors for the sparse graph") |
| argparser.add_argument("--dropout", type=float, default=0.1, help="dropout level; 0.0 means no dropout") |
| argparser.add_argument("--backbone_noise", type=float, default=0.2, help="amount of noise added to backbone during training") |
| argparser.add_argument("--rescut", type=float, default=3.5, help="PDB resolution cutoff") |
| argparser.add_argument("--debug", type=bool, default=False, help="minimal data loading for debugging") |
| argparser.add_argument("--gradient_norm", type=float, default=-1.0, help="clip gradient norm, set to negative to omit clipping") |
| argparser.add_argument("--mixed_precision", type=bool, default=True, help="train with mixed precision") |
| ``` |
| ----------------------------------------------------------------------------------------------------- |
| For example to make a conda environment to run ProteinMPNN: |
| * `conda create --name mlfold` - this creates conda environment called `mlfold` |
| * `source activate mlfold` - this activate environment |
| * `conda install pytorch torchvision torchaudio cudatoolkit=11.3 -c pytorch` - install pytorch following steps from https://pytorch.org/ |
| ----------------------------------------------------------------------------------------------------- |
| Models provided for the vanilla MPNN were trained with default flags: |
| * `v_48_002.pt` - `--num_neighbors 48 --backbone_noise 0.02 --num_epochs 150` |
| * `v_48_010.pt` - `--num_neighbors 48 --backbone_noise 0.10 --num_epochs 150` |
| * `v_48_020.pt` - `--num_neighbors 48 --backbone_noise 0.20 --num_epochs 150` |
| ----------------------------------------------------------------------------------------------------- |
| ``` |
| @article{dauparas2022robust, |
| title={Robust deep learning--based protein sequence design using ProteinMPNN}, |
| author={Dauparas, Justas and Anishchenko, Ivan and Bennett, Nathaniel and Bai, Hua and Ragotte, Robert J and Milles, Lukas F and Wicky, Basile IM and Courbet, Alexis and de Haas, Rob J and Bethel, Neville and others}, |
| journal={Science}, |
| volume={378}, |
| number={6615}, |
| pages={49--56}, |
| year={2022}, |
| publisher={American Association for the Advancement of Science} |
| } |
| ``` |
| ----------------------------------------------------------------------------------------------------- |
| |
