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dauparas / ProteinMPNN

Code for the ProteinMPNN paper
MIT License
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ProteinMPNN

ProteinMPNN Read ProteinMPNN paper.

To run ProteinMPNN clone this github repo and install Python>=3.0, PyTorch, Numpy.

Full protein backbone models: vanilla_model_weights/v_48_002.pt, v_48_010.pt, v_48_020.pt, v_48_030.pt, soluble_model_weights/v_48_010.pt, v_48_020.pt.

CA only models: ca_model_weights/v_48_002.pt, v_48_010.pt, v_48_020.pt. Enable flag --ca_only to use these models.

Helper scripts: helper_scripts - helper functions to parse PDBs, assign which chains to design, which residues to fix, adding AA bias, tying residues etc.

Code organization:

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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/
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These are provided `examples/`:
* `submit_example_1.sh` - simple monomer example 
* `submit_example_2.sh` - simple multi-chain example
* `submit_example_3.sh` - directly from the .pdb path
* `submit_example_3_score_only.sh` - return score only (model's uncertainty)
* `submit_example_3_score_only_from_fasta.sh` - return score only (model's uncertainty) loading sequence from fasta files
* `submit_example_4.sh` - fix some residue positions
* `submit_example_4_non_fixed.sh` - specify which positions to design
* `submit_example_5.sh` - tie some positions together (symmetry)
* `submit_example_6.sh` - homooligomer example
* `submit_example_7.sh` - return sequence unconditional probabilities (PSSM like)
* `submit_example_8.sh` - add amino acid bias
* `submit_example_pssm.sh` - use PSSM bias when designing sequences
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Output example:

3HTN, score=1.1705, global_score=1.2045, fixed_chains=['B'], designed_chains=['A', 'C'], model_name=v_48_020, git_hash=015ff820b9b5741ead6ba6795258f35a9c15e94b, seed=37 NMYSYKKIGNKYIVSINNHTEIVKALNAFCKEKGILSGSINGIGAIGELTLRFFNPKTKAYDDKTFREQMEISNLTGNISSMNEQVYLHLHITVGRSDYSALAGHLLSAIQNGAGEFVVEDYSERISRTYNPDLGLNIYDFER/NMYSYKKIGNKYIVSINNHTEIVKALNAFCKEKGILSGSINGIGAIGELTLRFFNPKTKAYDDKTFREQMEISNLTGNISSMNEQVYLHLHITVGRSDYSALAGHLLSAIQNGAGEFVVEDYSERISRTYNPDLGLNIYDFER T=0.1, sample=1, score=0.7291, global_score=0.9330, seq_recovery=0.5736 NMYSYKKIGNKYIVSINNHTEIVKALKKFCEEKNIKSGSVNGIGSIGSVTLKFYNLETKEEELKTFNANFEISNLTGFISMHDNKVFLDLHITIGDENFSALAGHLVSAVVNGTCELIVEDFNELVSTKYNEELGLWLLDFEK/NMYSYKKIGNKYIVSINNHTDIVTAIKKFCEDKKIKSGTINGIGQVKEVTLEFRNFETGEKEEKTFKKQFTISNLTGFISTKDGKVFLDLHITFGDENFSALAGHLISAIVDGKCELIIEDYNEEINVKYNEELGLYLLDFNK T=0.1, sample=2, score=0.7414, global_score=0.9355, seq_recovery=0.6075 NMYKYKKIGNKYIVSINNHTEIVKAIKEFCKEKNIKSGTINGIGQVGKVTLRFYNPETKEYTEKTFNDNFEISNLTGFISTYKNEVFLHLHITFGKSDFSALAGHLLSAIVNGICELIVEDFKENLSMKYDEKTGLYLLDFEK/NMYKYKKIGNKYVVSINNHTEIVEALKAFCEDKKIKSGTVNGIGQVSKVTLKFFNIETKESKEKTFNKNFEISNLTGFISEINGEVFLHLHITIGDENFSALAGHLLSAVVNGEAILIVEDYKEKVNRKYNEELGLNLLDFNL

* `score` - average over residues that were designed negative log probability of sampled amino acids
* `global score` - average over all residues in all chains negative log probability of sampled/fixed amino acids
* `fixed_chains` - chains that were not designed (fixed)
* `designed_chains` - chains that were redesigned
* `model_name/CA_model_name` - model name that was used to generate results, e.g. `v_48_020`
* `git_hash` - github version that was used to generate outputs
* `seed` - random seed
* `T=0.1` - temperature equal to 0.1 was used to sample sequences
* `sample` - sequence sample number 1, 2, 3...etc
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@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} }


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