Add Protein model

.gitignore

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+mmCIF/
+.vscode/
+ckpt/
+multirun/
+wandb/
+*.pdb
+*.csv
+*.fa
+*pdbs.jsonl
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+# pyenv
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+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/
+data/data/
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+
+#PDB processed dataset
+data/processed_pdb/
+data/processed_pdb_openfold/
+
+#PDBBind dataset
+DiffusionProteinLigand/data/PDBBind*
+
+#pynb for debug code
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+
+# keep track of the results example
+!results_example/
+!results_example/length_50/*
+!results_example/length_50/sample_0/*
+!results_example/length_50/sample_0/self_consistency/*
+!results_example/length_50/sample_0/self_consistency/esmf/*
+!results_example/length_50/sample_0/self_consistency/seqs/*

ProteinMPNN/LICENSE

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+MIT License
+
+Copyright (c) 2022 Justas Dauparas
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

ProteinMPNN/README.md

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+# ProteinMPNN
+![ProteinMPNN](https://docs.google.com/drawings/d/e/2PACX-1vTtnMBDOq8TpHIctUfGN8Vl32x5ISNcPKlxjcQJF2q70PlaH2uFlj2Ac4s3khnZqG1YxppdMr0iTyk-/pub?w=889&h=358)
+Read [ProteinMPNN paper](https://www.biorxiv.org/content/10.1101/2022.06.03.494563v1).
+
+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`.
+
+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:
+* `protein_mpnn_run.py` - the main script to initialialize and run the model.
+* `protein_mpnn_utils.py` - utility functions for the main script.
+* `examples/` - simple code examples.
+* `inputs/` - input PDB files for examples
+* `outputs/` - outputs from examples
+* `colab_notebooks/` - Google Colab examples
+* `training/` - code and data to retrain the model
+-----------------------------------------------------------------------------------------------------
+Input flags for `protein_mpnn_run.py`:
+```
+    argparser.add_argument("--ca_only", action="store_true", default=False, help="Parse CA-only structures and use CA-only models (default: false)")
+    argparser.add_argument("--path_to_model_weights", type=str, default="", help="Path to model weights folder;")
+    argparser.add_argument("--model_name", type=str, default="v_48_020", help="ProteinMPNN model name: v_48_002, v_48_010, v_48_020, v_48_030; v_48_010=version with 48 edges 0.10A noise")
+    argparser.add_argument("--seed", type=int, default=0, help="If set to 0 then a random seed will be picked;")
+    argparser.add_argument("--save_score", type=int, default=0, help="0 for False, 1 for True; save score=-log_prob to npy files")
+    argparser.add_argument("--save_probs", type=int, default=0, help="0 for False, 1 for True; save MPNN predicted probabilites per position")
+    argparser.add_argument("--score_only", type=int, default=0, help="0 for False, 1 for True; score input backbone-sequence pairs")
+    argparser.add_argument("--conditional_probs_only", type=int, default=0, help="0 for False, 1 for True; output conditional probabilities p(s_i given the rest of the sequence and backbone)")
+    argparser.add_argument("--conditional_probs_only_backbone", type=int, default=0, help="0 for False, 1 for True; if true output conditional probabilities p(s_i given backbone)")
+    argparser.add_argument("--unconditional_probs_only", type=int, default=0, help="0 for False, 1 for True; output unconditional probabilities p(s_i given backbone) in one forward pass")
+    argparser.add_argument("--backbone_noise", type=float, default=0.00, help="Standard deviation of Gaussian noise to add to backbone atoms")
+    argparser.add_argument("--num_seq_per_target", type=int, default=1, help="Number of sequences to generate per target")
+    argparser.add_argument("--batch_size", type=int, default=1, help="Batch size; can set higher for titan, quadro GPUs, reduce this if running out of GPU memory")
+    argparser.add_argument("--max_length", type=int, default=200000, help="Max sequence length")
+    argparser.add_argument("--sampling_temp", type=str, default="0.1", help="A string of temperatures, 0.2 0.25 0.5. Sampling temperature for amino acids. Suggested values 0.1, 0.15, 0.2, 0.25, 0.3. Higher values will lead to more diversity.")
+    argparser.add_argument("--out_folder", type=str, help="Path to a folder to output sequences, e.g. /home/out/")
+    argparser.add_argument("--pdb_path", type=str, default='', help="Path to a single PDB to be designed")
+    argparser.add_argument("--pdb_path_chains", type=str, default='', help="Define which chains need to be designed for a single PDB ")
+    argparser.add_argument("--jsonl_path", type=str, help="Path to a folder with parsed pdb into jsonl")
+    argparser.add_argument("--chain_id_jsonl",type=str, default='', help="Path to a dictionary specifying which chains need to be designed and which ones are fixed, if not specied all chains will be designed.")
+    argparser.add_argument("--fixed_positions_jsonl", type=str, default='', help="Path to a dictionary with fixed positions")
+    argparser.add_argument("--omit_AAs", type=list, default='X', help="Specify which amino acids should be omitted in the generated sequence, e.g. 'AC' would omit alanine and cystine.")
+    argparser.add_argument("--bias_AA_jsonl", type=str, default='', help="Path to a dictionary which specifies AA composion bias if neededi, e.g. {A: -1.1, F: 0.7} would make A less likely and F more likely.")
+    argparser.add_argument("--bias_by_res_jsonl", default='', help="Path to dictionary with per position bias.")
+    argparser.add_argument("--omit_AA_jsonl", type=str, default='', help="Path to a dictionary which specifies which amino acids need to be omited from design at specific chain indices")
+    argparser.add_argument("--pssm_jsonl", type=str, default='', help="Path to a dictionary with pssm")
+    argparser.add_argument("--pssm_multi", type=float, default=0.0, help="A value between [0.0, 1.0], 0.0 means do not use pssm, 1.0 ignore MPNN predictions")
+    argparser.add_argument("--pssm_threshold", type=float, default=0.0, help="A value between -inf + inf to restric per position AAs")
+    argparser.add_argument("--pssm_log_odds_flag", type=int, default=0, help="0 for False, 1 for True")
+    argparser.add_argument("--pssm_bias_flag", type=int, default=0, help="0 for False, 1 for True")
+    argparser.add_argument("--tied_positions_jsonl", type=str, default='', help="Path to a dictionary with tied positions")
+
+```
+-----------------------------------------------------------------------------------------------------
+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/
+-----------------------------------------------------------------------------------------------------
+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_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
+-----------------------------------------------------------------------------------------------------
+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
+-----------------------------------------------------------------------------------------------------
+```
+@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}
+}
+```
+-----------------------------------------------------------------------------------------------------

ProteinMPNN/colab_notebooks/README.md

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+<a href="https://colab.research.google.com/github/dauparas/ProteinMPNN/blob/main/colab_notebooks/quickdemo.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>