{ "info": { "author": "Magnus Haraldson H\u00f8ie & Alissa Hummer", "author_email": "maghoi@dtu.dk", "bugtrack_url": null, "classifiers": [ "Development Status :: 5 - Production/Stable", "Intended Audience :: Science/Research", "Operating System :: POSIX :: Linux", "Programming Language :: Python :: 3.10" ], "description": "AntiFold\n==============================\n\nAntiFold predicts sequences which fit into antibody variable domain structures. The tool outputs residue log-likelihoods in [CSV format](https://github.com/oxpig/AntiFold/blob/master/output/single_pdb/6y1l_imgt.csv), and can sample sequences to a [FASTA format](https://github.com/oxpig/AntiFold/blob/master/output/single_pdb/6y1l_imgt.fasta) directly. Sampled sequences show high structural agreement with experimental structures.\n\nAntiFold is based on the ESM-IF1 model and is fine-tuned on solved and predicted antibody structures from SAbDab and OAS.\n\n- Paper: [arXiv pre-print](https://arxiv.org/abs/2405.03370)\n- Webserver: [OPIG webserver](https://opig.stats.ox.ac.uk/webapps/antifold/)\n- Colab: [![Open In Colab](images/colab-badge.svg)](https://colab.research.google.com/drive/1oEDJCHcwxGBeCiYsCm62_OHBXDHnlhb9)\n- Model: [model.pt](https://opig.stats.ox.ac.uk/data/downloads/AntiFold/models/model.pt)\n- License: [BSD 3-Clause](https://opig.stats.ox.ac.uk/data/downloads/AntiFold/LICENSE)\n\n## Webserver\n\nTo try AntiFold without installing it, please see our OPIG webserver:\n[https://opig.stats.ox.ac.uk/webapps/antifold/](https://opig.stats.ox.ac.uk/webapps/antifold/)\n\n## Install and run AntiFold\n\n#### Download and install from Github source (recommended - latest release)\n```bash\nconda create --name antifold python=3.10 -y && conda activate antifold\nconda install -c conda-forge pytorch\ngit clone https://github.com/oxpig/AntiFold && cd AntiFold\npip install .\n```\n\nGPU only: install using environment.yml\n\n```bash\nconda create env -f environment.yml\npython -m pip install .\n```\n\nDepending on your CUDA version you may need to change the dependency `pytorch-cuda=12.1` in the environment.yml file.\nDetailed instructions on how to correctly install pytorch for your system can be found [here](https://pytorch.org/get-started/locally/)\n\n#### Run AntiFold (inverse-folding probabilities, sample sequences)\n```bash\n# Run AntiFold on single PDB/CIF file\n# Nb: Assumes first chain heavy, second chain light\npython antifold/main.py \\\n --pdb_file data/pdbs/6y1l_imgt.pdb\n\n# Antibody-antigen complex\npython antifold/main.py \\\n --pdb_file data/antibody_antigen/3hfm.pdb \\\n --heavy_chain H \\\n --light_chain L \\\n --antigen_chain Y\n\n# Nanobody or single-chain\npython antifold/main.py \\\n --pdb_file data/nanobody/8oi2_imgt.pdb \\\n --nanobody_chain B\n\n# Folder of PDB/CIFs\n# Nb: Assumes first chain heavy, second light\npython antifold/main.py \\\n --pdb_dir data/pdbs\n\n# Specify chains to run in a CSV file (e.g. antibody-antigen complex)\npython antifold/main.py \\\n --pdb_dir data/antibody_antigen \\\n --pdbs_csv data/antibody_antigen.csv\n\n# Sample sequences 10x\npython antifold/main.py \\\n --pdb_file data/pdbs/6y1l_imgt.pdb \\\n --heavy_chain H \\\n --light_chain L \\\n --num_seq_per_target 10 \\\n --sampling_temp \"0.2\" \\\n --regions \"CDR1 CDR2 CDR3\"\n\n# Run all chains with ESM-IF1 model weights\npython antifold/main.py \\\n --pdb_dir data/pdbs \\\n --esm_if1_mode\n```\n\n## Jupyter notebook\nNotebook: notebook.ipynb\n\nColab: [![Open In Colab](images/colab-badge.svg)](https://colab.research.google.com/drive/1oEDJCHcwxGBeCiYsCm62_OHBXDHnlhb9)\n\n```python\nimport antifold\nimport antifold.main\n\n# Load model\nmodel = antifold.main.load_model()\n\n# PDB directory\npdb_dir = \"data/pdbs\"\n\n# Assumes first chain heavy, second chain light\npdbs_csv = antifold.main.generate_pdbs_csv(pdb_dir, max_chains=2)\n\n# Sample from PDBs\ndf_logits_list = antifold.main.get_pdbs_logits(\n model=model,\n pdbs_csv_or_dataframe=pdbs_csv,\n pdb_dir=pdb_dir,\n)\n\n# Output log probabilites\ndf_logits_list[0]\n```\n\n## Input parameters\nRequired parameters:\n```text\nInput PDBs should be antibody variable domain structures (IMGT positions 1-128).\n\nIf no chains are specified, the first two chains will be assumed to be heavy light.\nIf custom_chain_mode is set, all (10) chains will be run.\n\n- Option 1: PDB file (--pdb_file). We recommend specifying heavy and light chain (--heavy_chain and --light_chain)\n- Option 2: PDB folder (--pdb_dir) + CSV file specifying chains (--pdbs_csv)\n- Option 3: PDB folder, infer 1st chain heavy, 2nd chain light\n```\n\nParameters for generating new sequences:\n```text\nPDBs should be IMGT annotated for the sequence sampling regions to be valid.\n\n- Number of sequences to generate (--num_seq_per_target)\n- Region to mutate (--region) based on inverse folding probabilities. Select from list in IMGT_dict (e.g. 'CDRH1 CDRH2 CDRH3')\n- Sampling temperature (--sampling_temp) controls generated sequence diversity, by scaling the inverse folding probabilities before sampling. Temperature = 1 means no change, while temperature ~ 0 only samples the most likely amino-acid at each position (acts as argmax).\n```\n\nOptional parameters:\n```text\n- Multi-chain mode for including antigen or other chains (--custom_chain_mode)\n- Extract latent representations of PDB within model (--extract_embeddings)\n- Use ESM-IF1 instead of AntiFold model weights (--esm_if1_mode), enables custom_chain_mode\n```\n\n## Example output\nFor example webserver output, see:\n[https://opig.stats.ox.ac.uk/webapps/antifold/results/example_job/](https://opig.stats.ox.ac.uk/webapps/antifold/results/example_job/)\n\nOutput CSV with residue log-probabilities: Residue probabilities: 6y1l_imgt.csv\n- pdb_pos - PDB residue number\n- pdb_chain - PDB chain\n- aa_orig - PDB residue (e.g. 112)\n- aa_pred - Top predicted residue by AntiFold (i.e. argmax) for this position\n- pdb_posins - PDB residue number with insertion code (e.g. 112A)\n- perplexity - Inverse folding tolerance (higher is more tolerant) to mutations. See paper for more details.\n- Amino-acids - Inverse folding scores (log-likelihood) for the given position\n```csv\npdb_pos,pdb_chain,aa_orig,aa_pred,pdb_posins,perplexity,A,C,D,E,F,G,H,I,K,L,M,N,P,Q,R,S,T,V,W,Y\n2,H,V,M,2,1.6488,-4.9963,-6.6117,-6.3181,-6.3243,-6.7570,-4.2518,-6.7514,-5.2540,-6.8067,-5.8619,-0.0904,-6.5493,-4.8639,-6.6316,-6.3084,-5.1900,-5.0988,-3.7295,-8.0480,-7.3236\n3,H,Q,Q,3,1.3889,-10.5258,-12.8463,-8.4800,-4.7630,-12.9094,-11.0924,-5.6136,-10.9870,-3.1119,-8.1113,-9.4382,-6.2246,-13.3660,-0.0701,-4.9957,-10.0301,-6.8618,-7.5810,-13.6721,-11.4157\n4,H,L,L,4,1.0021,-13.3581,-12.6206,-17.5484,-12.4801,-9.8792,-13.6382,-14.8609,-13.9344,-16.4080,-0.0002,-9.2727,-16.6532,-14.0476,-12.5943,-15.4559,-16.9103,-17.0809,-10.5670,-13.5334,-13.4324\n...\n```\n\nOutput FASTA file with sampled sequences: 6y1l_imgt.fasta\n- T: Temperature used for design\n- score: average log-odds of residues in the sampled region\n- global_score: average log-odds of all residues (IMGT positions 1-128)\n- regions: regions selected for design\n- seq_recovery: # mutations / total sequence length\n- mutations: # mutations from original PDB sequence\n```fasta\n>6y1l_imgt , score=0.2934, global_score=0.2934, regions=['CDR1', 'CDR2', 'CDRH3'], model_name=AntiFold, seed=42\nVQLQESGPGLVKPSETLSLTCAVSGYSISSGYYWGWIRQPPGKGLEWIGSIYHSGSTYYN\nPSLKSRVTISVDTSKNQFSLKLSSVTAADTAVYYCAGLTQSSHNDANWGQGTLVTVSS/V\nLTQPPSVSAAPGQKVTISCSGSSSNIGNNYVSWYQQLPGTAPKRLIYDNNKRPSGIPDRF\nSGSKSGTSATLGITGLQTGDEADYYCGTWDSSLNPVFGGGTKLEIKR\n> T=0.20, sample=1, score=0.3930, global_score=0.1869, seq_recovery=0.8983, mutations=12\nVQLQESGPGLVKPSETLSLTCAVSGASITSSYYWGWIRQPPGKGLEWIGSIYYSGSTYYN\nPSLKSRVTISVDTSKNQFSLKLSSVTAADTAVYYCAGLYGSPWSNPYWGQGTLVTVSS/V\nLTQPPSVSAAPGQKVTISCSGSSSNIGNNYVSWYQQLPGTAPKRLIYDNNKRPSGIPDRF\nSGSKSGTSATLGITGLQTGDEADYYCGTWDSSLNPVFGGGTKLEIKR\n...\n```\n\n## Usage\n```bash\nusage:\n # Predict on example PDBs in folder\npython antifold/main.py \\\n --pdb_file data/antibody_antigen/3hfm.pdb \\\n --heavy_chain H \\\n --light_chain L \\\n --antigen_chain Y # Optional\n\nPredict inverse folding probabilities for antibody variable domain, and sample sequences with maintained fold.\nPDB structures should be IMGT-numbered, paired heavy and light chain variable domains (positions 1-128).\n\nFor IMGT numbering PDBs use SAbDab or https://opig.stats.ox.ac.uk/webapps/sabdab-sabpred/sabpred/anarci/\n\noptions:\n -h, --help show this help message and exit\n --pdb_file PDB_FILE Input PDB file (for single PDB predictions)\n --heavy_chain HEAVY_CHAIN\n Ab heavy chain (for single PDB predictions)\n --light_chain LIGHT_CHAIN\n Ab light chain (for single PDB predictions)\n --antigen_chain ANTIGEN_CHAIN\n Antigen chain (optional)\n --pdbs_csv PDBS_CSV Input CSV file with PDB names and H/L chains (multi-PDB predictions)\n --pdb_dir PDB_DIR Directory with input PDB files (multi-PDB predictions)\n --out_dir OUT_DIR Output directory\n --regions REGIONS Space-separated regions to mutate. Default 'CDR1 CDR2 CDR3H'\n --num_seq_per_target NUM_SEQ_PER_TARGET\n Number of sequences to sample from each antibody PDB (default 0)\n --sampling_temp SAMPLING_TEMP\n A string of temperatures e.g. '0.20 0.25 0.50' (default 0.20). Sampling temperature for amino acids. Suggested values 0.10, 0.15, 0.20, 0.25, 0.30. Higher values will lead to more diversity.\n --limit_variation Limit variation to as many mutations as expected from temperature sampling\n --extract_embeddings Extract per-residue embeddings from AntiFold / ESM-IF1\n --custom_chain_mode Run all specified chains (for antibody-antigen complexes or any combination of chains)\n --exclude_heavy Exclude heavy chain from sampling\n --exclude_light Exclude light chain from sampling\n --batch_size BATCH_SIZE\n Batch-size to use\n --num_threads NUM_THREADS\n Number of CPU threads to use for parallel processing (0 = all available)\n --seed SEED Seed for reproducibility\n --model_path MODEL_PATH\n Alternative model weights (default models/model.pt). See --use_esm_if1_weights flag to use ESM-IF1 weights instead of AntiFold\n --esm_if1_mode Use ESM-IF1 weights instead of AntiFold\n --verbose VERBOSE Verbose printing\n```\n\n## IMGT regions dict\nUsed to specify which regions to mutate in an IMGT numbered PDB\n- IMGT numbered PDBs: [https://opig.stats.ox.ac.uk/webapps/sabdab-sabpred/sabdab](https://opig.stats.ox.ac.uk/webapps/sabdab-sabpred/sabdab)\n- Renumber existing PDBs with ANARCI: [https://github.com/oxpig/ANARCI](https://github.com/oxpig/ANARCI)\n- Read more: [https://www.imgt.org/IMGTScientificChart/Numbering/IMGTIGVLsuperfamily.html](https://www.imgt.org/IMGTScientificChart/Numbering/IMGTIGVLsuperfamily.html)\n\n```python\nIMGT_dict = {\n \"all\": range(1, 128 + 1),\n \"allH\": range(1, 128 + 1),\n \"allL\": range(1, 128 + 1),\n \"FWH\": list(range(1, 26 + 1)) + list(range(40, 55 + 1)) + list(range(66, 104 + 1)),\n \"FWL\": list(range(1, 26 + 1)) + list(range(40, 55 + 1)) + list(range(66, 104 + 1)),\n \"CDRH\": list(range(27, 39)) + list(range(56, 65 + 1)) + list(range(105, 117 + 1)),\n \"CDRL\": list(range(27, 39)) + list(range(56, 65 + 1)) + list(range(105, 117 + 1)),\n \"FW1\": range(1, 26 + 1),\n \"FWH1\": range(1, 26 + 1),\n \"FWL1\": range(1, 26 + 1),\n \"CDR1\": range(27, 39),\n \"CDRH1\": range(27, 39),\n \"CDRL1\": range(27, 39),\n \"FW2\": range(40, 55 + 1),\n \"FWH2\": range(40, 55 + 1),\n \"FWL2\": range(40, 55 + 1),\n \"CDR2\": range(56, 65 + 1),\n \"CDRH2\": range(56, 65 + 1),\n \"CDRL2\": range(56, 65 + 1),\n \"FW3\": range(66, 104 + 1),\n \"FWH3\": range(66, 104 + 1),\n \"FWL3\": range(66, 104 + 1),\n \"CDR3\": range(105, 117 + 1),\n \"CDRH3\": range(105, 117 + 1),\n \"CDRL3\": range(105, 117 + 1),\n \"FW4\": range(118, 128 + 1),\n \"FWH4\": range(118, 128 + 1),\n \"FWL4\": range(118, 128 + 1),\n}\n```\n\n## Citing this work\n\nThe code and data in this package is based on the following paper AntiFold. If you use it, please cite:\n\n```tex\n@misc{antifold,\n title={AntiFold: Improved antibody structure-based design using inverse folding},\n author={Magnus Haraldson H\u00f8ie and Alissa Hummer and Tobias H. Olsen and Broncio Aguilar-Sanjuan and Morten Nielsen and Charlotte M. 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