AntiFold は、抗体の可変ドメイン構造に適合する配列を予測します。このツールは残差対数尤度を CSV 形式で出力し、シーケンスを FASTA 形式に直接サンプリングできます。サンプリングされた配列は、実験構造と高い構造的一致を示します。
AntiFold は ESM-IF1 モデルに基づいており、SAbDab および OAS から解析および予測された抗体構造に基づいて微調整されています。
AntiFold をインストールせずに試すには、OPIG Web サーバーを参照してください: https://opig.stats.ox.ac.uk/webapps/antifold/
conda create --name antifold python=3.10 -y && conda activate antifold
conda install -c conda-forge pytorch
git clone https://github.com/oxpig/AntiFold && cd AntiFold
pip install .GPU のみ:environment.yml を使用してインストールします
conda create env -f environment.yml
python -m pip install . CUDA のバージョンによっては、environment.yml ファイル内の依存関係pytorch-cuda=12.1変更する必要がある場合があります。システムに pytorch を正しくインストールする方法の詳細な手順は、ここで見つけることができます。
# Run AntiFold on single PDB/CIF file
# Nb: Assumes first chain heavy, second chain light
python antifold/main.py
--pdb_file data/pdbs/6y1l_imgt.pdb
# Antibody-antigen complex
python antifold/main.py
--pdb_file data/antibody_antigen/3hfm.pdb
--heavy_chain H
--light_chain L
--antigen_chain Y
# Nanobody or single-chain
python antifold/main.py
--pdb_file data/nanobody/8oi2_imgt.pdb
--nanobody_chain B
# Folder of PDB/CIFs
# Nb: Assumes first chain heavy, second light
python antifold/main.py
--pdb_dir data/pdbs
# Specify chains to run in a CSV file (e.g. antibody-antigen complex)
python antifold/main.py
--pdb_dir data/antibody_antigen
--pdbs_csv data/antibody_antigen.csv
# Sample sequences 10x (paired VH/VL only)
python antifold/main.py
--pdb_file data/pdbs/6y1l_imgt.pdb
--heavy_chain H
--light_chain L
--num_seq_per_target 10
--sampling_temp " 0.2 "
--regions " CDR1 CDR2 CDR3 "
# Run all chains with ESM-IF1 model weights
python antifold/main.py
--pdb_dir data/pdbs
--esm_if1_modeノートブック：notebook.ipynb
コラボ：
import antifold
import antifold . main
# Load model
model = antifold . main . load_model ()
# PDB directory
pdb_dir = "data/pdbs"
# Assumes first chain heavy, second chain light
pdbs_csv = antifold . main . generate_pdbs_csv ( pdb_dir , max_chains = 2 )
# Sample from PDBs
df_logits_list = antifold . main . get_pdbs_logits (
model = model ,
pdbs_csv_or_dataframe = pdbs_csv ,
pdb_dir = pdb_dir ,
)
# Output log probabilites
df_logits_list [ 0 ]必須パラメータ:
Input PDBs should be antibody variable domain structures (IMGT positions 1-128).
If no chains are specified, the first two chains will be assumed to be heavy light.
If custom_chain_mode is set, all (10) chains will be run.
- Option 1: PDB file (--pdb_file). We recommend specifying heavy and light chain (--heavy_chain and --light_chain)
- Option 2: PDB folder (--pdb_dir) + CSV file specifying chains (--pdbs_csv)
- Option 3: PDB folder, infer 1st chain heavy, 2nd chain light
新しいシーケンスを生成するためのパラメータ:
PDBs should be IMGT annotated for the sequence sampling regions to be valid.
- Number of sequences to generate (--num_seq_per_target)
- Region to mutate (--region) based on inverse folding probabilities. Select from list in IMGT_dict (e.g. 'CDRH1 CDRH2 CDRH3')
- 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).
オプションのパラメータ:
- Multi-chain mode for including antigen or other chains (--custom_chain_mode)
- Extract latent representations of PDB within model (--extract_embeddings)
- Use ESM-IF1 instead of AntiFold model weights (--esm_if1_mode), enables custom_chain_mode
Web サーバーの出力例については、https://opig.stats.ox.ac.uk/webapps/antifold/results/example_job/ を参照してください。
残差対数確率を含む CSV の出力: 残差確率: 6y1l_imgt.csv
pdb_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
2,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
3,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
4,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
...
サンプリングされたシーケンスを含む出力 FASTA ファイル: 6y1l_imgt.fasta
>6y1l_imgt , score=0.2934, global_score=0.2934, regions=['CDR1', 'CDR2', 'CDRH3'], model_name=AntiFold, seed=42
VQLQESGPGLVKPSETLSLTCAVSGYSISSGYYWGWIRQPPGKGLEWIGSIYHSGSTYYN
PSLKSRVTISVDTSKNQFSLKLSSVTAADTAVYYCAGLTQSSHNDANWGQGTLVTVSS/V
LTQPPSVSAAPGQKVTISCSGSSSNIGNNYVSWYQQLPGTAPKRLIYDNNKRPSGIPDRF
SGSKSGTSATLGITGLQTGDEADYYCGTWDSSLNPVFGGGTKLEIKR
> T=0.20, sample=1, score=0.3930, global_score=0.1869, seq_recovery=0.8983, mutations=12
VQLQESGPGLVKPSETLSLTCAVSGASITSSYYWGWIRQPPGKGLEWIGSIYYSGSTYYN
PSLKSRVTISVDTSKNQFSLKLSSVTAADTAVYYCAGLYGSPWSNPYWGQGTLVTVSS/V
LTQPPSVSAAPGQKVTISCSGSSSNIGNNYVSWYQQLPGTAPKRLIYDNNKRPSGIPDRF
SGSKSGTSATLGITGLQTGDEADYYCGTWDSSLNPVFGGGTKLEIKR
...
usage:
# Predict on example PDBs in folder
python antifold/main.py
--pdb_file data/antibody_antigen/3hfm.pdb
--heavy_chain H
--light_chain L
--antigen_chain Y # Optional
Predict inverse folding probabilities for antibody variable domain, and sample sequences with maintained fold.
PDB structures should be IMGT-numbered, paired heavy and light chain variable domains (positions 1-128).
For IMGT numbering PDBs use SAbDab or https://opig.stats.ox.ac.uk/webapps/sabdab-sabpred/sabpred/anarci/
options:
-h, --help show this help message and exit
--pdb_file PDB_FILE Input PDB file (for single PDB predictions)
--heavy_chain HEAVY_CHAIN
Ab heavy chain (for single PDB predictions)
--light_chain LIGHT_CHAIN
Ab light chain (for single PDB predictions)
--antigen_chain ANTIGEN_CHAIN
Antigen chain (optional)
--pdbs_csv PDBS_CSV Input CSV file with PDB names and H/L chains (multi-PDB predictions)
--pdb_dir PDB_DIR Directory with input PDB files (multi-PDB predictions)
--out_dir OUT_DIR Output directory
--regions REGIONS Space-separated regions to mutate. Default ' CDR1 CDR2 CDR3H '
--num_seq_per_target NUM_SEQ_PER_TARGET
Number of sequences to sample from each antibody PDB (default 0)
--sampling_temp SAMPLING_TEMP
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.
--limit_variation Limit variation to as many mutations as expected from temperature sampling
--extract_embeddings Extract per-residue embeddings from AntiFold / ESM-IF1
--custom_chain_mode Run all specified chains (for antibody-antigen complexes or any combination of chains)
--exclude_heavy Exclude heavy chain from sampling
--exclude_light Exclude light chain from sampling
--batch_size BATCH_SIZE
Batch-size to use
--num_threads NUM_THREADS
Number of CPU threads to use for parallel processing (0 = all available)
--seed SEED Seed for reproducibility
--model_path MODEL_PATH
Alternative model weights (default models/model.pt). See --use_esm_if1_weights flag to use ESM-IF1 weights instead of AntiFold
--esm_if1_mode Use ESM-IF1 weights instead of AntiFold
--verbose VERBOSE Verbose printingIMGT 番号付き PDB でどの領域を変更するかを指定するために使用されます
IMGT_dict = {
"all" : range ( 1 , 128 + 1 ),
"allH" : range ( 1 , 128 + 1 ),
"allL" : range ( 1 , 128 + 1 ),
"FWH" : list ( range ( 1 , 26 + 1 )) + list ( range ( 40 , 55 + 1 )) + list ( range ( 66 , 104 + 1 )),
"FWL" : list ( range ( 1 , 26 + 1 )) + list ( range ( 40 , 55 + 1 )) + list ( range ( 66 , 104 + 1 )),
"CDRH" : list ( range ( 27 , 39 )) + list ( range ( 56 , 65 + 1 )) + list ( range ( 105 , 117 + 1 )),
"CDRL" : list ( range ( 27 , 39 )) + list ( range ( 56 , 65 + 1 )) + list ( range ( 105 , 117 + 1 )),
"FW1" : range ( 1 , 26 + 1 ),
"FWH1" : range ( 1 , 26 + 1 ),
"FWL1" : range ( 1 , 26 + 1 ),
"CDR1" : range ( 27 , 39 ),
"CDRH1" : range ( 27 , 39 ),
"CDRL1" : range ( 27 , 39 ),
"FW2" : range ( 40 , 55 + 1 ),
"FWH2" : range ( 40 , 55 + 1 ),
"FWL2" : range ( 40 , 55 + 1 ),
"CDR2" : range ( 56 , 65 + 1 ),
"CDRH2" : range ( 56 , 65 + 1 ),
"CDRL2" : range ( 56 , 65 + 1 ),
"FW3" : range ( 66 , 104 + 1 ),
"FWH3" : range ( 66 , 104 + 1 ),
"FWL3" : range ( 66 , 104 + 1 ),
"CDR3" : range ( 105 , 117 + 1 ),
"CDRH3" : range ( 105 , 117 + 1 ),
"CDRL3" : range ( 105 , 117 + 1 ),
"FW4" : range ( 118 , 128 + 1 ),
"FWH4" : range ( 118 , 128 + 1 ),
"FWL4" : range ( 118 , 128 + 1 ),
}このパッケージのコードとデータは、次の論文 AntiFold に基づいています。使用する場合は以下を引用してください。
@misc{antifold,
title={AntiFold: Improved antibody structure-based design using inverse folding},
author={Magnus Haraldson Høie and Alissa Hummer and Tobias H. Olsen and Broncio Aguilar-Sanjuan and Morten Nielsen and Charlotte M. Deane},
year={2024},
eprint={2405.03370},
archivePrefix={arXiv},
primaryClass={q-bio.BM}
}
