AntiFold memprediksi urutan yang sesuai dengan struktur domain variabel antibodi. Alat ini mengeluarkan kemungkinan log residu dalam format CSV, dan dapat mengambil sampel urutan ke format FASTA secara langsung. Urutan sampel menunjukkan kesesuaian struktural yang tinggi dengan struktur eksperimental.
AntiFold didasarkan pada model ESM-IF1 dan disesuaikan dengan struktur antibodi yang terpecahkan dan diprediksi dari SAbDab dan OAS.
Untuk mencoba AntiFold tanpa menginstalnya, silakan lihat server web OPIG kami: 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 .Khusus GPU: instal menggunakan environment.yml
conda create env -f environment.yml
python -m pip install . Bergantung pada versi CUDA Anda, Anda mungkin perlu mengubah ketergantungan pytorch-cuda=12.1 di file environment.yml. Petunjuk terperinci tentang cara menginstal pytorch dengan benar untuk sistem Anda dapat ditemukan di sini
# 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_modeBuku catatan: notebook.ipynb
Kolaborasi:
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 ]Parameter yang diperlukan:
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
Parameter untuk menghasilkan urutan baru:
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).
Parameter opsional:
- 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
Untuk contoh keluaran server web, lihat: https://opig.stats.ox.ac.uk/webapps/antifold/results/example_job/
Keluaran CSV dengan probabilitas log residu: Probabilitas residu: 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
...
Keluarkan file FASTA dengan urutan sampel: 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 printingDigunakan untuk menentukan wilayah mana yang akan dimutasi dalam PDB bernomor IMGT
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 ),
}Kode dan data dalam paket ini didasarkan pada kertas AntiFold berikut. Jika Anda menggunakannya, harap kutip:
@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}
}
