antifold 0.3.1

Description:

antifold 0.3.1

AntiFold
AntiFold predicts sequences which fit into antibody variable domain structures. The tool outputs residue log-likelihoods in CSV format, and can sample sequences to a FASTA format directly. Sampled sequences show high structural agreement with experimental structures.
AntiFold is based on the ESM-IF1 model and is fine-tuned on solved and predicted antibody structures from SAbDab and OAS.

Paper: arXiv pre-print
Webserver: OPIG webserver
Colab:
Model: model.pt
License: BSD 3-Clause

Webserver
To try AntiFold without installing it, please see our OPIG webserver:
https://opig.stats.ox.ac.uk/webapps/antifold/
Install and run AntiFold
Download and install from Github source (recommended - latest release)
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 only: install using environment.yml
conda create env -f environment.yml
python -m pip install .

Depending on your CUDA version you may need to change the dependency pytorch-cuda=12.1 in the environment.yml file.
Detailed instructions on how to correctly install pytorch for your system can be found here
Run AntiFold (inverse-folding probabilities, sample sequences)
# 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
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

Jupyter notebook
Notebook: notebook.ipynb
Colab:
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]

Input parameters
Required parameters:
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

Parameters for generating new sequences:
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).

Optional parameters:
- 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

Example output
For example webserver output, see:
https://opig.stats.ox.ac.uk/webapps/antifold/results/example_job/
Output CSV with residue log-probabilities: Residue probabilities: 6y1l_imgt.csv

pdb_pos - PDB residue number
pdb_chain - PDB chain
aa_orig - PDB residue (e.g. 112)
aa_pred - Top predicted residue by AntiFold (i.e. argmax) for this position
pdb_posins - PDB residue number with insertion code (e.g. 112A)
perplexity - Inverse folding tolerance (higher is more tolerant) to mutations. See paper for more details.
Amino-acids - Inverse folding scores (log-likelihood) for the given position

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
...

Output FASTA file with sampled sequences: 6y1l_imgt.fasta

T: Temperature used for design
score: average log-odds of residues in the sampled region
global_score: average log-odds of all residues (IMGT positions 1-128)
regions: regions selected for design
seq_recovery: # mutations / total sequence length
mutations: # mutations from original PDB sequence

>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
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 printing

IMGT regions dict
Used to specify which regions to mutate in an IMGT numbered PDB

IMGT numbered PDBs: https://opig.stats.ox.ac.uk/webapps/sabdab-sabpred/sabdab
Renumber existing PDBs with ANARCI: https://github.com/oxpig/ANARCI
Read more: https://www.imgt.org/IMGTScientificChart/Numbering/IMGTIGVLsuperfamily.html

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),
}

Citing this work
The code and data in this package is based on the following paper AntiFold. If you use it, please cite:
@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}
}