Official repository for IgFold: Fast, accurate antibody structure prediction from deep learning on massive set of natural antibodies.
The code and pre-trained models from this work are made available for non-commercial use (including at commercial entities) under the terms of the JHU Academic Software License Agreement. For commercial inquiries, please contact Johns Hopkins Tech Ventures at [email protected].
Try antibody structure prediction in Google Colab.
Updating to IgFold v0.4.0 is strongly recommended for proper PDB output formatting.
- Version 0.4.0
- Fix PDB output formatting issues
For easiest use, create a conda environment and install IgFold via PyPI:
$ pip install igfoldTo access the latest version of the code, clone and install the repository:
$ git clone [email protected]:Graylab/IgFold.git
$ pip install IgFoldNote: Due to the missing of weight file in this source repository, you should download and extract weight file from PyPI first before building from source code.
pip download igfold --no-deps --dest . --no-binary :all:
tar -zxvf igfold-*.tar.gz
mv igfold-*/igfold/trained_models/IgFold igfold/trained_models/IgFold
rm -rf igfold-*Two refinement methods are supported for IgFold predictions. To follow the manuscript, PyRosetta should be installed following the instructions here. If PyRosetta is not installed, refinement with OpenMM will be attempted. For this option, OpenMM must be installed and configured before running IgFold as follows:
$ conda install -c conda-forge openmm==7.7.0 pdbfixerAntibody renumbering requires installation of AbNumber. To install AbNumber, run the following command:
$ conda install -c bioconda abnumberPaired antibody sequences can be provided as a dictionary of sequences, where the keys are chain names and the values are the sequences.
from igfold import IgFoldRunner
from igfold.refine.pyrosetta_ref import init_pyrosetta
init_pyrosetta()
sequences = {
"H": "EVQLVQSGPEVKKPGTSVKVSCKASGFTFMSSAVQWVRQARGQRLEWIGWIVIGSGNTNYAQKFQERVTITRDMSTSTAYMELSSLRSEDTAVYYCAAPYCSSISCNDGFDIWGQGTMVTVS",
"L": "DVVMTQTPFSLPVSLGDQASISCRSSQSLVHSNGNTYLHWYLQKPGQSPKLLIYKVSNRFSGVPDRFSGSGSGTDFTLKISRVEAEDLGVYFCSQSTHVPYTFGGGTKLEIK"
}
pred_pdb = "my_antibody.pdb"
igfold = IgFoldRunner()
igfold.fold(
pred_pdb, # Output PDB file
sequences=sequences, # Antibody sequences
do_refine=True, # Refine the antibody structure with PyRosetta
do_renum=True, # Renumber predicted antibody structure (Chothia)
)To predict a nanobody structure (or an individual heavy or light chain), simply provide one sequence:
from igfold import IgFoldRunner
from igfold.refine.pyrosetta_ref import init_pyrosetta
init_pyrosetta()
sequences = {
"H": "QVQLQESGGGLVQAGGSLTLSCAVSGLTFSNYAMGWFRQAPGKEREFVAAITWDGGNTYYTDSVKGRFTISRDNAKNTVFLQMNSLKPEDTAVYYCAAKLLGSSRYELALAGYDYWGQGTQVTVS"
}
pred_pdb = "my_nanobody.pdb"
igfold = IgFoldRunner()
igfold.fold(
pred_pdb, # Output PDB file
sequences=sequences, # Nanobody sequence
do_refine=True, # Refine the antibody structure with PyRosetta
do_renum=True, # Renumber predicted antibody structure (Chothia)
)To predict a structure without refinement, set do_refine=False:
from igfold import IgFoldRunner
sequences = {
"H": "QVQLQESGGGLVQAGGSLTLSCAVSGLTFSNYAMGWFRQAPGKEREFVAAITWDGGNTYYTDSVKGRFTISRDNAKNTVFLQMNSLKPEDTAVYYCAAKLLGSSRYELALAGYDYWGQGTQVTVS"
}
pred_pdb = "my_nanobody.pdb"
igfold = IgFoldRunner()
igfold.fold(
pred_pdb, # Output PDB file
sequences=sequences, # Nanobody sequence
do_refine=False, # Refine the antibody structure with PyRosetta
do_renum=True, # Renumber predicted antibody structure (Chothia)
)RMSD estimates are calculated per-residue and recorded in the B-factor column of the output PDB file. These values are also returned from the fold method.
from igfold import IgFoldRunner
from igfold.refine.pyrosetta_ref import init_pyrosetta
init_pyrosetta()
sequences = {
"H": "EVQLVQSGPEVKKPGTSVKVSCKASGFTFMSSAVQWVRQARGQRLEWIGWIVIGSGNTNYAQKFQERVTITRDMSTSTAYMELSSLRSEDTAVYYCAAPYCSSISCNDGFDIWGQGTMVTVS",
"L": "DVVMTQTPFSLPVSLGDQASISCRSSQSLVHSNGNTYLHWYLQKPGQSPKLLIYKVSNRFSGVPDRFSGSGSGTDFTLKISRVEAEDLGVYFCSQSTHVPYTFGGGTKLEIK"
}
pred_pdb = "my_antibody.pdb"
igfold = IgFoldRunner()
out = igfold.fold(
pred_pdb, # Output PDB file
sequences=sequences, # Antibody sequences
do_refine=True, # Refine the antibody structure with PyRosetta
do_renum=True, # Renumber predicted antibody structure (Chothia)
)
out.prmsd # Predicted RMSD for each residue's N, CA, C, CB atoms (dim: 1, L, 4)Representations from IgFold may be useful as features for machine learning models. The embed method can be used to surface a variety of antibody representations from the model:
from igfold import IgFoldRunner
sequences = {
"H": "EVQLVQSGPEVKKPGTSVKVSCKASGFTFMSSAVQWVRQARGQRLEWIGWIVIGSGNTNYAQKFQERVTITRDMSTSTAYMELSSLRSEDTAVYYCAAPYCSSISCNDGFDIWGQGTMVTVS",
"L": "DVVMTQTPFSLPVSLGDQASISCRSSQSLVHSNGNTYLHWYLQKPGQSPKLLIYKVSNRFSGVPDRFSGSGSGTDFTLKISRVEAEDLGVYFCSQSTHVPYTFGGGTKLEIK"
}
igfold = IgFoldRunner()
emb = igfold.embed(
sequences=sequences, # Antibody sequences
)
emb.bert_embs # Embeddings from AntiBERTy final hidden layer (dim: 1, L, 512)
emb.gt_embs # Embeddings after graph transformer layers (dim: 1, L, 64)
emb.structure_embs # Embeddings after template incorporation IPA (dim: 1, L, 64)Refinement with OpenMM can be prioritized over PyRosetta by setting use_openmm=True.
from igfold import IgFoldRunner
sequences = {
"H": "EVQLVQSGPEVKKPGTSVKVSCKASGFTFMSSAVQWVRQARGQRLEWIGWIVIGSGNTNYAQKFQERVTITRDMSTSTAYMELSSLRSEDTAVYYCAAPYCSSISCNDGFDIWGQGTMVTVS",
"L": "DVVMTQTPFSLPVSLGDQASISCRSSQSLVHSNGNTYLHWYLQKPGQSPKLLIYKVSNRFSGVPDRFSGSGSGTDFTLKISRVEAEDLGVYFCSQSTHVPYTFGGGTKLEIK"
}
pred_pdb = "my_antibody.pdb"
igfold = IgFoldRunner()
igfold.fold(
pred_pdb, # Output PDB file
sequences=sequences, # Antibody sequences
do_refine=True, # Refine the antibody structure with PyRosetta
use_openmm=True, # Use OpenMM for refinement
do_renum=True, # Renumber predicted antibody structure (Chothia)
)To demonstrate the capabilities of IgFold for large-scale prediction of antibody structures, we applied the model to two sets of natural paired antibody sequences.
The first set contains 104K non-redundant paired antibody sequences from the Observed Antibody Space database. These predicted structures are made available for use online.
$ wget https://data.graylab.jhu.edu/OAS_paired.tar.gzThe second set contains 1.3M unique paired antibodies from four human donors, collected by Jaffe et al.. These predicted structures are made available for use online.
$ wget https://data.graylab.jhu.edu/Jaffe2022.tar.gzIf you run into any problems while using IgFold, please create a Github issue with a description of the problem and the steps to reproduce it.
@article{ruffolo2023fast,
title={Fast, accurate antibody structure prediction from deep learning on massive set of natural antibodies},
author={Ruffolo, Jeffrey A and Chu, Lee-Shin and Mahajan, Sai Pooja and Gray, Jeffrey J},
journal={Nature communications},
volume={14},
number={1},
pages={2389},
year={2023},
publisher={Nature Publishing Group UK London}
}
@article{ruffolo2021deciphering,
title = {Deciphering antibody affinity maturation with language models and weakly supervised learning},
author = {Ruffolo, Jeffrey A and Gray, Jeffrey J and Sulam, Jeremias},
journal = {arXiv},
year= {2021}
}
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