Awesome
IgLM
Official repository for IgLM: Generative Language Modeling for Antibody Design.
The code and pre-trained models from this work are made available for non-commercial use under the terms of the JHU Academic Software License Agreement. For commercial inquiries, please contact Johns Hopkins Tech Ventures at awichma2@jhu.edu
. The training and test datasets for IgLM are available for download here.
Try antibody sequence generation in Google Colab.
Setup
To use IgLM, install via pip:
pip install iglm
Alternatively, you can clone this repository and install the package locally:
$ git clone git@github.com:Graylab/IgLM.git
$ pip install IgLM
Command line usage
IgLM supports sequence infilling, sequence generation (with prompting), and sequence evaluation from the command line.
Re-design spans of an antibody sequence
To use IgLM to re-design spans of an antibody sequence, supply the fasta file, the fasta record ID corresponding to the sequence to design, the start index of the span (0-indexed), and the end index of the span (0-indexed, exclusive).
To generate 100 unique sequences of the anti-tissue factor antibody (1JPT) heavy chain with an IgLM-designed CDR3:
iglm_infill data/antibodies/1jpt/1jpt.fasta :H 98 106 --chain_token [HEAVY] --species_token [HUMAN] --num_seqs 100
Full antibody sequence generation
IgLM can be used to generate full antibody sequences while conditioning on the chain type and species-of-origin.
To generate 100 unique human heavy chain sequences starting with EVQ:
iglm_generate --prompt_sequence EVQ --chain_token [HEAVY] --species_token [HUMAN] --num_seqs 100
To generate 100 unique nanobody sequences starting with QVQ:
iglm_generate --prompt_sequence QVQ --chain_token [HEAVY] --species_token [CAMEL] --num_seqs 100
Sequence evaluation
IgLM can be used to calculate the log likelihood of a sequence given a chain type and species-of-origin.
Full sequence log likelihood calculation:
iglm_evaluate data/antibodies/1jpt/1jpt.fasta :H --chain_token [HEAVY] --species_token [HUMAN]
Infilled sequence log likelihood calculation:
iglm_evaluate data/antibodies/1jpt/1jpt.fasta :H --start 98 --end 106 --chain_token [HEAVY] --species_token [HUMAN]
Package usage
IgLM may also be used as a Python package, enabling the above use cases and more flexible usage.
Re-design spans of an antibody sequence
To use IgLM to re-design spans of an antibody sequence, supply the sequence to design, the start index of the span (0-indexed), and the end index of the span (0-indexed, exclusive).
To generate 100 unique sequences of the anti-tissue factor antibody (1JPT) heavy chain with an IgLM-designed CDR3:
from iglm import IgLM
iglm = IgLM()
parent_sequence = "EVQLVESGGGLVQPGGSLRLSCAASGFNIKEYYMHWVRQAPGKGLEWVGLIDPEQGNTIYDPKFQDRATISADNSKNTAYLQMNSLRAEDTAVYYCARDTAAYFDYWGQGTLVTVS"
chain_token = "[HEAVY]"
species_token = "[HUMAN]"
infill_range = (98, 106)
num_seqs = 100
generated_seqs = iglm.infill(
parent_sequence,
chain_token,
species_token,
infill_range=infill_range,
num_to_generate=num_seqs,
)
Full antibody sequence generation
IgLM can be used to generate full antibody sequences while conditioning on the chain type and species-of-origin.
To generate 100 unique human heavy chain sequences starting with EVQ:
from iglm import IgLM
iglm = IgLM()
prompt_sequence = "EVQ"
chain_token = "[HEAVY]"
species_token = "[HUMAN]"
num_seqs = 100
generated_seqs = iglm.generate(
chain_token,
species_token,
prompt_sequence=prompt_sequence,
num_to_generate=num_seqs,
)
To generate 100 unique nanobody sequences starting with QVQ:
from iglm import IgLM
iglm = IgLM()
prompt_sequence = "QVQ"
chain_token = "[HEAVY]"
species_token = "[CAMEL]"
num_seqs = 100
generated_seqs = iglm.generate(
chain_token,
species_token,
prompt_sequence=prompt_sequence,
num_to_generate=num_seqs,
)
Sequence evaluation
IgLM can be used to calculate the log likelihood of a sequence given a chain type and species-of-origin.
Full sequence log likelihood calculation:
import math
from iglm import IgLM
iglm = IgLM()
sequence = "EVQLVESGGGLVQPGGSLRLSCAASGFNIKEYYMHWVRQAPGKGLEWVGLIDPEQGNTIYDPKFQDRATISADNSKNTAYLQMNSLRAEDTAVYYCARDTAAYFDYWGQGTLVTVS"
chain_token = "[HEAVY]"
species_token = "[HUMAN]"
log_likelihood = iglm.log_likelihood(
sequence,
chain_token,
species_token,
)
perplexity = math.exp(-log_likelihood)
Infilled sequence log likelihood calculation:
import math
from iglm import IgLM
iglm = IgLM()
sequence = "EVQLVESGGGLVQPGGSLRLSCAASGFNIKEYYMHWVRQAPGKGLEWVGLIDPEQGNTIYDPKFQDRATISADNSKNTAYLQMNSLRAEDTAVYYCARDTAAYFDYWGQGTLVTVS"
chain_token = "[HEAVY]"
species_token = "[HUMAN]"
infill_range = (98, 106)
log_likelihood = iglm.log_likelihood(
sequence,
chain_token,
species_token,
infill_range=infill_range,
)
perplexity = math.exp(-log_likelihood)
Citing this work
@article{shuai2021generative,
title={Generative language modeling for antibody design},
author={Shuai, Richard W and Ruffolo, Jeffrey A and Gray, Jeffrey J},
journal={bioRxiv},
pages={2021--12},
year={2021},
publisher={Cold Spring Harbor Laboratory}
}