This repository is a work in progress.
This repository contains code and pre-trained model checkpoints for AbMAP, a Protein Language Model (PLM) customized for antibodies as featured in Learning the Language of Antibody Hypervariability (Singh, Im et al. 2023) Link. AbMAP leverages information from foundational PLMs as well as antibody structure and function, offering a multi-functional tool useful for predicting structure, functional properties, and analyzing B-cell repertoires.
pip install abmap # (recommended) latest release from PyPI
pip install git+https://github.com/rs239/ablm.git # the live main branchAfter installation, AbMAP can be easily imported into your python projects or run from the command line. Please see examples/demo.ipynb for common use cases. Instructions for running via CLI are below.
Instructions In Progress Given a sequence, generate a foundational PLM embedding augmented with in-silico mutagenesis and CDR isolation.
Given a dataset of labeled pairs of sequences and their augmented embeddings, train the AbMAP model on downstream prediction tasks.
Given fasta sequences and a pre-trained AbMAP model, generate their AbMAP embeddings (fixed or variable).
[1] Madeira, Fábio, et al. "Search and sequence analysis tools services from EMBL-EBI in 2022." Nucleic acids research 50.W1 (2022): W276-W279. (Transeq)