AMULETY: A Python package to embed adaptive immune receptor sequences

Abstract

Large language models have been developed to capture relevant features of adaptive immune receptors, each with unique potential applications. However, the diversity in available models presents challenges in accessibility and usability for downstream applications. Here we present AMULETY (Adaptive imMUne receptor Language model Embedding Tool), a Python-based software package to generate language model embeddings for adaptive immune receptor sequences, enabling users to leverage the strengths of different models without the need for complex configuration. AMULETY offers functions for embedding adaptive immune receptor amino acid sequences using pre-trained protein or antibody language models for paired B-cell receptor heavy-light, T-cell receptor alpha-beta or gamma-delta chains, or single chain sequences. We showcase the variability on the embedding space for several embeddings on a dataset of antibody binders to several SARS-CoV-2 epitopes as well as T-cell receptors binding to several epitopes and showed that different models may be effective at capturing different aspects of the distinctions between epitope groups. AMULETY is freely available under GPLv3 license from https://github.com/immcantation/amulety or via pip from the Python Package Index (PyPI) from https://pypi.org/project/amulety/.