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Identification of Mouse and Human Antibody Repertoires by Next-Generation Sequencing

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Published on: March 15, 2019

AMULETY: A Python package to embed adaptive immune receptor sequences.

Meng Wang¹, Yuval Kluger^1,2,3, Steven H Kleinstein^1,2,4

¹Program in Computational Biology and Biomedical Informatics, Yale University, New Haven, CT, USA.

Biorxiv : the Preprint Server for Biology

|April 8, 2025

Summary

This summary is machine-generated.

AMULETY is a new Python tool that simplifies using adaptive immune receptor language models. It generates embeddings for immune receptor sequences, making advanced analysis more accessible for researchers.

Keywords:

B cell receptor computational immunology embedding machine learning next generation sequencing

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Area of Science:

Immunoinformatics
Computational Biology
Bioinformatics

Background:

Large language models (LLMs) show promise for analyzing adaptive immune receptors.
Diverse LLMs exist, but their varied accessibility hinders widespread application in immunology research.
Standardizing LLM usage is crucial for leveraging their full potential in adaptive immune receptor sequence analysis.

Purpose of the Study:

To introduce AMULETY, a Python software package for generating language model embeddings of adaptive immune receptor sequences.
To provide a user-friendly interface for accessing and utilizing various pre-trained protein and antibody language models.
To facilitate downstream applications by simplifying the integration of LLM-derived features for immune receptor data.

Main Methods:

Developed AMULETY as a Python-based software package.
Implemented functions to generate embeddings for amino acid sequences of adaptive immune receptors.
Supported embedding generation using pre-trained protein or antibody language models for both paired and single immune receptor chains.

Main Results:

Demonstrated AMULETY's capability to generate embeddings for adaptive immune receptor sequences.
Showcased the variability in embedding spaces generated by different language models using SARS-CoV-2 epitope binder data.
Highlighted that distinct LLMs capture different biological features relevant to antibody-epitope interactions.

Conclusions:

AMULETY enhances accessibility and usability of LLM embeddings for adaptive immune receptor sequences.
The tool enables researchers to leverage diverse LLMs without complex setup, promoting broader adoption.
AMULETY facilitates nuanced analysis of immune receptor repertoires and antibody-antigen interactions.