Predicting B Cell Receptor Substitution Profiles Using Public Repertoire Data

TL;DR

This paper introduces SPURF, a novel penalized tensor regression method that predicts clonal-family-specific B cell receptor substitution profiles by leveraging large public datasets and related family information, aiding antibody research.

Contribution

The paper presents SPURF, a new framework that integrates multiple data sources to accurately predict B cell receptor substitution profiles from single sequences.

Findings

SPURF improves prediction accuracy over existing methods.

Leveraging related clonal families enhances substitution profile predictions.

The approach is validated on large public datasets and an external dataset.

Abstract

B cells develop high affinity receptors during the course of affinity maturation, a cyclic process of mutation and selection. At the end of affinity maturation, a number of cells sharing the same ancestor (i.e. in the same "clonal family") are released from the germinal center, their amino acid frequency profile reflects the allowed and disallowed substitutions at each position. These clonal-family-specific frequency profiles, called "substitution profiles", are useful for studying the course of affinity maturation as well as for antibody engineering purposes. However, most often only a single sequence is recovered from each clonal family in a sequencing experiment, making it impossible to construct a clonal-family-specific substitution profile. Given the public release of many high-quality large B cell receptor datasets, one may ask whether it is possible to use such data in a prediction model for clonal-family-specific substitution profiles. In this paper, we present the method "Substitution Profiles Using Related Families" (SPURF), a penalized tensor regression framework that integrates information from a rich assemblage of datasets to predict the clonal-family-specific substitution profile for any single input sequence. Using this framework, we show that substitution profiles from similar clonal families can be leveraged together with simulated substitution profiles and germline gene sequence information to improve prediction. We fit this model on a large public dataset and validate the robustness of our approach on an external dataset. Furthermore, we provide a command-line tool in an open-source software package (https://github.com/krdav/SPURF) implementing these ideas and providing easy prediction using our pre-fit models.