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 two external datasets. 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.
Highlights
In the therapeutic antibody discovery and engineering field, researchers commonly isolate antibodies from animal or human immunizations and screen for functional properties such as binding to a target protein
Antibody engineering can be greatly informed by knowledge about the underlying affinity maturation process
As such this can be probed by sequencing, but in practice often only one member of the clonal family is sequenced, making it difficult to determine a set of possible amino acid mutations that would retain the original antibody antigen binding affinity
Summary
In the therapeutic antibody discovery and engineering field, researchers commonly isolate antibodies from animal or human immunizations and screen for functional properties such as binding to a target protein. Following the initial screening process, a small number of wellbehaving antibodies (hits) are isolated for more rigorous examination of their biophysical properties in order to determine their potential as a therapeutic After this stage, only a few final antibodies remain as lead candidates. Referred to as “rational”, the choice of which amino acid to use for a site-directed mutation is often made using 1) the germline as a reference, 2) biochemical similarity between amino acids, or 3) the highest probability amino acid from a generic substitution matrix (e.g. BLOSUM) [9] Neither of these three methods are explicitly designed to conserve antibody functionality (i.e. binding to the same epitope with the same kinetics), so mutations are likely to have negative side effects on affinity. These considerations motivate a prediction problem: given a B cell receptor (BCR) sequence, which positions can be modified, and to which amino acids, without drastically changing the binding properties of the resulting BCR?
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
