Predictability of antigen binding based on short motifs in the antibody CDRH3.

Autor: Scheffer L; Department of Informatics, University of Oslo, Gaustadalléen 23B, 0373 Oslo, Norway., Reber EE; Department of Informatics, University of Oslo, Gaustadalléen 23B, 0373 Oslo, Norway., Mehta BB; Department of Immunology, University of Oslo, Sognsvannsveien 20, Rikshospitalet, 0372 Oslo, Norway., Pavlović M; Department of Informatics, University of Oslo, Gaustadalléen 23B, 0373 Oslo, Norway., Chernigovskaya M; Department of Immunology, University of Oslo, Sognsvannsveien 20, Rikshospitalet, 0372 Oslo, Norway., Richardson E; La Jolla Institute for Immunology, 9420 Athena Cir, La Jolla, CA, United States., Akbar R; Department of Immunology, University of Oslo, Sognsvannsveien 20, Rikshospitalet, 0372 Oslo, Norway., Lund-Johansen F; Department of Immunology, University of Oslo, Sognsvannsveien 20, Rikshospitalet, 0372 Oslo, Norway., Greiff V; Department of Immunology, University of Oslo, Sognsvannsveien 20, Rikshospitalet, 0372 Oslo, Norway., Haff IH; Department of Mathematics, University of Oslo, Niels Henrik Abels hus, Moltke Moes vei 35, 0851 Oslo, Norway., Sandve GK; Department of Informatics, University of Oslo, Gaustadalléen 23B, 0373 Oslo, Norway.
Jazyk: angličtina
Zdroj: Briefings in bioinformatics [Brief Bioinform] 2024 Sep 23; Vol. 25 (6).
DOI: 10.1093/bib/bbae537
Abstrakt: Adaptive immune receptors, such as antibodies and T-cell receptors, recognize foreign threats with exquisite specificity. A major challenge in adaptive immunology is discovering the rules governing immune receptor-antigen binding in order to predict the antigen binding status of previously unseen immune receptors. Many studies assume that the antigen binding status of an immune receptor may be determined by the presence of a short motif in the complementarity determining region 3 (CDR3), disregarding other amino acids. To test this assumption, we present a method to discover short motifs which show high precision in predicting antigen binding and generalize well to unseen simulated and experimental data. Our analysis of a mutagenesis-based antibody dataset reveals 11 336 position-specific, mostly gapped motifs of 3-5 amino acids that retain high precision on independently generated experimental data. Using a subset of only 178 motifs, a simple classifier was made that on the independently generated dataset outperformed a deep learning model proposed specifically for such datasets. In conclusion, our findings support the notion that for some antibodies, antigen binding may be largely determined by a short CDR3 motif. As more experimental data emerge, our methodology could serve as a foundation for in-depth investigations into antigen binding signals.
(© The Author(s) 2024. Published by Oxford University Press.)
Databáze: MEDLINE
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