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A compact vocabulary of paratope-epitope interactions enables predictability of antibody-antigen binding.

Authors :
Akbar R
Robert PA
Pavlović M
Jeliazkov JR
Snapkov I
Slabodkin A
Weber CR
Scheffer L
Miho E
Haff IH
Haug DTT
Lund-Johansen F
Safonova Y
Sandve GK
Greiff V
Source :
Cell reports [Cell Rep] 2021 Mar 16; Vol. 34 (11), pp. 108856.
Publication Year :
2021

Abstract

Antibody-antigen binding relies on the specific interaction of amino acids at the paratope-epitope interface. The predictability of antibody-antigen binding is a prerequisite for de novo antibody and (neo-)epitope design. A fundamental premise for the predictability of antibody-antigen binding is the existence of paratope-epitope interaction motifs that are universally shared among antibody-antigen structures. In a dataset of non-redundant antibody-antigen structures, we identify structural interaction motifs, which together compose a commonly shared structure-based vocabulary of paratope-epitope interactions. We show that this vocabulary enables the machine learnability of antibody-antigen binding on the paratope-epitope level using generative machine learning. The vocabulary (1) is compact, less than 10 <superscript>4</superscript> motifs; (2) distinct from non-immune protein-protein interactions; and (3) mediates specific oligo- and polyreactive interactions between paratope-epitope pairs. Our work leverages combined structure- and sequence-based learning to demonstrate that machine-learning-driven predictive paratope and epitope engineering is feasible.<br />Competing Interests: Declaration of interests E.M. declares holding shares in aiNET GmbH. V.G. declares advisory board positions in aiNET GmbH and Enpicom B.V.<br /> (Copyright © 2021 The Author(s). Published by Elsevier Inc. All rights reserved.)

Details

Language :
English
ISSN :
2211-1247
Volume :
34
Issue :
11
Database :
MEDLINE
Journal :
Cell reports
Publication Type :
Academic Journal
Accession number :
33730590
Full Text :
https://doi.org/10.1016/j.celrep.2021.108856