Back to Search
Start Over
A compact vocabulary of paratope-epitope interactions enables predictability of antibody-antigen binding.
- 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