Deep geometric representations for modeling effects of mutations on protein-protein binding affinity.
PLoS Comput Biol
; 17(8): e1009284, 2021 08.
Article
in English
| MEDLINE | ID: covidwho-1341479
ABSTRACT
Modeling the impact of amino acid mutations on protein-protein interaction plays a crucial role in protein engineering and drug design. In this study, we develop GeoPPI, a novel structure-based deep-learning framework to predict the change of binding affinity upon mutations. Based on the three-dimensional structure of a protein, GeoPPI first learns a geometric representation that encodes topology features of the protein structure via a self-supervised learning scheme. These representations are then used as features for training gradient-boosting trees to predict the changes of protein-protein binding affinity upon mutations. We find that GeoPPI is able to learn meaningful features that characterize interactions between atoms in protein structures. In addition, through extensive experiments, we show that GeoPPI achieves new state-of-the-art performance in predicting the binding affinity changes upon both single- and multi-point mutations on six benchmark datasets. Moreover, we show that GeoPPI can accurately estimate the difference of binding affinities between a few recently identified SARS-CoV-2 antibodies and the receptor-binding domain (RBD) of the S protein. These results demonstrate the potential of GeoPPI as a powerful and useful computational tool in protein design and engineering. Our code and datasets are available at https//github.com/Liuxg16/GeoPPI.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Proteins
/
Amino Acid Substitution
/
Models, Chemical
Type of study:
Experimental Studies
/
Prognostic study
Language:
English
Journal:
PLoS Comput Biol
Journal subject:
Biology
/
Medical Informatics
Year:
2021
Document Type:
Article
Affiliation country:
Journal.pcbi.1009284
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