Epitope profiling using computational structural modelling demonstrated on coronavirus-binding antibodies.
PLoS Comput Biol
; 17(12): e1009675, 2021 12.
Article
in English
| MEDLINE | ID: covidwho-1619980
ABSTRACT
Identifying the epitope of an antibody is a key step in understanding its function and its potential as a therapeutic. Sequence-based clonal clustering can identify antibodies with similar epitope complementarity, however, antibodies from markedly different lineages but with similar structures can engage the same epitope. We describe a novel computational method for epitope profiling based on structural modelling and clustering. Using the method, we demonstrate that sequence dissimilar but functionally similar antibodies can be found across the Coronavirus Antibody Database, with high accuracy (92% of antibodies in multiple-occupancy structural clusters bind to consistent domains). Our approach functionally links antibodies with distinct genetic lineages, species origins, and coronavirus specificities. This indicates greater convergence exists in the immune responses to coronaviruses than is suggested by sequence-based approaches. Our results show that applying structural analytics to large class-specific antibody databases will enable high confidence structure-function relationships to be drawn, yielding new opportunities to identify functional convergence hitherto missed by sequence-only analysis.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Epitopes, B-Lymphocyte
/
SARS-CoV-2
/
COVID-19
/
Antigens, Viral
Limits:
Animals
/
Humans
Language:
English
Journal:
PLoS Comput Biol
Journal subject:
Biology
/
Medical Informatics
Year:
2021
Document Type:
Article
Affiliation country:
Journal.pcbi.1009675
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