Details

Autor(en) / Beteiligte

• Robinson, Sarah A
• Raybould, Matthew I J
• Schneider, Constantin
• Wong, Wing Ki
• Marks, Claire
• Deane, Charlotte M
• Fraternali, Franca

Titel

Epitope profiling using computational structural modelling demonstrated on coronavirus-binding antibodies

Ist Teil von

• PLoS computational biology, 2021-12, Vol.17 (12), p.e1009675-e1009675

Ort / Verlag

United States: Public Library of Science

Erscheinungsjahr

2021

Link zum Volltext

Quelle

MEDLINE

Beschreibungen/Notizen

• 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.