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

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.

Primary exposure to SARS-CoV-2 variants elicits convergent epitope specificities, immunoglobulin V gene usage and public B cell clones

“…Importantly, we also observed convergences that are not encompassed within the standard definition of a public clone, consistent with structural modeling and clustering demonstrating that high CDR H3 sequence similarity and even convergent V genes are not required for antibodies to target overlapping epitopes using comparable binding conformations (72). As a specific example, we identified antibodies with a previously described IGHD3-22encoded YYDRxG motif that can result in broad neutralization of divergent sarbecoviruses (54).…”

Fast, accurate antibody structure prediction from deep learning on massive set of natural antibodies

“…However, incorporation of structural context has proven valuable, particularly for identification of sequence-disimilar binders to common epitopes (47). For example, grafting-based methods have been used to identify sequence-diverse but structurally similar antibodies against SARS-CoV-2 (48). The increased accuracy of IgFold, coupled with its speed, will make such methods more effective.…”

ABlooper: fast accurate antibody CDR loop structure prediction with accuracy estimation

“…With the arrival of B-cell receptor repertoire sequencing, the number of publicly available paired antibody sequence data is rapidly increasing ( Kovaltsuk et al , 2018 ; Olsen et al , 2022 ). Fast accurate tools such as ABlooper provide the opportunity for structural studies (such as Robinson et al , 2021 ) at previously infeasible scales. The model used for ABlooper is available at: https://github.com/oxpig/ABlooper .…”

“…Computational modelling tools have allowed researchers to bridge this gap by predicting large numbers of antibody structures to a high level of accuracy ( Leem et al , 2016 ; Ruffolo et al , 2021 ). For example, models of antibody structures have recently been used for virtual screening ( Schneider et al , 2021 ) and to identify coronavirus-binding antibodies that bind the same epitope with very different sequences ( Robinson et al , 2021 ).…”