Epitope profiling using computational structural modelling demonstrated on coronavirus-binding antibodies
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 e...
| Main Authors: | , , , , , |
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| Format: | Journal article |
| Language: | English |
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Public Library of Science
2021
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| _version_ | 1797082110346919936 |
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| author | Robinson, SA Raybould, MIJ Schneider, C Wong, WK Marks, C Deane, CM |
| author_facet | Robinson, SA Raybould, MIJ Schneider, C Wong, WK Marks, C Deane, CM |
| author_sort | Robinson, SA |
| collection | OXFORD |
| description | 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. |
| first_indexed | 2024-03-07T01:23:35Z |
| format | Journal article |
| id | oxford-uuid:912c9ca5-6851-4c9b-bf31-b4d0cd29cb27 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T01:23:35Z |
| publishDate | 2021 |
| publisher | Public Library of Science |
| record_format | dspace |
| spelling | oxford-uuid:912c9ca5-6851-4c9b-bf31-b4d0cd29cb272022-03-26T23:17:10ZEpitope profiling using computational structural modelling demonstrated on coronavirus-binding antibodiesJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:912c9ca5-6851-4c9b-bf31-b4d0cd29cb27EnglishSymplectic ElementsPublic Library of Science2021Robinson, SARaybould, MIJSchneider, CWong, WKMarks, CDeane, CMIdentifying 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. |
| spellingShingle | Robinson, SA Raybould, MIJ Schneider, C Wong, WK Marks, C Deane, CM Epitope profiling using computational structural modelling demonstrated on coronavirus-binding antibodies |
| title | Epitope profiling using computational structural modelling demonstrated on coronavirus-binding antibodies |
| title_full | Epitope profiling using computational structural modelling demonstrated on coronavirus-binding antibodies |
| title_fullStr | Epitope profiling using computational structural modelling demonstrated on coronavirus-binding antibodies |
| title_full_unstemmed | Epitope profiling using computational structural modelling demonstrated on coronavirus-binding antibodies |
| title_short | Epitope profiling using computational structural modelling demonstrated on coronavirus-binding antibodies |
| title_sort | epitope profiling using computational structural modelling demonstrated on coronavirus binding antibodies |
| work_keys_str_mv | AT robinsonsa epitopeprofilingusingcomputationalstructuralmodellingdemonstratedoncoronavirusbindingantibodies AT raybouldmij epitopeprofilingusingcomputationalstructuralmodellingdemonstratedoncoronavirusbindingantibodies AT schneiderc epitopeprofilingusingcomputationalstructuralmodellingdemonstratedoncoronavirusbindingantibodies AT wongwk epitopeprofilingusingcomputationalstructuralmodellingdemonstratedoncoronavirusbindingantibodies AT marksc epitopeprofilingusingcomputationalstructuralmodellingdemonstratedoncoronavirusbindingantibodies AT deanecm epitopeprofilingusingcomputationalstructuralmodellingdemonstratedoncoronavirusbindingantibodies |