Improved Antibody-Specific Epitope Prediction Using AlphaFold and AbAdapt.
Chembiochem
; 23(18): e202200303, 2022 09 16.
Article
in English
| MEDLINE | ID: covidwho-1958520
ABSTRACT
Antibodies recognize their cognate antigens with high affinity and specificity, but the prediction of binding sites on the antigen (epitope) corresponding to a specific antibody remains a challenging problem. To address this problem, we developed AbAdapt, a pipeline that integrates antibody and antigen structural modeling with rigid docking in order to derive antibody-antigen specific features for epitope prediction. In this study, we systematically assessed the impact of integrating the state-of-the-art protein modeling method AlphaFold with the AbAdapt pipeline. By incorporating more accurate antibody models, we observed improvement in docking, paratope prediction, and prediction of antibody-specific epitopes. We further applied AbAdapt-AF in an anti-receptor binding domain (RBD) antibody complex benchmark and found AbAdapt-AF outperformed three alternative docking methods. Also, AbAdapt-AF demonstrated higher epitope prediction accuracy than other tested epitope prediction tools in the anti-RBD antibody complex benchmark. We anticipate that AbAdapt-AF will facilitate prediction of antigen-antibody interactions in a wide range of applications.
Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Antibodies
/
Antigens
Type of study:
Prognostic study
Language:
English
Journal:
Chembiochem
Journal subject:
Biochemistry
Year:
2022
Document Type:
Article
Affiliation country:
Cbic.202200303
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