Volume 91, Issue 2 pp. 171-182
RESEARCH ARTICLE

Mapping of antibody epitopes based on docking and homology modeling

Israel T. Desta

Israel T. Desta

Department of Biomedical Engineering, Boston University, Boston, Massachusetts, USA

Contribution: Conceptualization, Methodology, ​Investigation, Writing - original draft, Writing - review & editing, Validation

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Sergei Kotelnikov

Sergei Kotelnikov

Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York, USA

Contribution: Conceptualization, Methodology, ​Investigation, Writing - original draft, Writing - review & editing

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George Jones

George Jones

Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York, USA

Contribution: Methodology, Validation, ​Investigation

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Usman Ghani

Usman Ghani

Department of Biomedical Engineering, Boston University, Boston, Massachusetts, USA

Contribution: Methodology, Validation, ​Investigation

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Mikhail Abyzov

Mikhail Abyzov

Innopolis University, Innopolis, Russia

Contribution: Validation, Methodology, Writing - review & editing

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Yaroslav Kholodov

Yaroslav Kholodov

Innopolis University, Innopolis, Russia

Contribution: ​Investigation, Validation, Writing - review & editing

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Daron M. Standley

Daron M. Standley

Department of Genome Informatics, Osaka University, Osaka, Japan

Center for Infectious Disease Education and Research, Osaka University, Osaka, Japan

Contribution: ​Investigation, Validation, Writing - review & editing

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Maria Sabitova

Maria Sabitova

Department of Mathematics, CUNY Queens College, Flushing, New York, USA

Contribution: Writing - review & editing, Formal analysis, Writing - original draft

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Dmitri Beglov

Dmitri Beglov

Department of Biomedical Engineering, Boston University, Boston, Massachusetts, USA

Contribution: Validation, Methodology

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Sandor Vajda

Corresponding Author

Sandor Vajda

Department of Biomedical Engineering, Boston University, Boston, Massachusetts, USA

Correspondence

Sandor Vajda, Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA.

Email: [email protected]

Dima Kozakov, Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY 11794, USA.

Email: [email protected]

Contribution: Writing - review & editing, Writing - original draft, Conceptualization, ​Investigation, Validation, Methodology

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Dima Kozakov

Corresponding Author

Dima Kozakov

Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York, USA

Correspondence

Sandor Vajda, Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA.

Email: [email protected]

Dima Kozakov, Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY 11794, USA.

Email: [email protected]

Contribution: Conceptualization, Writing - original draft, Writing - review & editing, Methodology, ​Investigation, Validation

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First published: 11 September 2022
Citations: 2

Israel T. Desta and Sergei Kotelnikov Joint first authors.

Funding information: National Institutes of Health; National Science Foundation, Grant/Award Number: DMS 2054251; National Institute of General Medical Sciences, Grant/Award Numbers: R43GM134769, RM1135136, R01 GM140098, R35GM118078

Abstract

Antibodies are key proteins produced by the immune system to target pathogen proteins termed antigens via specific binding to surface regions called epitopes. Given an antigen and the sequence of an antibody the knowledge of the epitope is critical for the discovery and development of antibody based therapeutics. In this work, we present a computational protocol that uses template-based modeling and docking to predict epitope residues. This protocol is implemented in three major steps. First, a template-based modeling approach is used to build the antibody structures. We tested several options, including generation of models using AlphaFold2. Second, each antibody model is docked to the antigen using the fast Fourier transform (FFT) based docking program PIPER. Attention is given to optimally selecting the docking energy parameters depending on the input data. In particular, the van der Waals energy terms are reduced for modeled antibodies relative to x-ray structures. Finally, ranking of antigen surface residues is produced. The ranking relies on the docking results, that is, how often the residue appears in the docking poses' interface, and also on the energy favorability of the docking pose in question. The method, called PIPER-Map, has been tested on a widely used antibody–antigen docking benchmark. The results show that PIPER-Map improves upon the existing epitope prediction methods. An interesting observation is that epitope prediction accuracy starting from antibody sequence alone does not significantly differ from that of starting from unbound (i.e., separately crystallized) antibody structure.

CONFLICT OF INTEREST

The PIPER docking program has been licensed by Boston University to Acpharis Inc. Acpharis, in turn, offers commercial sublicenses of PIPER. Dima Kozakov and Sandor Vajda consult for Acpharis and own stock in the company, Dmitri Beglov is the CEO of the company. However, the ClusPro server, implementing the PIPER program, is freely available to non-commercial use at https://cluspro.bu.edu/.

DATA AVAILABILITY STATEMENT

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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