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An Expanded Benchmark for Antibody-Antigen Docking and Affinity Prediction Reveals Insights into Antibody Recognition Determinants

78 Pages Posted: 8 Apr 2020 Publication Status: Published

See all articles by Johnathan D. Guest

Johnathan D. Guest

University of Maryland - Institute for Bioscience and Biotechnology Research

Thom Vreven

University of Massachusetts Worcester - Program in Bioinformatics and Integrative Biology (BIB)

Jing Zhou

Johns Hopkins University

Iain Moal

GlaxoSmithKline

Jeliazko Jeliazkov

Johns Hopkins University

Jeffrey J. Gray

Johns Hopkins University

Zhiping Weng

University of Massachusetts Worcester - Program in Bioinformatics and Integrative Biology (BIB)

Brian G. Pierce

University of Maryland - Institute for Bioscience and Biotechnology Research

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Abstract

Accurate predictive modeling of antibody-antigen complex structures and structure-based antibody design remain major challenges in computational biology, with implications in biotherapeutics, immunity, and vaccines. Through a systematic search for high resolution structures of antibody-antigen complexes and unbound antibody and antigen structures, in conjunction with identification of experimentally determined binding affinities, we have assembled a non-redundant set of test cases for antibody-antigen docking and affinity prediction. This benchmark more than doubles the number of antibody-antigen complexes and corresponding affinities available in our previous benchmarks, providing an unprecedented view of the determinants of antibody recognition and insights into molecular flexibility. Initial assessments of docking and affinity prediction tools highlight the challenges posed by this diverse set of cases, which includes camelid nanobodies, therapeutic monoclonal antibodies, and broadly neutralizing antibodies targeting viral glycoproteins. This dataset will enable development of advanced predictive modeling and design methods for this therapeutically relevant class of protein-protein interactions.

Keywords: protein-protein docking, antibody design, camelid, nanobody, affinity prediction

Suggested Citation

Guest, Johnathan D. and Vreven, Thom and Zhou, Jing and Moal, Iain and Jeliazkov, Jeliazko and Gray, Jeffrey J. and Weng, Zhiping and Pierce, Brian G., An Expanded Benchmark for Antibody-Antigen Docking and Affinity Prediction Reveals Insights into Antibody Recognition Determinants. Available at SSRN: https://ssrn.com/abstract=3564997 or http://dx.doi.org/10.2139/ssrn.3564997
This version of the paper has not been formally peer reviewed.

Johnathan D. Guest

University of Maryland - Institute for Bioscience and Biotechnology Research

United States

Thom Vreven

University of Massachusetts Worcester - Program in Bioinformatics and Integrative Biology (BIB)

368 Plantation St.
Worcester, MA 01605
United States

Jing Zhou

Johns Hopkins University

Baltimore, MD 20036-1984
United States

Iain Moal

GlaxoSmithKline

5 Moore Drive
RTP, NC 27709
United States

Jeliazko Jeliazkov

Johns Hopkins University

Baltimore, MD 20036-1984
United States

Jeffrey J. Gray

Johns Hopkins University

Baltimore, MD 20036-1984
United States

Zhiping Weng

University of Massachusetts Worcester - Program in Bioinformatics and Integrative Biology (BIB)

368 Plantation St.
Worcester, MA 01605
United States

Brian G. Pierce (Contact Author)

University of Maryland - Institute for Bioscience and Biotechnology Research ( email )

United States

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