An expanded benchmark for antibody-antigen docking and affinity prediction reveals insights into antibody recognition determinants

Johnathan D Guest; Thom Vreven; Jing Zhou; Iain Moal; Jeliazko R Jeliazkov; Jeffrey J Gray; Zhiping Weng; Brian G Pierce

doi:10.1016/j.str.2021.01.005

An expanded benchmark for antibody-antigen docking and affinity prediction reveals insights into antibody recognition determinants

Structure. 2021 Jun 3;29(6):606-621.e5. doi: 10.1016/j.str.2021.01.005. Epub 2021 Feb 3.

Authors

Johnathan D Guest¹, Thom Vreven², Jing Zhou³, Iain Moal⁴, Jeliazko R Jeliazkov⁵, Jeffrey J Gray⁶, Zhiping Weng⁷, Brian G Pierce⁸

Affiliations

¹ University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, MD 20850, USA; Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, MD 20742, USA.
² Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, MA 01605, USA.
³ Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21218, USA.
⁴ Computational Sciences, GlaxoSmithKline Research and Development, Stevenage SG1 2NY, UK.
⁵ Program in Molecular Biophysics, Johns Hopkins University, Baltimore, MD 21218, USA.
⁶ Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21218, USA; Program in Molecular Biophysics, Johns Hopkins University, Baltimore, MD 21218, USA.
⁷ Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, MA 01605, USA. Electronic address: [email protected].
⁸ University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, MD 20850, USA; Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, MD 20742, USA. Electronic address: [email protected].

Abstract

Accurate predictive modeling of antibody-antigen complex structures and structure-based antibody design remain major challenges in computational biology, with implications for 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: affinity prediction; antibody design; biotherapeutics; monoclonal antibodies; nanobody; protein-protein docking; viruses.

Publication types

Research Support, N.I.H., Extramural
Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

Algorithms
Antibodies / chemistry*
Antibodies / metabolism*
Antibodies, Monoclonal / chemistry
Antibodies, Monoclonal / metabolism
Antibodies, Viral / chemistry
Antibodies, Viral / metabolism
Antigen-Antibody Complex / chemistry
Antigens / chemistry*
Antigens / metabolism*
Benchmarking
Broadly Neutralizing Antibodies / chemistry
Broadly Neutralizing Antibodies / metabolism
Computational Biology / methods
Molecular Docking Simulation
Protein Binding
Protein Conformation
Single-Domain Antibodies / chemistry
Single-Domain Antibodies / metabolism
Software
Structure-Activity Relationship

Substances

Antibodies
Antibodies, Monoclonal
Antibodies, Viral
Antigen-Antibody Complex
Antigens
Broadly Neutralizing Antibodies
Single-Domain Antibodies

Abstract

Publication types

MeSH terms

Substances

Grants and funding