Information-Driven Antibody-Antigen Modelling with HADDOCK

Francesco Ambrosetti; Zuzana Jandova; Alexandre M J J Bonvin

doi:10.1007/978-1-0716-2609-2_14

Information-Driven Antibody-Antigen Modelling with HADDOCK

Francesco Ambrosetti, Zuzana Jandova, Alexandre M J J Bonvin^*

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Chapter › Academic › peer-review

Abstract

In the recent years, therapeutic use of antibodies has seen a huge growth, "due to their inherent proprieties and technological advances in the methods used to study and characterize them. Effective design and engineering of antibodies for therapeutic purposes are heavily dependent on knowledge of the structural principles that regulate antibody-antigen interactions. Several experimental techniques such as X-ray crystallography, cryo-electron microscopy, NMR, or mutagenesis analysis can be applied, but these are usually expensive and time-consuming. Therefore computational approaches like molecular docking may offer a valuable alternative for the characterization of antibody-antigen complexes.Here we describe a protocol for the prediction of the 3D structure of antibody-antigen complexes using the integrative modelling platform HADDOCK. The protocol consists of (1) the identification of the antibody residues belonging to the hypervariable loops which are known to be crucial for the binding and can be used to guide the docking and (2) the detailed steps to perform docking with the HADDOCK 2.4 webserver following different strategies depending on the availability of information about epitope residues.

Original language	English
Title of host publication	Methods in Molecular Biology
Editors	Kouhei Tsumoto, Daisuke Kuroda
Place of Publication	New York
Publisher	Humana Press
Pages	267-282
Number of pages	16
Volume	2552
Edition	1
ISBN (Electronic)	978-1-0716-2609-2
ISBN (Print)	978-1-0716-2608-5, 978-1-0716-2611-5
DOIs	https://doi.org/10.1007/978-1-0716-2609-2_14
Publication status	Published - 10 Nov 2023

Publication series

Name	Methods in Molecular Biology
Volume	2552
ISSN (Print)	1064-3745
ISSN (Electronic)	1940-6029

Bibliographical note

Publisher Copyright:
© 2023, Springer Science+Business Media, LLC, part of Springer Nature.

Funding

This work is supported by the European Union Horizon 2020 BioExcel (grant # 675728 and 823830), EOSC-hub (grant # 777536) and EGI-ACE (grant # 101017567) projects.

Funders	Funder number
EGI-ACE	101017567
EOSC-hub	777536
European Union Horizon 2020 BioExcel	823830, 675728

Keywords

Antibody
HADDOCK
Information-driven docking

Access to Document

10.1007/978-1-0716-2609-2_14

978-1-0716-2609-2_14Final published version, 704 KBLicence: Taverne

Cite this

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Information-Driven Antibody-Antigen Modelling with HADDOCK. / Ambrosetti, Francesco; Jandova, Zuzana; Bonvin, Alexandre M J J.
Methods in Molecular Biology. ed. / Kouhei Tsumoto; Daisuke Kuroda. Vol. 2552 1. ed. New York: Humana Press, 2023. p. 267-282 (Methods in Molecular Biology; Vol. 2552).

Research output: Chapter in Book/Report/Conference proceeding › Chapter › Academic › peer-review

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Information-Driven Antibody-Antigen Modelling with HADDOCK

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