@inbook{f1c93734870742d7b3e3969cb0ccde7c,
title = "Information-Driven Antibody-Antigen Modelling with HADDOCK",
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.",
keywords = "Antibody, HADDOCK, Information-driven docking",
author = "Francesco Ambrosetti and Zuzana Jandova and Bonvin, {Alexandre M J J}",
note = "Publisher Copyright: {\textcopyright} 2023, Springer Science+Business Media, LLC, part of Springer Nature.",
year = "2023",
month = nov,
day = "10",
doi = "10.1007/978-1-0716-2609-2_14",
language = "English",
isbn = "978-1-0716-2608-5",
volume = "2552",
series = "Methods in Molecular Biology",
publisher = "Humana Press",
pages = "267--282",
editor = "Tsumoto, {Kouhei } and Kuroda, {Daisuke }",
booktitle = "Methods in Molecular Biology",
edition = "1",
}