proABC-2: PRediction of AntiBody contacts v2 and its application to information-driven docking.

Bioinformatics
Francesco AmbrosettiA M J J Bonvin

Abstract

Monoclonal antibodies are essential tools in the contemporary therapeutic armory. Understanding how these recognize their antigen is a fundamental step in their rational design and engineering. The rising amount of publicly available data is catalyzing the development of computational approaches able to offer valuable, faster and cheaper alternatives to classical experimental methodologies used for the study of antibody-antigen complexes. Here, we present proABC-2, an update of the original random-forest antibody paratope predictor, based on a convolutional neural network algorithm. We also demonstrate how the predictions can be fruitfully used to drive the docking in HADDOCK. The proABC-2 server is freely available at: https://wenmr.science.uu.nl/proabc2/. Supplementary data are available at Bioinformatics online.

References

Aug 20, 1987·Journal of Molecular Biology·C Chothia, A M Lesk
May 23, 2009·British Journal of Pharmacology·Patrick ChamesDaniel Baty
Aug 23, 2011·Annual Review of Genetics·David G Schatz, Patrick C Swanson
Jun 19, 2014·Proceedings of the National Academy of Sciences of the United States of America·Hung-Pin PengAn-Suei Yang
Sep 28, 2015·Journal of Molecular Biology·G C P van ZundertA M J J Bonvin
Dec 7, 2018·Journal of Computational Biology : a Journal of Computational Molecular Cell Biology·Andreea DeacPietro Sormanni
Dec 6, 2018·MAbs·Hélène Kaplon, Janice M Reichert
Oct 19, 2019·Briefings in Bioinformatics·Richard A NormanKonrad Krawczyk
Nov 16, 2019·Structure·Francesco AmbrosettiAlexandre M J J Bonvin

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Citations

Aug 17, 2021·Frontiers in Molecular Biosciences·Rodrigo V HonoratoAlexandre M J J Bonvin

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Methods Mentioned

BETA
X-ray

Software Mentioned

Pred
proABC
HADDOCK
HV
Parapred
Surf

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