proABC-2: PRediction Of AntiBody Contacts v2 and its application to information-driven docking

F Ambrosetti; T H Olsen; P P Olimpieri; B Jiménez-García; E Milanetti; P Marcatilli; A M J J Bonvin

doi:10.1093/bioinformatics/btaa644

proABC-2: PRediction Of AntiBody Contacts v2 and its application to information-driven docking

F Ambrosetti, T H Olsen, P P Olimpieri, B Jiménez-García, E Milanetti, P Marcatilli, A M J J Bonvin

Department of Health Technology, Technical University of Denmark, Kgs. Lyngby, Denmark.
Department of Physics, Sapienza University, Piazzale Aldo Moro 5, 00184, Rome, Italy.
Center for Life Nano Science@Sapienza, Istituto Italiano di Tecnologia, Viale Regina Elena 291, 00161 Rome, Italy.

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Abstract

MOTIVATION: Monoclonal antibodies (mAbs) 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.

RESULTS: 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.

AVAILABILITY: The proABC-2 server is freely available at: https://wenmr.science.uu.nl/proabc2/.

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Original language	English
Pages (from-to)	5107-5108
Journal	Bioinformatics
Volume	36
Issue number	20
DOIs	https://doi.org/10.1093/bioinformatics/btaa644
Publication status	Published - 15 Oct 2020

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title = "proABC-2: PRediction Of AntiBody Contacts v2 and its application to information-driven docking",

abstract = "MOTIVATION: Monoclonal antibodies (mAbs) 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.RESULTS: 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.AVAILABILITY: The proABC-2 server is freely available at: https://wenmr.science.uu.nl/proabc2/.SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.",

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doi = "10.1093/bioinformatics/btaa644",

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AU - Ambrosetti, F

AU - Olsen, T H

AU - Olimpieri, P P

AU - Jiménez-García, B

AU - Milanetti, E

AU - Marcatilli, P

AU - Bonvin, A M J J

PY - 2020/10/15

Y1 - 2020/10/15

N2 - MOTIVATION: Monoclonal antibodies (mAbs) 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.RESULTS: 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.AVAILABILITY: The proABC-2 server is freely available at: https://wenmr.science.uu.nl/proabc2/.SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

AB - MOTIVATION: Monoclonal antibodies (mAbs) 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.RESULTS: 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.AVAILABILITY: The proABC-2 server is freely available at: https://wenmr.science.uu.nl/proabc2/.SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

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proABC-2: PRediction Of AntiBody Contacts v2 and its application to information-driven docking

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