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bioRxiv · 10.1101/2020.03.18.967828

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

Ambrosetti, F.·Olsed, T. H.·Olimpieri, P. P.·Jimenez-Garcia, B.·Milanetti, E.·Marcatilli, P.·Bonvin, A. M. J. J.
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Abstract

Monoclonal antibodies (mAbs) are essential tools in the contemporary therapeutic armoury. Understanding how these recognize their antigen is a fundamental step in their rational design and engineering. The rising amount of publicly available data is catalysing 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://bianca.science.uu.nl/proabc2/.

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Ambrosetti, F., Olsed, T. H., Olimpieri, P. P., Jimenez-Garcia, B., Milanetti, E., Marcatilli, P., Bonvin, A. M. J. J.. 2020-03-18. proABC-2: PRediction Of AntiBody Contacts v2 and its application to information-driven docking. https://doi.org/10.1101/2020.03.18.967828

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