Motivation: 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.
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 and implementation: The proABC-2 server is freely available at: https://wenmr.science.uu.nl/proabc2/.
Supplementary information: Supplementary data are available at Bioinformatics online.
© The Author(s) 2020. Published by Oxford University Press.