Francesco Ambrosetti1,2, Tobias Hegelund Olsen3, Pier Paolo Olimpieri1, Brian Jiménez-García2, Edoardo Milanetti1,4, Paolo Marcatilli3, Alexandre M J J Bonvin2. 1. Department of Physics, Sapienza University, 00184 Rome, Italy. 2. Department of Chemistry, Faculty of Science, Computational Structural Biology Group, Bijvoet Centre for Biomolecular Research, Utrecht University, Utrecht 3584CH, The Netherlands. 3. Department of Health Technology, Technical University of Denmark, Kgs. Lyngby 2800, Denmark. 4. Fondazione Istituto Italiano di Tecnologia (IIT), Center for Life Nano Science, 00161 Rome, Italy.
Abstract
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.
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.
Authors: Edgar Liberis; Petar Velickovic; Pietro Sormanni; Michele Vendruscolo; Pietro Liò Journal: Bioinformatics Date: 2018-09-01 Impact factor: 6.937
Authors: G C P van Zundert; J P G L M Rodrigues; M Trellet; C Schmitz; P L Kastritis; E Karaca; A S J Melquiond; M van Dijk; S J de Vries; A M J J Bonvin Journal: J Mol Biol Date: 2015-09-26 Impact factor: 5.469
Authors: Richard A Norman; Francesco Ambrosetti; Alexandre M J J Bonvin; Lucy J Colwell; Sebastian Kelm; Sandeep Kumar; Konrad Krawczyk Journal: Brief Bioinform Date: 2020-09-25 Impact factor: 11.622
Authors: Rahmad Akbar; Habib Bashour; Puneet Rawat; Philippe A Robert; Eva Smorodina; Tudor-Stefan Cotet; Karine Flem-Karlsen; Robert Frank; Brij Bhushan Mehta; Mai Ha Vu; Talip Zengin; Jose Gutierrez-Marcos; Fridtjof Lund-Johansen; Jan Terje Andersen; Victor Greiff Journal: MAbs Date: 2022 Jan-Dec Impact factor: 5.857
Authors: Shunsuke Teraguchi; Dianita S Saputri; Mara Anais Llamas-Covarrubias; Ana Davila; Diego Diez; Sedat Aybars Nazlica; John Rozewicki; Hendra S Ismanto; Jan Wilamowski; Jiaqi Xie; Zichang Xu; Martin de Jesus Loza-Lopez; Floris J van Eerden; Songling Li; Daron M Standley Journal: Comput Struct Biotechnol J Date: 2020-07-17 Impact factor: 7.271