J Jiménez1, S Doerr1, G Martínez-Rosell1, A S Rose2, G De Fabritiis1,3. 1. Computational Biophysics Laboratory (GRIB-IMIM), Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB), 08003 Barcelona, Spain. 2. San Diego Supercomputer Center, UC San Diego, MC 0505, 9500 Gilman Drive, La Jolla, CA 92093-0505. USA. 3. ICREA, 08010 Barcelona, Spain.
Abstract
MOTIVATION: An important step in structure-based drug design consists in the prediction of druggable binding sites. Several algorithms for detecting binding cavities, those likely to bind to a small drug compound, have been developed over the years by clever exploitation of geometric, chemical and evolutionary features of the protein. RESULTS: Here we present a novel knowledge-based approach that uses state-of-the-art convolutional neural networks, where the algorithm is learned by examples. In total, 7622 proteins from the scPDB database of binding sites have been evaluated using both a distance and a volumetric overlap approach. Our machine-learning based method demonstrates superior performance to two other competitive algorithmic strategies. AVAILABILITY AND IMPLEMENTATION: DeepSite is freely available at www.playmolecule.org. Users can submit either a PDB ID or PDB file for pocket detection to our NVIDIA GPU-equipped servers through a WebGL graphical interface. CONTACT: gianni.defabritiis@upf.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
MOTIVATION: An important step in structure-based drug design consists in the prediction of druggable binding sites. Several algorithms for detecting binding cavities, those likely to bind to a small drug compound, have been developed over the years by clever exploitation of geometric, chemical and evolutionary features of the protein. RESULTS: Here we present a novel knowledge-based approach that uses state-of-the-art convolutional neural networks, where the algorithm is learned by examples. In total, 7622 proteins from the scPDB database of binding sites have been evaluated using both a distance and a volumetric overlap approach. Our machine-learning based method demonstrates superior performance to two other competitive algorithmic strategies. AVAILABILITY AND IMPLEMENTATION: DeepSite is freely available at www.playmolecule.org. Users can submit either a PDB ID or PDB file for pocket detection to our NVIDIA GPU-equipped servers through a WebGL graphical interface. CONTACT: gianni.defabritiis@upf.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Authors: Vladimir Gligorijević; P Douglas Renfrew; Tomasz Kosciolek; Julia Koehler Leman; Daniel Berenberg; Tommi Vatanen; Chris Chandler; Bryn C Taylor; Ian M Fisk; Hera Vlamakis; Ramnik J Xavier; Rob Knight; Kyunghyun Cho; Richard Bonneau Journal: Nat Commun Date: 2021-05-26 Impact factor: 14.919