DeepSite: protein-binding site predictor using 3D-convolutional neural networks

• Published in: Bioinformatics (Oxford, England) Vol. 33; no. 19; pp. 3036 - 3042
• Main Authors: Jiménez, J, Doerr, S, Martínez-Rosell, G, Rose, A S, De Fabritiis, G
• Format: Journal Article
• Language: English
• Published: England 01-10-2017
• Subjects: Algorithms; Binding Sites; Drug Design; Machine Learning; Neural Networks, Computer; Protein Conformation; Proteins - chemistry; Software
• ISSN: 1367-4803; 1367-4811
• DOI: 10.1093/bioinformatics/btx350

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.
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.
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.
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Supplementary data are available at Bioinformatics online.