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Bioinformatics. 2017 Oct 1;33(19):3036-3042. doi: 10.1093/bioinformatics/btx350.

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

Author information

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.

PMID:
28575181
DOI:
10.1093/bioinformatics/btx350
[Indexed for MEDLINE]

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