DeepSite: protein-binding site predictor using 3D-convolutional neural networks
| Autor: | Alexander S. Rose, José Jiménez, G. De Fabritiis, Gerard Martínez-Rosell, Stefan Doerr |
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| Rok vydání: | 2017 |
| Předmět: |
0301 basic medicine
Statistics and Probability Protein Conformation Computer science Druggability Protein Data Bank (RCSB PDB) Plasma protein binding 010402 general chemistry computer.software_genre 01 natural sciences Biochemistry Convolutional neural network Machine Learning 03 medical and health sciences Software Binding site Molecular Biology Binding Sites Artificial neural network business.industry Proteins 0104 chemical sciences Computer Science Applications Computational Mathematics 030104 developmental biology Computational Theory and Mathematics Drug Design Neural Networks Computer Data mining business computer Algorithms |
| Zdroj: | Bioinformatics. 33:3036-3042 |
| ISSN: | 1367-4811 1367-4803 |
| Popis: | 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. Supplementary information Supplementary data are available at Bioinformatics online. |
| Databáze: | OpenAIRE |
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