In Bioinformatics (Oxford, England)
MOTIVATION : The knowledge of potentially druggable binding sites on proteins is an important preliminary step towards the discovery of novel drugs. The computational prediction of such areas can be boosted by following the recent major advances in the deep learning field and by exploiting the increasing availability of proper data.
RESULTS : In this paper, a novel computational method for the prediction of potential binding sites is proposed, called DeepSurf. DeepSurf combines a surface-based representation, where a number of 3 D voxelized grids are placed on the protein's surface, with state-of-the-art deep learning architectures. After being trained on the large database of scPDB, DeepSurf demonstrates superior results on three diverse testing datasets, by surpassing all its main deep learning-based competitors, while attaining competitive performance to a set of traditional non-data-driven approaches.
AVAILABILITY : The source code of the method along with trained models are freely available at https://github.com/stemylonas/DeepSurf.git.
SUPPLEMENTARY INFORMATION : Supplementary data are available at Bioinformatics online.
Mylonas Stelios K, Axenopoulos Apostolos, Daras Petros