DeepSurf: A surface-based deep learning approach for the prediction of ligand binding sites on proteins

• Published in: arXiv.org
• Main Authors: Mylonas, Stelios K, Axenopoulos, Apostolos, Daras, Petros
• Format: Paper Journal Article
• Language: English
• Published: Ithaca Cornell University Library, arXiv.org 16.02.2021
• Subjects: Binding sites; Computer Science - Learning; Deep learning; Machine learning; Proteins; Quantitative Biology - Biomolecules
• ISSN: 2331-8422
• DOI: 10.48550/arxiv.2002.05643

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. 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 3D 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.