Mylonas Stelios K, Axenopoulos Apostolos, Daras Petros
Information Technologies Institute, Centre for Research and Technology Hellas, Thessaloniki 57001, Greece.
Bioinformatics. 2021 Jul 19;37(12):1681-1690. doi: 10.1093/bioinformatics/btab009.
The knowledge of potentially druggable binding sites on proteins is an important preliminary step toward 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 article, 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.
The source code of the method along with trained models are freely available at https://github.com/stemylonas/DeepSurf.git.
Supplementary data are available at Bioinformatics online.
了解蛋白质上潜在的可成药结合位点是发现新型药物的重要初步步骤。随着深度学习领域的最新重大进展以及合适数据的日益丰富，此类区域的计算预测可以得到加强。
本文提出了一种预测潜在结合位点的新型计算方法，称为DeepSurf。DeepSurf将基于表面的表示（其中在蛋白质表面放置多个3D体素化网格）与最先进的深度学习架构相结合。在scPDB大型数据库上进行训练后，DeepSurf在三个不同的测试数据集上展示了卓越的结果，超过了所有主要的基于深度学习的竞争对手，同时在一组传统的非数据驱动方法中也取得了有竞争力的性能。
该方法的源代码以及训练好的模型可在https://github.com/stemylonas/DeepSurf.git上免费获取。
补充数据可在《生物信息学》在线获取。
