DeepSite: protein-binding site predictor using 3D-convolutional neural networks
DeepSite: protein-binding site predictor using 3D-convolutional neural networks
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DOI：
10.1093/bioinformatics/btx350
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发表时间：
2017-10-01
期刊：
影响因子：
5.8
通讯作者：
De Fabritiis, G.
中科院分区：
文献类型：
--
作者：
Jimenez, J.;Doerr, S.;De Fabritiis, G.
动机：基于结构的药物设计的一个重要步骤是预测可药用的结合位点。几种算法用于检测结合腔，那些可能会绑定到一个小的药物化合物，多年来已经开发了巧妙利用的几何，化学和进化特征的蛋白质。结果：在这里，我们提出了一种新的基于知识的方法，使用国家的最先进的卷积神经网络，算法是通过例子学习。总之，7622蛋白从scPDB数据库的结合位点已被评估使用的距离和体积重叠的方法。我们的机器学习为基础的方法表现出上级性能的其他两个竞争对手的算法策略。
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.