DeepSite：使用 3D 卷积神经网络的蛋白质结合位点预测器。
DeepSite: protein-binding site predictor using 3D-convolutional neural networks.
机构信息
Computational Biophysics Laboratory (GRIB-IMIM), Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB), 08003 Barcelona, Spain.
San Diego Supercomputer Center, UC San Diego, MC 0505, 9500 Gilman Drive, La Jolla, CA 92093-0505. USA.
出版信息
Bioinformatics. 2017 Oct 1;33(19):3036-3042. doi: 10.1093/bioinformatics/btx350.
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.
CONTACT
SUPPLEMENTARY INFORMATION
Supplementary data are available at Bioinformatics online.
动机
基于结构的药物设计的一个重要步骤包括预测可成药的结合部位。多年来，人们巧妙地利用蛋白质的几何形状、化学和进化特征，开发出了几种用于检测结合腔的算法，这些结合腔可能与小分子药物化合物结合。
结果
本文提出了一种新的基于知识的方法，该方法使用了最先进的卷积神经网络，通过示例来训练算法。总共对 scPDB 数据库中的 7622 个具有结合位点的蛋白质进行了评估，使用了距离和体积重叠两种方法。我们的基于机器学习的方法在性能上优于另外两种具有竞争力的算法策略。
可用性和实现
DeepSite 可在 www.playmolecule.org 上免费获取。用户可以通过 WebGL 图形界面将 PDB ID 或 PDB 文件提交到我们的 NVIDIA GPU 服务器上，进行口袋检测。
联系方式
补充信息
补充数据可在 Bioinformatics 在线获取。
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