Division of Electrical and Computer Engineering, Louisiana State University, Baton Rouge, LA 70803, USA.
Department of Biological Sciences, Louisiana State University, Baton Rouge, LA 70803, USA.
Bioinformatics. 2020 May 1;36(10):3077-3083. doi: 10.1093/bioinformatics/btaa094.
Fast and accurate classification of ligand-binding sites in proteins with respect to the class of binding molecules is invaluable not only to the automatic functional annotation of large datasets of protein structures but also to projects in protein evolution, protein engineering and drug development. Deep learning techniques, which have already been successfully applied to address challenging problems across various fields, are inherently suitable to classify ligand-binding pockets. Our goal is to demonstrate that off-the-shelf deep learning models can be employed with minimum development effort to recognize nucleotide- and heme-binding sites with a comparable accuracy to highly specialized, voxel-based methods.
We developed BionoiNet, a new deep learning-based framework implementing a popular ResNet model for image classification. BionoiNet first transforms the molecular structures of ligand-binding sites to 2D Voronoi diagrams, which are then used as the input to a pretrained convolutional neural network classifier. The ResNet model generalizes well to unseen data achieving the accuracy of 85.6% for nucleotide- and 91.3% for heme-binding pockets. BionoiNet also computes significance scores of pocket atoms, called BionoiScores, to provide meaningful insights into their interactions with ligand molecules. BionoiNet is a lightweight alternative to computationally expensive 3D architectures.
BionoiNet is implemented in Python with the source code freely available at: https://github.com/CSBG-LSU/BionoiNet.
Supplementary data are available at Bioinformatics online.
快速准确地对蛋白质中的配体结合位点进行分类，这对于自动注释大量蛋白质结构数据集的功能，以及蛋白质进化、蛋白质工程和药物开发等项目来说，都是非常有价值的。深度学习技术已经成功应用于解决各个领域的挑战性问题，非常适合用于分类配体结合口袋。我们的目标是证明现成的深度学习模型可以在最小的开发工作下用于识别核苷酸和血红素结合位点，其准确性可与高度专业化的体素方法相媲美。
我们开发了 BionoiNet，这是一个新的基于深度学习的框架，实现了一种流行的用于图像分类的 ResNet 模型。BionoiNet 首先将配体结合位点的分子结构转换为 2D Voronoi 图，然后将其作为预训练的卷积神经网络分类器的输入。ResNet 模型对未见数据具有很好的泛化能力，核苷酸结合口袋的准确率达到 85.6%，血红素结合口袋的准确率达到 91.3%。BionoiNet 还计算了口袋原子的 BionoiScores 得分，以提供对它们与配体分子相互作用的有意义的见解。BionoiNet 是一种轻量级的替代方案，计算成本比昂贵的 3D 架构要低。
BionoiNet 是用 Python 实现的，源代码可在以下网址获得：https://github.com/CSBG-LSU/BionoiNet。
补充数据可在生物信息学在线获得。
