Feinstein Joseph, Shi Wentao, Ramanujam J, Brylinski Michal
Center for Computation and Technology, Louisiana State University, Baton Rouge, LA, USA.
Division of Electrical and Computer Engineering, Louisiana State University, Baton Rouge, LA, USA.
Methods Mol Biol. 2021;2266:299-312. doi: 10.1007/978-1-0716-1209-5_17.
Bionoi is a new software to generate Voronoi representations of ligand-binding sites in proteins for machine learning applications. Unlike many other deep learning models in biomedicine, Bionoi utilizes off-the-shelf convolutional neural network architectures, reducing the development work without sacrificing the performance. When initially generating images of binding sites, users have the option to color the Voronoi cells based on either one of six structural, physicochemical, and evolutionary properties, or a blend of all six individual properties. Encouragingly, after inputting images generated by Bionoi into the convolutional autoencoder, the network was able to effectively learn the most salient features of binding pockets. The accuracy of the generated model is evaluated both visually and numerically through the reconstruction of binding site images from the latent feature space. The generated feature vectors capture well various properties of binding sites and thus can be applied in a multitude of machine learning projects. As a demonstration, we trained the ResNet-18 architecture from Microsoft on Bionoi images to show that it is capable to effectively classify nucleotide- and heme-binding pockets against a large dataset of control pockets binding a variety of small molecules. Bionoi is freely available to the research community at https://github.com/CSBG-LSU/BionoiNet.
Bionoi是一款新软件，用于生成蛋白质中配体结合位点的Voronoi表示，以用于机器学习应用。与生物医学中的许多其他深度学习模型不同，Bionoi使用现成的卷积神经网络架构，在不牺牲性能的情况下减少了开发工作。在最初生成结合位点图像时，用户可以选择根据六种结构、物理化学和进化特性中的任何一种，或这六种单独特性的混合来为Voronoi单元着色。令人鼓舞的是，将Bionoi生成的图像输入卷积自动编码器后，该网络能够有效地学习结合口袋的最显著特征。通过从潜在特征空间重建结合位点图像，从视觉和数值上评估生成模型的准确性。生成的特征向量很好地捕捉了结合位点的各种特性，因此可以应用于众多机器学习项目。作为演示，我们在Bionoi图像上训练了微软的ResNet-18架构，以表明它能够针对结合各种小分子的大量对照口袋数据集，有效地对核苷酸结合口袋和血红素结合口袋进行分类。研究社区可在https://github.com/CSBG-LSU/BionoiNet上免费获取Bionoi。
