Protein 3D structure-based neural networks highly improve the accuracy in compound-protein binding affinity prediction

• URL: http://arxiv.org/abs/2204.12586v1
• Date: Wed, 30 Mar 2022 00:44:15 GMT
• Title: Protein 3D structure-based neural networks highly improve the accuracy in compound-protein binding affinity prediction
• Authors: Binjie Guo, Hanyu Zheng, Huan Huang, Haohan Jiang, Xiaodan Li, Naiyu Guan, Yanming Zuo, Yicheng Zhang, Hengfu Yang, Xuhua Wang
• Abstract summary: We develop Fast Evolutional Attention and Thoroughgoing-graph Neural Networks (FeatNN) to facilitate the application of protein 3D structure information for predicting compound-protein binding affinities (CPAs) FeatNN considerably outperforms various state-of-the-art baselines in CPA prediction with the Pearson value elevated by about 35.7%.
• Score: 7.059949221160259
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Theoretically, the accuracy of computational models in predicting compound-protein binding affinities (CPAs) could be improved by the introduction of protein 3D structure information. However, most of these models still suffer from a low accuracy due to the lack of an efficient approach to encode informative protein features. The major challenge is how to combine the multi-modal information such as the residue sequence of the protein, residue atom coordinates and the torsion angles. To tackle this problem, we develop Fast Evolutional Attention and Thoroughgoing-graph Neural Networks (FeatNN) to facilitate the application of protein 3D structure information for predicting CPAs. Specifically, we established a novel end-to-end architecture to jointly embed torsion matrix, discrete distance matrix, and sequence information of protein and extract compound features with deep graph convolution layers. In addition, a new pairwise mapping attention mechanism is introduced to comprehensively learn potential interaction information between proteins and compounds. FeatNN considerably outperforms various state-of-the-art baselines in CPA prediction with the Pearson value elevated by about 35.7%. Thus, FeatNN provides an outstanding method for highly accurate CPA prediction and facilitates high-throughput virtual screening of drug candidates.

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