Predicting Protein-Ligand Binding Affinity Using Fusion Model of Spatial-Temporal Graph Neural Network and 3D Structure-Based Complex Graph.

IF 3.9 2区 生物学 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Gaili Li, Yongna Yuan, Ruisheng Zhang
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引用次数: 0

Abstract

The investigation of molecular interactions between ligands and their target molecules is becoming more significant as protein structure data continues to develop. In this study, we introduce PLA-STGCNnet, a deep fusion spatial-temporal graph neural network designed to study protein-ligand interactions based on the 3D structural data of protein-ligand complexes. Unlike 1D protein sequences or 2D ligand graphs, the 3D graph representation offers a more precise portrayal of the complex interactions between proteins and ligands. Research studies have shown that our fusion model, PLA-STGCNnet, outperforms individual algorithms in accurately predicting binding affinity. The advantage of a fusion model is the ability to fully combine the advantages of multiple different models and improve overall performance by combining their features and outputs. Our fusion model shows satisfactory performance on different data sets, which proves its generalization ability and stability. The fusion-based model showed good performance in protein-ligand affinity prediction, and we successfully applied the model to drug screening. Our research underscores the promise of fusion spatial-temporal graph neural networks in addressing complex challenges in protein-ligand affinity prediction. The Python scripts for implementing various model components are accessible at https://github.com/ligaili01/PLA-STGCN.

利用时空图神经网络和基于三维结构的复杂图的融合模型预测蛋白质配体结合亲和力
随着蛋白质结构数据的不断发展,配体与其目标分子之间的分子相互作用研究变得越来越重要。在本研究中,我们介绍了 PLA-STGCNnet,这是一种深度融合时空图神经网络,旨在基于蛋白质配体复合物的三维结构数据研究蛋白质配体之间的相互作用。与一维蛋白质序列或二维配体图不同,三维图表示法能更精确地描述蛋白质和配体之间复杂的相互作用。研究表明,我们的融合模型 PLA-STGCNnet 在准确预测结合亲和力方面优于单个算法。融合模型的优势在于能够充分结合多个不同模型的优势,并通过结合其特征和输出来提高整体性能。我们的融合模型在不同的数据集上都表现出了令人满意的性能,这证明了它的泛化能力和稳定性。基于融合的模型在蛋白质配体亲和力预测中表现出色,我们成功地将该模型应用于药物筛选。我们的研究强调了融合时空图神经网络在解决蛋白质配体亲和性预测复杂难题方面的前景。用于实现各种模型组件的 Python 脚本可在 https://github.com/ligaili01/PLA-STGCN 上获取。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Interdisciplinary Sciences: Computational Life Sciences
Interdisciplinary Sciences: Computational Life Sciences MATHEMATICAL & COMPUTATIONAL BIOLOGY-
CiteScore
8.60
自引率
4.20%
发文量
55
期刊介绍: Interdisciplinary Sciences--Computational Life Sciences aims to cover the most recent and outstanding developments in interdisciplinary areas of sciences, especially focusing on computational life sciences, an area that is enjoying rapid development at the forefront of scientific research and technology. The journal publishes original papers of significant general interest covering recent research and developments. Articles will be published rapidly by taking full advantage of internet technology for online submission and peer-reviewing of manuscripts, and then by publishing OnlineFirstTM through SpringerLink even before the issue is built or sent to the printer. The editorial board consists of many leading scientists with international reputation, among others, Luc Montagnier (UNESCO, France), Dennis Salahub (University of Calgary, Canada), Weitao Yang (Duke University, USA). Prof. Dongqing Wei at the Shanghai Jiatong University is appointed as the editor-in-chief; he made important contributions in bioinformatics and computational physics and is best known for his ground-breaking works on the theory of ferroelectric liquids. With the help from a team of associate editors and the editorial board, an international journal with sound reputation shall be created.
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