Enhancing Drug-Target Binding Affinity Prediction through Deep Learning and Protein Secondary Structure Integration

蛋白质二级结构 蛋白质测序 卷积神经网络 机器学习 深度学习 药物发现 序列(生物学) 特征(语言学) 人工神经网络 伪氨基酸组成 化学 计算生物学 计算机科学 人工智能 模式识别(心理学) 肽序列 生物化学 生物 氨基酸 基因 哲学 语言学 二肽
作者
Runhua Zhang,Baozhong Zhu,Tengsheng Jiang,Zhiming Cui,Hongjie Wu
出处
期刊:Current Bioinformatics [Bentham Science]
卷期号:19 (10): 943-952 被引量:3
标识
DOI:10.2174/0115748936285519240110070209
摘要

Background: Conventional approaches to drug discovery are often characterized by lengthy and costly processes. To expedite the discovery of new drugs, the integration of artificial intelligence (AI) in predicting drug-target binding affinity (DTA) has emerged as a crucial approach. Despite the proliferation of deep learning methods for DTA prediction, many of these methods primarily concentrate on the amino acid sequence of proteins. Yet, the interactions between drug compounds and targets occur within distinct segments within the protein structures, whereas the primary sequence primarily captures global protein features. Consequently, it falls short of fully elucidating the intricate relationship between drugs and their respective targets. Objective: This study aims to employ advanced deep-learning techniques to forecast DTA while incorporating information about the secondary structure of proteins. Methods: In our research, both the primary sequence of protein and the secondary structure of protein were leveraged for protein representation. While the primary sequence played the role of the overarching feature, the secondary structure was employed as the localized feature. Convolutional neural networks and graph neural networks were utilized to independently model the intricate features of target proteins and drug compounds. This approach enhanced our ability to capture drugtarget interactions more effectively Results: We have introduced a novel method for predicting DTA. In comparison to DeepDTA, our approach demonstrates significant enhancements, achieving a 3.9% increase in the Concordance Index (CI) and a remarkable 34% reduction in Mean Squared Error (MSE) when evaluated on the KIBA dataset. Conclusion: In conclusion, our results unequivocally demonstrate that augmenting DTA prediction with the inclusion of the protein's secondary structure as a localized feature yields significantly improved accuracy compared to relying solely on the primary structure.
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