Identification of Protein Cryptic Sites via Conformational Dynamics Capturing and Water-Based Pocket Characterization in Molecular Dynamics Simulations

分子动力学 动力学(音乐) 表征(材料科学) 鉴定(生物学) 蛋白质动力学 计算生物学 生物系统 计算机科学 化学 纳米技术 生物 物理 材料科学 计算化学 生态学 声学
作者
Tianming Qu,Steven L. Austin,Lianqing Zheng,J. Zhang,Wei Yang
出处
期刊:Journal of Chemical Theory and Computation [American Chemical Society]
卷期号:21 (17): 8571-8582
标识
DOI:10.1021/acs.jctc.5c01019
摘要

Employing molecular dynamics (MD) simulation to study the formation of novel protein cryptic sites has attracted increasing interest in the field of drug discovery. One specific challenge in this area is finding a viable method to accurately identify and characterize cryptic site transitions from MD simulation results while minimizing the need for extensive human input. Since the formation of cryptic sites often involves significant conformational changes in the protein structure, a method capable of capturing and describing these dynamic pocket transitions with precision is essential. In this paper, we present a new procedure, Conformational Dynamics Capturing and Water-Based Characterization (CDC-WBC). This procedure dynamically identifies the cryptic site region by tracking protein conformational changes observed during molecular dynamics (MD) simulations. The procedure also incorporates water density information to enhance the characterization of cryptic sites across frames. We evaluate the CDC-WBC procedure by applying it to characterize the opening process of the two well-studied cryptic sites in TEM1 β-lactamase. The results demonstrate that the CDC-WBC method accurately captures the open-closed transitions of the two cryptic sites. For comparison, three commonly used protein cavity detection methods in cryptic sites studies, POVME2, Epock, and MDpocket, are applied to identify the "CBT" cryptic site in TEM1 β-lactamase. The results show that the CDC-WBC method outperforms these methods in characterizing the transitions of the "CBT" cryptic site. Additionally, using a benchmark set of 84 protein systems (93 cryptic pockets) from the CryptoSite data set, CDC-WBC consistently shows better performance in distinguishing between the open and closed states of cryptic sites, further highlighting its capability for precise characterization of dynamic cryptic site transitions. The detailed implementation of the CDC-WBC procedure and demo data sets are uploaded to GitHub: https://github.com/TianmingQu/CDC-WBC.git.
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