Suppr超能文献

使用非平衡候选蒙特卡罗和分子动力学采样结合配体的构象变化。

Sampling Conformational Changes of Bound Ligands Using Nonequilibrium Candidate Monte Carlo and Molecular Dynamics.

机构信息

Department of Pharmaceutical Sciences, University of California, Irvine, California 92697, United States.

Department of Chemistry, University of California, Irvine, California 92697, United States.

出版信息

J Chem Theory Comput. 2020 Mar 10;16(3):1854-1865. doi: 10.1021/acs.jctc.9b01066. Epub 2020 Feb 24.

Abstract

Flexible ligands often have multiple binding modes or bound conformations that differ by rotation of a portion of the molecule around internal rotatable bonds. Knowledge of these binding modes is important for understanding the interactions stabilizing the ligand in the binding pocket, and other studies indicate it is important for calculating accurate binding affinities. In this work, we use a hybrid molecular dynamics (MD)/nonequilibrium candidate Monte Carlo (NCMC) method to sample the different binding modes of several flexible ligands and also to estimate the population distribution of the modes. The NCMC move proposal is divided into three parts. The flexible part of the ligand is alchemically turned off by decreasing the electrostatics and steric interactions gradually, followed by rotating the rotatable bond by a random angle and then slowly turning the ligand back on to its fully interacting state. The alchemical steps prior to and after the move proposal help the surrounding protein and water atoms in the binding pocket relax around the proposed ligand conformation and increase move acceptance rates. The protein-ligand system is propagated using classical MD in between the NCMC proposals. Using this MD/NCMC method, we were able to correctly reproduce the different binding modes of inhibitors binding to two kinase targets-c-Jun N-terminal kinase-1 and cyclin-dependent kinase 2-at a much lower computational cost compared to conventional MD and umbrella sampling. This method is available as a part of the BLUES software package.

摘要

柔性配体通常具有多种结合模式或构象,这些构象通过分子内部可旋转键的旋转而有所不同。了解这些结合模式对于理解稳定配体在结合口袋中的相互作用非常重要,其他研究表明,它对于计算准确的结合亲和力也很重要。在这项工作中,我们使用混合分子动力学 (MD)/非平衡候选蒙特卡罗 (NCMC) 方法来采样几个柔性配体的不同结合模式,并估计模式的种群分布。NCMC 移动提案分为三部分。通过逐渐降低静电和立体相互作用,将配体的柔性部分化学关闭,然后随机旋转可旋转键,然后慢慢将配体转回其完全相互作用的状态。在移动提案之前和之后的化学步骤有助于结合口袋中的周围蛋白质和水分子围绕所提出的配体构象松弛,并提高移动接受率。在 NCMC 提案之间使用经典 MD 来传播蛋白质-配体系统。使用这种 MD/NCMC 方法,与传统 MD 和伞状采样相比,我们能够以更低的计算成本正确再现抑制剂与两个激酶靶标(c-Jun N-末端激酶-1 和细胞周期蛋白依赖性激酶 2)结合的不同结合模式。该方法作为 BLUES 软件包的一部分提供。

相似文献

5
Reversibly Sampling Conformations and Binding Modes Using Molecular Darting.使用分子镖射技术来可逆地采样构象和结合模式。
J Chem Theory Comput. 2021 Jan 12;17(1):302-314. doi: 10.1021/acs.jctc.0c00752. Epub 2020 Dec 8.
6
Enhancing Side Chain Rotamer Sampling Using Nonequilibrium Candidate Monte Carlo.利用非平衡候选蒙特卡罗方法增强侧链构象抽样。
J Chem Theory Comput. 2019 Mar 12;15(3):1848-1862. doi: 10.1021/acs.jctc.8b01018. Epub 2019 Feb 11.
8
Enhancing Sampling of Water Rehydration on Ligand Binding: A Comparison of Techniques.增强配体结合时水合作用的采样:技术比较
J Chem Theory Comput. 2022 Mar 8;18(3):1359-1381. doi: 10.1021/acs.jctc.1c00590. Epub 2022 Feb 11.

引用本文的文献

6
WaterKit: Thermodynamic Profiling of Protein Hydration Sites.水套件:蛋白质水合部位的热力学分析。
J Chem Theory Comput. 2023 May 9;19(9):2535-2556. doi: 10.1021/acs.jctc.2c01087. Epub 2023 Apr 24.
8
An overview of the SAMPL8 host-guest binding challenge.SAMPL8 亲合作用结合挑战概述。
J Comput Aided Mol Des. 2022 Oct;36(10):707-734. doi: 10.1007/s10822-022-00462-5. Epub 2022 Oct 14.
9
Enhancing Sampling of Water Rehydration on Ligand Binding: A Comparison of Techniques.增强配体结合时水合作用的采样:技术比较
J Chem Theory Comput. 2022 Mar 8;18(3):1359-1381. doi: 10.1021/acs.jctc.1c00590. Epub 2022 Feb 11.

本文引用的文献

1
Binding Modes and Metabolism of Caffeine.咖啡因的结合模式和代谢。
Chem Res Toxicol. 2019 Jul 15;32(7):1374-1383. doi: 10.1021/acs.chemrestox.9b00030. Epub 2019 Jun 11.
3
Enhancing Side Chain Rotamer Sampling Using Nonequilibrium Candidate Monte Carlo.利用非平衡候选蒙特卡罗方法增强侧链构象抽样。
J Chem Theory Comput. 2019 Mar 12;15(3):1848-1862. doi: 10.1021/acs.jctc.8b01018. Epub 2019 Feb 11.
10
OpenMM 7: Rapid development of high performance algorithms for molecular dynamics.OpenMM 7:分子动力学高性能算法的快速开发。
PLoS Comput Biol. 2017 Jul 26;13(7):e1005659. doi: 10.1371/journal.pcbi.1005659. eCollection 2017 Jul.

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验