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1
Editorial: Mechanisms, thermodynamics and kinetics of ligand binding revealed from molecular simulations and machine learning.社论：从分子模拟和机器学习揭示的配体结合机制、热力学和动力学
Front Mol Biosci. 2023 Jan 17;10:1139471. doi: 10.3389/fmolb.2023.1139471. eCollection 2023.
2
Ligand Gaussian Accelerated Molecular Dynamics 2 (LiGaMD2): Improved Calculations of Ligand Binding Thermodynamics and Kinetics with Closed Protein Pocket.配体高斯加速分子动力学 2（LiGaMD2）：改进了结合自由能和结合动力学的计算方法，同时考虑了蛋白质口袋的封闭性。
J Chem Theory Comput. 2023 Feb 14;19(3):733-745. doi: 10.1021/acs.jctc.2c01194. Epub 2023 Jan 27.
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The ligand binding mechanism to purine nucleoside phosphorylase elucidated via molecular dynamics and machine learning.通过分子动力学和机器学习阐明配体与嘌呤核苷磷酸化酶的结合机制。
Nat Commun. 2015 Jan 27;6:6155. doi: 10.1038/ncomms7155.
4
Advances in computational methods for ligand binding kinetics.配体结合动力学计算方法的进展
Trends Biochem Sci. 2023 May;48(5):437-449. doi: 10.1016/j.tibs.2022.11.003. Epub 2022 Dec 22.
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Computationally predicting binding affinity in protein-ligand complexes: free energy-based simulations and machine learning-based scoring functions.计算预测蛋白质-配体复合物中的结合亲和力：基于自由能的模拟和基于机器学习的评分函数。
Brief Bioinform. 2021 May 20;22(3). doi: 10.1093/bib/bbaa107.
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Switches of hydrogen bonds during ligand-protein association processes determine binding kinetics.配体与蛋白质结合过程中氢键的转换决定结合动力学。
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Beware of machine learning-based scoring functions-on the danger of developing black boxes.警惕基于机器学习的评分函数——开发黑盒的危险。
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Toward High-Throughput Predictive Modeling of Protein Binding/Unbinding Kinetics.迈向蛋白质结合/解离动力学的高通量预测建模
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Development of a machine-learning model to predict Gibbs free energy of binding for protein-ligand complexes.开发一种用于预测蛋白-配体复合物结合自由能的机器学习模型。
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Ligand Gaussian Accelerated Molecular Dynamics 3 (LiGaMD3): Improved Calculations of Binding Thermodynamics and Kinetics of Both Small Molecules and Flexible Peptides.配体高斯加速分子动力学 3（LiGaMD3）：提高小分子和柔性肽的结合热力学和动力学计算。
J Chem Theory Comput. 2024 Jul 23;20(14):5829-5841. doi: 10.1021/acs.jctc.4c00502. Epub 2024 Jul 13.

本文引用的文献

1
Challenges and frontiers of computational modelling of biomolecular recognition.生物分子识别计算建模的挑战与前沿
QRB Discov. 2022;3. doi: 10.1017/qrd.2022.11. Epub 2022 Aug 19.
2
Unsupervised Learning Methods for Molecular Simulation Data.无监督学习方法在分子模拟数据中的应用。
Chem Rev. 2021 Aug 25;121(16):9722-9758. doi: 10.1021/acs.chemrev.0c01195. Epub 2021 May 4.
3
Recent progress in molecular simulation methods for drug binding kinetics.药物结合动力学的分子模拟方法的最新进展。
Curr Opin Struct Biol. 2020 Oct;64:126-133. doi: 10.1016/j.sbi.2020.06.022. Epub 2020 Aug 6.
4
Brownian Dynamics Simulations of Biological Molecules.生物分子的布朗动力学模拟
Trends Chem. 2019 Nov;1(8):727-738. doi: 10.1016/j.trechm.2019.07.008. Epub 2019 Aug 28.
5
Improving the accuracy of predicting protein-ligand binding-free energy with semiempirical quantum chemistry charge.用半经验量子化学电荷提高预测蛋白质 - 配体结合自由能的准确性。
Future Med Chem. 2019 Feb;11(4):303-321. doi: 10.4155/fmc-2018-0207. Epub 2019 Feb 25.
6
Rate Constants and Mechanisms of Protein-Ligand Binding.蛋白质-配体结合的速率常数与机制
Annu Rev Biophys. 2017 May 22;46:105-130. doi: 10.1146/annurev-biophys-070816-033639. Epub 2017 Mar 30.
7
Understanding ligand-receptor non-covalent binding kinetics using molecular modeling.使用分子建模理解配体-受体非共价结合动力学。
Front Biosci (Landmark Ed). 2017 Jan 1;22(6):960-981. doi: 10.2741/4527.
8
Molecular recognition and ligand association.分子识别与配体结合。
Annu Rev Phys Chem. 2013;64:151-75. doi: 10.1146/annurev-physchem-040412-110047. Epub 2013 Mar 5.

Editorial: Mechanisms, thermodynamics and kinetics of ligand binding revealed from molecular simulations and machine learning.

作者信息

Miao Yinglong, Chang Chia-En A, Zhu Weiliang, McCammon J Andrew

机构信息

Center for Computational Biology and Department of Molecular Biosciences, University of Kansas, Lawrence, KS, United States.

Department of Chemistry, University of California, Riverside, Riverside, CA, United States.

出版信息

Front Mol Biosci. 2023 Jan 17;10:1139471. doi: 10.3389/fmolb.2023.1139471. eCollection 2023.

DOI:10.3389/fmolb.2023.1139471
PMID:36733435
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9887283/
Abstract
摘要