Enhanced-Sampling Simulations for the Estimation of Ligand Binding Kinetics: Current Status and Perspective

被引：39

作者：

Ahmad, Katya ^{[1
]}

Rizzi, Andrea ^{[1
,2
]}

Capelli, Riccardo ^{[3
]}

Mandelli, Davide ^{[1
]}

Lyu, Wenping ^{[4
,5
]}

Carloni, Paolo ^{[1
,6
]}

机构：

[1] Forschungszentrum Julich, Computat Biomed IAS 5 INM 9, Julich, Germany

[2] Ist Italiano Tecnol, Atomist Simulat, Genoa, Italy

[3] Politecn Torino, Dept Appl Sci & Technol DISAT, Turin, Italy

[4] Chinese Univ Hong Kong Shenzhen, Warshel Inst Computat Biol, Sch Life & Hlth Sci, Shenzhen, Peoples R China

[5] Univ Sci & Technol China, Sch Chem & Mat Sci, Hefei, Peoples R China

[6] Forschungszentrum Julich, Mol Neurosci & Neuroimaging INM-11, Julich, Germany

来源：

FRONTIERS IN MOLECULAR BIOSCIENCES | 2022年 / 9卷

基金：

中国国家自然科学基金; 欧盟地平线“2020”;

关键词：

kinetics; drug discovery; QM; MM; parallel computing; machine learning; enhanced sampling; molecular dynamics; MOLECULAR-DYNAMICS SIMULATIONS; TARGET RESIDENCE TIME; ATOMICALLY DETAILED SIMULATIONS; FREE-ENERGY CALCULATIONS; CHARGE FORCE-FIELD; P38 MAP KINASE; DRUG DISCOVERY; TRANSITION-STATES; SIDE-CHAIN; INHIBITOR BINDING;

D O I：

10.3389/fmolb.2022.899805

中图分类号：

Q5 [生物化学]; Q7 [分子生物学];

学科分类号：

071010 ; 081704 ;

摘要：

The dissociation rate (k(off)) associated with ligand unbinding events from proteins is a parameter of fundamental importance in drug design. Here we review recent major advancements in molecular simulation methodologies for the prediction of k(off). Next, we discuss the impact of the potential energy function models on the accuracy of calculated k(off) values. Finally, we provide a perspective from high-performance computing and machine learning which might help improve such predictions.

引用

页数：17

共 25 条

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