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

[11] Effective Estimation of Ligand-Binding Affinity Using Biased Sampling Method
Son Tung Ngo
Vu, Khanh B.
Le Minh Bui
Vu, Van V.
ACS OMEGA, 2019, 4 (02): : 3887 - 3893
[12] Binding kinetics in drug discovery - A current perspective
Georgi, Victoria
Andres, Dorothee
Fernandez-Montalvan, Amaury E.
Stegmann, Christian M.
Becker, Andreas
Mueller-Fahrnow, Anke
FRONTIERS IN BIOSCIENCE-LANDMARK, 2017, 22 : 21 - 47
[13] Prediction of Protein-Ligand Binding Structures by Replica-Exchange Umbrella Sampling Simulations: Application to Kinase Systems
Kokubo, Hironori
Tanaka, Toshimasa
Okamoto, Yuko
JOURNAL OF CHEMICAL THEORY AND COMPUTATION, 2013, 9 (10) : 4660 - 4671
[14] Enhanced sampling of glutamate receptor ligand-binding domains
Lau, Albert Y.
NEUROSCIENCE LETTERS, 2019, 700 : 17 - 21
[15] Enhanced Sampling Approach to the Induced-Fit Docking Problem in Protein-Ligand Binding: The Case of Mono-ADP-Ribosylation Hydrolase Inhibitors
Zhao, Qianqian
Capelli, Riccardo
Carloni, Paolo
Luscher, Bernhard
Li, Jinyu
Rossetti, Giulia
JOURNAL OF CHEMICAL THEORY AND COMPUTATION, 2021, 17 (12) : 7899 - 7911
[16] The Interaction of Factor Xa and IXa with Non-Activated Antithrombin in Michaelis Complex: Insights from Enhanced-Sampling Molecular Dynamics Simulations
Balogh, Gabor
Bereczky, Zsuzsanna
BIOMOLECULES, 2023, 13 (05)
[17] Estimating the ligand-binding affinity via λ-dependent umbrella sampling simulations
Ngo, Son Tung
JOURNAL OF COMPUTATIONAL CHEMISTRY, 2021, 42 (02) : 117 - 123
[18] On-the-Fly Learning and Sampling of Ligand Binding by High-Throughput Molecular Simulations
Doerr, S.
De Fabritiis, G.
JOURNAL OF CHEMICAL THEORY AND COMPUTATION, 2014, 10 (05) : 2064 - 2069
[19] Entropic and Enthalpic Contributions to Stereospecific Ligand Binding from Enhanced Sampling Methods
Lai, Balder
Nagy, Gabor
Antonio Garate, Jose
Oostenbrink, Chris
JOURNAL OF CHEMICAL INFORMATION AND MODELING, 2014, 54 (01) : 151 - 158
[20] Estimation of Protein-Ligand Unbinding Kinetics Using Non-Equilibrium Targeted Molecular Dynamics Simulations
Wolf, Steffen
Amaral, Marta
Lowinski, Maryse
Vallee, Francois
Musil, Djordje
Gueldenhaupt, Joern
Dreyer, Matthias K.
Bomke, Joerg
Frech, Matthias
Schlitter, Juergen
Geiwert, Klaus
JOURNAL OF CHEMICAL INFORMATION AND MODELING, 2019, 59 (12) : 5135 - 5147

← 1 2 3 →