Molecular dynamics simulations and novel drug discovery

被引:287
|
作者
Liu, Xuewei [1 ,2 ]
Shi, Danfeng [1 ,2 ]
Zhou, Shuangyan [3 ]
Liu, Hongli [3 ]
Liu, Huanxiang [3 ]
Yao, Xiaojun [1 ,2 ]
机构
[1] Lanzhou Univ, State Key Lab Appl Organ Chem, Lanzhou, Gansu, Peoples R China
[2] Lanzhou Univ, Dept Chem, Lanzhou, Gansu, Peoples R China
[3] Lanzhou Univ, Sch Pharm, Lanzhou, Gansu, Peoples R China
基金
中国国家自然科学基金;
关键词
Molecular dynamics simulation; amyloidosis; drug discovery; virtual screening; drug resistance; AMYLOID-BETA-PROTEIN; HUMAN PRION PROTEIN; PROTECTIVE A2T MUTATION; ALPHA-SYNUCLEIN PROTEIN; FREE-ENERGY LANDSCAPES; C VIRUS NS5B; ALZHEIMERS-DISEASE; HIGH-THROUGHPUT; WILD-TYPE; RESISTANCE MECHANISM;
D O I
10.1080/17460441.2018.1403419
中图分类号
R9 [药学];
学科分类号
1007 ;
摘要
Introduction: Molecular dynamics (MD) simulations can provide not only plentiful dynamical structural information on biomacromolecules but also a wealth of energetic information about protein and ligand interactions. Such information is very important to understanding the structure-function relationship of the target and the essence of protein-ligand interactions and to guiding the drug discovery and design process. Thus, MD simulations have been applied widely and successfully in each step of modern drug discovery. Areas covered: In this review, the authors review the applications of MD simulations in novel drug discovery, including the pathogenic mechanisms of amyloidosis diseases, virtual screening and the interaction mechanisms between drugs and targets. Expert opinion: MD simulations have been used widely in investigating the pathogenic mechanisms of diseases caused by protein misfolding, in virtual screening, and in investigating drug resistance mechanisms caused by mutations of the target. These issues are very difficult to solve by experimental methods alone. Thus, in the future, MD simulations will have wider application with the further improvement of computational capacity and the development of better sampling methods and more accurate force fields together with more efficient analysis methods.
引用
收藏
页码:23 / 37
页数:15
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