Investigating Cryptic Binding Sites by Molecular Dynamics Simulations

被引:97
作者
Kuzmanic, Antonija [1 ,2 ]
Bowman, Gregory R. [4 ]
Juarez-Jimenez, Jordi [5 ]
Michel, Julien [5 ]
Gervasio, Francesco L. [1 ,2 ,3 ]
机构
[1] UCL, Dept Chem, London WC1E 0AJ, England
[2] UCL, Inst Struct & Mol Biol, London WC1E 0AJ, England
[3] Univ Geneva, Pharmaceut Sci, CH-1211 Geneva, Switzerland
[4] Washington Univ, Sch Med, Dept Biochem & Mol Biophys, St Louis, MO 63110 USA
[5] Univ Edinburgh, EaStCHEM Sch Chem, Edinburgh EH9 9FJ, Midlothian, Scotland
基金
英国工程与自然科学研究理事会; 欧盟地平线“2020”;
关键词
MARKOV STATE MODELS; INDUCED FIT; CONFORMATIONAL SELECTION; DISCOVERY; FORCE; IDENTIFICATION; INHIBITOR; PROTEINS; POCKETS;
D O I
10.1021/acs.accounts.9b00613
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
CONSPECTUS: This Account highlights recent advances and discusses major challenges in investigations of cryptic (hidden) binding sites by molecular simulations. Cryptic binding sites are not visible in protein targets crystallized without a ligand and only become visible crystallographically upon binding events. These sites have been shown to be druggable and might provide a rare opportunity to target difficult proteins. However, due to their hidden nature, they are difficult to find through experimental screening. Computational methods based on atomistic molecular simulations remain one of the best approaches to identify and characterize cryptic binding sites. However, not all methods are equally efficient. Some are more apt at quickly probing protein dynamics but do not provide thermodynamic or druggability information, while others that are able to provide such data are demanding in terms of time and resources. Here, we review the recent contributions of mixed-solvent simulations, metadynamics, Markov state models, and other enhanced sampling methods to the field of cryptic site identification and characterization. We discuss how these methods were able to provide precious information on the nature of the site opening mechanisms, to predict previously unknown sites which were used to design new ligands, and to compute the free energy landscapes and kinetics associated with the opening of the sites and the binding of the ligands. We highlight the potential and the importance of such predictions in drug discovery, especially for difficult ("undruggable") targets. We also discuss the major challenges in the field and their possible solutions.
引用
收藏
页码:654 / 661
页数:8
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