Abstract
Background: Glycogen synthase kinase-3 (GSK3) is associated with various key biological processes and has been considered as an important therapeutic target for the treatment of many diseases. Great efforts have been made on the development of GSK3 inhibitors, especially ATP-competitive GSK3β inhibitor, but it is still a great challenge to develop selective GSK3β inhibitors because of the high sequence homology with other kinases.
Objective: In order to reveal the selectivity mechanisms of GSK3β inhibition at the molecular level, a series of ATP-competitive GSK3β inhibitor was analyzed by a systematic computational method, combining 3DQSAR, molecular docking, molecular dynamic simulations and free energy calculations. Methods: Firstly, 3D-QSAR with CoMFA was built to explore the general structure activity relationships. Secondly, CDOCKER and Flexible docking were employed to predicted the reasonable docking poses of all studied inhibitors. And then, both GSK3β and CDK2 complexes were selected to conduct molecular dynamics simulations. Finally, the free energy calculations were employed to find the key selective-residues. Results: CoMFA model suggested the steric, hydrophobic fields play key roles in the bioactivities of inhibitors, and the binding mechanisms were well analyzed through molecular docking. The binding free energies predicted are in good agreement with the experimental bioactivities and the free energy calculations showed that the binding of GSK3β/inhibitors was mainly contributed from hydrogen bonding and hydrophobic interaction. Conclusion: Some key residues for selective binding were highlighted, which may afford important guidance for the rational design of novel ATP-competitive GSK3β inhibitors.Keywords: GSK3β ATP-competitive inhibitors, selective inhibitors, 3D-QSAR CoMFA, molecular docking, molecular dynamics simulation, free energy calculation.
Graphical Abstract
[http://dx.doi.org/10.1021/acschemneuro.8b00252] [PMID: 29944335]
[http://dx.doi.org/10.1042/bj3590001] [PMID: 11563964]
[http://dx.doi.org/10.7150/thno.14334] [PMID: 26941849]
[http://dx.doi.org/10.1038/35096075] [PMID: 11584304]
[http://dx.doi.org/10.1101/gad.12.22.3499] [PMID: 9832503]
[http://dx.doi.org/10.1016/j.bbrc.2017.06.018] [PMID: 28602697]
[http://dx.doi.org/10.2174/1381612823666170714141450] [PMID: 28714403]
[http://dx.doi.org/10.1242/bio.030874] [PMID: 29212798]
[http://dx.doi.org/10.1038/nrd1415] [PMID: 15173837]
[http://dx.doi.org/10.4161/15384101.2014.974439] [PMID: 25616418]
[http://dx.doi.org/10.1074/jbc.R800077200] [PMID: 19064989]
[http://dx.doi.org/10.1016/j.neurobiolaging.2017.05.010] [PMID: 28609678]
[http://dx.doi.org/10.1371/journal.pntd.0004506] [PMID: 26942720]
[http://dx.doi.org/10.1158/2159-8290.CD-16-0512] [PMID: 27872130]
[http://dx.doi.org/10.2174/1381612043452668] [PMID: 15078145]
[http://dx.doi.org/10.1002/med.10011] [PMID: 12111750]
[http://dx.doi.org/10.1111/cbdd.12907] [PMID: 27863047]
[http://dx.doi.org/10.2174/1381612811319260007] [PMID: 23260024]
[http://dx.doi.org/10.2174/138161210792389225] [PMID: 20642432]
[http://dx.doi.org/10.2174/1573409911666150617113933] [PMID: 26081557]
[http://dx.doi.org/10.1021/ci100427j] [PMID: 21338122]
[http://dx.doi.org/10.1111/cbdd.12203] [PMID: 23941500]
[http://dx.doi.org/10.1186/s12918-017-0385-5] [PMID: 28361711]
[http://dx.doi.org/10.1002/minf.201400045] [PMID: 27486081]
[http://dx.doi.org/10.1111/j.1747-0285.2011.01291.x] [PMID: 22168279]
[http://dx.doi.org/10.1007/s11030-013-9483-5] [PMID: 24081608]
[http://dx.doi.org/10.1021/jm201724m] [PMID: 22489897]
[http://dx.doi.org/10.1002/jcc.20290] [PMID: 16200636]
[http://dx.doi.org/10.1002/jcc.20035] [PMID: 15116359]
[http://dx.doi.org/10.1002/jcc.10349] [PMID: 14531054]
[http://dx.doi.org/10.1007/s10654-004-1025-0] [PMID: 15678789]
[http://dx.doi.org/10.1021/acscentsci.7b00419] [PMID: 29202023]
[http://dx.doi.org/10.1039/C7CP07623A] [PMID: 29785435]
[PMID: 30582283]
[http://dx.doi.org/10.1021/ci100275a] [PMID: 21117705]
[http://dx.doi.org/10.1016/S0022-2836(03)00610-7] [PMID: 12850155]
[http://dx.doi.org/10.1093/bioinformatics/btr294] [PMID: 21586518]
[http://dx.doi.org/10.1039/C4CP03179B] [PMID: 25205360]
[http://dx.doi.org/10.1261/rna.065896.118] [PMID: 29930024]
[http://dx.doi.org/10.2174/1573409911309010006] [PMID: 22734712]
[http://dx.doi.org/10.1039/C6MB00252H] [PMID: 27301448]
[http://dx.doi.org/10.1021/acs.jcim.8b00283] [PMID: 29993249]
[http://dx.doi.org/10.1039/c3mb70168a] [PMID: 23881296]
[http://dx.doi.org/10.1039/c2mb25408e] [PMID: 23340525]
[http://dx.doi.org/10.2174/138620712800563918] [PMID: 22263860]
[http://dx.doi.org/10.1039/C3MB70314B] [PMID: 24336903]
[http://dx.doi.org/10.1021/ci500414b] [PMID: 25233367]
[http://dx.doi.org/10.1021/ci400382r] [PMID: 24010823]
[http://dx.doi.org/10.1021/ci800147v] [PMID: 18717540]