Study on Homology Modeling of eEF 2K Protein and Virtual Screening of Its Inhibitors Molecules
- VernacularTitle:eEF2K蛋白同源模建及其抑制剂小分子的虚拟筛选研究
- Author:
Yumei LI
1
;
Yan KONG
2
;
Dayong YU
1
;
Yu SONG
1
;
Chuan TANG
1
;
Liying SHI
3
Author Information
1. School of Life Sciences and Technology，Dalian University，Liaoning Dalian 116622，China
2. Dept. of Oncology，Dalian Third People’s Hospital，Liaoning Dalian 116033，China
3. Institute of Materia Medica，Dalian University，Liaoning Dalian 116622，China
- Publication Type:Journal Article
- Keywords:
eEF2K;
Inhibitor molecule;
Homology modeling;
Hypogen pharmacophore;
Molecular docking;
Virtual screening
- From:
China Pharmacy
2019;30(16):2199-2205
- CountryChina
- Language:Chinese
-
Abstract:
OBJECTIVE： To screen potential eEF2K inhibitor molecules， and to provide reference for the design and R&D of eEF2K inhibitor. METHODS： The eEF2K crystal structure model was constructed by homology modeling technique. The model was optimized by Loop optimization and molecular dynamics. With the help of SAVES online server， the above models were evaluated from three aspects such as Verify_3D， EERAT and Laplace diagram. Totally 55 eEF2K inhibitor molecules were collected. Hypogen pharmacophore model with activity prediction ability was constructed based on 28 of them （odd number， as training set） by Insight Ⅱ software and validated by other 27 （even number， as test set）. The optimal pharmacophore model was screened by fitting the predicted and experimental values of activity [i.e. negative logarithm of half inhibitory concentration （pIC50）] and using Ligand profiler thermogram. The virtual screening of small molecules of eEF2K inhibitors was carried out by combining the above pharmacophore model， Lipinski’s five rules and molecular docking method. RESULTS & CONCLUSIONS： The overall quality factor score of the crystal structure model of eEF2K protein was 93.697. Among them， 83.33％ of the amino acid Verify_3D score was more than or equal to 0.2， and 1.7％ of the total amino acids were located in the non-permissible region. The amino acid conformation and skeleton structure of the model were reasonable and the reliability of the model was high. Totally 9 Hypogen pharmacophore models （No. 02-10） with active predictive function were constructed， among which No. 03 pharmacophore model included 2 hydrogen bond receptors and 2 conjugated aromatic rings， which could better distinguish active and inactive molecules. The predicted value of pIC50 fitted the experimental value best （the correlation coefficient was 0.665 3）， and it had good predictive ability and high reliability. Finally， 9 potential eEF2K inhibitor molecules were obtained through virtual screening （pIC50 ranged from 1.074 to 1.185， and Dcoking-score of protein-molecule interaction ranged from －9.730 to －7.467）. Pro268， Asp267， Gln171， Phe121 and Glu212 may be the key amino acids for the interaction between eEF2K inhibitors and target proteins， including hydrogen bonds， salt bridges and hydrophobicity. These 9 molecules are expected to be the lead compounds for the development of eEF2K inhibitors.
