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Acta Physico-Chimica Sinca  2016, Vol. 32 Issue (10): 2606-2619    DOI: 10.3866/PKU.WHXB201606202
ARTICLE     
Improved Docking-Based Virtual Screening Using the Score Correction Strategy for Specific Endothelial Lipase Inhibitors Identification
Qi-Yao LUO,Zi-Yun WANG,Hong-Wei JIN,Zhen-Ming LIU*(),Liang-Ren ZHANG*()
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Abstract  

Endothelial lipase (EL) has been implicated in high-density lipoprotein (HDL) metabolism and the pathogenetic progress of atherosclerosis, so its specific inhibitors are expected to be useful for the treatment of cardiovascular disease. In addition to the high homology of EL with other lipases such as lipoprotein lipase (LPL), the scoring bias of current docking programs toward large molecules and large protein-binding pockets also makes it difficult to find specific EL inhibitors by docking-based virtual screening. Herein, we conducted docking-based virtual screening of the Specs database for EL and LPL firstly, and we found the scoring bias phenomenon. From the docking results of the Specs database, we established standard curves for the binding energies of EL and LPL based on heavy atom number and contact area to correct the dock energy score statistically. We then validated the correctional effects of these curves in the screening of a validation set. Furthermore, the traditional Chinese medicine database (TCMD) was screened by docking using the score correction strategy. The dock ranks before and after correction were compared to confirm the screening effectiveness. Moreover, some compounds exhibiting better affinity for EL than LPL after correction as well as some compounds with antihyperlipidemic activity that may be specific EL inhibitors were analyzed to study their interaction mechanisms. The developed score correction strategy should be helpful to improve the hit rate in docking-based virtual screening. The molecules we identified should be useful for experimental scientists to prioritize drug candidates and provide groundwork for potential therapies of hyperlipidemia and atherosclerosis.



Key wordsEndothelial lipase      Lipoprotein lipase      Specific inhibitor      Molecular docking      Score correction      Standard curve     
Received: 21 April 2016      Published: 20 June 2016
MSC2000:  O641  
Fund:  the National Natural Science Foundation of China(21572010,21272017)
Corresponding Authors: Zhen-Ming LIU,Liang-Ren ZHANG     E-mail: zmliu@bjmu.edu.cn;liangren@bjmu.edu.cn
Cite this article:

Qi-Yao LUO,Zi-Yun WANG,Hong-Wei JIN,Zhen-Ming LIU,Liang-Ren ZHANG. Improved Docking-Based Virtual Screening Using the Score Correction Strategy for Specific Endothelial Lipase Inhibitors Identification. Acta Physico-Chimica Sinca, 2016, 32(10): 2606-2619.

URL:

http://www.whxb.pku.edu.cn/10.3866/PKU.WHXB201606202     OR     http://www.whxb.pku.edu.cn/Y2016/V32/I10/2606

Fig 1 Work flow of the score correction strategy in docking-based screening applied to specific EL inhibitors identification
Fig 2 Comparison of EL binding site and LPL binding site (a) the EL binding site shown as surface pattern, (b) the LPL binding site shown as surface pattern; (c) the pharmacophore features of EL binding site; (d) the pharmacophore features of LPL binding site. The red ball represents a“hydrogen bond acceptor”, the blue ball represents a“hydrogen bond donor”, and the gray ball represents a van derWaals and hydrophobic contact.
Fig 3 Docking energy score distribution of Specs compounds (a) the distribution of EL binding energy with heavy atom number; (b) the distribution of LPL binding energy with heavy atom number; (c) the distribution of EL binding energy with contact area; (d) the distribution of LPL binding energy with contact area
Fig 4 Binding energy standard curves (a) the EL and LPL binding energy standard curves based on heavy atom number; (b) the EL and LPL binding energy standard curves based on contact area
Fig 5 EL binding energy distribution of validation set (a) the distribution of EL binding energy with heavy atom number; (b) the distribution of EL binding energy with contact area
Fig 6 ROC curves showing the enrichment of EL inhibitors in the docking-based screening adopting score correction by HA,score correction by CA,and original score The area under the curve (AUC) values of correction by HA, correction by CA, and original score are 0.579, 0.530, and 0.374, respectively.
Fig 7 Plot of the LPL binding energy (y-axis) versus the EL binding energy (x-axis) of validation set (a) the original EL and LPL binding energy; (b) the EL and LPL energy scores corrected by the binding energy standard curves based on heavy atom number; (c) the EL and LPL energy scores corrected by the binding energy standard curves based on contact area
Fig 8 Comparison of the distribution of score rank adopting differenct strategies with heavy atom number The distribution of (a) original score rank, (b) correctional score rank (based on heavy atom number), (c) correctional score rank (based on contact area) with heavy atom bumber of the entire TCMD compounds. The distribution of (d) original score rank, (e) correctional score rank (based on heavy atom number), (f) correctional score rank (based on contact area) with heavy atom number of the top 100 TCMD compounds.
Fig 9 Plot of ΔE′HA (y-axis) versus EL_E′HA (x-axis) The points inside the circle represent the potential specific EL inhibitors.
CompoundHAEL_ELPL_EEL_E′HALPL_E′HAΔEΔE′HAStructure
(kJ?mol-1)(kJ?mol-1)(kJ?mol-1)(kJ?mol-1)(kJ?mol-1)(kJ?mol-1)
1881727-39.8-36.9-10.1-4.2-2.9-6.3
1750035-41.5-39.8-10.1-4.6-1.7-5.4
993128-39.8-33.9-9.6-0.8-5.9-9.2
1896937-41.5-40.2-9.6-4.6-1.3-5.4
2041025-38.5-35.2-9.6-3.4-3.4-6.3
393127-39.0-36.0-9.2-3.4-2.9-6.3
1409527-38.5-32.7-8.80.0-5.9-9.2
2173120-36.0-29.7-8.8-0.8-6.3-8.0
1073426-37.7-35.2-8.4-2.9-2.5-5.4
2031326-37.7-35.6-8.4-3.4-2.1-5.0
Table 1 The best 10 compounds found via virtual screeening from TCMD
CompoundHAEL_ELPL_EEL_E′HALPL_E′HAΔEΔE′HAStructure
(kJ?mol-1)(kJ?mol-1)(kJ?mol-1)(kJ?mol-1)(kJ?mol-1)(kJ?mol-1)
840142-39.0-40.6-6.7-4.21.7-2.5
1183424-33.9-33.5-5.0-2.1-0.4-2.9
1313721-32.7-32.7-5.0-2.90.0-2.1
2139243-37.3-41.5-4.6-4.64.20.0
210220-31.8-31.0-4.6-2.1-0.8-2.5
1831722-32.7-33.1-4.6-2.90.4-1.7
237941-36.9-41.1-4.6-4.64.20.0
933722-32.3-33.9-4.2-3.81.7-0.4
1800121-31.8-31.8-4.2-2.10.0-2.1
1164823-31.8-30.6-3.40.4-1.3-3.8
Table 2 The top 10 anti-hyperlipidemia compounds in TCMD
CompoundQED_WEIGHTEDMWALOGPHBAHBDPSAROTB
188170.77369.372.447169.620
175000.23470.516.966499.381
99310.72380.440.485162.231
189690.39492.614.934264.362
204100.67334.411.153032.780
39310.70364.441.134042.011
140950.69364.440.654042.011
217310.74272.252.375386.991
107340.71350.410.904153.010
203130.46349.292.236074.300
84010.26566.514.96104151.985
118340.48328.363.965061.833
131370.60286.242.1764107.221
213920.15594.522.21137212.668
21020.69270.242.415386.991
183170.51302.241.6375127.451
23790.24552.484.74105162.984
93370.53300.260.8164107.220
180010.89284.262.375275.992
116480.67316.261.8674116.452
Table 3 The drug likeness and main properties of hits in TCMD
Fig 10 Binding modes and interactions of EL with its potential inhibitors (a) TCMD 18817; (b) TCMD 13137. The 2D interaction depictions of EL binding site residues with (c) TCMD 18817; (d) TCMD 13137. Hydrogen bonds are shown as black dashed lines between the interaction partners on either side. Hydrophobic interactions are illustrated as green smooth contour lines between the respective amino acids and the ligand. color online
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