Stepwise high-throughput virtual screening of Rho kinase inhibitors from natural product library and potential therapeutics for pulmonary hypertension

Figures & data

Figure 1. Schematic representation of integrating stepwise virtual screening and kinase assay to identify potent Rho inhibitors from various natural products.

Figure 2. Re-docking of two inhibitors fasudil (A) and Y-27632 (B) into the active pocket of Rho kinase domain using AutoDock and DOCK methods; the docked conformations were superposed onto corresponding native cocrystallized ligands.

Figure 3. Plot of DOCK, AutoDock, and QSAR scores against experimental activity pIC₅₀ for the six sophisticated Rho inhibitors.

Table 1. Comparison of DOCK, AutoDock, and QSAR scores with inhibitory activity for the six sophisticated Rho inhibitors.

Inhibitor	Structure	DOCK score	AutoDock score	QSAR score	IC50 (μM)^a
Y-27632		−9.5	−11.6	5.7	0.15
Fasudil		−13.3	−8.7	5.4	0.66
RKI-1342		−14.7	−7.4	8.2	0.03
Unnamed		−8.2	−14.2	7.0	0.65
Unnamed		−10.6	−12.5	8.8	0.015
Unnamed		−12.5	−15.3	7.4	0.011

^aIC₅₀ values were taken from the BindingDB (Liu et al., 2007) and PDBbind (Wang et al., 2004) databases.

Figure 4. Diagrammatic representation of QSAR scores over the 1000 top natural product candidates.

Table 2. The six identified natural products.

Natural product	Chemical structure	QSAR score	IC50 (μM)
Baicalein		7.48	0.95
Kinetin		7.65	ND^a
Magnolol		7.42	240
Phloretin		7.59	0.22
Tetrahydropapaverine		7.65	4
Isoliquiritigenin		7.70	45

^aNot determined.

Liu T, Lin Y, Wen X, et al. (2007). BindingDB: A web-accessible database of experimentally determined protein–ligand binding affinities. Nucleic Acids Res 35:D198–201

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Wang R, Fang X, Lu Y, Wang S. (2004). The PDBbind database: Collection of binding affinities for protein–ligand complexes with known three-dimensional structures. J Med Chem 47:2977–80

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