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Bioorg Med Chem. 2009 Apr 1;17(7):2759-66. doi: 10.1016/j.bmc.2009.02.041. Epub 2009 Feb 26.

IKKbeta inhibitors identification part I: homology model assisted structure based virtual screening.

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Center for Chemoinformatics Research, Life Sciences Division, Korea Institute of Science and Technology, PO Box 131, Cheongryang, Seoul 130-650, Republic of Korea.


Control of NF-kappaB release through the inhibition of IKKbeta has been identified as a potential target for the treatment of inflammatory and autoimmune diseases. We have employed structure based virtual screening scheme to identify lead like molecule from ChemDiv database. Homology models of IKKbeta enzyme were developed based on the crystal structures of four kinases. The efficiency of the homology model has been validated at different levels. Docking of known inhibitors library revealed the possible binding mode of inhibitors. Besides, the docking sequence analyses results indicate the responsibility of Glu172 in selectivity. Structure based virtual screening of ChemDiv database has yielded 277 hits. Top scoring 75 compounds were selected and purchased for the IKKbeta enzyme inhibition test. From the combined approach of virtual screening followed by biological screening, we have identified six novel compounds that can work against IKKbeta, in which 1 compound had highest inhibition rate 82.09% at 10 microM and IC(50) 1.76 microM and 5 compounds had 25.35-48.80% inhibition.

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