Abstract
BackgroundDesigning small-molecule kinase inhibitors with desirable selectivity profiles is a major challenge in drug discovery. A high-throughput screen for inhibitors of a given kinase will typically yield many compounds that inhibit more than one kinase. A series of chemical modifications are usually required before a compound exhibits an acceptable selectivity profile. Rationalizing the selectivity profile for a small-molecule inhibitor in terms of the specificity-determining kinase residues for that molecule can be an important step toward the goal of developing selective kinase inhibitors.ResultsHere we describe S-Filter, a method that combines sequence and structural information to predict specificity-determining residues for a small molecule and its kinase selectivity profile. Analysis was performed on seven selective kinase inhibitors where a structural basis for selectivity is known. S-Filter correctly predicts specificity determinants that were described by independent groups. S-Filter also predicts a number of novel specificity determinants that can often be justified by further structural comparison.ConclusionS-Filter is a valuable tool for analyzing kinase selectivity profiles. The method identifies potential specificity determinants that are not readily apparent, and provokes further investigation at the structural level.
Highlights
Designing small-molecule kinase inhibitors with desirable selectivity profiles is a major challenge in drug discovery
Designing selective kinase inhibitors is a major challenge in drug discovery and development
The selectivity issues associated with small molecules that bind to the ATP catalytic binding site are challenging as most kinases have the same active-site chemistry
Summary
Prediction of specificity-determining residues for small-molecule kinase inhibitors. Address: 1Pfizer Research Technology Center, 620 Memorial Drive, Cambridge, MA 02139, USA and 2Pfizer Global Research and Development, 10777 Science Center Drive, San Diego, CA 92121, USA. Published: 25 November 2008 BMC Bioinformatics 2008, 9:491 doi:10.1186/1471-2105-9-491
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