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| Jeff Wiseman, Locus Pharmaceuticals |
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| Dr. Jeffrey Wiseman holds a degree in Chemistry from Harvard University and trained as a Postdoctoral Research Fellow at Stanford University and Brandeis University prior to commencing his 26 year career in the pharmaceutical industry. While at GlaxoSmithKline, where he was most recently Global Vice President of Cheminformatics, Dr. Wiseman helped create and develop the discipline of Cheminformatics. Since 2003, Dr.Wiseman has led efforts at Locus Pharmaceuticals to develop computational technology for the structure-based design of protein ligands.
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Structure-Based Design of Allosteric Protein Kinase Inhibitors
Jeffrey S. Wiseman, Locus Pharmaceuticals, Four Valley Square, 512 Township Line Road, Blue Bell, PA 19422, USA
The Grand Canonical Ensemble model from statistical thermodynamics provides a method to compute theoretically rigorous free energies of binding that is fast enough for practical application to drug design. The result is an accurate, completely general scoring function for the prediction of ligand binding energies.
The method is exemplified by the design of inhibitors for p38 kinase, with extension to inhibition of new kinases across the kinome. Starting with a known SAR that is excessively hydrophobic and non-drugable, the design approach yielded highly novel, potent, orally active inhibitors of p38 that are 500-fold or more selective against 190 kinases assayed to date. By addition of interactions with the more highly conserved ATP site, the selective, pure allosteric site ligands can be converted to semi-selective inhibitors that provide leads to new kinases with novel selectivity profiles, which in themselves have desirable pharmacological activities.
While an accurate scoring function is a pre-requisite for structure-based design, the scoring function by itself is insufficient. The demand that constructing thermodynamic ensembles places on computational speed makes efficient sampling of the protein surface a potentially limiting element of the process. This aspect of the computation is discussed in terms of the concept of limiting entropy.
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