Targeting Plague Virulence Factors:  A Combined Machine Learning Method and Multiple Conformational Virtual Screening for the Discovery of Yersinia Protein Kinase A Inhibitors

Xin Hu,† Gerd Prehna,† and C. Erec Stebbins*
Laboratory of Structural Microbiology, The Rockefeller University, New York, New York 10021
J. Med. Chem., 2007, 50 (17), pp 3980–3983
DOI: 10.1021/jm070645a
Publication Date (Web): August 3, 2007
Copyright © 2007 American Chemical Society
†

 These authors contributed equally to this work.

,
*

 To whom correspondenc should be addressed. Phone:  212-327-7190. Fax:  212-327-9171. E-mail:  stebbins@rockefeller.edu.

Abstract

Abstract Image

Yersinia spp. is currently an antibiotic resistance concern and a re-emerging disease. The essential virulence factor Yersinia protein kinase A (YpkA) contains a Ser/Thr kinase domain whose activity modulates pathogenicity. Here, we present an approach integrating a machine learning method, homology modeling, and multiple conformational high-throughput docking for the discovery of YpkA inhibitors. These first reported inhibitors of YpkA may facilitate studies of the pathogenic mechanism of YpkA and serve as a starting point for development of anti-plague drugs.

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  • Published In Issue August 23, 2007
  • Received June 5, 2007

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