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Virulence Inhibitors

The YpkA Virulence Factor of the Plague Bacterium

Many Gram-negative bacterial pathogens utilize a type III secretion system (TTSS) to inject effector proteins into the cytosol of host cells. These virulence factors play an important role in bacterial pathogenesis by modulating the host processes that regulate actin cytoskeletal assembly. With the emergence of antibiotic resistance and the threat of such bacteria being used as biological weapons, targeting virulence proteins for antibiotic design is attractive, as such compounds are unlikely to be cross-resistant or to induce resistance.

This project centered on the discovery of inhibitors for the Yersinia protein kinase A (YpkAa). YpkA is an essential virulence determinant in Yersinia spp., which includes the causative agent of plague. The protein contains a chaperone binding/membrane localization domain, a Ser/Thr kinase domain, a GDI-like domain that interacts with the Rho family of small GTPases, and a C-terminal subdomain responsible in part for actin binding and kinase activation. Because the kinase activity of YpkA has been shown to directly correlate to virulence by phosphorylating the small G protein Gαq, inhibition of YpkA could yield new antiplague therapeutics.

Protein kinase inhibitor design is a challenging problem because of the high similarity and plasticity of the catalytic site. In this study, we applied an approach combining a machine learning method and multiple conformational high-throughput docking for the discovery of YpkA inhibitors. The screening strategy employed is illustrated in Figure 1. First, we developed a machine learning support vector machine (SVM) model using a data set of known kinase inhibitors from a diverse kinase collection. The ligand-based SVM model was used as a kinase filter to prioritize the large size of chemical databases, and a target-focused library was obtained. Second, we constructed homology models of YpkA based on the MAPK templates and further performed MD simulations to sample different protein conformations characterized in the catalytic site to account for protein flexibility. Finally, with an ensemble of protein structures and the kinase inhibitor-enriched library, multiple conformational high-throughput docking was performed and a number of potent and selective inhibitors of YpkA have been successfully identified.

 

X. Hu, G. Prehna, C.E. Stebbins. (2007) "Targeting plague virulence factors: a combined machine learning method and multiple conformational virtual screening for the discovery of Yersinia protein kinase A inhibitors."  J Med Chem. 23;50(17):3980-3. PMID: 17676727.  [Abstract] [pdf]

 

The YopH Virulence Factor of the Plague Bacterium

Bacterial pathogens such as Yersinia and Salmonella represent an important medical concern, causing human diseases ranging from gastrointestinal disease to the Plague. The development of novel treatments of these bacterial infections has gained high priority recently due to the emergence of antibiotic resistance in these pathogens and the threat of the use of microbial agents as biological weapons. YopH of Yersinia and SptP of Salmonella are virulence factors that belong to the family of protein tyrosine phosphatases (PTPs). A great challenge remains in the design of selective PTP inhibitors due to their highly conserved active site. We are pursuing computational (virtual or in silico) compound screening and traditional HTS to find YopH inhibitors.

X. Hu, M. Vujanac , and C.E. Stebbins. (2004). "Computational Analysis of Tyrosine Phosphatase Inhibitor Selectivity for the Virulence Factors YopH and SptP" J Mol Graph Model, 2004 Oct;23(2):175-87. [Abstract] [pdf]

X. Hu and C.E. Stebbins. (2005). "Molecular docking and 3D-QSAR studies of Yersinia protein tyrosine phosphatase YopH inhibitors." Bioorg Med Chem. 13, 1101-9. [Abstract] [pdf]

X. Hu and C.E. Stebbins. (2006). "Dynamics of the WPD Loop of the Yersinia Protein Tyrosine Phosphatase." Biophys J. 91(3):948-56. [Abstract] [pdf]

X. Hu, G. Prehna, C.E. Stebbins. (2007) "Targeting plague virulence factors: a combined machine learning method and multiple conformational virtual screening for the discovery of Yersinia protein kinase A inhibitors." J Med Chem. 23;50(17):3980-3. PMID: 17676727 [Abstract] [pdf]

 

Drs. Gerd Prehna, Milos Vujanac, and Xin Hu were the lead scientists