Abstract
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.
Publication types
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Research Support, N.I.H., Extramural
MeSH terms
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Amino Acid Sequence
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Anthraquinones / chemistry
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Anti-Bacterial Agents / chemistry*
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Artificial Intelligence
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Bacterial Proteins / antagonists & inhibitors
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Bacterial Proteins / chemistry*
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Indoles / chemistry
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Mitogen-Activated Protein Kinases / chemistry
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Models, Molecular
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Molecular Conformation
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Molecular Sequence Data
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Plague / microbiology*
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Protein Kinase C / chemistry
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Protein Kinase Inhibitors / chemistry*
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Protein Serine-Threonine Kinases / antagonists & inhibitors
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Protein Serine-Threonine Kinases / chemistry*
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Pyrimidines / chemistry
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Quantitative Structure-Activity Relationship
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Sequence Homology, Amino Acid
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Virulence Factors / antagonists & inhibitors
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Virulence Factors / chemistry*
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Yersinia / enzymology*
Substances
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Anthraquinones
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Anti-Bacterial Agents
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Bacterial Proteins
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Indoles
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Protein Kinase Inhibitors
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Pyrimidines
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Virulence Factors
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ypkA protein, Yersinia
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Protein Serine-Threonine Kinases
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Protein Kinase C
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Mitogen-Activated Protein Kinases