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Identification of proteins associated with Mycobacterium tuberculosis virulence pathway by their polar profile

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Polanco C., Castañón-González J., Mancilla R., Buhse T., Samaniego J., Gimbel A.
Acta Biochimica Polonica
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2015
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vol. 62
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issue 2
191-196
EN
With almost one third of the world population infected, tuberculosis is one of the most devastating diseases worldwide and it is a major threat to any healthcare system. With the mathematical-computational method named "Polarity Index Method", already published by this group, we identified, with high accuracy (70%), proteins related to Mycobacterium tuberculosis bacteria virulence pathway from the Tuberculist Database. The test considered the totality of proteins cataloged in the main domains: fungi, bacteria, and viruses from three databases: Antimicrobial Peptide Database (APD2), Tuberculist Database, Uniprot Database, and four antigens of Mycobacterium tuberculosis: PstS-1, 38-kDa, 19-kDa, and H37Rv ORF. The method described was calibrated with each database to achieve the same performance, showing a high percentage of coincidence in the identification of proteins associated with Mycobacterium tuberculosis bacteria virulence pathway located in the Tuberculist Database, and identifying a polar pattern regardless of the group studied. This method has already been used in the identification of diverse groups of proteins and peptides, showing that it is an effective discriminant. Its metric considers only one physico-chemical property, i.e. polarity.
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Possible computational filter to detect proteins associated to influenza A subtype H1N1

80%
Polanco C., Buhse T., Castañón-González J., Samaniego J.
Acta Biochimica Polonica
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2014
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vol. 61
|
issue 4
693-698
EN
The design of drugs with bioinformatics methods to identify proteins and peptides with a specific toxic action is increasingly recurrent. Here, we identify toxic proteins towards the influenza A virus subtype H1N1 located at the UniProt database. Our quantitative structure-activity relationship (QSAR) approach is based on the analysis of the linear peptide sequence with the so-called Polarity Index Method that shows an efficiency of 90% for proteins from the Uniprot Database. This method was exhaustively verified with the APD2, CPPsite, Uniprot, and AmyPDB databases as well as with the set of antibacterial peptides studied by del Rio et al. and Oldfield et al.
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