Enterobacteria virulence factor prediction server

  • Authors

    • S Gnanendra
    • M Thirunavu kkarasu
    • K Dinakaran
    • E N. Sathishkumar
    https://doi.org/10.14419/ijet.v7i1.1.11242
  • Virulence prediction, amino acid composition, data mining, classifiers, SMO.

  • Abstract

    The continuous usage of antibiotics has resulted in the increase of multidrug resistance in the bacteria. The drastic increase in the bacterial genome projects has paved a path for the identification of potentially novel virulence-associated factors and their possibility as novel drug targets. Thus in the present study, we have implemented SMO classifiers for the better prediction of proteins based on individual protein sequences amino acid composition (AAC) and the performance of evaluation was checked via threshold dependent parameters such as Sensitivity, Specificity, Accuracy, and Mathew correlation coefficient. The predictions are based on the dataset incorporated in the database of five major virulence factors from six pathogens of Enterobacteriacae. This comprehensive database can serve as a source for the selection of significant virulence factor based on the intellectual Gene ontology terms that play a critical role in the pathogenesis and its surveillance in the host.

     

  • References

    1. [1] Mulder NJ, Mazandu GK & Rapano HA, “Using Host-Pathogen Functional Interactions for Filtering Potential Drug Targets in Mycobacterium tuberculosisâ€, J. Mycobac. Dis, (2013). https://doi.org/10.7196/SAMJ.5437.

      [2] Warner DF & Mizrahi V, “Approaches to target identification and validation for tuberculosis drug discovery: a UCT perspectiveâ€, South African Medical Journal, Vol.102, (2012), pp.457-460.

      [3] Waldvogel FA, “Infectious diseases in the 21st century: old challenges and new opportunitiesâ€, Int. J. Infect. Dis., Vol.8, (2004), pp.5–12. https://doi.org/10.1016/j.ijid.2003.01.001.

      [4] Weiss RA, “Virulence and pathogenesisâ€, Trends in Microbiology, Vol.10, (2002), pp.314–317. https://doi.org/10.1016/S0966-842X(02)02391-0.

      [5] Hogan D & Kolter R, “Why are bacteria refractory to antimicrobials?†Curr. Opin. Microbiol, Vol.5, (2002), pp.472–477. https://doi.org/10.1016/S1369-5274(02)00357-0.

      [6] Byarugaba DK, “Antimicrobial resistance in developing countries and responsible risk factorsâ€, Int. J. Antimicrob. Agents, Vol.24, (2004), pp.105–110. https://doi.org/10.1016/j.ijantimicag.2004.02.015.

      [7] Docampo R, “New and reemerging infectious diseasesâ€, Emerg. Infect. Dis., (2003), pp.1030– 1033. https://doi.org/10.3201/eid0908.030324.

      [8] Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P & Witten IH, “The WEKA data mining software: an updateâ€, ACM SIGKDD explorations newsletter, Vol.11, No.1, (2009).

      [9] Saha S & Raghava GP, “Prediction of continuous Bâ€cell epitopes in an antigen using recurrent neural networkâ€, Proteins: Structure, Function, and Bioinformatics, Vol.65, No.1, (2006). https://doi.org/10.1002/prot.21078.

      [10] Kalita MK, Nandal UK, Pattnaik A, Sivalingam A, Ramasamy G, Kumar M, Raghava GP & Gupta D, “CyclinPred: a SVM-based method for predicting cyclin protein sequencesâ€, PloS one., Vol.3, No.7, (2008). https://doi.org/10.1371/journal.pone.0002605.

      [11] Tsai CT, Huang WL, Ho SJ, Shu LS & Ho SY, “Virulent-GO: prediction of virulent proteins in bacterial pathogens utilizing gene ontology termsâ€, Development, (2009).

      [12] Matthews BW, “Comparison of the predicted and observed secondary structure of T4 phage lysozymeâ€, Biochimica ET Biophysica Acta (BBA)-Protein Structure, Vol.405, No.2, (1975), pp.442-451. https://doi.org/10.1016/0005-2795(75)90109-9.

      [13] Morens DM, Folkers GK & Fauci AS, “The challenge of emerging and re-emerging infectious diseasesâ€, Nature, Vol.430, (2004), pp.242–249. https://doi.org/10.1038/nature02759.

      [14] Pragash DS, Ragunathan L, Banoo S, Rayapu V & Shaker IA, “Occurrence of CTX-M and SHV Genes in ESBL Producing Gram Negative Organisms Causing Pyogenic Infections in a Tertiary Care Hospital in Puducherryâ€, Int J Pharm Bio Sci, Vol.3, No.4, (2012).

      [15] Toth IK, Pritchard L & Birch PR, “Comparative genomics reveals what makes an enterobacterial plant pathogenâ€, Annu. Rev. Phytopathol., Vol.44, (2006). https://doi.org/10.1146/annurev.phyto.44.070505.143444.

      [16] Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT & Harris MA, “Gene Ontology: tool for the unification of biologyâ€, Nature genetics, Vol.25, No.1, (2000), pp.25-29. https://doi.org/10.1038/75556.

      [17] Apweiler R, Bairoch A & Wu CH, “Protein sequence Databasesâ€, Curr. Opin Chem. Biol., Vol.8, (2004), pp.76-80. https://doi.org/10.1016/j.cbpa.2003.12.004.

      [18] Chen L, Yang J, Yu J, Yao Z, Sun L, Shen Y & Jin Q, “VFDB: a reference database for bacterial virulence factorsâ€, Nucleic Acids Res., Vol.33, (2005).

  • Downloads

  • How to Cite

    Gnanendra, S., Thirunavu kkarasu, M., Dinakaran, K., & N. Sathishkumar, E. (2017). Enterobacteria virulence factor prediction server. International Journal of Engineering & Technology, 7(1.1), 435-438. https://doi.org/10.14419/ijet.v7i1.1.11242

    Received date: 2018-04-07

    Accepted date: 2018-04-07