Possible computational filter to detect proteins associated to influenza A subtype H1N1.
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.
Facultad de Ciencias de la Salud, Universidad Anáhuac, Col. Lomas Anáhuac, Huixquilucan Estado de México, México.
Carlos
C
Polanco
Centro de Investigaciones Químicas, Universidad Autónoma del Estado de Morelos, Cuernavaca Morelos, México.
Thomas
T
Buhse
Facultad de Ciencias de la Salud, Universidad Anáhuac, Col. Lomas Anáhuac, Huixquilucan Estado de México, México.
Jorge Alberto
JA
Castañón-González
Facultad de Ciencias de la Salud, Universidad Anáhuac, Col. Lomas Anáhuac, Huixquilucan Estado de México, México.
José Lino
JL
Samaniego
D019295:Computational Biology / Q000379:methods*; D053118:Influenza A Virus, H1N1 Subtype / Q000378:metabolism*; D021281:Quantitative Structure-Activity Relationship; D014764:Viral Proteins / Q000737:chemistry* / Q000378:metabolism*
D014764:Viral Proteins