tetano
Editor, Senior Moderator
Acta Biochim Pol. 2014 Nov 7. [Epub ahead of print]
Possible computational filter to detect proteins associated to influenza A subtype H1N1.
Polanco C1, Buhse T2, Casta??n-Gonz?lez JA1, Samaniego JL1.
Author information
Abstract
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.
PMID:
25379569
[PubMed - as supplied by publisher]
Free full text
http://www.ncbi.nlm.nih.gov/pubmed/25379569
Possible computational filter to detect proteins associated to influenza A subtype H1N1.
Polanco C1, Buhse T2, Casta??n-Gonz?lez JA1, Samaniego JL1.
Author information
Abstract
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.
PMID:
25379569
[PubMed - as supplied by publisher]
Free full text
http://www.ncbi.nlm.nih.gov/pubmed/25379569