AntiBP2: improved version of antibacterial peptide prediction.

BMC Bioinformatics
Sneh LataG P S Raghava

Abstract

Antibacterial peptides are one of the effecter molecules of innate immune system. Over the last few decades several antibacterial peptides have successfully approved as drug by FDA, which has prompted an interest in these antibacterial peptides. In our recent study we analyzed 999 antibacterial peptides, which were collected from Antibacterial Peptide Database (APD). We have also developed methods to predict and classify these antibacterial peptides using Support Vector Machine (SVM). During analysis we observed that certain residues are preferred over other in antibacterial peptide, particularly at the N and C terminus. These observation and increased data of antibacterial peptide in APD encouraged us to again develop a new and more robust method for predicting antibacterial peptides in protein from their amino acid sequence or given peptide have antibacterial properties or not. First, the binary patterns of the 15 N terminus residues were used for predicting antibacterial peptide using SVM and achieved accuracy of 85.46% with 0.705 Mathew's Correlation Coefficient (MCC). Then we used the binary pattern of 15 C terminus residues and achieved accuracy of 85.05% with 0.701 MCC, latter on we developed prediction method by combini...Continue Reading

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Citations

Mar 19, 2013·Bioinformatics·Catherine MooneyDenis C Shields
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Methods Mentioned

BETA
peptide based prediction

Software Mentioned

pLOGOs
_ Light
AntiBP2
SVM
pLOGO
PERL
NKM
AntiBP
Solaris

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