A Cumulative Strategy to Predict and Characterize Antimicrobial Peptides (AMPs) from Protein Database

Abstract

Antimicrobial peptides (AMPs) are indivisible part of the innate immune system in invertebrates. AMPs have been proven to have crucial role with a wide range of biological activities, mainly with immunomodulatory and broad spectrum of antimicrobial activity against various pathogens. The unique and salient features of the AMPs show its exceptional nature of therapeutic activity and serves as an alternative agent for conventional antibiotics. The search for potential AMPs persist, as the emergence of multiple drug resistant bacterial strains has been spreading in higher number. Here, the putative antimicrobial peptide sequences were identified from 19,915 sequences of prawn transcriptome and analyzed with various in silico tools such as EXPASY, AMPA, and helical wheel projection and so on. The characteristic antimicrobial properties have been determined for 660 putative AMPs with above mentioned tools. We have demonstrated an efficient bioinformatics approach to derive and analyze the AMPs from the transcriptome data of Macrobrachium rosenbergii. Even though, 660 peptide regions were identified among those five peptide sequences were demonstrated comprehensively with each characteristic property contributes the antimicrobial activity. In this study, we have proposed a rapid and successful protocol that would help to predict AMP in sequential procedure using various in silico methods. Also, we have shown a distinctive method to shortlist the AMPs based on their various physico-chemical properties. Until now, no sequential protocol has been developed to identify and characterize the AMPs from protein database.