DBAASP Special prediction as a tool for the prediction of antimicrobial potency against particular target species

Boris Vishnepolsky,Margarita Karapetian,Malak Pirtskhalava,Maya Grigolava,Giorgi Zaalishvili

doi:10.3390/ecmc-4-05608

Boris Vishnepolsky, Margarita Karapetian + Show 3 more

Open Access

https://doi.org/10.3390/ecmc-4-05608

Copy DOI

Publication Date: Oct 31, 2018
Citations: 2	License type: cc-by

Affiliation: Agricultural University of Georgia

Abstract

Antimicrobial peptides (AMPs) have been identified as a potentially new class of antibiotics. There is a lot of computational methods of AMP prediction. Although most of them can predict antimicrobial potency against any microbe (microbe is not identified) with rather high accuracy, prediction quality of these tools against particular bacterial strains is low (Bioinformatics, 2018, in press; Journal of Chemical Information and Modeling 58, 1141-1151). Special prediction is a tool for the prediction of antimicrobial potency of peptides against particular target species with high accuracy. This tool is included into the Database of Antimicrobial Activity and Structure of Peptides (DBAASP, https://dbaasp.org, Nucleic acids research 44 (D1), D1104-D1112). In this presentation we describe this tool and predictive models for some Gram+ bacterial strains (Staphylococcus aureus ATCC 25923 and Bacillus subtilis) and a model for the prediction of hemolytic activity. Predictive model for Gram- Escherichia coli ATCC 25922 was presented earlier (Journal of Chemical Information and Modeling 58, 1141-1151, In 2nd Int. Electron. Conf. Med. Chem. 01-30 November 2016; Sciforum, 2016, A031). Special prediction tool can be used for the design of peptides being active against particular strain. To demonstrate the capability of the tool, peptides predicted as active against E. coli ATCC 25922 and S. aureus ATCC 25923 have been synthesized, and tested in vitro. The results have shown the justification of using special prediction tool for the design of new AMPs.

Highlights

S: 1. They can be available for the action of proteases for a short time and the peptides do not show antimicrobial activity
At the moment many Antimicrobial peptides (AMPs) prediction tools are available. Most of these tools can only predict if a peptide has any antimicrobial activity, but cannot predict antimicrobial potency against particular strains [1,2]
One of the main problems in the design of new peptides is the lack of effective predictive models capable of showing high performance when designing new amino acid sequences with a high therapeutic effect against specific strains of bacteria

Summary

Introduction

S: 1. They can be available for the action of proteases for a short time and the peptides do not show antimicrobial activity. For task-oriented design of new AMPs, tools for prediction of antimicrobial activities of peptides are needed. Current presentation describes predictive models for some Gram positive (Staphylococcus aureus ATCC 25923 and Bacillus subtilis) bacterial strains and a model for the prediction of hemolytic activity, which are based on the same algorithm as for Escherichia coli ATCC 25922

Results

Conclusion