Binary Response Models for Recognition of Antimicrobial Peptides

Authors:
Elena G. Randou;Daniel Veltri;Amarda Shehu
Affiliations:
Dept. of Mathematical Sciences, George Mason University;School of Systems Biology, George Mason University;Dept. of Computer Science, Dept. of Bioengineering, School of Systems Biology, George Mason University
Venue:
Proceedings of the International Conference on Bioinformatics, Computational Biology and Biomedical Informatics
Year:
2013

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Abstract

There is now great urgency in developing new antibiotics to combat bacterial resistance. Recent attention has turned to naturally-occurring antimicrobial peptides (AMPs) that can serve as templates for antibacterial drug research. As natural AMPs have a wide range of activity against various bacteria, current research is focusing on modifying existing peptides or designing new ones to increase potency. This paper presents a computational approach to further our understanding of what physicochemical properties or features confer to a peptide antimicrobial activity. One of the contributions of this paper is the ability to rigorously test the relevance of features obtained by biological or computational researchers in the context of AMP recognition. A second contribution is the construction of a predictive model that employs relevant features and their combinations to associate with a novel peptide sequence a probability to have antimicrobial activity. Taken together, the work in this paper seeks to help researchers elucidate features of importance for antimicrobial activity. This is an important first step towards modification or design of novel AMPs for treatment. With this goal in mind, we provide access to the proposed methodology through a web server, which allows users to replicate the findings here or evaluate their own feature set.