In the recent decades, antibacterial peptides have occupied a strategic position for pharmaceutical drug applications and became subject of intense research activities since they are used to strengthen the immune system of all living organisms by protecting them from pathogenic bacteria. This work proposes a simple and easy statistical/computational method through a peptide polarity index measure by which an antibacterial peptide subgroup can be efficiently identified, that is, characterized by a high toxicity to bacterial membranes but presents a low toxicity to mammal cells. These peptides also have the feature not to adopt to an alpha-helicoidal structure in aqueous solution. The double-blind test carried out to the whole Antimicrobial Peptide Database (November 2011) showed an accuracy of 90% applying the polarity index method for the identification of such antibacterial peptide groups. 1. Introduction The increasing resistance of pathogen agents towards multiple drugs has oriented parts of the investigation in bioinformatics to fast and efficient techniques that can predict the remarkable impact of antibacterial peptide action. These techniques can help to enhance the sometimes cumbersome chemical synthetic approach as well as the subsequent trial and error experiments to identify the peptide performance. Among the proposed various classifications of peptides, one of it refers to the alpha-helicoidal versus beta-sheet conformation that the peptides can adopt in aqueous solution. This classification refers to the predominance of certain amino acids in the linear sequence of the peptides such as proline-arginine, cathelicidin, or cysteine. It is important to note that such classification appears to be without any influence on the toxicity or selectivity of the peptide once it got in contact with the target membrane [1, 2]. Although nature was used as the main source of peptides with antibacterial properties in the past [3], parts of the research efforts are now more directed towards synthetic strategies. One of these synthetic approaches generate the peptides by replacing and/or removing constitutive amino acids from a natural peptide known for its antibacterial action [4], thus trying to reduce its size while keeping or increasing its toxicity [5]. Another technique consists of joining two peptides that individually do not exhibit antibacterial properties but combined turn out to be highly toxic [6]. To obtain efficient antibacterial peptides by measuring the potential action of each altered peptide with the-above described methods would result in a
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