Prediction of Linear Cationic Antimicrobial Peptides Based on Characteristics Responsible for Their Interaction with the Membranes

Most available antimicrobial peptides (AMP) prediction methods use common approach for different classes of AMP. Contrary to available approaches, we suggest that a strategy of prediction should be based on the fact that there are several kinds of AMP that vary in mechanisms of action, structure, mode of interaction with membrane, etc. According to our suggestion for each kind of AMP, a particular approach has to be developed in order to get high efficacy. Consequently, in this paper, a particular but the biggest class of AMP, linear cationic antimicrobial peptides (LCAP), has been considered and a newly developed simple method of LCAP prediction described. The aim of this study is the development of a simple method of discrimination of AMP from non-AMP, the efficiency of which will be determined by efficiencies of selected descriptors only and comparison the results of the discrimination procedure with the results obtained by more complicated discriminative methods. As descriptors the physicochemical characteristics responsible for capability of the peptide to interact with an anionic membrane were considered. The following characteristics such as hydrophobicity, amphiphaticity, location of the peptide in relation to membrane, charge density, propensities to disordered structure and aggregation were studied. On the basis of these characteristics, a new simple algorithm of prediction is developed and evaluation of efficacies of the characteristics as descriptors performed. The results show that three descriptors, hydrophobic moment, charge density and location of the peptide along the membranes, can be used as discriminators of LCAPs. For the training set, our method gives the same level of accuracy as more complicated machine learning approaches offered as CAMP database service tools. For the test set accuracy obtained by our method gives even higher value than the one obtained by CAMP prediction tools. The AMP prediction tool based on the considered method is available at http://www.biomedicine.org.ge/dbaasp/.