Prediction of linear cationic antimicrobial peptides based on characteristics responsible for their interaction with the membranes

TL;DR

This paper introduces a simple, descriptor-based method for predicting linear cationic antimicrobial peptides (LCAP), focusing on physicochemical properties to distinguish AMP from non-AMP with comparable or better accuracy than complex machine learning models.

Contribution

The study develops a straightforward prediction algorithm for LCAPs based on key physicochemical descriptors, outperforming complex models in certain metrics.

Findings

Hydrophobic moment, charge, and membrane location are effective discriminators.

The method achieves comparable accuracy to machine learning approaches.

Sensitivity on test data exceeds existing CAMP prediction tools.

Abstract

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 which are 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, propensity to disordered structure 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 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 sensitivity obtained by our method gives even higher value than the one obtained by CAMP prediction tools.