Combined charge and hydrophobicity-guided screening of antibacterial peptides: two-level approach to predict antibacterial activity and efficacy.

Antibacterial peptides can be a potential game changer in the fight against antibiotic resistance. In order for these peptides to become successful antibiotic alternatives, it is essential that they possess high efficacy in addition to just being antibacterial. In this study, we have developed a two-level SVM-based binary classification approach to predict the antibacterial activity of a given peptide (model 1) and thereafter classify its antibacterial efficacy as high/low (model 2) with respect to minimum inhibitory concentration (MIC) values against Staphylococcus aureus, one of the most common pathogens. Based on charge and hydrophobicity of amino acids, we developed a sequence-based combined charge and hydrophobicity-guided triad (CHT) as a new method for obtaining features of any peptide. Model 1 with a combination of CHT and amino acid composition (AAC) as the feature representation method resulted in the highest accuracy of 96.7%. Model 2 with CHT as the feature representation method yielded the highest accuracy of 70.9%. Thus, CHT is found to be a potential feature representation method for classifying antibacterial peptides based on both activity and efficacy. Furthermore, we have also used an explainable machine learning algorithm to extract various insights from these models. These insights are found to be in excellent agreement with experimental findings reported in the literature, thus enhancing the dependability of the proposed models.