An SVM Model Based on Physicochemical Properties to Predict Antimicrobial Activity from Protein Sequences with Cysteine Knot Motifs

Abstract

AbstractThe cysteine knot motifs are widely spread in several classes of peptides including those with antimicrobial functions. These motifs offer a major stability to the protein structure. Nevertheless, the antimicrobial activity is modulated by physicochemical properties. In this paper, we create a model of support vector machine to predict antimicrobial activity from sequences with similar motifs, based on physicochemical properties: net charge, ratio between hydrophobic and charged residues, average hydrophobicity and hydrophobic moment. The support vector machine model was trained with 146 antimicrobial peptides with six cysteines from the antimicrobial peptides database and an equal number of random sequences predicted as transmembrane proteins. The polynomial kernel shows the best accuracy (77.4%) on 10-fold cross validation. Testing in a blind dataset, we observe an accuracy of 83.02%. Through this model, proteins of varied size with a cysteine knot motif can be predicted with good reliability.KeywordsSupport Vector MachineAntimicrobial PeptidesPhysicochemical PropertiesCysteine Knot MotifMachine Learning