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

Abstract

Abstract. The 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.

Prediction and Rational Design of Antimicrobial Peptides

“…The system achieved an accuracy of 90%. Indeed, the aggregation propensity was seen to be crucial for this method in much the same way as the hydrophobic moment in the method developed by Porto et al (2010). The aggregation propensity changes if the sequence is shuffled, but the other six properties do not.…”

“…In fact, these studies played a critical role in identifying the AMP properties involved in antimicrobial activity. These properties served as the basis for developing approaches for antimicrobial activity prediction, through several methods, such as support vector machine (SVM, Lata et al, 2007;Porto et al, 2010;Thomas et al, 2010), artificial neural network (ANN, Fjell et al, 2009;Torrent et al, 2011) and quantitative structure-activity relationship (QSAR, Jenssen et al, 2007) as will be further detailed. By using machine learning methods, this field became more scientific than descriptive.…”

CS-AMPPred: An Updated SVM Model for Antimicrobial Activity Prediction in Cysteine-Stabilized Peptides

“…Recently, it has been proposed that physicochemical properties can be used as descriptors to predict the antimicrobial activity of cysteine-stabilized peptides by means of machine learning methods [20]. Several studies have applied machine learning methods for antimicrobial activity prediction [20]–[26].…”

“…Several studies have applied machine learning methods for antimicrobial activity prediction [20]–[26]. These methods aim to identify AMPs prior to in vitro tests, so that antimicrobial sequences can be identified directly from protein databases and further expressed in heterologous systems or synthesized [21], [26].…”

“…The answer will in fact be obtained only through in vitro and/or in vivo tests; however, the prediction methods can provide an indication of activity, improving the search methods. Bearing this in mind, here the CS-AMPPred (Cysteine-Stabilized Antimicrobial Peptides Predictor) is presented, as an updated version of the support vector machine (SVM) model proposed by our group [20] for antimicrobial activity prediction in cysteine-stabilized peptides.…”