Design of a machine learning-aided screening framework for antibiofilm peptides

Biofilms are formed by multicellular colonies of microorganisms that are protected by hard extracellular matrices. Eradication of biofilms is a challenging task due to their recalcitrant nature and thus biofilm formation poses a global threat to public health. In this regard, antibiofilm peptides are a promising class of therapeutics that are active against biofilms. However, large-scale experimental screening and testing of peptides for antibiofilm activity is a resource-intensive task. In this study, a machine learning-aided design framework is proposed to aid in screening of antibiofilm peptides. An SVM-based binary classification model is developed using amino acid compositions, sequence, and physicochemical properties of peptides as independent features. The physicochemical property-based model developed in this study achieved the highest accuracy of 97.9%, which is found to be substantially higher than the other feature representation techniques. The explainability of this model is performed using SHAP analysis. Results obtained show that amphiphilicity, aliphaticity and cationicity have positive correlation whereas steric parameter, length, and volume have negative correlation with antibiofilm activity of peptides. The developed model can be accessed freely via web tool: AntiBFP.