Sequence-based analysis and prediction of lantibiotics: A machine learning approach

•Lantibiotics can be accurately predicted by their sequence via machine learning.•SVM and SMO algorithms generated the most accurate lantibiotic predictor models.•Feature selection and sequence logo revealed significant distinctions in lantibiotics sequences.•The existence of leucine in position 10 from C-termini of most lantibiotics is a meaningful difference.•Glutamic acid residue frequency is higher in lantibiotics.•Lysin/Arginine residues are mostly distributed in C-termini.