AmPEP: Sequence-based prediction of antimicrobial peptides using distribution patterns of amino acid properties and random forest.

ABSTRACT: Antimicrobial peptides (AMPs) are promising candidates in the fight against multidrug-resistant pathogens owing to AMPs' broad range of activities and low toxicity. Nonetheless, identification of AMPs through wet-lab experiments is still expensive and time consuming. Here, we propose an accurate computational method for AMP prediction by the random forest algorithm. The prediction model is based on the distribution patterns of amino acid properties along the sequence. Using our collection of large and diverse sets of AMP and non-AMP data (3268 and 166791 sequences, respectively), we evaluated 19 random forest classifiers with different positive:negative data ratios by 10-fold cross-validation. Our optimal model, AmPEP with the 1:3 data ratio, showed high accuracy (96%), Matthew's correlati

SUBMITTER: Bhadra P 

PROVIDER: S-EPMC5785966 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature