Antimicrobial peptide similarity and classification through rough set theory using physicochemical boundaries.

ABSTRACT:

Background

Antimicrobial peptides attract considerable interest as novel agents to combat infections. Their long-time potency across bacteria, viruses and fungi as part of diverse innate immune systems offers a solution to overcome the rising concerns from antibiotic resistance. With the rapid increase of antimicrobial peptides reported in the databases, peptide selection becomes a challenge. We propose similarity analyses to describe key properties that distinguish between active and non-active peptide sequences building upon the physicochemical properties of antimicrobial peptides. We used an iterative supervised machine learning approach to classify active peptides from inactive peptides with low false discovery rates in a relatively short computational search time.

Results</

SUBMITTER: Boone K 

PROVIDER: S-EPMC6282327 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature