Computational antimicrobial peptide design and evaluation against multidrug-resistant clinical isolates of bacteria.

ABSTRACT: There is a pressing need for new therapeutics to combat multidrug- and carbapenem-resistant bacterial pathogens. This challenge prompted us to use a long short-term memory (LSTM) language model to understand the underlying grammar, i.e. the arrangement and frequencies of amino acid residues, in known antimicrobial peptide sequences. According to the output of our LSTM network, we synthesized 10 peptides and tested them against known bacterial pathogens. All of these peptides displayed broad-spectrum antimicrobial activity, validating our LSTM-based peptide design approach. Our two most effective antimicrobial peptides displayed activity against multidrug-resistant clinical isolates of Escherichia coli, Acinetobacter baumannii, Klebsiella pneumoniae, Pseudomonas aeru

SUBMITTER: Nagarajan D 

PROVIDER: S-EPMC5846155 | biostudies-literature | 2018 Mar

REPOSITORIES: biostudies-literature