In vitro and in vivo activities of antimicrobial peptides developed using an amino acid-based activity prediction method.

ABSTRACT: To design and discover new antimicrobial peptides (AMPs) with high levels of antimicrobial activity, a number of machine-learning methods and prediction methods have been developed. Here, we present a new prediction method that can identify novel AMPs that are highly similar in sequence to known peptides but offer improved antimicrobial activity along with lower host cytotoxicity. Using previously generated AMP amino acid substitution data, we developed an amino acid activity contribution matrix that contained an activity contribution value for each amino acid in each position of the model peptide. A series of AMPs were designed with this method. After evaluating the antimicrobial activities of these novel AMPs against both Gram-positive and Gram-negative bacterial strains, DP7 was chosen

SUBMITTER: Wu X 

PROVIDER: S-EPMC4135812 | biostudies-literature | 2014 Sep

REPOSITORIES: biostudies-literature