Development of the model of in silico design of AMPs active against Staphylococcus aureus 25923

Abstract

Emerging bacterial resistance to the existing antibiotics makes the development of new types of antibiotics an increasingly important challenge. Antimicrobial peptides (AMPs) can be considered as novel and efficient type of antibiotics that are hard to acquire resistance against. We have developed an algorithm to design peptides that are active against certain species. The prediction is based on clusterization of peptides with known biological activities by physicochemical properties. The Database of Antimicrobial Activity and Structure of Peptides (DBAASP, https://dbaasp.org) now includes Special Prediction (SP) tool, which allows to apply this algorithm to any amino acid sequence to predict whether this peptide is active against particular microbes. To verify the efficiency of the algorithm, we designed several variants of active peptides and tested them in vitro against two strains Escherichia coli ATCC 25922 and Staphylococcus aureus 25923. Prediction precision for the designed peptides against Escherichia coli ATCC was 95% and against Staphylococcus aureus was 68%. To improve prediction precision against Staphylococcus aureus, we applied the linear regression analysis based on binary classification. This approach allows us to improve the prediction precision of the peptides designed for Staphylococcus aureus 25923 up to 92%.

DBAASP v3: database of antimicrobial/cytotoxic activity and structure of peptides as a resource for development of new therapeutics

“…Its utility was previously demonstrated when it was used to predict peptides active against Escherichia coli strain ATCC 25922 and Staphylococcus aureus strain ATCC 25923 but not against red blood cells. The results of subsequent in vitro characterization of the designed peptides justify the tool’s use in the design of new AMPs ( 16 , 17 ). At present the tool is capable of providing predictions of activity against seven microbial species or strains ( Escherichia coli ATCC 25922 , Pseudomonas aeruginosa ATCC 27853 , Staphylococcus aureus ATCC 25923 , Klebsiella pneumoniae, Bacillus subtilis, Candida albicans and Saccharomyces cerevisiae ) in addition to human erythrocytes.…”

“…DBAASP continues to expand through the addition of new content and additional user services, and from 2016 to 2019 the number of entries increased to >15 000. The DBAASP team continues to develop predictive tools for use in the de novo design of peptides having desired activities, and in vitro tests of several synthetic peptides have demonstrated that DBAASP predictions can be effectively used in the design of peptide-based antimicrobial agents against both gram-negative and gram-positive bacteria ( 16 , 17 ).…”

In silico Approaches for the Design and Optimization of Interfering Peptides Against Protein–Protein Interactions

“…This database has continuously developed its predictive tools to be employed in the de novo design of peptide-based drugs. Its efficacy for the design of peptide-based antimicrobial agents against both gram-positive and gram-negative bacteria has been experimentally confirmed ( Vishnepolsky et al, 2019a , b ). The BaAMPs database was developed for AMPs with the property of disrupting microbial biofilms ( Di Luca et al, 2015 ).…”