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%.
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Development of the model of in silico design of AMPs active against Staphylococcus aureus 25923
Published: 01 November 2019 by MDPI in 5th International Electronic Conference on Medicinal Chemistry session ECMC-5
Keywords: Antimicrobial peptides; AMP prediction; Design of AMPs