Molecular dynamics based antimicrobial activity descriptors for synthetic cationic peptides

Abstract

Abstract : There is an urgent need to identify novel antimicrobial drugs in light of the development of resistance by the bacteria for a broad spectrum of antibiotics. Antimicrobial peptides are proving to be an effective remedy to which bacteria have not been able to develop resistance easily. With the goal of progressing towards a rational design of AMPs, we developed a neural network based quantitative model relating their physicochemical properties to their activity. A set of synthetic cationic polypeptides (CAMEL-s) (Mee et al. in J. Peptide Res. 49:89, 1997) which were studied systematically in experiments was used in the development of our model. Intuitive variables derived from short molecular dynamics simulations in octanol were used as descriptors, resulting in a good prediction of activity and underscoring the possibility of a rational design. Graphical abstract : Synopsis The dynamic properties of peptides calculated from molecular dynamics simulation are used as descriptors for the artificial neural network to predict the biological activity of the antimicrobial peptides. [Figure not available: see fulltext.].