Molecular dynamics based antimicrobial activity descriptors for synthetic cationic peptides

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. .