Abstract

HHC-36 is an antimicrobial peptide, designed through neural network algorithms. It has been tested in vivo and in vitro, and has proved to be strongly effective against strains of multidrug-resistant P. aeruginosa, methicillin-resistant Staphylococcus aureus, and a few other ‘superbugs’ (Cherkasov et al., ACS Chem. Biol., 2009, 4 (1), pp 65-74). The peptide has also been observed through in vivo tests to be greatly pathogen-specific, hence proving to be a great candidate for developing future antibiotics.To understand the mechanism of activity of this peptide against bacterial membranes, we have performed a number of all-atom simulations, together with a series of circular dichroism spectroscopy (CD) and isothermal titration calorimetry (ITC) experiments. The small size (9 amino acids) and great charge density of HHC-36 make it problematic (if not unreliable) to find the structure of HHC-36 through conventional spectroscopy and/or crystallography methods. We have thus performed microsecond-scale molecular dynamics simulations, starting from an unfolded structure of the peptide, to find its folded structure. An amphipathic turn structure has been obtained, which was observed to be very stable over few hundred nanosecond timescales of simulation. This result has been compared to circular dichroism spectroscopy results, and the presence of the turn structure has been verified. To assess the stability of the observed structure, we have also performed temperature-dependent simulations and CD measurements, which have shown the stability of the turn structure at close-to-physiological temperatures.The obtained structure is then used in peptide-membrane simulations with a few different membrane compositions mimicking both bacterial and animal cell membranes. Profiles of the potential of mean force have been obtained, and the relevant binding parameters extracted from these simulations are then compared with the binding free energies obtained from ITC experiments.

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