Antimicrobial peptides (AMPs) represent a class of bioactive agents that can be used to target multidrug resistant bacteria. Their mode of action is diverse and not fully understood, and innovative peptide design strategies are needed to generate novel AMPs with improved properties. Here we present the computer-assisted de novo design of AMPs. We selected two potent AMPs, protonectin and decoralin, as seed peptides and synthesized randomly scrambled derivatives based on these two templates. Membrane disruption was tested using large unilamellar vesicles (LUVs) fluorescence assay. Some of the scrambled peptides exhibited significant discriminatory activity for zwitterionic vesicles or anionic vesicles and presented a broader spectrum of antibacterial activity. For de novo design, we encoded the above tested peptides and 10000 pseudo-random peptides composed by the same amino acid distribution as the known actives by the PEPCATS descriptor and projected the distribution on a self-organizing map (SOM, Kohonen network). New peptides that fell together with tested active peptides in the same cluster were selected for synthesis and testing. Three synthesized peptides showed significant activity in the LUVs assays and two of them even exhibited stronger membrane lytic activities than the seeds. Inducible a-helix structure in circular dichroism emerged as a critical feature for those membrane active peptides. Surface plasmon resonance and atomic force microscopy were also used to explore peptide-membrane interaction. Isothermal titration calorimetry suggested an entropy-driven mechanism for peptide-membrane interaction. The study demonstrates the sustained potential of advanced computer-assisted methods for designing peptides with desired properties and activity.

De novo design antimicrobial peptides using self-organizing maps

247th National Meeting (2014)

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