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Linearized esculentin-2EM shows pH dependent antibacterial activity with an alkaline optimum.

Erum MalikDavid A PhoenixTimothy J SnapeFrederick HarrisJaipaul SinghLeslie H G MortonSarah R Dennison
Published in: Molecular and cellular biochemistry (2021)
Here the hypothesis that linearized esculentin 2EM (E2EM-lin) from Glandirana emeljanovi possesses pH dependent activity is investigated. The peptide showed weak activity against Gram-negative bacteria (MLCs ≥ 75.0 μM) but potent efficacy towards Gram-positive bacteria (MLCs ≤ 6.25 μM). E2EM-lin adopted an α-helical structure in the presence of bacterial membranes that increased as pH was increased from 6 to 8 (↑ 15.5-26.9%), whilst similar increases in pH enhanced the ability of the peptide to penetrate (↑ 2.3-5.1 mN m-1) and lyse (↑ 15.1-32.5%) these membranes. Theoretical analysis predicted that this membranolytic mechanism involved a tilted segment, that increased along the α-helical long axis of E2EM-lin (1-23) in the N → C direction, with -  < µH > increasing overall from circa - 0.8 to - 0.3. In combination, these data showed that E2EM-lin killed bacteria via novel mechanisms that were enhanced by alkaline conditions and involved the formation of tilted and membranolytic, α-helical structure. The preference of E2EM-lin for Gram-positive bacteria over Gram-negative organisms was primarily driven by the superior ability of phosphatidylglycerol to induce α-helical structure in the peptide as compared to phosphatidylethanolamine. These data were used to generate a novel pore-forming model for the membranolytic activity of E2EM-lin, which would appear to be the first, major reported instance of pH dependent AMPs with alkaline optima using tilted structure to drive a pore-forming process. It is proposed that E2EM-lin has the potential for development to serve purposes ranging from therapeutic usage, such as chronic wound disinfection, to food preservation by killing food spoilage organisms.
Keyphrases
  • gram negative
  • multidrug resistant
  • electronic health record
  • risk assessment
  • human health
  • big data
  • mass spectrometry
  • deep learning
  • anaerobic digestion