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Molecular dynamics methods to predict peptide locations in membranes: LAH4 as a stringent test case.

A Farrotti1, G Bocchinfuso1, A Palleschi1

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Summary

Molecular dynamics simulations reveal how peptide protonation affects membrane structure. Charged histidine residues in the LAH4 peptide induce membrane defects, explaining its antimicrobial activity.

Keywords:
Coarse grained force fieldsMembrane-active peptidesMolecular dynamics simulationsPotential of mean forceSolid-state NMR

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Area of Science:

  • Biophysics
  • Computational Chemistry
  • Membrane Biology

Background:

  • Understanding membrane-active peptide structure within lipid bilayers is crucial for elucidating their function.
  • Molecular dynamics (MD) simulations offer atomistic detail but require experimental validation.

Purpose of the Study:

  • To assess the reliability of self-assembling (minimum-bias) and potential of mean force (PMF) simulation approaches for studying peptide-lipid interactions.
  • To investigate the influence of histidine protonation state on the orientation and membrane interaction of the LAH4 peptide.

Main Methods:

  • Utilized all-atom (AA) and coarse-grained (CG) molecular dynamics force fields.
  • Employed minimum-bias and PMF simulation techniques.
  • Validated simulation results with solid-state NMR data.

Main Results:

  • Neutral histidine residues inserted into the membrane, leading to transmembrane orientations for LAH4.
  • Charged histidine residues induced membrane defects in AA simulations and showed varied localization in CG simulations.
  • PMF calculations accurately predicted higher membrane affinity for the neutral-His peptide and revealed minima consistent with minimum-bias simulations.

Conclusions:

  • Peptide protonation state significantly dictates membrane interaction and orientation.
  • The charged-His LAH4 peptide's interaction with lipid headgroups may promote membrane defects and facilitate translocation.
  • Minimum-bias simulations provide reliable structural insights comparable to more computationally intensive PMF calculations.