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Membrane/Toxin Interaction Energetics via Serial Multiscale Molecular Dynamics Simulations.

Chze Ling Wee1, Martin B Ulmschneider1, Mark S P Sansom1

  • 1Department of Biochemistry and Oxford Centre for Integrative Systems Biology, University of Oxford, South Parks Road, Oxford, OX1 3QU, United Kingdom.

Journal of Chemical Theory and Computation
|November 28, 2015
PubMed
Summary

Coarse-grained (CG) and atomistic (AT) molecular dynamics simulations were combined to accurately predict peptide-membrane interactions. This hybrid approach improves free energy calculations for complex biomolecular systems.

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Area of Science:

  • Biomolecular simulations
  • Computational biophysics
  • Membrane protein interactions

Background:

  • Atomistic (AT) molecular dynamics (MD) simulations face challenges in sampling and convergence for complex biomolecular systems.
  • Coarse-grained (CG) MD offers simulations of larger systems and longer timescales, but thermodynamic accuracy is uncertain.
  • Accurate free energy calculations are crucial for understanding peptide-membrane interactions.

Purpose of the Study:

  • To re-estimate the free energy profile (potential of mean force) of a peptide toxin (VSTx1) interacting with a lipid bilayer.
  • To compare free energy calculations using CG MD, AT MD with explicit solvent/membrane, and AT MD with implicit solvent (GBIM).
  • To establish a framework for combining CG and AT simulations for peptide-membrane interaction free energy estimation.

Main Methods:

  • Coarse-grained (CG) molecular dynamics (MD) simulations to compute the potential of mean force (PMF).
  • Atomistic (AT) MD simulations with explicit membrane and solvent.
  • Generalized Born implicit membrane and solvent (GBIM) model with explicit peptide.
  • Serial combination of CG and AT simulations, using CG results to guide AT simulations.

Main Results:

  • CG and AT simulations showed conserved topology in PMF profiles but differed in free energy magnitudes.
  • CG and AT simulations predicted membrane/water interface free energy wells of -27 and -23 kcal/mol, respectively.
  • The GBIM model yielded a reduced interfacial free energy well (-12 kcal/mol) and higher free energy barriers.
  • AT simulations predicted a significantly smaller free energy barrier (+26 kcal/mol) for toxin insertion compared to CG (+61 kcal/mol) and GBIM (+96 kcal/mol).

Conclusions:

  • A framework for serially combining CG and AT simulations effectively estimates peptide-membrane interaction free energies.
  • Hybrid simulation approaches combining different levels of granularity are vital for studying complex membrane/protein systems.
  • While CG models show promise, AT simulations remain essential for accurate quantitative thermodynamic predictions.