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Updated: Jun 10, 2026

Realistic Membrane Modeling Using Complex Lipid Mixtures in Simulation Studies
Published on: September 1, 2023
Using one-step perturbation to predict the effect of changing force-field parameters on the simulated folding
Zhixiong Lin1, Haiyan Liu, Wilfred F van Gunsteren
1Laboratory of Physical Chemistry, Swiss Federal Institute of Technology, ETH 8093, Zurich, Switzerland.
Abstract:
Computer simulation using molecular dynamics is increasingly used to simulate the folding equilibria of peptides and small proteins. Yet, the quality of the obtained results depends largely on the quality of the force field used. This comprises the solute as well as the solvent model and their energetic and entropic compatibility. It is, however, computational very expensive to perform test simulations for each combination of force-field parameters. Here, we use the one-step perturbation technique to predict the change of the free enthalpy of folding of a beta-peptide in methanol solution due to changing a variety of force-field parameters. The results show that changing the solute backbone partial charges affects the folding equilibrium, whereas this is relatively insensitive to changes in the force constants of the torsional energy terms of the force field. Extending the cut-off distance for nonbonded interactions beyond 1.4 nm does not affect the folding equilibrium. The same result is found for a change of the reaction-field permittivity for methanol from 17.7 to 30. The results are not sensitive to the criterion, e.g., atom-positional RMSD or number of hydrogen bonds, that is used to distinguish folded and unfolded conformations. Control simulations with perturbed Hamiltonians followed by backward one-step perturbation indicated that quite large perturbations still yield reliable results. Yet, perturbing all solvent molecules showed where the limitations of the one-step perturbation technique are met. The evaluated methodology constitutes an efficient tool in force-field development for molecular simulation by reducing the number of required separate simulations by orders of magnitude.
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