Abstract

The one-step free energy perturbation approach can be applied to obtain conformational state-specific free energy differences (FEDs) associated with changes in force field parameters, and thus offers the possibility to consider conformational equilibria during force field parameterization. In this work, using the alanine decapeptide in explicit water solution as a model, the α-helical and β-hairpin state-specific FEDs associated with force field changes between two widely used parameter sets of the GROMOS force field, namely, 43A1 and 53A6, were determined using one-step perturbation. The results mostly deviated by only 1 kJ mol(−1) in absolute or a few percent in relative values from thermodynamic integration results, suggesting that the convergence ranges of one-step perturbation were large enough to cover the substantial changes in nonbonded parameters between the two parameter sets. It was also found that one-step perturbation may give larger errors when the changes from the reference state include a large decrease in van der Waals radius, as indicated by the result for the β-hairpin state-specific free energy change going from 53A6 to 43A1. According to the free energy results, the α-helical state of the alanine decapeptide is destabilized by 15 kJ mol(−1), i.e., 1.5 kJ mol(−1) per residue, relative to the β-hairpin state when going from 43A1 to 53A6, in agreement with previous direct simulations in which native α-helices were often found to be unstable in simulations using 53A6, despite that the 53A6 parameters better reproduce a range of thermodynamic properties of small molecular systems. By applying one-step perturbation to analyze the effects of perturbing individual parameters, the differential stabilization of the two secondary structure states can be traced to the changes in van der Waals parameters, especially a van der Waals parameter involved in third-neighbor interactions. This study provides an example of the efficiency of one-step perturbation in force field development, reducing the computational cost by orders of magnitude.

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