Abstract
Computational simulations, akin to wetlab experimentation, are subject to statistical fluctuations. Assessing the magnitude of these fluctuations, that is, assigning uncertainties to the computed results, is of critical importance to drawing statistically reliable conclusions. Here, we use a simulation box size as an independent variable, to demonstrate how crucial it is to gather sufficient amounts of data before drawing any conclusions about the potential thermodynamic and kinetic effects. In various systems, ranging from solvation free energies to protein conformational transition rates, we showcase how the proposed simulation box size effect disappears with increased sampling. This indicates that, if at all, the simulation box size only minimally affects both the thermodynamics and kinetics of the type of biomolecular systems presented in this work.
Highlights
Molecular simulations sample well defined thermodynamic ensembles, providing a representation of the physical world in silico
We closely examine box size effects in different systems varying from the solvation free energy of a small molecule to the kinetics of a protein conformational change
Since the free energy is a thermodynamic state variable, it does not depend on whether a physical or alchemical pathway has been used for calculation
Summary
Molecular simulations sample well defined thermodynamic ensembles, providing a representation of the physical world in silico. A frequently noted shortcoming of molecular dynamics (MD) simulations is its dependence on the force field, that is, simplified representation of the electronic ground state potential energy. Large efforts are continuously dedicated to improve force field accuracy (Lindorff-Larsen et al, 2012). Another major simulation accuracy determining factor is the sampling convergence. Considering the stochastic nature of common sampling algorithms such as molecular dynamics simulations, biomolecular trajectories represent a multidimensional random walk of which the analysis is especially prone to suffer from sampling deficiencies (Hess, 2002)
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