10,586 research outputs found
Bayesian selection for coarse-grained models of liquid water
The necessity for accurate and computationally efficient representations of
water in atomistic simulations that can span biologically relevant timescales
has born the necessity of coarse-grained (CG) modeling. Despite numerous
advances, CG water models rely mostly on a-priori specified assumptions. How
these assumptions affect the model accuracy, efficiency, and in particular
transferability, has not been systematically investigated. Here we propose a
data driven, comparison and selection for CG water models through a
Hierarchical Bayesian framework. We examine CG water models that differ in
their level of coarse-graining, structure, and number of interaction sites. We
find that the importance of electrostatic interactions for the physical system
under consideration is a dominant criterion for the model selection. Multi-site
models are favored, unless the effects of water in electrostatic screening are
not relevant, in which case the single site model is preferred due to its
computational savings. The charge distribution is found to play an important
role in the multi-site model's accuracy while the flexibility of the
bonds/angles may only slightly improve the models. Furthermore, we find
significant variations in the computational cost of these models. We present a
data informed rationale for the selection of CG water models and provide
guidance for future water model designs
Atomic radius and charge parameter uncertainty in biomolecular solvation energy calculations
Atomic radii and charges are two major parameters used in implicit solvent
electrostatics and energy calculations. The optimization problem for charges
and radii is under-determined, leading to uncertainty in the values of these
parameters and in the results of solvation energy calculations using these
parameters. This paper presents a new method for quantifying this uncertainty
in implicit solvation calculations of small molecules using surrogate models
based on generalized polynomial chaos (gPC) expansions. There are relatively
few atom types used to specify radii parameters in implicit solvation
calculations; therefore, surrogate models for these low-dimensional spaces
could be constructed using least-squares fitting. However, there are many more
types of atomic charges; therefore, construction of surrogate models for the
charge parameter space requires compressed sensing combined with an iterative
rotation method to enhance problem sparsity. We demonstrate the application of
the method by presenting results for the uncertainties in small molecule
solvation energies based on these approaches. The method presented in this
paper is a promising approach for efficiently quantifying uncertainty in a wide
range of force field parameterization problems, including those beyond
continuum solvation calculations.The intent of this study is to provide a way
for developers of implicit solvent model parameter sets to understand the
sensitivity of their target properties (solvation energy) on underlying choices
for solute radius and charge parameters
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