952 research outputs found
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
Variational Methods for Biomolecular Modeling
Structure, function and dynamics of many biomolecular systems can be
characterized by the energetic variational principle and the corresponding
systems of partial differential equations (PDEs). This principle allows us to
focus on the identification of essential energetic components, the optimal
parametrization of energies, and the efficient computational implementation of
energy variation or minimization. Given the fact that complex biomolecular
systems are structurally non-uniform and their interactions occur through
contact interfaces, their free energies are associated with various interfaces
as well, such as solute-solvent interface, molecular binding interface, lipid
domain interface, and membrane surfaces. This fact motivates the inclusion of
interface geometry, particular its curvatures, to the parametrization of free
energies. Applications of such interface geometry based energetic variational
principles are illustrated through three concrete topics: the multiscale
modeling of biomolecular electrostatics and solvation that includes the
curvature energy of the molecular surface, the formation of microdomains on
lipid membrane due to the geometric and molecular mechanics at the lipid
interface, and the mean curvature driven protein localization on membrane
surfaces. By further implicitly representing the interface using a phase field
function over the entire domain, one can simulate the dynamics of the interface
and the corresponding energy variation by evolving the phase field function,
achieving significant reduction of the number of degrees of freedom and
computational complexity. Strategies for improving the efficiency of
computational implementations and for extending applications to coarse-graining
or multiscale molecular simulations are outlined.Comment: 36 page
A generalized Poisson and Poisson-Boltzmann solver for electrostatic environments
The computational study of chemical reactions in complex, wet environments is
critical for applications in many fields. It is often essential to study
chemical reactions in the presence of applied electrochemical potentials,
taking into account the non-trivial electrostatic screening coming from the
solvent and the electrolytes. As a consequence the electrostatic potential has
to be found by solving the generalized Poisson and the Poisson-Boltzmann
equation for neutral and ionic solutions, respectively. In the present work
solvers for both problems have been developed. A preconditioned conjugate
gradient method has been implemented to the generalized Poisson equation and
the linear regime of the Poisson-Boltzmann, allowing to solve iteratively the
minimization problem with some ten iterations of a ordinary Poisson equation
solver. In addition, a self-consistent procedure enables us to solve the
non-linear Poisson-Boltzmann problem. Both solvers exhibit very high accuracy
and parallel efficiency, and allow for the treatment of different boundary
conditions, as for example surface systems. The solver has been integrated into
the BigDFT and Quantum-ESPRESSO electronic-structure packages and will be
released as an independent program, suitable for integration in other codes
A Continuum Poisson-Boltzmann Model for Membrane Channel Proteins
Membrane proteins constitute a large portion of the human proteome and
perform a variety of important functions as membrane receptors, transport
proteins, enzymes, signaling proteins, and more. The computational studies of
membrane proteins are usually much more complicated than those of globular
proteins. Here we propose a new continuum model for Poisson-Boltzmann
calculations of membrane channel proteins. Major improvements over the existing
continuum slab model are as follows: 1) The location and thickness of the slab
model are fine-tuned based on explicit-solvent MD simulations. 2) The highly
different accessibility in the membrane and water regions are addressed with a
two-step, two-probe grid labeling procedure, and 3) The water pores/channels
are automatically identified. The new continuum membrane model is optimized (by
adjusting the membrane probe, as well as the slab thickness and center) to best
reproduce the distributions of buried water molecules in the membrane region as
sampled in explicit water simulations. Our optimization also shows that the
widely adopted water probe of 1.4 {\AA} for globular proteins is a very
reasonable default value for membrane protein simulations. It gives an overall
minimum number of inconsistencies between the continuum and explicit
representations of water distributions in membrane channel proteins, at least
in the water accessible pore/channel regions that we focus on. Finally, we
validate the new membrane model by carrying out binding affinity calculations
for a potassium channel, and we observe a good agreement with experiment
results.Comment: 40 pages, 6 figures, 5 table
Improvements to the APBS biomolecular solvation software suite
The Adaptive Poisson-Boltzmann Solver (APBS) software was developed to solve
the equations of continuum electrostatics for large biomolecular assemblages
that has provided impact in the study of a broad range of chemical, biological,
and biomedical applications. APBS addresses three key technology challenges for
understanding solvation and electrostatics in biomedical applications: accurate
and efficient models for biomolecular solvation and electrostatics, robust and
scalable software for applying those theories to biomolecular systems, and
mechanisms for sharing and analyzing biomolecular electrostatics data in the
scientific community. To address new research applications and advancing
computational capabilities, we have continually updated APBS and its suite of
accompanying software since its release in 2001. In this manuscript, we discuss
the models and capabilities that have recently been implemented within the APBS
software package including: a Poisson-Boltzmann analytical and a
semi-analytical solver, an optimized boundary element solver, a geometry-based
geometric flow solvation model, a graph theory based algorithm for determining
p values, and an improved web-based visualization tool for viewing
electrostatics
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