1,660 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
Utilizing Machine Learning to Greatly Expand the Range and Accuracy of Bottom-Up Coarse-Grained Models Through Virtual Particles
Coarse-grained (CG) models parameterized using atomistic reference data,
i.e., 'bottom up' CG models, have proven useful in the study of biomolecules
and other soft matter. However, the construction of highly accurate, low
resolution CG models of biomolecules remains challenging. We demonstrate in
this work how virtual particles, CG sites with no atomistic correspondence, can
be incorporated into CG models within the context of relative entropy
minimization (REM) as latent variables. The methodology presented, variational
derivative relative entropy minimization (VD-REM), enables optimization of
virtual particle interactions through a gradient descent algorithm aided by
machine learning. We apply this methodology to the challenging case of a
solvent-free CG model of a 1,2-dioleoyl-sn-glycero-3-phosphocholine (DOPC)
lipid bilayer and demonstrate that introduction of virtual particles captures
solvent-mediated behavior and higher-order correlations which REM alone cannot
capture in a more standard CG model based only on the mapping of collections of
atoms to the CG sites.Comment: 35 pages, 9 figure
Free energy landscape and characteristic forces for the initiation of DNA unzipping
DNA unzipping, the separation of its double helix into single strands, is
crucial in modulating a host of genetic processes. Although the large-scale
separation of double-stranded DNA has been studied with a variety of
theoretical and experimental techniques, the minute details of the very first
steps of unzipping are still unclear. Here, we use atomistic molecular dynamics
(MD) simulations, coarse-grained simulations and a statistical-mechanical model
to study the initiation of DNA unzipping by an external force. The calculation
of the potential of mean force profiles for the initial separation of the first
few terminal base pairs in a DNA oligomer reveal that forces ranging between
130 and 230 pN are needed to disrupt the first base pair, values of an order of
magnitude larger than those needed to disrupt base pairs in partially unzipped
DNA. The force peak has an "echo," of approximately 50 pN, at the distance that
unzips the second base pair. We show that the high peak needed to initiate
unzipping derives from a free energy basin that is distinct from the basins of
subsequent base pairs because of entropic contributions and we highlight the
microscopic origin of the peak. Our results suggest a new window of exploration
for single molecule experiments.Comment: 25 pages, 6 figures , Accepted for publication in Biophysical Journa
Mesoscopic Model for Free Energy Landscape Analysis of DNA sequences
A mesoscopic model which allows us to identify and quantify the strength of
binding sites in DNA sequences is proposed. The model is based on the
Peyrard-Bishop-Dauxois model for the DNA chain coupled to a Brownian particle
which explores the sequence interacting more importantly with open base pairs
of the DNA chain. We apply the model to promoter sequences of different
organisms. The free energy landscape obtained for these promoters shows a
complex structure that is strongly connected to their biological behavior. The
analysis method used is able to quantify free energy differences of sites
within genome sequences.Comment: 7 pages, 5 figures, 1 tabl
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