38,180 research outputs found
MSM/RD: Coupling Markov state models of molecular kinetics with reaction-diffusion simulations
Molecular dynamics (MD) simulations can model the interactions between
macromolecules with high spatiotemporal resolution but at a high computational
cost. By combining high-throughput MD with Markov state models (MSMs), it is
now possible to obtain long-timescale behavior of small to intermediate
biomolecules and complexes. To model the interactions of many molecules at
large lengthscales, particle-based reaction-diffusion (RD) simulations are more
suitable but lack molecular detail. Thus, coupling MSMs and RD simulations
(MSM/RD) would be highly desirable, as they could efficiently produce
simulations at large time- and lengthscales, while still conserving the
characteristic features of the interactions observed at atomic detail. While
such a coupling seems straightforward, fundamental questions are still open:
Which definition of MSM states is suitable? Which protocol to merge and split
RD particles in an association/dissociation reaction will conserve the correct
bimolecular kinetics and thermodynamics? In this paper, we make the first step
towards MSM/RD by laying out a general theory of coupling and proposing a first
implementation for association/dissociation of a protein with a small ligand (A
+ B C). Applications on a toy model and CO diffusion into the heme cavity
of myoglobin are reported
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Digging into Lipid Membrane Permeation for Cardiac Ion Channel Blocker d-Sotalol with All-Atom Simulations.
Interactions of drug molecules with lipid membranes play crucial role in their accessibility of cellular targets and can be an important predictor of their therapeutic and safety profiles. Very little is known about spatial localization of various drugs in the lipid bilayers, their active form (ionization state) or translocation rates and therefore potency to bind to different sites in membrane proteins. All-atom molecular simulations may help to map drug partitioning kinetics and thermodynamics, thus providing in-depth assessment of drug lipophilicity. As a proof of principle, we evaluated extensively lipid membrane partitioning of d-sotalol, well-known blocker of a cardiac potassium channel Kv11.1 encoded by the hERG gene, with reported substantial proclivity for arrhythmogenesis. We developed the positively charged (cationic) and neutral d-sotalol models, compatible with the biomolecular CHARMM force field, and subjected them to all-atom molecular dynamics (MD) simulations of drug partitioning through hydrated lipid membranes, aiming to elucidate thermodynamics and kinetics of their translocation and thus putative propensities for hydrophobic and aqueous hERG access. We found that only a neutral form of d-sotalol accumulates in the membrane interior and can move across the bilayer within millisecond time scale, and can be relevant to a lipophilic channel access. The computed water-membrane partitioning coefficient for this form is in good agreement with experiment. There is a large energetic barrier for a cationic form of the drug, dominant in water, to cross the membrane, resulting in slow membrane translocation kinetics. However, this form of the drug can be important for an aqueous access pathway through the intracellular gate of hERG. This route will likely occur after a neutral form of a drug crosses the membrane and subsequently re-protonates. Our study serves to demonstrate a first step toward a framework for multi-scale in silico safety pharmacology, and identifies some of the challenges that lie therein
Capturing the essence of folding and functions of biomolecules using Coarse-Grained Models
The distances over which biological molecules and their complexes can
function range from a few nanometres, in the case of folded structures, to
millimetres, for example during chromosome organization. Describing phenomena
that cover such diverse length, and also time scales, requires models that
capture the underlying physics for the particular length scale of interest.
Theoretical ideas, in particular, concepts from polymer physics, have guided
the development of coarse-grained models to study folding of DNA, RNA, and
proteins. More recently, such models and their variants have been applied to
the functions of biological nanomachines. Simulations using coarse-grained
models are now poised to address a wide range of problems in biology.Comment: 37 pages, 8 figure
Monovalent ions modulate the flux through multiple folding pathways of an RNA pseudoknot
The functions of RNA pseudoknots (PKs), which are minimal tertiary structural
motifs and an integral part of several ribozymes and ribonucleoprotein
complexes, are determined by their structure, stability and dynamics.
Therefore, it is important to elucidate the general principles governing their
thermodynamics/folding mechanisms. Here, we combine experiments and simulations
to examine the folding/unfolding pathways of the VPK pseudoknot, a variant of
the Mouse Mammary Tumor Virus (MMTV) PK involved in ribosomal frameshifting.
Fluorescent nucleotide analogs (2-aminopurine and pyrrolocytidine) placed at
different stem/loop positions in the PK, and laser temperature-jump approaches
serve as local probes allowing us to monitor the order of assembly of VPK with
two helices with different intrinsic stabilities. The experiments and molecular
simulations show that at 50 mM KCl the dominant folding pathway populates only
the more stable partially folded hairpin. As the salt concentration is
increased a parallel folding pathway emerges, involving the less stable hairpin
structure as an alternate intermediate. Notably, the flux between the pathways
is modulated by the ionic strength. The findings support the principle that the
order of PK structure formation is determined by the relative stabilities of
the hairpins, which can be altered by sequence variations or salt
concentrations. Our study not only unambiguously demonstrates that PK folds by
parallel pathways, but also establishes that quantitative description of RNA
self-assembly requires a synergistic combination of experiments and
simulations.Comment: Supporting Information include
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