47,945 research outputs found
Polymer chain generation for coarse-grained models using radical-like polymerization
An innovative method is proposed to generate configurations of coarse grained
models for polymer melts. This method, largely inspired by chemical ``radical
polymerization'', is divided in three stages: (i) nucleation of radicals
(reacting molecules caching monomers); (ii) growth of chains within a solvent
of monomers; (iii) termination: annihilation of radicals and removal of
residual monomers. The main interest of this method is that relaxation is
performed as chains are generated. Pure mono and poly-disperse polymers melts
are generated and compared to the configurations generated by the Push Off
method from Auhl et al.. A detailed study of the static properties (gyration
radius, mean square internal distance, entanglement length) confirms that the
radical-like polymerization technics is suitable to generate equilibrated
melts. The method is flexible, and can be adapted to generate nano-structured
polymers, namely diblock and triblock copolymers.Comment: 9 pages, 12 figure
Reaction Brownian Dynamics and the effect of spatial fluctuations on the gain of a push-pull network
Brownian Dynamics algorithms are widely used for simulating soft-matter and
biochemical systems. In recent times, their application has been extended to
the simulation of coarse-grained models of cellular networks in simple
organisms. In these models, components move by diffusion, and can react with
one another upon contact. However, when reactions are incorporated into a
Brownian Dynamics algorithm, attention must be paid to avoid violations of the
detailed-balance rule, and therefore introducing systematic errors in the
simulation. We present a Brownian Dynamics algorithm for reaction-diffusion
systems that rigorously obeys detailed balance for equilibrium reactions. By
comparing the simulation results to exact analytical results for a bimolecular
reaction, we show that the algorithm correctly reproduces both equilibrium and
dynamical quantities. We apply our scheme to a ``push-pull'' network in which
two antagonistic enzymes covalently modify a substrate. Our results highlight
that the diffusive behaviour of the reacting species can reduce the gain of the
response curve of this network.Comment: 25 pages, 7 figures, submitted to Journal of Chemical Physic
Transferable neural networks for enhanced sampling of protein dynamics
Variational auto-encoder frameworks have demonstrated success in reducing
complex nonlinear dynamics in molecular simulation to a single non-linear
embedding. In this work, we illustrate how this non-linear latent embedding can
be used as a collective variable for enhanced sampling, and present a simple
modification that allows us to rapidly perform sampling in multiple related
systems. We first demonstrate our method is able to describe the effects of
force field changes in capped alanine dipeptide after learning a model using
AMBER99. We further provide a simple extension to variational dynamics encoders
that allows the model to be trained in a more efficient manner on larger
systems by encoding the outputs of a linear transformation using time-structure
based independent component analysis (tICA). Using this technique, we show how
such a model trained for one protein, the WW domain, can efficiently be
transferred to perform enhanced sampling on a related mutant protein, the GTT
mutation. This method shows promise for its ability to rapidly sample related
systems using a single transferable collective variable and is generally
applicable to sets of related simulations, enabling us to probe the effects of
variation in increasingly large systems of biophysical interest.Comment: 20 pages, 10 figure
The macroscopic effects of microscopic heterogeneity
Over the past decade, advances in super-resolution microscopy and
particle-based modeling have driven an intense interest in investigating
spatial heterogeneity at the level of single molecules in cells. Remarkably, it
is becoming clear that spatiotemporal correlations between just a few molecules
can have profound effects on the signaling behavior of the entire cell. While
such correlations are often explicitly imposed by molecular structures such as
rafts, clusters, or scaffolds, they also arise intrinsically, due strictly to
the small numbers of molecules involved, the finite speed of diffusion, and the
effects of macromolecular crowding. In this chapter we review examples of both
explicitly imposed and intrinsic correlations, focusing on the mechanisms by
which microscopic heterogeneity is amplified to macroscopic effect.Comment: 20 pages, 5 figures. To appear in Advances in Chemical Physic
High-throughput Binding Affinity Calculations at Extreme Scales
Resistance to chemotherapy and molecularly targeted therapies is a major
factor in limiting the effectiveness of cancer treatment. In many cases,
resistance can be linked to genetic changes in target proteins, either
pre-existing or evolutionarily selected during treatment. Key to overcoming
this challenge is an understanding of the molecular determinants of drug
binding. Using multi-stage pipelines of molecular simulations we can gain
insights into the binding free energy and the residence time of a ligand, which
can inform both stratified and personal treatment regimes and drug development.
To support the scalable, adaptive and automated calculation of the binding free
energy on high-performance computing resources, we introduce the High-
throughput Binding Affinity Calculator (HTBAC). HTBAC uses a building block
approach in order to attain both workflow flexibility and performance. We
demonstrate close to perfect weak scaling to hundreds of concurrent multi-stage
binding affinity calculation pipelines. This permits a rapid time-to-solution
that is essentially invariant of the calculation protocol, size of candidate
ligands and number of ensemble simulations. As such, HTBAC advances the state
of the art of binding affinity calculations and protocols
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