45 research outputs found
Conformational Entropy as Collective Variable for Proteins
Many enhanced sampling methods, such as Umbrella Sampling, Metadynamics or
Variationally Enhanced Sampling, rely on the identification of appropriate
collective variables. For proteins, even small ones, finding appropriate
collective variables has proven challenging. Here we suggest that the NMR
order parameter can be used to this effect. We trace the validity of this
statement to the suggested relation between and entropy. Using the
order parameter and a surrogate for the protein enthalpy in conjunction with
Metadynamics or Variationally Enhanced Sampling we are able to reversibly fold
and unfold a small protein and draw its free energy at a fraction of the time
that is needed in unbiased simulations. From a more conceptual point of view
this implies describing folding as a resulting from a trade off between entropy
and enthalpy. We also use in combination with the free energy flooding
method to compute the unfolding rate of this peptide. We repeat this
calculation at different temperatures to obtain the unfolding activation
energy.Comment: 4 pages, 3 figure
Enhancing entropy and enthalpy fluctuations to drive crystallization in atomistic simulations
Crystallization is a process of great practical relevance in which rare but
crucial fluctuations lead to the formation of a solid phase starting from the
liquid. Like in all first order first transitions there is an interplay between
enthalpy and entropy. Based on this idea, to drive crystallization in molecular
simulations, we introduce two collective variables, one enthalpic and the other
entropic. Defined in this way, these collective variables do not prejudge the
structure the system is going to crystallize into. We show the usefulness of
this approach by studying the case of sodium and aluminum that crystallize in
the bcc and fcc crystalline structure, respectively. Using these two generic
collective variables, we perform variationally enhanced sampling and well
tempered metadynamics simulations, and find that the systems transform
spontaneously and reversibly between the liquid and the solid phases.Comment: 4 pages, 2 figure
Frequency adaptive metadynamics for the calculation of rare-event kinetics
The ability to predict accurate thermodynamic and kinetic properties in
biomolecular systems is of both scientific and practical utility. While both
remain very difficult, predictions of kinetics are particularly difficult
because rates, in contrast to free energies, depend on the route taken and are
thus not amenable to all enhanced sampling methods. It has recently been
demonstrated that it is possible to recover kinetics through so called
`infrequent metadynamics' simulations, where the simulations are biased in a
way that minimally corrupts the dynamics of moving between metastable states.
This method, however, requires the bias to be added slowly, thus hampering
applications to processes with only modest separations of timescales. Here we
present a frequency-adaptive strategy which bridges normal and infrequent
metadynamics. We show that this strategy can improve the precision and accuracy
of rate calculations at fixed computational cost, and should be able to extend
rate calculations for much slower kinetic processes.Comment: 15 pages, 2 figures, 2 table
Chemical Potential Calculations in Non-Homogeneous Liquids
The numerical computation of chemical potential in dense, non-homogeneous
fluids is a key problem in the study of confined fluids thermodynamics. To this
day several methods have been proposed, however there is still need for a
robust technique, capable of obtaining accurate estimates at large average
densities. A widely established technique is the Widom insertion method, that
computes the chemical potential by sampling the energy of insertion of a test
particle. Non-homogeneity is accounted for by assigning a density dependent
weight to the insertion points. However, in dense systems, the poor sampling of
the insertion energy is a source of inefficiency, hampering a reliable
convergence.
We have recently presented a new technique for the chemical potential
calculation in homogeneous fluids. This novel method enhances the sampling of
the insertion energy via Well-Tempered Metadynamics, reaching accurate
estimates at very large densities. In this paper we extend the technique to the
case of non-homogeneous fluids. The method is successfully tested on a confined
Lennard-Jones fluid. In particular we show that, thanks to the improved
sampling, our technique does not suffer from a systematic error that affects
the classic Widom method for non-homogeneous fluids, providing a precise and
accurate result.Comment: 16 pages, 4 figures Contains a Supplementary Information fil
Manifold Learning in Atomistic Simulations: A Conceptual Review
Analyzing large volumes of high-dimensional data requires dimensionality
reduction: finding meaningful low-dimensional structures hidden in their
high-dimensional observations. Such practice is needed in atomistic simulations
of complex systems where even thousands of degrees of freedom are sampled. An
abundance of such data makes gaining insight into a specific physical problem
strenuous. Our primary aim in this review is to focus on unsupervised machine
learning methods that can be used on simulation data to find a low-dimensional
manifold providing a collective and informative characterization of the studied
process. Such manifolds can be used for sampling long-timescale processes and
free-energy estimation. We describe methods that can work on datasets from
standard and enhanced sampling atomistic simulations. Unlike recent reviews on
manifold learning for atomistic simulations, we consider only methods that
construct low-dimensional manifolds based on Markov transition probabilities
between high-dimensional samples. We discuss these techniques from a conceptual
point of view, including their underlying theoretical frameworks and possible
limitations
Free-energy landscape of polymer-crystal polymorphism
Polymorphism rationalizes how processing can control the final structure of a
material. The rugged free-energy landscape and exceedingly slow kinetics in the
solid state have so far hampered computational investigations. We report for
the first time the free-energy landscape of a polymorphic crystalline polymer,
syndiotactic polystyrene. Coarse-grained metadynamics simulations allow us to
efficiently sample the landscape at large. The free-energy difference between
the two main polymorphs, and , is further investigated by
quantum-chemical calculations. The two methods are in line with experimental
observations: they predict as the more stable polymorph at standard
conditions. Critically, the free-energy landscape suggests how the
polymorph may lead to experimentally observed kinetic traps. The combination of
multiscale modeling, enhanced sampling, and quantum-chemical calculations
offers an appealing strategy to uncover complex free-energy landscapes with
polymorphic behavior.Comment: 10 pages, 4 figure
Electrostatic versus Resonance Interactions in Photoreceptor Proteins: The Case of Rhodopsin
Light sensing in photoreceptor proteins is subtly modulated by the multiple interactions between the chromophoric unit and its binding pocket. Many theoretical and experimental studies have tried to uncover the fundamental origin of these interactions but reached contradictory conclusions as to whether electrostatics, polarization, or intrinsically quantum effects prevail. Here, we select rhodopsin as a prototypical photoreceptor system to reveal the molecular mechanism underlying these interactions and regulating the spectral tuning. Combining a multireference perturbation method and density functional theory with a classical but atomistic and polarizable embedding scheme, we show that accounting for electrostatics only leads to a qualitatively wrong picture, while a responsive environment can successfully capture both the classical and quantum dominant effects. Several residues are found to tune the excitation by both differentially stabilizing ground and excited states and through nonclassical 'inductive resonance' interactions. The results obtained with such a quantum-in-classical model are validated against both experimental data and fully quantum calculations
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Molecular Simulation Strategies for Understanding the Degradation Mechanisms of Acrylic Polymers
Article describes how acrylic polymers, commonly used in paints, can degrade over time by several different chemical and physical mechanisms, depending on structure and exposure conditions. In this work, the authors studied the effects of different degradation mechanisms and agents on properties of acrylic polymers found in artists’ acrylic paints for the first time using atomistic molecular dynamics simulations