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Register a new userMinimum energy paths for conformational changes of viral capsids
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In this work we study how a viral capsid can change conformation using techniques of Large Deviations Theory for stochastic differential equations. The viral capsid is a model of a complex system in which many units - the proteins forming the capsomers - interact by weak forces to form a structure with exceptional mechanical resistance. The destabilization of such a structure is interesting both per se, since it is related either to infection or maturation processes, and because it yields insights into the stability of complex structures in which the constitutive elements interact by weak attractive forces. We focus here on a simplified model of a dodecahederal viral capsid, and assume that the capsomers are rigid plaquettes with one degree of freedom each. We compute the most probable transition path from the closed capsid to the final configuration using minimum energy paths, and discuss the stability of intermediate states.
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We investigate statistics of lead changes of the maxima of two discrete-time random walks in one dimension. We show that the average number of lead changes grows as $pi^{-1}ln(t)$ in the long-time limit. We present theoretical and numerical evidence that this asymptotic behavior is universal. Specifically, this behavior is independent of the jump distribution: the same asymptotic underlies standard Brownian motion and symmetric Levy flights. We also show that the probability to have at most n lead changes behaves as $t^{-1/4}[ln t]^n$ for Brownian motion and as $t^{-beta(mu)}[ln t]^n$ for symmetric Levy flights with index $mu$. The decay exponent $beta(mu)$ varies continuously with the Levy index when $0<mu<2$, while $beta=1/4$ for $mu>2$.
Many modern techniques employed in physics, such a computation of path integrals, rely on random walks on graphs that can be represented as Markov chains. Traditionally, estimates of running times of such sampling algorithms are computed using the number of steps in the chain needed to reach the stationary distribution. This quantity is generally defined as mixing time and is often difficult to compute. In this paper, we suggest an alternative estimate based on the Kolmogorov-Sinai entropy, by establishing a link between the maximization of KSE and the minimization of the mixing time. Since KSE are easier to compute in general than mixing time, this link provides a new faster method to approximate the minimum mixing time that could be interesting in computer sciences and statistical physics. Beyond this, our finding will also be of interest to the out-of-equilibrium community, by providing a new rational to select stationary states in out-of-equilibrium physics: it seems reasonable that in a physical system with two simultaneous equiprobable possible dynamics, the final stationary state will be closer to the stationary state corresponding to the fastest dynamics (smallest mixing time).Through the empirical link found in this letter, this state will correspond to a state of maximal Kolmogorov-Sinai entropy. If this is true, this would provide a more satisfying rule for selecting stationary states in complex systems such as climate than the maximization of the entropy production.
We call emph{Alphabet model} a generalization to N types of particles of the classic ABC model. We have particles of different types stochastically evolving on a one dimensional lattice with an exchange dynamics. The rates of exchange are local but under suitable conditions the dynamics is reversible with a Gibbsian like invariant measure with long range interactions. We discuss geometrically the conditions of reversibility on a ring that correspond to a gradient condition on the graph of configurations or equivalently to a divergence free condition on a graph structure associated to the types of particles. We show that much of the information on the interactions between particles can be encoded in associated emph{Tournaments} that are a special class of oriented directed graphs. In particular we show that the interactions of reversible models are corresponding to strongly connected tournaments. The possible minimizers of the energies are in correspondence with the Hamiltonian cycles of the tournaments. We can then determine how many and which are the possible minimizers of the energy looking at the structure of the associated tournament. As a byproduct we obtain a probabilistic proof of a classic Theorem of Camion cite{Camion} on the existence of Hamiltonian cycles for strongly connected tournaments. Using these results we obtain in the case of an equal number of k types of particles new representations of the Hamiltonians in terms of translation invariant $k$-body long range interactions. We show that when $k=3,4$ the minimizer of the energy is always unique up to translations. Starting from the case $k=5$ it is possible to have more than one minimizer. In particular it is possible to have minimizers for which particles of the same type are not joined together in single clusters.
The formation of a viral capsid -- the highly-ordered protein shell that surrounds the genome of a virus -- is the canonical example of self-assembly. The capsids of many positive-sense RNA viruses spontaneously assemble from in vitro mixtures of the coat protein and RNA. The high yield of proper capsids that assemble is remarkable, given their structural complexity: 180 identical proteins must arrange into three distinct local configurations to form an icosahedral capsid with a triangulation number of 3 (T = 3). Despite a wealth of data from structural studies and simulations, even the most fundamental questions about how these structures assemble remain unresolved. Experiments have not determined whether the assembly pathway involves aggregation or nucleation, or how the RNA controls the process. Here we use interferometric scattering microscopy to directly observe the in vitro assembly kinetics of individual, unlabeled capsids of bacteriophage MS2. By measuring how many coat proteins bind to individual MS2 RNA strands over time scales from 1 ms to 900 s, we find that the start of assembly is broadly distributed in time and is followed by a rapid increase in the number of bound proteins. These measurements provide strong evidence for a nucleation-and-growth pathway. We also find that malformed structures assemble when multiple nuclei appear on the same RNA before the first nucleus has finished growing. Our measurements reveal the complex assembly pathways for viral capsids around RNA in quantitative detail, including the nucleation threshold, nucleation time, growth time, and constraints on the critical nucleus size. These results may inform strategies for engineering synthetic capsids or for derailing the assembly of pathogenic viruses.
When noninteracting fermions are confined in a $D$-dimensional region of volume $mathrm{O}(L^D)$ and subjected to a continuous (or piecewise continuous) potential $V$ which decays sufficiently fast with distance, in the thermodynamic limit, the ground state energy of the system does not depend on $V$. Here, we discuss this theorem from several perspectives and derive a proof for radially symmetric potentials valid in $D$ dimensions. We find that this universality property holds under a quite mild condition on $V$, with or without bounded states, and extends to thermal states. Moreover, it leads to an interesting analogy between Andersons orthogonality catastrophe and first-order quantum phase transitions.
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