抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1（PfPMP1）与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员，通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

Stochastic thermodynamics as reviewed here systematically provides a framework for extending the notions of classical thermodynamics such as work, heat and entropy production to the level of individual trajectories of well-defined non-equilibrium ensembles. It applies whenever a non-equilibrium process is still coupled to one (or several) heat bath(s) of constant temperature. Paradigmatic systems are single colloidal particles in time-dependent laser traps, polymers in external flow, enzymes and molecular motors in single molecule assays, small biochemical networks and thermoelectric devices involving single electron transport. For such systems, a first-law like energy balance can be identified along fluctuating trajectories. For a basic Markovian dynamics implemented either on the continuum level with Langevin equations or on a discrete set of states as a master equation, thermodynamic consistency imposes a local-detailed balance constraint on noise and rates, respectively. Various integral and detailed fluctuation theorems, which are derived here in a unifying approach from one master theorem, constrain the probability distributions for work, heat and entropy production depending on the nature of the system and the choice of non-equilibrium conditions. For non-equilibrium steady states, particularly strong results hold like a generalized fluctuation–dissipation theorem involving entropy production. Ramifications and applications of these concepts include optimal driving between specified states in finite time, the role of measurement-based feedback processes and the relation between dissipation and irreversibility. Efficiency and, in particular, efficiency at maximum power can be discussed systematically beyond the linear response regime for two classes of molecular machines, isothermal ones such as molecular motors, and heat engines such as thermoelectric devices, using a common framework based on a cycle decomposition of entropy production. (Some figures may appear in colour only in the online journal) This article was invited by Erwin Frey.

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Stochastic thermodynamics, fluctuation theorems and molecular machines

We describe a global optimization technique using “basin-hopping” in which the potential energy surface is transformed into a collection of interpenetrating staircases. This method has been designed to exploit the features that recent work suggests must be present in an energy landscape for efficient relaxation to the global minimum. The transformation associates any point in configuration space with the local minimum obtained by a geometry optimization started from that point, effectively removing transition state regions from the problem. However, unlike other methods based upon hypersurface deformation, this transformation does not change the global minimum. The lowest known structures are located for all Lennard-Jones clusters up to 110 atoms, including a number that have never been found before in unbiased searches.

/pdf/global-optimization-by-basin-hopping-and-the-lowest-energy-2s1wxtsj18.pdf

Global Optimization by Basin-Hopping and the Lowest Energy Structures of Lennard-Jones Clusters Containing up to 110 Atoms

A protein molecule is a covalent chain of amino acid residues. Although it is topologically linear, in physiological conditions it folds into a unique (though flexible) three-dimensional structure. This structure, which has been determined by x-ray crystallography and nuclear magnetic resonance for many proteins (Bernstein et al., 1977; Abola et al., 1987), is referred to as the native structure. As demonstrated by the experiments of Anfinsen and co-workers (Anfinsen et al., 1961; Anfinsen, 1973), at least some protein molecules, when denatured (unfolded) by disrupting conditions in their environment (such as acidity or high temperature) can spontaneously refold to their native structures when proper physiological conditions are restored. Thus, all of the information necessary to determine the native structure can be contained in the amino acid sequence.

Computational Complexity, Protein Structure Prediction, and the Levinthal Paradox

The term molecular mechanics (MM) refers to the use of simple potential-energy functions (e.g., harmonic oscillator or Coulombic potentials) to model molecular systems. Molecular mechanics approaches are widely applied in molecular structure refinement, molecular dynamics (MD) simulations, Monte Carlo (MC) simulations, and ligand-docking simulations.

Typically, molecular mechanics models consist of spherical atoms connected by springs which represent bonds. Internal forces experienced in the model structure are described using simple mathematical functions. For example, Hooke’s law is commonly used to describe bonded interactions, and the nonbonded atoms might be treated as inelastic hard spheres or may interact according to a Lennard-Jones potential. Using these simple models, a molecular dynamics simulation numerically solves Newton’s equations of motion, thus allowing structural fluctuations to be observed with respect to time.

Dynamic simulation methods are widely used to obtain information on the time evolution of conformations of proteins and other biological macromolecules1–4 and also kinetic and thermodynamic information. Simulations can provide fine detail concerning the motions of individual particles as a function of time. They can be utilized to quantify the properties of a system at a precision and on a time scale that is otherwise inaccessible, and simulation is, therefore, a valuable tool in extending our understanding of model systems. Theoretical consideration of a system additionally allows one to investigate the specific contributions to a property through “computational alchemy”,5 that is, modifying the simulation in a way that is nonphysical but nonetheless allows a model’s characteristics to be probed. One particular example is the artificial conversion of the energy function from that representing one system to that of another during a simulation. This is an important technique in free-energy calculations.6 Thus, molecular dynamics simulations, along with a range of complementary computational approaches, have become valuable tools for investigating the basis of protein structure and function.

This review offers an outline of the origin of molecular dynamics simulation for protein systems and how it has developed into a robust and trusted tool. This review then covers more recent advances in theory and an illustrative selection of practical studies in which it played a central role. The range of studies in which MD has played a considerable or pivotal role is immense, and this review cannot do justice to them; MD simulations of biomedical importance were recently reviewed.4 Particular emphasis will be placed on the study of dynamic aspects of protein recognition, an area where molecular dynamics has scope to provide broad and far-ranging insights. This review concludes with a brief discussion of the future potential offered to advancement of the biological and biochemical sciences and the remaining issues that must be overcome to allow the full extent of this potential to be realized.

1.1. Historical Background
MD methods were originally conceived within the theoretical physics community during the 1950s. In 1957, Alder and Wainwright7 performed the earliest MD simulation using the so-called hard-sphere model, in which the atoms interacted only through perfect collisions. Rahman8 subsequently applied a smooth, continuous potential to mimic real atomic interactions. During the 1970s, as computers became more widespread, MD simulations were developed for more complex systems, culminating in 1976 with the first simulation of a protein9,10 using an empirical energy function constructed using physics-based first-principles assumptions. MD simulations are now widely and routinely applied and especially popular in the fields of materials science11,12 and biophysics.

As will be discussed later in this review, a variety of experimental conditions may be simulated with modern theories and algorithms. The initial simulations only considered single molecules in vacuo. Over time, more realistic or at least biologically relevant simulations could be performed. This trend is continuing today.

The initial protein MD simulation, of the small bovine pancreatic trypsin inhibitor (BPTI), covered only 9.2 ps of simulation time. Modern simulations routinely have so-called equilibration periods much longer than that, and production simulations of tens of nanoseconds are routine, with the first microsecond MD simulation being reported in 1998.13 In addition, the original BPTI simulation included only about 500 atoms rather than the 104-106 atoms that are common today. While much of this advancement results from an immense increase in availability of computing power, major theoretical and methodological developments also contribute significantly.

The number of publications regarding MD theory and application of MD to biological systems is growing at an extraordinary pace. A single review cannot do justice to the recent applications of MD. Using data from ISI Web of Science, the authors estimate that during 2005 at least 800 articles will be published that discuss molecular dynamics and proteins. The historical counts are shown in Figure 1.






Open in a separate window


Figure 1


Articles matching ISI Web of Science query “TS=(protein) AND TS=(molecular dynamics)”.

/pdf/molecular-dynamics-survey-of-methods-for-simulating-the-45fcz5gua9.pdf

Molecular dynamics: survey of methods for simulating the activity of proteins.

Performance of the diffusion equation method in searches for optimum structures of clusters of Lennard-Jones atoms

A model of barrier crossing dynamics governed by fractional Gaussian noise and the generalized Langevin equation is used to study the reaction kinetics of single enzymes subject to conformational fluctuations. The direct application of Kramers’s flux-over-population method to this model yields analytic expressions for the time-dependent transmission coefficient and the distribution of waiting times for barrier crossing. These expressions are found to reproduce the observed trends in recent simulations and experiments.

Complex chemical kinetics in single enzyme molecules: Kramers’s model with fractional Gaussian noise

In this article, we suggest simple alternatives to the methods recently used by Jain and Sebastian [J. Phys. Chem. B 2016, 120, 3988] and Chechkin et al. [Phys. Rev. X 2017, 7, 021002] to treat a model of non-Gaussian Brownian diffusion based on the dynamics of a particle governed by Ornstein–Uhlenbeck modulated white noise. In addition to substantiating these authors’ earlier findings (which show that a particle can execute a simple random walk even when the distribution of its displacements deviates from Gaussianity), our approach identifies another process, two-state white noise, that exhibits the same “anomalous” Brownian behavior. Indeed, we find that the modulation of white noise by any stochastic process whose time correlation function decays exponentially is likely to behave similarly, suggesting that the occurrence of such behavior can be widespread and commonplace.

Non-Gaussian Brownian Diffusion in Dynamically Disordered Thermal Environments.

Perturbation calculations are performed for 〈R2〉, 〈S2〉, 〈RH〉, A2, and A3 for polymers near the theta point using a continuous curve representation of the three‐parameter theory of Yamakawa. Both dimensional regularization and cut‐off schemes are employed, but only the latter method is shown to be in accord with observed polymer properties, demonstrating the necessity of retaining a cut‐off (or its equivalent) when ternary interactions are incorporated into the three dimensional model. An effective binary interaction parameter is defined to vanish along with the second virial coefficient under theta conditions. Renormalization group calculations are then combined with the perturbation expansions to extend the predictions for these polymer properties to larger ternary interactions while still remaining in the vicinity of the theta point where the effective binary interaction is small. A comparison of calculated values of A3 and the ratios 〈S2〉1/2/RH and 〈R2〉/〈S2〉 with data from experiment and computer simul...

Effect of residual interactions on polymer properties near the theta point

Vibrational lifetime and spectral shift data for the asymmetric CO stretching mode of $W(CO)_6$ in supercritical ethane, carbon dioxide, and fluoroform as a function of density at two temperatures are presented, and the lifetime $(T_1)$ measurements are compared to theory. The data and theory are refinements of previous work. Measurements at zero density allow the contribution from solute-solvent interactions to be separated from strictly intramolecular contributions to $T_1$. The results in the polyatomic SCFs are compared to data in Ar. The density functional/thermodynamic $theory^1$ has been extended to include contributions at large wavevector (k). Very good, quantitative agreement between theory and data taken in ethane and fluoroform is achieved, but the agreement for data taken in carbon dioxide, while reasonable, is not as good. The theory uses a variety of input information on the SCF properties obtained from the fluids' equations of state and other tabulated thermodynamic data. The solute-solvent spatial distribution is described in terms of hard spheres. The theory is able to reproduce the data without solute-solvent attractive interactions or clustering in the calculation of the spatial distribution.

/pdf/vibrational-lifetimes-and-spectral-shifts-in-supercritical-i5nutcw879.pdf

Binny J. Cherayil

Papers

Performance of the diffusion equation method in searches for optimum structures of clusters of Lennard-Jones atoms

Complex chemical kinetics in single enzyme molecules: Kramers’s model with fractional Gaussian noise

Non-Gaussian Brownian Diffusion in Dynamically Disordered Thermal Environments.

Effect of residual interactions on polymer properties near the theta point

Vibrational Lifetimes and Spectral Shifts in Supercritical Fluids as a Function of Density: Experiments and Theory