Quantum-classical model of retinal photoisomerization reaction in visual pigment rhodopsin

A quantum-classical model of photoisomerization of the visual pigment rhodopsin chromophore is proposed. At certain (and more realistic) parameter value combinations, the model is shown to accurately reproduce a number of independent experimental data on the photoreaction dynamics: the quantum yield, the time to reach the point of conical intersection of potential energy surfaces, the termination time of the evolution of quantum subsystem, as well as the characteristic low frequencies of retinal molecular lattice fluctuations during photoisomerization. In addition, the model behavior is in good accordance with experimental data about coherence and local character of quantum transition.     

The Davydov/Scott Model for Energy Storage and Transport in Proteins

The current status of the Davydov/Scott model for energy transfer in proteins is reviewed. After a brief introduction to the theoretical framework and to the basic results, the problems of finite temperature dynamics and of the full quantum and mixed quantum-classical approximations are described, as well as recent results obtained within each of these approximations. A short survey of experimental evidence in support of the Davydov/Scott model is made and absorption spectra are calculated that show the same temperature dependence as that measured in crystalline acetanilide. Future applications of the Davydov/Scott model to protein folding and function and to misfolding diseases are outlined.     

Gas phase infrared spectra via the phase integration quasi-classical method

We apply the recently developed phase integration method (PIM) (Monteferrante et al. Mol Phys. 2011;109:3015–3027) to the calculation of infrared spectra of gas phase molecules. The PIM combines a generalised Monte Carlo sampling of the exact thermal density of the system with classical molecular dynamics to obtain approximate time quantum correlation functions. To describe the molecules, we adopt very simple analytical potentials that have, however, proved interesting, and surprisingly challenging, benchmarks for approximate quantum dynamical schemes. We show that, in contrast with two other commonly applied methods, our spectra do not exhibit spurious features or unphysical shifts depending on the temperature. Identifying the positions of the peaks requires only a few tens of trajectories, while an accurate evaluation of the relative intensities of the peaks is computationally more demanding.     

A stochastic model of autocatalytic reaction networks

Autocatalytic cycles are rather widespread in nature and in several theoretical models of catalytic reaction networks their emergence is hypothesized to be inevitable when the network is or becomes sufficiently complex. Nevertheless, the emergence of autocatalytic cycles has been never observed in wet laboratory experiments. Here, we present a novel model of catalytic reaction networks with the explicit goal of filling the gap between theoretical predictions and experimental findings. The model is based on previous study of Kauffman, with new features in the introduction of a stochastic algorithm to describe the dynamics and in the possibility to increase the number of elements and reactions according to the dynamical evolution of the system. Furthermore, the introduction of a temporal threshold allows the detection of cycles even in our context of a stochastic model with asynchronous update. In this study, we describe the model and present results concerning the effect on the overall dynamics of varying (a) the average residence time of the elements in the reactor, (b) both the composition of the firing disk and the concentration of the molecules belonging to it, (c) the composition of the incoming flux.     

Modeling the lipid component of membranes

During the past several years, there have been a number of advances in the computational and theoretical modeling of lipid bilayer structural and dynamical properties. Molecular dynamics (MD) simulations have increased in length and time scales by about an order of magnitude. MD simulations continue to be applied to more complex systems, including mixed bilayers and bilayer self-assembly. A critical problem is bridging the gap between the still very small MD simulations and the time and length scales of experimental observations. Several new and promising techniques, which use atomic-level correlation and response functions from simulations as input to coarse-grained modeling, are being pursued.     

Lagrangian molecular dynamics using selected conformational degrees of freedom,with application to the pseudorotation dynamics of furanose rings

Using internal conformational degrees of freedom for biopolymers as natural variables, and introducing a Lagrangian dynamics approach, one can simulate time-dependent processes over a much longer time scale than in classical Newtonian molecular dynamics (MD) techniques. Two factors contribute to this: a substantial reduction in the number of degrees of freedom and a very large increase in the size of the time step. We present the Lagrangian equations of motion for repuckering transitions in model furanose ( F ), ribose ( R ), and 2′-deoxyribose ( dR ) ring systems using the pseudorotation phase angle as the single dynamic variable. As in most Lagrangian analyses, the effective masses for the R and dR models are dependent on conformation, and we test the behavior of this variable mass (VM) model. Since the variation in effective mass is small, the VM model is compared with a simplified constant mass (CM) model, which is shown to be an excellent approximation. The equations of motion for the CM and VM models are integrated with the leapfrog and the iterative leapfrog algorithms, respectively. The Lagrangian dynamics approach reduces the number of degrees of freedom from about 40 to 1, and allows the use of time steps on the order of 20 fs, about an order of magnitude greater than is used in conventional MD simulations. © 1994 John Wiley & Sons, Inc.     

Solvent-induced free energy landscape and solute-solvent dynamic coupling in a multielement solute

Molecular dynamics simulations using a simple multielement model solute with internal degrees of freedom and accounting for solvent-induced interactions to all orders in explicit water are reported. The potential energy landscape of the solute is flat in vacuo. However, the sole untruncated solvent-induced interactions between apolar (hydrophobic) and charged elements generate a rich landscape of potential of mean force exhibiting typical features of protein landscapes. Despite the simplicity of our solute, the depth of minima in this landscape is not far in size from free energies that stabilize protein conformations. Dynamical coupling between configurational switching of the system and hydration reconfiguration is also elicited. Switching is seen to occur on a time scale two orders of magnitude longer than that of the reconfiguration time of the solute taken alone, or that of the unperturbed solvent. Qualitatively, these results are unaffected by a different choice of the water-water interaction potential. They show that already at an elementary level, solvent-induced interactions alone, when fully accounted for, can be responsible for configurational and dynamical features essential to protein folding and function.     

Consequences of plant-herbivore coevolution on the dynamics and functioning of ecosystems

The potential consequences of plant-herbivore coevolution for ecosystem functioning are investigated using a simple nutrient-limited ecosystem model in which plant and herbivore traits are subject to adaptive dynamics. Although the ecological model is very simple and always reaches a stable equilibrium in the absence of evolution, coevolution can generate a great diversity of dynamical behaviors. The evolutionary dynamics can lead to a stable equilibrium. If the evolution of plants is fast enough, certain values of the trade-off parameters lead to complex evolutionary cycles bounded by physiological constraints. The dynamical behavior of the model is very different when the dynamics of inorganic nutrient is ignored and plant competition is modeled by a logistic growth function. This emphasizes the importance of including explicit nutrient dynamics in studies of plant-herbivore coevolution.     

The origin of large molecules in primordial autocatalytic reaction networks

Large molecules such as proteins and nucleic acids are crucial for life, yet their primordial origin remains a major puzzle. The production of large molecules, as we know it today, requires good catalysts, and the only good catalysts we know that can accomplish this task consist of large molecules. Thus the origin of large molecules is a chicken and egg problem in chemistry. Here we present a mechanism, based on autocatalytic sets (ACSs), that is a possible solution to this problem. We discuss a mathematical model describing the population dynamics of molecules in a stylized but prebiotically plausible chemistry. Large molecules can be produced in this chemistry by the coalescing of smaller ones, with the smallest molecules, the 'food set', being buffered. Some of the reactions can be catalyzed by molecules within the chemistry with varying catalytic strengths. Normally the concentrations of large molecules in such a scenario are very small, diminishing exponentially with their size. ACSs, if present in the catalytic network, can focus the resources of the system into a sparse set of molecules. ACSs can produce a bistability in the population dynamics and, in particular, steady states wherein the ACS molecules dominate the population. However to reach these steady states from initial conditions that contain only the food set typically requires very large catalytic strengths, growing exponentially with the size of the catalyst molecule. We present a solution to this problem by studying 'nested ACSs', a structure in which a small ACS is connected to a larger one and reinforces it. We show that when the network contains a cascade of nested ACSs with the catalytic strengths of molecules increasing gradually with their size (e.g., as a power law), a sparse subset of molecules including some very large molecules can come to dominate the system.     

4D bioinformatics: a new look at the ribosome as an example

We have adapted the Java Molecular Viewer (JMV) to virtual reality display environments, through a number of extensions to the Java 3D code. Phylogenetic information derived from multiple alignments (temporal information) can be overlaid onto molecule structures (spatial information). The number of sequences included in the underlying multiple alignment can be changed instantaneously, resulting in dynamical updates of the displayed information. JMV was also extended to handle an infinite number of objects (molecules) in the same display. The objects can be manipulated in six degrees of freedom simultaneously or independently. We have used the small subunit ribosomal RNA to demonstrate the system (http:// cave.ucalgary.ca), which can be used for any molecule with a resolved structure.     

Biological complexity,quantum coherent states and the problem of efficient transmission of information inside a cell

The intracellular channel of information transmission was analyzed from the point of view of complexity. The most important steps in the transfer of information within a cell are the folding, transport and recognition of proteins. It was shown that the large number of conformational degrees of freedom that proteins possess can paradoxically lead to an information channel with an exponentially small capacity. To resolve this paradox, a model, which assumes a quantum collective behavior of biologically important molecules, was proposed. Experiments to test the quantum nature of the intracellular transfer of information were also proposed.     

Nonlinearities in mating sounds of American crocodiles

We use nonlinear time series analysis methods to analyze the dynamics of the sound-producing apparatus of the American crocodile (Crocodylus acutus). We capture its dynamics by analyzing a recording of the singing activity during mating time. First, we reconstruct the phase space from the sound recording and thereby reveal that the attractor needs no less than five degrees of freedom to fully evolve in the embedding space, which suggests that a rather complex nonlinear dynamics underlies its existence. Prior to investigating the dynamics more precisely, we test whether the reconstructed attractor satisfies the notions of determinism and stationarity, as a lack of either of these properties would preclude a meaningful further analysis. After positively establishing determinism and stationarity, we proceed by showing that the maximal Lyapunov exponent of the recording is positive, which is a strong indicator for the chaotic behavior of the system, confirming that dynamical nonlinearities are an integral part of the examined sound-producing apparatus. At the end, we discuss that methods of nonlinear time series analysis could yield instructive insights and foster the understanding of vocal communication among certain reptile species.     

Quantum-dynamical picture of a multistep enzymatic process: reaction catalyzed by phospholipase A(2)

A quantum-classical molecular dynamics model (QCMD), applying explicit integration of the time-dependent Schr?dinger equation (QD) and Newtonian equations of motion (MD), is presented. The model is capable of describing quantum dynamical processes in complex biomolecular systems. It has been applied in simulations of a multistep catalytic process carried out by phospholipase A(2) in its active site. The process includes quantum-dynamical proton transfer from a water molecule to histidine localized in the active site, followed by a nucleophilic attack of the resulting OH(-) group on a carbonyl carbon atom of a phospholipid substrate, leading to cleavage of an adjacent ester bond. The process has been simulated using a parallel version of the QCMD code. The potential energy function for the active site is computed using an approximate valence bond (AVB) method. The dynamics of the key proton is described either by QD or classical MD. The coupling between the quantum proton and the classical atoms is accomplished via Hellmann-Feynman forces, as well as the time dependence of the potential energy function in the Schr?dinger equation (QCMD/AVB model). Analysis of the simulation results with an Advanced Visualization System revealed a correlated rather than a stepwise picture of the enzymatic process. It is shown that an sp(2)--> sp(3) configurational change at the substrate carbonyl carbon is mostly responsible for triggering the activation process.     

The dynamics of protein hydration water: a quantitative comparison of molecular dynamics simulations and neutron-scattering experiments

We present results from an extensive molecular dynamics simulation study of water hydrating the protein Ribonuclease A, at a series of temperatures in cluster, crystal, and powder environments. The dynamics of protein hydration water appear to be very similar in crystal and powder environments at moderate to high hydration levels. Thus, we contend that experiments performed on powder samples are appropriate for discussing hydration water dynamics in native protein environments. Our analysis reveals that simulations performed on cluster models consisting of proteins surrounded by a finite water shell with free boundaries are not appropriate for the study of the solvent dynamics. Detailed comparison to available x-ray diffraction and inelastic neutron-scattering data shows that current generation force fields are capable of accurately reproducing the structural and dynamical observables. On the time scale of tens of picoseconds, at room temperature and high hydration, significant water translational diffusion and rotational motion occur. At low hydration, the water molecules are translationally confined but display appreciable rotational motion. Below the protein dynamical transition temperature, both translational and rotational motions of the water molecules are essentially arrested. Taken together, these results suggest that water translational motion is necessary for the structural relaxation that permits anharmonic and diffusive motions in proteins. Furthermore, it appears that the exchange of protein-water hydrogen bonds by water rotational/librational motion is not sufficient to permit protein structural relaxation. Rather, the complete exchange of protein-bound water molecules by translational displacement seems to be required.     

Multiple time step diffusive Langevin dynamics for proteins

We present an algorithm for simulating the long time scale dynamics of proteins and other macromolecules. Our method applies the concept of multiple time step integration to the diffusive Langevin equation, in which short time scale dynamics are replaced by friction and noise. The macromolecular force field is represented at atomic resolution. Slow motions are modeled by constrained Langevin dynamics with very large time steps, while faster degrees of freedom are kept in local thermal equilibrium. In the limit of a sufficiently large molecule, our algorithm is shown to reduce the CPU time required by two orders of magnitude. We test the algorithm on two systems, alanine dipeptide and bovine pancreatic trypsin inhibitor (BPTI), and find that it accurately calculates a variety of equilibrium and dynamical properties. In the case of BPTI, the CPU time required is reduced by nearly a factor of 60 compared to a conventional, unconstrained Langevin simulation using the same force field. Proteins 30:215–227, 1998. © 1998 Wiley-Liss, Inc.     

Dynamic Finite Size Effects in Spiking Neural Networks

We investigate the dynamics of a deterministic finite-sized network of synaptically coupled spiking neurons and present a formalism for computing the network statistics in a perturbative expansion. The small parameter for the expansion is the inverse number of neurons in the network. The network dynamics are fully characterized by a neuron population density that obeys a conservation law analogous to the Klimontovich equation in the kinetic theory of plasmas. The Klimontovich equation does not possess well-behaved solutions but can be recast in terms of a coupled system of well-behaved moment equations, known as a moment hierarchy. The moment hierarchy is impossible to solve but in the mean field limit of an infinite number of neurons, it reduces to a single well-behaved conservation law for the mean neuron density. For a large but finite system, the moment hierarchy can be truncated perturbatively with the inverse system size as a small parameter but the resulting set of reduced moment equations that are still very difficult to solve. However, the entire moment hierarchy can also be re-expressed in terms of a functional probability distribution of the neuron density. The moments can then be computed perturbatively using methods from statistical field theory. Here we derive the complete mean field theory and the lowest order second moment corrections for physiologically relevant quantities. Although we focus on finite-size corrections, our method can be used to compute perturbative expansions in any parameter.     

Evolutionary Behaviour,Trade-Offs and Cyclic and Chaotic Population Dynamics

Many studies of the evolution of life-history traits assume that the underlying population dynamical attractor is stable point equilibrium. However, evolutionary outcomes can change significantly in different circumstances. We present an analysis based on adaptive dynamics of a discrete-time demographic model involving a trade-off whose shape is also an important determinant of evolutionary behaviour. We derive an explicit expression for the fitness in the cyclic region and consequently present an adaptive dynamic analysis which is algebraic. We do this fully in the region of 2-cycles and (using a symbolic package) almost fully for 4-cycles. Simulations illustrate and verify our results. With equilibrium population dynamics, trade-offs with accelerating costs produce a continuously stable strategy (CSS) whereas trade-offs with decelerating costs produce a non-ES repellor. The transition to 2-cycles produces a discontinuous change: the appearance of an intermediate region in which branching points occur. The size of this region decreases as we move through the region of 2-cycles. There is a further discontinuous fall in the size of the branching region during the transition to 4-cycles. We extend our results numerically and with simulations to higher-period cycles and chaos. Simulations show that chaotic population dynamics can evolve from equilibrium and vice-versa.