Multiple active zones in hybrid QM/MM molecular dynamics simulations for large biomolecular systems.

Spatially clustered synaptic inputs enable local dendritic computations important for learning, memory, and sensory processing. In the mammalian visual system, individual retinal ganglion cell (RGC) axons form clustered terminal boutons containing multiple active zones onto relay cell dendrites in the dorsal lateral geniculate nucleus (dLGN). This mature architecture arises through the addition of release sites, which strengthens selected afferents while weaker inputs are pruned. Following eye-opening, spontaneous activity and visual experience promote synaptic refinement and bouton clustering after binocular inputs have segregated. However, anatomical changes in release site addition and spatial patterning during earlier stages of eye-specific competition are not well understood. To investigate this, we examined the spatial organization of eye-specific active zones in wild type mice and a mutant line with disrupted cholinergic retinal waves. Using volumetric super-resolution single-molecule localization microscopy and electron microscopy, we found that individual retinogeniculate boutons begin forming multiple nearby presynaptic active zones during the first postnatal week. Both eyes generate these “multi-active-zone” (mAZ) inputs throughout refinement, but the dominant-eye forms more numerous mAZ contacts, each with more active zones and larger vesicle pools. At the height of competition, the non-dominant-eye projection adds many single active zone (sAZ) synapses. Mutants with abnormal cholinergic retinal waves still form mAZ inputs, but develop fewer sAZ synapses and show reduced synapse clustering in projections from both eyes. Together, these findings reveal activity-dependent, eye-specific differences in release site addition during synaptic competition in circuits essential for visual perception and behavior.

We report improved results of atomistic modeling of V-Fe alloys. We introduced an electronic structure embedding approach to improve the description of the point defects in first-principles calculations, by including the semicore electrons in some V atoms (those near the interstitial where the semicore levels are broadened) but not those further from the point defect. This enables us to combine good accuracy for the defect within large supercells and to expand the data set of first-principles point defect calculations in vanadium with and without small amounts of iron. Based on these data, previous first-principles work, and new calculations on the alloy liquid, we fitted an interatomic potential for the V-Fe system which describes the important configurations likely to arise when such alloys are exposed to radiation. This potential is in a form suitable for molecular dynamics (MD) simulations of large systems. Using the potential, we have calculated the migration barriers of vacancies in the presence of iron, showing that these are broadly similar. On the other hand, MD simulations show that V self-diffusion at high temperatures and Fe diffusion are greatly enhanced by the presence of interstitials.

Interactions between lipids and membrane proteins play a key role in determining the nanoscale dynamic and structural properties of biological membranes. Molecular dynamics (MD) simulations provide a valuable tool for studying membrane models, complementing experimental approaches. It is now possible to simulate large membrane systems, such as simplified models of bacterial and viral envelope membranes. Consequently, there is a pressing need to develop tools to visualize and quantify the dynamics of these immense systems, which typically comprise millions of particles. To tackle this issue, we have developed visual and quantitative analyses of molecular positions and their velocity field using path line, vector field and streamline techniques. This allows us to highlight large, transient flow-like movements of lipids and to better understand crowding within the lipid bilayer. The current study focuses on visualization and analysis of lipid dynamics. However, the methods are flexible and can be readily applied to e.g. proteins and nanoparticles within large complex membranes. The protocols developed here are readily accessible both as a plugin for the molecular visualization program VMD and as a module for the MDAnalysis library.

We present an optimized parallelization scheme for molecular dynamics simulations of large biomolecular systems, implemented in the production-quality molecular dynamics program NAMD. With an object-based hybrid force and spatial decomposition scheme, and an aggressive measurement-based predictive load-balancing framework, we have attained speeds and speedups that are much higher than any reported in literature so far.The paper first summarizes the broad methodology we are pursuing, and the basic parallelization scheme we used. It then describes the optimizations that were instrumental in increasing performance, and presents performance results on benchmark simulations.

Fine-Grained Parallel and Distributed Spatial Stochastic Simulation of Biological Reactions

The parallel implementation of a recently developed hybrid scheme for molecular dynamics (MD) simulations (Milano and Kawakatsu, J Chem Phys 2009, 130, 214106) where self-consistent field theory (SCF) and particle models are combined is described. Because of the peculiar formulation of the hybrid method, considering single particles interacting with density fields, the most computationally expensive part of the hybrid particle-field MD simulation can be efficiently parallelized using a straightforward particle decomposition algorithm. Benchmarks of simulations, including comparisons of serial MD and MD-SCF program profiles, serial MD-SCF and parallel MD-SCF program profiles, and parallel benchmarks compared with efficient MD program GROMACS 4.5.4 are tested and reported. The results of benchmarks indicate that the proposed parallelization scheme is very efficient and opens the way to molecular simulations of large scale systems with reasonable computational costs.

The opener muscle in the walking legs of the crayfish (Procambarus clarkii) is innervated by only one excitatory motor neuron, yet excitatory postsynaptic potentials (EPSPs) of proximal fibers are eightfold larger than those of central muscle fibers at low frequencies of activation, due in large measure to differences in presynaptic properties. We investigated quantal release properties, calcium signals, and ultrastructure of presynaptic terminals to elucidate factors that could account for the physiological differences. Focal macropatch electrodes were placed over individual visualized terminal varicosities to obtain records of quantal contributions to the excitatory junctional current (EJC). At low frequencies of activation, mean quantal content is greater for proximal than for central varicosities. This difference is due to a higher probability of release per synapse, and not to a larger number of active synapses. Recorded varicosities were labeled with fluorescent beads deposited by the electrode. These beads adhered to the muscle fibers, outlining the recorded site for subsequent serial thin sectioning and reconstruction from electron micrographs. Comparisons of structure and function were made for individual varicosities. The number of active zones per terminal surface area and the number of synapses with multiple active zones (complex synapses) were greater in high-output varicosities. Calcium indicators were loaded into proximal and central nerve terminals by axonal injection to compare the relative differences in calcium buildup during stimulation. Presynaptic calcium signals were larger for proximal varicosities than for central varicosities.(ABSTRACT TRUNCATED AT 250 WORDS)

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)”.

The linear-scaling divide-and-conquer (DC) quantum chemical methodology is applied to the density-functional tight-binding (DFTB) theory to develop a massively parallel program that achieves on-the-fly molecular reaction dynamics simulations of huge systems from scratch. The functions to perform large scale geometry optimization and molecular dynamics with DC-DFTB potential energy surface are implemented to the program called DC-DFTB-K. A novel interpolation-based algorithm is developed for parallelizing the determination of the Fermi level in the DC method. The performance of the DC-DFTB-K program is assessed using a laboratory computer and the K computer. Numerical tests show the high efficiency of the DC-DFTB-K program, a single-point energy gradient calculation of a one-million-atom system is completed within 60 s using 7290 nodes of the K computer. © 2016 Wiley Periodicals, Inc.

Molecular dynamics simulation of realistic systems

Current simulation tools for multibody dynamics are not problem dependent, they use the same modeling process to all cases regardless of their characteristics. In addition, real-time simulation of small multibody systems is achievable by existing simulation tools, however, real-time simulation of large and complex systems is not possible with existing methods. This is a challenge that needs to be addressed before further advances in mechanical simulation with hardware-in-the-loop and man-in-the-loop, as well as virtual prototyping are made possible. This paper addresses the issue of how the modeling process-dependent versus independent coordinates, and descriptor form versus state-space form of the equations of motion-affects the dynamic simulation of multibody systems and how it may be taken into account and added to the concept of intelligent simulation. With this new concept all the factors involved in the simulation process — modeling, equations, solution, etc. — are chosen and combined depending upon the characteristics of the system to be simulated. It is envisioned that this concept will lead to faster and more robust real-time simulators.

Molecular dynamics (MD) simulations based on the implicit solvent generalized Born (GB) models can provide significant computational advantages over the traditional explicit solvent simulations. However, the standard GB becomes prohibitively expensive for all-atom simulations of large structures; the model scales poorly, ∼n2, with the number of solute atoms. Here we combine our recently developed optimal point charge approximation (OPCA) with the hierarchical charge partitioning (HCP) approximation to present an ∼n log n multiscale, yet fully atomistic, GB model (GB-HCPO). The HCP approximation exploits the natural organization of biomolecules (atoms, groups, chains, and complexes) to partition the structure into multiple hierarchical levels of components. OPCA approximates the charge distribution for each of these components by a small number of point charges so that the low order multipole moments of these components are optimally reproduced. The approximate charges are then used for computing electrostatic interactions with distant components, while the full set of atomic charges are used for nearby components. We show that GB-HCPO can deliver up to 2 orders of magnitude speedup compared to the standard GB, with minimal impact on its accuracy. For large structures, GB-HCPO can approach the same nominal speed, as in nanoseconds per day, as the highly optimized explicit-solvent simulation based on particle mesh Ewald (PME). The increase in the nominal simulation speed, relative to the standard GB, coupled with substantially faster sampling of conformational space, relative to the explicit solvent, makes GB-HCPO a suitable candidate for MD simulation of large atomistic systems in implicit solvent. As a practical demonstration, we use GB-HCPO simulation to refine a ∼1.16 million atom structure of 30 nm chromatin fiber (40 nucleosomes). The refined structure suggests important details about spatial organization of the linker DNA and the histone tails in the fiber: (1) the linker DNA fills the core region, allowing the H3 histone tails to interact with the linker DNA, which is consistent with experiment; (2) H3 and H4 tails are found mostly in the core of the structure, closer to the helical axis of the fiber, while H2A and H2B are mostly solvent exposed. Potential functional consequences of these findings are discussed. GB-HCPO is implemented in the open source MD software NAB in Amber 2016.

Advances in Atomic-Level Simulations of Large-Scale Functional Motions of Membrane Transporters

To effectively archive configuration data during molecular dynamics (MD) simulations of polymer systems, we present an efficient compression method with good numerical accuracy that preserves the topology of ring-linear polymer blends. To compress the fraction of floating-point data, we used the Jointed Hierarchical Precision Compression Number - Data Format (JHPCN-DF) method to apply zero padding for the tailing fraction bits, which did not affect the numerical accuracy, then compressed the data with Huffman coding. We also provided a dataset of well-equilibrated configurations of MD simulations for ring-linear polymer blends with various lengths of linear and ring polymers, including ring complexes composed of multiple rings such as polycatenane. We executed 109 MD steps to obtain 150 equilibrated configurations. The combination of JHPCN-DF and SZ compression achieved the best compression ratio for all cases. Therefore, the proposed method enables efficient archiving of MD trajectories. Moreover, the publicly available dataset of ring-linear polymer blends can be employed for studies of mathematical methods, including topology analysis and data compression, as well as MD simulations.

The paper focus on the problem of aggregating induction motor models into a single unit model aiming to become computationally feasible for the dynamic behavior simulation of large electrical systems. A new aggregation method is developed based on the usual modeling of induction motors allowing the existing computer simulation programs to be kept in operation. The absence of drastic simplifications, the proper handling of motor mechanical load, and an adequate treatment of equivalent inertia constant allow the aggregate model to show very good accuracy. A small example is presented to illustrate the application of the method. Validation test results are also presented including motors at different buses in a real-life industrial system. >