Shapes of Semiflexible Polymers in Confined Spaces

We investigate the conformations of a semiflexible polymer confined to a square box. Results of Monte Carlo simulations show the existence of a shape transition when the persistence length of the polymer becomes comparable to the dimensions of box. An order parameter is introduced to quantify this behavior. A simple mean-field model is constructed to study the effect of the shape transition on the effective persistence length of the polymer.Comment: 8 pages, 20 figure

Reaction Path Averaging: Characterizing the Structural Response of the DNA Double Helix to Electron Transfer

A polarizable environment, prominently the solvent, responds to electronic changes in biomolecules rapidly. The knowledge of conformational relaxation of the biomolecule itself, however, may be scarce or missing. In this work, we describe in detail the structural changes in DNA undergoing electron transfer between two adjacent nucleobases. We employ an approach based on averaging of tens to hundreds of thousands of nonequilibrium trajectories generated with molecular dynamics simulation, and a reduction of dimensionality suitable for DNA. We show that the conformational response of the DNA proceeds along a single collective coordinate that represents the relative orientation of two consecutive base pairs, namely, a combination of helical parameters shift and tilt. The structure of DNA relaxes on time scales reaching nanoseconds, contributing marginally to the relaxation of energies, which is dominated by the modes of motion of the aqueous solvent. The concept of reaction path averaging (RPA), conveniently exploited in this context, makes it possible to filter out any undesirable noise from the nonequilibrium data, and is applicable to any chemical process in general.Comment: 45 pages, 20 figures, published, added Supplementary informatio

Exploring the Photophysical Properties of Molecular Systems Using Excited State Accelerated ab Initio Molecular Dynamics.

In the present work, we employ excited state accelerated ab initio molecular dynamics (A-AIMD) to efficiently study the excited state energy landscape and photophysical topology of a variety of molecular systems. In particular, we focus on two important challenges for the modeling of excited electronic states: (i) the identification and characterization of conical intersections and crossing seams, in order to predict different and often competing radiationless decay mechanisms, and (ii) the description of the solvent effect on the absorption and emission spectra of chemical species in solution. In particular, using as examples the Schiff bases formaldimine and salicylidenaniline, we show that A-AIMD can be readily employed to explore the conformational space around crossing seams in molecular systems with very different photochemistry. Using acetone in water as an example, we demonstrate that the enhanced configurational space sampling may be used to accurately and efficiently describe both the prominent features and line-shapes of absorption and emission spectra

Efficient coarse-grained brownian dynamics simulations for dna and lipid bilayer membrane with hydrodynamic interactions

The coarse-grained molecular dynamics (CGMD) or Brownian dynamics (BD) simulation is a particle-based approach that has been applied to a wide range of biological problems that involve interactions with surrounding fluid molecules or the so-called hydrodynamic interactions (HIs). From simple biological systems such as a single DNA macromolecule to large and complicated systems, for instances, vesicles and red blood cells (RBCs), the numerical results have shown outstanding agreements with experiments and continuum modeling by adopting Stokesian dynamics and explicit solvent model. Finally, when combined with fast algorithms such as the fast multipole method (FMM) which has nearly optimal complexity in the total number of CG particles, the resulting method is parallelizable, scalable to large systems, and stable for large time step size, thus making the long-time large-scale BD simulation within practical reach. This will be useful for the study of a large collection of molecules or cells immersed in the fluids. This dissertation can be divided into three main subjects: (1) An efficient algorithm is proposed to simulate the motion of a single DNA molecule in linear flows. The algorithm utilizes the integrating factor method to cope with the effect of the linear flow of the surrounding fluid and applies the Metropolis method (MM) in [N. Bou-Rabee, A. Donev, and E. Vanden-Eijnden, Multiscale Model. Simul. 12, 781 (2014)] to achieve more efficient BD simulation. More importantly, this proposed method permits much larger time step size than methods in previous literature while still maintaining the stability of the BD simulation, which is advantageous for long-time BD simulation. The numerical results on λ-DNA agree very well with both experimental data and previous simulation results. (2) Lipid bilayer membranes have been extensively studied by CGMD simulations. Numerical efficiencies have been reported in the cases of aggressive coarse-graining, where several lipids are coarse-grained into a particle of size 4 ~ 6 nm so that there is only one particle in the thickness direction. In [H. Yuan et al., Phys. Rev. E, 82, 011905 (2010)], Yuan et al. proposed a pair-potential between these one-particle-thick coarse-grained lipid particles to capture the mechanical properties of a lipid bilayer membrane, such as gel-fluid-gas phase transitions of lipids, diffusion, and bending rigidity. This dissertation provides a detailed implementation of this interaction potential in LAMMPS to simulate large-scale lipid systems such as a giant unilamellar vesicle (GUV) and RBCs. Moreover, this work also considers the effect of cytoskeleton on the lipid membrane dynamics as a model for RBC dynamics, and incorporates coarse-grained water molecules to account for hydrodynamic interactions. (3) An action field method for lipid bilayer membrane model is introduced where several lipid molecules are represented by a Janus particle with corresponding orientation pointing from lipid head to lipid tail. With this level of coarse-grained modeling, as the preliminary setup, the lipid tails occupy a half sphere and the lipid heads take the other half. An action field is induced from lipid-lipid interactions and exists everywhere in the computational domain. Therefore, a hydrophobic attraction energy can be described from utilizing the variational approach and its minimizer with respect to the action field is the so-called screened Laplace equation. For the numerical method, the well-known integral equation method (IEM) has great capability to solve exterior screened Laplace equation with Dirichlet boundary conditions. Finally, one then can obtain the lipid dynamics to validate the self-assembly property and other physical properties of lipid bilayer membrane. This approach combines continuum modeling with CGMD and gives a different perspective to the membrane energy model from the traditional Helfrich membrane free energy

Characterizing the conformational dynamics of metal-free PsaA using molecular dynamics simulations and electron paramagnetic resonance spectroscopy

Prokaryotic metal-ion receptor proteins, or solute-binding proteins, facilitate the acquisition of metal ions from the extracellular environment. Pneumococcal surface antigen A (PsaA) is the primary Mn2+-recruiting protein of the human pathogen Streptococcus pneumoniae and is essential for its in vivo colonization and virulence. The recently reported high-resolution structures of metal- free and metal-bound PsaA have provided the first insights into the mechanism of PsaA-facilitated metal binding. However, the conformational dynamics of metal-free PsaA in solution remain unknown. Here, we use continuous wave electron paramagnetic resonance (EPR) spectroscopy and molecular dynamics (MD) simulations to study the relative flexibility of the structural domains in metal-free PsaA and its distribution of conformations in solution. The results show that the crystal structure of the metal-free PsaA is a good representation of the dominant conformation in solution, but the protein also samples structurally distinct conformations that are not captured by the crystal structure. Further, these results suggest that the metal binding site is larger and more solvent exposed than indicated by the metal-free crystal structure. Collectively, this study provides atomic-resolution insight into the conformational dynamics of PsaA prior to metal binding and lays the groundwork for future EPR and MD based studies of PsaA in solution

Programmable Control of Nucleation for Algorithmic Self-Assembly

Algorithmic self-assembly, a generalization of crystal growth processes, has been proposed as a mechanism for autonomous DNA computation and for bottom-up fabrication of complex nanostructures. A `program' for growing a desired structure consists of a set of molecular `tiles' designed to have specific binding interactions. A key challenge to making algorithmic self-assembly practical is designing tile set programs that make assembly robust to errors that occur during initiation and growth. One method for the controlled initiation of assembly, often seen in biology, is the use of a seed or catalyst molecule that reduces an otherwise large kinetic barrier to nucleation. Here we show how to program algorithmic self-assembly similarly, such that seeded assembly proceeds quickly but there is an arbitrarily large kinetic barrier to unseeded growth. We demonstrate this technique by introducing a family of tile sets for which we rigorously prove that, under the right physical conditions, linearly increasing the size of the tile set exponentially reduces the rate of spurious nucleation. Simulations of these `zig-zag' tile sets suggest that under plausible experimental conditions, it is possible to grow large seeded crystals in just a few hours such that less than 1 percent of crystals are spuriously nucleated. Simulation results also suggest that zig-zag tile sets could be used for detection of single DNA strands. Together with prior work showing that tile sets can be made robust to errors during properly initiated growth, this work demonstrates that growth of objects via algorithmic self-assembly can proceed both efficiently and with an arbitrarily low error rate, even in a model where local growth rules are probabilistic.Comment: 37 pages, 14 figure

Characterization, modeling, and simulation of multiscale directed-assembly systems

Nanoscience is a rapidly developing field at the nexus of all physical sciences which holds the potential for mankind to gain a new level of control of matter over matter and energy altogether. Directed-assembly is an emerging field within nanoscience in which non-equilibrium system dynamics are controlled to produce scalable, arbitrarily complex and interconnected multi-layered structures with custom chemical, biologically or environmentally-responsive, electronic, or optical properties. We construct mathematical models and interpret data from direct-assembly experiments via application and augmentation of classical and contemporary physics, biology, and chemistry methods. Crystal growth, protein pathway mapping, LASER tweezers optical trapping, and colloid processing are areas of directed-assembly with established experimental techniques. We apply a custom set of characterization, modeling, and simulation techniques to experiments to each of these four areas. Many of these techniques can be applied across several experimental areas within directed-assembly and to systems featuring multiscale system dynamics in general. We pay special attention to mathematical methods for bridging models of system dynamics across scale regimes, as they are particularly applicable and relevant to directed-assembly. We employ massively parallel simulations, enabled by custom software, to establish underlying system dynamics and develop new device production methods