Assortative mixing in close-packed spatial networks

Background In recent years, there is aroused interest in expressing complex systems as networks of interacting nodes. Using descriptors from graph theory, it has been possible to classify many diverse systems derived from social and physical sciences alike. In particular, folded proteins as examples of self-assembled complex molecules have also been investigated intensely using these tools. However, we need to develop additional measures to classify different systems, in order to dissect the underlying hierarchy. Methodology and Principal Findings In this study, a general analytical relation for the dependence of nearest neighbor degree correlations on degree is derived. Dependence of local clustering on degree is shown to be the sole determining factor of assortative versus disassortative mixing in networks. The characteristics of networks constructed from spatial atomic/molecular systems exemplified by self-organized residue networks built from folded protein structures and block copolymers, atomic clusters and well-compressed polymeric melts are studied. Distributions of statistical properties of the networks are presented. For these densely-packed systems, assortative mixing in the network construction is found to apply, and conditions are derived for a simple linear dependence. Conclusions Our analyses (i) reveal patterns that are common to close-packed clusters of atoms/molecules, (ii) identify the type of surface effects prominent in different close-packed systems, and (iii) associate fingerprints that may be used to classify networks with varying types of correlations

Assortative Mixing in Close-Packed Spatial Networks

Background: In recent years, there is aroused interest in expressing complex systems as networks of interacting nodes. Using descriptors from graph theory, it has been possible to classify many diverse systems derived from social and physical sciences alike. In particular, folded proteins as examples of self-assembled complex molecules have also been investigated intensely using these tools. However, we need to develop additional measures to classify different systems, in order to dissect the underlying hierarchy. Methodology and Principal Findings: In this study, a general analytical relation for the dependence of nearest neighbor degree correlations on degree is derived. Dependence of local clustering on degree is shown to be the sole determining factor of assortative versus disassortative mixing in networks. The characteristics of networks constructed from spatial atomic/molecular systems exemplified by self-organized residue networks built from folded protein structures and block copolymers, atomic clusters and well-compressed polymeric melts are studied. Distributions of statistical properties of the networks are presented. For these densely-packed systems, assortative mixing in the network construction is found to apply, and conditions are derived for a simple linear dependence. Conclusions: Our analyses (i) reveal patterns that are common to close-packed clusters of atoms/molecules, (ii) identify the type of surface effects prominent in different close-packed systems, and (iii) associate fingerprints that may be used t

Self Assembly in Soft Matter

The term “soft matter” applies to a variety of physical systems, such as liquids, colloids, polymers, foams, gels, and granular materials. The most fascinating aspect of soft matter lies in the fact that they are not atomic or molecular in nature. They are instead macromolecular aggregates, whose spatial extent lies in the domain 1 nm to 1 micron. Some of the most important examples of soft matter are polymers, which exhibit intriguing and useful physical properties. In this work, the adsorption and self assembly of linear and star polymers on smooth surfaces are studied using coarse-grained, bead-springmolecular models and Langevin dynamics computer simulations. The aim is to gain insight on atomic-forcemicroscopy images of polymer films on mica surfaces, adsorbed from dilute solution following a good solvent-to-bad solvent quenching procedure. In the case of linear polymers, under certain experimental conditions, a bimodal cluster distribution is observed. It is demonstrated that this type of distribution can be reproduced in the simulations, and rationalized on the basis of the polymer structures prior to the quench. In addition to providing insight on experimental observations, the simulation results support a number of predicted scaling laws such as the decay of the monomer density as a function of distance from the surface, and the scaling of the filmheight with the strength of the polymer-surface interactions. Star polymers represent a special class of polymers, in which one end of each linear chain is tethered to a small central core to forma single particle. The discovery of these molecules led to the synthesis of a wide range of new materials. Their structures are effectively considered as intermediate between those of colloids and linear polymers. We explore the behaviour of the star polymers (which are like “soft colloids”) in the proximity of a surface, using Langevin dynamics simulations. A number of different measurements such as the height, radius of gyration, and asphericity of adsorbed stars with different number of arms, are shown to provide valuable insights on experimental findings. The simplest soft matter systems consist of spherical, rigid colloidal particles. Examples of such particles are chemically synthesized polystyrene or silica particles. We investigated the neighbour distribution in a two-dimensional polydisperse harddisk fluid, corresponding physically to a colloidal monolayer. The disk diameter distribution was defined by a power-law with the aim of realizing a scale-free nearneighbour network. Scale-free (power-law) behaviour is found in many important networks, for example, in transportation systems, biochemical reactions, scientific and movie-actor collaborations, and sexual contacts. We have provided the first example of a scale-free network in amodel condensed-matter system. Finally, we use genetic algorithms, a method for efficiently searching for minima on energy landscapes, to investigate the ordered equilibrium structures formed by binary mixtures of anisotropic dipolar particles confined on a plane, under the presence of an external magnetic field. The anisotropy of the interparticle forces is controlled by tilting the external magnetic field with respect to the plane. Initially, as the field is tilted the structures are only slightly perturbed, but once the anisotropy exceeds a critical value, completely new structures emerge

Computational methods to design biophysical experiments for the study of protein dynamics

In recent years, new software and automated instruments have enabled us to imagine autonomous or "self-driving" laboratories of the future. However, ways to design new scientific studies remain unexplored due to challenges such as minimizing associated time, labor, and expense of sample preparation and data acquisition. In the field of protein biophysics, computational simulations such as molecular dynamics and spectroscopy-based experiments such as double electron-electron resonance and Fluorescence resonance energy transfer techniques have emerged as critical experimental tools to capture protein dynamic behavior, a change in protein structure as a function of time which is important for their cellular functions. These techniques can lead to the characterization of key protein conformations and can capture protein motions over a diverse range of timescales. This work addresses the problem of the choice of probe positions in a protein, which residue-pairs should experimentalists choose for spectroscopy experiments. For this purpose, molecular dynamics simulations and Markov state models of protein conformational dynamics are utilized to rank sets of labeled residue-pairs in terms of their ability to capture the conformational dynamics of the protein. The applications of our experimental study design methodology called OptimalProbes on different types of proteins and experimental techniques are examined. In order to utilize this method for a previously uncharacterized protein, atomistic molecular dynamics simulations are performed to study a bacterial di/tri-peptide transporter a typical representative of the Major Facilitator Superfamily of membrane proteins. This was followed by ideal double electron-electron resonance experimental choice predictions based on the simulation data. The predicted choices are superior to the residue-pair choices made by experimentalists which failed to capture the slowest dynamical processes in the conformational ensemble obtained from our long timescale simulations. For molecular dynamics simulations based design of experimental studies to succeed both ensembles need to be comparable. Since this has not been the case for double electron-electron resonance distance distributions and molecular simulations, we explore possible reasons that can lead to mismatches between experiments and simulations in order to reconcile simulated ensembles with experimentally obtained distance traces. This work is one of the first studies towards integrating spectroscopy experiment design into a computational method systematically based on molecular simulations

Network characterization of packing architecture for condensed matter systems

Networks have currently been used to model real life complex systems and they have provided additional understanding for characterizing structure-functiondynamics relationships of these complex architectures. Here we investigate statistical and spectral properties and the connections between local motifs and global behavior of networks that are formed from condensed matter systems, particularly proteins, as well as micelles, polymeric melts and Lennard-Jones clusters. Proteins are considered as interacting residue networks. Pathways for information transfer manifested in the average path lengths are analyzed, where the energy of residue-residue interactions are imposed as edge weights in networks. Systematic removal of ''low energy'' interactions reveals that the network contains significant number of redundancies that provide high local clustering. The information transfer is achieved by a small number of highly clustered groups of residues, which makes the hub architecture different from that of scale-free networks. This result is then extended to protein complexes, where two proteins (ligand and receptor) interact, in order to identify essential pair-wise interactions between two proteins. In the presence of local clustering, establishing a relationship between local structure and global properties is far from trivial. But for certain cases, applying a bottom-up approach, a relation between nearest neighbors and next-to-nearest neighbors is obtained and this relation is observed in different networks formed from condensed matter systems, as well as perfect lattice models. To further investigate the association between local order and global structure, residue networks are considered in further detail. To outline local order, we compared residue networks to perfect lattice systems by creating self-avoiding chains on chains via Metropolis Monte Carlo method that capture three dimensional structure of protein chains as much as possible. Results show that, proteins conform to close packed ordered structures with significant voids irrespective of the underlying lattice bases. Finally, we analyzed the spectral properties of networks used throughout the thesis. Spectral changes while breaking and rewiring the edges revealed the importance and roles of short and long-ranged contacts in determining the network structure. Comparison of spectra distributions of different networks constructed from condensed matter systems supported the result from statistical parameters that these systems have structural similarities

Bioinformatic studies of small disulphide-rich proteins (SDPs)

Ph.DDOCTOR OF PHILOSOPH

Structural Characterization Of The Novel Flightin Domain Wyr And Its Defining Role In The Thick Filament Structure And Mechanics

The evolutionary success of Insecta has been attributed largely to the development of efficient means of motility: flight powered by muscle architecture harboring a largely conserved yet tunable system of power relay. The indirect flight muscle (IFM) of Drosophila melanogaster is a well-studied model for dissection of the structural and mechanical means by which muscle operates and evolves. Striated muscle, conserved throughout Animalia, is demarcated by an ordered array of thick- and thin-filaments prominently composed of the proteins myosin and actin. Flightin (fln) is a myosin binding thick filament protein essential for IFM stability, structure and function. The manner by which fln contacts myosin and relevance of its highly conserved domain (WYR) has not been fully elucidated. This dissertation presents the culmination of an effort to elucidate fln’s role in the thick filament and the nature and involvement of the novel WYR domain. Cardiac myosin binding protein-C (cMyBP-C), exclusive to vertebrates, and fln, exclusive to Pancrustacea bind a common site in the light meromyosin (LMM) region of myosin and have been hypothesized to have partially overlapping functions within the thick filament. To evaluate this, IFM sarcomeres and thick filaments from D. melanogaster mutant and transgenic strains with and without additional cMyBP-C expression were examined by transmission electron microscopy (TEM) and atomic force microscopy (AFM), respectively. cMyBP-C, like fln, is found to influence sarcomere length and contribute to thick filament flexural rigidity. This suggests a shared influence on thick filament properties though cMyBP-C did not fully rescue the fln0 phenotype. Adding depth to the fln-LMM relationship, we examined the structure and function of WYR. The structure of WYR, determined by circular dichroism (CD), is mostly aperiodic, with 30% antiparallel β content. A putative model of WYR secondary structure is presented, derived from CD findings and interpreted on the basis of WYR’s primary sequence and the potential contributions of its aromatic and polar residue electronic state transitions. Employing both cosedimentation and CD, we find that WYR binds the LMM and induces structural change. The WYR-LMM structure depict the LMM as decreasing in ɑ-helical nature and increasing in coiled-coil character and sedimentation assays demonstrate increased prevalence of macroscopic assemblies upon the association. Data from a structural study of the waterbug IFM thick filament was processed to reveal fln association to regions depicting coiled-coil unwinding. The portions of the LMM interfacing with fln were associated to the myosin sequence, revealing specific amino acids over which fln is in close proximity. We identify five interfaces, one of which is heptad mapped and reveals an LMM binding region shared between fln and cMyBP-C. Given the importance of fln to IFM function and the conservation of the WYR domain through Pancrustacea, the convergent effects of fln and cMyBP-C along with LMM structural change induced by WYR presents a positional and structural basis over which the thick filament experiences context-dependent tuning. Our findings depict fln as a cinch connecting multiple myosin dimers via the LMM, and support its intimate involvement in thick filament assembly. This work describes WYR on a multiscale, considering the nanoscopic mechanisms that underpin macroscopic biological phenomena. WYR is an important agent by which structural and mechanical adaptations are incorporated into the IFM hierarchy, relevant to the rise of flight within Insecta. Further dissection of WYR’s function and relationship to the LMM should provide insight pertinent to the scaling of mechanical processes by structural design and have bearing in studies beyond the IFM and insect adaptation

Catalysis and biocatalysis program

The annual report presents the fiscal year (FY) 1990 research activities and accomplishments for the Catalysis and Biocatalysis Program of the Advanced Industrial Concepts Division (AICD), Office of Industrial Technologies of the Department of Energy (DOE). The mission of the AICD is to create a balanced program of high risk, long term, directed interdisciplinary research and development that will improve energy efficiency and enhance fuel flexibility in the industrial sector. The Catalysis and Biocatalysis Program's technical activities were organized into five work elements: the Molecular Modeling and Catalysis by Design element; the Applied Microbiology and Genetics element; the Bioprocess Engineering element; the Separations and Novel Chemical Processes element; and the Process Design and Analysis element