Multiscale approaches to protein-mediated interactions between membranes - Relating microscopic and macroscopic dynamics in radially growing adhesions

Macromolecular complexation leading to coupling of two or more cellular membranes is a crucial step in a number of biological functions of the cell. While other mechanisms may also play a role, adhesion always involves the fluctuations of deformable membranes, the diffusion of proteins and the molecular binding and unbinding. Because these stochastic processes couple over a multitude of time and length scales, theoretical modeling of membrane adhesion has been a major challenge. Here we present an effective Monte Carlo scheme within which the effects of the membrane are integrated into local rates for molecular recognition. The latter step in the Monte Carlo approach enables us to simulate the nucleation and growth of adhesion domains within a system of the size of a cell for tens of seconds without loss of accuracy, as shown by comparison to $10^6$ times more expensive Langevin simulations. To perform this validation, the Langevin approach was augmented to simulate diffusion of proteins explicitly, together with reaction kinetics and membrane dynamics. We use the Monte Carlo scheme to gain deeper insight to the experimentally observed radial growth of micron sized adhesion domains, and connect the effective rate with which the domain is growing to the underlying microscopic events. We thus demonstrate that our technique yields detailed information about protein transport and complexation in membranes, which is a fundamental step toward understanding even more complex membrane interactions in the cellular context

Essential dynamics of proteins using geometrical simulations and subspace analysis

Essential dynamics is the application of principal component analysis to a dynamic trajectory derived from a simulation protocol in order to extract biologically relevant information contained in the high dimensional data. In this work, we apply the methodology of essential dynamics to protein trajectories derived from geometrical simulations, which are based on the perturbation of geometrical constraints inherent in a protein. Specifically, we show that the geometrical simulation model is highly efficient for the determination of native state dynamics. Furthermore, by the application of subspace analysis to the essential subspaces of multiple sets of proteins that were simulated under multiple modeling paradigms, we show that the geometrical modeling paradigm is internally consistent and provides results that are qualitatively and quantitatively similar to results obtained from the more commonly employed methods of elastic network models and molecular dynamics. The geometrical paradigm is therefore established as a viable alternative or co-model for the investigation of native state protein dynamics with application to both small, single domain proteins as well as large, multi domain systems

Dynamic torsional modeling and analysis of a fluid mixer

Mixers and agitators are used in a variety of processing industries. Each application has its own uniqueness requiring a high degree of customization in process design and mechanical design. Many of the processing and mechanical performance characteristics of mixers are derived from torque cell and tachometer measurements usually located between the motor and speed reducer. This thesis deals with the development of a dynamic modeling and analysis procedure to simulate the torsional response of mixers. This procedure will allow for the characterization of the torsional response at any point within the system, as well as relate the response as observed at the measurement location on full scale tests to any point of interest within the system. Various modeling options were developed for each of the mixing subsystems and compared to determine which configurations more accurately describe the system torsional dynamics. The developed modeling options were simulated using Simulink and MATLAB. For torsional frequency verification of the simulation model, a finite element model was constructed, analyzed, and compared to the simulation model. Also, the results of a full scale test were obtained and compared to the simulation model. Recommendations for usage, further study, and model development are also discussed

Structural Studies of Bile Salt Aggregation by Nuclear Magnetic Resonance Spectroscopy and Mass Spectrometry

Bile salts are biomolecules that are produced in the liver and are responsible for a range of functions in the process of digestion, primarily the emulsification of dietary fat and fat-soluble vitamins. Despite their importance in biological chemistry, the structure and dynamics of bile salt aggregation are not well understood. The efforts described herein attempt to enhance the understanding of cholate aggregation numbers (AN), critical micelle concentration (CMC), micellar structure(s), and interactions with a binaphthyl probe molecule. Cholate is the most common bile salt in mammals and is, therefore, a decent model for describing bile salt aggregation. CMC determination is achieved by observing the 1H NMR chemical shift perturbation of 1,1’-binaphthyl-2,2’-diyl hydrogen phosphate (R,S-BNDHP), a probe molecule for bile salt aggregation, when exposed to increased concentrations of sodium cholate. Using NMR and a phase-transition model to determine CMCs for pH 12.0 sodium cholate results in the observation of three unique CMC values at 6.1, 11.0, and ~25 mM. Using 1H-13C heteronuclear single quantum coherence (HSQC) spectroscopy, a two-dimensional NMR experiment, it appears that anti-parallel cholate dimers are not strictly collinear, but rather a skew exists between the two-cholate monomers. The existence of a skew is surprising as it would be incongruent with a well-known model of bile salt aggregation proposed by Donald Small proposed in 1968. HSQC also showed evidence that R- and S-BNDHP attack different edges of a cholate aggregate, possibly explaining the chiral selectivity exhibited by sodium cholate aggregates in earlier micellar electrokinetic chromatography experiments and confirming previous two-dimensional nuclear Overhauser effect (NOE) NMR data. HSQC data also suggest evidence for the interactions responsible for the aggregation of predicted aggregates by Small’s model. High-resolution negative ion electrospray ionization mass spectrometry (ESI-MS) data suggest that cholate is capable of forming several aggregates of sufficient stability for mass analysis, the most massive of which is an aggregate with an aggregation number of 18. With these data it is clear that this system has several complexities that affect aggregation that may not be accounted for in previous bile salt aggregation models

Prediction of Oxidation States of Cysteines and Disulphide Connectivity

Knowledge on cysteine oxidation state and disulfide bond connectivity is of great importance to protein chemistry and 3-D structures. This research is aimed at finding the most relevant features in prediction of cysteines oxidation states and the disulfide bonds connectivity of proteins. Models predicting the oxidation states of cysteines are developed with machine learning techniques such as Support Vector Machines (SVMs) and Associative Neural Networks (ASNNs). A record high prediction accuracy of oxidation state, 95%, is achieved by incorporating the oxidation states of N-terminus cysteines, flanking sequences of cysteines and global information on the protein chain (number of cysteines, length of the chain and amino acids composition of the chain etc.) into the SVM encoding. This is 5% higher than the current methods. This indicates to us that the oxidation states of amino terminal cysteines infer the oxidation states of other cysteines in the same protein chain. Satisfactory prediction results are also obtained with the newer and more inclusive SPX dataset, especially for chains with higher number of cysteines. Compared to literature methods, our approach is a one-step prediction system, which is easier to implement and use. A side by side comparison of SVM and ASNN is conducted. Results indicated that SVM outperform ASNN on this particular problem. For the prediction of correct pairings of cysteines to form disulfide bonds, we first study disulfide connectivity by calculating the local interaction potentials between the flanking sequences of the cysteine pairs. The obtained interaction potential is further adjusted by the coefficients related to the binding motif of enzymes during disulfide formation and also by the linear distance between the cysteine pairs. Finally, maximized weight matching algorithm is applied and performance of the interaction potentials evaluated. Overall prediction accuracy is unsatisfactory compared with the literature. SVM is used to predict the disulfide connectivity with the assumption that oxidation states of cysteines on the protein are known. Information on binding region during disulfide formation, distance between cysteine pairs, global information of the protein chain and the flanking sequences around the cysteine pairs are included in the SVM encoding. Prediction results illustrate the advantage of using possible anchor region information

Manipulating graphene's lattice to create pseudovector potentials, discover anomalous friction, and measure strain dependent thermal conductivity

Graphene is a single atomic sheet of graphite that exhibits a diverse range of unique properties. The electrons in intrinsic graphene behave like relativistic Dirac fermions; graphene has a record high Young's modulus but extremely low bending rigidity; and suspended graphene exhibits very high thermal conductivity. These properties are made more intriguing because with a thickness of only a single atomic layer, graphene is both especially affected by its environment and readily manipulated. In this dissertation the interaction between graphene and its environment as well as the exciting new physics realized by manipulating graphene's lattice are investigated. Lattice manipulations in the form of strain cause alterations in graphene's electrical dispersion mathematically analogous to the vector potential associated with a magnetic field. We complete the standard description of the strain-induced vector potential by explicitly including the lattice deformations and find new, leading order terms. Additionally, a strain engineered device with large, localized, plasmonically enhanced pseudomagnetic fields is proposed to couple light to pseudomagnetic fields. Accurate strain engineering requires a complete understanding of the interactions between a two dimensional material and its environment, particularly the adhesion and friction between graphene and its supporting substrate. We measure the load dependent sliding friction between mono-, bi-, and trilayer graphene and the commonly used silicon dioxide substrate by analyzing Raman spectra of circular, graphene sealed microchambers under variable external pressure. We find that the sliding friction for trilayer graphene behaves normally, scaling with the applied load, whereas the friction for monolayer and bilayer graphene is anomalous, scaling with the inverse of the strain in the graphene. Both strain and graphene's environment are expected to affect the quadratically dispersed out of plane acoustic phonon. Although this phonon is believed to provide the majority of graphene's very high thermal conductivity, its contributions have never been isolated. By measuring strain and pressure dependent thermal conductivity, we gain insight into the mechanism of graphene's thermal transport

Glosarium Matematika

273 p.; 24 cm