Matching Items (47)
Description
The continuous random network (CRN) model of network glasses is widely accepted as a model for materials such as vitreous silica and amorphous silicon. Although it
has been more than eighty years since the proposal of the CRN, there has not been conclusive experimental evidence of the structure of glasses and amorphous
materials. This has now changed with the advent of two-dimensional amorphous materials. Now, not only the distribution of rings but the actual atomic ring
structure can be imaged in real space, allowing for greater charicterization of these types of networks. This dissertation reports the first work done
on the modelling of amorphous graphene and vitreous silica bilayers. Models of amorphous graphene have been created using a Monte Carlo bond-switching method
and MD method. Vitreous silica bilayers have been constructed using models of amorphous graphene and the ring statistics of silica bilayers has been studied.
has been more than eighty years since the proposal of the CRN, there has not been conclusive experimental evidence of the structure of glasses and amorphous
materials. This has now changed with the advent of two-dimensional amorphous materials. Now, not only the distribution of rings but the actual atomic ring
structure can be imaged in real space, allowing for greater charicterization of these types of networks. This dissertation reports the first work done
on the modelling of amorphous graphene and vitreous silica bilayers. Models of amorphous graphene have been created using a Monte Carlo bond-switching method
and MD method. Vitreous silica bilayers have been constructed using models of amorphous graphene and the ring statistics of silica bilayers has been studied.
ContributorsKumar, Avishek (Author) / Thorpe, Michael F (Thesis advisor) / Ozkan, Sefika B (Committee member) / Beckstein, Oliver (Committee member) / Treacy, Michael Mj (Committee member) / Arizona State University (Publisher)
Created2014
Description
The properties of materials depend heavily on the spatial distribution and connectivity of their constituent parts. This applies equally to materials such as diamond and glasses as it does to biomolecules that are the product of billions of years of evolution. In science, insight is often gained through simple models with characteristics that are the result of the few features that have purposely been retained. Common to all research within in this thesis is the use of network-based models to describe the properties of materials. This work begins with the description of a technique for decoupling boundary effects from intrinsic properties of nanomaterials that maps the atomic distribution of nanomaterials of diverse shape and size but common atomic geometry onto a universal curve. This is followed by an investigation of correlated density fluctuations in the large length scale limit in amorphous materials through the analysis of large continuous random network models. The difficulty of estimating this limit from finite models is overcome by the development of a technique that uses the variance in the number of atoms in finite subregions to perform the extrapolation to large length scales. The technique is applied to models of amorphous silicon and vitreous silica and compared with results from recent experiments. The latter part this work applies network-based models to biological systems. The first application models force-induced protein unfolding as crack propagation on a constraint network consisting of interactions such as hydrogen bonds that cross-link and stabilize a folded polypeptide chain. Unfolding pathways generated by the model are compared with molecular dynamics simulation and experiment for a diverse set of proteins, demonstrating that the model is able to capture not only native state behavior but also partially unfolded intermediates far from the native state. This study concludes with the extension of the latter model in the development of an efficient algorithm for predicting protein structure through the flexible fitting of atomic models to low-resolution cryo-electron microscopy data. By optimizing the fit to synthetic data through directed sampling and context-dependent constraint removal, predictions are made with accuracies within the expected variability of the native state.
ContributorsDe Graff, Adam (Author) / Thorpe, Michael F. (Thesis advisor) / Ghirlanda, Giovanna (Committee member) / Matyushov, Dmitry (Committee member) / Ozkan, Sefika B. (Committee member) / Treacy, Michael M. J. (Committee member) / Arizona State University (Publisher)
Created2011
Description
Conformational changes in biomolecules often take place on longer timescales than are easily accessible with unbiased molecular dynamics simulations, necessitating the use of enhanced sampling techniques, such as adaptive umbrella sampling. In this technique, the conformational free energy is calculated in terms of a designated set of reaction coordinates. At the same time, estimates of this free energy are subtracted from the potential energy in order to remove free energy barriers and cause conformational changes to take place more rapidly. This dissertation presents applications of adaptive umbrella sampling to a variety of biomolecular systems. The first study investigated the effects of glycosylation in GalNAc2-MM1, an analog of glycosylated macrophage activating factor. It was found that glycosylation destabilizes the protein by increasing the solvent exposure of hydrophobic residues. The second study examined the role of bound calcium ions in promoting the isomerization of a cis peptide bond in the collagen-binding domain of Clostridium histolyticum collagenase. This study determined that the bound calcium ions reduced the barrier to the isomerization of this peptide bond as well as stabilizing the cis conformation thermodynamically, and identified some of the reasons for this. The third study represents the application of GAMUS (Gaussian mixture adaptive umbrella sampling) to on the conformational dynamics of the fluorescent dye Cy3 attached to the 5' end of DNA, and made predictions concerning the affinity of Cy3 for different base pairs, which were subsequently verified experimentally. Finally, the adaptive umbrella sampling method is extended to make use of the roll angle between adjacent base pairs as a reaction coordinate in order to examine the bending both of free DNA and of DNA bound to the archaeal protein Sac7d. It is found that when DNA bends significantly, cations from the surrounding solution congregate on the concave side, which increases the flexibility of the DNA by screening the repulsion between phosphate backbones. The flexibility of DNA on short length scales is compared to the worm-like chain model, and the contribution of cooperativity in DNA bending to protein-DNA binding is assessed.
ContributorsSpiriti, Justin Matthew (Author) / van der Vaart, Arjan (Thesis advisor) / Chizmeshya, Andrew (Thesis advisor) / Matyushov, Dmitry (Committee member) / Fromme, Petra (Committee member) / Arizona State University (Publisher)
Created2011
Description
Molecular dynamics (MD) simulations provide a particularly useful approach to understanding conformational change in biomolecular systems. MD simulations provide an atomistic, physics-based description of the motions accessible to biomolecular systems on the pico- to micro-second timescale, yielding important insight into the free energy of the system, the dynamical stability of contacts and the role of correlated motions in directing the motions of the system. In this thesis, I use molecular dynamics simulations to provide molecular mechanisms that rationalize structural, thermodynamic, and mutation data on the interactions between the lac repressor headpiece and its O1 operator DNA as well as the ERK2 protein kinase. I performed molecular dynamics simulations of the lac repressor headpiece - O1 operator complex at the natural angle as well as at under- and overbent angles to assess the factors that determine the natural DNA bending angle. I find both energetic and entropic factors contribute to recognition of the natural angle. At the natural angle the energy of the system is minimized by optimization of protein-DNA contacts and the entropy of the system is maximized by release of water from the protein-DNA interface and decorrelation of protein motions. To identify the mechanism by which mutations lead to auto-activation of ERK2, I performed a series of molecular dynamics simulations of ERK1/2 in various stages of activation as well as the constitutively active Q103A, I84A, L73P and R65S ERK2 mutants. My simulations indicate the importance of domain closure for auto-activation and activity regulation. My results enable me to predict two loss-of-function mutants of ERK2, G83A and Q64C, that have been confirmed in experiments by collaborators. One of the powerful capabilities of MD simulations in biochemistry is the ability to find low free energy pathways that connect and explain disparate structural data on biomolecular systems. An extention of the targeted molecular dynamics technique using constraints on internal coordinates will be presented and evaluated. The method gives good results for the alanine dipeptide, but breaks down when applied to study conformational changes in GroEL and adenylate kinase.
ContributorsBarr, Daniel Alan (Author) / van der Vaart, Arjan (Thesis advisor) / Matyushov, Dmitry (Committee member) / Wolf, George (Committee member) / Shumway, John (Committee member) / Arizona State University (Publisher)
Created2011
Description
Molecular dynamics simulations were used to study properties of water at the interface with nanometer-size solutes. We simulated nonpolar attractive Kihara cavities given by a Lennard-Jones potential shifted by a core radius. The dipolar response of the hydration layer to a uniform electric field substantially exceeds that of the bulk. For strongly attractive solutes, the collective dynamics of the hydration layer become slow compared to bulk water, as the solute size is increased. The statistics of electric field fluctuations at the solute center are Gaussian and tend toward the dielectric continuum limit with increasing solute size. A dipolar probe placed at the center of the solute is sensitive neither to the polarity excess nor to the slowed dynamics of the hydration layer. A point dipole was introduced close to the solute-water interface to further study the statistics of electric field fluctuations generated by the water. For small dipole magnitudes, the free energy surface is single-welled, with approximately Gaussian statistics. When the dipole is increased, the free energy surface becomes double-welled, before landing in an excited state, characterized again by a single-welled surface. The intermediate region is fairly broad and is characterized by electrostatic fluctuations significantly in excess of the prediction of linear response. We simulated a solute having the geometry of C180 fullerene, with dipoles introduced on each carbon. For small dipole moments, the solvent response follows the results seen for a single dipole; but for larger dipole magnitudes, the fluctuations of the solute-solvent energy pass through a second maximum. The juxtaposition of the two transitions leads to an approximately cubic scaling of the chemical potential with the dipole strengh. Umbrella sampling techniques were used to generate free energy surfaces of the electric potential fluctuations at the heme iron in Cytochrome B562. The results were unfortunately inconclusive, as the ionic background was not effectively represented in the finite-size system.
ContributorsFriesen, Allan Dwayne (Author) / Matyushov, Dmitry V (Thesis advisor) / Angell, C Austen (Thesis advisor) / Beckstein, Oliver (Committee member) / Mujica, Vladimiro (Committee member) / Arizona State University (Publisher)
Created2012
Description
Sample delivery is an essential component in biological imaging using serial diffraction from X-ray Free Electron Lasers (XFEL) and synchrotrons. Recent developments have made possible the near-atomic resolution structure determination of several important proteins, including one G protein-coupled receptor (GPCR) drug target, whose structure could not easily have been determined otherwise (Appendix A). In this thesis I describe new sample delivery developments that are paramount to advancing this field beyond what has been accomplished to date. Soft Lithography was used to implement sample conservation in the Gas Dynamic Virtual Nozzle (GDVN). A PDMS/glass composite microfluidic injector was created and given the capability of millisecond fluidic switching of a GDVN liquid jet within the divergent section of a 2D Laval-like GDVN nozzle, providing a means of collecting sample between the pulses of current XFELs. An oil/water droplet immersion jet was prototyped that suspends small sample droplets within an oil jet such that the sample droplet frequency may match the XFEL pulse repetition rate. A similar device was designed to use gas bubbles for synchronized “on/off” jet behavior and for active micromixing. 3D printing based on 2-Photon Polymerization (2PP) was used to directly fabricate reproducible GDVN injectors at high resolution, introducing the possibility of systematic nozzle research and highly complex GDVN injectors. Viscous sample delivery using the “LCP injector” was improved with a method for dealing with poorly extruding sample mediums when using full beam transmission from the Linac Coherent Light Source (LCLS), and a new viscous crystal-carrying medium was characterized for use in both vacuum and atmospheric environments: high molecular weight Polyethylene Glycol.
ContributorsNelson, Garrett Charles (Author) / Spence, John C (Thesis advisor) / Weierstall, Uwe J (Thesis advisor) / Schmidt, Kevin E (Committee member) / Beckstein, Oliver (Committee member) / Arizona State University (Publisher)
Created2015
Description
Molecular docking serves as an important tool in modeling protein-ligand interactions. Most of the docking approaches treat the protein receptor as rigid and move the ligand in the binding pocket through an energy minimization, which is an incorrect approach as proteins are flexible and undergo conformational changes upon ligand binding. However, modeling receptor backbone flexibility in docking is challenging and computationally expensive due to the large conformational space that needs to be sampled.
A novel flexible docking approach called BP-Dock (Backbone Perturbation docking) was developed to overcome this challenge. BP-Dock integrates both backbone and side chain conformational changes of a protein through a multi-scale approach. In BP-Dock, the residues along a protein chain are perturbed mimicking the binding induced event, with a small Brownian kick, one at a time. The fluctuation response profile of the chain upon these perturbations is computed by Perturbation Response Scanning (PRS) to generate multiple receptor conformations for ensemble docking. To evaluate the performance of BP-Dock, this approach was applied to a large and diverse dataset of unbound structures as receptors. Furthermore, the protein-peptide docking of PICK1-PDZ proteins was investigated. This study elucidates the determinants of PICK1-PDZ binding that plays crucial roles in numerous neurodegenerative disorders. BP-Dock approach was also extended to the challenging problem of protein-glycan docking and applied to analyze the energetics of glycan recognition in Cyanovirin-N (CVN), a cyanobacterial lectin that inhibits HIV by binding to its highly glycosylated envelope protein gp120. This study provide the energetic contribution of the individual residues lining the binding pocket of CVN and explore the effect of structural flexibility in the hinge region of CVN on glycan binding, which are also verified experimentally. Overall, these successful applications of BP-Dock highlight the importance of modeling backbone flexibility in docking that can have important implications in defining the binding properties of protein-ligand interactions.
Finally, an induced fit docking approach called Adaptive BP-Dock is presented that allows both protein and ligand conformational sampling during the docking. Adaptive BP-Dock can provide a faster and efficient docking approach for the virtual screening of novel targets for rational drug design and aid our understanding of protein-ligand interactions.
A novel flexible docking approach called BP-Dock (Backbone Perturbation docking) was developed to overcome this challenge. BP-Dock integrates both backbone and side chain conformational changes of a protein through a multi-scale approach. In BP-Dock, the residues along a protein chain are perturbed mimicking the binding induced event, with a small Brownian kick, one at a time. The fluctuation response profile of the chain upon these perturbations is computed by Perturbation Response Scanning (PRS) to generate multiple receptor conformations for ensemble docking. To evaluate the performance of BP-Dock, this approach was applied to a large and diverse dataset of unbound structures as receptors. Furthermore, the protein-peptide docking of PICK1-PDZ proteins was investigated. This study elucidates the determinants of PICK1-PDZ binding that plays crucial roles in numerous neurodegenerative disorders. BP-Dock approach was also extended to the challenging problem of protein-glycan docking and applied to analyze the energetics of glycan recognition in Cyanovirin-N (CVN), a cyanobacterial lectin that inhibits HIV by binding to its highly glycosylated envelope protein gp120. This study provide the energetic contribution of the individual residues lining the binding pocket of CVN and explore the effect of structural flexibility in the hinge region of CVN on glycan binding, which are also verified experimentally. Overall, these successful applications of BP-Dock highlight the importance of modeling backbone flexibility in docking that can have important implications in defining the binding properties of protein-ligand interactions.
Finally, an induced fit docking approach called Adaptive BP-Dock is presented that allows both protein and ligand conformational sampling during the docking. Adaptive BP-Dock can provide a faster and efficient docking approach for the virtual screening of novel targets for rational drug design and aid our understanding of protein-ligand interactions.
ContributorsBolia, Ashini (Author) / Ozkan, Sefika Banu (Thesis advisor) / Ghirlanda, Giovanna (Thesis advisor) / Beckstein, Oliver (Committee member) / Wachter, Rebekka (Committee member) / Arizona State University (Publisher)
Created2015
Description
In a typical living cell, millions to billions of proteins—nanomachines that fluctuate and cycle among many conformational states—convert available free energy into mechanochemical work. A fundamental goal of biophysics is to ascertain how 3D protein structures encode specific functions, such as catalyzing chemical reactions or transporting nutrients into a cell. Protein dynamics span femtosecond timescales (i.e., covalent bond oscillations) to large conformational transition timescales in, and beyond, the millisecond regime (e.g., glucose transport across a phospholipid bilayer). Actual transition events are fast but rare, occurring orders of magnitude faster than typical metastable equilibrium waiting times. Equilibrium molecular dynamics (EqMD) can capture atomistic detail and solute-solvent interactions, but even microseconds of sampling attainable nowadays still falls orders of magnitude short of transition timescales, especially for large systems, rendering observations of such "rare events" difficult or effectively impossible.
Advanced path-sampling methods exploit reduced physical models or biasing to produce plausible transitions while balancing accuracy and efficiency, but quantifying their accuracy relative to other numerical and experimental data has been challenging. Indeed, new horizons in elucidating protein function necessitate that present methodologies be revised to more seamlessly and quantitatively integrate a spectrum of methods, both numerical and experimental. In this dissertation, experimental and computational methods are put into perspective using the enzyme adenylate kinase (AdK) as an illustrative example. We introduce Path Similarity Analysis (PSA)—an integrative computational framework developed to quantify transition path similarity. PSA not only reliably distinguished AdK transitions by the originating method, but also traced pathway differences between two methods back to charge-charge interactions (neglected by the stereochemical model, but not the all-atom force field) in several conserved salt bridges. Cryo-electron microscopy maps of the transporter Bor1p are directly incorporated into EqMD simulations using MD flexible fitting to produce viable structural models and infer a plausible transport mechanism. Conforming to the theme of integration, a short compendium of an exploratory project—developing a hybrid atomistic-continuum method—is presented, including initial results and a novel fluctuating hydrodynamics model and corresponding numerical code.
Advanced path-sampling methods exploit reduced physical models or biasing to produce plausible transitions while balancing accuracy and efficiency, but quantifying their accuracy relative to other numerical and experimental data has been challenging. Indeed, new horizons in elucidating protein function necessitate that present methodologies be revised to more seamlessly and quantitatively integrate a spectrum of methods, both numerical and experimental. In this dissertation, experimental and computational methods are put into perspective using the enzyme adenylate kinase (AdK) as an illustrative example. We introduce Path Similarity Analysis (PSA)—an integrative computational framework developed to quantify transition path similarity. PSA not only reliably distinguished AdK transitions by the originating method, but also traced pathway differences between two methods back to charge-charge interactions (neglected by the stereochemical model, but not the all-atom force field) in several conserved salt bridges. Cryo-electron microscopy maps of the transporter Bor1p are directly incorporated into EqMD simulations using MD flexible fitting to produce viable structural models and infer a plausible transport mechanism. Conforming to the theme of integration, a short compendium of an exploratory project—developing a hybrid atomistic-continuum method—is presented, including initial results and a novel fluctuating hydrodynamics model and corresponding numerical code.
ContributorsSeyler, Sean L (Author) / Beckstein, Oliver (Thesis advisor) / Chamberlin, Ralph (Committee member) / Matyushov, Dmitry (Committee member) / Thorpe, Michael F (Committee member) / Vaiana, Sara (Committee member) / Arizona State University (Publisher)
Created2017
Description
In this dissertation two kinds of strongly interacting fermionic systems were studied: cold atomic gases and nucleon systems. In the first part I report T=0 diffusion Monte Carlo results for the ground-state and vortex excitation of unpolarized spin-1/2 fermions in a two-dimensional disk. I investigate how vortex core structure properties behave over the BEC-BCS crossover. The vortex excitation energy, density profiles, and vortex core properties related to the current are calculated. A density suppression at the vortex core on the BCS side of the crossover and a depleted core on the BEC limit is found. Size-effect dependencies in the disk geometry were carefully studied. In the second part of this dissertation I turn my attention to a very interesting problem in nuclear physics. In most simulations of nonrelativistic nuclear systems, the wave functions are found by solving the many-body Schrödinger equations, and they describe the quantum-mechanical amplitudes of the nucleonic degrees of freedom. In those simulations the pionic contributions are encoded in nuclear potentials and electroweak currents, and they determine the low-momentum behavior. By contrast, in this work I present a novel quantum Monte Carlo formalism in which both relativistic pions and nonrelativistic nucleons are explicitly included in the quantum-mechanical states of the system. I report the renormalization of the nucleon mass as a function of the momentum cutoff, an Euclidean time density correlation function that deals with the short-time nucleon diffusion, and the pion cloud density and momentum distributions. In the two nucleon sector the interaction of two static nucleons at large distances reduces to the one-pion exchange potential, and I fit the low-energy constants of the contact interactions to reproduce the binding energy of the deuteron and two neutrons in finite volumes. I conclude by showing that the method can be readily applied to light-nuclei.
ContributorsMadeira, Lucas (Author) / Schmidt, Kevin E (Thesis advisor) / Alarcon, Ricardo (Committee member) / Beckstein, Oliver (Committee member) / Erten, Onur (Committee member) / Arizona State University (Publisher)
Created2018
Description
The structure of glass has been the subject of many studies, however some
details remained to be resolved. With the advancement of microscopic
imaging techniques and the successful synthesis of two-dimensional materials,
images of two-dimensional glasses (bilayers of silica) are now available,
confirming that this glass structure closely follows the continuous random
network model. These images provide complete in-plane structural information
such as ring correlations, and intermediate range order and with computer
refinement contain indirect information such as angular distributions, and
tilting.
This dissertation reports the first work that integrates the actual atomic
coordinates obtained from such images with structural refinement to enhance
the extracted information from the experimental data.
The correlations in the ring structure of silica bilayers are studied
and it is shown that short-range and intermediate-range order exist in such networks.
Special boundary conditions for finite experimental samples are designed so atoms
in the bulk sense they are part of an infinite network.
It is shown that bilayers consist of two identical layers separated by a
symmetry plane and the tilted tetrahedra, two examples of
added value through the structural refinement.
Finally, the low-temperature properties of glasses in two dimensions
are studied. This dissertation presents a new approach to find possible
two-level systems in silica bilayers employing the tools of rigidity theory
in isostatic systems.
details remained to be resolved. With the advancement of microscopic
imaging techniques and the successful synthesis of two-dimensional materials,
images of two-dimensional glasses (bilayers of silica) are now available,
confirming that this glass structure closely follows the continuous random
network model. These images provide complete in-plane structural information
such as ring correlations, and intermediate range order and with computer
refinement contain indirect information such as angular distributions, and
tilting.
This dissertation reports the first work that integrates the actual atomic
coordinates obtained from such images with structural refinement to enhance
the extracted information from the experimental data.
The correlations in the ring structure of silica bilayers are studied
and it is shown that short-range and intermediate-range order exist in such networks.
Special boundary conditions for finite experimental samples are designed so atoms
in the bulk sense they are part of an infinite network.
It is shown that bilayers consist of two identical layers separated by a
symmetry plane and the tilted tetrahedra, two examples of
added value through the structural refinement.
Finally, the low-temperature properties of glasses in two dimensions
are studied. This dissertation presents a new approach to find possible
two-level systems in silica bilayers employing the tools of rigidity theory
in isostatic systems.
ContributorsSadjadi, Seyed Mahdi (Author) / Thorpe, Michael F (Thesis advisor) / Beckstein, Oliver (Committee member) / Schmidt, Kevin E (Committee member) / Treacy, Michael Mj (Committee member) / Arizona State University (Publisher)
Created2018