Matching Items (13)
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- All Subjects: Biophysics
- Creators: Vaiana, Sara
Description
This thesis describes several experiments based on carbon nanotube nanofludic devices and field-effect transistors. The first experiment detected ion and molecule translocation through one single-walled carbon nanotube (SWCNT) that spans a barrier between two fluid reservoirs. The electrical ionic current is measured. Translocation of small single stranded DNA oligomers is marked by large transient increases in current through the tube and confirmed by a PCR (polymerase chain reaction) analysis. Carbon nanotubes simplify the construction of nanopores, permit new types of electrical measurement, and open new avenues for control of DNA translocation. The second experiment constructed devices in which the interior of a single-walled carbon nanotube field-effect transistor (CNT-FET) acts as a nanofluidic channel that connects two fluid reservoirs, permitting measurement of the electronic properties of the SWCNT as it is wetted by an analyte. Wetting of the inside of the SWCNT by water turns the transistor on, while wetting of the outside has little effect. This finding may provide a new method to investigate water behavior at nanoscale. This also opens a new avenue for building sensors in which the SWCNT functions as an electronic detector. This thesis also presents some experiments that related to nanofabrication, such as construction of FET with tin sulfide (SnS) quantum ribbon. This work demonstrates the application of solution processed IV-VI semiconductor nanostructures in nanoscale devices.
ContributorsCao, Zhai (Author) / Lindsay, Stuart (Thesis advisor) / Vaiana, Sara (Committee member) / Ros, Robert (Committee member) / Marzke, Robert (Committee member) / Shumway, John (Committee member) / Arizona State University (Publisher)
Created2011
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
The goal of this theoretical study of infrared spectra was to ascertain to what degree molecules may be identified from their IR spectra and which spectral regions are best suited for this purpose. The frequencies considered range from the lowest frequency molecular vibrations in the far-IR, terahertz region (below ~3 THz or 100 cm-1) up to the highest frequency vibrations (~120 THz or 4000 cm-1). An emphasis was placed on the IR spectra of chemical and biological threat molecules in the interest of detection and prevention. To calculate IR spectra, the technique of normal mode analysis was applied to organic molecules ranging in size from 8 to 11,352 atoms. The IR intensities of the vibrational modes were calculated in terms of the derivative of the molecular dipole moment with respect to each normal coordinate. Three sets of molecules were studied: the organophosphorus G- and V-type nerve agents and chemically related simulants (15 molecules ranging in size from 11 to 40 atoms); 21 other small molecules ranging in size from 8 to 24 atoms; and 13 proteins ranging in size from 304 to 11,352 atoms. Spectra for the first two sets of molecules were calculated using quantum chemistry software, the last two sets using force fields. The "middle" set used both methods, allowing for comparison between them and with experimental spectra from the NIST/EPA Gas-Phase Infrared Library. The calculated spectra of proteins, for which only force field calculations are practical, reproduced the experimentally observed amide I and II bands, but they were shifted by approximately +40 cm-1 relative to experiment. Considering the entire spectrum of protein vibrations, the most promising frequency range for differentiating between proteins was approximately 600-1300 cm-1 where water has low absorption and the proteins show some differences.
ContributorsMott, Adam J (Author) / Rez, Peter (Thesis advisor) / Ozkan, Banu (Committee member) / Shumway, John (Committee member) / Thorpe, Michael (Committee member) / Vaiana, Sara (Committee member) / Arizona State University (Publisher)
Created2012
Description
CpG methylation is an essential requirement for the normal development of mammals, but aberrant changes in the methylation can lead to tumor progression and cancer. An in-depth understanding of this phenomenon can provide insights into the mechanism of gene repression. We present a study comparing methylated DNA and normal DNA wrt its persistence length and contour length. Although, previous experiments and studies show no difference between the physical properties of the two, the data collected and interpreted here gives a different picture to the methylation phenomena and its effect on gene silencing. The study was extended to the artificially reconstituted chromatin and its interactions with the methyl CpG binding proteins were also probed.
ContributorsKaur, Parminder (Author) / Lindsay, Stuart (Thesis advisor) / Ros, Robert (Committee member) / Tao, Nongjian (Committee member) / Vaiana, Sara (Committee member) / Beckenstein, Oliver (Committee member) / Arizona State University (Publisher)
Created2012
Description
Transportation of material across a cell membrane is a vital process for maintaininghomeostasis. Na+/H+ antiporters, for instance, help maintain cell volume and regulate
intracellular sodium and proton concentrations. They are prime drug targets, since
dysfunction of these crucial proteins in humans is linked to heart and neurodegenerative
diseases. Due to their placement in a cell membrane, their study is particularly difficult
compared to globular proteins, which is likely the reason the transport mechanisms
for these proteins are not entirely known. This work focuses on the electrogenic
bacterial homologs Thermus thermophilus NapA (TtNapA) and Echerichia coli NhaA
(EcNhaA), each transporting one sodium from the interior of the cell for two protons
on outside of the cell. Even though X-ray crystal structures for both of these systems
have been resolved, their study through molecular dynamics (MD) simulations is
limited. The dynamic protonation and deprotonation of the binding site residues is
a fundamental process in the transport cycle, which currently cannot be explored
intuitively with standard MD methodologies. Apart from this limitation, simulation
performance is only a fraction of what is needed to understand the full transport
process, particularly when it comes to global conformational changes. This work
seeks to overcome these limitations through the development and application of a
multiscale thermodynamic and kinetic framework for constructing models capable of
predicting experimental observables, such as the dependence of transporter turnover
on membrane voltage. These models allow interpretation of the effects of individual
processes on the function as a whole. This procedure is demonstrated for TtNapA and
the connection between structure and function is shown by computing cycle turnover
across a range of non-equilibrium conditions.
ContributorsKenney, Ian Michael (Author) / Beckstein, Oliver (Thesis advisor) / Ozkan, Sefika Banu (Committee member) / Heyden, Matthias (Committee member) / Vaiana, Sara (Committee member) / Arizona State University (Publisher)
Created2022
Description
Protein interactions with the environment are crucial for proper function, butinteraction mechanisms are not always understood. In G protein-coupled receptors
(GPCRs), cholesterol modulates the function in some, but not all, GPCRs. Coarse
grained molecular dynamics was used to determine a set of contact events for each
residue and fit to a biexponential to determine the time scale of the long contacts
observed in simulation. Several residues of interest were indicated in CCK1 R near
Y140, which is known to render CCK1 R insensitive to cholesterol when mutated to
alanine. A difference in the overall residence time between CCK1 R and its cholesterol
insensitive homologue CCK2 R was also observed, indicating the ability to predict
relative cholesterol binding for homologous proteins.
Occasionally large errors and poor fits to the data were observed, so several
improvements were made, including generalizing the model to include K exponential
components. The sets of residence times in the improved method were analyzed using
Bayesian nonparametrics, which allowed for error estimations and the classification of
contact events to the individual components. Ten residues in three GPCRs bound to
cholesterol in experimental structures had large tau. Slightly longer overall interaction
time for the cholesterol sensitive CB1 R over its insensitive homologue CB2 R was also
observed.
The interactions between the cystic fibrosis transmembrane conductance regulator
(CFTR) and GlyH-101, an open-channel blocker, were analyzed using molecular
dynamics. The results showed the bromine in GlyH-101 was in constant contact with
F337, which is just inside the extracellular gate. The simulations also showed an
insertion of GlyH-101 between TM1 and TM6 deeper than the starting binding pose.
Once inserted deeper between TMs 1 and 6, the number of persistent contacts also
increased. This proposed binding pose may help in future investigations of CFTR
and help determine an open-channel structure for the protein, which in turn may help
in the development of treatments for various medical conditions. Overall, the use
of molecular dynamics and state of the art analysis tools can be useful in the study
of membrane proteins and eventuallyin the development of treatments for ailments
stemming from their atypical function.
ContributorsSexton, Ricky (Author) / Beckstein, Oliver (Thesis advisor) / Presse, Steve (Committee member) / Ozkan, Sefika B. (Committee member) / Hariadi, Rizal (Committee member) / Arizona State University (Publisher)
Created2022
Description
Proteins, the machinery of life, perform a vast array of essential biochemical functions, evolving over time to acquire diverse roles within biological systems. This evolution, primarily driven by mutations within protein sequences, can profoundly impact protein function, potentially leading to various diseases. This thesis aims to dissect the intricate mechanisms through which genetic mutations influence protein functionality, focusing on the dynamic alterations induced by single and combined mutations. Employing a suite of computational tools, including molecular dynamics (MD) simulations and proven analysis metrics like the Dynamic Flexibility Index (DFI) and Dynamic Coupling Index (DCI), I analyze protein dynamics to uncover the common dynamic effects associated with disease causation and compensatory mechanisms. This analysis extends to exploring the concept of epistasis through the lens of protein dynamics, showing how combinations of mutations interact within the protein's 3D structure to either exacerbate or mitigate the functional impacts of individual mutations. The use of EpiScore, a computational tool designed to quantify the epistatic effects of mutations, provides insight on the combined dynamic effects two mutations might have. This is particularly evident in the analysis of rare alleles within human populations, where certain allele combinations, despite their individual rarity, frequently co-occur, suggesting a mechanism of dynamic compensation. This phenomenon is further investigated in the context of the SARS-CoV-2 spike protein, providing insights into viral evolution and the adaptive significance of specific mutations. Additionally, I delve into the role of Intrinsically Disordered Regions (IDRs) in protein function and mutation compensation, highlighting the need for sophisticated dynamics analysis tools to capture the full spectrum of mutation effects. By integrating these analyses, this thesis unveils a complex picture of how proteins' dynamic properties, shaped by mutations, underpin their functional evolution and disease outcomes.
ContributorsOse, Nicholas James (Author) / Ozkan, Sefika Banu (Thesis advisor) / Hariadi, Rizal (Committee member) / Beckstein, Oliver (Committee member) / Vaiana, Sara (Committee member) / Arizona State University (Publisher)
Created2024
Description
Calcitonin Gene-Related Peptide (CGRP) is an intrinsically disordered protein
that has no regular secondary structure, but plays an important role in vasodilation and pain transmission in migraine. Little is known about the structure and dynamics of the monomeric state of CGRP or how CGRP is able to function in the cell, despite the lack of regular secondary structure. This work focuses characterizing the non-local structural and dynamical properties of the CGRP monomer in solution, and understanding how these are affected by the sequence and the solution environment. The unbound, free state of CGRP is measured using a nanosecond laser-pump spectrophotometer, which allows measuring the end-to-end distance (a non-local structural property) and the rate of end-to-end contact formation (intra-chain diffusional dynamics). The data presented in this work show that electrostatic interactions strongly modulate the structure of CGRP, and that peptide-solvent interactions are sequence and charge dependent and can have a significant effect on the internal dynamics of the peptide. In the last few years migraine research has shifted focus to disrupting the CGRP-receptor pathway through the design of pharmacological drugs that bind to either CGRP or its receptor, inhibiting receptor activation and therefore preventing or reducing the frequency of migraine attacks. Understanding what types of intra- and inter-chain interactions dominate in CGRP can help better design drugs that disrupt the binding of CGRP to its receptor.
that has no regular secondary structure, but plays an important role in vasodilation and pain transmission in migraine. Little is known about the structure and dynamics of the monomeric state of CGRP or how CGRP is able to function in the cell, despite the lack of regular secondary structure. This work focuses characterizing the non-local structural and dynamical properties of the CGRP monomer in solution, and understanding how these are affected by the sequence and the solution environment. The unbound, free state of CGRP is measured using a nanosecond laser-pump spectrophotometer, which allows measuring the end-to-end distance (a non-local structural property) and the rate of end-to-end contact formation (intra-chain diffusional dynamics). The data presented in this work show that electrostatic interactions strongly modulate the structure of CGRP, and that peptide-solvent interactions are sequence and charge dependent and can have a significant effect on the internal dynamics of the peptide. In the last few years migraine research has shifted focus to disrupting the CGRP-receptor pathway through the design of pharmacological drugs that bind to either CGRP or its receptor, inhibiting receptor activation and therefore preventing or reducing the frequency of migraine attacks. Understanding what types of intra- and inter-chain interactions dominate in CGRP can help better design drugs that disrupt the binding of CGRP to its receptor.
ContributorsSizemore, Sara (Author) / Vaiana, Sara (Thesis advisor) / Ghirlanda, Giovanna (Committee member) / Ros, Robert (Committee member) / Lindsay, Stuart (Committee member) / Ozkan, Sefika (Committee member) / Arizona State University (Publisher)
Created2015
Description
Proteins are essential for most biological processes that constitute life. The function of a protein is encoded within its 3D folded structure, which is determined by its sequence of amino acids. A variation of a single nucleotide in the DNA during transcription (nSNV) can alter the amino acid sequence (i.e., a mutation in the protein sequence), which can adversely impact protein function and sometimes cause disease. These mutations are the most prevalent form of variations in humans, and each individual genome harbors tens of thousands of nSNVs that can be benign (neutral) or lead to disease. The primary way to assess the impact of nSNVs on function is through evolutionary approaches based on positional amino acid conservation. These approaches are largely inadequate in the regime where positions evolve at a fast rate. We developed a method called dynamic flexibility index (DFI) that measures site-specific conformational dynamics of a protein, which is paramount in exploring mechanisms of the impact of nSNVs on function. In this thesis, we demonstrate that DFI can distinguish the disease-associated and neutral nSNVs, particularly for fast evolving positions where evolutionary approaches lack predictive power. We also describe an additional dynamics-based metric, dynamic coupling index (DCI), which measures the dynamic allosteric residue coupling of distal sites on the protein with the functionally critical (i.e., active) sites. Through DCI, we analyzed 200 disease mutations of a specific enzyme called GCase, and a proteome-wide analysis of 75 human enzymes containing 323 neutral and 362 disease mutations. In both cases we observed that sites with high dynamic allosteric residue coupling with the functional sites (i.e., DARC spots) have an increased susceptibility to harboring disease nSNVs. Overall, our comprehensive proteome-wide analysis suggests that incorporating these novel position-specific conformational dynamics based metrics into genomics can complement current approaches to increase the accuracy of diagnosing disease nSNVs. Furthermore, they provide mechanistic insights about disease development. Lastly, we introduce a new, purely sequence-based model that can estimate the dynamics profile of a protein by only utilizing coevolution information, eliminating the requirement of the 3D structure for determining dynamics.
ContributorsButler, Brandon Mac (Author) / Ozkan, S. Banu (Thesis advisor) / Vaiana, Sara (Committee member) / Ghirlanda, Giovanna (Committee member) / Ros, Robert (Committee member) / Arizona State University (Publisher)
Created2016
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