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- All Subjects: Biophysics
- Creators: Presse, Steve
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
OP50 Esherichia coli is a Gram-negative bacterium with a fast replication rate and can be easily manipulated, making it a model species for many science disciplines. To probe this bacterium’s search strategy, cultures were starved and the cell velocity was probed at various points later in time after perturbing the buffer in which the bacteria were located. To start, we added E.coli OP50 filtrate. In yet another experiment filtrate from a Bdellovibrio bacteriovorus (Gram-negative predator) culture was added to monitor the OP50’s differential response to cues from its environment. Using MATLAB code, thousands of E.coli tracks were measured.
ContributorsSanchez, Alec Jesus (Author) / Presse, Steve (Thesis director) / Gile, Gillian (Committee member) / School of Molecular Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
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
Transition metal ions such as Zn2+, Mn2+, Co2+, and Fe2+ play crucial roles in organisms from all kingdoms of life. The homeostasis of these ions is mainly regulated by a group of secondary transporters from the cation diffusion facilitator (CDF) family. The mammalian zinc transporters (ZnTs), a subfamily of CDF, have been an important target for study as they are associated with several diseases, such as diabetes, delayed growth and osteopenia, Alzheimer’s disease, and Parkinsonism. The bacterial homolog of ZnTs, YiiP, is the first CDF transporter with a determined structure and is used as a model for studying the structural and mechanistic properties of CDF transporters. On the other hand, Molecular dynamics simulation has emerged as a valuable computational tool for exploring the physical basis of biological macromolecules' structure and function with atomic precision at femtosecond resolution. This work aims to elucidate the roles of the three Zn$2+ binding sites found on each YiiP protomer and the role of protons in the transport process of CDFs, which remain under debate despite previous thermodynamic and structural studies on YiiP. Cryo-EM, microscale thermophoresis (MST) and molecular dynamics (MD) simulations were used to address these questions. With a Zn2+ model that accurately reproduces experimental structures of the binding clusters, the dynamical influence of zinc binding on the transporter was accessed through MD simulations, which was consistent with the new cryo-EM structures. Zinc binding affinities obtained through MST were used to infer the stoichiometry of Zn2+/H+ antiport in combination with a microscopic thermodynamic model and constant pH simulations. The most likely microstates of H$^+$ and Zn2+ binding indicated a transport stoichiometry of 1 Zn2+ to 2-3 H+ depending on the external pH. A model describing the entire transport cycle of YiiP was finally built on these findings, providing insight into the structural and mechanistic properties of CDF transporters.
ContributorsFan, Shujie (Author) / Beckstein, Oliver (Thesis advisor) / Ozkan, Banu (Committee member) / Heyden, Matthias (Committee member) / Van Horn, Wade (Committee member) / Arizona State University (Publisher)
Created2023
Description
The Bayesian paradigm provides a flexible and versatile framework for modeling complex biological systems without assuming a fixed functional form or other constraints on the underlying data. This dissertation explores the use of Bayesian nonparametric methods for analyzing fluorescence microscopy data in biophysics, with a focus on enumerating diffraction-limited particles, reconstructing potentials from trajectories corrupted by measurement noise, and inferring potential energy landscapes from fluorescence intensity experiments. This research demonstrates the power and potential of Bayesian methods for solving a variety of problems in fluorescence microscopy and biophysics more broadly.
ContributorsBryan IV, J Shepard (Author) / Presse, Steve (Thesis advisor) / Ozkan, Banu (Committee member) / Wadhwa, Navish (Committee member) / Shepherd, Doug (Committee member) / Arizona State University (Publisher)
Created2023
Description
Contrary to the traditional structure-function paradigm for proteins, intrinsically disorderedproteins (IDPs) and regions (IDRs) are highly disordered sequences that lack a fixed
crystal structure yet perform various biological activities such as cell signaling, regulation,
and recognition. The interactions of these disordered regions with water molecules are essential
in the conformational distribution. Hence, exploring their solvation thermodynamics
is crucial for understanding their functions, which are challenging to study experimentally.
In this thesis, classical Molecular Dynamics (MD), 3D-Two Phase Thermodynamics (3D-
2PT), and umbrella sampling have been employed to gain insights into the behaviors of
intrinsically disordered proteins (IDPs) and water.
In the first project, local and total solvation thermodynamics around the K-18 domain
of the intrinsically disordered protein Tau were compared, and simulated with four pairs
of modified and standard force fields. In empirical force fields, an imbalance between
intramolecular protein interactions and protein-water interactions often leads to collapsed
IDP structures in simulations. To counter this, various methods have been devised to refine
protein-water interaction models. This research applied both standard and adapted force
fields in simulations, scrutinizing the effects of each adjustment on solvation free energy.
In the second project, the MD-based 3D-2PT analysis was utilized to examine variations
in local entropy and number density of bulk water in response to an electric field, focusing
on the vicinity of reference water molecules.
In the third project, various peptide sequences were examined to quantify the free energy
involved when specific sequences, known as alpha-MoRFs (alpha-Molecular Recognition
Features), transition from intrinsically disordered states to structured secondary motifs
like the alpha-helix. The low folding free energy penalty of these sequences can be exploited
to design peptide-based or small-molecule drugs. Upon binding to alpha-MoRFs,
these drugs can stabilize the helix structure through a binding-induced folding mechanism.
Alpha-MoRFs were juxtaposed with entirely disordered sequences from known proteins,
with findings benchmarked against leading structure prediction models. Additionally, the
binding free energies of various alpha-MoRFs in their folded conformation were assessed
to discern if experimental binding free energies reflect the separate contributions of folding
and binding, as obtained from umbrella sampling simulations.
ContributorsMaiti, Sthitadhi (Author) / Heyden, Matthias (Thesis advisor) / Ozkan, S. Banu (Committee member) / Sulc, Petr (Committee member) / Arizona State University (Publisher)
Created2024
Description
Computational biophysics is a powerful tool for observing and understanding the microscopic machinery that underpins the biological world. Molecular modeling and simulations can help scientists understand a cell’s behavior and the mechanisms that drive it. Empirical evidence can provide information on the structure and organization of biomolecular machines, which serve as the backbone of biomolecular modeling. Experimental data from probing the cell’s inner workings can provide modelers with an initial structure from which they can hypothesize and independently verify function, complex formation, and response. Additionally, molecular data can be used to drive simulations toward less probable but equally interesting states. With the advent of machine learning, researchers now have an unprecedented opportunity to take advantage of the wealth of data collected in a biomolecular experiment. This dissertation presents a comprehensive review of atomistic modeling with cryo-electron microscopy and the development of new simulation strategies to maximize insights gained from experiments. The review covers the integration of cryo-EM and molecular dynamics, highlighting the evolution of their relationship and the recent history of MD innovations in cryo-EM modeling. It also covers the discoveries made possible by the integration of molecular modeling with cryo-EM. Next, this work presents a method for fitting small molecules into cryo-electron microscopy maps, which uses neural network potentials to parameterize a diverse set of ligands. The method obtained fitted structures commensurate with, if not better than, the structures submitted to the Protein Data Bank. Additionally, the work describes the data-guided Multi- Map methodology for ensemble refinement of molecular movies. The method shows that cryo-electron microscopy maps can be used to bias simulations along a specially constructed reaction coordinate and capture conformational transitions between known intermediates. The simulated pathways appear reversible with minimal hysteresis and require only low-resolution density information to guide the transition. Finally, the study analyzes the SARS-CoV-2 spike protein and the conformational heterogeneity of its receptor binding domain. The simulation was guided along an experimentally determined free energy landscape. The resulting motions from following a pathway of low-energy states show a degree of openness not observed in the static models. This sheds light on the mechanism by which the spike protein is utilized for host infection and provides a rational explanation for the effectiveness of certain therapeutics. This work contributes to the understanding of biomolecular modeling and the development of new strategies to provide valuable insights into the workings of cellular machinery.
ContributorsVant, John Wyatt (Author) / Singharoy, Abhishek (Thesis advisor) / Heyden, Matthias (Committee member) / Presse, Steve (Committee member) / Arizona State University (Publisher)
Created2024
Description
In this work, I present a Bayesian inference computational framework for the analysis of widefield microscopy data that addresses three challenges: (1) counting and localizing stationary fluorescent molecules; (2) inferring a spatially-dependent effective fluorescence profile that describes the spatially-varying rate at which fluorescent molecules emit subsequently-detected photons (due to different illumination intensities or different local environments); and (3) inferring the camera gain. My general theoretical framework utilizes the Bayesian nonparametric Gaussian and beta-Bernoulli processes with a Markov chain Monte Carlo sampling scheme, which I further specify and implement for Total Internal Reflection Fluorescence (TIRF) microscopy data, benchmarking the method on synthetic data. These three frameworks are self-contained, and can be used concurrently so that the fluorescence profile and emitter locations are both considered unknown and, under some conditions, learned simultaneously. The framework I present is flexible and may be adapted to accommodate the inference of other parameters, such as emission photophysical kinetics and the trajectories of moving molecules. My TIRF-specific implementation may find use in the study of structures on cell membranes, or in studying local sample properties that affect fluorescent molecule photon emission rates.
ContributorsWallgren, Ross (Author) / Presse, Steve (Thesis advisor) / Armbruster, Hans (Thesis advisor) / McCulloch, Robert (Committee member) / Arizona State University (Publisher)
Created2019
Description
The cell is a dense environment composes of proteins, nucleic acids, as well as other small molecules, which are constantly bombarding each other and interacting. These interactions and the diffusive motions are driven by internal thermal fluctuations. Upon collision, molecules can interact and form complexes. It is of interest to learn kinetic parameters such as reaction rates of one molecule converting to different species or two molecules colliding and form a new species as well as to learn diffusion coefficients.
Several experimental measurements can probe diffusion coefficients at the single-molecule and bulk level. The target of this thesis is on single-molecule methods, which can assess diffusion coefficients at the individual molecular level. For instance, super resolution methods like stochastic optical reconstruction microscopy (STORM) and photo activated localization microscopy (PALM), have a high spatial resolution with the cost of lower temporal resolution. Also, there is a different group of methods, such as MINFLUX, multi-detector tracking, which can track a single molecule with high spatio-temporal resolution. The problem with these methods is that they are only applicable to very diluted samples since they need to ensure existence of a single molecule in the region of interest (ROI).
In this thesis, the goal is to have the best of both worlds by achieving high spatio-temporal resolutions without being limited to a few molecules. To do so, one needs to refocus on fluorescence correlation spectroscopy (FCS) as a method that applies to both in vivo and in vitro systems with a high temporal resolution and relies on multiple molecules traversing a confocal volume for an extended period of time. The difficulty here is that the interpretation of the signal leads to different estimates for the kinetic parameters such as diffusion coefficients based on a different number of molecules we consider in the model. It is for this reason that the focus of this thesis is now on using Bayesian nonparametrics (BNPs) as a way to solve this model selection problem and extract kinetic parameters such as diffusion coefficients at the single-molecule level from a few photons, and thus with the highest temporal resolution as possible.
Several experimental measurements can probe diffusion coefficients at the single-molecule and bulk level. The target of this thesis is on single-molecule methods, which can assess diffusion coefficients at the individual molecular level. For instance, super resolution methods like stochastic optical reconstruction microscopy (STORM) and photo activated localization microscopy (PALM), have a high spatial resolution with the cost of lower temporal resolution. Also, there is a different group of methods, such as MINFLUX, multi-detector tracking, which can track a single molecule with high spatio-temporal resolution. The problem with these methods is that they are only applicable to very diluted samples since they need to ensure existence of a single molecule in the region of interest (ROI).
In this thesis, the goal is to have the best of both worlds by achieving high spatio-temporal resolutions without being limited to a few molecules. To do so, one needs to refocus on fluorescence correlation spectroscopy (FCS) as a method that applies to both in vivo and in vitro systems with a high temporal resolution and relies on multiple molecules traversing a confocal volume for an extended period of time. The difficulty here is that the interpretation of the signal leads to different estimates for the kinetic parameters such as diffusion coefficients based on a different number of molecules we consider in the model. It is for this reason that the focus of this thesis is now on using Bayesian nonparametrics (BNPs) as a way to solve this model selection problem and extract kinetic parameters such as diffusion coefficients at the single-molecule level from a few photons, and thus with the highest temporal resolution as possible.
ContributorsJazani, Sina (Author) / Presse, Steve (Thesis advisor) / Matyushov, Dmitry (Committee member) / Levitus, Marcia (Committee member) / Fricks, John (Committee member) / Arizona State University (Publisher)
Created2020