Matching Items (74)
Description
Surgery as a profession requires significant training to improve both clinical decision making and psychomotor proficiency. In the medical knowledge domain, tools have been developed, validated, and accepted for evaluation of surgeons' competencies. However, assessment of the psychomotor skills still relies on the Halstedian model of apprenticeship, wherein surgeons are observed during residency for judgment of their skills. Although the value of this method of skills assessment cannot be ignored, novel methodologies of objective skills assessment need to be designed, developed, and evaluated that augment the traditional approach. Several sensor-based systems have been developed to measure a user's skill quantitatively, but use of sensors could interfere with skill execution and thus limit the potential for evaluating real-life surgery. However, having a method to judge skills automatically in real-life conditions should be the ultimate goal, since only with such features that a system would be widely adopted. This research proposes a novel video-based approach for observing surgeons' hand and surgical tool movements in minimally invasive surgical training exercises as well as during laparoscopic surgery. Because our system does not require surgeons to wear special sensors, it has the distinct advantage over alternatives of offering skills assessment in both learning and real-life environments. The system automatically detects major skill-measuring features from surgical task videos using a computing system composed of a series of computer vision algorithms and provides on-screen real-time performance feedback for more efficient skill learning. Finally, the machine-learning approach is used to develop an observer-independent composite scoring model through objective and quantitative measurement of surgical skills. To increase effectiveness and usability of the developed system, it is integrated with a cloud-based tool, which automatically assesses surgical videos upload to the cloud.
ContributorsIslam, Gazi (Author) / Li, Baoxin (Thesis advisor) / Liang, Jianming (Thesis advisor) / Dinu, Valentin (Committee member) / Greenes, Robert (Committee member) / Smith, Marshall (Committee member) / Kahol, Kanav (Committee member) / Patel, Vimla L. (Committee member) / Arizona State University (Publisher)
Created2013
Description
Cyanovirin-N (CVN) is a cyanobacterial lectin with potent anti-HIV activity, mediated by binding to the N-linked oligosaccharide moiety of the envelope protein gp120. CVN offers a scaffold to develop multivalent carbohydrate-binding proteins with tunable specificities and affinities. I present here biophysical calculations completed on a monomeric-stabilized mutant of cyanovirin-N, P51G-m4-CVN, in which domain A binding activity is abolished by four mutations; with comparisons made to CVNmutDB, in which domain B binding activity is abolished. Using Monte Carlo calculations and docking simulations, mutations in CVNmutDB were considered singularly, and the mutations E41A/G and T57A were found to impact the affinity towards dimannose the greatest. 15N-labeled proteins were titrated with Manα(1-2)Manα, while following chemical shift perturbations in NMR spectra. The mutants, E41A/G and T57A, had a larger Kd than P51G-m4-CVN, matching the trends predicted by the calculations. We also observed that the N42A mutation affects the local fold of the binding pocket, thus removing all binding to dimannose. Characterization of the mutant N53S showed similar binding affinity to P51G-m4-CVN. Using biophysical calculations allows us to study future iterations of models to explore affinities and specificities. In order to further elucidate the role of multivalency, I report here a designed covalent dimer of CVN, Nested cyanovirin-N (Nested CVN), which has four binding sites. Nested CVN was found to have comparable binding affinity to gp120 and antiviral activity to wt CVN. These results demonstrate the ability to create a multivalent, covalent dimer that has comparable results to that of wt CVN.
WW domains are small modules consisting of 32-40 amino acids that recognize proline-rich peptides and are found in many signaling pathways. We use WW domain sequences to explore protein folding by simulations using Zipping and Assembly Method. We identified five crucial contacts that enabled us to predict the folding of WW domain sequences based on those contacts. We then designed a folded WW domain peptide from an unfolded WW domain sequence by introducing native contacts at those critical positions.
WW domains are small modules consisting of 32-40 amino acids that recognize proline-rich peptides and are found in many signaling pathways. We use WW domain sequences to explore protein folding by simulations using Zipping and Assembly Method. We identified five crucial contacts that enabled us to predict the folding of WW domain sequences based on those contacts. We then designed a folded WW domain peptide from an unfolded WW domain sequence by introducing native contacts at those critical positions.
ContributorsWoodrum, Brian William (Author) / Ghirlanda, Giovanna (Thesis advisor) / Redding, Kevin (Committee member) / Wang, Xu (Committee member) / Arizona State University (Publisher)
Created2014
Description
Spider dragline silk is an outstanding biopolymer with a strength that exceeds steel by weight and a toughness greater than high-performance fibers like Kevlar. For this reason, structural and dynamic studies on the spider silk are of great importance for developing future biomaterials. The spider dragline silk comprises two silk proteins, Major ampullate Spidroin 1 and 2 (MaSp1 and 2), which are synthesized and stored in the major ampullate (MA) gland of spiders. The initial state of the silk proteins within Black Widow MA glands was probed with solution-state NMR spectroscopy. The conformation dependent chemical shifts information indicates that the silk proteins are unstructured and in random coil conformation. 15N relaxation parameters, T1, T2 and 15N-{1H} steady-state NOE were measured to probe the backbone dynamics for MA silk proteins. These measurements indicate fast sub-nanosecond timescale backbone dynamics for the repetitive core of spider MA proteins indicating that the silk proteins are unfolded, highly flexible random coils in the MA gland. The translational diffusion coefficients of the spider silk proteins within the MA gland were measured using 1H diffusion NMR at 1H sites from different amino acids. A phenomenon was observed where the measured diffusion coefficients decrease with an increase in the diffusion delay used. The mean displacement along the external magnetic field was found to be 0.35 μm and independent of the diffusion delay. The results indicate that the diffusion of silk protein was restricted due to intermolecular cross-linking with only segmental diffusion observable.
To understand how a spider converts the unfolded protein spinning dope into a highly structured and oriented in the super fiber,the effect of acidification on spider silk assembly was investigated on native spidroins from the major ampullate (MA) gland fluid excised from Latrodectus hesperus (Black Widow) spiders. The in vitro spider silk assembly kinetics were monitored as a function of pH with a 13C solid-state Magic Angle Spinning (MAS) NMR approach. The results confirm the importance of acidic pH in the spider silk self-assembly process with observation of a sigmoidal nucleation-elongation kinetic profile. The rates of nucleation and elongation and the percentage of β-sheet structure in the grown fibers depend on pH.
The secondary structure of the major ampullate silk from Peucetia viridians (Green Lynx) spiders was characterized by X-ray diffraction (XRD) and solid-state NMR spectroscopy. From XRD measurement, β-sheet nano-crystallites were observed that are highly oriented along the fiber axis with an orientational order of 0.980. Compare to the crystalline region, the amorphous region was found to be partially oriented with an orientational order of 0.887. Further, two dimensional 13C-13C through-space and through-bond solid-state NMR experiments provide structural analysis for the repetitive amino acid motifs in the silk proteins. The nano-crystallites are mainly alanine-rich β-sheet structures. The total percentage of crystalline region is determined to be 40.0±1.2 %. 18±1 % of alanine, 60±2 % glycine and 54±2 % serine are determined to be incorporated into helical conformations while 82±1 % of alanine, 40±3 % glycine and 46±2 % serine are in the β-sheet conformation.
To understand how a spider converts the unfolded protein spinning dope into a highly structured and oriented in the super fiber,the effect of acidification on spider silk assembly was investigated on native spidroins from the major ampullate (MA) gland fluid excised from Latrodectus hesperus (Black Widow) spiders. The in vitro spider silk assembly kinetics were monitored as a function of pH with a 13C solid-state Magic Angle Spinning (MAS) NMR approach. The results confirm the importance of acidic pH in the spider silk self-assembly process with observation of a sigmoidal nucleation-elongation kinetic profile. The rates of nucleation and elongation and the percentage of β-sheet structure in the grown fibers depend on pH.
The secondary structure of the major ampullate silk from Peucetia viridians (Green Lynx) spiders was characterized by X-ray diffraction (XRD) and solid-state NMR spectroscopy. From XRD measurement, β-sheet nano-crystallites were observed that are highly oriented along the fiber axis with an orientational order of 0.980. Compare to the crystalline region, the amorphous region was found to be partially oriented with an orientational order of 0.887. Further, two dimensional 13C-13C through-space and through-bond solid-state NMR experiments provide structural analysis for the repetitive amino acid motifs in the silk proteins. The nano-crystallites are mainly alanine-rich β-sheet structures. The total percentage of crystalline region is determined to be 40.0±1.2 %. 18±1 % of alanine, 60±2 % glycine and 54±2 % serine are determined to be incorporated into helical conformations while 82±1 % of alanine, 40±3 % glycine and 46±2 % serine are in the β-sheet conformation.
ContributorsXu, Dian (Author) / Yarger, Jeffery L (Thesis advisor) / Holland, Gregory P (Thesis advisor) / Wang, Xu (Committee member) / Liu, Yan (Committee member) / Arizona State University (Publisher)
Created2015
Description
The apolipoprotein E (APOE) e4 genotype is the most prevalent known genetic risk factor for Alzheimer's disease (AD). In this paper, we examined the longitudinal effect of APOE e4 on hippocampal morphometry in Alzheimer's Disease Neuroimaging Initiative (ADNI). Generally, atrophy of hippocampus has more chance occurs in AD patients who carrying the APOE e4 allele than those who are APOE e4 noncarriers. Also, brain structure and function depend on APOE genotype not just for Alzheimer's disease patients but also in health elderly individuals, so APOE genotyping is considered critical in clinical trials of Alzheimer's disease. We used a large sample of elderly participants, with the help of a new automated surface registration system based on surface conformal parameterization with holomorphic 1-forms and surface fluid registration. In this system, we automatically segmented and constructed hippocampal surfaces from MR images at many different time points, such as 6 months, 1- and 2-year follow up. Between the two different hippocampal surfaces, we did the high-order correspondences, using a novel inverse consistent surface fluid registration method. At each time point, using Hotelling's T^2 test, we found significant morphological deformation in APOE e4 carriers relative to noncarriers in the entire cohort as well as in the non-demented (pooled MCI and control) subjects, affecting the left hippocampus more than the right, and this effect was more pronounced in e4 homozygotes than heterozygotes.
ContributorsLi, Bolun (Author) / Wang, Yalin (Thesis advisor) / Maciejewski, Ross (Committee member) / Liang, Jianming (Committee member) / Arizona State University (Publisher)
Created2015
Description
An animal's ability to produce protein-based silk materials has evolved independently in many different arthropod lineages, satisfying various ecological necessities. However, regardless of their wide range of uses and their potential industrial and biomedical applications, advanced knowledge on the molecular structure of silk biopolymers is largely limited to those produced by spiders (order Araneae) and silkworms (order Lepidoptera). This thesis provides an in-depth molecular-level characterization of silk fibers produced by two vastly different insects: the caddisfly larvae (order Trichoptera) and the webspinner (order Embioptera).
The molecular structure of caddisfly larval silk from the species Hesperophylax consimilis was characterized using solid-state nuclear magnetic resonance (ss-NMR) and Wide Angle X-ray Diffraction (WAXD) techniques. This insect, which typically dwells in freshwater riverbeds and streams, uses silk fibers as a strong and sticky nanoadhesive material to construct cocoons and cases out available debris. Conformation-sensitive 13C chemical shifts and 31P chemical shift anisotropy (CSA) information strongly support a unique protein motif in which phosphorylated serine- rich repeats (pSX)4 complex with di- and trivalent cations to form rigid nanocrystalline β-sheets. Additionally, it is illustrated through 31P NMR and WAXD data that these nanocrystalline structures can be reversibly formed, and depend entirely on the presence of the stabilizing cations.
Nanofiber silks produced by webspinners (order Embioptera) were also studied herein. This work addresses discrepancies in the literature regarding fiber diameters and tensile properties, revealing that the nanofibers are about 100 nm in diameter, and are stronger (around 500 MPa mean ultimate stress) than previous works suggested. Fourier-transform Infrared Spectroscopy (FT-IR), NMR and WAXD results find that approximately 70% of the highly repetitive glycine- and serine-rich protein core is composed of β-sheet nanocrystalline structures. In addition, FT-IR and Gas-chromatography mass spectroscopy (GC-MS) data revealed a hydrophobic surface coating rich in long-chain lipids. The effect of this surface coating was studied with contact angle techniques; it is shown that the silk sheets are extremely hydrophobic, yet due to the microstructural and nanostructural details of the silk surface, are surprisingly adhesive to water.
The molecular structure of caddisfly larval silk from the species Hesperophylax consimilis was characterized using solid-state nuclear magnetic resonance (ss-NMR) and Wide Angle X-ray Diffraction (WAXD) techniques. This insect, which typically dwells in freshwater riverbeds and streams, uses silk fibers as a strong and sticky nanoadhesive material to construct cocoons and cases out available debris. Conformation-sensitive 13C chemical shifts and 31P chemical shift anisotropy (CSA) information strongly support a unique protein motif in which phosphorylated serine- rich repeats (pSX)4 complex with di- and trivalent cations to form rigid nanocrystalline β-sheets. Additionally, it is illustrated through 31P NMR and WAXD data that these nanocrystalline structures can be reversibly formed, and depend entirely on the presence of the stabilizing cations.
Nanofiber silks produced by webspinners (order Embioptera) were also studied herein. This work addresses discrepancies in the literature regarding fiber diameters and tensile properties, revealing that the nanofibers are about 100 nm in diameter, and are stronger (around 500 MPa mean ultimate stress) than previous works suggested. Fourier-transform Infrared Spectroscopy (FT-IR), NMR and WAXD results find that approximately 70% of the highly repetitive glycine- and serine-rich protein core is composed of β-sheet nanocrystalline structures. In addition, FT-IR and Gas-chromatography mass spectroscopy (GC-MS) data revealed a hydrophobic surface coating rich in long-chain lipids. The effect of this surface coating was studied with contact angle techniques; it is shown that the silk sheets are extremely hydrophobic, yet due to the microstructural and nanostructural details of the silk surface, are surprisingly adhesive to water.
ContributorsAddison, John Bennett (Author) / Yarger, Jeffery L (Thesis advisor) / Holland, Gregory P (Thesis advisor) / Wang, Xu (Committee member) / Ros, Robert (Committee member) / Arizona State University (Publisher)
Created2014
Description
Detecting anatomical structures, such as the carina, the pulmonary trunk and the aortic arch, is an important step in designing a CAD system of detection Pulmonary Embolism. The presented CAD system gets rid of the high-level prior defined knowledge to become a system which can easily extend to detect other anatomic structures. The system is based on a machine learning algorithm --- AdaBoost and a general feature --- Haar. This study emphasizes on off-line and on-line AdaBoost learning. And in on-line AdaBoost, the thesis further deals with extremely imbalanced condition. The thesis first reviews several knowledge-based detection methods, which are relied on human being's understanding of the relationship between anatomic structures. Then the thesis introduces a classic off-line AdaBoost learning. The thesis applies different cascading scheme, namely multi-exit cascading scheme. The comparison between the two methods will be provided and discussed. Both of the off-line AdaBoost methods have problems in memory usage and time consuming. Off-line AdaBoost methods need to store all the training samples and the dataset need to be set before training. The dataset cannot be enlarged dynamically. Different training dataset requires retraining the whole process. The retraining is very time consuming and even not realistic. To deal with the shortcomings of off-line learning, the study exploited on-line AdaBoost learning approach. The thesis proposed a novel pool based on-line method with Kalman filters and histogram to better represent the distribution of the samples' weight. Analysis of the performance, the stability and the computational complexity will be provided in the thesis. Furthermore, the original on-line AdaBoost performs badly in imbalanced conditions, which occur frequently in medical image processing. In image dataset, positive samples are limited and negative samples are countless. A novel Self-Adaptive Asymmetric On-line Boosting method is presented. The method utilized a new asymmetric loss criterion with self-adaptability according to the ratio of exposed positive and negative samples and it has an advanced rule to update sample's importance weight taking account of both classification result and sample's label. Compared to traditional on-line AdaBoost Learning method, the new method can achieve far more accuracy in imbalanced conditions.
ContributorsWu, Hong (Author) / Liang, Jianming (Thesis advisor) / Farin, Gerald (Committee member) / Ye, Jieping (Committee member) / Arizona State University (Publisher)
Created2011
Description
Glycosaminoglycans (GAGs) are a class of complex biomolecules comprised of linear, sulfated polysaccharides whose presence on cell surfaces and in the extracellular matrix involve them in many physiological phenomena as well as in interactions with pathogenic microbes. Decorin binding protein A (DBPA), a Borrelia surface lipoprotein involved in the infectivity of Lyme disease, is responsible for binding GAGs found on decorin, a small proteoglycan present in the extracellular matrix. Different DBPA strains have notable sequence heterogeneity that results in varying levels of GAG-binding affinity. In this dissertation, the structures and GAG-binding mechanisms for three strains of DBPA (B31 and N40 DBPAs from B. burgdorferi and PBr DBPA from B. garinii) are studied to determine why each strain has a different affinity for GAGs. These three strains have similar topologies consisting of five α-helices held together by a hydrophobic core as well as two long flexible segments: a linker between helices one and two and a C-terminal tail. This structural arrangement facilitates the formation of a basic pocket below the flexible linker which is the primary GAG-binding epitope. However, this GAG-binding site can be occluded by the flexible linker, which makes the linker a negative regulator of GAG-binding. ITC and NMR titrations provide KD values that show PBr DBPA binds GAGs with higher affinity than B31 and N40 DBPAs, while N40 binds with the lowest affinity of the three. Work in this thesis demonstrates that much of the discrepancies seen in GAG affinities of the three DBPAs can be explained by the amino acid composition and conformation of the linker. Mutagenesis studies show that B31 DBPA overcomes the pocket obstruction with the BXBB motif in its linker while PBr DBPA has a retracted linker that exposes the basic pocket as well as a secondary GAG-binding site. N40 DBPA, however, does not have any evolutionary modifications to its structure to enhance GAG binding which explains its lower affinity for GAGs. GMSA and ELISA assays, along with NMR PRE experiments, confirm that structural changes in the linker do affect GAG-binding and, as a result, the linker is responsible for regulating GAG affinity.
ContributorsMorgan, Ashli M (Author) / Wang, Xu (Thesis advisor) / Allen, James (Committee member) / Yarger, Jeffery (Committee member) / Arizona State University (Publisher)
Created2015
Description
This dissertation will investigate two of the most promising high-capacity anode
materials for lithium-based batteries: silicon (Si) and metal lithium (Li). It will focus on
studying the mechanical behaviors of the two materials during charge and discharge and
understanding how these mechanical behaviors may affect their electrochemical
performance.
In the first part, amorphous Si anode will be studied. Despite many existing studies
on silicon (Si) anodes for lithium ion batteries (LIBs), many essential questions still exist
on compound formation, composition, and properties. Here it is shown that some
previously accepted findings do not truthfully reflect the actual lithiation mechanisms in
realistic battery configurations. Furthermore the correlation between structure and
mechanical properties in these materials has not been properly established. Here, a rigorous
and thorough study is performed to comprehensively understand the electrochemical
reaction mechanisms of amorphous-Si (a-Si) in a realistic LIB configuration. In-depth
microstructural characterization was performed and correlations were established between
Li-Si composition, volumetric expansion, and modulus/hardness. It is found that the
lithiation process of a-Si in a real battery setup is a single-phase reaction rather than the
accepted two-phase reaction obtained from in-situ TEM experiments. The findings in this
dissertation establish a reference to quantitatively explain many key metrics for lithiated a
Si as anodes in real LIBs, and can be used to rationally design a-Si based high-performance
LIBs guided by high-fidelity modeling and simulations.
In the second part, Li metal anode will be investigated. Problems related to dendrite
growth on lithium metal anodes such as capacity loss and short circuit present major
barriers to the next-generation high-energy-density batteries. The development of
successful mitigation strategies is impeded by the incomplete understanding of the Li
dendrite growth mechanisms. Here the enabling role of plating residual stress in dendrite
initiation through novel experiments of Li electrodeposition on soft substrates is confirmed,
and the observations is explained with a stress-driven dendrite growth model. Dendrite
growth is mitigated on such soft substrates through surface-wrinkling-induced stress
relaxation in deposited Li film. It is demonstrated that this new dendrite mitigation
mechanism can be utilized synergistically with other existing approaches in the form of
three-dimensional (3D) soft scaffolds for Li plating, which achieves superior coulombic
efficiency over conventional hard copper current collectors under large current density.
materials for lithium-based batteries: silicon (Si) and metal lithium (Li). It will focus on
studying the mechanical behaviors of the two materials during charge and discharge and
understanding how these mechanical behaviors may affect their electrochemical
performance.
In the first part, amorphous Si anode will be studied. Despite many existing studies
on silicon (Si) anodes for lithium ion batteries (LIBs), many essential questions still exist
on compound formation, composition, and properties. Here it is shown that some
previously accepted findings do not truthfully reflect the actual lithiation mechanisms in
realistic battery configurations. Furthermore the correlation between structure and
mechanical properties in these materials has not been properly established. Here, a rigorous
and thorough study is performed to comprehensively understand the electrochemical
reaction mechanisms of amorphous-Si (a-Si) in a realistic LIB configuration. In-depth
microstructural characterization was performed and correlations were established between
Li-Si composition, volumetric expansion, and modulus/hardness. It is found that the
lithiation process of a-Si in a real battery setup is a single-phase reaction rather than the
accepted two-phase reaction obtained from in-situ TEM experiments. The findings in this
dissertation establish a reference to quantitatively explain many key metrics for lithiated a
Si as anodes in real LIBs, and can be used to rationally design a-Si based high-performance
LIBs guided by high-fidelity modeling and simulations.
In the second part, Li metal anode will be investigated. Problems related to dendrite
growth on lithium metal anodes such as capacity loss and short circuit present major
barriers to the next-generation high-energy-density batteries. The development of
successful mitigation strategies is impeded by the incomplete understanding of the Li
dendrite growth mechanisms. Here the enabling role of plating residual stress in dendrite
initiation through novel experiments of Li electrodeposition on soft substrates is confirmed,
and the observations is explained with a stress-driven dendrite growth model. Dendrite
growth is mitigated on such soft substrates through surface-wrinkling-induced stress
relaxation in deposited Li film. It is demonstrated that this new dendrite mitigation
mechanism can be utilized synergistically with other existing approaches in the form of
three-dimensional (3D) soft scaffolds for Li plating, which achieves superior coulombic
efficiency over conventional hard copper current collectors under large current density.
ContributorsWang, Xu (Author) / Jiang, Hanqing (Thesis advisor) / Yu, Hongbin (Thesis advisor) / Chan, Candace (Committee member) / Wang, Liping (Committee member) / Qiong, Nian (Committee member) / Arizona State University (Publisher)
Created2018
Description
Unsupervised learning of time series data, also known as temporal clustering, is a challenging problem in machine learning. This thesis presents a novel algorithm, Deep Temporal Clustering (DTC), to naturally integrate dimensionality reduction and temporal clustering into a single end-to-end learning framework, fully unsupervised. The algorithm utilizes an autoencoder for temporal dimensionality reduction and a novel temporal clustering layer for cluster assignment. Then it jointly optimizes the clustering objective and the dimensionality reduction objective. Based on requirement and application, the temporal clustering layer can be customized with any temporal similarity metric. Several similarity metrics and state-of-the-art algorithms are considered and compared. To gain insight into temporal features that the network has learned for its clustering, a visualization method is applied that generates a region of interest heatmap for the time series. The viability of the algorithm is demonstrated using time series data from diverse domains, ranging from earthquakes to spacecraft sensor data. In each case, the proposed algorithm outperforms traditional methods. The superior performance is attributed to the fully integrated temporal dimensionality reduction and clustering criterion.
ContributorsMadiraju, NaveenSai (Author) / Liang, Jianming (Thesis advisor) / Wang, Yalin (Thesis advisor) / He, Jingrui (Committee member) / Arizona State University (Publisher)
Created2018
Description
The physiological phenomenon of sensing temperature is detected by transient
receptor (TRP) ion channels, which are pore forming proteins that reside in the
membrane bilayer. The cold and hot sensing TRP channels named TRPV1 and TRPM8
respectively, can be modulated by diverse stimuli and are finely tuned by proteins and
lipids. PIRT (phosphoinositide interacting regulator of TRP channels) is a small
membrane protein that modifies TRPV1 responses to heat and TRPM8 responses to cold.
In this dissertation, the first direct measurements between PIRT and TRPM8 are
quantified with nuclear magnetic resonance and microscale thermophoresis. Using
Rosetta computational biology, TRPM8 is modeled with a regulatory, and functionally
essential, lipid named PIP2. Furthermore, a PIRT ligand screen identified several novel
small molecular binders for PIRT as well a protein named calmodulin. The ligand
screening results implicate PIRT in diverse physiological functions. Additionally, sparse
NMR data and state of the art Rosetta protocols were used to experimentally guide PIRT
structure predictions. Finally, the mechanism of thermosensing from the evolutionarily
conserved sensing domain of TRPV1 was investigated using NMR. The body of work
presented herein advances the understanding of thermosensing and TRP channel function
with TRP channel regulatory implications for PIRT.
receptor (TRP) ion channels, which are pore forming proteins that reside in the
membrane bilayer. The cold and hot sensing TRP channels named TRPV1 and TRPM8
respectively, can be modulated by diverse stimuli and are finely tuned by proteins and
lipids. PIRT (phosphoinositide interacting regulator of TRP channels) is a small
membrane protein that modifies TRPV1 responses to heat and TRPM8 responses to cold.
In this dissertation, the first direct measurements between PIRT and TRPM8 are
quantified with nuclear magnetic resonance and microscale thermophoresis. Using
Rosetta computational biology, TRPM8 is modeled with a regulatory, and functionally
essential, lipid named PIP2. Furthermore, a PIRT ligand screen identified several novel
small molecular binders for PIRT as well a protein named calmodulin. The ligand
screening results implicate PIRT in diverse physiological functions. Additionally, sparse
NMR data and state of the art Rosetta protocols were used to experimentally guide PIRT
structure predictions. Finally, the mechanism of thermosensing from the evolutionarily
conserved sensing domain of TRPV1 was investigated using NMR. The body of work
presented herein advances the understanding of thermosensing and TRP channel function
with TRP channel regulatory implications for PIRT.
ContributorsSisco, Nicholas John (Author) / Van Horn, Wade D (Thesis advisor) / Mills, Jeremy H (Committee member) / Wang, Xu (Committee member) / Yarger, Jeff L (Committee member) / Arizona State University (Publisher)
Created2018