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- All Subjects: Biophysics
- All Subjects: Machine Learning
- Creators: Yan, Hao
Description
Solution conformations and dynamics of proteins and protein-DNA complexes are often difficult to predict from their crystal structures. The crystal structure only shows a snapshot of the different conformations these biological molecules can have in solution. Multiple different conformations can exist in solution and potentially have more importance in the biological activity. DNA sliding clamps are a family of proteins with known crystal structures. These clamps encircle the DNA and enable other proteins to interact more efficiently with the DNA. Eukaryotic PCNA and prokaryotic β clamp are two of these clamps, some of the most stable homo-oligomers known. However, their solution stability and conformational equilibrium have not been investigated in depth before. Presented here are the studies involving two sliding clamps: yeast PCNA and bacterial β clamp. These studies show that the β clamp has a very different solution stability than PCNA. These conclusions were reached through various different fluorescence-based experiments, including fluorescence correlation spectroscopy (FCS), Förster resonance energy transfer (FRET), single molecule fluorescence, and various time resolved fluorescence techniques. Interpretations of these, and all other, fluorescence-based experiments are often affected by the properties of the fluorophores employed. Often the fluorescence properties of these fluorophores are influenced by their microenvironments. Fluorophores are known to sometimes interact with biological molecules, and this can have pronounced effects on the rotational mobility and photophysical properties of the dye. Misunderstanding the effect of these photophysical and rotational properties can lead to a misinterpretation of the obtained data. In this thesis, photophysical behaviors of various organic dyes were studied in the presence of deoxymononucleotides to examine more closely how interactions between fluorophores and DNA bases can affect fluorescent properties. Furthermore, the properties of cyanine dyes when bound to DNA and the effect of restricted rotation on FRET are presented in this thesis. This thesis involves studying fluorophore photophysics in various microenvironments and then expanding into the solution stability and dynamics of the DNA sliding clamps.
ContributorsRanjit, Suman (Author) / Levitus, Marcia (Thesis advisor) / Lindsay, Stuart (Committee member) / Yan, Hao (Committee member) / Arizona State University (Publisher)
Created2013
Description
Single molecule DNA Sequencing technology has been a hot research topic in the recent decades because it holds the promise to sequence a human genome in a fast and affordable way, which will eventually make personalized medicine possible. Single molecule differentiation and DNA translocation control are the two main challenges in all single molecule DNA sequencing methods. In this thesis, I will first introduce DNA sequencing technology development and its application, and then explain the performance and limitation of prior art in detail. Following that, I will show a single molecule DNA base differentiation result obtained in recognition tunneling experiments. Furthermore, I will explain the assembly of a nanofluidic platform for single strand DNA translocation, which holds the promised to be integrated into a single molecule DNA sequencing instrument for DNA translocation control. Taken together, my dissertation research demonstrated the potential of using recognition tunneling techniques to serve as a general readout system for single molecule DNA sequencing application.
ContributorsLiu, Hao (Author) / Lindsay, Stuart M (Committee member) / Yan, Hao (Committee member) / Levitus, Marcia (Committee member) / Arizona State University (Publisher)
Created2013
Description
The F1Fo ATP synthase is required for energy conversion in almost all living organisms. The F1 complex is a molecular motor that uses ATP hydrolysis to drive rotation of the γ–subunit. It has not been previously possible to resolve the speed and position of the γ–subunit of the F1–ATPase as it rotates during a power stroke. The single molecule experiments presented here measured light scattered from 45X91 nm gold nanorods attached to the γ–subunit that provide an unprecedented 5 μs resolution of rotational position as a function of time. The product of velocity and drag, which were both measured directly, resulted in an average torque of 63±8 pN nm for the Escherichia coli F1-ATPase that was determined to be independent of the load. The rotational velocity had an initial (I) acceleration phase 15° from the end of the catalytic dwell, a slow (S) acceleration phase during ATP binding/ADP release (15°–60°), and a fast (F) acceleration phase (60°–90°) containing an interim deceleration (ID) phase (75°–82°). High ADP concentrations decreased the velocity of the S phase proportional to 'ADP-release' dwells, and the F phase proportional to the free energy derived from the [ADP][Pi]/[ATP] chemical equilibrium. The decreased affinity for ITP increased ITP-binding dwells by 10%, but decreased velocity by 40% during the S phase. This is the first direct evidence that nucleotide binding contributes to F1–ATPase torque. Mutations that affect specific phases of rotation were identified, some in regions of F1 previously considered not to contribute to rotation. Mutations βD372V and γK9I increased the F phase velocity, and γK9I increased the depth of the ID phase. The conversion between S and F phases was specifically affected by γQ269L. While βT273D, βD305E, and αR283Q decreased the velocity of all phases, decreases in velocity due to βD302T, γR268L and γT82A were confined to the I and S phases. The correlations between the structural locations of these mutations and the phases of rotation they affect provide new insight into the molecular basis for F1–ATPase γ-subunit rotation.
ContributorsMartin, James (Author) / Frasch, Wayne D (Thesis advisor) / Chandler, Douglas (Committee member) / Gaxiola, Roberto (Committee member) / Yan, Hao (Committee member) / Arizona State University (Publisher)
Created2012
Description
Proteins and peptides fold into dynamic structures that access a broad functional landscape, however, designing artificial polypeptide systems continues to be a great chal-lenge. Conversely, deoxyribonucleic acid (DNA) engineering is now routinely used to build a wide variety of two dimensional and three dimensional (3D) nanostructures from simple hybridization based rules, and their functional diversity can be significantly ex-panded through site specific incorporation of the appropriate guest molecules. This dis-sertation describes a gentle methodology for using short (8 nucleotide) peptide nucleic acid (PNA) linkers to assemble polypeptides within a 3D DNA nanocage, as a proof of concept for constructing artificial catalytic centers. PNA-polypeptide conjugates were synthesized directly using microwave assisted solid phase synthesis or alternatively PNA linkers were conjugated to biologically expressed proteins using chemical crosslinking. The PNA-polypeptides hybridized to the preassembled DNA nanocage at room tempera-ture or 11 ⁰C and could be assembled in a stepwise fashion. Time resolved fluorescence anisotropy and gel electrophoresis were used to determine that a negatively charged az-urin protein was repelled outside of the negatively charged DNA nanocage, while a posi-tively charged cytochrome c protein was retained inside. Spectroelectrochemistry and an in-gel luminol oxidation assay demonstrated the cytochrome c protein remained active within the DNA nanocage and its redox potential decreased modestly by 10 mV due to the presence of the DNA nanocage. These results demonstrate the benign PNA assembly conditions are ideal for preserving polypeptide structure and function, and will facilitate the polypeptide-based assembly of artificial catalytic centers inside a stable DNA nanocage. A prospective application of assembling multiple cyclic γ-PNA-peptides to mimic the oxygen-evolving complex (OEC) catalytic active site from photosystem II (PSII) is described. In this way, the robust catalytic capacity of PSII could be utilized, without suffering the light-induced damage that occurs by the photoreactions within PSII via triplet state formation, which limits the efficiency of natural photosynthesis. There-fore, this strategy has the potential to revolutionize the process of designing and building robust catalysts by leveraging nature's recipes, and also providing a flexible and con-trolled artificial environment that might even improve them further towards commercial viability.
ContributorsFlory, Justin David (Author) / Fromme, Petra (Thesis advisor) / Yan, Hao (Committee member) / Buttry, Daniel (Committee member) / Ghirlanda, Giovanna (Committee member) / Arizona State University (Publisher)
Created2014
Description
Image-based process monitoring has recently attracted increasing attention due to the advancement of the sensing technologies. However, existing process monitoring methods fail to fully utilize the spatial information of images due to their complex characteristics including the high dimensionality and complex spatial structures. Recent advancement of the unsupervised deep models such as a generative adversarial network (GAN) and generative adversarial autoencoder (AAE) has enabled to learn the complex spatial structures automatically. Inspired by this advancement, we propose an anomaly detection framework based on the AAE for unsupervised anomaly detection for images. AAE combines the power of GAN with the variational autoencoder, which serves as a nonlinear dimension reduction technique with regularization from the discriminator. Based on this, we propose a monitoring statistic efficiently capturing the change of the image data. The performance of the proposed AAE-based anomaly detection algorithm is validated through a simulation study and real case study for rolling defect detection.
ContributorsYeh, Huai-Ming (Author) / Yan, Hao (Thesis advisor) / Pan, Rong (Committee member) / Li, Jing (Committee member) / Arizona State University (Publisher)
Created2019
Description
Transfer learning is a sub-field of statistical modeling and machine learning. It refers to methods that integrate the knowledge of other domains (called source domains) and the data of the target domain in a mathematically rigorous and intelligent way, to develop a better model for the target domain than a model using the data of the target domain alone. While transfer learning is a promising approach in various application domains, my dissertation research focuses on the particular application in health care, including telemonitoring of Parkinson’s Disease (PD) and radiomics for glioblastoma.
The first topic is a Mixed Effects Transfer Learning (METL) model that can flexibly incorporate mixed effects and a general-form covariance matrix to better account for similarity and heterogeneity across subjects. I further develop computationally efficient procedures to handle unknown parameters and large covariance structures. Domain relations, such as domain similarity and domain covariance structure, are automatically quantified in the estimation steps. I demonstrate METL in an application of smartphone-based telemonitoring of PD.
The second topic focuses on an MRI-based transfer learning algorithm for non-invasive surgical guidance of glioblastoma patients. Limited biopsy samples per patient create a challenge to build a patient-specific model for glioblastoma. A transfer learning framework helps to leverage other patient’s knowledge for building a better predictive model. When modeling a target patient, not every patient’s information is helpful. Deciding the subset of other patients from which to transfer information to the modeling of the target patient is an important task to build an accurate predictive model. I define the subset of “transferrable” patients as those who have a positive rCBV-cell density correlation, because a positive correlation is confirmed by imaging theory and the its respective literature.
The last topic is a Privacy-Preserving Positive Transfer Learning (P3TL) model. Although negative transfer has been recognized as an important issue by the transfer learning research community, there is a lack of theoretical studies in evaluating the risk of negative transfer for a transfer learning method and identifying what causes the negative transfer. My work addresses this issue. Driven by the theoretical insights, I extend Bayesian Parameter Transfer (BPT) to a new method, i.e., P3TL. The unique features of P3TL include intelligent selection of patients to transfer in order to avoid negative transfer and maintain patient privacy. These features make P3TL an excellent model for telemonitoring of PD using an At-Home Testing Device.
The first topic is a Mixed Effects Transfer Learning (METL) model that can flexibly incorporate mixed effects and a general-form covariance matrix to better account for similarity and heterogeneity across subjects. I further develop computationally efficient procedures to handle unknown parameters and large covariance structures. Domain relations, such as domain similarity and domain covariance structure, are automatically quantified in the estimation steps. I demonstrate METL in an application of smartphone-based telemonitoring of PD.
The second topic focuses on an MRI-based transfer learning algorithm for non-invasive surgical guidance of glioblastoma patients. Limited biopsy samples per patient create a challenge to build a patient-specific model for glioblastoma. A transfer learning framework helps to leverage other patient’s knowledge for building a better predictive model. When modeling a target patient, not every patient’s information is helpful. Deciding the subset of other patients from which to transfer information to the modeling of the target patient is an important task to build an accurate predictive model. I define the subset of “transferrable” patients as those who have a positive rCBV-cell density correlation, because a positive correlation is confirmed by imaging theory and the its respective literature.
The last topic is a Privacy-Preserving Positive Transfer Learning (P3TL) model. Although negative transfer has been recognized as an important issue by the transfer learning research community, there is a lack of theoretical studies in evaluating the risk of negative transfer for a transfer learning method and identifying what causes the negative transfer. My work addresses this issue. Driven by the theoretical insights, I extend Bayesian Parameter Transfer (BPT) to a new method, i.e., P3TL. The unique features of P3TL include intelligent selection of patients to transfer in order to avoid negative transfer and maintain patient privacy. These features make P3TL an excellent model for telemonitoring of PD using an At-Home Testing Device.
ContributorsYoon, Hyunsoo (Author) / Li, Jing (Thesis advisor) / Wu, Teresa (Committee member) / Yan, Hao (Committee member) / Hu, Leland S. (Committee member) / Arizona State University (Publisher)
Created2018
Description
In eukaryotes, DNA is packed in a highly condensed and hierarchically organized structure called chromatin, in which DNA tightly wraps around the histone octamer consisting of one histone 3-histone 4 (H3-H4) tetramer and two histone 2A- histone 2B (H2A-H2B) dimers with 147 base pairs in an almost two left handed turns. Almost all DNA dependent cellular processes, such as DNA duplication, transcription, DNA repair and recombination, take place in the chromatin form. Based on the critical importance of appropriate chromatin condensation, this thesis focused on the folding behavior of the nucleosome array reconstituted using different templates with various controllable factors such as histone tail modification, linker DNA length, and DNA binding proteins. Firstly, the folding behaviors of wild type (WT) and nucleosome arrays reconstituted with acetylation on the histone H4 at lysine 16 (H4K16 (Ac)) were studied. In contrast to the sedimentation result, atomic force microscopy (AFM) measurements revealed no apparent difference in the compact nucleosome arrays between WT and H4K16 (Ac) and WT. Instead, an optimal loading of nucleosome along the template was found necessary for the Mg2+ induced nucleosome array compaction. This finding leads to the further study on the role of linker DNA in the nucleosome compaction. A method of constructing DNA templates with varied linker DNA lengths was developed, and uniformly and randomly spaced nucleosome arrays with average linker DNA lengths of 30 bp and 60 bp were constructed. After comprehensive analyses of the nucleosome arrays' structure in mica surface, the lengths of the linker DNA were found playing an important role in controlling the structural geometries of nucleosome arrays in both their extended and compact forms. In addition, higher concentration of the DNA binding domain of the telomere repeat factor 2 (TRF2) was found to stimulate the compaction of the telomeric nucleosome array. Finally, AFM was successfully applied to investigate the nucleosome positioning behaviors on the Mouse Mammary Tumor Virus (MMTV) promoter region, and two highly positioned region corresponded to nucleosome A and B were identified by this method.
ContributorsFu, Qiang (Author) / Lindsay, Stuart M (Thesis advisor) / Yan, Hao (Committee member) / Ghirlanda, Giovanna (Committee member) / Arizona State University (Publisher)
Created2010
Description
With a quantum efficiency of nearly 100%, the electron transfer process that occurs within the reaction center protein of the photosynthetic bacteria Rhodobacter (Rh.) sphaeroides is a paragon for understanding the complexities, intricacies, and overall systemization of energy conversion and storage in natural systems. To better understand the way in which photons of light are captured, converted into chemically useful forms, and stored for biological use, an investigation into the reaction center protein, specifically into its cascade of cofactors, was undertaken. The purpose of this experimentation was to advance our knowledge and understanding of how differing protein environments and variant cofactors affect the spectroscopic aspects of and electron transfer kinetics within the reaction of Rh. sphaeroides. The native quinone, ubiquinone, was extracted from its pocket within the reaction center protein and replaced by non-native quinones having different reduction/oxidation potentials. It was determined that, of the two non-native quinones tested—1,2-naphthaquinone and 9,10- anthraquinone—the substitution of the anthraquinone (lower redox potential) resulted in an increased rate of recombination from the P+QA- charge-separated state, while the substitution of the napthaquinone (higher redox potential) resulted in a decreased rate of recombination.
ContributorsSussman, Hallie Rebecca (Author) / Woodbury, Neal (Thesis director) / Redding, Kevin (Committee member) / Lin, Su (Committee member) / School of Molecular Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2015-12
Description
Uncertainty quantification is critical for engineering design and analysis. Determining appropriate ways of dealing with uncertainties has been a constant challenge in engineering. Statistical methods provide a powerful aid to describe and understand uncertainties. This work focuses on applying Bayesian methods and machine learning in uncertainty quantification and prognostics among all the statistical methods. This study focuses on the mechanical properties of materials, both static and fatigue, the main engineering field on which this study focuses. This work can be summarized in the following items: First, maintaining the safety of vintage pipelines requires accurately estimating the strength. The objective is to predict the reliability-based strength using nondestructive multimodality surface information. Bayesian model averaging (BMA) is implemented for fusing multimodality non-destructive testing results for gas pipeline strength estimation. Several incremental improvements are proposed in the algorithm implementation. Second, the objective is to develop a statistical uncertainty quantification method for fatigue stress-life (S-N) curves with sparse data.Hierarchical Bayesian data augmentation (HBDA) is proposed to integrate hierarchical Bayesian modeling (HBM) and Bayesian data augmentation (BDA) to deal with sparse data problems for fatigue S-N curves. The third objective is to develop a physics-guided machine learning model to overcome limitations in parametric regression models and classical machine learning models for fatigue data analysis. A Probabilistic Physics-guided Neural Network (PPgNN) is proposed for probabilistic fatigue S-N curve estimation. This model is further developed for missing data and arbitrary output distribution problems. Fourth, multi-fidelity modeling combines the advantages of low- and high-fidelity models to achieve a required accuracy at a reasonable computation cost. The fourth objective is to develop a neural network approach for multi-fidelity modeling by learning the correlation between low- and high-fidelity models. Finally, conclusions are drawn, and future work is outlined based on the current study.
ContributorsChen, Jie (Author) / Liu, Yongming (Thesis advisor) / Chattopadhyay, Aditi (Committee member) / Mignolet, Marc (Committee member) / Ren, Yi (Committee member) / Yan, Hao (Committee member) / Arizona State University (Publisher)
Created2022
Description
Molecular engineering is an emerging field that aims to create functional devices for modular purposes, particularly bottom-up design of nano-assemblies using mechanical and chemical methods to perform complex tasks. In this study, we present a novel method for constructing an RNA clamp using circularized RNA and a broccoli aptamer for fluorescence sensing. By designing a circular RNA with the broccoli aptamer and a complementary DNA strand, we created a molecular clamp that can stabilize the aptamer. The broccoli aptamer displays enhanced fluorescence when bound to its ligand, DFHBI-1T. Upon induction with this small molecule, the clamp can exhibit or destroy fluorescence. We demonstrated that we could control the fluorescence of the RNA clamp by introducing different complementary DNA strands, which regulate the level of fluorescence. Additionally, we designed allosteric control by introducing new DNA strands, making the system reversible. We explored the use of mechanical tension to regulate RNA function by attaching a spring-like activity through the RNA clamp to two points on the RNA surface. By adjusting the stiffness of the spring, we could control the tension between the two points and induce reversible conformational changes, effectively turning RNA function on and off. Our approach offers a simple and versatile method for creating RNA clamps with various applications, including RNA detection, regulation, and future nanodevice design. Our findings highlight the crucial role of mechanical forces in regulating RNA function, paving the way for developing new strategies for RNA manipulation, and potentially advancing molecular engineering. Although the current work is ongoing, we provide current progress of both theoretical and experimental calculations based on our findings.
ContributorsJoseph, Joel (Author) / Yan, Hao (Thesis director) / Stephanopoulos, Nicholas (Committee member) / Lapinaite, Audrone (Committee member) / Barrett, The Honors College (Contributor) / Historical, Philosophical & Religious Studies, Sch (Contributor) / School of Molecular Sciences (Contributor)
Created2023-05