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Quantifying Nonequilibrium Thermodynamics of Proteins Using Single Molecule Spectroscopy
Get PDFDepartment of Biomedical EngineeringProteins are dynamic molecules, and their structural fluctuations play a crucial role in their function. Quantification of these motions is hence central to our understanding of a protein???s function. The timescales of these fluctuations span over 12 orders of magnitude from ultrafast picoseconds vibrational motions to slow seconds long global changes. While the ultrafast and slow motions have been well characterized, the intermediate conformational changes occurring at microsecond to millisecond timescales remain to be clearly understood. This thesis explores the use of single molecule multidimensional fluorescence spectroscopy to investigate protein conformational changes occurring at microsecond to millisecond timescales. The information of conformational transition rates gives insight into their operating principles and the mechanisms behind their biological functions.
Single-molecule Two-Dimensional Fluorescence Lifetime Correlation Spectroscopy (sm-2DFLCS) is used to resolve distinct conformations of protein molecules based on different fluorescence lifetimes. Time resolved fluorescence photon traces are converted to two-dimensional emission delay correlation maps which are then converted to lifetime correlation spectra using a nonlinear optimization algorithm. Key advancements in the technique were achieved by adapting analytical strategies from 2D NMR analysis protocols which convert the underlying optimization problem from constrained to unconstrained optimization resulting in a fast and robust 2D Inverse Laplace Transformation algorithm (2D-ILT). This enables monitoring of forward and reverse conformational transitions in a protein independently.
Nonequilibrium thermodynamics of pump protein Bacteriorhodopsin is characterized using the improvised 2D-ILT algorithm. Independent measurement of forward and reverse conformational transitions reveals microscopically irreversible transitions in the reaction cycle of the proton pump. Nonequilibrium thermodynamic properties such as entropy production, flux and affinity are quantified through portions of the reaction cycle. It is shown that the rate of irreversible transitions shows an inverse dependence on temperature and fitting the trend with Gibbs-Helmholtz relation yields experimentally determined enthalpy of transition.
Coupling of proteins internal reaction coordinate and surrounding solvent is investigated by monitoring microsecond equilibrium fluctuations in the chromophore pocket of eGFP. It is observed that the dynamics of local rearrangements around the chromophore are coupled to the bulk viscosity of the solvent. The dependence is observed to deviate from Kramer???s scaling and the deviation is attributed to proteins??? internal friction.clos
Study on ATAD5-mediated PCNA Regulation in Safeguarding Genomic Integrity and Orchestrating ESC Differentiation
No full textDepartment of Biological Sciencesclos
Tailoring the Intrinsic Electronic Structure and Crystal Facet of Transition Metal-based Materials for Energy Conversion
No full textSchool of Energy and Chemical Engineering (Energy Engineering)clos
Surface structuring on transparent crystalline silicon solar cells for effective light management
No full textSchool of Energy and Chemical Engineering (Energy Engineering)clos
Nitrate Salt-Based Inorganic Electrolyte for High-Performance Lithium-Air Batteries
No full textSchool of Energy and Chemical Engineering (Energy Engineering (Battery Science and Technology))clos
The battery pack diagnosis approach Using advanced cell modeling And parallel-connected battery module simulation
Get PDFSchool of Energy and Chemical Engineering (Energy Engineering (Battery Science and Technology))In proportion to the increase in usage of large-scale applications such as EVs(Electric Vehicles) and ESS(Energy Storage System), the number of accidents caused by batteries is also increasing. In this background, the demand for diagnosing the state of health of the battery is also increasing. But the battery diagnosis remains still at the cell stage, and the battery pack level diagnosis is rarely proposed. In this study, an approach to the battery pack level diagnosis was proposed, and necessary prerequisites for the approach were proposed and developed. Through the proposed approach "Backward Estimation", battery pack data can be decomposed into cell data. And conventional cell diagnosis methods can be applied using the decomposed cell data. In this study, highly accurate ECM model-based cell modeling method and parallel-connection module profile simulation using that cell modeling(???Forward Estimation???), which are two necessary prerequisites for backward estimation, were developed and verified.clos
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No full textSchool of Energy and Chemical Engineering (Chemical Engineering)clos
Generating Natural 3D Human in Indoor Scene
Get PDFGraduate School of Artificial IntelligenceAs the demand for metaverse experiences grows rapidly, there is a growing need to investigate the interactions of 3D human avatars within indoor environments. This study aims to tackle the challenge of generating scene-aware 3D human avatars that interact with their surroundings in a natural manner. To achieve this goal, I focus on the importance of geometric alignment between 3D human avatars and the scene in order to address physical human-scene interaction. To address this issue, I propose a novel framework that incorporates geometric alignment on potential contact areas between 3D human avatars and their surroundings. Moreover, I introduce a compact and effective human pose classifier that identifies the human pose and determines potential contact areas, which allows us to adaptively apply geometric alignment loss based on the classified pose. My method outperforms existing state-of-the-art approaches by generating physically and semantically plausible 3D human avatars that interact with 3D scenes in a natural manner, without requiring additional post-processing. Qualitative and quantitative analysis demonstrates that my approach improves the physical plausibility and diversity of human-scene interactions compared to previous research.clos
Sequential Recommendation with Link-Prediction on Graphs Meta-Learning
Get PDFGraduate School of Artificial IntelligenceIn this paper, we propose a novel framework called Sequential Graph Meta-learning (SGM) to address the problem of sequential recommendation, which involves predicting the next item based on a user???s historical behavior. SGM introduces a graph-based representation that captures the relationships between users and items, leveraging them as nodes and their interactions as edges. By extracting meaningful node embeddings, our model effectively encodes the complex relationships within the graph. Furthermore, we utilize subgraphs that represent user-item interactions with meta-learning, enabling the model to adapt and reflect as time change. Specifically, our approach focuses on link prediction, aiming to predict whether a user will interact with a specific item in the future. Through extensive experiments, we demonstrate that our SGM framework outperforms previous models in most scenarios by significant margins. This highlights the effectiveness of our proposed approach in addressing the challenges of sequential recommendation and enhancing recommendation accuracy.clos
A Real-time Sparsity-Aware 3D-CNN Processor for Mobile Hand Gesture Recognition
No full textGraduate School of Artificial Intelligenceclos