Single-molecule Kinetics and Kinematics of Rotary ATPases

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Description
Across the tree of life, rotary molecular motors like the F1FO ATP synthase utilize a transmembrane nonequilibrium proton gradient to synthesize adenosine triphosphate (ATP), the biological energy currency. The catalytic portion of rotary motors, such as the F1 complex from…

Across the tree of life, rotary molecular motors like the F1FO ATP synthase utilize a transmembrane nonequilibrium proton gradient to synthesize adenosine triphosphate (ATP), the biological energy currency. The catalytic portion of rotary motors, such as the F1 complex from E. coli and the V1 complex from S. cerevisiae, was purified and studied during ATP hydrolysis. Single-molecule assays utilized gold nanorods to investigate the kinetics of the F1-ATPase catalytic dwell, the biophysics of V1-ATPase, and the kinematics of the F1-ATPase power stroke. Observation of oscillatory rotor motion during the F1 catalytic dwell provided new insight as to how energy from ATP binding is stored during its three stages. That motion indicated a ratchet mechanism, in which F1 changed states according to first-order kinetics with a time constant τ = 0.182, showing that Stage-1 represents a pre-hydrolysis state and Stage-2 represents a post-hydrolysis state. F1 was then observed to return to 0° prior to its next power stroke (Stage-3), which explained why the three catalytic dwells remain 120° apart after many revolutions. Analysis of the 120° power stroke following Stage-3 was conducted in both V1 and F1, allowing comparative biology to elucidate defects in the ATPase mechanism, such as ADP inhibition and faltering rotation. It is noteworthy that the V1 rotary positions of ADP release and ATP binding are the opposite of F1, and that less elastic energy is stored in the V1 rotor due to differences in its catch loop. In both rotary ATPases, energy contributed by binding and hydrolysis can dissipate at multiple points. When the F1 catch loop contact between F1 βD305 and γQ269 was mutated, the elastic energy stored in the rotor dissipated dramatically. Dissipation was clearly shown by sustained Phase-1 decelerations, the distribution of ATP-binding dwells, and high-amplitude oscillations in γQ269L. These findings clarify evolutionary similarities and differences between eukaryotic V1, which is exclusively a hydrolase, and F1, which can both hydrolyze and synthesize ATP.
Date Created
2024
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Label-Free Functional Imaging of Single Molecules and Single Cells Using Surface- Enhanced Scattering Microscopy

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Description
Recent breakthroughs in optical scattering-based imaging have enabledvisualization of entities as small as single proteins. Leveraging our innovation, Surface Enhanced Scattering Microscopy (SESM), detection of single protein binding kinetics and single DNA conformational changes have been achieved, showcasing the feasibility…

Recent breakthroughs in optical scattering-based imaging have enabledvisualization of entities as small as single proteins. Leveraging our innovation, Surface Enhanced Scattering Microscopy (SESM), detection of single protein binding kinetics and single DNA conformational changes have been achieved, showcasing the feasibility of single molecule imaging. In this dissertation, I aim to harness the potential of SESM and extend its relevance in the biomedical realm. My first goal is to conduct multiplexed protein detection and parallel binding kinetics analysis with label-free digital single- molecule counting. My second goal is focused on accurate quantification of cell force. An elastic model has been developed to quantify the cell-substrate interactions and have continuously tracked cell force evolutions upon small-molecule drugs (for example, acetylcholine) stimulation, achieving a temporal resolution of approximately 60 ms over the course of 30 min without attenuating the signals. The third goal is to achieve real- time tracking of DNA self-assembly dynamics. I have demonstrated SESM's capability to image individual DNA origami monomers and established an on-chip temperature annealing system to monitor the real-time progression of DNA self-assembly. The applications of the imaging method, spanning single proteins, single DNA origami, and single cells, are poised to impact the field of biology
Date Created
2024
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Empowering Biomolecular Modeling with Neural Networks and Empirical Evidence: An Integrative Approach to Hypothesis Generation

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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…

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.
Date Created
2024
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Learning Continuous 2D Diffusion Maps from Particle Trajectories without Data Binning

Description
Diffusion coefficients often vary across regions, such as cellular membranes, and quantifying their variation can provide valuable insight into local membrane properties such as composition and stiffness. Toward quantifying diffusion coefficient spatial maps and uncertainties from particle tracks, we use…

Diffusion coefficients often vary across regions, such as cellular membranes, and quantifying their variation can provide valuable insight into local membrane properties such as composition and stiffness. Toward quantifying diffusion coefficient spatial maps and uncertainties from particle tracks, we use a Bayesian method and place Gaussian Process (GP) Priors on the maps. For the sake of computational efficiency, we leverage inducing point methods on GPs arising from the mathematical structure of the data giving rise to non-conjugate likelihood-prior pairs. We analyze both synthetic data, where ground truth is known, as well as data drawn from live-cell single-molecule imaging of membrane proteins. The resulting tool provides an unsupervised method to rigorously map diffusion coefficients continuously across membranes without data binning.
Date Created
2024-05
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Analyzing Molecular Interactions of Membrane Proteins by Computational Methods

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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…

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.
Date Created
2022
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Advancing Biophysics Research with Bayesian Methods: Novel Applications and Insights into Biological Systems' Behavior

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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…

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.
Date Created
2023
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Comparison of Bdellovibrio bacteriovorus in vivo vs in vitro

Description

Bdellovibrio bacteriovorus (B. bacteriovorus) is a predatory bacterium that preys on other gram-negative bacteria. In order to survive and reproduce, B. bacteriovorus invades the periplasm of other bacterial cells creating the potential for it to act as a “living antibiotic”.…

Bdellovibrio bacteriovorus (B. bacteriovorus) is a predatory bacterium that preys on other gram-negative bacteria. In order to survive and reproduce, B. bacteriovorus invades the periplasm of other bacterial cells creating the potential for it to act as a “living antibiotic”. In this work, a comparison was made between the rates of predation of B. bacteriovorus in vitro and in vivo. In vitro, the behavior of B. bacteriovorus was examined in the presence of prey. In vivo, the behavior of B. bacteriovorus was examined in the presence of prey and a living host, Caenorhabditis elegans (C. elegans). C. elegans were infected with Escherichia coli (E. coli) and treated with B. bacteriovorus. In previous studies that analyzed B. bacteriovorus in vitro, a decrease in concentrations of bacteria has been observed after introduction of B. bacteriovorus. In vivo, B. bacteriovorus were found to not have a net reduction of E. coli but to reproducibly raise the level of fluctuations in E. coli concentrations.

Date Created
2023-05
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Addition of Predatory Bacteria Decreases the Mortality of C. elegans

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Description

Bdellovibrio bacteriovorus (BB) is a gram negative predatory bacteria that uses other gram negative bacteria to proliferate non-binarily. Due to the predatory nature of BB researchers have proposed to use it as a potential biocontrol agent against other gram negative…

Bdellovibrio bacteriovorus (BB) is a gram negative predatory bacteria that uses other gram negative bacteria to proliferate non-binarily. Due to the predatory nature of BB researchers have proposed to use it as a potential biocontrol agent against other gram negative bacteria. The in vivo effect of predatory bacteria on a living host lacks thorough investigation. This paper explores BB inside and outside of the C. elegans. BB acts internally by pre- infecting C. elegans with E. coli and then treating the worms with BB. After BB treatment worm survivavbility increased and morbidity decreased. Ex- ternally, BB modulated the environment around the nematode which reduced infection rates and increased nematode lifespan and survivability. Together, the internal and external results suggest BB has the capability to act as a living antibiotic acting topically and internally to reduce infection rates.

Date Created
2022-05
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Quantitative Image Corrections for EMCCD Camera

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Description

Electron Multiplying Charge Coupled Device (EMCCD) cameras are widely used for fluorescence microscopy experiments. However, the quantitative determination of biological parameters uniquely depends on characteristics of the unavoidably inhomogenous illumination profile as it gives rise to an image. It is…

Electron Multiplying Charge Coupled Device (EMCCD) cameras are widely used for fluorescence microscopy experiments. However, the quantitative determination of biological parameters uniquely depends on characteristics of the unavoidably inhomogenous illumination profile as it gives rise to an image. It is therefore of interest to learn this inhomogenous illumination profiles that can dramatically vary across images alongside the camera parameters though a detailed camera model. In this manuscript we create a detailed model to learn inhomogeneous illumination profiles as well as learn all associated camera parameters. We achieve this within a Bayesian paradigm allowing us to determine full distributions over the unknowns.

Date Created
2022-05
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A Bayesian Approach to Single-Photon Single-Molecule FRET data

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Description

Single molecule FRET experiments are important for studying processes that happen on the molecular scale. By using pulsed illumination and collecting single photons, it is possible to use information gained from the fluorescence lifetime of the chromophores in the FRET…

Single molecule FRET experiments are important for studying processes that happen on the molecular scale. By using pulsed illumination and collecting single photons, it is possible to use information gained from the fluorescence lifetime of the chromophores in the FRET pair to gain more accurate estimates of the underlying FRET rate which is used to determine information about the distance between the chromophores of the FRET pair. In this paper, we outline a method that utilizes Bayesian inference to learn parameter values for a model informed by the physics of a immobilized single-molecule FRET experiment. This method is unique in that it combines a rigorous look at the photophysics of the FRET pair and a nonparametric treatment of the molecular conformational statespace, allowing the method to learn not just relevant photophysical rates (such as relaxation rates and FRET rates), but also the number of molecular conformational states.

Date Created
2021-05
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