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Description
Photosystem II (PSII) is a large protein-cofactor complex. The first step in
photosynthesis involves the harvesting of light energy from the sun by the antenna (made
of pigments) of the PSII trans-membrane complex. The harvested excitation energy is
transferred from the antenna complex to the reaction center of the PSII, which leads to a
light-driven charge separation event, from water to plastoquinone. This phenomenal
process has been producing the oxygen that maintains the oxygenic environment of our
planet for the past 2.5 billion years.
The oxygen molecule formation involves the light-driven extraction of 4 electrons
and protons from two water molecules through a multistep reaction, in which the Oxygen
Evolving Center (OEC) of PSII cycles through 5 different oxidation states, S0 to S4.
Unraveling the water-splitting mechanism remains as a grant challenge in the field of
photosynthesis research. This requires the development of an entirely new capability, the
ability to produce molecular movies. This dissertation advances a novel technique, Serial
Femtosecond X-ray crystallography (SFX), into a new realm whereby such time-resolved
molecular movies may be attained. The ultimate goal is to make a “molecular movie” that
reveals the dynamics of the water splitting mechanism using time-resolved SFX (TRSFX)
experiments and the uniquely enabling features of X-ray Free-Electron Laser
(XFEL) for the study of biological processes.
This thesis presents the development of SFX techniques, including development of
new methods to analyze millions of diffraction patterns (~100 terabytes of data per XFEL
experiment) with the goal of solving the X-ray structures in different transition states.
ii
The research comprises significant advancements to XFEL software packages (e.g.,
Cheetah and CrystFEL). Initially these programs could evaluate only 8-10% of all the
data acquired successfully. This research demonstrates that with manual optimizations,
the evaluation success rate was enhanced to 40-50%. These improvements have enabled
TR-SFX, for the first time, to examine the double excited state (S3) of PSII at 5.5-Å. This
breakthrough demonstrated the first indication of conformational changes between the
ground (S1) and the double-excited (S3) states, a result fully consistent with theoretical
predictions.
The power of the TR-SFX technique was further demonstrated with proof-of principle
experiments on Photoactive Yellow Protein (PYP) micro-crystals that high
temporal (10-ns) and spatial (1.5-Å) resolution structures could be achieved.
In summary, this dissertation research heralds the development of the TR-SFX
technique, protocols, and associated data analysis methods that will usher into practice a
new era in structural biology for the recording of ‘molecular movies’ of any biomolecular
process.
photosynthesis involves the harvesting of light energy from the sun by the antenna (made
of pigments) of the PSII trans-membrane complex. The harvested excitation energy is
transferred from the antenna complex to the reaction center of the PSII, which leads to a
light-driven charge separation event, from water to plastoquinone. This phenomenal
process has been producing the oxygen that maintains the oxygenic environment of our
planet for the past 2.5 billion years.
The oxygen molecule formation involves the light-driven extraction of 4 electrons
and protons from two water molecules through a multistep reaction, in which the Oxygen
Evolving Center (OEC) of PSII cycles through 5 different oxidation states, S0 to S4.
Unraveling the water-splitting mechanism remains as a grant challenge in the field of
photosynthesis research. This requires the development of an entirely new capability, the
ability to produce molecular movies. This dissertation advances a novel technique, Serial
Femtosecond X-ray crystallography (SFX), into a new realm whereby such time-resolved
molecular movies may be attained. The ultimate goal is to make a “molecular movie” that
reveals the dynamics of the water splitting mechanism using time-resolved SFX (TRSFX)
experiments and the uniquely enabling features of X-ray Free-Electron Laser
(XFEL) for the study of biological processes.
This thesis presents the development of SFX techniques, including development of
new methods to analyze millions of diffraction patterns (~100 terabytes of data per XFEL
experiment) with the goal of solving the X-ray structures in different transition states.
ii
The research comprises significant advancements to XFEL software packages (e.g.,
Cheetah and CrystFEL). Initially these programs could evaluate only 8-10% of all the
data acquired successfully. This research demonstrates that with manual optimizations,
the evaluation success rate was enhanced to 40-50%. These improvements have enabled
TR-SFX, for the first time, to examine the double excited state (S3) of PSII at 5.5-Å. This
breakthrough demonstrated the first indication of conformational changes between the
ground (S1) and the double-excited (S3) states, a result fully consistent with theoretical
predictions.
The power of the TR-SFX technique was further demonstrated with proof-of principle
experiments on Photoactive Yellow Protein (PYP) micro-crystals that high
temporal (10-ns) and spatial (1.5-Å) resolution structures could be achieved.
In summary, this dissertation research heralds the development of the TR-SFX
technique, protocols, and associated data analysis methods that will usher into practice a
new era in structural biology for the recording of ‘molecular movies’ of any biomolecular
process.
ContributorsBasu, Shibom, 1988- (Author) / Fromme, Petra (Thesis advisor) / Spence, John C.H. (Committee member) / Wolf, George (Committee member) / Ros, Robert (Committee member) / Fromme, Raimund (Committee member) / Arizona State University (Publisher)
Created2015
Description
Serial femtosecond crystallography (SFX) uses diffraction patterns from crystals delivered in a serial fashion to an X-Ray Free Electron Laser (XFEL) for structure determination. Typically, each diffraction pattern is a snapshot from a different crystal. SFX limits the effect of radiation damage and enables the use of nano/micro crystals for structure determination. However, analysis of SFX data is challenging since each snapshot is processed individually.
Many photosystem II (PSII) dataset have been collected at XFELs, several of which are time-resolved (containing both dark and laser illuminated frames). Comparison of light and dark datasets requires understanding systematic errors that can be introduced during data analysis. This dissertation describes data analysis of PSII datasets with a focus on the effect of parameters on later results. The influence of the subset of data used in the analysis is also examined and several criteria are screened for their utility in creating better subsets of data. Subsets are compared with Bragg data analysis and continuous diffuse scattering data analysis.
A new tool, DatView aids in the creation of subsets and visualization of statistics. DatView was developed to improve the loading speed to visualize statistics of large SFX datasets and simplify the creation of subsets based on the statistics. It combines the functionality of several existing visualization tools into a single interface, improving the exploratory power of the tool. In addition, it has comparison features that allow a pattern-by-pattern analysis of the effect of processing parameters. \emph{DatView} improves the efficiency of SFX data analysis by reducing loading time and providing novel visualization tools.
Many photosystem II (PSII) dataset have been collected at XFELs, several of which are time-resolved (containing both dark and laser illuminated frames). Comparison of light and dark datasets requires understanding systematic errors that can be introduced during data analysis. This dissertation describes data analysis of PSII datasets with a focus on the effect of parameters on later results. The influence of the subset of data used in the analysis is also examined and several criteria are screened for their utility in creating better subsets of data. Subsets are compared with Bragg data analysis and continuous diffuse scattering data analysis.
A new tool, DatView aids in the creation of subsets and visualization of statistics. DatView was developed to improve the loading speed to visualize statistics of large SFX datasets and simplify the creation of subsets based on the statistics. It combines the functionality of several existing visualization tools into a single interface, improving the exploratory power of the tool. In addition, it has comparison features that allow a pattern-by-pattern analysis of the effect of processing parameters. \emph{DatView} improves the efficiency of SFX data analysis by reducing loading time and providing novel visualization tools.
ContributorsStander, Natasha (Author) / Fromme, Petra (Thesis advisor) / Zatsepin, Nadia (Thesis advisor) / Kirian, Richard (Committee member) / Liu, Wei (Committee member) / Arizona State University (Publisher)
Created2019
Description
Machine learning is the process of training a computer with algorithms to learn from data and make informed predictions. In a world where large amounts of data are constantly collected, machine learning is an important tool to analyze this data to find patterns and learn useful information from it. Machine learning applications expand to numerous fields; however, I chose to focus on machine learning with a business perspective for this thesis, specifically e-commerce.
The e-commerce market utilizes information to target customers and drive business. More and more online services have become available, allowing consumers to make purchases and interact with an online system. For example, Amazon is one of the largest Internet-based retail companies. As people shop through this website, Amazon gathers huge amounts of data on its customers from personal information to shopping history to viewing history. After purchasing a product, the customer may leave reviews and give a rating based on their experience. Performing analytics on all of this data can provide insights into making more informed business and marketing decisions that can lead to business growth and also improve the customer experience.
For this thesis, I have trained binary classification models on a publicly available product review dataset from Amazon to predict whether a review has a positive or negative sentiment. The sentiment analysis process includes analyzing and encoding the human language, then extracting the sentiment from the resulting values. In the business world, sentiment analysis provides value by revealing insights into customer opinions and their behaviors. In this thesis, I will explain how to perform a sentiment analysis and analyze several different machine learning models. The algorithms for which I compared the results are KNN, Logistic Regression, Decision Trees, Random Forest, Naïve Bayes, Linear Support Vector Machines, and Support Vector Machines with an RBF kernel.
The e-commerce market utilizes information to target customers and drive business. More and more online services have become available, allowing consumers to make purchases and interact with an online system. For example, Amazon is one of the largest Internet-based retail companies. As people shop through this website, Amazon gathers huge amounts of data on its customers from personal information to shopping history to viewing history. After purchasing a product, the customer may leave reviews and give a rating based on their experience. Performing analytics on all of this data can provide insights into making more informed business and marketing decisions that can lead to business growth and also improve the customer experience.
For this thesis, I have trained binary classification models on a publicly available product review dataset from Amazon to predict whether a review has a positive or negative sentiment. The sentiment analysis process includes analyzing and encoding the human language, then extracting the sentiment from the resulting values. In the business world, sentiment analysis provides value by revealing insights into customer opinions and their behaviors. In this thesis, I will explain how to perform a sentiment analysis and analyze several different machine learning models. The algorithms for which I compared the results are KNN, Logistic Regression, Decision Trees, Random Forest, Naïve Bayes, Linear Support Vector Machines, and Support Vector Machines with an RBF kernel.
ContributorsMadaan, Shreya (Author) / Meuth, Ryan (Thesis director) / Nakamura, Mutsumi (Committee member) / Computer Science and Engineering Program (Contributor, Contributor) / Dean, W.P. Carey School of Business (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05