A study of structure and function of two enzymes in pyrimidine biosynthesis

Thesis advisor: Evan R. KantrowitzNucleotides, the building blocks for nucleic acids, are essential for cell growth and replication. In E. coli the enzyme responsible for the regulation of pyrimidine nucleotide biosynthesis is aspartate transcarbamoylase (ATCase), which catalyzes the committed step in this pathway. ATCase is allosterically inhibited by CTP and UTP in the presence of CTP, the end products of the pyrimidine pathway. ATP, the end product of the purine biosynthetic pathway, acts as an allosteric activator. ATCase undergoes the allosteric transition from the low-activity and low-affinity T state to the high-activity and high-affinity R state upon the binding of the substrates. In this work we were able to trap an intermediate ATCase along the path of the allosteric transition between the T and R states. Both the X-ray crystallography and small-angle X-ray scattering in solution clearly demonstrated that the mutant ATCase (K164E/E239K) exists in an intermediate quaternary structure shifted about one-third toward the canonical R structure from the T structure. The structure of this intermediate ATCase is helping to understand the mechanism of the allosteric transition on a molecular basis. In this work we also discovered that a metal ion, such as Mg2+, was required for the synergistic inhibition by UTP in the presence of CTP. Therefore, the metal ion also had significant influence on how other nucleotides effect the enzyme. A more physiological relevant model was proposed involving the metal ion. To better understand the allosteric transition of ATCase, time-resolved small-angle X-ray scattering was utilized to track the conformational changes of the quaternary structure of the enzyme upon reaction with the natural substrates, PALA and nucleotide effectors. The transition rate was increased with an increasing concentration of the natural substrates but became over one order of magnitude slower with addition of PALA. Addition of ATP to the substrates increased the rate of the transition whereas CTP or the combination of CTP and UTP exhibited the opposite effect. In this work we also studied E. coli dihydroorotase (DHOase), which catalyzes the following step of ATCase in the pyrimidine biosynthetic pathway. A virtual high throughput screening system was employed to screen for inhibitors of DHOase, which may become potential anti-proliferation and anti-malarial drug candidates. Upon the discovery of the different conformations of the 100's loop of DHOase when substrate or product bound at the active site, we've genetically incorporated an unnatural fluorescent amino acid to a site on this loop in the hope of obtaining a better understanding of the catalysis that may involve the movement of the 100's loop.Thesis (PhD) — Boston College, 2012.Submitted to: Boston College. Graduate School of Arts and Sciences.Discipline: Chemistry

Semi-supervised Road Updating Network (SRUNet): A Deep Learning Method for Road Updating from Remote Sensing Imagery and Historical Vector Maps

A road is the skeleton of a city and is a fundamental and important geographical component. Currently, many countries have built geo-information databases and gathered large amounts of geographic data. However, with the extensive construction of infrastructure and rapid expansion of cities, automatic updating of road data is imperative to maintain the high quality of current basic geographic information. However, obtaining bi-phase images for the same area is difficult, and complex post-processing methods are required to update the existing databases.To solve these problems, we proposed a road detection method based on semi-supervised learning (SRUNet) specifically for road-updating applications; in this approach, historical road information was fused with the latest images to directly obtain the latest state of the road.Considering that the texture of a road is complex, a multi-branch network, named the Map Encoding Branch (MEB) was proposed for representation learning, where the Boundary Enhancement Module (BEM) was used to improve the accuracy of boundary prediction, and the Residual Refinement Module (RRM) was used to optimize the prediction results. Further, to fully utilize the limited amount of label information and to enhance the prediction accuracy on unlabeled images, we utilized the mean teacher framework as the basic semi-supervised learning framework and introduced Regional Contrast (ReCo) in our work to improve the model capacity for distinguishing between the characteristics of roads and background elements.We applied our method to two datasets. Our model can effectively improve the performance of a model with fewer labels. Overall, the proposed SRUNet can provide stable, up-to-date, and reliable prediction results for a wide range of road renewal tasks.Comment: 22 pages, 8 figure

Refined Equivalent Pinhole Model for Large-scale 3D Reconstruction from Spaceborne CCD Imagery

In this study, we present a large-scale earth surface reconstruction pipeline for linear-array charge-coupled device (CCD) satellite imagery. While mainstream satellite image-based reconstruction approaches perform exceptionally well, the rational functional model (RFM) is subject to several limitations. For example, the RFM has no rigorous physical interpretation and differs significantly from the pinhole imaging model; hence, it cannot be directly applied to learning-based 3D reconstruction networks and to more novel reconstruction pipelines in computer vision. Hence, in this study, we introduce a method in which the RFM is equivalent to the pinhole camera model (PCM), meaning that the internal and external parameters of the pinhole camera are used instead of the rational polynomial coefficient parameters. We then derive an error formula for this equivalent pinhole model for the first time, demonstrating the influence of the image size on the accuracy of the reconstruction. In addition, we propose a polynomial image refinement model that minimizes equivalent errors via the least squares method. The experiments were conducted using four image datasets: WHU-TLC, DFC2019, ISPRS-ZY3, and GF7. The results demonstrated that the reconstruction accuracy was proportional to the image size. Our polynomial image refinement model significantly enhanced the accuracy and completeness of the reconstruction, and achieved more significant improvements for larger-scale images.Comment: 24 page

Sea Ice Extraction via Remote Sensed Imagery: Algorithms, Datasets, Applications and Challenges

The deep learning, which is a dominating technique in artificial intelligence, has completely changed the image understanding over the past decade. As a consequence, the sea ice extraction (SIE) problem has reached a new era. We present a comprehensive review of four important aspects of SIE, including algorithms, datasets, applications, and the future trends. Our review focuses on researches published from 2016 to the present, with a specific focus on deep learning-based approaches in the last five years. We divided all relegated algorithms into 3 categories, including classical image segmentation approach, machine learning-based approach and deep learning-based methods. We reviewed the accessible ice datasets including SAR-based datasets, the optical-based datasets and others. The applications are presented in 4 aspects including climate research, navigation, geographic information systems (GIS) production and others. It also provides insightful observations and inspiring future research directions.Comment: 24 pages, 6 figure

Theoretical Insight into the Spectral Characteristics of Fe(II)-Based Complexes for Dye-Sensitized Solar Cells—Part I: Polypyridyl Ancillary Ligands

The design of light-absorbent dyes with cheaper, safer, and more sustainable materials is one of the key issues for the future development of dye-sensitized solar cells (DSSCs). We report herein a theoretical investigation on a series of polypyridyl Fe(II)-based complexes of FeL2(SCN)2, [FeL3]2+, [FeL′(SCN)3]-, [FeL′2]2+, and FeL′′(SCN)2 (L = 2,2′-bipyridyl-4,4′-dicarboxylic acid, L′ = 2,2′,2″-terpyridyl-4,4′,4″-tricarboxylic acid, L″ = 4,4‴-dimethyl-2,2′ : 6′,2″ :6″,2‴-quaterpyridyl-4′,4″-biscarboxylic acid) by density functional theory (DFT) and time-dependent DFT (TD-DFT). Molecular geometries, electronic structures, and optical absorption spectra are predicted in both the gas phase and methyl cyanide (MeCN) solution. Our results show that polypyridyl Fe(II)-based complexes display multitransition characters of Fe → polypyridine metal-to-ligand charge transfer and ligand-to-ligand charge transfer in the range of 350–800 nm. Structural optimizations by choosing different polypyridyl ancillary ligands lead to alterations of the molecular orbital energies, oscillator strength, and spectral response range. Compared with Ru(II) sensitizers, Fe(II)-based complexes show similar characteristics and improving trend of optical absorption spectra along with the introduction of different polypyridyl ancillary ligands

Mitochondrial Ferritin Deletion Exacerbates β

Mitochondrial ferritin (FtMt) is a mitochondrial iron storage protein which protects mitochondria from iron-induced oxidative damage. Our previous studies indicate that FtMt attenuates β-amyloid- and 6-hydroxydopamine-induced neurotoxicity in SH-SY5Y cells. To explore the protective effects of FtMt on β-amyloid-induced memory impairment and neuronal apoptosis and the mechanisms involved, 10-month-old wild-type and Ftmt knockout mice were infused intracerebroventricularly (ICV) with Aβ25–35 to establish an Alzheimer’s disease model. Knockout of Ftmt significantly exacerbated Aβ25–35-induced learning and memory impairment. The Bcl-2/Bax ratio in mouse hippocampi was decreased and the levels of cleaved caspase-3 and PARP were increased. The number of neuronal cells undergoing apoptosis in the hippocampus was also increased in Ftmt knockout mice. In addition, the levels of L-ferritin and FPN1 in the hippocampus were raised, and the expression of TfR1 was decreased. Increased MDA levels were also detected in Ftmt knockout mice treated with Aβ25–35. In conclusion, this study demonstrated that the neurological impairment induced by Aβ25–35 was exacerbated in Ftmt knockout mice and that this may relate to increased levels of oxidative stress

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data