Phosphopantetheine adenylyl transferase catalyzes a rate limiting penultimate step of the multistep reaction which produces coenzyme A (CoA) as a final product. CoA is required as an essential cofactor in a number of metabolic reactions. Therefore inhibiting the function of this enzyme will lead to cell death in bacteria. Acinetobacter baumannii is multi drug resistant pathogen and causes infections in immunocompromised patients. AbPPAT has been cloned, expressed, purified and crystallized and structures of two complexes of AbPPAT with dephospho coenzyme A (dPCoA) and coenzyme A (CoA) have been determined. Both dPCoA and CoA molecules are observed in the substrate binding site of AbPPAT. A comparison with the structures of the complexes of PPAT from other species shows that the orientations of dPCoA are identical in all the structures. On the other hand, as observed from the structures of the complexes of CoA with PPAT, the orientations of CoA are found to differ considerably. This shows that the substrates occupy identical positions in the substrate binding sites of enzymes whereas the positions of inhibitors may differ. The binding studies carried out using fluorescence method and surface plasmon resonance techniques showed that binding affinity of CoA towards AbPPAT is nearly three times higher than that of dPCoA.
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http://dx.doi.org/10.1016/j.ijbiomac.2019.09.090 | DOI Listing |
J Infect Dev Ctries
December 2024
Faculdade de Medicina de Campos, Campos dos Goytacazes, Brazil.
Introduction: Despite efforts by health organizations to share evidence-based information, fake news hindered the promotion of social distancing and vaccination during the coronavirus disease 2019 (COVID-19) pandemic. This study analyzed COVID-19 knowledge and practices in a vulnerable area in northern Rio de Janeiro, acknowledging the influence of the complex social and economic landscape on public health perceptions.
Methodology: This cross-sectional study was conducted in Novo Eldorado - a low-income, conflict-affected neighborhood in Campos dos Goytacazes - using a structured questionnaire, following the peak of COVID-19 deaths in Brazil (July-December 2021).
BMC Genomics
January 2025
Henan Collaborative Innovation Center of Modern Biological Breeding, College of Agronomy, Henan Institute of Science and Technology, Xinxiang, 453003, China.
Background: The Sec14 domain is an ancient lipid-binding domain that evolved from yeast Sec14p and performs complex lipid-mediated regulatory functions in subcellular organelles and intracellular traffic. The Sec14 family is characterized by a highly conserved Sec14 domain, and is ubiquitously expressed in all eukaryotic cells and has diverse functions. However, the number and characteristics of Sec14 homologous genes in soybean, as well as their potential roles, remain understudied.
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January 2025
Department of Physics, Khalifa University of Science and Technology, 127788, Abu Dhabi, United Arab Emirates.
In this study, biopolymer composites based on chitosan (CS) with enhanced optical properties were functionalized using Manganese metal complexes and black tea solution dyes. The results indicate that CS with Mn-complexes can produce polymer hybrids with high absorption, high refractive index and controlled optical band gaps, with a significant reduction from 6.24 eV to 1.
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January 2025
Division of Infectious Diseases, Department of Medicine, Columbia University Irving Medical Center, 622 West 168th Street, Ste. 876, New York, NY, 10032, USA.
The COVID-19 pandemic may have exacerbated mental health conditions by introducing and/or modifying stressors, particularly in university populations. We examined longitudinal patterns, time-varying predictors, and contemporaneous correlates of moderate-severe psychological distress (MS-PD) among college students. During 2020-2021, participants completed self-administered questionnaires quarterly (T1 = 562, T2 = 334, T3 = 221, and T4 = 169).
View Article and Find Full Text PDFSci Data
January 2025
Remote Sensing Centre for Earth System Research (RSC4Earth), Leipzig University, Leipzig, 04103, Germany.
With climate extremes' rising frequency and intensity, robust analytical tools are crucial to predict their impacts on terrestrial ecosystems. Machine learning techniques show promise but require well-structured, high-quality, and curated analysis-ready datasets. Earth observation datasets comprehensively monitor ecosystem dynamics and responses to climatic extremes, yet the data complexity can challenge the effectiveness of machine learning models.
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