Properties of the metal ion binding sites of Zn-transcription factor IIIA (TFIIIA) were investigated to understand the potential of this type of zinc finger to undergo reactions that remove Zn(2+) from the protein. Zn-TFIIIA was purified from E. coli containing the cloned sequence for Xenopus laevis oocyte TFIIIA and its stoichiometry of bound Zn(2+) was shown to depend on the details of the isolation process. The average dissociation constant of Zn(2+) in Zn-TFIIIIA was 10(-7). The dissociation constant for Zn-F3, the third finger from the N-terminus of TFIIIA, was 1.0 x 10(-8). The reactivity of Zn-TFIIIA with a series of metal binding ligands, including 2-carboxy-2'-hydroxy-5'-sulfoformazylbenzene (zincon), 4-(2-pyridylazo)-resorcinol (PAR), and 3-ethoxy-2-oxo-butyraldehyde-bis-(N(4)-dimethylthiosemicarbazone) (H(2)KTSM(2)) revealed similar kinetics. The reactivity of PAR with Zn-TFIIIA declined substantially when the protein was bound to the internal control region (ICR) of the 5S ribosomal DNA. Both Cd(2+) and Pb(2+) disrupt TFIIIA binding to its cognate DNA sequence. The Pb(2+) dissociation constant of Pb-F3 was measured as 2.5 x 10(-8). According to NMR spectroscopy, F3 does not fold into a regular conformation in the presence of Pb(2+).
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http://dx.doi.org/10.1016/j.jinorgbio.2004.01.014 | DOI Listing |
J Phys Chem A
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Institute of Atomic and Molecular Physics, Sichuan University, Chengdu 610065, China.
High-level multireference configuration interaction plus Davidson correction (MRCI + Q) calculation method was employed to determine the potential energy curves (PECs) of 10 Λ-S states, which come from the first and second dissociation channels of the SbP molecule, as well as 34 Ω states considering the spin-orbit coupling (SOC) effect. By solving the Schrödinger equation for nuclear motion, spectroscopic constants for the ground state XΣ and low-lying excited states were obtained and compared with experimental data. The excellent agreement indicates the reliability of our calculations.
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Dept of Biochemistry & Center for Biophysics and Quantitative Biology, University of Illinois Urbana-Champaign, Urbana, IL, USA.
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View Article and Find Full Text PDFJ Phys Chem A
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Ufa Institute of Chemistry, Ufa Federal Research Centre of the Russian Academy of Sciences, Laboratory of Physicochemical Methods of Analysis, 69 Prospekt Oktyabrya, Ufa 450054, Russian Federation.
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Machine learning (ML) models now play a crucial role in predicting properties essential to drug development, such as a drug's logscale acid-dissociation constant (p). Despite recent architectural advances, these models often generalize poorly to novel compounds due to a scarcity of ground-truth data. Further, these models lack interpretability.
View Article and Find Full Text PDFEnviron Res
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Key Laboratory of the Three Gorges Reservoir Region's Eco-Environment, Ministry of Education, Chongqing University, Chongqing, 400045, China. Electronic address:
Residual antibiotics in aquatic environments pose health and ecological risks due to their persistence and resistance to biodegradation. Thus, it is crucial to develop efficient technologies for the degradation of such antibiotics. This study presents a novel approach using a nano zero-valent iron/graphitic carbon nitride (nZVI/g-CN)-enhanced dielectric barrier discharge (DBD) plasma process for the degradation of ciprofloxacin (CIP).
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