Tabtoxin resistance protein (TTR) is an enzyme that renders tabtoxin-producing pathogens, such as Pseudomonas syringae, tolerant to their own phytotoxins. Here, we report the crystal structure of TTR complexed with its natural cofactor, acetyl coenzyme A (AcCoA), to 1.55A resolution. The binary complex forms a characteristic "V" shape for substrate binding and contains the four motifs conserved in the GCN5-related N-acetyltransferase (GNAT) superfamily, which also includes the histone acetyltransferases (HATs). A single-step mechanism is proposed to explain the function of three conserved residues, Glu92, Asp130 and Tyr141, in catalyzing the acetyl group transfer to its substrate. We also report that TTR possesses HAT activity and suggest an evolutionary relationship between TTR and other GNAT members.
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http://dx.doi.org/10.1016/s0022-2836(02)01284-6 | DOI Listing |
Inorg Chem
March 2025
Key Laboratory of High-Temperature and High-Pressure Study of the Earth's Interior, Institute of Geochemistry, Chinese Academy of Sciences, Guiyang, Guizhou 550081, China.
In situ high-pressure single-crystal X-ray diffraction and Raman spectroscopy analyses were performed on a natural bismutotantalite with an α-BiTaO structure. The results indicate that α-BiTaO transforms into an orthorhombic phase (HP γ-BiTaO), likely through an intermediate orthorhombic phase (HP β-BiTaO). The transition pressures are 11.
View Article and Find Full Text PDFJ Am Chem Soc
March 2025
School of Chemistry, The University of New South Wales, Sydney, New South Wales 2052, Australia.
The presence of defects can significantly improve catalytic activity and stability, as they influence the binding of the reactants, intermediates, and products to the catalyst. Controlling defects in the structures of nanocrystal catalysts is synthetically challenging. In this study, we demonstrate the ability to control the growth of Ir nanocrystals, enabling the tuning of both structural and surface defects.
View Article and Find Full Text PDFPhys Chem Chem Phys
March 2025
Ivanovo State University of Chemistry and Technology, Department of Physical and Colloidal Chemistry, Sheremetevskiy Ave, 7, 153000 Ivanovo, Russia.
A gas-phase electron diffraction analysis combined with mass-spectrometry (GED/MS) of (2,9)--2,9-diiodo-13-(triflyl)-13-azabicyclo[8.2.1]tridec-5-ene was performed and the results were compared with those studied earlier by GED/MS and X-ray analysis of triflamide derivatives, that is 3,7,9-tris(triflyl)-3,7,9-triazabicyclo[3.
View Article and Find Full Text PDFLangmuir
March 2025
Department of Information and Electrical Engineering and Applied Mathematics, University of Salerno, via Giovanni Paolo II 132, Fisciano, Salerno 84084, Italy.
The interaction between organic molecules and nanomaterials leads to complexation or the functionalization of later and modification of their properties, which are promising for electronics, terahertz technology, photonics, medical imaging, drug delivery, and other applications. Based on theoretical and experimental (THz, Raman, and fluorescence spectroscopy) studies, we analyzed the main spectroscopic characteristics of a weakly bound van der Waals complex of -stilbene (TS) molecule and hexagonal boron nitride (hBN). Raman scattering was demonstrated to be the most effective tool to confirm complex formation, exhibiting blue-shifted TS fingerprint lines in the TS + hBN Raman spectrum with respect to the spectra of pure TS or BN.
View Article and Find Full Text PDFAngew Chem Int Ed Engl
March 2025
Southeast University, School of Chemistry and Chemical Engineering, Moling Street, Jiangning District, 211189, Nanjing, CHINA.
Co-crystal engineering is of interest for many applications in pharmaceutical, chemistry and material fields, but rational design of co-crystals is still challenging. Although artificial intelligence has brought major changes in the decision-making process for materials design, yet limitations in generalization and mechanistic understanding remain. Herein, we sought to improve prediction of co-crystal by combining mechanistic thermodynamic modeling with machine learning.
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