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Heavy metal pollution, which is one of the most important environmental problems, has a significant effect on plant growth and development. Plants influence all living things because of their role in the food chain. Therefore, in this study, antioxidant enzyme activities and metallothionein (MT) gene expression levels were investigated to determine the protective role of copper (Cu) and ascorbic acid (AsA) against chromium (Cr) stress. In order to determine these parameters, sunflower seeds were grown in hydroponic system after germination. Two weeks later, AsA (200 mg/L) treatment was made as foliar spray. After 24 h of the treatment, the hydroponic nutrient medium was used as the treatment solution containing different concentrations of metals (0.25 mM Cu (CuSO4·5H2O); 1 mM Cr (K2Cr2O7). The results have reduced the overall growth of plants with Cr heavy metals. Cr stress increased the amount of malondialdehyde. Furthermore, while superoxide dismutase and peroxidase activities were increased, no significant change in catalase activity was observed. Cu and AsA applied with Cr treatment significantly reduced Cr toxicity. Expression levels of MT genes (HanMT2-1 and HanMT4) were determined by qRT-PCR analysis. From the study result, it was determined that the expression levels of MT genes under Cr stress changed according to the plant tissues and were upregulated in the roots. The protective roles of Cu and AsA against Cr stress were observed. From the results of this study, it was confirmed that MT2-1 could be an effective gene resource in Cr remediation-related plant breeding programs. [ABSTRACT FROM AUTHOR]

G protein-coupled receptors (GPCRs) play a central role in cellular signaling and are linked to many diseases. Accordingly, computational methods to explore potential allosteric sites for this class of proteins to facilitate the identification of potential modulators are needed. Importantly, the availability of rich structural data providing the locations of the orthosteric ligands and allosteric modulators targeting different GPCRs allows for the validation of approaches to identify new allosteric binding sites. Here, we validate the combination of two computational techniques, the residue interaction network (RIN) model and the site identification by ligand competitive saturation (SILCS) method, to predict putative allosteric binding sites of class A GPCRs. RIN analysis identifies hub residues that mediate allosteric signaling within a receptor and have a high capacity to alter receptor dynamics upon ligand binding. The known orthosteric (and allosteric) binding sites of 18 distinct class A GPCRs were successfully predicted by RIN through a dataset of 105 crystal structures (91 ligand-bound, 14 unbound) with up to 77.8% (76.9%) sensitivity, 92.5% (95.3%) specificity, 51.9% (50%) precision, and 86.2% (92.4%) accuracy based on the experimental and theoretical binding site data. Moreover, graph spectral analysis of the residue networks revealed that the proposed sites were located at the interfaces of highly interconnected residue clusters with a high ability to coordinate the functional dynamics. Then, we employed the SILCS-Hotspots method to assess the druggability of the novel sites predicted for 7 distinct class A GPCRs that are critical for a variety of diseases. While the known orthosteric and allosteric binding sites are successfully explored by our approach, numerous putative allosteric sites with the potential to bind drug-like molecules are proposed. The computational approach presented here promises to be a highly effective tool to predict putative allosteric sites of GPCRs to facilitate the design of effective modulators., Competing Interests: The authors declare the following competing financial interest(s): ADM Jr., is co-founder and CSO of SilcsBio LLC. The other authors declare no competing financial interest., (© 2024 The Authors. Published by American Chemical Society.)