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Frontiers in Chemistry 2024Many enzymes in nature utilize a free arginine (L-Arg) amino acid to initiate the biosynthesis of natural products. Examples include nitric oxide synthases, which... (Review)
Review
Many enzymes in nature utilize a free arginine (L-Arg) amino acid to initiate the biosynthesis of natural products. Examples include nitric oxide synthases, which generate NO from L-Arg for blood pressure control, and various arginine hydroxylases involved in antibiotic biosynthesis. Among the groups of arginine hydroxylases, several enzymes utilize a nonheme iron(II) active site and let L-Arg react with dioxygen and -ketoglutarate to perform either C-hydroxylation, C-hydroxylation, C-hydroxylation, or C-C-desaturation. How these seemingly similar enzymes can react with high specificity and selectivity to form different products remains unknown. Over the past few years, our groups have investigated the mechanisms of L-Arg-activating nonheme iron dioxygenases, including the viomycin biosynthesis enzyme VioC, the naphthyridinomycin biosynthesis enzyme NapI, and the streptothricin biosynthesis enzyme OrfP, using computational approaches and applied molecular dynamics, quantum mechanics on cluster models, and quantum mechanics/molecular mechanics (QM/MM) approaches. These studies not only highlight the differences in substrate and oxidant binding and positioning but also emphasize on electronic and electrostatic differences in the substrate-binding pockets of the enzymes. In particular, due to charge differences in the active site structures, there are changes in the local electric field and electric dipole moment orientations that either strengthen or weaken specific substrate C-H bonds. The local field effects, therefore, influence and guide reaction selectivity and specificity and give the enzymes their unique reactivity patterns. Computational work using either QM/MM or density functional theory (DFT) on cluster models can provide valuable insights into catalytic reaction mechanisms and produce accurate and reliable data that can be used to engineer proteins and synthetic catalysts to perform novel reaction pathways.
PubMed: 38406558
DOI: 10.3389/fchem.2024.1365494 -
ACS Omega Apr 2024Colorectal cancer (CRC) has witnessed a concerning increase in incidence and poses a significant therapeutic challenge due to its poor prognosis. There is a pressing...
Colorectal cancer (CRC) has witnessed a concerning increase in incidence and poses a significant therapeutic challenge due to its poor prognosis. There is a pressing demand to identify novel drug therapies to combat CRC. In this study, we addressed this need by utilizing the pharmacological profiles of anticancer drugs from the Genomics of Drug Sensitivity in Cancer (GDSC) database and developed QSAR models using the Support Vector Machine (SVM) algorithm for prediction of alternative and promiscuous anticancer compounds for CRC treatment. Our QSAR models demonstrated their robustness by achieving a high correlation of determination () after 10-fold cross-validation. For 12 CRC cell lines, ranged from 0.609 to 0.827. The highest performance was achieved for SW1417 and GP5d cell lines with values of 0.827 and 0.786, respectively. Further, we listed the most common chemical descriptors in the drug profiles of the CRC cell lines and we also further reported the correlation of these descriptors with drug activity. The KRFP314 fingerprint was the predominantly occurring descriptor, with the KRFPC314 fingerprint following closely in prevalence within the drug profiles of the CRC cell lines. Beyond predictive modeling, we also confirmed the applicability of our developed QSAR models via methods by conducting descriptor-drug analyses and recapitulating drug-to-oncogene relationships. We also identified two potential anti-CRC FDA-approved drugs, viomycin and diamorphine, using QSAR models. To ensure the easy accessibility and utility of our research findings, we have incorporated these models into a user-friendly prediction Web server named "ColoRecPred", available at https://project.iith.ac.in/cgntlab/colorecpred. We anticipate that this Web server can be used for screening of chemical libraries to identify potential anti-CRC drugs.
PubMed: 38680332
DOI: 10.1021/acsomega.4c01195