The identification of high-affinity ligands for PPAR gamma has revealed the role of this receptor as the molecular target for the antidiabetic activity of the thiazolidinediones. The surprising observation that agonists of an adipogenic transcription factor reverse the obesity-associated disease of diabetes highlights the power of using potent and selective ligands to study receptor-mediated biology. Similarly, the observation that PGD2 and its cyclopentenone metabolites compounds are microM PPAR ligands suggests that these receptors may have a physiological role in mediating prostaglandin signaling in the spleen.
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http://dx.doi.org/10.1111/j.1749-6632.1996.tb18622.x | DOI Listing |
Biosci Biotechnol Biochem
January 2025
Division of Food Science and Biotechnology, Graduate School of Agriculture, Kyoto University, Kyoto, 606-8502, Japan.
Protein kinase C (PKC) is a family of serine/threonine kinases, and PKC ligands have the potential to be therapeutic seeds for cancer, Alzheimer's disease, and human immunodeficiency virus infection. However, in addition to desired therapeutic effects, most PKC ligands also exhibit undesirable pro-inflammatory effects. The discovery of new scaffolds for PKC ligands is important for developing less inflammatory PKC ligands, such as bryostatins.
View Article and Find Full Text PDFPharmacol Ther
January 2025
School of Pharmacy, China Pharmaceutical University, Nanjing 211198, China; School of Life Science and Technology, China Pharmaceutical University, Nanjing 211198, China.
G protein-coupled receptors (GPCRs) can transmit signals via G protein-dependent or independent pathways due to the conformational changes of receptors and ligands, which is called biased signaling. This concept posits that ligands can selectively activate a specific signaling pathway after receptor activation, facilitating downstream signaling along a preferred pathway. Biased agonism enables the development of ligands that prioritize therapeutic signaling pathways while mitigating on-target undesired effects.
View Article and Find Full Text PDFCell
January 2025
Program in Bioinformatics, Boston University, Boston, MA 02215, USA; Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 1A8, Canada; Center for Network Systems Biology, Boston University, Boston, MA 02218, USA; Department of Chemistry, Boston University, Boston, MA 02215, USA; Department of Chemical Physiology and Biochemistry, Division of Oncological Sciences, Knight Cancer Institute, Oregon Health and Science University, Portland, OR, USA. Electronic address:
Knowledge of protein-metabolite interactions can enhance mechanistic understanding and chemical probing of biochemical processes, but the discovery of endogenous ligands remains challenging. Here, we combined rapid affinity purification with precision mass spectrometry and high-resolution molecular docking to precisely map the physical associations of 296 chemically diverse small-molecule metabolite ligands with 69 distinct essential enzymes and 45 transcription factors in the gram-negative bacterium Escherichia coli. We then conducted systematic metabolic pathway integration, pan-microbial evolutionary projections, and independent in-depth biophysical characterization experiments to define the functional significance of ligand interfaces.
View Article and Find Full Text PDFExpert Opin Drug Discov
January 2025
Department of Radiology, Molecular Imaging Innovations Institute (MI3), Weill Cornell Medicine, New York, NY, USA.
Introduction: Technological advancements in virtual screening (VS) have rapidly accelerated its application in drug discovery, as reflected by the exponential growth in VS-related publications. However, a significant gap remains between the volume of computational predictions and their experimental validation. This discrepancy has led to a rise in the number of unverified 'claimed' hits which impedes the drug discovery efforts.
View Article and Find Full Text PDFPharmaceuticals (Basel)
January 2025
Centro de Química Médica, Facultad de Medicina Clínica Alemana, Universidad del Desarrollo, Santiago 7780272, Chile.
Acute myeloid leukemia (AML) presents significant therapeutic challenges, particularly in cases driven by mutations in the FLT3 tyrosine kinase. This study aimed to develop a robust and user-friendly machine learning-based quantitative structure-activity relationship (QSAR) model to predict the inhibitory potency (pIC values) of FLT3 inhibitors, addressing the limitations of previous models in dataset size, diversity, and predictive accuracy. Using a dataset which was 14 times larger than those employed in prior studies (1350 compounds with 1269 molecular descriptors), we trained a random forest regressor, chosen due to its superior predictive performance and resistance to overfitting.
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