4 results match your criteria: "School of Sciences - Pontifical Catholic University of Rio Grande do Sul (PUCRS)[Affiliation]"
Computational Analysis of Dipyrone Metabolite 4-Aminoantipyrine As A Cannabinoid Receptor 1 Agonist.
Curr Med Chem
September 2020
Laboratory of Computational Systems Biology, School of Sciences - Pontifical Catholic University of Rio Grande do Sul (PUCRS), Av. Ipiranga, 6681, Porto Alegre-RS 90619-900, Brazil.
Background: Cannabinoid receptor 1 has its crystallographic structure available in complex with agonists and inverse agonists, which paved the way to establish an understanding of the structural basis of interactions with ligands. Dipyrone is a prodrug with analgesic capabilities and is widely used in some countries. Recently some evidence of a dipyrone metabolite acting over the Cannabinoid Receptor 1has been shown.
View Article and Find Full Text PDFCyclin-Dependent Kinase 2 in Cellular Senescence and Cancer. A Structural and Functional Review.
Curr Drug Targets
July 2020
Graduate Program in Cellular and Molecular Biology, The Pontifical Catholic University of Rio Grande do Sul (PUCRS). Av. Ipiranga, 6681 Porto Alegre/RS 90619-900, Brazil.
Background: Cyclin-dependent kinase 2 (CDK2) has been studied due to its role in the cell-cycle progression. The elucidation of the CDK2 structure paved the way to investigate the molecular basis for inhibition of this enzyme, with the coordinated efforts combining crystallography with functional studies.
Objective: Our goal here is to review recent functional and structural studies directed to understanding the role of CDK2 in cancer and senescence.
Optimized Virtual Screening Workflow: Towards Target-Based Polynomial Scoring Functions for HIV-1 Protease.
Comb Chem High Throughput Screen
January 2019
Laboratory of Computational Systems Biology, School of Sciences, Pontifical Catholic University of Rio Grande do Sul, Av. Ipiranga, 6681 Partenon Porto Alegre-RS, 90619-900, Brazil.
Article Synopsis
- The study focuses on creating a computational approach to predict how molecules interact with the HIV-1 protease to help develop new inhibitors.
- Researchers built a scoring function using existing data and machine-learning techniques aimed at predicting how effectively potential drugs can bind to the HIV-1 protease.
- The new methodology demonstrated improved accuracy in simulations compared to previous methods, suggesting it could enhance the drug design process targeting HIV-1 protease.
Supervised machine learning techniques to predict binding affinity. A study for cyclin-dependent kinase 2.
Biochem Biophys Res Commun
December 2017
Laboratory of Computational Systems Biology, School of Sciences - Pontifical Catholic University of Rio Grande do Sul (PUCRS), Av. Ipiranga, 6681, Porto Alegre, RS 90619-900, Brazil; Graduate Program in Cellular and Molecular Biology, Pontifical Catholic University of Rio Grande do Sul (PUCRS), Av. Ipiranga, 6681, Porto Alegre, RS 90619-900, Brazil. Electronic address:
Here we report the development of a machine-learning model to predict binding affinity based on the crystallographic structures of protein-ligand complexes. We used an ensemble of crystallographic structures (resolution better than 1.5 Å resolution) for which half-maximal inhibitory concentration (IC) data is available.
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