(Na(+)+K(+))-ATPase is a target receptor of digitalis (cardiac glycoside) drugs. It has been demonstrated that the H1-H2 domain of the alpha-subunit of the (Na(+)+K(+))-ATPase is one of the digitalis drug interaction sites of the enzyme. Despite the extensive studies of the inhibitory effect of digitalis on the (Na(+)+K(+))-ATPase, the functional property of the H1-H2 domain of the enzyme and its role in regulating enzyme activity is not completely understood. Here we report a surprise finding: instead of inhibiting the enzyme, binding of a specific monoclonal antibody SSA78 to the H1-H2 domain of the (Na(+)+K(+))-ATPase elevates the catalytic activity of the enzyme. In the presence of low concentration of ouabain, monoclonal antibody SSA78 significantly protects enzyme function against ouabain-induced inhibition. However, higher concentration of ouabain completely inactivates the (Na(+)+K(+))-ATPase even in the presence of SSA78. These results suggest that the H1-H2 domain of the (Na(+)+K(+))-ATPase is capable of regulating enzyme function in two distinct ways for both ouabain-sensitive and -resistant forms of the enzyme: it increases the activity of the (Na(+)+K(+))-ATPase during its interaction with an activator; it also participates in the mechanism of digitalis or ouabain-induced inhibition of the enzyme. Understanding the dual activity of the H1-H2 domain will help better understand the structure-function relationships of the (Na(+)+K(+))-ATPase and the biological processes mediated by the enzyme.
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http://dx.doi.org/10.1016/j.bbrc.2008.09.137 | DOI Listing |
Sci Rep
November 2024
School of Business and Management, Xiamen University of Technology, Xiamen, 361024, China.
As medical informatization continues to progress, the sophistication of smart medicine (SM) systems has led to a marked enhancement in the caliber of medical services provided. Nevertheless, the existing body of literature is sparse when it comes to frameworks for evaluating service quality, specifically within the domain of smart medicine (SM). Drawing on a hybrid multi-criteria decision-making model that fuses the best-worst method (BWM) with the VIKOR approach, this research has crafted an innovative framework for assessing the service quality of SM.
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May 2024
Key Laboratory of Medical Molecular Virology (MOE/NHC/CAMS), School of Basic Medical Sciences, and Shanghai Institute of Infectious Disease and Biosecurity, Fudan University, Shanghai, China.
The coronavirus disease 2019 (COVID-19) pandemic, caused by the novel coronavirus severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2), has rapidly spread worldwide since its emergence in late 2019. Its ongoing evolution poses challenges for antiviral drug development. Coronavirus nsp6, a multiple-spanning transmembrane protein, participates in the biogenesis of the viral replication complex, which accommodates the viral replication-transcription complex.
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March 2024
Institute of Pharmaceutical Innovation, Hubei Province Key Laboratory of Occupational Hazard Identification and Control, School of Medicine, Wuhan University of Science and Technology, Wuhan 430065, China.
The farnesoid X receptor (FXR) has been recognized as a potential drug target for the treatment of non-alcoholic fatty liver disease (NAFLD). FXR agonists benefit NAFLD by modulating bile acid synthesis and transport, lipid metabolism, inflammation, and fibrosis pathways. However, there are still great challenges involved in developing safe and effective FXR agonists.
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February 2024
MRC Laboratory of Molecular Biology, Francis Crick Avenue, Cambridge CB2 0QH, United Kingdom. Electronic address:
Protein Eng Des Sel
January 2023
Protabit LLC, 111 Waverly Drive, Pasadena, CA 91105, USA.
Computational modeling and design of antibodies has become an integral part of today's research and development in antibody therapeutics. Here we describe the Triad Antibody Homology Modeling (TriadAb) package, a functionality of the Triad protein design platform that predicts the structure of any heavy and light chain sequences of an antibody Fv domain using template-based modeling. To gauge the performance of TriadAb, we benchmarked against the results of the Second Antibody Modeling Assessment (AMA-II).
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