a(MESH:VU0357017)
More recently, the M1-selective allosteric agonist VU0357017 was discovered, which displayed improved potency via binding to a novel allosteric site on the M1 mAChR. VU0357017 significantly blocked scopolamine-impaired contextual fear conditioning and enhanced spatial and contextual fear learning PubMed:24511233
More recently, the M1-selective allosteric agonist VU0357017 was discovered, which displayed improved potency via binding to a novel allosteric site on the M1 mAChR. VU0357017 significantly blocked scopolamine-impaired contextual fear conditioning and enhanced spatial and contextual fear learning PubMed:24511233
More recently, the M1-selective allosteric agonist VU0357017 was discovered, which displayed improved potency via binding to a novel allosteric site on the M1 mAChR. VU0357017 significantly blocked scopolamine-impaired contextual fear conditioning and enhanced spatial and contextual fear learning PubMed:24511233
During the past few years, the M1 mAChR allosteric agonists VU0184670 and VU0357017 have been screened out, and have more exciting properties. Both compounds have high solubility in aqueous solutions as well as good CNS penetration, without any agonist or antagonist activity for the M2 and M5 subtypes PubMed:24590577
Moreover, VU0184670 potentiates neuronal NMDAR-mediated currents in hippocampal brain slices and VU0357017 reverses the cognitive deficits induced by an mAChR antagonist in a contextual fear conditioning paradigm, exhibiting improvement of hippocampus-dependent learning[110, 123]. PubMed:24590577
Moreover, VU0184670 potentiates neuronal NMDAR-mediated currents in hippocampal brain slices and VU0357017 reverses the cognitive deficits induced by an mAChR antagonist in a contextual fear conditioning paradigm, exhibiting improvement of hippocampus-dependent learning[110, 123]. PubMed:24590577
BEL Commons is developed and maintained in an academic capacity by Charles Tapley Hoyt and Daniel Domingo-Fernández at the Fraunhofer SCAI Department of Bioinformatics with support from the IMI project, AETIONOMY. It is built on top of PyBEL, an open source project. Please feel free to contact us here to give us feedback or report any issues. Also, see our Publishing Notes and Data Protection information.
If you find BEL Commons useful in your work, please consider citing: Hoyt, C. T., Domingo-Fernández, D., & Hofmann-Apitius, M. (2018). BEL Commons: an environment for exploration and analysis of networks encoded in Biological Expression Language. Database, 2018(3), 1–11.