6 research outputs found

    Efecto del AMG9810 sobre el desarrollo folicular y la pubertad de la cobaya

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    “Las células de la teca de los folículos ováricos expresan receptores TRPV1 y al administrar 1 o 10nM de capsaicina in situ los folículos atrésicos disminuyeron, mientras que con 1µM aumentó el número de ellos. En esta tesis mostramos que cuando administramos en la bolsa ovárica 1nM, 10nM o 1µM de AMG9810, un antagonista de los receptores TRPV1, el número de folículos atrésicos disminuyó y la edad de la primera apertura vaginal no se modificó, además con 1 nM de AMG9810, el número de células TRPV1-positvas aumentó en los folículos sanos y no cambió con 10nM o 1µM del mismo antagonista. Estos resultados sugieren que el AMG9819 protege de la atresia folicular y que los receptores TRPV1 son reguladores locales que modulan el desarrollo de los folículos ováricos, pero no la edad para iniciar la pubertad de las cobayas.

    Structural Insight into Tetrameric hTRPV1 from Homology Modeling, Molecular Docking, Molecular Dynamics Simulation, Virtual Screening, and Bioassay Validations

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    The transient receptor potential vanilloid type 1 (TRPV1) is a heat-activated cation channel protein, which contributes to inflammation, acute and persistent pain. Antagonists of human TRPV1 (hTRPV1) represent a novel therapeutic approach for the treatment of pain. Developing various antagonists of hTRPV1, however, has been hindered by the unavailability of a 3D structure of hTRPV1. Recently, the 3D structures of rat TRPV1 (rTRPV1) in the presence and absence of ligand have been reported as determined by cryo-EM. rTRPV1 shares 85.7% sequence identity with hTRPV1. In the present work, we constructed and reported the 3D homology tetramer model of hTRPV1 based on the cryo-EM structures of rTRPV1. Molecular dynamics (MD) simulations, energy minimizations, and prescreen were applied to select and validate the best model of hTRPV1. The predicted binding pocket of hTRPV1 consists of two adjacent monomers subunits, which were congruent with the experimental rTRPV1 data and the cyro-EM structures of rTRPV1. The detailed interactions between hTRPV1 and its antagonists or agonists were characterized by molecular docking, which helped us to identify the important residues. Conformational changes of hTRPV1 upon antagonist/agonist binding were also explored by MD simulation. The different movements of compounds led to the different conformational changes of monomers in hTRPV1, indicating that TRPV1 works in a concerted way, resembling some other channel proteins such as aquaporins. We observed that the selective filter was open when hTRPV1 bound with an agonist during MD simulation. For the lower gate of hTRPV1, we observed large similarities between hTRPV1 bound with antagonist and with agonist. A five-point pharmacophore model based on several antagonists was established, and the structural model was used to screen <i>in silico</i> for new antagonists for hTRPV1. By using the 3D TRPV1 structural model above, the pilot <i>in silico</i> screening has begun to yield promising hits with activity as hTRPV1 antagonists, several of which showed substantial potency

    CRC Platform: A Colorectal Cancer Domain-specific Chemogenomics Knowledgebase for Polypharmacology and Target Identification Research

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    Colorectal cancer (CRC) is the third most common cancer, causing more than 600,000 deaths worldwide annually. Due to the involvement of complicated signaling pathways, epigenetic changes and genetic/genomic alterations, it is still challenging to develop effective treatments to reverse CRC progression. In order to facilitate developing new drugs for CRC treatment and revealing the mechanisms of CRC drug action at molecular level, we have constructed a computational CRC Platform (http://www.cbligand.org/CRC/), a domain-specific chemogenomics knowledgebase. The CRC platform consists of four database modules, e.g. 762 CRC related genes and proteins, 411 known CRC drugs and chemicals, 168383 CRC related bioassays, and 269 CRC pathways, as well as searching tools for multi-function retrieval. It is also featured with powerful cloud computation technologies and computational tools to expedite target identification, polypharmacology and drug synergy analysis for CRC research. We have also demonstrated the application of the CRC platform in the case studies: (1) computational exploration of FDA-approved CRC drugs for polypharmacology and drug synergy analysis; (2) in silico target identification of small chemical molecules from natural products with anti-CRC bioactivity; and (3) target identification and experimental validation for our in-house compounds. CRC platform will not only enrich our knowledge of CRC target identification, polypharmacology analysis, and biomarkers investigation, but also enhance the CRC chemogenomics data sharing and information exchange globally, and assist new drug design discovery and development for CRC treatment
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