Identification of potential antagonists of CRF1R for possible treatment of stress and anxiety neuro-disorders using structure-based virtual screening and molecular dynamics simulation

Abdullahi Ibrahim Uba, Chun Wu

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

G protein-coupled-receptors (GPCRs) are the largest family of cell surface receptors with tremendous therapeutic potential. They mediate signal transduction activities via G protein-dependent signaling pathways, G protein-independent signaling pathways, and other complicated regulatory processes. The corticotropin-releasing factor receptor type 1 (CRF1R) is a member of class B GPCRs that is predominantly found in the central nervous system, where it plays a key role in stress-related neuro-disorders. To date, no drug targeting this receptor has been approved, partly due to inadequate understanding of the activation mechanism of class B GPCRs. Previously, using MD simulation, we demonstrated that the CRF1R complexed with a small-molecule antagonist CP-376395 maintains a conformation of its transmembrane domain (TMD). Here, using the most abundant structures derived from those simulations, we carried out a structure-based virtual screening of ZINC15 “Druglike” library containing approximately 17 million compounds. The docking complexes of the CRF1R with the top 30 hits were submitted to MD simulation to examine the stability of ligand binding mode. Furthermore, MM-GBSA binding energy calculations were performed on all the complexes to rank them with improving accuracy. Hit 1 (ZINC000046079839) and hit 20 (ZINC000032907937) span the allosteric site of the CRF1R, persistently forming interactions with transmembrane helices 3 and 6. These interactions are likely to keep the receptor in an inactive state since both transmembrane helices play a critical role in the activation of the receptor.

Original languageEnglish (US)
Article number107743
JournalComputational Biology and Chemistry
Volume100
DOIs
StatePublished - Oct 2022

All Science Journal Classification (ASJC) codes

  • Structural Biology
  • Biochemistry
  • Organic Chemistry
  • Computational Mathematics

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