Literature DB >> 28750813

Methods for the Development of In Silico GPCR Models.

Paula Morales1, Dow P Hurst2, Patricia H Reggio2.   

Abstract

The Reggio group has constructed computer models of the inactive and G-protein-activated states of the cannabinoid CB1 and CB2 receptors, as well as, several orphan receptors that recognize a subset of cannabinoid compounds, including GPR55 and GPR18. These models have been used to design ligands, mutations, and covalent labeling studies. The resultant second-generation models have been used to design ligands with improved affinity, efficacy, and subtype selectivity. Herein, we provide a guide for the development of GPCR models using the most recent orphan receptor studied in our lab, GPR3. GPR3 is an orphan receptor that belongs to the Class A family of G-protein-coupled receptors. It shares high sequence similarity with GPR6, GPR12, the lysophospholipid receptors, and the cannabinoid receptors. GPR3 is predominantly expressed in mammalian brain and oocytes and it is known as a Gαs-coupled receptor activated constitutively in cells. GPR3 represents a possible target for the treatment of different pathological conditions such as Alzheimer's disease, oocyte maturation, or neuropathic pain. However, the lack of potent and selective GPR3 ligands is delaying the exploitation of this promising therapeutic target. In this context, we aim to develop a homology model that helps us to elucidate the structural determinants governing ligand-receptor interactions at GPR3. In this chapter, we detail the methods and rationale behind the construction of the GPR3 active-and inactive-state models. These homology models will enable the rational design of novel ligands, which may serve as research tools for further understanding of the biological role of GPR3.
© 2017 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Cannabinoid receptors; Docking studies; GPCR; GPR3; Homology model; Molecular modeling

Mesh:

Substances:

Year:  2017        PMID: 28750813      PMCID: PMC5809125          DOI: 10.1016/bs.mie.2017.05.005

Source DB:  PubMed          Journal:  Methods Enzymol        ISSN: 0076-6879            Impact factor:   1.600


  110 in total

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