Drug repurposing on G protein-coupled receptors using a computational profiling approach
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De Felice, Alessandra
ORCID
Euler Institute (EUL), Università della Svizzera italiana, Switzerland
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Aureli, Simone
ORCID
Euler Institute (EUL), Università della Svizzera italiana, Switzerland
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Limongelli, Vittorio
ORCID
Euler Institute (EUL), Università della Svizzera italiana, Switzerland - Department of Pharmacy, University of Naples “Federico II”, Naples, Italy
Published in:
- Frontiers in molecular biosciences . - 2021, vol. 8, p. 673053
English
G protein-coupled receptors (GPCRs) are the largest human membrane receptor family regulating a wide range of cell signaling. For this reason, GPCRs are highly desirable drug targets, with approximately 40% of prescribed medicines targeting a member of this receptor family. The structural homology of GPCRs and the broad spectrum of applications of GPCR-acting drugs suggest an investigation of the cross-activity of a drug toward different GPCR receptors with the aim of rationalizing drug side effects, designing more selective and less toxic compounds, and possibly proposing off-label therapeutic applications. Herein, we present an original in silico approach named “Computational Profiling for GPCRs” (CPG), which is able to represent, in a one-dimensional (1D) string, the physico-chemical properties of a ligand–GPCR binding interaction and, through a tailored alignment algorithm, repurpose the ligand for a different GPCR. We show three case studies where docking calculations and pharmacological data confirm the drug repurposing findings obtained through CPG on 5-hydroxytryptamine receptor 2B, beta-2 adrenergic receptor, and M2 muscarinic acetylcholine receptor. The CPG code is released as a user-friendly graphical user interface with numerous options that make CPG a powerful tool to assist the drug design of GPCR ligands.
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Collections
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Language
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Classification
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Pharmacology, therapeutics, toxicology
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License
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CC BY
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Open access status
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gold
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Identifiers
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Persistent URL
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https://n2t.net/ark:/12658/srd1328323
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