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A Structure- and Ligand-Based Virtual Screening of a Database of “Small” Marine Natural Products for the Identification of “Blue” Sigma-2 Receptor Ligands

Sigma receptors are a fascinating receptor protein class whose ligands are actually under clinical evaluation for the modulation of opioid analgesia and their use as positron emission tomography radiotracers. In particular, peculiar biological and therapeutic functions are associated with the sigma-...

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Detalles Bibliográficos
Autores principales: Floresta, Giuseppe, Amata, Emanuele, Barbaraci, Carla, Gentile, Davide, Turnaturi, Rita, Marrazzo, Agostino, Rescifina, Antonio
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6212963/
https://www.ncbi.nlm.nih.gov/pubmed/30322188
http://dx.doi.org/10.3390/md16100384
Descripción
Sumario:Sigma receptors are a fascinating receptor protein class whose ligands are actually under clinical evaluation for the modulation of opioid analgesia and their use as positron emission tomography radiotracers. In particular, peculiar biological and therapeutic functions are associated with the sigma-2 (σ(2)) receptor. The σ(2) receptor ligands determine tumor cell death through apoptotic and non-apoptotic pathways, and the overexpression of σ(2) receptors in several tumor cell lines has been well documented, with significantly higher levels in proliferating tumor cells compared to quiescent ones. This acknowledged feature has found practical application in the development of cancer cell tracers and for ligand-targeting therapy. In this context, the development of new ligands that target the σ(2) receptors is beneficial for those diseases in which this protein is involved. In this paper, we conducted a search of new potential σ(2) receptor ligands among a database of 1517 “small” marine natural products constructed by the union of the Seaweed Metabolite and the Chemical Entities of Biological Interest (ChEBI) Databases. The structures were passed through two filters that were constituted by our developed two-dimensional (2D) and three-dimensional Quantitative Structure-Activity Relationship (3D-QSAR) statistical models, and successively docked upon a σ(2) receptor homology model that we built according to the FASTA sequence of the σ(2)/TMEM97 (SGMR2_HUMAN) receptor.