@misc{10481/71159,
year = {2021},
month = {8},
url = {http://hdl.handle.net/10481/71159},
abstract = {Background: The perception of taste is a prime example of complex signal transduction at the subcellular level,
involving an intricate network of molecular machinery, which can be investigated to great extent by the tools
provided by Computational Molecular Modelling. The present review summarises the current knowledge on the
molecular mechanisms at the root of taste transduction, in particular involving taste receptors, highly specialised
proteins driving the activation/deactivation of specific cell signalling pathways and ultimately leading to the
perception of the five principal tastes: sweet, umami, bitter, salty and sour. The former three are detected by
similar G protein-coupled receptors, while the latter two are transduced by ion channels.
Scope and approach: The main objective of the present review is to provide a general overview of the molecular
structures investigated to date of all taste receptors and the techniques employed for their molecular modelling.
In addition, we provide an analysis of the various ligands known to date for the above-listed receptors, including
how they are activated in the presence of their target molecule.
Key findings and conclusions: In the last years, numerous advances have been made in molecular research and
computational investigation of ligand-receptor interaction related to taste receptors. This work aims to outline
the progress in scientific knowledge about taste perception and understand the molecular mechanisms involved
in the transfer of taste information.},
organization = {European Union's Horizon 2020 research and innovation program under the Marie Sklodowska-Curie-RISE Grant	872181},
publisher = {Elsevier},
keywords = {Taste receptors},
keywords = {Molecular modelling},
keywords = {Molecular dynamics},
keywords = {Taste},
keywords = {GPCR},
keywords = {Ion channel},
title = {On the human taste perception: Molecular-level understanding empowered by computational methods},
doi = {10.1016/j.tifs.2021.07.013},
author = {Pallante, Lorenzo and Martos Núñez, María Vanesa},
}