001     884251
005     20210130005849.0
037 _ _ |a FZJ-2020-03150
100 1 _ |a Alfonso-Prieto, Mercedes
|0 P:(DE-Juel1)169976
|b 0
|e Corresponding author
|u fzj
111 2 _ |a XXIXth Annual Meeting of the European Chemoreception Research Organization
|g ECRO 2019
|c Trieste
|d 2019-09-11 - 2019-09-14
|w Italy
245 _ _ |a S14- A computational view on coffee perception: modeling and simulations of chemosensory receptors
260 _ _ |c 2020
336 7 _ |a Conference Paper
|0 33
|2 EndNote
336 7 _ |a Other
|2 DataCite
336 7 _ |a INPROCEEDINGS
|2 BibTeX
336 7 _ |a conferenceObject
|2 DRIVER
336 7 _ |a LECTURE_SPEECH
|2 ORCID
336 7 _ |a Conference Presentation
|b conf
|m conf
|0 PUB:(DE-HGF)6
|s 1601644116_14867
|2 PUB:(DE-HGF)
|x Invited
520 _ _ |a Bitterness is an organoleptic quality commonly linked to coffee. At low levels, bitterness may help to counteract coffee acidity and is associated by consumers with the boost they get from caffeine. On the contrary, at high levels, bitterness can overpower the other flavors present in coffee and produce rejection in consumers. The first step in bitter taste perception is the detection of bitter molecules by their target receptors in the tongue. Humans have twenty-five bitter taste receptors (also known as taste 2 receptors or TAS2Rs) that are able to recognize around 1,000 different bitter compounds. Unraveling this complex combinatorial code of receptor-ligand pairs at the molecular level has been hindered by the lack of experimental structures of bitter taste receptors. Hence, we combine bioinformatics and multiscale molecular dynamics simulations to generate models of receptor-ligand complexes and then validate them by comparison with experimental mutagenesis and ligand data. Our integrated computational- experimental pipeline provides molecular insights into the ligand selectivity determinants of bitter taste receptors. Such information may offer clues for the design of new bitter masking compounds and for the understanding of perceptual differences across the population.
536 _ _ |a 574 - Theory, modelling and simulation (POF3-574)
|0 G:(DE-HGF)POF3-574
|c POF3-574
|f POF III
|x 0
700 1 _ |a Giorgetti, Alejandro
|0 P:(DE-Juel1)165199
|b 1
|u fzj
700 1 _ |a Carloni, Paolo
|0 P:(DE-Juel1)145614
|b 2
|u fzj
856 4 _ |u https://doi.org/10.1093/chemse/bjaa007
909 C O |o oai:juser.fz-juelich.de:884251
|p VDB
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 0
|6 P:(DE-Juel1)169976
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 1
|6 P:(DE-Juel1)165199
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 2
|6 P:(DE-Juel1)145614
913 1 _ |a DE-HGF
|b Key Technologies
|l Decoding the Human Brain
|1 G:(DE-HGF)POF3-570
|0 G:(DE-HGF)POF3-574
|2 G:(DE-HGF)POF3-500
|v Theory, modelling and simulation
|x 0
|4 G:(DE-HGF)POF
|3 G:(DE-HGF)POF3
914 1 _ |y 2020
920 _ _ |l yes
920 1 _ |0 I:(DE-Juel1)IAS-5-20120330
|k IAS-5
|l Computational Biomedicine
|x 0
920 1 _ |0 I:(DE-Juel1)INM-9-20140121
|k INM-9
|l Computational Biomedicine
|x 1
980 _ _ |a conf
980 _ _ |a VDB
980 _ _ |a I:(DE-Juel1)IAS-5-20120330
980 _ _ |a I:(DE-Juel1)INM-9-20140121
980 _ _ |a UNRESTRICTED


LibraryCollectionCLSMajorCLSMinorLanguageAuthor
Marc 21