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000884251 037__ $$aFZJ-2020-03150
000884251 1001_ $$0P:(DE-Juel1)169976$$aAlfonso-Prieto, Mercedes$$b0$$eCorresponding author$$ufzj
000884251 1112_ $$aXXIXth Annual Meeting of the European Chemoreception Research Organization$$cTrieste$$d2019-09-11 - 2019-09-14$$gECRO 2019$$wItaly
000884251 245__ $$aS14- A computational view on coffee perception: modeling and simulations of chemosensory receptors
000884251 260__ $$c2020
000884251 3367_ $$033$$2EndNote$$aConference Paper
000884251 3367_ $$2DataCite$$aOther
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000884251 3367_ $$0PUB:(DE-HGF)6$$2PUB:(DE-HGF)$$aConference Presentation$$bconf$$mconf$$s1601644116_14867$$xInvited
000884251 520__ $$aBitterness 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.
000884251 536__ $$0G:(DE-HGF)POF3-574$$a574 - Theory, modelling and simulation (POF3-574)$$cPOF3-574$$fPOF III$$x0
000884251 7001_ $$0P:(DE-Juel1)165199$$aGiorgetti, Alejandro$$b1$$ufzj
000884251 7001_ $$0P:(DE-Juel1)145614$$aCarloni, Paolo$$b2$$ufzj
000884251 8564_ $$uhttps://doi.org/10.1093/chemse/bjaa007
000884251 909CO $$ooai:juser.fz-juelich.de:884251$$pVDB
000884251 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)169976$$aForschungszentrum Jülich$$b0$$kFZJ
000884251 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)165199$$aForschungszentrum Jülich$$b1$$kFZJ
000884251 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)145614$$aForschungszentrum Jülich$$b2$$kFZJ
000884251 9131_ $$0G:(DE-HGF)POF3-574$$1G:(DE-HGF)POF3-570$$2G:(DE-HGF)POF3-500$$3G:(DE-HGF)POF3$$4G:(DE-HGF)POF$$aDE-HGF$$bKey Technologies$$lDecoding the Human Brain$$vTheory, modelling and simulation$$x0
000884251 9141_ $$y2020
000884251 920__ $$lyes
000884251 9201_ $$0I:(DE-Juel1)IAS-5-20120330$$kIAS-5$$lComputational Biomedicine$$x0
000884251 9201_ $$0I:(DE-Juel1)INM-9-20140121$$kINM-9$$lComputational Biomedicine$$x1
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