DOI: 10.1002/ijch.201400027

Computational Studies of Smell and Taste ReceptorsAntonella Di Pizio[a, b] and Masha Y. Niv*[a, b]

1 Introduction

G-protein coupled receptors (GPCRs) constitute a largefamily of seven-transmembrane proteins located at theplasma membrane. Much of the information from theoutside world or from other cells is processed by GPCRs.There are over 800 GPCRs in the human genome, andthey respond to a broad spectrum of chemical entities,ranging from photons, protons, and small organic mole-cules to peptides and glycoproteins. The most commonly-used classification system, based on physiological andstructural features, divides GPCRs into six classes: classA (rhodopsin-like), consisting of over 80% of all GPCRsin humans; class B (secretin-like); class C (metabotropicglutamate receptors); class D (pheromone receptors);class E (cAMP receptors); and class F (frizzled/smooth-ened family).[1] The “Glutamate, Rhodopsin, Adhesion,Frizzled/Taste2, and Secretin” (GRAFS) system is an al-ternative classification for GPCRs, based on phylogeneticstudies.[2] Due to the central role of GPCRs in multiplephysiological processes, they represent the largest groupof targets for drug discovery for a broad spectrum of dis-eases.[3] Interest in these targets has led to the establish-ment of many freely available databases and servers, re-cently reviewed in reference [4], which provide usefulsources of information.

With the advance of X-ray crystallography of GPCRs(celebrated by the 2012 Nobel Prize in Chemistry to

Robert Lefkowitz and Brian Kobilka), more and moredetails about GPCR signaling are being revealed.[5] Theincreasing number of GPCR crystal structures providesa great advantage for the rational design of GPCR li-gands.[6]

Multiscale computational methods are being applied tothe study of GPCR signaling, including normal modeanalysis (e.g., reference [7]), docking (e.g., reference [8]),fragment-based design (e.g., reference [9]), molecular dy-namics (MD) simulations (e.g., reference [10]), and freeenergy of binding estimations (e.g., reference [11]).

Many of these computational approaches were devel-oped thanks to the pioneering works of the 2013 NobelPrize in Chemistry laureates (Martin Karplus, MichaelLevitt, and Arieh Warshel), including the development offorce fields[12] and of computational techniques, such as

Abstract : Smell and taste are among the basic senses withwhich we perceive the world around us. In addition to ena-bling recognition of chemical moieties that provide social ornutritional clues, taste and smell receptors are expressed inmany extraoral tissues, including the gastrointestinal, respi-ratory, and reproductive systems. It is, therefore, likely thattaste and smell receptors have additional physiologicalroles, which are currently under intensive study. Most of thetaste modalities, as well as olfaction, are mediated by G-pro-tein coupled receptors (GPCRs). Recent breakthroughs incrystallography and signaling studies of GPCRs (celebratedby the 2012 Nobel Prize in Chemistry to Robert Lefkowitzand Brian Kobilka) provide excellent opportunities for apply-ing this information towards furthering our understandingof taste and smell signaling.

No crystal structures of odorant or taste receptors are cur-rently available. However, computational techniques, manyof which stem from the pioneering contributions of the2013 Nobel Prize in Chemistry laureates, Martin Karplus,Michael Levitt, and Arieh Warshel, can shed light on thefunction of taste and olfactory GPCRs. In this review, wehighlight examples of iterative combinations of simulationand experiment that were successfully applied toward delin-eating binding modes of tastants and odorants and towardpredicting additional ligands.Further studies are required in order to answer remainingquestions regarding receptor promiscuity versus selectivity,the details of receptor coupling to G-proteins, and the rolesof oligomerization and of allosteric modulation in taste andsmell transduction.

Keywords: odorant · proteins · receptors · tastant · theoretical chem

[a] A. Di Pizio, M. Y. NivInstitute of Biochemistry, Food Science and NutritionRobert H. Smith Faculty of Agriculture, Food and EnvironmentThe Hebrew UniversityRehovot 76100 (Israel)e-mail: masha.niv@mail.huji.ac.il

[b] A. Di Pizio, M. Y. NivFritz Haber Center for Molecular DynamicsThe Hebrew UniversityJerusalem 91904 (Israel)

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minimization[13] and coarse-grained[3,14] and atomistic mo-lecular dynamics.[15]

The present review, which is part of the special issue oncomputational chemistry of biomolecules, in a volumededicated to the 2013 Nobel Prize in Chemistry, focuseson chemosensory receptors and on discoveries in smelland taste signaling that were enabled by incorporatingmolecular modeling and other computational approaches.

2 Smell and Taste

The sense of smell, or olfaction, is mediated by olfactory(odorant) receptors (ORs), which detect airborne organic

compounds of less than 400 Da, called odorants.[16] ORsare located in the nasal cavity, in the main olfactory epi-thelium (MOE), and in the vomeronasal organ (VNO).From these organs, neurons project their axons to the ol-factory bulb, where other neurons are activated to createan odor spatial map.[17]

Other specialized chemoreceptors (belonging to GPCRfamily) have been discovered in the nasal cavity: traceamine-associated receptors (TAARs) in the MOE[18] andvomeronasal receptors (V1Rs and V2Rs) in the VNO.[19]

Only five TAAR genes have been found in humans, anda few agonists (volatile amines) are known.[20] Humanshave only five functional V1R genes and no intact V2Rgenes (chemosensory receptor database [CRDB;[21] http://zldev.ccbr.utoronto.ca/CRDB/]). Vomeronasal receptorsseem to specialize in the perception of pheromones, whileORs detect mainly odorants. The relationship amongthem, however, is not yet clear.[22]

Figure 1 presents the phylogenetic tree of GPCRs, withORs highlighted in blue. ORs constitute the largest groupof GPCRs; more than 50 % of the GPCRs codified inmammalian genomes are olfactory receptors.[23] There areclose to 400 functional receptors in humans (388 basedon the GPCR network data[24] [http://gpcr.scripps.edu/],391 in the human olfactory data explorer [HORDE;[25]

http://genome.weizmann.ac.il/horde/], and 396 inCRDB[21] [http://zldev.ccbr.utoronto.ca/CRDB/]). In mice,there are over 1,000 functional ORs (1,035 reported inreference [26] and 1,037 in the CRDB[21]). The humangenome contains hundreds of additional genes predictedto be non-functional OR genes.[27] It has been hypothe-sized that humans lost these receptors with the develop-ment of visual perception, which was accompanied by re-duced dependency on smell.[28] Humans lost some tastereceptors as well, presumably because our changed dieteliminated the need to sense certain chemicals in food.[29]

The taste, or gustation, system detects tastants by tastereceptor cells (TRCs) present in special structures, calledtaste buds, embedded within taste papillae on the tongue.Other taste buds are located in the palate, epiglottis,pharynx, and larynx. When sensory cells are stimulated,the information is transmitted from the receptors to thebrain, where it is processed as taste perception.[30]

Humans can distinguish at least five basic tastes: the at-tractive sweet (indicative of carbohydrate content),umami (savory taste, indicative of protein content), andsalty (sodium is needed for ion balance) taste modalitiesand the aversive bitter and sour tastes, which are thoughtto guard against intake of toxic or spoiled food.[31] Re-cently, GPCRs have been shown to be involved in themediation of the taste of fat,[32] which is proposed as thesixth basic taste modality.[33] Sweet, umami, and bittertastes are mediated by GPCRs, sweet and umami byT1Rs (in orange in Figure 1) and bitter by T2Rs (in greenin Figure 1). T1Rs belong to class C GPCRs,[34] containingan extracellular Venus flytrap (VFT), as an N-terminal

Antonella Di Pizio received her Ph.D. inpharmaceutical chemistry at the Uni-versity of Chieti (Italy) in 2012. Her re-search activity was carried out in Italy,under the supervision of Dr. Mariange-la Agamennone, and in Germany, atthe University of Marburg, under thesupervision of Prof. Dr. Gerhard Klebe.She gained considerable experience incrystallography and molecular model-ing, exploiting the integration of experi-mental and computer-aided drugdesign methods in order to identifynovel inhibitors of Zn-containing enzymes. Currently she is a post-doctoral fellow in Prof. Masha Niv’s lab, where she is investigatingthe molecular basis of odorant and tastant recognition by chemo-sensory receptors.

Masha Niv completed her B.Sc. cumlaude, in 1994, and the direct Ph.D.program, in Prof. Benny Gerber’s lab,in the chemistry department of theHebrew University, in 2001. After lead-ing the bioinformatics team at Keryx Bi-opharmaceuticals in 2001–2003, shewas a postdoctoral fellow in Prof. HarelWeinstein’s lab (2003–2005) and in-structor (2005–2007) at Weill MedicalCollege of Cornell University in NewYork. In 2007, Prof. Niv established herlab at the Institute of Biochemistry,Food Science and Nutrition at the Robert H. Smith Faculty of Agri-culture, Food and Environment at the Hebrew University, where sheis currently an Associate Professor. Computational studies ofGPCRs, in particular of bitter taste receptors, are a major focus ofthe Niv lab, leading, for example, to the establishment of BitterDB,a freely available electronic database of bitter compounds, and elu-cidation of molecular recognition of bitter signals. Prof. Niv isa member of the Fritz Haber Center for Molecular Dynamics at theHebrew University and management committee member of the Eu-ropean COST Action CM1207 on GPCRs. In 2010, Prof. Niv receivedthe Krill Prize for Excellence in Scientific Research from the WolfFoundation.

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domain, linked to the transmembrane (TM) domain viaa short cysteine-rich (CR) domain. The VFT, the ligand-binding domain, consists of two lobes that can assume anopen (inactive) or closed (active) conformation. Umamitaste is mediated by a heterodimer composed of T1R1and T1R3, and sweet is sensed by a heterodimer of T1R2and T1R3.[35] The bitter receptor family is larger, with 25

T2Rs in humans, representing about 4 % of the GPCRsand the second largest sensory GPCR family. T2Rs havea short N-terminus and are typically considered class AGPCR family members.[36] Because of low sequence simi-larity of T2Rs with class A GPCRs (less than 20%), theirclassification is ambiguous. Sometimes they are consid-ered a distinct family[37] or grouped with the frizzled re-

Figure 1. Phylogenetic tree of GPCRs. T1Rs, T2Rs, and ORs are highlighted in orange, green, and blue, respectively. The picture of theGPCR tree was furnished by the GPCR Network[24] (http://gpcr.scripps.edu/).

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ceptors.[38] Nordstrçm et al.[39] and our results (see below)support the classification of T2Rs as class A GPCRs.

In recent years, it became clear that both ORs andtaste receptors are expressed in numerous tissues andhave multiple physiological roles, including nutrient sens-ing, autophagy, and regulation of gut motility, as well ascontrol of protective airway reflexes and bronchodila-tion.[40] Chemosensory receptors may also have a role insexual reproduction.[41] For example, it has been shownthat a testicular odorant receptor mediates human spermchemotaxis[41b] and that a genetic absence of both theT1R3 and gustducin a-subunit GNAT3 leads to male-spe-cific sterility in mice.[42] Bitter taste receptors have beenproposed as human targets for bacterial signals in the air-ways[43] and are currently considered as novel targets forasthma.[44] Recently, odorant receptors have been shownto be expressed in human airway epithelia and to respondto volatile chemical stimuli.[45] T2Rs present in the gastro-intestinal tract are involved in the digestive process andin glucose and insulin homeostasis in the gut.[46] The roleof sweet taste receptors in the intestinal tract is of partic-ular interest for diabetes and obesity.[47]

Thus, smell and taste receptors are interesting modelsfor fundamental questions of molecular recognition, im-portant mediators of perception related to nutrition andsocial behavior, and have multiple additional roles in ex-traoral tissues, possibly emerging as novel targets for drugdiscovery.

3 Modeling 3D Structures of the Receptors

3.1 3D Structures of GPCRs: State-of-the-Art

Because of their flexibility, GPCRs are very difficult tar-gets for crystallization. Furthermore, membrane proteinsare inserted into lipid bilayers surrounding the cell and itssub-compartments, and the lipid bilayer influences thebiophysical conditions of these proteins. Thus, when iso-lated from membranes, these proteins are generally lessstable.[5a,48] Despite great progress in GPCR structure de-termination[48c] and the fact that several olfactory recep-tors are being targeted for crystallization in the Stevenslab (http://gpcr.scripps.edu/tracking_status.htm), no exper-imental structure of a chemosensory receptor is currentlyavailable. Computational modeling represents an alterna-tive approach to predict membrane protein 3D structures.

Ab initio (or template-free) approaches calculate the3D structure of a protein, starting from its amino acid se-quence, without using the structure of another protein asa template. Several different ab initio methods have beendeveloped, so far, for the modeling of membrane proteinstructures: ROSETTA Membrane[49] uses a fragment as-sembly method to build up membrane protein structures;FILM3[50] generates membrane protein models using frag-ment assembly and correlated mutation analysis; EVfold_membrane[51] is based on evolutionary constraints and

predicts 3D structures of membrane proteins using onlythe genomic sequencing; PREDICT[52] searches for themost stable GPCR structure among multiple “decoy”protein conformations; and MembStruk[53] provides 3Dstructures for GPCRs through atomistic simulations, suchas molecular dynamics and Monte Carlo.

Homology modeling (HM) predicts 3D protein struc-ture starting from the structure(s) of one or more homol-ogous proteins. HM methods require less computing timethan do ab initio methods but rely on the availability ofa template. Crucial steps in homology modeling arechoosing the best template and producing reliable align-ments between the target and the template.[54] The fre-quently used HM programs, such as MODELLER,[55]

have been successfully tested for membrane protein mod-eling,[48b,56] but were originally developed for water-solu-ble proteins. To take the constraints imposed by the mem-brane on protein structure into account, packages specifi-cally for modeling membrane proteins are being devel-oped. MEDELLER[57] identifies the core structure (in themiddle of the membrane) shared by the template andtarget proteins and then copies the identified template co-ordinates to build the core model, thereby outperformingMODELLER, at least for some membrane proteins.

Memoir[58] is a new server for homology modeling ofmembrane proteins that integrates MEDELLER with:iMembrane,[59] which predicts the degree of insertion ofa membrane protein within a lipid bilayer; MP-T,[60]

which performs sequence alignment guided by the mem-brane information furnished by iMembrane; andFREAD,[61] a loop-modeling tool.

GPCR-I-TASSER[62] is a novel algorithm that specializ-es in modeling GPCR structures. The I-TASSER pro-gram[63] is a successful fragment-based method in whichfragments are excised from template structures and reas-sembled based on threading alignments. Compared to theoriginal I-TASSER, improvements in GPCR-I-TASSERinclude: i) an ab initio transmembrane helix folding pro-gram,[64] in addition to the default search of putative re-lated template structures in the PDB;[65] ii) the GPCR re-search database (GPCRRD), a spatial restraint database(which collects all experimental restraints, such as residueorientation, contact, and distance maps); iii) an improvedforce field for Monte Carlo simulations exploited tosearch the conformations space (restricted by all the in-formation above); and iv) the fragment-guided moleculardynamics (FG-MD) refinement protocol[66] to rebuild thefinal atomic structure model.

Recently, a novel method for the specific generation ofGPCRs, called GPCRM, has been described.[67] To buildthe final model, GPCRM integrates various programsand approaches, including: i) template detection andalignment generation (alignment with all template se-quences in the GPCRM database, using MUSCLE[68] andClustalW2[69]) ; ii) model building with MODELLER[55] ;iii) loop refinement (the best 10 models are selected for

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a loop refinement in ROSETTA); and iv) refinement inthe all-atom ROSETTA[70] force field. GPCRM incorpo-rates a Z-coordinate-based filter to generate only suchGPCR models in which extra- and intra-cellular loops donot penetrate the membrane.

The most straightforward evaluation of the differentmodeling methods assesses their ability to predicta GPCR structure prior to its publication. There werethree related evaluations so far: i) GPCR Dock 2008[71]

(in which the adenosine A2A receptor bound to the ligandZM241385 was analyzed); ii) GPCR Dock 2010[1b] (inwhich three complexes were investigated: dopamine D3receptor bound to the ligand eticlopride, CXCR4 chemo-kine receptor bound to the ligand IT1t, and CXCR4bound to the cyclopeptide CVX15); and iii) GPCR Dock2013 (a summary of the outcome of this competition isnot yet available). The results of GPCR Dock 2008 and2010 demonstrate the success of hypothesis-driven ap-proaches that take available experimental informationabout the target and its ligands into consideration,[1b] em-phasizing the advantages of insights from the scientist-driven versus the fully automated approaches.

3.2 3D Structures of Smell and Taste GPCRs

Many of these approaches have been applied to modelingof chemosensory receptors. Man et al. proposed a protocolto model ORs in a highly cited 2004 publication.[72]

Launay et al. recently reviewed the computational ap-proaches applied to model the 3D structures of ORs andpredict the binding modes of their ligands.[73] The firststudies of ORs used the rhodopsin structure as a tem-plate.[74] Today, more and more GPCR structures are be-coming available and are used to obtain increasingly ac-curate models.[75]

In some cases, such as with the bitter taste receptorfamily of T2Rs, low similarity to other GPCRs leads toambiguous classification within the GPCR subfamily andto difficulties in choosing optimal templates and in pro-ducing the correct alignment. The majority of disclosedcrystal structures are class A GPCRs,[76] but recently,structures of class B GPCRs[77] and a frizzled receptor[76l]

have been determined. The availability of newly discov-ered X-ray structures increases the number of possibletemplates for chemosensory receptor modeling. To evalu-ate all available experimentally determined GPCR struc-tures as templates for T2R modeling, we applied twodifferent approaches—HHpred[78] and GPCR-I-TASSER[62]—to T2R1, as an example. HHpred is basedon hidden Markov models[79] and is implemented ina server for searching and aligning query sequences withsequences from PDB structures. The predicted high-rank-ing score templates are class A GPCRs; family B and friz-zled receptors appear at the bottom of the ranked tem-plates. GPCR-I-TASSER, as described above, is a frag-ment-based HM tool that uses a GPCR-specific template

library (http://zhanglab.ccmb.med.umich.edu/GPCRSD/)for template detection. Using this software forT2R1 modeling, family A GPCRs are still the best amongthe top templates, while the smoothened structure (PDBID: 4JKV[76l]) ranked fourth and family B structures wereexcluded.

The successful use of class A GPCRs to build T2Rmodels was supported by site-directed mutagenesis stud-ies.[80] These data support grouping of the T2R familywith class A GPCRs (as suggested by Nordstrçmet al.[39]), although inclusion of templates from other fami-lies may improve fragment-based models.

T1Rs are class C GPCRs. Until now, knowledge aboutclass C GPCRs relied on structures of extracellular do-mains of metabotropic glutamate receptors (mGluRs),[81]

which were used as templates for sweet and umami struc-ture modeling.[82] Very recently, the VFT structure of theGABAB homodimeric subunits[83] was determined, andnow the full structure of an mGlu1 receptor bound to anallosteric modulator was published by Stevens and cow-orkers.[84] These new developments furnish exciting op-portunities for modeling class C GPCRs, including sweetand umami receptors.

4 Interactions of Smell and Taste GPCRs withTheir Ligands

The modeled structures of most GPCRs are typically oflow resolution.[1b,51] Interestingly, even low-resolutionmodels may be useful for binding site prediction. Skol-nick et al. recently reviewed the ligand homology model-ing approach compared to high-resolution and traditionaldocking methods[85] and demonstrated that homologymodels can capture the majority of fundamental interac-tions with ligands and can be used to guide screening pro-tocols, as successfully implemented in FINDSITEcomb.[86]

Recent studies suggest that binding site optimization,which implements functional knowledge in the modelbuilding process, may greatly improve model quality andapplicability for ligand screening and design,[87] and there-fore, the applicability of homology models is high. In par-ticular, docking approaches are often used to predict thebinding modes of ligands with their receptors and to iden-tify additional ligands. Protein-ligand docking aims topredict and rank the complexes arising from the associa-tion between a given ligand and a target protein.[88] Dock-ing methods prove successful in many cases, despite thefact that they typically use a simplified energy model tocalculate the binding energy, such as an empirical scoringfunction with a simple solvent model. Docking and scor-ing comprise the first step in investigation of the bindingmode, applicable also to homology models ofGPCRs.[48b,c]

Docking approaches have been applied to predict thebinding modes of odorants and tastants to their corre-

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sponding smell and taste receptors.[89] Using an approachthat integrates homology modeling, docking, and site-di-rected mutagenesis, we could identify binding modes ofdifferent bitter taste receptors with various ligands.[56b,80a]

The experimental data was used to manually adjust thetarget-template alignment (reference [80a] and unpublish-ed data). The adjusted alignment was used to producea homology model of the receptor, into which ligandswere docked. The docked poses were analyzed, and thesuggested interacting residues were mutated and testedfor receptor activation in vitro.[56b,80a] In some cases, theexperimental results supported the predicted modes ofligand-receptor interaction,[80a] while in other cases theyled to reevaluation of the predicted binding mode (un-published data). These results started to shed light on thepuzzling question of how hundreds or thousands of bitter

(and potentially toxic) compounds[90] can be recognizedby only dozens of bitter taste receptors.[91] The ligandsrecognized by the same receptor may have completelydifferent chemical structures,[92] and the receptive rangeof receptors in the same family may differ from narrowlytuned to broad or multispecific.[80a,90,93] We found that theligand binding pocket of bitter taste receptors coincideswith the canonical binding site of family A GPCRs (seeFigure 2A for schematic location); the multispecificity isachieved by using subsites within the binding pocket anddifferent types of interactions for different ligands.[80a,b]

Indeed, we have shown that T2R10 receptor uses the or-thosteric binding pocket to bind different ligands (such asstrychnine and denatonium benzoate; see Figure 2B).Single point mutations at the binding site can improve af-finity towards one ligand while reducing affinity towards

Figure 2. Multispecificity strategies. (A) Orthosteric binding site of a bitter taste receptor. (B) Different binding modes of denatonium ben-zoate (1) and parthenolide (2) in the same binding region of the T2R10 receptor.[80a] (C) Different binding sites of various ligands in sweetand umami taste receptors. VFT domains are represented schematically in their closed (holo) and open (apo) conformations.

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a different ligand, suggesting that the binding site hasevolved to optimally accommodate multiple agonists atthe expense of reduced affinity.[80a] Interestingly, somebitter compounds are recognized by multiple bitter tastereceptors. This may be an important backup mechanismto ensure protection against potent poisons. Interestingly,the interaction modes of the poison strychnine with itsbitter taste receptors T2R10 and T2R46 are very differen-t.[80a,b] The lack of conservation of the binding mode sug-gests independent acquisition of agonist specificities byreceptor subtypes during evolution.[80a]

Odorant receptors present a similar multispecificitypuzzle, since many odorants bind to more than one ORand a single odorant receptor can interact with more thanone odorant. Matching ORs to ligands has seen limitedsuccess; ligands have been published for only 22 humanodorant receptors.[41b,89a,94] Similarly to T2Rs, the ORbinding site seems to coincide with that of many otherclass A GPCRs,[72,74a] and the multispecificity could bedue to mechanisms similar to those proposed for bitterreceptors. For example, different binding modes of differ-ent odorants have been discovered for the hOR2AG1 re-ceptor.[95] By combining computational techniques withsite-directed mutagenesis and functional analysis, Geliset al. have provided a molecular model able to predictthe activation capability of odorants towardshOR2AG1.[95] The activity of six odorants (amylbutyrate,phenylethylacetate, phenirate, prenylacetate, isopentyla-cetate, and isoamylbenzoate) correlated with modeled ac-tivities. A similar binding mode was described for phenyl-ethylacetate and amylbutyrate, while phenirate, despitebinding to the same cavity, established different interac-tions.[95] The studies of promiscuity of the orthosteric(canonical) binding site of bitter taste and odorant recep-tors prompted us to investigate this binding site in thegeneral context of GPCRs. Using statistical methods ap-plied to GPCRs with known X-ray structures and theirantagonists, we found that GPCR promiscuity (quantifiedby a wide range of physicochemical properties of antago-nists) correlates with small number of H-bond donors,high hydrophobic content, and high exposure of the or-thosteric binding site.[96]

The T1R2/T1R3 heterodimer is activated by diversecompounds, spanning from low molecular weight sweet-eners to sweet proteins. Mouse T1R1/T1R3 is broadly ac-tivated by most L-amino acids, whereas human T1R1/T1R3 specifically responds to L-Glu.[97] The selectivity be-tween sweet and umami tastes indicates that the bindingsite of these receptors should be localized on the taste-specific monomers, T1R2 for sweet and T1R1 for umamireceptors. Indeed, integrated computational approachesand mutagenesis studies identified the binding site forsugars in the VFT domain of T1R2[98] and for glutamatein the VFT domain of T1R1.[97,99]

Multiple binding sites for different ligands have beenidentified on the sweet taste receptor (Figure 2C). Small

agonist molecules, such as sucralose, aspartame, and sac-charin, bind to the active site (hinge region) in the T1R2VFT domain.[100] Sweet proteins, such as brazzein andthaumatin, seem to bind on the receptor surface, estab-lishing interactions with the VFT domain of T1R2.[101] Ithas been also shown that sweet receptors can work by anallosteric, synergistic mechanism; sweet taste enhancers,such as SE-2, SE-3 and SE-4, are tasteless molecules butcan potentiate the taste of sweeteners.[102] Applying mo-lecular modeling and mutagenesis studies, the bindingmode of these enhancers has been discovered; they bindin the entrance of the VFT domain of T1R2 and can sta-bilize the active conformation of the receptor.[82b] Similar-ly, different binding pockets have been found in umamitaste receptors (Figure 2C). Nucleosides, such as guano-sine-5’-monophosphate (GMP), can enhance the umamitaste.[103] The consensus between computational[82a] andmutagenesis[103b] studies supports the hypothesis thatGMP binds at the entrance of the binding cleft of theVFT. Computational studies suggested a mechanistic ex-planation for the reduced activity following stimulation ofumami receptors with monosodium glutamate, with par-ticular signal-nucleotide polymorphisms (SNPs)[104] thatled to two amino acid mutations in the T1R1-VFTdomain. Though not directly involved in the glutamatebinding site, mutations of these residues induce confor-mational changes that might decrease the receptor func-tional activity.

Another binding site for small molecules has beenidentified in the TM domain of the T1R3 monomer.These small molecules (cyclamate and lactisole) act as al-losteric modulators for both sweet and umami receptors.Cyclamate and lactisole bind to the TM domains, in a sim-ilar location, but show opposite activity; cyclamate acts asan allosteric enhancer and lactisole as a negative modula-tor of T1R1/T1R3 and T1R2/T1R3 receptors.[105]

5 MD of Smell and Taste Receptors

Protein flexibility is one of the major issues in the studyof macromolecular systems. From seminal contributions,such as references [12b,15a,106], to the current state-of-the-art (reviewed, for example, in reference [107]), MDprovide glimpses into the dynamic behavior of proteins,including insights into allosteric modulation,[10b] pH-de-pendent effects,[108] channel gating mechanisms,[109] drugdesign,[107c] and more.

The advances of this computational technique and ofavailable computer technology have made the applicationof MD studies to GPCRs increasingly useful—enablinginvestigation of G-protein activation and allosteric modu-lation[10b,110]—and, now, approachable even via MD speci-alized web-software[111] (http://gpcr.usc.es). Recent workssuggest that apo-forms of GPCRs sample conformationsthat resemble some of the structural characteristics of the

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active states of the receptors.[112] Proteins, includingGPCRs, exist in a manifold of different conformations;[113]

inactive GPCR conformations do not activate the G-pro-tein, while active ones are responsible for basal activity.[5a]

The binding of the agonist alone may not be sufficient toactivate the receptor, but it may influence the dynamicproperties of the receptor (e.g., b2AR) and increase theprobability of active state conformations.[114] Only G-pro-tein binding, however, stabilizes the fully activestate.[5a,112,114]

MD simulations also represent useful tools for model-ing the complex membrane environment and, thus, fortreating GPCRs in more “physiological” conditions, al-lowing the study of biophysical properties of GPCRs andGPCR oligomers.[115] The oligomerization of GPCRscould influence their signaling and has been studied withboth experimental[116] and computational approaches,mostly MD simulations.[117]

Because of the relevance of protein flexibility in ligandbinding, MD simulations have been used to identify andcharacterize ligand-protein interactions[118] and, in virtualscreening (VS) campaigns, to define enrichment forGPCRs.[119] Explicit-solvent MD simulations in lipid bilay-ers have been exploited to generate an ensemble of pro-tein conformations for the X-ray structures and homologymodels of both aminergic and peptidergic GPCRs, includ-ing the chemokine CXCR4, dopamine D3, histamine H4,and serotonin 5HT6 holo receptors. Frames selected fromthe MD trajectory could outperform X-ray structures andhomology models, in terms of enrichment factors andAUC values.[119]

A hybrid molecular mechanics/coarse-grained (MM/CG) technique has been developed and applied toGPCRs, in order to improve the quality of structural pre-dictions and the investigations of GPCR-ligand com-plexes.[120] Another useful application of MD is represent-ed by the balloon expansion simulation exploited to studythe binding site of GPCRs, since the structure obtainedby HM is often not suitable to accommodate ligands.[121]

Applying MD simulations to homology models was re-viewed in reference [48b]. It is not surprising that, basedon these successes, MD protocols are now being appliedto chemosensory receptors as well. Integrating dockingcalculations with MD simulations allows analysis of bind-ing mode on an energetic basis and better characteriza-tion of ligand-protein interactions. MD simulations ap-plied to odorant receptors were essential to define thebinding sites and to evaluate the residues in contact withthe ligands;[93c,95,122] the difference observed between theinitial structure and the structure obtained after simula-tion was significant.[93c,122a]

MD simulations have been exploited to study the bind-ing of sweet[82b] and umami[82a,99] modulators to orthostericand allosteric pockets. MD simulations suggest that GMPbinds at the entrance of the VFT domain, likely after the

binding of glutamate, and stabilizes the active state ofumami receptor.[82a]

Ligand-induced conformational changes were investi-gated by MD in bitter taste receptors. MD studies onT2R1 revealed a conformational change induced byligand binding and the opening of the switch between theintracellular loop (ICL2) and the cytoplasmic end ofTM3, hypothesized to be necessary for the activation ofT2R1.[123] Combining the MM/CG approach[120] and muta-genesis studies, the roles of residues in the predictedbinding site of bitter taste receptor T2R38 were ana-lyzed.[80e]

It seems that applying MD to low-resolution modelscan be beneficial for homology models of GPCRs, in gen-eral, and of chemosensory receptors, in particular. MDcan be exploited to account for protein flexibility and toinvestigate the conformational space of protein activestates and the influence of the solvent on ligand binding.It would be interesting to quantify the effects of deviationbetween the initial homology model and the X-ray struc-ture and of uncertainties related to loops on the useful-ness of MD simulations. These effects could be evaluatedbased on enrichment factors and on ability to reproduceexperimental data (e.g., known binding modes), in com-parison with results obtained using a single homologymodel or multiple homology models not originating fromMD.[124]

Such evaluation would be helpful in choosing the mostsuitable protocols for chemosensory receptors and otherGPCRs currently lacking experimental structures.

6 Predicting Additional Ligands

It is now becoming clear that smell and taste receptorsare also expressed extraorally. Identifying additional tasteand smell compounds may help to identify ligands thatare relevant for extraoral roles of these receptors. VS isa commonly employed tool for ligand identification.[125] Itallows selection of predicted active molecules from largelibraries of chemical structures.[126]

VS can use structure-based (SBVS) or ligand-based(LBVS) approaches. SBVS is based on structure determi-nation and binding site identification. Thus, for chemo-sensory receptors, it currently requires homology model-ing and binding site prediction (as described above). Thescreening may be based on high-throughput docking,[127]

where molecules are ranked based on their affinities forthe target (predicted by applying a scoring function), oron structure-based pharmacophore modeling,[128] whereligand-protein interactions are converted in reciprocalligand space defining a pharmacophore model (i.e., “thegeometric organization of steric and electronic featuresthat is necessary to ensure the optimal supramolecular in-teractions with a specific biological target and to trigger[or block] its biological response”).[129] Structure-based

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pharmacophore models represent valuable tools for VSbecause, as they do not require calculation of ligand-pro-tein affinities, they are faster than docking, and they canbe used even in cases where ligand information isscarce.[130] Once the correlation of predicted and experi-mental biological activities validates the models, they canbe applied to screen libraries of unknown compounds. Asshown above, by integrating computational and experi-mental methods, the binding modes of several odorantsand tastants have been determined, and the amino acidsinvolved in the binding have been identified.[80a,b,95] Thus,structure-based approaches can now be applied to identi-fy additional ligands. Indeed, our preliminary results (un-published data) indicate successful identification ofT2R14 bitter agonists using structure-based pharmaco-phore models. Importantly, in cases where different li-gands activate the same receptor by using different sub-sites and interaction types (e.g., reference [80a], as illus-trated in Figure 2B), multiple structure-based models,representing the various binding modes, will be needed toidentify different families of ligands.

Another well-established VS approach is to disregardthe binding site and to focus only on the features ofknown ligands. LBVS is generally very fast and exploresa wide chemical space. Active compounds can be used astemplates, and in this case, the screening is based on 2Dor 3D molecular similarity (quantitatively measured usingmolecular descriptor similarity, shape similarity, etc.).[131]

Alternatively, features essential for ligand activity areused to build a pharmacophore model. Ligand-based ap-proaches have been used extensively to characterize andidentify GPCR ligands[132] and are useful for chemosenso-ry receptors as well. Through 2D fingerprints and 3Dpharmacophore models, structure-activity relationships(SARs) of flavonoids and isoflavonoids towards T2R14and T2R39 were detected.[133] This study discovered themolecular determinants of ligand activity and selectivitybetween T2R14 and T2R39; T2R39 requires one moreH-bond donor than does T2R14, whereas the hydropho-bic interaction is more important for T2R14 than it is forT2R39.[133]

Ligand-based pharmacophore models and shape andelectrostatics VS approaches have enabled us to identifynovel agonists of the T2R14 receptor among approvedand experimental drugs. The shape and electrostaticsmethod ROCS[134] had higher success rates than theligand-based pharmacophore method (HipHop algorithmimplemented in Catalyst (Discovery Studio)[135]), but thelatter was more successful in identifying novel chemo-types. Many of the newly discovered agonists inhibit thehERG potassium channel, highlighting the potential im-portance of hERG as an off-target for bitter compounds,possibly relating bitterness and toxicity.[136]

An alternative to screening compounds that activatea particular bitter receptor is the development of predic-tors of bitterness in general. This is clearly very challeng-

ing, as bitter compounds are extremely variable in theirchemicophysical properties.[90] Nevertheless, Rodgerset al. , analyzing bitter molecules by MOLPRINT 2D cir-cular fingerprints, information gain-based feature selec-tion, and the na�ve Bayes classifier, provided a classifica-tion model for bitterness.[137]

Khan et al. developed a ligand-based method to predictodor pleasantness through molecular structures. They de-fined the 2D odorant space, in which all known odorantsare represented. Projecting unknown molecules ontothese principal components allowed prediction of theirpleasantness.[138] A 3D-quantitative structure-activity rela-tionship (QSAR) study of OR1G1 ligands[139] developedpharmacophore models that could discriminate OR1G1agonists and antagonists, as validated experimentally.

7 Summary and Outlook

Despite the breathtaking progress in structure determina-tion of GPCRs, no structure is yet available for chemore-ceptors. Computational methods are, therefore, essentialfor this research field.

Table 1 summarizes the computational techniques ap-plied to chemosensory receptors, reporting the main goalof the research, the main programs used (as detailed inthe main text of this review), the target receptor, and thereference. Most of the works aim to determine the bind-ing modes of taste and smell molecules to their receptorsand to identify additional ligands. Current data suggestthat the monomeric family A or family A-like receptorsfor smell and bitter taste use single binding sites for theirligands, which coincide with the canonical binding sites offamily A GPCRs. Multispecificity can be achieved byusing different subsites or different types of interactionsfor dissimilar ligands. On the other hand, family C hetero-meric receptors for sweet and umami tastes use severalbinding sites to bind their ligands. Furthermore, the VFTdomain of the family C chemosensory receptors has al-ready been successfully targeted by allosteric modulators.Allosteric modulation of family A fatty acid receptors hasbeen recently highlighted,[140] while allosteric modulationof bitter taste[141] and odorant GPCRs has not beenwidely explored. Allosteric modulators may be more se-lective among GPCR subtypes with nearly identical or-thosteric sites and may represent a valid clinical alterna-tive to the conventional orthosteric drugs.[142] To date, twoallosteric ligands for GPCRs have been approved as ther-apeutics.[142–143] A high-resolution structure, mutagenesis,and pharmacology of the d-opioid receptor revealed thepresence of a sodium ion and its role in mediating alloste-ric control of receptor functional selectivity and constitu-tive activity.[144] Allosteric modulation of bitter, odorant,and fatty receptors comprises an exciting research direc-tion.

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In summary, chemosensory receptors represent attrac-tive research targets because of their complex molecularrecognition patterns, their key role in driving consumeracceptability of foods and fragrances, and their involve-ment in diverse physiological processes. The integrationof computational tools and experimental studies is essen-tial, and sometimes successful, in furthering the under-standing of the mechanisms of signal transduction and thefunctional roles of GPCRs in signal transduction path-ways. Advanced in silico tools may facilitate rationaldesign of target-selective chemosensation modulators andprovide novel testable hypotheses for chemosensorysignal transduction.

Acknowledgments

The Israel Science Foundation (No. 432/12), Chief Scien-tist of Agriculture (No. 820-0296), and the German Re-search Foundation DFG (ME 1024/8-1) grants to M. Y. N.are gratefully acknowledged. M. Y. N. participates in theEuropean COST Action CM1207 (GLISTEN). We thankDr. Louise Slade for stimulating discussions and Dr. Tali

Yarnitzky and Dr. Michal Slutzki for critical reading ofthe manuscript.

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Received: January 26, 2014Accepted: March 26, 2014

Published online: July 28, 2014

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