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SuperScent--a database of flavors and scents

Dunkel Mathias Schmidt Ulrike Struck Swantje Berger Lena Gruening BjoernHossbach Julia Jaeger Ines S Effmert Uta Piechulla Birgit Eriksson Roger KnudsenJette Preissner RobertPublished inNucleic Acids Research

DOI101093nargkn695

2009

Link to publication

Citation for published version (APA)Dunkel M Schmidt U Struck S Berger L Gruening B Hossbach J Jaeger I S Effmert U PiechullaB Eriksson R Knudsen J amp Preissner R (2009) SuperScent--a database of flavors and scents NucleicAcids Research 37 D291-D294 httpsdoiorg101093nargkn695

Total number of authors12

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Nucleic Acids Research 2008 1ndash4doi101093nargkn695

SuperScentmdasha database of flavors and scentsMathias Dunkel1 Ulrike Schmidt1 Swantje Struck1 Lena Berger1 Bjoern Gruening1

Julia Hossbach1 Ines S Jaeger12 Uta Effmert3 Birgit Piechulla3 Roger Eriksson4

Jette Knudsen5 and Robert Preissner1

1Structural Bioinformatics Group Institute of Molecular Biology and Bioinformatics Charite ndash University MedicineBerlin Arnimallee 22 14195 Berlin 2Department of Cardiology and Angiology Charite ndash University Medicine BerlinSchumannstr 2021 10117 Berlin 3Institute of Biological Sciences Biochemistry University of Rostock 18059Rostock Germany 4Department of Plant and Environmental Sciences University of Gothenburg SE 413 19Goteborg and 5Department of Ecology Lund University SE 223 62 Lund Sweden

Received August 15 2008 Revised September 24 2008 Accepted September 25 2008

ABSTRACT

Volatiles are efficient mediators of chemical com-munication acting universally as attractant repel-lent or warning signal in all kingdoms of lifeBeside this broad impact volatiles have in naturescents are also widely used in pharmaceuticalfood and cosmetic industries so the identificationof new scents is of great industrial interest Despitethis importance as well as the vast number anddiversity of volatile compounds there is currentlyno comprehensive public database providing infor-mation on structure and chemical classification ofvolatiles Therefore the database SuperScent wasestablished to supply users with detailed informa-tion on the variety of odor components The versionof the database presented here comprises the 2D3D structures of approximately 2100 volatiles andaround 9200 synonyms as well as physicochemicalproperties commercial availability and referencesThe volatiles are classified according to theirorigin functionality and odorant groups The infor-mation was extracted from the literature and webresources SuperScent offers several searchoptions eg name Pubchem ID number speciesfunctional groups or molecular weight SuperScentis available online at httpbioinformaticscharitedesuperscent

INTRODUCTION

In scientific terms scents are mixtures of volatile com-pounds with a high vapor pressure and a molecularweight which is usually lt300 gmol1 (1) Human beingsoften associate scents with volatiles that can be perceivedby the human nose and have a pleasant smell But the

entire group of volatile compounds comprises thousandsof inorganic and organic compounds stemming frommajor pathways of secondary metabolisms of manyorganisms (1) These volatiles may affect living organismsin one way or the other Over the past decades scientificinvestigations have revealed that volatiles play a key rolein life by acting as semiochemicals mediating inter- andintraspecies interactions of living organismsVolatiles allow animals to recognize or detect individ-

uals The volatiles called pheromones are indispensablefor mating choices sexual behavior and fertilization andalso for nursing They are important for the maintenanceof social relationships especially in animal communitiessuch as hives ant colonies prairie dog towns or evenpackspridesherds of larger animals Volatiles also sup-port foraging and the detection of prey They can alsoserve as signals to warn kin in situations of danger(alarm pheromones) or they are even used to defendagainst predators The relevance of volatiles is notrestricted to the animal kingdom Volatile semiochemicalsare involved in plantndashplant interactions (2) and many playa crucial role in plantndashanimal interactions eg pollination(3) herbivory (4) and the plantsrsquo response to defendagainst herbivores (5) Recently it was shown that bac-teria also emit a wealth of volatiles with an impact onplants fungi animals and bacteria (M Kai et alBacterial volatiles and their action potential AppliedMicrobiology Biotechnology submitted for publication)Last but not least scent components are of tremendous

commercial interest resulting in many applications ofvolatiles in science and industry Certain pleasant odorshave a positive effect on customer and are therefore usedin shopping malls for wellness applications and for theproduction of perfumes cosmetics and household cleaningagents to name a fewFor the recognition of odorant molecules a large

variety of olfactory receptors is known in humans andanimals To discriminate between scent components

To whom correspondence should be addressed Tel +49 30 8445 1649 Fax +49 30 8445 1551 Email robertpreissnercharitede

2008 The Author(s)This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (httpcreativecommonsorglicensesby-nc20uk) which permits unrestricted non-commercial use distribution and reproduction in any medium provided the original work is properly cited

Nucleic Acids Research Advance Access published October 17 2008

each receptor has affinities for a range of molecules andby combination of activated receptors many differentsmells can be perceived (6) All olfactory receptors aremembers of class A rhodopsin-like family of G-protein-coupled receptors (GPCRs) (7) a group of seventransmembrane domain receptors activating signal trans-ductions in cells Crystal structures are currently availablefor rhodopsin (8) and b2-adrenoreceptor (9) An excellentsource for information about olfactory receptors is theOlfactory Receptor database (10) This database providesinformation about the perceiving proteins and complexesand a database gathering comprehensive knowledge onvolatiles should give information about their ligandsThe work of Schmuker et al (1112) bridges the gapbetween the structure of volatile compounds and thereceptor response by prediction via machine learning tech-niques using experimental data from in vivo receptorrecordingsHitherto a comprehensive compiliation of volatiles was

not publicly available for scientific use It was thereforeour goal to establish the database lsquoSuperScentrsquo A varietyof database resources of scent components is alreadyknown but they are limited in scope and focus on certainsubgroups of scents eg the OdorDB part of the neu-roscience Senselab project (13) centers on odorant mole-cules experimentally shown to bind olfactory receptorproteins whereas the Pherobase database (httpwwwpherobasecom) focuses on pheromones but mean-while also covers a broad variety of semiochemicals Acompilation of floral scent components is found in theScentBase (httpwww2dpesguseSCENTbasehtml)and the Flavornet (httpwwwflavornetorgflavornethtml) summarizes volatile compounds found in thehuman olfactory perception space Consequently thesedatabases are useful for special purposes but there isstill a need for a comprehensive listing of volatiles regard-ing their properties and their uses in science and industryThe SuperScent database which is presented here com-

prises more than 2100 compounds together with severalclassification criteria These volatile compounds were col-lected from a variety of sources eg the literature or otherdatabases leading to a comprehensive dataset of scentcomponents together with information about their chemi-cal properties and commercial availability These featuresprovide the user with an extensive database together withsubstantial options such as a search within particulargroup odor subgroups

THE DATABASE

With 2147 compounds 9214 synonyms and references tomore than 20 different suppliers SuperScent provides thelargest diversity of volatile compounds and correspondinginformation available online The data are accessible withtwo different search options lsquoScent Searchrsquo and lsquoStructureSearchrsquoThe lsquoScent Searchrsquo enables the user to look for com-

pounds by means of the PubChem-ID or the nameAdditionally by choosing certain functional groups

species or range of molecular weights all database entriesmeeting the search criteria can be accessed

The second way to screen the database requires a molec-ular structure (lsquoStructure Searchrsquo) Here a SMILES code(Simplified Molecular Input Line Entry System) or aMOL-file of the search compound can be uploadedWith the help of MarvinView (httpwwwchemaxoncom) the user can draw either the whole structure of thesearch molecule or parts of it (I-A in Figure 1) Thus it ispossible to screen the database with self-edited moleculestructures In order to find resembling structures in thedatabase a similarity search is performed The 10 mostsimilar database entries are listed in the order of similarity(II in Figure 1) For each compound the name thePubChem-ID the 2D structure and the Tanimoto coeffi-cient are presented Furthermore a similarity search tofind the 10 20 or 30 most similar compounds is provided

Additional information is available in a separatelsquoPropertiesrsquo window (II-AB in Figure 1) The user canfind synonyms functional groups the molecular weightand all species in which the compound has been foundtogether with the corresponding reference Furthermorethe compounds are classified according to their structure(eg benzenoids) chemical features (Figure 2) quality ofscent (eg fruity-peach) and ordering information (sup-plier ID)

Another useful feature of the web interface is the lsquoScentTreersquo (I-B in Figure 1) Here the database entries havebeen clustered according to the quality of their aromaThere are 29 classes such as balsamic floral or spicywhich are again divided into several subclasses Forinstance the fruity scents include subclasses such asapple banana or coconut leading to an overall numberof 121 different groups

A manually verified upload option allows the scientificcommunity to contribute to the database Here the usercan import a MOL-file together with corresponding infor-mation of the compound The SuperScent database will beupdated twice a year

METHODS

Data were collected from the literature and various webresources such as a collection of floral scents (1) a reviewoutlining bacterial volatiles (14) and PubChem (httppubchemncbinlmnihgov) Abstracts of the literaturedatabase PubMed were filtered for relevant articles usingspecific keywords The abstracts were screened againstnames and synonyms of chemical compounds as well asa distinct set of substrings of IUPAC names The textpassages containing matches were manually curated by ascientific team of biologists that confirmed the matchingcompounds and verified them Several web resourceseg the Riechstoff-Lexikon (httpomikron-onlinede)and the Flavors and Fragrances catalog (httpsafcsupplysolutionscom) supplied by Sigma Aldrich werechecked

SuperScent is designed as a relational database which isimplemented on a MySQL server For chemical function-ality the MyChem package which aims to provide a

2 Nucleic Acids Research 2008

Figure 1 Flow chart of a scent search in the SuperScent database (I) Different search options are provided (A) Structure search upload an MOL-file or SMILES code but it is also possible to draw onersquos own structure (B) Scent tree structures are clustered into scent classes The branch can beexpanded by clicking on the nodes and a click on one subclass shows a list of compounds (II) Result table of a scent search The detailed view fortwo molecules is depicted (A) synthetic musk (B) natural musk

Nucleic Acids Research 2008 3

complete set of functions for handling chemical datawithin MySQL is added Most of the functions used byMyChem depend upon Open Babel (15) The fingerprintalgorithm implemented in Open Babel follows theDaylight approach (httpwwwdaylightcomdayhtmldoctheory) As similarity index the Tanimoto coefficientis used calculating the number of bit positions set to 1 inboth fingerprints divided by the number of bit positionsset to 1 in at least one of the fingerprints If a set bit isconsidered as a feature present in the molecule theTanimoto coefficient is a measure of the number ofcommon features in both molecules (16) A Tanimotocoefficient of gt085 indicates that two molecules mayhave similar activities (17) For displaying 3D structuresJmol an open-source Java viewer for chemical structuresin 3D (httpwwwjmolorg) is used Marvin ChemSketchwas applied for the built-in molecule editor which allowsstructural screening with self-edited molecules The web-site is built with php and javascript and web access isenabled via Apache HTTP Server 22

CONCLUSION AND FUTURE DIRECTION

The SuperScent database has become a useful tool toretrieve information about scents or to get an overviewof the known volatile organic compounds The includeddata on purchasability of scents will enable systematicexperimental approaches on the relation between struc-tural similarities and scent classes Furthermore structurecomparisons of self-edited molecules with the annotatedscents may allow a first rough estimation of the potentialaroma of new chemicals The SuperScent database is a freeresource with embedded screening functions for chemicalcompounds The extension of the database allows thescientific community simple access to a growing numberof available scents We plan to extend the database byincluding olfactory receptors in the near future

FUNDING

Deutsche Forschungsgemeinschaft (SFB 449) Investi-tionsbank Berlin (IBB) International Research TrainingGroup (IRTG) Berlin-Boston-Kyoto and Deutsche Kreb-shilfe Funding for open access charge SFB 449

Conflict of interest statement None declared

REFERENCES

1 KnudsenJT ErikssonR GershenzonJ and StahlB (2006)Diversity and distribution of floral scent Bot Rev 72 1ndash120

2 BaldwinIT HalitschkeR PascholdA von DahlCC andPrestonCA (2006) Volatile signaling in plant-plant interactionslsquolsquotalking treesrsquorsquo in the genomics era Science 311 812ndash815

3 DobsonHEM (2006) Relationship between floral fragrancecomposition and type of pollinator In DudarevaN andPicherskyE (eds) Biology of Floral Scent Taylor and FrancisBoca Raton FL pp 147ndash198

4 VetLEM and DickeM (1992) Ecology of infochemical use bynatural enemies in a tritrophic contextAnnu Rev Entomol 37 141ndash172

5 HeilM and Silva BuenoJC (2007) Within-plant signaling byvolatiles leads to induction and priming of an indirect plant defensein nature Proc Natl Acad Sci USA 104 5467ndash5472

6 FiresteinS (2001) How the olfactory system makes sense of scentsNature 413 211ndash218

7 KrautwurstD (2008) Human olfactory receptor families and theirodorants Chem Biodivers 5 842ndash852

8 PalczewskiK KumasakaT HoriT BehnkeCA MotoshimaHFoxBA Le TrongI TellerDC OkadaT StenkampRE et al(2000) Crystal structure of rhodopsin a G protein-coupled receptorScience 289 739ndash745

9 CherezovV RosenbaumDM HansonMA RasmussenSGThianFS KobilkaTS ChoiHJ KuhnP WeisWIKobilkaBK et al (2007) High-resolution crystal structure of anengineered human beta2-adrenergic G protein-coupled receptorScience 318 1258ndash1265

10 CrastoC MarencoL MillerP and ShepherdG (2002) OlfactoryReceptor Database a metadata-driven automated population fromsources of gene and protein sequences Nucleic Acids Res 30354ndash360

11 SchmukerM de BruyneM HahnelM and SchneiderG (2007)Predicting olfactory receptor neuron responses from odorantstructure Chem Cent J 1 11

12 SchmukerM and SchneiderG (2007) Processing and classificationof chemical data inspired by insect olfaction Proc Natl Acad SciUSA 104 20285ndash20289

13 CrastoCJ MarencoLN LiuN MorseTM CheungKHLaiPC BahlG MasiarP LamHY LimE et al (2007)SenseLab new developments in disseminating neuroscience infor-mation Brief Bioinform 8 150ndash162

14 SchulzS and DickschatJS (2007) Bacterial volatiles the smell ofsmall organisms Nat Prod Rep 24 814ndash842

15 GuhaR HowardMT HutchisonGR Murray-RustPRzepaH SteinbeckC WegnerJ and WillighagenEL (2006) TheBlue Obelisk-interoperability in chemical informatics J Chem InfModel 46 991ndash998

16 DelaneyJS (1996) Assessing the ability of chemical similaritymeasures to discriminate between active and inactive compoundsMol Divers 1 217ndash222

17 PattersonDE CramerRD FergusonAM ClarkRD andWeinbergerLE (1996) Neighborhood behavior a useful conceptfor validation of lsquolsquomolecular diversityrsquorsquo descriptors J Med Chem39 3049ndash3059

Figure 2 Pie chart of chemical classes found in the SuperScentdatabase

4 Nucleic Acids Research 2008

complete set of functions for handling chemical datawithin MySQL is added Most of the functions used byMyChem depend upon Open Babel (15) The fingerprintalgorithm implemented in Open Babel follows theDaylight approach (httpwwwdaylightcomdayhtmldoctheory) As similarity index the Tanimoto coefficientis used calculating the number of bit positions set to 1 inboth fingerprints divided by the number of bit positionsset to 1 in at least one of the fingerprints If a set bit isconsidered as a feature present in the molecule theTanimoto coefficient is a measure of the number ofcommon features in both molecules (16) A Tanimotocoefficient of gt085 indicates that two molecules mayhave similar activities (17) For displaying 3D structuresJmol an open-source Java viewer for chemical structuresin 3D (httpwwwjmolorg) is used Marvin ChemSketchwas applied for the built-in molecule editor which allowsstructural screening with self-edited molecules The web-site is built with php and javascript and web access isenabled via Apache HTTP Server 22

CONCLUSION AND FUTURE DIRECTION

The SuperScent database has become a useful tool toretrieve information about scents or to get an overviewof the known volatile organic compounds The includeddata on purchasability of scents will enable systematicexperimental approaches on the relation between struc-tural similarities and scent classes Furthermore structurecomparisons of self-edited molecules with the annotatedscents may allow a first rough estimation of the potentialaroma of new chemicals The SuperScent database is a freeresource with embedded screening functions for chemicalcompounds The extension of the database allows thescientific community simple access to a growing numberof available scents We plan to extend the database byincluding olfactory receptors in the near future

FUNDING

Deutsche Forschungsgemeinschaft (SFB 449) Investi-tionsbank Berlin (IBB) International Research TrainingGroup (IRTG) Berlin-Boston-Kyoto and Deutsche Kreb-shilfe Funding for open access charge SFB 449

Conflict of interest statement None declared

REFERENCES

1 KnudsenJT ErikssonR GershenzonJ and StahlB (2006)Diversity and distribution of floral scent Bot Rev 72 1ndash120

2 BaldwinIT HalitschkeR PascholdA von DahlCC andPrestonCA (2006) Volatile signaling in plant-plant interactionslsquolsquotalking treesrsquorsquo in the genomics era Science 311 812ndash815

3 DobsonHEM (2006) Relationship between floral fragrancecomposition and type of pollinator In DudarevaN andPicherskyE (eds) Biology of Floral Scent Taylor and FrancisBoca Raton FL pp 147ndash198

4 VetLEM and DickeM (1992) Ecology of infochemical use bynatural enemies in a tritrophic contextAnnu Rev Entomol 37 141ndash172

5 HeilM and Silva BuenoJC (2007) Within-plant signaling byvolatiles leads to induction and priming of an indirect plant defensein nature Proc Natl Acad Sci USA 104 5467ndash5472

6 FiresteinS (2001) How the olfactory system makes sense of scentsNature 413 211ndash218

7 KrautwurstD (2008) Human olfactory receptor families and theirodorants Chem Biodivers 5 842ndash852

8 PalczewskiK KumasakaT HoriT BehnkeCA MotoshimaHFoxBA Le TrongI TellerDC OkadaT StenkampRE et al(2000) Crystal structure of rhodopsin a G protein-coupled receptorScience 289 739ndash745

9 CherezovV RosenbaumDM HansonMA RasmussenSGThianFS KobilkaTS ChoiHJ KuhnP WeisWIKobilkaBK et al (2007) High-resolution crystal structure of anengineered human beta2-adrenergic G protein-coupled receptorScience 318 1258ndash1265

10 CrastoC MarencoL MillerP and ShepherdG (2002) OlfactoryReceptor Database a metadata-driven automated population fromsources of gene and protein sequences Nucleic Acids Res 30354ndash360

11 SchmukerM de BruyneM HahnelM and SchneiderG (2007)Predicting olfactory receptor neuron responses from odorantstructure Chem Cent J 1 11

12 SchmukerM and SchneiderG (2007) Processing and classificationof chemical data inspired by insect olfaction Proc Natl Acad SciUSA 104 20285ndash20289

13 CrastoCJ MarencoLN LiuN MorseTM CheungKHLaiPC BahlG MasiarP LamHY LimE et al (2007)SenseLab new developments in disseminating neuroscience infor-mation Brief Bioinform 8 150ndash162

14 SchulzS and DickschatJS (2007) Bacterial volatiles the smell ofsmall organisms Nat Prod Rep 24 814ndash842

15 GuhaR HowardMT HutchisonGR Murray-RustPRzepaH SteinbeckC WegnerJ and WillighagenEL (2006) TheBlue Obelisk-interoperability in chemical informatics J Chem InfModel 46 991ndash998

16 DelaneyJS (1996) Assessing the ability of chemical similaritymeasures to discriminate between active and inactive compoundsMol Divers 1 217ndash222

17 PattersonDE CramerRD FergusonAM ClarkRD andWeinbergerLE (1996) Neighborhood behavior a useful conceptfor validation of lsquolsquomolecular diversityrsquorsquo descriptors J Med Chem39 3049ndash3059

Figure 2 Pie chart of chemical classes found in the SuperScentdatabase

4 Nucleic Acids Research 2008