Background: G protein-coupled receptors (GPCRs) play important physiological roles transducing extracellular signals into intracellular responses. Approximately 50% of all marketed drugs target a GPCR. There remains considerable interest in effectively predicting the function of a GPCR from its primary sequence. Findings: Using techniques drawn from data mining and proteochemometrics, an alignment-free approach to GPCR classification has been devised. It uses a simple representation of a protein's physical properties. GPCRTree, a publicly-available internet server, implements an algorithm that classifies GPCRs at the class, sub-family and sub-subfamily level. Conclusion: A selective top-down classifier was developed which assigns sequences within a GPCR hierarchy. Compared to other publicly available GPCR prediction servers, GPCRTree is considerably more accurate at every level of classification. The server has been available online since March 2008 at URL: http://igrid-ext.cryst.bbk.ac.uk/gpcrtree

Alex A Freitas

Andrew Secker

Darren R Flower Open Access

David S Moss

Edward Clark

Jon Timmis

Mark Halling-brown

Matthew N Davies

English

PubMed

Davies, M.N.

Secker, A.

Halling-Brown, Mark D.

Moss, David S.

Freitas, A.A.

Timmis, J.

Clark, E.

Flower, D.R.

Name not available

GPCRTree: online hierarchical classification of GPCR function

Birkbeck Institutional Research Online

A compendium of specific motifs for diagnosing GPCR subtypes. Trends Pharmacol Sci

Attwood TK: Integrative bioinformatics for functional genome annotation: trawling for G protein-coupled receptors. Semin Cell Dev Biol

Brazma A: Expression Profiler. In The Analysis of Gene Expression Data: Methodsand Software Edited by: Parmigiani

Classifying G-protein coupled receptors with bagging classification tree. Computational Biology and Chemistry

Comparing several approaches for hierarchical classification of proteins with decision trees.

Conformational changes of G protein-coupled receptors during their activation by agonist binding.

de Carvalho ACPLF: A Tutorial on Hierarchical Classification with Applications in Bioinformatics.

DR: On the hierarchical classification of G Protein Coupled Receptors. Bioinformatics

DR: On the hierarchical classification of G protein-coupled receptors. Bioinformatics

Drug design strategies for targeting Gprotein-coupled receptors. ChemBioChem

Fast fourier transform-based support vector machine for prediction of G-protein coupled receptor subfamilies. Acta Biochim Biophys Sin (Shanghai)

Flower DR: Proteomic applications of automated GPCR classification. Proteomics

Frank E: Data Mining: Practical Machine Learning Tools and Techniques.

G-protein-coupled receptor heterodimers: pharmacology, function and relevance to drug discovery. Drug Discov Today

GRIFFIN: a system for predicting GPCR-G-protein coupling selectivity using a support vector machine and a hidden Markov model. Nucleic Acids Res

Gustafsson MG: Unbiased descriptor and parameter selection confirms the potential of proteochemometric modelling.

Hamodrakas SJ: A method for GPCRs coupling specificity to G-proteins using refined profile Hidden Markov Models.

Hamodrakas SJ: PRED-GPCR: GPCR recognition and family classification server. Nucleic Acids Res

Haussler D: Classifying G-protein coupled receptors with support vector machines. Bioinformatics

JR: Identification of novel multi-transmembrane proteins from genomic databases using quasi-periodic structural properties. Bioinformatics

Kenakin T: G protein-coupled receptor allosterism and complexing. Pharmacol Rev

Modelling G-protein-coupled receptors for drug design. Biochim Biophys Acta

Raghava GP: GPCRpred: an SVM-based method for prediction of families and subfamilies of G-protein coupled receptors. Nucleic Acids Res

Raghava GP: GPCRsclass: a web tool for the classification of amine type of G protein-coupled receptors. Nucleic Acids Res

SG: Structural basis for activation of G-protein-coupled receptors. Pharmacol Toxicol

Sonnhammer EL: A general model of G protein-coupled receptor sequences and its application to detect remote homologs. Protein Sci

Vriend G: GPCRDB information system for G protein-coupled receptors. Nucleic Acids Res

WEKA Classification Algorithms,

Wikberg JE: Proteochemometrics modeling of the interaction of amine G-protein coupled receptors with a diverse set of ligands. Mol Pharmacol

Abstract Background G protein-coupled receptors (GPCRs) play important physiological roles transducing extracellular signals into intracellular responses. Approximately 50% of all marketed drugs target a GPCR. There remains considerable interest in effectively predicting the function of a GPCR from its primary sequence. Findings Using techniques drawn from data mining and proteochemometrics, an alignment-free approach to GPCR classification has been devised. It uses a simple representation of a protein's physical properties. GPCRTree, a publicly-available internet server, implements an algorithm that classifies GPCRs at the class, sub-family and sub-subfamily level. Conclusion A selective top-down classifier was developed which assigns sequences within a GPCR hierarchy. Compared to other publicly available GPCR prediction servers, GPCRTree is considerably more accurate at every level of classification. The server has been available online since March 2008 at URL: http://igrid-ext.cryst.bbk.ac.uk/gpcrtree/.</p

Timmis Jon

Freitas Alex A

Moss David S

Halling-Brown Mark

Secker Andrew

Davies Matthew N

Clark Edward

Flower Darren R

Directory of Open Access Journals

BMC Research Notes

Background: G protein-coupled receptors (GPCRs) play important physiological roles transducing extracellular signals into intracellular responses. Approximately 50% of all marketed drugs target a GPCR. There remains considerable interest in effectively predicting the function of a GPCR from its primary sequence. Findings: Using techniques drawn from data mining and proteochemometrics, an alignment-free approach to GPCR classification has been devised. It uses a simple representation of a protein's physical properties. GPCRTree, a publicly-available internet server, implements an algorithm that classifies GPCRs at the class, sub-family and sub-subfamily level. Conclusion: A selective top-down classifier was developed which assigns sequences within a GPCR hierarchy. Compared to other publicly available GPCR prediction servers, GPCRTree is considerably more accurate at every level of classification. The server has been available online since March 2008 at URL: http://igrid-ext. cryst.bbk.ac.uk/gpcrtree/.</p

Davies, Matthew N.

Secker, Andrew

Halling-Brown, Mark

Freitas, Alex A.

Timmis, Jon

Clark, Edward

Flower, Darren R.

Aberystwyth Research Portal

GPCRTree:Online hierarchical classification of GPCR function

G protein-coupled receptors (GPCRs) play important physiological roles transducing extracellular signals into intracellular responses. Approximately 50% of all marketed drugs target a GPCR. There remains considerable interest in effectively predicting the function of a GPCR from its primary sequence

Flower, Darren R

Aston Publications Explorer

BioMed CentralBMC Research NotesssOpen AcceTechnical NoteGPCRTree: online hierarchical classification of GPCR functionMatthew N Davies*1, Andrew Secker2, Mark Halling-Brown3, David S Moss3, Alex A Freitas2, Jon Timmis4, Edward Clark4 and Darren R Flower1Address: 1The Jenner Institute, University of Oxford, Compton, Newbury, Berkshire, RG20 7NN, UK, 2Department of Computing and Centre for BioMedical Informatics, University of Kent, Canterbury, Kent, CT2 7NF, UK, 3Department of Crystallography, Birkbeck College, University of London, Malet Street, London, WC1E 7HX, UK and 4Departments of Computer Science and Electronics, University of York, Heslington, York, YO10 5DD, UKEmail: Matthew N Davies* - m.davies@mail.cryst.bbk.ac.uk; Andrew Secker - andysecker@gmail.com; Mark Halling-Brown - m.halling-brown@mail.cryst.bbk.ac.uk; David S Moss - d.moss@mail.cryst.bbk.ac.uk; Alex A Freitas - A.A.Freitas@kent.ac.uk; Jon Timmis - jt517@ohm.york.ac.uk; Edward Clark - edclark@cs.york.ac.uk; Darren R Flower - darren.flower@jenner.ac.uk* Corresponding author    AbstractBackground: G protein-coupled receptors (GPCRs) play important physiological rolestransducing extracellular signals into intracellular responses. Approximately 50% of all marketeddrugs target a GPCR. There remains considerable interest in effectively predicting the function ofa GPCR from its primary sequence.Findings: Using techniques drawn from data mining and proteochemometrics, an alignment-freeapproach to GPCR classification has been devised. It uses a simple representation of a protein'sphysical properties. GPCRTree, a publicly-available internet server, implements an algorithm thatclassifies GPCRs at the class, sub-family and sub-subfamily level.Conclusion: A selective top-down classifier was developed which assigns sequences within aGPCR hierarchy. Compared to other publicly available GPCR prediction servers, GPCRTree isconsiderably more accurate at every level of classification. The server has been available onlinesince March 2008 at URL: http://igrid-ext.cryst.bbk.ac.uk/gpcrtree/.BackgroundThe G protein-coupled receptors (GPCR) comprise adiverse range of integral membrane proteins regulatingmany important physiological functions [1-3]. Ligandbinding to a GPCR on the cell surface initiates cell signal-ing. An extremely heterogeneous set of molecules act asGPCR ligands. The GPCRs are a common target for thera-peutic drugs and approximately 50% of all marketeddrugs target GPCRs [4,5]. In spite of their functional andsequence diversity, GPCRs share certain common struc-tural features, but show a far greater conservation of three-dimensional structure than primary sequence [6]. Thismakes it difficult to develop for GPCR subtypes a compre-hensive classification system based on sequence [7]. Themost commonly-used system of classification is thatimplemented in the GPCRDB database [8], which dividesthe GPCRs into six classes (Class A: Rhodopsin-like, withover 80% of all GPCRs in humans; Class B: Secretin-like;Class C: Metabotropic glutamate receptors; Class D: Phe-romone receptors; Class E: cAMP receptors; and the muchsmaller Class F: Frizzled/smoothened family). Classes A,B, C and F are found in mammalian species while Class Dproteins are found only in fungi and Class E proteins areexclusive to Dictyostelium. The six classes are furtherPublished: 21 August 2008BMC Research Notes 2008, 1:67 doi:10.1186/1756-0500-1-67Received: 8 August 2008Accepted: 21 August 2008This article is available from: http://www.biomedcentral.com/1756-0500/1/67© 2008 Davies et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.Page 1 of 5(page number not for citation purposes)BMC Research Notes 2008, 1:67 http://www.biomedcentral.com/1756-0500/1/67divided into sub-divisions and sub-sub-divisions basedon the function of a GPCR and its specific ligand.Previous attempts at classifying the GPCRs from its pri-mary sequence have included motif-based classificationtools [9,10] and machine learning methods such as Hid-den Markov Models [11,12] and Support Vector Machines(SVMs) [13]. Several publicly-available SVM-based GPCRclassifiers exist: PRED-GPCR [14,15], GPCR-PRED [16]and GPCRsclass [17]. Some predictive techniques haveused a combination of SVMs and HMMs [18]. Otherapproaches towards GPCR Classification have includedSelf-Organising Maps [19], Quasi-predictor Feature Clas-sifiers [20] and Decision Trees [21]. GPCRTree is a newpublicly-available server based on the idea of selecting thebest classifier (from a set of candidate classifiers) at eachnode of the GPCR class tree.FindingsAlgorithmA previously-constructed comprehensive GPCR sequencesdataset was used to train and test the classifier [22]. Pro-teins shorter than 280 amino acids were removed, elimi-nating incomplete protein sequences. All identicalsequences were removed to avoid redundancy and classeswith fewer than 10 examples were also removed. The data-set used to train the server contains 8222 proteinsequences in 5 classes at the family level (A-E), 38 classesat the sub-family level, and 87 classes at the sub-subfamilylevel. Class F was not considered since it contains too fewsequences to develop an accurate classification model.The system uses an alignment-independent classificationsystem based on amino acids physical properties. Proteo-chemometrics uses 5 "z-values" (z1–z5) derived from 26real physiochemical properties using principal compo-nent analysis [23,24]. These five values are calculated foreach amino acid in the sequence and are used to generatethe 15 attribute values described in [17], giving a purelynumerical description of the protein.The GPCRTree server classifies at the GPCR Class, Sub-family and Sub-Subfamily level. Hierarchical classifica-tion of a sequence is performed using a selective top-downapproach, whereby each group of sibling nodes in theGPCR class tree becomes a flat classification problemsolved using a standard classifier [25,26], obviating theneed to devise a novel classifier. The full dataset trains theroot classifier, while only relevant subsets of the data areused to train classifiers at the subfamily and sub-sub-family levels. When an unclassified sequence is presentedto the algorithm, the root level classifier assigns it to aclass, which is then passed down to an appropriate classi-fier at the next level until it is assigned to a subfamily anda sub-subfamily [27]. Instead of a single classificationalgorithm being used at each node of the class tree, manyclassifiers are trained using a subset of the training setcalled the sub-training set, and then tested using a sepa-rate part of the training set called the validation set. Theclassifier with the highest classification accuracy on thevalidation set is selected for that node. Eight standard clas-sification algorithms were used as candidate classifiers ateach node of the GPCR tree. All code was written using theopen source WEKA data mining package [28,29] and thedefault parameters were used for each algorithm.TestingThe GPCRTree server has been validated against threeother predictive GPCR servers [22]. The GPCRTree serverwas trained using the full GPCRtree dataset, and thentested with each GPCR server dataset as test data.GPCRTree produced accuracies of 97% at the Class level,84% at the Sub-family and 75% at the Sub-Subfamilylevel. This exceeded the PRED-GPCR server at the Classlevel and is comparable at the Sub-family level. It exceedsthe GPCRPred server at all levels of the hierarchy. TheGPCRsclass server was the most successful classifier at themost specific (sub-sub-family) level; this may be becausethe classifier is overly specialised, being applicable only tothe Class A Amine sub-subfamily level. Of servers applica-ble to all GPCR classes, GPCRTree is the most accurateGPCR prediction server currently available.ImplementationGPCRTree is available through a web interface – http://igrid-ext.cryst.bbk.ac.uk/gpcrtree/. It was implementedusing PHP, dHTML and a java client. The PHP interfaceaffords a simple and straightforward method to submit aprotein sequence for evaluation. The code for the selectivetop-down approach, as previously published, requiredseveral changes to facilitate its effective integration intothe server environment. Training was modified such thatall GPCR proteins belonging to a class with 10 or moreexamples (protein sequences) were used. The algorithmthen pauses and waits for input that will come as an aux-iliary program making a TCP socket connection with theselective top down classifier. Upon connection, the auxil-iary program will send the protein sequence to be classi-fied and then pause. The classifier will make a predictionand then return the result. A TCP connection has beenused for several reasons. It can allow multiple users toaccess the classifier. Separate users can run separate auxil-iary programs, and so the classifier can queue theserequests ensuring that only one will invoke the classifierat any given time. The remainder will be queued and serv-iced in the order of submission. Moreover, this architec-ture promotes portability. It may be necessary, forresource or security reasons, to run the classifier on differ-ent hardware. In this case, the server can invoke the auxil-iary program which can connect via network connectionto the separate machine running the classifier.Page 2 of 5(page number not for citation purposes)BMC Research Notes 2008, 1:67 http://www.biomedcentral.com/1756-0500/1/67A user enters a protein sequence in plain or fasta formatand submits the job (Figure 1). The interface then sendsan AJAX call to the java client. The GPCRTree java clientsubmits sequences to the GPCRTree server, where they areclassified and the classification returned to the java clientwhich in turn passes this result to the interface. Thesequence and classification is then displayed below thesubmission button (Figure 2).Where non-standard residues are included within thesequences, substitutions are made: a sequence containinga 'B' (asparagine or aspartic acid) is assigned as an aspar-agine 'N'; a 'Z' (glutamine or glutamic acid) is assigned asa glutamine 'Q'; and a 'U' (selenocysteine) is assigned as acysteine 'C'. All unknown residues 'X' were assigned asalanines 'A'.ConclusionGPCR classification is among the most challenging prob-lems in bioinformatics due to the sequence diversity ofthe GPCR superfamily and the uneven distribution of itsvarious family subgroups. GPCRTree is the first server toimplement an alignment-independent representation ofprotein sequences and is also the first to classify sequencesusing a classifier specifically selected for each group of sib-ling nodes in the GPCR functional classification tree. Byselecting the best classifier (from a set of candidate classi-fiers) at each GPCR class tree node, the selective top-downmethod effectively exploits the fact that different classifi-ers have different biases that are more suitable for differ-ent classification problems. GPCRTree is currently themost accurate publicly-available server for the predictionof GPCR sequence classification and it utilises a simple yetrobust interface that can undertake multiple classifica-tions simultaneously.Availability and requirementsProject name: GPCRTreeProject home page: http://igrid-ext.cryst.bbk.ac.uk/gpcrtree/Operating system(s): Platform independentInput page for GPCRTree serverFig re 1Input page for GPCRTree server.Page 3 of 5(page number not for citation purposes)BMC Research Notes 2008, 1:67 http://www.biomedcentral.com/1756-0500/1/67Programming language: PHP, dHTML, JavaOther requirements: NoneLicense: NoneAny restrictions to use by non-academics: NoneAbbreviationsGPCR: G protein coupled receptor; TCP: TranmissionControl Protocol; WEKA: Waikato Environment forKnowledge Analysis; SVM: Support Vector MachineCompeting interestsThe authors declare that they have no competing interests.Authors' contributionsMD Built GPCRtree datatset, created alignment-free repre-sentations of protein sequences, and wrote the paper. ASDesigned and implemented the selective top-downmethod for hierarchical classification. Implementedmethod of turning raw protein sequences into numericalattributes. Assisted in writing the paper and implementa-tion of the code on the GPCRTree web server. MHB Con-structed and implemented GPCRTree server, currentlymaintains server at Birkbeck College, University of Lon-don DM Supervised construction of GPCRTree server atBirkbeck College AF Supervised the design of the selectivetop-down method for hierarchical classification. JT Super-vised a mathematical analysis of data mining algorithmsfor hierarchical classification EC Performed a mathemati-cal analysis of data mining algorithms for hierarchicalclassification. DRF Supervised design and construction ofGPCRtree dataset, development of representations of pro-Results page for the GPCRTree server showing the prediction for the sequence of Chemokine CCR4 receptorFig re 2Results page for the GPCRTree server showing the prediction for the sequence of Chemokine CCR4 receptor.Page 4 of 5(page number not for citation purposes)BMC Research Notes 2008, 1:67 http://www.biomedcentral.com/1756-0500/1/67Publish with BioMed Central   and  every scientist can read your work free of charge"BioMed Central will be the most significant development for disseminating the results of biomedical research in our lifetime."Sir Paul Nurse, Cancer Research UKYour research papers will be:available free of charge to the entire biomedical communitypeer reviewed and published immediately upon acceptancecited in PubMed and archived on PubMed Central yours — you keep the copyrightSubmit your manuscript here:http://www.biomedcentral.com/info/publishing_adv.aspBioMedcentraltein sequences, and co-wrote the paper. All authors readand approved the paper.AcknowledgementsThe authors should like to gratefully acknowledge funding under the ESPRC grant EP/D501377/1 and the European Union ImmunoGrid project FP6-2004-IST-4 (contract no. 028069).References1. Christopoulos A, Kenakin T: G protein-coupled receptor allos-terism and complexing.  Pharmacol Rev 2002, 54:323-374.2. Gether U, Asmar F, Meinild AK, Rasmussen SG: Structural basisfor activation of G-protein-coupled receptors.  Pharmacol Toxi-col 2002, 91:304-312.3. Bissantz C: Conformational changes of G protein-coupledreceptors during their activation by agonist binding.  J ReceptSignal Transduct Res 2003, 23:123-153.4. Flower DR: Modelling G-protein-coupled receptors for drugdesign.  Biochim Biophys Acta 1999, 1422:207-234.5. Klabunde T, Hessler G: Drug design strategies for targeting G-protein-coupled receptors.  ChemBioChem 2002, 3:928-944.6. 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GPCRTree:online hierarchical classification of GPCR function

G protein-coupled receptors (GPCRs) play important physiological roles transducing extracellular signals into intracellular responses. Approximately 50% of all marketed drugs target a GPCR. There remains considerable interest in effectively predicting the function of a GPCR from its primary sequence. FINDINGS: Using techniques drawn from data mining and proteochemometrics, an alignment-free approach to GPCR classification has been devised. It uses a simple representation of a protein's physical properties. GPCRTree, a publicly-available internet server, implements an algorithm that classifies GPCRs at the class, sub-family and sub-subfamily level. CONCLUSION: A selective top-down classifier was developed which assigns sequences within a GPCR hierarchy. Compared to other publicly available GPCR prediction servers, GPCRTree is considerably more accurate at every level of classification

Secker, Andrew D.

Kent Academic Repository

Mark Halling-Brown

Darren R Flower

Springer - Publisher Connector

Crossref

http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.275.6987

GPCRTree: online hierarchical classification of GPCR function

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