classification schemes for protein structure and function

© 2003 Nature Publishing Group

508 | JULY 2003 | VOLUME 4 www.nature.com/reviews/genetics

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elements (for example, fold types or enzyme proper-ties) that are used for the classification, the definition ofa METRIC (similarity or distance, which are derived onthe basis of the features), a set of algorithms that gener-ate the metric and perform clustering (for example,DISTANCE-BASED HIERARCHICAL CLUSTERING) and, finally, theinterpretation of intra- and inter-cluster relationships,which are usually tightly linked to the performanceevaluation of the entire procedure.

Several recent reviews on the classification of pro-teins4–6 discuss the properties and contents of a set ofresources for the analysis of protein structure. Here, weprovide a critical assessment of these resources andexpand the scope by including an overview of thefunctional classifications of proteins. Our main pointis that the scope of all classification schemes is similarand that a more rigorous comparative analysis of theseschemes might ultimately lead to a single widelyacceptable taxonomy of molecular types and familiesfor all living organisms.

Approaches to classificationWe begin by describing in detail several approaches forthe classification of proteins and their functions thathave been developed over the past few decades. We pro-vide both the original and latest citations for each classi-fication or resource, to give an indication of how longthe corresponding project has been established.

Biology has a long tradition of classification, which isthe definition and naming of groups1. Classification isbased on the comparative analysis of groups, which waspioneered by Aristotle (384–322 BC) who, for the firsttime, understood the challenge of grouping livingorganisms into meaningful classes on the basis of theiranatomy and physiology2. This tradition was rediscov-ered and further expanded 20 centuries later by a seriesof great naturalists, including John Ray (1628–1705),Carl von Linné (1707–1778), Jean Baptiste de Monet,Chevalier de Lamarck (1744–1829) and Charles Darwin(1809–1882)2. This long and brilliant tradition of nat-ural classification schemes for biological species had asignificant impact on the modern molecular biology ofthe last century, in particular on the organization andfurther classification of molecular systems, includingmetabolic pathways and protein families or folds.

The recent revolution in high-throughput technolo-gies in molecular biology has produced vast amounts ofinformation about the structure, function and evolu-tion of biological macromolecules at the genome scale3.To deduce possible clues about the action and interac-tions of these molecules in the cell, it is necessary toclassify them into meaningful categories that are collec-tively linked to existing biological knowledge. In techni-cal terms, clustering and classification refer to certainelements to be classified (for example, protein structureand function), the features (or attributes) of these

CLASSIFICATION SCHEMES FORPROTEIN STRUCTURE ANDFUNCTIONChristos A. Ouzounis*, Richard M. R. Coulson*, Anton J. Enright‡, Victor Kunin* and José B. Pereira-Leal*

We examine the structural and functional classifications of the protein universe, providing anoverview of the existing classification schemes, their features and inter-relationships. We argue thata unified scheme should be based on a natural classification approach and that more comparativeanalyses of the present schemes are required both to understand their limitations and to helpdelimit the number of known protein folds and their corresponding functional roles in cells.

*Computational GenomicsGroup, The EuropeanBioinformatics Institute,EMBL CambridgeOutstation, CambridgeCB10 1SD, UK.‡Computational BiologyCenter, Memorial Sloan-Kettering Cancer Center,1275 York Avenue,New York 10021, USA.Correspondence to C.A.O.e-mail: [email protected]:10.1038/nrg1113

METRIC

A criterion or set of criteria thatare stated in quantifiable terms.

DISTANCE-BASED

HIERARCHICAL CLUSTERING

Clustering is the process ofgrouping objects on the basis oftheir similarity. Distance-basedhierarchical clustering is used toconstruct a tree of nestedclusters on the basis of theproximity (or distance) betweendata points.


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and assign them to particular groups. The automaticclassifications rely on the execution of an algorithm thatgenerates metrics for similarity or distance, which aresubsequently processed to identify these groups. Oneadvantage of curation is the high quality of the clustering;however, the disadvantage is that the end result might not be reproducible and scalable to high volumes ofincoming data. Conversely, automation might generatemore inaccurate assignments; yet, this approach is fullyreproducible and should be scalable, given sufficientcomputational resources. Ideally, automation should be

Fundamental distinctions. Classification schemes can bebroadly divided into curated versus automatic, andstructural versus functional, in a continuum of semi-automatic and structure-to-function types of approach.Here, we define this distinction more precisely, becauseit is useful for our analysis, comparison and evaluationof these different approaches.

Curation versus automation. The curated classificationstake into account human expertise, guided by computeranalyses, to identify similarities between proteins

Table 1 | Resources for structural classifications of proteins

Scheme Description Automation Type Comments(algorithm)

SCOP Structural classification of Low Fold level and below High quality, highly curated, possible proteins problems with scalability

CATH Class, architecture, topology, Low/medium Fold, topology similarity Comprehensive, some degree of humanhomology (SSAP) intervention

FSSP Fold assignment using DALI High (DALI) Fold, topology similarity Highly automated, highly scalable

DSSP Database of secondary High (DSSP) Secondary structure The first algorithmic definition of secondarystructure of proteins of proteins structure

HSSP Homology and secondary High (MaxHom) Secondary structures plus One of the first databases of sequencestructure of proteins sequence alignments alignments

Pfam Domain-level classification of Medium (HMMER) Domain database, sensitive Human intervention required for the seedproteins detection of homologues by set, highly comprehensive collection of

hidden Markov models domains

PRINTS Fingerprint information for Low Motif database, multiple Human intervention required, limited setprotein sequences levels of sequence similarity with specific families, highly curated

SMART Mobile domains in proteins Low/medium Short motif database Impressive collection of motifs, especially fornuclear, signalling and extracellular proteins

PROSITE Motif definition Low Pattern and sequence Contributions by the community, frequently profile database updated

TIGRFAMS Protein family database Medium (HMMER) Annotated protein family Concentrates on genomes published bydatabase, pointers to TIGRgene ontology

PRODOM Protein domain database Medium (BLAST) Protein domain database Recent versions encompass completed genome sequences

BLOCKS Multiple-alignment blocks High (BLIMPS, Multiple-alignment database Seed set supplied by PROSITE has beenLAMA, extended to encompass sequences fromBLOCKSSEARCH) other databases

eMOTIF Protein motif database High Motif database, derived Powerful pattern definition, although databasederivative (MOTIFMAKER) from PRINTS and BLOCKS has not been updated recently

Bio-Dictionary Pattern-based classification High (Teiresias) Pattern discovery and Highly scalable, unsupervised motifdetection discovery, high coverage

SYSTERS Protein families Medium Protein sequence family Comprehensive resource, recently extendeddatabase to encompass expressed sequence tags

ClustR Clusters of related proteins High (Smith- Protein family database Highly comprehensive, extensively cross-Waterman dynamic referenced, computationally demandingprogramming) construction of protein families

COGS Clusters of orthologous Low/medium, Attempt to construct lists COGS (families) classified into functionalgroups BLAST similarity of orthologous genes classes, difficult to scale up

across genomes

ProtoMap Hierarchical classification Medium/high Protein families detected Hierarchical clustering of SwissProtof proteins by a graph representing

sequence-similarity relationships

MetaFam A meta-database of Medium/high An integrated database for Detailed schema and powerful queryprotein families several protein family capabilities

databases

TRIBES Protein family database High (TRIBE-MCL) Protein sequence family Scales well owing to an efficientdatabase for complete clustering algorithm, problems with genomes and SwissProt high granularity



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Table 2 | Resources for functional classifications of proteins

Scheme Description Automation Type Comments(algorithm)

EC Enzyme classification Low Enzyme Commission hierarchical Refers to reaction types, not explicitly toclassification proteins

YPD* Functional classification Low Species-specific function classification Derivatives are species specificfor yeast proteins, also databaselocalization

SGD Functional classification Low Species-specific function classification One of the most comprehensive species- for yeast proteins, also database, community based specific resources available so farlocalization, mutantsand so on

MIPS Munich Information Medium Functional classifications derived Initially a database of functional analysis ofCenter for Protein (PEDANT) manually, highly curated, inferred by the yeast genome, later expanded toSequences function similarity encompass many other speciesdatabase

WIT/ Acronym for ‘what is Low/medium Functional associations discovered Encompasses a wide range of genomesERGO* there?’ by gene clusters

STRING Search tool for the Medium/high Functional associations discovered by Server provides seamless access to theretrieval of interacting gene clusters, gene fusions and principal methods for the detection ofgenes/proteins phylogenetic profiles associations using genome constraints

AllFuse Differential fusion High Functional associations discovered by Scalable, computation intensive(DifFuse) gene clusters, gene fusions and

phylogenetic profiles

Predictome Putative functional Medium/high Functional associations discovered by Contains information about conserved genelinks between proteins computational methods clusters, gene fusions and phylogenetic

profiles

Riley Riley’s functional Low Details a number of functional classes, The first functional classification of an entireclassification scheme explicit classifications of gene products genome, that of E. colifor Escherichia coli

GeneQuiz Large-scale automatic High Shallow hierarchy of three superclasses Highly automated, opts for low coverageannotation system (EUCLID) and fourteen classes and high precision, unique among this

group

GO Gene ontology Low A classification for molecular Expanded to encompass a large number offunction, biological process species and cellular component forDrosophila melanogaster

BioCyc EcoCyc, MetaCyc Medium A database architecture for genome and Performs automatic metabolic and derivatives (PathTools) pathway information reconstruction using genome-derived

sequence annotations

KEGG Kyoto encyclopaedia Low An integrated database of gene and Contains implicit information about functionalof genes and genomes metabolic pathway information for associations of genes in terms of pathway

many species and reaction participation

DIP Database of Low/medium A collection of interacting proteins from The first protein-interaction database, alreadyinteracting proteins different species, highly curated, aided by includes thousands of entries. Not clear if

some text mining from Medline abstracts scalable, but certainly a good gold-standardfor computation with protein-interaction data

YPL.db Localization database Low/medium A database of localization for yeast Not densely populated at present, for yeast proteins, includes images ultimate goal is to annotate all yeast

proteins

TRIPLES Transposon-insertion Low Information on mutants and subcellular Impressive contents for mutation analysisphenotypes, localization localization for the yeast genomeand expression in Saccharomyces

MINT Molecular interactions Low A molecular-interaction database, Not densely populated at presentdatabase containing information both for genetic

and protein interactions

BIND Biomolecular Low/medium Detailed schema for molecular interactions A sophisticated database schema forinteractions database molecular interactions, not highly populated,

scalable

PIM* Protein interaction Medium Prototyped to facilitate analysis of two- Probably extensible to other species, gearedmanager (PIMRider) hybrid interaction analysis of towards two-hybrid experiments

Helicobacter pylori

CellZome CellZome data Medium Information extracted from TAP-MS Prototyped on the yeast genome, applicable db* experiments to other organisms

*Commercial, no access without registration and/or license fee. For further information on these resources see links in online links box. SGD, Saccharomyces GenomeDatabase; TAP-MS, tandem affinity purification–mass spectrometry; YPD, Yeast Proteome Database.



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This is a hierarchical classification with different levels,folds (protein domains of similar topology and struc-ture without detectable sequence similarity), superfami-lies (similar structures with weak sequence similarity)and families (in cases in which sequence similarity isreadily detectable)8.

CATH follows a similar principle and classifies pro-teins according to four main classes (α, β, α–β and ‘other’in terms of secondary-structure content), multiplearchitectural classes (based on the orientations of sec-ondary-structure elements ignoring their sequence con-nectivity), topology (in which sequence connectivity istaken into account) and finally, homology classes (withdetectable sequence similarity)10. CATH relies both onmanual inspection and the SSAP structure-comparisonalgorithm13.

Finally, the FSSP database relies on an exhaustive all-against-all comparison of protein structures12,which is performed by the DALI algorithm14,15. Of thesethree databases, FSSP is the most automated and possi-bly most scalable, whereas SCOP contains the highestamount of manual curation. These databases allow thedetection of common and unusual folds in protein-structure space16,17 and represent invaluable tools forSTRUCTURAL GENOMICS18.

Other databases of protein structure include theDictionary of Secondary Structures of Proteins (DSSP)database19 and the Homology-derived SecondaryStructure of Proteins (HSSP) database20,21. DSSP uses aset of three-dimensional (3D) coordinates as input anddefines the secondary-structure elements on the basisof hydrogen-bonding patterns and other geometricalfeatures19. HSSP uses a POSITION-WEIGHTED DYNAMIC

PROGRAMMING METHOD for sequence profile alignment,which is called MaxHom20. By aligning protein sequenceswith definitions of secondary structure that are derivedfrom DSSP, HSSP enhances the set of known structuresby adding sequence-similarity information21.

The primary structure (protein sequence) classifica-tion schemes focus on the detection of homologues insequence databases and the accurate definition of proteinfamilies. Some of the best-known classifications includemotif or domain databases — for example, the ProteinFamilies database (Pfam)22,23, Protein Fingerprints(PRINTS)24,25, the Simple Modular Architecture ResearchTool (SMART)26,27, the PROSITE database of proteinfamilies and domains28,29, the TIGRFAMS protein-familydatabase30,31, the ProDom database32,33, the BLOCKs data-base34,35 and the eMOTIF collection36,37 — and protein-family databases, such as the Bio-Dictionary resource38,the SYSTERS database39, ClustR40,41, COGS 42,43,ProtoMap44,45, MetaFam46,47 and TRIBES48.

The Pfam database is constructed by SEED ALIGNMENTS

that are subsequently extended by using HIDDEN MARKOV

MODEL (HMM) searches against the full protein data-base23. The PRINTS database is a highly curated databaseof motifs that are diagnostic of the functional propertiesof proteins at different levels of sequence similarity25.The SMART system describes mobile domains, which areprincipally found in eukaryotes26, on the basis of sensitivedatabase searches and multiple alignment27.

the ultimate goal of all classifiers, building on experiencefrom manual curation on a smaller scale. Indeed, manycuration projects use a host of different algorithms thatassist human experts in the classification process.Anothermore subtle distinction is that curated classifications are‘supervised’, in the sense that the classes are usuallydefined by experts, whereas automatic classifications are usually ‘unsupervised’ — the number of classes isdetermined by the algorithm and is not necessarily fixed.

Structure versus function. The distinction between struc-ture and function is less obvious. The traditional view hasdistinguished systems that classify protein sequences intosequence families, as distinct from the classification ofprotein structures into structure folds. However, in ourview, this distinction is not fundamentally different:sequence families and structure folds represent differentlevels of the protein-structure hierarchy — the primaryand tertiary level, respectively.According to this view, clas-sifications that take into account secondary structureshould also be considered as part of this approach. Thedefining factor for structure-based classifications is theability to derive measures of molecular similarity exclu-sively on the basis of different levels of protein structure.The function classification systems do (or should) not, inprinciple, take structure into account. They draw on thefeatures that characterize the individual elements fromother lines of evidence, including experimental informa-tion about participation in specific pathways, networks orphases of the cell cycle, sets of constraints obtained fromgenome structure and evolution, textual information interms of supporting literature and database records, orsimply a priori schemes of biological roles for macromol-ecules. In summary, function classification should beindependent from structure attributes, and allow struc-turally (or evolutionarily) unrelated molecular types to beassigned to the same functional class on the basis ofshared cellular roles or other features.

Overview of classification schemesBelow, we describe the classification schemes for protein structure and function, and simultaneouslyassess their particular features according to the distinc-tive elements mentioned earlier. We then present someexamples of proteins that have been classified with dif-ferent levels of success, and explore possible avenuesfor future improvement and integration of these classi-fications. Our examples include cases for individualstructure or (where possible) function associationsagainst the available schemes.

Structural classifications. Structural classifications derivegroups on the basis of molecular similarity in terms ofprimary or tertiary structure (TABLE 1). Three of the mostwidely known tertiary structural classifications are theStructural Classification of Proteins (SCOP)7,8, the Class /Architecture/Topology/Homology (CATH) data-base9,10 and the Fold Classification based on Structure–Structure Alignment of Proteins (FSSP) database11,12.

SCOP is based on the visual inspection of proteinfolds and manual curation of the corresponding groups.

STRUCTURAL GENOMICS

Initiatives to solve thestructures of proteins that areencoded in an entire genome byhigh-throughput methods.

POSITION-WEIGHTED DYNAMIC

PROGRAMMING

Dynamic programming is analgorithmic approach to solvesequential or multi-stagedecision problems, such asfinding optimal protein-sequence alignments. Theposition-weighted dynamic-programming methodincorporates a matrix ofsubstitution frequencies betweenamino acids, weighted by thedegree of conservation ofparticular residues.

SEED ALIGNMENTS

Hand-edited multiple sequencealignments that incorporatesequences that are described inthe literature as belonging to thesame family. From these seedalignments, hidden Markovmodels can be created that canin turn be used to searchdatabases and identify newmembers of the family.

HIDDEN MARKOV MODEL

(HMM). A pattern-recognitionapproach that is used inbioinformatics forDNA/protein feature detectionand sequence comparison.HMMs are based on transitionprobabilities for discrete states.These probabilities are usuallyderived from training sets suchas seed alignments.


COGS is a resource that documents the ORTHOLOGOUS

genes across entire genomes42. ProtoMap is a compre-hensive resource of protein families, which are createdusing similarity graphs that are generated from differentsequence-similarity detection methods44,45. MetaFam isa resource that allows the querying and integration of various family databases46,47. Finally, TRIBEs is adatabase of protein families48 that are detected by theTRIBE-MCL algorithm for entire genomes and Swiss-Prot51.

Functional classifications. Functional classificationsderive groups on the basis of functional similarity interms of enzyme reaction mechanisms, participationin biochemical pathways, functional roles and cellularlocalization (TABLE 2). Following our definitions, weconsider the sets of functional classifications thatmake no reference to structural similarities, to obtaina more balanced view of the classification approaches.In other words, we do not consider structural classifi-cations with associated functional information,because related proteins often have related functions.Instead, we attempt to examine the available schemesfor the classification of function independently fromstructural information.

One of the oldest classification schemes is theEnzyme Commission (EC) hierarchical classification,which defines six principal classes of enzymes52,53. Thisscheme groups reactions into categories with similar

The PROSITE database is one of the oldest curatedmotif databases with contributions from the commu-nity28, which has now been extended to encompass a largenumber of patterns and sequence profiles29. The TIGR-FAMS database is a collection of manually curated pro-tein families that are identified by HMMs. It containscomments and functional classification assignments31.The ProDom database contains a comprehensive set ofprotein domain families that are automatically gener-ated from sequence databases32 and complete genomesequences33. These six motif or domain collections havebeen integrated into a single resource, the InterProdatabase49. The BLOCKS database contains a set of gap-free multiple alignments of protein familiesthat have been contained in PROSITE34. The eMOTIFdatabase is a derivative of the BLOCKS and PRINTSdatabases37, under a unified language for amino-acidsimilarity and pattern definition36.

The Bio-Dictionary resource detects the presence ofsequence patterns using Teiresias, which is an unsuper-vised COMBINATORIAL PATTERN-DISCOVERY algorithm50, andsubsequently allows the automated annotation of theproteins that contain these patterns38. The SYSTERS data-base performs automated clustering and classification ofprotein sequences into non-overlapping families andsuperfamilies39. ClustR represents the effort to hierarchi-cally classify protein sequences into families using theSmith–Waterman dynamic-programming alignmentalgorithm at different levels of sequence similarity40,41.

Box 1 | Best practice example for protein annotation

An archetype of consistent annotation of the well-characterized Escherichia coliprotein TrpCF tryptophan biosynthesis enzyme IGPS-PRAI is shown below. Thisexample represents a case of ‘best practice’ for protein annotation. The figureshows a three-dimensional view of the enzyme, in which the two domains arehighlighted in different colours.


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COMBINATORIAL PATTERN

DISCOVERY

An approach that produces allpatterns in any given data set inan efficient way that avoids theexplicit enumeration of theentire pattern space.

ORTHOLOGUES

Genes of common origin thathave diverged throughspeciation rather thanduplication. This term issometimes ambiguously used todenote functionally equivalentgenes that are of common originin different organisms.

Database Entry Description

SWISSPROT TRPC_ECOLI Tryptophan biosynthesis protein trpCF [Includes: Indole-3-glycerol phosphatesynthase (EC 4.1.1.48) (IGPS); N-(5′-phospho-ribosyl)anthranilate isomerase (EC 5.3.1.24) (PRAI)]

INTERPRO, BLOCKS, E-MOTIF Indole-3-glycerol phosphate synthase(IPB001468); PROSITE (PS00614); PROTOMAP (Cluster #737), Tribes (TR-0000000188), PFAM(PF00218); COG (COG0134)

PDB, CATH, SCOP, FSSP, DSSP, 1PII N-(5′phosphoribosyl)anthranilate isomerase HSSP synthase

PFAM (PF00697); COG (COG0135) PRAI N-(5′phosphoribosyl)anthranilate (PRA) isomerase

ENZYME 4.1.1.48 Indole-3-glycerol-phosphate synthase. 1-(2-carboxyphenylamino)-1-deoxy- D-ribulose 5-phosphate = 1-(indol-3-yl)glycerol 3-phosphate + CO(2) + H(2)O

ENZYME 5.3.1.24 Phosphoribosylanthranilate isomerase: N-(5-phospho-β-D-ribosyl)-anthranilate = 1-(2-carboxyphenylamino)-1-deoxy-D-ribulose 5-phosphate

PRINTS, TIGRFAMS, CLUSTR, No entries availablePREDICTOME, MINT, BIND

TRIPLES, YPLDB, CELLZOME Species specific, not covering Escherichia coli



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BIND

MINT

TRIPLES

YPL.db

DIP

KEGG

BioCyc

GO

GeneQuiz

Predictome

DifFuse

STRING

MIPS

SGD

Enzyme

TRIBES 84

ProtoMap 3.0

COGS

ClustR

SYSTERS 3.0

Bio-Dictionary 07-02

BLOCKS 13.0

eMOTIF 3.6

PRODOM 2002.1

TIGRFAMS 2.1

PROSITE 17.39

SMART 3.4

PRINTS 35.0

Pfam A+B 8.0

HSSP 02-03

DSSP 03-03

FSSP 06-02

CATH 2.4

SCOP 1.61

1 10 100 1,000 10,000 100,000 1,000,000

Proteins Classes

Str

uctu

ral c

lass

ifica

tio

n re

sour

ces

Func

tio

nal c

lass

ifica

tio

n re

sour

ces

Figure 1 | Coverage of protein sequence space by structural versus functional classifications. Coverage of the knownprotein sequence space in terms of entries (blue) and classes (red) for structural and functional classification schemes. The x-axiscorresponds to the number of entries in logarithmic scale, clipped at one million entries. The y-axis indicates the resource orclassification scheme (version numbers, for example, SCOP 1.61, or release dates, for example, FSSP 06-02, are provided wherepossible). All structural classification schemes from TABLE 1, with the exception of MetaFam, are listed; only publicly availablefunctional classification schemes are listed. The number of classes for functional classification schemes are not necessarilycomparable, for example, ENZYME corresponds to the total number of three-level classifications, EcoCyc and KEGG classescorrespond to enzymatic reactions, GeneQuiz and Gene Ontology (GO) contain functional classes, and protein interactions arelisted as raw counts. It is clear by comparing the total number of protein entries (represented by the blue bars) that structuralclassifications provide a better coverage of protein sequence space than functional classifications, which are less automated.



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subcellular localization information. Another signifi-cant resource that provides a widely used functional clas-sification scheme is the Munich Information Center forProtein Sequences (MIPS)58,59. A similar resource wasWhat Is There? (WIT)60, which provided information onconserved gene clusters and metabolic reconstructions.Similar to YPD, it was also commercialized, by IntegratedGenomics, Inc., and renamed ERGO.

Several other resources provide genome-wide func-tional associations that are detected by comparativegenomics3. The most comprehensive of those is theSearch Tool for the Retrieval of InteractingGenes/Proteins (STRING)61,62, which was initially devel-oped for conserved gene clusters61 and later expanded62

to cover gene fusions63 and phylogenetic profiles64.Other more specialized databases that classify proteins

properties, and further sub-classifies them with respectto reaction mechanisms, reactants and products, andspecificities, by assigning them EC numbers. Althoughthese widely used EC numbers are associated with pro-teins (enzymes), it is worth noting that they refer toreactions and not proteins (different proteins mighthave similar EC numbers and vice versa).

Another widely used database with a significantamount of curation is the Yeast Proteome Database(YPD)54,55, which was originally created for theSaccharomyces cerevisiae genome and later expanded toother species, before it was acquired by IncyteGenomics. The definitive public-domain resource forS. cerevisiae is, at present, the Saccharomyces GenomeDatabase (SGD), which contains a wealth of informa-tion about yeast proteins56,57, including mutant and

Gene Ontology (GO) functional classification of families

Ligand binding or carrier

Nucleic-acid binding

Enzyme

Transporter

Storage protein

Structural protein

Signal transducer

Motor

Class unknown or not assigned

Figure 2 | Overlaying structural and functional classifications for a group of interconnected proteins. The largestinterconnected group of protein families from the Swiss-Prot protein database (237 protein families; 21,727 sequences intotal) is shown. Circles represent protein families. Lines show sequence similarities between families. Circles are colouredaccording to the GeneOntology functional classes73 (where available). This is an example of two independent classifications,one based on sequence similarity (structural) and the other based on functional categorization. Figure modified withpermission from REF. 51.



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A different way of classifying genes and proteins isby their participation or association with metabolicpathways. One such system is the EcoCyc database, anencyclopaedia of E. coli genes and metabolism74,which has also been used for metabolic predictions ofHaemophilus influenzae 75 and other species by theincorporation of other pathways in the MetaCycdatabase76. Another important metabolic database isthe Kyoto Encyclopaedia of Genes and Genomes(KEGG)77,78.

Finally, another set of implicit classifications can bederived from protein interaction and cellular-localizationinformation. The groups of proteins that are defined asinteracting can be viewed as classes in a functional clas-sification scheme, the classes of which have not neces-sarily been defined. Resources here include theDatabase of Interacting Proteins (DIP)79,80, the YeastProtein Localization database (YPL.db)81, the TRIPLESdatabase82,83, the Molecular INTeraction database(MINT)84, the BIND database85,86, the ProteinInteraction Manager (PIM) resource for two-hybridexperiments in Helicobacter pylori 87 and the CellZomeyeast-interaction database88. Of the above, DIP, MINTand BIND are public-domain resources that allow therecording and annotation of protein interactionsfrom various species, which greatly facilitates patterndiscovery in protein-association networks 89.

General features of classificationsThese classification schemes cover a vast spectrum ofbiological properties (TABLES 1,2). Structural classifi-cations range from short motifs and promiscuousdomains to full-length protein-sequence families to secondary-structure libraries, alignments and proteinfolds. Functional classifications range from cellular rolesand localization to phenotype data to biochemical path-ways and protein-interaction networks. Although thisdiversity is welcome in principle, it is not useful in theabsence of a theme that will unite these classificationsunder one roof, as the scope of these schemes is, in fact,similar. Well-characterized proteins indicate the ulti-mate goal of all classification schemes: the generation ofconsistent and ideally complementary levels of struc-tural and functional information (see BOX 1 for anexample of best practice).

into functionally associated groups are the AllFuseresource65, which is based on the detection of gene-fusion events66, and Predictome67, which is similar incontent to STRING68.

One of the most influential schemes of a priorifunctional classification was a hierarchy of propertiesfor the gene products of Escherichia coli 69, which waslater extended to include multifunctional classifica-tions70. Inspired by this classification, a shallow hierar-chy of protein function was devised for the automatedgenome-annotation system GeneQuiz71, based on thekeyword mapping of protein families to 14 functionalclasses, called EUCLID72. Another complex classifica-tion scheme, which comprises three unique sub-schemes on molecular function, biological processand cellular component, is the Gene Ontology (GO)classification73.

Nucleus

MitochondriaCytoplasm

Cell wall

Vacuole

Peroxisome

Golgi

ER

Endosometransport vesicles

Cytoskeleton

Plasma membrane

15.2

6.2

6.23.3

1.5

1.5

4.5

2.3

2.2

23.2 32.7

Figure 3 | Relative frequencies of subcellular localization labels for Saccharomycescerevisiae gene products of known function. Subcellular localization represents a functionalclassification scheme that is independent of structural information, such as sequence similarity.Data obtained from the Munich Information Center for Protein Sequences (MIPS) ComprehensiveYeast Genome Database (CYGD)59. ER, endoplasmic reticulum.

Box 2 | Coverage of structure and function classification schemes

Examples of four randomly selected proteins from the Chlamydia trachomatis serovar D genome sequence97

and their annotations. The coverage for the 20 structural and functional classification schemes is shown as apercentage. For example, in the case of CT313, one-half of the structural classification schemes list this protein,compared with only one-third of the functional classification schemes. It is evident from this short list that proteinsfrom genome projects are more likely to be included in structural classification schemes than in functional schemes.

Protein ID Consistent annotations (%) Annotation

Structural Functional

CT080 25 15 Late transcription unit B hypotheticalprotein

CT094 45 25 tRNA pseudouridine synthase EC 4.2.1.70

CT313 50 30 Transaldolase EC 2.2.1.2

CT664 45 25 FHA domain-containing protein



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It is worth noting that structural classifications can begenerated automatically because the similarity relation-ships are, in principle, obtained by algorithms that facili-tate clustering and further annotation, whereas functionalclassifications are labour intensive, as they are oftenderived a priori or from large-scale experimentation.Consequently, structural classifications are comparativelymore shallow owing to the limitations of algorithms todetect any relationships beyond the protein family (ormotif) level92. By contrast, functional classifications,which are less constrained by this type of resolution limit,imply deep hierarchical classifications that potentiallyspan all levels of molecular and cellular processes (FIG. 2).

It is also conceivable that, at least on a species-specificlevel, all high-throughput experiments result in a set thatencompasses functional classifications on a genome-wide scale. According to our definitions, these associa-tions of proteins are not a result of molecular similarity (that is structural classes), as they reflect the various bio-logical processes as snapshots of cellular activity. Forinstance, experiments that provide information about

Certain elements of these classifications becomeapparent in this survey (TABLES 1,2). Structural classifi-cations contain fold, motif and protein-family infor-mation, whereas functional classifications refer tobiochemical and cellular roles, metabolic pathways,subcellular localization and molecular interactions.Evidently, the coverage of protein space might be dif-ferent and sometimes difficult to obtain (FIG. 1). Mostof these data collections were created more than adecade ago, a fact that also makes them susceptible tocertain legacy requirements (such as the format ofidentifiers and modes of distribution).

Although structural classifications are probably eas-ier to define on the basis of molecular-similarity crite-ria, their overlap is surprisingly limited6. Conversely,functional classifications encompass many processesand elements, which range from pathways to cellularcompartments, and have been shown to overlap con-siderably with each other90. This is exemplified by fourgene products that were randomly selected from theChlamydia trachomatis serovar D genome91 (BOX 2).

DifFuse

Genetic interactions (MIPS) Predictome

Immunoprecipitation (DIP)

Yeast two-hybrid

Yeast two-hybrid

Figure 4 | Topology and network structure of known protein interactions from yeast. The different resources include two yeasttwo-hybrid experiments (upper left panel98 and lower left panel99 ), immunoprecipitation or genetic interactions (middle), andcomputational predictions (right). Notice the diversity of these groupings, both in terms of the number of proteins involved in interactionsand the structure of the relationships, which might be attributable to the low coverage of interaction space for the yeast cell. Linesrepresent interactions; circles represent proteins.



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clustering of protein families cannot take the evolutionof proteins into account because ‘molecular phylogeny’can only be inferred. As another example, the clusteringof gene-expression patterns cannot, at present, trace theproduction of the corresponding mRNAs in real time,which is a process that might be termed ‘molecularONTOGENY’. Although the molecular ontogeny or phy-logeny criterion is desirable, our present knowledge ofthese processes is insufficient and, therefore, the classifi-cations are compromised by heuristic criteria that onlypartly reflect the physical reality.

It is an open problem to what extent the metrics thatare derived by our algorithms reflect a natural processand, therefore, to what extent our final classificationsreflect a more objective natural classification scheme.Note that a similar debate over the classification ofspecies has been continuing for decades95. Once a naturalclassification scheme is obtained, the challenge of unify-ing these schemes under a single theoretical frameworkwill be far more feasible.

Comparative analysis. The second problem is thatthe multitude of classifications requires consistencythat has not yet been achieved, perhaps because thisendeavour is still at an exploratory stage. To achieve ahigh level of consistency, more elaborate databaseschemas are required, which are known as ‘ONTOLOGIES’in computer science. These schemas necessitate strictdefinitions of the group (or ‘class’) definitions (forexample, what is a protein family?), in otherwise disparate database implementations (each schememight have a different idea of what a protein familyis). We argue that more research is required to realignthe diverse collection of protein classifications andachieve a desirable consensus. Integration projectssuch as InterPro49, MetaFam46 and GO73 point in theright direction, although a rigorous theoreticalframework is still missing.

Future perspectivesFrom the earlier discussion, it seems that there is aneed for a meta-classification as a base on which morerefined classifications (and ontologies) can be built.This classification must encompass all of the subtletiesand definitions of existing classification schemes toensure the communication and interchangeability ofinformation between them, while also reflecting ourgenuine level of biological understanding.

The structural classification schemes are variedand disparate, although many use the same terminol-ogy, including motifs, domains and sequence families.It will be interesting to see whether a more formalontology develops that encompasses these entitiesunder formal definitions. In that sense, functionalclassifications have been more successful under GOand other systems, and have generated a wide-rangingdictionary that can be used across species and molec-ular processes. It is also interesting that many functionalgenomics experiments use functional classes from GOto annotate groups of genes without committing tomore specific functional assignments96.

the subcellular localization of gene products should be considered as such classifications (FIG. 3). These resourcesshould be viewed as individual classification schemesthat will grow rapidly with time, reflecting the functionalproperties of molecular networks in the cell (FIG. 4). Notunlike crystallographic or NUCLEAR MAGNETIC RESONANCE

(NMR) experiments that deduce the 3D structure ofproteins — or DNA sequencing experiments that deci-pher the structure of genomes — large-scale experi-mentation for the detection of subcellular localization,protein interactions, phenotypic analysis and the like, isthe basis for further, sometimes implicit, functionalclassifications of proteins.

The analogy goes further: in structural analysis, theexperimental conditions (for example, temperature andpH) are strictly defined and perturbations involve the cre-ation of artificial constructs (for example, by point muta-tion) that might affect molecular structure. The fact thatmolecular structure is robust to such changes allows thereliable inference of properties by structural similarity.The time-dependent element is the phylogeny of mole-cules, which extends to millions of years. By contrast, infunctional analysis the experimental conditions are muchharder to define because they involve the study of anentire biological system, not a DNA sequence or anatomic crystal. The molecule or crystal is at a state of lowENTROPY compared with a snapshot of cell physiology. This‘entropy difference’ makes the comparison of functionalexperiments more difficult.Also, perturbations of physio-logical conditions involve system parameters that aredifficult to control, with unexpected results. The time-dependent element is the life cycle or developmental timeof the cell, which is orders of magnitude shorter than evo-lutionary time. Therefore, all snapshots of biological sys-tems that use functional genomics provide a view of theunderlying complexity of life that has not been availableby the examination of molecular ‘relics’.

From a computational perspective, this time-dependent element makes functional classifications par-ticularly prone to a lack of consistency, an issue that hasalready been discussed extensively in the literature93,94.

Challenges for classification schemesGiven the number of classification schemes, it would be desirable to unify them under a single theoreticalframework. In practical terms, that would require the re-engineering of these resources and their integration, sothat experimental biologists can find them more trans-parent and user-friendly. The problem, in our opinion, isthat we lack the highly desirable fundamental conceptualframework for the classification of protein structure andfunction that would facilitate the creation of a singlemeaningful classification. Moreover, to achieve this goal,research into the comparative analysis of these schemes isrequired. Certain steps towards achieving the goals ofunification and comparison have already been taken6,90.

Natural classification, unification. The first problemis the lack of natural classification schemes (most ofwhich rely on directly derived metrics that do notnecessarily have a physical meaning). For example, the

NUCLEAR MAGNETIC

RESSONANCE

(NMR). An analytical chemistrytechnique that is used to studymolecular structure anddynamics, which exploresspectrum differences that arecaused by the differentialalignment of atomic spins in thepresence of a strong magneticfield.

ENTROPY

A measure of the disorder orunavailability of energy within aclosed system.

ONTOGENY

The development and life cycleof a single organism.

ONTOLOGY

An explicit formal specificationof how to represent the objects,concepts and other entitieswithin a domain of discourse,and the relationships amongthem. Ontologies are designedto create agreed vocabularies forexchanging information.


Concluding remarksThe wealth of structure information (protein sequenceand fold) has been used to derive dynamic models (forexample, molecular dynamics from mutants, evolution-ary threading from folds, subfamily determinants frominteractions and genome sequences from evolution). Thisfield of exploring the evolutionary dynamics of proteins


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will expand with the availability of further primary andtertiary structures of proteins and their functional prop-erties. The most obvious applicability of dynamic mod-els, however, comes from functional analyses, in whichthe short timescales and amenability to highly detailedexperimentation render these systems ideally suited forthe development of time-dependent models.

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AcknowledgementsR.M.R.C. would like to acknowledge the Medical ResearchCouncil (UK) for support. J.P.L. would like to acknowledge theFoundation for Science and Technology (Portugal). We also thankC. Von Mering, I. Rigoutsos and colleagues at the EuropeanBioinformatics Institute (EBI) for providing information on Figure 1.

Online Links

FURTHER INFORMATIONAllFuse: http://maine.ebi.ac.uk:8000/services/allfuseBIND: http://cbm.bio.uniroma2.it/mintBioCyc: http://biocyc.orgBio-Dictionary:http://www.research.ibm.com/bioinformatics/metadata.phtml.htmlBLOCKS: http://www.blocks.fhcrc.orgCATH: http://www.biochem.ucl.ac.uk/bsm/cathCellZome: http://www.cellzome.comClustR: http://www.ebi.ac.uk/clustrCOGS: http://www.ncbi.nlm.nih.gov/COG CYGD: http://mips.gsf.de/proj/yeast/CYGD/dbDIP: http://dip.doe-mbi.ucla.eduDSSP: http://www.sander.ebi.ac.uk/dsspeMOTIF: http://motif.stanford.edu/emotifERGO: http://www.integratedgenomics.com/ergo_light/ergo_overview.htmlFSSP: http://www.ebi.ac.uk/dali/fsspGene Ontology: http://www.geneontology.orgGeneQuiz: http://jura.ebi.ac.uk:8765/ext-genequizHSSP: http://www.sander.ebi.ac.uk/hsspInterPro: http://www.ebi.ac.uk/interproISCB: http://www.iscb.orgKEGG: http://www.genome.ad.jp/kegg/kegg2.htmlMetaFam: http://metafam.ahc.umn.eduMINT: http://cbm.bio.uniroma2.it/mintMIPS: http://mips.gsf.dePfam: http://www.sanger.ac.uk/Software/Pfam/index.shtmlPIM: http://pim.hybrigenics.com/pimrider/pimriderlobby/PimRiderLobbyHpFull.jspPredictome: http://predictome.bu.eduPRINTS: http://bioinf.man.ac.uk/dbbrowser/PRINTS/PRINTS.htmlProDom:http://prodes.toulouse.inra.fr/prodom/2002.1/html/home.phpPROSITE: http://us.expasy.org/prositeProtoMap: http://protomap.cornell.edu/index.htmlSCOP: http://scop.mrc-lmb.cam.ac.uk/scopSGD: http://www.yeastgenome.orgSMART: http://smart.ox.ac.ukSTRING: http://www.bork.embl-heidelberg.de/STRINGSwiss-Prot: http://us.expasy.org/sprotSYSTERS: http://systers.molgen.mpg.deTIGRFAMS: http://www.tigr.org/TIGRFAMsTRIBEs: http://maine.ebi.ac.uk:8000/services/tribesTRIPLES: http://ygac.med.yale.edu/triples/triples.htmYPL.db: http://ypl.tugraz.atAccess to this interactive links box is free online.

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