Guilt by Association: Contextual Information inGenome AnalysisL. Aravind1

National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, Maryland 20894 USA

The genome sequences churned outby the “genomic revolution” have chal-lenged both computational and experi-mental biologists to come up with newmethods to decipher the secrets of theencoded proteins. The experimental bi-ologists have largely concentrated on avariety of large-scale methods to assaygene expression and protein–protein in-teractions (Brown and Botstein 1999;Uetz et al. 2000; Walhout et al. 2000).The computational biologists, however,have deeply mined the genomes for evo-lutionary information in the form of ho-mology between genes (Tatusov et al.1997; Koonin et al. 2000; Ponting et al.2000,). Over the past few years there hasbeen increasing interest in the kinds ofinformation that exist in the context inwhich a protein or a domain thereof isencoded in the genome (Mushegian andKoonin 1996; Dandekar et al. 1998). Re-cently, contextual information has beenoffered as a strong handle on the prob-lem of in silico inference of proteinfunction (Enright et al. 1999; Marcotteet al. 1999a,b; Overbeek et al. 1999; Pel-legrini et al. 1999; Huynen et al. 2000).Understanding the scope and limita-tions of the use of these methods may becritical for the experimental biologistsseeking to use computational guidelinesfor large-scale investigations of proteinfunction. Here, we outline the recent ad-vances in this direction and briefly illus-trate the new leads they provide in un-derstanding protein function.

Contextual information comes inseveral overlapping grades, each with adifferent degree of specificity with re-gards to a particular protein’s role (Fig.1). The most general form of contextual

information is a phyletic profile, that is,the pattern of occurrence of orthologs ofa particular gene in a set of genomes un-der comparison (Pellegrini et al. 1999;Tatusov et al. 2000). In this setup, thenull hypothesis would be that genes thatfunctionally interact in a particularpathway or complex would share a simi-lar phyletic profile. This hypothesis issupported by the phyletic distributionof components of the core cellular ma-chinery—the translation, transcription,and replication complexes, which inter-act very tightly—as well as those ofmetabolic pathways. For example, mostof the proteins with a shared phyleticpattern between the archaea and the eu-karyotes are components of one of themany protein complexes that have arole in the above-stated three-core cellu-lar processes. Thus, the detection of un-characterized proteins, such as the fam-ily typified by MJ0586 from Methanococ-cus jannaschii, with a similar phyleticprofile would implicate them in one ofthe core functions (Fig. 1). When the in-formation from sequence homology isapplied to these proteins, one can oftenarrive at rather precise functions forthese proteins. In the case of theMJ0586-like proteins, sequence com-parisons reveal that they have a DNAbinding helix-turn-helix domain (Ara-vind and Koonin 1999), suggesting thatit is a component of the basal transcrip-tion machinery similar to TFIIB or TBP,which share the same phyletic profile(Fig. 1). This inference is compatiblewith the recent implication of the eu-karyotic representative of this familyMBF1 in transcriptional regulation(Kabe et al. 1999). When a rare sharedphyletic pattern is seen for certain pro-teins whose sequence affinities suggest arelated function, a strong case can be

made for their interaction. One exampleof this is the typically eukaryotic chro-matin protein methyltransferase—theSET domain that is seen in the bacteriaChlamydiae and Bordatella pertussis,along with another eukaryotic chroma-tin protein domain, SWIB (Stephens etal. 1998). The rare phyletic profile ofthese proteins in bacteria suggests thatthe SET and SWIB domains probably in-teract not only in these bacteria but pos-sibly also in other organisms.

Since the pioneering works of Jacoband Monod, scientists have realized thatfunctionally linked genes are coregu-lated and occur in proximity to eachother on the chromosome. Genomecomparisons have supported this andshow that, in prokaryotes, functionallyinteracting genes are in clusters thatrange from the giant ribosomal operonsto gene pairs that survive over large phy-logenetic distances (Dandekar et al.1998; Overbeek et al. 1999). Thus, theoccurrence of an uncharacterized genein the neighborhood (the same operon)of genes with known functions couldpotentially betray its function. How-ever, the variability of operons and theinability to predict them with a highlevel of certainty causes a loss of speci-ficity of this form of contextual informa-tion. On this issue, Huynen et al. arguethat greater stringency in the criteria forgene neighborhood reduces false posi-tives in these inferences, with physicalinteractions between gene productsstrongly predicted by conservation ofgene order in an operon. Furthermore,as the number of gene neighborhoodcombinations are far from exhaustedwith the currently available genomes,this method is likely to improve in itsscope and confidence with the availabil-ity of more genomes in the future.

1E-MAIL aravind@ncbi.nlm.nih.gov; FAX (301)480-9241.

Insight/Outlook

1074 Genome Research 10:1074–1077 ©2000 by Cold Spring Harbor Laboratory Press ISSN 1088-9051/00 $5.00; www.genome.orgwww.genome.org

Cold Spring Harbor Laboratory Press on November 7, 2016 - Published by genome.cshlp.orgDownloaded from

While establishing functional interac-tions between the proteins, the operonconservation approach has a deep pre-dictive value only for protein complexesthat function in metabolism and coreprocesses. This is mainly because thefunctions of the uncharacterized pro-teins can be interpreted in light of afairly well-known biochemical pathway.There are several conserved operons thatencode proteins such as histidine ki-nases and chemotactic receptors and �

regulatory kinases and phosphatasesthat are likely to physically interact indistinct signaling pathways. Contrary tothe metabolic- and core-function pro-teins, in most such cases no specific bio-

logical role can be assigned for these pre-dicted signaling pathways (Fig. 1).

Protein domain fusions provide an-other form of information that couldhelp in unraveling uncharacterized pro-teins or domains. Here the ability to ex-tract useful information is very contin-gent on the proper classification of do-main fusions. In evolutionary termsdomain fusions can be classified as fol-lows.

First, there are the ancient fusionsthat have occurred in the common an-cestor of a major lineage of organisms,such as the domains comprising the�-subunit of the bacterial DNA polymer-ase III. In this case the protein is made

up of a phosphoesterase domain, a DNApolymerase region, and two DNA-binding modules—a Helix-hairpin-Helix(HhH) and an OB domain (Fig. 1). Thesedomains by themselves provide infor-mation only in the context of the DNApolymerase, rather than automaticallyextending to the other proteins inwhich they are found, such as the his-tidinol phosphatase that also has thesame phosphoesterase domain. Thus,they are of limited value in predictingpotential interaction partners of theseproteins.

Second are the recent fusions thathave occurred in the more terminalbranches of the tree of life (though they

Figure 1 A schematic representation of the different grades of contextual information in the rough hierarchy of their specificity. It should be kept in mindthat the grades overlap to some extent. The different specific examples that have been discussed in the text are illustrated for each grade. The simplifiedphylogenetic profiles follow. -AE stands for present only in archaea and eukaryotes, while -ChBpE stands for present in Chlamydiae, Bordetella, andEukaryotes but absent in other prokaryotes. PHP is the phosphoesterase domain found in the DNA polymerase III subunits and histidinol phosphatases andthe H for the HhH DNA binding module. In the case of the Death domain, the potential interactions between Death domains in different proteins presentin a given genome are shown. The enzymes of the aromatic amino acid biosynthesis pathway that are encoded by separate genes in bacteria but fusedinto a single polypeptide in yeast are shown as a case of gene fusion.

Insight/Outlook

Genome Research 1075www.genome.org

Cold Spring Harbor Laboratory Press on November 7, 2016 - Published by genome.cshlp.orgDownloaded from

may show a secondary dispersionthrough horizontal transfer). The sec-ond class of recent fusions may furtherbe divided into those driven by evolu-tionarily mobile domains (‘promiscuousdomains’; Marcotte et al. 1999a) andgene fusions that arise from fusions ofgenes that typically occur separately inseveral other genomes. A good numberof the commonly encountered mobiledomains, which include repetitive mod-ules such as �-propellers and �-� super-helices, function in numerous distinctprotein–protein interactions in differentcellular compartments and are of mini-mal value for predicting specific interac-tions. Other fusions between mobile do-mains such as helicases and nucleases il-lustrate a general tendency for thesedomains to physically interact (Aravindet al. 1999), but this does not imply thatall related nucleases and helicases inter-act. Some of the mobile domains such asthe Death domains in animal apoptoticsignaling mediate homophilic interac-tion, while cysteine knot domains ofteninteract with extracellular leucine-richrepeat domains. Hence, the presence ofsuch domains in proteins can often nar-row down, if not specify, the set of in-teraction partners in a given genome.Other mobile domains such as the HhHmodule or the ACT domain act as tagsspecifying interactions with nucleic ac-ids and small molecules, respectively,and predict the protein’s functional mi-lieu to some extent. Unlike the widespectrum of relatively imprecise predic-tive information offered by the mobiledomain fusions, the relatively recentgene fusions are generally direct indica-tors of physical interactions between theproteins encoded by separate versions ofthese genes (Marcotte et al. 1999a). Suchfusions are typical in proteins that areparts of the same complex or catalysts ofadjacent reactions in pathways (Fig. 1)and that are, in prokaryotes, extremecases of gene order conservation in op-erons. While this form of fusion has ahigh predictive value distinguishing itfrom other the forms, fusions may notnecessarily be straightforward in the ab-sence of a robust list of orthologs (Gal-perin and Koonin 2000).

What is the total amount of contex-

tual information with all its overlappinggrades that is available for a particulargenome? Huynen et al. (2000) deter-mine this for the smallest cellular ge-nome available to us, that of Mycoplasmagenitalium. They show that such infor-mation can be gleaned for up to 50% ofthe genes with gene order conservationcontributing the maximum informationand gene fusions, phyletic profiling, andco-occurrence in operons contributingmuch smaller, roughly equivalentamounts of information. The observa-tion that gene order conservation pro-vides most of the contextual informa-tion has serious implications for thegenerality of these methods because theoperonic organization of genes is a hall-mark of the prokaryotes. A large part ofthe critical regulatory interactions of eu-karyotic proteins, especially typical ofmulticellular forms, cannot avail itself ofthis form of contextual information. Apartial solution to this problem is theuse of coexpression, as determinedthrough transcription profiling, as anadditional form of nongenomic contex-tual information in the eukaryotes (Mar-cotte et al. 1999b). While expressionprofiles are far from being comparable tooperons in their predictive specificity,multiple combined approaches that ad-ditionally use the results of large-scalegenetic screens and mass spectroscopy–based characterization of protein com-plexes could increase the amount ofcontext information in eukaryotic sys-tems.

Finally, it can be seen that leads intofundamental biological processes suchas translation may be derived from con-textual methods if they are suitablycombined with robust homology-basedcharacterization of the concerned pro-teins. Different forms of contextual in-formation have pointed out the role ofat least two GTPases, one of the TdhFfamily (Huynen et al. 2000) and anotherof the OBG1 family fused with a pre-dicted RNA binding TGS domain (Gal-perin and Koonin 2000). They are likelyto participate in translation, suggestingas yet undiscovered GTP-dependentsteps. Using similar techniques, differ-ent enzymes, namely at least two meth-yltransferases (MG259 and MG347;

Huynen et al. 2000) and one Rossmannfold oxidoreductase of the GidA family(Marcotte et al. 1999b), have been im-plicated in translation-related functionsin bacteria and mitochondria. BothMG259 and MG347 are similar to RNAmethylases, and least one of them(MG259) has signatures typical ofnucleic acid adenine methylases. This,taken together with the link to transla-tion, implies that they are involved inmethylation (MG259 and MG347) andbase modification (GidA) of either tRNAor rRNA. Thus, in conjunction with ap-propriate homology studies, ‘guilt by as-sociation’ can be converted to clear-cuthypotheses that are testable with spe-cific experiments.

Experimental methods such ashigh-throughput two-hybrid screens(Uetz et al. 2000; Walhout et al. 2000)suffer from false positives arising be-cause of interactions among proteinsfrom different subcellular locations. Insuch cases, the contextual informationcould effectively cut down the searchspace for interacting partners andthereby improve the chances of findingbiologically relevant interactions. Withgenome sequence data pouring in, thesecomputational approaches are likely togo hand in hand with large-scale experi-mental efforts in the conquest of un-chartered biological systems.

REFERENCESAravind, L. and Koonin, E.V. 1999. Nucleic Acids

Res. 27: 4658–4670.Aravind, L., Walker, D.R., and Koonin, E.V. 1999.

Nucleic Acids Res. 27: 1223–1242.Brown, P.O. and Botstein, D. 1999. Nat. Genet.

21: 33–37.Dandekar, T., Snel, B., Huynen, M., and Bork, P.

1998. Trends Biochem. Sci. 23: 324–328.Enright, A.J., Iliopoulos, I., Kyrpides, N.C., and

Ouzounis, C. A. 1999. Nature 402: 86–90.Galperin, M.Y. and Koonin, E.V. 2000. Nat.

Biotechnol. 18: 609–613.Huynen, M., Snel, B., Lathe, W., and Bork, P.

2000. Genome Res. (this issue).Kabe, Y., Goto, M., Shima, D., Imai, T., Wada, T.,

Morohashi, K., Shirakawa, M., Hirose, S., andHanda, H. 1999. J. Biol. Chem. 274: 34196–34202.

Koonin, E.V., Wolf, Y.I., and Aravind, L. 2000.Adv. Protein Chem. 54: 245–275.

Marcotte, E.M., Pellegrini, M., Ng, H.L., Rice, D.W., Yeates, T.O., and Eisenberg, D. 1999a.Science 285: 751–753.

Marcotte, E.M., Pellegrini, M., Thompson, M.J.,

Aravind

1076 Genome Researchwww.genome.org

Cold Spring Harbor Laboratory Press on November 7, 2016 - Published by genome.cshlp.orgDownloaded from

Yeates, T.O. and Eisenberg, D. 1999b. Nature402: 83–86.

Mushegian, A.R. and Koonin, E.V. 1996. TrendsGenet. 12: 289–290.

Overbeek, R., Fonstein, M., D’Souza, M., Pusch, G.D., and Maltsev, N. 1999. Proc. Natl. Acad. Sci.96: 2896–2901.

Pellegrini, M., Marcotte, E.M., Thompson, M.J.,Eisenberg, D., and Yeates, T.O. 1999. Proc.Natl. Acad. Sci. 96: 4285–4288.

Ponting, C.P., Schultz, J., Copley, R.R., Andrade,M. A., and Bork, P. 2000. Adv. Protein Chem.54: 185–244.

Stephens, R.S., Kalman, S., Lammel, C., Fan, J.,Marathe, R., Aravind, L., Mitchell, W., Olinger,L., Tatusov, R.L., Zhao, Q. et al. 1998. Science282: 754–759.

Tatusov, R.L., Galperin, M.Y., Natale, D.A., andKoonin, E.V. 2000. Nucleic Acids Res.28: 33–36.

Tatusov, R.L., Koonin, E.V., and Lipman, D.J.1997. Science 278: 631–637.

Uetz, P., Giot, L., Cagney, G., Mansfield, T.A.,Judson, R.S., Knight, J.R., Lockshon, D.,Narayan, V., Srinivasan, M., Pochart, P. et al.2000. Nature 403: 623–627.

Walhout, A.J., Sordella, R., Lu, X., Hartley,J.L., Temple, G.F., Brasch, M.A., Thierry-Mieg, N., and Vidal, M. 2000. Science287: 116–122.

Insight/Outlook

Genome Research 1077www.genome.org

Cold Spring Harbor Laboratory Press on November 7, 2016 - Published by genome.cshlp.orgDownloaded from

10.1101/gr.10.8.1074Access the most recent version at doi:2000 10: 1074-1077 Genome Res. 

  L. Aravind  Guilt by Association: Contextual Information in Genome Analysis

  References

  http://genome.cshlp.org/content/10/8/1074.full.html#ref-list-1

This article cites 19 articles, 10 of which can be accessed free at:

  License

Commons Creative

  http://creativecommons.org/licenses/by-nc/3.0/.described at

a Creative Commons License (Attribution-NonCommercial 3.0 Unported License), as ). After six months, it is available underhttp://genome.cshlp.org/site/misc/terms.xhtml

first six months after the full-issue publication date (see This article is distributed exclusively by Cold Spring Harbor Laboratory Press for the

ServiceEmail Alerting

  click here.top right corner of the article or

Receive free email alerts when new articles cite this article - sign up in the box at the

http://genome.cshlp.org/subscriptionsgo to: Genome Research To subscribe to

Cold Spring Harbor Laboratory Press

Cold Spring Harbor Laboratory Press on November 7, 2016 - Published by genome.cshlp.orgDownloaded from