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482 nature structural biology • volume 8 number 6 • june 2001

structure-function relationships in thisnovel molecule.

Since the identification of H. pylorialmost 20 years ago, basic studies ofpathogenesis have identified numerousareas in which new concepts have beenuncovered, including discovery of autoly-sis as a mechanism for surface-associationof cytoplasmic proteins, host-mediatedphosphorylation of bacterial proteinsdelivered to human gastric epithelialcells14 and a novel family of penicillin-binding proteins15,16. The report by Haet al.1 adds another concept — short pep-tide segments added to existing structuresmay allow assembly of higher-orderoligomers with properties (in this case,ability to resist inactivation by acid) notseen in the original structures. The con-

clusions of this study thus take us one stepfurther toward understanding the abilityof H. pylori to resist acid and perhaps thedevelopment of new treatments for eradi-cating H. pylori infection.

Bruce E. Dunn is in the Department ofPathology, Medical College of Wisconsin,and Pathology and Laboratory MedicineService, Milwaukee Department of VeteransAffairs Medical Center, Milwaukee,Wisconsin 53295, USA. Markus G. Grütteris at Biochemisches Institut, UniversitätZürich, Zürich, Switzerland. Correspond-ence should be addressed to B.E.D. email:Bruce.Dunn@med.va.gov

1. Ha, N. et al. Nature Struct. Biol. 8, 505–509 (2001).2. Jabri, E., Carr, M. B., Hausinger, R.P. & Karplus, P.A.

Science 268, 998–1004 (1995).

3. Benini, S. et al. Structure Fold. Des. 7, 205–216(1999).

4. Dunn, B. E. et al. Infect. Immun. 65, 1181–1188(1997).

5. Phadnis, S. H. et al. Infect. Immun. 64, 905–912(1996).

6. Krishnamurthy, P. et al. Infect. Immun. 66,5060–5066 (1998).

7. Weeks, D., Eskandari,S., Scott, D.R. & Sachs, G.Science 287, 482-485 (2000).

8. Scott, D.R. et al. Gastroenterology. 114, 58-70(1998).

9. Miederer, S. E. & P. Grubel. Digestive Diseases Sci.41, 944–949 (1996).

10. Marshall, B. J. et al. Am. J. Gastroenterol 82, 200(1987).

11. Stingl, K. et al. Infect. Immun. 69, 1178–1180(2001).

12. Austin, J.W., Doig, P., Stewart, M. & Trust, T.J. J.Bacteriol. 174, 7470-7473 (1992).

13. Chu, S., Tanaka, S., Kaunitz, J.D. & Montrose, M.H. J.Clin. Invest. 103, 605–612 (1999).

14. Stein, M., Rappuoli, R. & Covacci, A. Proc. Natl.Acad. Sci. USA 97, 1263–1268 (2000).

15. Krishnamurthy, P. et al. J. Bacteriol. 181, 5107–5110(1999).

16. Mittl, P.R., Luthy, L., Hunziker, P. & Grütter, M.G. J.Biol. Chem. 275, 17693–17699 (2000).

Target practiceAndrej Sali

The scope of structural genomics has recently been estimated by simulation of several target selection strategiesbased on the currently known protein sequence families. Useful characterization of most protein sequences willbe possible by protein structure modeling, if structures of ~16,000 carefully selected protein domains aredetermined experimentally. In the absence of globally coordinated target selection, three times as manystructures may be required.

Structural genomics is a comprehensiveeffort toward characterizing the structuresof all proteins1–11. The first essential step instructural genomics is the selection of tar-get protein sequences for experimentalstructure determination such that all theremaining proteins are related to at leastone known structure at a useful level ofsimilarity (Fig. 1). On pages 559–566 ofthis issue of Nature Structural Biology,Vitkup et al.12 describe the scope of struc-tural genomics. The number of targets isestimated from similarities among thesequences in the Pfam database of 2,000family alignments of related proteindomains13. To relate 90% of the domainsequences in Pfam to a known structurewith >30% sequence identity, two struc-tures per Pfam family are needed. ThePfam domain families cover only a quarterof the domains in several representativegenomes. In practice, inefficiencies in tar-get selection are estimated to increase thenumber of targets by approximately a fac-tor of three relative to the optimal targetselection. Thus, the scope of structural

genomics corresponds up to 50,000 tar-gets, which is well within reach of thenascent global structural genomicseffort14.

A priori, structural genomics targets canbe qualified by two criteria. First, the tar-gets are likely to correspond to individualdomains rather than multidomain pro-teins. The reason is that the structure of asingle domain is usually easier to deter-mine by X-ray crystallography or NMRspectroscopy than that of a more flexiblemulti-domain protein. Second, domainsthat are not amenable to structure deter-mination are excluded from considera-tion. Such domains may includemembrane spanning domains, domainscontaining long regions of unusual aminoacid residue composition of low complex-ity, large flexible domains, domains thatrequire ligands for stability and variantsresulting from post-translational modifi-cations and alternative splicing.

Target selection is intimately tied to thespecific aim of structural genomics. Forexample, if the aim is to map distant evo-

lutionary relationships between all relateddomains15, only a relatively low-densitysampling of the protein space is required.In contrast, the inability of protein struc-ture modeling to reliably predict func-tional differences between homologs ledothers to include close homologs on thetarget list (for example, 70% sequenceidentity); however, in that case the scope islimited to a single genome so that the pro-ject is still feasible9. Many additional targetselection strategies of different groupsinvolved in structural genomics initiativesare reviewed in ref. 15. For example, targetlists may correspond to the representativesof all fold families16,17, functional fami-lies7, all proteins from a genome3 or allunusual uncharacterized soluble proteinsin a small genome18. Domain families anddomain sequences may be prioritized byrelevance and feasibility criteria, such ascurrently perceived medical importanceand the absence of transmembrane span-ning helices, respectively. The target listsof the individual research groups are usu-ally limited to a certain type of protein

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nature structural biology • volume 8 number 6 • june 2001 483

(for example, cancer-related proteins) orto a subset of all protein sequences (forexample, a genome) to make the size ofthe individual projects reasonable. In con-trast to individual groups who can affordto focus on relatively small parts of theprotein space, the target selection of theglobal structural genomics effort mustcover all protein sequences that areamenable to structure determination.

For the purposes of target selection, it isconvenient to take a model-centric view ofstructural genomics: structural genomicsaims to produce useful comparative mod-els for most protein sequences12,19. Thisview is justified because the first step inmany structure-based annotations is thecalculation of a comparative model20,although there are trivial cases wheremodeling is not needed and difficult caseswhere modeling is not helpful. To obtain areasonable level of accuracy, the modelsmust be based on alignments with fewerrors. Such alignments can usually beobtained when the sequence identitybetween the modeled sequence and atleast one known structure is higher than30% (ref. 20). Thus, structural genomicsshould determine protein structures sothat most sequences in the genome data-bases match at least one structure with anoverall sequence identity of more than30% (refs 12, 19).

Vitkup et al.12 first estimate the numberof structural genomics targets for a welldefined set of 2,000 protein domain fami-lies in the Pfam 4.4 database. The targetsare selected by a ‘greedy’ coverage algo-rithm. This simple algorithm picks a tar-get iteratively by maximizing the numberof domain sequences that can be modeledbased on at least 30% sequence identity tothe selected target structure. The numberof targets required to cover all of the260,000 domain sequences in Pfam is17,000 (13,000 if the membrane spanningdomains are excluded). Above 30%sequence identity, the number of targets

increases by 10,000 per 10 percentagepoints of sequence identity.

As described below, Vitkup et al.12 quan-tify substantial reductions in the number oftargets that result from improving model-ing techniques and from relaxing the com-pleteness requirement. They also addressthe negative impacts of failure in structuredetermination and deviations from theoptimal target selection strategy.

The number of required targets would bereduced by a factor of two if the modelingtechniques were improved so that the accu-racy of comparative models based on 20%sequence identity equaled the current accu-racy at 30% sequence identity12. To achievethis aim, improvements in all aspects ofcomparative modeling are required,including fold assignment, sequence-struc-ture alignment, and modeling of inser-tions, core segments, and sidechains20.

A substantial reduction in the numberof targets can also be achieved if outliers inlarge families and some small families areinitially ignored. For example, when thecoverage requirement is relaxed from100% to 90% of all sequences in Pfam,only 4,000 targets (2 per family) instead of17,000 targets (8 per family) are required12.

On the downside, it might be expectedthat the efficiency of structural genomicsis decreased significantly by the low suc-cess rate of structure determination —that is, 10–20% for randomly picked pro-tein sequences9. However, the correspond-ing decrease in the coverage of domainsequences by structural genomics is only10% (ref. 12). The reason is that largefamilies provide many alternative targets,most of which are satisfactory becausethey allow modeling of many of theremaining family members. This resultsupports the class-directed approach tostructure determination, which is at thecore of structural genomics1.

The efficiency of structural genomics isalso reduced when the individual researchgroups apply different target selection crite-ria12. They may not all rigorously use the30% sequence identity cutoff and mayimpose additional filters, such as the

genome of origin and the biological signifi-cance of the target. As a consequence, the‘selection’ of targets for the global structuralgenomics effort does not minimize thenumber of targets required for structuralcharacterization of most protein sequences.The target selection efficiency in practice isexpected to correspond to that of selectingtargets randomly, but only if they have<30% sequence identity to an already deter-mined structure. In that case, three times asmany targets would be required as with theoptimal ‘greedy’ algorithm. This result pro-vides a strong incentive for global coordina-tion of target lists. Steps in this directioninclude the web sites of the individualresearch groups mandated by the NationalInstitutes of Health (NIH) in NorthAmerica14, web sites with comprehensivetarget lists (http://presage.berkeley.edu,http://www.structuralgenomics.org), andtools such as PartsList, a web based systemfor dynamically ranking domain foldsbased on more than 180 attributes21.

The final step in estimating the scope ofstructural genomics is to cautiouslyextrapolate from the number of targetsneeded for the current Pfam domain fam-ilies to the number of targets needed forall domain families12. It is necessary toassume that the modeling density in Pfamapplies to all domain families, includingthe currently unknown ones. Since onlyabout a quarter of all residues in the cod-ing regions of several representativegenomes match one of the 2,000 Pfamfamilies, the total number of proteindomains is estimated to be ∼ 8,000, whichis consistent with some other estimates22.Because 12,000 targets are required tocover 90% of sequences in the currentPfam database when using target selec-tion without global coordination, thescope of a comprehensive structuralgenomics effort is up to 50,000 targets(including the membrane spanningdomains). In other words, if the struc-tures of 50,000 target domains are deter-mined by experiment, it should bepossible to model ∼ 90% of all sequencesbased on at least 30% sequence identity.

Fig. 1 Selecting protein domain targets for the global structural genomics effort. Once thedomains appropriate for structure determination in all known proteins are defined, the onlyremaining step in target selection is to cluster these domains into the smallest possible number ofgroups using appropriate criteria. The targets for structural genomics are any one of the domainsin groups without any structurally defined members. The criteria for the clustering of domainsdetermine the number of targets; they depend on the modeling ability and the availableresources. The groups of domains might correspond to the different fold types, superfamilies ofdomains originating from the common ancestors, families of domains that share at least 30%sequence identity, or families of domains with the ‘same’ functions. The final result of structuralgenomics will be a map of the protein sequence-structure space (the light blue rectangle) thatrelates all similar domains (red and green dots) and provides structures for at least one member(green dots) of each domain group (dark blue circles), as well as 3D models for all of the remainingsequences (red dots).

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In comparison, the fraction of domainsthat can currently be modeled based on atleast 30% sequence identity to a knownstructure is only ∼ 10% (ref. 12). Thus, thecurrently known structures do not signif-icantly reduce the scope of structuralgenomics if at least 30% sequence identityis required for modeling.

At present, structural biologists are pro-ducing ∼ 500 protein structures qualifyingas structural genomics targets per year12.In a few years, the global structuralgenomics effort is likely to exceed thisnumber several fold. Thus, in my view it isconceivable that structures of 70% of allprotein domains within boundaries ofstructural genomics will be structurallycharacterized in less than five years. As aresult, application of the powerful princi-ples of structural biology to most biologi-cal problems is imminent.

AcknowledgmentsA.S. is grateful to S.K. Burley, J. Kuriyan, T. Gaasterland and other members of the New YorkStructural Genomics Research Consortium for manydiscussions about structural genomics, and to H.M. Moss and N. Eswar for comments on themanuscript. A.S. is an Irma T. Hirschl Trust CareerScientist. Support from The Merck Genome ResearchInstitute, Mathers Foundation, and NIH is alsoacknowledged.

Andrej Sali is in the Laboratories ofMolecular Biophysics, Pels Family Centerfor Biochemistry and Structural Biology,The Rockefeller University, 1230 YorkAvenue, New York, New York 10021, USA.email: sali@rockefeller.edu

1. Terwilliger, T.C. et al. Protein Sci. 7, 1851–1856 (1998).2. Sali, A. Nature Struct. Biol. 5, 1029–1032 (1998).3. Zarembinski, T.I. et al. Proc. Natl. Acad. Sci. USA 95,

15189–15193 (1998).4. Montelione, G.T. & Anderson, S. Nature Struct. Biol.

6, 11–12 (1999).5. Teichmann, S.A., Chothia, C. & Gerstein, M. Curr.

Opin. Struct. Biol. 9, 390–399 (1999).6. Burley, S.K. et al. Nature Genet. 23, 151–157 (1999).7. Cort, J.R., Koonin, E.V., Bash, P.A. & Kennedy, M.A.

Nucleic Acids Res. 27, 4018–4027 (1999).8. Brenner, S.E. & Levitt, M. Protein Sci. 9, 197–200

(2000).9. Christendat, D. et al. Nature Struct. Biol. 7, 903–909

(2000).10. Heinemann, U. Nature Struct. Biol. 7, 940–942 (2000).11. Yokoyama, S. Nature Struct. Biol. 7, 943–945 (2000).12. Vitkup, D., Malamud, E., Moult, J. & Sander,

C. Nature Struct. Biol. 8, 559–566 (2001).13. Bateman, A. et al. Nucleic Acids Res. 27, 263–266

(2000).14. Nature Struct. Biol. 7, 927–994 (2000).15. Brenner, S.E. Nature Struct. Biol. 7, 967–969 (2000).16. Mallick, P., Goodwill, K.E., Fitz-Gibbons, S., Miller,

J.H. & Eisenberg, D. Proc. Natl. Acad. Sci. USA 97,2450–2455 (2000).

17. Portugalyi, E. & Linial, M. Proc. Natl. Acad. Sci. USA97, 5161–5116 (2000).

18. Balasubramanian, S., Schneider, T., Gerstein, M. &Regan, L. Nucleic Acids Res. 28, 3075–3082 (2000).

19. Sánchez, R. et al. Nature Struct. Biol. 7, 986–990(2000).

20. Martí-Renom, M.A. et al. Rev. Biophys. Biomolec.Struct. 29, 291–325 (2000).

21. Qian, J. et al. Nucleic Acids. Res. 29, 1750–1764(2001).

22. Wolf, Y.I., Grishin, N.V. & Koonin, E.V. J. Mol. Biol.299, 897–905 (2000).

β-catenin and its multiple partners:promiscuity explainedLawrence Shapiro

β-catenin functions both in cadherin-mediated cell adhesion and the Wnt signaling pathway. In these roles, β-catenin interacts with a multitude of protein partners including cadherins, α-catenin, axin, APC, and Tcf familytranscription factors. Recent crystal structures show how β-catenin can achieve this remarkably diversefunctionality.

Some proteins seem to have too many dif-ferent jobs — so much so that it can bedifficult to understand how they do it all.β-catenin is a champion jack-of-all-trades: it functions both in cadherin-based cell adhesion and also as a centralcomponent of the developmentallyimportant Wnt signaling pathway, dys-function of which is a common cause ofhuman cancers. In these different roles, β-catenin has a multitude of specific bind-ing partners. As structural biologists, wecan be skeptical of such diverse functionsuntil we see at atomic level details of theirspecificity and possible mechanisms.Recent papers by Graham et al.1 andHuber and Weis2 in Cell clearly show howβ-catenin recognizes two of its mostimportant targets: the transcription factorTcf, which is the ultimate activator ofdownstream Wnt signaling, and the cyto-plasmic region of E-cadherin. Thesestructures show that β-catenin recognizesat least a subset of its binding partners as

elongated peptides, through a set of‘quasi-independent’ subsites that may beregulated independently of one another.This suggests how β-catenin can play sucha central role in its different functions.

The many roles of β-cateninIn its cell adhesion role, β-catenin associ-ates with the cytoplasmic domain of cad-herin cell–cell adhesion proteins, such asE-cadherin (Fig. 1). Through a distinctbinding site, β-catenin also binds to theunrelated protein α-catenin, which inturn binds to filamentous actin. Thus, β-catenin provides the linkage betweentransmembrane cadherin adhesion pro-teins and the cytoskeleton. This linkage tothe cytoskeleton is crucial to the cell adhe-sion function of cadherins because cad-herins cannot mediate adhesion in cellsthat lack either α- or β-catenin.

But that’s just part of the story. In its sig-naling role, β-catenin binds specifically toadenomatous polypsosis coli (APC), the

product of the infamous colon cancergene whose main function is to degrade β-catenin. Wnt signaling is an importantdevelopmental pathway, regulated by Wntfamily growth factors (wingless is theDrosophila Wnt). In the absence of a Wntsignal, phosphorylation of β-catenin byglycogen synthase kinase-3β (GSK-3β)targets β-catenin for degradation. Thisphosphorylation event occurs in a multi-protein complex that contains APC andaxin, both of which bind β-catenin. In thepresence of a Wnt signal, GSK-3β phos-phorylation is inhibited, and the cytoplas-mic pool of β-catenin is stabilized. Thisfree β-catenin moves to the nucleus whereit binds to Tcf transcription factors. Theβ-catenin–Tcf complex activates tran-scription of downstream genes that effectthe Wnt signaling program.

So, β-catenin binds to (at least) the fol-lowing partners: cadherins, α-catenin,axin, APC, and the Tcf transcription fac-tors, which share little apparent sequence

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