Progress and Prospects

18 Pages·2005·0.16 MB·English

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March24,2001 12:13 AnnualReviews AR128-08
Annu.Rev.Biophys.Biomol.Struct.2001.30:173–89
Copyright(cid:176)c 2001byAnnualReviews.Allrightsreserved
A I P S P :
B NITIO ROTEIN TRUCTURE REDICTION
Progress and Prospects
g Richard Bonneau and David Baker
or
ws. DepartmentofBiochemistry,Box357350,UniversityofWashington,Seattle,
e
vi Washington,98195;e-mail:[email protected],[email protected]
e
ualrnly.
no
s.anuse KeyWords structuralgenomics,proteinfolding,scoringfunctions,foldprediction
urnalonal n Abstract Considerablerecentprogresshasbeenmadeinthefieldofabinitiopro-
os
arjper tein structure prediction, as witnessed by the third Critical Assessment of Structure
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8E
3-1LL
7O
30:1L C CONTENTS
1.A
200DIC INTRODUCTION :::::::::::::::::::::::::::::::::::::::::::::::: 174
Struct. V ME RELDaUttiCceEDMoCdOelMsP:L::E:X:I:T:Y:M::O:D::E:L:S::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: 117756
ol. UNI DiscreteStateOff-LatticeModels ::::::::::::::::::::::::::::::::::: 176
omL NarrowingtheSearchwithLocalStructurePrediction::::::::::::::::::::: 176
s. BiNEL SCORINGFUNCTIONSFORREDUCEDCOMPLEXITYMODELS :::::::::: 177
phyOR Solvation-BasedScores :::::::::::::::::::::::::::::::::::::::::: 178
Bioy C PairInteractions:::::::::::::::::::::::::::::::::::::::::::::::: 178
v. b SequenceIndependentTerms/SecondaryStructureArrangement ::::::::::::: 179
e
nnu. R THhigehU-RseesoofluMtiuolntipSlteruScetuqrueenPcreedAicltiigonnm—ePntrsosinpeTcetsrtifaorryRPerfiendeicmtieonnt:::::::::::::::::::::::::: 118801
A THEROSETTAMETHOD ::::::::::::::::::::::::::::::::::::::::: 181
RELATIONSHIPTOPROTEINFOLDING?::::::::::::::::::::::::::::: 183
APPLICATIONSOFABINITIOSTRUCTUREPREDICTION ::::::::::::::: 183
GenomeAnnotation ::::::::::::::::::::::::::::::::::::::::::::: 183
HomologyModeling :::::::::::::::::::::::::::::::::::::::::::: 184
StructuresfromLimitedConstraintSets::::::::::::::::::::::::::::::: 184
CONCLUSION:::::::::::::::::::::::::::::::::::::::::::::::::: 185
1056-8700/01/0610-0173$14.00 173
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INTRODUCTION
Thegoalofabinitiostructurepredictionissimple:Givenaprotein’saminoacid
sequence predict the structure of its native state. It is generally assumed that a
proteinsequencefoldstoanativeconformationorensembleofconformationsthat
isatorneartheglobalfree-energyminimum.Thus,theproblemoffindingnative-
likeconformationsforagivensequencecanbedecomposedintotwosubproblems:
(a) developing an accurate potential and (b) developing an efficient protocol for
g
or searchingtheresultantenergylandscape.
s.
w Todate,themostsuccessfulmethodsforstructurepredictionhavebeenhomo-
e
evi logy-based comparative modeling and fold recognition (58). When homologous
ualrnly. orweaklyhomologoussequencesofknownstructurearenotavailable,themost
no
s.anuse successful structure prediction methods have been those that predict secondary
urnalonal stitmruect(u1r2e,a4n0d,4lo3c,4al6,s7tr4u)c.tuTrheismroetvifise;wthfeosceusmeesthoondcsuhrarevnetbmeeenthaovdasilfaobrlepfroedriscotimnge
os
arjper tertiarystructureintheabsenceofhomologytoaknownstructureanddiscusses
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8E fromthePDB.Inordertotesttheperformanceofanyoneoftheseapproaches,one
3-1LL must carefully remove sequences that are homologous to proteins in the test set
7O
30:1L C fromalldatabasesusedbythemethodinquestion.Anyerrorsoroversightsmade
1.A atthisstagecouldleadtooverestimatesofsuccess.Forthesereasons,theCritical
0C
20DI Assessment of Structure Prediction (CASP), a biannual, community-wide blind
uct. ME testofpredictionmethods,wasconceivedandimplemented(4,6,27).Threesuch
omol. StrL UNIV tTehstrsouhgavheouotccthuirsrerdevainedwt,hoeufroausrstehswsmaseunntdoefrtthaekepnertfhoisrmsuamncmeeorf(iv.aer.,io2u0s00m)e(t2h0o,d3s9i)s.
BiEL influencedbytheresultsofthesecommunity-widetests,especiallyCASP3(58).
ys. RN CASP1andCASP2showedthatinspiteofsomesuccessreportedintheliterature,
ophCO littlesuccesswasseenforproteinsofthesizeandtypebeingsolvedbystructural
Biy
v. b biologists (50). The consensus after these first two experiments was that the ab
Re initiostructurepredictionproblemwouldmostprobablybesolvedtoolatetobe
nu. appliedtoanyrealbiologicalproblems(20,50).CASP3showedsomereversalof
n
A thisconsensus.Severalmethodsmadegoodpredictionsintheabinitiocategory,
and some ab initio methods outperformed fold recognition methods for certain
proteinsinthefoldrecognitioncategory(61,63,64).
Inspiteofrecentprogress,manyissuesmuststillberesolvedifaconsistently
reliableabinitiopredictionschemeistobedeveloped.Noonemethodperforms
consistently across all classes of proteins (most methods perform worse on all-
betaproteins),andallmethodsexaminedseemtofailtotallyonsequenceslonger
than 150 residues in length (the longest contiguous blind predictions to date are
»100residuesinlength).Inaddition,thesuccessfulpredictionmethods,asjudged
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ABINITIOPREDICTION 175
byperformanceatCASP3andbyperformancereportedintheliterature,showa
large diversity in their formulation. Our goal here is to review the common fea-
turesoftheserecentmethodsinordertohighlightcurrentchallengesandfuture
possibilitiesforthefieldofabinitiostructureprediction.
Themostnaturalstartingpointforsimulatingproteinfoldingisstandardmolec-
ular dynamics (MD) simulation (numerically integrating Newton’s equations of
motionforthepolypeptidechain)usingaphysicallyreasonablepotentialfunction.
Thisapproachhasalonghistoryandisstillpopular,asillustratedperhapsmost
g
or dramatically by IBM’s Blue Gene project, which will apparently be devoted, at
s.
w leastinpart,tosuchanendeavor.Thereareseveralratherobviousproblemsthat
e
vi havelimitedthesuccessofsuchapproachesthusfar.First,MDiscomputationally
e
ualrnly. expensive—withexplicitrepresentationofsufficientwatermoleculestominimally
no
urnals.anonal use staaonkldveasint»ecrt4eh0aes0efhsoolidnuirnasvgoacinlhaaabilcneu,craroenmnatpnsuoitnseegrclpeoonpwdreoMrcehDsasvsoeirm.cAounldasvtiidaonenrcaeobsflyiane1sx0itm0en-urdleaestdiidosuniemsputrrlaoattteiegoinyn
os
arjper times; for example, Kollman and coworkers carried out a microsecond simula-
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3-1LL lackofconsensusastothebestcomputationallytractableyetphysicallyrealistic
7O
30:1L C model for water (a number of quite different models for water, both polarizable
1.A andnonpolarizable,havebeenusedincurrentsimulationmethods),andsomeun-
0C
20DI certainty exists as to the values of the parameters used in molecular mechanics
uct. ME potentials(partialatomiccharges,Lennard-Joneswelldepthsandradii).Accurate
omol. StrL UNIV rgerpereeosefnptoatliaornizoabfielilteyctorofswtaattiecrs,itshaelslaorgaecodnifsfiedreernacbeleinchthaellednigeleecgtirviecnptrhoepherigtihesdoe-f
BiEL thesolventandtheprotein,andtheuncertaintiesinthemagnitudeandlocationof
s. N atomicpartialcharges.Becausethefreeenergyofaproteinrepresentsadelicate
yR
ophCO balanceoflargeandopposingcontributions,theseproblemssignificantlyreduce
v. Biby the likelihood that the native state will be found at the global free-energy mini-
Re mumusingcurrentpotentials(2).ThebestcurrentuseofMDmethodsmaybein
u. refininganddiscriminatingamongmodelsproducedbylower-resolutionmethods
n
n
A (28a,49).
REDUCEDCOMPLEXITYMODELS
To overcome the sampling problems mentioned above, most methods for fold
predictiontodatehaveinvolvedsomesignificantcomplexityreduction.Methods
for reducing protein structure to discrete low-complexity models can be divided
intotwomajorclasses:latticeandoff-latticemodels.
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LatticeModels
Latticemodelshavealonghistoryinthemodelingofpolymersduetotheiranalyt-
icalandcomputationalsimplicity(22).Theevaluationofenergiesonalatticecan
beachievedquiteefficiently(integermathcanbemadequitefast),andmethods
involvingexhaustivesearchesoftheavailableconformationalspacebecomefea-
sible (30,35,85). However, lattice methods have a somewhat restricted ability
to represent subtle geometric considerations (strand twist, secondary structure
g propensities, and packings) and can reproduce the backbone with accuracies no
or
s. greater than approximately half the lattice spacing (73). The most common sys-
w
vie tematicerrorobservedforavarietyoflatticemodelsistheirinabilitytoreproduce
e
ualrnly. helices, and most lattice models exhibit various degrees of secondary structure
no bias(70).Giventheimportanceofregularsecondarystructureinproteins,thisis
s.anuse clearlyaproblem.Recentsuccessfultests,however,haveshownthatthecompu-
urnalonal tationaladvantagesoflatticemodelsmayoutweightheproblemsassociatedwith
os
arjper theirsystematicbiases(30,47,48,73).
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3-18LLE atoms(82,87).Discretestatemodelsoftheproteinbackboneusuallyfixallside
7O chaindegreesoffreedomandlimitthebackbonetospecificPhi/Psipairs:Models
30:1L C containingfrom4to32Phi/Psistatesrepresentingvariousstrand,helix,andloop
1.A
0C conformationshavebeendescribedintheliterature(70).Properlyoptimizedsix-
20DI
Struct. V ME satsatsetrmanoddse,lhse(lii.cee.,sm,aonddelcsatnhoantiaccacloluonotpfso)rclaoncarlefperaotduurecseonbasteivrevecdoinntapcrtost,epinressseurcvhe
ol. UNI secondary structure, and fit the overall coordinates of the native state as well as
omL 18-state lattice models that do not account for such protein-specific information
BiEL (70).
s. N
yR
phO
oC
Biy NarrowingtheSearchwithLocalStructurePrediction
v. b
e
R Local structures excised from proteins can fold independent of the full protein,
nu. demonstratingthatstronglocalstructuralbiasescanexistforshortsequenceseg-
n
A ments(8,15,54,60).Severalexamplesofexcisedfragmentshavinglittleobserv-
able structure also exist, indicating that the strength and multiplicity of these
localbiasesarehighlysequencedependent.Somesequencesareobservedtofoldto
differentconformationsdependingontheirglobalsequencecontext,againdemon-
strating the possible multiplicity of local structure biases (17,42). Bystroff et al
developed a method that recognizes sequence motifs (ISITES) with strong ten-
dencies to adopt a single local conformation that was used to make some good
local structure predictions in CASP2 (12–14, 29). Despite the ambiguities in
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ABINITIOPREDICTION 177
local sequence-structure relationships, secondary structure prediction methods
havebeensteadilyimproving(63,64).
Theabovementionedexperimentsandobservationssuggestthatanymethodat-
temptingtouselocalsequence-structurebiasestoguidecomplexityreductionswill
havetobeadaptivetothestrengthandtothemultiplicityofdifferentsequence-
structure patterns. The majority of methods proving successful at CASP3 used
secondarystructurepredictionsinonewayoranother.Inonecase,predictedsec-
ondarystructureelementswerefittotheresultsofinitiallattice-basedexhaustive
g
or enumeration,thuserasinganypossiblesecondarystructurebiasintheinitiallat-
s.
w ticemodelpriortoall-atomrefinement(1).TheRosettamethodusedsecondary
e
vi structuretobiastheselectionoffragmentsofknownstructurefromthePDB.Yet
e
ualrnly. another paradigm is, given a secondary structure string, to reduce the problem
no
s.anuse ofpredictingthetertiaryfoldtotheproblemofhowtoassemblerigidsecondary
urnalonal sintrduecptuenredeenletmoefnstesc(o2n4d).arAydsdtirtuiocntuarlempertehdoidctsiothnataldgeotreirtmhminse(lboycaclasltcruulcattuirnegbtihaeseses
os
arjper biasesduringthefoldingsimulation)havealsobeendescribed(86).
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3-1LL modelswithsecondarystructurepredictedmoreaccuratelythanispossiblewith
7O
30:1L C traditionalsecondarystructuremethods.
1.A
0C
20DI SCORINGFUNCTIONSFORREDUCED
uct. ME COMPLEXITYMODELS
StrV
ol. UNI
omL Onceamodelforrepresentingtheproteinischosenthatsufficientlyreducesthe
BiEL complexityoftheconformationalsearch,ascoringorenergyfunctionthatworks
ys. RN inthechosenlow-complexityspacemustbedeveloped.Theenergyfunctionmust
ophCO adequatelyrepresenttheforcesresponsibleforproteinstructure:solvation,strand
Biy
v. b hydrogenbonding,etc.Giventhatmostlow-complexitymodelsdonotexplicitly
e
R represent all atoms and can reproduce even the native state backbone with only
nu. limited accuracy, any energy function designed to work in the low-complexity
n
A regime must represent these forces in a manner robust to such systematic error
(thesystematiclimitationsofthemodel).Last,thesefunctionsmustbecomputa-
tionallyefficient,forduringtheinitialstagesofanyconformationalsearch,huge
numbers of energy evaluations are necessary. Because of the shortcomings of
molecularmechanics-basedpotentials,andtheconsiderationsabove,manymeth-
odsdevelopedinthelasttenyearsutilizescoringfunctionsderivedfromtheprotein
databasethatinessencefavorarrangementsofresiduesfrequentlyfoundinknown
proteinstructuresanddisfavorrarelyseenarrangements.
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Solvation-BasedScores
Ithasbeenlongthoughtthatthehydrophobiceffectistheprincipaldrivingforce
behindproteinfolding(3).Manydiversemethodsforjudgingthefitnessofcon-
formationsbasedonsolvationorhydrophobicpackingexist,andthedebateover
the proper functional form for representing solvation effects represents an open
question of considerable importance and interest (69). A common approach is
to classify sites in proteins according to their degree of solvent exposure (either
g throughthesurfaceareaorthenumberofnearbyresidues)(11)andtodetermine
or
s. thefrequenciesofoccurrenceoftheaminoacidsineachtypeofsite.Theenergyor
w
vie scoreofanaminoacidatasiteisthentakentobethelogarithmoftheaminoacid’s
e
ualrnly. frequencyofoccurrenceatthattypeofsite.Thistypeofresidue-environmentterm
no favorsplacementofhydrophobicaminoacidsatburiedpositionsandofhydrophilic
s.anuse aminoacidsatexposedpositions.
urnalonal Anadditionalcommonlyusedclassoffunctionalformsconsistsofglobalmea-
os
arjper suresofhydrophobicarrangement.Onesimpleglobalquantityisaresidue’sdis-
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8E
3-1LL is that they assume that proteins are ideally spherical in shape when in actual-
30:17L CO iptryoancahtivuesepsraonteeilnlispseoxihdiablitapaprmouxcimhaltairogneorfrtahnegsehaopfesohfapthees.hAydrmopohreobfliecxciobrleetahpa-t
1.A
200DIC doesnotrequireasignificantincreaseincomputationandaidsintheselectionof
uct. ME near-nativeconformationsfromdecoysetscontainingahighnumberofprotein-
StrV likeyetincorrectcompactconformations(9).Theproblemassociatedwiththese
ol. UNI functionsisthattheywillinevitablyexcludeasmallpercentageofproteinstruc-
omL turesthatdeviatefromtheirassumptionsconcerningshapeandthusfailwhena
s. BiNEL protein is divided into small subdomains or contains large invaginations (1HQI
yR
phO is a toroid). In spite of this potential downfall, they have demonstrated their
oC
Biy usefulnessinseveralmethodsowingtotheirabilitytorecognizethemajorityof
v. b smallhydrophobiccores,theirsimplicity,andthespeedwithwhichtheycanbe
e
R
u. computed.
n
n
A
PairInteractions
Manylow-resolutionpotential/scoringfunctionsutilizeanempiricallyderivedpair
potentialinplaceoforinadditiontotheresidue-environmenttermdescribedabove.
Themostcommonofthesepotentialsarefunctionsofthepositionofasinglecenter
perresidue(C ,C ,orcentroid/unitedatomcenter)andarethusquitecomputa-
fi fl
tionallyefficient;all-atomfunctionshavealsobeenused(77).Manyvariationsof
pair terms have been developed, with the two main branches of methods being
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ABINITIOPREDICTION 179
distancedependentandcontactbased(57,84).Liketheresidue-environmentterm
mentionedabove,thesescoringfunctionsaresometimesjustifiedbypositingthat
thearrangementsofresiduesinproteinsfollowaBoltzmandistribution:E(x) D
kTlnP(x),wherexisafeaturesuchastheoccurrenceoftworesiduesseparated
byadistancelessthanr.Alternatively,thesescoringfunctionsmaybeseenpurely
asprobabilitydistributionfunctions(23,83).Intheformercase,theoptimization
may be viewed as a search for the lowest energy configurations; in the latter, a
search for the highest probability configurations. For most applications, there is
g
or little practical difference between the two viewpoints. The issue becomes more
s.
w substantive,however,whensuchdatabase-derivedscoringfunctionsarecombined
e
vi withphysics-basedpotentials,aswilllikelybecomeincreasinglyusefuloverthe
e
ualrnly. nextseveralyears.
no
s.anuse Severalproblemsareassociatedwithstatisticallyderivedpairpotentials.The
urnalonal ainstseurmacptitoionnstihsantoftregeeneenrearlglyievsacliadnabcerorsesparellseinntteerdacbtyionsusmprmesinegntoivneprrcooteminpso,naenndt
os
arjper thus,thebasicfunctionalformmaynotbeadequatetorepresentthefreeenergyof
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3-1LL titioning,specificinteractionssuchaselectrostaticattractionbetweenoppositely
7O
30:1L C charged residues dominate the pair scoring/energy functions, and hydrophobic
1.A interactions make relatively modest contributions. The pair term, in this case, is
0C
20DI perhapsbestviewedasthesecondterminaseriesexpansionfortheresidue-residue
uct. ME distributionsinthedatabaseinwhichtheresidue-environmentdistributionsarethe
omol. StrL UNIV firsStotemrme.oftheearliestcomprehensivetestsofthediscriminatorypowerofthese
BiEL pairpotentialsweredoneinthecontextofthreadingself-recognition(62).Later
s. N workdemonstratedthattheself-recognitionproblemwasnotasufficientlychal-
yR
ophCO lengingtestofscoringfunctionsandfocusedontheperformanceofmultiplepair-
v. Biby wiseenergyfunctionsonlarger,morediversesetsofconformations(24,38,41,68,
Re 69). The performance of the various energy functions at recognizing native-like
u. structuresinlargeensemblesofincorrectdecoysishighlydependentonthemeth-
n
n
A odsusedtocreatethedecoysets,highlightingthefactthatanenergyfunctionthat
works well in the context of one method will not necessarily work well given a
decoysetcreatedusinganorthogonalmethod.
SequenceIndependentTerms/SecondaryStructure
Arrangement
Manyfeaturesofproteins,suchastheassociationofbetastrandsintosheets,canbe
described by sequence independent scoring functions. Several early approaches
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180 BONNEAU ¥ BAKER
to folding all-beta proteins were protocols dominated by initial low-resolution
combinatorialsearchesofpossiblestrandarrangementsandwereconcernedonly
withtheprobabilityofdifferentstrandarrangements(16,18,19,72).Theseearly
methodsnarrowedtheconformationalspacebyconsideringsequencespecificef-
fects only in the context of highly probable strand arrangements. Several of the
relatively successful methods at CASP3 incorporated secondary structure pack-
ing terms (51,81). Ortiz et al used an explicit hydrogen bond term in combi-
nation with a flfifl and a flflfi chirality term to ensure protein-like secondary
g
or structureformation.Itcannotbeexpectedthatlow-complexitylatticemodelspro-
s.
w ducethecorrectchiralityorsubtlehigher-ordereffectssuchasstrandtwist,and
e
vi these rules sensibly correct for these expected shortcomings (65). The Rosetta
e
ualrnly. method used three terms that monitored strand-strand pairing, sheet forma-
no
s.anuse tion, and helix-strand interactions to ensure protein-like secondary structure
urnalonal arrangements.
os
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3-1LL onerelativelysuccessfulmethodatCASP3(67).Theaccuracyofthesecovariance
7O
30:1L C methodsisnotgreat(63,64);variousmethods,includingthefittingofsecondary
1.A structuretoinitialconstraintsinordertoobtainadditionalconstraints,wereused
0C
20DI to increase the robustness of ab initio protocols based on such information to
uct. ME expectederrorlevels(66).PreviousmethodshaveusedMSAstopredicttheburial
omol. StrL UNIV ooffsveaqriuaetniocne(p5o,s7it)i.onsinthequerysequencebasedonhydrophobicityandpatterns
BiEL AnotherwaytouseMSAsisintheselectionoflow-resolutiondecoysbyrequir-
s. N ingthatalldecoysbeconsistentwithallsequencesinthefamily.Inoneprocedure,
yR
ophCO the aligned sequences are mapped onto decoys one at a time and the energy of
v. Biby each decoy averaged over all aligned sequences is computed (2). In another ap-
Re proach, simulations of multiple aligned homologs are coupled and carried out
u. simultaneously(45).Inthesetwomethods,themodelswiththelowestscoreare
n
n
A selected as the best models. In a recent method for applying MSA information
to fold prediction, an independent simulation is carried out for each aligned se-
quence.Onlyaftermultiplesimulationsforeachalignedsequencearecompleted
is the multiple sequence alignment information utilized by simultaneously clus-
tering all of the structures generated from the different aligned sequences. The
largest and most diverse cluster often contains the best model (9). In all above
describedcases,significantimprovementswereseenintheabilityofthepredic-
tion methods to select good models using highly simplified representations of
proteins.
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ABINITIOPREDICTION 181
High-ResolutionStructurePrediction—Prospects
forRefinement
Reducedcomplexityapproaches,asdescribedabove,cannotbeexpectedtoconsis-
tentlygeneratepredictionswithresolutionsofbetterthan3–7 A˚.Low-resolution
methodsareperhapsbestviewedasnarrowingthepossibleconformationsfroman
exponentiallylargenumbertoanumbersmallenoughthatmorecomputationally
expensivemethodscanbeapplied.High-resolutionpotentialsmustbeimproved
org in order for significant progress to be made in the field of ab initio protein fold
ws. prediction.
e
vi Someprogresshasbeenmadeinmodelingtwoofthemoredifficulttermsin
e
ualrnly. the potential—solvation and electrostatics—in the context of full atom models.
urnals.annonal use o Bwofeatceteanrudsaeerestchcreiobrterredalnaustsfeeidnr-gwfrsietuehrfesanocelevrgeainretes-aao–cfbcaesssmesiadblllmemesotuhlreofcdaucsl.eeWsarfeersaos,mosnonl&ovnatpEiooislnaernebnsoeerrlgvgeiefnostusanrtdoe
os
arjper thattheadditionofasolvent-accessiblesurfacearea–basedterm,theparameters
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3-1LL generalizedBornmodelremediesthisproblembytreatingthenonpolarinteractions
7O
1.30:1AL C wtoitdheaalsuwriftahcethaeredae–sboalsveadtiotenrmofwchhialerguessinagndagchenaregrealipzaaitrisoninofthtehepBrootrenineqinutaetriioonr
0C
20DI (37,52,88).Thismethodhasproventobeusefulformodelingloopsasdescribed
uct. ME aboveandisfastenoughtobeincludedinalmostanydiscriminationorrefinement
StrV procedure (71). An alternative implicit solvation model that is able to partially
ol. UNI discriminatebetweennative-likeandincorrectdecoyswasdevelopedbyLazaridis
BiomELL &Karplus(48a).
s. N Fromtheabovediscussionitisapparentthatmostcurrentenergy/scoringfunc-
yR
phO tionsarebasedeitheronsmall-moleculeparameterizedfunctionswithformssug-
oC
Biy gested by physical chemistry or on protein database statistics. We believe that
ev. b progresswillcomefromacombinationofthesetwoapproaches.
R
u.
n
n
A
THEROSETTAMETHOD
Howcanabinitiostructurepredictionmethodsproducereasonablemodelsgiven
theinadequaciesofcurrentpotentialfunctions?Onepossiblesolutionistolimit
the conformational space searched to that compatible with the local sequence-
structurepropensitiesoftheproteinsequence.Suchaprocedureisconsistentwith
a view of the folding process in which local structure propensities influence the
conformationssampledbyshortsequencesegments,andfoldinginvolvesasearch
P1:GDL
March24,2001 12:13 AnnualReviews AR128-08
182 BONNEAU ¥ BAKER
through these local conformations for a stable tertiary structure simultaneously
consistent with these local biases and with nonlocal constraints (compactness,
electrostatics,hydrophobicburial,etc.).Localbiasesareinfluencedbyrelatively
subtleinteractions(side-chain/main-chainhydrogenbonding,sidechainconfigu-
rational entropy losses, etc.) as witnessed by the debate over the origins of sec-
ondary structure propensities; current physical chemistry–based models do not
capture all of the subtleties. To circumvent this problem, the Rosetta procedure
makestheassumptionthatthedistributionofconfigurationssampledbyapeptide
g
or segment are reasonably well-approximated by the distribution of configurations
s.
w observedintheproteindatabase.TertiarystructuresaregeneratedusingaMonte
e
vi Carlo search of the possible combinations of likely local structures, minimizing
e
ualrnly. a scoring function that accounts for nonlocal interactions such as compactness,
no
s.anuse hydrophobic burial, specific pair interactions (disulfides and electrostatics), and
urnalonal smtraannydspuabitrliyndgif(fseereenFtivgeurrseio1n)s. oWfiethsselinsttisalolfy2si5mfirlaagrmloecnatlsstpreurctsuerqeusemncaeybpeosrietpioren-,
os
arjper sented;thus,thenumberofstatesperpositioniseffectivelymuchlowerthan25.
9. Downloaded from GE on 03/24/05. For Ofsrtarpagtnimmdesin,zatatnsioedtnsootphfreonrdopunrclooetcseaislntir-nulitcketeruarfceetsaiotwunrsiethwsibtthuharitineadcroehn,yfbodyrrmocpoahntiosobtnriuacclrtseiposanidc,ueceodsne,fispinsaetierdendbtywbteihtthea
8E
3-1LL
7O
30:1L C
1.A
0C
20DI
uct. ME
StrV
ol. UNI
omL
BiEL
s. N
yR
phO
oC
Biy
v. b
e
R
u.
n
n
A
Figure1 VenndiagramillustratingtheconceptualbasisforRosetta.Near-nativestructuresare
labeledN.