1. Comparative studies of RNA: inferring higher-order
structure from patterns of sequence variation
Robin R. Gutell
University of Colorado, Boulder, USA
RNA structural chemistry and evolutionary biology, long considered
disparate fields of study, are the topics of this review. Evolution transcends
all of the sciences, adding a dimension that enriches every science
it touches, and in its enrichment of those fields contributes back to
evolutionary thought. RNA, on the other hand, is a molecule with
an inordinate number of possible conformations; knowing which to
evaluate experimentally is problematic. Analysis of RNA sequences from
an evolutionary perspective reveals patterns of variation and sequence
constraints, and suggests how an RNA sequence is folded up into its
higher-order structure.
Current Opinion in Structural Biology 1993, 3:313-322
Introduction
“How far back the historian wishes to place the origins
and antecedentsof the GlassBead Game is, ultimately,
a matterof his personal choice. For like everygreat idea
it has no real beginning; rather, it has always been, at
leastthe ideaof it.” HermannHesse- MagisterLudi (The
GlassBeadGame)
Imagine you’re a graduate student, and your thesis
project is to fold up a large RNA molecule (let us say
hypothetically, 16sand 23s ribosomal RNA[rRNA]) into
its correct secondary and tertiary structure. And if you
have some extra time, deduce something about the
functionally important regions of these RNAmolecules.
Unfortunately, your own laboratory experimentation is
producing little useful information. You ask: Can these
intrinsically complex molecules be folded up? How will
I be able to saysomething profound about its function.
And most important, will I be able to obtain an advanced
degreebefore the year 2000?
And then one day, as if in a Herman Hessenovel, you
arethrilled to learn that the experiments havebeen done
for you (CRWoese,personal communication). The task
now is to find the notebooks containing these results,
question how this information is stored, and wonder
how to interpret it. To keep a long story short, the
notebooks are found and are filled with homologous
nucleic acidsequenceinformation, andwith the sequenc-
ing and computer information revolutions upon us, you
find a large number of RNAsequences,all in computer-
readableform.
The interpretation of this sequenceinformation is at first
glance not readily apparent. Upon reflecting on your
graduate course in evolutionary biology, which at the
time was taken simply for the fun of it, not for the se-
rious molecular biologist, you come up with two simple
but powerful conclusions:
(a) These RNAmolecules evolved to their present form,
starting from a simpler lessconstrained state and being
progressivelydefined and relined in structure and func-
tion. Each structure existing today is thus highly tuned
for its own structure/function and for its complex and
dynamic interactions with the outside world. (It should
be noted that this refinement in RNA structure is far
more subtle than we can fully appreciate with today’s
experimental methodology [1,2].>
(b) Principles of RNAstructure, specific functional con-
straints, and overall cellular mechanics mold the evolu-
tion of these molecules. Although not necessarily im-
plicit in its RNA structure, these principles, constraints
and mechanics are encoded in the actual sequence of
nucleotides.
Decoding this collection of nucleic acid sequencesbe-
gins with the realization that molecules performing sim-
ilar functions (e.g. tRNAs) must have a similar three-
dimensional structure. And knowing that different base-
pair types(e.g.AU and GC) compose homologous struc-
tural elements,e.g.a helix, it follows that similar higher-
order structures can be derived from different primary
structures. (The details and extent of these realizations
and deductions remain to be elucidated and fully appre-
ciated.)
The scientific logic used here to deduce structural and
functional relationships is tierent from conventional
experimental analysis.Instead of designing and execut-
ing experiments to test the hypothesis, here the exper-
Abbreviations
RNase-ribonuclease; rRNA-ribosomal RNA.
@ Current Biology Ltd ISSN 095940X 313

2. 314 Nucleic acids
iments havealreadybeen done, and the sequencesare
the answers!Now we areasking:what are the questions,
what are the hypothesisbeing tested,and how were the
experimentsdone?Thesequestionsunderlie the analysis
of our homologous RNAsequencedatasets.Patternsof
consemtion and variation are analyzed,our initial goal
beiig to infer higher-order structure.
Comparative sequence methods in practice
‘Since all of the sRNAs[soluble RNAs]in the processof
transferingtheir respectiveamino acidsto protein on the
surfaceof ribosomesprobably haveto meetsimilar,if not
identical,spatialconstraints,it would seemreasonableto
conclude that all sRNAswill be capableof adopting es-
sentiallyidentical three-dimensionalstructures.” [31
“Figure 3 shows,however,that it is possible to construct
very similar base-pairedstructures in spite of the limited
similaritiesin sequences.”[4]
Transfer RNA
Shortly after the first few tRNA sequenceswere deter-
mined, comparative sequence analysiswas applied to
thesesequences,resulting in the well known cloverleaf
secondarystructure [3-6]. For this analysis,secondary
structure helicescontaining only canonical (e.g.AU and
GC) and GU pairings in common with all sequencesin
this datasetwere identiIied and scored positively. The
cloverleafwasthe only structure to satisfythis condition
for a molecule76 (or so) nucleotides in length.
5S ribosomal RNA
Nearly a decade later: comparative sequence methods
were again called upon to fold 5s rRNA, a molecule
120 nucleotides in length, into its secondary structure
[7]. Criteria similar to those applied to tRNA were used
here.Whereasexperimental methods resulted in several
different secondarystructure models, thesecomparative
methods,when applied to 10phylogeneticallydiversese-
quences,resultedin a single analogoussecondarystruc-
ture.
16s and 23s ribosomal RNA
In I978 and 1980,the first complete sequencesfor ES-
cbericbiucoli I6S and 23srRNAwere determined IS-lo],
ad shortlythereafter,comparativestructurestudieswere
initiated to fold theseRNAsof length 1542and 2904nu-
cleondes,the largest RNA.sso far for which structures
have been provided using these methods. Criteria sim-
ilar to thoseused on tRNAand 5SrRNAresultedin asin-
gle secondarystructure shared by all membersof their
respectiverRNAdatasets(lb& 111-131;23s, [lP161).
During this time, a helix was considered comparatively
proven if two or more base pairs within the potential
helix each contained at least one covanation. As the
number and diversity of sequencesincreased, the I6S
and 23s rRNAsecondarystructures modelswere refined,
and variations in the different rRNAmodelswere largely
. resolved.The most recentrefinementsof the 16sand 23s
rRNAstructures are basedon a large and diversecollec-
tion of sequences[17.,18*,19].At this time, the question
of comparative proof can accesseach base pair in the
model; the vast majority of all base pairs in the higher-
order structure can now be considered proven!
Although appreciatedby some,this methodologywasnot
widely acceptedduring the early1980sandwasevencast
negativelyby some:“I don’t know how you can suggest
structure by just iooking at sequences”.With the success
and attention given to the rRNAs,attitudeschanged.Many
of us started(mis)pronouncing genus-speciesnameswe
knew nothing about, except for some RNA sequence.
Many of us became(un)certilied microbiologist, proto-
zoologist and the like.
Other RNAs
During the 198Os,comparativesequenceanalysiswasap-
plied to other functionally important RNAmolecules,re-
sulting in secondarystructure models for each of these
comparativesequencedatasets.This list includes:group I
[20,21] and II [22] introns, ribonuclease (RNase)P RNA
[23,24], U-PNAs(Ul, U2, U4, U5 and u6 [25]), 7SSRP
RNA [261,and telomeraseRNA [271.
Are these comparatively derived structures congruent
with formal laboratory experimentation? The quick an-
swer is ‘yes’,although the question cannot be addressed
for all F@IAsnoted above,and the long detailed answer
is beyond the scope of this review. Sufficeit to say,all
of the comparatively inferred secondary structure base
pairs were present in the yeast tRNAphecrystal struc-
ture 128,291,revealing the authenticity of this approach.
Chemical probing experiments of the entire I6S rRNA
(301were largely consistent with the comparatively de-
rived secondarystructure, suggestingsuch a methodol-
ogy could also be applied to large RNAs,although it
doesn’t by itself prove this model. Other experiments,
discussedin part in the following sections, lend addi-
tional support for the higher-order interactionsproposed
with thesecomparativestudies.
Searching for tertiary interactions
Transfer RNAs
With the strong implication that comparative methods
can correctly deduce secondary structure, we can now
askcan such a methodology alsodeduce tertiary interac-
tions?The first attemptwasmadeon tRNA[31], resulting
in, a few correct and a few incorrect tertiary interaction
proposals (when comparedwith the crystalstructure so-
lution 128,291).But with significantly larger and diverse
tRNAdatasets,and relined correlation analysismethods,a
larger proportion of the higher-order structure can now
be correctly inferred [32,33,34*,35].

3. Comparative studies of RNA Gutell 315
16s and 23s ribosomal RNAs
A searchfor tertiaryinteractions in the 16s and 23srRNAs
wasinitiated in the early 1980sand resulted in severalre-
finementsin secondary-structurepairings and a few can-
didatesfor tertiary pairings [36]. The best candidate in-
volved positions 570and 866 (E. cofi numbering) in 16.5
rRNA 1371,forming a pseudoknot structure (see Fig. 1).
As the l6.S and 23S-rRNAdatabasesgrew in number and
diversity, the number and variety of tertiary interactions
increased(see Figs 1 and 2 and next section; for recent
work on 16sand 23siRNA,see [17*,18*,19,38]).Genetic
and biochemical experimental analyseshave addressed
and substantiateda number of these higher-order inter-
actions (see also next section) [39*,40,41=,42,43,44].
Fig. 1. Secondary structure diagrams for E. co/i 165 rRNA. All nu-
cleotides are replaced with small open circles. Higher-order inter-
actions more complex than the secondary structure helices are
denoted with a thick line or a large filled circle. Adapted from
117.1 (for details, see I17*, X3*1).
Group I introns
Covarianceanalysisof the group I intron databasehas
been most impressive,producing a well establishedsec-
ondary and tertiary structure model [21] and forming
the basis for a detailed three-dimensional model upon
which functional experimental analysis can be based.
Severalproposed base-triples [45], pseudoknots, and
non-canonical,pairings [46] have been substantiated us-
ing site-directedmutagenesis.
Ribonuclease P RNA
The RNA component of the RNaseP ribonuclease has
been extensively studied by comparative and experi-
mental methodologies. An evaluation of the phyloge-
netic commonality and diversity in RNaseP RNAslead
to the development of a mini-P RNA,a minimally con-
figured RNAwith normal enzymatic activity [47]. Several
comparatively derived base pairings in the two pseudo-
knot structures were testedand substantiated using site-
directed mutagenesis[48=].
Emerging principles of RNA structure
“If you’ve seenone helix, you’ve seen them all.”
“If I see one more secondary structure model, I’ll
scream!” (Comments overheard at a ribosome confer-
ence)
Fortunately, there is more to higher-order structure than
just secondary-structure helices. Now we can ask what
are these additional RNA structure principles and can
they be inferred from comparative methods? Altema-
tively we can ask what types of structural features are
discernable using comparative methods?Before we ad-
dress these questions, it is important to step back and
evaluatethesemethods, albeit in a most brief fashion.
Initial searchesfor tertiary interactions haveusedanewer
covarianceanalysisalgorithm, Merent from those used
for inferring RNAsecondarystructure. The most notable
differences are: correlating positions are identilied re-
gardlessof the pairing type,in contrast to previous meth-
ods that specifically looked for canonical and GU pairs;
and the current algorithm only looks for correlating pairs,
independent of surrounding structure, which is in sharp
contrast with older methods that only identified pairings
within a potential secondary-structure helix. Analysis of
the 16s and 23s rRNAdatasetsusing this newer algorithm,
and without any knowledge of previous secondary-struc-
ture proposals, identified the vast majority of all previ-
ously suspectedsecondary-strucm-repairings (RRGutell,
unpublished data)‘[34*]. Thus, searchingonly for corre-
lating positions resulted in the two basic and underlying
principles of RNAstructure: namely,AU, GCand GU pair-
ings, and the antiparallel and contiguous arrangementof
thesepairings!
It is of interest to note that the majority of all pairings in
the 16s and 23s rRNAdatasetsidentilied using our most
recent analysisalgorithms (RRGutell, unpublished data)
1341 are canonical or GU, and that the tiajority of these
are found in the conventional secondarystructure helix.
Only a small percentageof all correlating pairs are non-
canonical.Only a smallpercentageof all correlating pairs
lie outside of the secondarystructure, and these usually
form pseudoknot structures or heliceswith a single base
pair. These exceptional 16s and 23s rRNA interactions
are emphasizedin Figs 1 and 2, and discussedbelow.

4. (a)
316 Nucleic acids
JFig. 2. Secondary structure diagrams for E. co/i 235 rRNA: (a) 5’ half; fb) 3’ half. All nucleotides are replaced with small open circles.
Higher-order interactions more complex than the secondary structure helices are denoted with a thick line or a large filled circle. Dashed
lines represent tentative tertiary interactions. Adapted from 117*1(for details, see 1170, 18.1).
Non-canonical pairs
Of the 16 possible pairing types, the six canonical and
GU pairings account for the vast majority of all com-
paratively’derived base pairs. In the majority of phylo-
geneticbase-pairreplacements,one of thesesix typesis
replacedwith anotherof thesesix. Ten of the 16possible
pairings occur infrequently; however, specific classesof
pairing typesand their phylogenetic replacementsarebe-
ginning to emerge,with the most salient ones described
below (others havebeen identified [17*]).
A:C+tC:A
Severalexamples of this replacement type occur in
the rRN& One occurs in 16s rRNAbetween positions
1357 and 1365, at the end of a helix [2,17*]. Within
(eu)bacteria, chloroplasts and mitochondria, this pair
interchangesbetween AG and GA; within Archue and
Eucurya, the interchangeoccurs solely between canon-
ical pairing types.A second good example is found in
23srRNAbetweenpositions 2112and 2169,which along
with severalcanonical pairings forms a parallel structure
(also seebelow) [170,381.Most interestingly,thesesame
two nucleotides are associatedwith the E site in trans-
lation [49], suggestingthat this unusual pairing and/or
the parallel structure are functionally important aswell
asstructurally unique.
A:A+-L:C
Correlations between thesetwo pairing typeshavebeen
found in 16s rRNAbetween positions 722 and 733 [38],
and in 5s rRNAbetweenpositions 76and 100(RRGutell,
unpublished data).A similar setof correlated pairings is
present in the HIV1 Revbinding region of an in vitro
genetically selectedRNA 1501.In these three cases,this
pairing is found in an internal loop, immediatelyadjacent
to a helical structure. The Revprotein binding suggests
that this non-canonicalpairing could be ageneralprotein
recognition motif.
u:u+K:c
Severalexamples of these correlated replacementsare
found in 16s and 23s rRNA The two found in 16s lie
in different internal loops, immediately adjacent to the
end of the helix. These same types of correlated pair-
ings associatedomains 4 and 5 of 23s rRNA [38]. Ther-
modynamic studies have addressedthese pairing types
and found that UU and CC+ pairs can stabilize a du-

5. Comparative studies of RNA Gutell 317
plex [51]. Interestingly, these two pairings Can form an
isomot+ic structure when one of the cytosines are pro-
tonated, whereas the unprotonated form of the CC pair
is destabilizing and not isomorphic in structure.
C:UctA:C
A percentage of the GU helical base pairings are very con-
served, and for a percentage of these, replacement yields
an AC pair. The three best examples are found in 16s
rRNA and all lie at the end of one helical element and in
close proximity to the end of an associated helix [17.1. In
the translational decoding site of 16s rRNA, there is a CA
pairing at positions 1402 and 1500 in the overwhelming
majority of all 16s (and 16S-like) rRNA sequences. A few
phylogenetically distinct mitochondria change this pair to
a UG base pair [18*]. A GU and an AC base pair can form
isomorphic structures when the adenine (of the AC) is
protonated. The thermostability of this A+C pair is close
to that of an AU pair [52**] suggesting that AC pairs, with
the adenine protonated, may well be paired in a specific
structural context.
Tetra loops
The hairpin loop of four bases is a common feature in
the rRNAs, occuring in over 50% and 40% of all 16s and
23s hairpin loops, respectively. Among the 256 different
loop sequences of size four, there is a strong bias in the
rRNAs for three major classes: UUCG, GNRA, and CUUG
[531. Thermodynamic analysis revealed that these loops
are surprisingly very stable [54,55], and structural analy-
sis of these loops reveals an unusually compact structure
[56,57].
Pseudoknots
Pseudoknots are a popular and fashionable class of RNA
structure, delined as a set of base pairings that cross an
existing secondary-structure helix. Comparative methods
have been used to elucidate many examples in 16s and
235 rRNAs [17.1, aswell as in many other RNA molecules
[58-*]. Within the i-RN& these structures vary from one
to three base pairs in length, and are situated immedl-
ately adjacent to another helical structure on one or both
ends of its helix, suggesting a possible coaxial stack. NMR
studies of a simple pseudoknot structure have revealed
coaxial stacking of the helices [59].
Site-directed mutagenesis has substantiated several of
these pseudoknot interactions. In 16s rRNA, a helix of
three base pairs formed between a side bulge at posi-
tion 505 and the apex of the hairpin loop at position
525 is nested between other helices, forming a complex
pseudoknotted structure (assuming each helix occurs si-
multaneously) with multiple coaxial stacking possibilities.
This same region is highly conserved in primary and sec-
ondary structure and strongly implicated in translational
function. A series of elegant site-directed mutagenesis ex-
periments has addressed the structure of this proposed
helix [39*] and revealed that this helix is not only struc-
turalIy correct, but also is directly implicated in trans-
lational function, streptomycin binding, and binding to
ribosomal protein S12 (also see below).
A complex pseudoknot structure situated with multiple
coaxial stacking possibilities involves 235 rRNA positions
1343-1344 with 1403-1404 (E. coli numbering). This
general region of domain III is the binding site for an
essential early-assembling ribosomal protein. This helix
has recently been experimentally altered in a Saccha-
romyces cerevtie in vitro protein binding system, using
site-directed mutagenesis. The results clearly show that
canonical pairing in this short helix is required for proper
protein binding [41l]_Other comparatively derived pseu-
doknot structures have been proposed in RNaseP,group
I introns, and telomerase RNA [GO]. Some of these struc-
tural elements have been strongly implicated in catalytic
function, and have been evaluated and substantiated by
site-directed genetic analysis [46,48*]. It is interesting
to note that the lengths of non-ribosomal RNA pseu-
doknot helices are usually greater than those found in
rRNk Other naturally occuring pseudoknot structures
have been suggested by comparative and experimental
criteria, and discussed in some detail [589-l. More re-
cently, the analysis of a collection of sequences derived
from an in vitro amplification and selection for binding
to HIV1 reverse transcrlptase has ident.i&i a pseudoknot-
motif binding site [61].
Coaxial stacking
The concept of comparative evidence for a coaxial stack
was initially proposed a decade ago [2] and states that
two adjoining helices that vary in length might be coaxi-
alIy stacked upon one another if their combined length
remains constant. Based on simple spatial considera-
tions, many secon% and pseudoknot helices in the
16s and 23s rPNA can potentially stack upon one an-
other; however, comparative evidence is lacking for all
but two that do satisfy this condition. The first exam-
ple of coaxial stacking involves helices 500-504/541-545
and 5ll-515/536-540 in 16s rRNA, the second is at the
base of the cr-sarcin loop in 23s rRNA, involving he-
lices 26462652/2668-2674 and- 2675-2680/2727-2732
[170,18*]. For both proposed coaxial stackings, the
lengths of the underlying helices remain the same in
all (eu)bacteria, chloroplast and mitochondria, but are of
different lengths in the Arcbaeand Eucarya phylogenetic
domains. Both of these rRNA regions have been directly
implicated in translational function [39**,49] and some of
these functions are overlapping, suggesting that if these
coaxial stacks do occur, they could be associated with
ribosomal function and with each other in acoordinated
manner. As noted earlier, the re@on of 16s rRNA between
positions 500 and 545 is considered to be quite com-
plex, with a pseudoknot structure and potential coaxial
stacking. The coaxial stack proposed here on the basis of
comparative evidence would only make this region more
complex, and for alI of these suggested coaxial stackings
to occur, conformational rearrangements would be re-
quired (i.e. not all of them can occur simultaneously).

6. 318 Nucleic acids
This should be aninteresting set of ideasto testexperi-
mentally.
Parallel interactions
comparative evidenceexists for two setSof parallel in-
teractions in rENA.The more interesting of the two is
found in the 23s rRNA,involving three pairings arranged
in parallel: 2112-2169,2113-2170,and 2117-2172.The
iirst pair covaries between an AG and a GA pairing,
whereasthe latter two change from one canonical pair
to another [17*]. This region is structured further with
the interaction between position 2111 and the first and
lastnucleotide of the ten-aloop at 2144-2147 [19]. This
unusual structure is associatedwith the translational E-
site [48].
Base triples
Transfer ENA, a molecule 76 nucleotides in length,
contains three base-triple interactions (28,291.These
have been partially predicted by comparative methods
[31-33,340,35].No convincing base triples have been
uncovered(so far) within the RNAs,nor haveany been
identified (yet) in RNaseP RNAand the LJ-RNAs.Com-
parative analysisof the group I introns have revealed
howeverseveralbasetriples 121,451,and thesehavebeen
substantiatedby site-directedmutagenesisand modeled
in three dimensions. More recently, these two group I
intron basetriples havebeen characterizedand substan-
tiatedby NMR[62].
Conclusions
“The G:U basepair in the upper stem might be impor-
tant. Is it possible that the activatingenzymescould ex-
tract enough information from the G:U pair and other
featuresof this double-stranded region for it to act asa
recognition site?”[4]
An important question to askat this juncture iswhat type
of information can be obtained from comparative se-
quenceanalysis?Severalexamplesof largestructuresand
variousstructuralelementshavebeen inferred from such
analysii, and many of these are consistent with and/or
proven with experimental methods. Beyond these, are
additional constraints present in RNAmolecules,and if
SO, whit are they?Do they suggestnew typesof higher-
order structural motifs, recognition sitesfor proteins or
other RNAmoleculesor do theyrepresentsubtle thermo-
dynamic and/or structural reiinements?Wii we be able
to decode this information from sequenceinformation
alone?
Addressing such questions will require additional se-
quences and diversity for each RNA dataset (e.g. 16s
rRNA). In parallel, the computer tools used for com-
parativesequenceanalysiswill need to be expanded and
relined. Quantitation correlation analysisalgorithms are
capableof uncovering subtle constraints [32,33,34*,35].
The most recent application of these methods is be-
ginning to identify structural constraints beyond simple
pairings (secondary and tertiary), and suggestsin some
c&s that certain base-pair types (or simply bases) in-
fluence the types of pairings (or bases) in close three-
dimensional proximity (i.e. context eifect; see Figs 3
and 4) [34-j. At the moment, these quantitative cor-
relation analysismethods do not incorporate the num-
ber of phylogenetic eventsunderlying each coordinated
basechange(i.e. the number of compensatorychanges
that have occurred throughout the phylogenetic tree;
the larger this number, the more significant the set of
changes),although such eventshave been incorporated
into a non-quantitative prologbased covarianceanalysis
program [63]. Incorporating a ‘phylogenetic events’fac-
tor into quantitative correlation algorithms could well
improve these quantitative methods and help identify
or strengthen the argument for new structural elements
and/or new.structural principles.
O-3’
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Fig. 3. Secondary-structure diagram of tRNA (yeast Phe number-
ing) highlighting those positions correlating best with position 13
(identified with filled triangle). Two large filled circles identify the
two highest correlating positions (22 and 461, and smaller filled
circles identify the next five highest correlating positions with
position 13. Adapted from f34.1.
What is called comparativesequenceanalysis(or phylo-
genetic analysisby manywho are referring to structural
analysisof thetypediscussedhere) goesbeyond the anal-
ysis of a single RNAmolecule. It should be appreciated
that such analysiscan and should encompassthe com-
parison of different RNAmoleculesor subsetsof a given
molecular database,when there is biological rationalefor
doing so. For example, the complete tFWAsequence

7. Comparative studies of RNA &tell 319
VARIABLE LOQP VARIABLE LOOP
Fig. 4. Stereo pair of the three-dimensional structure for this tRNA, with the seven best correlating positions with position 13 identified
as in Fig. 3. Adapted from I34e1.
databasecan be subdivided into the 20 amino acid ac-
ceptors and analyzed for subtle structural differences,
which could be the recognition signals for the differ-
ent amino acyl synthetases[64,65]. The first attempt at
this wascompleted in 1966 [4], and is surprisingly good
given the small sequencedatasetused. An example of
intermolecular analysis includes a 16%23s rRNA com-
parison. These two moIecules are befieved to interact
during translation, and thus any significant intermolec-
ular correlation could wetl be pointing at a structurally
and functionally important site.
Comparative structure analysis is not only generating
higher-order structures that are widely accepted in var-
ious RNAfields,it is alsoestablishing an agendafor vari-
ous experimental designs.I havenoted how this method
hasidentified manyof the sign&ant RNAstructure prin-
ciples,including Watson-Crick and GU pairings, antipar-
allel and contiguous arrangement of these pairings, te-
tra loops, pseudoknots, severalclassesof non-canonical
pairings (and their replacements),helix coaxial stacking,
base-tripleinteractions,and setsof pairs that form paral-
lel structures,The majority of thesestructures havenow
beenevalulatedand substantiatedin one form or another
usingexperimental methods.When astructure of interest
is atafunctional site,that function can be experimentally
evaluatedin the light of that structure. Thus a compara-
tively derived higher-order structure can and should be
considered a hypothesis,testedwith each new rRNAse-
quence,evaluatedexperimentally, and subject to molec-
ular modeling.
Superimposinghomologous RNAstructures,for example
16s and 16S-likerRNA,allows us the opportunity to eval-
uate more than its higher-order structure, presenting
us with a glimpse of what structural features are con
served throughout evolution or part thereof, which in
turn suggestsstructural elements of possible functional
significance.When the number and diversity of struc-
tures is sufficient, as it is for 16s and 23s rRNA (there
now exist over 2200 16s and 16S-likeand over 200 23s
and 23Slike sequences), evolutionary events and path-
ways can be mapped in great detail. Not only can the
reconstruction of these events be played like a Disney
movie flip book (i.e. a frame by frame snapshot anima-
tion of (r)RNA evolving), but underlying constraints on
structure and function can be deduced,from which prin-
ciples and refinementsof RNAstructure and function can
be inferred.
Darwinian or natural evolution has generated a won-
derfully diverse collection of RNA molecules for us to
compare and contrast utilizing the comparative meth-
ods discussedabove. Recently,the advent of new tech-
niques in biochemistry has atlowed the molecular biol-
ogist to practise a little evolutionary home brewing for
themselves.Starting with a very large collection of ran-
dom oligonucleotide sequences,one can subject these
macromoleculesto multiple rounds of selection and am-
plification, enricl-+g for those sequences that best sat-
isfy the constraint conditions. Such methodology now
puts some of Mother Nature’s authority into the hands
of the research scientist. But, instead of designing ter-
ribly complex and obtuse experiments, as it appears
to us mere mortals, the scientist can now select and
enrich for something far simpler, such as a small fig
and or protein-binding site on an RNA molecule, or
RNA molecules capable of defined catalytic functional-
ity [66**,67**]. Such newer methodology lets the genie
out of the bottle. ~Althoughthe sequencesare the an
swers,we now have a better appreciation of how the
experiments were done; for we ask the questions and
know what the underlying hypothesis is. The sequences
resulting from this work will now have a Yii associated
with its Yang. The next few yearsshould be an exciting
and rewarding time. (And people like me canstill saythat
the experiments havebeen done for us.)

8. 320 Nucleic acids
So,in closing,we.cansit backand marvelabout the rapid
technologicaladvanceshappening all around us.The se-
quencing revolution makes it possible to fill volumes
of notebooks with homologous sequenceinformation.
Computersand their networks allow us to readily store,
manipulate, access,and analyze these notebooks. The
comparativesequence/structure analysisparadigm puts
somemeaningand dimensions to this information. It is
aparadigmthatis itselfstill being definedanddeveloped.
Acknowledgements
This work was supponed by the NIH (GM 48207). RRGutell is an
Associatein the Program in Evolutionary Biology of the Canadian
Institute of AdvancedResearch.I wish to thank the WM Keck Foun-
dation for their generous suppop of RNA science on the Boulder
campus, SUN Microsystemsfor their donation of computer equip-
ment, and B@ Weiser,Tom Mackeand others Fordeveloping much
OFthe computer code used to analyze and present RNA structural
information.
References and recommended reading
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.
. .
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
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of outstanding interest
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