Alternative Watson–Crick SyntheticGenetic Systems
Steven A. Benner, Nilesh B. Karalkar, Shuichi Hoshika, Roberto Laos,Ryan W. Shaw, Mariko Matsuura, Diego Fajardo, and Patricia Moussatche
The Westheimer Institute for Science and Technology, The Foundation for Applied Molecular Evolution,Alachua, Florida 32615
Correspondence: [email protected]
In its “grand challenge” format in chemistry, “synthesis” as an activity sets out a goal that issubstantially beyond current theoretical and technological capabilities. In pursuit of thisgoal, scientists are forced across uncharted territory, where they must answer unscriptedquestions and solve unscripted problems, creating new theories and new technologies inways that would not be created by hypothesis-directed research. Thus, synthesis drives dis-covery and paradigm changes in ways that analysis cannot. Described here are the productsthat have arisen so far through the pursuit of one grand challenge in synthetic biology:Recreate the genetics, catalysis, evolution, and adaptation that we value in life, but usinggenetic and catalytic biopolymers different from those that have been delivered to us bynatural history on Earth. The outcomes in technology include new diagnostic tools that havehelped personalize the care of hundreds of thousands of patients worldwide. In science, theeffort has generated a fundamentally different view of DNA, RNA, and how they work.
On the occasion of the 90th birthday of Albert Eschen-moser, a master of synthesis.
Many have noted that the phrase “synthetic bi-ology” has had no consistent meaning amongthe communities that have used it over the past40 years (Brent 2004). This inconsistency is re-flected in the literature. To some, synthetic biol-ogy means simply “synthesizing a lot of DNA,”perhaps even entire genomes (Ellington 2016;Glass 2016). To others, synthetic biology is anew name for the much older field of metabolicengineering, but on a grander scale than mod-estly constructing a microbe that manufacturesa single natural product using a single heterol-ogously expressed gene (Lechner et al. 2016).
To others, “synthetic biology” is the redirectingof information in living systems, perhaps to cre-ate a microbial platform for further engineering(Gaj et al. 2016). To these can be added conceptsnot represented in this series, such as the con-struction of devices that use natural nucleicacids and proteins as biobricks (Smolke 2009;Win et al. 2009), perhaps to test a theory abouthow those parts work together naturally (Pre-hoda et al. 2000; Dueber et al. 2004). Others use“synthetic biology” as suggested by Eric Kool, tomean the use of unnatural molecules in the con-text of natural biological systems (Rawls 2000).In its original definition, synthetic biologymeant the creation of artificial life (Leduc 1912).
Editors: Daniel G. Gibson, Clyde A. Hutchison III, Hamilton O. Smith, and J. Craig Venter
Additional Perspectives on Synthetic Biology available at www.cshperspectives.org
Copyright # 2016 Cold Spring Harbor Laboratory Press; all rights reserved; doi: 10.1101/cshperspect.a023770
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We have ourselves long held the view thatsynthesis is not a field, but rather an activity, anactivity that derives value not only from prod-ucts that it creates, but also from what is learnedas the syntheses are attempted (Sismour andBenner 2005a). Indeed, the heroes of synthesisin classical organic chemistry often chose tar-gets that had no product value at all (Woodward1968; Kishi 1989). Instead they chose targetsthat would present a “grand challenge,” a mol-ecule whose synthesis was beyond current capa-bilities, whose pursuit would therefore chal-lenge molecular theory.
Here, synthesis does something that “hy-pothesis-directed research” cannot. When sci-entists control the hypotheses that they test, theyoften strategically limit their activities to safehypotheses that are likely to be true. If they donot, then their funding agencies will. Thus, “hy-pothesis-based research” tends to not challengecore convictions.
In contrast, synthesis in pursuit of a “grandchallenge” forces scientists across unchartedgrounds, where they must ask and answer un-scripted questions. Thus, a well-selected syn-thetic grand challenge tests all of the theoriesand assumptions that go into any strategic syn-thetic plan. Many of these are unstated; the sci-entists involved might not even realize that theyare making them. As a result, synthesis can drivediscovery and paradigm change in ways that hy-pothesis cannot.
For example, in his classic defense of grandchallenge synthesis, Woodward(1968) discussedhis choice, with Albert Eschenmoser, of vitaminB12 as a synthetic target. In 1965 (and in somesense still), B12 was the most complicated non-polymeric natural product known. The productof the synthesis itself had no commercial value;fermentationwas already generating B12 for pen-nies per unit. However, the effort led to the dis-covery of the intimate relation between molecu-lar reactivity and molecular orbital structure.That discovery, captured as the Woodward–Hoffman rules (1970), was later recognized bya Nobel Prize (Fukui 1982; Hoffmann 1982).
The celebrated total synthesis of the genomeof a bacterium by Venter and his coworkers(Gibson et al. 2010) was, of course, nothing
more (and nothing less) than the total synthesisof a natural product, one that happens to beinvolved in microbial inheritance. Venter re-portedly said that he undertook this challengebecause someone told him it “could not bedone.” This “someone” was certainly not a syn-thetic organic chemist. The credo of chemistry,for at least 50 years, has held that if a molecularstructure can be drawn, and if the arrangementof atoms that it represents is an energy mini-mum, then the molecule can be synthesizedgiven enough effort and money. That credo restson many successful syntheses; some examplesinclude tetrodotoxin (which is barely an energyminimum) (Kishi et al. 1972) and palytoxin(whose complexity is almost boring) (Fig. 1)(Kishi 1989).
Despite that credo, chemists in the early1980s fully understood that “structure theory”in chemistry had broad deficiencies. In partic-ular, that theory could not tell us “what” mol-ecules to synthesize to create a desired molecu-lar behavior. This was especially true if thegrand challenge goal related to artificial life (Le-duc 1912). Chemical theory could not then, andstill cannot, meet the following easy-to-expresschallenge: “Draw me structures of some mole-cules that, if synthesized, will together have theproperties that we value in living systems.”
Indeed, even simpler tasks were (in 1980)and remain (today) beyond the power of chem-ical theory. Despite advanced computers, ad-vanced theory (Pople 1999), and decades of ef-fort, we still cannot predict the solubility of saltsin water, the packing of organic crystals (Dunitzand Bernstein 1995), or the freezing point ofwater. Yet these “simple” processes are the ele-ments of the molecular interactions that gener-ate the molecular behaviors that we value inbiology. We cannot create medicines by directdesign. We cannot create molecular electronicdevices by direct design. We cannot engineerself-assembling materials by direct design. Weget these today only by trial, error, intuition,and experiment.
However, DNA has long appeared to be spe-cial. At first glance, the Watson–Crick model forthe double helix did appear to allow design ofmolecules that bound to other molecules in wa-
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Alternative Watson–Crick Synthetic Genetic Systems
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ter (a problematic solvent). Indeed, such designwas being performed almost routinely by mo-lecular biologists who had no training in the fra-ternity of organic chemistry. An entire industry(“antisense therapeutics”) (Miller and T’so 1988)was based on the notion that the Watson–Crickmodel of DNA–DNA binding could be easilytransferred to other backbones, in particular, tononionic backbones. Millions of dollars werespent to show that this transfer was not easy.
In contrast with models for other molecularsystems in chemistry, the Watson–Crick modelis almost trivially simple. First, the model holdsthat molecular recognition depends entirely ontwo very simple rules of complementarity: sizecomplementarity (big purines pair with smallpyrimidines) and hydrogen-bonding comple-mentarity (hydrogen bond donors pair with hy-drogen bond acceptors) (Fig. 2). High schoolstudents (ourselves included) were taught“how genetics works” using paper cutouts toillustrate these rules. Generations of future mo-lecular biologists came to view the structure ofDNA as “obviously correct.”
Yet, even a newly minted Ph.D. in 1980 couldsee the multiple absurdities in this model. Onehardlyexpects to get good molecular recognitionfrom complementary hydrogen bonding in wa-ter; water as a solvent is “nothing but” compet-ing hydrogen bonds. Further, the size comple-mentarity required for Watson–Crick pairing is,at first glance, not likely to be enforced by theflexible backbone. Indeed, as Kool et al. (2000),Romesberg and collaborators (Malyshev et al.
2009, 2014), Hirao and collaborators. (Kimotoet al. 2011), Heuberger and Switzer (2008), andothers (Doi et al. 2008) later showed, the back-bone can easily adjust itself to accommodategeometries other than edge-on contacts (Fig.3). Further, as “obvious” as nucleobase stackingseems (pennies stacked in a roll is a commonanalogy) (Bowman and Williams 2011), simplearomatic solids (benzene is an archetype) donot stack like pennies in a roll (Fig. 3).
Once one begins down this path of reason-ing, horrors pile on top of horrors. Adenine is“missing” a hydrogen-bonding group, perhapsbecause of how it emerged in prebiotic process-es (Fig. 3) (Orgel 2004); the resulting instabilityof the A:T pair creates unending problems inbiotechnology (Wei et al. 2012). Cytosine suf-fers spontaneous deamination in water, requir-ing constant repair in our genomes; so do ade-nine and guanine, at slower rates (Shapiro1987). DNA sequences with consecutive dGsare so pathological that, at some point, com-mercial supply houses hesitated to make them(Mizusawa et al. 1986). And things get worsewith RNA sequences that have consecutive Gs(Davis 2004).
These realizations helped make the 1980s agood time to expand synthesis past natural bio-products to include unnatural bioproducts.Here, the “grand challenge” centered on ques-tions “why?” and “why not?” Why did DNAhave these molecular perplexities? What othermolecular systems can “do” genetics? Could wesynthesize alternative genetic molecules thatperform better? The grand challenge questioncould even be put fancifully: If we encounteredan alien species capable of Darwinian evolution,would their “DNA” be DNA or some other mo-lecular system?
This article discusses the synthesis of alter-native Watson–Crick systems (Benner et al.1998; Benner 2004). As it turns out, the alter-native molecular recognition systems do them-selves have value as products. However, themajor value of the synthetic effort to create un-natural genetic systems came from what waslearned as the challenge was undertaken, underterms that did not allow failure to be an option(Bostick 2010).
GC
AT
Figure 2. How genetics works. The cartoon that “ex-plains everything”—paper cutouts that taught gen-erations of schoolchildren that molecular geneticswere simple chemistry.
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REARRANGING THE SYNTHESIS OFHYDROGEN-BONDING UNITS—GENERATIONS OF ARTIFICIAL WATSON–CRICKERY
Even 30 years ago, one could easily draw nucle-obase pairs that retained the Watson–Crick
pairing “concept,” but had hydrogen-bondingunits rearranged to give, at first glance, newWatson–Crick pairs. As shown in Figure 4,this rearrangement could readily generate 12hypothetical nucleobases that might form a to-tal of six orthogonal Watson–Crick pairs.
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Alternative Watson–Crick Synthetic Genetic Systems
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The structures in Figure 4 came to be called“artificially expanded genetic informationsystems” (AEGIS). This term covers pairs thatfully retain the Watson–Crick pairing concept;it distinguishes this concept from strategiesfrom the Kool laboratory (in which interstrandhydrogen bonding is dispensed with entirely),the Hirao laboratory (in which size comple-mentarity is key) (Hirao et al. 2002; Hirao2006; Kimoto et al. 2009, 2013), and theRomesberg and Schultz laboratories (in whichcombinatorics defined the scope of polymer-ase–DNA interactions in a particularly interest-ing way) (McMinn et al. 1999; Malyshev et al.2009).
We were not the first to hypothesize that anexpanded genetic alphabet might be obtainedby shuffling hydrogen-bonding units. AlexRich, some 20 years earlier, had recognizedthat isoguanine (a natural product) and isocy-tosine might possibly form a third pair (Rich1962) (the first-generation S:B pair in Fig. 1).Independently, Geoffrey Zubay (1988) pro-posed another alternative pair (Fig. 5), not rec-ognizing that his hypothetical structure for thesmall component of the new pair lacked thearomatic planar geometry that the Watson–Crick model suggested was necessary for nucle-obase stacking. Interestingly, even the writers ofthe movie E.T. the Extra-Terrestrial understoodthe possibility of an expanded genetic alphabet;E.T. has DNA built from six nucleotides, “ino-sine and a pyrimidine we cannot identify”(Mathison 1982).
However, synthesis as an activity was neces-sary to determine whether nucleobase pairingwas as simple as the Watson–Crick model im-plied. In this undertaking, it soon became clearthat more than one heterocyclic system wouldsupport, or “implement,” any particular hydro-gen-bonding pattern. For example, among nat-ural nucleobases, uridine and pseudouridineboth present a hydrogen bond acceptor–do-nor–acceptor hydrogen-bonding pattern (Fig.5). Those seeking to meet the grand challenge ofcreating an artificial genetic system needed todecide which heterocyclic system to synthesizeto implement each of the orthogonal hydrogen-bonding patterns.
In many cases, the first heterocyclic systemprepared to implement each of the four addi-tional hydrogen-bonding patterns turned outnot to be the best heterocycle to support genet-ics. Several first-generation AEGIS componentssuffered from chemical defects, indicated inmagenta in Figure 4. For example, the pyrazineheterocycles used first to implement the pyADDand pyDDA hydrogen-bonding patterns epi-merized rapidly (Fig. 5) (Voegel et al. 1993a,b;Voegel and Benner 1994, 1996a,b; von Krosigkand Benner 2004). The purine ring system usedto implement the puDDA hydrogen-bondingpatterns had a substantial amount of a minortautomer that created nucleobase-pairing am-biguity (Sepiol et al. 1976; Sismour et al. 2004).In a long process documented in the literature,second-generation implementations of variousbonding patterns were then synthesized to fixproblems in the new DNA (Benner 2009).
These second-generation improvements aresummarized in Figure 4. The effort producedmuch new knowledge in heterocyclic chemistry.Within the framework of the Watson–Crickpair, a rather comprehensive view of what het-erocycles can support genetics has nowemerged.We can now even make a good guess about the“pyrimidine that we cannot identify” in E.T.’sDNA. We also know that if E.T. indeed had ino-sine in his/her genome, (s)he would have haddifficulty surviving to the point where his/herspecies could attempt interplanetary travel.
NUCLEOBASE PAIRING ISCONSTRUCTIVELY AS SIMPLE AS THEWATSON–CRICK MODEL SUGGESTS
Once synthesis delivered heterocycles that ade-quately implemented the four extra hydrogen-bonding pairing patterns, it was relatively easyto adapt the then-emerging phosphoramidite-based solid-phase-synthesis chemistry (the keyenabling technology for synthetic biology) tocreate DNA containing AEGIS components.These supported experiments to determinehow AEGIS pairing contributes to overall du-plex stability.
Remarkably, these studies found the Wat-son–Crick concept to be quite robust with re-
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Alternative Watson–Crick Synthetic Genetic Systems
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C
N
OH
HH
H3C
No
oxid
atio
n,bu
t epi
mer
izat
ion
OC
RO
RO
NC
CH
C
N
OH
HH
O2N
No
epim
eriz
atio
n,de
prot
onat
ion
OC
RO
RO
NC
C
HC
N
O
HH
O2N
OC
RO
RO
NC
CH
C
N
OH
O
RO
RO
NC
HC
N
O
H
HH
HH
NC NCN
o ox
idat
ion,
slow
er e
pim
eriz
atio
n
0 g
ener
atio
n
AB
CD
1st
gen
erat
ion
2nd
gen
erat
ion
N
NN N R
O
NH
H
H
N
NN
NN R
O
NH
H
H
N
N
N RO
NH
H
H
N
NN
N RO
NH
H
H
N
NN N R
O
NH
H
N
NN
NN R
O
NH
H
N
N
N RO
NH
H
N
NN
N RO
NH
H
H HH H
Figu
re5.
Dif
fere
nt
rin
gsy
stem
sh
avin
gd
iffe
ren
tp
rop
erti
esca
nim
ple
men
tth
esa
me
Wat
son
–C
rick
hyd
roge
n-b
on
din
gp
atte
rn.(
A)
Zu
bay
’sri
ng
syst
emfo
rmal
lyim
ple
men
tsa
hyd
roge
nb
on
dd
on
or–
acce
pto
r–d
on
or
pat
tern
,bu
tw
ith
ou
tth
ep
lan
arar
om
atic
syst
emth
atth
eW
atso
n–
Cri
ckm
od
elim
pli
es(Z
ub
ay19
88);
toge
tb
oth
,o
ne
mu
stm
ake
aC
-gly
cosi
de
(Ben
ner
etal
.198
7).(
B)
Inn
atu
re,u
rid
ine
and
pse
ud
ou
rid
ine
imp
lem
ent
the
sam
eh
ydro
gen
-bo
nd
ing
pat
tern
,th
efi
rst
asan
N-g
lyco
sid
e,th
ese
con
das
aC
-gly
cosi
de.
(C)
Ob
tain
ing
ah
eter
ocy
cle
toim
ple
men
tth
ep
uD
DA
hyd
roge
n-b
on
din
gp
atte
rnw
ases
pec
iall
ych
alle
ngi
ng,
asva
rio
us
C-g
lyco
sid
esar
eea
syto
oxi
diz
eo
reas
ily
epim
eriz
ed.(
D)
Var
iou
sd
iffe
ren
t5,6
-ri
ng
syst
ems
imp
lem
ent
the
pu
(DD
A)
hyd
roge
n-b
on
din
gp
atte
rn,
wit
hd
iffe
ren
tam
ou
nts
of
am
ino
rta
uto
mer
icfo
rm,
wh
ich
imp
lem
ents
ad
iffe
ren
tp
u(D
AD
)h
ydro
gen
-bo
nd
ing
pat
tern
com
ple
men
tary
toth
ymid
ine.
S.A. Benner et al.
8 Cite this article as Cold Spring Harb Perspect Biol 2016;8:a023770
on October 13, 2021 - Published by Cold Spring Harbor Laboratory Press http://cshperspectives.cshlp.org/Downloaded from
spect to the shuffling of hydrogen-bondingunits. In a study that looked at some 300 du-plexes, pairs joined by three hydrogen bondsgenerally contributed more to duplex stabilitythan pairs joined by just two; pairs joined by justone hydrogen bond contributed no stability to aduplex in competition with alternative interac-tions with bulk solvent (Geyer et al. 2003).
A set of second-order rules could also bediscerned. For example, an uncompensatedC¼O group was not particularly disfavoredin a pair (Fig. 6). However, an uncompensated–NH2 group was. Further, adding a proton anda positive charge to a nucleobase to create for-mal Watson–Crick complementarity was gen-erally accepted. In contrast, losing a proton toput a negative charge on the nucleobase desta-bilized a formally Watson–Crick complement.These observations were used to develop a “self-avoiding molecular recognition system” (Ho-shika et al. 2010), another unnatural DNAthat is gaining in multiplex diagnostic systemsand isothermal DNA amplification architec-tures (Sharma et al. 2014; Glushakova et al.2015; Yang et al. 2015).
The flexibility of the DNA backbone, and byimplication its inability to enforce size comple-mentarity, was also evident in these studies. Forexample, pairing of two small nucleobase ana-logs could stabilize the duplex if they werejoined by three hydrogen bonds (Fig. 3B). In-deed, a small:small pair joined by three hydro-gen bonds stabilized the duplex as much as asmall:large pair joined by just two hydrogenbonds (Geyer et al. 2003). The observationthat these pairs might compete with standard
small:large pairs remains an important con-straint on the design of artificial genetic systems.
These biophysical studies were followed bycrystallographic studies that showed that AEGIScomponents did in fact pair with Watson–Crick geometry. For example, introduction ofa single Z:P pair into the stem of an RNA ribo-switch marginally increased the stability of thatstem; a crystal structure showed essentially nogeometric perturbation (Fig. 7, right) (Hernan-dez et al. 2015). In DNA, the crystal structureof a single Z:P pair likewise shows no substan-tial deviation from Watson–Crick geometry(Zhang et al. 2015) (Fig. 7, left). Indeed, duplex-es with four or six Z:P pairs retain their overallWatson–Crick geometry (Fig. 7, center) (Geor-giadis et al. 2015).
THE USE OF ORTHOGONAL AEGISBINDING IN DIAGNOSTICS
Even as “why?” and “why not?” questions werebeing pursued in a grand challenge effort fordiscovery, not for technology, the intrinsicability of AEGIS to fit within the canonicalWatson–Crick structure was proving to be im-portant for many applications. For example,Mickey Urdea and Thomas Horn at Chiron(Emeryville, CA) sought to create a branchedDNA (b-DNA) assay kit to measure viralloads in patients infected with HIV, hepatitisB, and hepatitis C viruses (Bushnell et al.1999). Viral load measurements are critical todetermining when the management of a viralinfection starts to fail because of mutation ofthe virus to create resistance to a drug being
NN
N
N O
O
NC
NN
N
HH
HH
H
R
RN
N
N
C O
NC
N+N
N
HH
HH
H
R
R
-
HH
NN
N
NN
NN
O
O
H3C HH
H
R
R
A negative charge ina stack is unfavorable
An uncompensated aminogroup is unfavorable
An uncompensated C=Ogroup is acceptable
Figure 6. Some second-order Watson–Crick pairing rules obtained via artificially expanded genetic informationsystems (AEGIS) development (Geyer et al. 2003). A negative charge in the nucleobase stack destabilizing (left).An uncompensated C–NH2 unit is destabilizing (center). An uncompensated C¼O unit is acceptable (right).
Alternative Watson–Crick Synthetic Genetic Systems
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35.1
36.0
AB
CD
2.6
3.1
3.3 2.
5
2.7
2.7
P1
Figu
re7.
Th
ree
crys
tals
tru
ctu
res
wit
hZ
:Par
tifi
cial
lyex
pan
ded
gen
etic
info
rmat
ion
syst
ems
(AE
GIS
)p
airs
.An
iso
late
dp
air
ina
sho
rt,
A-f
orm
DN
Ad
up
lex
crys
tall
ized
wit
hth
eai
do
fa
sele
niu
msu
bst
itu
tio
n(l
eft)
(Zh
ang
etal
.20
15).
A16
-mer
du
ple
xw
ith
six
con
secu
tive
Z:P
pai
rs(c
ente
r)(G
eorg
iad
iset
al.2
015)
.Asi
ngl
eZ
:Pp
air
inan
RN
Ari
bo
swit
chin
fou
rvi
ews
A,
B,
C,
and
Dea
chro
tate
d90
˚(ri
ght)
(Her
nan
dez
etal
.20
15).
S.A. Benner et al.
10 Cite this article as Cold Spring Harb Perspect Biol 2016;8:a023770
on October 13, 2021 - Published by Cold Spring Harbor Laboratory Press http://cshperspectives.cshlp.org/Downloaded from
administered. Here, instead of amplifying thenucleic acid target, the b-DNA assay uses AEGIScomponents to assemble a signaling nanostruc-ture (Fig. 8).
Standard Watson–Crick pairing was usedto capture the viral sequence on the solid sup-port. Then, in a “sandwich” format, standardWatson–Crick pairing allowed the immobilizedviral sequence to capture a second DNA mole-cule. The second DNA molecule then captureda b-DNA molecule, which, in turn, capturedmultiple signaling molecules. The b-DNA assaydid not amplify the target viral nucleic acidsequence, creating a downstream contamina-tion problem. Thus, the b-DNA assay becamean alternative to a polymerase chain reaction(PCR) that required less skill to perform andless expertise to interpret.
Unfortunately, the b-DNA architecture withonly natural nucleic acids failed to detect targetnucleic acid sequence with low noise. Naturalbiological samples (e.g., blood) contain manynucleic acids, some of which partially comple-ment “any” DNA sequences that might be used
to assemble the signaling nanostructure. Thesecan interact with the signaling molecules, per-haps mismatched, perhaps via concatenation,to immobilize signaling molecules on the sur-face of the support even in the absence of thetarget virus molecule.
This background noise was mitigated byputting AEGIS components (first-generationS and B) into the sequences that self-assembledto give the signaling nanostructure. AEGIS oli-gonucleotides cannot complement any naturaloligonucleotides. Therefore, they cannot formstructures that generate background noise. Thisallowed the b-DNA assay to become FDA ap-proved with a level of detection at 30 moleculesand serve millions of patients (Elbeik et al.2004a,b). This represents the first examples ofusing DNA to construct commercially usefulnanostructures.
The nonenzymatic hybridization of oligo-nucleotides containing AEGIS componentsshowed that the simple Watson–Crick modelcould be generalized to increase the number oforthogonal pairs. In parallel, synthesis going on
Capture probe (solution)
Target RNA
Microwell
Capture probe (microwell)
Target probe
Preamplifier
Amplifier with hybridizedLabel probes
Figure 8. The branched DNA assay (Bushnell et al. 1999). When artificially expanded genetic informationsystems (AEGIS) nucleotides (in this case, first-generation S and B) are placed in the amplifier nanostructure,the noise is dramatically decreased (Elbeik et al. 2004a,b).
Alternative Watson–Crick Synthetic Genetic Systems
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at the same time was showing the importance ofthe repeating charge in the backbone to molec-ular recognition (Steinbeck and Richert 1998;Benner and Hutter 2002). Here, various neutralbackbone analogs of DNA, including polyamidenucleic acid (PNA) (Nielsen et al. 1991), weremade and studied. The outcome of these studieswas to show that to support Darwinian evolu-tion, a linear genetic biopolymer was likely torequire a repeating backbone charge. This “poly-electrolyte theory of the gene” (Benner and Hut-ter 2002) allowed the physical properties of thepolymer to be largely independent of sequence,a property that is unusual in all molecular sys-tems. Indeed, the repeating backbone charge isthe reason why DNA molecules with expectedproperties are so easy to design.
At the same time, synthesis was showing thedelicate and unpredictable impact of changingthe structure of the sugar ring (Schneider andBenner 1990; Freier and Altmann 1997; Wildset al. 2002). Here, a wide diversity of DNA an-alogs with different backbone carbohydrates(Eschenmoser 1999; Declercq et al. 2002; Wildset al. 2002; Horhota et al. 2005) were synthe-sized and studied. Some had prebiotic signifi-cance (Krishnamurthy 2015).
Discussion of alternative backbone units forDNA and RNA is regrettably beyond the scopeof this article. Nevertheless, the point was anal-ogous: By the activity of synthesis, nucleobasepairing, at the core of genetics, was found to bemore malleable than the phosphates and carbo-hydrates backbone units, the “uprights” in the“ladder” that the standard model had relegatedas largely incidental to the performance of DNAin genetics.
CREATING A MOLECULAR BIOLOGYTO SUPPORT AEGIS
But could AEGIS components do more thanjust bind to other AEGIS components? Couldan AEGIS DNA strand also dynamically partic-ipate in the enzymatic synthesis of its comple-ment, a key property that we value in naturalbiomolecules?
Here, the grandness of the challenge arosefrom natural history. To serve in a genetic
system, a polymerase must take instructiononly from the template with extraordinaryfidelity. Achieving this was no small trick.Polymerases have four natural substrates:A(template):T-triphosphate, T(template):A-triphosphate, G(template):C-triphosphate, andC(template):G-triphosphate. Thus, any directcontact between a polymerase and the nucleo-base is “dangerous.” For example, contact in themajor groove might cause a polymerase to pre-fer some nucleotides over others, disregardingthe instructions from the template, as naturalnucleobases differ greatly in the moieties thatthey present to the major groove.
The minor groove is different in this respect.As noted by Joyce and Steitz (1994), all of thefour standard nucleobases present electron den-sity to the minor groove. This density is deliv-ered by the exocyclic C¼O moieties of C andT and by the N3 nitrogens of A and G (Fig. 4).Crystallographic analysis of many polymerasesidentifies side chains that contact this electrondensity in both the template and primer inprimer–template–enzyme complexes.
For just one AEGIS pair, both componentspresent analogous electron density to the minorgroove. Z has an exocyclic C¼O moiety; itspartner P carries electron density on its N3. Inthe three other second-generation pairs, thesmall component presents a hydrogen bond do-nating moiety (-NH2) to the minor groove.Thus, the Joyce–Steitz “minor groove scanninghypothesis” suggested that polymerases wouldreadily synthesize duplex DNA containing Z:Ppairs from templates and triphosphates, where-as relatively few would synthesize the S:B, K:X,and V:J pairs; those pairs would be synthesizedbest by polymerases that had been mutated.
This proved to be the case empirically.Nearly all polymerases examined over the pastdecade do a reasonable, sometimes acceptable,and occasionally an excellent job replicating Z:Ppairs. In contrast, the S:B (Sismour and Benner2005b) and K:X (Sismour et al. 2004) pairs,which lack this electron density, are often bestreplicated by polymerases that are first mutated.
To create polymerase variants that acceptAEGIS components, a directed evolution tooldeveloped by Tawfik and Griffiths (1998) and
S.A. Benner et al.
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Nes
ted
PC
R w
here
poly
mer
ase
ampl
ifies
its o
wn
gene
with
exte
rnal
AE
GIS
prim
ers
Hea
t lys
es c
ells
brin
ging
intr
acel
lula
rpo
lym
eras
es in
toco
ntac
t with
prim
ers
and
dNT
Ps
inth
e w
ater
use
d to
mak
eth
e em
ulsi
on;
begi
n ne
sted
PC
R
dNT
Ps
dNT
Ps
dNT
Ps
dNT
Ps
dNT
Ps
Prim
ers
Prim
ers
Em
ulsi
ficat
ion,
one
cel
l per
dro
p
Cel
lsex
pres
sing
activ
e an
din
activ
epo
lym
eras
es
Rec
lone
gen
esfo
r be
st c
atal
ysts
for
next
rou
nd
Bre
ak e
mul
sion
s
Prim
ers
Prim
ers
Prim
ers
Rou
nd 1
am
plic
ons
have
AE
GIS
tags
. Afte
r ch
imer
icpr
imer
s ar
e co
nsum
ed,
ampl
ifica
tion
is c
arrie
d by
exte
rnal
prim
ers.
Pol
ymer
ase
exte
nds
Str
ands
sep
arat
e, r
ever
se p
rime
Pol
ymer
ase
exte
nds
16
5′P
PP
P
18 m
er, w
ith 4
AE
GIS
AE
GIS
exte
rnal
prim
er
AE
GIS
exte
rnal
prim
er
AE
GIS
com
plem
ent
Targ
et5′
PP
PP
5′P
PP
P5′ Z
ZZ
Z
PP
PP
PPP
PP
PP
PP
PP
P
5′P
PP
P
Figu
re9.
Sch
emat
ico
fco
lon
yse
lf-r
epli
cati
on
(CSR
)(G
had
essy
etal
.200
1)fo
rth
ese
lect
ion
ofp
oly
mer
ases
that
amp
lify
AE
GIS
pai
rs,h
ere
ina
nes
ted
po
lym
eras
ech
ain
reac
tio
n(P
CR
)ar
chit
ectu
re.A
lib
rary
of
gen
esen
cod
ing
acti
ve(b
lue)
and
inac
tive
(red
)p
oly
mer
ases
iscl
on
edin
tob
acte
rial
cell
s,w
hic
har
ed
isp
erse
d(o
ne
cell
per
dro
ple
t)in
toan
emu
lsio
nw
her
eth
eex
trac
ellu
lar
bu
ffer
con
tain
sP
CR
pri
mer
san
dtr
iph
osp
hat
es.T
he
init
ialh
eatc
ycle
pla
ces
the
po
lym
eras
esin
con
tact
wit
hth
eP
CR
pri
mer
san
dtr
iph
osp
hat
es.T
he
dro
ple
tske
epth
ep
oly
mer
ase
vari
ant
asso
ciat
edw
ith
its
own
enco
din
gge
ne;
ifth
ege
ne
isto
be
amp
lifi
ed,i
tm
ust
be
amp
lifi
edb
yit
sen
cod
edp
oly
mer
ase.
Aft
erth
eta
rget
-sp
ecifi
cp
rim
ers
are
con
sum
ed,
the
nes
ted
PC
Ris
“car
ried
”b
yex
tern
alp
rim
ers
con
tain
ing
AE
GIS
com
po
nen
ts.
Th
ege
nes
enco
din
gp
oly
mer
ases
that
are
able
toco
py
AE
GIS
nu
cleo
tid
es(t
he
blu
ear
cs)
are
enri
ched
inth
ep
rod
uct
po
ol.
Th
ep
rod
uct
sar
eth
enre
clo
ned
,an
dth
ese
lect
ion
isre
pea
ted
.
Alternative Watson–Crick Synthetic Genetic Systems
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adapted to polymerases by Holliger (Ghadessyet al. 2001) proved to be useful. Called compart-mentalized self-replication (CSR) (Fig. 9), bac-teria containing plasmids encoding polymerasevariants are placed in water droplets emulsifiedin oil. The water droplets carry buffer, primers,and triphosphates necessary for PCR. The bac-teria biosynthesize the encoded variant poly-merase, which is released with its encodinggene to the extracellular PCR mixture in the firstheat step. If the variant can replicate its owngene, then that gene is amplified and the ampli-fied gene presented to subsequent cycles of lab-oratory evolution. If, however, the variant can-not replicate its own gene, it does not survive.By placing AEGIS components strategicallyinto a CSR experiment, polymerases that copythem with increased efficiency are evolved in thelaboratory.
Polymerases contain too many importantamino acid residues to expect good results toemerge via random variation. Evidence ofthis comes, for example, from the fact that alibrary of 108 polymerase variants having 4–6amino-acid replacements does not generate anythat can be recovered in a laboratory selectionexperiment that has useful activity (Laos et al.2014).
However, the natural history of polymeraseevolution can rationally improve polymerase li-braries. Sites that display unusual evolutionarybehavior (such as heterotachy, homoplasy, andparallelism) (Fig. 10) are more productively al-tered in a nonrandom library (Chen et al. 2010).As a consequence, many polymerase systems arenow available that replicate many different six-letter AEGIS alphabets (Sismour et al. 2004,2005; Yang et al. 2010; Laos et al. 2014; Sefahet al. 2014).
USING AEGIS MOLECULAR BIOLOGY
With the availability of polymerases that repli-cate AEGIS pairs, the orthogonality of AEGISpairing seen in the b-DNA assay can be exploit-ed in useful processes and products that includeAEGIS PCR. Consider, for example, the multi-plexed PCR problem. In most cases, PCR am-plicons can be extracted from a complex biolog-
ical mixture by adding a single pair of primers.However, as the number of primer pairs is in-creased to target more and more amplicons, theprimers interact with each other, find off-targetsites in a complex genomic environment tobind, and create spurious amplicons.
AEGIS proved able to manage this problemin a nested PCR format (Fig. 11). Here, the PCRis initiated with low concentrations of chimericprimers with 30-ends complementary to the tar-get (natural) sequence, and a 50-AEGIS tag. Theinitial rounds of PCR create amplicons carryingcomplementary AEGIS sequences at their ends.Therefore, after the small amounts of chimericprimers are consumed, the PCR is carried bylarge amounts of “external” AEGIS primerscomplementary to these tags. Even thoughthey are present at high concentration, the AE-GIS external primers cannot find any naturalDNA to bind off-target, even in very complexbiological mixtures. Therefore, AEGIS nestedPCR is very clean, even in a multiplexed form(Fig. 11) (Yang et al. 2010).
AEGIS orthogonality gained further usewith the invention of “conversion” (Yang et al.2013). Conversion copies a standard DNA mol-ecule in a solution that lacks, for example, dCTP.Instead, the solution contains dZTP. With thecorrect polymerase, the correct buffer compo-nents, and the correct pH, the polymerase isforced to put in Z opposite G in the template(Yang et al. 2013). This creates a GAZT product,which cannot complement any natural xenonucleic acid (XNA) sequence in any biologicalsample, no matter how complex. This allowsclean and uniform capture of AEGIS DNA.
The use of conversion is shown in Figure 12,in a single assay that targets 22 different arbo-viral RNA sequences that might be presentin a single mosquito carcass (Glushakova et al.2015). Self-avoiding primers (Hoshika et al.2010) are used with AEGIS external primers tocleanly create amplicons. Then, primers are ex-tended with conversion to create an AEGIS tagthat is the only oligonucleotide in the mixturecomplementary to a CTPA (computed tomog-raphy pulmonary angiogram) probe carried bya Luminex bead. This gives high and uniformdetection of the amplicons arising from what-
S.A. Benner et al.
14 Cite this article as Cold Spring Harb Perspect Biol 2016;8:a023770
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AA
AA
VV
VV
AA
VV
VV Spl
it (c
onse
rved
but
diff
eren
t)
VV
VV
AA
VV
AA
AA
VV
Hom
opla
sy (
para
llel,
conv
erge
nce)
VV
AA
RV
VM
VV
MV
Mut
abili
ty h
igh
in a
ll br
anch
es. S
ites
whe
re r
epla
cem
ent n
ot li
kely
to d
estr
oyac
tivity
, but
will
hav
e lit
tle u
sefu
l im
pact
Het
erot
achy
: mut
abili
ty d
iffer
ent i
nbr
anch
es. S
ites
whe
re r
epla
cem
ent w
illha
ve a
n im
pact
, but
not
des
troy
act
ivity
VV
VV
AA
VV
MM
RV
Figu
re10
.T
he
pat
tern
of
amin
oac
idre
pla
cem
ent
inn
atu
ral
his
tory
.R
epre
sen
ted
her
eb
ya
ph
ylo
gen
etic
tree
for
anin
div
idu
alsi
te,
the
pat
tern
can
be
use
dto
guid
ep
rote
inen
gin
eeri
ng
and
dir
ecte
dev
olu
tio
nex
per
imen
ts.
(A)
Am
ino
acid
rep
lace
men
tat
site
sw
ith
low
-lev
elo
frep
lace
men
tsty
pic
ally
lead
sto
inac
tiva
tio
no
fth
ep
oly
mer
ase.
(B)
Ho
mo
pla
syin
the
form
ofp
aral
lele
volu
tio
nin
dic
atin
gp
uri
fyin
gse
lect
ion
rem
oves
vari
atio
nth
atm
oves
bey
on
da
smal
lset
ofa
min
oac
ids;
site
sd
isp
layi
ng
this
vari
atio
nm
ayb
ere
pla
ced
ina
con
tro
lled
way
.(C
)Su
bst
anti
alva
riat
ion
ind
icat
esa
site
exp
erie
nci
ng
litt
lep
uri
fyin
gse
lect
ive
pre
ssu
re;r
epla
cem
enta
tth
issi
teis
un
like
lyto
hav
eli
ttle
inte
rest
ing
imp
acto
np
oly
mer
ase
beh
avio
r.(D
)H
eter
ota
chy,
inw
hic
hd
iffe
ren
tb
ran
ches
hav
ed
iffe
ren
tra
tes
ofa
min
oac
idre
pla
cem
ent,
ind
icat
esch
angi
ng
fun
ctio
nal
con
stra
ints
ata
site
;th
ese
site
sar
eth
efo
cio
fth
em
ost
succ
essf
ull
yd
esig
ned
pro
tein
engi
nee
rin
gli
bra
ries
(Ch
enet
al.2
010)
.
Alternative Watson–Crick Synthetic Genetic Systems
Cite this article as Cold Spring Harb Perspect Biol 2016;8:a023770 15
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Am
plic
on “
mes
s” in
nest
ed P
CR
with
out
AE
GIS
in th
e ta
gs
Cle
an a
mpl
ifica
tion
in n
este
d P
CR
wit
hA
EG
IS in
the
tags
12
34
56
78
5′-P
5′-P
PP
P
PP
Ext
end
chim
eric
prim
er
with
dN
TP
s
Str
and
sepa
rate
Str
and
sepa
rate
Tagg
ed a
mpl
icon
Ext
end
chim
eric
prim
er
with
dN
TP
s, d
ZT
P
Ext
end
exte
rnal
prim
er
w
ith d
NT
Ps,
dZ
TP
Com
mon
ext
erna
l prim
ers
in h
igh
conc
entr
atio
n“a
naly
te-s
peci
fic”
chim
eric
prim
ers
in lo
w c
once
ntra
tion
P
5′-P
PP
P5′-
PP
PP
PP
PP
-5′
PP
PP
-5′
PP
PP
-5′
5′-P
P
PP
5′-P
P
PP
5′-P
P
PP
ZZ
ZZ
5′-P
P
PP
ZZ
ZZ
5′-P
P
PP
ZZ
ZZ
PP
PP
-5′
PP
PP
-5′
P ZZ
ZZ
PP
P-5
′
PP
PP
-5′
Figu
re11
.Alt
ho
ugh
syn
thes
ized
ina
“gra
nd
chal
len
ge”
toas
k“w
hat
if?”
and
“wh
yn
ot?
”q
ues
tio
ns,
arti
fici
ally
exp
and
edge
net
icin
form
atio
nsy
stem
s(A
EG
IS)
com
po
nen
tstu
rned
ou
tto
hav
ep
ract
ical
valu
e.H
ere,
AE
GIS
ort
ho
gon
alit
ycr
eate
sve
rycl
ean
mu
ltip
lexe
dp
oly
mer
ase
chai
nre
acti
on
(PC
R)
(rig
ht)
ina
nes
ted
PC
Rar
chit
ectu
re(l
eft)
.Wit
hA
EG
ISn
ucl
eoti
des
inth
eex
tern
alp
rim
ers,
PC
Ris
clea
n,e
ven
inco
mp
lex
bio
logi
calm
ixtu
res
com
par
edw
ith
the
sam
eP
CR
bu
tw
ith
exte
rnal
pri
mer
sh
avin
go
nly
stan
dar
dn
ucl
eoti
des
(Yan
get
al.
2010
).
S.A. Benner et al.
16 Cite this article as Cold Spring Harb Perspect Biol 2016;8:a023770
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ever RNAvirus might be present. These combi-nations are now being used in assays to detectmany infectious disease agents, from NiV (Ni-pah virus) and SARS (severe acute respiratorysyndrome) virus to MERS (Middle East respi-ratory syndrome) virus and Zika virus (Benneret al. 2015; Yang et al. 2015).
USING AEGIS INSTEAD OF NATURALNUCLEOTIDES
This kind of synthetic biology shows that tech-nology need not be constrained by the structureof the biopolymers that prebiotic chemistry(and four billion years of subsequent historicalaccident examined by natural selection) on
Earth has delivered to us. Peter Schultz and oth-ers have made the parallel point with respect tothe protein lexicon (Chatterjee et al. 2013).
The two can be joined. More nucleotide“letters” in a genetic alphabet should allow thewriting of more amino acid “words” in the pro-tein “lexicon.” Indeed, using the first-genera-tion AEGIS S and B “letters” to support thecodon:anticodon interaction in mRNA andtRNA molecules allowed an AEGIS mRNA mol-ecule to encode a 21st amino acid in in vitrotranslation (Bain et al. 1992). One outcome ofthis synthesis was a deeper understanding of therole played by release factors in preventingframe shifting during translation termination.Parallel experiments using the second-genera-
VEE 19
Background0
1000
2000
3000
4000
MF
IM
FI
MF
I
MF
I
MF
I
MF
I
MF
I
MF
I
MF
I
MF
I
MF
I
MF
I
MF
I
MF
I
MF
IM
FI
MF
I
5000
6000
7000
8000
0
2000
4000
6000
8000
10000
2000
3000
4000
1000
0
5000
60007000
8000
900010000
12000
0
500
1000
1500
2000
2500
6000 3000 7000 10000 9000
0
500
1000
1500
2000
250012,000
10,000
10,000
9000
80007000
6000
5000
4000
3000
2000
1000
0
8000
7000
6000
5000
4000
3000
2000
1000
0
8000
6000
4000
2000
0
8000
7000
6000
5000
4000
3000
2000
1000
0
9000
8000
7000
6000
5000
4000
3000
2000
1000
0
6000
5000
4000
3000
2000
1000
0
2500
2000
1500
1000
500
0
5000
4000
3000
2000
1000
0
2000
1800
1600
1400
1200
1000
800
600
400
200
0
MF
I
MF
IM
FI
MF
I
MF
I
5000 600
500
400
300
200
100
0
4500
4000
3500
3000
2500
2000
1500
1000
500
0Conversion/vent(exo-) Background
BackgroundBackground
California encephalitis Jamestown canyon San Angelo Primers
Keystone La Cross encephalitisRocioSnowshoe HareMelaoSerra do Navio
Venezuelan Equineencephalitis
Dengue 1 Dengue 2 Dengue 3 Dengue 4 Murray valley encephalitis
Western Equineencephalitis
Eastern Equineencephalitis
Saint Louis encephalitisJapanese encephalitisYellow feverWest Nile
WN
150
50
150
Multiplex nested SAMRS-AEGIS RT-PCR
50
150
50
YF JE SLE EEE WEE VEE D1 D2 D3 D4 MVE SN Mel SSH Rocio KS LAC CE JTC SA
Background Background Background Background
Conversion/vent(exo-)
Conversion/vent(exo-)Conversion/vent(exo-)
Background Conversion/vent(exo-)
Background Conversion/vent(exo-) Conversion/vent(exo-)Background BackgroundConversion Background Conversion Background
Conversion/vent(exo-)
Background Conversion/vent(exo-) Background Conversion/vent(exo-) Background Conversion/vent(exo-)
Conversion/vent(exo-) Background Conversion/vent(exo-)
Background Conversion/vent(exo-)Background Conversion/vent(exo-)
Conversion/vent(exo-)Conversion/vent(exo-) Conversion/vent(exo-)
Background BackgroundConversion/vent(exo-) Conversion/vent(exo-)
LAC 51
Mel 77
JTC2 25EEE 53
D1 79
MB 27
D3 55
KS 85
CE 31
JE 57
SA 89
MVE 39Racio 59
D2 91
JTC1 41
SN 61SLE 95
SSH 43 D4 47
WEE 75WN 71
YF 99
0
1000
2000
3000
4000
5000
6000
7000 1400 2500 4500 8000 6000
5000
4000
3000
2000
1000
0
7000
6000
5000
4000
3000
2000
1000
0
4000
3500
3000
2500
2000
1500
1000
500
0
2000
1500
1000
500
0
1200
1000
800
600
400
200
0
Figure 12. Artificially expanded genetic information systems (AEGIS) conversion supports an assay that allows22 mosquito-borne viruses to be sought in a single mosquito carcass (for details, see Glushakova et al. 2015).
Alternative Watson–Crick Synthetic Genetic Systems
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tion Z:P pair, in which the AEGIS tRNA wascharged by flexizymes (Morimoto et al. 2011)did the same. Flexizymes are RNA catalysts thatcharge tRNA molecules with amino acids; theinspiration for their design was the RNA en-zymes that presumably charged tRNA in theRNAworld that invented ribosomal translation.
AEGIS IN LARGE-SCALE DNA SYNTHESIS
AEGIS can also be used for another goal dis-cussed in this volume: the synthesis of largeDNA constructs from short fragments. Here,AEGIS components are placed in the overhangsof synthetic fragments that are mixed in a mul-ticomponent ligation to create a large DNAmolecule (Fig. 13). Because AEGIS nucleotidesdo not pair with standard nucleotides, theseoverhangs cannot form hairpins or other unde-sired secondary structures, either within theirfragment or between fragments; they thereforeare free to hybridize as designed. After ligation,conversion in the reverse direction replaces theAEGIS nucleotides by standard nucleotides,completing the synthesis of a large DNA con-struct. This was shown in a “one-pot” synthesisof a gene encoding kanamycin resistance (Mer-ritt et al. 2014).
AEGIS AS A PLATFORM FOR EVOLUTION
Natural DNA and RNA (collectively XNA) canperform functions beyond genetics (Ellingtonand Szostak 1992; Bartel and Szostak 1993;Breaker and Joyce 1994; Schneider et al. 1995;Kraemer et al. 2011). RNA catalysis may havesupported the first forms of life on Earth; a cur-rent model for natural history holds that an ear-lier episode of life on Earth (the “RNA world”)used RNA as its only genetically encoded cata-lytic component (Benner et al. 1989). Indeed,the design of flexizymes to charge AEGIS tRNAwith unnatural amino acids was based on theRNA-world model (Morimoto et al. 2011).
Adding replicable nucleobases should in-crease the binding and catalytic potential ofthe XNA libraries. However, adding nucleobasesalso expands the “sequence space” accessible toa biopolymer, from 4n species in a library n
nucleotides in length to 6n in an XNA specieswith six building blocks, and 12n if the AEGIS iscompleted. It remains open whether AEGIShelps or hurts the search for functional nucleicacids, as the success of that search depends onthe “density” of functional behavior in thatspace. Here, we might be advised to follow thePerrin or Silverman strategy of simply function-alizing the four standard nucleotides (Hollen-stein et al. 2009a,b; Zhou et al. 2016).
The availability of polymerases that repli-cate AEGIS nucleotides makes it possible to ap-ply laboratory in vitro evolution (LIVE) exper-iments to address that question (Fig. 14). Theapproach follows in vitro selection experimentsapplied to four-letter nucleic acids by Szostak,Ellington, Gold, Joyce, and others (Ellingtonand Szostak 1992; Bartel and Szostak 1993;Breaker and Joyce 1994; Schneider et al. 1995;Kraemer et al. 2011). These experiments haveshown that natural nucleic acids are relativelypoor reservoirs of receptors, ligands, and cata-lysts, perhaps because of their limited numberand functionality of their monomers.
AEGIS-LIVE is in its infancy, now with justfour examples. Nevertheless, it appears as ifadding functionalized AEGIS nucleotides to aDNA library delivers better and more specificreceptors and ligands. In one example (Zhanget al. 2015), a GACTZP DNA library was used inan AEGIS-LIVE experiment to find AEGIS mol-ecules that bind to HepG2 liver cancer cells. Acounterselection against untransformed livercells (Hu1545V) was used to remove nonspecif-ic binders (Fig. 14) (Zhang et al. 2015). Bindingwas seen in the bulk pools after 12 rounds ofaffirmative selection. Four rounds of negativeselection were embedded in the process, whichalso included 200 cycles of PCR. Sequencingrecovered 17 motifs that contributed from0.14% to 26% of the total surviving population.These were resynthesized and their affinities forHepG2 cells were measured (specificity data areshown in Fig. 15).
Several features of the data are striking. First,AEGIS-LIVE delivered cell-binding moleculesthat had more than one Z and/or P. These in-clude several that had Z and P nearby (ZnP andPnnZ, where “n” is any nucleotide), multiple Z
S.A. Benner et al.
18 Cite this article as Cold Spring Harb Perspect Biol 2016;8:a023770
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BT
S B
G C
T A
B SN
NN
NN
NT
S B
G C
S B
T A
C G
NN
NN
NN
NN
NN
NN
NB S
T A
S B
C G
B SG
NN
NN
NN
NN
NN
NA
AS
C5′
Pol
ymer
ase
exte
nsio
n
3′
NN
GN
TN
5′3′
BT
S B
G C
T A
B SN
NN
NN
NT
S B
G C
S B
T A
C G
NN
NN
NN
NN
NN
NN
NB S
T A
S B
C G
B SG
NN
NN
NN
NN
NN
NA
AS
C5′
NG
GN
TN
5′
NN
NN
NN
NN
NN
NN
NN
NN
NN
NG
GN
TN
NN
NN
NN
NN
NN
NN
Liga
tion
BT
S B
G C
T A
B SN
NN
NN
NT
S B
G C
S B
T A
C G
NN
NN
NN
NN
NN
NN
NB S
T A
S B
C G
B SG
NN
NN
NN
NN
NN
NA
AS
C5′
NG
GN
TN
5′N
NN
NN
N
NN
NN
NN
NN
NN
NN
NG
GN
TN
NN
NN
NN
NN
NN
NN
3′
3′ 3′
PC
R a
mpl
ifica
tion
with
con
vers
ion
AT
T A
G C
T A
A TN
NN
NN
NT
T A
G C
T A
T A
C G
NN
NN
NN
NN
NN
NN
NA T
T A
T A
C G
A TG
NN
NN
NN
NN
NN
NA
AT
C5′
NG
GN
TN
5′N
NN
NN
N
NN
NN
NN
NN
NN
NN
NG
GN
TN
NN
NN
NN
NN
NN
NN
3′
3′
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ne
NN
N
N
NH
O
R
H
NN
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H
RCH
3
NN
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O
R
H
H
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l for
m o
f B
NN
O NRC
H3
HH
Com
plem
enta
ry to
T
Ket
o fo
rm o
f B
Com
plem
enta
ryto
isoC
90:1
0
Do
no
r
Acc
epto
r
Do
no
r
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no
r
Acc
epto
r
Do
no
r
Acc
epto
r
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epto
r
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epto
r
Acc
epto
r
Do
no
r
Do
no
r
SB
Maj
orta
utom
er
Min
orta
utom
er
Figu
re13
.Ass
emb
lyo
fla
rge
DN
Aco
nst
ruct
su
sin
gar
tifi
cial
lyex
pan
ded
gen
etic
info
rmat
ion
syst
ems
(AE
GIS
)p
airs
toas
sem
ble
the
stra
nd
sw
ith
ou
tth
eir
form
ing
hai
rpin
so
ro
ther
un
des
ired
stru
ctu
res.
Her
e,th
ead
ded
info
rmat
ion
den
sity
ofa
six-
lett
erp
oly
mer
dim
inis
hes
asse
mb
lyam
big
uit
y.A
fter
the
asse
mb
lyis
com
ple
te,
AE
GIS
nu
cleo
tid
esar
eco
nve
rted
tost
and
ard
nu
cleo
tid
es,
givi
ng
anen
tire
lyn
atu
ral
con
stru
ct(M
erri
ttet
al.
2014
).T
his
allo
ws
sho
rter
frag
men
tsto
be
use
d,
wh
ich
,in
turn
,re
cogn
izes
that
the
lon
ger
the
syn
thet
icfr
agm
ent,
the
grea
ter
the
chan
ceo
fer
ror.
Alternative Watson–Crick Synthetic Genetic Systems
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T A G
C P
Z
Syn
thes
ize
six-
nuce
lotid
ess
DN
A li
brar
y
Six
-nuc
elot
ide
PC
R
Sur
vivo
rs
Per
form
six
-nuc
elot
ide
deep
seq
uenc
ing
Rem
ove
nons
peci
fic D
NA
Neg
ativ
e se
lect
ion
Cou
nter
cel
ls
Sur
vivo
rs a
fter
posi
tive
sele
ctio
n
Ext
ract
–bou
nd D
NA
DN
A–c
ell c
ompl
ex
Rem
ove
unbo
und
DN
A
Targ
et c
ells
Pos
itive
sel
ectio
n
Six
-nuc
elot
ide
ssD
NA
libr
ary
AE
GIS
cell-
SE
LE
X
Scr
een
and
char
acte
rize
pote
ntia
l apt
amer
s
...A
PC
TZ
GT
CG
Z...
...T
CG
CZ
PAT
CG
......
CT
GZ
GP
TAG
T...
...T
GC
PAZ
ZG
TC
...
Figu
re14
.Sch
emat
ico
fth
ece
ll-t
arge
ted
arti
fici
ally
exp
and
edge
net
icin
form
atio
nsy
stem
s–la
bo
rato
ryin
vitr
oev
olu
tio
n(A
EG
IS-L
IVE
)re
po
rted
inZ
han
get
al.
(201
5).
AG
AC
TZ
PD
NA
lib
rary
wit
ha
ran
do
miz
edre
gio
nfl
anke
db
yp
rim
erb
ind
ing
site
sw
asin
cub
ated
wit
hth
eta
rget
cell
s.U
nb
ou
nd
seq
uen
ces
wer
eth
enw
ash
edaw
ay.B
ou
nd
seq
uen
ces
wer
eel
ute
dfr
om
the
cell
s,an
dth
esu
per
nat
ant
enri
ched
inA
EG
ISD
NA
mo
lecu
les
(“su
rviv
ors
”)h
avin
gaf
fin
ity
for
the
cell
sw
asco
llec
ted
.C
ou
nte
rsel
ecti
on
sw
ere
per
form
edag
ain
stu
ntr
ansf
orm
edli
ver
cell
s.T
he
surv
ivo
rsw
ere
po
lym
eras
ech
ain
reac
tio
n(P
CR
)-am
pli
fied
usi
ng
afl
uo
resc
ein
iso
thio
cyan
ate
(FIT
C)-
lab
eled
pri
mer
and
ab
ioti
nyl
ated
pri
mer
.Si
ngl
e-st
ran
ded
DN
Aw
asm
ade
fro
mth
eP
CR
pro
du
cts
usi
ng
the
bio
tin
han
dle
and
ente
red
into
the
nex
tro
un
do
fse
lect
ion
.In
each
rou
nd
,su
rviv
or
po
ols
wer
em
on
ito
red
for
the
app
eara
nce
of
“bu
lk”
bin
din
g.A
fter
this
was
seen
,su
rviv
ors
wer
ese
qu
ence
dan
dre
syn
thes
ized
for
stu
dy.
S.A. Benner et al.
20 Cite this article as Cold Spring Harb Perspect Biol 2016;8:a023770
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LZH
114
±3
24±
3
24±
5
36±
5
41±
7
47±
5
55±
8
96±
10
214±
26
234±
53
247±
25
298±
37
326±
53
346±
50
727±
84
>10
00
>10
000.
54%
0.74
%
1.25
%
1.27
%
0.91
%
0.14
%
0.27
%
1.35
%
25.6
7%
0.65
%
23.0
4%
2.15
%
0.66
%
0.50
%
7.84
%
0.28
%
0.40
%
LZH
2
LZH
3
LZH
4
LZH
5
LZH
6
LZH
7
LZH
8
LZH
9
LZH
10
LZH
11
LZH
12
LZH
13
LZH
14
LZH
15
LZH
16
LZH
17~
CC
AC
CT
AA
GC
TC
TG
GT
TT
CC
CG
TG
G~
~T
CC
CT
AC
AT
GC
GA
GT
AC
CA
CC
CC
TG
~
~T
TG
CG
CA
TG
CC
AC
CA
CC
TA
CC
AG
GC
~
~C
CA
AC
CT
GC
GA
CC
CA
CA
AC
CC
TA
TG
~
~G
TG
CG
GC
CA
CC
AT
AC
CC
TC
CT
GG
GC
~
~C
GG
CT
TG
AC
AG
ACP
GC
ATZ
GA
TC
AG
~
~C
GG
CC
GCZ
GA
GC
AG
GP
CC
CC
CC
CC
G~
~C
GC
CC
AC
GG
AA
GA
GT
CT
CT
GC
GG
CC
~
~T
AT
CP
GT
TG
CC
CT
TA
AA
GG
CT
AT
GG
~
~T
AT
TA
GT
AC
GG
CT
TA
AC
CCP
CA
TG
G~
~C
AA
TA
AT
TC
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Alternative Watson–Crick Synthetic Genetic Systems
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and P units nearby (e.g., PnnZnP), and evenone with an adjacent Z and P (PZ). This sug-gests that the AEGIS-LIVE experiment searchedmuch of the substantially larger “sequencespace” of the GACTZP system. The binderswere also more specific (Fig. 15).
Binders also emerged from the same ex-periment that had neither Z nor P. However,these had systematically weaker affinity forthe cells, all with Kdiss values that were.200 nM. Those with Z and P typicallyhad Kdiss values that were , 50 nM. These indi-cate that the additional functionality presentedon Z, or possibly the additional informationdensity of a six-letter nucleic acid, helped im-prove the quality of a DNA library as a reservoirof functional molecules. The nitro group is a“universal binding moiety” (remembering theability of nitrocellulose to bind many proteins).Increasing the information density in an oligo-nucleotide manages folding ambiguity, a majorproblem in DNA catalysts that have been stud-ied in detail (Carrigan et al. 2004).
MOVING AEGIS INTO LIVING CELLS
These results showed that the rather simple Wat-son–Crick model not only supported an en-larged molecular recognition system and an en-larged molecular biology, but also a geneticsystem that has enlarged functional potential.Further, if combined with natural enzymes, AE-GIS provides many of the properties that wevalue in a genetic system. AEGIS forms duplexeswith sequence specificity, directs its own repli-cation, can adapt under selective pressure, andcan evolve. Further, AEGIS has value, not onlyfor what it has taught us about the intimateconnection between molecular structure andgenetics, but also in human diagnostics, patho-gen surveillance, and in a platform to createreceptors, ligands, and catalysts “on demand.”
Thus, AEGIS fits closely the Kool definitionfor synthetic biology (unnatural parts workingin the context of natural systems) (Rawls 2000).However, AEGIS has not been placed into livingcells. Here, the much more “unnatural” pairdescribed by Romesberg has been replicatedfor �15 hr in Escherichia coli (Malyshev et al.
2014), as a single exemplar. Unfortunately, thistour de force required that an engineered E. colicell be fed presynthesized triphosphates pre-sented in the growth medium.
Fortunately, efforts to meet the granderchallenge, a robust bacterial system that repli-cates AEGIS-containing plasmids, are well un-derway. Mutants of kinases that add a singlephosphate to a nucleoside to make the nucleo-side monophosphate, and then further trans-form the monophosphate to the di- and tri-phosphates inside of living cells, have beenprepared (Matsuura et al. 2016). Cells havebeen engineered to manage the unnatural func-tional groups that AEGIS carries. The construc-tion of a cell that makes a fifth and sixth triphos-phate is teaching us much about how cellsregulate this key machinery required for life.Perhaps someday E.T. will be present on Earth,not by interstellar travel, but rather by the handsof the synthetic biologist.
CONCLUSIONS
“Synthetic biology” means different things indifferent communities. Most communities,however, remain focused on activities that in-volve only rearranging natural bioparts. This iscertainly pragmatic. Natural DNA can be or-dered inexpensively from Integrated DNATech-nologies (IDT). Natural polymerases that man-age natural DNA can be likewise obtainedinexpensively. Moving beyond “the natural” re-quires that one reinvent much of molecular bi-ology, including polymerases, restriction en-zymes (Chen et al. 2011), and other tools thatbiotechnologists take for granted. Moving be-yond the natural also requires the developmentof new synthetic pipelines to create triphos-phates and phosphoramidites and new analyt-ical chemistry tools, including AEGIS sequenc-ing technology (Yang et al. 2013). Thus, there islittle wonder that most synthetic biology usesnatural biological parts.
However, moving synthetic biology beyond“the natural” is not without benefits. As withclassical synthesis, the effort to recreate theproperties that we value in life, but with unnat-ural products, has provided (and is providing)
S.A. Benner et al.
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unexpected insights into how biomoleculeswork in terran systems, and how biomolecularstructure is intimately connected to biomolec-ular behavior.
But moving synthetic biology beyond thenatural also benefits technology. Natural DNAis the product of prebiotically constrained start-ing molecules whose molecular structures havebeen conserved despite their multiple defects(Fig. 3). Those who do not move beyond thenatural are condemned to suffer (in perpetuity)from these defects, which lead to failed micro-arrays (Wei et al. 2012), PCR multiplexed messes(Yang et al. 2010), expensive diagnostics (Glu-shakova et al. 2015), laborious DNA constructsynthesis (Merritt et al. 2014), poor quality ap-tamers (Sefah et al. 2014), slow DNAzymes(Zhang et al. 2015), ambiguously folded DNAnanostructures, and proteins with only 20 (or21) different kinds of amino acids (Chatterjeeet al. 2013).
In return for its added effort, the unnaturalkind of synthetic biology solves these prob-lems. Indeed, given the advantages of rede-signed DNA that “fixes God’s mistakes,” itmight soon be surprising to find anyone whouses the natural stuff anymore.
ACKNOWLEDGMENTS
We are indebted to the National Aeronauticsand Space Administration (NASA) (Experi-mental Approaches to Potential Alien Molecu-lar Biologies: A Two-Biopolymer DarwinianSystem, Grant No. NNX14AK37G and Expand-ed Alphabets for Constructing EvolutionaryMachines, Grant No. NNX15AF46G), The De-fense Threat Reduction Agency (DTRA) (Ap-tamers from Artificial Genetic Systems, GrantNo. HDTRA1-13-1-0004), and the TempletonWorld Charity Foundation (TWCF) (Doing Bi-oinformation Differently: A Two-BiopolymerSynthetic Life Form without Encoding orInstruction, with Bidirectional InformationFlow, Grant No. TWCF0092/AB57) for theirsupport of this work. This notwithstanding,the opinions expressed herein are those of theauthors, and not of the Federal Government,NASA, the Department of Defense, or the TWCF.
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