Alternative Watson–Crick Synthetic Genetic Systems

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

Cite this article as Cold Spring Harb Perspect Biol 2016;8: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-

S.A. Benner et al.

2 Cite this article as Cold Spring Harb Perspect Biol 2016;8:a023770



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Alternative Watson–Crick Synthetic Genetic Systems

Cite this article as Cold Spring Harb Perspect Biol 2016;8:a023770 3



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).

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Figure 2. How genetics works. The cartoon that “ex-plains everything”—paper cutouts that taught gen-erations of schoolchildren that molecular geneticswere simple chemistry.

S.A. Benner et al.




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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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-

S.A. Benner et al.




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n

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1st

gen

erat

ion

2nd

gen

erat

ion

N

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Figu

re5.

Dif

fere

nt

rin

gsy

stem

sh

avin

gd

iffe

ren

tp

rop

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esca

nim

ple

men

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me

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son

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rick

hyd

roge

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ne

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ake

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de

(Ben

ner

etal

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7).(

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re,u

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ine

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ud

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ine

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nd

ing

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tern

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rst

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tary

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ymid

ine.

S.A. Benner et al.




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

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H

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R

-

HH

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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).





35.1

36.0

AB

CD

2.6

3.1

3.3 2.

5

2.7

2.7

P1

Figu

re7.

Th

ree

crys

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ctu

res

wit

hZ

:Par

tifi

cial

lyex

pan

ded

gen

etic

info

rmat

ion

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ems

(AE

GIS

)p

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ang

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15).

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ith

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and

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chro

tate

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ght)

(Her

nan

dez

etal

.20

15).

S.A. Benner et al.




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).





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.




Nes

ted

PC

R w

here

poly

mer

ase

ampl

ifies

its o

wn

gene

with

exte

rnal

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GIS

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ers

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t lys

es c

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ging

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ers

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e w

ater

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eth

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on;

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n ne

sted

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es

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r be

st c

atal

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for

next

rou

nd

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ak e

mul

sion

s

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ers

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nd 1

am

plic

ons

have

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GIS

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. Afte

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e co

nsum

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ifica

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d by

exte

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ase

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nds

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ands

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e, r

ever

se p

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ase

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nds

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ith 4

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exte

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5′P

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ZZ

Z

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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

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amp

lify

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ina

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ted

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ain

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lib

rary

of

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ases

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eth

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tain

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san

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pla

ces

the

po

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con

tact

wit

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ple

tske

epth

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ant

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ith

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ifth

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ust

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ases

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ear

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are

enri

ched

inth

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isre

pea

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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.




AA

AA

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VV

AA

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VV Spl

it (c

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rved

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t)

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re11

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ho

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syn

thes

ized

ina

“gra

nd

chal

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toas

k“w

hat

if?”

and

“wh

yn

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arti

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and

edge

net

icin

form

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s(A

EG

IS)

com

po

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mer

sh

avin

go

nly

stan

dar

dn

ucl

eoti

des

(Yan

get

al.

2010

).

S.A. Benner et al.




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

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4000

3500

3000

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2000

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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

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WN

150

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Multiplex nested SAMRS-AEGIS RT-PCR

50

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Background Background Background Background

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Background Conversion/vent(exo-)

Background Conversion/vent(exo-) Conversion/vent(exo-)Background BackgroundConversion Background Conversion Background

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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

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MB 27

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0

1000

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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).





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.




BT

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ral

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2014

).T

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S.A. Benner et al.




LZH

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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.




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.

REFERENCES�Reference is also in this collection.

Bain JD, Chamberlin AR, Switzer CY, Benner SA. 1992.Ribosome-mediated incorporation of non-standardamino acids into a peptide through expansion of thegenetic code. Nature 356: 537–539.

Bartel DP, Szostak JW. 1993. Isolation of new ribozymesfrom a large pool of random sequences. Science 261:1411–1418.

Benner SA. 2004. Understanding nucleic acids using syn-thetic chemistry. Accounts Chem Res 37: 784–797.

Benner SA. 2009. The life, the universe and the scientificmethod. FfAME, Gainesville, FL.

Benner SA, Hutter D. 2002. Phosphates, DNA, and thesearch for nonterrean life. A second generation modelfor genetic molecules. Bioorg Chem 30: 62–80.

Benner SA, Allemann RK, Ellington AD, Ge L, Glasfeld A,Leanz GF, Krauch T, Macpherson LJ, Moroney SE, Picci-rilli JA. 1987. Natural selection, protein engineering andthe last riboorganism. Rational model building in bio-chemistry. Cold Spring Harbor Symp Quant Biol 52: 53–63.

Benner SA, Ellington AD, Tauer A. 1989. Modern metabo-lism as a palimpsest of the RNAworld. Proc Natl Acad Sci86: 7054–7058.

Benner SA, Battersby TR, Kodra J, Switzer CY, Moroney SE,Voegel J, MacPherson L, von Krosigk U, Hammer C,Richert C, et al. 1998. Redesigning nucleic acids. PureAppl Chem 70: 263–266.

Benner SA, Kim HJ, Merritt KB, Yang Z, McLendon C,Hoshika S, Hutter D. 2015. Next-generation DNA inpathogen detection, surveillance, and CLIA-waivable di-agnostics. Advances in Global Health through SensingTechnologies 2015, doi: 10.1117/12.2183481.

Bostick J. 2010. Origin of Apollo 13 quote: “Failure is not anoption,” www.thespacereview.com/article/357/1.

Bowman JC, Williams LD. 2011. Nucleic acids. In Encyclo-pedia of astrobiology, pp. 1141–1147. Springer, Berlin.

Breaker RR, Joyce GF. 1994. A DNA enzyme that cleavesRNA. Chem Biol 1: 223–229.

Brent RL. 2004. A partnership between biology and engi-neering. Nat Biotechnol 22: 1211–1214.

Bushnell S, Budde J, Catino T, Cole J, Derti A, Kelso R,Collins ML, Molino G, Sheridan P, Monahan J, et al.1999. ProbeDesigner for the design of probesets forbranched DNA (bDNA) signal amplification assays. Bio-informatics 15: 348–355.

Carrigan MA, Ricardo A, Ang DN, Benner SA. 2004. Quan-titative analysis of a deoxyribonucleotide catalyst ob-tained via in vitro selection. A DNA ribonuclease. Bio-chemistry 43: 11446–11459.

Chatterjee A, Sun SB, Furman JL, Xiao H, Schultz PG. 2013.A versatile platform for single- and multiple-unnaturalamino acid mutagenesis in Escherichia coli. Biochemistry52: 1828–1837.

Chen F, Gaucher EA, Leal NA, Hutter D, Havemann SA,Govindarajan S, Ortlund EA, Benner SA. 2010. Recon-structed evolutionary adaptive paths give polymerases




http://www.thespacereview.com/article/357/1




accepting reversible terminators for sequencing and SNPdetection. Proc Natl Acad Sci 107: 1948–1953.

Chen F, Yang ZY, Yang MC, Alvarado JB, Wang GG, BennerSA. 2011. Recognition of an expanded genetic alphabetby type-II restriction endonucleases and their applicationto analyze polymerase fidelity. Nucleic Acids Res 39:3949–3961.

Cox EG, Cruickshank DWJ, Smith JAS. 1958. The crystalstructure of benzene at 23˚C. Proc R Soc A 247: 1–21.

Davis JT. 2004. G quartets 40 years later: From 50-GMP tomolecular biology and supramolecular chemistry. AngewChem Int Ed Engl 43: 668–898.

Declercq R, Van Aerschot A, Read RJ, Herdewijn P, VanMeervelt L. 2002. Crystal structure of double helical hex-itol nucleic acids. J Am Chem Soc 124: 928–933.

Doi Y, Chiba J, Morikawa T, Inouye M. 2008. Artificial DNAmade exclusively of nonnatural C-nucleosides with fourtypes of nonnatural bases. J Am Chem Soc 130: 8762–8768.

Dueber JE, Yeh BJ, Bhattacharyya RP, Lim WA. 2004. Rewir-ing cell signaling: The logic and plasticity of eukaryoticprotein circuitry. Curr Opin Struct Biol 14: 690–699.

Dunitz JD, Bernstein J. 1995. Disappearing polymorphs. AccChem Res 28: 193–200.

Elbeik T, Markowitz N, Nassos P, Kumar U, Beringer S,Haller B, Ng V. 2004a. Simultaneous runs of the BayerVERSANT HIV-1 version 3.0 and HCV bDNAversion 3.0quantitative assays on the system 340 platform providereliable quantitation and improved work flow. J Clin Mi-crobiol 42: 3120–3127.

Elbeik T, Surtihadi J, Destree M, Gorlin J, Holodniy M,Jortani SA, Kuramoto K, Ng V, Valdes R, Valsamakis A,et al. 2004b. Multicenter evaluation of the performancecharacteristics of the Bayer VERSANT HCV RNA 3.0assay (bDNA). J Clin Microbiol 42: 563–569.

� Ellington AD. 2016. Review on DNA synthesis and assemblyapproaches. Cold Spring Harb Perspect Biol doi: 10.1101/cshperspect.a023812.

Ellington AD, Szostak JW. 1992. Selection in vitro of single-stranded molecules that fold into specific ligand-bindingstructures. Nature 355: 850–852.

Eschenmoser A. 1999. Chemical etiology of nucleic acidstructure. Science 284: 2118–2124.

Freier SM, Altmann KH. 1997. The ups and downs of nu-cleic acid duplex stability. Structure-stability studies onchemically-modified DNA:RNA duplexes. Nucleic AcidsRes 25: 4429–4443.

Fukui K. 1982. Role of frontier orbitals in chemical reac-tions. Science 218: 747–754.

� Gaj T, Sirk SJ, Shui SI, Liu J. 2016. Genome editing technol-ogies: Principles and applications. Cold Spring Harb Per-spect Biol doi: 10.1101/cshperspect.a023754.

Gao JM, Liu HB, Kool ET. 2004. Expanded-size bases innaturally sized DNA: Evaluation of steric effects in Wat-son–Crick pairing. J Am Chem Soc 126: 11826–11831.

Georgiadis MM, Singh I, Kellett WF, Hoshika S, Benner SA,Richards NGJ. 2015. Crystal structures of non-naturalnucleobase pairs in A- and B-DNA. J Am Chem Soc137: 6947–6955.

Geyer CR, Battersby TR, Benner SA. 2003. Nucleobase pair-ing in expanded Watson–Crick like genetic informationsystems. The nucleobases. Structure 11: 1485–1498.

Ghadessy FJ, Ong JL, Holliger P. 2001. Directed evolution ofpolymerase function by compartmentalized self-replica-tion. Proc Natl Acad Sci 98: 4552–4557.

Gibson DG, Glass JI, Lartigue C, Noskov VN, Chuang RY,Algire MA, Benders GA, Montague MG, Ma L, MoodieMM, et al. 2010. Creation of a bacterial cell controlled bya chemically synthesized genome. Science 329: 52–56.

� Glass J. 2016. Minimal cells. Cold Spring Harb Perspect Bioldoi: 10.1101/cshperspect.a023861.

Glushakova LG, Bradley A, Bradley KM, Alto BW, HoshikaS, Hutter D, Sharma N, Yang Benner SA. 2015. High-throughput multiplexed xMAP Luminex array panelfor detection of twenty two medically important mos-quito-borne arboviruses based on innovations in syn-thetic biology. J Virol Meth 214: 60–74.

Hernandez AR, Shao Y, Hoshika S, Yang Z, Shelke SA, Her-rou J, Kim HJ, Kim MJ, Piccirilli JA, Benner SA. 2015. Acrystal structure of a functional RNA molecule contain-ing an artificial nucleobase pair. Angew Chem Int Ed Engl54: 9853–9856.

Heuberger BD, Switzer C. 2008. An alternative nucleobasecode: Characterization of purine–purine DNA doublehelices bearing guanine–isoguanine and diaminopurine7-deazaxanthine base pairs. ChemBioChem 9: 2779–2783.

Hirao I. 2006. Unnatural base pair systems for DNA/RNA-based biotechnology. Curr Opin Chem Biol 10: 622–627.

Hirao I, Ohtsuki T, Fujiwara T, Mitsui T, Yokogawa T, OkuniT, Nakayama H, Takie K, Yabuki T, Kigawa T, et al. 2002.An unnatural base pair for incorporating amino acidanalogs into proteins. Nat Biotechnol 20: 177–182.

Hoffmann R. 1982. Building bridges between inorganic andorganic chemistry (Nobel Lecture). Angew Chem Int EdEngl 21: 711–724.

Hollenstein M, Hipolito CJ, Lam CH, Perrin DM. 2009a. ADNAzyme with three protein-like functional groups. En-hancing catalytic efficiency of M2þ-independent RNAcleavage. ChemBioChem 10: 1988–1992.

Hollenstein M, Hipolito CJ, Lam CH, Perrin DM. 2009b. Aself-cleaving DNA enzyme modified with amines, guani-dines and imidazoles operates independently of divalentmetal cations (M2þ). Nucleic Acids Res 37: 1638–1649.

Horhota A, Zou KY, Ichida JK, Yu B, McLaughlin LW, Szo-stak JW, Chaput JC. 2005. Kinetic analysis of an efficientDNA-dependent TNA polymerase. J Amer Chem Soc 127:7427–7434.

Hoshika S, Leal N, Chen F, Benner SA. 2010. Artificial ge-netic systems. Self-avoiding DNA in PCR and multi-plexed PCR. Angew Chem Int Ed Engl 49: 5554–5557.

Joyce CM, Steitz TA. 1994. Function and structure relation-ships in DNA polymerases. Ann Rev Biochem 63: 777–822.

Kimoto M, Kawai R, Mitsui T, Yokoyama S, Hirao I. 2009.An unnatural base pair system for efficient PCR amplifi-cation and functionalization of DNA molecules. NucleicAcids Res 37: e14.

S.A. Benner et al.




Kimoto M, Cox RS, Hirao I. 2011. Unnatural base pairsystems for sensing and diagnostic applications. ExpertRev Mol Diagn 11: 321–331.

Kimoto M, Yamashige R, Matsunaga K, Yokoyama S, HiraoI. 2013. Generation of high-affinity DNA aptamers usingan expanded genetic alphabet. Nat Biotechnol 31: 453–457.

Kishi Y. 1989. Natural products synthesis. Palytoxin. PureAppl Chem 61: 313–324.

Kishi Y, Fujuyama T, Aratani M, Nakatsubo F, Goto T, InoueS, Tanini H, Sugiura S, Kakoi H. 1972. Synthetic studieson tetrodotoxin and related compounds. IV: Stereospe-cific total synthesis of DL-tetrodotoxin. J Am Chem Soc 94:9219–9221.

Kool ET, Morales JC, Guckian KM. 2000. Mimicking thestructure and function of DNA. Insights into DNA stabil-ity and replication. Angew Chem Int Ed Engl 39: 990–1009.

Kraemer S, Vaught JD, Bock C, Gold L, Katilius E, KeeneyTR, Kim N, Saccomano NA, Wilcox SK, Zichi D, et al.2011. From SOMAmer-based biomarker discovery to di-agnostic and clinical applications: A SOMAmer-based,streamlined multiplex proteomic assay. PLoS ONE 6:e26332.

Krishnamurthy R. 2015. On the emergence of RNA. IsraelJ Chem 55: 837–850.

Laos R, Thomson JM, Benner SA. 2014. DNA polymerasesengineered by directed evolution to incorporate non-standard nucleotides. Front Microbiol 5: 565.

� Lechner A, Brunk E, Keasling JD. 2016. The need for inte-grated approaches in metabolic engineering. Cold SpringHarb Perspect Biol doi: 10.1101/cshperspect.a023903.

Leduc S. 1912. La biologie synthetique. Poinat, Paris.

Malyshev DA, Seo YJ, Ordoukhanian P, Romesberg FE.2009. PCR with an expanded genetic alphabet. J AmChem Soc 131: 14620–14621.

Malyshev DA, Dhami K, Lavergne T, Chen T, Dai N, FosterJM, Correa IR Jr, Romesberg FE. 2014. A semi-syntheticorganism with an expanded genetic alphabet. Nature 509:385–388.

Mathison M. 1982. E.T. the Extra-Terrestrial. Directed bySteven Spielberg, Universal Pictures.

Matsuura MF, Shaw RW, Moses JD, Kim HJ, Kim MJ, KimMS, Benner SA. 2016. Assays to detect the formation oftriphosphates of unnatural nucleotides: Application toEscherichia coli nucleoside diphosphate kinase. ACSSynth Biol 5: 234–240.

McMinn DL, Ogawa AK, Wu Y, Liu J, Schultz PG, Romes-berg FE. 1999. Efforts toward expansion of the geneticalphabet. DNA polymerase recognition of a highly stable,self-pairing hydrophobic base. J Am Chem Soc 121:11585–11586.

Merritt KB, Bradley KM, Hutter D, Benner SA. 2014. Wholegenes from autonomous self-assembly of synthetic oligo-nucleotides incorporating artificial nucleotides. BeilsteinJ Org Chem 10: 2348–2360.

Miller PS, Ts’o POP. 1988. Oligonucleotide inhibitors ofgene-expression in living cells. New opportunities indrug design. Ann Rep Med Chem 23: 295–304.

Mizusawa S, Nishimur S, Seela F. 1986. Improvement of thedideoxy chain termination method of DNA sequencing

by use of deoxy-7-deazaguanosine triphosphate in placeof dGTP. Nucleic Acids Res 14: 1319–1324.

Morimoto J, Hayashi Y, Iwasaki K, Suga H. 2011. Flexizymes:Their evolutionary history and the origin of catalyticfunction. Acc Chem Res 44: 1359–1368.

Nielsen PE, Egholm M, Berg RH, Buchardt O. 1991. Se-quence-selective recognition of DNA by strand displace-ment with a thymine-substituted polyamide. Science 254:1497–1500.

Orgel LE. 2004. Prebiotic adenine revisited: Eutectics andphotochemistry. Orig Life Evol Biosph 34:: 361–369.

Pople JA. 1999. Nobel lecture. Quantum mechanicalmodels. Rev Mod Phys 71:: 1267–1274.

Prehoda KE, Scott JA, Mullins RD, Lim WA. 2000. Integra-tion of multiple signals through cooperative regulation ofthe N-WASP-Arp2/3 complex. Science 290: 801–806.

Rawls RL. 2000. “Synthetic biology” makes its debut. ChemEng News 78: 49–53.

Rich A. 1962. On the problems of evolution and bio-chemical information transfer. In Horizons in biochemis-try (ed. Kasha M, Pullmann B), pp. 103–126. Academic,New York.

Schneider KC, Benner SA. 1990. Oligonucleotides contain-ing flexible nucleoside analogs. J Am Chem Soc 112: 453–455.

Schneider DJ, Feigon J, Hostomsky Z, Gold L. 1995. High-affinity ssDNA inhibitors of the reverse transcriptase oftype 1 human immunodeficiency virus. Biochemistry 34:9599–9610.

Sefah K, Yang Z, Bradley KM, Hoshika S, Jimenez E, Zhu G,Shanker S, Yu F, Tan WH, Benner SA. 2014. In vitroselection with artificial expanded genetic informationsystems (AEGIS). Proc Natl Acad Sci 111: 1449–1456.

Sepiol J, Kazimierczuk Z, Shugar DZ. 1976. Tautomerism ofiso-guanosine and solvent-induced keto-enol equilibri-um. Z Naturforsch 31: 361–370.

Shapiro R. 1987. Origins: A skeptic’s guide to the creation oflife on Earth. Bantam Dell, New York.

Sharma N, Hoshika S, Hutter D, Bradley KM, Benner SA.2014. Recombinase-based isothermal amplification ofnucleic acids with self-avoiding molecular recognitionsystems (SAMRS). ChemBioChem 15: 2268–2274.

Sismour AM, Benner SA. 2005a. Synthetic biology. ExpertOpin Biol Ther 5: 1409–1414.

Sismour AM, Benner SA. 2005b. The use of thymidine an-alogs to improve the replication of an extra DNA basepair. A synthetic biological system. Nucleic Acids Res 33:5640–5646.

Sismour AM, Lutz S, Park J-H, Lutz MJ, Boyer PL, HughesSH, Benner SA. 2004. PCR amplification of DNA con-taining non-standard base pairs by variants of reversetranscriptase from human immunodeficiency virus-1.Nucleic Acids Res 32: 728–735.

Smolke CD. 2009. Building outside of the box: iGEM andthe BioBricks Foundation. Nat Biotechnol 27: 1099–1102.

Steinbeck C, Richert C. 1998. The role of ionic backbones inRNA structure. An unusually stable non-Watson–Crickduplex of a nonionic analog in an apolar medium. J AmChem Soc 120: 11576–11580.





Stoker HS. 1988. General, organic and biological chemistry,p. 745. McGraw-Hill, New York.

Tawfik DS, Griffiths AD. 1998. Man-made cell-like com-partments for molecular evolution. Nat Biotechnol 16:652–656.

Voegel JJ, Benner SA. 1994. Non-standard hydrogen bond-ing in duplex oligonucleotides. The base pair between anacceptor–donor–donor pyrimidine analog and a do-nor–acceptor–acceptor purine analog. J Am Chem Soc116: 6929–6930.

Voegel JJ, Benner SA. 1996a. Synthesis and characterizationof non-standard nucleosides and nucleotides bearing theacceptor–donor–donor pyrimidine analog 6-amino-3-methylpyrazin-2(1H )-one. Helv Chim Acta 79: 1863–1880.

Voegel JJ, Benner SA. 1996b. Synthesis, molecular recogni-tion and enzymology of oligonucleotides containing thenon-standard base pair between 5-aza-7-deaza-isogua-nine and 6-amino-3-methylpyrazin-2(1H )-one, a do-nor–acceptor–acceptor purine analog and an accep-tor–donor–donor pyrimidine analog. Helv Chim Acta79: 1881–1898.

Voegel JJ, Altorfer MM, Benner SA. 1993a. The donor–ac-ceptor–acceptor purine analog: Transformation of 5-aza-7-deaza-1H-isoguanine (¼4 aminoimidazo-[1,2-a]-1,3,5-triazin-2(1H )-one) to 20-deoxy-5-aza-7-deaza-isoguanosine using purine nucleoside phosphorylase.Helv Chim Acta 76: 2061–2069.

Voegel JJ, von Krosigk U, Benner SA. 1993b. Synthesis andtautomeric equilibrium of 6-amino-5-benzyl-3-methyl-pyrazin-2-one. An acceptor–donor–donor nucleosidebase analog. J Org Chem 58: 7542–7547.

von Krosigk U, Benner SA. 2004. Expanding the geneticalphabet. Pyrazine nucleosides that support a donor–donor–acceptor hydrogen bonding pattern. Helv ChimActa 87: 1299–1324.

Wei T, Pearson MN, Armstrong K, Blohm D, Liu J. 2012.Analysis of crucial factors resulting in microarray hybrid-ization failure. Mol Biosyst 8: 1325–1339.

Wilds CJ, Wawrzak Z, Krishnamurthy R, Eschenmoser A,Egli M. 2002. Crystal structure of a B-form DNAduplex containing (L)-a-threofuranosyl (30!20) nucle-osides: A four-carbon sugar is easily accommodated intothe backbone of DNA. J Am Chem Soc 124: 13716–13721.

Win MN, Liang JC, Smolke CD. 2009. Frameworks for pro-gramming biological function through RNA parts anddevices. Chem Biol 16: 298–310.

Winnacker M, Kool ET. 2013. Artificial genetic sets com-posed of size-expanded base pairs. Angew Chem Int EdEngl 52: 12498–12508.

Woodward RB. 1968. Recent advances in the chemistry ofnatural products. Pure Appl Chem 17: 519–547.

Woodward RB, Hoffmann R. 1970. The conservation of or-bital symmetry. Springer, Heidelberg.

Yang Z, Chen F, Chamberlin SG, Benner SA. 2010. Expandedgenetic alphabets in the polymerase chain reaction. An-gew Chem Int Ed Engl 49: 177–180.

Yang Z, Durante M, Glushakova LG, Sharma N, Leal NA,Bradley KM, Chen F, Benner SA. 2013. Conversion strat-egy using an expanded genetic alphabet to assay nucleicacids. Anal Chem 85: 4705–4712.

Yang Z, McLendon C, Hutter D, Bradley KM, Frye C, BennerSA. 2015. Helicase dependent isothermal amplificationof DNA and RNA using self-avoiding molecular recogni-tion systems. ChemBioChem 16: 1365–1370.

Zhang L, Yang Z, Sefah K, Bradley KM, Hoshika S, Zhu G,Cansiz S, Teng IT, Champanhac C, McLendon C, et al.2015. Evolution of functional six-nucleotide DNA. J AmChem Soc 137: 6734–6737.

Zhou C, Avins JL, Klauser PC, Brandsen BM, Lee Y, Silver-man SK. 2016. DNA-catalyzed amide hydrolysis. J AmChem Soc 138: 2106–2109.

Zubay G. 1988. The roots of modern biochemistry (ed. Klein-kauf H, von Doehren H, Jaenicke L), pp. 911–916. Walterde Gruyter, Berlin.

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