Compared with conventional technologies, manynatural molecular machines systems displaytremendous abilities. The molecular machinery

of green plants, for example, converts more energy andsynthesizes a greater tonnage of organic compoundsthan does humanity’s entire chemical industry, and doesso cleanly and by using cheap raw materials. At a conservative megabyte or so per genome, the digitalstorage capacity of the millions of bacteria in the dirton a typical computer far exceeds that of the advertisedcomponents. Although we spend billions of dollars on dense digital storage systems, nature places far denser systems in the same boxes free of charge, butunintended and unusable.

From megaton-per-year product streams tomegabyte-per-cubic-micron storage systems, naturalmolecular machinery has outperformed anything wecurrently know how to build. Perhaps, then, we shouldlearn to build molecular machine systems ourselves,aiming to make a wider range of products, includingcomputer components. This is the first in a series ofarticles organized around the theme of nanotechnol-ogy. Other authors will describe a range of micro- andnanoscale systems – some useful today, others demon-strating components and techniques with promise forthe future. I will outline how these trends can buildtoward a molecular machine technology delivering(and even exceeding) the technological promisedemonstrated by the molecular machinery of nature.

What are molecular machine systems?Speaking of molecular machines is not a metaphor.

If something has moving parts and does useful work,we call it a machine. If something is nanometers in scaleand has a precise arrangement of bonded atoms, we callit a molecule, or a molecular assembly. If somethingmatches both these descriptions, we can properly callit a molecular machine; if it comprises many parts, eachworthy of the name ‘machine’, it may be betterdescribed as a molecular machine system.

Such descriptions don’t define sharp boundaries.Although it seems hard not to view the bacterial flagellar motor as a molecular machine, somewhere onthe path towards simplicity – from ribosome toenzyme, organometallic catalyst or solvated ion – theterm ‘machine’ loses its utility. The very fuzziness ofthe boundary, however, emphasizes that no barrier sep-arates the simpler systems we can design and build fromthe more intricate and capable systems we can as yetonly sketch and analyse. Development can proceed byincrements rather than by breakthroughs.

As is so often the case in technology, engineeringdesign and analysis can describe at least some of the possibilities. As one might expect, however, the easiestsystems to analyse are not the easiest to synthesize.

Biological systems and modern synthetic techniquescan most easily make polymeric structures, but thesemust fold appropriately if they are to form compact,stable molecular objects. Designing and modeling poly-mers that fold and function in solution, however, pre-sents severe challenges. Flexibility multiplies the poss-ible configurations beyond any hope of an exhaustiveanalysis, and (with help from the solvent) ensures thatthe driving forces for molecular interactions dependstrongly on the entropic components of the free energy.Accordingly, theoretical studies of molecular machinesystems1–3 have focused on inflexible covalent structures– graphitic and diamondoid materials – working in thesimplest possible medium, a vacuum. Among thedevices analysed are gears, bearings, motors and logicgates, and systems using them, including nanoscale sepa-rators, conveyors and assemblers for manipulating mol-ecules, and sensors, signal channels and computers formanipulating bits.

A straightforward analysis based on well-establishedphysical principles shows that these advanced devicescan process matter, energy, and information precisely,efficiently and with high productivity1. Theoreticalstudies have explored synthetic strategies for such struc-tures, guiding reactions by the atomically precise posi-tioning of highly reactive species3. These syntheticstrategies, however, themselves rely on preexisting mol-ecular machinery to do the positioning – a good wayto build on an initial success, perhaps, but not a goodway to start. Diamondoid nanomachines seem anappealing goal for the long term (and they make a finetheoretician’s playground today), but they can’t be syn-thesized using current or next-generation laboratorytechiques.

Concrete progress must build on existing technolo-gies. As the most attractive approaches involve solvated,self-assembled systems of folded macromolecules, itshould come as no surprise that biotechnology and its allied fields are well positioned to exploit the early opportunities. Both current developments andfuture possibilities have been explored in a series ofconferences sponsored by the Foresight Institute4–6.

Molecular design and fiddlingThe most complex molecular machine systems yet

shaped by human ingenuity are proteins. But, althoughcurrent biotechnology modifies and shuffles naturalproteins, it makes little use of de novo designs. This limitation stems in part from real difficulties, but bolder design efforts may also be inhibited by a tacitmisconception of the nature of the design problems.

For classic example, ‘the protein-folding problem,’ isbetter thought of as two quite different problems, thoseof fold prediction and fold design. The first is scien-tific: given the primary structure of a natural protein,can we infer the tertiary structure (without help fromknowledge of a homologous protein)? The second istechnological: given a desired tertiary structure, can

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Building molecular machine systemsK. Eric Drexler

K. E. Drexler is at the Institute for Molecular Manufacturing, 123 Fremont Ave., Los Altos, CA 94022, USA.

we design a primary structure that will fold to produceit (with help from whatever choices of objective andapproach prove most useful)? Because evolution –unlike human design – doesn’t aim to develophumanly predictable folds, it seemed clear early on thatthe technological problem should prove easier7.Indeed, despite an early consensus that nature must be understood before engineering could succeed, fold design has advanced more quickly than fold prediction.

The lesson here is that technologists can cheat. In a technological context, the right approach to a diffi-cult scientific question is often to go around it, to learnthe basics from nature and then do something differ-ent. In protein design, for example, the numerous~1 kcal mole21 uncertainties in the stabilizing or desta-bilizing effect of residue-level choices can be buried byincorporating an unnaturally high density of interac-tions, each of which is expected (although not known)to be stabilizing. The individual uncertainties aren’teliminated, but are instead accommodated by allowingan overall margin for error.

Another escape from the paralysis-inducing con-templation of our scientific limitations is to exploitevolutionary methods. Nature, after all, has developedcomplex molecular machine systems without usinggraduate students or any other mechanism for design-ing and modeling proposed structures. In vitro evolu-tionary systems for peptides and nucleic acids show thevalue of large-scale trial and error, as does the recentexplosion of combinatorial chemistry. Knowledge andclever design can shape experimental objectives andtechniques while still relying on brute-force searches

to find molecular solutions to a particular problem ofbinding or catalysis. With further ingenuity, thesesearch techniques might be extended to aid the de-velopment of components for molecular machine systems.

Yet another way around problems is to work withmore tractable biological materials. Protein folds are, inpart, difficult to design because individual amino acidshave no strong, natural complementarity. Designingself-assembling sticky-ended DNA structures, by con-trast, is utterly routine. Elaborating this principle inmore difficult directions has led to the design and syn-thesis of branched, three-dimensional structures(including a cube-like framework containing eight Yjunctions) and a growing range of successors8. Thiswork indicates that nucleic acids can be engineered toserve as scaffolds for complex molecular systems.

Biomimetic systemsNature relies chiefly on proteins and nucleic acids for

molecular machine components, but evolution hasresponded to incentives that differ from ours and hasbeen locked into the same basic chemistry for billionsof years. By learning from nature and then applying thetools of organic synthesis to realize quite differentdesigns, we can gain still more freedom to avoid problems and implement solutions.

When building protein-like molecular objects,adding amino acids from outside the geneticallyencoded set can allow better core packings, comple-mentary interactions, novel modes of cross linking, anda wider range of surface moieties9. Adding stabilizinginteractions enables stable folds in shorter chains,

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reducing the synthetic challenges. Entirely replacingthe standard peptide backbone can also make synthesis easier; some choices also facilitate the formation ofhelical segments, producing so-called ‘foldamers’. Likepeptide chains, nonbiological foldamers can presum-ably serve as building blocks for tertiary and quaternaryassemblies of the complexity necessary to implementmolecular machine systems.

Fully synthetic nonbiological structures may seem farfrom the concerns of biotechnologists, but the actualoverlap is quite broad. At the outset, good technologi-cal objectives will often stem from a biological inspir-ation. Then, after an excursion through nonbiologicaltechniques to forge covalent structures, many of the keyproblems and techniques tend to reconverge. Thepurification, characterization and manipulation of sol-vated macromolecules and macromolecular assemblieswill be as important here as in molecular biology; manyof the same instruments and intellectual tools will beessential to the enterprise.

ToolsRegardless of whether specifically biological mol-

ecules remain the preferred choice for implementingdesign concepts, ongoing advances in tools for biotech-nology will boost molecular machine research. Thegrowth of combinatorial chemistry has strengthenedthe drive towards high-throughput, automated systemsfor chemical synthesis and analysis. Improved micro-fluidic systems – ‘labs on a chip’ – will make experi-ments faster and cheaper, expanding the utility of trialand error in overcoming limited predictive knowledgeand design methods. Micromechanical systems in theform of scanning-probe microscopes now enable thedirect visualization and manipulation of individualmacromolecules.

Powered by the exponential explosion in micro-processor performance, advances in molecular model-ing software have expanded the range of systems thatcan be effectively simulated. Here again, the distinctionbetween scientific and technological questions makes adifference. For example, no molecular modeling sys-tem can correctly predict the equilibrium crystal struc-ture of every organic molecule, if only because thechoice between structures of different symmetry maydepend on arbitrarily small differences in free energy.If modeling is viewed as a scientific effort to develop acomprehensive, predictive theory, this is a grave short-coming. If modeling is seen as a means of searching forextraordinarily stable structures, however, substantialerrors in energy calculations may be acceptable. Inmany areas of molecular engineering, designs for whichmodeling gives ambiguous results will be those lackingoverall robustness. Better, then, to fix the design thanperfect the model.

Toward biomimetic machineryWith computational modeling to aid rational design,

and faster, cheaper cycles of synthesis and analysis tocorrect mistakes, it seems that emerging technologieswill eventually enable the routine fabrication of diversemacromolecular objects comparable in function to pro-teins. As proteins in nature form molecular machinesystems, it seems worth considering what analogoussystems could do for us.

Current technological approaches have shaped plansto spend billions of dollars in a worldwide, multi-yeareffort to read the human genome. The scale of thiseffort seems odd, because every person working in ithas a body with trillions of cells, each containing ahuman genome and a set of molecular machines ableto read and copy it in a matter of hours. These DNAreaders transfer genetic information to other molecules,rather than to a conventional database, but this reflectsevolutionary, not physical constraints. Many techniques(optical, mechanical and electrical) are now known forsensing changes in single molecules. Thus, an earlyproduct of a molecular machine technology could bea DNA reader using arrays of devices comparable to abacterial DNA polymerase in size (~6 nm) andthroughput (~10 bases s21) but bound to a solid sur-face and interfaced to microelectronics. At the currentcost of sequence data, even molecular complexes labo-riously nudged together and monitored using scan-ning-probe systems could prove to be economical.

Of more direct use to molecular machine technologyitself would be a device with ribosome-like utility, able to piece together sequences of nonbiologicalmonomers that fold to make stable, functional prod-ucts. Like the DNA polymerases, natural ribosomestransfer information from one molecular medium toanother, and here again, a direct link to the macro-scopic world would be useful. Biology makes little useof a mechanism ubiquitous in chemical technology:building structures by exposure to a sequence of chemi-cal environments with differing temperature, pressure,pH, reagents and so forth; solid-phase synthesisschemes provide good examples. Temporal sequenc-ing could likewise be used to control the sequence ofmonomers added by a simple machine. Ideally, ofcourse, a sequence-builder would comprise a set of self-assembling molecules of the sort that it itself canbuild.

There is no great technological divide betweenadding monomers to a chain or to a dendrimer, a sheetor a sturdy cross-linked block. External signals can drivecomplex sequences of actions in simple nanoscale sys-tems1, allowing them to place monomers in patternsthat form systems that are larger and more intricate.The interactions between small molecular parts are assimple as the interactions between transistors, butmicroprocessors show that patterns of simple inter-actions can enable microscopic systems to performcomplex, programmable behaviors. It will be similarwith molecular machine systems, and the limits are hardto see.

References1 Drexler, K. E. (1992) Nanosystems: Molecular Machinery,

Manufacturing, and Computation, Wiley–Interscience2 Tuzun, R. E., Noid, D. W. and Sumpter, B. G. (1995)

Nanotechnology 6, 64–743 Merkle, R. C. (1997) Nanotechnology 8, 149–1624 Whole issue (1991) Nanotechnology 2, 111–2195 Crandall, B. C. and Lewis, J., eds (1992) Nanotechnology: Research and

Perspectives, MIT Press6 Whole issue (1998) Nanotechnology 9, 143–3047 Drexler, K. E. (1981) Proc. Natl. Acad. Sci. U. S. A. 78, 5275–52788 Seeman, N. C. et al. (1998) Nanotechnology 9, 257–2739 Drexler, K. E. (1994) Annu. Rev. Biophys. Biomol. Struct. 23,

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