»Reprogrammed: This is the structure of the endonuclease
I-MsoI computationally redesigned to target a new DNA
site. The redesigned enzyme displays altered site specificity with a level of target discrimination comparable to that ofwild type (see ref. 8). Such methods are currently being applied
to more ambitious targets, such as disease-gene hot spots.

Proteins
by Design

New functional proteins are buing built on advances
in modeling and structure prediction
By David Baker

26 The Scientist July 2006

organism, scientists could also characterize the structure of eachof its proteins. This would unveil biomolecular interactions, structural homologies, functional roles, and potential drug targets thatmight never be found from gene sequence alone.
Although there is still a long way to go, with improvements toalgorithms and increases in computing power, exciting progress
is being made in both prediction and design.

STRUCTURE PREDICTION
Now-classic experiments conducted with RNAase in the early
1970s demonstrated that all the information necessary to fold aprotein resides in its amino acid sequence. This suggested that
protein-structure prediction could be fairly straightforward. But
going from sequence to structure has proven phenomenally difficult – biology’s version of predicting the weather – at least in partΩ

Courtesy Justin Ashworth

Imagine having the power to create a brand new protein – abiosensor for any small molecule, say, or a novel enzyme– on demand. It’s not pure fantasy. Computational structural
biology is poised to put this power into our hands.
Along with a team of research groups around the world, wehave begun designing novel proteins and folds from scratch, computing amino acid sequences that will fold to create enzymatic
activities never before seen in nature. The possibilities are limited
only by our imaginations: Picture an endonuclease designed
to thwart malaria, molecular sensors for bioterror agents, or avaccine that HIV is less likely to evolve around.
The mechanics of these engineering feats are closely related,perhaps not surprisingly, to their logical inverse: structure prediction. Scientists have for years tried to develop methods for predicting a protein’s structure simply from its amino acid sequence.
Imagine that in the time it takes to sequence the genome of anJuly 2006 The Scientist	 27

proteins by design

because even a relatively small protein
can assume a vast number of possible
conformations. According to “Levinthal’sParadox,” which assumes each amino acidhas three rotational degrees of freedom(and that’s an underestimate), a 100residue sequence could adopt at least 3100
possible conformations.
To attack this problem, we havedeveloped a computer program called
ROSETTA, which has at its core amethod for computing the energy of agiven protein conformation. Eliminating
unlikely structures that have, for instance,hydrophobic residues exposed to solvent,the program intelligently samples the total
protein-folding landscape, testing perhaps
a million or so possible conformations forthe lowest energy structure.
To benchmark our progress, we havesince 1998 been entering ROSETTA predictions in a worldwide structure-prediction
experiment called CASP (Critical Assessment of techniques for protein Structure
Prediction; www.predictioncenter.org).
Instituted more than a decade ago, CASP isa sort of structural biology proving ground,a community experiment/competition inwhich participants are asked to predict theshape of proteins whose structures havebeen elucidated but not yet published.
The predictions are then compared to thecorrect structures, and a meeting is held todiscuss the results and identify the mostpromising methods and the most important problems remaining to be solved.
No algorithm has yet produced a precisely correct structure, but ROSETTA has
performed very well in these tests, and ourpredictions (and those of other groups) get
closer every year. A highlight of the mostrecent event, CASP6, for instance, was theprediction by Phil Bradley in my group of a
76-residue protein to within 1.5 Angstromsof the correct structure. Phil followed up
on this achievement by showing we could
predict structures with this accuracy for anumber of small proteins.1

DE NOVO DESIGN
Protein design is essentially the inverseof prediction; here, we are asking for the
2 8 The Scientist July 2006

amino acid sequence that will fold in sucha way as to create a protein structure thatcarries out a desired function.
The field has made exciting progressdesigning proteins with new structuresand functions. In 1998, Stephen Mayoand coworkers at the California Instituteof Technology computed a novel sequencethat folded into a naturally occurring zincfinger structure. In 2003, Brian Kuhlman,
now a professor at the University ofNorth Carolina, Chapel Hill, and Gautam
Dantas in my group went a step further,using ROSETTA to design an exceptionally stable protein called Top7, which hasa sequence and structure unrelated to
any known protein. The 93-amino acidprotein was found to be monomeric andfolded, and it’s X-ray structure lined up
remarkably well with our prediction, demonstrating that modern protein-design
methodology can design brand-new proteins with atomic-level accuracy.2
While the creation of completely newstructures is exciting, current efforts inthe protein-design field have primarily
taken aim at giving existing proteins functions found elsewhere in protein chemistry. Mayo, along with Daniel Bolon, forinstance, used the catalytically inactive
Escherichia coli protein, thioredoxin, as a
scaffold for a novel enzyme capable of catalyzing the histidine-mediated hydrolysisof p-nitrophenyl acetate.3 Bill DeGrado’sgroup at the University of Pennsylvania
engineered a metalloenzyme site into adesigned four-helix bundle protein, while
Homme Hellinga of Duke University
Medical Center in Durham, NC, and colleagues have used members of the E. coli
periplasmic binding protein superfamily
as scaffolds upon which to design new
biosensors for, among other molecules,trinitrotoluene (TNT) and glucose.4
Remarkably, Hellinga and coworkers
succeeded in coupling their new designed
biosensors to cellular signaling pathways
to generate bacteria that turn blue when
exposed to TNT; this work may lead to
new detection methods for the land mines
plaguing much of the world. More recently,
Hellinga’s team successfully converted anotherwise inactive ribose-binding protein

into an extremely active catalyst of thetriose phosphate isomerase reaction.5
The next step is the important but
formidable challenge of creating enzymes
to catalyze chemical reactions not performed by naturally occurring proteins(see “Designing a New Catalyst”). Ourgroup is heading up a worldwide teamof researchers representing the wide
range of expertise that will be required
for success. The team includes the designgroups of Mayo, Hellinga, Kuhlman, andJens Meiler; the computational chemistryexpertise of William Jorgensen’s groupat Yale; Ken Houk’s quantum chemistrygroup at the University of California, Los
Angeles, which brings the ability to accurately compute structures of active sites
optimal for stabilizing reaction transition
states; the molecular evolution and catalytic antibody expertise of Don Hilvert’sgroup in Switzerland; and other groups
with expertise ranging from computerscience and physical chemistry to biochemistry and molecular biology. Althougha tremendous challenge, with this stellar
team, brought together and funded by
the Defense Advanced Research Projects
Agency (DARPA), I am optimistic we will
see some real breakthroughs.

INTERACTION DESIGN
Beyond de novo design of catalysts, reengineering the interface of macromolecularinteractions, whether between proteinsor between a protein and a nucleic acid,is an important goal. Macromolecularinteractions play critical regulatory andfunctional roles in the cell, and redesigning these could lead to the developmentof new drug compounds, research tools,
and/or diagnostics. As with de novo
protein design, we have made exciting
progress in this area.
Lukasz Joachimiak, a graduate student
in my group, and Tanja Kortemme, nowa professor at the University of California, San Francisco, used as a test bed theinteraction between colicin E7 (a nonspecific DNAase) and its inhibitor, immunity
protein Im7. We first identified contacts on
one partner in the pair that would destabi-lize the complex, and then identified compensatory changes we could make in thesecond partner to restore the interaction.
In this way, we developed new proteinprotein pairs that interact with each otherwith subnanomolar affinities, but which
do not interact with their cognate wildtype partners.6
We are also working to design newprotein-DNA interactions. In 2002, forexample, working with the groups ofRay Monnat at the University of Washington and Barry Stoddard at the Fred
Hutchinson Cancer Research Center inWhile the creation of completely
new structures is exciting, current
efforts in the protein-design field havelargely aimed at giving existing proteinsfunctions found elsewhere.
Seattle, we developed a novel endonuclease, E-DreI, by fusing domains from two
separate homing enzymes, I-DmoI andI-CreI.7 The new enzyme has a DNAbinding specificity that is a hybrid of thetwo parent enzymes.
Recently, Jim Havranek in my groupextended the ROSETTA design methodology to the reengineering of proteinDNA interaction specificity, and graduate
student Justin Ashworth computationallyredesigned the DNA-binding interface of
the I-MsoI homing endonuclease to cleavea new DNA sequence.8 Justin’s experimental characterization of the redesigned
enzyme showed that it efficiently cleaves
the new site, but not the original site,
and the high-resolution crystal structure
confirmed the accuracy of the design (seeimage, p. 27).
We are now using this computational
design approach to try to create therapeutically useful endonucleases. Designedenzymes could be introduced into mutant
cells or organisms, together with a wildtype copy of the mutant gene, to drive
gene therapy, for instance. Following
cleavage of the disease gene by the novelΩ

July 2006 The Scientist	 2 9

proteins by design

enzyme, the wild-type sequence would beused to drive DNA repair, thereby fixing
the genetic defect. In collaboration witha team of researchers worldwide we arealso seeking to design new endonucleases
that would inactivate the genes in mosquitoes required for the malaria parasiteto survive and propagate; such engineered
enzymes could play roles in malaria eradication programs.

OTHER APPLICATIONS
Protein design methodology can be
useful in unanticipated ways. A numberof significant human diseases, including
Alzheimer and Parkinson, are associ-ated with proteins that misfold to form
amyloid fibrils. David Eisenberg’s groupat UCLA made a major breakthroughlast year in the understanding of how
this occurs when they reported the firsthigh-resolution structure of an amyloidforming peptide. 9 The study revealed a
set of interactions that seem very likely
to be general to most, if not all, amyloidstructures. John Karanicolas in my grouphas been collaborating with Eisenberg’sgroup to try to predict the portions ofproteins responsible for amyloid fiberformation. The design methodology in
ROSETTA was used to identify sequencescompatible with a generalized model of
their amyloid structure.10

Designing a New Catalyst
In a recent demonstration of computational protein-design principles, we approached an intramolecular aldol reaction whose substrate is
not catalyzed by naturally occurring enzymes. The aldol reaction constitutes one of the most powerful tools for the formation of carboncarbon bonds both in nature and the lab. Our goal is to design a non-natural aldolase able to catalyze reactions of non-natural substrates.

Target Reaction:
Hajos-Parrish-Eder-Sauer-Wiechert intramolecular aldol reaction.

EnzymeTS ModelLys 201
Site Description: The proposed catalytic
residues in the active site are based on three
key residues – Lysine 167, Aspartate 102, andLysine 201 – found in the native deoxyribosephosphate aldolase (DERA). The transition state(TS) model (yellow) and optimal functionalgroup positions of the catalytic residues (green)
are calculated by quantum mechanical (QM)
methods.

3 0 The Scientist July 2006

Asp 102

Lys 167

Protein design also has commercial
implications. Xencor, a protein-engineering
firm in Monrovia, Calif., for instance, hasused its Protein Design Automation technology to develop XPro-1595, a dominantnegative inhibitor of tumor necrosis factor
alpha, which is expected to enter clinical
trials in 2006. Researchers at Sangamo
Biosciences recently announced theirsuccess in repairing genes in vivo using adesigned, zinc-finger-based enzyme. Biosensors, like Hellinga’s novel designs, could
be used to build chips, or even to regulate
an implantable artificial pancreas. And
then there are the nearly limitless options
in organic chemistry for enzymes capableof accelerating complicated synthesis reac-tions, such as that of the antimalarial drug,artemisinin.
Yet we remain constrained by computational power. The calculations required
to probe and refine a protein’s structure
are intensive, as they must account for all
the various rotational and conformation
degrees of freedom. It took 15 processordays (3.2 GHz CPUs) to model the docking
interface between two proteins for a community experiment similar to CASP, theCritical Assessment of Prediction of Interactions (CAPRI, http://capri.ebi.ac.uk). The
same calculations would have taken a year
on decade-old hardware. It took approximately 150 CPU-days to make the folding
predictions for our 2005 Science paper.1ROSETTAΩDesign: Rosetta algorithms search for new active sites
in a library of protein scaffolds and design the residues
surrounding these potential active sites to further stabilize the TS model. The protein should fold in a way thatwill place the desired catalytic groups in the active siteand have pocket-shape complementary to the TS model.

Experimentation: This designed protein
retains the desired catalytic geometry fromthe QM calculation and the pocket to bindthe TS model. The next steps would include
synthesizing the designed protein, evaluating
its catalytic activity, and crystallizing it forcomparison with the predicted structure.
Such experiments will further test our understanding of catalytic sites.

July 2006 The Scientist	 3 1

proteins by design

To enable the searching of the huge
conformational and sequence spaces
associated with protein-structure prediction and design, David Kim in my group
has developed a distributed computing
version of ROSETTA, called rosetta@
home, which harnesses the collective
computing power of tens of thousands ofcomputers around the world.
At last count rosetta@home hadsome 67,000 users worldwide, yielding a
combined 29 teraflops of computational
power – good enough for eighth place on
the November 2005 list of the world’s top
500 supercomputers. The resulting webof computing power is greatly increasing the rate at which we can improve theROSETTA structure-prediction methodology, as we can test new ideas much morequickly. Currently rosetta@home users are
field-testing our new methods on the justreleased prediction targets for the ongoing
CASP7 structure-prediction experiment,
which closes August 4.
When CASP7 is completed, we will
also direct our users’ computational power
to important design problems, such as thedevelopment of a vaccine for HIV. The
reason people have difficulty fighting off
HIV is that the major viral coat protein,
gp120, has many highly variable loops.
Immune responses to the virus tend to be
aimed at these variable regions and hence
are relatively ineffective against the virus.
There are, however, a few key regions ofgp120 that cannot change because they are
critical to virus infectivity. Bill Schief in mygroup, in collaboration with the groups ofPeter Kwong, Rich Wyatt, and Gary Nabelat the National Institutes of Health, Leo
Stamatatos at the Seattle Biomedical
Research Institute, Roland Strong at theFred Hutchinson Cancer Research Center,and Dennis Burton at Scripps ResearchInstitute, is now designing a series of novel
protein vaccines designed to mimic theseAchilles-heel regions of the virus. Our collaborators will be testing whether antibodies made against these potential vaccines
can neutralize virus infectivity.

3 2 The Scientist July 2006

These examples are just the beginning.
I would guess the potential for protein
design is nearly as vast as the diversity ofbiology itself. With the power to create
unheard of catalysts, improved biomolecular interactions, and genuinely useful
new proteins, I consider myself privileged
to be part of a field limited merely byimagination and computing power. And I
am even more privileged to have had theopportunity to work with the wonderfulstudents and postdoctoral fellows who
have come through my research group,
many of whom continue at their respective universities to develop the ROSETTA
prediction and design methodology andapply it to problems I never would havedreamed of. nDavid Baker is a professor of biochemistry and a
Howard Hughes Medical Institute investigator at
the University of Washington, Seattle. You can sign
up for the rosetta@home project from his lab Web
page, www.bakerlab.org
dbaker@the-scientist.com

References
1. P. Bradley et al., “Toward high-resolution de novo
structure prediction for small proteins,” Science,
309:1868–71, 2005.
2. B. Kuhlman et al., “Design of a novel globularprotein fold with atomic level accuracy,” Science,
302:1364–8, 2003.
3. D.N. Bolon, S.L. Mayo, “Enzyme-like proteinsby computational design,” Proc Natl Acad Sci,
98:14274–9, 2001.
4. L.L. Looger et al., “Computational designof receptor and sensor proteins with novelfunctions,” Nature, 423:185–90, 2003.
5. M.A. Dwyer et al, “Computational design of abiologically active enzyme,” Science, 304:1967–71,2004.
6. T. Kortemme et al., “Computational redesign ofprotein-protein interaction specificity,” Nat StructMol Biol, 11:371–9, 2004.
7. B.S. Chevalier et al., “Design, activity, and structureof a highly specific artificial endonuclease,” Mol
Cell, 10:895–905, 2002.
8. J. Ashworth et al., “Computational redesignof endonuclease DNA binding and cleavage
specificity,” Nature, in press, 2006.
9. R. Nelson, “Structure of the cross-b spine ofamyloid-like fibrils,” Nature, 435:773–8, 2005.
10. M.J. Thompson et al., “The 3D profile method foridentifying fibril-forming segments of proteins,”
Proc Natl Acad Sci, 103:4074–8, March 14, 2006.