Letter

https://doi.org/10.1038/s41586-019-1274-4

De novo protein design by citizen scientists
Brian Koepnick1,2, Jeff Flatten3, Tamir Husain3, Alex Ford1,2, Daniel-Adriano Silva1,2, Matthew J. Bick1,2, Aaron Bauer3,
Gaohua Liu4,5, Yojiro Ishida6, Alexander Boykov11, Roger D. Estep11, Susan Kleinfelter11, Toke Nørgård-Solano11, Linda Wei11,
Foldit Players10, Gaetano T. Montelione4,6, Frank DiMaio1,2, Zoran Popović3, Firas Khatib7, Seth Cooper8 & David Baker1,2,9*Online citizen science projects such as GalaxyZoo1, Eyewire2 andPhylo3 have proven very successful for data collection, annotationand processing, but for the most part have harnessed human patternrecognition skills rather than human creativity. An exception is thegame EteRNA4, in which game players learn to build new RNA
structures by exploring the discrete two-dimensional space of
Watson–Crick base pairing possibilities. Building new proteins,
however, is a more challenging task to present in a game, as both therepresentation and evaluation of a protein structure are intrinsicallythree-dimensional. We posed the challenge of de novo protein
design in the online protein-folding game Foldit5. Players were
presented with a fully extended peptide chain and challenged to craft
a folded protein structure and an amino acid sequence encoding thatstructure. After many iterations of player design, analysis of the topscoring solutions and subsequent game improvement, Foldit players
can now—starting from an extended polypeptide chain—generate
a diversity of protein structures and sequences that encode them insilico. One hundred forty-six Foldit player designs with sequences
unrelated to naturally occurring proteins were encoded in synthetic
genes; 56 were found to be expressed and soluble in Escherichia coli,
and to adopt stable monomeric folded structures in solution. The
diversity of these structures is unprecedented in de novo protein
design, representing 20 different folds—including a new fold notobserved in natural proteins. High-resolution structures were
determined for four of the designs, and are nearly identical tothe player models. This work makes explicit the considerable
implicit knowledge that contributes to success in de novo protein
design, and shows that citizen scientists can discover creative new
solutions to outstanding scientific challenges such as the protein
design problem.
The principle underlying de novo protein design is that proteins
fold to their lowest free-energy state6; hence, designing a new protein structure requires finding an amino acid sequence with its lowest
energy state in the prescribed structure. In practice, this challenge canbe divided into two subproblems: first, crafting a protein backbonethat is designable (that is, that could be the lowest energy state of somesequence); and second, finding a sequence with its lowest energy statein the crafted structure. One of the challenges of protein design isthe exponentially increasing number of conformations available to apolypeptide chain, which is huge even for a modestly sized protein of
60–100 residues. Thus, the first subproblem of crafting a plausible backbone is extremely open-ended, and the second subproblem is difficult
because it is not tractable to explicitly check that a designed sequence
has lower energy in the crafted structure than in any other structure.
There has been considerable progress in de novo protein design inrecent years7–10, but it is unclear whether all of the contributions tothis success have been made explicit in the protocols used to design
proteins, and how much implicit knowledge resides in the expertise of

the designers. Disentangling the role of expert knowledge is particularly difficult for the extremely open-ended challenge posed by the first
subproblem (that is, crafting a plausible backbone), for which there area practically unlimited number of solutions. Because full computer
enumeration of backbones is not possible, there is considerable roomfor human creativity and intuition in generating and designing new
protein structures.
To investigate how crowd-based creativity could contribute to solving
the de novo protein design problem, we incorporated de novo design
tools into the protein-folding game Foldit. Foldit is a free online computer game developed to crowdsource problems in protein modelling,
and provides full control over the three-dimensional structure of a protein model5 (Fig. 1). Players compete to build a model with the lowest
free energy, as calculated using the Rosetta energy function11. In thepast, Foldit has been primarily applied to protein structure prediction
problems, in which players are presented with an unstructured aminoacid sequence and challenged to determine its native conformation5,12.
In one case, Foldit players redesigned a loop region of an already folded
structure13, but the de novo design of an entire protein is a far more
expansive challenge.
We repeatedly challenged Foldit players to design stably folded proteins from scratch, and iteratively improved the game on the basis of
their results. In each challenge, players were provided with a polyisoleucine backbone in a fully extended conformation (60–100 residues
in length) and were given 7 days to fold the backbone into a compact
structure and identify a sequence specifying this backbone. Initially,
most top-scoring (low-energy) Foldit player designs were highly
extended, lacked a solvent-inaccessible core and were composed
entirely of polar residues (Extended Data Fig. 1); such extended, fully
α-helical structures have more favourable hydrogen bonding, electrostatic and local torsional energies than collapsed structures, which must
contort to create a buried core. Whereas polylysine and other extended
polar sequences resembling these initial Foldit solutions are often
α-helical in solution14,15, the lack of long-range interactions precludes
specific folding into a single stable structure16. This highlights a limitation of using absolute energy as an optimization criterion for protein
design: a low-energy design does not guarantee structural specificity,which arises only if all other alternative conformations have higher
energy. To favour the design of globular solvent-excluding protein folds,
with sequences that uniquely encode them, we introduced three supplementary design rules into Foldit: a ‘core exists’ rule that requires aminimum proportion of residues (for example, 30%) to be solvent-inaccessible in the designed structure; a ‘secondary structure design’rule that prohibits glycine and alanine in all secondary structure elements; and a ‘residue interaction energy’ rule to penalize large residues
that make insufficient intramolecular interactions in the designed
structure. With the addition of these rules to Foldit, subsequent
top-scoring designs from Foldit players were compact globular proteins.

1

Department of Biochemistry, University of Washington, Seattle, WA, USA. 2Institute for Protein Design, University of Washington, Seattle, WA, USA. 3Center for Game Science, Paul G. Allen Schoolof Computer Science and Engineering, University of Washington, Seattle, WA, USA. 4Department of Molecular Biology and Biochemistry, Rutgers University The State University of New Jersey,
Piscataway, NJ, USA. 5Nexomics Biosciences, Bordentown, NJ, USA. 6Department of Biochemistry, Robert Wood Johnson Medical School, Rutgers The State University of New Jersey, Piscataway,
NJ, USA. 7Department of Computer and Information Science, University of Massachusetts Dartmouth, Dartmouth, MA, USA. 8Khoury College of Computer Sciences, Northeastern University,
Boston, MA, USA. 9Howard Hughes Medical Institute, University of Washington, Seattle, WA, USA. 10A list of participants appears in the Supplementary Information. 11Unaffiliated: Alexander
Boykov, Roger D. Estep, Susan Kleinfelter, Toke Nørgård-Solano, Linda Wei. *e-mail: dabaker@uw.edu
N A T U R E | www.nature.com/nature

RESEARCH Letter
a

f

b

c

d

e

Fig. 1 | The Foldit user interface. a, The Foldit score is the Rosetta energy
with a negative multiplier, so that better models yield higher scores.
b, The design palette allows players to change the identity of the aminoacid residue at any position of the model. c, The ‘pull’ tool allows players
to manipulate the 3D structure of the model. d, The ‘undo’ graph tracks
the score as a model is developed, and allows players to backtrack andload previous versions of a model. e, Additional Foldit tools (from left toa
0.00

Relative energybDesign trajectoriesiii

right): full-structure minimization, sidechain minimization, backbone
minimization, auto-design sidechains, repack sidechains, translate
or rotate model, secondary structure assignment, idealize secondary
structure, manually design sidechains, delete residues, insert residues,
insert cutpoint and idealize peptide bond geometry. f, Foldit players
explore diverse structures that have no sequence or structural homology
to natural proteins.

i

ii

iii

iv

v

vi

iii
v

–0.25
–0.50
–0.75
iv
–1.00

vi
0

1
2
3
Mutations per residuec4i,ii,iii

iv,vd
× 5,000
× 10,000

Fig. 2 | Comparison of Foldit player and automated design-samplingstrategies. a, Single trajectories (ignoring abandoned branches) for three
Foldit player-designed proteins in red (Foldit1), blue (Peak6) and green(Ferredog-Diesel); and design trajectories for four Rosetta-designed proteinsin grey. The y axis is the Rosetta energy rescaled so that the final design has
a value of −1.00, and positive energies are shown as zero. Foldit players
are willing to undergo large increases in energy to explore new regions; bycontrast, the Rosetta protocol has a limited ability to escape local energy
minima. Red circles correspond to structures shown in b. b, Snapshots fromthe design trajectory of Foldit1: (i) the initial extended chain of polyisoleucine;(ii) development of secondary structure; (iii) development of folded tertiary
N A T U R E | www.nature.com/naturevi

structure; (iv) sequence design of folded structure, with inset showing
favourable packing at positions 13 and 45; (v) high-energy intermediate
design, with inset showing redesign at positions 13 and 45, which results insteric clashes with the protein backbone; (vi) the final refined design, withinset showing renewed favourable interactions at positions 13 and 45. c, The
design strategy for Foldit1 represented as a graph, showing all branch points
where multiple design trajectories were spawned from a single intermediate.
The final design was reached after 17 branch points. Node colours correspondto five different cooperating Foldit players, and the final design is marked witha star. d, Similar representation of a Rosetta design trajectory—there are only
two branch points.

Letter RESEARCHbc –160

d 1.2
1.0
0.8
0.6
0.4
0.2
0.0

–180
–200

I

–220

2002949_0000

VIII

Rosetta energy

V–VI

6

8 10 12

–170
–180
0

2

4

6

8 10 12

–140
–160
–180

IX–X

–200

XI
XII–XIV

4

–130
–140
–150
–160

II–IV

VII

2

0

2002766_0001

2

4

6

XVI–XX–200–220

Structuralclustering–240

Fold groups
2003594_S028

0

5

10

15

0

2

4

6 8 10 12
Cα r.m.s.d. (ɯ)

g 0

10

–2

–5

0

5

10

15

20

1.2
1.0
0.8
0.6
0.4
0.2
0.0
0

5

10

15

–10

20

20

1.2
1.0
0.8
0.6
0.4
0.2
0.0

–180

f0
–4

0

1.2
1.0
0.8
0.6
0.4
0.2
0.0

8 10 12–160

XV

Normalized A280 (AU)02003169_S953

e 20

Mean residue ellipticity (103 deg cm2 dmol–1)

a

220

240

260

50

75

100

–15

0

0

20

–2

–5

10

–4

–10

0

–6

–10

220

240

260

25

50

75

100

–20

60

0

40

–5

0

–10

–10

–15

–20

20
0
–20
–40

200

220

240

–10

50

75

ΔG = 7.4
kcal mol–1–30
0 1 2 3 4 5 6 7
100

–2

0
ΔG = 12.3–5 kcal mol–1

–4

–10

0

–6

–15

–8
200 220 240 260
Wavelength (nm)

ΔG = 7.8
kcal mol–1
0 1 2 3 4 5 6 7

10

–20
260
25

0

0 1 2 3 4 5 6 7

–15

–8
200

10

5
10
15
20
Elution volume (ml)

25

30

20

0

–10

–6
200

ΔG = 14.4
kcal mol–1

25

50
75
100
Temperature (°C)–20

0 1 2 3 4 5 6 7Guanidinium HCl (M)Fig. 3 | Structural characterization of Foldit player-designed proteins.
a, Dendrogram showing all 56 folded Foldit player designs clustered bystructural similarity (TM-align26), with coloured circles highlighting thefour designs characterized in b–g. The stacked bars show the 20 different
folds among the clustered designs (Extended Data Fig. 5). Fold XX(see design 2003594_S028) is a new fold, previously unobserved in naturalproteins. b–g, Cartoon depiction of four select Foldit designs (b); thegraphs in c–g correspond to these four structures. c, Rosetta@home abinitio calculations show that the sequence for each design has an energy
landscape that is strongly funnelled towards the design structure.
y axis, Rosetta energy; x axis, Cα r.m.s.d. to the designed structure; points
represent lowest-energy structures sampled starting from an extended
chain (red points) and starting from the Foldit design model(green points). d, Size-exclusion chromatography traces (absorbanceat 280 nm) show that designs are monomeric in solution. e, Circulardichroism spectra indicate that the designs adopt the expected secondary
structure content in solution at 25 °C (blue trace), when heated to 95 °C(red trace) and when cooled again to 25 °C (green trace). f, Circulardichroism mean residue ellipticity at 220 nm as temperature is increased
from 25 °C to 95 °C; the designs do not denature with increasing
temperature. g, Cooperative unfolding during titration with guanidiniumhydrochloride. Blue circles show circular dichroism mean residue
ellipticity at 220 nm with increasing concentration of denaturant, and theblack curve shows a two-state unfolding model fit to the data. Free energyof unfolding (ΔGunf ) was determined by linear extrapolation using the fit
model parameters27.

We obtained custom synthetic genes encoding 12 player designs
for which structure prediction calculations converged on the player-designed conformation17. The sequences of these proteins have nohomology to any known protein (Supplementary Table 1). The de novo
designs were expressed in E. coli and purified by metal-affinity andsize-exclusion chromatography. Analysis by chromatography andcircular dichroism indicated that 6 of the 12 designs were monomericand folded in solution, with helical secondary structure consistent withthe players’ models (Supplementary Fig. 1). All of the experimentallytested proteins described in this paper are entirely the work of Foldit
players.
During gameplay, the Foldit application uploads the player’s latest
model to the Foldit server every 2–5 minutes; from these snapshots
we can reconstruct the process by which a Foldit player develops aprotein design (Fig. 2). Foldit players use more-varied and complex
exploration strategies than standard Rosetta automated design protocols, and frequently revert to a previous iteration of their model toexplore an alternative path, resulting in a highly branched search tree.
A typical automated design protocol, by contrast, includes only two
branch points18. In addition, Foldit players regularly sample muchhigher energy states than the automated protocol, which has only alimited ability to escape local energy minima.
Encouraged by the success of Foldit players in designing stable
proteins from scratch, we made additions to the game to encourage
players to explore more-diverse protein structures. Up until this point,
all top-scoring Foldit designs had consisted of either three or fourα-helices connected by minimal loops. Indeed, Foldit players had
determined that designs with β-sheets did not score as well as α-helicalbundles (Extended Data Fig. 2), and competitive players had abandoned any attempt to design more varied folds. This is an interesting

parallel to protein design by practicing scientists, which has also
focused much more on helical bundles than on other classes of protein
folds19–22. To encourage the design of a wider variety of folds, we introduced a ‘secondary structure’ rule, stipulating that no more than 50% of
residues may form α-helices. Foldit players responded by designing a
multitude of mixed α/β-proteins, which were indistinguishable fromexpert designs on visual inspection. However, structure prediction calculations for these α/β design sequences showed poor sampling close to
the target design structure, which suggests that the designed sequences
did not strongly encode their local structures17. Further analysis showedthat these player designs contained many residues with locally strained
backbone conformations (backbone φ and ψ torsions in unfavoured
regions of the Ramachandran plot23,24). That such designs had very
low energies revealed a problem in the Rosetta energy function at thetime: because Rosetta users typically sampled backbones starting fromfragments of native proteins, unfavourable local conformations wererarely encountered—therefore, it had not been discovered that theenergies associated with local-backbone strain were being underestimated. We addressed this flaw in the Rosetta model by increasing the steepness of the energetic penalties associated with strained
local-backbone geometry; this is now implemented in the latest Rosettaenergy function11. We also added to Foldit an ‘ideal loops’ rule that
restricted players to a set of 19 unstrained reverse-turn conformations7, and incorporated new tools to aid generation of unstrained
backbones: a fragment lookup-based loop-closure tool, an interactive
Ramachandran map and a protein blueprint scheme for drag-and-drop
assembly of secondary structure elements and common loop conformations (Extended Data Fig. 3). Together, these upgrades brought abouta marked improvement in the local-backbone quality of Foldit playerdesigned proteins (Extended Data Fig. 4).
N A T U R E | www.nature.com/nature

RESEARCH Lettera

Foldit1

b

Peak6

c

Ferredog-DieseldFoldit3

Fig. 4 | High-resolution structures of Foldit player-designed proteins.
a, The Foldit1 design (fold V in Fig. 3: three β-strands with sheet order
1–2–3) model backbone (rainbow) aligns to the crystal structure (grey)with Cα r.m.s.d. of 1.1 Å. b, The Peak6 design (fold III: four strands, sheetorder 1–2–4–3) model backbone aligns to the crystal structure with Cα
r.m.s.d. of 0.9 Å. c, The Ferredog-Diesel design (fold I: four strands, sheetorder 4–1–3–2) model backbone aligns to the crystal structure with Cα

r.m.s.d. of 1.7 Å. Cross sections show core-residue sidechains, with thecomposite omit 2mFo − DFc map contoured at 2.0σ (a, b) or 1.0σ (c).
d, The Foldit3 design model (fold XVII: four strands, sheet order 2–1–3–4)
and NMR ensemble. The design model aligns to the representative(medoid) NMR model with a Cα r.m.s.d. of 1.1 Å. Cross sections compare
core sidechains in the design model (rainbow) and representative NMR
model (grey).

The importance of reducing local-backbone strain was borne out
in experimental characterization. Before the backbone modelling
improvements described in the previous paragraph, only 4 of 37 Foldit
α/β-designs tested (11%) were monomeric and structured in solution.
Following the backbone modelling additions, 46 of 97 (47%) weremonomeric and exhibited the expected secondary structure in solution. Most showed exceptional stability in thermal and chemical denaturation experiments, with some free energies of unfolding (ΔGunf )exceeding 20 kcal mol−1; indeed, 32 designed proteins remained completely folded at 95 °C (Fig. 3, Supplementary Fig. 1). This success ratesurpasses that in previous reports of designed α/β-proteins7,12.
Overall, the 56 successful Foldit designs are diverse in structure,
representing 20 different protein folds (Fig. 3, Extended Data Fig. 5),
one of which is a new fold that is previously unobserved in naturalproteins. The success of Foldit designs is not attributed to just one
or two exceptional Foldit players, but is shared broadly by the Foldit
community (Supplementary Table 1). The 56 successful designs werecreated by 36 different Foldit players (the most prolific player created
10 successful designs); 19 designs were created collaboratively by atleast 2 cooperating players; and 5 successful designs were not topscoring, but were nevertheless flagged by players as personal
favourites. Foldit players lack formal expertise in protein modelling(Extended Data Fig. 6, Supplementary Notes), but knowledge andintuition gained from playing protein structure prediction puzzles
in Foldit translated to success in de novo protein design (Extended
Data Fig. 7).

We succeeded in solving high-resolution structures of four Foldit
player-designed proteins. X-ray crystal structures of three designed
proteins (named by their designers Foldit1, Peak6 and Ferredog-Diesel)closely match the designed conformations, with Cα root mean squaredeviations (r.m.s.d.) of 1.1, 0.9 and 1.7 Å, respectively (Fig. 4). Wellresolved electron density in the protein core of Foldit1 and Peak6 showsthat most sidechains adopt the intended rotamers and preserve thedesigned packing interactions. The electron density of Ferredog-Diesel
is less clear, but the protein backbone adopts the designed fold, andmany core sidechains appear to pack as intended. The solution nuclear
magnetic resonance (NMR) structure of a fourth design, Foldit3,
also closely matches the design conformation, with a Cα r.m.s.d. of
1.1 Å between the design model and the medoid conformer25 of theensemble.
From these results, we can draw several general conclusions about
scientific models, citizen science and the interplay between the two.
First, a scientific model that holds within the domain space consideredby practicing scientists may not hold outside of this domain. This ismost vividly illustrated by the highly extended structures generated byFoldit players in their first de novo design efforts, and later by the structures with strained local geometry not previously sampled by Rosetta
users. Second, for citizen scientists to make essential and creative scientific contributions through online gaming, the scoring function of
the game must be an accurate representation of the science. In our
initial iterations, Foldit did not present to players a sufficiently accurate and general model to allow them to robustly design new proteins,

N A T U R E | www.nature.com/natureLetter RESEARCH
even though the underlying Rosetta software had been used for protein
design by practicing scientists. Third and most importantly, citizen
science offers a powerful way to systematically improve a scientificmodel through iterations of model trial and model improvement.
Human game players are exceptionally capable at finding and exploiting
unanticipated solutions that are otherwise unexplored by experienced
scientists, whose focus is not on getting a high score, but rather onsolving their specific scientific problem.
We have demonstrated that non-expert citizen scientists, playing theonline computer game Foldit, can accurately design completely newprotein structures from scratch. Locally, players’ solutions are physicallyplausible and resemble natural proteins, but globally, they are creativeand diverse. Proteins designed by citizen-scientist Foldit players are
by no measure inferior to those of expert protein designers: they foldaccurately to the intended conformation, show exceptional folding stability and span a wide diversity of structures. This result is all the more
impressive given that de novo protein design was an almost completely
unsolved problem just a few years ago, and the diversity in protein folds
spanned by the successful Foldit players’ models considerably exceedsthat in any previous protein design report, to our knowledge. The sustained success of Foldit players over a wide diversity of protein folds
highlights the power of human creativity when guided by scientificunderstanding presented in a readily comprehensible form.

Online content

Any methods, additional references, Nature Research reporting summaries, source
data, statements of data availability and associated accession codes are available athttps://doi.org/10.1038/s41586-019-1274-4.

Received: 19 October 2018; Accepted: 14 May 2019;
Published online xx xx xxxx.
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Acknowledgements We thank all Foldit players for their gameplay
contributions, and for feedback offered on the Foldit website (https://fold.it).
We thank A. Kang, S. A. Rettie, C. Chow and L. Carter for help with experiments;
D. Alonso, L. Goldschmidt, P. Vecchiato and D. Kim for computer support;
and Rosetta@home (https://boinc.bakerlab.org) volunteers for computing
resources. We thank G. Rocklin, V. Mulligan and other members of the Baker
laboratory for discussions. This material is based on work supported by the
National Science Foundation (NSF) Graduate Research Fellowship under
grant no. DGE-1256082, NSF grant no. 1629879, National Institutes of Health(NIH) grant 1UH2CA203780, and NIH grants 1S10 OD018207 and 5R01
GM120574 (to G.T.M.) and HHMI (D.B.). The ALS-ENABLE beamlines aresupported in part by the NIH, National Institute of General Medical Sciences,
grant P30 GM124169-01. The Advanced Light Source is a DOE User Facility
under contract no. DE-AC02-05CH11231. Foldit3 was a nominated target of
the CASP COMMONS Community Outreach program.
Reviewer information Nature thanks Jérôme Waldispuhl and the otheranonymous reviewer(s) for their contribution to the peer review of this work.
Author contributions B.K., Z.P., F.K., S.C. and D.B. designed the study. B.K., J.F.,
T.H., A.F., D.-A.S. and S.C. developed Foldit software tools. A. Boykov, R.D.E., S.K.,
T.N.-S. and L.W., along with the other Foldit players, designed all proteins. B.K.,
F.K., A.F. and A. Bauer analysed Foldit player designs. B.K. performed biophysicalcharacterization. B.K., M.J.B. and F.D. determined crystal structures. G.L., Y.I.
and G.T.M. determined the NMR structure. B.K. and D.B. wrote the manuscriptwith input from all authors. Foldit players contributed extensively through their
feedback and gameplay, which generated the data for this paper.
Competing interests G.T.M. is a co-founder of Nexomics Biosciences.
Additional information
Extended data is available for this paper at https://doi.org/10.1038/s41586019-1274-4.
Supplementary information is available for this paper at https://doi.org/
10.1038/s41586-019-1274-4.
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RESEARCH Letter
Methods

Foldit protein design puzzles. Foldit puzzles were set up with a model polyisoleucine in fully extended conformation, with fixed length ranging from 60 to 100
residues. Each puzzle was posted online for seven days, during which Foldit players
competed to develop a protein model with the lowest energy, as calculated by the
Rosetta energy function. Foldit puzzles used the talaris2013_cart scorefunction
with the following modifications: (1) the cart_bonded scoreterm was upweighted(increased from 0.5 to 2.0) to ensure realistic bond lengths and angles as players cut
and splice the backbone chain; (2) a penalty-only envsmooth scoreterm (weighted
at 2.0) was added to supplement the Rosetta solvation treatment, and to discourage
the design of buried polar and exposed nonpolar residues; (3) the reference energyof alanine was modified (increased to 3.0) to discourage the excessive design ofalanine. Configuration files for all Foldit puzzles are provided in the Supplementary
Data. Each Foldit puzzle was accompanied by a brief description, along with an
explanation of any supplementary rules enforced in the puzzle. Design puzzles wereaccessible to all Foldit users; Foldit user registration is free and open to the public,
at http://fold.it. Models were collected continuously as Foldit players worked on the
puzzles, as the Foldit application automatically uploads the user’s latest model toa server every 2–5 min. This study was approved by the University of Washington
Institutional Review Board, and informed consent for this research was obtained
from all Foldit users at the time of user registration.
Protein design selection. After the end of each puzzle, we selected player models
for further analysis as follows: first, we selected the lowest-energy model fromeach of the ten top-ranked groups, in which independent players were treatedas individual groups (designs named with suffix ‘0000-9’). Second, we selected
the lowest-energy model from the ten top-ranked solo players, which includes
independent players as well as group members that developed a model without
assistance from their group (suffix ‘s000-9’). Third, we visually inspected models that were flagged by Foldit players for special consideration, and selected any
models that appeared plausible (suffix ‘S∗∗∗’). Last, we ranked and pruned the setof remaining models by removing any models that align to a better-scoring modelwith Cα r.m.s.d. less than 2.5 Å. We visually inspected the 50 top-ranked models
in the pruned set and selected any models that appeared plausible (suffix ‘100150’). Models deemed ‘implausible’ typically lacked secondary structure, contained
buried polar residues or included long stretches of completely polar residues. At
each step, we used TM-align26 to eliminate duplicate models (TM-score >0.98) that
had already been selected (for example, models that were top-ranking and flaggedby players). In rounds 2 and 3, the top-ranked group and solo models were automatically selected for further analysis, without visual inspection. The sequencesof selected models were subjected to Rosetta ab initio structure prediction17, using
the distributed computing platform Rosetta@home. If ab initio predictions identified any decoy structures with energy comparable to (or lower than) the designed
structure, or if ab initio predictions were unable to sample the designed structure,
the design was rejected. All other designs were selected for experimental characterization. See Extended Data Table 1 for summary statistics on design selection.
The majority of experimentally tested designs (96 of 146) were top-ranked groupor solo designs, which were selected ‘blindly’ (without visual inspection). Models
and FASTA sequences of all tested designs are shown in the Supplementary Data.
Protein expression and purification. A 6×His tag with TEV-cleavable linker(sequence MGHHHHHHGWSENLYFQGS) was prepended to the N terminusof each design selected for experimental characterization. Plasmids containing
the encoded genes were ordered from Genscript in pET15 (designs with prefix
between 997258 and 1998925), in pET21 (1998555–2002990) or from Twist in
pET29 (2003048-2003594) vectors. Plasmids were transformed into E. coli BL21
Star (DE3) cells (Invitrogen), and grown overnight in 4 ml Luria–Bertani (LB)
medium with 50 μg/ml carbenicillin (for pET15, pET21 vectors) or 30 μg/ml kanamycin (for pET29). Overnight cultures were used to inoculate 0.5 l auto-induction
medium, and grown at 37 °C for 18 h. Cultures were pelleted and resuspended
in 25 ml lysis buffer (20 mM Tris pH 8.0, 300 mM NaCl, 1 mg/ml lysozyme, 0.1 mg/ml
DNase, 1 mM PMSF), and lysed by microfluidization. The cell lysate was pelleted
and supernatant was filtered with a 0.22-μm filter before loading onto a 2 ml nickel-affinity gravity column. Protein bound to the column was washed with 20 ml
wash buffer (20 mM Tris pH 8.0, 500 mM NaCl, 30 mM imidazole) and eluted
in 10 ml elution buffer (20 mM Tris pH 8.0, 500 mM NaCl, 250 mM imidazole).
Purified protein was dialysed into TBS (20 mM Tris pH 8.0, 300 mM NaCl) at 4 °C
overnight to remove imidazole and further purified by size-exclusion chromatography on an AKTAxpress (GE Healthcare) with a Superdex S75 10/300 GL column(GE Healthcare). For proteins containing cysteine, dialysis and gel filtration werecarried out in TBS with 1 mM tris(2-carboxyethyl)phosphine (TCEP). Protein
expression and solubility was determined from SDS–PAGE and mass spectrometry.
Oligomeric state was determined by size-exclusion chromatography.
Circular dichroism. Purified protein was dialysed into 50 mM sodium phosphate
pH 7.4 at 4 °C overnight (plus 500 μM TCEP for proteins containing cysteine).
All circular dichroism data were collected on an AVIV Model 420 spectrometer.

Far UV spectra and temperature melts were measured with 11–62 μM protein
in a quartz cuvette with path length of 1 mm. Protein concentration was determined by absorbance at 280 nm using a NanoDrop spectrophotometer (Thermo
Scientific), using predicted extinction coefficients. Wavelength spectra were measured between 195 and 260 nm at 25 °C, 95 °C and again after cooling to 25 °C. For
temperature melts, ellipticity at 220 nm was monitored as temperature increased
from 25 °C to 95 °C in increments of 2 °C. Chemical titrations were carried out
with 1.0–21 μM protein in a quartz cuvette with path length of 10 mm. Ellipticity
at 220 nm was monitored at concentrations of guanidinium chloride increasingfrom 0 to 7 M, in increments of 0.25 M. Denaturation curves were fitted withnonlinear regression to two-state unfolding model with six parameters: the folding
free energy, m-value, and slope and y intercept for baseline curves27.
X-ray crystallography. Prior to X-ray crystallography, the N-terminal 6×His
tag was cleaved from protein samples by incubation with 250 μg TEV proteaseat 25 °C for 4 h in 20 mM Tris pH 8.0, 300 mM NaCl, 1 mM DTT. The reaction
product was dialysed into TBS overnight at 4 °C to remove DTT and flowed over a
2 ml metal-affinity gravity column to remove TEV protease and residual histidine
tag. The cleaved protein was further purified by gel filtration as described above.
Purified protein was concentrated to 20–100 mg/ml in 20 mM Tris pH 8.0, 300 mM
NaCl. Crystallization screening was carried out with a variety of 96-condition spare
matrix suites available from Qiagen or Hampton Research. A Mosquito Crystal
nanolitre robot (TTP Labtech) was used to prepare screens in 3-well sitting drop
plates, with 200 nl drops and protein:precipitant ratios of 1:1, 1:2 and 2:1.
Foldit1 (2002949_0000) was crystallized at 20 mg/ml in 50 mM HEPES pH
7.5, 0.2 M potassium chloride, 35% v/v pentaerythritol propoxylate. Crystals were
flash-frozen in liquid nitrogen without further cryo-protection. X-ray diffraction
was collected to a resolution of 1.18 Å.
Peak6 (2003333_0006) was crystallized at 40 mg/ml in 0.1 M sodium acetate
pH 4.5, 0.2 M lithium sulphate, 50% w/v PEG 400. Crystals were briefly soaked
in mother liquor plus 20% PEG 200, then flash-frozen in liquid nitrogen. X-ray
diffraction was collected to a resolution of 1.54 Å.
Ferredog-Diesel (2003169_S953) was crystallized with 6×His tag intact, at 80
mg/ml in 0.1 M citrate pH 4.0, 3.0 M NaCl. Crystals were dehydrated by soaking
in 5 μl mother liquor in open air for 10 min, then flash-frozen in liquid nitrogen.
X-ray diffraction was collected to a resolution of 1.92 Å.
X-ray diffraction datasets were collected at the Advanced Light Source(Berkeley, CA). Data was processed with HKL200028. Crystal structures weresolved by molecular replacement with Phaser29, using the backbone of the original
designed model with sidechains truncated to the β-carbon (Foldit1 and Peak6),
or using the backbone of a model predicted ab initio from the design sequence(Ferredog-Diesel). Models were built and refined in iterative cycles using Cootand PHENIX30,31. Diffraction data and refinement statistics are listed in Extended
Data Table 2.
NMR spectroscopy. NMR studies were performed using uniformly 15N,13Cenriched protein samples. A synthetic gene for Foldit3 (2003265_s008) was
obtained from Genscript already incorporated into plasmid pET15TEV_NESG,
which includes a N-terminal 6×His purification tag, followed by a TEV proteasecleavage site (sequence MGHHHHHHGWSENLYFQGS). E. coli BL21(DE3) cells
containing plasmid pET15TEV_NESG-Foldit3 were grown in 1 l MJ9 minimal
medium32, supplemented with 100 µg/ml ampicillin at 37 °C. To produce uniformly
15
N and 13C-enriched protein samples, 1 g/l 15NH4 salts and 2g/l U-13C glucose
were added as sole nitrogen and carbon sources, respectively. When OD600 reachedaround 0.5 units, the culture was transferred to 18 °C, and protein production was
induced by addition of 1 mM IPTG. After overnight incubation, the cells were collected and resuspended in 20 ml binding buffer (20 mM Tris-HCl pH 8.0, 500 mM
NaCl and 20 mM imidazole). After passing the cells through 900–1000 psi Frenchpress twice, cell debris were removed by 10,000 r.p.m. for 30 min. The supernatant
was further spun down at 40,000 r.p.m. for 1 h. The obtained supernatant (soluble fraction) was mixed with 1 ml of Ni-resin and incubated at 4 °C for 1 h. The
non-specific binding proteins were removed by 20 ml binding buffer and washingbuffer (20 mM Tris-HCl pH 8.0, 500 mM NaCl and 50 mM imidazole) and thetarget protein was eluted by 5 ml elution buffer (20 mM Tris-HCl pH 8.0, 500 mM
NaCl and 300 mM imidazole). The protein was dialysed against GF buffer (20 mM
Tris-HCl pH 8.0, 100 mM NaCl) for overnight and gel filtration was carried out
using AKTA express with high-load 26/600 Superdex 200 pg column. Homogeneity(>97%) was validated by SDS polyacrylamide gel electrophoresis. The purified
protein was dialysed against 20 mM potassium phosphate (pH 6.5), and the protein
concentration was adjusted to between 0.3–0.4 mM for NMR studies.
All NMR spectra were recorded at 25 °C using cryogenic NMR probes. All
NMR data were collected on the Bruker AVANCE III 600 MHz spectrometersand processed using the program NMRPipe33, and analysed using the programs
SPARKY and XEASY34. Spectra were referenced to external DSS. Sequence-specificresonance assignments were determined using AutoAssign software together withinteractive manual analysis, as previously described35. Backbone dihedral angle

Letter RESEARCH
constraints were derived from the chemical shifts using the program TALOS_N36
for residues located in well-defined secondary structure elements. The programs
ASDP37 and CYANA38,39 were used to automatically assign NOEs and to calculate
structures. RPF analysis37,40 was used in parallel to guide iterative cycles of noise
and artefact peak removal, peak picking, and NOESY peak assignments. The 20
conformers with the lowest target CYANA function value were then refined in
explicit water41 using the program CNS42. The structural statistics and global
structure quality factors (Extended Data Table 3) including Verify3D43, ProsaII44,
PROCHECK45, and MolProbity46 raw and statistical Z-scores were computed using
the PSVS47 v.1.5 and PDBStat48 software packages. The global goodness-of-fit of
the final structure ensembles with the NOESY peak list data, the NMR DP score,
was determined using the RPF analysis program40.
Reporting summary. Further information on research design is available in
the Nature Research Reporting Summary linked to this paper.

Data availability

The atomic coordinates of Foldit1, Peak6 and Ferredog-Diesel crystal structures
and the Foldit3 NMR structure have been deposited in the RCSB Protein Data
Bank (PDB) with accession numbers 6MRR, 6MRS, 6NUK and 6MSP, respectively. Chemical shift and NOESY peak list data for Foldit3 were deposited in the
Biological Magnetic Resonance Data Bank with accession number 30527.

Code availability

Because Foldit crowdsourcing relies on regulated, fair competition between participants, the source code of the Foldit user interface is not open. The underlying Rosetta macromolecular modelling suite (https://www.rosettacommons.
org) is freely available to academic and non-commercial users, and commercial
licenses are available via the University of Washington CoMotion Express License
Program. Analysis scripts used in this paper are available in the Supplementary
Information.
28. Otwinowski, Z. & Minor, W. Processing of X-ray diffraction data collected inoscillation mode. Methods Enzymol. 276, 307–326 (1997).
29. McCoy, A. J. et al. Phaser crystallographic software. J. Appl. Crystallogr. 40,
658–674 (2007).
30. Emsley, P., Lohkamp, B., Scott, W. G. & Cowtan, K. Features and development of
Coot. Acta Crystallogr. D 66, 486–501 (2010).
31. Afonine, P. V. et al. Towards automated crystallographic structure refinementwith phenix.refine. Acta Crystallogr. D 68, 352–367 (2012).
32. Jansson, M. et al. High-level production of uniformly 15N- and 13C-enrichedfusion proteins in Escherichia coli. J. Biomol. NMR 7, 131–141 (1996).

33. Delaglio, F. et al. NMRPipe: a multidimensional spectral processing system
based on UNIX pipes. J. Biomol. NMR 6, 277–293 (1995).
34. Bartels, C., Xia, T. H., Billeter, M., Güntert, P. & Wüthrich, K. The program XEASY
for computer-supported NMR spectral analysis of biological macromolecules.
J. Biomol. NMR 6, 1–10 (1995).
35. Liu, G. et al. NMR data collection and analysis protocol for high-throughput
protein structure determination. Proc. Natl Acad. Sci. USA 102, 10487–10492(2005).
36. Shen, Y., Delaglio, F., Cornilescu, G. & Bax, A. TALOS+: a hybrid method forpredicting protein backbone torsion angles from NMR chemical shifts.
J. Biomol. NMR 44, 213–223 (2009).
37. Huang, Y. J., Tejero, R., Powers, R. & Montelione, G. T. A topology-constrained
distance network algorithm for protein structure determination from NOESY
data. Proteins 62, 587–603 (2006).
38. Güntert, P., Mumenthaler, C. & Wüthrich, K. Torsion angle dynamics for NMR
structure calculation with the new program DYANA. J. Mol. Biol. 273, 283–298(1997).
39. Herrmann, T., Güntert, P. & Wüthrich, K. Protein NMR structure determination
with automated NOE assignment using the new software CANDID and thetorsion angle dynamics algorithm DYANA. J. Mol. Biol. 319, 209–227 (2002).
40. Huang, Y. J., Powers, R. & Montelione, G. T. Protein NMR recall, precision, andF-measure scores (RPF scores): structure quality assessment measures basedon information retrieval statistics. J. Am. Chem. Soc. 127, 1665–1674 (2005).
41. Linge, J. P., Williams, M. A., Spronk, C. A., Bonvin, A. M. & Nilges, M. Refinementof protein structures in explicit solvent. Proteins 50, 496–506 (2003).
42. Brünger, A. T. et al. Crystallography & NMR system: a new software suite formacromolecular structure determination. Acta Crystallogr. D 54, 905–921(1998).
43. Lüthy, R., Bowie, J. U. & Eisenberg, D. Assessment of protein models withthree-dimensional profiles. Nature 356, 83–85 (1992).
44. Sippl, M. J. Recognition of errors in three-dimensional structures of proteins.
Proteins 17, 355–362 (1993).
45. Laskowski, R. A., Macarthur, M. W., Moss, D. S. & Thornton, J. M. Procheck—a
program to check the stereochemical quality of protein structures. J. Appl.
Crystallogr. 26, 283–291 (1993).
46. Word, J. M., Bateman, R. C., Jr, Presley, B. K., Lovell, S. C. & Richardson, D. C.
Exploring steric constraints on protein mutations using MAGE/PROBE. Protein
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47. Bhattacharya, A., Tejero, R. & Montelione, G. T. Evaluating protein structures
determined by structural genomics consortia. Proteins 66, 778–795 (2007).
48. Tejero, R., Snyder, D., Mao, B., Aramini, J. M. & Montelione, G. T. PDBStat: auniversal restraint converter and restraint analysis software package for protein
NMR. J. Biomol. NMR 56, 337–351 (2013).
49. Trifonov, E. N. in Structure and Methods, Vol. 1: The Proceedings of the Sixth
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50. Holm, L. & Laakso, L. M. Dali server update. Nucleic Acids Res. 44 (W1),
W351–W355 (2016).

RESEARCH Letter

Extended Data Fig. 1 | Initial top-ranking Foldit player designs.
When challenged to design a protein with only the talaris2013 scorefunction (and no additional rules), Foldit players discovered low-energy
models that are unlikely to fold as designed. a, An extended α-helix,
composed entirely of lysine and glutamate, has very favourable energies
for hydrogen-bonding, electrostatic and backbone torsions, but isunlikely to fold cooperatively into a single stable structure. This type ofdesign is discouraged with the ‘core exists’ rule. b, Owing to their greater
surface area, large aromatic sidechains can make more interactions thansmaller aliphatic sidechains, even when underpacked or solvent-exposed.
This type of design is discouraged with the ‘residue interaction energy’rule. c, A design with an alanine- and glycine-saturated core can makefavourable van der Waals interactions between closely packed backbone
atoms; however, the burial of these small sidechains is associated with aweaker hydrophobic effect, and the lack of interdigitation allows exchange
between multiple conformations with similar core packing energies (thatis, molten globule behaviour). These designs are discouraged with the‘secondary structure design’ rule.

Letter RESEARCH

Extended Data Fig. 2 | Rosetta energy of top Foldit player designs.
Rosetta energy of top-ranking designs was calculated with the talaris2013
score function and normalized by residue count. a, Energy of top-tenranked designs from: initial Foldit puzzles (round 0; n = 30 designs),
round 1 puzzles (n = 170), round 2 puzzles (n = 510) and round 3 puzzles(n = 250). The introduction of supplementary rules in round 1 and round2 resulted in higher-energy designs (P < 10−6 and P < 0.01, respectively;
Wilcoxon rank-sum test). The backbone modelling improvements inround 3 resulted in lower-energy designs (P < 10−15; Wilcoxon rank-sumtest). b, Energy of top-ten-ranked designs from round three all-α puzzles(n = 30) or α/β-puzzles using the ‘secondary structure’ rule (n = 220).
All-α designs tend to have lower energy than α/β-designs (P < 10−10;
Wilcoxon rank-sum test). Box plots show: centre line, median; box limits,
upper and lower quartiles; whiskers, 1.5× interquartile range; points,
outliers.

RESEARCH Letter

Extended Data Fig. 3 | New backbone-modelling tools in Foldit. a, The‘remix’ tool allows players to select a region of the model and search alibrary of backbone fragments for a conformation that can be substituted.
b, An interactive Ramachandran map allows players to easily identify
residues with outlier backbone conformations. Players can also click anddrag points on the Ramachandran map to set the backbone torsions ofindividual residues. c, A ‘blueprint’ panel shows the primary sequence andsecondary structure content of the model. Residues are coloured according
to the ABEGO quadrants of the Ramachandran plot7. d, Players can dragand-drop modular building blocks onto the blueprint panel to insert
common turn conformations into their model.

Letter RESEARCH

Extended Data Fig. 4 | Improvement of backbone quality in round
3 Foldit designs. MolProbity24 was used to calculate the proportion ofresidues with unfavored or outlier backbone torsions in: high-resolution
crystal structures of native proteins (n = 6,342), de novo design models
from a previous study7 (n = 72), and top-ranking Foldit player designs
from before (n = 680) and after (n = 250) improvements to Foldit
backbone-modelling tools. Initial Foldit player designs contained
significantly more unfavoured torsions than native proteins or otherde novo designs from a previous study7 (P < 10−15, two-tailed t-test).
Improvements to Foldit’s backbone-modelling tools led Foldit players to
produce designs with fewer unfavoured torsions (P < 10−15, two-tailed
t-test). Box plots show: centre line, median; box limits, upper and lower
quartiles; whiskers, 1.5× interquartile range; points, outliers.

RESEARCH Letter

Extended Data Fig. 5 | Protein folds represented by successful Foldit
player designs. Each fold has a unique arrangement and connectivity of
secondary structure elements, depicted in cartoon diagrams. Diagrams
are labelled with Roman numerals as in Fig. 3. Fold XX is a new fold,previously unobserved in natural proteins; TM-align26 and DALI50
alignments of design 2003594_S028 against the entire PDB found no
structural homologues with this fold.

Letter RESEARCH

Extended Data Fig. 6 | Foldit player demographics. All players who participated in Foldit protein design puzzles and who had not opted out of
Foldit-related email were solicited for survey questions. Data are shown for n = 324 responding Foldit players.

RESEARCH Letter

Extended Data Fig. 7 | Category rankings of Foldit players. Foldit player
rankings are strongly correlated in the design and prediction categories(Spearman’s rank correlation coefficient of 0.84). This suggests that skills
developed playing Foldit structure prediction puzzles carry over to design
puzzles and vice versa.

Letter RESEARCH
Extended Data Table 1 | Success rates of Foldit player-designed proteins*Linguistic sequence complexity49 was calculated from the top-ten-ranked models in all puzzles, using word lengths of 1, 2 and 3.
†Rosetta energy is the talaris2013 energy normalized by residue count. Values shown are mean and standard deviation for the ten top-ranked models in all puzzles. See Extended Data Fig. 2 forsample sizes.
‡Includes redundant models, as very similar models can appear in two or more categories (top, shared and clustered). See Methods for details on model selection.

RESEARCH Letter
Extended Data Table 2 | X-ray crystallography data and refinement statistics

Values in parentheses are for highest resolution shell. X-ray diffraction data for each protein structure were collected on a single crystal and processed as described in the Methods.

Letter RESEARCH
Extended Data Table 3 | NMR and refinement statistics for protein structures∗

Pairwise r.m.s.d. was calculated among 20 refined structures for ‘well-defined’ residues 21–45, 48–54, 58–76, 81–87 and 90–96.

Reporting Summary
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When statistical analyses are reported, confirm that the following items are present in the relevant location (e.g. figure legend, table legend, main
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The exact sample size (n) for each experimental group/condition, given as a discrete number and unit of measurement
An indication of whether measurements were taken from distinct samples or whether the same sample was measured repeatedly
The statistical test(s) used AND whether they are one- or two-sided
Only common tests should be described solely by name; describe more complex techniques in the Methods section.

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A full description of the statistics including central tendency (e.g. means) or other basic estimates (e.g. regression coefficient) AND
variation (e.g. standard deviation) or associated estimates of uncertainty (e.g. confidence intervals)
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Give P values as exact values whenever suitable.

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State explicitly what error bars represent (e.g. SD, SE, CI)
Our web collection on statistics for biologists may be useful.

Software and code
Policy information about availability of computer code
Data collection

The pre-compiled Foldit game is freely available for download at https://fold.it for Windows, Linux, and Mac. A standalone version of
Foldit is also freely available for academic use; for details visit https://fold.it/standalone. Foldit configuration files for all design puzzles
are included in the Supplementary Information. The Rosetta software suite was used to perform ab initio prediction calculations; Rosetta
is freely available for academic users on Github, and can be licensed for commercial use by the University of Washington CoMotion
Express License Program.

Data analysis

Custom Python scripts written to analyze circular dichroism data are included in the Supplementary Information. Protein structures wereanalyzed with MolProbity (version 4.2). Crystallographic data were analyzed with PHENIX (release 1.101.1-2155) and Coot (v0.8.7 EL).
NMR data were analyzed with SPARKY, XEASY, TALOS_N, ASDP, CYANA, PDBStat and PSVS (version 1.5).
April 2018

For manuscripts utilizing custom algorithms or software that are central to the research but not yet described in published literature, software must be made available to editors/reviewers
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Policy information about availability of data
All manuscripts must include a data availability statement. This statement should provide the following information, where applicable:- Accession codes, unique identifiers, or web links for publicly available datasets- A list of figures that have associated raw data
- A description of any restrictions on data availability
The atomic coordinates of Foldit1, Peak6, and Ferredog-Diesel crystal structures, and the Foldit3 NMR structure, have been deposited in the RCSB Protein Data Bank
with accession numbers 6MRR, 6MRS, 6NUK and 6MSP, respectively. Chemical shift and NOESY peak list data for Foldit3 were deposited in the Biological Magnetic
Resonance Bank (BMRB ID 30527).

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Life sciences study design
All studies must disclose on these points even when the disclosure is negative.
Sample size

Sample size for protein characterization was determined by estimated work load. In total, 146 protein designs were tested, from 97 Foldit
puzzles. This was deemed sufficient due to the high number of successfully folded designs in our testing. For in silico designed-backbone
analysis, the sample sizes of (n = 717 or 250) was considered sufficient. The inclusion of additional samples is not expected to affect the
distribution of measured values.

Data exclusions

No data were excluded from analysis.

Replication

Puzzle configurations were used repeatedly in replicated Foldit puzzles to ensure reproducibility. The final puzzle configuration was used for
25 replicate Foldit puzzles. Protein expression and solubility was tested in duplicate. Structural characterization were performed once or twicewith internal statistical validation. If positive results (e.g. protein expression, solubility, etc.) could not be replicated, they are reported as
negative results.

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There was no randomized sample allocation in this work. All tested protein designs received identical treatment.

Blinding

Blinding was not relevant to this work, since all tested proteins received identical treatment.

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Participation was open and free to the public, and we did not control for participant demographics. See Extended Data Fig. 6 fordemographic data from a voluntary (non-obligatory) participant survey.

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