Article

doi:10.1038/nature23912

Massively parallel de novo protein designfor targeted therapeutics

Aaron Chevalier1,2*, Daniel-Adriano Silva1,2*, Gabriel J. Rocklin1,2*, Derrick R. Hicks1,2,3, Renan Vergara1,2,4, Patience Murapa5,
Steffen M. Bernard6,7, Lu Zhang8,9, Kwok-Ho Lam10, Guorui Yao10, Christopher D. Bahl1,2, Shin-Ichiro Miyashita11,12,Inna Goreshnik1, James T. Fuller5, Merika T. Koday5,13, Cody M. Jenkins5, Tom Colvin1, Lauren Carter1,2, Alan Bohn5,
Cassie M. Bryan1,2, D. Alejandro Fernández-Velasco4, Lance Stewart2, Min Dong11,12, Xuhui Huang9, Rongsheng Jin10,
Ian A. Wilson6,7, Deborah H. Fuller5 & David Baker1,2

De novo protein design holds promise for creating small stable proteins with shapes customized to bind therapeutictargets. We describe a massively parallel approach for designing, manufacturing and screening mini-protein binders,integrating large-scale computational design, oligonucleotide synthesis, yeast display screening and next-generation
sequencing. We designed and tested 22,660 mini-proteins of 37–43 residues that target influenza haemagglutinin andbotulinum neurotoxin B, along with 6,286 control sequences to probe contributions to folding and binding, and identified
2,618 high-affinity binders. Comparison of the binding and non-binding design sets, which are two orders of magnitudelarger than any previously investigated, enabled the evaluation and improvement of the computational model. Biophysicalcharacterization of a subset of the binder designs showed that they are extremely stable and, unlike antibodies, do not
lose activity after exposure to high temperatures. The designs elicit little or no immune response and provide potent
prophylactic and therapeutic protection against influenza, even after extensive repeated dosing.
Small (4–12 kDa) binding proteins have the potential to bridge thegap between monoclonal antibodies and small molecule drugs1–3,
with advantages of stability and amenability to chemical synthesis overmonoclonal antibodies, and of selectivity and designability over small
molecules. Directed evolution, starting from naturally ­occurring small
protein scaffolds, has previously been used to g­ enerate new b­ inding
proteins4. While powerful, such approaches have l­imitations: theycannot modify the overall shape of the starting scaffold p­ rotein(s),they can sample only a very small fraction of sequence space, and­naturally occurring disulfide mini-proteins can be difficult to express.
Computational protein design has the potential to overcome theselimitations by efficiently sampling both shape and sequence space ona much larger scale, and by generating readily producible ­proteins,
as recently demonstrated by the design of stapled mini-­protein­scaffolds with a wide range of shapes5. Despite this potential, thehigh cost of s­ ynthesizing genes for each designed protein has, until
recently6, ­limited testing to small numbers (tens) of designs for any one­application, which is too few to systematically explore the determinantsof protein binding and folding and provide feedback to improve the­computational model7,8.
Here, we describe an integrated computational and experimental
approach that enables the rapid design and testing of tens of thousandsof de novo mini-protein binders. Our approach exploits advances inboth DNA manufacturing and protein design that have led to a fortunate convergence between the upper limit of the size of oligonucleotides(230 bp) that can be synthesized as pools of 10,000 or larger9,10, andthe lower limit of the size of genetically encodable computationallydesigned proteins (roughly 40 amino acids). To generate binders
for a given target, we used Rosetta11 to design thousands of protein­scaffolds with varying shapes, dock these onto the target, optimize theresidues at the interface for high-affinity binding, and identify, fromthe r­ esulting pool of hundreds of thousands of designs, approximately
10,000 with high predicted stability and affinity. This large pool ofcomputational designs, together with controls probing aspects of thedesign p­ rocedure, was then experimentally evaluated by encoding eachindividual sequence in a single oligonucleotide, manufacturing theoligonucleotides in parallel, sorting yeast libraries displaying thedesigns labelled with fluorescent targets, and using deep sequencing
to identify the designs most enriched for binding (Fig. 1).

High-throughput computational design

As targets, we selected Influenza A H1 haemagglutinin (HA), as
this virus remains a serious public health concern12, and botulinum
neurotoxin B (BoNT/B), which causes the acute neuroparalytic syndrome of botulism and is one of the most lethal natural toxins known(with a lethal dose of approximately 1 ng per kg)13. We generated virtual
scaffold libraries with over 4,000 backbone geometries in five different
topologies: HHH, EHEE, HEE, EEHE, and HEEH (where H indicatesan α​-helix and E a β​-strand) with or without5,6 diverse disulfide connectivities (Fig. 1a). To design binding interfaces, we superimposed
helical segments of the scaffolds on interface helices in previously
solved HA and BoNT/B complexes (the previously designed HAbinders HB36.6 and HB80.414–16, and the natural target of BoNT/B
synaptotagmin-II (Syt-II)17), seeded the newly formed interfaces

1

Department of Biochemistry, University of Washington, Seattle, Washington 98195, USA. 2Institute for Protein Design, University of Washington, Seattle, Washington 98195, USA. 3Molecular andCellular Biology Program, University of Washington, Seattle, Washington 98195, USA. 4Facultad de Medicina, Universidad Nacional Autónoma de México (UNAM), Ciudad Universitaria, México
City 04510, Mexico. 5Department of Microbiology, University of Washington, Seattle, Washington 98109, USA. 6Department of Integrative Structural and Computational Biology, The Scripps
Research Institute, 10550 North Torrey Pines Road, La Jolla, California 92037, USA. 7The Skaggs Institute for Chemical Biology, The Scripps Research Institute, 10550 North Torrey Pines Road, La
Jolla, California 92037, USA. 8State Key Laboratory of Structural Chemistry, Fujian Institute of Research on the Structure of Matter, Chinese Academy of Sciences, Fuzhou, Fujian 350002, China.
9
Department of Chemistry and State Key Laboratory of Molecular Neuroscience, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China. 10Departmentof Physiology and Biophysics, University of California, Irvine, California 92697, USA. 11Department of Urology, Boston Children’s Hospital, Boston, Massachusetts 02115, USA. 12Department ofMicrobiology and Immunobiology and Department of Surgery, Harvard Medical School, Boston, Massachusetts 02115, USA. 13Virvio Inc., Seattle, Washington 98195, USA.
*These authors contributed equally to this work.
7 4 | N A T U R E | V O L 5 5 0 | 5 o c to b er 2 0 1 7© 2017 Macmillan Publishers Limited, part of Springer Nature. All rights reserved.

Article RESEARCH
e

Bot

HB2

HB1

Virtual scaffold library
HHH

HEEH

HEE

Deep sequencing

Binding enrichmenta

HA

Screen for function
Expression
and binding

BoNT
Design ID

EHEE

EEHE

d

Transform into yeast
Yeast adaptor

b

HB1.1

HB2.1

Yeast adaptor

Bot.1

Amplify oligo pools

Genetically encode 16,968 mini-proteins
HB1.2

HB2.2

Bot.2

c

HB1.3

HB2.3

Fraction

0.10
Bot.3

Helicalfragments
alone

0.05

0
5,619

6,038

Additional
designed
contacts

5,311

1,000
1,400
1,800
Interface buried surface area (ɯ2)

Figure 1 | Massively parallel binding protein design. a, Hundreds of
37–43 residue mini-protein backbones with different secondary structureelements, orientations and loop lengths were matched with hotspot
binding motifs for HA (HB1 and HB2) and BoNT (Bot) by identifying
compatible mini-protein local backbone segments, superimposing them
onto the hotspot motif–target complex, and discarding docks with miniprotein/target backbone clashes. Each topology included designs withmany different disulfide configurations; several possibilities are illustrated.
b, For each non-clashing dock of each scaffold onto each target, themonomer and interaction energies were optimized with Rosetta sequence
design. Representative models are shown at the left of each column. Rightcolumns show a top view of the target with the hotspot interaction areas
coloured as above and new contact areas generated by Rosetta sequence

design coloured yellow; the total number of unique designs generated isindicated at the bottom. c, Designed contacts substantially increase theinterface buried surface area of the designs beyond the starting hotspot
residues. d, Genes encoding 16,968 mini-protein designs, including 6,286
controls, were synthesized using DNA oligo pool synthesis (see Methods).
e, The oligo pools were recombined into yeast display vectors andtransformed into yeast (see Methods), and binding of the designs HA or
BoNT at different concentrations was assessed by FACS. For each sorting
condition, enriched designs were identified by comparing the frequencies
in the original and sorted populations using deep sequencing. These data
were used to guide improvement of the computational design model, andthe entire design, synthesis and testing cycle was iterated.

with hotspot residues from these helices (Extended Data Fig. 1) anddesigned the remainder of the residues to maximize binding affinity
and monomer stability using Rosetta combinatorial sequence optimization (Supplementary Fig. 1). The designs interact with the targets ina myriad of ways (Fig. 1b), with a wide range of buried surface areas at
the interface (Fig. 1c) resulting from variations in the Rosetta designed
interactions outside the hotspot residues. As HA and BoNT differ inshape (the HA epitope is more concave and the BoNT epitope is flatter),the design protocol favoured different topologies in the two cases (α​–β​for HA and all α​for BoNT).
For experimental characterization, we selected 7,276 designs against
HA and 3,406 designs against BoNT (see Methods). To probe contributions to folding and binding, we also included a variety of control sequences in which the amino acids outside the helical interface
motif were randomly permuted, the core residues were randomly permuted, all the core residues were mutated to valine, the loop residues
were mutated to Gly-Ser, or the designed binding sites were omitted.
Oligo pools encoding all of the design and control sequences (16,968
in total) were synthesized, amplified, and co-transformed into yeast,
along with a linearized yeast display vector (Fig. 1d). The resulting yeast
libraries, displaying the 16,968 proteins, were incubated with a rangeof concentrations of fluorescently labelled target, in some cases after
protease treatment to remove poorly folded designs. Cells displaying
designs that bound the target were retrieved by fluorescence-activatedcell sorting (FACS, Fig. 1e). The frequency of each design and control
sequence in each pool was determined by deep sequencing the sorted

cell populations, and the sequences were categorized on the basis of
the sorting condition in which they were most enriched (Extended
Data Fig. 2).
Deep sequencing of the initial yeast-transformed pools showed nearcomplete representation of full-length genes; the HA pool contained
11,002 of the 11,657 sequences ordered (94.4%) and the BoNT poolcontained 5,306 of the 5,311 sequences ordered (99.9%). Sorting thepools under conditions of increasing stringency (decreasing concentration of target) sharply reduced the number of distinct sequences
recovered (Fig. 2). For the BoNT pool, after sorting at target concentrations of 100, 10 and 1 nM, the enriched binder populations contained
2,685, 987 and 355 distinct sequences, respectively; a final higher stringency screen that involved incubating with protease before assaying
for binding (to favour highly stable designs) reduced the population to
57 distinct sequences. At 1 nM of BoNT with no protease treatment,
7.8% of the designed sequences, 5.1% of the scrambled core variants,
1.4% of the valine core variants and none of the Gly-Ser loop
variants were enriched. For the HA pool, after sorting against HA (frominfluenza strain A/PuertoRico/8/1934 (PR8)) concentrations of 1,000,
100 and 10 nM, the enriched populations contained 115, 41 and 29
distinct sequences, respectively. For both targets, the population fraction of the computationally designed mini-proteins increased over thatof the scrambled control sequences as selection stringency increased;
computational design considerably increased the probability of binding
the target with high affinity. The simplest explanation for this is that asubstantial fraction of the proteins fold into the designed structures.
5 o c to b er 2 0 1 7 | V O L 5 5 0 | N A T U R E | 7 5© 2017 Macmillan Publishers Limited, part of Springer Nature. All rights reserved.

RESEARCH Article
Negative controls

Influenza HA

Affinity

Scrambles

Designs+Disulfides

Totalsequences

Designs +1–3 disulfides

High (10 nM)

29Medium (100 nM)

41

Low (1,000 nM)

115

All designs
0

2,000

4,000

6,000

Scaffolds Designs

0.5

1.0

11,657

α topology
αβ topology

Design controls

Negative controls

0

10,0008,000+Scrambled core

Designs +1 disulfide

Syt-II
Scrambles

BoNT HCBHigh + Protease+GS loops+Valine core
57

High (1 nM)

355Medium (10 nM)

987

Low (100 nM)

2,685

All designs
0

1,000

2,000

3,000

4,000

5,000

Design index

0

0.5
1.0
Population fraction

5,311

Figure 2 | Massively parallel evaluation of binding. Vertical bars indicate
FACS binding enrichment at different target concentrations for eachof the 16,968 designs and 6,286 controls for Influenza H1 HA (a) and
BoNT HCB (b). All α​-helical designs are in green; mixed α​–β​ topologies,
in orange. The mini-proteins are grouped by type as indicated by thehorizontal bars and text at the top of the panels. ‘High+​Protease’ indicates
5 min incubation with trypsin (18.5 μ​g ml−1) followed by incubation with
1 nM target. Right panels indicate normalized population fraction of eachtype of design (colour scheme as in corresponding left panel) for each of

the selection conditions at the far left (Extended Data Table 2); the total
number of surviving designs is indicated by the numbers at the far right.
For example, after incubation of the HA mini-protein population with
100 nM HA, FACS and deep sequencing, the population fractions of both
non-disulfide (blue) and disulfide (yellow) designs doubled comparedto the starting population, while that of the non-disulfide scrambles
decreased approximately fivefold and the disulfide scrambles completely
disappeared.

The design population included 3,264 BoNT designs with single
disulfides and 3,594 HA designs with multiple disulfides in geometrically allowed positions. For both targets, designs containing disulfides
had similar success rates to those in which disulfides were absent (26.2%
versus 25.5% for BoNT and 0.5% versus 0.8% for HA), ­consistent with alate and non-instructive role for disulfides in protein folding. However,when the design libraries were treated with trypsin before binding
selection, only disulfide-stabilized designs were r­ ecovered; although
they do not guide protein folding, the disulfides clearly confer stability
against proteolysis (Extended Data Table 1).

(see Methods). Although there was little correlation with the ­overall
structural deviation from the design model, the interface hotspot
residues fluctuated to a lesser extent in the binding designs than in
the non-binding designs (Fig. 3d), suggesting that binding site pre-­organization is important for binding and that molecular dynamics
simu­lations capture this property reasonably well (Extended Data
Fig. 3).
To investigate the sequence dependence of folding and function in
more detail, we generated and screened single-site saturation mutagenesis (SSM) libraries for six HA and two BoNT designs, with every
position mutated to every amino acid, one at a time (a total of 6,126
mutants). Substitutions at the binding interface and in the protein core
were more disruptive than substitutions at surface positions, and almost
all the cysteines were highly conserved in designs containing disulfides(Fig. 3e–f and Supplementary Fig. 3a–h). Rosetta designed interactions
outside the hotspot regions were found to make important contributions to binding (Supplementary Fig. 3a–h); mutations of the nonhotspot HB1.6928.2 residues Ala11, Trp19, and Tyr24 (see Fig. 4a), forexample, greatly decreased binding affinity. The effects of each mutation on both ­binding energy and monomer stability were estimatedusing Rosetta design calculations, and a reasonable correlation was
found between the predicted and experimentally determined susceptibility of positions to mutation for three of the six designs for HB1 andboth of the two BoNT designs (Supplementary Fig. 2). Finally, the SSM
datasets were used to guide generation of higher affinity HA and BoNT
binders (see Extended Data Table 2 and Methods).

Assessment of the computational model

The measured binding activity of a design reflects both the extent towhich the protein is folded and the binding affinity of its folded state
to the target. In general, sequences with binding activity had lower
computed folding energies and binding energies (Fig. 3a): this may be
the largest-scale confirmation to date of the ability of a computationalmodel to recapitulate protein–protein interactions. The ­second-orderfeatures most strongly associated with binding were local sequence–
structure compatibility and the numbers of contacts across the i­ nterface(Fig. 3b). On the basis of these results, we updated the design p­ rotocol(see Methods) and generated 11,420 new HA designs for a second
round of experimental testing (Fig. 3c), in which the success rateincreased from 1.4% to 3.1% (producing 342 new HA binders). The
improvement was particularly marked in the subset of HB2 seeded
designs, improving the success rate almost tenfold from 0.23% to 1.9%.
Iterations of this design–build–test cycle should reveal additional contributions that are missing in the current model, which can then be
captured in the energy function and design protocol.
The large dataset provides an opportunity to determine whether
extensive molecular dynamics simulations in explicit solvent can reproducibly distinguish binding and non-binding designs. We s­ imulated
143 randomly selected non-binders and 146 binders that were evenly
split between HA and BoNT, for a total simulation time of 108 μ​s

Individual characterization of designed binders

Eight BoNT and six HA binders, a mix of affinity-matured and ­original
designs, were chemically synthesized or expressed in Escherichia coli,
purified, and characterized in solution (Extended Data Table 2). All
14 designs had circular dichroism spectra consistent with the designmodels, and melting temperatures greater than 70 °C (Extended Data
Fig. 4a). Designs containing disulfides did not unfold at 95 °C and were

7 6 | N A T U R E | V O L 5 5 0 | 5 o c to b er 2 0 1 7© 2017 Macmillan Publishers Limited, part of Springer Nature. All rights reserved.

Article RESEARCH

0.01

–4
–4

0
0
4
10
Side-chain probability

AUC = 0.77
90
8

HB1

Experimental SSM

Mean per-positionchange in binding
0
–2
–2
0
Computed (Rosetta)
SSM

0

1.0

b

HA1

HA2

Bot.671.2

0
190
250
Interface atom count

Design

10

HB2

e

0.35

Binding designs
Non-binding designs

20

HB1.6928.2.3

Design

0.02

2.0
3.0
4.0
Average hotspot r.m.s.d. (Å)

fExperimental SSM

100

0

0.4

0

Generation 1
Generation 2

252

0

0.04

d

0.05

Success ratec–2
Binding energy

140

5.0

Mean per-positionchange in binding
1
0
–1
–2
0
Computed (Rosetta)
SSM

Figure 3 | Experiment-based assessment of computational models.
a, Computed energies of folding and binding for binding designs (orange)
and non-binding designs (grey); x-axis is binding energy per nm2 andy-axis is monomer (folding) energy per residue, both in kcal per mol.
b, Kernel density estimates for HA (top) and BoNT (bottom) show thatdesigns that bind target (blue) have better local sequence–structurecompatibility, quantified by the Rosetta side-chain probability score-p_aa_pp, and higher interface atom counts than non-binding designs(red). Design success rate (dark green) is shown with 1σ confidence
interval (light green). c, Inset: Receiver–operator characteristic curve fordiscriminating first generation HA binders using a five-factor logistic
regression. A second generation of HA binder design incorporating
filtering on these five features (see Methods) had an increased success rate
(y-axis); the numbers of successes are indicated above the bars. d, Interface
residue fluctuations in molecular dynamics simulations are smaller forbinding designs than non-binders (see Methods and Extended Data Fig. 3).
e, f, Left, design models of Bot.671.2 (e) and HB1.6928.2.3 (f) bound totheir targets, coloured by the mean change in binding at each positionin the comprehensive mutagenesis pools; conserved residues (blue) are
shown as sticks, non-conserved positions in red. Right, the experimentallyobserved mean changes in binding at each position (y-axis) correlate withthose computed from the structures (x-axis) (Pearson cross-correlation
test: e, r =​  0.76; f, r =​  0.64).

c

d
1.0

102

0.5

0.0

FI6v3
Bot.671.2
HB1.6928.2.3
0

20

40

Min

60

Bot.671.2

BoNT HCB

Influenza H1 neutralization
>100

100
10–2
10–4

+HB36.6+FI6v3

CA09 H1N1+HB1

PR8 H1N1e

BoNT/B (20nM) neutralization
80
40
0

(+)

+Syt-II
(nM)

100

50

0

(+)

600

BoNT
Probability density–2

Number of designs

BoNT
Monomer energy

060
Non-binders
Binders
0.2

BoNT/B binding (%)

10

200
600

4

60
200
600

0

–2

0

VAMP2 cleavage (%)

0

Y24 W19

EC50 (µg ml–1)

–4

0.03

0.2

180º

HB1.6928.2.3

Success rate

Non-binders
Binders

–4

a

0.1

Success rate

HA
Monomer energy–2

Designs only
0.04

Binding activity(normalized)b

All sequences
0

HA
Probability densitya+Bot.671.2(nM)

Figure 4 | Characterization of structure, stability and activity of designs.
a, Left, comparison of design model with X-ray structure of HB1.6928.2.3
in complex with PR8 H1 HA (HA used in design calculations is essentially
identical to crystal structure and not shown). Right, close-up of the
HB1.6928.2.3 X-ray structure with designed residues conserved in the
SSMs outside of the hotspot seed indicated in sticks; these residues makeboth packing (e.g. W19 and Y24) and electrostatic interactions with
HA. b, Left, as in a, but for Bot.671.2 in complex with BoNT HCB.
c, Binding activity remaining following incubation of the indicatedmolecules at 80 °C for different durations (x-axis), measured by biolayerinterferometry. The designs are considerably more robust than themAb FI6v3 antibody. d, HB1.6928.2.3 (HB1) more effectively prevents
influenza infection of Madin–Darby canine kidney cells than do FI6v3
or the previously designed binder HB36.6 (see also Extended Data Fig.
8). n =​ 3 independent virus titrations were performed for each condition.
Dots show raw values for each test and whiskers show ±​ 1 s.d. e, Bot.671.2
better protects cultured rat cortical neurons against degradation of VAMP2
than does Syt-II, and it prevents binding of the toxin to neurons. n =​  4
independent samples for each condition, dots show raw values for eachcondition and whiskers show ±​ 1 s.d.

Bot.671.2.1, and the haemagglutinin-targeting antibody mAb FI6v3
at 80 °C for various times before performing binding assays to theirrespective targets. The mini-protein binders showed no detectable
loss of binding after 1 hour at high temperature, while FI6v3 ­binding­activity was reduced by approximately 74% (Fig. 4c). These results suggest that therapeutics relying on small designed proteins could bypass
the requirement for cold chain management for monoclonal antibodies.
In vitro assays were carried out for BoNT and influenza. HB1.6928.2.3,
resistant to trypsin (Extended Data Fig. 5); reduction of the disulfides
with the reducing agent tris(2-carboxyethyl)phosphine (TCEP) con- an affinity-matured, disulfide-containing design, strongly neutra­
siderably decreased their stability (Extended Data Fig. 4b,c). The lized PR8 and CA09 influenza viruses after 48 hours in culture, with
BoNT binders bound to HCB with affinities ranging from 1 to 20 nM a half-maximal effective concentration (EC50) value for Cal09 (CA09)
by biolayer interferometry, and also bound the serotype G of botulinum more than 100-fold lower than the broadly neutralizing antibody
neurotoxin (BoNT/G) (see Methods and Supplementary Fig. 4). FI6v318, or the previously designed HB36.614, on the basis of mass
The HA binders bound to HA proteins from two H1N1 influenza (Fig. 4d; the EC50 is similar to the antibody on a molar basis). Parallelingstrains, PR8 and A/California/04/2009 (CA09); the three affinity-­ this, Bot.671.2 protected rat cortical neurons against the entry of the­ embrane
matured binders had affinities against CA09 below 10 nM (Extended BoNT/B toxin and against cleavage of the vesicle-associated m
Data Table 2). We succeeded in solving crystal structures of both protein 2 (VAMP2; the intracellular target of BoNT/B) (Fig. 4e) at
HA and BoNT HCB binders (HB1.6928.2.3 and Bot.671.2.1, respec- lower concentrations than Syt-II (the BoNT/B receptor), which contively) in complex with their targets. In both cases the complexes tains the same hotspot residues (Extended Data Fig. 7). The increase
were in ­excellent agreement with the computational design models in ­protection is likely to reflect both the reduction in conformational(monomer-Cα root-mean-square deviation (r.m.s.d.) =​  0.94 Å for entropy of the binding motif and the additional designed interface
HB1.6928.2.3, and 0.82 Å for Bot.671.2.1, see Fig. 4a–b, Extended Data contacts.
HB1.6928.2.3 protected mice from influenza both pre- and post-­
Fig. 6 and Supplementary Tables 1 and 2).
To compare the ability of the designs to survive high temperature exposure. Intranasal administration of HB1.6928.2.3 twenty-four hours
exposure with that of antibodies, we incubated HB1.6928.2.3, before lethal challenge with CA09 influenza resulted in 100% survival
5 o c to b er 2 0 1 7 | V O L 5 5 0 | N A T U R E | 7 7© 2017 Macmillan Publishers Limited, part of Springer Nature. All rights reserved.

RESEARCH Articleab
100
Weight change (%)

Weight change (%)

100
90
80
70

60

0.10 mg kg–1 i.n.

70400.10 mg kg–1 i.n.

20

3.30 mg kg–1 i.n.

0.10 mg kg–1 i.v.
3.30 mg kg–1 i.v.
0

2

c

80
Survival (%)

Survival (%)

0.03 mg kg–1 i.n.

FI6 mAb HB1.6928.2.3

PBS

80

Control

60

+1d

40

+2d HB1.6928.2.3–1+3d (3.0 mg kg )
+4d

20

4
6
8
10
Time since challenge (days)

0

14

0

2

4
6
8
10
Time since challenge (days)

12

14

12

14

d

Intranasal

100
Weight change (%)

Intravenous

12

2.0
Optical density (450 nm)

80

100

100

0

90

1.0

90
80
70
0
100

0
2
Dose number

3

mIgG
hIgG
HB1.6928.2.3
Bot.2110.4
Bot.3194.4

1

2
Dose number

80

3

PBS
BSA
HB1.6928.2.3
HB1.6394.2.3

Survival (%)

1

60
40
20
0

0

4×
pre-exposurei.n.
(0.3 mg kg–1)

Bot.2110.4 i.v.
Bot.3194.4 i.v.

Bot.2110.4
Bot.3194.4

PBS
HB1.6928.2.3 i.n.
HB1.6928.2.3 i.v.

HB1.6928.2.3

2

4
6
8
10
Time since challenge (days)

Figure 5 | In vivo efficacy and immunogenicity. a, b, d, Weight change(top) and survival (bottom) of BALB/c mice receiving influenza binder.
a, Prophylactic efficacy: mice received HB1.6928.2.3 (orange) or FI6v3
mAb (green) intranasally or intravenously 24 h before challenge with 2
MLD50 (fifty per cent mouse lethal dose) of H1N1 CA09 virus (n =​  10,except 0.03 mg kg−1, n =​ 5), see also Supplementary Fig. 6. b, Therapeutic
efficacy: mice were first challenged with 2 MLD50 of CA09 virus andthen received HB1.6928.2.3 intranasally 1–4 days post-challenge(n =​ 5). The mini-proteins have remarkable therapeutic efficiency evenif administered after three days. c, Immune (IgG) responses in BALB/cmice (n =​ 5) that received three intravenous doses (3 mg kg−1) of miniproteins, human IgG (hIgG) or mouse IgG (mIgG) spaced three weeks
apart (left) or three intranasal doses of mini-proteins or bovine serum

albumin (BSA; 3 mg kg−1) spaced two weeks apart (right). IgG responses
in both cases were measured by enzyme-linked immunosorbent assay(ELISA, 1:500 serum) two weeks after each dose. d, Prophylactic
efficacy is not reduced by repeated dosing: Mice received four doses(weeks 0, 3, 6, and 12, 3 mg kg−1) of either HB1.6828.3.2, a Bot protein(mock dosing controls), or buffer (PBS), followed by a fifth intranasal
dose of HB1.6828.3.2 or a Bot protein (0.3 mg kg−1) nine days after
the fourth administration. Twenty-four hours after the fifth dose, mice
were challenged with 2 MLD50 of H1N1 CA09 flu virus. HB1.6928.2.3
remains equally protective after repeated administration when compared
to protection with no prior dosing. In all panels, whiskers show ±​ 1
s.e.m. Raw data for all the experiments in this figure are available in the
Supplementary Information. i.n., intranasal; i.v., intravenous.

at doses as low as 0.03 mg kg−1, which is 100-fold lower on the basis ofmass than the dose of FI6v3 required for equivalent protection (Fig. 5aand Supplementary Fig. 6). Therapeutic administration of a single
3 mg kg−1 dose of HB1.6928.2.3 twenty-four hours after virus challenge
resulted in 100% survival and little (less than 10%) weight loss (Fig. 5b);the same dose administered 72 hours after challenge imparted complete
protection and 100% ­survival (Fig. 5b). Intravenous administrationof HB1.6928.2.3, ­however, resulted in little protection, indicating that(as with the on-market drug Zanamivir19) intranasal administration is
likely to be the optimal ­delivery route for these mini-proteins.
Three sequential doses of the mini-proteins, administered by intranasal or intravenous delivery every two weeks, induced little or no
antibody response (Fig. 5c): the low levels of antibody detected werecomparable to those induced by mouse IgG (negative control), andsubstantially less than levels induced by human IgG (positive ­control).
Intranasal administration of the influenza mini-protein binders
24 hours prior to viral challenge still achieved 100% prophylactic efficacy even in mice that had previously received four repeated intranasal or intravenous doses of the mini-protein over a space of twelve

weeks (Fig. 5d), indicating that any immune response and clearance isminimal and not sufficient to interfere with antiviral potency. The low
immunogenicity is likely to be a consequence of the very small size andhyperstability of the mini-proteins, and suggests that the binders could
be used for prophylactic protection against influenza over an extended
period of time. To our knowledge, this is the first investigation of theimmunogenicity of de novo designed proteins.

Conclusions

The high throughput of our pipeline from computational design toexperimental testing enables the characterization of computationallydesigned binding proteins on a scale that is orders of magnitude greaterthan those of previous studies, providing insights into the contributions
to folding and binding on thousands of test cases simultaneously. For
example, our observation that substituting designed loop sequenceswith generic Gly-Ser linkers reduced binding fitness to a greater degree
than did substituting the designed core residues with valine (or scrambling their order) suggests that loops may play an underappreciated,
instructive role in the folding of proteins of this size. Different topologies

7 8 | N A T U R E | V O L 5 5 0 | 5 o c to b er 2 0 1 7© 2017 Macmillan Publishers Limited, part of Springer Nature. All rights reserved.

Article RESEARCH
were found to be optimal for the BoNT and HA interfaces, supporting
the hypothesis that no single protein topology or shape is the best fit
for all interfaces. The massively parallel design process succeeded ingenerating folded high-affinity binding proteins despite uncertainties
in designing 40-residue proteins with multiple hydrophobic residues(which potentially complicate folding) on their surfaces. Iteration
between data-driven model improvement and experimental testing islikely to improve both the computational design methodology and ourunderstanding of the determinants of folding and binding: the limited
number of native protein structures from which much of our current
knowledge is derived is dwarfed by the nearly unlimited number ofde novo proteins that can be designed and tested using our approach.
De novo protein design has the potential to generate pharmaceutically superior molecules that combine the specificity of antibodies withthe high stability and manufacturability of small molecules. Designed
protein binders have previously been produced by re-­engineering
naturally occurring proteins; although these achieve some of
the above goals (such as accurate site-specific binding), they are
not more robust than antibodies. The de novo designed binders
described here exhibit much greater stability to incubation at elevated temperatures and b­ etter neutralization than comparable antibodies and natural protein derivatives, have approxi­mately 1/30thof the molecular weight, and are readily ­chemically synthesizable, which enables the introduction of a wide variety of c­ hemicalfunctionality. Probably as a result of their small size and very high­stability, they elicit little immune response even without explicit
negative design20, and the best of the HA designs provides prophylactic
and therapeutic protection against influenza infection in vivo with apotency rivalling or surpassing that of antibodies. Unlike antibodies,
the mini-proteins do not contain the effector recruiting IgG Fc region;
our results show the latter is not required for potent protection against
influenza, and therapeutics which lack the Fc could avoid issues of antibody enhanced infectivity. More generally, hyperstable designed mini-­
proteins show promise for both therapeutic and diagnostic applications.
Online Content Methods, along with any additional Extended Data display items andSource Data, are available in the online version of the paper; references unique tothese sections appear only in the online paper.
received 25 January; accepted 17 August 2017.
Published online 27 September 2017.
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Supplementary Information is available in the online version of the paper.
Acknowledgements We thank M. Levitt and M. Zhang for discussions,
A. Ford for data analysis advice, and Rosetta@Home participants for donating
computing time. D.-A.S. thanks T. J. Brunette, J. E. Hsu and M. J. Countryman for
their assistance. R.J. thanks K. Perry for X-ray data collection. We acknowledge
funding support from: Life Sciences Discovery Fund Launch grant 9598385(A.C.); PEW Latin-American fellow in the biomedical sciences and a CONACyT
postdoctoral fellowship (D.-A.S.); Merck fellow of the Life Sciences Research
Foundation (G.J.R.); CONACyT and Doctorado en Ciencias Bioquímicas
UNAM (R.V.); NIH (R56AI117675) and Molecular Basis of Viral Pathogenesis
Training Grant (T32AI007354-26A1) (S.M.B.); Investigator in the Pathogenesis
of Infectious Disease award from the Burroughs Wellcome Fund and NIH(1R01NS080833) (M.D.); CoMotion Mary Gates Innovation Fellow program(T.C.); generous gift from Rocky and Genie Higgins (C.B.); Shenzhen Scienceand Technology Innovation Committee (JCYJ20170413173837121), Hong
Kong Research Grant Council C6009-15G and AoE/P-705/16 (X.H.); PAPIIT
UNAM (IN220516), CONACyT (254514) and Facultad de Medicina UNAM
(D.A.F.-V.); NIAID grants (AI091823, AI123920, and AI125704) (R.J.); NIAID
grant 1R41AI122431 (M.T.K. and D.H.F.); NIAID grant 1R21AI119258 and Life
Sciences Discovery Fund grant 20040757 (D.H.F.). We acknowledge computing
resources provided by the Supercomputing Laboratory at King Abdullah
University of Science and Technology and the Hyak supercomputer systemfunded by the STF at the University of Washington. The Berkeley Center for
Structural Biology is supported in part by the NIH, NIGMS, and HHMI. The
Advanced Light Source is a DOE Office of Science User Facility under contract
no. DE-AC02-05CH11231. The Northeastern Collaborative Access Team
beamlines are funded by NIGMS grant P41 GM103403 and a NIH-ORIP HEI
grant (S10OD021527). Advanced Photon Source is a U.S. DOE Office of Science
User Facility operated by Argonne National Laboratory under Contract No.
DE-AC02-06CH11357.
Author Contributions A.C., D.-A.S., G.J.R., D.H.F. and D.B. designed the research;
A.C., D.-A.S., and G.J.R. contributed equally; D.R.H., R.V., and P.M. contributed
equally; A.C., D.-A.S., G.J.R., D.R.H., R.V., and C.D.B. designed proteins. A.C.,
D.-A.S., D.R.H., R.V., performed binding experiments; S.M.B. solved influenza
co-crystal structures; P.M., M.T.K., A.B., C.M.J. and J.T.F. performed influenza
experiments; L.Z. performed molecular dynamics simulations; K.-H.L. and
G.Y. solved BoNT co-crystal structures; S.-I.M. performed BoNT neutralization
assays; I.G. and C.M.B. prepared yeast and next generation sequencing; T.C.
performed protease-resistance characterization; L.C. performed protein
purification. All authors analysed data. D.A.F.-V., L.S., M.D., X.H., R.J., I.A.W., D.H.F.
and D.B. supervised research. A.C., D.-A.S., G.J.R., D.R.H., D.H.F. and D.B. wrote
the manuscript.
Author Information Reprints and permissions information is available atwww.nature.com/reprints. The authors declare competing financial interests:
details are available in the online version of the paper. Readers are welcome tocomment on the online version of the paper. Publisher’s note: Springer Natureremains neutral with regard to jurisdictional claims in published maps andinstitutional affiliations. Correspondence and requests for materials should be
addressed to D.B. (dabaker@uw.edu).
Reviewer Information Nature thanks G. Nabel and the other anonymous
reviewer(s) for their contribution to the peer review of this work.

5 o c to b er 2 0 1 7 | V O L 5 5 0 | N A T U R E | 7 9© 2017 Macmillan Publishers Limited, part of Springer Nature. All rights reserved.

RESEARCH Article
Methods

Unless stated otherwise, the experiments were not randomized and the investigators were not blinded to allocation during experiments and outcome assessment.
Mini-protein binders design. Mini-protein design began by defining a variety of
mixed α​–β​ and α​-only scaffold topologies using the RosettaRemodel ‘blueprint’
format21 with the requirement of at least one 10–14-residue helix. The blueprints
were used to generate backbones using the Rosetta Monte Carlo-based fragment
assembly protocol22,23. Between one and three disulfides were added to a ­subsetof these backbones at geometrically allowed positions. Sequence design was
performed using the FastDesign protocol with layer control active, alternating
between side-chain rotamer optimization and gradient-descent-based energy
minimization. For each topology, over 10,000 structures were generated andfiltered on overall energy per residue and score terms related to backbone quality,
compactness and disulfide quality (see Supplementary Appendix).
To match the mini-protein scaffolds with the desired target helix-binding
motifs, we used the Rosetta MotifGraft Mover7,24. The inputs were composed of:(1) HB36, HB80 or Syt-II helical binding motifs (Protein Databank (PDB) IDs:
3R2X, 4EEF and 2NM1, respectively); (2) the context target protein (influenza
HA or BoNT HCB); and (3) the above described library of de novo mini-protein
scaffolds. Matching parameters were set to perform full backbone alignment of the
input motif, with a maximum backbone r.m.s.d. =​ 1 Å, endpoints r.m.s.d. =​ 1 Å,
clash_score_cutoff =​ 5 and enabling revert_graft_to_native_sequence. In the caseof BoNT/B’s Syt-II binding domain, the hotspots were defined as: Met1, Phe2,
Leu5, Lys6, Lys8, Phe9, Phe10, Glu12, Ile13 (see Extended Data Fig. 1). For the
influenza HA HB80.4 (HB1) binding domain, the hotspots were defined as: Phe1,
Ile5, Ile9, Phe13. For the influenza HA HB36.6 (HB2) binding domain, the hotspots
were defined as: Phe1, Met5, Trp9, Phe13. Following MotifGraft, we performed
Rosetta’s sequence repack of interface neighbouring residues (except hotspots),
Cartesian minimization and filtering using the scoring function Talaris2013 orTalaris2014.
After the first round of HA design and testing, the Kolmogorov–Smirnov
two-sample test was used to determine P values for the null hypothesis that thecomputational metrics of the binding versus non-binding designs were drawn fromthe same underlying distribution. Using the metrics that correlated strongly withsuccess (such as those shown Fig. 3b), a second round of HA design was performed
which incorporated more stringent filtering on a broader range of metrics. The
metrics used to select the first round of HA designs were delta G of binding (ddgfilter), shape complementarity (sc), and interface buried surface area (SASA). The
additional metrics used to select the second generation HA designs and shown tobe highly predictive of round one success in the logistic regression model (Fig. 3cinset) were average degree (degree), side-chain probability given phi-psi (p_aa_pp),
per cent core by side-chain neighbours, phi-psi probability given side-chain (rama)
and more stringent shape complementarity. Design models are available online(https://zenodo.org, http://dx.doi.org/10.5281/zenodo.838815).
Software analysis. All amino acid sequences were reverse translated and codon
optimized for yeast using DNAworks 2.025. Sequence identity calculations were
performed with a subset of designs using PRALINE26 after PSI-BLAST globalalignment. Sequencing pairing after Illumina deep sequencing was performed
using PEAR27. Plots and visualizations were created using Seaborn statisticalvisualization tools28, Python (Python Software Foundation) and Python’s scikitlearn (INRIA).
Gene pools. Oligo library pools ordered from either CustomArray or Agilent
with all genes 3′​and 5′​flanked with common 20-bp adaptor segments to allow
amplification. We obtained conventional oligonucleotides (PCR primers andsequencing primers) from Integrated DNA Technologies. The raw oligonucleotide
pools were amplified with Kapa HiFi Hotstart Ready Mix (Kapa Biosystems) using
extension primers to add pETCON yeast homologous recombination segments(40 bp) to each end. All amplifications were performed using real-time PCR on
a MiniOpticon (Bio-Rad) for between 9 and 20 cycles. Quantitative PCR amplification was critical as over-amplification of gene pools resulted in low transformation efficiency. Amplified pools were size-selected on a 2% agarose gel andcleaned (Qiagen QIAquick Gel Extraction Kit). A second round of quantitative
PCR amplification was performed with the same primers on the size-selected pools
to generate 2–4μ​g of DNA. Yeast EBY100 cells were transformed with library DNA
and linearized pETCON plasmid29 using an established protocol30. After transformation (minimum 1 ×​  107 transformants), cells were grown overnight in SDCAA
medium in 30-ml cultures at 30 °C, passaged once, and stored in 20 mM HEPES
150 mM NaCl pH 7.5, 20% (w/v) glycerol in 1 ×​  107-cell aliquots at −​80 °C.
Yeast display and deep sequencing. Cell aliquots were thawed on ice, centrifuged
at 13,000 r.p.m. for 30 s, resuspended in 1 ×​  107 cells per ml of C-Trp-Ura medium
and grown at 30 °C for 16 h. Cells were then centrifuged at 13,000 r.p.m. for 1 min
and resuspended at 1 ×​  107 cells per ml SGCAA medium and induced at 30 °C for

16–24 h. Cells were labelled with either biotinylated BoNT HCB, PR8 haemag­
glutinin, or CA09 haemagglutinin, washed, secondarily labelled with streptavidin–
phycoerythrin (SAPE, Invitrogen) and anti-c-Myc fluorescein isothiocyanate(FITC, Miltenyi Biotech), and sorted by fluorescent gates under various stringency
conditions using a Sony SH800. HA target proteins were produced as previously
described16. Cells were recovered overnight at 2.5 ×​  105 collected cells per ml
SDCAA medium, whereupon at least 1 ×​  107 cells were spun down at 13,000 r.p.m.
for 1 min and stored as cell pellets at −​80 °C before library preparation for deep
sequencing. Between 1 ×​  107 and 4 ×​  107 yeast cells were barcoded and preparedfor deep sequencing for each library as previously described15. Enhanced score
files including all sequences ordered, computational metrics, and enrichments areavailable online (https://zenodo.org, http://dx.doi.org/10.5281/zenodo.838815).
SSM and affinity maturation. SSM libraries for eight designs (Supplementary Figs 2
and 3a–h) were constructed from Agilent gene pools, and yeast display selections
performed as described above, using target protein concentrations of 1,000 nM,
100 nM, 10 nM and 1 nM for HA, and 100 nM, 10 nM, 1 nM, 1nM + (trypsin at
18.5 µg ml−1) for BoNT HCB. Upon deep sequencing, the five most beneficial
mutations at nine positions in each of the HA designs predicted to result in higher
affinity were combined into high-diversity libraries (<​1 ×​  107) using wobble bases
as guided by SwiftLib31. A DNA library for each design was constructed fromassembly PCR using Ultramer oligonucleotides (Integrated DNA Technology) toencode the variable region. Primers and sequences are listed in Supplementary
Table 3. These libraries went through three increasing stringency sorts: rd1 100 nM,
rd2 10 nM, and rd3 1 nM against CA09. Promising constructs were identified
through Sanger sequencing of a subset of the final rd3 pool. Versions of the BoNT
designs were directly generated from SSMs by incorporating the best beneficial
mutations to create high-affinity variants; no selections were used.
Mini-protein expression and peptide synthesis. Genes encoding the designed
protein sequences were synthesized and cloned into pET-28b(+​) E. coli plasmid
expression vectors (GenScript, N-terminal 6 ×​His tag and thrombin cleavagesite). Plasmids were then transformed into chemically competent E. coli Lemo21
cells (NEB). Protein expression was then induced with 1 mM of isopropyl
β​-d-­thiogalactopyranoside (IPTG) at 18 °C. After overnight expression, cells
were ­collected and purified by nickel affinity followed by size-exclusion fast protein l­ iquid chromatography (Superdex 75 10/300 GL, GE Healthcare) and mass
­spectrum verification of the molecular weight of the species in solution (Thermo
Scientific). Peptide sequences were synthesized from commercial vendors Biomatikor CS Bio in 50 mg quantities with 70% purity requirements. Sequences containing
cysteines underwent standard natural oxidation performed by vendor.
Circular dichroism. Far-ultraviolet circular dichroism measurements were ­carriedout with an AVIV spectrometer model 420 in PBS buffer (pH 7.4) in a 1 mm
path-length cuvette with protein concentration of approximately 0.25 mg ml−1
(unless otherwise mentioned in the text). Temperature melts were from25 to 95 °C and monitored absorption signal at 222 nm (steps of 2 °C per min, 30 sof equilibration by step). Wavelength scans (195–260 nm) were collected at 25 °C
and 95 °C, and again at 25 °C after fast refolding (roughly 5 min). Four chemically­synthesized, disulphide-containing mini-proteins (see Extended Data Fig. 4b) werealso ­characterized at a concentration of approximately 0.2 mg ml−1 in the absenceor presence of 2.5 mM of the reducing agent TCEP5.
Biolayer Interferometry. Binding data were collected in an Octet RED96(ForteBio) and processed using the instrument’s integrated software using a 1:1
binding model. For BotNT/B binding proteins, the target BoNT heavy chain
protein domains (HCB or HCG) were covalently linked to amine-reactive s­ ensors(ARG2, ForteBio) at 5 μ​g ml−1 in acetate buffer for 300 s, while the binding
proteins to assay were diluted from a concentrated stock into binding buffer(10 mM HEPES, pH 7.4, 150 mM NaCl, 3 mM EDTA, 0.05% surfactant P20, 0.5%
non-fat dry milk). For influenza binding proteins, streptavidin-coated biosensors
were dipped in wells containing biotinylated HA proteins (100 nM) in binding
buffer for immobilization for 300 s, while the binding proteins to assay were diluted
from a concentrated stock into binding buffer (1 ×​phosphate buffered saline (PBS)
pH 7.4, 0.01% BSA, 0.002% Tween 20). After baseline measurement in binding
buffer alone, the binding kinetics were monitored by dipping the biosensors inwells containing defined concentrations of the designed protein (association) andthen dipping the sensors back into baseline wells (dissociation). For heat-timecourses, the proteins were incubated for defined times at a concentration of 160 nM
in PBS buffer (150 nM NaCl, pH =​ 7.4) and then diluted to 8 nM in the final buffer
and assayed as described above.
BoNT/B (HCB) co-crystal structures. HCB was expressed and purified as previously described32. For X-ray crystallography, the HCB-Bot.671.2 c­ omplex was
prepared by mixing HCB with the binder at a molar ratio of 1:1.5 and the complex
was further purified by a Superdex 200 SEC (GE Healthcare) in a buffer containing
150 mM NaCl, 10 mM Hepes pH 7.0, 1 mM TCEP. Initial crystallization screens© 2017 Macmillan Publishers Limited, part of Springer Nature. All rights reserved.

Article RESEARCH
were carried out using a Gryphon crystallization robot (Art Robbins Instrument)
and commercial high-throughput crystallization screen kits from Hampton
Research and Qiagen. After extensive manual optimization, the best crystals weregrown by sitting-drop vapour diffusion at 18 °C. The protein (5 mg ml−1) was
mixed in 2:1 ratio with a reservoir solution containing 100 mM Tris pH 8.0, 25%polyethylene glycol (PEG) 400. The crystals were cryoprotected in the original
mother liquor and flash-cooled in liquid nitrogen. X-ray diffraction data werecollected at 100 K at beamline 24-ID-E, Advanced Photon Source (APS). The
data were processed using XDS32,33. The structure was determined by molecularreplacement software Phaser using the structure of the HCB (PDB code: 2NM1) as
the search model32. The manual model building and refinement were performed
in COOT34 and PHENIX35 in an iterative manner. The refinement progress wasmonitored with the Rfree value36 using a 5% randomly selected test set. The structures were validated through the MolProbity web server36,37 and showed excellent
stereochemistry. Data collection and structural refinement statistics are listed in
Supplementary Table 1.
Influenza H1 co-crystal structure. For the HA-HB1.6928.2.3 complex,
A/PuertoRico/8/1934 HA and HB1.6928.2.3 peptide (in 25 mM Tris pH 8.0,
150 mM NaCl) were mixed at a 1:4 molar ratio at a final concentration of 10 mg
ml−1 HA in 25 mM Tris pH 8.0, 150 mM NaCl. Crystals were grown with a wellsolution of 5% PEG 3000, 30% PEG 200, 100 mM MES pH 6.0 using the sitting drop
vapour diffusion method and directly flash cooled in liquid nitrogen. Data werecollected at ALS beamline 5.0.3 and processed with HKL200038. Phaser 35,38 wasused for molecular replacement within Phenix39 using a single protomer of PR8 H1
HA (PDB ID: 1RVX40) as a search model. The HB1.6928.2.3 peptide was manuallybuilt into FO−​FC and 2FO−​FC maps using clearly defined aromatic residues anddisulfide bonds to confirm the register. The model was refined through iterativerounds of model building in COOT34 and refinement in Phenix. TLS groups wereautomatically identified by Phenix. Glycans and waters were manually added andedited in COOT. The final model was assessed with quality metrics within the
Phenix.refine interface which utilizes MolProbity37. Data collection and refinement
statistics are listed in Supplementary Table 2.
Molecular dynamics simulations. A total of 289 independent mini-protein­binders (without the target) were simulated (143 for BoNT, and 146 for Influenza
H1 HA see Extended Data Fig. 3) using Gromacs 5.0441 and Amber99sb-ILDN
force field42. Each protein was simulated in a triclinic box with explicit water­solvent (TIP3P43), with box edges at least 10 Å from the protein. Counterions(Na+ and Cl−) were used to neutralize the system. Integration time step was 2 ps
and LINCS44 was applied to constrain all the bonds. Long-range electrostatics(greater than 12 Å) were treated with the particle-mesh Ewald method45. Van
der Waals interactions were smoothly switched off between 10 Å and 12 Å. After
minimization (10,000 steps), the system was position restrained for 200 ps in an
NVT ensemble (only heavy atoms, restraint =​  10 kJ per mol ×​ Å−2, T =​  310 K),
followed by 500 ps of NPT (T =​  310 K, restraint =​  10 kJ per mol ×​ Å−2, 1 bar) using
Berendsen thermostat and barostat46. For each protein, we then performed fiveindependent NPT production simulations (T =​ 310K, 1 bar) with 500 ps of initial
temperature annealing (T0 =​  50, Tfinal =​ 310 K) using V-rescale thermostat47 andParrinello-Rahman Barostat48. Each production simulation was in the length of
100 ns for BoNT and 50 ns for influenza binders (see Supplementary Information).
Snapshots were recorded every 50 ps, and all of them were used for subsequent data
analysis (Extended Data Fig. 3).
Influenza neutralization assays. One hundred TCID50 units of virus andhalf-log dilutions of binders were incubated in quadruplicate at 37 °C for twohours in 200 μ​l neutralization assay medium (‘NAM’: medium 199, 0.3% BSA,
10 mM HEPES,1 mM CaCl2, penicillin-streptomycin). Ninety-six-well plates withconfluent monolayers of Madin–Darby canine kidney epithelial cells (ATCC) were
washed twice with PBS followed by addition of 50 μ​l of 5 μ​g ml−1 TPCK-trypsin in
NAM and the virus/binder neutralization mix. Plates were incubated for 48 h andvirus detected by combining 50 μ​l each of assay supernatants and 0.5% turkey red
blood cells (TRBC). Virus-positive wells that haemagglutinated the TRBC wereidentified, and the EC50 was calculated using Reed–Muench method.
Botulinum neurotoxin neutralization assays. All procedures were conductedin accordance with the guidelines approved by the Institute Animal Care and Use
Committee (IACUC) at Boston Children’s Hospital (#3030). Timed-pregnant
rats (Sprague Dawley strain, purchased from Charles River) were euthanizedand p­ rimary rat cortical neurons were prepared from E18–19 embryos using apapain dissociation kit (Worthington Biochemical). For immunoblot analyses,
neurons were exposed to BoNT/B (20 nM) or BoNT/A (10 nM) with GST–Syt-II
or Bot.671.2 for 10 min in high [K+] buffer. Subsequently, the neurons were
washed and incubated in culture medium for 10 h (BoNT/B) or 6 h (BoNT/A).
Cells were then lysed (lysis buffer: PBS, 1% Triton X-100, 0.05% sodium dodecyl
sulfate (SDS), plus protease inhibitor cocktail (Roche)). Lysates were centrifuged(4 °C) for 5 min at maximum speed using a microcentrifuge. The supernatants

were collected for immunoblot analysis against BoNT/A, BoNT/B or actin.
For i­ mmunocytochemistry, the neurons were seeded on cover glass in 24-wellplates. The cells were then exposed to 20 nM BoNT/B for 10 min (middle panel,­positive control), or with 20 nM BoNT/B and 600 nM Bot.671.2 for 10 min. Cells
were washed with PBS and fixed with 4% paraformaldehyde for 20 min. The cells
were then blocked with PBS containing 10% goat serum for 45 min and exposed toprimary antibody (rabbit anti-BoNT/B antibody) for 1 h and secondary antibody(Alexa-488) for 1 h. The coverslips were then mounted on a slide and images
collected using a fluorescence microscope. See Extended Data Fig. 7.
In vivo immunogenicity and influenza challenge. Animal studies were approvedby the University of Washington Institutional Animal Care and Use Committee.
BALB/c mice (female, 6–8 weeks old, n =​ 5–10 per group) were randomly separated
into groups, anaesthetized and then were dosed either intranasally or intravenouslywith PBS (negative control), the antibody FI6 (SFFV-FI6v3 IgG, Molecular design& Therapeutics) or mini-protein binders (HB1.6928.2.3 or HB36.6). Between
24 and 96 h after being dosed (see Fig. 5), the mice were anaesthetized with 2.5%
isoflurane and challenged intranasally with 2 MLD50 of CA09. Following ­challenge,
the mice were monitored twice daily for weight loss and survival until up to
14 days post-infection. Animals that lost 30% of their body weight (as a proportion of their initial weight) were euthanized by carbon dioxide in accordance with
our animal protocols. For the immunogenicity experiment, BALB/c mice (female,
6–8 weeks old, n =​ 5 per group) were randomly separated into groups, anaesthetized and dosed with (intranasal or intravenous) PBS, mini-proteins (HB1.5702.3.3,
HB1.6928.2.3, HB1.6394.2.3, HB36.6, Bot2110.4 or Bot3194.4), or monoclonal
antibodies mIgG (Innovative IR-MSBC-GF) or hIgG (Innovative IR-HU-GF-ED).
A total of three or four doses were administered two weeks apart for both theintranasal and intravenous doses. Blood was collected two weeks after each doseby retro orbital bleed using micro-haematocrit capillary tubes (Fisher). Serum wasseparated by centrifuging the blood samples in polymer gel chemistry tubes. For
mouse experiments, researchers were not blinded to animal identity.
ELISA. HB36.6, HB1.6928.2.3, HB1.6394.2.3, Bot 2110.4, Bot 3194.4, mIgG,
hIgG and BSA-specific IgG antibody levels in mouse serum were assessed by
ELISA. Maxisorp (Thermo Scientific-Nunc) were coated with 100 ng per wellof HB36.6, HB1.5702.3.3, HB1.6928.2.3, HB1.6394.2.3, Bot 2110.4, Bot 3194.4,
mIgG (Innovative IR-MSBC-GF), hIgG (Innovative IR-HU-GF-ED) or BSA(LAMPIRE Biological laboratories, cat no. 7500804) in PBS overnight at 4 °C. Plates
were blocked with 5% nonfat milk powder in PBS for 1 h at room ­temperature,and then washed three times with wash buffer (PBS-T; phosphate-bufferedsaline containing 0.05% Tween 20). Samples were diluted in a buffer containing
1% nonfat milk powder in PBS-T, added to the wells, and incubated for 1 hr atroom temperature. Following three washes with PBS-T, plates were incubatedwith horseradish-­peroxidase conjugated goat anti-mouse IgG (1/5,000 dilution)­secondary antibodies (ThermoFisher 62-6520) for 1 h at room temperature. After
five washes with PBS-T, TMB substrate (KPL 52-00-03) was added to the wells for
30 min at room temperature. Colour development was stopped by the additionof 50 μ​l HCl (1 M), and the plates were read at 450 nm to measure relative opticaldensities. The average optical density of blank wells was subtracted to calculate
the reported values.
Statistical and power analyses. For animal studies, mice were randomly assigned
to either treatment or control groups. In mouse influenza challenge studies,
investigators were blinded to which treatment group each animal was assigned
to during measurement of weight loss and survival. For all other analyses, investigators were not blinded to treatment groups or in the data analysis. A group sizeof n =​ 10 mice was determined based on power analysis, yielding 80% power todiscern a ­difference of 5% weight-change between groups at an alpha value of 0.05,­considering a standard deviation of 4% in weight-change at 4–7 days after infection.
Survival analyses were performed using the Kaplan–Meier log-rank test. A P valueof <​0.05 was considered to be significant. For mice, the minimum group size wasdetermined using weight loss data with 70% of control mice becoming infectedwith CA09. Comparisons in antibody responses were performed using unpaired
student t-test. With a standard deviation of 2% in weight loss, a group size of n =​  5
yields >​80% power to detect a minimum of a 10% difference between groups inweight loss using a two-sided t-test with an alpha value of 0.05.
Code availability. Computational protein designs and filtering were ­performed
using RosettaScripts. The respective XML code is available as part of the
Supplementary Information Appendices A–C.
Data availability. Computational design models, Rosetta-metrics, and experimental
results that support the findings in this study have been deposited in the online
zenodo repository (https://zenodo.org. http://dx.doi.org/10.5281/zenodo.838815).
Structures have been submitted to the Research Collaboratory for Structural
Bioinformatics Protein Databank under the codes 5VLI (HB1.6928.2.3 co-­crystal
with influenza HA), 5VID (Bot.671.2 co-crystal with BoNT HCB) and 5VMR
(Bot.2110.4 co-crystal with BoNT HCB).

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RESEARCH Article
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Article RESEARCH

Extended Data Figure 1 | Target proteins architecture and interactions
with anti-BoNT/B and anti-influenza motifs. a, Full complex of BoNT,
showing heavy chain binding domain (HCB) target epitope position in
relation to catalytic and translocation domains. Inset shows inhibitory
fragment Syt-II (in orange) bound to HCB with hotspots shown as sticks,
and grey areas excluded from design calculations. b, Crystal structure of
SC1918/H1 showing HA1 and HA2 subunits in complex with HB36.3.

Inset shows detailed view of HB36.3 (in green) bound to stem region
epitope with hotspots shown as sticks, and grey areas excluded from design
calculations. c, Crystal structure of SC1918/H1 showing HA1 and HA2subunits in complex with HB80.4. Inset shows detailed view of HB80.4(in magenta) bound to stem region epitope with hotspots shown as sticks,
and grey areas excluded from design calculations.

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RESEARCH Article

Extended Data Figure 2 | Categorization of binders from highthroughput sequencing data of yeast-display FACS-sorted yeast
pools. a, Schematic representation of a resulting yeast pool experimenttransformed with four genes, corresponding to four different binder
designs (colours: blue, orange, grey, yellow). The first column represents
the initial yeast pool, which presents some variability in the initial numberof cells transformed with each gene. Subsequently, the cells are subject todifferent stringencies of selection condition (display, high, medium andlow target concentrations). The number of cells selected during FACS
(see Methods) is proportional to both the binding affinity and thefractional population of the design. b, Instead of observing a ‘classical’
readout where each measurement is directly proportional to the amountof binding, the result is a convoluted readout (using high-throughput
sequencing of each FACS of selected yeast pools under different
conditions, see Methods) of both the population fraction and the binding
strength. c, Our method of analysing the strength of an individual design
is to assign each of them to a binding condition (category) if they produce
a peak in its enrichment (as compared to its own initial population in
the unselected, but displaying, population). Since, at higher categories,‘better’ binders will always out-compete weaker ones, this method clusters
binders into categories of binding (for example, weak, medium, or strong).
If protease is used to further select the populations for stability, the same
concept applies (see Fig. 2).

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Article RESEARCH

Extended Data Figure 3 | Molecular dynamics simulations to assess
the flexibility of mini-protein binder designs, their binding motifs
and hotspots. a, Schematic representations of the helical segments
and hotspots used to calculate the average r.m.s.d. for mini-proteinbinders containing binding motifs from HB36, HB80 and Syt-II. The
four conserved hotspots (orange) used to calculate the average r.m.s.d.
of each binding motif are also shown. b, Top, average r.m.s.d.s (withrespect to the designed bound conformation) of the whole mini-proteinsversus those of the hotspots. The results for non-binders and binders areshown in black and red, respectively. Bottom panel, same as top, exceptthat the x-axis displays the r.m.s.d.s of the entire helical motif. These
results were obtained from an aggregation of 108 μ​s molecular dynamics
simulations, from a representative sample of designs (143 for BoNT and
146 for influenza, see Methods for details). The r.m.s.d. values for hotspot
residues were calculated using a subset of side-chain heavy atoms thatare invariant to the rotation of the aromatic ring (CG and CZ for Phe andTyr). The backbone heavy atoms were used for the r.m.s.d. calculationsof ‘binding helical motif ’ and ‘whole protein’. c, The convergence ofmolecular dynamics simulations discriminates binders and non-binders
as a function of simulation length (30 ns, 40 ns, 50 ns and 100 ns), subject
to a similar amount of total sampling. The results show that simulations of
50 ns in duration are sufficient to discriminate the stability of binders andnon-binders, even though longer molecular dynamics simulations (suchas 100 ns) may further improve the discrimination power. Ten randomly
selected mini-proteins designed against BoNT (which are also included in b)
were used in this figure. d, Similar to Fig. 3d, the normalized traces of thehistograms (fitted using a normal probability density function) show that,for both targets, the designs that are binders (cyan, yellow and red lines)
show trends with smaller fluctuations in hotspot residues thannon-binders (blue lines); however, no particular trend is observed
regarding strength of binding.

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RESEARCH Article

Extended Data Figure 4 | Circular dichroism studies. a, Designed miniproteins that were co-crystallized in complex with their respective targets(as shown in Fig. 4). Designed anti-HA mini-protein HB1.6928.2.3 does
not denature up to a temperature of 95 °C. Designed anti-BoNT/B miniprotein shows partial denaturation at 95 °C that is completely reversible
after fast-cooling to 25 °C. Black shows the circular dichroism spectrumat 25 °C, red at 95 °C, and yellow at 25 °C (after fast refolding, 5 min).
Proteins were measured at 0.25 mg ml−1 in PBS buffer pH 7 (see Methods).
b, Proteins that were solubly expressed or chemically synthesized. Plots
are analogous to a. HB1.10027.3 contains two disulfides, HB1.6394.2.3

contains three disulfides, Bot.6782.4, Bot.6827.4, Bot.7075.4, Bot.4024.4,
Bot.3318.4, Bot.5721.4, and Bot.5916.4, each contain one disulfide bond.
The rest of the proteins were designed without disulfide bonds. c, Three
disulfide-containing proteins with and without reducing agent. Plots areanalogous to a. Proteins were measured at 0.25 mg ml−1 in PBS buffer
pH 7 without (top row) and with (bottom row) the reducing agent TCEP.
The disulfides are shown to be crucial for the thermal stability of these
disulfide-containing proteins (HB1.6928.2.3 contains two disulfides,
Bot.2110.4 and Bot.3194.4 each contain one disulfide).

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Article RESEARCH

Extended Data Figure 5 | Trypsin resistance of HA binders. Chemicallysynthesized HA binder (0.3 mg ml−1) was incubated in PBS with various
dilutions of trypsin (52 μ​M stock) for 20 min at room temperature.
Reactions were quenched with addition of 1% weight per volume BSA andsamples run on SDS–PAGE gel. The relative concentrations of trypsin are
shown at the top. ImageJ was used to quantify the intensity of each band

(below the band). a, Both HB36.6 and HB1.5702.3 show weaker gel bands
at trypsin concentrations higher than 0.055 stock (2.86 μ​M), indicating
proteolytic degradation. HB1.6928.2 and HB1.6394.2, both of whichcontain disulfides, show no degradation at any trypsin concentration.
b, Scatter plot of gel intensities in a.

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RESEARCH Article

Extended Data Figure 6 | Omit map of HB1.6928.2.3. a, A simulated
annealing FO−​FC omit map for HB1.6928.2.3 (green) residues 10–22(contoured at 3σ) shows clear density for amino-acid side chains at theinterface (dark blue HA1, light blue HA2). A single residue (Asn32), ina loop between the first and second β​–strands, is not observed in theelectron density. b, 2FO−​FC map for Bot.671.2 (green) residues 2-13
(contoured at 2σ) shows clear density for side chains at the interface except
for the flexible lysine residue. BoNT HCB is shown in dark blue. The entire
backbone, interface, and core residues for Bot.671.2 are all well resolved in
the electron density map.

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Article RESEARCH

Extended Data Figure 7 | In vitro neutralization of BoNT/B.
a, b, Immunoblots of cultured primary rat cortical neurons that were
exposed to BoNT/B (20 nM) or BoNT/A (10 nM) with or without
GST–Syt-II or Bot.671.2 (see Methods). The supernatants of lysed neurons
were collected for immunoblot analysis to detect the indicated proteins,and actin served as control for loading. The designed mini-protein appears
to confer protection against degradation of VAMP2, but not against
degradation of the negative control, SNAP25 (the intracellular target of

BoNT/A). c, Immunocytochemistry for detection of BoNT/B in neurons(see Methods). Left, negative control (no toxin); middle, positive control(cells incubated with 20 nM of BoNT/B for 10 min); right, near-total
protective effect against 20 nM of BoNT/B conferred by co-incubating thecells with 600 nM of the design Bot.671.2. Top panels show a representative
image of fluorescence microscopy for the detection of BoNT/B; bottom
panels show backfield illumination microscopy for the same area.

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RESEARCH Article

Extended Data Figure 8 | In vitro neutralization of influenza.
Comparison of in vitro neutralization of influenza viruses by HB36.6,
FI6v3 and the designed mini-protein HB1.6928.2.3. Each antiviral wascompared for its efficiency (EC50) in inhibiting the infection of Madin–
Darby canine kidney cells by a range of influenza strains. It is clear that
HB1.6928.2.3 most efficiently inhibited infection for all of the group-1

influenza strains tested (H1N1, H5N1 and H6N2). As expected,
no neutralization was observed against H3N2 (group 2). In all
experiments, n =​ 3 independent samples were tested for each condition,
except for T/Mass/1965 (H6N2) and HK/ X31 (H3N2), for which n =​  2
samples were tested. Dots show raw values for independent tests andwhiskers show ±​ 1 s.d.

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Article RESEARCH
Extended Data Table 1 | Number of designs and fraction surviving under each selection condition for mini-protein binders designed against
influenza HA and BoNT

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© 2017 Macmillan Publishers Limited, part of Springer Nature. All rights reserved.

!

RESEARCH Article
Extended Data Table 2 | Physicochemical properties characterized in solution for a selected pool of top binding designs
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`

Experimental design

1. Sample size
Describe how sample size was determined.

Survival analyses were performed by using the Kaplan-Meier log-rank test. A P
value of <0.05 was considered to be significant. For mice, the minimum group size
was determined using weight loss data with 70% of control mice becoming
infected with CA09. Comparisons in antibody responses were performed using
unpaired student t-test. Based on a standard deviation of 2% in weight loss, agroup size of n = 5 yields >80% power to detect a minimum of a 10% difference
between groups in weight loss using a two-sided t-test with an alpha value of 0.

nature research | life sciences reporting summary

Corresponding author(s): David Baker

2. Data exclusions
Describe any data exclusions.

None

3. Replication
Describe whether the experimental findings werereliably reproduced.

Experimental finders were statistically significant and no attempt at reproduction
was performed.

4. Randomization
Describe how samples/organisms/participants wereallocated into experimental groups.

Female, 6–8 week-old BALB/c mice (female, 6-8 weeks old, n=5-10/group) wererandomly separated into groups

5. Blinding
Describe whether the investigators were blinded togroup allocation during data collection and/or analysis.

For mouse experiments, researchers were not blinded to animal identity.

Note: all studies involving animals and/or human research participants must disclose whether blinding and randomization were used.

6. Statistical parameters
For all figures and tables that use statistical methods, confirm that the following items are present in relevant figure legends (or in the
Methods section if additional space is needed).
n/a Confirmed
The exact sample size (n) for each experimental group/condition, given as a discrete number and unit of measurement (animals, litters, cultures, etc.)
A description of how samples were collected, noting whether measurements were taken from distinct samples or whether the same
sample was measured repeatedly
A statement indicating how many times each experiment was replicated
The statistical test(s) used and whether they are one- or two-sided (note: only common tests should be described solely by name; more
complex techniques should be described in the Methods section)

The test results (e.g. P values) given as exact values whenever possible and with confidence intervals noted

June 2017

A description of any assumptions or corrections, such as an adjustment for multiple comparisons

A clear description of statistics including central tendency (e.g. median, mean) and variation (e.g. standard deviation, interquartile range)
Clearly defined error bars
See the web collection on statistics for biologists for further resources and guidance.

1Policy information about availability of computer code

7. Software
Describe the software used to analyze the data in thisstudy.

All amino-acid sequences were reverse translated and codon optimized for yeast
using DNAworks 2.025. Sequence identity calculations were performed with asubset of designs using PRALINE after PSI-BLAST global alignment. Sequencing
pairing after Illumina deep sequencing was performed by PEAR. Plots andvisualizations were created using Seaborn statistical visualization tools, Python(Python Software Foundation), and python’s scikit-learn (INRIA).

For manuscripts utilizing custom algorithms or software that are central to the paper but not yet described in the published literature, software must be madeavailable to editors and reviewers upon request. We strongly encourage code deposition in a community repository (e.g. GitHub). Nature Methods guidance forproviding algorithms and software for publication provides further information on this topic.

` Materials and reagents
Policy information about availability of materials

8. Materials availability
Indicate whether there are restrictions on availability of
unique materials or if these materials are only available
for distribution by a for-profit company.

No known restrictions

nature research | life sciences reporting summary` Software

9. Antibodies
Describe the antibodies used and how they were validated mIgG (Innovative IR-MSBC-GF) hIgG (Innovative IR-HU-GF-ED)
for use in the system under study (i.e. assay and species).

10. Eukaryotic cell lines`

a. State the source of each eukaryotic cell line used.

ATCC

b. Describe the method of cell line authentication used.

Authenticated by vendor (ATCC). We did not do any additional authentication.

c. Report whether the cell lines were tested for
mycoplasma contamination.

Cell line was tested by vendor for mycoplasma contamination. We did not perform
any additional testing for mycoplasma.

d. If any of the cell lines used are listed in the databaseof commonly misidentified cell lines maintained by
ICLAC, provide a scientific rationale for their use.

None

Animals and human research participants

Policy information about studies involving animals; when reporting animal research, follow the ARRIVE guidelines

11. Description of research animals
Provide details on animals and/or animal-derived
materials used in the study.

Female, 6–8 week-old BALB/c mice (female, 6-8 weeks old, n=5-10/group)

Policy information about studies involving human research participants

12. Description of human research participants
Describe the covariate-relevant population
characteristics of the human research participants.

None

June 20172

Initial submission

Revised version

Final submission

Flow Cytometry Reporting Summary
Form fields will expand as needed. Please do not leave fields blank.

`

Data presentation

For all flow cytometry data, confirm that:
1. The axis labels state the marker and fluorochrome used (e.g. CD4-FITC).
2. The axis scales are clearly visible. Include numbers along axes only for bottom left plot of group (a 'group' is an analysis ofidentical markers).
3. All plots are contour plots with outliers or pseudocolor plots.
4. A numerical value for number of cells or percentage (with statistics) is provided.

`

Methodological details

5. Describe the sample preparation.

Cell aliquots were thawed on ice, centrifuged at 13,000 r.p.m. for 30 s,
resuspended in 1e7 cells per ml of C-Trp-Ura media and grown at 30 °C for
16 h. Cells were then centrifuged for 13,000 r.p.m. and resuspended at
1e7 cells per ml SGCAA media and induced at 30°C for 16–24 h. Cells werelabeled with either biotinylated BoNT HCB, PR8 hemagglutinin, or
CA/04/09 hemagglutinin, washed, secondary labeled with SAPE
(Invitrogen) and anti-cmyc FITC (Miltenyi Biotech).

6. Identify the instrument used for data collection.

Sony SH800

7. Describe the software used to collect and analyze
the flow cytometry data.

Sonh SH800 instrument software

8. Describe the abundance of the relevant cell
populations within post-sort fractions.

This is listed in extended data table 2.

9. Describe the gating strategy used.

FSC/SSC gates were set loosely to capture single cells. Sorting was set tosem-purity on SH800. Binding gates were defined as binding signal above
negative control cells(displaying cells non-binding).

nature research | flow cytometry reporting summary

Corresponding author(s): David Baker

Tick this box to confirm that a figure exemplifying the gating strategy is provided in the Supplementary Information.

June 20171