De novo design of buttressed loops for sculpting protein functions

Abstract

In natural proteins, structured loops have central roles in molecular recognition, signal transduction and enzyme catalysis. However, because of the intrinsic flexibility and irregularity of loop regions, organizing multiple structured loops at protein functional sites has been very difficult to achieve by de novo protein design. Here we describe a solution to this problem that designs tandem repeat proteins with structured loops (9–14 residues) buttressed by extensive hydrogen bonding interactions. Experimental characterization shows that the designs are monodisperse, highly soluble, folded and thermally stable. Crystal structures are in close agreement with the design models, with the loops structured and buttressed as designed. We demonstrate the functionality afforded by loop buttressing by designing and characterizing binders for extended peptides in which the loops form one side of an extended binding pocket. The ability to design multiple structured loops should contribute generally to efforts to design new protein functions.

Full text

Nature Chemica Bioogy
nature chemical biology
https://doi.org/10.1038/s41589-024-01632-2
Article
De novo design of buttressed loops for
sculpting protein functions
Hanlun Jiang 1,2,8, Kevin M. Jude 3,4,8, Kejia Wu1,2,5,8, Jorge Fallas1,2,
George Ueda 1,2, T. J. Brunette1,2, Derrick R. Hicks1,2, Harley Pyles1,2,
Aerin Yang 4, Lauren Carter1,2, Mila Lamb1,2, Xinting Li1,2, Paul M. Levine1,2,
Lance Stewart 1,2, K. Christopher Garcia 3,4,6 & David Baker 1,2,7
In natural proteins, structured loops have central roles in molecular
recognition, signal transduction and enzyme catalysis. However, because of
the intrinsic exibility and irregularity of loop regions, organizing multiple
structured loops at protein functional sites has been very dicult to achieve
by de novo protein design. Here we describe a solution to this problem
that designs tandem repeat proteins with structured loops (9–14 residues)
buttressed by extensive hydrogen bonding interactions. Experimental
characterization shows that the designs are monodisperse, highly soluble,
folded and thermally stable. Crystal structures are in close agreement with
the design models, with the loops structured and buttressed as designed.
We demonstrate the functionality aorded by loop buttressing by designing
and characterizing binders for extended peptides in which the loops form
one side of an extended binding pocket. The ability to design multiple
structured loops should contribute generally to eorts to design new
protein functions.
While antibodies still have central roles in protein therapeutics, pro-
gress has been made in drug development using nonantibody-binding
proteins that show superior properties in thermal/pH stability, binding
affinities, tissue delivery and industrial-scale manufacture
1–3
. The two
main approaches are random library selection methods and computa-
tional protein design. Perhaps the most successful scaffold for random
library selection has been the ankyrin repeat
4,5
; libraries of designed
ankyrin repeat proteins (DARPins) have been used to identify high-
affinity binding proteins via high-throughput screening methods,
which have had multiple successes in preclinical studies
3,5,6
. Ankyrin
repeat proteins have a repeating architecture with structured, hair-
pin-shaped loops extending from the helices to an extended binding
groove that is geometrically compatible with many globular protein
targets. Despite these successes, the global shape diversity of DARPins
is limited by the use of a single base scaffold. Computational design
of binding proteins does not have this limitation as a wide range of
scaffolds can be used, with shapes more optimal to bind the target
protein of interest. However, this advantage thus far has come with
a different limitation—because of the inherent flexibility and lack of
extensive backbone hydrogen bonding of long loop regions, protein
binder design has focused on scaffolds and binding sites primarily
composed of α helical7 or β strand8 secondary structure, which has
limited the achievable local shape diversity.
Results
Design approach
Here we set out to overcome the challenges in de novo design of long
loops on the one hand, and the limitations of ankyrin scaffolds in global
Received: 18 February 2023
Accepted: 29 April 2024
Published online: xx xx xxxx
Check for updates
1Department of Biochemistry, University of Washington, Seattle, WA, USA. 2Institute for Protein Design, University of Washington, Seattle, WA, USA.
3Howard Hughes Medical Institute, Stanford University School of Medicine, Stanford, CA, USA. 4Department of Molecular and Cellular Physiology,
Stanford University School of Medicine, Stanford, CA, USA. 5Biological Physics, Structure and Design Graduate Program, University of Washington,
Seattle, WA, USA. 6Department of Structural Biology, Stanford University School of Medicine, Stanford, CA, USA. 7Howard Hughes Medical Institute,
University of Washington, Seattle, WA, USA. 8These authors contributed equally: Hanlun Jiang, Kevin M. Jude, Kejia Wu. e-mail: kcgarcia@stanford.edu;
dabaker@uw.edu
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Nature Chemica Bioogy
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While these methods can generate considerable diversity, we found
that they did not provide sufficient control over backbone positions for
designing loop buttressing. Instead, we developed a parametric repeat
protein generation method that enables precise control over backbone
placement. We generated diverse repeat units consisting of two ideal-
ized helices by systematically sampling the lengths of the helices (from
12 to 28 residues) and the six rigid-body degrees of freedom between
the two helices. We next sampled the six rigid-body degrees of freedom
between repeat units and applied the same transform repeatedly to
generate a disconnected repeat protein model. Finally, we connected
pairs of sequence-adjacent helices using a native-protein-based loop
lookup protocol that grafts on loops (from three to six residues) that
best fit onto the termini of the helices10. In extensive model-building
shape diversity on the other, by computationally designing repeat
proteins with multiple long loops buttressed by loop–loop interactions
(Fig. 1a). To achieve this goal, we divided the problem into the following
two subproblems: first, the generation of repeating scaffold backbone
conformations compatible with loop buttressing, and second, the
generation of loop backbone conformations compatible with a dense
network of hydrogen bonds and hydrophobic interactions between
pairs of loops and between the loops and the underlying scaffold.
We developed a computational method for generating a wide
range of repeat protein backbones that are geometrically compatible
with the insertion of long loops (Fig. 1a, top row). Previous approaches
to designing helical repeat proteins have used fragment assembly
methods to assemble repeat units with short loops connecting them
9
.
Generate
helical repeat
proteins
Build loop
in each repeat
unit
Select loop
backbone
models
Intrarepeat
rigid-body
transform
Interrepeat
rigid-body
transform
Repeat
propagation
β-Turn motifs
Helix-capping motifs
Motif-based
kinematic closure
Single loop Repeat
propagation
of loop
Multiple loops
Loop–helix
hydrogen bonds
Loop–helix
hydrophobic contacts
Intraloop
hydrogen bonds
Interloop
hydrogen bonds
Helical repeat protein
Helical repeat protein
with buttressed loops
cb
a
Short interloop distances
favorable for buttressing
Loops too far
for interactions
Helix-capping
motifs (4 amino
acids)
KIC residues
(5–10 amino acids)
β-Turn
motifs (4 amino acids)
Design sequence
for loop
buttressing
Fig. 1 | Computational design of RBLs. a, Design strategy for generating and stabilizing multiple loops in helical repeat proteins. b, A gallery of diverse designed
proteins that pass the in silico design filters. c, Loop buttressing hydrogen bonds in the designed proteins.
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experiments, we found that to enable the installation of long loops onto
these parametrically generated models, the termini of the helices had
to be less than 18 Å apart, and we removed backbone models where
the distance between termini was greater than this value. We also
eliminated poorly packed models with fewer than 28% of the residues
in a buried core. The resulting repeat protein models have well-defined
core regions and are slightly curved with little or no twisting between
the repeat units.
We next sought to develop a general method for building multiple
long loops that buttress one another onto protein scaffolds (Fig. 1a, sec-
ond and third rows). Surveying the structured, long loops in natural pro-
teins, we observed that they frequently contain β turns with strand-like
hydrogen bonds flanking the turn residues, which contribute to the
stabilization of the specific loop conformation. We also observed that
natural proteins often use helix-capping interactions between the
sidechain or backbone on the loop residue and the backbone of the
helix from which it emanates; this feature helps specify the orienta-
tion of the loop as it leaves the helix. Based on these observations, we
constructed and curated libraries of β-turn motifs and helix-capping
motifs by clustering four-residue native-protein fragments, and we
selected the clusters that fulfilled the requirements of hydrogen bonds
as described in Methods. During the loop sampling, these motifs were
randomly selected and incorporated into a single loop growing from
the C-terminus of a helix. Using generalized kinematic closure11, we
then connected the C-terminus of the loop to the N-terminus of the next
helix in the backbone model. The resulting loop was then propagated
to each repeat unit to generate a complete repeat protein model with
multiple long loops. To specifically favor loops that could be buttressed
with hydrogen bond networks, we required that models have at least
two intraloop backbone-to-backbone hydrogen bonds within each
repeat unit and at least one interloop backbone-to-backbone hydrogen
bond between the repeat unit neighbors. To favor interactions between
the long loops and the helices, we further filtered the models by requir-
ing at least five residues within 8 Å of the closest helical residues. The
remaining backbone models following these filtering steps contain
long loops arranged in sheet-like structures ready for the installation
of additional sidechain-based buttressing interactions.
We designed sequences onto these backbones, focusing on further
loop stabilization through buttressing (Fig. 1a, bottom row). We began
by scanning each position on the long loops for Asn, Asp, His or Gln
placements that form backbone-sidechain bidentate hydrogen bonds
between loops or between a loop and a helix, and for Val, Leu, Ile, Met
or Phe placements that form loop–helix hydrophobic contacts; amino
acids meeting these criteria were kept fixed in subsequent design steps.
We then performed four rounds of full combinatorial Rosetta protein
sequence design with slowly ramped-up fa_rep weight to promote core
packing. A slight compositional bias toward proline was used in the long
loop to increase rigidity. The design models were filtered in Rosetta
by the number of buried unsatisfied heavy atoms (≤3), core residue
hole score (≤−0.015), total score per residue (≤−2), packstat (≥0.5) and
average hydrogen bond energy per residue (≤−1) in the buttressed long
loops. The rigidity of the design models was evaluated using molecular
dynamics simulations and the extent to which the designed sequence
encodes the structure by AlphaFold
12,13
. The in silico validated designs
span a diverse range of shapes with different repeat protein curvatures
and loop geometries (Fig. 1b) and adopt multiple loop buttressing
strategies using loop–helix hydrogen bond networks and loop–loop
bidentate hydrogen bonds (Fig. 1c). These designed buttressed loops
have significantly more diverse structures than the long, hairpin loops
in the native ankyrins (Extended Data Fig. 1) and contain more backbone
hydrogen bonds (Extended Data Fig. 2).
Experimental characterization
We expressed 102 selected designs (which we call repeat proteins
with buttressed loops (RBLs)) in Escherichia coli and purified them
by His tag-immobilized metal affinity chromatography. In total, 77 of
the purified proteins were soluble (representative models shown in
Fig. 2a), 52 were monodisperse and 46 were monomeric, as indicated
by multi-angle light scattering coupled with size-exclusion chroma-
tography (SEC–MALS; Fig. 2b). Forty-four of these proteins showed the
expected α-helical circular dichroism (CD) spectrum at 25 °C, remained
at least partially folded at 95 °C and recovered nearly all the CD signal
when cooled down to 25 °C (Fig. 2c). Fourteen designs were further
validated by small-angle X-ray scattering (SAXS; Fig. 2d and Extended
Data Fig. 3). The experimental scattering curves agreed with profiles
computed from the design models.
We determined the crystal structure of design RBL4 at 1.8 Å resolu-
tion (Fig. 3a–e). RBL4 contains four helix–long-loop–helix repeat units
that are sandwiched by two terminal capping helices. Each long loop
is anchored on top of the neighboring helices and stabilized by inter-
loop Asn-mediated bidentate hydrogen bond networks as designed,
and the design model is in good agreement with the crystal structure
with a Cα root-mean-square deviation (RMSD) of 1.7 Å (Fig. 3a). The
primary discrepancy between the crystal structure and design model
is in the inter-repeat transformation—the design model is slightly
curved (smaller superhelical radius), while the crystal structure is
nearly flat (larger superhelical radius). Within individual repeat units,
there is very close agreement between the crystal and design model,
with repeat unit Cα RMSDs for different repeat units ranging from
0.48 to 0.61 Å (Fig. 3b). The designed loop buttressing interactions—
the bidentate interloop hydrogen bonds (Fig. 3c) and loop–helix salt
bridges (Fig. 3d)—were accurately recapitulated in the crystal structure.
B-factors for the loop residues are elevated compared to the helix resi-
dues (Extended Data Fig. 4a), but the fit to the electron density shows
that the loops are well ordered (Extended Data Fig. 4b).
Design RBL7 has a similar overall geometry as RBL4, but with a
smaller superhelical radius. This design was highly stable and mono-
meric, with an overall fold validated by SAXS (Fig. 2, second row). We
obtained crystals that diffracted poorly with the highest resolution
at 4.2 Å. As previous studies suggested that synthetic oligomeriza-
tion can sometimes assist crystallization
14
, we sought to generate a
dimeric form of RBL7 by introducing a hydrophobic dimer interface.
The redesigned protein, RBL7_C2_3, was soluble and dimeric, and we
were able to solve the crystal structure at 3 Å resolution. The crystal
structure closely matches the design model, with a Cα RMSD over the
dimer of 2.9 Å (Fig. 3f) and over the monomer of 1.6 Å (Fig. 3g). The
main discrepancies between the crystal and designed structures were
in the terminal helices. Similar to design RBL4, the crystal structure
confirmed the accuracy of the designed loop buttressing interactions
in RBL7_C2_3 (Fig. 3h–j). All of the designed interloop hydrogen bonds
at the β turns of long loops were recapitulated in the crystal structure
(Fig. 3h). These hydrogen bonds are likely crucial for positioning the
long loops and contribute to the close matching between the loops in
the design model and those in the crystal structure. Again, the B-factor
values of the buttressed loops are slightly elevated in the loops com-
pared to the helices (Extended Data Fig. 4c), but with a good fit to the
electron density (Extended Data Fig. 4d).
Design of peptide-binding RBLs
An exciting application of our designed RBLs is to use them as starting
points for the computational design of high-affinity binding proteins.
This could enable the design of DARPin-like binders for a wide range of
targets without the need for large-scale library selection methods. The
ability to design a wide diversity of repeating scaffolds with buttressed
loops could considerably expand the space of targets. As a first step
toward investigating the design of RBL-based binders, we redesigned
the extended groove bordered by the buttressed loops to bind extended
peptides. To take advantage of the repeating nature of RBLs, we chose
to focus on peptides with a repeating sequence motif—in this case,
once a repeat unit is designed to bind a particular short peptide, repeat
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proteins containing multiple copies of this unit should bind peptides
with multiple copies of the motif, provided the register between the
repeat protein and the peptide can be maintained. Generalizing from
the observation that some ankyrin family proteins can bind peptides
with a PxLPxI/L (x can be any amino acid) sequence motif15, we sought
to design binders for peptide sequences of the form (XYZ)
n
, where n
is the number of repeats, X is a polar residue interacting with residues
in the buttressed loop β turns and Y and Z are hydrophobic residues
interacting with the helices and the helix–loop joint of RBLs (see Fig. 4b
for an example of one peptide repeat unit interacting with an RBL-based
peptide binder).
To design binders of (XYZ)n peptides, we first docked tripep-
tide repeats in the polyproline II helix conformation to the binding
grooves of RBLs guided by the interactions in peptide-binding ankyrin
family proteins in the Protein Data Bank (PDB; Methods), and car-
ried out rigid-body perturbations to diversify the docked poses. For
each resulting pose, we used the Rosetta sequence design to gener-
ate sequences of both RBL and peptide for optimized binding. We
Normalized A280
RBL4RBL7RBL21RBL35RBL79RBL92
MRE (103 deg·cm2 dmol−1)
Retention time (min) Wavelength (nm)
log I(q)
q (Å–1)
1.0
0.8
0.6
0.4
0
a b c d
5 10 15 20
20
10
–10
0
–20
200 210 220 230
25 °C
95 °C
Cooling 25 °C
240
1 × 10−2
0 0.100.05 0.15 0.20 0.25 0.30
1.0
0.8
0.6
0.4
0 5 10 15 20
1.0
0.8
0.6
0.4
0 5 10 15 20
1.0
0.8
0.6
0.4
0 5 10 15 20
1.0
0.8
0.6
0
0.4
0.2
0 5 10 15 20
1.0
0.8
0.6
0
0.4
0.2
0 5 10 15 20
20
10
–10
0
–20
200 210 220 230
25 °C
95 °C
Cooling 25 °C
240
20
10
–10
0
–20
200 210 220 230
25 °C
95 °C
Cooling 25 °C
240
20
10
–10
0
–20
200 210 220 230
25 °C
95 °C
Cooling 25 °C
240
20
10
–10
0
–20
200 210 220 230
25 °C
95 °C
Cooling 25 °C
240
20
10
–10
0
–20
200 210 220 230
25 °C
95 °C
Cooling 25 °C
240
1 × 10−1
1 × 100
1 × 101
1 × 102
1 × 10−2
0 0.100.05 0.15 0.20 0.25 0.30
1 × 10−1
1 × 100
1 × 101
1 × 102
1 × 10−2
0 0.100.05 0.15 0.20 0.25 0.30
1 × 10−1
1 × 100
1 × 101
1 × 102
1 × 10−1
0 0.100.05 0.15 0.20 0.25 0.30
1 × 100
1 × 101
1 × 103
1 × 102
1 × 10−2
0 0.100.05 0.15 0.20 0.25 0.30
1 × 10−1
1 × 100
1 × 101
1 × 102
1 × 10−2
0 0.100.05 0.15 0.20 0.25 0.30
1 × 10−1
1 × 100
1 × 101
1 × 102
Fig. 2 | Biophysical characterization of designed helical RBLs. a, Design models of six representative designs. b, SEC traces monitoring absorbance at 280 nm.
c, CD spectra collected at 25 °C (blue), 95 °C (orange) and 25 °C after cooling from 95 °C (green). d, Overlay of experimental (black) and theoretical (red) SAXS profiles.
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designed 34 proteins to bind six-repeat peptides ((DLP)6, (KLP)6 or
(DLS)6), screened them in silico based on protein–protein interac-
tion design filters including AlphaFold12,13 structure recapitulation,
obtained synthetic genes encoding the designs and purified the
proteins from E. coli expression. In the initial binding screens using
split-luciferase assay, seven designs showed clear binding signals.
From these designs, we selected the strongest binders for each pep-
tide target and performed fluorescence polarization measurements,
which showed the protein–peptide interactions are orthogonal and
have high affinities (Fig. 4). All the selected binders (Fig. 4a,d,g) were
based on RBL4 with the peptides in nearly identical binding modes.
Unlike the natural peptide-binding ankyrins, the designed peptide
binders interact specifically with every peptide residue side chain,
with the Asp/Lys at the X position forming salt bridges with charged
residues from the β-turn tip of RBL, the Leu at the Y position fully bur-
ied in the hydrophobic interface and the Pro/Ser interacting with the
residues on the bottom of helices (Fig. 4b,e,h). Both the (DLP)6 binder
and the (KLP)
6
binder bound their target peptides with high affinities
(K
d
= 1.2 nM and <0.3 nM, respectively) and high specificity (Fig. 4c,f).
Neither binder strongly bound (DLS)6, suggesting Pro in the peptide
was crucial in the protein–peptide interactions. We sought to rescue
the (DLS)
6
binding by installing Gln-mediated bidentate hydrogen
bonds (Fig. 4h). The resulting design bound (DLS)6 with high affinity
(Kd = 2.9 nM) but retained affinity for (DLP)6 (Fig. 4i).
Discussion
There are two primary routes forward for engineering new functions
using our designed RBLs. First, by analogy with the many DARPins
obtained starting from stabilized consensus ankyrin repeat proteins,
it should be readily possible to create binders from RBLs by random
library generation in conjunction with yeast display and other selection
methods for binding to targets of interest. Second, as demonstrated by
our design of peptide-binding proteins, computational design meth-
ods can be used to generate binders to a wide variety of targets, taking
dc
b
aIndividual repeat unit
Cα RMSDs: 0.48–0.61 Å
e
ih
gf
j
Fig. 3 | Structural characterization by X-ray crystallography. a,
Superimposition of crystal structure (yellow) onto the design model of RBL4
(gray). b, Alignment of individual repeat units. c–e, Accurately designed loop
buttressing interactions: bidentate interloop hydrogen bonds (c), loop–helix salt
bridge (d) and loop–helix hydrophobic contacts (e). f, Superimposition of crystal
structure (blue) onto the design model of RBL7_C2_3 (gray). g, Superimposition
of a monomer unit in the crystal structure onto the design model. h–j, Accurately
designed loop buttressing interactions: intraloop and interloop hydrogen
bonds (h), bidentate interloop hydrogen bonds (i) and loop–helix hydrophobic
contacts (j).
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advantage of the diverse geometries that can be achieved with different
buttressed loops on different repeat protein scaffolds.
From a fundamental design perspective, the crystal structures
presented here show that computational protein design has advanced
to the point that proteins with multiple ordered long loops can now
be designed. Key to this success was the design of dense networks
of hydrogen bonding and nonpolar interactions within and between
the loops and between the loops and the underlying secondary struc-
tural elements. Our approach, alone or integrated with additional
recent progress in loop design
16
and recently developed deep learning
approaches for protein design17–21 (which do not directly address the
challenge of designing structured long loops), should enable the design
of structured loops for binding functions and beyond in a wide variety
of scaffolds. For example, for enzyme design, multiple loops emanat-
ing from designed TIM barrels
22–24
could be built to stabilize each other
and, together with the residues emerging from the top of the β strands
and helices in the TIM barrel structure, form an extensively buttressed
catalytic site and associated substrate/transition state binding site.
Online content
Any methods, additional references, Nature Portfolio reporting sum-
maries, source data, extended data, supplementary information,
acknowledgements, peer review information; details of author contri-
butions and competing interests; and statements of data and code avail-
ability are available at https://doi.org/10.1038/s41589-024-01632-2.
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a
d
g
b
e
h
c
f
i
Concentration (M)
(DLP)6 (KLP)6 (DLS)6
(DLP)6binder–(DLP)6
(KLP)6binder–(KLP)6
(KLP)6binder
(DLS)6binder–(DLS)6
(DLS)6binder
1.2 nM
~1.0 µM
4.6 nM
<0.3 nM
0.7 nM
2.9 nM
~1.8 µM
Fluorescence
anisotropy
0.30
0.25
0.20
0.15
0.10
0.05
10–11 10–8 10–5 10–2
(DLP)6binder
0.30
0.25
0.20
0.15
0.10
0.05
10–11 10–8 10–5 10–2
0.30
0.25
0.20
0.15
0.10
0.05
10–11 10–8 10–5 10–2
Fig. 4 | Designed repeat peptide-binding RBLs. a,d,g, Design models of
peptide-binding proteins in complex with (DLP)6 (a), (KLP)6 (d) and (DLS)6
(g). b,e,h, Sequence-specific interactions in the binder–peptide complexes:
(DLP)6binder–(DLP)6 (b), (KLP)6binder–(KLP)6 (e) and (DLS)6binder–(DLS)6 (h).
c,f,i, Fluorescence polarization measurement of binding between (DLP)6binder–
(DLP)6 (c), (KLP)6binder–(KLP)6 (f) and (DLS)6binder–(DLS)6 (i). For each binder,
a titration curve is plotted for the binding of each peptide (blue, (DLP)6; orange,
(KLP)6; and green, (DLS)6).
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Methods
Computational design method
We developed our computational protein design protocols using
Rosetta
25,26
(2019.01) and PyRosetta4 (release 2019.22)
27
. Our protocol
of parametric repeat protein generation started by building an ideal
helix H1 (with a length of 12–28 residues) with the MakeBundleHelix
mover in Rosetta
25,26
and placing it away from the z axis with a given
radius and an angle corresponding to its orientation. A second helix,
H2 (with a length of 12–28 residues), was then modeled and placed
according to the specification of the six rigid-body degrees of freedom
for geometry transformation from H1 to H2. By combining H1 and H2
into one pose, we built the first repeat unit R1. Subsequently, we used
user-specified six rigid-body degrees of freedom between repeat units
to perform a geometric transformation to obtain the second unit R2.
We propagated the repeat units based on the number of repeats desired
to generate the helical repeat protein backbones. We then connected
pairs of sequence-adjacent helices with loops of three to six residues
using ConnectChainMover
10
. To filter the generated repeat protein
backbones, we required a maximum distance of 18 Å between the ter-
mini of the helices to be connected by buttressed long loops. We also
removed the low-quality backbone models with fewer than 28% of the
residues in a buried core.
To design buttressed loops, we developed a hybrid method that
assembles native structural motifs via kinematic loop closure. To
guide the sampling toward the hairpin-shaped conformations, we
constructed a motif library that consists of native β turns. A β-turn motif
is defined by having a backbone-to-backbone hydrogen bond between
the carbonyl group of residue i and the amine group of residue i + 3
(refs. 28,29). In this work, we searched for native β-turn fragments by
mining a set of selected PDBs based on 90% maximum sequence iden-
tity and a 1.6 Å resolution cutoff from PISCES30. The collected β turns
were further clustered by the K-centers algorithm31 at a maximum clus-
ter distance of 0.63 Å, resulting in 180 motif clusters. Using the same
approach, we compiled a library of native helical capping motifs to
guide the sampling of loops connecting helices in the repeat proteins.
We used GeneralizedKIC
11
for loop closure. An extended loop frag-
ment was first constructed by stitching native helical capping motifs
(four amino acids), β-turn motifs (four amino acids) and KIC residues
(five to ten amino acids) with randomized backbone torsion angles. We
chose these lengths because we found limited structural diversity for
loops with lengths less than nine amino acids. When the loop length
exceeded 14 amino acids, it became significantly more difficult to
design buttressing interactions to stabilize the entire loop. The torsion
angles of β turns were set according to the motifs sampled from the
β-turn library, and the Φ/Ψ torsion angles of nonpivot KIC residues were
sampled from the Ramachandran distribution, with omega torsion
angles fixed at 180°. All the bond lengths were kept fixed at the ideal
lengths. The position of the β-turn was randomly sampled in the loop.
In each step of GeneralizedKIC, kinematic loop closure was performed
to connect the loop to the intended insertion site. Loop conformations
were filtered by backbone steric clashes. We further filtered the models
by selecting loops with at least two intraloop backbone-to-backbone
hydrogen bonds. To avoid helical conformations, we removed the
models predicted to have more than five consecutive helical residues by
DSSP32. This ensured the extended β-hairpin shape, which contributed
to the loop stability and compatibility for buttressing.
To install the loops of the same conformation in each unit of
repeat proteins, we used the RepeatPropagationMover in Rosetta
25,26
.
After filtering out the loops with steric clashes, we computed three
metrics to help select the best loop conformations for buttressing—
number of interloop backbone-to-backbone hydrogen bonds, loop
motif score and direction score. We required at least one interloop
backbone-to-backbone hydrogen bond between each pair of neighbor-
ing loops to enhance the sequence-independent loop buttressing. To
select loops with loop–helix hydrophobic contacts, the motif scores
were computed by matching the selected pairs of residues to the known
contacting native hydrophobic residue pairs (Val, Leu, Ile, Met and Phe)
in PDB33. The scores for the matched residue pairs in the loop regions
were then summed to one total score. Only the loops with a negative
total motif score were selected. The direction score described the rela-
tive orientation of the loops from the rest of the input repeat proteins.
Specifically, we defined the following two vectors: vector a started from
the center of mass of the two loop terminal residues and pointed to the
farthest Cα atom of the loop; vector b started from the same point as a
but pointed toward the center of mass of the repeat unit. The direction
score was derived by computing the angle between the two vectors.
Direction score =cos−1
a⋅
⋅
⋅b
|a||
|b|
|
The accepted angles ranged from 45° to 135°. We also required at
least five residues within 8 Å of the closest helical residues.
Next, we performed a fast sequence design task to identify loop
conformations compatible with interloop bidentate hydrogen bond
networks. From each propagated set of loops, the loop on the sec-
ond repeat unit was selected for sequence design. One packing step
using PackRotamersMover25,26 was conducted separately for each
residue on this loop using amino acids that are compatible with form-
ing sidechain-to-backbone bidentate hydrogen bonds—Asn, Asp, Gln
or His. We excluded amino acids with longer side chains (Arg and Lys),
as their high entropic cost might diminish the free energy contribution
of buttressing. After each packing step, bidentate hydrogen bonds
between the packed residue and its neighboring residues were counted.
A bidentate hydrogen bond was defined as two separate hydrogen
bonds forming between atoms in the functional group of the sidechain
from a residue on the loop and the backbone of a neighboring repeat
unit. The selected amino acid was kept only if it formed interloop
bidentate hydrogen bonds; otherwise, the original amino acid (by
default, Ala) was kept. In the case where the one-step packing approach
failed to generate any interloop bidentate hydrogen bonds, we used an
alternative three-stage scheme to maximize the sampling efficiency of
bidentate hydrogen bonds—identifying pseudo-bidentate hydrogen
bonds, performing constrained minimization for building hydrogen
bonds and evaluating the resulting bidentate hydrogen bonds. We
defined that a pseudo hydrogen bond has a donor–acceptor distance
<3 Å and a hydrogen bond angle >120°. After propagating the designed
residue to all the repeat units, we imposed a harmonic distance con-
straint between each donor and acceptor atoms with a target distance
of 2 Å and a s.d. of 0.5 Å. At the minimization stage, we performed
symmetric minimization of the loops to improve the interactions of
potential hydrogen bonds. Finally, we used the Rosetta score function
to examine if the bidentate hydrogen bonds formed in the minimized
loop conformations.
To guide the sequence design, we used LayerSelector to define the
core, the boundary and the surface layers and specified the allowed
amino acids for each layer. We added residue type constraints to fix
the identity of the residues participating in loop buttressing bidentate
hydrogen bonds, so the stabilizing interactions obtained during loop
sampling would be maintained throughout sequence design. Next, we
performed four rounds of sequence design using the FastDesign mover
under the repeat-symmetric constraints to ensure the repeat units had
the same structures and sequences. To improve the solubility and fold-
ing of the designs, we subsequently performed one round of FastDesign
to remove the solvent-exposed hydrophobic residues on the terminal
repeat units. Only polar residues such as Glu, Gln, Lys and Arg were
allowed for this round of design. The designed structures were then
refined by minimization in Cartesian space and subsequently filtered
by the number of buried unsatisfied heavy atoms (≤3), hole score nor-
malized by total number of core residues (≤−0.015), total score normal-
ized by total number of residues (<−2), packstat (≥0.5) and hydrogen
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Nature Chemica Bioogy
Article https://doi.org/10.1038/s41589-024-01632-2
bonding energy of each loop residue (≤−1). Top 10% scoring structures
were further tested by in silico validation methods such as molecular
dynamics simulations (Cα RMSD < 3 Å), AlphaFold
12,13
(PLDDT > 80, Cα
RMSD < 3 Å) or RoseTTAFold34 (PLDDT > 80, Cα RMSD < 3 Å). Structural
similarity between native ankyrin loops and the designed RBL loops
was computed by TM-align35.
We performed molecular dynamics simulations using GROMACS
(2018.4)36 with the Amber99SB-ILDN force field37. The design models
were solvated in dodecahedron boxes of the explicit TIP3P
38
waters
with the net charge neutralized. We treated long-range electrostatic
interactions with the Particle-Mesh Ewald method
39
. Both short-range
electrostatic interactions and van der Waals interactions used a cutoff
of 10 Å. Energy minimization was performed using the steepest descent
algorithm. A 1-ns equilibration under the NPT ensemble was subse-
quently performed with position restraints on the heavy atoms. We
used Parrinello–Rahman barostat
40
and velocity-rescaling thermostat
41
for pressure coupling (1 atm) and temperature coupling (310 K), respec-
tively. For the production runs, we launched three 20-ns trajectories
under the NPT ensemble for each design model. The Cα atom RMSD
against the design model was computed for analysis.
Protein expression and characterization
Genes encoding the in silico validated designs were synthesized (IDT)
and cloned into pET-29b expression vectors. The plasmids were trans-
formed into Lemo21 (DE3) expression E. coli strain (NEB). Protein expres-
sion was performed using the auto-induction protocol
42
at 37 °C for 24 h
in 50 ml or 100 ml culture. During the purification, cells were pelleted
at 4,000g for 10 min and resuspended in 25 ml lysis buffer (25 mM
Tris–HCl (pH = 8), 150 mM NaCl, 30 mM imidazole, 1 mM DNase and
10 mM lysozyme with Pierce Protease Inhibitor Tablets (Thermo Fisher
Scientific)). Sonication was subsequently performed for 2.5 min (10 s
on and 10 s off per cycle). The lysate was then centrifuged at 16,000g for
30 min. The supernatant was applied to a gravity flow column packed
with Ni-NTA resin (Qiagen), followed by 20 ml wash buffer (25 mM
Tris–HCl (pH = 8), 150 mM NaCl and 30 mM imidazole) and 5 ml elution
buffer (25 mM Tris–HCl (pH = 8), 150 mM NaCl and 400 mM imida-
zole). The eluted protein was then concentrated and injected into an
Akta Pure FPLC device with a flow rate of 0.75 ml min
−1
in the running
buffer (25 mM Tris–HCl (pH = 8) and 150 mM NaCl). The typical yield
of a monodisperse and thermally stable designed RBL is 1–6 g l
−1
. To
perform SEC–MALS, we prepared the purified protein at ~2 mg ml−1 and
injected 100 μl of sample into a Superdex 200 10/300GL column and
measured the light scattering signals using a miniDAWN TREOS device
(Wyatt Technology). To measure the CD signals, we first prepared the
sample at ~0.2 mg ml
−1
in 25 mM phosphate buffer in a 1 mm cuvette.
A Jasco J-1500 CD spectrometer was used for all CD measurements.
We set the range of wavelength from 190 nm to 260 nm and scanned
over a three-temperature (25 °C, 95 °C and cooling back to 25 °C) set
for each sample. We submitted all samples for SAXS
43,44
to Advanced
Light Source, LBNL for data collection at the SIBYLS 12.3.1 beamline.
Design and characterization of repeat peptide-binding
proteins
We used the recently developed protein interface design method7 for
in silico binder docking and design experiments. Docking of repeat
peptides to the binder scaffold was guided by the geometric transfor-
mation between native ankyrins and their peptide targets in the crystal
structures from PDB
15
. Symmetric sequence design was performed for
each docked peptide–protein pair following the same protocol used
for designing RBLs. All the designed complexes were computation-
ally tested by AlphaFold with a cutoff of PAE_interaction ≤15 before
experimental characterization.
Split-luciferase assay was performed using the Nano-Glo Lucif-
erase Assay System (Promega). The coding sequence of small-BiT
was fused to the gene of peptide binders, and the coding sequence
of large-BiT was fused to the coding sequence of the target peptide
(GenScript). The BiT-fused proteins and peptides were expressed
and purified with the same protocol for RBLs. The purified peptide
binders and target peptides were titrated in the presence of Nano-Glo
substrate in 96-well plates, and the luminescence was measured on
a Synergy Neo2 plate reader (Agilent Technologies). To conduct the
fluorescence polarization binding assays, we synthesized the repeat
peptide fragments with N-terminal tetramethylrhodamine labels.
Fluorescence polarization measurements were performed at 25 °C in
a Synergy Neo2 plate reader (Agilent Technologies) with a 530/590 nm
filter. A series of twofold dilutions of binder–peptide 80-μl mixture
were performed in 25 mM Tris–HCl (pH = 8), 150 mM NaCl and 0.05%
(vol/vol) Tween 20 in 96-well assay plates. The protein concentrations
ranged from 4 μM to 0.47 pM, and the concentration of N-terminal
tetramethylrhodamine-labeled peptide was kept at 0.3 nM. The sam-
ples were incubated for 3 h before measurement.
Structural characterization by X-ray crystallography
RBL4 was concentrated to 150 mg ml
−1
and crystallized by vapor dif-
fusion. Initial crystals formed in the MCSG-2 crystallization screen
(Anatrace) and optimized crystals were grown in 100 mM sodium
acetate, pH 4.4, and 2% polyethylene glycol 4000. The crystal was
cryoprotected with 30% ethylene glycol and flash-cooled in liquid
nitrogen. Diffraction was measured at the Advanced Photon Source
beamline 23 ID-B. Reflections were indexed, integrated and scaled with
autoPROC (1.0.5)
45
. The structure was solved by molecular replacement
in Phaser (2.8.3)
46
. Initial attempts using the predicted model were
unsuccessful due to clashes. A subsequent search for eight copies
of a single helix–loop–helix repeat (76–118 residues) identified two
copies of the protein in the asymmetric unit. The model was rebuilt
using Phenix AutoBuild (1.18.2_3874)47 and completed by iterative
rounds of interactive refinement in Coot (0.9.5)
48
and reciprocal space
refinement in Phenix (1.19.1_4122)49–52. The final refinement strategy
included reciprocal space refinement, individual atomic displacement
parameters, Translation/Libration/Screw refinement using parameters
determined with TLSMD (13 June 2012)
53
and occupancy refinement of
alternate conformations. Model geometry was assessed with MolPro-
bity (implemented in Phenix 1.19.1_4122)
54
. The final model included
99.5% of residues in the favored region of the Ramachandran plot with
no outliers.
RBL7_C2_3 was concentrated to 119 mg ml−1 and crystallized by
vapor diffusion in 2.4 M sodium malonate, pH 7.0, using the MCSG-1
crystallization screen (Anatrace). The crystal was cryoprotected by
the addition of ten volumes of 3.4 M sodium malonate, pH 7.0, and
flash-cooled in liquid nitrogen. Reflections were indexed, integrated
and scaled with XDS (5 February 2021)55. To solve the structure by
molecular replacement, an ensemble of monomer structures was
generated by AlphaFold and used as a search ensemble in Phaser (2.8.3).
The solution contained eight molecules that formed four homodimers.
The model was rebuilt with Phenix AutoBuild (1.19.2_4158) with mor-
phing and completed by iterative rounds of interactive refinement in
Coot (0.9.8.6) and reciprocal space refinement in Buster (2.10.4)56 or
Phenix (1.20.1_4487). The final refinement strategy in Phenix included
reciprocal space refinement, individual atomic displacement param-
eters, noncrystallographic symmetry restraints and Translation/Libra-
tion/Screw refinement using one group per chain. Model geometry
was assessed with MolProbity (implemented in Phenix 1.20.1_4487)54.
The final model had 98.22% of residues in the favored regions of the
Ramachandran plot with no outliers. Composite omit maps were gener-
ated in Phenix by sequentially omitting 5% of the final structure model
and performing simulated annealing from 5,000 K. Crystallographic
software was installed and maintained using SBGrid57.
Data analysis and visualization were performed using Python
(3.7)58, seaborn (0.11.2)59, Matplotlib (3.1.3)60, Pandas (0.24.2)61,62 and
PyMOL (2.4.1)63.
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Nature Chemica Bioogy
Article https://doi.org/10.1038/s41589-024-01632-2
Reporting summary
Further information on research design is available in the Nature
Portfolio Reporting Summary linked to this article.
Data availability
All the design models, protein sequences and DNA sequences are
available at: https://files.ipd.uw.edu/pub/2023_buttressed_loops/
data.tar.gz and Zenodo64. Crystal structures and reflection data have
been deposited in the RCSB Protein Data Bank with accession IDs 8FRE
(RBL4) and 8FRF (RBL7_C2_3). X-ray diffraction images have been
deposited in the SBGrid Data Bank (8FRE and 8FRF).
Code availability
The design scripts for parametric repeat protein generation and but-
tressed loop designs are available at https://github.com/hanlunj/but-
tressed_loops.git and Zenodo64.
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Acknowledgements
We thank F. Praetorius, P. Leung and S. Vazquez for advice on
luorescence polarization assay; B. Wicky and I. Lutz for advice on
split-luciferase assay; the Wysocki group at Ohio State University for
the support with native mass spectrometry; ALS SIBYLS beamline
at Lawrence Berkeley National Laboratory for SAXS data collection;
K. VanWormer for wet lab support; L. Goldschmidt for computing
support; and D. Silva, C. Xu, H. Bai, C. Norn, P. Salveson, D. Sahtoe,
R. Kibler, B. Weitzner, F. DiMaio, P. Bradley, B. Stoddard, K. Lee and F.
Content courtesy of Springer Nature, terms of use apply. Rights reserved

Nature Chemica Bioogy
Article https://doi.org/10.1038/s41589-024-01632-2
Pardo-Avila for helpful discussions. Funding for this work is provided
by the Open Philanthropy Project Improving Protein Design Fund
(GF129460 to H.J. and D.B.) and the Audacious Project at the Institute
for Protein Design (to K.W. and D.B.). Use of the Stanford Synchrotron
Radiation Lightsource, SLAC National Accelerator Laboratory, is
supported by the US Department of Energy, Oice of Science, Oice
of Basic Energy Sciences under contract (DE-AC02-76SF00515 to
K.M.J. and K.C.G.). The SSRL Structural Molecular Biology Program
is supported by the DOE Oice of Biological and Environmental
Research, and by the National Institutes of Health (NIH) and the
National Institute of General Medical Sciences (P30GM133894 to
K.M.J. and K.C.G.). GM/CA@APS has been funded by the National
Cancer Institute (ACB-12002) and the National Institute of General
Medical Sciences (AGM-12006 and P30GM138396 to K.M.J. and
K.C.G.). This research used resources from the Advanced Photon
Source, a US Department of Energy (DOE) Oice of Science User
Facility operated for the DOE Oice of Science by Argonne National
Laboratory under contract (DE-AC02-06CH11357 to K.M.J. and K.C.G.).
The Eiger 16M detector at GM/CA-XSD was funded by an NIH grant
(S10 OD012289 to K.M.J. and K.C.G.).
Author contributions
H.J., K.C.G. and D.B. designed the research. H.J. developed the
computational loop design protocol and the parametric repeat protein
design method based on the work by T.J.B., D.R.H. and H.P. H.J., J.F. and
G.U. performed protein expression and puriication. H.J. performed
CD experiments and analyzed the results of CD and SAXS data.
A.Y. performed yeast protein display. L.C. and M.L. performed and
analyzed SEC–MALS. X.L. performed and analyzed mass spectrometry
analysis. K.M.J. performed X-ray crystallography and determined
the structures of RBL4 and RBL7_C2_3. H.J. and K.W. designed and
experimentally characterized repeat peptide-binding proteins. X.L.
and P.M.L. synthesized target peptides. L.S. and D.B. supervised the
research. H.J., K.M.J. and D.B. wrote the paper.
Competing interests
The authors declare no competing interests.
Additional information
Extended data is available for this paper at
https://doi.org/10.1038/s41589-024-01632-2.
Supplementary information The online version
contains supplementary material available at
https://doi.org/10.1038/s41589-024-01632-2.
Correspondence and requests for materials should be addressed to
K. Christopher Garcia or David Baker.
Peer review information Nature Chemical Biology thanks Kale Kundert
for their contribution to the peer review of this work.
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Nature Chemica Bioogy
Article https://doi.org/10.1038/s41589-024-01632-2
Extended Data Fig. 1 | TM scores between native ankyrin loops and the designed loops from RBLs. Higher TM scores indicate higher structural similarity.
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Nature Chemica Bioogy
Article https://doi.org/10.1038/s41589-024-01632-2
Extended Data Fig. 2 | Comparison of hydrogen bonds and buried unsatisfied
loop heavy atoms between ankyrin loops and the designed loops in RBLs. (a)
Distribution of the number of backbone-to-backbone hydrogen bonds involving
one long loop in each structure normalized by the loop length. (b) Distribution of
the number of backbone-to-side chain hydrogen bonds involving one long loop
in each structure normalized by the loop length. (c) Number of buried unsatisfied
loop heavy atoms in one long loop in each structure.
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Nature Chemica Bioogy
Article https://doi.org/10.1038/s41589-024-01632-2
Extended Data Fig. 3 | Characterization of RBLs by small-angle X-ray scattering. The experimental profiles are shown in black, and the theoretical profiles are
shown in red.
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Nature Chemica Bioogy
Article https://doi.org/10.1038/s41589-024-01632-2
Extended Data Fig. 4 | Characterization of the buttressed loops by X-ray
crystallography. (a,c) Residue-wise B-factor values of the crystal structures of
RBL4 (a) and RBL7_C2_3 (c). The regions corresponding to the buttressed loops
are highlighted in pink. (b,d) Simulated annealing composite omits maps of RBL4
(b) and RBL7_C2_3 (d). Details of the boxed area showing cross-eyed stereo views
of 2mFo-DFc electron density maps are contoured at 1σ over the designed loops.
Grid spacing of the maps is 0.25× the resolution of the structure.
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Content courtesy of Springer Nature, terms of use apply. Rights reserved

Content courtesy of Springer Nature, terms of use apply. Rights reserved

Content courtesy of Springer Nature, terms of use apply. Rights reserved

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