Rational design of topographical helix mimics as potent inhibitors of protein-protein interactions

Article abstract of DOI:10.1021/ja502310r

Protein-protein interactions encompass large surface areas, but often a handful of key residues dominate the binding energy landscape. Rationally designed small molecule scaffolds that reproduce the relative positioning and disposition of important binding residues, termed hotspot residues , have been shown to successfully inhibit specific protein complexes. Although this strategy has led to development of novel synthetic inhibitors of protein complexes, often direct mimicry of natural amino acid residues does not lead to potent inhibitors. Experimental screening of focused compound libraries is used to further optimize inhibitors but the number of possible designs that can be efficiently synthesized and experimentally tested in academic settings is limited. We have applied the principles of computational protein design to optimization of nonpeptidic helix mimics as ligands for protein complexes. We describe the development of computational tools to design helix mimetics from canonical and noncanonical residue libraries and their application to two therapeutically important protein-protein interactions: p53-MDM2 and p300-HIF1α. The overall study provides a streamlined approach for discovering potent peptidomimetic inhibitors of protein-protein interactions.

Full text of DOI:10.1021/ja502310r

Article
pubs.acs.org/JACS
Rational Design of Topographical Helix Mimics as Potent Inhibitors
of Protein−Protein Interactions
Brooke Bullock Lao,†,§ Kevin Drew,‡,§ Danielle A. Guarracino,† Thomas F. Brewer,† Daniel W. Heindel,†
Richard Bonneau,*,‡ and Paramjit S. Arora*,†
†Department of Chemistry and ‡Departments of Biology and Computer Science, New York University, New York, New York 10003,
United States
S
* Supporting Information
ABSTRACT: Protein−protein interactions encompass large
surface areas, but often a handful of key residues dominate the
binding energy landscape. Rationally designed small molecule
scaffolds that reproduce the relative positioning and
disposition of important binding residues, termed “hotspot
residues”, have been shown to successfully inhibit specific
protein complexes. Although this strategy has led to
development of novel synthetic inhibitors of protein
complexes, often direct mimicry of natural amino acid residues
does not lead to potent inhibitors. Experimental screening of
focused compound libraries is used to further optimize inhibitors but the number of possible designs that can be efficiently
synthesized and experimentally tested in academic settings is limited. We have applied the principles of computational protein
design to optimization of nonpeptidic helix mimics as ligands for protein complexes. We describe the development of
computational tools to design helix mimetics from canonical and noncanonical residue libraries and their application to two
therapeutically important protein−protein interactions: p53-MDM2 and p300-HIF1α. The overall study provides a streamlined
approach for discovering potent peptidomimetic inhibitors of protein−protein interactions.
mimics can play in affording small molecule inhibitors of
protein−protein interactions for which no inhibitors are
currently known. The classical examples of helix surface mimics
were described by Hamilton et al.27−29 and contained aromatic
scaffolds displaying protein-like functionality.3 Inspired by this
work, we proposed oligooxopiperazines as a new class of helix
mimetics (Figure 1).23 The advantage of oxopiperazine-based
scaffolds is that they offer chiral backbones and can be easily
assembled from α-amino acids allowing rapid diversification of
the scaffold. We were also attracted to the piperazine motif
because 2-oxopiperazines and diketopiperazines have a rich
history in medicinal chemistry.30−35
The potential of oxopiperazine helix mimetics (OHMs) to
target protein−protein interactions was recently established in
biochemical, cell culture, and in vivo assays.36 We showed that
OHMs that mimic a key α-helix from hypoxia inducible factor
1α (HIF1α) can inhibit the interactions of this transcription
factor with coactivator p300/CBP. Significantly, the designed
compounds downregulate the expression of a specific set of
genes and reduce tumor burden in mouse xenograft models.
Encouraged by this success, we sought to develop a
computational approach to design and optimize oxopiperazine
analogues with natural and nonnatural amino acid residues.
INTRODUCTION
■
Protein−protein interactions are often mediated by amino acid
residues organized on secondary structures.1 Designed
oligomeric ligands that can mimic the array of protein-like
functionality at interfaces offer an attractive approach to target
therapeutically important interactions.2 Efforts to mimic
interfacial α-helices have resulted in three overarching synthetic
strategies: helix stabilization, helical foldamers, and helical
surface mimetics.3,4 Helix stabilization employs side chain
cross-links5,6 or hydrogen-bond surrogates7 to preorganize
amino acid residues and initiate helix formation. Helical
foldamers are nonnatural oligomers that adopt defined helical
conformations;8,9 prominent examples include β-peptide10−12
and peptoid helices.13 Helical surface mimetics utilize
conformationally restricted scaffolds with attached functional
groups that mimic the topography of α-helical side chains. With
the exception of some elegant examples,14−18 surface mimetics
typically impart functionality from one face of the helix, while
stabilized peptide helices and foldamers are able to reproduce
functionality present on multiple faces of the target helix.19
key advantage of helix surface mimicry is that it affords low
molecular weight compounds as modulators of protein
interactions.20−25
A recent survey of protein−protein complexes in the Protein
Data Bank (PDB) suggests that a significant portion of interface
helices use one face to target the binding partner.19,26 This
analysis points to the meaningful role that topographical helix
A
Received: July 8, 2013
© XXXX American Chemical Society
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Our second model system for computational design
validation is the interaction between HIF1α and coactivators
p300/CBP, which mediate transactivation of hypoxia-inducible
genes.56,57 The HIF signaling pathway is intimately linked to
angiogenesis and metastasis in cancer.58,59 The design of
oxopiperazine analogues described previously36 was based on
mimicry of natural residues and resulted in submicromolar
inhibitors. Here we show that application of the new Rosetta-
based peptidomimetic design strategy with noncanonical
residues affords compounds that are greater than an order of
magnitude more potent than the scaffolds that displayed wild-
type residues.
Overall, the study offers a generalized approach for
discovering topographical helix mimetics consisting of wild-
type and noncanonical residues that can target helical protein−
protein interactions with high affinities.
RESULTS AND DISCUSSION
■
Peptidomimetic Design with Rosetta. We investigated a
computational approach that combines success in computa-
tional protein design60−62 with peptidomimetic scaffolds to
develop OHMs as PPI inhibitors. Protein design is the process
of predicting an amino acid sequence that will fold into a
desired structure or carry out a desired function.60 Computa-
tional protein design techniques have made significant strides in
recent years.63 A short list of successful applications includes an
experimentally validated protein fold not seen in nature,61
redesign of protein−protein and protein−DNA interfaces,64
hyper stabilization of proteins,65 and design of enzymatic and
ligand binding activities.62,66−72 We sought to use protein
design principles to optimize the affinity of oxopiperazine
mimetics using Rosetta (https://www.rosettacommons.org/).73
The basic design protocol in Rosetta uses a fixed backbone
target and flexible ligand, with the goal of identifying the set of
residues and side chain conformations with the lowest energy
(Figure 2). To reduce the computational complexity required
to model side chain degrees of freedom, the side chains are
represented as “rotamers”, discrete side chain conformations
located at the centroids of chi angle clusters, as determined by
analyzing experimental protein structures. Recent extensions of
the Rosetta framework enable modeling and design of
noncanonical amino acids74 on nonnatural scaffolds such as
peptoids.75,76 Implementation of oxopiperazine design in
Rosetta has been recently described, and the protocols are
available on the web (http://rosie.rosettacommons.org).77
Here we expand on our previous Web server implementation
by allowing larger rigid-body sampling and designs, which
include noncanonical amino acids.
There were several significant challenges involved in
modifying Rosetta to enable modeling and design of
oxopiperazine scaffolds. Specifically, we modified Rosetta’s
protein centric score function to account for the OHM
backbone, we employed recent methods to incorporate
noncanonical amino acids in designs, we built core descriptions
of oxopiperazine molecules in Rosetta’s internal molecular
representation, and last we built methods for conformational
sampling that efficiently sample oxopiperazine conformations.
We were aided in this endeavor by two key recent
developments in the broader Rosetta developers community.
A new molecular mechanics-based score function was recently
added to Rosetta that does not rely on the protein centric
knowledge-based score terms.74 Additionally, a redevelopment
of the Rosetta software suite73 has provided key flexibility in the
Figure 1. (a) Design of oxopiperazine helix mimetics. (b) Overlay of
an 8-mer canonical α-helix and an oxopiperazine dimer (left).
Predicted low-energy structure of an oxopiperazine dimer (right).
Side chain groups are depicted as spheres.
The objective of computational molecular design is to reduce
the total number of possible designs to a manageable number
that can be efficiently synthesized and experimentally tested.
For example an oxopiperazine dimer has four variable positions,
and assuming a standard library of 17 amino acids (20
canonical amino acids without Cys, Gly and Pro), the total
number of possible designs would be >83 500. This calculation
does not account for noncanonical amino acids, whose
inclusion significantly raises the number of potential designs.
Experimentally synthesizing and testing this many designs
would be difficult for typical academic laboratories. Computa-
tional design offers a means of reducing the number of total
designs one must synthesize to obtain potent ligands and
streamlines the process of finding a high-affinity binder.
Contemporary computational methods for design of PPI
inhibitors often emphasize fragment-based screening.37,38 As a
complementary approach, peptidomimetic design seeks to graft
appropriate side chains on stable synthetic backbones, i.e.,
helical or β-sheet scaffolds.
We utilized a new computational protocol to develop
nanomolar ligands for two different protein−protein inter-
actions. We designed oxopiperazine dimers that mimic the p53
activation domain and HIF1α to develop ligands for Mdm2 and
p300/CBP, respectively. The p53−Mdm2 interaction is an
attractive target for cancer therapeutics39,40 as well as a model
system for evaluating rational design strategies for inhibitor
discovery. The activation domain of p53 adopts an α-helical
conformation when bound to Mdm2,41 and several classes of
stabilized helices and helix mimetics have been shown to target
this interaction.20,21,25,42−47 In addition, several potent small
molecule inhibitors of this interaction are known and are being
evaluated for their in vivo efficacy in advanced preclinical
models.48−51 Lastly, a wealth of structural data on the p53−
Mdm2 interaction makes it well suited for development of
computational strategies52 for ligand optimization.41,53−55
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Table 1. Design and Mdm2 Binding Properties of
Preliminary Oxopiperazine-Derived Helix Mimetics
a
mimetic
R1
R2
R3
R4
X
Kd (μM)
65
≥200
1
2
3
4
5
6
7
8
Phe
Phe
Phe
Phe
Phe
Phe
Lys
Phe
Trp
Trp
Trp
Trp
Trp
Trp
Trp
Ala
Ala
Lys
Leu
Phe
Phe
Phe
Phe
Phe
Leu
Leu
Leu
Leu
Leu
Lys
Leu
Leu
OH
OH
8
OH
7.9 0.5
6.9 1.3
2.9 0.1
≥200
OH
NH2
NH2
NH2
NH2
≥200
64
7
a
Binding affinity for Mdm2 as determined by a competitive
fluorescence polarization assay.
Figure 2. Key steps in the inhibitor design protocol. The protocol is
initiated with identification of hotspot residues at the native interface
by computational alanine scans. Positions on the scaffold are identified
to mimic hotspot residues, and the scaffold featuring the hotspot
mimics is experimentally validated. Computational steps including
optimization of the ligand−protein complex conformation and design
of hotspot analogues are performed using Rosetta. Top designs are
inspected for proper binding of the target interface, and proper designs
are experimentally validated.
data structures to enable modeling diverse sets of molecules
other than proteins and nucleic acids. Finally, we have added
new functionality into Rosetta that efficiently samples various
oxopiperazine conformations, including a puckering of the
oxopiperazine ring.76 This work was supported by quantum
mechanical exploration of the backbone conformations to
validate backbone energy terms.76
Design of p53/Mdm2 Inhibitors. The p53 activation
domain targets Mdm2 with three hydrophobic residues Phe19,
Trp23, and Leu26 forming key contacts; we began by modeling
these residues onto the oxopiperazine scaffold (Figure 1). An
oxopiperazine dimer displays four amino acid side chains. Our
modeling studies suggest that the first, second, and the fourth
side chains, labeled as R1, R2, and R4, respectively, in Figure 1,
overlay well on the i, i + 4 and i + 7 side chains of the α-helix.
This leaves R3 potentially available for placement of solubilizing
groups or small noninteracting side chains, as our preliminary
analysis predicted that this residue does not directly contact the
receptor. Accordingly, we designed and synthesized mimetic 1
(with the sequence FWAL) and 2 (FWKL), which featured the
wild-type residues at the equivalent positions on the non-
peptidic scaffold but alanine or lysine residues at the R3 position
(Table 1). OHMs were prepared using a solid phase synthesis
methodology as described (Scheme S1).36
Figure 3. Determination of oxopiperazine analogue binding to His6-
tagged Mdm2 by a fluorescence polarization assay. Binding curves for
selected compounds in Table 1 are shown.
properties, we designed a series of compounds where this
position was changed to hydrophobic, anionic, cationic
residues. These studies were performed in the context of the
dimers (1−4) as well as oxopiperazine monomers linked to
uncyclized dipeptides (Table 2). Together these preliminary
studies showed that a hydrophobic group such as Leu or Phe at
position R3 is preferred. Importantly, comparisons of the
Table 2. Mdm2 Binding Properties of Oxopiperazine-
Dipeptide analogues
We utilized a previously described fluorescence polarization
competition assay with a fluorescein-labeled p53 peptide to
probe the binding affinity of the mimetics.78,79 Competitive
displacement of the p53 peptides provides a strong indication
that the designed nonpeptidic ligands are occupying the p53
binding pocket on Mdm2. In this assay, mimetic 1 bound
Mdm2 with a dissociation constant, Kd, of 65 μM, while 2
displayed an appreciably lower affinity (Table 1 and Figure 3).
To examine the affect of the R3 position on the binding
a
mimetic
R1
R2
R3
R4
Kd (μM)
9
Phe
Phe
Phe
Phe
Trp
Trp
Trp
Trp
Ser
Asp
Leu
Phe
Leu
Leu
Leu
Leu
>400
10
11
12
125 73
94 16
49 10
a
Binding affinity for Mdm2 as determined by a competitive
fluorescence polarization assay.
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dimers 3 (FWLL) and 4 (FWFL) with the monomer-dipeptide
sequences 11 and 12 support our hypothesis that cyclization of
dipeptides in oxopiperazine rings provides a significant boost to
the ability of these helix mimetics to target protein pockets.
Ramachandran plots80 derived from quantum mechanical
calculations further illustrate the flexibility of the uncyclized
derivative as compared to the cyclic dimer (Figure S1).
In these preliminary investigations, we also studied the effect
of modulating the C-terminal functional group from a
carboxylic acid to a carboxamide. Comparison of 4 and 5
illustrates that C-terminal functionalities do not significantly
alter the binding profile of the molecules. Mimetic 5 binds
Mdm2 with a dissociation constant of roughly 3 μM.
Importantly, substitution of the Trp, Phe, and Leu residues at
positions R1, R2, and R4, respectively, with alanine or lysine
leads to substantial decrease in the binding affinities (5 versus
6−8); these results suggest that the residues in these positions
on the dimer are making substantial contacts with the target
interface, as expected, from mimickry of p53 Phe19, Trp23, and
Leu26 residues within the Mdm2 pocket (Figure 4). We
observed. However, we cannot rule out the possibility that both
cis and trans conformations contribute to the overall binding
affinity. To fully dissect the contribution of the cis and trans
conformations, amide bond isosteres where each of the
conformations can be controlled will need to be examined in
future studies.
We began our computational design protocol in Rosetta by
building a model of 4 and by analyzing the experimental
structure activity relationships shown in Table 1. Compound 4
was docked to align with the p53 hotspot residues, and our
oxopiperazine docking protocol was used to optimize the rigid-
body conformations of the ligand and the protein based on
Rosetta’s molecular mechanics energy function (see methods
for details). Figures 5a and 8a show that 4 makes several
energetically favorable contacts with the Mdm2 interface,
suggesting proper mimicry of the p53 hotspot residues. The R1
residue, Phe, of 4 (Figure 5b) is involved in good packing
Figure 4. Docking of the oxopiperazine scaffold (cyan) with sidechains
shown in orange in p53 binding pocket of Mdm2. Figure shows the
relative positioning of the oxopiperazine dimer side chains R1−R4 and
p53 hotspot residues Phe19, Trp23, and Leu26 within the protein
pocket.
expected that the low micromolar dissociation constants
obtained for this new class of helix mimetic scaffold can be
further optimized, in keeping with previous studies with p53
mimics which showed that minor changes to contact residues
can provide a significant improvement in binding.81 However,
we were concerned that cis−trans amide bond isomerization
may be contributing to lower affinity. The amide bond linking
the R2 residue to the R3 oxopiperazine ring may adopt a trans
or a cis conformation. Computational studies suggest that the
trans conformation is preferred over the cis conformation by
roughly 1.0 kcal/mol or more depending on the identity of the
R2 and R3 residues;23 similar to the energy difference observed
with proline. The fact that a hydrophobic group is favored over
charged residues at the R3 position suggests that this residue
may be occupying the Leu26 binding site in Mdm2 as opposed
to the R4 residue. This alternative-binding mode would be
possible if the cis-amide conformation was accessed in the
complex. Mimetic 6 (Table 1) explicitly tests this possibility. If
the R4 group is solvent accessible and R3 binds in the Mdm2
hydrophobic pocket, 6 would be expected to bind Mdm2 with a
similar affinity as 5, instead of being a rather poor binder as
Figure 5. (a) Predicted conformation of Mimetic 4 in Mdm2 pocket.
Binding modes of (b) Phe, (c) Trp, and (d) Leu residues (R1, R2, and
R4 positions) of 4 are shown.
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interactions with the Mdm2 interface (residues Ile61, Met62,
and Tyr67), including a potential stacking interaction with
Tyr67. The R2 residue, Trp (Figure 5c), is well packed in the
same pocket as the p53 hotspot Trp23 contacting Mdm2
residues Leu54, Leu57, Gly58, Ile61, Phe86, Phe91, Val93,
Ile99, and Ile103. Lastly, the R4 residue, Leu (Figure 5d), also
properly mimics the p53 hotspot residue (Leu26), packing well
into a pocket formed by several Mdm2 hydrophobic interface
residues including Leu54, Val93, His96, Ile99, and Ile103.
Next, we developed an algorithm to predict high-affinity
oxopiperazine dimers for Mdm2 using Rosetta and a library of
noncanonical amino acids (Table S1). We used the starting
conformation of the ligand−Mdm2 complex (developed as in
our modeling of compound 5, Figure 5a) as input for Rosetta
calculations and designed a two-step iterative protocol
consisting of conformation and sequence optimization steps.
The conformation optimization step attempts to find a low-
energy conformation between the scaffold and the target
protein. During this step, the protocol performs a Monte Carlo
search of conformational space making random changes to the
rigid-body orientation, oxopiperazine backbone (including ring
puckering), and side chain repacking to both the scaffold and
target interface. In the sequence optimization step, we make
side chain substitutions from a library of both natural and
noncanonical amino acids to find the lowest-energy oxopiper-
azine sequence.
here are generally soluble at millimolar concentrations in
aqueous solutions.)
The goal of computational peptidomimetic design is to
produce a handful of top designs that can be experimentally
evaluated. Rosetta predicts a large number of binders for Mdm2
and provides a filtered and ranked list of predicted high-affinity
binders composed of natural and noncanonical residues. Figure
6 shows a violin plot in gray indicating the distribution of
predicted oxopiperazine ligands spanning the Rosetta binding
energy score spectrum. The gray area represents the top 1000
scores from Rosetta’s evaluation of 30 000 designs. This
spectrum provides a background on which to compare possible
high-affinity Rosetta designs. Figure S2 correlates experimental
binding affinity for Mdm2 with Rosetta binding energy score
for the subset of sequences we tested using our competition
binding assay. Although our goal here is to use Rosetta to
suggest designs with high likelihood of success, these data
illustrate that Rosetta predictions correlate well with exper-
imental dissociation constants for the oxopiperazines and are
competitive with current computational methods.82−85
To show that our Rosetta binding energy protocol enriches
for high-affinity binders, selected designs were synthesized and
evaluated using the fluorescence polarization competition assay
described above. The Rosetta results suggest that the
tryptophan residue at position R2 is optimized for that position
so we began by synthesizing the variants at each of the other
three positions (Table 3 and Figure 7). Mimetic 13 contains a
This two-step protocol is repeated for a large number of
substitutions and low-energy oxopiperazine sequences (de-
signs), and their 3D models are saved. Low-energy designs were
sorted based on calculated binding energy (Figure 6 and Table
Table 3. Computationally Predicted Oxopiperazine p53
Mimics and Their Potential to Target Mdm2
a
mimetic
R1
R2
R3
R4
Kd (μM)
13
14
15
16
17
18
19
20
Phe
Phe
Trp
Trp
Trp
Trp
Trp
Trp
Trp
Trp
Phe
Tyr
Phe
Phe
Phe
Phe
Phe
Phe
Nle
Leu
Leu
Leu
Leu
Leu
Leu
Leu
2.5 0.5
3.1 0.2
0.9 0.1
0.4 0.05
0.3 0.01
0.3 0.04
2.6 0.04
1.3 0.1
Nap
Tyr
Tyr(O-Me)
Phe(3-Cl)
Phe(3-Me)
Phe(4-Cl)
a
Binding affinity for Mdm2 as determined by a competitive
Figure 6. A violin plot showing distribution of the predicted
oxopiperazine analogues for their potential to target Mdm2. The
binding affinity is expressed as Rosetta binding energy units (REUs).
The plot shows the energy scores for the top scoring 1000 designs
selected from 30 000 random Rosetta designs (gray violin) as well as
experimentally tested designs (dots). The Rosetta score discriminates
between good binders (green and yellow label) and weak binders (red
label).
fluorescence polarization assay.
norleucine residue at position R4 in place of the leucine in 5,
while compound 14 features a tyrosine group at R3 in place of
phenylalanine. Two derivatives, 15 and 16, containing
naphthylalanine and tyrosine residues, respectively, at position
R1 were synthesized. Binding studies indicate that substitutions
at the R3 and R4 positions of dimers do not lead to higher
affinity compounds. In contrast, substitutions at the R1 position
provided improvements predicted by Rosetta. The naphthyl
analogue, 15, binds Mdm2 with a 3-fold higher affinity than 5,
while substitution with tyrosine to obtain 16 provides a 400 nM
ligand for Mdm2. Based on these results, we prepared and
tested two more derivatives of phenylalanine at the R1 position.
Mimetic 17 contains a methylated tyrosine group, while 18
S2), and the top designs were selected for manual inspection.
Manual inspection included verifying that (1) the oxopiper-
azine scaffold occupied the same pockets as the p53 helix
hotspots to ensure inhibition, (2) the conformation entailed
good packing among side chains from both sides of interface,
and (3) the design was synthetically tractable and likely soluble,
etc. (It should be noted that oxopiperazine dimers described
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Figure 7. Determination of binding affinity for computationally
optimized oxopiperazine analogue binding to His6-tagged Mdm2 by a
fluorescence-polarization assay. Binding curves for selected com-
pounds in Table 3 are shown.
features a 3-chloro-phenylalanine residue. Both of these
analogues proved to be better than 16. Overall, our designs
involving changes at the R1 position yielded a roughly 10-fold
improvement over the unoptimized derivative 5.
Analyses of the minimized complexes show that the Phe
residue at the R1 position of 5 is wedged in a pocket formed by
Ile61, Met62, Tyr67, and Gln72 of Mdm2. Tyr67 and Gln72
reside on a flexible loop allowing different-sized analogues of
Phe to be accommodated in the pocket (Figure 8a). The
predicted orientation of the R1 residue for compounds 5, 15,
16, and 18 is shown in Figure 8 and illustrates the plasticity of
the pocket. We designed two control compounds, 19 and 20, to
investigate the specificity of the pocket for 3-chloro-phenyl-
alanine group. Mimetic 19 contains a bulkier methyl group in
place of the chlorine atom, while 20 features the chlorine atom
at the 4-position. We find that replacement of the chlorine
atom with the methyl group causes an 8-fold decrease in
binding affinity and moving it to the para-position on the
phenyl ring leads to a 4-fold reduction. These results suggest
that the 3-chlorophenyl group makes specific steric and
electronic contacts within the pocket, consistent with our
computational model of this interaction.
The full list of Rosetta scores and experimental binding
affinities for the p53 mimics is included in Table S2. The
Rosetta algorithm produced a pool dramatically enriched for
high-affinity binders. The lack of a perfect correlation between
experimental and computational results within a narrow
window of affinities is not surprising in these preliminary
studies that represent the first test of Rosetta on a novel
backbone that includes noncanonical amino acids. (We find
correlation equal to 0.64 and 0.79 for the two interfaces
described here.) It is interesting to note that the poor binder,
KWFL (7), scored better than expected by Rosetta.
Examination of the Mdm2 bound structure of KWFL reveals
that the lysine residue does not occupy the p53 Phe19 hotspot
pocket, violating our first rule of manual inspection described
above (Figure S3). It is not surprising that this compound leads
to poor inhibition since the Phe19 pocket offers an important
contact for p53. The result underscores the importance of
targeting the interaction interface when developing an inhibitor.
The algorithm correctly predicts that mimetic 6, in which a
lysine group resides in place of leucine, will be a poor binder.
We envisage a better correlation in future studies when a larger
set of experimental data is available as a training set.82
Figure 8. Examination of the N-terminal residue-binding pocket in
Mdm2. (a) The phenylalanine residue at the R1 position of 4 (cyan)
resides in a flexible pocket consisting of Ile-61, Met62, Tyr67, and
Gln72 of Mdm2 (green). (b) Predicted orientations of phenylalanine
and analogues (c) naphthylalanine of mimic 15, (d) tyrosine of mimic
16, and (e) 3-chlorophenylalanine of mimic 18. Electrostatic surface of
Mdm2 is modeled by Pymol.
To confirm that 18 binds to Mdm2 in the p53 binding
1
pocket, we performed H−15N HSQC NMR titration experi-
ments with 18 and uniformly 15N-labeled Mdm2. Addition of
18 to 50 μM Mdm2 in Mdm2:18 ratios of 1:0.2 and 1:0.5
provided a concentration-dependent shift in resonances of
several Mdm2 residues (Figures 9 and S4). Specifically,
addition of 18 leads to shifts in resonances of residues
corresponding to the hydrophobic cleft into which the native
p53 helix binds. Overall, the NMR results support the Rosetta
derived model of the complex.
Design of HIF1α/p300(CBP) Inhibitors. To further
validate the potential of Rosetta oxopiperazine design protocol,
we chose to develop inhibitors of a different transcriptional
complex. We have recently shown that stabilized peptide
helices86 and small molecule oxopiperazine analogues36 that
mimic a key helical domain of HIF1α can inhibit hypoxia
inducible signaling in cell culture and animal models. The C-
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Leu822, and Gln824, can be mimicked by oxopiperazine
dimers consisting of the appropriate building blocks (Figure
10). Based on this analysis, we designed and synthesized
analogues of HIF1α to inhibit its binding with p300/CBP.
OHM 21 contains projections representing all three wild-type
residues from HIF1α: R1 as Leu818, R2 as Leu822, and R4 as
Gln824 (Table 4). The R3 position of the oxopiperazine
Table 4. Oxopiperazine HIF Mimics Targeting the CH1
Domain of p300/CBP
a
mimetic
R1
R2
R3
R4
Kd (μM)
21
22
23
24
25
Leu
Leu
Leu
Met
Hle
Leu
Ala
Ala
Ala
Ala
Ala
Ala
Gln
Gln
Gln
Gln
Gln
0.53 0.14
>10
Nle
Met
Hle
0.03 0.01
0.24 0.04
0.16 0.06
Figure 9. Model depicts the results of a 1H−15N HSQC NMR
titration experiment. Mdm2 residues undergoing chemical shift
perturbations upon addition of 18 are shown in colors that match
the magnitude of the chemical shift change in the scale. Compound 18
is represented in blue. The computationally predicted model of the
complex is shown.
a
Binding affinity for p300-CH1 was determined using an intrinsic
tryptophan fluorescence assay.
scaffold was not predicted to make contacts with the target
protein; an alanine residue was inserted at this position. OHM
22 was designed as a single mutant of 21 with the R2 position
substituted with an alanine residue.
terminal domain of HIF1α utilizes two short α-helices to bind
to the CH1 domain of p300/CBP (Figure 10). Computational
alanine scanning64 studies on the complex reveal that four
helical residues from the HIF1α helix816−824 (Leu818, Leu822,
Asp823, and Gln824) make close contacts with the CH1
domain of p300/CBP. Three of these residues, Leu818,
Results with 21 and 22 have been previously described.36 We
found that oxopiperazine 21, consisting of the wild-type
residues, bound the CH1 domain of p300 with an affinity of
533 24 nM; whereas, the negative control 22 displayed a very
weak affinity for p300-CH1, with Kd value of >10 μM (Table 4
and Figure 11). The binding affinities of OHMs for p300-CH1
Figure 11. Determination of binding affinity for p300-CH1 by a
tryptophan fluorescence spectroscopy. Binding curves for compounds
21−25 in Table 4 are shown.
domain were evaluated using intrinsic tryptophan fluorescence
spectroscopy, as described previously.86,87 Because Trp403 lies
in the binding cleft of p300/CBP where a native HIF1α816−824
helix binds, it offers a probe for investigating mimetics of this
helix. As part of this earlier study, we also characterized the
interaction of OHM 21 with p300-CH1 domain using 1H−15N
HSQC NMR titration experiments with the uniformly 15N-
Figure 10. Design of HIF1α mimetics as ligands for p300-CH1. (a)
Overlay of HIF1α helix776−826 (in magenta) and OHM 21 (cyan) in
complex with CH1 domain of p300/CBP (PDB code 1L8C). The R1,
R2 and R4 positions of 21 access the same p300 molecular pockets as
Leu818, Leu822, and Gln824 of the HIF1α C-terminal activation
domain.
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labeled CH1. Addition of OHM 21 led to consistent shifts in
resonances of residues corresponding to the HIF1α816−824
binding pocket. OHM 21 efficiently downregulated HIF
signaling in cell culture at micromolar levels and reduced
tumor levels in triple-negative breast cancer cell line MDA-MB-
231 mouse xenograft models. Importantly, microarray gene
expression profiling data showed that the designed oxopiper-
azine helix mimetic despite its low molecular weight and a
limited number of contacts with the intended target protein
shows high specificity on a genome-wide scale.
The encouraging results with OHM 21 provide a platform to
test the potential of our Rosetta peptidomimetic design strategy
to further optimize design of HIF mimetics. Specifically, we
wanted to determine if the computational approach could
rapidly suggest noncanonical residues that may boost the
binding affinity for p300-CH1. We analyzed the p300/OHM
21 binding using our established protocol, with a library of
noncanonical amino acids (Table S3). The computational
predictions suggested inclusion of longer aliphatic side chains in
place of the isobutyl group of leucine would lead to better
contacts with the hydrophobic pocket. Specifically, substitution
with noncanonical side chains at the R2 position of OHM 21
was predicted to lead to an optimized binder (Figure 12).
Figure 13. A violin plot showing distribution of the predicted
oxopiperazine analogues for their potential to target the CH1 domain
of p300/CBP. The binding affinity is expressed as Rosetta binding
energy unit (REU). The plot shows the energy scores for the top
scoring 1000 designs selected from 30 000 random Rosetta designs
(gray violin) as well as experimentally tested designs (dots). The
Rosetta score discriminates between good binders (green and yellow
label) and weak binders (red label).
higher affinity than the parent OHM 21, with OHM 23
providing a 13-fold enhancement in binding affinity (Kd = 30.2
1.87 nM).
We find a strong correlation between the experimental
results for p300/CBP and Rosetta predictions (Figure S5 and
Table S4) further highlighting the success of the computational
design protocol. Since the fluorescence-binding assay uses a
native tryptophan residue in the target molecular pocket, it
provides a stringent test for the binding site specificity.
Characterization of the interaction of OHM 21 with p300-
CH1 domain using 1H−15N HSQC NMR titration experiments
further confirms the target pocket for the designed analogues.
Cross-Specificities of the Designed Compounds for
Mdm2 and p300. Analyses of protein−protein interaction
networks suggest that the human interactome consists of
hundreds of thousands of different PPIs.88,89 Our own analysis
of the high-resolution protein complexes available in the
Protein Data Bank reveals that up to 60% of such protein
complexes contain an interfacial α-helix.19,26,90 Thus, a central
question in the design of helix mimetics as PPI inhibitors
pertains to their specificity on the genome-wide scale. We
recently probed the specificity of OHM 21, designed to be a
transcriptional inhibitor for off-target regulation, using the
Affymetrix Human Gene ST 1.0 arrays containing oligonucleo-
tide sequences representing over 28 000 transcripts.36 This
compound was found to be remarkably specific given the
limited number of contacts it offers.
In the present study, we have computationally designed
potent small molecule helix mimetics that feature noncanonical
side chains as potential inhibitors of protein−protein
interactions. As a preliminary analysis of Rosetta’s ability to
predict the specificity of OHMs against unintended targets, we
determined the binding affinity of the p53 OHM mimic 18
against p300-CH1 and that of HIF OHM mimics 23 and 25 for
Mdm2 (Table 5 and Figure S6). These analogues were chosen
because they represent the highest-affinity ligands obtained for
their respective targets and contain noncanonical residues.
Calculations with the modified version of Rosetta, described
Figure 12. Analysis of the R2 position of HIF OHM mimics in p300-
CH1 binding pocket. Space-filling model reveals longer hydrophobic
side chains form better packing in the p300-CH1 pocket, natively
inhabited by the Leu822 of HIF1α: (a) leucine of 21, (b) alanine of
22, (c) homoleucine of 25, and (d) norleucine of 23.
Figure 13 shows a violin plot for OHMs targeting the CH1
domain. The gray area represents the top 1000 scores from
Rosetta’s evaluation of 30 000 designs. The predicted high-
affinity designs feature norleucine (Nle) and homoleucine
(Hle) residues in place of the wild-type leucine analogues and
are substantially lower in energy than the rest of the sequences
tested by Rosetta. Substitution of the two leucine residues with
methionines is predicted to be less effective than with
noncanonical residues, suggesting that space-filling and polarity
of side chain groups are necessary for optimal results. Other
combinations of homoleucine, norleucine, and leucine residues
were also examined (Table S4). To experimentally evaluate the
predictions, we prepared three analogues representing top
designs in which both leucine groups of 21 were substituted
with methionine, norleucine, or homoleucine to obtain OHMs
23−25 (Table 4). Each of theses compounds bound p300 with
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specifically for oxopiperazine inhibitors. The protocol iterates between
a perturbation phase (conformational optimization), attempting to
find the lowest-energy conformation of bound ligand and target
protein given the current residue identities, and a design phase, which
attempts to find residue substitutions including noncanonical
analogues that lower the energy given the current conformation.
The perturbation phase consists of (a) rigid-body rotation and
translation moves, (b) small angle moves of phi and psi, and (c)
pucker moves of the oxopiperazine rings. Perturbations were only
allowed to the scaffold leaving the target’s backbone fixed. All residues
at the interface on both target and ligand were allowed to sample side-
chain rotamer space. The design phase consists of residue identity
substitutions at positions along the scaffold and rotamer repacking.
Substitutions were defined in the Rosetta resfile. Finally, minimization
of all degrees of freedom in the complex was performed.
For modeling analysis, we used the same design protocol except
residues were fixed to the identities of interest in the Rosetta residue
input file (i.e., resfile). Fixing residue identities only allows side chain
optimization during the “design” phase. 5000 independent runs (i.e.,
decoys) were computed for each sequence. For design runs of p53
mimic, substitutions that were allowed included noncanonical amino
acids that were derivative of the original hotspot residue (e.g., R1
phenylalanine was designed with 3-methyl-phenylalanine, etc.) See
Table S1 for the NCAA_library list. For each position on the scaffold,
>10 000 runs were carried out allowing the single position to vary
leaving the other positions fixed. This was repeated for each position
on the oxopiperazine scaffold. The SVN Revision: 52345 version of
Rosetta used was for these studies. For design runs of HIF1α mimic,
R1 and R2 were substituted with all hydrophobic noncanonical amino
acids in Table S3 except for proline analogues. Detailed protocols
including command lines have been previously described76 and can be
found in the Supporting Information.
Table 5. Cross-Specificities of Oxopiperazine p53 and HIF
Mimics Against p300-CH1 and Mdm2
a
b
ligand
p300 Kd (μM)
Mdm2 Kd (μM)
18
23
25
>30
0.3 0.04
>50
0.03 0.01
0.16 0.06
>50
a
The binding affinities for p300-CH1 were determined using an
b
intrinsic tryptophan fluorescence assay. Affinities for Mdm2 were
measured using a competitive fluorescence polarization assay with Flu-
p53 as a probe.
above, predict that the p53 mimetic 18 is a poor ligand for
p300-CH1 and that HIF mimetics 23 and 25 are not optimal
designs for Mdm2. Specifically, the calculated Rosetta binding
energy (REU) for OHM 18/p300-CH1 binding is the same as
that calculated for 22, a negative control designed for the HIF/
p300 interaction (Figure 13). Likewise, Rosetta predicts
compounds 23 and 25 to have a high-energy interaction with
Mdm2; >6 REU’s when compared to 18 the high-affinity
Mdm2 ligand (Figure 6). We confirmed these predictions in
experimental binding assays.
We tested the binding of oxopiperazine derivatives using the
assays described above. As expected the compounds are specific
for their cognate receptors (Table 5), with each showing more
than 100-fold specificity for the desired protein surface. These
results provide support for our hypotheses that the computa-
tional strategy developed herein can be used to ultimately
predict specificity of the designed peptidomimetics on the
genome-wide scale.
Top designs were selected based on filtering the lowest 5% of total
energy decoys and sorting by Rosetta binding energy score. The
Rosetta binding energy score was calculated by the equation:
CONCLUSIONS
■
Protein−protein interactions are attractive targets for drug
design because of their fundamental role in human biology and
disease progression. These large interfaces are often dismissed
as “undruggable”; however, the past decade has seen emerging
methods to inhibit these complexes. Here we describe the
potential of small molecule helix mimetics derived from the
oxopiperazine scaffold to target protein complexes where one
face of the interfacial helix contributes significantly to binding.
We find that the affinity of the designed ligands can be
enhanced significantly using a combination of computational
design and experimental structure−activity relationship data.
Central to the present efforts was a novel combination of
rational design (i.e., hotspot mimicry) and a new set of Rosetta
functionalities for computational design with noncanonical side
chains and backbones. We expect that the tools and algorithms
developed here will be applicable for targeting PPIs that remain
intractable for synthetic inhibition. All computational tools
described in this work are freely available to academic
researchers via the RosettaCommons (rosettacommons.org).
Our efforts show that the principles of computational protein
design can be transferred to nonnatural scaffolds featuring
noncanonical amino acid residues.
Binding_energy_score = total_score − unbound_score
The unbound score was calculated by separating the scaffold from
the target receptor, then repacking the side chains and finally
calculating the total Rosetta energy of the unbound complex.
Rosetta Binding Discrimination Analysis. A random set of
designs against target proteins Mdm2 and p300 were generated from a
set of 30 000 Rosetta design runs where all four positions of an
oxopiperazine dimer were allowed to vary to any canonical amino acid
excluding Cys, Gly, and Pro. The top 1000 of models by total Rosetta
score made up the total random set. This random set is shown as a
gray histogram (violin plot) in Figures 6 and 13.
The top binding energy score for designs with experimental binding
affinities were determined from a set of 5000 decoy structures. As
described above, the top 1000 of decoys by total score was then sorted
by Rosetta binding energy score, and the lowest Rosetta binding
energy score was used.
Quantum Mechanics Calculations. Quantum mechanics calcu-
lations were done using the Gaussian 09 (EM64L-G09RevC.01,
version date: 2011-09-23) software package.91 An initial optimization
using “HF 6-31G(d) Opt SCRF=PCM SCF=Tight” parameters was
done for each model structure. The resulting optimized structure was
then used for further energy calculations with parameters “B3LYP 6-
31G(d) Geom=Check SCRF=PCM SCF=Tight” and “MP2(full) 6-
31G(d) Geom=Check SCRF=PCM SCF=Tight”.
Description of Mdm2 Binding Studies. The relative affinities of
OHMs for N-terminal His6-tagged Mdm2 (25−117) were determined
using fluorescence polarization-based competitive binding assay with
fluorescein labeled p53 peptide, Flu-p53. The polarization experiments
were performed with a DTX 880 Multimode Detector (Beckman) at
25 °C, with excitation and emission wavelengths at 485 and 535 nm,
respectively. All samples were prepared in 96 well plates in 0.1%
pluronic F-68 (Sigma). Prior to the competition experiments, the
affinity of the Flu-p53 for Mdm2 was determined by monitoring
polarization of the fluorescent probe upon binding Mdm2 (Figure S7).
EXPERIMENTAL SECTION
■
Docking and Design Protocol in Rosetta. The oxopiperazine
dimer scaffold was initially docked by aligning Cβ atoms on the
scaffold positions corresponding to hotspot residues on P53 (R1,
Phe19, R2, Trp23, R3, Leu26) and HIF1α (R1, Leu818, R2, Leu822, R4,
Gln824) using the PDB structure: 1YCR and 1L8C, respectively. The
Rosetta relax w/constraints application was run on this initial structure
to relieve any clashes that may hinder score analysis. The relaxed
complex was then modeled and designed using a protocol developed
I
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For competition binding experiments, appropriate concentrations of
the peptides (1 nM−100 μM) were added to the Mdm2-Flu-p53
mixture, and the resulting solution was incubated at 25 °C for 1 h
before measuring the degree of dissociation of Flu-p53 by polarization.
The binding affinity (KD) values reported for each peptide are the
averages of 3−5 individual experiments and were determined by fitting
the experimental data to a sigmoidal dose−response nonlinear
regression model on GraphPad Prism 5.0.92
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Description of p300-CH1 Binding Studies. Relative affinities
for of OHMs were determined using a tryptophan fluorescence assay.
Spectra were recorded on a QuantaMaster 40 spectrofluorometer
(Photon Technology International) in a 10 mm quartz fluorometer
cell at 25 °C with 4 nm excitation and 4 nm emission slit widths from
200 to 400 nm at intervals of 1 nm/s. Samples were excited at 295 nm,
and fluorescence emission was measured from 200 to 400 nm and
recorded at 335 nm. OHM stock solutions were prepared in DMSO.
Aliquots containing 1 μL DMSO stocks were added to 400 μL of 1
μM p300-CH1 in 50 mM Tris and 100 mM NaCl (pH 8.0). After each
addition, the sample was allowed to equilibrate for 5 min before UV
analysis. Background absorbance and sample dilution effects were
corrected by titrating DMSO into p300-CH1 in an analogous manner.
Final fluorescence is reported as the absolute value of [(F1 − F0)/
F1]*100, where F1 is the final fluorescence upon titration, and F0 is the
fluorescence of the blank DMSO titration. EC50 values for each
peptide were determined by fitting the experimental data to a
sigmoidal dose−response nonlinear regression model on GraphPad
Prism 5.0, and the dissociation constants, KD, were obtained from
equation.
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ASSOCIATED CONTENT
■
S
* Supporting Information
Scheme S1, Figures S1−S5, and Tables S1−S4. Compound
characterization including NMR spectra and analytical HPLC
traces. This material is available free of charge via the Internet
at http://pubs.acs.org.
(25) Lee, J. H.; Zhang, Q.; Jo, S.; Chai, S. C.; Oh, M.; Im, W.; Lu, H.;
Lim, H. S. J. Am. Chem. Soc. 2011, 133, 676.
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14, 341.
AUTHOR INFORMATION
■
Corresponding Authors
arora@nyu.edu
bonneau@nyu.edu
(28) Yin, H.; Hamilton, A. D. Angew. Chem., Int. Ed. 2005, 44, 4130.
(29) Orner, B. P.; Ernst, J. T.; Hamilton, A. D. J. Am. Chem. Soc.
2001, 123, 5382.
Author Contributions
§These authors contributed equally.
Notes
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E.; Del Rio, J.; Herranz, R. J. Org. Chem. 2002, 67, 3866.
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Konishi, N.; Terashita, Z.; Naka, T. J. Med. Chem. 2001, 44, 2438.
(34) Sugihara, H.; Fukushi, H.; Miyawaki, T.; Imai, Y.; Terashita, Z.;
Kawamura, M.; Fujisawa, Y.; Kita, S. J. Med. Chem. 1998, 41, 489.
(35) Borthwick, A. D.; Davies, D. E.; Exall, A. M.; Hatley, R. J. D.;
Hughes, J. A.; Irving, W. R.; Livermore, D. G.; Sollis, S. L.; Nerozzi, F.;
Valko, K. L.; Allen, M. J.; Perren, M.; Shabbir, S. S.; Woollard, P. M.;
Price, M. A. J. Med. Chem. 2006, 49, 4159.
(36) Lao, B. B.; Grishagin, I.; Mesallati, H.; Brewer, T. F.; Olenyuk,
B. Z.; Arora, P. S. Proc. Natl. Acad. Sci. USA 2014, DOI: 10.1073/
pnas.1402393111.
(37) Schneider, G.; Fechner, U. Nat. Rev. Drug Discovery 2005, 4,
649.
The authors declare no competing financial interest.
ACKNOWLEDGMENTS
■
This work was financially supported by the National Science
Foundation (CHE-1151554 to P.S.A.) and the National
Institute for Health (RC4-AI092765, PN2-EY016586,
IU54CA143907-01, EY016586-06 to R.B.). B.N.B. thanks the
New York University for a Dean’s Dissertation Fellowship.
Support from the National Science Foundation (CHE-
0958457) in the form of an instrumentation grant is gratefully
acknowledged.
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