Article

https://doi.org/10.1038/s41467-024-45826-7

A druggable conformational switch in thec-MYC transactivation domain
Received: 12 January 2023
Accepted: 6 February 2024

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Dilraj Lama 1,6 , Thibault Vosselman1,6, Cagla Sahin 1,2, Judit Liaño-Pons
Carmine P. Cerrato 1, Lennart Nilsson 3, Kaare Teilum 2, David P. Lane1,
Michael Landreh 1,4 & Marie Arsenian Henriksson 1,5

1,

The c-MYC oncogene is activated in over 70% of all human cancers. The
intrinsic disorder of the c-MYC transcription factor facilitates molecularinteractions that regulate numerous biological pathways, but severely limits
efforts to target its function for cancer therapy. Here, we use a reductionist
strategy to characterize the dynamic and structural heterogeneity of thec-MYC protein. Using probe-based Molecular Dynamics (MD) simulations andmachine learning, we identify a conformational switch in the c-MYC aminoterminal transactivation domain (termed coreMYC) that cycles between aclosed, inactive, and an open, active conformation. Using the polyphenol
epigallocatechin gallate (EGCG) to modulate the conformational landscape of
coreMYC, we show through biophysical and cellular assays that the inductionof a closed conformation impedes its interactions with the transformation/transcription domain-associated protein (TRRAP) and the TATA-box binding
protein (TBP) which are essential for the transcriptional and oncogenic activities of c-MYC. Together, these ﬁndings provide insights into structure-activity
relationships of c-MYC, which open avenues towards the development of
shape-shifting compounds to target c-MYC as well as other disordered transcription factors for cancer treatment.

The gene encoding the c-MYC transcription factor is the most frequently deregulated oncogene across the majority of humancancers1–3, with its overexpression and/or activation of downstream
target genes in the vast majority of the cancer types analysed4.
Deregulation of c-MYC can occur through elevated transcription of
the gene, ampliﬁcation, chromosomal translocation, point mutation,or protein stabilization, all of which release the otherwise tightcontrol and increase expression levels of the protein in the cell1,5.
Inhibition of c-MYC, on the other hand, has been observed to promote apoptosis, growth arrest, differentiation, senescence, metabolic changes, as well as tumour regression in several human cancer

models, clearly indicating its potential as a target for anti-cancer
therapeutics6–9.
Targeting c-MYC using traditional structure-based drug design
has remained challenging, due to its lack of a stable tertiary fold10.
Intrinsically disordered proteins (IDPs) like c-MYC exist in an
ensemble of conformational states, which facilitate their interaction
with multiple binding partners. This feature is illustrated by the
structural characterization of peptide derivatives from different
regions of c-MYC10. The amino-terminal residues 13–30 form a fuzzy
complex with the protein phosphatase 1 nuclear targeting subunits(PNUTS)11. Residues 55–68 predominantly adopt a polyproline II (PPII)

1

Department of Microbiology, Tumor and Cell Biology (MTC), Karolinska Institutet, Biomedicum, SE-17165 Stockholm, Sweden. 2Department of Biology,
Structural Biology and NMR Laboratory and the Linderstrøm-Lang Centre for Protein Science, University of Copenhagen, DK-2200 Copenhagen, Denmark.
3
Department of Biosciences and Nutrition, Karolinska Institutet, SE-14813 Huddinge, Sweden. 4Department of Cell- and Molecular Biology, Uppsala University,
SE-751 24 Uppsala, Sweden. 5Division of Translational Cancer Research, Department of Laboratory Medicine, Lund University, SE-221 00 Lund, Sweden.
6
These authors contributed equally: Dilraj Lama, Thibault Vosselman.
e-mail: dilraj.lama@ki.se; michael.landreh@ki.se; marie.arsenian.henriksson@ki.se

Nature Communications | (2024)15:18651

Article
conformation in complex with the tumour suppressor MYC boxdependent-interacting protein 1 (BIN1)12. Downstream residues 96–111
form an amphipathic alpha-helix when bound into the groove formedby the TATA binding protein (TBP) and the amino-terminal domain of
TBP-associated factor 1 (TAF1TAND1)13. A peptide fragment (residues
260–267) from the central region binds the chromatin-associatedtryptophan-aspartic acid (WD) repeat-containing protein 5 (WDR5) inan extended conformation by occupying a shallow hydrophobic clefton the surface of the protein14. Importantly, the carboxy-terminal
region (residues 351–439) of c-MYC dimerizes with its obligate binding
partner MYC-associated factor X (MAX) for DNA recognition by
adopting a basic-helix-loop-helix leucine-zipper (bHLHZip)
conformation15. Furthermore, the peptides derived from c-MYC andthe transformation/transcription domain-associated protein (TRRAP)
have been reported to undergo a disorder to order transition in theformation of a structurally-stable complex16. In addition, the structureof residues 61–89 from MYCN (a member of the MYC family) has been
revealed in complex with the protein kinase Aurora-A, in which theamino-terminus of the peptide adopts an elongated state followed byan alpha helix towards its carboxy-terminus17. Hence, the protein
requires speciﬁc local conformations to exert its biological activity: in a
dynamic ensemble, the disordered c-MYC would ﬂuctuate between
metastable “active” conformations for interacting with its binding
partners, and “inactive”, resting states not amenable for binding.
Molecular interventions that can stabilize or propagate such “inactive”
conﬁgurations would thereby have a signiﬁcant impact on its activity.
In fact, structural ensemble modulation of IDPs has been recognized asa useful strategy for their inhibition18,19. A comprehensive understanding of the structural features will be essential to design ligandsthat can efﬁciently drive the conformational transition of the target
proteins. However, most high-resolution structure determination
methods do not allow a detailed characterization of disordered protein states. Hence, a different conceptual framework is required fortheir structural exploration and targeting for therapy. This is exempliﬁed by that despite continuous efforts, the only promising drugcandidate in clinical trials to date is the OMOMYC peptide (clinicaltrials.gov identiﬁer: NCT04808362)20. This mini-protein is derived
from the bHLHZip domain of MYC with four rationally incorporated
mutations that renders the peptide to preferentially bind to MAX or toitself and will thus modulate MYC-mediated transcription by inhibiting
MYC:MAX dimerization and DNA-binding20,21.
In this work, we have taken a distinct approach to address thesechallenges and developed a peptide-based computational strategy toidentify a conformational switch in c-MYC. The transition samples
different structural conﬁgurations which are directly related to theassociation of c-MYC with proteins required for its transcriptional
activity, and thus present an opportunity for therapeutic intervention.
We have further employed probe-based molecular dynamics (MD)simulations, mass spectrometry, and cell assays to demonstrate thattargeting this conformational switch using a model compound inhibitsinteractions between c-MYC and its binding partners, establishing a
potentially druggable feature.

Results
c-MYC harbors a conditionally folded switch
The high conformational ﬂexibility of c-MYC makes it very challenging
to structurally characterize the native full-length protein. Hence, we
undertook a reductionist approach by dividing the protein into 50residue non-overlapping peptides resulting in nine derivatives(MYC1–50 to MYC401–439, Fig. 1a). Except for the last peptide, which is 39
residues in length, all other peptides consist of 50 residues. We thensubjected each peptide to all-atom MD simulations with ff19SB forceﬁeld and OPC water model (see Supplementary Information for
details), and calculated the radius of gyration (Rg) for the structuresgenerated through their independent microsecond simulations toNature Communications | (2024)15:1865

https://doi.org/10.1038/s41467-024-45826-7

create a probability distribution proﬁle (Fig. 1b, black curves). Analysisof these plots shows that the peptides can be classiﬁed into two majorgroups based on their nature and scale of Rg distribution. The group of
the ﬁve peptides spanning amino acids 1–50, 51–100, 151–200,
351–400, and 401–439 has narrow normal distributions roughly centered around a mean of 15 Å, suggesting that they are mostly compact
and have a low degree of ﬂuctuation. However, peptides coveringresidues 101–150, 201–250, 251–300, and 301–350 display variabletypes of distributions exhibiting a wider range of Rg values up to 35 Å
(Fig. 1b, black curves). This indicates that these peptides have a greater
ﬂexibility than the other parts of the protein and sample relatively
more extended states. Overall, the peptides derived from the c-MYC
protein exhibit variable sampling properties.
Mixed-solvent MD simulations of proteins is a technique applied instructure-based drug discovery for the identiﬁcation of cryptic ligand
binding pockets, fragment screening, location of hotspot interactions,and assessment of druggability22,23. We therefore chose to explore theinﬂuence of additives on the conformational distributions of the ninedifferent c-MYC peptides. We selected benzene as the co-solvent as it is
one of the most commonly used small organic probes in such
simulations24,25. All the mixed-solvent MD simulations were initiatedfrom the same starting structure in pure water with the addition of
0.4 M benzene. By comparing the resulting Rg distributions with wateronly based simulations, we mostly observed proﬁles that are qualitatively very similar with moderate variations between the two solvent
conditions (Fig. 1b, red curves). The only exception is the peptide covering residues 101–150. Here, the presence of the benzene probe has asigniﬁcant impact on the conformational sampling. This region undergoes a pronounced transformation from a wide Rg distribution range
from 5 to 35 Å to a narrow range between 5 and 15 Å with the mean at
12 Å. This clearly indicates that the peptide is much more compact in thepresence of benzene. The benzene probe-based peptide screening of
c-MYC was further expanded with eight additional 50-residue derivatives (MYC25–74 to MYC375–424) that overlap with the initial library of nine
peptides (MYC1–50 to MYC401–439; Supplementary Fig. 3a). The conformational distributions of the overlapping peptides indicate that theyshow relative transition towards a compact state to different degrees
with the addition of benzene (Supplementary Fig. 3b), but none of themundergo a signiﬁcant ensemble modulation between the two environments as was observed for MYC101–150.
Intrinsically disordered proteins (IDPs) are generally characterized by amino-acid compositions with high proportions of hydrophilic
and charged residues. To understand the divergent behavior of theregion between residues 101 and 150, we calculated the disorder andhydrophobicity scores of c-MYC. Notably, and as expected, we foundan inverse correlation between the two variables, i.e., a high percentage of c-MYC observed in disordered regions correlating withhydrophilic stretches (Fig. 1c). Interestingly though, the peptide
spanning amino acids 101–150, which is predicted to be ordered is also
the most hydrophobic segment in the protein. This observation iscorroborated with multiple algorithms, albeit with different scales(Supplementary Fig. 4). A closer examination shows that 50% of theresidues in this region are hydrophobic and bulky. This analysis highlights that the region is an epitope within the disordered c-MYC, which
does not possess a prototypical IDP sequence composition. We
explored a rationale for the conformational divergence induced bybenzene by computing the enthalpic energy (E) of all the simulated
structures in combination with their corresponding Rg values. To thisend, we generated a two-dimensional “Rg-E” distribution proﬁle forresidues 101–150 (Fig. 1d). It is evident from this plot that compact
conformations are distributed along the energy scale, ranging fromfavorable (low energy score) to unfavorable (high energy score), while
extended conformations are associated predominantly with unfavorable states. The bulky hydrophobic residues tend to shield themselvesfrom the polar environment, and hence drive the formation of the2Articlehttps://doi.org/10.1038/s41467-024-45826-7

c-MYC sequence
MYC1-50 MYC51-100

c

Frq

Water

TAD
MBI

Benzene

MYC351-400 MYC401-439

Conformational sampling

b 1.2

1.2

1.2

0.6

0.6

0.6

Disorder Score

MD simulations

25

0.0
35 5
1.2

0.6
0.0

15

25

35

0.6

5

15

25

35

0.0

0.0

5
1.2

15

25

0.6

0.2

15

25

35

0.0
5

1.2

1.2

1.2

0.6

0.6

0.6

0.0

0.0
35 5

0.0
35
5

0

15

25

15
25
Rg (Å)

100
200
300
Residue number

400
150

d 1.0
15

25

35

0.5

0.0
5

4

101

0.6
5

5

0.4

35

Energy Score

Frequency Score

15

6

0.8

0
0.0
5
1.2

DBD
MBIV bHLHZip

1

Rg

Complexity reduction

CR
MBII MBIII

Hydrophobicity Score

a

15

25

35

5

15

25
Rg (Å)

35

Fig. 1 | Identiﬁcation of a conformational switch in c-MYC. a Overview of the MD
strategy employed to identify regions that exhibit probe-induced conformationalchanges. b Normalized frequency distribution of Rg computed from the ensembleof structures generated from explicit solvent MD simulations in water (black) andwater + benzene (red) for all nine c-MYC derived peptides as indicated. c Disorder(VSL2 algorithm) and hydrophobicity (Miyazawa hydrophobicity scale) proﬁle ofc-MYC. The modular architecture (TAD: Transactivation Domain, CR: Central
Region, DBD: DNA Binding Domain) of c-MYC, MYC Boxes I–IV (MBI – IV) and thebHLHZip domain is shown at the top. The hydrophobic residues between positions
101 and 150 are highlighted in yellow background. d Rg-E (energy normalized in thescale from 0 to 1) distribution plots of coreMYC in water (black) and water +benzene (red) solvents. Representative models of the compact and extended statesof coreMYC from the ensemble of MD generated structures are indicated. The
hydrophobic residues and the benzene probes are shown as yellow and red
spheres, respectively.

hydrophobic collapsed state of the peptide. The addition of benzene
completely abolishes the extended conﬁgurations of the peptide. Thischange is due to interactions between benzene and the hydrophobic
residues, acting as a molecular “glue” that drives the equilibrium
towards the compact state (Fig. 1d). A comparative analysis of similar“Rg-E” scatter plots, along with the time-dependent evolution of Rg
across all the peptide derivatives of c-MYC show that except for anotable extent in MYC75–124, which overlaps with MYC101–150, theaddition of benzene does not induce a similarly pronounced transition
elsewhere in the protein (Supplementary Figs. 5 and 6). This distinct
feature of the epitope was further examined by subjecting the peptide
to mixed-solvent simulations with three different chemical probes,
which include propane (hydrophobic: aliphatic), methanol (hydrophilic: h-bond donor), and acetaldehyde (hydrophilic: h-bond acceptor). We ﬁnd that propane shifts the structural ensemble of the peptide
towards a compact conformation as observed for benzene (hydrophobic: aromatic), while the hydrophilic probes (methanol and acetaldehyde) marginally increase the population of the compact state, buthave no signiﬁcant impact on the epitope (Supplementary Fig. 7).
These data further underscores the distinct sequence composition of
the region within c-MYC, which makes it particularly susceptible toconformational modulation by certain molecular probes. We conclude
that the peptide covering residues 101–150, which we refer to as“coreMYC” (COnformational REgulator of c-MYC), is susceptible toligand-induced conformational changes.

viable small molecules that could induce similar conformational shifts
but are better suited for experimental validation. We selected Epigallocatechin gallate (EGCG), a polyphenolic compound that is abundant in green tea (Fig. 2a). Due to its high biocompatibility, EGCG is
considered a pro-drug with anti-cancer activity26. It is widely employed
as a non-speciﬁc inhibitor of protein aggregation27 and has more
recently been shown to modulate the conformational ensembles of
IDPs28, including the disordered transactivation domain (TAD) of p5329.
To investigate whether EGCG could elicit similar effects as thebenzene probe, we turned to Ion Mobility spectrometry in combination with native Mass Spectrometry (IM-MS). In native MS (nMS),
protein complexes are gently transferred from near-physiologicalsolutions to the gas phase using electrospray ionization (ESI), preserving their three-dimensional fold and non-covalent interactions. Massmeasurements of the desolvated, intact complexes can then be
employed to determine the stoichiometries of their components. Withion mobility separation (IM), the complexes are separated in a gasﬁlled cell according to their collision cross section (CCS), which isanalogous to the Rg, although the charging and desolvation processes
can cause additional conformational changes30. By combining IM with
nMS, we can monitor the conformational states of individual protein
populations, e.g., for protein-ligand complexes with varying stoichiometries. nMS of recombinantly produced coreMYC indicated thepresence of the peptide in its monomeric form with partial cleavage ofthe amino-terminal methionine as well as 1–4 sodium adducts (Fig. 2b).
Ion mobility analysis revealed the presence of several populations,representing extended, compact, and intermediate conformations,with the compact state, composed of two populations, being the least
abundant (Fig. 2c). We then added increasing concentrations of EGCG

EGCG binding induces compaction of coreMYC
The ﬁnding that the benzene probe induced compaction of coreMYC
in MD simulations led us to explore more soluble and physiologicallyNature Communications | (2024)15:1865

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Articlehttps://doi.org/10.1038/s41467-024-45826-7

ab Native mass spectrometry
4+

1

5+

2 EGCGc Ion mobility spectrometry

EGCG

4+

3 EGCG

200 uM

150 uM

100 uM

50 uM
Epigallocatechin gallate(EGCG)No EGCG

1500

2000
m/z

Energy Score

d

2

4

6

8

Arrival Time (ms)

e

1.0

0.5

0.0
5

coreMYC
coreMYC + 1 EGCG
153525
Rg (Å)

Energy Score

1.0

0.5
coreMYC
coreMYC + 2 EGCG

0.0
5

15

25

35

Rg (Å)
Fig. 2 | Characterization of the interaction between coreMYC and EGCG. a The
chemical structure of EGCG. b Native mass spectra of coreMYC in the presence ofincreasing concentrations of EGCG. The observed charged states and number of
EGCG molecules bound to coreMYC are indicated. c Arrival time distributions forthe 4+ charge state of coreMYC in different concentrations of EGCG. d Rg-E (energy
normalized in the scale from 0 to 1) distribution plots of coreMYC generated fromMD simulations of the peptide with one (upper panel) or two (lower panel) EGCG
molecules (red), respectively. Rg-E distributions of coreMYC in water (black) is

shown for reference. e Representative structures from MD simulations of coreMYC
bound to one (left) or two (right) EGCG molecules, respectively. The residues from
coreMYC with contact score >1 with EGCG are shown in spheres and the EGCG is
depicted in stick representation (green). See Supplementary Figs. 9e, f and 10e, f for
more details on the residues. Side-chain carbon atoms of hydrophobic residues areshown in yellow and hydrophilic residues in orange. Oxygen and nitrogen atoms
are shown in red and blue respectively.

and monitored the interaction by nMS. Binding of one EGCG molecule
to coreMYC was readily observed at an EGCG concentration of 50 μM.
At concentrations of 100 μM and above, we found two EGCG adductswith the third adduct observed at the highest tested concentration of
200 µM (Fig. 2b). Plotting the signal intensity of the 1:1 complex as afunction of EGCG concentration showed no apparent saturation within
the concentration regime that could be tested by nMS, indicating nonspeciﬁc binding (Supplementary Fig. 8a)31. Strikingly, IM-MS analysis of
the complexes between EGCG and coreMYC showed a strong shift inconformations from zero to two bound molecules; binding of one
single EGCG compound almost completely depleted the most extended population, while binding of two EGCG molecules resulted in analmost exclusively compact state (Fig. 2c). For the 5+ charge state, we
furthermore observed an increase in compact coreMYC withoutbound EGCG, which indicates that some of the peptide-ligand

complexes dissociate during the transfer to the gas phase (Supplementary Fig. 8b).
To gain more insights into the structural effects of EGCG on
coreMYC, we conducted MD simulations of the peptide in the presence of one or two EGCG molecules. The Rg-E distribution plots fromthe simulations clearly show that the peptide is predominantly in thecompact conformation (Fig. 2d), analogous to the benzene mixedsolvent simulations (Fig. 1d). This conformational landscape is consistently observed in both replicates for each of the individual systems(Supplementary Figs. 9a, b, 10a, b and 11). To directly correlate theseﬁndings with experimental data, we computed the theoretical CCS
values for the compact states of coreMYC in the MD simulations using
IMPACT32 and compared them to the experimentally determinedTW
CCSN2 values of coreMYC:EGCG complexes. Indeed, theexperimental TWCCSN2 values of 820, 825, and 897 Å2 are in reasonable

Nature Communications | (2024)15:1865

4

Articleagreement with the theoretical CCSs of 820, 881, and 923 Å2, respectively for zero, one, or two EGCG molecules bound. We additionallysought to validate the effect of EGCG on coreMYC using solution
nuclear magnetic resonance (NMR) spectroscopy. However, the chemical shift perturbations observed in coreMYC during EGCG titrations
could not be separated with sufﬁcient conﬁdence from potential
titration artefacts. The data has therefore not been included inthis study.
Lastly, to obtain a detailed model for EGCG-induced compactionof coreMYC, we compared the IM-MS data to the contacts between
EGCG and coreMYC from simulation trajectories. The MD data indicate a dynamic mode of association with multiple residues involvedin the interaction. However, the speciﬁc residues that are involved in
the recognition are not exactly identical and could vary between
replicates (Supplementary Figs. 9c–f and 10c–f). This indicates (aswas also deduced from the nMS measurements) that EGCG associateswith coreMYC in a non-speciﬁc manner, which is further highlighted
with the multiple bound conformations observed from clustering
based on the residues that form maximum contact during the MD
simulations (Fig. 2e; Supplementary Figs. 9g, h and 10g, h). It isinteresting to note that in the 1:1 complex, EGCG is wedged into analmost cavity-like structure, but associates in distinct regions of
the peptide in the 1:2 complex. These conformations are observed tobe relatively stable (Supplementary Fig. 12), which indicate that they
are potential representatives of the multiple metastable complexes
formed during the dynamic interaction between EGCG and coreMYC.
In summary, the combination of MS, and MD provide compelling
evidence at the molecular level that EGCG associates with coreMYC
in a heterogenous manner leading to the induction of a compact
conformation.

Conformational switching of coreMYC regulates access to
MYC Box II
The c-MYC protein is characterized by the presence of at least four
highly evolutionary conserved sequence motifs termed MYC Box I – IV
(MBI – IV), where MYC Box II (MBII; residues 128–143) is present within
coreMYC (Fig. 1c). This motif serves as a binding site for multiple
proteins that regulate the transcriptional activity of c-MYC2,33,34; yet
how these interactions are controlled in the disordered c-MYC is notknown. We therefore asked whether the conformational transitions of
coreMYC between compact and extended states could translate into
regulating the interactions of MBII with its binding partners. For thispurpose, we created a dataset of eighteen direct interactors of c-MYC(Supplementary Table 1), which have been experimentally characterized to recognize the protein either through MBII or other parts of theamino-terminal region which includes coreMYC33,34; and turned toAlphaFold2 (AF2), which besides predicting highly accurate protein
structures35,36, has also been demonstrated to be an excellent tool for
peptide: protein docking37,38. We examined its utility by predicting
complex structures from sequences from all structurally resolved
c-MYC derived peptides bound to different interactors (Supplementary Fig. 13a–e). The accuracy of the modeled complexes varied todifferent degrees (ipTM score 0.3–0.9), however and more importantly for our study, the conformation (measured in Rg) of the interacting c-MYC peptides showed a good agreement for all complexes
between the experimental and the AF2 structures (Supplementary
Fig. 13f). Based on this benchmarking exercise, we then modeled
coreMYC in complex with the eighteen selected partner proteins(Supplementary Fig. 14) and computed the Rg of the peptide. Comparing the conformational distributions of coreMYC in each complex
and in MD simulations shows that the peptides have a notably higher
Rg when bound to a partner protein (Fig. 3a). Inspection of the residuewise binding energy contribution involved in the predicted complexes
further demonstrated that coreMYC interacts preferentially via
hydrophobic residues (Fig. 3b), and the compaction of coreMYC is

Nature Communications | (2024)15:1865

https://doi.org/10.1038/s41467-024-45826-7

driven by hydrophobic collapse (Fig. 1d). This ﬁnding suggests that the
peptide has to adopt an extended, open conformation to regulate
access to MBII in order to recognize its binding partners.
To test this observation experimentally, we selected the adapter
protein TRRAP, a critical and well characterized cofactor of c-MYC34,39.
The minimal binding region of TRRAP with c-MYC (residues
2033–2088) has been described to undergo a disorder-to-order transition when complexed with MBII16. We investigated the complex formation of coreMYC and a peptide from TRRAP spanning amino acids
2038–2087 (TRRAP2038–2087) using nMS. Under near-physiologicalsolution conditions, both peptides have well-resolved spectra showing
only monomeric species (Fig. 3c). Mass spectra of an equimolar
amount of coreMYC and TRRAP2038–2087 show peaks corresponding inmass to a 1:1 complex (Fig. 3c; Supplementary Fig. 15). Importantly, noother stoichiometries were observed, suggesting a speciﬁc association. To test the effect of EGCG on this interaction, we brieﬂy incubatedcoreMYC with 150 μM EGCG prior to the addition of TRRAP2038–2087
and recorded native mass spectra. Strikingly, the presence of EGCG
completely inhibited complex formation. Instead, we observed binding of up to two EGCG molecules to both peptides (Fig. 3c). We conclude that association with EGCG inhibits the ability of coreMYC tointeract with TRRAP2038–2087.
To elucidate whether the inhibitory effect of EGCG is due tocompaction of coreMYC, we analysed the AF2 predicted structures of
coreMYC:TRRAP2038–2087 complex. The AF2 model suggests an elongated and predominantly helical structure of the two peptides in thecomplex state (Supplementary Fig. 16). Interestingly, similar peptide
derivatives from c-MYC and TRRAP has been shown to form a structurally stable complex with high helical content16. The highest ranked
model from the predicted structures was then subjected to multi-copy
MD simulations. Binding energy calculations from the replicates showthat the hydrophobic residues in both peptides contribute themajority of energetically favorable interactions (Fig. 3d, e; Supplementary Fig. 17), indicating that the association is predicted to be
driven by hydrophobic interaction. Importantly, residue W135 from
coreMYC, which has been reported to be involved in the recognition of
TRRAP16, is part of the hydrophobic interaction network in the modeled complexes (Fig. 3d, e; Supplementary Fig. 17). We also created insilico glycine mutant derivatives of W135 as well as other hydrophobic
residues in the MBII motif of coreMYC and generated AF2 models of
their complexes with TRRAP2038–2087. The binding energy computedfrom the highest ranked models indicate that the potency of interaction with TRRAP2038–2087 decreases with the replacement of hydrophobic residues (Supplementary Fig. 18). Furthermore, from MD
simulations, we conclude that coreMYC engages in nonspeciﬁc interactions with EGCG (Fig. 2e; Supplementary Figs. 9e–h and 10e–h) that
reduce its ability to interact with TRRAP2038–2087.
Next, having observed that EGCG inhibits complex formation of
coreMYC with the disordered TRRAP2038–2087 peptide, we also
investigated its impact on the binding of the epitope to a folded
protein. For this purpose, we selected TBP, an integral component of
the multimeric transcription factor IID (TFIID) protein assembly40.
nMS of full-length, human TBP showed the formation of 1:1 complexes with coreMYC (Fig. 3f), as anticipated from structural andbiochemical studies, which have shown direct association of residues
96–125 (the amino terminal half of coreMYC) with a binary complex
of TBP and TAF113. Pre-incubation of coreMYC with a 10-fold excess of
EGCG almost completely abolished complex formation, conﬁrmingthat EGCG indeed interferes with the interaction by affecting coreMYC directly. A modeled complex of coreMYC with human TBP(Fig. 3g) shows that the recognition occurs in an extended conformation predominantly through the hydrophobic residues present
in this region (Fig. 3h; Supplementary Fig. 19), which are also involvedin EGCG binding and compaction of coreMYC (Fig. 2d, e and Supplementary Figs. 9–11).
5

Articlehttps://doi.org/10.1038/s41467-024-45826-7

a Free coreMYC (MD)bBound coreMYC (AF2)1.0
20hydrophobichydrophilic

PIM1

SKP2

0.8
Energy Score

Rg (Å)

18
16
14

BAF53

0.4

SNIP1
0.2

12
10

0

0
1
Frequencyc

0.6

Residues

coreMYC

4+

10dcoreMYCe
0
Energy (kcal/mol)

TRRAP
TRRAP

4+

6+

coreMYC + TRRAPResidues
V102
M110
I128
F138
M101
L105
F124
W135
L149

-2
-4
-6
-8
ResidueshcoreMYC + TRRAP+ EGCG

0

1250

1500

1750

Energy (kcal/mol)

1000

2000

m/z

f

TBP

L105

12+

g
coreMYC + TBP

coreMYC + TBP+ EGCG

L106

I116
F115

F124
C117

-2
-4
-6
-8

21+

13+

V102

M110

TBP

12+

13+
coreMYC

2600

3000

3400
m/z

3800

4200

Fig. 3 | Active conformation of coreMYC and inhibition of coreMYC interactions. a Rg values of coreMYC computed from AF2 modeled complexes with its
binding partners mapped with the Rg distribution obtained from MD simulations(insert to the left). b Cumulative residue-wise binding energy score (normalized in
the scale from 0 to 1) of coreMYC in the bound complexes. Representative AF2modeled complexes are shown with coreMYC rendered as orange cartoon and thebinding partners in gray surface (see Supplementary Fig. 14 for the complete set ofmodeled complexes). c Native mass spectra of coreMYC (orange circles),
TRRAP2038–2087 (violet circles), coreMYC with TRRAP2038–2087, and coreMYC with
TRRAP2038–2087 after pre-incubation with EGCG. The zoom region from m/z
1800–2000 shows the 1:1 coreMYC:TRRAP2038–2087 complex formed in the absenceof EGCG. d Representative structure of the complex between coreMYC (orange
ribbon) and TRRAP2038–2087 (blue surface) from MD simulations initiated from theAF2 generated model. Residues from coreMYC that contribute signiﬁcantly tobinding (≤ −2 kcal/mol) are rendered as yellow spheres. e Residue-wise binding

Nature Communications | (2024)15:1865

energy contribution from coreMYC. Only residues with energetic contribution ≤ −2
kcal/mol is shown. Data are represented as Average ± S.D. computed withn = 1000 simulated structures. See Supplementary Fig. 17 for the complete set ofenergetic contribution from both peptides. f Native mass spectra show that humanfull-length TBP (brown circles) forms a 1:1 complex with coreMYC (orange circles),Pre-incubation of coreMYC with EGCG suppresses complex formation (arrows).
g Representative model from MD simulation of the ﬁrst 24 residues from coreMYC(residues: 101–124, orange ribbon) and the core domain of human TBP (brownsurface). Residues from coreMYC that contribute signiﬁcantly to binding (≤ −2 kcal/mol) are rendered as yellow spheres. h Residue-wise binding energy contribution
from coreMYC. Data are represented as Average ± S.D. computed withn = 1000 simulated structures. Only residues with energetic contribution ≤ −2 kcal/
mol is shown. See Supplementary Fig. 19 for the complete set of binding energy
contribution from coreMYC.

6

Articlehttps://doi.org/10.1038/s41467-024-45826-7

25 um

30

20

10

20

10

25 um

0

G
EG
C

R

A
P

-T
-T

YC
M

-T

M

YC

M

YC

YC

-M

A

R

X

R

A

P

PC

G

B

M

P

YC

EG

-T

X
A
-M
YC
25 um

M

Untreated

25 um

d

e

uM EGCG

25 um

25 um

25 um

25 um

25 um

25 um

20
ns

10

55

0

25

50
MYC
TBP
MAX
β-actin

430

TRRAP

55

α-tubulin

A

-M

25 um

10

25 um

M

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YC
M
25 um

YC

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M

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kDa
70
55
25

-M

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YC
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-M
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C

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PLA signals/nucleus

30

25 uM EGCG

c-MYC-TBP

25 um

C

B

P

PC

0

R

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c
PLA signals/nucleus

b 30

Merge

M

DAPI

c-MYC-MAX

PLA signal

PLA signals/nucleusaFig. 4 | EGCG inhibits interactions of c-MYC with TBP and TRRAP in cancer cells.
a Representative images of isPLA for c-MYC in SH-SY5Y cells from n = 3 biologicallyindependent experiments. Top row: PLA positive control of c-MYC: MAX complex(green) co-localized with nuclear DAPI staining (blue). Middle row: PLA signal for cMYC: TBP interaction (green) and nuclei (blue) in untreated cells. Insert: Enlargement of representative cells showing nuclear PLA signal (arrow heads). Bottom row:
PLA for c-MYC: TBP complex (green) in cells treated with 25 µM EGCG shows areduced nuclear signal for the complex formation compared to control. Insert:
Enlargement of representative cells with weak or no nuclear PLA signal (arrowheads). Yellow indicates merge between respective PLA signal with DAPI. Scale bar
is 25 µm. b Quantiﬁcation of nuclear c-MYC:TBP signal (n ≥ 10 samples/biologicalexperiment) in untreated and 25 µM EGCG treated cells showing reduced PLA signal
compared to control; n = 3 biologically independent experiments. MYC-MAX(positive control, PC) versus MYC-TBP 0 µM EGCG p = 0.0190; PC versus MYC-TBP
25 µM EGCG p = <0.0001. c Quantiﬁcation of nuclear c-MYC:TRRAP signal in
untreated and 25 µM EGCG treated cells showing reduced PLA signal compared tocontrol; n = 3 biologically independent experiments. PC versus MYC-TRRAP 0 µM
EGCG p = < 0.0001; PC versus MYC-TRRAP 25 µM EGCG p = < 0.0001.
d Quantiﬁcation of nuclear c-MYC:MAX signal in untreated and EGCG-treated cells
shows that interaction is not reduced with 25 µM EGCG; n = 3 biologically independent experiments. MYC-MAX 0 µM EGCG versus MYC-MAX 25 µM EGCG
p = 0.0882 (n.s.), versus MYC negative control (MYC NC) p = < 0.0001. n.s. = notsigniﬁcant. *p = < 0.05 and ****p = < 0.0001 (Statistics for b–d were run using oneway ANOVA, Dunnett’s multiple comparison test with a single pooled variance).
e Western blot analysis shows that MYC, TBP, TRRAP, and MAX levels are unaffected by EGCG treatment. β-Actin is used as loading control for MYC, TBP, andMAX, and α-tubulin for TRRAP. Molecular weight markers (kDa) are indicated to theleft. One representative blot is shown out of n = 3 biologically independent
experiments (quantiﬁcation is presented in Supplementary Fig. 21d). For western
blot and PLA assays, the samples were derived from the same biological replicate
and processed in parallel.

Based on these ﬁndings, we conclude that the extended state of
coreMYC with exposed hydrophobic residues constitutes an active
recognition module that enables coreMYC interaction with its binding
partners, while the compact conformation represents an inactive stateof the peptide.

with up to 25 μM EGCG, beyond which we observed a dose-dependent
decrease in survival, as judged by live-cell imaging (Supplementary
Fig. 20a). Similarly, analysis with the WST1 assay showed only a minor
decrease in metabolic activity in the presence of 20 μM EGCG (Supplementary Fig. 20b). These ﬁndings indicate that although higher
concentrations of EGCG are toxic to the cells, treatment with 25 μM
EGCG is well-tolerated. We therefore focused on the signals from theisPLA assay in the presence of 25 μM EGCG, and found a substantial
loss of PLA signals from nuclear c-MYC:TBP complexes at this EGCG
concentration as compared to untreated cells (Fig. 4b). We next analyzed whether EGCG could also disrupt the complex between c-MYC
and TRRAP. Our data show that a small fraction of TRRAP was engaged
in a direct nuclear interaction with c-MYC, and this binding wasinhibited in the presence of 25 μM EGCG (Fig. 4c). Lastly, we examined
whether EGCG would also affect the carboxy-terminal bHLHZip interaction between c-MYC and MAX. Importantly, we found that treatment
with EGCG did not affect the amount of nuclear PLA signals for c-MYCMAX complexes, supporting the idea that EGCG predominantly affects
interactions involving MBII (Fig. 4d).
The robustness of the assay was also demonstrated in control
experiments where no PLA signal was observed in the negative control(with only c-MYC antibody; Supplementary Fig. 21a), while the positive

EGCG prevents coreMYC dependent recruitment of TBP and
TRRAP in cancer cells
Having established that the conformation of coreMYC regulates
binding of target proteins and can be modulated by EGCG, we sought
to explore the basic mechanism in a cellular context. To this end, wemonitored the protein–protein interaction (PPI) of c-MYC with TBP
and with TRRAP using in situ proximity ligation assays (isPLAs) in thehuman neuroblastoma cell line SH-SY5Y, which constitutively overexpresses c-MYC and is a well-established model for c-MYC activity intumorigenesis41,42.
The isPLA showed a distinct signal for c-MYC:TBP complexes
predominantly in the nucleus but also in the cytoplasm (Fig. 4a). Next,
we analysed whether this complex formation could be disrupted byexposure to EGCG. To this end, cells were treated with EGCG concentrations ranging from 0 to 100 µM over a 24 h period. The SH-SY5Y
cells did not display any signiﬁcant change in viability when treated

Nature Communications | (2024)15:1865

7

Article

Closed, inactive
Open, active

control, measuring the interaction between c-MYC and MAX, showed astrong signal inside the nuclei in the absence of EGCG (Fig. 4a).
Quantiﬁcation of the PLA signals showed a dose-dependent reductionof nuclear c-MYC:TBP and c-MYC:TRRAP complexes with increasing
EGCG concentration (Fig. 4b, c; Supplementary Fig. 21). In order toverify that the reduction in PLA signals was not due to reduced protein
levels in the cell, we analysed the expression of c-MYC, MAX, TBP, andTRRAP after EGCG incubation using Western blot. Cells were treatedwith 0–50 μM EGCG and protein expression was analyzed after 24 h(same conditions as for the isPLA). We found that treatment with 25 µM
EGCG did not affect expression of any of the studied proteins (Fig. 4e;
Supplementary Fig. 21d). Furthermore, EGCG did not affect c-MYC
mRNA levels (Supplementary Fig. 21e). Thus, these data conﬁrm thatthe observed reduction in PLA signals is due to inhibition of interaction
between c-MYC:TBP and c-MYC:TRRAP and not due to reduced protein expression. Taken together, our ﬁndings indicate that ECGC efﬁciently disrupts c-MYC:TBP and c-MYC:TRRAP complex formation incancer cells.

https://doi.org/10.1038/s41467-024-45826-7

c-MYC

TBP

EGCG

TRRAP

Discussion
Here, we have used a hybrid MD-MS strategy to identify a conformational switch region within the structurally protected amino-terminal
TAD of c-MYC43. The peptide termed “coreMYC” includes the functionally important MBII motif and switches between an active, extended state and a compact, inactive conﬁguration that can be induced by
the biocompatible ligand EGCG. Our study provides a blueprint for theidentiﬁcation and characterization of similar ligand-binding interfaces
in other IDPs regarded as “undruggable”.
Small molecule inhibitors of IDPs are generally more hydrophobic
and aromatic than those that target folded proteins44. EGCG belongs to
the family of polyphenols45 and possesses the necessary pharmacophoric properties to interact with disordered proteins. It has in fact
been reported to bind and modulate the aggregation properties of
IDPs such as α-synuclein46, Aβ47, and tau48 by inducing the formation of
structural states that prevent the formation of toxic assemblies. In
addition, it was recently shown to interact with the disordered aminoterminal TAD of the tumor suppressor p53 and to induce a compact
conformation, which prevented its interaction with MDM229. c-MYC
has been shown to be downregulated by EGCG targeting β-cateninsignaling49, though any direct association between this molecule andc-MYC has not been previously demonstrated. Here, by employing a
combination of MD simulations and IM-MS, we show that EGCG
interacts at multiple sites in the coreMYC peptide and drives theconformational landscape towards a more compact conﬁguration that
affects the accessibility of MBII, a major interaction site for binding
partners (Fig. 5).
The ﬁnding of the apparent “speciﬁcity” in which EGCG disrupts
complexes of MYC with TBP and TRRAP, but not with MAX appears
surprising, considering the generally non-speciﬁc nature of EGCG
binding. Even at high concentrations, EGCG engages <50% of the
coreMYC population, yet completely abolishes binding to TBP and
TRRAP, as judged by nMS. To better understand the basis for theseobservations, we considered the nature of MBII. MD analysis showsthat MBII is mostly unstructured, indicating enhanced plasticity whichenables a variety of binding modes that are a hallmark of fuzzy complexes. Fuzzy complex formation is a stochastic process that is controlled by the conformational landscape of disordered protein
regions50. A prominent example is the TAD of p53, whose interactions
with its folded binding partners are strongly dependent on transient
structural ﬂuctuations of a 10-residue disordered motif51. As shown
here and elsewhere29, EGCG modulates the conformations of disordered proteins through non-speciﬁc, often short-lived, contacts.
These interactions are nevertheless sufﬁcient to induce an imbalancein the conformational landscape of MBII that disrupts interactions with
TBP and TRRAP2038–2087. The non-speciﬁc nature of the interactions
Nature Communications | (2024)15:1865

Fig. 5 | Schematic representation of the proposed mechanism of action for
coreMYC and its modulation by EGCG. The inactive state of coreMYC adopts acompact conformation via contacts between hydrophobic residues (yellow
spheres), while the active extended state with exposed hydrophobic residues
enables binding to c-MYC interactors that recognizes this region of c-MYC TAD
such as TBP (brown) or TRRAP (blue). EGCG stabilizes the compact state of
coreMYC by interacting with the hydrophobic residues in a non-speciﬁc manner,
inhibiting the formation of an open state and thus its interaction with binding
partners.

implies that EGCG may also affect complex formation by changing theconformation of TRRAP2038–2087. MAX, on the other hand, binds to thebHLHZip motif, which is more structured and therefore less likely to beaffected by EGCG.
The most commonly used pharmacological intervention strategy
for IDPs or proteins with intrinsically disordered regions has been totarget their ordered binding partners, inhibiting their interactions
based on conventional structure-based ligand design52–54. Alternatively, ligands that can directly interact and modulate the structure
ensemble distribution of IDPs and thereby impede their interaction
with endogenous binding partners is promising but largely unexploredarea for targeting these challenging proteins. The identiﬁcation andmodulation of switch regions, such as the c-MYC TAD (as reported inthis study) and p53 by EGCG are attractive developments for thisstrategy. In a similar manner, the small molecule SJ403 which displaces
the disordered D2 domain of p27 from CDK2 has been shown tointeract with D2 by causing a shift in its conformation55. Based on theseobservations, it is tempting to speculate that additional IDPs containconformational switches that regulate access to binding sites. The
major challenge with this approach, however, is the characterization of
IDP-ligand interactions in fuzzy complexes, which can be dynamic withmultiple binding sites. We believe that the application of probe-based
MD simulations and IM-MS will provide an efﬁcient framework for
future investigations.
Finally, major efforts in the development of drugs that target
c-MYC have focused on impeding the dimerization of c-MYC and MAX,
either directly, or by preventing the interaction of the heterodimer
with DNA56. A promising development in this regard has been the entry
into clinical trials of the OMOMYC mini-protein20,21, which is derived
from the carboxy-terminus of c-MYC. The conformational switch
identiﬁed in this study is located at the amino-terminal TAD of c-MYC,
which is known to interact with multiple binding partners importantfor its transcriptional and oncogenic activities. In this context, we
8Article
provide a proof of principle that ligands can induce conformationalchanges in c-MYC, opening interesting avenues for the development of
c-MYC based therapeutics. Despite the lack of speciﬁcity of EGCG
binding, the insights gained from our investigation provide a potential
opportunity for rational design of more speciﬁc and stronger binders
to c-MYC. We envision that adding functional groups to the EGCG
scaffold that speciﬁcally interact with key residues in the “pseudocore” and increase its stability would be one potential approach. Our
efforts in evoking a conformational switch hold promise for further
studies aiming at identifying inducers of the inactive state of c-MYC.
Our work also opens the prospects to employ similar strategies fortargeting other desirable but challenging IDPs for therapeutics.

Methods
Modeling c-MYC peptide derivatives and system setup for MD
simulations
The initial structure of all the c-MYC peptide derivatives was generated
in an extended conformation using the TLEAP module of AMBER 1857.
The amino-terminus was acetylated and the carboxy-terminus wasamidated. All structures were subjected to a short MD simulation in
vacuum for 200 ps and then placed in the center of a truncated octahedral box whose dimension was ﬁxed by setting a minimum distanceof 8 Å between any peptide atom and the box boundaries. The force
ﬁeld parameters of probe molecules (benzene, propane, methanol,
and acetaldehyde) and EGCG with bcc charges was derived using the
GAFF2 force-ﬁeld58 through the ANTECHAMBER module of AMBER 18.
The appropriate number of probe molecules equivalent to 0.4 M
concentration was computed based on the volume of the simulationbox (Supplementary Table 2). The program packmol59 was used togenerate the initial distribution of the probe molecules within a sphereof radius 35 Å from the center-of-mass (COM) of the peptides. The
coreMYC peptide (encompassing amino acids 101–150) system with
one or two EGCG molecule was also generated with packmol by placing EGCG within a spherical distance of 15 Å from the COM of thepeptide. The volume of the simulation box was kept identical for thesystems with free peptides, peptides+probes, and peptide+EGCG.

Molecular dynamics simulations
The OPC water model60 was used for solvation and the net charge ofthe different peptides was neutralized by adding appropriate numbersof chloride/sodium counterions. MD simulations was carried out using
the PMEMD module of AMBER 18 employing ff19SB force-ﬁeld
parameters61. All the systems were initially relaxed through energy
minimization using steepest descent followed by conjugate gradientalgorithms. They were then heated to 300 K over 30 ps and equilibrated for 200 ps under NVT and NPT ensembles respectively. Production dynamics was run under NPT conditions for 5 µs each for all
the peptides in pure water and mixed solvent with the probe molecules, amounting to a total cumulative simulation time of 195 µs. The
two systems of coreMYC with one or two EGCG molecules were also
simulated for 5 µs each in duplicates. Simulation temperature of 300 K
and pressure of 1 atm was maintained using Langevin dynamics62(collision frequency: 1.0 ps−1) and weak-coupling63 (relaxation time:
1 ps), respectively. Periodic Boundary Conditions (PBC) were appliedon the primary simulation box and long range electrostatic interactions were computed using Particle Mesh Ewald (PME) method64. All
the bonds involving hydrogen atoms were constrained using SHAKE
algorithm65. The equation of motion was solved with an integration
time step of 4 fs after applying the hydrogen-mass repartitioning66.
The nine peptides (MYC1–50 to MYC401–439) were also subjected to MD
simulations with ff14SB67 and ff14IDPSFF68 force-ﬁelds after solvation
with TIP3P69 water model. These peptides were simulated for 1µs eachfollowing the same protocol and parameters as described above. The
four representative coreMYC: EGCG complexes (SupplementaryFigs. 9g, h and 10g, h) were used as starting structures for additional

Nature Communications | (2024)15:1865

https://doi.org/10.1038/s41467-024-45826-7

MD simulations. These complexes were placed in the center of atruncated octahedral box whose dimensions were ﬁxed by maintaining
a minimum distance of 10 Å between any protein atom and the box
boundaries. They were then solvated with OPC water model, neutralized by adding 5 Na+ counter ions, energy minimized, heated andequilibrated with the same parameters as described above. The ﬁnal
production dynamics was done for 100 ns in ﬁve replicates for each of
the four complexes.

Modeling with AlphaFold2
The Alphafold2 (AF2) modeling in this work was done through thejupyter notebook for Colabfold (version 1.3.0)70. The MSA_mode was set
to MMseqs2 (UniRef+Environment), pair_mode selected as “unpaired+paired”, model type employed was “AlphaFold-multimer-v2” with threenumber of recycles, no template information was used and amberrelaxation of models was disabled except in the case of coreMYC:
TRRAP2038–2087 complex. The input sequences for the modeling of
c-MYC peptide derivatives in complex with PNUTS, BIN1, MAX, TBP, andWDR5 were obtained from the PDB “FASTA sequence” ﬁle of eachstructure. In case of generating the bound state conformation of coreMYC with the selected binding partners (Supplementary Table 1), thesequence of the isoform which has been described as canonical in the
Uniprot database (Uniprot ID is shown in Supplementary Table 1) is used
for model building. The sequence of the residues from 2038–2087 of
TRRAP was used for predicting the peptide complex of coreMYC with
TRRAP2038–2087 and its glycine mutants (sequences shown in Supplementary Fig. 18). All the modeled complexes were ranked by multimer
and the highest ranked model was selected as a representative structure
for subsequent analysis.

MD simulation of coreMYC: TRRAP2038–2087 dimer
The highest ranked AF2 generated complex models of coreMYC with
TRRAP2038–2087 and its glycine mutant derivatives were used as starting
structures for MD simulations and energy minimization respectively.
These complexes were placed in the center of a truncated octahedralbox whose dimensions was ﬁxed by maintaining a minimum distance of
6 Å between any protein atom and the box boundaries. The amino- andcarboxy-terminal ends were acetylated and methylated respectively,
solvated with OPC water model and, the net charge of the system wasneutralized by adding one Na+ counter ion. The energy minimization of
coreMYC: TRRAP2038–2087 mutants and MD simulation of coreMYC:
TRRAP2038–2087 was performed using the same protocol and parameters as described above. The ﬁnal production dynamics of coreMYC
with TRRAP2038–2087 was run for 1 µs in duplicate.

Molecular modeling and MD simulation of coreMYC: TBP
complex
The complex model of initial 24 amino acids of coreMYC (residues
101–124) and the core domain of human TBP (residues 159–337: uniport ID: P20226) was built guided by the structural and biochemical
data reported by ref. 13. The modeled structure was then placed in thecenter of a truncated octahedral box whose dimensions was ﬁxed bymaintaining a minimum distance of 6 Å between any protein atom andthe box boundaries. The amino- and carboxy-terminal ends wereacetylated and methylated respectively, solvated with OPC watermodel and the net charge of the system was neutralized by adding 8 Clcounter ions. The simulation protocol and parameters were similar toas described above. A harmonic positional restrain of 5 kcal/mol wasapplied to all the backbone (N, CA, C, O) heavy atoms. The ﬁnal production dynamics was run for 500 ns in duplicate.

Computation of enthalpic and binding energies
The enthalpic energy of the seventeen c-MYC peptide derivatives
and the residue-wise binding energy decomposition of
coreMYC:TRRAP2038–2087 and coreMYC:TBP structure ensemble
9Articlehttps://doi.org/10.1038/s41467-024-45826-7

generated from MD simulations were computed using Molecular
Mechanics/Generalized Born Surface Area (MM/GBSA) method71 using
the following equations:
ΔGBinding = ½Gcomplex  ½GpeptideA  ½GpeptideBð1Þ

G = Ebon + Evdw + Eele + Epol + Enpol

ð2Þ

Where, Ebon is the summation of bond, angle, and dihedral energy
components, Evdw and Eele are the non-bonded Van der Waals andelectrostatic interaction energies respectively. These energies arecalculated using Molecular Mechanics (MM) force ﬁeld expressions of
AMBER. Epol is the polar solvation energy which is obtained by solving
the Generalized Born (GB) solvation model. Enonpol is the non-polarsolvation energy estimated through an empirical linear relationship(γ*SASA), where “γ” is the surface-tension and “SASA” is the Solvent
Accessible Surface Area. For these calculations, 5000 (for the
peptides) and 1000 (for the complexes) structures covering the entire
simulation period were extracted, and the explicit solvent molecules
were stripped. An implicit GB model (IGB = 2) was then used to model
the continuum solvent environment. The dielectric constant for thesolvent was set to 80, γ = 0.0072 kcal/mol/Å2 and salt concentration
was set to 150 mM respectively. The MMPBSA.py script72 available
through AMBER 18 was employed for computation.

Peptide sequences of coreMYC and TRRAP2038–2087
MGHis6-coreMYC (expressed and puriﬁed in-house)
MGHHHHHHMVTELLGGDMVNQSFICDPDDETFIKNIIIQDCMWSGFSAAAKLVSEKLATRRAP2038–2087 synthetic peptide (produced by GenScript, Rijswijk, Netherlands)
Ac-GVNSVSSSIKRGLSVDSAQEVKRFRTATGAISAVFGRSQSLPGADS
LLAK-NH2

Recombinant human TATA binding protein (ab81897, Abcam,
Cambridge, UK)
MDQNNSLPPYAQGLASPQGAMTPGIPIFSPMMPYGTGLTPQPIQNTNSLSILEEQQRQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQ
QQQQAVAAAAVQQSTSQQATQGTSGQAPQLFHSQTLTTAPLPGTTPLYPSPMTPMTPITPATPASESSGIVPQLQNIVSTVNLGCKLDLKTIALRARNAEYNPKRFAAVIMRIREPRTTALIFSSGKMVCTGAKSEEQSRLAARKYARVVQKLGFPAKFLDFKIQNMVGSCDVKFPIRLEGLVLTHQQFSSYEPELFPGLIYRMIKPRIVLLIFVSGKVVLTGAKVRAEIYEAFENIYPILKGFRKTTExpression of coreMYC
The gene encoding MGHis6-coreMYC was cloned into a pET-26b(+)
vector (GenScript, Rijswijk, The Netherlands). Transformation was
performed into chemically competent BL21 (DE3) E. coli cells (NewEngland Biolabs, Ipswich, MA, USA). Cultures grown overnight were
inoculated at 1:50 ratio into Luria-Bertani medium containing 70 mg/L
kanamycin. 500 mL cultures were grown at +37 °C and induced at an
OD600 of 0.6–0.7 with 1 mM isopropyl-β-D-thiogalactopyranoside(IPTG). Expression was carried out overnight at +37 °C with continuous shaking at 150 rpm. The cultures were harvested by 20 min
centrifugation at 4000 x g and the pellets were stored at −80 °C until
puriﬁcation.

Puriﬁcation of coreMYC
After thawing at room temperature, the pellet was resuspended in
10 mL lysis buffer consisting of 0,1 g/L lysozyme supplemented withcomplete mini EDTA-free protease inhibitor tablets (Roche Diagnostics GmBH, Mannheim, Germany) in Milli-Q water, followed bysonication using a probe sonicator (Branson, Brookﬁeld, CT, USA) on
ice for 10 min at 50% amplitude with pulses on/off for 5 s. The lysate

Nature Communications | (2024)15:1865

was centrifuged at +4 °C for 30 min at 12,000 x g. The supernatant was
discarded and the pellet was washed with three different washingbuffers, buffer 1 (1% Triton X-100, 5 mM imidazole and protease inhibitor in PBS), buffer 2 (500 mM NaCl, 1% Triton X-100, 20 mM TRIS
base and 35 mM imidazole in Milli-Q water) and buffer 3 (500 mM
NaCl, 1% Triton X-100, 20 mM TRIS base, 35 mM imidazole and 1,6 M
urea in Milli-Q water), each time followed by centrifugation at +4 °C for
30 min at 12,000 x g. The pellet was solubilized overnight at +4 °C with
continuous shaking at low speed, in a solubilization buffer containing
500 mM NaCl, 1% Triton X-100, 20 mM TRIS base, 35 mM imidazole
and 7 M urea at pH 11. The solution was cleared by centrifugation for
30 min at 11,750 x g, +4 °C to remove undissolved pellet and debris.
The supernatant was loaded on a His SpinTrap column (GE Healthcare,
Chicago, IL, USA) equilibrated in Milli-Q water with 10 mM imidazole
and 6 M urea and eluted with an elution buffer containing 250 mM
imidazole and 5 M urea in Milli-Q water. The eluted fractions were
evaluated on Mini-PROTEAN TGX Stain-Free 4–15% SDS-PAGE gels
(BioRad, Hercules, CA, USA) and stored at −20 °C until further use.

Native and ion mobility-mass spectrometry
MGHis6-coreMYC (hereafter referred to as coreMYC) was bufferexchanged into 100 mM ammonium acetate, pH 7 using BioSpin6
columns (BioRad, Hercules, CA, USA). Recombinant human TBP(ab81897, Abcam, Cambridge, UK) was buffer-exchanged into 1 M
ammonium acetate through double use of ZebaTM Micro Spin Desalting
Columns (Thermo Fisher Scientiﬁc Inc., Waltham, MA, USA). The lyophilized TRRAP2038–2087 peptide (GenScript, Rijswijk, Netherlands) was
directly dissolved into 100 mM ammonium acetate. Concentrations
for coreMYC, TRRAP peptide, and TBP were 31 μM, 480 μM, and 13 μM,
respectively. The TRRAP2038–2087 peptide stock was further diluted inammonium acetate to a ﬁnal concentration of 30 μM, to match theﬁnal concentration of coreMYC. For MS analysis, coreMYC (31 μM),
TRRAP peptide (30 μM) and a mixture of both (15,5 μM coreMYC and15 μM TRRAP2038–2087 peptide) were prepared directly in ammonium
acetate. MS analysis of coreMYC (31 mM) TBP (13 mM), or a mixture ofboth (6 mM coreMYC and 10 mM TBP) were prepared directly inammonium acetate. To study the effect of EGCG, coreMYC and EGCG
were mixed thoroughly by pipetting and incubated for 30 min at+37 °C. The EGCG-coreMYC solution was then added to the
TRRAP2038–2087 peptide to a ﬁnal concentration of 15 mM for each
peptide and 100 mM EGCG or added to TBP to a ﬁnal concentration of
6 mM coreMYC, 10 mM TBP, and 100 mM EGCG. Samples were loaded
into nESI capillaries (Thermo Fisher Scientiﬁc Inc., Waltham, MA, USA).
Mass spectra were recorded in positive ionization mode on a Waters
Synapt G1 traveling-wave IM mass spectrometer (MS Vision, Almere,
The Netherlands). The capillary voltage was maintained at 1,5 kV andthe sample cone was 10 V for TRRAP and 50 V for TBP measurements.
The extraction cone voltage was 4 V. The source temperature was+30 °C. The trap and transfer collision energies were 10 V. The trap gas
was argon at a ﬂow rate of 4 mL/h, the IMS gas was nitrogen at a ﬂow
rate of 30 mL/h. The ion mobility-mass spectrometry settings were:
IMS wave height 12 V and IMS wave velocity 350 m/s. Human insulin
was used as T-Wave calibrant73. Data was analyzed with MassLynx
version 4.1 (Waters) and Pulsar version 2.074. Theoretical CCS values
from pdb ﬁles were computed using IMPACT32.

Cell culture
Human neuroblastoma SH-SY5Y cells were cultured in 1:1 of F12 andminimal essential medium (MEM) (Cat No. 21090055, 21765037,
Thermo Fisher Scientiﬁc, Waltham, MA, USA), supplemented with 10%
fetal bovine serum (FBS) (Cat No. SV30160.03, Cytiva HyClone, Logan,
UT, USA), 1% (v/v) L-glutamine, 1% (v/v) non-essential amino acids (CatNo. SH30034.01 and SH30238.01, Cytiva HyClone, Logan, UT, USA),and 1% (v/v) penicillin-streptomycin (Cat No. 15140122, Gibco, Life
Technologies, Waltham, MA, USA) in a humidiﬁed environment at
10Article+37 °C and 5% CO2. This cell line was validated using short tandem
repeat (STR) analysis (23/10/2023) (AuthentiCell, European Collectionof Authenticated Cell Cultures (ECACC), UK Health Security Agency,
Salisbury, UK).

WST-1 cell viability assay
The viability of SH-SY5Y cells was analyzed using the WST-1 colorimetric assay (Cat No. 11644807001, Roche Diagnostics GmBH, Mannheim, Germany). Cells were seeded in transparent ﬂat-bottom 96-wellplate at a density of 18,000 cells per well and after 48 h the cells weretreated with increasing concentrations of EGCG for a duration of 24 h.
Following this period, 10 μL of WST-1 was added to each well and after
one hour of incubation the absorbance was measured in a Tecan Spark
20 M multiplate reader (Männedorf, Switzerland) at 450 nm and
650 nm (as reference wavelength and subtracted during analysis)
employing Tecan’s SparkControl magellan version 1.2. The cell viability
experiment was performed in three independent biological experiments with four replicates per repeat. Results were normalized overthe 0 μM EGCG non-treated condition and statistical analysis was
performed by one-way ANOVA, Dunnett´s Multiple Comparison Test,by use of GraphPad Prism version 5.0.4 (GraphPad Software Inc., San
Diego, CA, USA). p = < 0.05 (*p < 0.05); p = < 0.005 (***p < 0.005).
Results are displayed as mean±S.D. from n = 3 biologically independent experiments.

IncuCyte live-cell imaging
SH-SY5Y cells were seeded in standard 96-well plates (TPP, SigmaAldrich, St. Louis, MO, USA) at a cell density of 18,000 cells per well andgrown for 48 h before treatment with increasing concentrations of
EGCG. Cells were then continuously imaged over a repeated schedulewith image collection every two hours in the IncuCyte system (Essen
Bioscience Inc., Ann Arbor, MI, USA). Analysis was performed with
IncuCyte software 2021 C. For the live-cell imaging viability, n = 3 biologically independent experiments with four technical replicates per
experimental repeat were performed. Data were processed by using
GraphPad Prism version 5.0.4 (Graphpad Software Inc., San Diego, CA,
USA). Results were normalized compared to starting conﬂuence foreach timepoint and condition to indicate the relative increase/decrease in conﬂuence over time, and displayed as mean±S.D. fromn = 3 biologically independent experiments. Statistical analysis was
performed by two-way ANOVA with Bonferroni post-test. p = < 0.0001(****p < 0.0001) indicates signiﬁcance of the ﬁnal data points. Results
are displayed as mean±S.D. from n = 3 biologically independent
experiments.

In situ proximity ligation assay
SH-SY5Y cells were seeded in standard six-well tissue culture dishes(60 mm diameter) with coverslips at a cell density of 300,000 cells per
well. After 48 h, cells were treated with increasing concentrations of
EGCG for 24 h and ﬁxed in 4% paraformaldehyde for 15 min at room
temperature. After blocking and permeabilization (3% BSA, 0.25%
Triton X-100 in PBS 1X) for 1 h at room temperature, coverslips wereincubated with primary antibodies overnight in a wet chamber at+4 °C. The following antibodies were used: mouse monoclonal anti-cMYC (1:200, 9E10, sc-40), rabbit polyclonal anti-MAX (1:200, C-17, sc197) from Santa Cruz Biotechnology (Dallas, TX, USA), rabbit polyclonal anti-TBP (1:200, ab63766) from Abcam (Cambridge, UK), andrabbit polyclonal anti-TRRAP (1:400, SAB1300444) from Sigma-Aldrich(St. Louis, MO, USA) Following the manufacturer’s protocol, secondary
antibodies (anti-rabbit PLUS probe DUO92002 or anti-mouse MINUS
probe DUO92004, Sigma-Aldrich, St. Louis, MO, USA) were used at 1X,
and interactions detected using the Duolink® In Situ Detection
Reagents Green (DUO92014). When the two proteins are at maximum
distance of 40 nm, rolling circle ampliﬁcation was triggered by the

Nature Communications | (2024)15:1865

https://doi.org/10.1038/s41467-024-45826-7

subsequent additions. Ampliﬁed DNA was detected by a probe containing a ﬂuorophore (λex 495 nm; λem 527 nm) and visualized using thesame ﬁlter as FITC. Nuclei were counter-stained with Duolink® In Situ
Mounting Medium with DAPI (Cat No. DUO82040-5ML, Sigma-Aldrich,
St. Louis, MO, USA). Images were acquired with a Nikon Eclipse Ti
series confocal microscope (Nikon Melville, NY, USA) equipped with a
S PLAN ELWD 60X/0,70 objective (Nikon Melville, NY, USA) and a Zyla
sCMOS camera (Andor, Oxford Instruments, Belfast, UK). Images wereanalyzed and PLA-positive signals were quantiﬁed using the NISElements Advanced Research 5.02.03 software (Nikon, Melville, NY,
USA). A minimum of 10 ﬁelds were captured for quantiﬁcation for eachcondition. To analyze PLA signals, a minimum of 100 cells were randomly selected from each condition and the PLA puncta in nuclei(ﬁxed constant area with 5 µm radius) of each cell were counted. The
total number of PLA puncta was divided by the total number of nuclei
for each condition to obtain an average PLA signal per nucleus. The
fold decrease exhibited by EGCG treated cells were statistically compared with an ordinary one-way ANOVA, Dunnett’s multiple comparison test with a single pooled variance: MYC-MAX (positive control, PC)versus MYC-TBP 0 µM EGCG p = 0.0190; MYC-MAX (PC) versus MYCTBP 25 µM EGCG p = < 0.0001, MYC-TBP 50 µM EGCG p = 0.0754 (n.s),
MYC-TBP 100 µM EGCG p = < 0.0067, or MYC negative control (NC)p = < 0.0001. For TRRAP statistical analysis: MYC-MAX (PC) versus
MYC-TRRAP 0 µM EGCG p = < 0.0001; MYC-MAX (PC) versus MYCTRRAP 25 µM EGCG p = < 0.0001, MYC-TRRAP 50 µM EGCG p = 0.0038,
MYC-TRRAP 100 µM EGCG p = < 0.0001, or MYC negative control (NC)p = < 0.0001. p = < 0.0001 (**p = 0.0038; ****p = < 0.0001). For MYCMAX with EGCG statistical analysis: MYC-MAX 0 µM EGCG versus MYCMAX 25 µM EGCG p = 0.0882 (n.s.), MYC-MAX 50 µM EGCG p = 0.9665(n.s.), MYC negative control (MYC NC) p = < 0.0001, or MAX negative
control (MAX NC) p = < 0.0001. p = < 0.0001 (****p = < 0.0001). Data
were processed by use of GraphPad Prism version 9.5.1 (Graphpad
Software Inc., San Diego, CA, USA), and results are shown as mean ±S.D. from n = 3 biologically independent experiments. n.s= nonsigniﬁcant.

Western blot
Whole-cell lysates were prepared using RIPA lysis buffer supplemented
with protease and phosphatase inhibitor cocktail (Cat No. 78442,
Thermo Fisher Scientiﬁc, MA, USA). Samples were boiled in 1× Laemmli
buffer at + 95 °C for 5 min and separated in a Bolt™ 4–12% Bis-tris Plusgel (for MYC, MAX, TBP and β-actin) and a NuPAGE™ 3 to 8% TrisAcetate gel (for TRRAP and α-tubulin) (Cat No. NW04120BOX andEA0375BOX, Invitrogen, Waltham, MA, USA). The PageRuler™ Plus
Prestained protein ladder was used to visualize molecular weights (CatNo. 26620, Thermo Fisher Scientiﬁc, Waltham, MA, USA). Next, separated proteins were transferred into nitrocellulose membranes (TransBlot Turbo Transfer Pack, Cat No. 1704159, Bio-Rad, CA, USA). Membranes were blocked for 1 h in 5% non-fat milk (Cat No. A0830.5000,
AppliChem GmbH, Darmstadt, Germany), and then incubated at +4 °C
overnight with the following primary antibodies: goat anti-TRRAP(1:1000, sc-5405), rabbit anti-MAX mouse (1:1000, sc-197), mouse antiβ-actin (1:3000, sc-47778), and mouse anti-α-tubulin (1:2000, sc32293) from Santa Cruz Biotechnology (Dallas, TX, USA); rabbit anti-cMYC (1:2000, #9402) from Cell Signaling Technology (Danvers, MA,
USA), and rabbit anti-TBP (1:1000, ab63766) from Abcam (Cambridge,
UK). Next day, membranes were incubated with horseradish peroxidase tagged anti-mouse, anti-rabbit, or anti-goat secondary antibodies(1:3000 Cat No. P044801-2, P044701-2, or P0449, Agilent Technologies, North Billerica, MA, USA). Signals were developed using SuperSignal™ West Dura (Cat No. 34076, Thermo Fisher Scientiﬁc, MA, USA)in a ChemiDoc XRS+ System (Bio-Rad, Hercules, CA, USA). Three biologically independent replicates were performed and quantiﬁed with
ImageJ software (NIH, Bethesda, MD, USA)75. Results were represented

11

Article
as protein level relative to protein loading control (β-actin or α-tubulin) and normalized against 0 μM EGCG. Graph shows the mean ±S.D.
from n = 3 biologically independent replicates. Statistical analysis: twoway ANOVA with * indicating p < 0.05.

Real-time quantitative polymerase chain reaction (RT-qPCR)
Total RNA was isolated using TRIzol reagent and the DirectZol RNA
Miniprep Kit (Zymo Research, Irvine, CA, USA) following the instructions of the manufacturer, including DNase treatment. cDNA wassynthesized using the iScript cDNA synthesis kit (Cat No. 1725037, BioRad, Hercules, CA, USA). The mRNA expression was analysed with RTqPCR using iTaq universal SYBR Green supermix (Cat No. 1725124, BioRad, Hercules, CA, USA) on the StepOnePlus Real-Time PCR system(Applied Biosystems, Waltham, MA, USA). Samples were run in triplicate and the relative expression calculated using the ΔΔCT method
after normalization to the mRNA levels of the internal control β-2microglobulin (B2M). Data was represented relative to the control(0 μM EGCG), as mean ±S.D. from n = 3 biologically independent
experiments, not signiﬁcant between conditions. Statistical analysis:
two-tailed unpaired t test. Forward (F) and reverse (R) primers used arelisted in Supplementary Table 3.

Reporting summary
Further information on research design is available in the Nature
Portfolio Reporting Summary linked to this article.

Data availability
All data supporting the ﬁndings of this manuscript are available fromthe corresponding authors upon request. The molecular dynamics
simulation trajectories reported in this study have been deposited in
the Figshare repository (https://ﬁgshare.com/articles/journal_
contribution/coreMYC/25020956). Source data are provided withthis paper.

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Acknowledgements
The research in this study is supported by the Swedish Cancer Society
grants 201288, 190480, and 190510 + 22 2266 to D.P.L., M.L. and M.A.H.;
by the Swedish Research Council grants 2013–08807, 2019–01961, and2018–02580 to D.P.L., M.L. and M.A.H.; by KI faculty grants to D.P.L.,
J.L.P., C.S., M.L. and M.A.H.; and by the Novo Nordisk Foundation to the
NMR infrastructure facility, cOpenNMR, grant NNF18OC0032996 to K.T.
C.S. is supported by a Novo Nordisk Foundation Postdoctoral Fellowship(NNF19OC0055700), J.L.P. by a postdoctoral position from the Swedish
Cancer Society (22 0539), D.P.L. by a Swedish Research Council grant13

Articlefor Internationally recruited Scientists (2013–08807), and M.L. by twoconsecutive KI faculty-funded Career Positions.

Author contributions
DL conceived the study with input from CS, KT, DPL, ML, and MAH. DL
performed all modeling analyses with computational support from LN.
TV and CS puriﬁed proteins, designed and performed MS experiments,
and analyzed the data. TV and JLP designed and conducted isPLA, TV
and CPC performed confocal microscopy, and CPC performed data
analysis. JLP performed RT-q-PCR and Western blot assays and TV cell
viability experiments. DL wrote the manuscript with input from ML and
MAH. All authors edited and approved the ﬁnal version.

Funding
Open access funding provided by Karolinska Institute.

Competing interests
The authors declare no competing interests.

Additional information
Supplementary information The online version contains
supplementary material available athttps://doi.org/10.1038/s41467-024-45826-7.
Correspondence and requests for materials should be addressed to
Dilraj Lama, Michael Landreh or Marie Arsenian Henriksson.

Nature Communications | (2024)15:1865

https://doi.org/10.1038/s41467-024-45826-7
Peer review information Nature Communications thanks Marie-Eve
Beaulieu, Agnieszka Bronowska, and the other, anonymous, reviewers
for their contribution to the peer review of this work. A peer review ﬁle isavailable.
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