# An integrative characterization of proline cis and trans conformers in a disordered peptide

Alice J Pettitt, Vaibhav Kumar Shukla, Angelo Miguel Figueiredo, Lydia S Newton, Stephen Mccarthy, Alethea B Tabor, Gabriella T Heller, Christian D Lorenz, D. Flemming Hansen

► To cite this version:

Alice J Pettitt, Vaibhav Kumar Shukla, Angelo Miguel Figueiredo, Lydia S Newton, Stephen Mccarthy, et al.. An integrative characterization of proline cis and trans conformers in a disordered peptide. Biophysical Journal, 2024, 123 (21), pp.3798-3811. 10.1016/j.bpj.2024.09.028. hal-04781090

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# An integrative characterization of proline *cis* and *trans* conformers in a disordered peptide

Alice J. Pettitt,¹,³,⁴ Vaibhav Kumar Shukla,¹,⁴ Angelo Miguel Figueiredo,¹ Lydia S. Newton,¹ Stephen McCarthy,² Alethea B. Tabor,² Gabriella T. Heller,¹ Christian D. Lorenz,³,\* and D. Flemming Hansen¹,⁴,\*

¹Department of Structural and Molecular Biology, Division of Biosciences, London, United Kingdom; ²Department of Chemistry, Faculty of Mathematical and Physical Sciences, London, United Kingdom; ³Department of Engineering, Faculty of Natural, Mathematical and Engineering Sciences, King's College London, London, United Kingdom; and ⁴The Francis Crick Institute, London, United Kingdom

**ABSTRACT** Intrinsically disordered proteins (IDPs) often contain proline residues that undergo *cis/trans* isomerization. While molecular dynamics (MD) simulations have the potential to fully characterize the proline *cis* and *trans* subensembles, they are limited by the slow timescales of isomerization and force field inaccuracies. NMR spectroscopy can report on ensemble-averaged observables for both the *cis*-proline and *trans*-proline states, but a full atomistic characterization of these conformers is challenging. Given the importance of proline *cis/trans* isomerization for influencing the conformational sampling of disordered proteins, we employed a combination of all-atom MD simulations with enhanced sampling (metadynamics), NMR, and small-angle x-ray scattering (SAXS) to characterize the two subensembles of the ORF6 C-terminal region (ORF6<sub>CTR</sub>) from SARS-CoV-2 corresponding to the proline-57 (P57) *cis* and *trans* states. We performed MD simulations in three distinct force fields: AMBER03ws, AMBER99SB-*disp*, and CHARMM36m, which are all optimized for disordered proteins. Each simulation was run for an accumulated time of 180–220 µs until convergence was reached, as assessed by blocking analysis. A good agreement between the *cis*-P57 populations predicted from metadynamic simulations in AMBER03ws was observed with populations obtained from experimental NMR data. Moreover, we observed good agreement between the radius of gyration predicted from the metadynamic simulations in AMBER03ws and that measured using SAXS. Our findings suggest that both the *cis*-P57 and *trans*-P57 conformations of ORF6<sub>CTR</sub> are extremely dynamic and that interdisciplinary approaches combining both multiscale computations and experiments offer avenues to explore highly dynamic states that cannot be reliably characterized by either approach in isolation.

**SIGNIFICANCE** This study employs MD simulations (with metadynamics), NMR spectroscopy, and SAXS to elucidate the individual *cis*-proline and *trans*-proline conformations of ORF6<sub>CTR</sub> from SARS-CoV-2. The good agreement on proline *cis/trans* populations observed in experiments (NMR) and those calculated from simulations in the AMBER03ws force field (with SAXS reweighting) showcases the efficiency of this interdisciplinary approach, which can be used to characterize highly dynamic disordered protein states, even for very slow processes. Furthermore, our study emphasizes the importance of considering both computational and experimental methodologies to gain a more holistic understanding of highly dynamic proteins. The presented integrative approach sets a precedent for future studies aiming to explore complex and dynamic biological systems with slow transitions such as proline isomerizations.

## INTRODUCTION

Intrinsically disordered proteins (IDPs) and disordered regions, which represent at least 30% of the human proteome (1), are particularly common in cancer-associated proteins, with up to 80% containing disordered regions (2), and in vi-

ruses, where their coverage ranges from 3 to 55% depending on the viral species (3). Unlike folded proteins, disordered proteins are highly dynamic, and they often exist as an ensemble of diverse heterogeneous conformations that lack a single three-dimensional (3D) structure. Compared with folded proteins, the primary sequences of disordered proteins have a nearly 2-fold increase of proline residues (4), which are well-known to reduce the formation of secondary structure in proteins (5). In particular, proline residues in disordered proteins have been shown to play key

<footnote>Submitted June 9, 2024, and accepted for publication September 25, 2024.</footnote>

<footnote>*Correspondence: chris.lorenz@kcl.ac.uk or d.hansen@ucl.ac.uk</footnote>

<footnote>Editor: Jianhan Chan.</footnote>

<footnote>https://doi.org/10.1016/j.bpj.2024.09.028</footnote>

<footnote>© 2024 The Author(s). Published by Elsevier Inc. on behalf of Biophysical Society.</footnote>

<footnote>This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).</footnote>
roles in regulating protein-protein interactions (6,7), post-
translational modifications (8), and liquid-liquid phase sep-
aration (9).

Most peptide bonds within proteins exist almost exclusively
in the energetically favorable *trans* conformation. However,
for proline residues, the free energy difference between the
*cis* and *trans* isomers is lower due to the cyclic structure of
this amino acid. Given the high energy barrier to rotation,
approximately 84 kJ mol⁻¹ (10), proline isomerization is
generally a slow process (11), occurring at a rate of 10⁻³–
10⁻² s⁻¹ at room temperature, depending on the adjacent res-
idues (12,13). The *cis*-proline population typically ranges
between 5 and 10% in disordered proteins (4), but this can
vary substantially depending on the length and composition
of the amino acid sequence (6,14,15). Consequently, multiple
*cis*-proline conformations may be present within polyproline
disordered protein ensembles. These ensembles sample a
vast conformational space of very slowly exchanging con-
formers, further increasing their complexity (16).

Molecular dynamics (MD) simulations are often used to
characterize the ensemble of disordered proteins as they
can resolve individual conformations within an ensemble
at atomic resolution, which is a challenge for many experi-
mental techniques. Significant progress has been made over
the last decade to optimize force fields for modeling disor-
dered proteins (17–19), as well as advances in the integra-
tion of MD simulations and experimental data to improve
their accuracy (20,21). Despite these advances, sampling
the full configurational energy landscape of disordered pro-
tein ensembles in all-atom explicit solvent MD simulations
is extremely computationally expensive. Proline *cis/trans*
isomerization presents an additional challenge due to the
slow timescales of this process (12,13), which are generally
not accessible in brute-force MD simulations alone (22),
even on today’s most powerful computers. However, when
suitable collective variables (CVs) can be identified, meta-
dynamics, an enhanced sampling approach, offers an effec-
tive method for sampling slow motions (23,24). Indeed,
metadynamics has been used to encourage exploration of
the full configurational space of disordered proteins (7,25)
and proline *cis/trans* isomerization in simulations of dipep-
tides and folded systems (26,27). In the latter cases, the ζ
angle (Cαᵢ₋₁, Oᵢ₋₁, Cδᵢ, Cαᵢ, where i = proline) was em-
ployed as one CV for the isomerization and pyramidaliza-
tion of the amide nitrogen and the ψ angle (Nᵢ, Cαᵢ, C'ᵢ,
Nᵢ₊₁) was employed as an additional CV to control the
amide orientation, which may affect the rate of transition
between the *cis*-proline and *trans*-proline conformations.
Both CVs are required to enhance proline *cis/trans* sampling
as they compensate for each other.

NMR spectroscopy is a well-suited experimental tech-
nique to characterize ensemble-averaged properties of disor-
dered proteins at atomic resolution under physiological
conditions (pH, temperature, and salt concentrations) (28).
Furthermore, NMR can uniquely characterize and quantify

the populations of *cis*-proline and *trans*-proline conforma-
tions. The distinct chemical environments for the two proline
isomers, coupled with their slow exchange, can result in the
detection of two separate peaks for neighboring residues or
the proline itself. NMR has therefore not only been used to
characterize the overall ensemble of disordered proteins
(29,30), but NMR has also been used extensively to charac-
terize the structural propensities and dynamics of *cis*-proline
conformations in disordered proteins (6,14,15,31).

Another experimental technique that can report on the en-
sembles of disordered proteins in solution is small-angle
x-ray scattering (SAXS). This technique can provide coarse
structural information relating to a protein's size and shape.
The capability to predict SAXS profiles from atomic coordi-
nates makes it possible to compare conformational ensem-
bles from MD simulations with experimental SAXS data
(32). While SAXS measurements offer powerful global in-
formation, they report ensemble-averaged states and cannot
generally distinguish between the *cis*-proline and *trans*-pro-
line configurations. Complementary approaches, such as
NMR, are essential for providing detailed experimental in-
formation at the local scale.

Here, we used an integrative approach anchored in all-
atom explicit solvent metadynamic simulations to charac-
terize the C-terminal region of open reading frame 6
(ORF6<sub>CTR</sub>) from severe acute respiratory syndrome corona-
virus 2 (SARS-CoV-2). This region of ORF6 is predicted
to be disordered (Fig. S1, A and B) and binds to host
proteins via an essential methionine residue at position 58
(M58), leading to suppression of the innate immune response
(33–35). Moreover, this 21-residue peptide contains a single
proline residue at position 57 (P57), which may influence its
binding to host proteins as this residue is at a preceding posi-
tion to M58 (33,34). We sampled the conformational space
of ORF6<sub>CTR</sub> using three different force fields, each optimized
for disordered proteins: AMBER03ws (a03ws) (17),
AMBER99SB-*disp* (a99SB-*disp*) (18), and CHARMM36m
(C36m) (19). We employed metadynamics to enhance
sampling (23,24), using various local and global CVs,
including those on the P57 ζ and ψ angles (26,27). To re-
weight and validate resulting conformational ensembles, we
compared ensemble-averaged properties from each force
field to NMR and SAXS data. Specifically, we employed
NMR chemical shifts to report on the local properties and
populations of the *cis*-P57 and *trans*-P57 states, NMR diffu-
sion experiments to compare the global properties of both
states, and NMR spin-relaxation experiments to probe dy-
namics. Moreover, SAXS data were used to select the most
accurate force field for predicting the ORF6<sub>CTR</sub> global
conformational ensemble. To further refine the conforma-
tional ensembles, we updated the statistical reweighting using
a Bayesian/maximum entropy (BME) approach (21,36).

By integrating metadynamic simulations, SAXS, and
NMR, we can characterize the highly dynamic *cis*-P57
and *trans*-P57 subensembles of ORF6<sub>CTR</sub>. We show that
metadynamics with the P57 ζ and ψ angle CVs enhances sampling of P57 isomerization, and we observe convergence of these two CVs for the a03ws and C36m force fields. We find that a03ws most accurately predicts the *cis*-P57 and *trans*-P57 populations in the ORF6<sub>CTR</sub>. By employing SAXS BME reweighting (21,36) and two independent a03ws runs, we obtain *cis*-P57 populations in the a03ws force field that match those from NMR. NMR diffusion experiments suggest that the *cis*-P57 subensemble is slightly more compact than the *trans*-P57 subensemble, in qualitative agreement with the metadynamic simulation predictions. Furthermore, NMR spin-relaxation experiments and metadynamic simulations indicate that both the *cis*-P57 and the *trans*-P57 conformations of ORF6<sub>CTR</sub> are extremely dynamic. We anticipate that this interdisciplinary approach can be broadly applied to the many disordered proteins that undergo complex dynamics across varying timescales.

# MATERIALS AND METHODS

## ORF6<sub>CTR</sub> peptide synthesis

The ORF6<sub>CTR</sub> with sequence SKSLTENKYSQLDEEQPMEID was initially made by solid-phase peptide synthesis using a MultiSynTech Syro Peptide Synthesiser. Fmoc-Asp(O'Bu)-NovaSyn TGT resin and standard Fmoc-amino acids, coupling, and deprotection conditions were used. All residues were double-coupled. The resin was cleaved using a cleavage mixture of TFA/TIPS/H₂O (95:2.5:2.5) and the peptide was isolated by precipitation from diethyl ether, centrifugation, and lyophilization. A portion (4 mg) of the crude peptide was purified by semipreparative HPLC to give pure ORF6<sub>CTR</sub> (1.5 mg) (see Fig. S2, A and B in the supporting material).

## N-Acetylated ORF6<sub>CTR</sub> peptide synthesis

The unlabeled N-acetylated ORF6<sub>CTR</sub> peptide (NAc-ORF6<sub>CTR</sub>) with sequence Ac-SKSLTENKYSQLDEEQPMEID was produced synthetically (>96.3% purity) by GenScript (GenScript Biotech UK, Oxford, UK).

## Expression and purification of the isotopically labeled ORF6<sub>CTR</sub> peptide

The uniformly isotopically labeled ORF6<sub>CTR</sub> with sequence SKSLTENKYSQLDEEQPMEID was produced by recombinant protein expression with an N-terminal glutathione S-transferase (GST) tag followed by a tobacco etch virus protease cleavage site in the pGEX-6P-1 expression vector (GenScript Biotech UK, Oxford, UK). ORF6<sub>CTR</sub> was expressed in *Escherichia coli* BL21(DE3) strain. The cells were grown at 37°C in minimal M9 medium containing 1 g/L ¹⁵NH₄Cl as the sole nitrogen source and 10 g/L glucose for the ¹⁵N-labeled ORF6<sub>CTR</sub> peptide. For the ¹³C,¹⁵N-labeled ORF6<sub>CTR</sub> peptide, 1 g/L ¹⁵NH₄Cl was used as the sole nitrogen source and 3 g/L ¹³C-glucose as the sole carbon source. Cultures were grown at 37°C with vigorous shaking. Expression was induced at an OD₆₀₀ of 0.5–0.6 by addition of 1 mM isopropyl β-D-thiogalactopyranoside and left shaking for 4 h at 37°C.

The cell pellet was collected by centrifugation and resuspended in lysis buffer containing 50 mM Tris (pH 8.0), 300 mM NaCl, 10 mM β-mercaptoethanol, and 5% glycerol. Prior to lysing the cells via sonication, 0.25% IGE-PAL was added to the mixture along with small amounts of DNase, lysozyme, and one protease inhibitor tablet per 50 mL. Next, the cell lysate was added to Glutathione Agarose 4B resin (Protino) and binding was al-

lowed to occur by gently rocking the mixture for 1 h at 4°C. The gravity columns were then washed with the lysis buffer to remove any unbound proteins. ORF6<sub>CTR</sub> was cleaved from the GST-tag via the addition of tobacco etch virus protease overnight, shaking at 22°C. The next day, the flowthrough containing the cleaved ORF6<sub>CTR</sub> was concentrated by ultracentrifugation through 1 kDa cutoff centricons (PALL, New York, NY) ultrafiltration membranes. For further purification, size-exclusion chromatography on a Superdex 75 column was carried out in NMR buffer containing 25 mM HEPES (pH 6.9), 150 mM NaCl at 5°C. Fractions containing ORF6<sub>CTR</sub> were pooled together and concentrated by 1 kDa cutoff centricons (PALL) ultrafiltration membranes. The yield of purified peptide was around 1.4–1.6 mg from 1 L of culture medium.

## NMR spectroscopy

Unless specified otherwise, NMR spectra were collected on uniformly ¹⁵N-labeled or ¹³C,¹⁵N-labeled ORF6<sub>CTR</sub> peptide samples at concentrations of 300 µM and unlabeled NAc-ORF6<sub>CTR</sub> at concentrations of 400 µM. All peptide samples were prepared in 25 mM HEPES buffer (pH 6.9), 150 mM NaCl, containing 5% D₂O, 1 mM sodium azide, and 1 mM EDTA. Before recording experiments, ORF6<sub>CTR</sub> samples were boiled in the NMR tube (sample volume ~600 µL) at 100°C for ~3 min to remove proteases. NMR data were recorded at 15°C unless stated otherwise. NMR data were acquired at three different static magnetic fields: 14.1 T (600 MHz) on a Bruker NEO spectrometer with a TXO cryoprobe, 18.8 T (800 MHz) on a Bruker Avance III HD spectrometer equipped with Z-gradient triple-resonance TCI cryoprobe, and 22.3 T (950 MHz) on a Bruker Avance NEO spectrometer equipped with a QCI-F cryoprobe.

Resonance assignments were obtained from a standard suite of double-resonance and triple-resonance experiments at a static magnetic field strength of 14.1 T. Backbone ¹⁵N relaxation rates, including R₁, R₁ₚ, and {¹H}-¹⁵N steady-state heteronuclear NOEs (hetNOEs), were measured at two static magnetic field strengths (14.1 and 18.8 T). The backbone ¹⁵N exchange-free relaxation rates (R₉₉) were recorded using a previously described method (37) at a static magnetic field strength of 14.1 T. Diffusion ordered spectroscopy (DOSY) experiments were measured using a pseudo-3D ¹H-¹⁵N heteronuclear single quantum coherence (HSQC)-type experiment at a static magnetic field strength of 22.3 T. Additional information regarding the NMR experiments can be found in the supporting material.

## NMR data analysis

All NMR spectra were processed using NMRPipe (38), and analyzed with NMRFAM-SPARKY software (39) and FuDA (37). To analyze residual secondary structure for the *cis*-P57 and *trans*-P57 configurations, we used the secondary structure propensity score algorithm (40), with the ORF6<sub>CTR</sub> ¹H<sup>α</sup>, ¹³C<sup>α</sup>, and ¹³C<sup>β</sup> chemical shifts as input. R₁ and R₁ₚ rate-constants were calculated by fitting the intensity profiles to monoexponential decay functions with FuDA (37). The {¹H}-¹⁵N hetNOEs were calculated as the ratios of the peak intensities in the saturated and reference subspectra. The transverse relaxation rate constants (R₂) were calculated from the R₁ and R₁ₚ relaxation rates (see Eqs. S1 and S2 in the supporting material). Peak intensities of the diffusion NMR data were obtained with FuDA (37) and diffusion coefficients were calculated (see Eq. S3 in the supporting material).

## SAXS measurements

The NAc-ORF6<sub>CTR</sub> was dissolved in 25 mM HEPES (pH 6.9), 150 mM NaCl and centrifuged at 5600 × g for 10 min at 4°C to give a final concentration of 2 mg/mL (800 µM). The SAXS data were obtained on Instrument B21 at Diamond Light Source (Didcot, UK) (41). Measurements were recorded at 37°C. Data sets of 26 frames with a frame exposure time of 1 s each were acquired. ScAtter IV (42) was used for buffer subtraction and
data reduction, in which the 26 frames were averaged. The Guinier peak analysis function in ScÄtter IV was used to calculate the radius of gyration ($R_g$). Furthermore, we rescaled the error bars for the SAXS intensities by a factor estimated through the Bayesian indirect Fourier transform using the BioXTAS Raw software (43).

## MD simulations

All-atom metadynamic simulations were performed using GROMACS 2021.2 (44) patched with the open-source, community-developed PLUMED library version 2.7.1 (45). Simulations were set up using three different force fields and their corresponding water models: a03ws force field (17) with the TIP4P/2005 water model (46), a99SB-disp (18) with the TIP4P-D water model (47), and C36m (19) with the CHARMM-modified TIP3P water model (48). ORF6<sub>CTR</sub> was N-acetylated for the simulations. The initial starting structure for NAc-ORF6<sub>CTR</sub> was prepared as a linear peptide using PyMOL (49). Topology and coordinate files were then generated for the peptide using the heavy hydrogen flag for hydrogen mass repartitioning (50). The system was then solvated in a rhombic dodecahedron box with an initial volume of 775 nm³. Ions were added to neutralize the charge of the system and to maintain a concentration of 150 mM NaCl to match experiments. The solvated system was subjected to an energy minimization simulation using the steepest descent method with a target maximum force of 2,000 kJ mol⁻¹ nm⁻¹. The extended structure was collapsed by running a high-temperature simulation at 600 K for 20 ns in the canonical (NVT) ensemble with a 2 fs time step. To ensure a sufficiently large box size, the $R_g$ was calculated for each frame in the 600 K NVT trajectory. From the 95th percentile of the $R_g$ distribution, a collapsed peptide starting structure was randomly selected. This was repeated for each force field.

The collapsed peptide structure was then resolvated and neutralized at a concentration of 150 mM NaCl in a new rhombic dodecahedron box with volumes between 290 and 380 nm³ and 9,600–12,200 explicit water molecules, depending on the force field. Each force field system was subjected to the energy minimization simulation by steepest descent with a target maximum force of 2,000 kJ mol⁻¹ nm⁻¹. All subsequent simulations employed either a 4 or 5 fs time step to enhance computational efficiency. A 20 ns simulation at 600 K in the NVT ensemble was performed to obtain 128 diverse starting structures. These 128 starting structures were taken at even intervals after 5 ns of equilibration time. Thermalization was implemented for 1,250 ps at 310 K in the canonical (NVT) ensemble using the Bussi-Donadio-Parrinello thermostat (51). Then the density of each system was equilibrated for 50 ns at 310 K in the isothermal-isobaric (NPT) ensemble using the Parrinello-Rahman barostat (52).

Production runs were executed in the NPT ensemble with a target pressure of 1 bar and temperature of 310 K, employing the Parrinello-Rahman barostat (52), and using a 5 fs time step (50). LINCS constraints on all bonds were used (53). A Verlet list cutoff scheme was used for the nonbonded interactions. The van der Waals and Coulomb interactions were cut off at 1.2 nm for all a03ws and a99SB-disp simulations and at 0.95 nm for C36m. Long-range electrostatic effects were treated with the particle-mesh Ewald method (54). Metadynamics was performed with the parallel-bias, well-tempered, and multiple-walkers protocols using a Gaussian deposition stride set to 2.5 ps, an initial height of 1.2 kJ mol⁻¹, and a bias factor of 30 for 128 replicas (23–25). Nine CVs were selected to enhance conformational sampling (see Eqs. S4–S8; Table S1). Metadynamic simulations were run for an accumulated time of 192 µs (a03ws run 1 and a03ws run 2), 181 µs (a99SB-disp), and 222 µs (C36m) until convergence was reached as assessed by blocking analysis for every CV (see Figs. S3, A–C and S4; Eq. S9) (32,55).

## Structural ensemble analysis

Statistical weights were calculated for each force field at the end of the simulation (see Eq. S10). Analysis was conducted using the open-source package MDTraj (56).

Chemical shift predictions were back-calculated from the simulations at each time step using CamShift (57). SAXS intensity curves were calculated for each structure using Pepsi-SAXS (58) and we followed a previously reported method to perform the fitting (36). We used the BME software to update the metadynamics weights to better match experimental SAXS data (21).

## RESULTS

### Sampling slowly exchanging proline cis/trans conformers with metadynamic simulations

To characterize the structural ensemble of ORF6<sub>CTR</sub>, all-atom explicit solvent MD simulations were set up with enhanced sampling provided by metadynamics (23,24). Given the sensitivity of simulations of disordered proteins to the force field used (59), we employed three diverse parameter sets (force fields) optimized for disordered proteins, capable of sampling both folded and disordered regions, or primarily extended conformations. These included the force fields a03ws (17), a99SB-disp (18), and C36m (19). The ORF6<sub>CTR</sub> peptide was N-acetylated (NAc-ORF6<sub>CTR</sub>) in the simulations to ensure a neutral charge at the N-terminus and to better match the peptide in its native context.

To obtain a good sampling of the highly heterogeneous free energy surface (FES) adopted by the expected disordered peptide (Fig. S1, A and B), we selected a variety of metadynamics CVs to explore both local and global properties of NAc-ORF6<sub>CTR</sub>. These CVs included total α-helical content, total β-sheet content, the radius of gyration ($R_g$), the number of salt bridges, the distance between the Cα of the first and last residue (end-to-end distance), the correlation between consecutive ψ dihedral angles (dihedral correlation), and the number of contacts between hydrophobic residues (25). To enhance proline cis/trans isomerization (Fig. 1 A), we also implemented two CVs on the P57 improper ζ dihedral angle and the P57 ψ dihedral angle (Fig. 1 B) (see the supporting material for more information on the CVs) (26,27). Each CV for each force field was assessed for convergence by blocking analysis (see Fig. S3, A–C; Eq. S9) (32,55). We observed that the FES standard-error plateaus at a constant value for the CVs listed above in all three force fields, except for the P57 ζ improper dihedral angle CV in the a99SB-disp force field.

For the a03ws and C36m force fields, convergence was observed for the P57 ζ improper dihedral angle (Fig. S3, A and C). This, combined with convergence of the ψ dihedral angle, suggests that P57 cis/trans isomerization was converged despite the very slow timescale generally observed for proline isomerization, typically 10–10³ s at room temperature (12,13). Sampling of the cis-P57 isomer was not observed in the simulation with the a99SB-disp force field (Fig. S3 B). This is potentially due to the exclusion of glycine and proline residues from torsion optimizations, along with the enhanced Lennard-Jones pair override
<figure><img src="image_2.png" /><figcaption>FIGURE 1 All three force fields predict NAc-ORF6<sub>CTR</sub> is disordered but only the a03ws and C36m force fields predict that NAc-ORF6<sub>CTR</sub> undergoes proline cis/trans isomerization. (A) cis-proline (pink) and trans-proline (green) conformations with the positions of C<sup>γ</sup> and C<sup>β</sup> indicated. (B) Definition of the ζ (gray) and ψ (purple) dihedral angles used as CVs to enhance proline cis/trans isomerization. (C) R<sub>g</sub> probability distributions were calculated using kernel density estimates to compare the a03ws run 1 (orange), a99SB-disp (red), and C36m (blue) ensembles. Ensemble-averaged R<sub>g</sub> are shown for each system. The associated error represents the standard deviation between the first and second halves of the analyzed trajectories. The experimental SAXS data and error (standard deviation from the Guinier analysis) are shown in gray. (D) Probability distributions for the ω angle about the Q56-P57 peptide bond in the a03ws run 1 (orange), a99SB-disp (red), and C36m (blue) ensembles. The thickness of the gray distribution represents the uncertainty. The cis-P57 populations are displayed for each system and the error represents the standard deviation between the first and second halves of the analyzed trajectories.</figcaption></figure>

(18). New and improved force fields, such as DES-Amber (60), have further advanced upon this and may be better suited for proteins with both ordered and disordered regions or for investigating a disordered protein in complex with a folded protein.

To assess the sampling of secondary structures, we calcu-
lated ensemble-averaged C<sup>α</sup> minimum distance contact
maps for all three force fields, which showed low probabil-
ities of α-helix and β-sheet content (Fig. S5, A–C), and thus
a lack of secondary structure. This result is consistent with
predictions from s2D (Fig. S1 A), a protein secondary struc-
ture propensity predictor trained on solution-based NMR
data (61), and the AlphaFold2 pLDDT score (62), in which
low confidence scores have been found to correlate with
disordered regions (Fig. S1 B) (63).

Assessing the conformational ensembles using
experimental data

Histograms of the R<sub>g</sub>, calculated from the ensembles ob-
tained with metadynamic simulations, show that NAc-

ORF6<sub>CTR</sub> adopts a continuum of states in all three force
fields, ranging from collapsed to extended, suggesting that
NAc-ORF6<sub>CTR</sub> exists as an ensemble of disordered confor-
mations (Fig. 1 C), in agreement with the lack of stable sec-
ondary structure (Fig. S5, A–C). It is well established that
various force fields for disordered proteins can predict
dramatically different features for the same disordered pro-
tein sequence, particularly in terms of ensemble-averaged
properties such as R<sub>g</sub> values (59). The three force fields
used herein predict slightly different R<sub>g</sub> values (a03ws run
1: 1.194 ± 0.002 nm, a99SB-disp: 1.131 ± 0.005 nm,
and C36m: 1.18179 ± 0.00002 nm; Fig. 1 C). To
assess which of the simulations, if any, was the most in
agreement with experimental measurements, we performed
SAXS experiments on NAc-ORF6<sub>CTR</sub> and determined
an ensemble-averaged R<sub>g</sub> of 1.27 ± 0.02 nm. Notably,
a03ws and C36m performed only marginally better than
the coarse-grained force field CALVADOS, which predicts
an R<sub>g</sub> of 1.180 ± 0.007 nm (64–66). Furthermore,
the coarse-grained disordered region ensemble predictor
ALBATROSS (67) predicts an R<sub>g</sub> of 1.29 nm, which is
closer in agreement with the experimental $R_g$ than any of the all-atom models tested here. Both coarse-grained force field predictors specifically used the $R_g$ parameter for optimization (66,67).

In contrast to SAXS data, which report on global properties, NMR chemical shifts report on local dihedral angles, ring current shifts, and electrostatics (68), and are regularly used to improve and assess the accuracy of simulation ensembles because they are sensitive to secondary structure (21,25). To this end, we recorded and assigned 2D ¹H-¹H total correlation spectroscopy (TOCSY) and ¹H-¹³C HSQC spectra of unlabeled NAc-ORF6<sub>CTR</sub> to obtain ¹³C<sup>α</sup>, ¹³C<sup>β</sup>, ¹H<sup>α</sup>, and ¹H<sup>N</sup> chemical shifts. Using CamShift (57), we back-calculated chemical shifts for all nonproline residues in our metadynamics ensembles for all three force fields. All experimentally determined chemical shifts were within the CamShift error (Fig. S6, A–C). To probe secondary structure in the Nac-ORF6<sub>CTR</sub> we recorded a 2D ¹H-¹H TOCSY temperature titration at eight temperature values between 5 and 37°C. The linear temperature coefficients of the ¹H<sup>N</sup> chemical shifts are indicative of the hydrogen bonding state of individual amides (69). For example, at higher temperatures hydrogen bonds are weakened, which causes the relative upfield shifting of the ¹H<sup>N</sup> chemical shift. All residues in the Nac-ORF6<sub>CTR</sub> have temperature coefficients more negative than -4.5 ppb K<sup>-1</sup> (Fig. S7), suggesting that none of the ¹H<sup>N</sup> are involved in a long-lived intramolecular hydrogen bond. This indicates that there is no substantial secondary structure in Nac-ORF6<sub>CTR</sub>, in agreement with the metadynamic simulations.

## Solution-state NMR confirms proline *cis/trans* isomerization

Like force-field-dependent variations predicted in the $R_g$ distributions, the *cis*-P57 population predictions also vary dramatically between different force fields (Fig. 1 D). Our analysis suggests *cis*-P57 populations of 18 ± 1, 0, and 1.6 ± 0.3% using the a03ws run 1, a99SB-disp, and C36m force fields, respectively. To assess the accuracy of these *cis*-P57 populations, we recombinantly expressed and purified uniformly ¹⁵N-labeled and ¹³C, ¹⁵N-labeled ORF6<sub>CTR</sub> for NMR experiments (see materials and methods). The production of isotopically labeled ORF6<sub>CTR</sub> in *E. coli* required a GST-tag, making it extremely difficult to N-acetylate the system. While N-acetylation has been shown to decrease the *cis*-proline population in small tetrapeptide systems, the effect of N-acetylation on *cis*-proline sampling is reduced upon increasing the length of the peptide (14). As the N-acetylation in ORF6<sub>CTR</sub> would occur at residue S41, we anticipate that potential *cis/trans* isomerization at residue P57, separated by 16 residues from the acetylation site, is unlikely to have its chemical environment influenced by the acetyl group (see below).

2D ¹H-¹⁵N HSQC spectra show sharp resonances with a limited chemical shift dispersion in the ¹H<sup>N</sup> dimension, suggesting that these resonances arise from disordered residues (Fig. 2 A). This finding agrees with spectra of the unlabeled, N-acetylated peptide (Fig. S8), and with the secondary structure predictions (Fig. S1, A and B). The chemical shifts in the ORF6<sub>CTR</sub> are very similar to those obtained from the NAc-ORF6<sub>CTR</sub> (see Table S2; Eq. S11). The a03ws and C36m simulations discussed above predict that the ORF6<sub>CTR</sub> conformational ensemble undergoes proline *cis/trans* isomerization about the Q56-P57 peptide bond. Indeed, in the 2D ¹H-¹⁵N HSQC spectrum, two distinct signals with different signal intensities are observed for residues in proximity to P57 (Fig. 2 A).

To verify that the additional set of minor peaks corresponds to residues in the *cis*-P57 conformational ensemble, we analyzed the ¹³C side-chain chemical shifts of P57 using a 3D CC(CO)NH experiment (Fig. 2 B) (70). The difference in chemical shifts between ¹³C<sup>β</sup> and ¹³C<sup>γ</sup> is highly diagnostic of a *cis*-proline (~9.5 ppm) or a *trans*-proline (~4.5 ppm) peptide bond conformation (31,71). Similarly, peaks from the *cis*-P57 ensemble were detected for residues E55, Q56, M58, and E59. Most *cis*-P57 peaks are relatively close to their corresponding *trans*-P57 peak. For Q56, the residue preceding P57, the change in the ¹⁵N chemical shift between the *cis* and *trans* states is ~3 ppm. A large change in chemical shift between *cis*-proline and *trans*-proline configurations is not uncommon for peaks of residues preceding the proline undergoing isomerization (71). Thus, NMR provides evidence of a *cis*-P57 conformation, while also enabling the characterization of the properties of both the proline *cis* and *trans* conformational subensembles. Initially, to characterize the secondary structure content of the *cis*-P57 and *trans*-P57 subensembles, we calculated the secondary structure propensity scores for the two configurations (40). These scores indicate that both conformations are disordered (Fig. 2 C), although there is a slight increase in β-strand propensity near the C-terminus for the *cis*-P57 subensemble.

In the slow-exchange regime, NMR peak intensities are proportional to the concentration of the species giving rise to the observed peaks (72). The population of the *cis*-P57 and *trans*-P57 conformations can thus be determined, assuming slow exchange and similar dynamics for the two conformations. The NMR experiments described above were recorded at 15°C to reduce signal loss due to backbone amide proton exchange with the solvent. The *cis*-P57 and *trans*-P57 peak intensities at 15°C were calculated for residues E55, Q56, M58, and E59 using the integrated peak volume or the peak height (Figs. 2 D and S9 A) (31). To enhance the *cis*-P57 population, study the system at a biologically relevant temperature, and match our metadynamic simulations, the *cis*-P57 and *trans*-P57 integrated peak volumes and peak heights were also analyzed at 37°C. Residue M58 was excluded from the analysis at 37°C since the two
<figure><img src="image_3.png" /></figure>

FIGURE 2 NMR verifies the disordered ORF6<sub>CTR</sub> undergoes proline *cis/trans* isomerization. (A) 2D ¹H-¹⁵N HSQC spectrum of 300 µM ¹⁵N-labeled ORF6<sub>CTR</sub> measured at 15°C, pH 6.9, and 14.1 T. Residues colored in green originate from the *trans*-P57 conformational ensemble, while residues colored in pink and labeled with an asterisk (*) report on the *cis*-P57 conformation. The black peak is unassigned. (B) 2D ¹H-¹³C strip taken from the 3D CC(CO)NH spectrum of the 300 µM ¹³C,¹⁵N-labeled ORF6<sub>CTR</sub> at the ¹⁵N chemical shift of M58*. (C) The ORF6<sub>CTR</sub> ¹³C<sup>α</sup>, ¹³C<sup>β</sup>, and ¹H<sup>α</sup> chemical shifts were used to calculate the secondary structure propensity scores for both the *cis*-P57 and *trans*-P57 subensembles. A positive value indicates α-helical propensity, a negative value indicates a β-strand propensity, whereas a value near zero indicates random coil. (D) The *cis*-P57 populations at 15°C (*solid fill*) and 37°C (*dashed fill*) are shown for each well-resolved *cis*-P57 and *trans*-P57 peak in the 2D ¹H-¹⁵N HSQC spectrum. The integrated peak volume was used to calculate the mean *cis*-P57 population and standard deviation across residues E55, Q56, and E59. (E) The van't Hoff analysis of P57 *cis/trans* isomerization determined from the integrated peak volume. The mean natural logarithm of the equilibrium constant for proline *cis/trans* isomerization about the Q56-P57 peptide bond as a function of temperature was calculated using residues E55, Q56, and E59. Error bars represent the standard deviation across the three residues used in the analysis. The van't Hoff linear fit yielded a *cis*-P57 population of 10 ± 2% at 37°C.

proline isomer peaks overlap at higher temperatures. The integrated peak volume analysis yielded a *cis*-P57 population of 5.4 ± 0.9% at 15°C and 8.8 ± 2.5% at 37°C (Fig. 2 D), which agrees most closely with the free energy difference between the *cis*-P57 and *trans*-P57 conformations in the a03ws run 1 metadynamic simulation.

To investigate the enthalpic and entropic factors influencing the formation of the *cis*-P57 state, we measured

the equilibrium constant K = [cis]/[trans] for residues E55, Q56, and E59 at 10 temperature values between 2.5 and 40°C. From the van't Hoff analysis, using integrated peak volumes, we observed that the *cis*-P57 conformation is enthalpically disfavored (ΔH = 16 ± 2 kJ mol⁻¹) but entropically favored (ΔS = 34 ± 7 J K⁻¹ mol⁻¹) over the *trans*-P57 conformation (Fig. 2 E). Similar results were obtained using peak heights, where ΔH = 14 ± 1 kJ mol⁻¹
and $\Delta S = 25 \pm 3 \text{ J K}^{-1} \text{ mol}^{-1}$ (Fig. S9 B). The *cis*-P57 population is consistent with results reported in the literature for other disordered protein systems (31) and is a reasonable population given that the Q56 residue, preceding P57 in ORF6<sub>CTR</sub>, is neither favorable nor unfavorable for proline isomerization (71).

## Refining the metadynamics ensembles using Bayesian/maximum entropy

Given that the a03ws force field (a03ws run 1) produced an ensemble that agreed best with the experimental *cis*-P57 populations and SAXS data, we repeated the a03ws simulation (a03ws run 2) to assess reproducibility. To do this, and to avoid positive bias for the *cis*-P57 configuration, we randomly selected 128 frames only from the *trans*-P57 configuration within the initial a03ws run 1 ensemble. We then repeated the metadynamic simulation until convergence was reached at an accumulated simulation time of 192 µs (Fig. S4). The a03ws run 2 conformational ensemble was again consistent with the NMR data (Fig. S10), gave a *cis*-P57 population of 13 ± 1% (Fig. S11 A), and agreed with the characterizations from a03ws run 1 (Figs. 1 C, S5 A, and S11, B and C). The robust sampling of the *cis*-P57 conformation, even when using starting structures with 100% *trans*-P57, and the consistency with both SAXS and chemical shift data from two separate metadynamic simulations, indicate that the a03ws force field produces an NAc-ORF6<sub>CTR</sub> ensemble that is both robust and in agreement with the experimental data for this system.

To further refine the conformational ensemble with minimal perturbation, we used BME reweighting with the SAXS data for the two independent a03ws ensembles and the C36m ensemble (21,36). While all three ensembles were already in quite good agreement with the SAXS data (Figs. 3 A and S12 A), we were interested in exploring whether reweighting using SAXS data could improve the *cis*-P57 population predictions. As expected, reweighting resulted in a more extended ensemble-averaged $R_g$ for the a03ws run 1 (1.292 ± 0.001 nm) and the a03ws run 2 (1.295 ± 0.005 nm) ensembles (Fig. 3 B), indicating that the reweighting was successful. Reweighting also increased the ensemble-averaged $R_g$ of the C36m ensemble (1.299 ± 0.003 nm) and shifted the C36m $R_g$ distribution to match the a03ws distributions (Fig. S12 B–D).

Across all three systems, we observed that SAXS BME reweighting led to a reduction in the *cis*-P57 populations (Figs. 3 C and S12 E). Although reweighting improved the consistency between predicted *cis*-P57 populations in both a03ws ensembles and the NMR data, it did not improve the prediction in the C36m ensemble. This discrepancy underscores the challenge of using BME reweighting to improve accuracy when experimental data are minimally informative on the desired observable, as in this case, where SAXS does not distinguish the *cis*-proline and *trans*-proline

<figure><img src="image_4.png" /></figure>

<figure><img src="image_5.png" /></figure>

<figure><img src="image_6.png" /></figure>

**FIGURE 3** The a03ws force field most accurately predicts the disordered NAc-ORF6<sub>CTR</sub> ensemble and P57 *cis/trans* populations after SAXS BME reweighting. (A) The calculated SAXS intensities from the a03ws run 1 metadynamic simulation (*orange*) and the SAXS BME reweighted simulation (*brown*) compared with the experimental SAXS intensities (*gray*), and the error associated with each intensity (*gray*). (B) $R_g$ probability distributions were calculated using kernel density estimates to compare the SAXS BME reweighted a03ws run 1 ensemble (*brown*) and the SAXS BME reweighted a03ws run 2 ensemble (*teal*). The associated error represents the standard deviation between the first and second halves of the analyzed trajectories. The experimental SAXS data and error (standard deviation from the Guinier analysis) are shown in *gray*. (C) Probability distributions for the $\omega$ angle about the Q56-P57 peptide bond in the SAXS BME reweighted a03ws run 1 ensemble (*brown*) and the SAXS BME reweighted a03ws run 2 ensemble (*teal*). The two reweighted independent a03ws ensembles were used to calculate the mean *cis*-P57 population and standard deviation. Additional plots for the SAXS BME reweighted a03ws run 2 and C36m ensembles can be found in the *supporting material*.
conformers. Using experimental data that are informative on both isomeric states could potentially improve the accuracy of the *cis*-proline population predictions. The reweighted ensembles were also compared with the chemical shift data and no significant changes were observed (Fig. S13, A–C). Again, this indicates that the chemical shifts alone are not sufficient for reweighting the ensembles, as changes to the global conformation have limited influence on this parameter. Taking the two SAXS BME reweighted a03ws ensembles we calculated a mean *cis*-P57 population of 13 ± 2%, which is in good agreement with the population derived from NMR (~10 ± 2%).

## ORF6<sub>CTR</sub> is highly dynamic in both proline configurations

Using the reweighted metadynamics ensembles, we next sought to elucidate the structural differences between the *cis*-P57 and *trans*-P57 states. We observed that both conformations have similar $R_g$ distributions (Fig. 4 A), with the *cis*-P57 state exhibiting a slightly smaller mean conformation (1.2510 ± 0.0004 nm) compared with the *trans*-P57 state (1.299 ± 0.003 nm). We then calculated ensemble-averaged $C^\alpha$ minimum distance contact maps for all 21 residues (Fig. 4 B) and the secondary structure populations for both P57 states (Fig. 4 C). Both analyses suggest an overall lack of stable secondary structure for the *cis*-P57 and the *trans*-P57 subensembles. Within the N-terminal region of ORF6<sub>CTR</sub>, the secondary structure propensity scores calculated from NMR chemical shifts and the secondary structure populations from the metadynamic simulations indicate the sampling of very transient $\alpha$-helical structures (Figs. 2 C, 4 C, S14 A–C, and S15 A–C). Whereas the chemical shifts near the P57 residue indicate a slight increase in $\beta$-strand (Fig. 2 C). These near-minuscule changes in secondary structure were not predicted by the metadynamic simulations (Figs. 4 C, S14 C, and S15 C).

We also wondered whether the metadynamic simulations could accurately predict both the *cis*-P57 and *trans*-P57 chemical shifts using CamShift (57). Thus, we compared $^1$H$^N$ and $^{15}$N chemical shifts for residues E55, Q56, M58, and E59 with those predicted by CamShift (Figs. 4 D, S14 D, and S15 D). The errors in CamShift are generally greater than the chemical shift differences between *cis*-P57 and *trans*-P57 states, except for the backbone nitrogen for residue Q56, which precedes P57. For this experimental observable, CamShift accurately predicts the value for the *trans*-P57 state, but inaccurately predicts that of the *cis*-P57 state (Figs. 4 D, S14 D, and S15 D). As more data for proline isomerization of disordered proteins becomes readily available, we anticipate that force field parameters for this important state will improve, as well as the prediction of chemical shifts for *cis*-proline states.

To qualitatively compare the global features of the *cis*-P57 and *trans*-P57 ORF6<sub>CTR</sub> subensembles predicted by

the metadynamic simulations to experiments, we recorded $^1$H-$^{15}$N-DOSY-HSQC experiments at a magnetic field strength of 22.3 T. The P57 *cis/trans* isomerization exchange rate is slow compared with the diffusion timescales ($\Delta$ = 200 ms), and slow compared with the longitudinal relaxation ($k_{ex} \ll R_1$), making it possible to measure distinct diffusion rates for the *cis*-P57 and *trans*-P57 subensembles. We determined the average diffusion coefficients for the *cis*-P57 and *trans*-P57 conformations using data from residues Q56, M58, and E59. The diffusion measurements for the $^{15}$N-labeled ORF6<sub>CTR</sub> at pH 6.9 and 15°C revealed a diffusion coefficient of (2.52 ± 0.09) × 10<sup>-10</sup> m<sup>2</sup> s<sup>-1</sup> for the *cis*-P57 conformation and (2.34 ± 0.05) × 10<sup>-10</sup> m<sup>2</sup> s<sup>-1</sup> for the *trans*-P57 conformation. The observed faster diffusion coefficient for the *cis*-P57 subensemble suggests a slightly more compact conformation compared with the *trans*-P57, with statistical significance of 98% (determined via a one-tailed Welch's *t*-test, df = 4, t = 3.179, p = 0.042). This finding is in qualitative agreement with our SAXS BME reweighted metadynamic simulations, which predict that the ORF6<sub>CTR</sub> *cis*-P57 subensemble is slightly more compact than the *trans*-P57 subensemble in the two independent a03ws simulations and the C36m simulation (Figs. 4 A, S14 A, and S15 A). However, small discrepancies are observed when comparing the relative differences in the predicted ensemble-averaged $R_g$ values between the *cis*-P57 and *trans*-P57 subensembles with their corresponding NMR diffusion coefficients, highlighting the accuracy limitations of all three metadynamic simulations.

To characterize the dynamics of the ORF6<sub>CTR</sub> experimentally, we recorded the standard set of backbone heteronuclear $^1$H-$^{15}$N relaxation experiments at 15°C and at two different static magnetic field strengths (14.1 and 18.8 T) (73). $^{15}$N $R_2$ rates were determined from the corresponding $^{15}$N $R_{1\rho}$ rates, to minimize the effect of off-resonance effects and potential microsecond exchange dynamics (see Eqs. S1 and S2) (74). The $^{15}$N $R_1$ and $R_2$ relaxation rates report on protein motions occurring at timescales faster than the effective rotational correlation time, which is usually on the nanosecond timescale for disordered proteins. The rates obtained for the ORF6<sub>CTR</sub> *cis*-P57 and *trans*-P57 subensembles are very similar, and both show the expected bell shape for a random coil disordered state (Figs. 5, A and B and S16, A and B) (31,75).

The $\{^1$H\}-$^{15}$N hetNOEs measured for ORF6<sub>CTR</sub> at a static magnetic field strengths of 14.1 T were mostly near 0 in the central region and decreased to -1 at the N- and C-termini, which is indicative of picosecond dynamics associated with a disordered protein (Fig. 5 C) (73). At higher static magnetic field strengths (18.8 T), the values of the steady-state hetNOEs increase relatively, suggesting the absence of slow motions in ORF6<sub>CTR</sub> (Fig. S16 C). We also evaluated the spectral density function, $J(\omega)$, at zero frequency, $J(0)$, on a per-residue basis by reduced spectral density mapping (Fig. 5 D) (37). This analysis indicated
<figure><img src="image_7.png" /><figcaption>FIGURE 4 NAc-ORF6<sub>CTR</sub> *cis*-P57 and *trans*-P57 subensembles predicted by the a03ws run 1 SAXS BME reweighted metadynamic simulation have very similar profiles. (A) *R*<sub>g</sub> probability distributions were calculated using kernel density estimates to compare the *cis*-P57 (pink) and *trans*-P57 (green) conformational subensembles. The associated error represents the standard deviation between the first and second halves of the analyzed trajectory. The experimental SAXS data and error (standard deviation from the Guinier analysis) are shown in gray. (B) Ensemble-averaged *C*<sup>α</sup> minimum distance contact maps for the *cis*-P57 (pink) and *trans*-P57 (green) conformations. Color intensity represents the probability of a contact between residues, with white indicating no contact, and increasing color intensity suggesting higher contact probabilities. (C) Secondary structure populations for all residues in the *cis*-P57 (pink) and the *trans*-P57 (green) conformations based on SAXS BME statistical weights. Coil populations are represented by solid lines and α-helical populations by dashed lines. β-strand represents less than 0.5% of the population for each residue so was not included. Error bars (black) represent the standard deviation between the first and second halves of the analyzed trajectory. (D) Consistency of the ¹H<sup>N</sup> and ¹⁵N *cis*-P57 and *trans*-P57 ORF6<sub>CTR</sub> experimental chemical shifts with the predicted chemical shifts from the reweighted NAc-ORF6<sub>CTR</sub> a03ws run 1 ensemble. The error in CamShift (silver) is shown. The standard deviation for predicted chemical shifts between the first and second halves of the analyzed trajectory were also plotted but are too small to see. See the *supporting material* for the a03ws run 2 and C36m analysis.</figcaption></figure>

that both P57 subensembles are highly dynamic on the nanosecond timescale and there are no substantial changes in secondary structure propensity between the two ORF6<sub>CTR</sub> P57 states.

Transverse ¹⁵N relaxation rates (*R*₂) also report on the potential chemical exchange contribution (*R*<sub>ex</sub>) due to dynamics occurring on the microsecond to millisecond timescale. One way of distinguishing between the contribu-

tions to the ¹⁵N *R*₂ rates arising from picosecond to nano-second dynamics and those associated with slower microsecond to millisecond dynamics is through the use of the ¹⁵N exchange-free relaxation rate (*R*<sub>dd</sub>) (37). Thus, we determined the *R*<sub>dd</sub> rates at a static magnetic field strength of 14.1 T (Fig. S17 A). We did not detect any exchange occurring on the microsecond to millisecond timescale (Fig. S17, B and C), suggesting minimal long-range
<figure><img src="image_8.png" /><figcaption>FIGURE 5 ORF6<sub>CTR</sub> is very dynamic in both the *trans*-P57 and *cis*-P57 conformations. (A) <sup>15</sup>N longitudinal relaxation rates (*R*<sub>1</sub>), (B) <sup>15</sup>N transverse relaxation rates (*R*<sub>2</sub>), (C) {<sup>1</sup>H}-<sup>15</sup>N steady-state hetNOEs, and (D) *J*(0) values of 300 µM <sup>15</sup>N-labeled ORF6<sub>CTR</sub> at 15°C and pH 6.9. The backbone motions are shown for all peaks corresponding to the *trans*-P57 conformation (*green*), and only for the well-resolved peaks with a measurable intensity corresponding to the *cis*-P57 (*pink*) conformation. Error bars represent the fitting of the NMR data in (A) and (C). The error bars for (B) were calculated by error propagation. The error bars for (D) represent the standard deviations for each residue, which were calculated using Monte Carlo uncertainty propagation. All experiments were recorded at a static magnetic field strength of 14.1 T.</figcaption></figure>

contacts and/or stabilization of secondary structures. This is
consistent with the lack of secondary structure observed for
both P57 ORF6<sub>CTR</sub> subensembles from the secondary struc-
ture propensity scores (Fig. 2 C).

DISCUSSION

Significant progress has been made over the last decade to
accurately characterize the conformational ensembles of
disordered proteins with MD simulations and experimental
techniques. The extensive amounts of data generated by
MD simulations, alongside the capabilities of deep-learning,
have now made it possible to predict some overall properties
of disordered proteins from just the amino acid sequence

alone (66,67). Using deep-learning techniques, global features such as the ensemble-averaged R_g can be determined within a couple of minutes. This approach achieves an accuracy that matches experimental data and surpasses the accuracy of all-atom explicit solvent MD simulations accumulated over hundreds of microseconds. While these coarse-grained deep-learning approaches have the potential to accurately characterize many global features of disordered proteins, atomic resolution is currently required to characterize local features, such as proline cis and trans configurations. Proline cis/trans isomerization, which in certain cases can dramatically alter the conformational ensemble of disordered proteins and thus regulate their function (6,7,15,31), is challenging to characterize with simulations given force field inaccuracies and the long simulation times required to reach convergence for this slow motion. Although NMR generally reports only on ensemble-averaged properties, it provides key insights into the local features of disordered proteins, specifically proline cis/trans isomerization, as these slow conversions result in separate NMR signals reporting on the individual proline conformations (14,31).

Using a combination of all-atom explicit solvent metad-
namic simulations, and experimental NMR and SAXS,
we have accurately characterized both the *cis*-P57 and
*trans*-P57 conformational subensembles of the 21-residue
ORF6<sub>CTR</sub> from SARS-CoV-2. This includes an accurate
description of many of the features of both *cis*-P57 and
*trans*-P57 subensembles, including secondary structure con-
tent, R<sub>g</sub>, and P57 *cis/trans* populations. Applying the ζ and ψ
CVs for P57 in ORF6<sub>CTR</sub> shows that it is possible to achieve
convergence for a process that generally occurs on a time-
scale of seconds to minutes (12,13) and in a system that is
highly computationally expensive to simulate given its het-
erogeneous FES. While all three force fields used here pro-
duced ensembles with R<sub>g</sub>, chemical shifts predictions, and
secondary structure content that agreed with experimental
data, only the a03ws force field generated an ensemble
with a *cis*-P57 population that agreed with those calculated
from NMR. The a03ws force field appears to agree best with
the experimental data in the case of the highly disordered
peptide presented above; however, further considerations
will be required if simulating other systems with distinct
features. Our integrative approach will be particularly rele-
vant for investigating the interactions of disordered protein
ensembles with ligands, where proline isomerization can
be influenced by binding. In the context of ORF6<sub>CTR</sub>, the
proximity of residue P57 to the essential M58 residue
(33–35) suggests that proline isomerization could play a
regulatory role in binding to host proteins. Although the po-
tential influence of proline isomerization has previously
been discussed for the isomeric configuration of P57 in
the bound-state crystal structure (34), the effects of proline
isomerization on binding free energy and potential long-
range dynamics remain open questions. We anticipate that
the ongoing accumulation of data will further enhance the accuracy of force fields to characterize the *cis*-proline state, and agreement between experimental ensemble-averaged *cis*-proline conformations and MD resolved *cis*-proline conformations will only continue to improve.

## DATA AND CODE AVAILABILITY

Code that supports the findings of this study is available from GitHub at https://github.com/hansenlab-ucl/orf6-ctr_cis_trans_conformers and data files are available from Zenodo at https://doi.org/10.5281/zenodo.13748215. NMR chemical shifts have been deposited in the Biological Magnetic Resonance Data Bank (BMRB) (www.bmrb.wisc.edu) under the following accession codes: 52459 for the ORF6<sub>CTR</sub> *cis*-P57 and *trans*-P57 configurations, and 52460 for the unlabeled NAc-ORF6<sub>CTR</sub>. Additional data related to this paper may be requested from the authors.

## ACKNOWLEDGMENTS

The authors acknowledge Carla Molteni, Kresten Lindorff-Larsen, Francesco Pesce, Charlie Buchanan, Carlo Camilloni, and Thomas Löhr for useful discussions. We acknowledge Greg Towers and Morten Andreas Govasli Larsen for providing the GenScript NAc-ORF6<sub>CTR</sub> peptide. This work used the ARCHER2 UK National Supercomputing Service (https://www.archer2.ac.uk) under the ARCHER2 Pioneer Projects proposal (project name e692). We acknowledge PRACE for awarding us access under the special allocation COVID-19 in the PRACE Call 21, on TGCC Joliot-Curie (project name COVID-1986). We acknowledge Diamond Light Source for time on beamline BL21 under proposal 32676, and we acknowledge Katsuaki Inoue and Nathan Cowieson for their assistance with the sample preparation, beamline experiments, and analysis. A.J.P. was supported by a Biotechnology and Biological Sciences Research Council (BBSRC) UK Research and Innovation (UKRI) funded studentship, BB/T008709/1, with the London Interdisciplinary Biosciences Consortium Doctoral Training Partnership. L.S.N. was supported by the UCL-Birkbeck Medical Research Council (MRC) DTP (MR/N013867/1). The Wellcome Trust is thanked for the award of a PhD studentship to S.M. (109073/Z/15/Z). G.T.H. was supported by Schmidt Science Fellows, Rosaland Franklin Research Fellowship from Newham College, Cambridge, and a BBSRC Discovery Fellowship (BB/X009955/1). D.F.H was supported by the Engineering and Physical Sciences Research Council (EPSRC) grant (EP/X036782/1). The BBSRC (BB/R000255/1), Wellcome Trust (ref. 101569/z/13/z), and the EPSRC are acknowledged for supporting the NMR facility at University College London. Access to ultra-high field NMR spectrometers was supported by the Francis Crick Institute through provision of access to the MRC Biomedical NMR Centre. The Francis Crick Institute receives its core funding from Cancer Research UK (FC001029), the UK Medical Research Council (FC001029), and the Wellcome Trust (FC001029). For the purpose of open access, the author has applied a Creative Commons Attribution (CC BY) licence to any Author Accepted Manuscript version arising. This research is supported by the UKRI and EPSRC.

## AUTHOR CONTRIBUTIONS

A.J.P., G.T.H., and C.D.L. performed and analyzed the metadynamic simulations. A.J.P., V.K.S., and S.M. produced the samples. A.J.P., A.M.F., L.S.N., V.K.S., and D.F.H. performed and analyzed NMR data. A.J.P. analyzed the SAXS data. A.B.T., G.T.H., C.D.L., and D.F.H. designed and supervised the research. All authors discussed the results and wrote the paper.

## DECLARATION OF INTERESTS

The authors declare no competing interests.

## SUPPORTING MATERIAL

Supporting material can be found online at https://doi.org/10.1016/j.bpj.2024.09.028.

## SUPPORTING CITATIONS

References (76–88) appear in the supporting material.

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Supplemental information

An integrative characterization of proline *cis* and *trans* conformers in a
disordered peptide

Alice J. Pettitt, Vaibhav Kumar Shukla, Angelo Miguel Figueiredo, Lydia S. Newton, Stephen McCarthy, Alethea B. Tabor, Gabriella T. Heller, Christian D. Lorenz, and D. Flemming Hansen
SUPPLEMENTAL MATERIALS AND METHODS

NMR assignment experiments

The two-dimensional (2D) ¹H-¹H total correlation spectroscopy (TOCSY) and 2D ¹H-¹³C heteronuclear single quantum coherence (HSQC) spectra were measured at a static magnetic field strength of 14.1 T (600 MHz) on the unlabelled N-acetylated (NAc-ORF6<sub>CTR</sub>) to assign the ¹H<sup>α</sup>, ¹H<sup>β</sup>, ¹³C<sup>α</sup>, and ¹³C<sup>β</sup> chemical shifts. These were used for comparison with the chemical shifts derived from the all-atom explicit solvent metadynamic simulations. The 2D ¹H-¹H TOCSY spectra were acquired using the standard dipsi2esgpph Bruker pulse sequence, with spectral widths of 14 ppm (direct dimension) and 12 ppm (indirect dimension), and 1,024 x 512 complex points, respectively. 32 scans were recorded, with an inter-scan delay of 1.5 s. The RF-carrier was set to the position of the water peak (¹H; 4.77 ppm) in both dimensions. The TOCSY mixing was applied with a field of 10 kHz for 80 ms. This experiment was repeated on the ORF6<sub>CTR</sub> sample using the same setup. For the 2D ¹H-¹³C spectra, the standard hsqcgpph Bruker sequence was employed, with 192 scans, spectral widths of 14 ppm (¹H) and 80 ppm for (¹³C), and 512 x 180 complex points, respectively. The recycle delay was set to 1.5 s, with carriers set to the position of the water peak (¹H; 4.77 ppm) and 35 ppm (¹³C) relative to tetramethylsilane (TMS). These experiments were measured at temperatures ranging from 5°C to 40°C, increasing by 5°C increments, and at 37°C.

The 2D ¹H-¹⁵N HSQC spectra were recorded on the uniformly ¹⁵N-labelled ORF6<sub>CTR</sub> at a static magnetic field strength of 14.1 T and at temperatures ranging from 5°C to 40°C, increasing by 5°C increments. Additionally, measurements were taken at 2.5°C and 37°C. The 2D ¹H-¹⁵N HSQC spectra were acquired using the hsqcetf3gpsi2 Bruker pulse sequence (1). Spectral widths were set to 16 ppm (¹H) and 20.5 ppm (¹⁵N), with 1,536 x 128 complex points acquired, respectively. Carriers were set to the position of the water peak (¹H; 4.77 ppm) and 121.5 ppm (¹⁵N), with 4 scans and a recycle delay of 1 s.

Uniformly ¹³C,¹⁵N-labelled and ¹⁵N-labelled samples of ORF6<sub>CTR</sub> were used for backbone and side chain chemical shift assignments. These assignments correspond to both the *trans*-P57 and *cis*-P57 conformations at a static magnetic field strength of 14.1 T and at 15°C. Standard double-resonance and triple-resonance experiments were employed for backbone assignment, including the 2D CON (2), and the three-dimensional (3D) HNCO (3), HN(CA)CO (4), and HNCACB (5) experiments. This was supplemented with the ¹⁵N-edited nuclear Overhauser effect spectroscopy HSQC (¹⁵N-NOESY-HSQC) (6) and ¹⁵N-TOCSY-HSQC (7) experiments to characterise intra- and inter-residue information. Side chain assignments were obtained from the 3D CC(CO)NH experiment (8).

The 2D CON spectra were acquired using the *c_con*_lasq Bruker pulse sequence. Spectral widths were set to 40 ppm in both dimensions, with carriers set to 173 ppm (¹³C) relative to TMS and 121.5 ppm (¹⁵N), and 512 x 128 complex points, respectively. 32 scans and a recycle delay of 2 s were applied. The 3D HNCO spectra were acquired using the *hncogp3d* Bruker pulse sequence and the 3D HN(CA)CO spectra were acquired using the *hncacogpwg3d* Bruker pulse sequence (4). Both spectra were recorded with 16 scans, 1,024 x 29 x 80 complex points (¹H, ¹⁵N, ¹³C), and a spectral width of 14 x 20.5 x 7 ppm, respectively. Carriers were set to the position of the water peak (¹H; 4.77 ppm), 121.5 ppm (¹⁵N) and 173 ppm (¹³C) relative to TMS, with a recycle delay of 1 s. The 3D HNCACB spectra were acquired using the *hncacbgp3d* Bruker pulse sequence (5). Spectra were recorded with 16 scans, 1,024 x 30 x 72 complex points (¹H, ¹⁵N, ¹³C), and a spectral width of 14 x 20.5 x 60.2 ppm, respectively. Carriers were set to the water peak (¹H; 4.77 ppm), 121.5 ppm (¹⁵N), and 43 ppm (¹³C), with a recycle delay of 1 s.

The ¹⁵N-NOESY-HSQC spectra were acquired using the noesyhsqcfpf3gpsi3d Bruker pulse sequence. Spectra were recorded with eight scans, 2,048 x 24 x 96 complex points (¹H, ¹⁵N, ¹H), and a
spectral width of 16 x 20.5 x 16 ppm, respectively. Carriers were set to the water peak (¹H; 4.77 ppm) and 121.5 ppm (¹⁵N). A recycle delay of 1.5 s and a mixing time of 0.25 s were used. The ¹⁵N-TOCSY-HSQC spectra were acquired using a pulse sequence based on the original Bruker dipsihsqcf3gpsi3d with a flip-flop spectroscopy (FLOPSY)-16 scheme. Spectra were acquired with 16 scans, 1,024 x 32 x 96 complex points (¹H, ¹⁵N, ¹H), and a spectral width of 16 x 20.5 x 16 ppm, respectively. Carriers were set to the water peak (¹H; 4.77 ppm) and 121.5 ppm (¹⁵N), with a recycle delay of 1 s. A TOCSY mixing time of 0.1 s was used and an 8 kHz TOCSY spin-lock was applied. The CC(CO)NH spectra were acquired using a pulse sequence based on the original Bruker ccconhgp3d.2 with a FLOPSY-16 scheme. Spectra were recorded with 16 scans, 1,024 x 30 x 84 complex points (¹H, ¹⁵N, ¹³C), and a spectral width of 14 x 20.5 x 71 ppm, respectively. Carriers were set to the water peak (¹H; 4.77 ppm), 121.5 ppm (¹⁵N) and 43 ppm (¹³C). A recycle delay of 1.5 s, along with a TOCSY mixing time of 18 ms were used.

## NMR relaxation experiments

¹⁵N-labelled ORF6<sub>CTR</sub> samples were used to measure the backbone longitudinal ($R_1$) and rotating frame ($R_{1p}$) relaxation rates, as well as the {¹H}-¹⁵N steady-state heteronuclear NOEs (hetNOEs), at two static magnetic field strengths (14.1 T and 18.8 T) and at 15°C.

Both $R_1$ and $R_{1p}$ were measured using established proton-detected pulse sequences based on gradient-selected, sensitivity-enhanced, refocused ¹⁵N sequences (9). Both spectra were recorded with 2,048 x 160 complex points and spectral widths as used before in the 2D ¹H-¹⁵N HSQC experiment. Gradient pulses were used to suppress the water signal and a long recycle delay of 3 s was employed. In the $R_1$ sequence, N-H cross-correlated relaxation pathways were suppressed by hard 180° pulses every 20 ms during the relaxation delay (10). The $R_1$ ¹⁵N-¹H planes were recorded with eight relaxation delays ranging from 20 ms to 700 ms. In the $R_{1p}$ sequence, cross-correlated relaxation was suppressed by hard 180° pulses during the ¹⁵N spin-lock. Magnetisation was explicitly aligned with the spin-lock field (11). N-H planes were recorded using eight relaxation delays ranging from 2 ms to 140 ms, with a ¹⁵N spin-lock field strength of 2 kHz.

The {¹H}-¹⁵N hetNOEs were recorded using a pseudo-3D experiment, with and without proton saturation (12). Amide proton magnetisation saturation was accomplished by using a 5 s train of high-power 120° pulses applied at 5 ms intervals. The reference and saturated spectra were alternately recorded. To ensure complete recovery of the initial magnetisation at the start of each increment of the reference experiment, a long recycle delay of 15 s was applied. The spectral widths were 16 ppm (¹H) and 20.5 ppm (¹⁵N), and 2,048 x 160 (¹H, ¹⁵N) complex points were recorded at a static magnetic field strength of 14.1 T.

Exchange-free ¹⁵N transverse relaxation, $R_{dd}$, rates were measured using pulse schemes for the four ¹H-¹⁵N relaxation rates: $R_{1p}(2\text{Hz}_z\text{N}'_z)$, $R_{1p}(2\text{Hz}'_z\text{N}'_z)$, $R_{1p}^2(2\text{Hz}'_z\text{N}''_z)$, and $R_1(2\text{Hz}_z\text{N}_z)$ at a static magnetic field strength of 14.1 T and at 15°C. A total of eight relaxation delays between 2 ms and 26 ms were used for all experiments (same delays used for each rate measurement) (13). A 10 kHz ¹H spin-lock and a 2 kHz ¹⁵N spin-lock were applied. Spectral widths were the same as those used before in {¹H}-¹⁵N hetNOE experiments, with 1,536 x 640 (¹H, ¹⁵N) complex points.
## NMR diffusion experiments

¹⁵N-labelled ORF6<sub>CTR</sub> samples were used to measure the pseudo-3D diffusion pulsed field-gradient spin echo (PFGSE) ¹H-¹⁵N-diffusion ordered spectroscopy-HSQC (¹H-¹⁵N-DOSY-HSQC) experiment with a bipolar gradient (14) at a static magnetic field strength of 22.3 T and at 15°C. Six experiments with gradient strengths of 0.963, 8.667, 16.370, 24.070, 31.780, and 39.480 G cm<sup>-1</sup> were acquired for each data set, with the gradient strengths increased linearly through the acquisition and all other delays and pulses held constant. Gradient pulses, δ, were applied for 3 ms and a diffusion delay, Δ, of 200 ms. 80 scans were acquired per gradient experiment with 1,536 x 176 complex points (¹H, ¹⁵N), using the same spectral widths and carriers as used before for the 2D ¹H-¹⁵N HSQC experiment.

## NMR analysis

The transverse relaxation rate-constants, R<sub>2</sub>, were calculated using the following equation:

$$R_{1\rho} = R_1 \cos^2\theta + R_2 \sin^2\theta \quad (S1)$$

Where θ is the tip-angle of the magnetisation-vector:

$$\theta = \arctan\left(\frac{v_1}{\Delta v}\right) \quad (S2)$$

and v<sub>1</sub> is the field strength of the spin-lock (Hz) and Δv is the offset relative to the frequency of the transmitter frequency (Hz).

Diffusion coefficients of the cis-P57 and trans-P57 peaks were calculated by fitting the signal decay of residues Q56, M58, and E59 against the Stejskal-Tanner equation (15):

$$\frac{I}{I_0} = e^{-D\gamma_H^2 g^2 \delta^2 \left(\Delta - \frac{\delta}{3} - \frac{\tau}{2}\right)} \quad (S3)$$

where I is the observed intensity, I<sub>0</sub> is the intensity of the unattenuated signal, D is the diffusion coefficient, and γ<sub>H</sub> is the gyromagnetic ratio of ¹H. Residue E55 was not included in the analysis due to low signal intensity.
## Metadynamics setup

The following nine collective variables (CVs) were used to enhance conformational sampling of the NAc-ORF6<sub>CTR</sub> with Gaussian widths for each CV shown in Table S1 below (16, 17):

1.) Total α-helix content, where the ALPHARMSD keyword in PLUMED is used to quantify this CV (18). In proteins, any chain of six contiguous residues can form an α-helix. This CV initially generates the set of all potential regions comprising six consecutive residues within the system. The root mean square deviation (RMSD) between the configuration where the residues are located and an idealised helix structure is then calculated. The sum of functions of the RMSD distances are used to compute this CV as follows:

$$S_{\alpha helix} = \sum_{i} \frac{1 - \left( \frac{\text{RMSD}_{i}}{\text{RMSD}_{0}} \right)^{8}}{1 - \left( \frac{\text{RMSD}_{i}}{\text{RMSD}_{0}} \right)^{12}} \quad (S4)$$

where the sum extends over all potential α-helix segments, i, and RMSD<sub>0</sub> = 0.08 nm.

2.) The sum of the combined parallel and anti-parallel β-sheet content, where the total parallel β-sheet content and total anti-parallel β-sheet content are calculated using the PARABETARMSD and ANTIBETARMSD keywords, respectively, in PLUMED. In a protein chain, two segments containing three contiguous residues can form an anti-parallel or parallel β-sheet if they are separated by a minimum of two or three residues, respectively, to accommodate a turn. This CV initially generates the set of all possible six residue sections that could form a parallel or anti-parallel β-sheet. It then calculates the RMSD between the configuration where the residues are located and the idealised parallel or anti-parallel β-sheet structure. The total parallel or anti-parallel β-sheet content is calculated using the equivalent Eq. S4 above, where the sum extends over all potential parallel and anti-parallel β-sheets. The CV is finally computed by summing the combined parallel and anti-parallel β-sheet contents (18).

3.) The R<sub>g</sub> (in nm) computed using only the positions of the C<sup>α</sup> carbon atoms with the GYRATION keyword in PLUMED. This CV is defined as:

$$S_{R_g} = \left( \frac{\sum_{i \in C_\alpha} m_i |\mathbf{r}_i - \mathbf{r}_{COM}|^2}{\sum_{i \in C_\alpha} m_i} \right)^{\frac{1}{2}} \quad (S5)$$

where m<sub>i</sub> and r<sub>i</sub> are the mass and position of the C<sup>α</sup> atom of the i<sup>th</sup> residue, respectively.

r<sub>COM</sub> is the coordinate of the centre of mass (COM) of the protein defined as:

$$\mathbf{r}_{\text{COM}} = \frac{\sum_{i \in C^{\alpha}} m_i \mathbf{r}_i}{\sum_{i \in C^{\alpha}} m_i} \quad (S6)$$
4.) The number of salt bridges. This CV is computed as the number of contacts within a 0.6 nm cutoff range between the atoms of NH₃⁺ groups of lysine or the -C(NH₂)₂⁺ of arginine and the -COO⁻ atom groups of aspartate or glutamate. This CV is computed using the COORDINATION key word in PLUMED (17). The following switching function was used:

$$S_{\text{Salt bridges}} = \sum_{i \in A} \sum_{j \in B} \frac{1 - \left(\frac{d_{ij}}{d_0}\right)^6}{1 - \left(\frac{d_{ij}}{d_0}\right)^{12}} \quad (S7)$$

where $d_0 = 0.6$ nm, $d_{ij}$ is the distance between atoms i and j, and A and B are the two groups of atoms between which contacts are calculated. For this example, group A contains the heavy atoms from arginine and lysine residues and group B contains the heavy atoms from aspartate and glutamate residues. Self-interactions are excluded from the calculation.

5.) The end-to-end distance (in nm) of the NAc-ORF6<sub>CTR</sub>. This CV is computed on the C<sup>a</sup> carbon of the first and last residue using the DISTANCES keyword in PLUMED, which calculates the distance between a pair of atoms.

6.) The correlation between consecutive ψ torsion angles (17). This CV is computed using the DIHCOR keyword in PLUMED. The following equation is applied for every residue, i, given by:

$$S_{\text{Dihedral correlation}} = \frac{1}{2} \sum_{i} [1 + \cos(\psi_{i} - \psi_{i+1})] \quad (S8)$$

7.) The improper dihedral angle ζ (in rad) for residue P57. This CV uses the TORSION keyword in PLUMED to compute the torsion between the following four atoms: C<sup>a</sup><sub>i-1</sub>, O<sup>i-1</sup>, C<sup>δ</sup><sub>i</sub>, C<sup>a</sup><sub>i</sub>, where i is the P57 residue (19, 20).

8.) The ψ torsional angle (in rad) for residue P57. This CV uses the TORSION keyword in PLUMED to compute the torsion between the following four atoms: N<sub>i</sub>, C<sup>a</sup><sub>i</sub>, C<sup>'</sup><sub>i</sub>, N<sub>i+1</sub>, where i is the P57 residue (19, 20).

9.) The number of contacts between hydrophobic residues (17). Hydrophobic residues isoleucine, leucine, methionine, proline, and tyrosine were used in this CV, which calculates the number of inter-C<sup>β</sup> atom distances less than 0.6 nm. To compute the CV, the COORDINATION keyword in PLUMED is applied and uses the switching function as defined by Eq. S7 above, except A and B are the same group. Self-interactions are not included in the calculation.
## Convergence model

The following equation was used to fit a bi-exponential curve to the FES blocking analysis for each CV:

$$ \text{Error (FES)} = A * (1 - B * e^{(-k1*t)} - C * e^{(-k2*t)}) \quad (S9) $$

The constant term, A, determined from the model, was used to approximate the FES standard-error at infinite time for each CV.

## Structural ensemble analysis

By using the weights $w(S_i)$ obtained from the bias potential $V_{PB}(S_i)$ at the end of the metadynamic simulations, observables were calculated as ensemble averages using the following equation:

$$ w(S_i) = \frac{e^{\frac{V_{PB}(S_i)}{k_B T}}}{\sum_{j=1}^{N} e^{\frac{V_{PB}(S_j)}{k_B T}}} \quad (S10) $$

where $S_i$ is the value of all CVs at time $i$, $N$ is the total number of time steps, and $k_B T$ is 2.5774 kJ mol⁻¹ (the product of the Boltzmann constant, $k_B$, and $T$, when $T = 310$ K) (17, 21).
SUPPLEMENTAL TABLES

<table><caption>Table S1. Gaussian widths and grid sizes for the nine CVs in both a03ws simulations (run 1 and run 2), the a99SB-disp simulation, and the C36m simulation.</caption><thead><tr><th rowspan="2">CV No. and name</th><th colspan="3">Gaussian width (in CV units)</th><th rowspan="2">Grid max</th><th rowspan="2">Grid min</th></tr><tr><th>a03ws</th><th>a99SB-disp</th><th>C36m</th></tr></thead><tbody><tr><td>1. α-helix</td><td>0.73</td><td>0.58</td><td>0.23</td><td>100</td><td>-1</td></tr><tr><td>2. β-sheet</td><td>0.12</td><td>0.13</td><td>0.24</td><td>100</td><td>-1</td></tr><tr><td>3. R<sub>g</sub></td><td>0.10</td><td>0.11</td><td>0.12</td><td>20</td><td>-1</td></tr><tr><td>4. Salt bridges</td><td>2.47</td><td>2.61</td><td>4.01</td><td>400</td><td>-1</td></tr><tr><td>5. End-to-end distance</td><td>0.48</td><td>0.54</td><td>0.63</td><td>10</td><td>-1</td></tr><tr><td>6. Dihedral correlation</td><td>1.03</td><td>1.00</td><td>1.06</td><td>50</td><td>-1</td></tr><tr><td>7. ζ angle</td><td>0.93</td><td>1.41</td><td>1.11</td><td>π</td><td>-π</td></tr><tr><td>8. ψ angle</td><td>0.85</td><td>0.56</td><td>0.57</td><td>π</td><td>-π</td></tr><tr><td>9. Hydrophobic contacts</td><td>0.07</td><td>0.08</td><td>0.06</td><td>10</td><td>-1</td></tr></tbody></table>

The upper and lower bounds for the grids are provided. These bounds represent the maximum and minimum values the CV can take. For example, for the P57 ψ dihedral angle (CV No. 8), the maximum value of the angle is π and the minimum value is -π. The units for the Gaussian widths and grid sizes are dimensionless unless specified otherwise in the CV definitions.
<table><caption>Table S2. Absolute differences for each residue in the ORF6<sub>CTR</sub> and NAc-ORF6<sub>CTR</sub> ¹H<sup>N</sup> and ¹H<sup>a</sup> chemical shifts in ppm.</caption><thead><tr><th>Residue</th><th>|Δδ<sub>H</sub><sup>a</sup>| (ppm)</th><th>|Δδ<sub>H</sub><sup>N</sup>| (ppm)</th></tr></thead><tbody><tr><td>S43</td><td>0.030</td><td>0.149</td></tr><tr><td>L44</td><td>0.018</td><td>0.092</td></tr><tr><td>T45</td><td>0.009</td><td>0.005</td></tr><tr><td>E46</td><td>0.002</td><td>0.008</td></tr><tr><td>N47</td><td>0.008</td><td>0.000</td></tr><tr><td>K48</td><td>0.003</td><td>0.005</td></tr><tr><td>Y49</td><td>0.002</td><td>0.001</td></tr><tr><td>S50</td><td>0.009</td><td>0.006</td></tr><tr><td>Q51</td><td>0.032</td><td>0.002</td></tr><tr><td>L52</td><td>0.005</td><td>0.007</td></tr><tr><td>D53</td><td>0.008</td><td>0.006</td></tr><tr><td>E54</td><td>0.013</td><td>0.001</td></tr><tr><td>E55</td><td>0.010</td><td>0.007</td></tr><tr><td>Q56</td><td>0.004</td><td>0.002</td></tr><tr><td>P57</td><td>0.018</td><td>N/A</td></tr><tr><td>M58</td><td>0.009</td><td>0.004</td></tr><tr><td>E59</td><td>0.002</td><td>0.003</td></tr><tr><td>I60</td><td>0.022</td><td>0.009</td></tr><tr><td>D61</td><td>0.017</td><td>0.010</td></tr></tbody></table>

The RMSD between ORF6<sub>CTR</sub> and NAc-ORF6<sub>CTR</sub> is 0.010 and 0.029 for ¹H<sup>a</sup> and ¹H<sup>N</sup>, respectively, as given by Eq. S11 shown below.
$$ \text{RMSD}(\mathbf{v}, \mathbf{w}) = \left( \frac{1}{N} \sum_{i=1}^{N} (v_i - w_i)^2 \right)^{\frac{1}{2}} \qquad (S11) $$

where $\mathbf{v}$ = ORF6<sub>CTR</sub> and $\mathbf{w}$ = NAc-ORF6<sub>CTR</sub> for the ¹H<sup>N</sup> or ¹H<sup>α</sup> chemical shifts assigned for residues 43-61.
SUPPLEMENTAL FIGURES

<figure><img src="image_9.png" /></figure>

<figure><img src="image_10.png" /><figcaption>**Figure S1.** The C-terminal region of ORF6 is predicted to be intrinsically disordered.</figcaption></figure>

(A) s2D prediction showing the percentage populations of coil, α-helix, β-stand, and polyproline-II for ORF6 (22). (B) AlphaFold2 predicted local distance difference test (pLDDT) score for ORF6 (23). The pLDDT score is between 0 and 100. A pLDDT greater than 90 suggests a very high model confidence and a pLDDT less than 50 suggests a very low model confidence. The confidence for ORF6<sub>CTR</sub> (residues 41-61) is very low indicating that this region is unstructured (24).
<figure><img src="image_11.png" /></figure>

<figure><img src="image_12.png" /><figcaption>Figure S2. ORF6<sub>CTR</sub> solid-phase peptide synthesis analysis.</figcaption></figure>

(A) Analytical HPLC trace of purified ORF6<sub>CTR</sub>. Analysis was performed using a gradient of Buffer B (0.1% (v/v) TFA in acetonitrile) in Buffer A (0.1% (v/v) TFA in water) (5-75% over 60 min) using a C8 RP-silica column from Dr. Maisch GmbH at 1 mL/min with detection at 214 nm. (B) ESI+ mass spectrum of purified ORF6<sub>CTR</sub> with annotations showing charge states. The spectrum was recorded with a Waters Acquity UPLC SQD LC-MS instrument equipped with a Hypersil GOLD C4, 5 µm particle, 50 mm x 2.1 mm column (Thermo Scientific).
<figure><img src="image_13.png" /><figcaption>Figure S3. Convergence analysis of NAc-ORF6<sub>CTR</sub> metadynamic simulations: a03ws run 1, a99SB-disp, and C36m ensembles.</figcaption></figure>

The free energy surface (FES) standard-error as a function of block size was plotted for all nine CVs in the (A) a03ws run 1 (orange), (B) a99SB-disp (red), and (C) C36m (blue) force fields (25, 26). The modelled blocking analysis curve from Eq. S9 (black) and the FES standard-error at infinite time (grey dashed line) are shown. See below for a03ws run 2.
<figure><img src="image_14.png" /><figcaption>**Figure S4.** Convergence analysis of NAc-ORF6<sub>CTR</sub> metadynamic simulations: a03ws run 2 ensemble. The FES standard-error as a function of block size was plotted for all nine CVs in the a03ws run 2 simulation (25, 26). The modelled blocking analysis curve from Eq. S9 (**black**) and the FES standard-error at infinite time (**grey dashed line**) are shown.</figcaption></figure>
A

<figure><img src="image_15.png" /></figure>

B

<figure><img src="image_16.png" /></figure>

C

<figure><img src="image_17.png" /></figure>

Figure S5. Ensemble-averaged C<sup>a</sup> minimum distance inter-residue contact maps predict the NAc-ORF6<sub>CTR</sub> to be disordered in all three force fields (a03ws run 1, a99SB-disp, and C36m before SAXS BME reweighting).

Contact maps are shown for (A) a03ws run 1 (orange), (B) a99SB-disp (red), and (C) C36m (blue) ensembles. The contact maps represent the probability of two C<sup>a</sup> atoms being 0.5 nm apart or closer (force field colour) or not in contact (white). The contact map for a03ws run 2 is shown below.
<figure><img src="image_18.png" /><figcaption>B</figcaption></figure>

<figure><img src="image_19.png" /><figcaption>C</figcaption></figure>

<figure><img src="image_20.png" /><figcaption>Figure S6. Assessment of metadynamic simulations (before SAXS BME reweighting) consistency with NMR chemical shift data: a03ws run 1, a99SB-disp, and C36m ensembles.</figcaption></figure>

Comparison of the ensemble-averaged predicted chemical shifts calculated using CamShift (27) for the (A) a03ws run 1 (orange), (B) a99SB-disp (red), and (C) C36m (blue) ensembles to experimental chemical shifts (black) using the ¹³Cª, ¹³Cβ, ¹Hª, and ¹HN chemical shifts. The error in CamShift (silver) is shown. The standard deviation between the chemical shifts predicted in the first and second halves of the analysed trajectories were also plotted but are smaller than the data points. The RMSD and its corresponding error between experimental and chemical shifts predicted from the simulations are displayed for each force field and chemical shift. The plot for a03ws run 2 is shown below.
<figure><img src="image_21.png" /><figcaption>**Figure S7.** NMR analysis indicates the amide protons in the NAc-ORF6<sub>CTR</sub> do not form stable intramolecular hydrogen bonds.</figcaption></figure>

Temperature coefficients for the *trans*-P57 conformation of 400 µM unlabelled NAc-ORF6<sub>CTR</sub> determined from ¹H-¹H TOCSY spectra at eight temperatures between 5-37°C. A value more negative than -4.5 ppb K<sup>-1</sup> (*grey dashed line*) indicates a lack of long-lived intramolecular hydrogen bonds. Error bars represent the uncertainty associated with the least squares polynomial fit for each residue. Data was collected at pH 6.9 and a static magnetic field strength of 14.1 T.
<figure><img src="image_22.png" /><figcaption>Figure S8. ORF6<sub>CTR</sub> and NAc-ORF6<sub>CTR</sub> 2D ¹H-¹H TOCSY spectra.</figcaption></figure>

¹H-¹H TOCSY spectrum for 300 µM ¹⁵N-labelled ORF6<sub>CTR</sub> (green) and 400 µM unlabelled NAc-ORF6<sub>CTR</sub> (purple) at 15°C. Narrow dispersion in the ¹H<sup>N</sup> dimension indicates that ORF6<sub>CTR</sub> is disordered. N-acetylation only substantially changes the chemical shifts for the residues closest to the N-terminus (S43 and L44). Spectra were acquired at a static magnetic field strength of 14.1 T and pH 6.9. Unassigned peaks have been marked with an asterisk (*).
<figure><img src="image_23.png" /><figcaption>**Figure S9. ORF6<sub>CTR</sub> cis-P57 population and thermodynamics analysis using peak height.**</figcaption></figure>

(A) The *cis*-P57 populations at 15°C (*solid fill*) and 37°C (*dashed fill*) are shown for each well-resolved *cis*-P57 and *trans*-P57 peak in the 2D ¹H-¹⁵N HSQC spectrum. The peak height was used to calculate the mean *cis*-P57 population and standard deviation across residues E55, Q56, and E59. (B) The van't Hoff analysis of P57 *cis/trans* isomerisation determined from the peak height. The mean natural logarithm of the equilibrium constant for proline *cis/trans* isomerisation about the Q56-P57 peptide bond as a function of temperature was calculated using residues E55, Q56, and E59. Error bars represent the standard deviation across the three residues used in the analysis. The van't Hoff linear fit yielded a *cis*-P57 population of 8 ± 1% at 37°C. Experiments were recorded at a static magnetic field strength of 14.1 T and pH 6.9.
a03ws run 2

<figure><img src="image_24.png" /><figcaption>**Figure S10.** Assessment of metadynamic simulations (before SAXS BME reweighting) consistency with NMR chemical shift data: a03ws run 2 ensemble.</figcaption></figure>

Comparison of the ensemble-averaged predicted chemical shifts calculated using CamShift (27) for the a03ws run 2 (*cyan*) ensemble to experimental chemical shifts (black) using the ¹³Cª, ¹³Cβ, ¹Hª, and ¹HN chemical shifts. The error in CamShift (*silver*) is shown. The standard deviation between the chemical shifts predicted in the first and second halves of the analysed trajectory were also plotted but are smaller than the data points. The RMSD and its corresponding error between experimental and chemical shifts predicted from the simulations are displayed.
A

<figure><img src="image_25.png" /></figure>

B

<figure><img src="image_26.png" /></figure>

C

<figure><img src="image_27.png" /></figure>

Figure S11. Consistency of the a03ws run 2 NAc-ORF6<sub>CTR</sub> ensemble characterisations with the a03ws run 1 ensemble (before SAXS BME reweighting).

(A) Probability distribution for the ω angle about the Q56-P57 peptide bond in the a03ws run 2 simulation. The thickness of the grey distribution represents the uncertainty. The *cis*-P57 population is shown, and the error represents the standard deviation between the first and second halves of the analysed trajectory. (B) The *R*<sub>g</sub> probability distribution was calculated using kernel density estimates to compare the a03ws run 1 simulation (orange) and the a03ws run 2 simulation (cyan). Ensemble-averaged *R*<sub>g</sub> are shown for each ensemble. The associated error represents the standard deviation between the first and second halves of the analysed trajectories. The experimental SAXS data and error (standard deviation from the Guinier analysis) are shown in grey. (C) Ensemble-averaged, residue-specific C<sup>α</sup> inter-residue minimum distance contact maps for a03ws run 2.
A

a03ws run 2 $\chi^2_{red} = 0.902$

<figure><img src="image_28.png" /></figure>

C36m

$\chi^2_{red} = 0.955$

<figure><img src="image_29.png" /></figure>

B

a03ws run 1

<figure><img src="image_30.png" /></figure>

1.194 ± 0.002 nm

1.27 ± 0.02 nm

1.292 ± 0.001 nm

C

a03ws run 2

<figure><img src="image_31.png" /></figure>

1.190 ± 0.002 nm

1.27 ± 0.02 nm

1.295 ± 0.005 nm

D

C36m

<figure><img src="image_32.png" /></figure>

1.18179 ± 0.00002 nm

1.27 ± 0.02 nm

1.299 ± 0.003 nm

E

C36m reweight

<figure><img src="image_33.png" /></figure>

$\omega$ (degrees)

**Figure S12.** Integration of a03ws run 1, a03ws run 2, and C36m NAc-ORF6<sub>CTR</sub> ensembles with SAXS data using the BME approach.

The calculated SAXS intensities from the metadynamic simulations and the reweighted simulation are compared to the experimental SAXS intensities and the error associated with each intensity (grey) for (A) the a03ws run 2 (left) and the C36m (right) ensembles (28–30). R<sub>g</sub> probability distributions were calculated using kernel density estimates to compare the distribution before (light shade) and after (dark shade) the SAXS BME reweighting for the (B) a03ws run 1, (C) a03ws run 2, and (D) C36m ensembles. Ensemble-averaged R<sub>g</sub> are shown for each system. The associated error represents the standard deviation between the first and second halves of the analysed trajectories. The R<sub>g</sub> determined by SAXS, and associated error (standard deviation from the Guinier analysis) are shown in grey. (E) Probability distribution for the $\omega$ angle about the Q56-P57 peptide bond in the C36m SAXS BME reweighted ensemble with the cis-P57 population shown. The error represents the standard deviation between the first and second halves of the analysed trajectory.
A

a03ws run 1 reweight

B

a03ws run 2 reweight

C

C36m reweight

<figure><img src="image_34.png" /><figcaption>Figure S13. Assessment of SAXS BME reweighted metadynamic simulations consistency with NMR chemical shift data: a03ws run 1, a03ws run 2, and C36m ensembles.</figcaption></figure>

Comparison of the ensemble-averaged predicted chemical shifts calculated using CamShift (27) for the (A) a03ws run 1 (brown), (B) a0ws run 2 (teal), and (C) C36m (dark blue) ensembles to experimental chemical shifts (black) using the ¹³Cª, ¹³Cβ, ¹Hª, and ¹HN chemical shifts. The error in CamShift (silver) is shown. The standard deviation between the chemical shifts predicted in the first and second halves of the analysed trajectory were also plotted but are smaller than the data points. The RMSD and its corresponding error between experimental and chemical shifts predicted from the simulations are displayed for each ensemble and chemical shift.
<figure><img src="image_35.png" /><figcaption>Figure S14. NAc-ORF6<sub>CTR</sub> *cis*-P57 and *trans*-P57 subensembles predicted by the a03ws run 2 SAXS BME reweighted metadynamic simulation have very similar profiles.</figcaption></figure>

(A) Rg probability distributions were calculated using kernel density estimates to compare the *cis*-P57 (pink) and *trans*-P57 (green) conformational subensembles. The associated error represents the standard deviation between the first and second halves of the analysed trajectory. The experimental SAXS data and error (standard deviation from the Guinier analysis) are shown in grey. (B) Ensemble-averaged C<sup>α</sup> minimum distance contact maps for the *cis*-P57 (pink) and *trans*-P57 (green) conformations. Colour intensity represents the probability of a contact between residues, with white indicating no contact and increasing colour intensity suggesting a higher contact probability. (C) Secondary structure populations for all residues in the *cis*-P57 (pink) and the *trans*-P57 (green) conformations. Coil populations are represented by solid lines and α-helical populations by dashed lines. β-strand represents less than 0.9% of the population for each residue so was not included. Error bars (black) represent the standard deviation between the first and second halves of the analysed trajectory. (D) Consistency of the ¹H<sup>N</sup> and ¹⁵N *cis*-P57 and *trans*-P57 ORF6<sub>CTR</sub> experimental chemical shifts with the predicted chemical shifts from the SAXS BME reweighted NAc-ORF6<sub>CTR</sub> a03ws run 2 ensemble. The error in CamShift (silver) is shown. The standard deviation for predicted chemical shifts between the first and second halves of the analysed trajectory were also plotted but are too small to see.
<figure><img src="image_36.png" /><figcaption>**Figure S15.** NAc-ORF6<sub>CTR</sub> cis-P57 and trans-P57 subensembles predicted by the C36m SAXS BME reweighted metadynamics simulation have very similar profiles. (The figure legend is continued on the next page).</figcaption></figure>
(A) *R*<sub>g</sub> probability distributions were calculated using kernel density estimates to compare the *cis*-P57 (pink) and *trans*-P57 (green) conformational subensembles. The associated error represents the standard deviation between the first and second halves of the analysed trajectory. The experimental SAXS data and error (standard deviation from the Guinier analysis) are shown in grey. (B) Ensemble-averaged *C*<sup>α</sup> minimum distance contact maps for the *cis*-P57 (pink) and *trans*-P57 (green) conformations. Colour intensity represents the probability of a contact between residues, with white indicating no contact and increasing colour intensity suggesting a higher contact probability. (C) Secondary structure populations for all residues in the *cis*-P57 (pink) and the *trans*-P57 (green) conformations based on SAXS BME statistical weights are shown for coil (top), α-helix (middle), and β-strand (bottom). Error bars (black) represent the standard deviation between the first and second halves of the analysed trajectory. (D) Consistency of the <sup>1</sup>H<sup>N</sup> and <sup>15</sup>N *cis*-P57 and *trans*-P57 ORF6<sub>CTR</sub> experimental chemical shifts with the predicted chemical shifts from the SAXS BME reweighted NAc-ORF6<sub>CTR</sub> C36m ensemble. The error in CamShift (silver) is shown. The standard deviation for predicted chemical shifts between the first and second halves of the analysed trajectory were also plotted but are too small to see.
<figure><img src="image_37.png" /><figcaption>Figure S16. The *cis*-P57 and *trans*-P57 subensembles exhibit similar backbone motions at a static magnetic field strength of 18.8 T.</figcaption></figure>

(A) ¹⁵N longitudinal relaxation rate, *R*₁, (B) ¹⁵N transverse relaxation rate, *R*₂, and (C) {¹H}·¹⁵N steady-state hetNOEs of 300 µM ¹⁵N-labelled ORF6<sub>CTR</sub> at 15°C. The backbone motions are shown for all peaks corresponding to the *trans*-P57 conformation (*green*), and only for the well-resolved peaks with a measurable intensity corresponding to the *cis*-P57 (pink) conformation. Error bars represent the fitting of the NMR data in (A) and (C). The errors for (B) were calculated by error propagation. All experiments were recorded at a static magnetic field strength of 18.8 T and pH 6.9.
<figure><img src="image_38.png" /><figcaption>Figure S17. Absence of microsecond to millisecond dynamics in the ORF6<sub>CTR</sub> *trans*-P57 conformational subensemble.</figcaption></figure>

(A) Exchange-free measure of dipole-dipole ¹⁵N transverse relaxation, $R_{dd}$, (B) zoomed-in view of chemical exchange analysis and (C) chemical exchange analysis for the *trans*-P57 conformation of 300 µM ¹⁵N-labelled ORF6<sub>CTR</sub> at 15°C (13). The error bars in (A) represent the error associated with the exponential decay model fit for each residue. The error bars for (B) and (C) represent the standard deviation for each residue, which was calculated using Monte Carlo uncertainty propagation. All experiments were recorded at a static magnetic field strength of 14.1 T and pH 6.9.
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