# Flexibility or uncertainty? A critical assessment of AlphaFold 2 pLDDT

Yann Vander Meersche, Julien Diharce, Jean-Christophe Gelly, Tatiana Galochkina

► To cite this version:

Yann Vander Meersche, Julien Diharce, Jean-Christophe Gelly, Tatiana Galochkina. Flexibility or uncertainty? A critical assessment of AlphaFold 2 pLDDT. Structure, 2025, 33 (12), pp.2157-2163.e2. 10.1016/j.str.2025.09.001. hal-05474189

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# Flexibility or uncertainty? A critical assessment of AlphaFold 2 pLDDT

Yann Vander Meersche¹, Julien Diharce¹, Jean-Christophe Gelly*¹, Tatiana Galochkina*¹

* Corresponding authors: jean-christophe.gelly@u-paris.fr, tatiana.galochkina@u-paris.fr

¹ Université Paris Cité and Université des Antilles and Université de la Réunion, INSERM, BIGR, DSIMB, F-75015 Paris, France

## Abstract

The release of AlphaFold 2 and subsequent development of AlphaFold 3 have had a profound impact on protein structure prediction, providing near-experimental accuracy from the amino acid sequences. However, the utility of AF2's confidence index (pLDDT) as indicators of protein flexibility remains underexplored and debated. In this large-scale study, we evaluate AF2's pLDDT as a predictor of protein flexibility by comparing it with flexibility metrics derived from molecular dynamics (MD) simulations stored in the ATLAS dataset, NMR ensembles, and experimental B-factor values. We also assess the efficiency of ESMFold pLDDT and AlphaFold 3 in this context. Our findings reveal that AF2 pLDDT correctly correlates with MD and NMR-derived flexibility metrics, but fails to capture flexibility in the presence of interacting partners, and therefore need to be cautiously interpreted. Furthermore, AF2 pLDDT appears more relevant than B-factor values for evaluation of protein flexibility. While AF3 shows slight improvements in capturing protein dynamics, MD simulations remain superior for comprehensive flexibility assessment.

## Introduction

AlphaFold 2 (AF2)¹ public release in 2021 drastically transformed the field of structural biology. It significantly outperformed previous protein structure prediction tools, reaching an accuracy similar to high-resolution experiments in particular thanks to its innovative neural network architecture, efficiently extracting coevolutionary information encoded in Multiple Sequences Alignments (MSA) and features of experimentally determined protein structures²,³. A new version, AlphaFold 3 (AF3)⁴, has been released in April 2024 in a web server format followed by source code release in November 2024, with an updated architecture allowing prediction of additional features such as protein-ligand interaction. In parallel, ESMFold⁵ emerged as a powerful alternative for protein structure prediction. Unlike AlphaFold 2, ESMFold does not perform any sequence alignment or template search, relying solely on language models to predict protein structures from a single amino acid sequence. This approach allows ESMFold to generate structures more rapidly, though with a slight lack for highly complex proteins as compared to AF2⁶.

To help users assess the quality of structural predictions, the AF2 team developed the pLDDT (predicted Local Distance Difference Test). This per-residue metric is based on LDDT⁷, evaluating the local distance differences between two protein structures. It computes the percentage of atom pairs whose distances fall within a certain distance threshold as compared to the reference structure⁷. The pLDDT score therefore estimates how closely the predicted model aligns with the theoretical experimental structure. Confidence of prediction is considered low for pLDDT values below 50²,³.

Although designed as a confidence of prediction, pLDDT values have shown potential for protein flexibility assessment. Indeed, it has been shown that residues with pLDDT scores below 50 are likely to be disordered³,⁸⁻¹⁰, indicating extreme flexibility. Additionally, it has been demonstrated on 11 proteins that the pLDDT correlates with the molecular dynamics' (MD) root mean square fluctuations (RMSF) of the backbone¹¹, suggesting its possible applicability for protein flexibility
assessment. On the other hand, the pLDDT is evaluated to poorly correlate with experimental B-factor flexibility measurement of globular proteins¹². Moreover, while the pLDDT seems to be a good indicator of protein flexibility on short loops, it performs drastically worse as the loop length increases¹³. Thus, ambiguous results emerged from the interpretation of pLDDT as a potential estimator of protein flexibility.

In this study, we performed the first large-scale assessment of the relationship between
confidence criterion of protein structure prediction by AF2 and ESMFold (pLDDT), and protein
flexibility observed in molecular dynamics (MD) simulations. We found a reasonable correlation
between pLDDT and flexibility descriptors derived from 1,390 MD trajectories in the recently
released ATLAS dataset¹⁴, as well as with structural NMR ensembles. This suggests that pLDDT
effectively assesses flexibility measurements, particularly RMSF. Additionally, comparisons with
experimental B-factor values revealed that pLDDT is more relevant for evaluating flexibility in MD
and NMR contexts than B-factors. However, AF2 pLDDT has difficulty detecting flexibility
variations induced by partner molecules. Finally, we observed that pLDDT values from AF3 exhibit
behaviour very similar to those from AF2.

Results

We have assessed protein flexibility observed in 1,390 MD trajectories at residue level using different metrics such as root mean square fluctuations of α-carbon atoms (RMSF), local deformability of protein chain according to the discrete structural alphabet of protein blocks¹⁵ (Neq, or Number of equivalent), standard deviation of the relative solvent accessible area (Std. RSA) and mean of local distance difference test⁷ (Mean LDDT) between the different conformations adopted by the protein and the initial frame. The same measures were also calculated for top 5 models generated by AlphaFold 2 and for NMR ensembles. The resulting values were then compared to confidence of prediction evaluated by AF2 and ESMFold in terms of predicted LDDT (pLDDT, see Materials & Methods for the details). Finally, AlphaFold 3 performances were benchmarked with similar analysis on a subset of proteins.

Does AlphaFold 2 pLDDT reflect protein flexibility estimated by MD?

According to our large-scale analysis of MD trajectories in the ATLAS database, MD flexibility metrics show moderate to high correlations with AF2 pLDDT (Fig. 1A). Interestingly, AF2 pLDDT demonstrates the most prominent correlation with RMSF values, and not with the average value of LDDT. Moreover, ESMFold's pLDDT values present comparable correlations with protein flexibility. Thus, confidence of AF2 and ESMFold predictions can be used for a correct but not perfect evaluation of expected RMSF at a given protein position.

RMSF seems also to be the only metric taking similar values when compared between MD
simulations and the conformational ensemble of top 5 AF2 generated models, but the correlation
considering the three other metrics is poor.

In order to verify consistency of our conclusions for proteins with starting conformations
obtained using different methods (see Materials & Methods for the details), we computed separate
correlation matrices for proteins modelled by AF2 (332 proteins with long gaps) and those with X-ray
structures completed by MODELLER (1,058 with gaps < 5 residue long) in Fig. S9. While the general
pattern remains very similar to Fig. 1, we observe a stronger correlation for proteins whose starting
structure for MD simulations was an AF2 model. Out of 332 proteins, 270 (81%) had gaps longer than
5 residues located at their extremities, i.e. N- or C-terminus. We attribute the observed effect to these
terminal gaps, as they systematically exhibit low pLDDT in AlphaFold2 models. This finding is
particularly consistent with MD simulations of these regions, as expected for those non-structured protein
segments. Our hypothesis is supported by correlation matrices calculated for protein regions resolved
in X-ray against those missing in X-ray structure (Fig. S10-S11). In the case of X-ray structure used
for MD simulations (Fig. S10), there is no significant difference in correlations. However, for AF2
models used for MD simulations (Fig. S11), we observe a stronger correlation between RMSF and
pLDDT for regions missing in the experimental structure (Fig. S11 Top right) as compared to the
resolved residues (Fig. S11 Top left). Moreover, the correlation is even stronger when we consider
“large” gaps: between 5 and 10 residues (Fig. S11 Bottom), which correspond to particularly flexible
regions often located at the extremities.

<figure><img src="image_2.png" /><figcaption>B</figcaption></figure>

<figure><img src="image_3.png" /></figure>

**Figure 1:** Protein-wise average of Pearson correlation coefficients calculated for prediction confidence metrics (pLDDT) and predicted structural ensembles (AF2 Top 5), against protein flexibility observed in MD simulations (A) and NMR ensembles (B). A. MD-derived flexibility metrics (rows) are computed for ATLAS dataset. Pairwise joint distributions of every metric are provided in Figs. S12-S15. B. Flexibility metrics are computed from NMR conformational ensembles for a subset of proteins (see Materials & Methods for the details). Pairwise joint distributions of every metric are provided in Figs. S16-S19. For A. and B., absolute values of average correlations were used for visualisation, and the two firsts and last residues of each protein were excluded. See Fig. S1 & S2 for complete matrices.

Does AlphaFold 2 pLDDT reflect protein flexibility observed in NMR ensembles?

To further explore the relevance of our results with respect to the experimentally measured protein
flexibility, we performed correlation analysis on a subset of ATLAS dataset proteins resolved in NMR
spectroscopy (Fig. 1B). AF2 pLDDT and ESMFold pLDDT values correlate well with NMR
flexibility, especially for RMSF values, and the AF2 Top 5 ensemble appears less relevant for
flexibility assessment, in accordance with our observations for the MD dataset. Nevertheless,
conformational mobility observed in MD remains the closest to the one observed in NMR ensembles.
Indeed, NMR RMSF, Neq, and Std. RSA show higher correlations with MD ensembles than AF2
pLDDT, with a statistically significant difference (p-values: 0.01, 0.03, and 0.01, respectively, using a
Wilcoxon signed-rank test).

On average, MD simulations seem to represent NMR flexibility better than pLDDT values (Fig. 2A, green zones) for all metrics except for LDDT and regardless of the domain architecture. In case of *M. brassicae*'s chemosensory protein (Fig. 2B Top), the correlation between NMR and MD for both Mean LDDT and RMSF values is particularly strong, while almost no correlation is observed between NMR RMSF and AF2 pLDDT (PCC: 0.71 vs. 0.22, respectively). While the experimental PDB structure is almost identical to the AF2 models, three 12-bromododecanol ligands are absent from the NMR and MD ensembles (Fig. 2A Right). Mosbah et al.¹⁶ demonstrated that these ligands are structurally important, and that large conformational change occurs upon binding. These movements are particularly visible in the N-ter loop at 1-16 positions and in the 35-59 region
containing a helix undergoing large deformation. Therefore, the flexibility observed in NMR is
consistent with the stabilising role of these ligands, confirming that their absence in the NMR and MD
ensembles leads to significant conformational movements. On the other hand, while AF2 predicts
these regions with a high degree of confidence, likely due to the availability of structural templates, it
fails to fully reflect the ligand-induced fluctuations. This highlights the limitations of AF2's static
predictions compared to dynamic experimental data such as NMR.

At the same time, for several proteins MD simulations are less reflective of the flexibility
observed in NMR structures as compared to AlphaFold 2 pLDDT. In particular, for E. coli's MerB
organomercurial lyase (Fig. 2B Bottom), the NMR ensemble's flexibility measurements correlate
better with AF2 pLDDT than with LDDT from the MD ensemble (PCC: 0.88 vs. 0.38, respectively).
The main difference concerns three loop regions corresponding to residue positions 66-69, 117-121,
and 176-184, identified as particularly flexible in MD simulations. However, experimental
information on dynamics of the corresponding residues remains ambiguous. Indeed, according to
Lafrance-Vanasse et al.¹⁷, these loops participate in MerB homodimer formation in the crystal
structure (Fig. 2A Left), while MerB can also exist in monomeric form in solution. Thus, high
confidence of AF2 prediction can be explained by the availability of the X-ray templates of the dimer
form of the protein with corresponding regions stabilised by partner interactions. Template availability
also provides a realistic model of 1-12 helix resolved in X-ray structure but absent from NMR, which
is observed to be rigid in MD simulations despite rather low pLDDT values. Interestingly, all the
loops involved in dimer formation appear as relatively rigid according to the analysis of NMR
structural ensembles, despite the lack of constraints during NMR resolution. In particular, region
176-184 forms part of a loop that demonstrates high backbone flexibility on fast timescales evidenced
by low 15N-1H heteronuclear NOE values¹⁸, which is in accordance with flexibility evaluated in MD
simulation but surprisingly corresponds to low RMSF between NMR conformers. These examples
highlight the complexity of the correct assessment of protein flexibility especially in case of partner
interactions and suggest that MD, NMR, and AF2 pLDDT can be complementary in assessing
flexibility.
<figure><img src="image_4.png" /><figcaption>**Figure 2:** pLDDT vs MD-derived metrics calculated on the NMR dataset. **A.** Center: Comparison of the Pearson correlation coefficients between flexibility measurements calculated on NMR ensembles and MD ensembles (X-axis), and Pearson correlation coefficients between NMR ensembles flexibility metrics and AF2 pLDDT values (Y-axis). Green zones indicate higher correlation between NMR ensembles and MD-derived metrics than that between NMR ensembles and AF2 pLDDT. Each point represents a protein chain and is coloured according to the type of its ECOD domain architecture. Raw pairwise distributions for each domain class are provided in Figs. S26-S33. Left: Structure of *M. brassicae*'s chemosensory protein in homodimer form (PDB ID for X-ray: 3F0P). Right: Structure of *E. coli*'s organomercurial lyase (PDB ID for X-ray: 1N8U) with 12-bromododecanol ligands in pink. **B.** Comparison of the flexibility profiles of *M. brassicae*'s chemosensory protein (Top) and *E. coli*'s organomercurial lyase (Bottom) with RMSF calculated from MD simulations, RMSF extracted from NMR ensembles and AlphaFold 2 pLDDT (inverted for visualisation). The thickness and the colour of the protein structure are proportional to the represented metric. The corresponding line plots of each metric are provided in Fig. S22-S23.</figcaption></figure>

Is AlphaFold 2 pLDDT in agreement with X-ray B-factor values?

For the majority of proteins resolved by X-ray crystallography, information on the expected stability of different regions can be deduced from analysis of B-factor values. Indeed, B-factor reflects atomic
thermal motion during crystallisation and is known to correlate with some MD-derived flexibility descriptors such as RMSF<sup>19,20</sup>, although it is not systematically the case. To make the analysis here, we only focus our analysis on residues of the ATLAS database for which a B-factor is known in the several structures.

According to our findings, B-factors correlate moderately with MD-derived RMSF and provide a very weak correlation with other MD-derived descriptors. However, even for the high-quality structures selected for our study (of good resolution and with few missing residues), B-factor values exhibit the weakest correlation with MD and NMR ensembles across all the flexibility estimators, as compared to correlations observed with AF2 and ESMFold pLDDT values (Fig. 3). This trend remains consistent even when analysing a subset of proteins crystallised without any partners (such as protein chains, ligands and ions) that influence B-factor values (Fig. S3).

Based on these results, we suggest that AF2 pLDDT may provide a better indication of the expected flexibility of different protein regions than available B-factor values, at least in the absence of interactions with partners.

<figure><img src="image_5.png" /><figcaption>**Figure 3:** Protein-wise average of Pearson correlation coefficients calculated for experimental B-factor values, prediction confidence metrics (pLDDT) and predicted structural ensembles (AF2 Top 5), against protein flexibility observed in MD simulations (A) and NMR ensembles (B). A. ATLAS dataset - Averages of the protein-wise Pearson correlations between flexibility metrics (Mean LDDT, RMSF, Neq & Avg. RSA) calculated on the MD ensembles (rows) and B-factor, AF2 pLDDT, ESM pLDDT & AF2 ensemble flexibility (AF2 Top 5). A. MD-derived flexibility metrics (rows) are computed for ATLAS dataset. B. Flexibility metrics are computed from NMR conformational ensembles for a subset of proteins (see Materials & Methods for the details). For A. and B., only the residues with available B-factor values were used for the analysis. Absolute values were used for visualisation, and the two first and last residues of each protein were excluded. See Fig. S4-S5 for complete matrices and Fig. S20-S21 for raw distribution.</figcaption></figure>

## Does AlphaFold 2 pLDDT capture influence of co-crystalised contacts on protein flexibility?

While proteins in the ATLAS dataset were simulated as individual chains in apo form, some of them were actually crystallised in contact with other protein chains or ligands. Since some of the intermolecular contacts contribute to stabilisation of protein structure (in particular, in case of crystallisation adjuvants), their removal from the MD simulations leads to the increased flexibility of the concerned regions (Fig. 4). This trend is particularly pronounced for residues forming an interface with other chains (Fig. 4, in green). At the same time, AF2 pLDDT distributions remain very similar regardless of the presence of co-crystallisation contacts. High confidence of AlphaFold 2 predictions for the corresponding regions must be explained by the abundant presence of the corresponding templates in the PDB. Template-free ESMFold in its turn provides a distribution of pLDDT values, which is closer to that observed in MD, even though the corresponding predicted structures are quite different from the starting frames of MD simulations (Fig. S6). Finally, B-factor values are slightly lower for residues participating in inter-molecular interactions, as expected taking into account the stabilising nature of the corresponding additives during the experimental setting.
<figure><img src="image_6.png" /><figcaption>**Figure 4:** Residue-wise normalised distributions of the different flexibility measurements calculated for MD trajectories of ATLAS dataset in comparison to AF2/ESM pLDDT values (inverted for visualisation) and experimental B-factors. Residues involved in co-crystalised contacts (Ligands, Protein Chains, Ions, Nucleotides) are coloured accordingly.</figcaption></figure>

**Is AlphaFold 3 pLDDT more convenient for protein flexibility assessment than AlphaFold 2 pLDDT when compared to NMR ensembles?**

One of the major advantages of AF3 as compared to AF2 is the possibility to explicitly take into account the presence or absence of partner interactions, which could impact protein stability. Nevertheless, according to our results, AF3 pLDDT values obtained for the single chain predictions in apo form correlate with flexibility metrics from the NMR ensemble almost identically to AF2 pLDDT (Fig. 5). Moreover, AF2 and AF3 pLDDT values are very close with a Pearson correlation of 0.87 (Fig. S2). At the same time, AF3 Top 5 ensembles are closer to NMR ensembles than AF2 Top 5 ensembles, especially on the RMSF metric, suggesting that AF3's new design produces models more representative of experimental protein fluctuations as observed in NMR ensembles, though still less informative than MD simulations.

<figure><img src="image_7.png" /><figcaption>**Figure 5:** Protein-wise average of Pearson correlation coefficients calculated for prediction confidence metrics (pLDDT) and predicted structural ensembles (AF2/AF3 Top 5), against protein flexibility observed in NMR ensembles. Absolute values were used for visualisation, and the two first and last residues of each protein were excluded. See Fig. S2 for complete matrices.</figcaption></figure>
Discussion

Large-scale analysis of all-atom MD simulations stored in the ATLAS dataset confirms the existence
of significant correlation between AF2 pLDDT values and flexibility of the corresponding protein
regions, in particular in terms of RMSF values. Nevertheless, pLDDT correlation with experimentally
observed flexibility metrics calculated from NMR ensembles remains lower than that of MD-derived
estimators. This trend is particularly pronounced for protein regions interacting with protein partner or
ligand in the resolved X-ray structure. Moreover, in most of the cases, pLDDT values correlate better
with MD descriptors than conformational variability computed from structural ensembles of top 5
AF2 models. Such results must be explained by the bias in the predictive model training set. Indeed,
the AF2 was trained on static protein structures, which are obtained under artificial conditions and
often stabilised by molecular interactions of different nature (ligands, ions, crystallisation additives,
chains, etc.). All those kinds of interactions are not explicitly included in the predictive model.

The difference in experimental conditions used for protein X-ray structure resolution also has
a major impact on B-factors values²⁰. As a result, confidence of AF2 and ESMFold predictions trained
on all the available experimental structures²¹,²² does poorly correlate with B-factor values obtained in
particular experiments. Moreover, we also show that the B-factor is also poorly correlated with almost
every other flexibility estimator derived from MD trajectories.

Even though AlphaFold2 was not trained on experimental dynamics data, the pLDDT
confidence score implicitly captures structural uncertainty, which may arise from local conformational
heterogeneity or disorder. These are often the same regions that exhibit structural variability in PDB
templates used by AF2 and have elevated B-factors or are unresolved, thus creating an indirect
correlation between pLDDT and dynamic behavior. At the same time, while low pLDDT values are
strong indicators of flexibility and of poor structural definition, the opposite is not necessarily true.
High pLDDT values may reflect confidence in a dominant conformation captured during training but
do not reflect underlying dynamical behavior, especially in functionally mobile regions (e.g.,
active-site loops or hinge domains). Additionally, AF2's training on static structures from the PDB
may induce a bias toward confident predictions in well-resolved regions, regardless of their true
biophysical mobility. This can lead to high pLDDT scores even in regions known to be flexible in
solution or under different functional states. It is also worth noting that B-factors primarily capture
atomic displacement due to thermal motion and crystal packing, whereas pLDDT may also reflect
alternative conformational states and model uncertainty arising from functional mobility,
poorly-structured termini segments or low sequence conservation.

Interestingly, flexibility according to the top five structures predicted by AF3, which was
trained taking into account a wider range of interactions, are significantly closer to NMR-derived
flexibility. However, the pLDDT given by AF3 seems to reflect almost identically the same trends
regarding the correlation with flexibility metrics as AF2 pLDDT, underlining no improvements of this
criterion relevance for protein dynamics. Such results can be explained by the nature of proteins used
in our study. Indeed, in order to make our MD simulations as uniform as possible, we exclusively used
single protein chains during the construction of the ATLAS dataset and prioritised proteins resolved in
monomeric form. For such proteins, AF2 and AF3 are known to yield highly comparable accuracy of
predictions with absolute gain of ~0.1–0.2 Å RMSD reduction²³. For the benchmarks including
CASP16 targets, AF3 achieved an average GDT_TS of ~87.1 compared to ~85.5 for AF2, a modest
gain of <1%. Moreover, these improvements correspond mainly to easy-to-predict targets, with little
to no gain for more complex targets and novel folds, and most important differences were observed
primarily for well-folded rigid regions of the protein²⁴. For more complex targets or more flexible
regions, AF3 provides no significant advantage over AF2, which is in accordance with our
observations.
Finally, our study is subject to several important limitations. First, our conclusions are based on the analysis of free globular protein dynamics in solution. Due to poor performance of AF2 and ESMFold for the prediction of membrane protein structure, we expect pLDDT value to be also less relevant in the assessment of their flexibility. Secondly, our dataset is composed of relatively short 100 ns MD simulations (in three replicates), empirically proven to provide realistic information on protein flexibility at least around the equilibrium state for proteins of medium size (the mean size of proteins in ATLAS dataset is 236 residues, https://www.dsimb.inserm.fr/ATLAS/about.html) but insufficient to sample major conformational changes of large proteins. Thirdly, while our comparison to NMR experiments was limited to the ensembles deposited in the PDB, other flexibility measurements such as NOE distances and order parameters would deepen our insights and will be incorporated into future studies. Finally, our analysis is limited to relatively rigid proteins with available high quality experimental structures, while omitting long intrinsically disordered regions. At the same time, with accelerating emergence of standardised MD datasets¹⁴,²⁵,²⁶ and protein ensemble prediction tools²⁷⁻³⁰, further improvements of protein flexibility assessment events would certainly appear in the near future. Indeed, according to our preliminary results, AlphaFlow³⁰ already demonstrates encouraging performances in terms of flexibility predictors (Figs. S24-S25). Moreover, the use of protein conformation resulting from AF2 sub-sampling as starting points for MD simulation was already proven to improve the conformational space exploration both at global and local level³¹,³². This will certainly improve the quality and coverage of the available MD data in the upcoming extended datasets.
# Materials and methods

## Structural ensembles datasets

**ATLAS dataset (MD)** - The Mean LDDT, RMSF, Neq and Std. RSA were computed using the ATLAS dataset's standardised all-atom molecular dynamics simulations, aiming to be representative of the structural diversity of the PDB. To provide uniform information, all ATLAS simulations were performed using consistent system settings, MD protocols, software and force field. Ligand molecules and partner chains were removed from the experimental structure used in MD simulations, making them comparable with AlphaFold 2 and ESMFold predictions, neither of which explicitly using ligand information.

**NMR dataset (NMR)** - An experimental structural ensemble of proteins was created for the analysis. Since a single point mutation can significantly alter the flexibility profile, only proteins with exactly the same protein sequence as those in the ATLAS dataset were selected. This resulted in the selection of 29 NMR conformational ensembles, each containing at least 10 molecular models.

**AF2 Top 5 & AF3 Top 5** - We also considered models predicted by AlphaFold 2 and AlphaFold 3. By default, AF2 and AF3 predict five models for the same protein, possibly capturing flexibility information in the variation of this ensemble.

Among all entries of the ATLAS dataset (1,390 structures), all the proteins containing more than 5 consecutive gaps (332 proteins) were entirely modeled using AlphaFold 2 (with templates). We have verified that resulting AF2 models are very close to the initial X-ray template (TM-score > 0.9). All proteins using AF2 structures to run MD simulations are marked as such in the database interface to avoid confusion for the users. Importantly, among the 29 proteins with available NMR structural ensembles, none was remodelled by AF2 (X-ray structures contained only short gaps filled by MODELLER).

## Flexibility measurements

### Metrics calculated on structural ensembles:

* **Mean LDDT** - Mean LDDT represents the LDDT⁷ (Local Distance Difference Test) over the molecular dynamics simulation. This metric quantifies the backbone fluctuation of the protein without being impacted by the alignment step, mandatory in standard RMSF calculation.

LDDT is a superposition-free score evaluating the local distance differences between the atoms of a pair of structures. By comparing the number of atoms inside different radius thresholds, the LDDT allows accurate protein structure similarity measurement at the residue scale, unaffected by domain motion. We calculated the average LDDT values for each frame of the conformational ensemble, using the first frame as reference, on the alpha-carbons. Calculations were performed using the LDDT executable of OpenStructure, with default parameters³³.

* **RMSF** - RMSF (Root Mean Square Fluctuation) represents the standard deviation of the atomic positions in the trajectory, and allows detecting subtle flexibility variations as it does not use distance thresholds like the LDDT. It however requires an alignment step, possibly leading to an overestimation of some positions for multi-domain proteins. It is calculated on α-carbons using GROMACS³⁴.
*   **Neq** - Neq is an entropy-based index measuring the local deformability of the backbone during the MD simulation. This metric effectively discriminates between mobility of rigid protein fragments, and true deformability. Neq represents the average number of protein blocks¹⁵ (PBs) at a given position, ranging from 1 (no PB variation) to 16 (fully random PB distribution). PBs are part of a structural alphabet which allows a discretisation of the local structure based on Phi / Psi angles, and is done using the PBxplore³⁵ software.

*   **Std. RSA** - Std. RSA measures the variation of relative accessible surface area of each residue through the trajectory. For each frame of the structural ensemble, the standard deviation of RSA values is computed, using MDtraj³⁶ for SASA calculation and Tien et al.'s 2013 theoretical scale³⁷ for normalisation. This provides a convenient and quite original flexibility measurement, revealing environmental solvent changes, ranging from side chain rotations to larger compaction or expansion of protein fragments.

To ensure robust results, flexibility measurements from the ATLAS dataset were averaged across the three MD replicates, as the measurements appeared consistent across replicates (Fig. S7). Given the dataset's extensive size, calculations were generally performed on 10,000 MD frames for each 100 ns replicate, except for Mean LDDT, which was calculated using 1,000 frames. Throughout our study we eliminate two first and two last residues of each protein from metrics computation in order to avoid over-optimistic correlations for these highly flexible regions. These four metrics each capture different aspects of protein flexibility, and the maximum theoretical correlations between the different metrics can be found in the supplementary materials (Fig. S8). Further details can be found in the ATLAS database publication¹⁴.

**Experimental measurement:**

*   **B-factor** - We considered the experimental isotropic B-factor values of Cα atoms normalised among each protein using Z-score. While comparing different flexibility metrics to B-factors, we excluded residues missing in the initial X-ray structures. This exclusion was applied to 6,403 out of 328,049 residues, representing 1.95 % of total amount.

**pLDDT computation**

**Dataset** - pLDDT values were computed using the sequences of the ATLAS dataset¹⁴, providing high-quality MD simulations with high-resolution initial structure and minimal missing residues, ensuring favourable conditions for AlphaFold 2 predictions¹,².

**Methods** - The AlphaFold 2 pLDDT values (AF2 pLDDT) for the proteins of the ATLAS dataset were obtained from the 3D structure predictions generated using the local version of AlphaFold 2 Colab³⁸ (ColabFold v1.5.1) with default settings. These settings include MMseqs2³⁹ for homology search (UniRef30+env) and free search for structural templates on the PDB70 via online API. The prediction process involves three recycling steps and OpenMM/Amber GPU relaxation to generate five models, which are then ranked based on their average pLDDT scores. The first-ranked model's pLDDT will be used as it generally demonstrates best stereochemical characteristics⁴⁰.

The ESMFold pLDDT values (ESM pLDDT) were extracted from the structures predicted using the local version of ESMFold, with the 4 recycling steps by default. Unlike AlphaFold 2, ESMFold does not perform any sequence alignment or template search.

For preliminary analysis, AlphaFold 3 pLDDT values of the NMR dataset were also retrieved from the predicted structures using the online AlphaFold Server in beta version, without indicating the presence of possible interacting partners.
## Acknowledgments

The authors thank the Ministry of Research (France), Université Paris Cité (France), National Institute for Health and Medical Research (INSERM, France) and IdEx [grant no. ANR-18-IDEX-0001], French National Research Agency [grant no. ANR-21-CE45-0019] and the Data Intelligence Institute of Paris (DiiP). A part of calculations was performed using high performance computing resources of TGCC (Très Grand Centre de Calcul) [grants no. A0140714131, A0140712053, A0160712053] funded by GENCI (Grand Equipement National de Calcul Intensif, France).

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