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Cite this:Phys.Chem.Chem.Phys.,

2017, 19, 10651

NMR probing and visualization of correlatedstructural fluctuations in intrinsically disorderedproteins†

Dennis Kurzbach, *ab Andreas Beier,c Agathe Vanas,c Andrea G. Flamm,c

Gerald Platzer,c Thomas C. Schwarzc and Robert Konrat*c

A novel statistical analysis of paramagnetic relaxation enhancement (PRE) and paramagnetic relaxation

interference (PRI) based nuclear magnetic resonance (NMR) data is proposed based on the computation

of correlation matrices. The technique is demonstrated with an example of the intrinsically disordered

proteins (IDPs) osteopontin (OPN) and brain acid soluble protein 1 (BASP1). The correlation analysis

visualizes in detail the subtleties of conformational averaging in IDPs and highlights the presence of

correlated structural fluctuations of individual sub-domains in IDPs.

Introduction

The concept of intrinsically disordered proteins (IDP) describesa class of proteins that retain biological function despite a lackof stable secondary and tertiary structures.1,2 Their physico-chemical properties challenge the views of classical (static)structural biology and require novel and innovative approachesfor their structural and dynamical characterization. As classicalcrystallography is not applicable to IDPs nuclear magneticresonance (NMR) based methods have emerged as a major toolfor the characterization of their structure and structure–functionrelationships.3,4 Especially, paramagnetic relaxation enhancement(PRE) techniques have provided remarkable insights.5 Thesemethods rely on covalent attachment of a spin label that carriesan unpaired electron to a protein. The presence of the electronenhances the transverse relaxation rate, R2, of any nucleus in itsvicinity broadening the NMR resonance lines and decreasingthe signal height. PRE-based methods allow the observation ofresidual structures and local compaction of the polypeptidechain in IDPs, yet, the existence of concerted motions andcooperatively folded segments cannot be detected. To circumventthis problem, we recently proposed a novel technique coined

paramagnetic relaxation interference (PRI) based on cross-correlation effects embracing pairs of spin labels.6 For thisapproach, labels are attached to a protein at two differentCys-mutation sites. Paramagnetic relaxation enhanced ratesfor backbone amide protons (1HN-G2) are measured for thetwo singly labelled forms, 1HN-G2(S1) and 1HN-G2(S2), as well asthe doubly spin labelled variant, 1HN-G2(D). We defined the PRIeffect as the difference between rates measured for the double-mutant and the sum of the individual PRE rates measured forthe respective single-mutants:

DG2 = 1HN-G2(D) � [1HN-G2(S1) + 1HN-G2(S2)] (1)

Note that by this definition individual phenomena stemmingfrom single spin labels are effectively subtracted from the data,such that the PRI rate, DG2, exclusively describes cooperativephenomena based on correlations between the two electrons.In our earlier work, we showed in detail that the PRI effect canbe traced back to cross-correlated relaxation (CCR) embracing anucleus and two electrons, which requires the concerted spatialproximity of all three involved spins, as the dipolar coupling(which underlies PRE) between a pair of spins depends steeplyon their distance by r�6.6 Since the dipole–dipole CCR isgoverned by a factor of 3 cos2(y) � 1, y denoting the anglebetween the two electron–nucleus vectors,7 the PRI value DG2

can adopt both positive and negative values depending on therelative orientation of the nuclear-electron dipolar vectors in aconcertedly compact state of an IDP.6

It is important to realize that PRE and PRI probe differentsub-states of the conformational ensemble of an IDP (Fig. 1).While PREs are sensitive to any form of local compaction of thepolypeptide chain, PRIs require correlated or concerted structuralfluctuations of larger segments and a simultaneous encounter of

a Departement de Chimie, Ecole Normale Superieure, PSL Research University,

UPMC Univ Paris 06, CNRS, Laboratoire des Biomolecules (LBM),

24 rue Lhomond, 75005 Paris, France. E-mail: kurzbach@ens.frb Sorbonne Universites, UPMC Univ Paris 06, Ecole Normale Superieure, CNRS,

Laboratoire des Biomolecules (LBM), Paris, Francec Department for Structural and Computational Biology, Max F. Perutz

Laboratories, University of Vienna, Vienna Biocenter Campus 5, 1030 Vienna,

Austria. E-mail: robert.konrat@univie.ac.at

† Electronic supplementary information (ESI) available. See DOI: 10.1039/c7cp00430c

Received 19th January 2017,Accepted 27th March 2017

DOI: 10.1039/c7cp00430c

rsc.li/pccp

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10652 | Phys. Chem. Chem. Phys., 2017, 19, 10651--10656 This journal is© the Owner Societies 2017

both spin labels in the vicinity of a nucleus (corresponding toconformer 3 and the intersect region in Fig. 1). The two experi-ments thus report on different subsets of the conformationalensemble and taken together provide a more comprehensivepicture of the structural dynamics of an IDP.

Here, we introduce a novel statistical analysis for the simulta-neous interpretation of PRE- and PRI-data. Data sets obtained fromdifferent paramagnetic protein mutants, are quantitativelyanalysed using correlation matrices. The underlying rationalof the methodology is the simultaneous interpretation of PRE/PRI effects. Similar approaches employing the computation ofcorrelation and covariance matrices have been employed toanalyse data from molecular dynamics,8 allosteric effects encodedNMR chemical shifts9 and graphs of NMR protein interactiondata.10,11 The representation of PRE and PRI data as correlationmatrices yields insights into the conformational ensemble andconcerted structural fluctuations of IDPs. The method is illustratedby application to the IDPs osteopontin (OPN)12 and brain acidsoluble protein 1 (BASP1). We show that our statistical analysisprovides unique insights into correlations between diverse transi-ently formed structural motifs of an IDP that would remainunnoticed by conventional means of data analysis.

Theory

The simultaneous interpretation of N residue-resolved data sets isachieved by a correlation matrix where correlation is defined as thequantitative relationship between two random variables X and Y:

covX;Y ¼ 1=NXN

i¼1Xi � Xh ið Þ Yi � Yh ið Þ (2a)

corrX,Y = covX,Y/(sxsy) (2b)

with N denoting the sample size and the angle brackets (hXi andhYi) indicating the mean value in the case of large numbers.

sx and sy are the corresponding standard deviations (see theESI† for the difference between covariance and correlationmatrices). For a residue-resolved set of PRE or PRI data includingN different labelling sites one may compute correlation coefficientsin a residue dependent manner such that X and Y indicate theparamagnetic relaxation rates (PRE or PRI) for the backboneamide proton, HN, of residues k and l: Xk = 1HN-G2,k, likewiseXl = 1HN-G2,l. The transverse paramagnetic relaxation rate 1HN-G2

can be expressed as:5,13–15

1HN-G2 = 1/15(m0gHgmB/4p)2S(S + 1)[4J(0) + 3J(oL)]hr�6i (3)

hri denotes the average distance between the unpaired electronand any HN of interest and J(0) and J(oL) denote spectral densityfunctions at zero frequencies and the Larmor frequency, oL. Allother symbols have their usual meaning. It should be noted thatwe do not aim at obtaining quantitative information about therotational dynamics of the protein (e.g. time scales of motions). In anIDP, conformational averaging leads to the modulation of electron–nucleus distances on different time scales. The unambiguousdissection of geometric (electron–nucleus distances) anddynamic parameters (correlation times) is thus not possibleleaving the exact form of J(o) unknown.

In contrast, here we focus on the correlated structuralfluctuations of residues obtained from paramagnetic NMRexperiments. Most importantly, the simultaneous interpreta-tion of both PRE and PRI experiments provides more compre-hensive information about the conformational ensemble than aconventional PRE analysis performed in the recent past.

Since PRE and PRI exploit distinctly different relaxationmechanisms, they probe different subsets of the conforma-tional ensemble (Fig. 1). While PREs probe all thermallyaccessible conformations, PRI experiments only probe globallycompacted states displaying sizeable compaction of the poly-peptide chain. PRI data thus report on globally compactedsubstates that would remain undetected in conventional PREexperiments. In the proposed correlation analysis approach,these states become evident by larger segments with positivecorrelation values.

The information content prevalent in the correlation analysis ofPRE and PRI data can be interpreted as follows: the individual spinlabel sites define reference points relative to which structuralfluctuations of individual residues are analysed. Two residuestypically appear as correlated (positive correlation) if they concertedlyfluctuate relative to the spin label reference point(s). In the caseof negative correlation coefficients, two residues undergo anti-correlated structural changes (e.g., orthogonal folding–unfoldingevents). The proposed correlation matrices provide a direct graphicaldisplay of the heterogeneity of the conformational ensemble of anIDP as they depict distinct conformations of structural sub-states.Arguably, these maps bear some resemblance to contact mapsderived from 3D structures of stably folded proteins and can thusbe regarded as dynamic counterparts.

The ESI† contains a detailed guide to the analysis of correla-tion coefficients obtained for prototypical PRE and PRI data. Insummary, in PRE experiments strong short-range PREs areobserved due to the proximity between a spin label and amino

Fig. 1 Schematic illustration of the different sampling properties of PREand PRI techniques. On the left, blue and yellow dots represent spinlabelling sites and the surrounding spheres indicate the ranges of obser-vable PREs. PRI effects can be expected at the intersection of two PREspheres. On the right, an IDP with three different conformations isschematically depicted. PRE experiments probe all conformations (I, IIand III), while PRI is only sensitive for the compact sub-state III in whichresidues located within the intersection of two PRE spheres would displaydeviations from the additive sum of pairwise PRE contributions.

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acids in the primary sequence. Depending on the local compac-tion of the polypeptide chain significant positive correlationsare found between residues that are spatially close to the spinlabel site. Anti-correlations to more distant sites result fromorthogonal protein backbone folding (in other words, mutuallyexclusive PREs). Positive correlations are only obtained betweenresidues that are subject to simultaneous long-range PREs fromdifferent labelling sites (simultaneous backbone compaction).

In contrast, PRI experiments report exclusively on globallycompacted states. Additionally, PRI data are influenced by a3 cos2(y) � 1 dependence of the underlying CCR (see above)such that the resulting correlation coefficients will also reporton the mutual orientations of residue pairs in the compactstates. In principle, positive and negative contributions to thecorrelation coefficient can result depending on the relativeorientation in the globally compacted substrate. However, weanticipate more positive correlation coefficients as two residuesthat are in spatial proximity in the compact state will experi-ence similar electron–nucleus dipole–dipole CCRs.

Methodological considerations

The change in the number of experiments (N) between PRE andPRI analyses influences the noise level of the correlationmatrices, which decreases with the number of input data setsN. We find that for N Z 3, the average noise level hni of thecorrelation matrices, given by the variance over all matrixentries, does not exceed 0.5. Thus, we set all values for which|corrx,y| o 0.5 to 0 in our analysis to avoid biases throughvarying N and correlation of noise. This allows one to quantita-tively compare the PRE- and PRI-derived correlation coeffi-cients. This would not be as straightforward for covariancematrices since they depend on the absolute values of the inputdata, although their noise level would be lower. Yet, covariance(eqn (2a)) and correlation (eqn (2b)) matrices feature similarinformation contents. The impact of the number of experi-ments used in PRE/PRI analyses on the obtained correlationmatrix is treated in detail in the ESI.†

We showed earlier10,11 that a covariance analysis of residue-resolved data can identify compacted/correlated patches alongthe protein with high precision as the noise level is typicallyreduced in comparison to the input data as the differentdata sets are combined into a single representation such thathnipON holds, as well known from other NMR applications.19

Note that the information content of the PRE-derived corre-lation matrix changes with N, as each labelling site gives rise tospecific local information. The PRI-derived matrix is less sensi-tive to the number of input data sets, as PRI is a collectivephenomenon that is represented in each data set (see the ESI†for a detailed discussion of this issue and for correlationanalyses of sparse input data).

For PRE data, the frequently reported changes in signalintensity due to the presence of a paramagnetic label wouldyield a similar correlation matrix. However, such data are notavailable for PRI, as only relaxation rates are amenable to the

above definition (cf. eqn (1)). For reasons of comparison, wethus propose to use relaxation rates throughout the analysis.

Results and discussion

As a first example, we applied our approach to a 220 aa longconstruct of OPN, an IDP involved in inflammation and metastasis.Previous investigations in our laboratory revealed the existence of aresidual structure in solution.6,10,16,17,20 Structural preformationwas found to be of relevance for binding to the oligosaccharideligand heparin. Its binding epitope was located between residues100 and 165 housing a direct binding site between positions 145and 165 and a compensatory site between 100 and 145. PRE andPRI data sets were obtained using four OPN labelling sites atresidues C54, C108, C188 and C247.16 Thus, for the PRE analysisN = 4 and for the PRI analysis N = 6 (cf. eqn (2)). Experimentaldetails of the PRE and PRI experiments have been describedelsewhere.6

The PRE and PRI correlation matrices are shown in Fig. 2.The PRE-derived matrix provides evidence for the existence of foursegments, I: between amino acids (aa) 50–90; II: aa 90–145; III:145–190; IV: aa 190–260. This grouping is substantiated by acluster analysis as shown in Fig. 2 that highlights the differentsegments. (For details see the ESI.†)

The unique content of the correlation map is obvious fromthe differential couplings of the four preformed structuralelements. Segment I appears to be largely uncoupled fromthe other segments (off-diagonal elements between segmentsI and II as well as III and IV equal zero) and seems not toparticipate in the formation of the compacted substrates ofOPN. In stark contrast, structural compaction of segments II–IVappears to be correlated and results in non-zero off-diagonalelements between the different segments remote to correla-tions that stem from short-range PREs of residues located closeto a labelling site in the primary sequence (red squares inFig. 2). The structural coupling of segments II–IV appears toinclude both, correlated and anti-correlated residues, high-lighting the complicated interplay between stabilizing long-range contacts in the heterogeneous conformational ensembleof OPN that lead to the formation of diverse compacted states.Interestingly, the structural compaction of segments II and IVseems to be anti-correlated. It should be noted that this anti-correlation is due to the mutually exclusive PRE effects ofmutants C108 and C247 and would not be revealed by aconventional PRE analysis. Interestingly, segment III (coveringresidues 145–165) houses the heparin-binding site and appearsto be largely anti-correlated to segment II. This suggests thatligand binding eventually leads to an expansion of OPN’spolypeptide chain that is in excellent agreement with earlierstudies concerning conformational adaptations in OPN uponcomplex formation.16,17,20

Note that NMR signals in the direct vicinity of MTSL labelsare typically broadened beyond detection. The areas around thelabelling sites are spanned by the red dots in Fig. 2. Correla-tions observed for the involved residues should be interpreted

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with caution. (The available information is reduced since onlythree input data sets (corresponding to N = 3) are effectivelyavailable for these sites. However, as explained above, theremoval of matrix elements with |corrx,y| 4 0.5 minimizesthe influence of varying N. Note that the average experimentalnoise level of the entire displayed matrix is 0.22, which issignificantly smaller than 0.5 as the residual structure canreduce the noise level and as N is larger than 3.)

The PRI-based correlation matrix for OPN depicted in Fig. 2(bottom), however, highlights distinctly different results.

Positive matrix elements are found almost throughout theentire polypeptide chain indicating the (transient) existence ofa globally and concertedly compacted state of OPN. Interestingly,a closer inspection reveals that OPN’s most compact substatecomprises two cooperatively folded structural segments with aflexible linker region around the heparin-binding site 150–160.This is in very good agreement with the observed flexibility inthe PRE correlation analysis (Fig. 2, top). Key to the proposed

approach is the following: the PRI experiment requires thesimultaneous presence of both spin labels near the 1HN nucleus.Concerted fluctuations of individual segments and the preformationof long-range structural correlations of IDPs can thus be directlyprobed. The OPN example illustrates how the combined analysis ofPRE and PRI data can reveal unprecedented and detailed informa-tion about accessible states of an IDP in solution.

A second example illustrating the applicability of ourapproach is given by BASP1, a tumour-suppressor playing animportant role in neuronal development.21,22 Experimentaldetails of the PRE and PRI experiments have been describedelsewhere.6 BASP1 was spin labelled at four positions: C3, C92,C136 and C205. As in the case of OPN, N = 4 for the PRE analysisand N = 6 for the PRI analysis (cf. eqn (2)). (Note that the averageexperimental noise level of the displayed matrix is 0.25, as inthe case of OPN significantly below 0.5.) Analogous to theexample of OPN, correlation matrices were calculated for PREand PRI data and are shown in Fig. 3 The PRE correlationmatrix (Fig. 3, top) points to the existence of partially foldedstructural segments comprising residues 1–70, 70–90, 90–140and, 170–200 with the N-terminus being the most compact one.Inter-segmental couplings are weak or even anti-correlated.These findings are in very good agreement with earlier studiesproviding evidence for an N-terminal compaction of BASP1 insolution, while the rest of the polypeptide chain displays randomcoil characteristics.11,23 Again, the PRI correlation matrix (Fig. 3,bottom) provides clear evidence for concerted fluctuationspredominantly employing aa 1–100 and thus highlighting thesensitivity of PRI for detecting non-random dynamics in IDPs.

Note that for both IDPs, OPN and BASP1, the correlationanalysis shows largely positive correlations. In regard to thedependence of the correlation coefficients on the sign of thePRI, this is not surprising for BASP1 as the PRI rates are alwaysnegative for this protein (see ref. 6 and the ESI†), while in thecase of OPN, where we observe positive as well as negative PRI,this finding can be interpreted as a hint towards a globalcooperative fluctuation, i.e., compaction.

The different findings obtained for both IDPs can be sum-marized as follows. While OPN transiently samples globallycompacted sub-states that entail correlated motions of differentsub-domains, BASP1 exists as a loosely folded polypeptidechain lacking significant tertiary interactions, except forconcerted fluctuations in its N-terminal domain (NTD). Thisfinding clearly shows the heterogeneity of proteins that arecurrently classified under the notions of IDPs. In the light ofthe importance of compact conformations for the biologicalactivities of IDPs, it is interesting to mention that OPN houses aheparin binding epitope that spans a large fraction of theprotein,17 which corresponds to the extensive positive correla-tions observed via the PRI analysis in Fig. 2, while BASP1 isknown to bind via its NTD to calmodulin and membranesurfaces (cf. Fig. 3).11 The reported concerted compaction inthese biologically vital domains hints towards peculiardynamics within these epitopes that are important for theprotein interactions and that are quite distinct from random-coil dynamics.

Fig. 2 Correlation analysis of PRE (top) and PRI (bottom) data obtained forOPN. Correlated (green), anti-correlated (blue) and uncorrelated (white)regions are indicated. The correlation matrices have been cut off at0.5/�0.5 to represent only correlations above the average noise level,hni, of the matrix. The red dots indicate the labelling sites. The dendrogramon top indicates that the PRE-based matrix can be subdivided into fourclusters, i.e., segments. (Note that the cluster analysis was performed onthe non-truncated data set.) The red squares indicate correlations due toshort-range PREs. The black box indicates an area of significant long-range correlations within residues 70–140.

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Compacted states of IDPs frequently form biologicallyactive epitopes like pre-structured binding regions,16 allostericeffector domains17 or enzymatically active sites.18 In thisrespect, the correlation analysis of PRE/PRI data has theadvantage of highlighting patches of residues that form locallycompacted sites along the entire protein as well as regions thatundergo correlated, concerted compactions. The possibility todiscriminate between simply compacted regions from concertedlyfluctuating, compacted regions offers exciting opportunities forfuture IDP research. Specifically, anti-correlations between tworegions that are subject to long-range PREs (as found for OPN)indicate that the two underlying compacted structural segmentsare formed in a mutually exclusive way and might be relevant for

subtle modulations of ligand interactions proceeding via complexconformational selection mechanisms.

Conclusions

The presented correlation matrix analysis of PRE and PRI datayields a graphical display of correlated fluctuations in theconformational ensemble of IDPs. This is particularly relevantin the context of structural preformation and conformationalselection processes in intrinsically disordered protein interactionevents. Structural preformation originates from the existence ofautonomously folded structural (sub)domains comprising basicstructural elements (e.g., super-secondary structure elements, closedloops)24–27 and leads to an enormous reduction of the accessibleconformational space. IDPs often mediate protein interactionsusing distinct linear motifs that are presumably structurally pre-formed to accommodate the various binding partners. Dependingon the context, these motifs are called short linear motifs (SLiM),28

eukaryotic linear motifs (ELM),29 molecular recognition features(MoRFs)30 or preformed structural elements (PSEs).31 The observa-tion of simultaneously occurring correlated and anti-correlatedstructural fluctuations in IDPs points to a hitherto unknownmechanism for allosteric control of protein interactions by allowingfor both folding-upon–binding as well as unfolding-upon-bindingevents. These features are of great relevance for events proceedingvia conformational selection where binding competent statesalready (pre)-exist in the free form.

Acknowledgements

D. K. acknowledges a Feodor-Lynen Scholarship by the HumboldtFoundation. This work was supported by the Austrian ScienceFoundation (FWF) (P26317 & I844 to R. K.).

Notes and references

1 H. J. Dyson and P. E. Wright, Nat. Rev. Mol. Cell Biol., 2005,6, 197–208.

2 J. Dogan, S. Gianni and P. Jemth, Phys. Chem. Chem. Phys.,2014, 16, 6323–6331.

3 T. Mittag and J. D. Forman-Kay, Curr. Opin. Struct. Biol.,2007, 17, 3–14.

4 R. Konrat, J. Magn. Reson., 2014, 241, 74–85.5 G. M. Clore and J. Iwahara, Chem. Rev., 2009, 109,

4108–4139.6 D. Kurzbach, A. Vanas, A. G. Flamm, N. Tarnoczi, G. Kontaxis,

N. Maltar-Strmecki, K. Widder, D. Hinderberger and R. Konrat,Phys. Chem. Chem. Phys., 2016, 18, 5753–5758.

7 L. E. Kay and T. E. Bull, J. Magn. Reson., 1992, 99, 615–622.8 S. F. Lienin and R. Bruschweiler, Phys. Rev. Lett., 2000, 84,

5439–5442.9 R. Selvaratnam, S. Chowdhury, B. VanSchouwen and

G. Melacini, Proc. Natl. Acad. Sci. U. S. A., 2011, 108,6133–6138.

10 D. Kurzbach, Protein Sci., 2016, 25, 1617–1627.

Fig. 3 Correlation analysis of PRE (top) and PRI (bottom) data obtained forBASP1. Correlated (green), anti-correlated (blue) and uncorrelated (white)regions are indicated. Residues 1–17 have been excluded from the analysisdue to possible intermolecular biases. The correlation matrices have beencut off at 0.5/�0.5 to represent only correlations above the average noiselevel of the matrix. The red dots indicate the labelling sites and spanresidues for which NMR signal intensities are weak due to PRE effects. Thedendrogram on top indicates that the PRE-based matrix can be subdividedinto two clusters, i.e., segments. (Note that the cluster analysis wasperformed on the non-truncated data set.) The red squares indicatecorrelations due to short-range PREs.

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11 D. Kurzbach, A. G. Flamm and T. Sara, Protein Sci., 2016, 25,1628–1636.

12 H. Rangaswami, A. Bulbule and G. C. Kundu, Trends CellBiol., 2006, 16, 79–87.

13 N. Bloembergen and L. O. Morgan, J. Chem. Phys., 1961, 34,842–850.

14 N. Bloembergen, J. Chem. Phys., 1957, 27, 572–573.15 I. Solomon, Phys. Rev., 1955, 99, 559–565.16 G. Platzer, A. Schedlbauer, A. Chemelli, P. Ozdowy, N. Coudevylle,

R. Auer, G. Kontaxis, M. Hartl, A. J. Miles, B. A. Wallace, O. Glatter,K. Bister and R. Konrat, Biochemistry, 2011, 50, 6113–6124.

17 D. Kurzbach, T. C. Schwarz, G. Platzer, S. Hofler, D. Hinderbergerand R. Konrat, Angew. Chem., Int. Ed., 2014, 53, 3840–3843.

18 C. Schulenburg and D. Hilvert, Top. Curr. Chem., 2013, 337,41–67.

19 G. S. Rule and T. K. Hitchens, Fundamentals of Protein NMRSpectroscopy, Springer, Dordrecht, 2006.

20 D. Kurzbach, G. Platzer, T. C. Schwarz, M. A. Henen,R. Konrat and D. Hinderberger, Biochemistry, 2013, 52,5167–5175.

21 M. Hartl, A. Nist, M. I. Khan, T. Valovka and K. Bister, Proc.Natl. Acad. Sci. U. S. A., 2009, 106, 5604–5609.

22 I. Korshunova, P. Caroni, K. Kolkova, V. Berezin, E. Bockand P. S. Walmod, J. Neurosci. Res., 2008, 86, 2201–2213.

23 L. Geist, M. A. Henen, S. Haiderer, T. C. Schwarz,D. Kurzbach, A. Zawadzka-Kazimierczuk, S. Saxena, S. Zerko,W. Kozminski, D. Hinderberger and R. Konrat, Protein Sci.,2013, 22, 1196–1205.

24 M. Levitt and C. Chothia, Nature, 1976, 261, 552–558.25 I. N. Berezovsky and E. N. Trifonov, J. Biomol. Struct. Dyn.,

2002, 20, 315–316.26 E. N. Trifonov and Z. M. Frenkel, Curr. Opin. Struct. Biol.,

2009, 19, 335–340.27 G. D. Rose, P. J. Fleming, J. R. Banavar and A. Maritan, Proc.

Natl. Acad. Sci. U. S. A., 2006, 103, 16623–16633.28 N. E. Davey, D. C. Shields and R. J. Edwards, Nucleic Acids

Res., 2006, 34, 3546–3554.29 V. Neduva and R. B. Russell, FEBS Lett., 2005, 579,

3342–3345.30 V. Vacic, C. J. Oldfield, A. Mohan, P. Radivojac, M. S.

Cortese, V. N. Uversky and A. K. Dunker, J. Proteome Res.,2007, 6, 2351–2366.

31 M. Fuxreiter, I. Simon, P. Friedrich and P. Tompa, J. Mol.Biol., 2004, 338, 1015–1026.

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