NMR spectroscopy offers the unique possibility to relate the structural propensities of disordered proteins and loop segments of folded peptides to biological function and aggregation behaviour. Backbone chemical shifts are ideally suited for this task, provided that appropriate reference data are available and idiosyncratic sensitivity of backbone chemical shifts to structural information is treated in a sensible manner. In the present paper, we describe methods to detect structural protein changes from chemical shifts, and present an online tool [ncSPC (neighbour-corrected Structural Propensity Calculator)], which unites aspects of several current approaches. Examples of structural propensity calculations are given for two well-characterized systems, namely the binding of α-synuclein to micelles and light activation of photoactive yellow protein. These examples spotlight the great power of NMR chemical shift analysis for the quantitative assessment of protein disorder at the atomic level, and further our understanding of biologically important problems.

Protein order, disorder and NMR chemical shifts

The fact that IDPs (intrinsically disordered proteins) are widespread in Nature and sustain various functions advances a new view within structural biochemistry, expanding far beyond the traditional ‘structure–function’ paradigm [1]. IDPs/intrinsically unstructured proteins are known for the lack of a well-defined tertiary structure and exist as a multitude of conformations that dynamically interconvert over time [2]. Their abundance puts forward the notion that protein dynamics has evolved for the adaptive benefit of higher organisms, and advances a ‘systems view’ of protein interaction networks [3]. IDPs control many important biological processes, effectively complementing the functional spectrum of ordered proteins [4]. The evaluation of the ‘flavours’ of disorder observed in IDPs [5] demonstrates that structural lability can help these entities to fulfil their functions in various ways. Interestingly, disordered segments of large proteins often act as flexible linkers between folded subunits in multidomain proteins. Alternatively, many disordered proteins function by specific binding to downstream partners, DNA or RNA. This phenomenon, referred to as coupled folding and binding, involves a temporary excursion to a more ordered state with a structurally defined binding interface [6]. Aside from their fundamental biological role, numerous IDPs are also implicated as causative agents in devastating human diseases, most notably in the area of neurodegeneration [7]. Thus there is a strong need to develop our understanding of the functional role of labile protein conformations in cellular processes, as well as their involvement in protein aggregation, oligomerization and fibril formation.

As explained in the seminal works of Dyson and Wright [8] and Dobson [2], dynamic disorder severely limits protein three-dimensional structure determination using X-ray crystallography, rendering our knowledge of the conformational state of IDPs rudimentary. However, among the available experimental approaches, NMR spectroscopy has proven to be unique in its capacity to study disordered proteins with atomic detail, in both the solid and the solution state [9]. Owing to the lack of a defined three-dimensional structure, the conformational state of IDPs is described by extensive ensembles, derived from a thoroughgoing analysis of various experimental data [10]. As an alternative to comprehensive NMR structure determination, which for large (>25 kDa) proteins may be a tedious task, it would be of significant value to identify a simple, sensitive and accurate proxy for changes in protein (dis)order and dynamics, and relate this to functional consequences. For example, what is the immediate structural consequence of a protein's contact with a membrane or a mutation? NMR chemical shifts are perfectly suited to help to answer such questions, since they reflect the conformational preferences of polypeptide chains with atomic resolution [11] and display exquisite sensitivity to dynamics [12,13]. Furthermore, chemical shifts are easy to measure experimentally and can be efficiently assigned to individual atoms in the protein molecule.

The advancement of experimental NMR spectroscopy of proteins stimulated the development of computational tools that utilize chemical shifts for secondary-structure assignment. Since the deviation of a measured chemical shift from its random-coil value indicates the relative tendency of the polypeptide chain to adopt either helical or extended structures at that point in the primary sequence, protein topology can be effectively computed from known experimental resonance assignments. One of the most prominent developments in chemical shift analysis is the CSI (chemical shift index) [14]. Furthermore, because chemical shifts are so sensitive to structure, even structurally labile regions can be classified and small propensities to transiently populate canonical types of secondary structure, such as α-helix or β-sheet, can be quantitatively determined. Marsh et al. [15] utilized this concept and proposed the SSP (Secondary Structure Propensity) as a measure of local preferences of a disordered chain to adopt canonical secondary structure, and demonstrated its application in structural characterization of an IDP family of synucleins [15]. Ultimately, chemical shift information can be used for the assessment of protein flexibility and structural order, as elegantly demonstrated in the study of Berjanskii and Wishart [13]. Importantly, all of the methods listed above rely heavily on random-coil chemical shift libraries, against which experimental data are compared. Multiple approaches have been proposed in order to provide the most reliable and comprehensive set of random-coil chemical shifts, for example by including nearest-neighbour effects on the backbone 15N chemical shifts in short polypeptides [16], sequence-corrected backbone chemical shift libraries for the polypeptides Ac-GGXAG-NH2 in 1.0 M urea at pH 5 [17] and Ac-GGXGG-NH2 in 8.0 M urea at pH 2.3 [18], as well as a more recent compilation for Ac-QQXQQ-NH2 [19]. Alternative random-coil chemical shift libraries have been derived from the chemical shifts observed for protein regions which were found to be outside regular secondary structure elements and turns (i.e. assigned as ‘coil’) as gauged from their PDB co-ordinates [20,21]. Our recent study of a novel chemical shift library compiled of experimental resonance assignments for IDPs, ncIDP, demonstrates that secondary chemical shift computation will benefit the most from the use of random-coil shifts derived from disordered proteins under native conditions [22].

From chemical shifts to structural propensities

With better predictive power of the chemical shifts of the random-coil state, it is now possible to more accurately detect local propensities for structure formation both for natively unfolded polypeptides, as well as for ordered proteins with disordered regions. We have used experimental resonance assignments for the well-characterized IDP human α-synuclein [15,2327] to demonstrate this fact.

Sequence-specific random-coil chemical shifts

For the reference random-coil state, we used the ncIDP chemical shift library, compiled specifically for IDPs [22], in which the random-coil chemical shift for a nucleus n∈{1HN, 1Hα, 13Cα, 13Cβ, 13CO, 15N} of amino acid residue α, within a tripeptide xay, is expressed as:

 
formula
(1)

where δn(a) is the random-coil chemical shift in the GaG reference sequence, and Δpn (x) and Δnn (y) are the neighbour corrections due to the preceding (p) and next (n) residue respectively.

The sequence-dependent deviations of experimental resonance assignments from the random-coil chemical shifts are known as secondary chemical shifts, and report on the increased sampling of compact (e.g. α-helix) or extended (e.g. β-strand) structures. Figure 1 displays the 13Cα and 15N secondary chemical shifts of human α-synuclein, using the RefDB [28] random-coil chemical shifts as utilized by Marsh et al. [15] (left), and those calculated from the ncIDP random-coil database (right) [22]. The differences in Figure 1 mainly arise from the absence of neighbour corrections in RefDB, which are particularly large in the case of 15N [16,22] and are also responsible for the significant residue-to-residue variation of the 13Cα chemical shifts. Using the more accurate IDP random-coil database correctly identifies most of the protein to be a random coil (right), whereas the neglect of sequence sensitivity of chemical shifts yields less reliable, or even biased, estimates of the conformational state of a disordered protein (left). As borne out by Figure 1, the success of assigning local conformational preferences is predicated on the fact that backbone and side-chain 13Cβ chemical shifts demonstrate a strong and idiosyncratic sensitivity to secondary structure. However, chemical shifts depend on structure in different ways. For example, the reliability of discriminating between α-helix ‘coil’ decreases in the order 13Cα>13CO>1Hα>13Cβ>15N>1HN, whereas the sensitivity to identify β-sheet structure follows the order 1Hα>13Cβ>1HN,15N,13Cα,13CO [20]. Consequently, the most reliable predictions can be made using a properly weighted combination of secondary chemical shifts.

Secondary chemical shifts of α-synuclein

Figure 1
Secondary chemical shifts of α-synuclein

Secondary chemical shifts were computed using RefDB (left) and an IDP-based random-coil chemical shift library (ncIDP) (right).

Figure 1
Secondary chemical shifts of α-synuclein

Secondary chemical shifts were computed using RefDB (left) and an IDP-based random-coil chemical shift library (ncIDP) (right).

Structural propensity calculations

The SSP algorithm was proposed by Marsh et al. [15], and used to explain the fibrillation propensities of human α- and γ-synuclein. In this procedure, the calculated secondary chemical shift for a particular nucleus is divided by that expected in the case of fully formed secondary structure, and yields a score between −1.0 (β-sheet) and +1.0 (α-helix). In their analysis of synucleins, 13Cα, 13Cβ and 1Hα appeared to be most useful, whereas the sensitivity of 15N chemical shifts to peptide conformation may have been missed due to lack of sequence consideration.

In the present paper, we refine the SSP score of Marsh et al. [15]. As in the original implementation, the contributions of different chemical shifts to the total SSP score Ψ(k) are weighted by their uneven sensitivity to α- or β-structure, but, in addition, weights are defined that reflect their relative sensitivity to backbone conformation (see below). Eqn (2) demonstrates how the SSP score for a residue k in the sequence of amino acids xay is then computed:

 
formula
(2)

Ωobsn (j,x - a - y) is the neighbour-corrected secondary chemical shift for amino acid a at the position j in the sequence, computed as a difference between the experimental and the predicted shift from eqn (1), and ΩSSn (j,a) is the tabulated average secondary chemical shift of fully formed secondary structure (α or β) for amino acid a, taken from RefDB [28]. Both terms are weighted by a standard deviation σSSn (j,a) of ΩSSn (j,a) as defined in RefDB. The parameter θSSn reflects the relative sensitivity of the chemical shift n to secondary structure of type SS={α,β}. Normalized values of θSSn are given in Table 1 and are based on a principal component analysis of the various contributions to backbone and 13Cβ chemical shift, performed by Neal et al. [28a]. The SSP score is calculated from a comprehensive set of 1HN, 1Hα, 13Cα, 13Cβ, 13CO and 15N chemical shifts. Eqn (2) may include a user-defined weighted average over 2w+1 residues, to identify consistent trends in the residual secondary structure around the investigated position k, as often used for the CSI. Secondary structure type discrimination in eqn (2) is performed using a test in which the sign of the product of Ωobsn (j,x - a - y) and ΩSSn (a) is calculated, as in eqn (3):

 
formula
(3)
Table 1.
Normalized weight parameters θSSn, reflecting relative sensitivity of chemical shifts to the canonical secondary structures

θSSn is given in arbitrary units.

Nucleus (nα-Helix β-Sheet 
1HN 0.15 0.30 
1Hα 1.00 1.00 
13CO 0.5 0.25 
13Cα 1.00 1.00 
13Cβ 1.00 1.00 
150.125 0.250 
Nucleus (nα-Helix β-Sheet 
1HN 0.15 0.30 
1Hα 1.00 1.00 
13CO 0.5 0.25 
13Cα 1.00 1.00 
13Cβ 1.00 1.00 
150.125 0.250 

Consequently, residual SSP scores of 1.0 and −1.0 reflect fully formed α- or β-structure respectively, whereas a score of 0.5 suggests that 50% of the conformers in the ensemble are helical at that position.

Being aware of the indispensable role of the ‘reference’ random-coil state in secondary structure assignment, along with the idiosyncratic sensitivity of the chemical shifts to structural features of the protein backbone, we implemented the rationale developed above into a neighbour-corrected Structural Propensity Calculator (ncSPC), which uses NMR chemical shift data as the sole input to determine the molecular conformation of proteins, in both the disordered and ordered state. The ncSPC algorithm is expected to provide reliable propensity calculations, as (i) the calculation of random-coil chemical shifts using the IDP random-coil database naturally includes these sequence corrections, and greatly improves the accuracy and completeness of secondary chemical shift calculations (see Figure 1), and (ii) numerically optimized weight factors for the secondary chemical shifts of the various nuclei afford the comprehensive and accurate calculation of structural propensities. Thus ncSPC can detect and classify areas of disorder more reliably than currently available methods because it can better predict random-coil chemical shifts of disordered proteins, and weigh the contributions of the different nuclei to the propensity score by the relative sensitivity of chemical shifts to secondary-structure effects.

The use of chemical shifts as proxies for protein conformational state is now illustrated using two examples, for which structural rearrangements can be readily linked to their biological function: (i) detection of structural propensities in the intrinsically disordered human protein α-synuclein under native and membrane-mimicking conditions; and (ii) the structural transition of the bacterial PYP (photoactive yellow protein) upon light excitation.

Example 1: Structural characterization of the IDP α-synuclein under native and membrane-mimicking conditions

Human α-synuclein is a small IDP of 140 amino acids that has attracted great interest [23,24,26,29,30]. Within neurons, α-synuclein is particularly localized in the nucleus and in the presynaptic terminals [31], where it is loosely associated with the distal pool of synaptic vesicles [32] and known to interact with the lipid rafts of the plasma membrane [33]. The propensity of α-synuclein to interact with neuron membranes in vivo was confirmed by extensive in vitro studies, including binding to membrane-mimicking agents [34,35]. At synapses, however, the distribution of the protein is highly dynamic and directly related to neuronal activity, with detectable rapid exchanges taking place among neighbouring synapses [36]. α-Synuclein is directly implicated in human neuropathology. In the brain of patients with neurodegenerative disorders, termed α-synucleinopathies, the protein is known to undergo conformational transitions yielding deposits of proteinaceous material [7,37]. Consequently, at the onset of neurodegenerative processes, malfunction of synapses develops.

Much effort has been put into the understanding of the fundamental causes of α-synuclein aggregation and the abrupt loss of functional membrane interactions. Several biophysical methods, including NMR [15,25,27,3841], EPR [42,43] and fluorescence spectroscopy [44], have provided valuable insights into the structural features of natively disordered and membrane-bound α-synuclein. Figure 2(A) shows the result of a structural propensity calculation from NMR chemical shift data, using the ncSPC web tool. The program detects enhanced β-propensity only in the C-terminal region, residues 105–140. The extended conformation of the C-terminus was confirmed from NMR residual dipolar couplings in an independent study and shown to result from electrostatic forces [38]. In contrast, as shown in Figures 2(B) and 2(C), in membrane-mimic states of the physiological situation at nerve termini, residues 1–97 form a broken loosely ordered helical structure while maintaining disorder in the C-terminal domain [43]. Moreover, the regions of decreased α-helical propensity can be reliably assigned to the presence of structural ‘breaks’ in well defined α-helices upon contact with SDS micelles, as gauged from RDC measurement by Ulmer et al. [45]. The ncSPC assignment of structural propensity for α-synuclein is in line with available structural data, offering the reliable detection of small, but meaningful, structural features of an IDP.

Structural interpretation of ncSPC for pure and micelle-bound α-synuclein

Figure 2
Structural interpretation of ncSPC for pure and micelle-bound α-synuclein

(A) Propensity plot for pure α-synuclein (upper panel), and α-synuclein in the presence of 50 mM SDS (lower panel). (B) Structural interpretation of ncSPC score in the context of micelle-bound NMR model of α-synuclein (PDB code 1XQ8). The positions of structural ‘kinks’ in the helices are annotated with green circles. (C) Schematic representation of partially folded α-synuclein upon binding to SDS micelle (grey). The cartoon representation of the protein is colour-coded according to the ncSPC score. Calculations were performed using 1Hα, 1HN, 13Cα, 13Cβ and 15N chemical shifts from in-house assigned α-synuclein and BMRB 5744.

Figure 2
Structural interpretation of ncSPC for pure and micelle-bound α-synuclein

(A) Propensity plot for pure α-synuclein (upper panel), and α-synuclein in the presence of 50 mM SDS (lower panel). (B) Structural interpretation of ncSPC score in the context of micelle-bound NMR model of α-synuclein (PDB code 1XQ8). The positions of structural ‘kinks’ in the helices are annotated with green circles. (C) Schematic representation of partially folded α-synuclein upon binding to SDS micelle (grey). The cartoon representation of the protein is colour-coded according to the ncSPC score. Calculations were performed using 1Hα, 1HN, 13Cα, 13Cβ and 15N chemical shifts from in-house assigned α-synuclein and BMRB 5744.

Example 2: light activation of PYP

The second example concerns the blue light photoreceptor PYP, involved in bacterial phototaxis, and a model for the action of PAS (Per/Arnt/Sim) signal transduction domains [46]. PYP is a 14 kDa water-soluble protein, which contains a thioester linked p-coumaric acid cofactor [47]. Upon light excitation, PYP undergoes a significant conformational change, leading to the dissolution of native structure around the chromophore [48]. Although the three-dimensional structure of the photocycle ground state was solved using NMR spectroscopy, the light-activated ‘signalling’ state has been elusive, showing ubiquitous line broadening as a result of slow (microsecond) protein conformational interconversion [48]. We have analysed the truncation mutant PYP Δ25, which displays a photocycle that is very similar to wild-type, and for which near-complete heteronuclear NMR resonance assignments are available [49]. Using the chemical shift data alone, we were able to detect and interpret the structural consequence of blue light irradiation on PYP, as shown in Figure 3: the helix at the PAS core domain, comprising residues AAEGDIT, dissolves and forms a region of extended structure. At the same time, an adjacent region with β-sheet propensity becomes consolidated further. Our analysis demonstrates that the long-lived intermediate of the PYP Δ25 photocycle in solution exhibits structural and dynamic disorder. Consequently, exposure of a new surface epitope can mediate signal transduction through protein–protein interaction with a downstream partner. The structural change described above then marks the start of the response of the bacterium to evade harmful blue light.

Investigation of secondary structure propensity changes in ground and photoactivated states of PYP Δ25

Figure 3
Investigation of secondary structure propensity changes in ground and photoactivated states of PYP Δ25

(A) Propensity plot for ground state PYP Δ25 (upper panel), and PYP Δ25 after photoactivation (lower panel). (B) Structural interpretation of ncSPC score in the context of NMR model of ground state PYP Δ25 (PDB code 1XFN). The position of structural rearrangement is indicated in yellow. The position of p-coumaric acid is marked with a green circle. Calculations performed using 1Hα, 1HN, 13Cα, 13Cβ and 15N chemical shifts from BMRB 6321 and 6322 respectively.

Figure 3
Investigation of secondary structure propensity changes in ground and photoactivated states of PYP Δ25

(A) Propensity plot for ground state PYP Δ25 (upper panel), and PYP Δ25 after photoactivation (lower panel). (B) Structural interpretation of ncSPC score in the context of NMR model of ground state PYP Δ25 (PDB code 1XFN). The position of structural rearrangement is indicated in yellow. The position of p-coumaric acid is marked with a green circle. Calculations performed using 1Hα, 1HN, 13Cα, 13Cβ and 15N chemical shifts from BMRB 6321 and 6322 respectively.

Conclusions

Structural changes in adaptable regions of proteins are widespread and have great significance in biology. However, they often remain elusive to the ‘standard’ tools of structural biochemistry. The above examples demonstrate that, given a judiciously compiled reference set of random-coil chemical shifts and a simple propensity calculation algorithm, NMR spectroscopy is able to detect meaningful conformational shifts in disordered proteins or labile regions of folded proteins. These conformational rearrangements mediate biological function, or distinguish benign from malign cellular protein interactions. The development of a web tool, that can instantly perform such analyses on any query protein for which assigned chemical shift data are available, will facilitate its use in the structural biology community.

ncSPC is available as a W3-compliant application and can be accessed at http://www.protein-nmr.org. The structural propensity computations are divided into three stages. The first phase is data upload. At this stage, experimental resonance assignments can be retrieved from the BioMagResBank or uploaded from a local workstation as a STAR 2.x/3.x file. Since correct chemical shift referencing is crucial in the analysis, manual or 13C automatic reference offset corrections, according to the method described by Marsh et al. [15], can be performed upon upload. The presence of deuterium isotope shifts for uniformly deuterated proteins [50] can be accounted for. Subsequently, a choice of random-coil chemical shift library, with five commonly available random-coil tables implemented with their neighbour-corrections, is made [16,18,20,22]. The last step transfers the user to a selection of features divided into three major groups: predictive, statistical and structural propensity computation. The calculations are concluded with a short summary of selected options and a list of downloadable ASCII and graphics files. The data and graphics presented in the present paper were prepared with ncSPC.

Intrinsically Disordered Proteins: A Biochemical Society Focused Meeting held at University of York, U.K., 26–27 March 2012. Organized and Edited by Jennifer Potts (York, U.K.) and Mike Williamson (Sheffield, U.K.).

Abbreviations

     
  • CSI

    chemical shift index

  •  
  • IDP

    intrinsically disordered protein

  •  
  • ncSPC

    neighbour-corrected Structural Propensity Calculator

  •  
  • PAS

    Per/Arnt/Sim

  •  
  • PYP

    photoactive yellow protein

  •  
  • SSP

    Secondary Structure Propensity

We thank Professor D.B. Janssen for his continued support of this project. Dr Renee Otten is gratefully acknowledged for 2H-effect correction routines.

Funding

This work was supported in part by a VIDI grant to F.A.A.M. from the Netherlands Organization for Scientific Research (NWO).

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