Random coil chemical shifts for serine, threonine and tyrosine phosphorylation over a broad pH range
Phosphorylation is one of the main regulators of cellular signaling typically occurring in flexible parts of folded proteins and in intrinsically disordered regions. It can have distinct effects on the chemical environment as well as on the structural properties near the modification site. Secondary chemical shift analysis is the main NMR method for detection of transiently formed secondary structure in intrinsically disordered proteins (IDPs) and the reliability of the analysis depends on an appropriate choice of random coil model. Random coil chemical shifts and sequence correction factors were previously determined for an Ac-QQXQQ-NH2-peptide series with X being any of the 20 common amino acids. However, a matching dataset on the phosphorylated states has so far only been incompletely determined or determined only at a single pH value. Here we extend the database by the addition of the random coil chemical shifts of the phosphorylated states of serine, threonine and tyrosine measured over a range of pH values covering the pKas of the phosphates and at several temperatures (www.bio.ku.dk/sbinlab/randomcoil). The combined results allow for accurate random coil chemical shift determination of phosphorylated regions at any pH and temperature, minimizing systematic biases of the secondary chemical shifts. Comparison of chemical shifts using random coil sets with and without inclusion of the phosphoryl group, revealed under/over estimations of helicity of up to 33%. The expanded set of random coil values will improve the reliability in detection and quantification of transient secondary structure in phosphorylation-modified IDPs.
KeywordsSecondary structure NMR Phosphorylation Post translational modification Secondary chemical shift analysis PTM IDP Random coil
Intrinsically disordered proteins (IDPs) are important components of the cellular signaling machinery (Wright and Dyson 2015) and they are abundant in most proteomes (Ward et al. 2004; Xue et al. 2012). They exist as an ensemble of interconverting dynamic conformations with varying amounts of transiently populated secondary structure. Phosphorylation mostly occurs in intrinsically disordered regions (Iakoucheva et al. 2004; Tyanova et al. 2013), and can have diverse effects on transiently populated secondary structures. In the literature, cases can be found where there is no effect on the secondary structure (Sibille et al. 2012), but also cases where varying degrees of (de)stabilization are seen (Miranda et al. 2004; Espinoza-Fonseca et al. 2008; Andrew et al. 2002; Bui and Gsponer 2014), depending in most instances on the immediate sequence context (Hendus-Altenburger et al. 2017). One of the most pronounced effects reported is the phosphorylation-induced global folding of the IDP 4EBP2, which is the major neural isoform of a family of three mammalian proteins that bind eIF4E and suppress cap-dependent translation initiation (Bah et al. 2015).
The most convenient and robust NMR method to identify secondary structures in proteins is based on the secondary chemical shift analysis (SCS, Δδ). Chemical shifts (δs, CSs) of the backbone nuclei and in particular those of Hα, Cα and C′ correlate strongly with local backbone structure. By comparison to random coil CSs, one can derive secondary structure propensities and specifically identify the position, length and population of these in proteins. Yet, random coil CSs of individual residues vary depending on their neighboring residues (Wishart et al. 1995) as well as on experimental conditions (salt, pH, temperature etc.) (Kjaergaard et al. 2011; Nielsen and Mulder 2018). This is critical, especially for IDPs as the transient nature of their secondary structure manifests in small SCSs, which means that small variations in the reference random coil libraries can lead to large biases. Currently, several datasets for reference CSs of commonly occurring amino acids exist that are based on either neighboring correction factors derived from peptide libraries (Kjaergaard et al. 2011; Kjaergaard and Poulsen 2011; Marsh et al. 2006), computational approaches (Camilloni et al. 2012), or curated CS data sets derived from disordered proteins (Nielsen and Mulder 2018; Tamiola et al. 2010). Posttranslational modifications (PTMs) affect the CSs, and the chemical effects of PTMs, like phosphorylation, on the random coil CSs are not yet included in any library. Previous studies have aimed to characterize the random coil CSs of phosphorylated amino acids using peptides (Bienkiewicz and Lumb 1999; Conibear et al. 2019). However, these studies were based on glycine peptides, and as glycines have unusual Ramachandran distributions, only some aspects of the neighbor-dependence are accounted for (Kjaergaard and Poulsen 2011). Furthermore, the effect of a PTM may extend further than to its immediate neighbors and previous work has not provided any correction factors for neighboring amino acid residues upon phosphorylation. Recent work on 18 differently modified amino acids measured the random coil CSs of phosphorylated serine, threonine and tyrosine in an Ac-GGXGG-NH2 context as well as reported neighboring effect, but only at pH 5.0 (Conibear et al. 2019). This pH is not compatible with most studies on IDPs, which are typically conducted around physiological pH and at, or above, the pI of the phosphates.
At present, the most precise way to extract the inducible effect of either phosphorylation, other PTMs, or non-natural amino acids is to use the chemically unfolded state as internal reference (δirc) to determine the SCSs (Modig et al. 2007). The use of the chemically unfolded state as internal reference does not require a reference library, as each protein can be used as its own internal reference. Thus, this approach has successfully been used in secondary structure analysis of a number of IDPs (Haxholm et al. 2015; Hendus-Altenburger et al. 2016; Kjaergaard et al. 2010), as well as to quantify the effect of multiple phosphorylations on secondary structure and the identification of a stabilizing phospho-motif in an IDP (Hendus-Altenburger et al. 2017). Yet, using the chemically unfolded state requires another round of assignment, homogeneous phosphorylations of all modified sites as well as identical sample conditions for all states, which is rather laborious. Moreover, the extent to which urea biases the CSs is not entirely clear (Elam et al. 2013; Whittington et al. 2005). Thus, peptide derived random coil shifts remain an efficient and accurate approach to examine the locally and globally induced structural changes of these modifications.
Here, we expand the previous dataset of random coil CSs and sequence correction factors recorded on the Ac-QQXQQ-NH2 peptide series (Kjaergaard and Poulsen 2011) by including the phosphorylated states of serine, threonine and tyrosine (referred to as pSer, pThr and pTyr). The results have been implemented in an online predictor at www.bio.ku.dk/sbinlab/randomcoil. We have explored the effects at various experimental conditions that are likely to be relevant for phosphorylations in IDPs, specifically the temperature- and pH dependence of the phosphorylated state random coil CSs covering the pKas of the phosphates. Although this dataset was determined with IDPs and the effect of their phosphorylation in mind, it should be equally applicable for folded proteins.
Materials and methods
Peptides with the sequence Ac-QQXQQ-NH2 were purchased from KJ Ross-Petersen ApS (Denmark) and from Schafer N (Denmark), where X was either serine, threonine or tyrosine without or with (pSer, pThr or pTyr) phosphorylation (≥ 95% purity by reversed phase HPLC, identities confirmed by mass spectrometry).
Peptide samples for circular dichroism (CD) analyses were prepared in 20 mM sodium phosphate buffer to a final approximate concentration of 250 µM (pH 6.5, with or without 150 mM NaF). Far-UV CD spectra were recorded from 260 to 190 nm on a Jasco 815 spectropolarimeter in 0.1 cm quartz cuvettes, and with Peltier controlled temperatures set to 5 °C or 35 °C. Each spectrum was recorded at a scan rate of 10 nm/min, band width 1 nm, and a response time of 2 s and averaged over 10 scans. To enable comparison at equal concentrations, especially as the serine, pSer, threonine, and pThr peptides lack absorbance at 280 nm, the signals were normalized using the HT level. Background spectra were recorded identically and subtracted. The final spectra were smoothed using the FFT function in the Jasco software.
NMR samples were prepared by dissolving 2–3 mg of peptide in 500 µL 20 mM sodium phosphate buffer pH 6.5 containing 5% (v/v) D2O, 3 mM NaN3, and 1 mM DSS. pH was adjusted to 6.5 by the addition of small quantities of HCl or NaOH or in urea as described (Hendus-Altenburger et al. 2017). All NMR spectra were acquired on either a Varian Unity 800 MHz spectrometer equipped with a room temperature probe or a 600 MHz Bruker Avance III HD spectrometer with a cryo-probe. CSs were referenced to internal DSS as previously described (Wishart et al. 1995). For each sample the following spectra were acquired at natural isotope abundance: 1D 1H (zgesgp), 1H–15N HSQC (hsqcetfpf3gpsi), 1H–13C HSQC (hsqcetgpsisp2.2), 2D TOCSY (Piotto et al. 1992) (mlevgpph19, mixing time 80 ms), 2D ROESY (roesygpph19.2, mixing time 300 ms) and 1Hα–13CO HSQC (HACO_hsqcetgpsi) (Kjaergaard et al. 2011). The 1Hα–13CO HSQC experiment correlates the Hα protons with the carbonyl resonances of the same and the preceding residue. For all peptides, data were recorded at 5 °C, 15 °C, 25 °C, and 35 °C. NMR data were processed using NMRPipe (Delaglio et al. 1995) and analyzed using CCPNMR Analysis (Vranken et al. 2005). The 3JHNHA coupling constants were measured by the peak splitting of the HN-signals in the 1D 1H NMR spectra.
δHA and δA represent the random coil CSs of the fully protonated and fully deprotonated species, respectively. Ka is the acid dissociation constant of the side chain. The CSs were fitted to Eq. (3), where Ka was treated as a global fitting parameter. The protonation/deprotonation of the N- and C-termini could be neglected due to N-terminal acetylation and C-terminal amidation.
To test the performance of the new predictor, the chemical shifts of six phosphorylated proteins were extracted from the BMRB database. These include the sodium proton exchanger 1 (NHE1) (BMRB 26755 and 27812) (Hendus-Altenburger et al. 2017; Hendus-Altenburger et al. 2016), the kinase inducible transactivation domain (KID) (BMRB 6784 and 6788) (Radhakrishnan et al. 1998), the transcriptional regulator protein Ash1 (BMRB 26719 and 26720) (Martin et al. 2016), the disordered cytosolic domain CD79a of the B cell receptor (BMRB 19644 and 19648) (Rosenlow et al. 2014), the regulatory region of the cystic fibrosis transmembrane conductance regulator (CFTR) (BMRB 15336 and 15340) (Baker et al. 2007) and the transcriptional activation domain of the transcription factor Elk-1 (BMRB 26762 and 26786) (Mylona et al. 2016).
Results and discussion
The change in NMR CSs upon PTMs of proteins is due to the changed local chemical environment but can in addition be caused by accompanying structural rearrangements induced by the PTM. While the chemical effect is not expected to reach further than 2–3 residues on either side of the modified residue in the random coil state, structural rearrangements or changes in the conformational ensembles of IDPs can have long-range effects (Hendus-Altenburger et al. 2017). In order to allow for secondary structure analysis in the presence of phosphorylation we extended the previously published Ac-QQXQQ-NH2 peptide random coil CS database to include those for phosphorylated serine (pSer), threonine (pThr) and tyrosine (pTyr) residues. Furthermore, the random coil CS were extracted for various temperatures and at pH values ranging from pH 4.0 to 8.0 to cover the pKas of the phosphates.
Random coil CSs at pH 6.5 and 5°C
The amide peaks were well resolved in the 1H–15N HSQC spectra of all three phosphorylated peptides (Fig. 1b) and their CSs were readily assigned as indicated in the 1H–13C–HSQC spectra (Fig. 1c). For pSer and pThr, strong downfield shifts of the backbone amides were observed compared to the unphosphorylated counterparts. In contrast, tyrosine phosphorylation did not induce a similar large downfield shift of the modified residue, likely due to the more distal position of the modified hydroxyl group in the side chain relative to the backbone amide (Bienkiewicz and Lumb 1999; Theillet et al. 2012) (Table 1). These are important observations, as in several cases (transient) hydrogen bonds between the phospho-group and the amide of the same or neighboring residues were observed upon phosphorylation (Du et al. 2005; Ramelot and Nicholson 2001; Kang et al. 2010). We note that Q2 of the pTyr peptide showed a strong down-field shift suggesting this residue to be more affected than the tyrosine amide itself, which indicates that the CS of the residue prior to a pTyr can be used diagnostically to identify phosphorylation of tyrosine residues by NMR.
Within error, the random coil shifts for pTyr as well as the shift of its neighboring glutamines were identical to those of the non-phosphorylated peptides, testifying to the random coil nature of the phosphorylated peptide. However, for pSer and pThr, the shifts deviated from those of the non-phosphorylated peptides and together with the downfield shift of the amide, this could indicate structure formation. Thus, to address if the phosphates in these peptides induce structure, we recorded far-UV CD spectra of the phosphorylated as well as non-phosphorylated peptides at different temperatures (5 °C and 35 °C) and in the absence and presence of 150 mM NaF (SI Fig. S1). The peptides were all in a random coil state as judged by the negative ellipticity at 198 nm and the slight positive signal at 215 nm. Besides a more pronounced negative ellipticity at 198 nm for the phosphorylated peptides, indicating slightly more extended structure, phosphorylation did not change the CD spectra, neither did the presence of 150 mM NaF. At 35 °C, we observed a minor change in the CD profile towards a slight redistribution away from polyproline II structure, as observed previously for IDPs (Kjaergaard et al. 2010). Finally, we compared the 1H,13C-HSQC spectra recorded on Ac-QQpSQQ-NH2 and Ac-QQpTQQ-NH2 in the absence and presence of 150 mM NaCl (SI Fig. S2), which showed the Cα, Cβ, and Hα CSs to be similar. Thus, the presence of salt at physiological concentrations does not change the conformational ensemble of these peptides.
An overlay of the 15N-HSQC spectra of the phosphorylated and non-phosphorylated peptides showed that the glutamine side chain resonances did not readily superimpose (SI Fig. S3), indicating that the phosphates changed the chemical environment of these and/or induced structure. Therefore, to further substantiate the random coil nature of the phosphorylated peptides, we analyzed ROESY spectra for connectivity beyond those of sequential origin. All phosphorylated peptides showed stronger Hα–HN (i, i + 1) inter-residue cross-peaks, and weaker HN–HN (i, i + 1) cross peaks (Fig. 1d), showing that the phosphorylated peptides are indeed random coil and have no secondary structure related interactions (Bienkiewicz and Lumb 1999; Dyson and Wright 1991). We repeated the ROESY spectra in the presence of 150 mM NaCl and 8 M urea. These changes did not alter the peak intensity patterns and thus the ensembles remained similar (SI Fig. S4).
Random coil CSs of the fully protonated and fully deprotonated phosphorylated residues at 5 °C
9.76 × 10−7
δA − δHA
5.00 × 10−7
δA − δHA
1.47 × 10−6
δA − δHA
Glutamine derived sequence correction factors at pH 6.5, 5 °C
Revisiting the CS analyses of phosphorylated IDPs
CSs of phosphorylated proteins available in the BMRB and used in this study
Phosphorylation sites (kinase)
Na+/H+ exchanger 1 (NHE1), disordered distal tail
Ser693, Ser723, Ser726, Ser771, Thr779, Ser785 (MAP kinase ERK2)
Kinase inducible transactivation (KID) domain of the transcription factor CREB
Ser133 (Protein kinase A, PKA)
6788 (Radhakrishnan et al. 1998)
Regulatory region of the cystic fibrosis transmembrane conductance regulator (CFTR)
Ser660, Ser700, Ser712, Ser737, Ser753, Ser768, Ser795, Ser813 (Protein kinase A, PKA)
15340 (Baker et al. 2007)
Transcriptional regulator protein Ash1
Ser424, Ser426, Thr429, Ser442, Thr450, Ser452, Ser455, Ser465, Ser469, Ser490 (cyclin A/Cdk2)
26720 (Martin et al. 2016)
Activation domain of the transcription factor Elk-1
Thr337, Thr354, Thr364, Thr369, Ser384, Ser390, Thr418, Ser423 (MAP kinase ERK2)
26786 (Mylona et al. 2016)
Disordered cytosolic domain CD79a of the B-cell receptor
Tyr182, Tyr188, Tyr199, Tyr210 (Src family kinase Fyn)
19648 (Rosenlow et al. 2014)
Taken together, the use of the new predictor of random coil CS values for phosphorylated proteins allowed for more accurate detection of the transient secondary structures of phosphoproteins. The effects of phosphorylation could now be directly separated from the chemical effect and enabled quantification of the structure modulating effects of phosphorylation. Generally, a phosphorylation N-terminal to transient helicity stabilized the helical structure, and when the phosphorylation site was positioned C-terminal to transient structure, helicity was destabilized, in agreement with previous observations (Andrew et al. 2002). Further, the new predictor allows for pH corrected CS predictions, which is critical as phosphate titrates in the physiological pH range with considerable effects on the carbon CSs, eliminating spurious spikes in the secondary CSs. The large effect of phosphorylation on threonine CSs combined with its strong pH sensitivity, warrants extra care in interpreting structural effects from threonine phosphorylation in general.
Extraction of local structure from CSs has been possible for 50 years (Markley et al. 1967) and the random coil CS databases and peptide-derived libraries continue to improve both in accuracy and precision of the correction factors for sequence and sample conditions like temperature and pH (Kjaergaard et al. 2011; Kjaergaard and Poulsen 2011; Schwarzinger et al. 2001). With the inclusion of a full set of random coil shifts for phosphorylated side chains in proteins and their pH and temperature dependence covering the range of the pKa values of the phosphates, we can more reliably analyze and decompose the effects of phosphorylations on the structural ensemble.
We thank Magnus Kjaergaard for fruitful discussions and Andreas Prestel for help with NMR recordings. This work was supported by Grants from the Danish Research Councils (to B.B.K./W.B.: 4181-00344), the Lundbeck Foundation (M.B.A.K./R.H-A), and the Novo Nordisk Foundation SYNERGY Grant (B.B.K) and Challenge Grant, REPIN (B.B.K). The Villumfonden is thanked for generous support for NMR instruments.
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