Identification of helix capping and β-turn motifs from NMR chemical shifts

We present an empirical method for identification of distinct structural motifs in proteins on the basis of experimentally determined backbone and ¹³C^β chemical shifts. Elements identified include the N-terminal and C-terminal helix capping motifs and five types of β-turns: I, II, I′, II′ and VIII. Using a database of proteins of known structure, the NMR chemical shifts, together with the PDB-extracted amino acid preference of the helix capping and β-turn motifs are used as input data for training an artificial neural network algorithm, which outputs the statistical probability of finding each motif at any given position in the protein. The trained neural networks, contained in the MICS (motif identification from chemical shifts) program, also provide a confidence level for each of their predictions, and values ranging from ca 0.7–0.9 for the Matthews correlation coefficient of its predictions far exceed those attainable by sequence analysis. MICS is anticipated to be useful both in the conventional NMR structure determination process and for enhancing on-going efforts to determine protein structures solely on the basis of chemical shift information, where it can aid in identifying protein database fragments suitable for use in building such structures.     

Prediction of Xaa-Pro peptide bond conformation from sequence and chemical shifts

We present a program, named Promega, to predict the Xaa-Pro peptide bond conformation on the basis of backbone chemical shifts and the amino acid sequence. Using a chemical shift database of proteins of known structure together with the PDB-extracted amino acid preference of cis Xaa-Pro peptide bonds, a cis/trans probability score is calculated from the backbone and ¹³C^β chemical shifts of the proline and its neighboring residues. For an arbitrary number of input chemical shifts, which may include Pro-¹³C^γ, Promega calculates the statistical probability that a Xaa-Pro peptide bond is cis. Besides its potential as a validation tool, Promega is particularly useful for studies of larger proteins where Pro-¹³C^γ assignments can be challenging, and for on-going efforts to determine protein structures exclusively on the basis of backbone and ¹³C^β chemical shifts.     

2DCSi: identification of protein secondary structure and redox state using 2D cluster analysis of NMR chemical shifts

Chemical shifts of amino acids in proteins are the most sensitive and easily obtainable NMR parameters that reflect the primary, secondary, and tertiary structures of the protein. In recent years, chemical shifts have been used to identify secondary structure in peptides and proteins, and it has been confirmed that ¹H^α, ¹³C^α, ¹³C^β, and ¹³C′ NMR chemical shifts for all 20 amino acids are sensitive to their secondary structure. Currently, most of the methods are purely based on one-dimensional statistical analyses of various chemical shifts for each residue to identify protein secondary structure. However, it is possible to achieve an increased accuracy from the two-dimensional analyses of these chemical shifts. The 2DCSi approach performs two-dimension cluster analyses of ¹H^α, ¹H^N, ¹³C^α, ¹³C^β, ¹³C′, and ¹⁵N^H chemical shifts to identify protein secondary structure and the redox state of cysteine residue. For the analysis of paired chemical shifts of 6 data sets, each of the 20 amino acids has its own 15 two-dimension cluster scattering diagrams. Accordingly, the probabilities for identifying helix and extended structure were calculated by using our scoring matrix. Compared with existing the chemical shift-based methods, it appears to improve the prediction accuracy of secondary structure identification, particularly in the extended structure. In addition, the probability of the given residue to be helix or extended structure is displayed, allows the users to make decisions by themselves. Electronic Supplementary Material The online version of this article (doi:) contains supplementary material, which is available to authorized users. Grant sponsor: National Science Council of ROC; Grant numbers: NSC-94-2323-B006- 001, NSC-93-2212-E-006.     

Uncovering symmetry-breaking vector and reliability order for assigning secondary structures of proteins from atomic NMR chemical shifts in amino acids

Unravelling the complex correlation between chemical shifts of ¹³ C ^α, ¹³ C ^β, ¹³ C′, ¹ H ^α, ¹⁵ N, ¹ H ^N atoms in amino acids of proteins from NMR experiment and local structural environments of amino acids facilitates the assignment of secondary structures of proteins. This is an important impetus for both determining the three-dimensional structure and understanding the biological function of proteins. The previous empirical correlation scores which relate chemical shifts of ¹³ C ^α, ¹³ C ^β, ¹³ C′, ¹ H ^α, ¹⁵ N, ¹ H ^N atoms to secondary structures resulted in progresses toward assigning secondary structures of proteins. However, the physical-mathematical framework for these was elusive partly due to both the limited and orthogonal exploration of higher-dimensional chemical shifts of hetero-nucleus and the lack of physical-mathematical understanding underlying those correlation scores. Here we present a simple multi-dimensional hetero-nuclear chemical shift score function (MDHN-CSSF) which captures systematically the salient feature of such complex correlations without any references to a random coil state of proteins. We uncover the symmetry-breaking vector and its reliability order not only for distinguishing different secondary structures of proteins but also for capturing the delicate sensitivity interplayed among chemical shifts of ¹³ C ^α, ¹³ C ^β, ¹³ C′, ¹ H ^α, ¹⁵ N, ¹ H ^N atoms simultaneously, which then provides a straightforward framework toward assigning secondary structures of proteins. MDHN-CSSF could correctly assign secondary structures of training (validating) proteins with the favourable (comparable) Q3 scores in comparison with those from the previous correlation scores. MDHN-CSSF provides a simple and robust strategy for the systematic assignment of secondary structures of proteins and would facilitate the de novo determination of three-dimensional structures of proteins.     

Triple-Resonance Methods for Complete Resonance Assignment of Aromatic Protons and Directly Bound Heteronuclei in Histidine and Tryptophan Residues

A set of three experiments is described which correlate aromatic resonances of histidine and tryptophan residues with amide resonances in ¹³C/¹⁵N-labelled proteins. Provided that backbone ¹H and ¹⁵N positions of the sequentially following residues are known, this results in sequence-specific assignment of histidine ¹H^δ2/¹³C^δ2 and ¹H^ε1/¹³C^ε1 as well as tryptophan ¹H^δ1/¹³C^δ1, ¹H^ζ2/¹³C^{ζ 2}, ¹H^η2/¹³C^η2, ¹H^ε3/¹³C^ε3, ¹H^ζ3/¹³C^ζ3 and ¹H^ε1/¹⁵N^ε1 chemical shifts. In the reverse situation, these residues can be located in the ¹H–¹⁵N correlation map to faciliate backbone assignments. It may be chosen between selective versions for either of the two amino acid types or simultaneous detection of both with complete discrimination against phenylalanine or tyrosine residues in each case. The linkages between δ-proton/carbon and the remaining aromatic as well as backbone resonances do not rely on through-space interactions, which may be ambiguous, but exlusively employ one-bond scalar couplings for magnetization transfer instead. Knowledge of these aromatic chemical shifts is the prerequisite for the analysis of NOESY spectra, the study of protein–ligand interactions involving histidine and tryptophan residues and the monitoring of imidazole protonation states during pH titrations. The new methods are demonstrated with five different proteins with molecular weights ranging from 11 to 28 kDa.     

Combining NMR ensembles and molecular dynamics simulations provides more realistic models of protein structures in solution and leads to better chemical shift prediction

While chemical shifts are invaluable for obtaining structural information from proteins, they also offer one of the rare ways to obtain information about protein dynamics. A necessary tool in transforming chemical shifts into structural and dynamic information is chemical shift prediction. In our previous work we developed a method for 4D prediction of protein ¹H chemical shifts in which molecular motions, the 4th dimension, were modeled using molecular dynamics (MD) simulations. Although the approach clearly improved the prediction, the X-ray structures and single NMR conformers used in the model cannot be considered fully realistic models of protein in solution. In this work, NMR ensembles (NMRE) were used to expand the conformational space of proteins (e.g. side chains, flexible loops, termini), followed by MD simulations for each conformer to map the local fluctuations. Compared with the non-dynamic model, the NMRE+MD model gave 6–17% lower root-mean-square (RMS) errors for different backbone nuclei. The improved prediction indicates that NMR ensembles with MD simulations can be used to obtain a more realistic picture of protein structures in solutions and moreover underlines the importance of short and long time-scale dynamics for the prediction. The RMS errors of the NMRE+MD model were 0.24, 0.43, 0.98, 1.03, 1.16 and 2.39 ppm for ¹Hα, ¹HN, ¹³Cα, ¹³Cβ, ¹³CO and backbone ¹⁵N chemical shifts, respectively. The model is implemented in the prediction program 4DSPOT, available at .     

Effects of side-chain orientation on the <Superscript>13</Superscript>C chemical shifts of antiparallel β-sheet model peptides

The dependence of the ¹³C chemical shift on side-chain orientation was investigated at the density functional level for a two-strand antiparallel β-sheet model peptide represented by the amino acid sequence Ac-(Ala)₃-X-(Ala)₁₂-NH₂ where X represents any of the 17 naturally occurring amino acids, i.e., not including alanine, glycine and proline. The dihedral angles adopted for the backbone were taken from, and fixed at, observed experimental values of an antiparallel β-sheet. We carried out a cluster analysis of the ensembles of conformations generated by considering the side-chain dihedral angles for each residue X as variables, and use them to compute the ¹³C chemical shifts at the density functional theory level. It is shown that the adoption of the locally-dense basis set approach for the quantum chemical calculations enabled us to reduce the length of the chemical-shift calculations while maintaining good accuracy of the results. For the 17 naturally occurring amino acids in an antiparallel β-sheet, there is (i) good agreement between computed and observed ¹³C^α and ¹³C^β chemical shifts, with correlation coefficients of 0.95 and 0.99, respectively; (ii) significant variability of the computed ¹³C^α and ¹³C^β chemical shifts as a function of χ¹ for all amino acid residues except Ser; and (iii) a smaller, although significant, dependence of the computed ¹³C^α chemical shifts on χ^ξ (with ξ ≥ 2) compared to χ¹ for eleven out of seventeen residues. Our results suggest that predicted ¹³C^α and ¹³C^β chemical shifts, based only on backbone (φ,ψ) dihedral angles from high-resolution X-ray structure data or from NMR-derived models, may differ significantly from those observed in solution if the dihedral-angle preferences for the side chains are not taken into account. Electronic supplementary material Supplementary material is available in the online version of this article at and is accessible for authorized users.     

GFT projection NMR based resonance assignment of membrane proteins: application to subunit c of <Emphasis Type="Italic">E. coli</Emphasis> F<Subscript>1</Subscript>F<Subscript>0</Subscript> ATP synthase in LPPG micelles

G-matrix FT projection NMR spectroscopy was employed for resonance assignment of the 79-residue subunit c of the Escherichia coli F₁F₀ ATP synthase embedded in micelles formed by lyso palmitoyl phosphatidyl glycerol (LPPG). Five GFT NMR experiments, that is, (3,2)D HNNCO, L-(4,3)D HNNC ^αβ C ^α, L-(4,3)D HNN(CO)C ^αβ C ^α, (4,2)D HACA(CO)NHN and (4,3)D HCCH, were acquired along with simultaneous 3D ¹⁵N, ¹³C^aliphatic, ¹³C^aromatic-resolved [¹H,¹H]-NOESY with a total measurement time of ∼43 h. Data analysis resulted in sequence specific assignments for all routinely measured backbone and ¹³C^β shifts, and for 97% of the side chain shifts. Moreover, the use of two G²FT NMR experiments, that is, (5,3)D HN{N,CO}{C ^αβ C ^α} and (5,3)D {C ^αβ C ^α}{CON}HN, was explored to break the very high chemical shift degeneracy typically encountered for membrane proteins. It is shown that the 4D and 5D spectral information obtained rapidly from GFT and G²FT NMR experiments enables one to efficiently obtain (nearly) complete resonance assignments of membrane proteins. Qi Zhang, Hanudatta S. Atreya, Douglas E. Kamen, Mark E. Girvin and Thomas Szyperski—New York Consortium on Membrane Protein Structure.     

SHIFTX2: significantly improved protein chemical shift prediction

A new computer program, called SHIFTX2, is described which is capable of rapidly and accurately calculating diamagnetic ¹H, ¹³C and ¹⁵N chemical shifts from protein coordinate data. Compared to its predecessor (SHIFTX) and to other existing protein chemical shift prediction programs, SHIFTX2 is substantially more accurate (up to 26% better by correlation coefficient with an RMS error that is up to 3.3× smaller) than the next best performing program. It also provides significantly more coverage (up to 10% more), is significantly faster (up to 8.5×) and capable of calculating a wider variety of backbone and side chain chemical shifts (up to 6×) than many other shift predictors. In particular, SHIFTX2 is able to attain correlation coefficients between experimentally observed and predicted backbone chemical shifts of 0.9800 (¹⁵N), 0.9959 (¹³Cα), 0.9992 (¹³Cβ), 0.9676 (¹³C′), 0.9714 (¹HN), 0.9744 (¹Hα) and RMS errors of 1.1169, 0.4412, 0.5163, 0.5330, 0.1711, and 0.1231 ppm, respectively. The correlation between SHIFTX2’s predicted and observed side chain chemical shifts is 0.9787 (¹³C) and 0.9482 (¹H) with RMS errors of 0.9754 and 0.1723 ppm, respectively. SHIFTX2 is able to achieve such a high level of accuracy by using a large, high quality database of training proteins (>190), by utilizing advanced machine learning techniques, by incorporating many more features (χ₂ and χ₃ angles, solvent accessibility, H-bond geometry, pH, temperature), and by combining sequence-based with structure-based chemical shift prediction techniques. With this substantial improvement in accuracy we believe that SHIFTX2 will open the door to many long-anticipated applications of chemical shift prediction to protein structure determination, refinement and validation. SHIFTX2 is available both as a standalone program and as a web server ().     

Sequential nearest-neighbor effects on computed <Superscript>13</Superscript>C<Superscript>α</Superscript> chemical shifts

To evaluate sequential nearest-neighbor effects on quantum-chemical calculations of ¹³C^α chemical shifts, we selected the structure of the nucleic acid binding (NAB) protein from the SARS coronavirus determined by NMR in solution (PDB id 2K87). NAB is a 116-residue α/β protein, which contains 9 prolines and has 50% of its residues located in loops and turns. Overall, the results presented here show that sizeable nearest-neighbor effects are seen only for residues preceding proline, where Pro introduces an overestimation, on average, of 1.73 ppm in the computed ¹³C^α chemical shifts. A new ensemble of 20 conformers representing the NMR structure of the NAB, which was calculated with an input containing backbone torsion angle constraints derived from the theoretical ¹³C^α chemical shifts as supplementary data to the NOE distance constraints, exhibits very similar topology and comparable agreement with the NOE constraints as the published NMR structure. However, the two structures differ in the patterns of differences between observed and computed ¹³C^α chemical shifts, Δ _ca,i , for the individual residues along the sequence. This indicates that the Δ _ca,i -values for the NAB protein are primarily a consequence of the limited sampling by the bundles of 20 conformers used, as in common practice, to represent the two NMR structures, rather than of local flaws in the structures.     

Backbone and Ile-δ1, Leu, Val Methyl 1H, 13C and 15N NMR chemical shift assignments for human interferon-stimulated gene 15 protein

Human interferon-stimulated gene 15 protein (ISG15), also called ubiquitin cross-reactive protein (UCRP), is the first identified ubiquitin-like protein containing two ubiquitin-like domains fused in tandem. The active form of ISG15 is conjugated to target proteins via the C-terminal glycine residue through an isopeptide bond in a manner similar to ubiquitin. The biological role of ISG15 is strongly associated with the modulation of cell immune function, and there is mounting evidence suggesting that many viral pathogens evade the host innate immune response by interfering with ISG15 conjugation to both host and viral proteins in a variety of ways. Here we report nearly complete backbone ¹H^N, ¹⁵N, ¹³C′, and ¹³C^α, as well as side chain ¹³C^β, methyl (Ile-δ1, Leu, Val), amide (Asn, Gln), and indole N–H (Trp) NMR resonance assignments for the 157-residue human ISG15 protein. These resonance assignments provide the basis for future structural and functional solution NMR studies of the biologically important human ISG15 protein.     

Improved pulse sequences for sequence specific assignment of aromatic proton resonances in proteins

Aromatic proton resonances of proteins are notoriously difficult to assign. Through-bond correlation experiments are preferable over experiments that rely on through-space interactions because they permit aromatic chemical shift assignments to be established independently of the structure determination process. Known experimental schemes involving a magnetization transfer across the C^β–C^γ bond in aromatic side chains either suffer from low efficiency for the relay beyond the C^δ position, use sophisticated ¹³C mixing schemes, require probe heads suitable for application of high ¹³C radio-frequency fields or rely on specialized isotopic labelling patterns. Novel methods are proposed that result in sequential assignment of all aromatic protons in uniformly ¹³C/¹⁵N labelled proteins using standard spectrometer hardware. Pulse sequences consist of routinely used building blocks and are therefore reasonably simple to implement. Ring protons may be correlated with β-carbons and, alternatively, with amide protons (and nitrogens) or carbonyls in order to take advantage of the superior dispersion of backbone resonances. It is possible to record spectra in a non-selective manner, yielding signals of all aromatic residues, or as amino-acid type selective versions to further reduce ambiguities. The new experiments are demonstrated with four different proteins with molecular weights ranging from 11 kDa to 23 kDa. Their performance is compared with that of (Hβ)Cβ(CγCδ)Hδ and (Hβ)Cβ(CγCδCɛ)Hɛ pulse sequences [Yamazaki et al. (1993) J Am Chem Soc 115:11054–11055]. Electronic Supplementary Material The online version of this article (doi: ) contains supplementary material, which is available to authorized users.     

Backbone assignment of human proliferating cell nuclear antigen

PCNA is an essential factor for DNA replication, repair, chromatin metabolism, and effector of cell-cycle regulatory signals. The assignment of backbone ¹H_N, ¹³C_α, ¹³CO, and ¹⁵N, and sidechain ¹³C_β resonances of the human PCNA homotrimeric ring (∼90 kDa, 261 residues) is reported here.     

Backbone 1H, 13C, and 15N NMR assignments of the tail domain of vinculin

Vinculin is an essential protein involved in linking the actin cytoskeleton to sites of cell-cell and cell-matrix adhesion. Here we report the majority of the backbone ¹H_N, ¹⁵N, ¹³C_α, ¹³CO, and side chain ¹³C_β NMR resonance assignments of the actin binding tail domain of vinculin (Vt).     

PASA – A Program for Automated Protein NMR Backbone Signal Assignment by Pattern-Filtering Approach

We present a new program, PASA (Program for Automated Sequential Assignment), for assigning protein backbone resonances based on multidimensional heteronuclear NMR data. Distinct from existing programs, PASA emphasizes a per-residue-based pattern-filtering approach during the initial stage of the automated ¹³C^α and/or ¹³C^β chemical shift matching. The pattern filter employs one or multiple constraints such as ¹³C^α/C^β chemical shift ranges for different amino acid types and side-chain spin systems, which helps to rule out, in a stepwise fashion, improbable assignments as resulted from resonance degeneracy or missing signals. Such stepwise filtering approach substantially minimizes early false linkage problems that often propagate, amplify, and ultimately cause complication or combinatorial explosion of the automation process. Our program (http://www.lerner.ccf.org/moleccard/qin/) was tested on four representative small-large sized proteins with various degrees of resonance degeneracy and missing signals, and we show that PASA achieved the assignments efficiently and rapidly that are fully consistent with those obtained by laborious manual protocols. The results demonstrate that PASA may be a valuable tool for NMR-based structural analyses, genomics, and proteomics. Electronic supplementary material Electronic supplementary material is available for this article at and accessible for authorised users.     

Extension of the HA-detection based approach: (HCA)CON(CA)H and (HCA)NCO(CA)H experiments for the main-chain assignment of intrinsically disordered proteins

Extensive resonance overlap exacerbates assignment of intrinsically disordered proteins (IDPs). This issue can be circumvented by utilizing ¹⁵N, ¹³C′ and ¹H^N spins, where the chemical shift dispersion is mainly dictated by the characteristics of consecutive amino acid residues. Especially ¹⁵N and ¹³C′ spins offer superior chemical shift dispersion in comparison to ¹³C^α and ¹³C^β spins. However, HN-detected experiments suffer from exchange broadening of amide proton signals on IDPs especially under alkali conditions. To that end, we propose here two novel HA-detected experiments, (HCA)CON(CA)H and (HCA)NCO(CA)H and a new assignment protocol based on panoply of unidirectional HA-detected experiments that enable robust backbone assignment of IDPs also at high pH. The new approach was tested at pH 6.5 and pH 8.5 on cancer/testis antigen CT16, a 110-residue IDP, and virtually complete backbone assignment of CT16 was obtained by employing the novel HA-detected experiments together with the previously introduced iH(CA)NCO scheme. Remarkably, also those 10 N-terminal residues that remained unassigned in our earlier HN-detection based assignment approach even at pH 6.5 were now readily assigned. Moreover, theoretical calculations and experimental results suggest that overall sensitivity of the new experiments is also applicable to small or medium sized globular proteins that require alkaline conditions.     

Band-selective 13C Homonuclear 3D Spectroscopy for Solid Proteins at High Field with Rotor-synchronized Soft Pulses

We demonstrate improved 3D ¹³C–¹³C–¹³C chemical shift correlation experiments for solid proteins, utilizing band-selective coherence transfer, scalar decoupling and homonuclear zero-quantum polarization transfer. Judicious use of selective pulses and a z-filter period suppress artifacts with a two-step phase cycle, allowing higher digital resolution in a fixed measurement time. The novel correlation of C^ali–C^ali–CX (C^ali for aliphatic carbons, CX for any carbon) reduces measurement time by an order of magnitude without sacrificing digital resolution. The experiment retains intensity from side-chain carbon resonances whose chemical shift dispersion is critical to minimize spectral degeneracy for large proteins with a predominance of secondary structure, such as β-sheet rich fibrillar proteins and α-helical membrane proteins. We demonstrate the experiment for the β1 immunoglobulin binding domain of protein G (GB1) and fibrils of the A30P mutant of α-synuclein, which is implicated in Parkinson’s disease. Selective pulses of duration comparable the rotor period give optimal performance, but must be synchronized with the spinning in non-trivial ways to minimize chemical shift anisotropy recoupling effects. Soft pulses with a small bandwidth-duration product are best for exciting the ~70 ppm bandwidth required for aliphatic-only dimensions.     

Hash: a program to accurately predict protein Hα shifts from neighboring backbone shifts

Chemical shifts provide not only peak identities for analyzing nuclear magnetic resonance (NMR) data, but also an important source of conformational information for studying protein structures. Current structural studies requiring H^α chemical shifts suffer from the following limitations. (1) For large proteins, the H^α chemical shifts can be difficult to assign using conventional NMR triple-resonance experiments, mainly due to the fast transverse relaxation rate of C^α that restricts the signal sensitivity. (2) Previous chemical shift prediction approaches either require homologous models with high sequence similarity or rely heavily on accurate backbone and side-chain structural coordinates. When neither sequence homologues nor structural coordinates are available, we must resort to other information to predict H^α chemical shifts. Predicting accurate H^α chemical shifts using other obtainable information, such as the chemical shifts of nearby backbone atoms (i.e., adjacent atoms in the sequence), can remedy the above dilemmas, and hence advance NMR-based structural studies of proteins. By specifically exploiting the dependencies on chemical shifts of nearby backbone atoms, we propose a novel machine learning algorithm, called Hash, to predict H^α chemical shifts. Hash combines a new fragment-based chemical shift search approach with a non-parametric regression model, called the generalized additive model, to effectively solve the prediction problem. We demonstrate that the chemical shifts of nearby backbone atoms provide a reliable source of information for predicting accurate H^α chemical shifts. Our testing results on different possible combinations of input data indicate that Hash has a wide rage of potential NMR applications in structural and biological studies of proteins.     

Alternative SAIL-Trp for robust aromatic signal assignment and determination of the χ<Subscript>2</Subscript> conformation by intra-residue NOEs

Tryptophan (Trp) residues are frequently found in the hydrophobic cores of proteins, and therefore, their side-chain conformations, especially the precise locations of the bulky indole rings, are critical for determining structures by NMR. However, when analyzing [U–¹³C,¹⁵N]-proteins, the observation and assignment of the ring signals are often hampered by excessive overlaps and tight spin couplings. These difficulties have been greatly alleviated by using stereo-array isotope labeled (SAIL) proteins, which are composed of isotope-labeled amino acids optimized for unambiguous side-chain NMR assignment, exclusively through the ¹³C–¹³C and ¹³C–¹H spin coupling networks (Kainosho et al. in Nature 440:52–57, 2006). In this paper, we propose an alternative type of SAIL-Trp with the [ζ2,ζ3-²H₂; δ1,ε3,η2-¹³C₃; ε1-¹⁵N]-indole ring ([¹²C_γ, ¹²C_ε2] SAIL-Trp), which provides a more robust way to correlate the ¹H_β, ¹H_α, and ¹H_N to the ¹H_δ1 and ¹H_ε3 through the intra-residue NOEs. The assignment of the ¹H_δ1/¹³C_δ1 and ¹H_ε3/¹³C_ε3 signals can thus be transferred to the ¹H_ε1/¹⁵N_ε1 and ¹H_η2/¹³C_η2 signals, as with the previous type of SAIL-Trp, which has an extra ¹³C at the C_γ of the ring. By taking advantage of the stereospecific deuteration of one of the prochiral β-methylene protons, which was ¹H_β2 in this experiment, one can determine the side-chain conformation of the Trp residue including the χ₂ angle, which is especially important for Trp residues, as they can adopt three preferred conformations. We demonstrated the usefulness of [¹²C_γ,¹²C_ε2] SAIL-Trp for the 12 kDa DNA binding domain of mouse c-Myb protein (Myb-R2R3), which contains six Trp residues.     

HA-detected experiments for the backbone assignment of intrinsically disordered proteins

We propose a new alpha proton detection based approach for the sequential assignment of natively unfolded proteins. The proposed protocol superimposes on following features: HA-detection (1) enables assignment of natively unfolded proteins at any pH, i.e., it is not sensitive to rapid chemical exchange undergoing in natively unfolded proteins even at moderately high pH. (2) It allows straightforward assignment of proline-rich polypeptides without additional proline-customized experiments. (3) It offers more streamlined and less ambiguous assignment based on solely intraresidual ¹⁵N(i)-¹³C′(i)-H^α(i) (or ¹⁵N(i)-¹³C^α(i)-H^α(i)) and sequential ¹⁵N(i + 1)-¹³C′(i)-H^α(i) (or ¹⁵N(i + 1)-¹³C^α(i)-H^α(i)) correlation experiments together with efficient use of chemical shifts of ¹⁵N and ¹³C′ nuclei, which show smaller dependence on residue type. We have tested the proposed protocol on two proteins, small globular 56-residue GB1, and highly disordered, proline-rich 47-residue fifth repeat of EspF_U. Using the proposed approach, we were able to assign 90% of ¹H^α, ¹³C^α, ¹³C′, ¹⁵N chemical shifts in EspF_U. We reckon that the HA-detection based strategy will be very useful in the assignment of natively unfolded proline-rich proteins or polypeptide chains.