A software tool for the prediction of Xaa-Pro peptide bond conformations in proteins based on 13C chemical shift statistics

The chemical shift difference ([¹³C] – [¹³C]) is a reference-independent indicator of the Xaa-Pro peptide bond conformation. Based on a statistical analysis of the ¹³C chemical shifts of 1033 prolines from 304 proteins deposited in the BioMagRes database, a software tool was created to predict the probabilities for cis or trans conformations of Xaa-Pro peptide bonds. Using this approach, the conformation at a given Xaa-Pro bond can be identified in a simple NOE-independent way immediately after obtaining its NMR resonance assignments. This will allow subsequent structure calculations to be initiated using the correct polypeptide chain conformation.     

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.     

SPARTA+: a modest improvement in empirical NMR chemical shift prediction by means of an artificial neural network

NMR chemical shifts provide important local structural information for proteins and are key in recently described protein structure generation protocols. We describe a new chemical shift prediction program, SPARTA+, which is based on artificial neural networking. The neural network is trained on a large carefully pruned database, containing 580 proteins for which high-resolution X-ray structures and nearly complete backbone and ¹³C^β chemical shifts are available. The neural network is trained to establish quantitative relations between chemical shifts and protein structures, including backbone and side-chain conformation, H-bonding, electric fields and ring-current effects. The trained neural network yields rapid chemical shift prediction for backbone and ¹³C^β atoms, with standard deviations of 2.45, 1.09, 0.94, 1.14, 0.25 and 0.49 ppm for δ¹⁵N, δ¹³C’, δ¹³C^α, δ¹³C^β, δ¹H^α and δ¹H^N, respectively, between the SPARTA+ predicted and experimental shifts for a set of eleven validation proteins. These results represent a modest but consistent improvement (2–10%) over the best programs available to date, and appear to be approaching the limit at which empirical approaches can predict chemical shifts.     

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.     

Measuring <Superscript>13</Superscript>C<Superscript>β</Superscript> chemical shifts of invisible excited states in proteins by relaxation dispersion NMR spectroscopy

A labeling scheme is introduced that facilitates the measurement of accurate ¹³C^β chemical shifts of invisible, excited states of proteins by relaxation dispersion NMR spectroscopy. The approach makes use of protein over-expression in a strain of E. coli in which the TCA cycle enzyme succinate dehydrogenase is knocked out, leading to the production of samples with high levels of ¹³C enrichment (30–40%) at C^β side-chain carbon positions for 15 of the amino acids with little ¹³C label at positions one bond removed (≈5%). A pair of samples are produced using [1-¹³C]-glucose/NaH¹²CO₃ or [2-¹³C]-glucose as carbon sources with isolated and enriched (>30%) ¹³C^β positions for 11 and 4 residues, respectively. The efficacy of the labeling procedure is established by NMR spectroscopy. The utility of such samples for measurement of ¹³C^β chemical shifts of invisible, excited states in exchange with visible, ground conformations is confirmed by relaxation dispersion studies of a protein–ligand binding exchange reaction in which the extracted chemical shift differences from dispersion profiles compare favorably with those obtained directly from measurements on ligand free and fully bound protein samples.     

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.     

Pressure-dependent <Superscript>13</Superscript>C chemical shifts in proteins: origins and applications

Pressure-dependent ¹³C chemical shifts have been measured for aliphatic carbons in barnase and Protein G. Up to 200 MPa (2 kbar), most shift changes are linear, demonstrating pressure-independent compressibilities. CH₃, CH₂ and CH carbon shifts change on average by +0.23, −0.09 and −0.18 ppm, respectively, due to a combination of bond shortening and changes in bond angles, the latter matching one explanation for the γ-gauche effect. In addition, there is a residue-specific component, arising from both local compression and conformational change. To assess the relative magnitudes of these effects, residue-specific shift changes for protein G were converted into structural restraints and used to calculate the change in structure with pressure, using a genetic algorithm to convert shift changes into dihedral angle restraints. The results demonstrate that residual ¹³Cα shifts are dominated by dihedral angle changes and can be used to calculate structural change, whereas ¹³Cβ shifts retain significant dependence on local compression, making them less useful as structural restraints.     

Spectroscopic Detection of Pseudo-Turns in Homodetic Cyclic Penta- and Hexapeptides Comprising β-Homoproline

β-Amino acids with side chains at C² and/or at C³ are of growing interest in drug design, as they may induce astonishing and unusual peptide conformations. Therefore it is of eminent importance to gather information on the consequences of β-amino acid incorporation on the three-dimensional structure of a peptide. This paper describes the synthesis and conformational analysis of cyclic penta- and hexapeptides comprising either (S)-Pro or (S)-β-Hpro. The conformational influence of the β-homoproline building block was analyzed by the combined application of CD, FT-IR and NMR. While the CD spectra of the proline containing peptides indicate the presence of inverse γ-turns and βII-turns, the CD spectra of the β-homoamino acid analogs are dominated by an unprecedented negative band near 205 nm associated with a pseudo-β-turn (Ψβ) or pseudo-γ-turn (Ψγ). These results were confirmed by FT-IR spectroscopy, which also indicates the formation of two internal hydrogen bonds in the cyclic peptides containing the β-homoproline. The conformations of the β-homoproline containing pentapeptides were additionally determined by NMR in combination with MD simulations in two different solvents. The conformation in trifluoroethanol (TFE) is characterized by a bifurcated hydrogen bond stabilizing a pseudo-γ-turn with β-homoproline in the central position, nested with a pseudo-β-turn with β-homoproline in the i+1 position. The combined CD/FT-IR studies clearly show that the replacement of proline by β-homoproline gives rise to a more flexible peptide backbone, and CD spectroscopy hints towards the presence of pseudo-β- or pseudo-γ-turns.     

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.     

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.     

Effect of disulfide bridge formation on the NMR spectrum of a protein: Studies on oxidized and reduced Escherichia coli thioredoxin

Summary As a prelude to complete structure calculations of both the oxidized and reduced forms of Escherchia coli thioredoxin (M_r 11 700), we have analyzed the NMR data obtained for the two proteins under identical conditions. The complete aliphatic ¹³C assignments for both oxidized and reduced thioredoxin are reported. Correlations previously noted between ¹³C chemical shifts and secondary structure are confirmed in this work, and significant differences are observed in the C and C shifts between cis- and trans-proline, consistent with previous work that identifies this as a simple and unambiguous method of identifying cis-proline residues in proteins. Reduction of the disulfide bond in the active-site Cys³²-Gly-Pro-Cys³⁵ sequence causes changes in the ¹H, ¹⁵N and ¹³C chemical shifts of residues close to the active site, some of them quite far distant in the amino acid sequence. Coupling constants, both backbone and side chain, show some differences between the two proteins, and the NOE connectivities and chemical shifts are consistent with small changes in the positions of several side chains, including the two tryptophan rings (Trp²⁸ and Trp³¹). These results show that, consistent with the biochemical behavior of thioredoxin, there are minimal differences in backbone configuration between the oxidized and reduced forms of the protein.     

Fractional <Superscript>13</Superscript>C enrichment of isolated carbons using [1-<Superscript>13</Superscript>C]- or [2-<Superscript>13</Superscript>C]-glucose facilitates the accurate measurement of dynamics at backbone C<Superscript>α</Superscript> and side-chain methyl positions in proteins

A simple labeling approach is presented based on protein expression in [1-¹³C]- or [2-¹³C]-glucose containing media that produces molecules enriched at methyl carbon positions or backbone C^α sites, respectively. All of the methyl groups, with the exception of Thr and Ile(δ1) are produced with isolated ¹³C spins (i.e., no ¹³C–¹³C one bond couplings), facilitating studies of dynamics through the use of spin-spin relaxation experiments without artifacts introduced by evolution due to large homonuclear scalar couplings. Carbon-α sites are labeled without concomitant labeling at C^β positions for 17 of the common 20 amino acids and there are no cases for which ¹³C^α−¹³CO spin pairs are observed. A large number of probes are thus available for the study of protein dynamics with the results obtained complimenting those from more traditional backbone ¹⁵N studies. The utility of the labeling is established by recording ¹³C R _1ρ and CPMG-based experiments on a number of different protein systems.     

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.     

Phylogenetic analysis of condensation domains in NRPS sheds light on their functional evolution

Background  

Non-ribosomal peptide synthetases (NRPSs) are large multimodular enzymes that synthesize a wide range of biologically active natural peptide compounds, of which many are pharmacologically important. Peptide bond formation is catalyzed by the Condensation (C) domain. Various functional subtypes of the C domain exist: An^LC_L domain catalyzes a peptide bond between two L-amino acids, a^DC_L domain links an L-amino acid to a growing peptide ending with a D-amino acid, a Starter C domain (first denominated and classified as a separate subtype here) acylates the first amino acid with a β -hydroxy-carboxylic acid (typically a β -hydroxyl fatty acid), and Heterocyclization (Cyc) domains catalyze both peptide bond formation and subsequent cyclization of cysteine, serine or threonine residues. The homologous Epimerization (E) domain flips the chirality of the last amino acid in the growing peptide; Dual E/C domains catalyze both epimerization and condensation.     

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.     

Backbone Structure Confirmation and Side Chain Conformation Refinement of a Bradykinin Mimic BKM-824 by Comparing Calculated 1H, 13C and 19F Chemical Shifts with Experiment

Abstract

Calculated and experimental ¹H, ¹³C and ¹⁹F chemical shifts were compared in BKM-824, a cyclic bradykinin antagonist mimic, c[Ava¹-Igl²-Ser³-DF5F⁴-Oic⁵-Arg⁶] (Ava=5-amino- valeric acid, Igl=α-(2-indanyl)glycine, DF5F=pentafluorophenylalanine, Oic=(2S,3aS,7aS)- octahydroindole-2-carboxylic acid). The conformation of BKM-824 has been studied earlier by NMR spectroscopy (M. Miskolzie et al., J. Biomolec. Struct. Dyn. 17, 947–955 (2000)). All NMR structures have qualitatively the same backbone structure but there is considerable variation in the side chain conformations. We have carried out quantum mechanical optimization for three representative NMR structures at the B3LYP/6–31G* level, constraining the backbone dihedral angles at their NMR structure values, followed by NMR chemical shift calculations at the optimized structures with the 6–311G** basis set. There is an intramolecular hydrogen bond at Ser³ in the optimized structures.

The experimental ¹³C chemical shifts at five C^α positions as well as at the C^β, C^γ and Cδ position of Ava¹, which forms part of the backbone, are well reproduced by the calculations, confirming the NMR backbone structure. A comparison between the calculated and experimental H^β chemical shifts in Igl² shows that the dominant conformation at this residue is gauche. Changes of proton chemical shifts with the scan of the χ1 angle in DF5F⁴ suggest that χ1 ≈180°. The calculated ¹H and ¹³C chemical shifts are in good agreement with experiment at the rigid residue Oic⁵. None of the models gives accurate results for Arg⁶, presumably because of its positive charge. Our study indicates that calculated NMR shifts can be used as additional constraints in conjunction with NMR data to determine protein conformations. However, to be computationally effective, a database of chemical shifts in small peptide fragments should be precalculated.     

Identification of N-terminal helix capping boxes by means of 13C chemical shifts

Summary We have examined the ¹³C and ¹³C chemical shifts of a number of proteins and found that their values at the N-terminal end of a helix provide a good predictor for the presence of a capping box. A capping box consists of a hydrogen-bonded cycle of four amino acids in which the side chain of the N-cap residue forms a hydrogen bond with the backbone amide of the N3 residue, whose side chain in turn may accept a hydrogen bond from the amide of the N-cap residue. The N-cap residue exhibits characteristic values for its backbone torsion angles, with and clustering around 94±15° and 167±5°, respectively. This is manifested by a 1–2 ppm upfield shift of the ¹³C resonance and a 1–4 ppm downfield shift of the ¹³C resonance, relative to their random coil values, and is mainly associated with the unusually large value of . The residues following the N-cap residue exhibit downfield shifts of 1–3 ppm for the ¹³C resonances and small upfield shifts for the ¹³C ones, typical of an -helix.     

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.     

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.     

Probabilistic Approach to Determining Unbiased Random-coil Carbon-13 Chemical Shift Values from the Protein Chemical Shift Database

We describe a probabilistic model for deriving, from the database of assigned chemical shifts, a set of random coil chemical shift values that are “unbiased” insofar as contributions from detectable secondary structure have been minimized (RCCS_u). We have used this approach to derive a set of RCCS_u values for ¹³C^α and ¹³C^β for 17 of the 20 standard amino acid residue types by taking advantage of the known opposite conformational dependence of these parameters. We present a second probabilistic approach that utilizes the maximum entropy principle to analyze the database of ¹³C^α and ¹³C^β chemical shifts considered separately; this approach yielded a second set of random coil chemical shifts (RCCS). Both new approaches analyze the chemical shift database without reference to known structure. Prior approaches have used either the chemical shifts of small peptides assumed to model the random coil state (RCCS_peptide) or statistical analysis of chemical shifts associated with structure not in helical or strand conformation (RCCS). We show that the RCCS values are strikingly similar to published RCCS_peptide and RCCS values. By contrast, the RCCS_u values differ significantly from both published types of random coil chemical shift values. The differences (RCCS_peptide−RCCS_u) for individual residue types show a correlation with known intrinsic conformational propensities. These results suggest that random coil chemical shift values from both prior approaches are biased by conformational preferences. RCCS_u values appear to be consistent with the current concept of the “random coil” as the state in which the geometry of the polypeptide ensemble samples the allowed region of (ϕ,ψ)-space in the absence of any dominant stabilizing interactions and thus represent an improved basis for the detection of secondary structure. Coupled with the growing database of chemical shifts, this probabilistic approach makes it possible to refine relationships among chemical shifts, their conformational propensities, and their dependence on pH, temperature, or neighboring residue type.Electronic Supplementary Material Supplementary material is available to authorised users in the online version of this article at .