Using the Chemical Shift Anisotropy Tensor of Carbonyl Backbone

5830

J. Phys. Chem. A 2010, 114, 5830–5837

Using the Chemical Shift Anisotropy Tensor of Carbonyl Backbone Nuclei As a Probe of Secondary Structure in Proteins S. Begam Elavarasi,† Amrita Kumari,‡ and Kavita Dorai*,‡ Department of Physics, Indian Institute of Technology Madras, Chennai 600036, India, and Department of Physics, Indian Institute of Science Education & Research (IISER) Mohali, Chandigarh 160019, India ReceiVed: NoVember 29, 2009; ReVised Manuscript ReceiVed: April 4, 2010

This study seeks to establish that the chemical shift anisotropy (CSA) tensor of the backbone carbonyl (13C′) nucleus is a useful indicator of secondary structure elements in proteins. The CSA tensors of protein backbone nuclei in different secondary structures were computed for experimentally determined dihedral angles using ab initio methods and by calculating the CSA tensor for a model peptide over the entire dihedral angle space. It is shown that 2D and 3D cluster plots of CSA tensor parameters for 13C′ nuclei are able to distinguish between different secondary structure elements with little to no overlap. As evidenced by multinuclear 2D plots, the CSA of the 13C′ nucleus when correlated with different CSA parameters of the other backbone nuclei (such as CR or 1HR) is also useful in secondary structure identification. The differentiation of R-helix versus β-sheet motifs (the most populated regions of the Ramachandran map) for experimentally determined values of the carbonyl CSA tensor for proteins ubiquitin and binase (obtained from the literature) agrees well with the quantum chemical predictions. Introduction Chemical shifts have been extensively used as sensitive probes of secondary structures in proteins as aids in the refinement of tertiary structures and to obtain precise conformational information.1-4 Several NMR studies have focused on the estimation of secondary structure content of biomolecules from a direct quantification of isotropic chemical shifts.5-15 An alternate route to secondary structure prediction focuses on utilizing information from the entire CSA tensor.16-21 Techniques used to characterize the complete CSA tensor include ab initio quantum chemistry calculations, solid-state NMR of single crystals as well as of static powders, and cross-correlated spin relaxation experiments in the liquid state.22-30 Apart from being used to obtain information about dihedral backbone angles, the CSA tensor has been utilized in MAS experiments to measure internuclear distances, to find the orientation of membrane proteins embedded in oriented lipid bilayers, to validate quantum-chemical calculations, and to exploit residual anisotropic chemical shifts in liquid-state NMR.31-35 It has been noted that CSA parameters of certain protein backbone nuclei are strongly correlated with secondary structure, with helices and sheet values showing distinct differences, and that a set of anisotropy measures obtained from theoretical anisotropy surfaces for the 13CR showed some promise in distinguishing secondary structure.36 Recently, 13CR chemical shift tensors were experimentally obtained for the protein GB1 in an attempt to show that accurate de novo structure determination is possible using chemical shift tensor information.37 Backbone amide 15N CSA tensors relate electronic structure to NMR observables and are highly sensitive to local structural parameters. In general, accurate predictions of 15N chemical shifts are more difficult than predictions for 13CR or 13C′ in * To whom correspondence should be addressed. E-mail: kavita@ iisermohali.ac.in. † Indian Institute of Technology. ‡ Indian Institute of Science Education & Research (IISER).

proteins. This is because the 15N CSA tensor is strongly influenced by a number of factors including backbone and sidechain torsion angles, hydrogen bonding at the amide site, nearest neighbor residues, and electrostatic interactions.38-41 Recently, research has focused on the variation of the carbonyl CSA tensor and its utility in structure prediction.42-48 Residue-specific experimental measurements of the carbonyl CSA parameters in different proteins such as binase and ubiquitin suggest that the variations in the isotropic chemical shifts, σiso, are strongly correlated with the CSA tensor component, σ22, which is almost parallel to the CdO internuclear bond vector. Most of these studies also conclude that the dispersion of the individual carbonyl CSA parameters may be due to variations in bond angles, variations in internuclear distances, and internal motions and that the average values of the CSA parameters depend a lot on the motional model for internal motion used. This work focuses on the carbonyl (13C′) nucleus and quantifies the role and utility of correlations between different CSA parameters in identifying secondary structure elements in proteins. We focus on the CSA of the 13C′ nucleus because many experimental studies already exist for this nucleus, whereas for nuclei such as 13CR with smaller CSA tensors, experimental data are not readily available. To this end, we have extracted secondary structure information from experimentally determined protein structures deposited in the Protein Data Bank (PDB)

Figure 1. CSA tensor configuration of peptide showing principal axes of carbonyl backbone nucleus.

10.1021/jp9113199  2010 American Chemical Society Published on Web 04/20/2010

CSA Tensor of Carbonyl Backbone Nuclei

J. Phys. Chem. A, Vol. 114, No. 18, 2010 5831 experimentally determined values of CSA tensors for the proteins ubiquitin and binase from the literature and extracted their relevant CSA parameters for carbonyl nuclei. The 2D CSA parameter correlation plots for the 13C′ nuclei in both of these proteins exhibit distinct clusters for helices and sheets. For both proteins, the correlation maps were able to identify each amino acid participating in either a helical or sheet conformation. This study indicates that multidimensional and multinuclear CSA parameter correlations involving backbone 13C′ nuclei are promising candidates for structure refinement, especially when isotropic chemical shifts give ambiguous information.

Figure 2. Peptide model For-L-Ala-NH2 used to compute CSA tensors for carbonyl. The dihedral angles φ and ψ are defined as φ ) C′ - N - CR - C′ and ψ ) N - CR - C′ - N.

database and computed the CSA tensor for each of the backbone nuclei of the alanine amino acid using ab initio methods. Multidimensional 2D and 3D correlated plots of the (σiso, Ω, κ) parameters were constructed from the computed values of the CSA tensors and show interesting correlations with secondary structure content. The carbonyl CSA tensor for an alanine peptide model over the entire dihedral angle database has also been constructed using ab initio methods. We find that the CSA tensor of the 13C′ nucleus is an unambiguous estimator of secondary structure content. The CSA tensor parameters of the other backbone protein nuclei, while containing useful information, do not make as clear distinctions between major secondary structure conformations. To quantify our results further, we used

Theory and Methods The isotropic chemical shift of a nucleus, σiso, is a scalar parameter related to the trace of the full CSA tensor, σ, and indicates the average shielding experienced by the nucleus

1 1 σiso ) Trσ ) (σ11 + σ22 + σ33) 3 3

(1)

where σ11, σ22, and σ33 are the principal values of the diagonalized tensor, that is, the eigenvalues of the symmetric part of the CSA tensor. The CSA tensor of a nucleus is completely characterized by the magnitude of its principal elements and by three Euler angles (R, β, γ) that describe the orientation of the principal axis system (PAS) in the molecular frame. Different CSA parameters such as the anisotropy (∆), the asymmetry (η), the span (Ω), and the skew (κ) can be constructed from the CSA tensor and are widely used in NMR. In this study, we have used the set of well-known anisotropy parameters span

Figure 3. Computed 2D and 3D CSA parameter correlations for the model alanine peptide spanning the RD, R-helix, β-strand, and PPII regions of the Ramachandran map. (a) The (Ω, κ) values plotted for the carbonyl 13C′ nucleus show well-resolved, distinct clusters for the helix and sheet secondary structures with no overlap. The R-helix and RD regions are also clearly distinguishable. There is some overlap of the PPII and sheet regions. (b) Adding the isotropic chemical shift δiso as another correlator along the third dimension helps marginally in resolving overlaps. The 2D and 3D correlations using the δ22 principal axis component are shown in parts c and d, respectively.

5832

J. Phys. Chem. A, Vol. 114, No. 18, 2010

Elavarasi et al.

Figure 4. Multinuclear 2D and 3D correlations of the computed CSA tensor for the alanine model peptide. The Ω CSA parameter of the 13C′ nucleus proves to be a “universal correlator”, and its correlations with the Ω parameters of the other backbone nuclei 13CR, 1HR, 1HN, and 15N are plotted in (a-d), respectively. The computed 3D Ω correlation map of 13C′- 13CR- 1HR is the best differentiator of inherent secondary structure.

(Ω) and skew (κ) and the (Ω, κ, σiso) triplet to describe the carbonyl CSA tensor

Ω ) σ33 - σ11,

(Ω g 0)

κ ) (2σ22 - σ11 - σ33)/Ω, (-1 e κ e +1) where the principal components of the CSA tensor are labeled according to the Herzfeld-Berger convention σ11 e σ22 e σ33. Computing CSA Tensor for Experimental Dihedral Angles. The structural information (the helical or sheet content) of the proteins considered in this study was derived from their X-ray crystal coordinates at a resolution e2.5A0, available in the PDB.49 A data set of experimental dihedral angles for alanine participating in different secondary structures was extracted from the PDB using a set of python programs. The selected structures provide the dihedral angles, φ and ψ, characterizing the backbone structure of the amino acid residues (Figure 1). The coordinates of the alanine amino acid (in different secondary structure conformations) were extracted from these structures. The CSA tensors for all five backbone nuclei, that is, 13C′, 13CR, 1 R 15 H , N, and 1HN, were computed using ab initio methods. Alanine has been used as a building block for computational models of chemical shift surfaces and CSA tensor computations,50 and amino acid residues with similar chemical shielding

surfaces have been grouped into five clusters.51 We conducted a similar study for chemical shift tensor values and found that the general trends of the Ω versus κ plots of all amino acids are similar to alanine. Quantum Chemical Calculations. The calculation of NMR CSA tensors was carried out in the gauge-including atomic orbitals (GIAO) framework52-54 and density functional (DFT) level of theory using the Gaussian03 program.55 The importance of using large basis sets for NMR chemical shielding calculations as well as the locally dense basis set method (which involves using a larger basis set on the neighboring atoms of the nucleus of interest) has been well established.56,57 Several different basis sets were evaluated for their accuracy, and all calculations used the B3LYP exchange-correlation potential. Gaussian03 calculations provide absolute shielding values, which were arbitrarily assigned such that σ11 e σ22 e σ33. The CSA tensor orientation in each case is described relative to the peptide backbone. The structures were geometry optimized, and the NMR parameter calculation was performed with the large 6-311++(d,p) basis set and the optimized coordinates. Computing CSA for a Model Peptide. To explore the CSA tensor and its dependence on dihedral angles, we used the peptide model For-L-Ala-NH2 (Figure 2) and constructed the full CSA tensor at different points on the Ramachandran map.


J. Phys. Chem. A, Vol. 114, No. 18, 2010 5833

Figure 5. Differentiation between the major secondary structure elements, namely, R-helices and β-sheets, is clearly seen via 2D CSA parameter correlations as computed for experimental dihedral angles extracted from a database. The Ω CSA parameter of 13C′ is a “universal correlator”, and when plotted against the Ω CSA parameters of the other backbone nuclei, it is able to unambiguously distinguish between helices and sheets.

To generate the CSA surface as a function of dihedral angles (φ, ψ), a 36 × 36 grid was used in the allowed regions of the Ramachandran map. The following regions of the Ramachandran map were selected for the computation: R-helix, -90 e φ e -40° and -60 e ψ e -10°; β-strand, -140 e φ e -100° and 110 e ψ e 150°; polyproline II (PPII), -90 e φ e -60° and 120 e ψ e 160°; left-handed R-helix (RD), 40 e φ e 80° and 10 e ψ e 60°. The (φ, ψ) angle pairs of the input structures were frozen, and all other geometrical parameters of the model structure were optimized using the Gaussian program. CSA tensors of all backbone nuclei were computed using DFT and HF levels of theory for all structures resulting from the constrained optimizations on the constructed (φ, ψ) grid. Results and Discussion Computed CSA Tensor for Model Peptide. The dependence of CSA on secondary structure has been investigated by computing CSA parameter pairs for a dense grid in the Ramachandran space of backbone torsional angles φ and ψ for a model peptide For-L-Ala-NH2. It has previously been noted that no individual CSA parameter alone is able to distinguish unambiguously between helix and sheet structures because there is a great amount of overlap in these conformational regions.

Because CSA parameter pairs such as (Ω, κ) contain complete anisotropy information about the shielding tensor of a nucleus, 2D correlation plots of such CSA pairs show clustering of secondary structure elements. The CSA parameters for the model peptide have been computed for four different secondary structures, namely, R-helix, left-handed helix (RD), β-sheet, and polyproline II helix (PPII). The 2D and 3D CSA parameter correlations for carbonyl are shown in Figure 3. Whereas the correlations are clearly able to distinguish between R-helices, β-sheets, and RD helices, there is some overlap between the PPII and β-sheet regions, which is to be expected because these structures are contiguous in Ramachandran space. As evidenced in Figure 3c, the correlations between the Ω of 13C′ and the δ22 principal component of the CSA tensor (that is almost parallel to the CdO bond vector) are better able to resolve overlaps in the β-sheet versus PPII regions. This observation corroborates previous studies that established that the δ22 tensor component is strongly correlated with the isotropic chemical shift, whereas the other two principal axis components remain almost invariant. Adding the isotropic chemical shift as a third correlator leads to a better visual depiction of the secondary structure clusters, as seen in Figure 3d. Multinuclear correlations (between the CSA parameters of the carbonyl nucleus and those of the other

5834


Elavarasi et al.

Figure 6. 3D structure of ubiquitin (76 amino acids, PDB entry 1g6j) and the amino acid sequence showing R-helix and β-strand regions. The secondary structure content of ubiquitin is 18% helical (two helices) and 31% β sheet (seven strands).

Figure 7. Experimental 2D and 3D CSA parameter correlations for ubiquitin spanning the R-helix and β-strand regions of the Ramachandran map. The (Ω, κ) map shown in (a) and the (Ω, κ, δiso) map shown in (b) are able to distinguish the R-helices from the β-sheet-like structures. The multinuclear (Ω, Ω) correlation maps of the 13C′ CSA parameter with those of the other backbone nuclei 15N and 1HN are shown in (c) and (d), respectively.

four backbone nuclei) for the computed CSA tensor of the model alanine peptide are given in Figure 4. The carbonyl CSA tensor span Ω can be thought of as a “universal correlator”, and maps of this parameter with the span of the other backbone nuclei CSA tensors seem promising as structure indicators. In particular, Ω(13C′) correlations with the Ω of 1HR show clearly distinguishable regions for the R-helices, the β-sheets, the RD

helices, and the PPII structures. Using the tensor span of the 13 R C nucleus as another correlator along the third dimension leads to an even better resolution of overlaps. CSA Tensor for Experimental Dihedral Angles. The CSA tensor parameters computed for experimentally determined dihedral angles are plotted in Figure 5. Only the major secondary structure elements such as R-helices and β-sheets are analyzed.


Figure 8. 3D structure of binase (108 amino acids, PDB entry 2rbi) and the amino acid sequence showing R-helix and β-strand regions. The secondary structure content of binase is 22% helical (four helices) and 23% β sheet (six strands).

The results mirror the same trends as those for the computed model peptide tensor, namely, that the CSA tensor span Ω of the carbonyl nucleus is a “universal correlator” and correlations with the tensor spans of other backbone nuclei show distinct clusters for the helical and sheet structures. Such multinuclear (Ω, Ω) plots hence show much promise for structure elucidation and require further investigation. Ubiquitin and Binase Experimental CSA Values. The CSA tensor for 13C′, 1HN, and 15N backbone nuclei of the protein ubiquitin (3D structure and secondary structure content shown in Figure 6) has been experimentally determined previously.31,42-44 We used the reported literature values of the backbone CSA

J. Phys. Chem. A, Vol. 114, No. 18, 2010 5835 tensor for ubiquitin and calculated the (Ω, κ) CSA parameter pair for all three backbone nuclei and for every amino acid. Furthermore, to see how well experimental measures match with theoretical calculations, we also computed the full CSA tensor for each amino acid using the Gaussian program (and previously described basis sets and DFT level of theory). Whereas we get overestimated values of the CSA tensor for the theoretical computation a propos the experimental values, the trends in terms of CSA parameter and multinuclei correlations are the same in both cases. The 2D and 3D correlations between different CSA parameters for Ubiquitin are shown in Figure 7. The correlations between the tensor span, Ω, and the tensor skew, κ, are able to distinguish between the helices and the sheets in the structure. Adding the δiso parameter along the third dimension is better able to resolve the overlaps. Multinuclear (Ω, Ω) correlations between 13C′- 15N and 13C′- 1HN are shown in Figure 3c,d, respectively. The correlation of the carbonyl nucleus with the amide proton is better able to distinguish helices from sheets, perhaps due to the fact that both nuclei encode information about backbone hydrogen bonding in the protein structure. The CSA tensor for the backbone carbonyl nuclei of the protein binase (3D structure and secondary structure content shown in Figure 8) has been previously experimentally determined.45 As done for ubiquitin, we extracted the relevant CSA parameter pairs for the experimentally reported carbonyl CSA tensors and computed the CSA tensor for each amino acid using the Gaussian program. Because the only experimental information available pertains to the carbonyl CSA tensor, we have not explored multinuclear correlations as structure indicators for this protein. The 2D and 3D correlation maps constructed for binase are shown in Figure 9. Whereas the (Ω, κ) correlation

Figure 9. (a,b) Experimental 2D CSA parameter correlations (Ω, κ) and (Ω, δiso), respectively, for 13C′ nucleus of binase. (c) Experimental 3D CSA parameter correlations for the protein binase, plotting (Ω, κ) of 13C′ in the (x, y) plane and δiso of 13C′ in the z direction. The addition of the isotropic chemical shift correlator in the third dimension helps resolve overlaps.

5836


is not able to distinguish between helices and sheets, the (Ω, δiso) correlation is able to distinguish helices from sheets (by a few ppm). Adding isotropic chemical shift as another correlator in the third dimension leads to a better resolution of secondary structure overlaps. For both ubiquitin and binase, the CSA correlation maps show discernible differences between different secondary structures, but the distinction between the helix and sheet regions is not dramatic. However, we believe that this is partially due to the fact that these CSA tensors have been determined from cross-correlated spin relaxation experiments and hence encode dynamic as well as structural information. This dynamic averaging of the CSA tensors during the measurement in liquid-state experiments makes it difficult to discern the clustering of different secondary structures subsequently. We hence feel that such multiparameter correlation maps will be potentially more useful as direct estimators of secondary structure content in proteins whose backbone CSA tensors have been determined from solid-state NMR experiments. Concluding Remarks The CSA tensor of a nucleus can, in principle, provide a wealth of structural and dynamic information, and CSA tensor information is increasingly being used to refine biomolecular structural constraints. Small changes in isotropic shift are often accompanied by much larger changes in the CSA, which is thus better correlated with changes in the molecular structure. The dependence of the anisotropy of the carbonyl chemical shielding tensor on dihedral angles has been investigated in detail in this work. Two-dimensional CSA parameter (Ω, κ) plots were constructed for protein backbone nuclei for the alanine residue participating in different secondary structures taken from the PDB, and the plots were analyzed as a function of the secondary structure. In particular, the 13C′ CSA tensor 2D plots show that helices and sheets fall into two distinct regions with very little overlap, and hence this tensor can be used to identify these structures unambiguously in a protein. One major difficulty in using 1D chemical shift information for secondary structure identification is the fact that there are severe overlaps between helices and sheets, which can be resolved by using multidimensional, multinuclei CSA correlation maps. In comparison with experimental measurements of the CSA tensor, it was shown that 2D cluster plots of CSA tensor parameters for the 13 C′ nuclei are a good way to quantify secondary structure content of proteins. The differentiation of helix versus β-sheet motifs for experimental values of the 13C′ CSA tensors agrees well with the quantum chemical calculations. This study hence suggests that carbonyl CSA tensor information may be useful as an additional tool for structure refinement, especially when isotropic chemical shifts of other nuclei give ambiguous information. The accurate experimental determination of CSA tensors of peptides and proteins is of vital importance, and understanding the variation of CSA tensors could play a pivotal role in the structure and dynamics determination of proteins using NMR spectroscopy. Acknowledgment. This work is funded by the Department of Biotechnology, Ministry of Science & Technology (India), under grant number BT/PR9446/BRB/10/558/2007. Supporting Information Available: Proteins considered for this study including the PDB reference code, the residue number, the dihedral angles (φ, ψ), the secondary structure, and the number of amino acids participating in the particular secondary structure. This material is available free of charge via the Internet at http://pubs.acs.org.

Elavarasi et al. References and Notes (1) Wishart, D. S.; Case, D. A. Methods Enzymol. 2002, 338, 3. (2) Cavalli, A.; Salvatella, X.; Dobson, C. M.; Vendruscolo, M. Proc. Natl. Acad. Sci. U.S.A. 2007, 104, 9615. (3) Vila, J. A.; Arnautova, Y. A.; Scheraga, H. A. Proc. Natl. Acad. Sci. U.S.A. 2008, 105, 1891. (4) Mielke, S. P.; Krishnan, V. V. Prog. Nucl. Magn. Reson. Spectrosc. 2009, 54, 141. (5) Wang, C.-C.; Chen, J.-H.; Lai, W.-C.; Chuang, W.-J. J. Biol. NMR 2007, 38, 57. (6) Neal, S.; Nip, A. M.; Zhang, H. Y.; Wishart, D. S. J. Biol. NMR 2003, 26, 215. (7) Kontaxis, G.; Delaglio, F.; Bax, A. Methods Enzymol. 2005, 394, 42. (8) Villegas, M. E.; Vila, J. A.; Scheraga, H. A. J. Biol. NMR 2007, 37, 137. (9) Eghbalnia, H. R.; Wang, L.; Bahramia, A.; Assadib, A.; Markley, J. L. J. Biol. NMR 2005, 32, 71. (10) Sibley, A. B.; Cosman, M.; Krishnan, V. V. Biophys. J. 2003, 84, 1223. (11) Birn, J.; Poon, A.; Mao, Y.; Ramamoorthy, A. J. Am. Chem. Soc. 2004, 126, 8529. (12) Tang, S.; Case, D. A. J. Biol. NMR 2007, 38, 255. (13) Chekmenev, E. Y.; Xu, R. Z.; Mashuta, M. S.; Wittebort, R. J. J. Am. Chem. Soc. 2002, 124, 11894. (14) Sun, H.; Sanders, L. K.; Oldfield, E. J. Am. Chem. Soc. 2002, 124, 5486. (15) Shen, Y.; Bax, A. J. Biol. NMR 2007, 38, 289. (16) Loth, K.; Abergel, D.; Pelupessy, P.; Delarue, M.; Lopes, P.; Ouazzani, J.; Duclert-Savatier, N.; Nilges, M.; Bodenhausen, G.; Stoven, V. Proteins 2006, 64, 931. (17) de Dios, A. C.; Pearson, J. G.; Oldfield, E. Science 1993, 260, 1491. (18) Fushman, D.; Cowburn, D. Methods Enzymol. 2001, 339, 109. (19) Fushman, D.; Cowburn, D. J. Biol. NMR 1999, 13, 139. (20) Wang, Y. J.; Jardetzky, O. J. Am. Chem. Soc. 2002, 124, 14075. (21) Cai, L.; Fushman, D.; Kosov, D. S. J. Biol. NMR 2008, 41, 77. (22) Heise, B.; Leppert, J.; Wenschuh, H.; Ohlenschlagger, O.; Garlach, M.; Ramachandran, R. J. Biol. NMR 2001, 19, 167. (23) Havlin, R. H.; Le, H.; Laws, D. D.; deDios, A. C.; Oldfield, E. J. Am. Chem. Soc. 1997, 119, 11951. (24) Case, D. A. Curr. Opin. Struct. Biol. 1998, 8, 624. (25) Sitkoff, D.; Case, D. A. Prog. Nucl. Magn. Reson. Spectrosc. 1998, 32, 165. (26) Strohmeier, M.; Grant, D. M. J. Am. Chem. Soc. 2004, 126, 966. (27) Stueber, D.; Guenneau, F. N.; Grant, D. M. J. Chem. Phys. 2001, 114, 9236. (28) Hall, J. B.; Fushman, D. J. Am. Chem. Soc. 2006, 128, 7855. (29) Wylie, B. J.; Rienstra, C. M. J. Chem. Phys. 2008, 128, 052207. (30) Wylie, B. J.; Franks, W. T.; Rienstra, C. M. J. Phys. Chem. B 2006, 110, 10926. (31) Cornilescu, G.; Bax, A. J. Am. Chem. Soc. 2000, 122, 10143. (32) Havlin, R. H.; Laws, D. D.; Bitter, H.-M. L.; Sanders, L. K.; Sun, H.; Grimley, J. S.; Wemmer, D. E.; Pines, A.; Oldfield, E. J. Am. Chem. Soc. 2001, 123, 10362. (33) Moon, S.; Case, D. A. J. Comput. Chem. 2006, 27, 825. (34) Wi, S.; Sun, H. H.; Oldfield, E.; Hong, M. J. Am. Chem. Soc. 2005, 127, 6451. (35) Ying, J.; Grishaev, A.; Bryce, D. L.; Bax, A. J. Am. Chem. Soc. 2006, 128, 11443. (36) Czinki, E.; Csaszar, A. G.; Magyarfalvi, G.; Schreiner, P. R.; Allen, W. D. J. Am. Chem. Soc. 2007, 129, 1568. (37) Wylie, B. J.; Schwieters, C. D.; Oldfield, E.; Rienstra, C. M. J. Am. Chem. Soc. 2009, 131, 985. (38) Brender, J. R.; Taylor, D. M.; Ramamoorthy, A. J. Am. Chem. Soc. 2001, 123, 914. (39) Poon, A.; Birn, J.; Ramamoorthy, A. J. Phys. Chem. B 2004, 108, 16577. (40) Walker, O.; Varadan, R.; Fushman, D. J. Magn. Reson. 2004, 168, 336. (41) Hall, J. B.; Fushman, D. Magn. Reson. Chem. 2003, 41, 837. (42) Cisnetti, F.; Loth, K.; Pelupessy, P.; Bodenhausen, G. ChemPhysChem 2004, 5, 807. (43) Loth, K.; Pelupessy, P.; Bodenhausen, G. J. Am. Chem. Soc. 2005, 127, 6062. (44) Burton, R. A.; Tjandra, N. J. Am. Chem. Soc. 2007, 129, 1322. (45) Pang, Y.; Zuiderweg, E. R. P. J. Am. Chem. Soc. 2000, 122, 4841. (46) Markwick, P. R. L.; Sattler, M. J. Am. Chem. Soc. 2004, 126, 11424. (47) Wang, T.; Weaver, D. S.; Cai, S.; Zuiderweg, E. R. P. J. Biol. NMR 2006, 36, 79. (48) Tjandra, N.; Bax, A. J. Am. Chem. Soc. 1997, 119, 8076.

CSA Tensor of Carbonyl Backbone Nuclei (49) Berman, H. M.; Westbrook, J.; Feng, Z.; Gilliland, G.; Bhat, T. N.; Weissig, H.; Shindyalov, I. N.; Bourne, P. E. Nucleic Acids Res. 2000, 28, 235. (50) Czinki, E.; Csaszar, A. G. J. Biol. NMR 2007, 38, 269. (51) Iwadate, M.; Asakura, T.; Williamson, M. P. J. Biol. NMR 1999, 13, 199. (52) Ditchfield, R. Mol. Phys. 1974, 27, 789. (53) Wolinski, K.; Hinton, J. F.; Pulay, P. J. Am. Chem. Soc. 1990, 112, 8251. (54) Rauhut, G.; Puyear, S.; Wolinski, K.; Pulay, P. J. Phys. Chem. 1996, 100, 6310. (55) Frisch, M. J.; Trucks, G. W.; Schlegel, H. B.; Scuseria, G. E.; Robb, M. A.; Cheeseman, J. R.; Montgomery, J. A., Jr.; Vreven, T.; Kudin, K. N.; Burant, J. C.; Millam, J. M.; Iyengar, S. S.; Tomasi, J.; Barone, V.; Mennucci, B.; Cossi, M.; Scalmani, G.; Rega, N.; Petersson, G. A.; Nakatsuji, H.; Hada, M.; Ehara, M.; Toyota, K.; Fukuda, R.; Hasegawa, J.; Ishida, M.; Nakajima, T.; Honda, Y.; Kitao, O.; Nakai,

J. Phys. Chem. A, Vol. 114, No. 18, 2010 5837 H.; Klene, M.; Li, X.; Knox, J. E.; Hratchian, H. P.; Cross, J. B.; Bakken, V.; Adamo, C.; Jaramillo, J.; Gomperts, R.; Stratmann, R. E.; Yazyev, O.; Austin, A. J.; Cammi, R.; Pomelli, C.; Ochterski, J. W.; Ayala, P. Y.; Morokuma, K.; Voth, G. A.; Salvador, P.; Dannenberg, J. J.; Zakrzewski, V. G.; Dapprich, S.; Daniels, A. D.; Strain, M. C.; Farkas, O.; Malick, D. K.; Rabuck, A. D.; Raghavachari, K.; Foresman, J. B.; Ortiz, J. V.; Cui, Q.; Baboul, A. G.; Clifford, S.; Cioslowski, J.; Stefanov, B. B.; Liu, G.; Liashenko, A.; Piskorz, P.; Komaromi, I.; Martin, R. L.; Fox, D. J.; Keith, T.; Al-Laham, M. A.; Peng, C. Y.; Nanayakkara, A.; Challacombe, M.; Gill, P. M. W.; Johnson, B.; Chen, W.; Wong, M. W.; Gonzalez, C.; Pople, J. A. Gaussian 03, revision C.02; Gaussian, Inc.: Wallingford, CT, 2004. (56) Chestnut, D. B.; Rusiloski, B. E.; Moore, K. D.; Egolf, D. A. J. Comput. Chem. 1993, 14, 1364. (57) Helgaker, T.; Jaszunski, M.; Ruud, K. Chem. ReV. 1999, 99, 293.

JP9113199

Using the Chemical Shift Anisotropy Tensor of Carbonyl Backbone

Recommend Documents