Chemical shift index

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Example of chemical shift index Chemical shift index.png
Example of chemical shift index

The chemical shift index or CSI is a widely employed technique in protein nuclear magnetic resonance spectroscopy that can be used to display and identify the location (i.e. start and end) as well as the type of protein secondary structure (beta strands, helices and random coil regions) found in proteins using only backbone chemical shift data [1] [2] The technique was invented by David S. Wishart in 1992 for analyzing 1Hα chemical shifts and then later extended by him in 1994 to incorporate 13C backbone shifts. The original CSI method makes use of the fact that 1Hα chemical shifts of amino acid residues in helices tends to be shifted upfield (i.e. towards the right side of an NMR spectrum) relative to their random coil values and downfield (i.e. towards the left side of an NMR spectrum) in beta strands. Similar kinds of upfield and downfield trends are also detectable in backbone 13C chemical shifts.

Contents

Implementation

The CSI is a graph-based technique that essentially employs an amino acid-specific digital filter to convert every assigned backbone chemical shift value into a simple three-state (-1, 0, +1) index. This approach generates a more easily understood and much more visually pleasing graph of protein chemical shift values. In particular, if the upfield 1Hα chemical shift (relative to an amino acid-specific random coil value) of a certain residue is > 0.1 ppm, then that amino acid residue is assigned a value of -1. Similarly, if the downfield 1Hα chemical shift of a certain amino acid residue is > 0.1 ppm then that residue is assigned a value of +1. If an amino acid residue's chemical shift is not shifted downfield or upfield by a sufficient amount (i.e. <0.1 ppm), it is given a value of 0. When this 3-state index is plotted as a bar graph over the full length of the protein sequence, simple inspection can allow one to identify beta strands (clusters of +1 values), alpha helices (clusters of -1 values), and random coil segments (clusters of 0 values). A list of the amino acid-specific random coil chemical shifts for CSI calculations is given in Table 1. An example of a CSI graph for a small protein is shown in Figure 1 with the arrows located above the black bars indicating locations of the beta strands and the rectangular box indicating the location of a helix.

Table 1. CSI Residue-Specific 1Hα Random Coil Shifts
Amino Acid1Hα random coil shift (ppm)Amino Acid1Hα RC shift random coil shift (ppm)
Ala (A)4.35Met (M)4.52
Cys (C)4.65Asn (N)4.75
Asp (D)4.76Pro (P)4.44
Glu (E)4.29Gln (Q)4.37
Phe (F)4.66Arg (R)4.38
Gly (G)3.97Ser (S)4.50
His (H)4.63Thr (T)4.35
Ile (I)3.95Val (V)3.95
Lys (K)4.36Trp (W)4.70
Leu (L)4.17Tyr (Y)4.60

Performance

Using only 1Hα chemical shifts and simple clustering rules (clusters of 3 or more vertical bars for beta strands and clusters of 4 or more vertical bars for alpha helices), the CSI is typically 75-80% accurate in the identification of secondary structures. [2] [3] [4] [5] This performance depends partly on the quality of the NMR data set as well as the technique (manual or programmatic) used to identify the protein secondary structures. As noted above, a consensus CSI method that filters upfield/downfield chemical shift changes in 13Cα, 13Cβ, and 13C' atoms in a similar manner to 1Hα shifts has also been developed. [2] The consensus CSI combines the CSI plots from backbone 1H and 13C chemical shifts to generate a single CSI plot. It can be up to 85-90% accurate. [5]

History

The link between protein chemical shifts and protein secondary structure (specifically alpha helices) was first described by John Markley and colleagues in 1967. [6] With the development of modern 2-dimensional NMR techniques, it became possible to measure more protein chemical shifts. With more peptides and proteins were being assigned in the early 1980s it soon became obvious that amino acid chemical shifts were sensitive not only to helical conformations, but also to β-strand conformations. Specifically, the secondary 1Hα chemical shifts of all amino acids exhibit a clear upfield trend on helix formation and an obvious downfield trend on β-sheet formation. [7] [8] By the early 1990s, a sufficient body of 13C and 15N chemical shift assignments for peptides and proteins had been collected to determine that similar upfield/downfield trends were evident for essentially all backbone 13Cα, 13Cβ, 13C', 1HN and 15N (weakly) chemical shifts. [9] [10] It was these rather striking chemical shift trends that were exploited in the development of the chemical shift index.

Limitations

The CSI method is not without some shortcomings. In particular, its performance drops if chemical shift assignments are mis-referenced or incomplete. It is also quite sensitive to the choice of random coil shifts used to calculate the secondary shifts [5] and it generally identifies alpha helices (>85% accuracy) better than beta strands (<75% accuracy) regardless of the choice of random coil shifts. [5] Furthermore, the CSI method does not identify other kinds of secondary structures, such as β-turns. Because of these shortcomings, a number of alternative CSI-like approaches have been proposed. These include: 1) a prediction method that employs statistically derived chemical shift/structure potentials (PECAN); [11] 2) a probabilistic approach to secondary structure identification (PSSI); [12] 3) a method that combines secondary structure predictions from sequence data and chemical shift data (PsiCSI), [13] 4) a secondary structure identification approach that uses pre-specified chemical shift patterns (PLATON) [14] and 5) a two-dimensional cluster analysis method known as 2DCSi. [15] The performance of these newer methods is generally slightly better (2-4%) than the original CSI method.

Utility

Since its original description in 1992, the CSI method has been used to characterize the secondary structure of thousands of peptides and proteins. Its popularity is largely due to the fact that it is easy to understand and can be implemented without the need for specialized computer programs. Even though the CSI method can be easily performed manually, a number of commonly used NMR data processing programs such as NMRView, [16] NMR structure generation web servers such as CS23D [17] as well as various NMR data analysis web servers such as RCI, [18] Preditor [19] and PANAV [20] have incorporated the CSI method into their software.

See also

Related Research Articles

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<span class="mw-page-title-main">Nuclear magnetic resonance spectroscopy</span> Laboratory technique

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<i>Journal of Biomolecular NMR</i> Academic journal

The Journal of Biomolecular NMR publishes research on technical developments and innovative applications of nuclear magnetic resonance spectroscopy for the study of structure and dynamic properties of biopolymers in solution, liquid crystals, solids and mixed environments. Some of the main topics include experimental and computational approaches for the determination of three-dimensional structures of proteins and nucleic acids, advancements in the automated analysis of NMR spectra, and new methods to probe and interpret molecular motions.

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<span class="mw-page-title-main">CS23D</span>

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Probabilistic Approach for protein NMR Assignment Validation (PANAV) is a freely available stand-alone program that is used for protein chemical shift re-referencing. Chemical shift referencing is a problem in protein nuclear magnetic resonance as >20% of reported NMR chemical shift assignments appear to be improperly referenced. For certain nuclei these referencing issues can cause systematic chemical shift errors of between 1.0 and 2.5 ppm. Chemical shift errors of this magnitude often make it very difficult to compare NMR chemical shift assignments between proteins. It also makes it very hard to structurally interpret chemical shifts. Unlike most other chemical shift re-referencing tools PANAV employs a structure-independent protocol. That is, with PANAV there is no need to know the structure of the protein in advance of correcting any chemical shift referencing errors. This makes PANAV particularly useful for NMR studies involving novel or newly assigned proteins, where the structure has yet to be determined. Indeed, this scenario represents the vast majority of assignment cases in biomolecular NMR. PANAV uses residue-specific and secondary structure-specific chemical shift distributions that were calculated over short fragments of correctly referenced proteins to identify mis-assigned resonances. More specifically, PANAV compares the initial chemical shift assignments to the expected chemical shifts based on their local sequence and expected/predicted secondary structure. In this way, PANAV is able to identify and re-reference mis-referenced chemical shift assignments. PANAV can also identify potentially mis-assigned resonances as well. PANAV has been extensively tested and compared against a large number of existing re-referencing or mis-assignment detection programs. These assessments indicate that PANAV is equal to or superior to existing approaches.

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ShiftX is a freely available web server for rapidly calculating protein chemical shifts from protein X-ray coordinates. Protein chemical shift prediction is particularly useful in verifying protein chemical shift assignments, adjusting mis-referenced chemical shifts, refining NMR protein structures and assisting with the NMR assignment of unassigned proteins that have either had their structures determined by X-ray or NMR methods.

Nitrogen-15 nuclear magnetic resonance spectroscopy is a version of nuclear magnetic resonance spectroscopy that examines samples containing the 15N nucleus. 15N NMR differs in several ways from the more common 13C and 1H NMR. To circumvent the difficulties associated with measurement of the quadrupolar, spin-1 14N nuclide, 15N NMR is employed in samples for detection since it has a ground-state spin of ½. Since14N is 99.64% abundant, incorporation of 15N into samples often requires novel synthetic techniques.

David S. Wishart is a Canadian researcher and a Distinguished University Professor in the Department of Biological Sciences and the Department of Computing Science at the University of Alberta. Wishart also holds cross appointments in the Faculty of Pharmacy and Pharmaceutical Sciences and the Department of Laboratory Medicine and Pathology in the Faculty of Medicine and Dentistry. Additionally, Wishart holds a joint appointment in metabolomics at the Pacific Northwest National Laboratory in Richland, Washington. Wishart is well known for his pioneering contributions to the fields of protein NMR spectroscopy, bioinformatics, cheminformatics and metabolomics. In 2011, Wishart founded the Metabolomics Innovation Centre (TMIC), which is Canada's national metabolomics laboratory.

References

  1. ↑ Wishart DS, Sykes BD, Richards FM (February 1992). "The chemical shift index: a fast and simple method for the assignment of protein secondary structure through NMR spectroscopy". Biochemistry. 31 (6): 1647–51. CiteSeerX   10.1.1.539.2952 . doi:10.1021/bi00121a010. PMID   1737021.
  2. 1 2 3 Wishart, David S.; Sykes, Brian D. (1994). "The 13C Chemical-Shift Index: A simple method for the identification of protein secondary structure using 13C chemical-shift data". Journal of Biomolecular NMR. 4 (2): 171–80. doi:10.1007/BF00175245. PMID   8019132. S2CID   42323147.
  3. ↑ Wishart DS, Case DA (2001). "Use of chemical shifts in macromolecular structure determination". Nuclear Magnetic Resonance of Biological Macromolecules Part A. Methods in Enzymology. Vol. 338. pp. 3–34. doi:10.1016/s0076-6879(02)38214-4. ISBN   9780121822392. PMID   11460554.
  4. ↑ Mielke SP, Krishnan VV (April 2009). "Characterization of protein secondary structure from NMR chemical shifts". Progress in Nuclear Magnetic Resonance Spectroscopy. 54 (3–4): 141–165. Bibcode:2009PNMRS..54..141M. doi:10.1016/j.pnmrs.2008.06.002. PMC   2766081 . PMID   20160946.
  5. 1 2 3 4 Wishart DS (February 2011). "Interpreting protein chemical shift data". Progress in Nuclear Magnetic Resonance Spectroscopy. 58 (1–2): 62–87. Bibcode:2011PNMRS..58...62W. doi:10.1016/j.pnmrs.2010.07.004. PMID   21241884.
  6. ↑ Markley JL, Meadows DH, Jardetzky O (July 1967). "Nuclear magnetic resonance studies of helix-coil transitions in polyamino acids". Journal of Molecular Biology. 27 (1): 25–40. doi:10.1016/0022-2836(67)90349-X. PMID   6033611.
  7. ↑ Clayden, N.J; Williams, R.J.P (1982). "Peptide group shifts". Journal of Magnetic Resonance. 49 (3): 383. Bibcode:1982JMagR..49..383C. doi:10.1016/0022-2364(82)90252-9.
  8. ↑ Pardi A, Wagner G, Wüthrich K (December 1983). "Protein conformation and proton nuclear-magnetic-resonance chemical shifts". European Journal of Biochemistry. 137 (3): 445–54. doi: 10.1111/j.1432-1033.1983.tb07848.x . PMID   6198174.
  9. ↑ Wishart DS, Sykes BD, Richards FM (November 1991). "Relationship between nuclear magnetic resonance chemical shift and protein secondary structure". Journal of Molecular Biology. 222 (2): 311–33. doi: 10.1016/0022-2836(91)90214-Q . PMID   1960729.
  10. ↑ Spera, Silvia; Bax, Ad (1991). "Empirical correlation between protein backbone conformation and Cα and Cβ 13C nuclear magnetic resonance chemical shifts". Journal of the American Chemical Society. 113 (14): 5490–2. doi:10.1021/ja00014a071. INIST   5389018.
  11. ↑ Eghbalnia HR, Wang L, Bahrami A, Assadi A, Markley JL (May 2005). "Protein energetic conformational analysis from NMR chemical shifts (PECAN) and its use in determining secondary structural elements". Journal of Biomolecular NMR. 32 (1): 71–81. doi:10.1007/s10858-005-5705-1. PMID   16041485. S2CID   31769093.
  12. ↑ Wang Y, Jardetzky O (April 2002). "Probability-based protein secondary structure identification using combined NMR chemical-shift data". Protein Science. 11 (4): 852–61. doi:10.1110/ps.3180102. PMC   2373532 . PMID   11910028.
  13. ↑ Hung LH, Samudrala R (February 2003). "Accurate and automated classification of protein secondary structure with PsiCSI". Protein Science. 12 (2): 288–95. doi:10.1110/ps.0222303. PMC   2312422 . PMID   12538892.
  14. ↑ Labudde D, Leitner D, Krüger M, Oschkinat H (January 2003). "Prediction algorithm for amino acid types with their secondary structure in proteins (PLATON) using chemical shifts". Journal of Biomolecular NMR. 25 (1): 41–53. doi:10.1023/A:1021952400388. PMID   12566998. S2CID   11900013.
  15. ↑ Wang CC, Chen JH, Lai WC, Chuang WJ (May 2007). "2DCSi: identification of protein secondary structure and redox state using 2D cluster analysis of NMR chemical shifts". Journal of Biomolecular NMR. 38 (1): 57–63. doi:10.1007/s10858-007-9146-x. PMID   17333485. S2CID   20041621.
  16. ↑ Johnson BA, Blevins RA (September 1994). "NMR View: A computer program for the visualization and analysis of NMR data". Journal of Biomolecular NMR. 4 (5): 603–14. doi:10.1007/BF00404272. PMID   22911360. S2CID   32728461.
  17. ↑ Wishart DS, Arndt D, Berjanskii M, Tang P, Zhou J, Lin G (July 2008). "CS23D: a web server for rapid protein structure generation using NMR chemical shifts and sequence data". Nucleic Acids Research. 36 (Web Server issue): W496–502. doi:10.1093/nar/gkn305. PMC   2447725 . PMID   18515350.
  18. ↑ Berjanskii MV, Wishart DS (July 2007). "The RCI server: rapid and accurate calculation of protein flexibility using chemical shifts". Nucleic Acids Research. 35 (Web Server issue): W531–7. doi:10.1093/nar/gkm328. PMC   1933179 . PMID   17485469.
  19. ↑ Berjanskii MV, Neal S, Wishart DS (July 2006). "PREDITOR: a web server for predicting protein torsion angle restraints". Nucleic Acids Research. 34 (Web Server issue): W63–9. doi:10.1093/nar/gkl341. PMC   1538894 . PMID   16845087.
  20. ↑ Wang B, Wang Y, Wishart DS (June 2010). "A probabilistic approach for validating protein NMR chemical shift assignments". Journal of Biomolecular NMR. 47 (2): 85–99. doi:10.1007/s10858-010-9407-y. PMID   20446018. S2CID   22564072.
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