Journal of Biomolecular NMR

, Volume 60, Issue 2–3, pp 131–146 | Cite as

CSI 2.0: a significantly improved version of the Chemical Shift Index

Article

Abstract

Protein chemical shifts have long been used by NMR spectroscopists to assist with secondary structure assignment and to provide useful distance and torsion angle constraint data for structure determination. One of the most widely used methods for secondary structure identification is called the Chemical Shift Index (CSI). The CSI method uses a simple digital chemical shift filter to locate secondary structures along the protein chain using backbone 13C and 1H chemical shifts. While the CSI method is simple to use and easy to implement, it is only about 75–80 % accurate. Here we describe a significantly improved version of the CSI (2.0) that uses machine-learning techniques to combine all six backbone chemical shifts (13Cα, 13Cβ, 13C, 15N, 1HN, 1Hα) with sequence-derived features to perform far more accurate secondary structure identification. Our tests indicate that CSI 2.0 achieved an average identification accuracy (Q3) of 90.56 % for a training set of 181 proteins in a repeated tenfold cross-validation and 89.35 % for a test set of 59 proteins. This represents a significant improvement over other state-of-the-art chemical shift-based methods. In particular, the level of performance of CSI 2.0 is equal to that of standard methods, such as DSSP and STRIDE, used to identify secondary structures via 3D coordinate data. This suggests that CSI 2.0 could be used both in providing accurate NMR constraint data in the early stages of protein structure determination as well as in defining secondary structure locations in the final protein model(s). A CSI 2.0 web server (http://csi.wishartlab.com) is available for submitting the input queries for secondary structure identification.

Keywords

Nuclear magnetic resonance Chemical shifts Secondary structure multi-class support-vector machine Markov model 

Notes

Acknowledgments

The authors would like to thank Yongjie Liang for his help in preparing the CSI 2.0 web server. Financial support from the Natural Sciences and Engineering Research Council (NSERC), the Alberta Prion Research Institute (APRI) and PrioNet is gratefully acknowledged.

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Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  1. 1.Department of Computing ScienceUniversity of AlbertaEdmontonCanada
  2. 2.Department of Biological SciencesUniversity of AlbertaEdmontonCanada

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