Biological Magnetic Resonance Data Bank

A Repository for Data from NMR Spectroscopy on Proteins, Peptides, Nucleic Acids, and other Biomolecules
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PROSHIFT: Protein chemical shift prediction using artificial neural networks

Jens Meiler

Department of Biochemistry, Box 357350, Seattle,
University of Washington,
Washington 98195-7350,

Abstract: The importance of protein chemical shift values for the determination of three-dimensional protein structure has increased in recent years because of the large databases of protein structures with assigned chemical shift data. These databases have allowed the investigation of the quantitative relationship between chemical shift values obtained by liquid state NMR spectroscopy and the three-dimensional structure of proteins. A neural network was trained to predict the 1H, 13C, and 15N of proteins using their three-dimensional structure as well as experimental conditions as input parameters. It achieves root mean square deviations of 0.3 ppm for hydrogen, 1.3 ppm for carbon, and 2.6 ppm for nitrogen chemical shifts. The model reflects important influences of the covalent structure as well as of the conformation not only for backbone atoms (as, e.g., the chemical shift index) but also for side-chain nuclei. For protein models with a RMSD smaller than 5 Å a correlation of the RMSD and the r.m.s. deviation between the predicted and the experimental chemical shift is obtained. Thus the method has the potential to not only support the assignment process of proteins but also help with the validation and the refinement of three-dimensional structural proposals. It is freely available for academic users at the PROSHIFT server at

Journal of Biomolecular NMR
26 (1): 25-37, May 2003
[PubMed PMID: 12766400]