|
|
|
PROSHIFT: Protein chemical shift prediction using artificial neural networks
Jens Meiler
Department of Biochemistry, Box 357350, Seattle,
University of Washington,
Washington 98195-7350,
U.S.A.
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 www.meilerlab.org
Journal of Biomolecular NMR
26 (1): 25-37, May 2003
[PubMed PMID: 12766400]
|
|
