Skip to content
Journal article

PROSHIFT: protein chemical shift prediction using artificial neural networks

Meiler J...(+1 more)

J.Biomol.NMR, vol. 26, issue 0925-2738 (Print) (2003) pp. 25-37

  • 1

    Readers

    Mendeley users who have this article in their library.
  • N/A

    Citations

    Citations of this article.
  • N/A

    Views

    ScienceDirect users who have downloaded this article.
Sign in to save reference

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 (1)H, (13)C, and (15)N 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 A 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: www.jens-meiler.de/proshift.html

Author-supplied keywords

  • Amino Acid Sequence
  • Biochemistry
  • CHEMICAL-SHIFT
  • CHEMICAL-SHIFTS
  • CONFORMATION
  • Carbon
  • Carbon Isotopes
  • Chemical Shift
  • Databases
  • Databases,Factual
  • Hydrogen
  • Isotopes
  • MODELS
  • Magnetic Resonance Spectroscopy
  • Models,Molecular
  • NETWORKS
  • NEURAL NETWORKS
  • NMR
  • NMR spectroscopy
  • NMR-SPECTROSCOPY
  • Nerve Net
  • Nitrogen
  • Nitrogen Isotopes
  • PREDICTION
  • PROTEIN-STRUCTURE
  • Protein
  • Protein Conformation
  • Proteins
  • Reproducibility of Results
  • Research
  • SERVER
  • SPECTROSCOPY
  • STATE
  • STATE NMR
  • Structure
  • Structures
  • assignment
  • chemistry
  • database
  • methods
  • model
  • protein models
  • protein structure
  • protein structures
  • refinement
  • side chain
  • three-dimensional structure

Find this document

  • PMID: 12766400

Cite this document

Choose a citation style from the tabs below