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PROSHIFT: protein chemical shift prediction using artificial neural networks.

Jens Meiler1

  • 1University of Washington, Department of Biochemistry, Box 357350, Seattle, Washington 98195-7350, USA. jens@jens-meiler.de

Journal of Biomolecular NMR
|May 27, 2003
PubMed
Summary

A new neural network predicts protein chemical shifts (H, C, N) using 3D structure and experimental conditions. This method aids in protein structure determination, validation, and refinement, offering accurate predictions for researchers.

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Area of Science:

  • Structural Biology
  • Computational Chemistry
  • Biophysics

Background:

  • Protein chemical shift data is crucial for determining 3D protein structures.
  • Large databases of protein structures with assigned chemical shifts enable quantitative analysis.
  • Understanding the relationship between NMR chemical shifts and protein structure is vital.

Purpose of the Study:

  • To develop a neural network model for predicting protein chemical shifts.
  • To utilize 3D protein structure and experimental conditions as input parameters for prediction.
  • To assess the model's accuracy and its potential applications in structural biology.

Main Methods:

  • A neural network was trained using protein 3D structures and experimental conditions.
  • The model predicts hydrogen (1H), carbon (13C), and nitrogen (15N) chemical shifts.

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  • Root mean square deviations (RMSD) were calculated to evaluate prediction accuracy.

    • Main Results:

    • The model achieved RMSDs of 0.3 ppm for hydrogen, 1.3 ppm for carbon, and 2.6 ppm for nitrogen chemical shifts.
    • The predictions reflect influences of covalent structure and conformation on backbone and side-chain nuclei.
    • A correlation was found between model RMSD and experimental chemical shift deviation for structures with RMSD < 5 Å.

    Conclusions:

    • The developed method accurately predicts protein chemical shifts.
    • This approach can support protein structure assignment, validation, and refinement.
    • The tool is available to academic users via the PROSHIFT server.