Protein backbone and sidechain torsion angles predicted from NMR chemical shifts using artificial neural networks

J Biomol NMR. 2013 Jul;56(3):227-41. doi: 10.1007/s10858-013-9741-y. Epub 2013 Jun 2.


A new program, TALOS-N, is introduced for predicting protein backbone torsion angles from NMR chemical shifts. The program relies far more extensively on the use of trained artificial neural networks than its predecessor, TALOS+. Validation on an independent set of proteins indicates that backbone torsion angles can be predicted for a larger, ≥90 % fraction of the residues, with an error rate smaller than ca 3.5 %, using an acceptance criterion that is nearly two-fold tighter than that used previously, and a root mean square difference between predicted and crystallographically observed (ϕ, ψ) torsion angles of ca 12º. TALOS-N also reports sidechain χ(1) rotameric states for about 50 % of the residues, and a consistency with reference structures of 89 %. The program includes a neural network trained to identify secondary structure from residue sequence and chemical shifts.

Publication types

  • Research Support, N.I.H., Intramural

MeSH terms

  • Algorithms
  • Models, Molecular
  • Neural Networks, Computer*
  • Nuclear Magnetic Resonance, Biomolecular*
  • Protein Conformation
  • Proteins / chemistry*
  • Reproducibility of Results
  • Software


  • Proteins