title: Protein backbone and sidechain torsion angles predicted from NMR chemical shifts using artificial neural networks.
abstract: 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.
authors.affiliation: Laboratory of Chemical Physics, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Building 5, Room 126 NIH, Bethesda, MD 20892-0520, USA.
authors.forename: Yang
authors.initials: Y
authors.lastname: Shen
authors.forename: Ad
authors.initials: A
authors.lastname: Bax
mesh_terms: D000465:Algorithms; D008958:Models, Molecular; D016571:Neural Networks, Computer; D019906:Nuclear Magnetic Resonance, Biomolecular; D011487:Protein Conformation; D011506:Proteins / Q000737:chemistry*; D015203:Reproducibility of Results; D012984:Software
chemical_list: D011506:Proteins