Protein backbone and sidechain torsion angles predicted from NMR chemical shifts using artificial neural networks
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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 χ1 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.
KeywordsHeteronuclear chemical shift Secondary structure Backbone and sidechain conformation Dynamics TALOS Order parameter Protein structure SPARTA
We thank Frank Delaglio for assistance and useful discussions. This work was funded by the Intramural Research Program of the NIDDK, NIH.
- Ester, M, Kriegel, HP, Sander, J and Xu, XW (1996) A density-based algorithm for discovering clusters in large spatial databases with noise. Proceedings of the second international conference on knowledge discovery and data mining (KDD-96). In: Simoudis E, Han J, Fayyad UM, AAAI Press: 226–231Google Scholar
- Koonin EV, Galperin MY (eds) (2003) Sequence - Evolution - Function: Computational Approaches in Comparative Genomics. Boston, Kluwer AcademicGoogle Scholar