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

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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 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.

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Acknowledgments

We thank Frank Delaglio for assistance and useful discussions. This work was funded by the Intramural Research Program of the NIDDK, NIH.

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Authors and Affiliations

  1. 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

    Yang Shen & Ad Bax

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  1. Yang Shen
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  2. Ad Bax
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Correspondence to Ad Bax.

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Shen, Y., Bax, A. Protein backbone and sidechain torsion angles predicted from NMR chemical shifts using artificial neural networks. J Biomol NMR 56, 227–241 (2013). https://doi.org/10.1007/s10858-013-9741-y

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