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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 (?, ?) torsion angles of ca 12º. TALOS-N also reports sidechain ?(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.

SUBMITTER: Shen Y

PROVIDER: S-EPMC3701756 | biostudies-literature | 2013 Jul

REPOSITORIES: biostudies-literature

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

Shen Yang Y Bax Ad A

Journal of biomolecular NMR 20130602 3

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 prev ...[more]

PMID: 23728592

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