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Protein backbone and sidechain torsion angles predicted from NMR chemical shifts using artificial neural networks
Open accessAbstract
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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Cites
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Protein backbone angle restraints from searching a database for chemical shift and sequence homology
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“…protein database for closely matching fragments was at the heart of the earlier TALOS and TALOS+ programs for predicting backbone φ/ψ angles, and provides a robust quality control step by eliminating local geometries that are sterically not feasible (Cornilescu et al. 1999; Shen et al. 2009a).”
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Metadata
- Authors
- Shen, Y., & Bax, A.
- Venue
- Journal of Biomolecular NMR
- Published
- 2013-06-02
- Volume
- 56
- Issue
- 3
- Pages
- 227-241
- ISSN
- 0925-2738 (ISSN-L)
- 1573-5001
- OpenAlex
- W2167785149
- Cited by
- 1,119
- References
- 3
- FWCI
- 22.32
- Type
- journal article
- Language
- en
- Open access
- green
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