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

By Yang Shen, A. Bax. 2013

1,083 citations recorded by the source corpus.

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.

Subfields: Materials Chemistry, Molecular Biology

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Citations in the corpus

De novo protein design by deep network hallucination (2020, methodology): “Peak intensities from 3D NOESY spectra, together with dihedral angle constraints determined from back-bone chemical shift data using TALOS-N 45 , were used as input for structure determination.”
PINE-SPARKY.2 for automated NMR-based protein structure research (2017, methodology): “2016) for validation of peak assignments; TALOS-N for shift based torsion angle restraints (Shen and Bax, 2013), CS-Rosetta (Shen et al.”
CS-ROSETTA (2018, methodology): “To overcome this bottleneck, CS-Rosetta makes use of TALOS-N (torsion angle likelihood obtained from shift and sequence similarity) (Shen & Bax, 2013), which takes as input the amino acid sequence and NMR chemical shifts to characterize the regions of rigidity and flexibility using random coil index”
Protein structure determination by combining sparse NMR data with evolutionary couplings (2015, positive): “Backbone dihedral angle restraints— Dihedral angle restraints may be generated automatically from backbone chemical shift using TALOS-N 41 (or TALOS+ 33 ), or defined by alternative automated and/or manual methods.”
Chemical shift-based methods in NMR structure determination (2018, methodology): “The key innovation in this procedure is a protein alignment module, Pomona, which uses TALOS-N [110] to estimate //w backbone torsion angle probability maps from 13Cα, 13Cβ, 13Cγ , 15N, 1Hα, and (1)HN chemical shifts for every amino acid in the query sequence.”
De novo design of covalently constrained mesosize protein scaffolds with unique tertiary structures (2017, methodology): “In addition to distance constraints, backbone and 1 dihedral angle constraints were derived from chemical shifts using the program TALOS(23) for residues located in well-defined secondary structure elements.”
Integrative NMR for biomolecular research (2016, positive): “…automated assignment (Bahrami et al. 2009), PONDEROSA-C/S for automated structure determination, TALOS-N for shift based torsion angle restraints (Shen and Bax 2013), the PACSY database (Lee et al. 2012), and the CS-Rosetta (Lange et al. 2012) compute server at the Biological Magnetic Resonance…”