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

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

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

J Biomol NMR. 2013 Jun 2;

Authors: Shen Y, Bax A

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.


PMID: 23728592 [PubMed - as supplied by publisher]



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