Journal of Biomolecular NMR

, Volume 48, Issue 1, pp 13–22 | Cite as

SPARTA+: a modest improvement in empirical NMR chemical shift prediction by means of an artificial neural network

Article

Abstract

NMR chemical shifts provide important local structural information for proteins and are key in recently described protein structure generation protocols. We describe a new chemical shift prediction program, SPARTA+, which is based on artificial neural networking. The neural network is trained on a large carefully pruned database, containing 580 proteins for which high-resolution X-ray structures and nearly complete backbone and 13Cβ chemical shifts are available. The neural network is trained to establish quantitative relations between chemical shifts and protein structures, including backbone and side-chain conformation, H-bonding, electric fields and ring-current effects. The trained neural network yields rapid chemical shift prediction for backbone and 13Cβ atoms, with standard deviations of 2.45, 1.09, 0.94, 1.14, 0.25 and 0.49 ppm for δ15N, δ13C’, δ13Cα, δ13Cβ, δ1Hα and δ1HN, respectively, between the SPARTA+ predicted and experimental shifts for a set of eleven validation proteins. These results represent a modest but consistent improvement (2–10%) over the best programs available to date, and appear to be approaching the limit at which empirical approaches can predict chemical shifts.

Keywords

Electric field Hydrogen bonding Torsion angles SHIFTX Structure database Camshift SPARTA 

Supplementary material

10858_2010_9433_MOESM1_ESM.pdf (234 kb)
Supplementary material 1 (PDF 233 kb)

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Copyright information

© US Government 2010

Authors and Affiliations

  1. 1.Laboratory of Chemical Physics, National Institute of Diabetes and Digestive and Kidney DiseasesNational Institutes of HealthBethesdaUSA

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