SPARTA+: a modest improvement in empirical NMR chemical shift prediction by means of an artificial neural network |
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Authors: | Yang Shen Ad Bax |
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Institution: | (1) Laboratory of Chemical Physics, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Building 5, room 126, NIH, Bethesda, MD 20892-0520, USA; |
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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. |
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