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, δ13Câ??, 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 (2â??10%) over the best programs available to date, and appear to be approaching the limit at which empirical approaches can predict chemical shifts.
- Content Type Journal Article
- DOI 10.1007/s10858-010-9433-9
- Authors
- Yang Shen, National Institutes of Health Laboratory of Chemical Physics, National Institute of Diabetes and Digestive and Kidney Diseases Building 5, room 126, NIH Bethesda MD 20892-0520 USA
- Ad Bax, National Institutes of Health Laboratory of Chemical Physics, National Institute of Diabetes and Digestive and Kidney Diseases Building 5, room 126, NIH Bethesda MD 20892-0520 USA
Source: Journal of Biomolecular NMR