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

By Yang Shen, A. Bax. Journal of Biomolecular NMR, 2010

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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.

Subfields: Materials Chemistry, Molecular Biology, Spectroscopy

View Paper: https://www.semanticscholar.org/paper/886bc4741a774b3b2d3749804d7688d888e4f793

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Citations in the corpus

ALS mutations disrupt phase separation mediated by -helical structure in the TDP-43 low complexity C-terminal domain (2016, methodology): “SPARTA+ derived simulated ensemble secondary chemical shift predictions(Shen and Bax, 2010) correlate well with those observed by experiment (Fig 2A, top) and the RMSD of 0.”
Protein backbone and sidechain torsion angles predicted from NMR chemical shifts using artificial neural networks (2013, methodology): “The reason that this works well lies in the observation that the chemical shifts predicted by SPARTA+ have the χ1 rotamer information already well encoded (SI Fig.”
SHIFTX2: significantly improved protein chemical shift prediction (2011, background): “SPARTA and its successor SPARTA? ( Shen and Bax 2010 ), have proven to be remarkably accurate, especially for predicting 13 C and 15 N backbone”
Chemical shift-based methods in NMR structure determination (2018, methodology): “Year Description (method) Reference 1993 NOE assignment and structure refinement approach (ASNO) [213] 1995 Introduction of ambiguous NOE distance restraints [144] 1997 Protein structure determination methods using fragment assembly (Ab initio) [57,58] 1997 NMR structure calculation using ambiguous ”