ArticleJournal of biomolecular NMR2010
SPARTA+: a modest improvement in empirical NMR chemical shift prediction by means of an artificial neural network.
Article in Journal of biomolecular NMR, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 295 papers.
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295 citing papers in PubMed.
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- Transient tertiary structure in intrinsically disordered proteins revealed by multithermal enhanced sampling.Nature communications · 2026Article
- Probing Solution Dynamics of Tissue Factor Using Molecular Dynamics Simulations Guided by NMR Chemical Shifts.The journal of physical chemistry. B · 2026Article
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- Efficient Machine Learning Prediction of Solvent-DependentJournal of computational chemistry · 2026Article
- Toward a unified framework for determining conformational ensembles of disordered proteins.Nature methods · 2026Review
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- Improving Protein Structure Determination by Integrating Ensemble-Driven Molecular Dynamics with Chemical Shift-Based Restraints.Journal of chemical information and modeling · 2026Article
- Automatic forward model parameterization with Bayesian inference of conformational populations.APL machine learning · 2026Article
- Accurate conformational ensembles of intrinsically disordered proteins using reweighting based on NMR chemical shifts.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Atomic resolution ensembles of intrinsically disordered proteins with Alphafold.Nature communications · 2026Article
- Resolving Conformational Heterogeneity in Intrinsically Disordered Proteins via Experimentally Guided Multi-Replica Simulations.bioRxiv : the preprint server for biology · 2026Article
- Top-Down Scoring of Spectral Fitness by Image Analysis for Protein Structure Validation.Journal of chemical information and modeling · 2026Article
- Residue-Specific Modulation of Aggregation-Associated Interactions by Spermine in Tau, α-Synuclein, and Aβ40.bioRxiv : the preprint server for biology · 2025Article
235 more citing papers are in PubMed but not listed here.
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2 authors.
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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 (13)C(beta) 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 (13)C(beta) atoms, with standard deviations of 2.45, 1.09, 0.94, 1.14, 0.25 and 0.49 ppm for delta(15)N, delta(13)C', delta(13)C(alpha), delta(13)C(beta), delta(1)H(alpha) and delta(1)H(N), 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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