Evidence map›Paper›PMID 12766419›Full record

ArticleJournal of biomolecular NMR2003

Rapid and accurate calculation of protein 1H, 13C and 15N chemical shifts.

Stephen Neal, Alex M Nip, Haiyan Zhang, David S Wishart

Abstract readEvaluation Study
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In one paragraph

Article in Journal of biomolecular NMR, 2003. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 241 papers.

0numbers the graph read from it
0cells of the map it votes in
241citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

241 citing papers in PubMed.

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181 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Stephen NealFaculty of Pharmacy & Pharmaceutical Sciences, University of Alberta, Edmonton, AB T6G 2N8, Canada.
Alex M Nip
Haiyan Zhang
David S Wishart

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A computer program (SHIFTX) is described which rapidly and accurately calculates the diamagnetic 1H, 13C and 15N chemical shifts of both backbone and sidechain atoms in proteins. The program uses a hybrid predictive approach that employs pre-calculated, empirically derived chemical shift hypersurfaces in combination with classical or semi-classical equations (for ring current, electric field, hydrogen bond and solvent effects) to calculate 1H, 13C and 15N chemical shifts from atomic coordinates. The chemical shift hypersurfaces capture dihedral angle, sidechain orientation, secondary structure and nearest neighbor effects that cannot easily be translated to analytical formulae or predicted via classical means. The chemical shift hypersurfaces were generated using a database of IUPAC-referenced protein chemical shifts--RefDB (Zhang et al., 2003), and a corresponding set of high resolution (<2.1 A) X-ray structures. Data mining techniques were used to extract the largest pairwise contributors (from a list of approximately 20 derived geometric, sequential and structural parameters) to generate the necessary hypersurfaces. SHIFTX is rapid (<1 CPU second for a complete shift calculation of 100 residues) and accurate. Overall, the program was able to attain a correlation coefficient (r) between observed and calculated shifts of 0.911 (1Halpha), 0.980 (13Calpha), 0.996 (13Cbeta), 0.863 (13CO), 0.909 (15N), 0.741 (1HN), and 0.907 (sidechain 1H) with RMS errors of 0.23, 0.98, 1.10, 1.16, 2.43, 0.49, and 0.30 ppm, respectively on test data sets. We further show that the agreement between observed and SHIFTX calculated chemical shifts can be an extremely sensitive measure of the quality of protein structures. Our results suggest that if NMR-derived structures could be refined using heteronuclear chemical shifts calculated by SHIFTX, their precision could approach that of the highest resolution X-ray structures. SHIFTX is freely available as a web server at http://redpoll.pharmacy.ualberta.ca.

Indexed as

AnimalsCarbon IsotopesHumansHydrogenNitrogen IsotopesNuclear Magnetic Resonance, BiomolecularProteinsSoftwareCarbon IsotopesHydrogenNitrogen IsotopesProteins

Identifiers

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.