| Title |
Protein backbone chemical shifts predicted from searching a database for torsion angle and sequence homology
|
|---|---|
| Published in |
Journal of Biomolecular NMR, July 2007
|
| DOI | 10.1007/s10858-007-9166-6 |
| Pubmed ID | |
| Authors |
Yang Shen, Ad Bax |
| Abstract |
Chemical shifts of nuclei in or attached to a protein backbone are exquisitely sensitive to their local environment. A computer program, SPARTA, is described that uses this correlation with local structure to predict protein backbone chemical shifts, given an input three-dimensional structure, by searching a newly generated database for triplets of adjacent residues that provide the best match in phi/psi/chi(1 )torsion angles and sequence similarity to the query triplet of interest. The database contains (15)N, (1)H(N), (1)H(alpha), (13)C(alpha), (13)C(beta) and (13)C' chemical shifts for 200 proteins for which a high resolution X-ray (< or =2.4 A) structure is available. The relative importance of the weighting factors for the phi/psi/chi(1) angles and sequence similarity was optimized empirically. The weighted, average secondary shifts of the central residues in the 20 best-matching triplets, after inclusion of nearest neighbor, ring current, and hydrogen bonding effects, are used to predict chemical shifts for the protein of known structure. Validation shows good agreement between the SPARTA-predicted and experimental shifts, with standard deviations of 2.52, 0.51, 0.27, 0.98, 1.07 and 1.08 ppm for (15)N, (1)H(N), (1)H(alpha), (13)C(alpha), (13)C(beta) and (13)C', respectively, including outliers. |
Mendeley readers
The data shown below were compiled from readership statistics for 247 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 7 | 3% |
| France | 3 | 1% |
| Belgium | 2 | <1% |
| United Kingdom | 2 | <1% |
| Colombia | 1 | <1% |
| Australia | 1 | <1% |
| Taiwan | 1 | <1% |
| Portugal | 1 | <1% |
| Russia | 1 | <1% |
| Other | 3 | 1% |
| Unknown | 225 | 91% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 66 | 27% |
| Researcher | 52 | 21% |
| Professor > Associate Professor | 21 | 9% |
| Student > Master | 16 | 6% |
| Student > Bachelor | 16 | 6% |
| Other | 44 | 18% |
| Unknown | 32 | 13% |
| Readers by discipline | Count | As % |
|---|---|---|
| Chemistry | 76 | 31% |
| Agricultural and Biological Sciences | 68 | 28% |
| Biochemistry, Genetics and Molecular Biology | 39 | 16% |
| Computer Science | 8 | 3% |
| Physics and Astronomy | 7 | 3% |
| Other | 13 | 5% |
| Unknown | 36 | 15% |
Attention Score in Context
This research output has an Altmetric Attention Score of 9. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 16 January 2024.
All research outputs
#3,833,166
of 24,224,854 outputs
Outputs from Journal of Biomolecular NMR
#39
of 567 outputs
Outputs of similar age
#8,338
of 70,682 outputs
Outputs of similar age from Journal of Biomolecular NMR
#1
of 1 outputs
Altmetric has tracked 24,224,854 research outputs across all sources so far. Compared to these this one has done well and is in the 84th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 567 research outputs from this source. They receive a mean Attention Score of 3.2. This one has done particularly well, scoring higher than 92% of its peers.
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We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them