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SHIFTX2: significantly improved protein chemical shift prediction
Open accessAbstract
A new computer program, called SHIFTX2, is described which is capable of rapidly and accurately calculating diamagnetic (1)H, (13)C and (15)N chemical shifts from protein coordinate data. Compared to its predecessor (SHIFTX) and to other existing protein chemical shift prediction programs, SHIFTX2 is substantially more accurate (up to 26% better by correlation coefficient with an RMS error that is up to 3.3× smaller) than the next best performing program. It also provides significantly more coverage (up to 10% more), is significantly faster (up to 8.5×) and capable of calculating a wider variety of backbone and side chain chemical shifts (up to 6×) than many other shift predictors. In particular, SHIFTX2 is able to attain correlation coefficients between experimentally observed and predicted backbone chemical shifts of 0.9800 ((15)N), 0.9959 ((13)Cα), 0.9992 ((13)Cβ), 0.9676 ((13)C'), 0.9714 ((1)HN), 0.9744 ((1)Hα) and RMS errors of 1.1169, 0.4412, 0.5163, 0.5330, 0.1711, and 0.1231 ppm, respectively. The correlation between SHIFTX2's predicted and observed side chain chemical shifts is 0.9787 ((13)C) and 0.9482 ((1)H) with RMS errors of 0.9754 and 0.1723 ppm, respectively. SHIFTX2 is able to achieve such a high level of accuracy by using a large, high quality database of training proteins (>190), by utilizing advanced machine learning techniques, by incorporating many more features (χ(2) and χ(3) angles, solvent accessibility, H-bond geometry, pH, temperature), and by combining sequence-based with structure-based chemical shift prediction techniques. With this substantial improvement in accuracy we believe that SHIFTX2 will open the door to many long-anticipated applications of chemical shift prediction to protein structure determination, refinement and validation. SHIFTX2 is available both as a standalone program and as a web server ( http://www.shiftx2.ca ).
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Basic local alignment search tool
Altschul, S. F., Gish, W., Miller, W., Myers, E. W., & Lipman, D. J. (1990) · Journal of Molecular Biology · doi:10.1006/jmbi.1990.9999 · Cited by 98,669
A decision-theoretic generalization of on-line learning and an application to boosting
Freund, Y., & Schapire, R. E. (1997) · Journal of Computer and System Sciences · doi:10.1006/jcss.1997.1504 · Cited by 20,466
The WEKA data mining software: an update
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I. H. (2009) · ACM SIGKDD Explorations Newsletter · doi:10.1145/1656274.1656278 · Cited by 17,750
Breiman, L. (1996) · Machine Learning · doi:10.1007/bf00058655 · Cited by 16,325
Wiederstein, M., & Sippl, M. J. (2007) · Nucleic Acids Research · doi:10.1093/nar/gkm290 · Cited by 5,909
AQUA and PROCHECK-NMR: Programs for checking the quality of protein structures solved by NMR
Laskowski, R., Rullmann, J. A., MacArthur, M., Kaptein, R., & Thornton, J. (1996) · Journal of Biomolecular NMR · doi:10.1007/bf00228148 · Cited by 5,502
The Strength of Weak Learnability
Schapire, R. E. (1990) · Machine Learning · doi:10.1007/bf00116037 · Cited by 4,600
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Finding Our Way in the Dark Proteome
Bhowmick, A., Brookes, D. H., Yost, S. R., Dyson, H. J., Forman-Kay, J. D., Gunter, D., Head-Gordon, M., Hura, G. L., Pande, V. S., Wemmer, D. E., et al. (2016) · Journal of the American Chemical Society · doi:10.1021/jacs.6b06543 · Cited by 128
Lindorff-Larsen, K., & Kragelund, B. B. (2021) · Journal of Molecular Biology · doi:10.1016/j.jmb.2021.167196 · Cited by 85
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Metadata
- Authors
- Han, B., Liu, Y., Ginzinger, S. W., & Wishart, D. S.
- Venue
- Journal of Biomolecular NMR
- Published
- 2011-03-30
- Volume
- 50
- Issue
- 1
- Pages
- 43-57
- ISSN
- 0925-2738 (ISSN-L)
- 1573-5001
- OpenAlex
- W2029120587
- Cited by
- 671
- References
- 7
- FWCI
- 17.92
- Type
- journal article
- Language
- en
- Open access
- hybrid
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