SHIFTX2: significantly improved protein chemical shift prediction
 

SHIFTX2: significantly improved protein chemical shift prediction


Abstract A new computer program, called SHIFTX2, is described which is capable of rapidly and accurately calculating diamagnetic 1H, 13C and 15N 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 3.3Ã? smaller) than the next best performing program. It also provides significantly more coverage (up to 10% more), is significantly faster (up to 8.5Ã?) and capable of calculating a wider variety of backbone and side chain chemical shifts (up to 6Ã?) 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 (15N), 0.9959 (13Cα), 0.9992 (13Cβ), 0.9676 (13Câ?²), 0.9714 (1HN), 0.9744 (1Hα) and RMS errors of 1.1169, 0.4412, 0.5163, 0.5330, 0.1711, and 0.1231 ppm, respectively. The correlation between SHIFTX2â??s predicted and observed side chain chemical shifts is 0.9787 (13C) and 0.9482 (1H) 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 (Ï?2 and Ï?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).

• Content Type Journal Article
• Pages 1-15
• DOI 10.1007/s10858-011-9478-4
• Authors
  • Beomsoo Han, Department of Computing Science, University of Alberta, Edmonton, AB, Canada
  • Yifeng Liu, Department of Computing Science, University of Alberta, Edmonton, AB, Canada
  • Simon W. Ginzinger, Department of Molecular Biology, Division of Bioinformatics, Center of Applied Molecular Engineering, University of Salzburg, Hellbrunnerstr. 34/3.OG, 5020 Salzburg, Austria
  • David S. Wishart, Department of Computing Science, University of Alberta, Edmonton, AB, Canada

• • Journal Journal of Biomolecular NMR
  • Online ISSN 1573-5001
  • Print ISSN 0925-2738

Source: Journal of Biomolecular NMR