caa a22 4500 445325690 CHVBK 20180317142719.0 cr unu---uuuuu 170323e20110501xx s 000 0 eng 10.1007/s10858-011-9478-4 doi (NATIONALLICENCE)springer-10.1007/s10858-011-9478-4 SHIFTX2: significantly improved protein chemical shift prediction [Elektronische Daten] [Beomsoo Han, Yifeng Liu, Simon Ginzinger, David Wishart] 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 3.3× 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 (15N), 0.9959 (13Cα), 0.9992 (13Cβ), 0.9676 (13C′), 0.9714 (1HN), 0.9744 (1Hα) and RMS errors of 1.1169, 0.4412, 0.5163, 0.5330, 0.1711, and 0.1231ppm, respectively. The correlation between SHIFTX2's predicted and observed side chain chemical shifts is 0.9787 (13C) and 0.9482 (1H) with RMS errors of 0.9754 and 0.1723ppm, 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 (χ2 and χ3 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 ). The Author(s), 2011 NMR nationallicence Protein nationallicence Chemical shift nationallicence Machine learning nationallicence Han Beomsoo Department of Computing Science, University of Alberta, Edmonton, AB, Canada aut Liu Yifeng Department of Computing Science, University of Alberta, Edmonton, AB, Canada aut Ginzinger Simon Department of Molecular Biology, Division of Bioinformatics, Center of Applied Molecular Engineering, University of Salzburg, Hellbrunnerstr. 34/3.OG, 5020, Salzburg, Austria aut Wishart David Department of Computing Science, University of Alberta, Edmonton, AB, Canada aut Journal of Biomolecular NMR Springer Netherlands 50/1(2011-05-01), 43-57 0925-2738 50:1<43 2011 50 10858 https://doi.org/10.1007/s10858-011-9478-4 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s10858-011-9478-4 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Han Beomsoo Department of Computing Science, University of Alberta, Edmonton, AB, Canada aut NATIONALLICENCE 700 1- Liu Yifeng Department of Computing Science, University of Alberta, Edmonton, AB, Canada aut NATIONALLICENCE 700 1- Ginzinger Simon Department of Molecular Biology, Division of Bioinformatics, Center of Applied Molecular Engineering, University of Salzburg, Hellbrunnerstr. 34/3.OG, 5020, Salzburg, Austria aut NATIONALLICENCE 700 1- Wishart David Department of Computing Science, University of Alberta, Edmonton, AB, Canada aut NATIONALLICENCE 773 0- Journal of Biomolecular NMR Springer Netherlands 50/1(2011-05-01), 43-57 0925-2738 50:1<43 2011 50 10858 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer