SHIFTX2 predicts both the backbone and side chain 1H, 13C and 15N chemical shifts for proteins using their structural (PDB) coordinates as input. SHIFTX2 combines ensemble machine learning methods with sequence alignment-based methods to calculate protein chemical shifts for backbone and side chain atoms. SHIFTX2 has been trained on a carefully selected set of 197 proteins and tested on a separate set of 61 proteins. Both the training and testing sets consisted of high resolution X-ray structures (<2.1 Angs) with carefully verified chemical shifts assignments. SHIFTX2 is able to attain correlation coefficients between experimentally observed and predicted backbone chemical shifts of 0.9800 (15N), 0.9959 (13CA), 0.9992 (13CB), 0.9676 (13CO), 0.9714 (1HN), 0.9744 (1HA) and RMS errors of 1.1169, 0.4412, 0.5163, 0.5330, 0.1711, and 0.1231 ppm, respectively. Comparisons to other chemical shift predictors using the same testing data set indicates that SHIFTX2 is substantially more accurate (up to 26% better by correlation coefficient with an RMS error that is up to 3.3X smaller) than any other program.
Please cite the following:
Beomsoo Han, Yifeng Liu, Simon Ginzinger, and David Wishart. (2011) SHIFTX2: significantly improved protein chemical shift prediction. Journal of Biomolecular NMR, volume 50, number 1, 43-57. doi: 10.1007/s10858-011-9478-4.