SHIFTX2: significantly improved protein chemical shift prediction

By Beomsoo Han, Yifeng Liu, Simon W. Ginzinger, David S. Wishart. Journal of Biomolecular NMR, 2011

632 citations recorded by the source corpus.

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 0.1231 ppm, 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 0.1723 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 (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).

Subfields: Materials Chemistry, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/028dafade5d0cf4261a94225583a85fc47922699

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Citations in the corpus

Protein backbone and sidechain torsion angles predicted from NMR chemical shifts using artificial neural networks (2013, background): “Han B, Liu YF, Ginzinger SW, Wishart DS. SHIFTX2: significantly improved protein chemical shift prediction.”
Using chemical shift perturbation to characterise ligand binding. (2013, background): “More sophisticated equations with more terms give better fits, currently up to about twice as good [24].”
NMRbox: A Resource for Biomolecular NMR Computation. (2017, background): “2), a metapackage for determining protein structures from unassigned 13C chemical shifts, found that new installations of their complex software (uti- lizing NMRPipe (17), ROSETTA (18), MODELER (19), SPARKY (20), and SHIFTX2 (21)) to be difficult, even within their own laboratory.”
Chemical shift-based methods in NMR structure determination (2018, methodology): “Year Description (method) Reference 1993 NOE assignment and structure refinement approach (ASNO) [213] 1995 Introduction of ambiguous NOE distance restraints [144] 1997 Protein structure determination methods using fragment assembly (Ab initio) [57,58] 1997 NMR structure calculation using ambiguous ”