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      SHIFTX2: significantly improved protein chemical shift prediction

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          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 3.3× 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 (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).

          Electronic supplementary material

          The online version of this article (doi:10.1007/s10858-011-9478-4) contains supplementary material, which is available to authorized users.

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          Most cited references38

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          1H, 13C and 15N chemical shift referencing in biomolecular NMR.

          A considerable degree of variability exists in the way that 1H, 13C and 15N chemical shifts are reported and referenced for biomolecules. In this article we explore some of the reasons for this situation and propose guidelines for future chemical shift referencing and for conversion from many common 1H, 13C and 15N chemical shift standards, now used in biomolecular NMR, to those proposed here.
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            Data mining in bioinformatics using Weka.

            The Weka machine learning workbench provides a general-purpose environment for automatic classification, regression, clustering and feature selection-common data mining problems in bioinformatics research. It contains an extensive collection of machine learning algorithms and data pre-processing methods complemented by graphical user interfaces for data exploration and the experimental comparison of different machine learning techniques on the same problem. Weka can process data given in the form of a single relational table. Its main objectives are to (a) assist users in extracting useful information from data and (b) enable them to easily identify a suitable algorithm for generating an accurate predictive model from it. http://www.cs.waikato.ac.nz/ml/weka.
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              • Record: found
              • Abstract: found
              • Article: not found

              VADAR: a web server for quantitative evaluation of protein structure quality.

              VADAR (Volume Area Dihedral Angle Reporter) is a comprehensive web server for quantitative protein structure evaluation. It accepts Protein Data Bank (PDB) formatted files or PDB accession numbers as input and calculates, identifies, graphs, reports and/or evaluates a large number (>30) of key structural parameters both for individual residues and for the entire protein. These include excluded volume, accessible surface area, backbone and side chain dihedral angles, secondary structure, hydrogen bonding partners, hydrogen bond energies, steric quality, solvation free energy as well as local and overall fold quality. These derived parameters can be used to rapidly identify both general and residue-specific problems within newly determined protein structures. The VADAR web server is freely accessible at http://redpoll.pharmacy.ualberta.ca/vadar.
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                Author and article information

                Contributors
                +780-492-0383 , david.wishart@ualberta.ca
                Journal
                J Biomol NMR
                Journal of Biomolecular Nmr
                Springer Netherlands (Dordrecht )
                0925-2738
                1573-5001
                30 March 2011
                30 March 2011
                May 2011
                : 50
                : 1
                : 43-57
                Affiliations
                [1 ]Department of Computing Science, University of Alberta, Edmonton, AB Canada
                [2 ]Department of Biological Sciences, University of Alberta, Edmonton, AB Canada
                [3 ]National Research Council, National Institute for Nanotechnology (NINT), Edmonton, AB T6G 2E8 Canada
                [4 ]Department of Molecular Biology, Division of Bioinformatics, Center of Applied Molecular Engineering, University of Salzburg, Hellbrunnerstr. 34/3.OG, 5020 Salzburg, Austria
                Article
                9478
                10.1007/s10858-011-9478-4
                3085061
                21448735
                d551a689-8d80-462e-81eb-e6804afe58f6
                © The Author(s) 2011
                History
                : 22 December 2010
                : 28 January 2011
                Categories
                Article
                Custom metadata
                © Springer Science+Business Media B.V. 2011

                Molecular biology
                machine learning,chemical shift,protein,nmr
                Molecular biology
                machine learning, chemical shift, protein, nmr

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