摘要: The importance of protein chemical shift values for the determination three-dimensional structure has increased in recent years because large databases structures with assigned data. These have allowed investigation quantitative relationship between obtained by liquid state NMR spectroscopy and proteins. A neural network was trained to predict 1H, 13C, 15N proteins using their as well experimental conditions input parameters. It achieves root mean square deviations 0.3 ppm hydrogen, 1.3 ppm carbon, 2.6 ppm nitrogen shifts. model reflects important influences covalent conformation not only backbone atoms (as, e.g., index) but also side-chain nuclei. For models a RMSD smaller than 5 A correlation r.m.s. deviation predicted is obtained. Thus method potential support assignment process help validation refinement structural proposals. freely available academic users at PROSHIFT server: www.jens-meiler.de/proshift.html
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meilerlab.org 参考文章(39)
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