Abstract
An important quantity that is measured in NMR spectro- scopy is the chemical shift. The interpretation of these data is mostly done by human experts. We present a method, named SimShiftDB, which identifies structural similarities between a protein of unknown structure and a database of resolved proteins based on chemical shift data. To evaluate the performance of our approach, we use a small but very reliable test set and compare our results to those of 123D and TALOS. The evaluation shows that SimShiftDB outperforms 123D in the majority of cases. For a significant part of the predictions made by TALOS, our method strongly reduces the error. SimShiftDB also assesses the statistical significance of each similarity identified.
This work was funded by the German Research Foundation (DFG, Bioinformatics Initiative).
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Ginzinger, S.W., Gräupl, T., Heun, V. (2007). SimShiftDB: Chemical-Shift-Based Homology Modeling. In: Hochreiter, S., Wagner, R. (eds) Bioinformatics Research and Development. BIRD 2007. Lecture Notes in Computer Science(), vol 4414. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71233-6_28
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DOI: https://doi.org/10.1007/978-3-540-71233-6_28
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