[nmrlearner]

Abstract Understanding the relationship between protein structure and biological function is a central theme in structural biology. Advances are severely hampered by errors in experimentally determined protein structures. Detection and correction of such errors is therefore of utmost importance. Electron densities in molecular structures obey certain rules which depend on the molecular environment. Here we present and discuss a new approach that relates electron densities computed from a structural model to densities expected from prior observations on identical or closely related molecular environments. Strong deviations of computed from expected densities reveal unrealistic molecular structures. Most importantly, structure analysis and error detection are independent of experimental data and hence may be applied to any structural model. The comparison to state-of-the-art methods reveals that our approach is able to identify errors that formerly remained undetected. The new technique, called RefDens, is accessible as a public web service at http://refdens.services.came.sbg.ac.at.

• Content Type Journal Article
• DOI 10.1007/s10858-010-9408-x
• Authors
  • Simon W. Ginzinger, University of Salzburg Department of Molecular Biology, Division of Bioinformatics, Center of Applied Molecular Engineering Hellbrunnerstr. 34 5020 Salzburg Austria
  • Christian X. Weichenberger, University of Salzburg Department of Molecular Biology, Division of Bioinformatics, Center of Applied Molecular Engineering Hellbrunnerstr. 34 5020 Salzburg Austria
  • Manfred J. Sippl, University of Salzburg Department of Molecular Biology, Division of Bioinformatics, Center of Applied Molecular Engineering Hellbrunnerstr. 34 5020 Salzburg Austria

• • Journal Journal of Biomolecular NMR
  • Online ISSN 1573-5001
  • Print ISSN 0925-2738
  • Journal Volume Volume 47
  • Journal Issue Volume 47, Number 1

Source: Journal of Biomolecular NMR