ArticleJournal of biomolecular NMR2009
SimShiftDB; local conformational restraints derived from chemical shift similarity searches on a large synthetic database.
Article in Journal of biomolecular NMR, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- The MPI bioinformatics Toolkit as an integrative platform for advanced protein sequence and structure analysis.Nucleic acids research · 2016Article
- Protein backbone and sidechain torsion angles predicted from NMR chemical shifts using artificial neural networks.Journal of biomolecular NMR · 2013Article
- A direct interaction between DCP1 and XRN1 couples mRNA decapping to 5' exonucleolytic degradation.Nature structural & molecular biology · 2012Article
- Nuclear magnetic resonance signal chemical shifts and molecular simulations: a multidisciplinary approach to modeling copper protein structures.Journal of biological inorganic chemistry : JBIC : a publication of the Society of Biological Inorganic Chemistry · 2012Article
- 4D prediction of protein (1)H chemical shifts.Journal of biomolecular NMR · 2009Article
- Biophysical characterizations of human mitochondrial transcription factor A and its binding to tumor suppressor p53.Nucleic acids research · 2009Article
- CheckShift improved: fast chemical shift reference correction with high accuracy.Journal of biomolecular NMR · 2009Article
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Authors and funding
2 authors.
Funding
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Abstract
We present SimShiftDB, a new program to extract conformational data from protein chemical shifts using structural alignments. The alignments are obtained in searches of a large database containing 13,000 structures and corresponding back-calculated chemical shifts. SimShiftDB makes use of chemical shift data to provide accurate results even in the case of low sequence similarity, and with even coverage of the conformational search space. We compare SimShiftDB to HHSearch, a state-of-the-art sequence-based search tool, and to TALOS, the current standard tool for the task. We show that for a significant fraction of the predicted similarities, SimShiftDB outperforms the other two methods. Particularly, the high coverage afforded by the larger database often allows predictions to be made for residues not involved in canonical secondary structure, where TALOS predictions are both less frequent and more error prone. Thus SimShiftDB can be seen as a complement to currently available methods.
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