title: SimShift: identifying structural similarities from NMR chemical shifts.
abstract: BACKGROUND
  An important quantity that arises in NMR spectroscopy experiments is the chemical shift. The interpretation of these data is mostly done by human experts; to our knowledge there are no algorithms that predict protein structure from chemical shift sequences alone. One approach to facilitate this process could be to compare two such sequences, where the structure of one protein has already been resolved. Our claim is that similarity of chemical shifts thereby found implies structural similarity of the respective proteins.
  
  
  RESULTS
  We present an algorithm to identify structural similarities of proteins by aligning their associated chemical shift sequences. To evaluate the correctness of our predictions, we propose a benchmark set of protein pairs that have high structural similarity, but low sequence similarity (because with high sequence similarity the structural similarities could easily be detected by a sequence alignment algorithm). We compare our results with those of HHsearch and SSEA and show that our method outperforms both in >50% of all cases.
authors.affiliation: LFE Bioinformatik, Institut für Informatik, Ludwig-Maximilians-Universität München Amalienstrasse 17, D-80333 München, Germany. Simon.Ginzinger@bio.ifi.lmu.de
authors.forename: Simon W
authors.initials: SW
authors.lastname: Ginzinger
authors.forename: Johannes
authors.initials: J
authors.lastname: Fischer
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D009682:Magnetic Resonance Spectroscopy / Q000379:methods*; D008969:Molecular Sequence Data; D010363:Pattern Recognition, Automated / Q000379:methods; D010449:Peptide Mapping / Q000379:methods*; D011506:Proteins / Q000032:analysis / Q000737:chemistry* / Q000145:classification; D016415:Sequence Alignment / Q000379:methods*; D020539:Sequence Analysis, Protein / Q000379:methods*; D017386:Sequence Homology, Amino Acid
chemical_list: D011506:Proteins