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@ARTICLE{Zimmermann:1115,
      author       = {Zimmermann, O. and Hansmann, U. H. E.},
      title        = {{LOCUSTRA}: {A}ccurate {P}rediction of {L}ocal {P}rotein
                      {S}tructure {U}sing a {T}wo-{L}ayer {S}upport {V}ector
                      {M}achine {A}pproach},
      journal      = {Journal of Chemical Information and Modeling},
      volume       = {48},
      issn         = {1549-9596},
      reportid     = {PreJuSER-1115},
      pages        = {1903 - 1908},
      year         = {2008},
      note         = {Record converted from VDB: 12.11.2012},
      abstract     = {Constraint generation for 3d structure prediction and
                      structure-based database searches benefit from fine-grained
                      prediction of local structure. In this work, we present
                      LOCUSTRA, a novel scheme for the multiclass prediction of
                      local structure that uses two layers of support vector
                      machines (SVM). Using a 16-letter structural alphabet from
                      de Brevern et al. (Proteins: Struct., Funct., Bioinf. 2000,
                      41, 271-287), we assess its prediction ability for an
                      independent test set of 222 proteins and compare our method
                      to three-class secondary structure prediction and direct
                      prediction of dihedral angles. The prediction accuracy is
                      $Q16=61.0\%$ for the 16 classes of the structural alphabet
                      and $Q3=79.2\%$ for a simple mapping to the three secondary
                      classes helix, sheet, and coil. We achieve a mean phi(psi)
                      error of 24.74 degrees (38.35 degrees) and a median RMSDA
                      (root-mean-square deviation of the (dihedral) angles) per
                      protein chain of 52.1 degrees. These results compare
                      favorably with related approaches. The LOCUSTRA web server
                      is freely available to researchers at
                      http://www.fz-juelich.de/nic/cbb/service/service.php.},
      keywords     = {Algorithms / Computer Simulation / Databases, Factual /
                      Models, Biological / Models, Molecular / Peptidyl
                      Transferases: chemistry / Predictive Value of Tests /
                      Protein Structure, Tertiary / Proteins: chemistry /
                      Quantitative Structure-Activity Relationship / Streptomyces:
                      enzymology / Proteins (NLM Chemicals) / Peptidyl
                      Transferases (NLM Chemicals) / J (WoSType)},
      cin          = {NIC},
      cid          = {I:(DE-Juel1)NIC-20090406},
      pnm          = {Scientific Computing},
      pid          = {G:(DE-Juel1)FUEK411},
      shelfmark    = {Chemistry, Multidisciplinary / Computer Science,
                      Information Systems / Computer Science, Interdisciplinary
                      Applications},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:18763837},
      UT           = {WOS:000259398500016},
      doi          = {10.1021/ci800178a},
      url          = {https://juser.fz-juelich.de/record/1115},
}