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  Local protein structure prediction using discriminative models

Sander, O., Sommer, I., & Lengauer, T. (2006). Local protein structure prediction using discriminative models. BMC Bioinformatics, 7, 1-13.

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Datensatz-Permalink: https://hdl.handle.net/11858/00-001M-0000-000F-235A-C Versions-Permalink: https://hdl.handle.net/11858/00-001M-0000-000F-235B-A
Genre: Zeitschriftenartikel

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 Urheber:
Sander, Oliver1, Autor           
Sommer, Ingolf1, Autor           
Lengauer, Thomas1, Autor           
Affiliations:
1Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society, ou_40046              

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 Zusammenfassung: Background In recent years protein structure prediction methods using local structure information have shown promising improvements. The quality of new fold predictions has risen significantly and in fold recognition incorporation of local structure predictions led to improvements in the accuracy of results. We developed a local structure prediction method to be integrated into either fold recognition or new fold prediction methods. For each local sequence window of a protein sequence the method predicts probability estimates for the sequence to attain particular local structures from a set of predefined local structure candidates. The first step is to define a set of local structure representatives based on clustering recurrent local structures. In the second step a discriminative model is trained to predict the local structure representative given local sequence information. Results The step of clustering local structures yields an average RMSD quantization error of 1.19 Å for 27 structural representatives (for a fragment length of 7 residues). In the prediction step the area under the ROC curve for detection of the 27 classes ranges from 0.68 to 0.88. Conclusion The described method yields probability estimates for local protein structure candidates, giving signals for all kinds of local structure. These local structure predictions can be incorporated either into fold recognition algorithms to improve alignment quality and the overall prediction accuracy or into new fold prediction methods.

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Sprache(n): eng - English
 Datum: 2007-03-302006
 Publikationsstatus: Erschienen
 Seiten: -
 Ort, Verlag, Ausgabe: -
 Inhaltsverzeichnis: -
 Art der Begutachtung: -
 Identifikatoren: eDoc: 314488
Anderer: Local-ID: C125673F004B2D7B-38F3C8B7D7CC7569C125721E004F57C6-Sander2006
 Art des Abschluß: -

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Titel: BMC Bioinformatics
Genre der Quelle: Zeitschrift
 Urheber:
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Ort, Verlag, Ausgabe: -
Seiten: - Band / Heft: 7 Artikelnummer: - Start- / Endseite: 1 - 13 Identifikator: ISBN: 14712105