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Secondary structure classification of amino-acid sequences using state-space modeling

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  • Brunnert, Marcus
  • Krahnke, Tillmann
  • Urfer, Wolfgang

Abstract

The secondary structure classification of amino acid sequences can be carried out by a statistical analysis of sequence and structure data using state-space models. Aiming at this classification, a modified filter algorithm programmed in S is applied to data of three proteins. The application leads to correct classifications of two proteins even when using relatively simple estimation methods for the parameters of the state-space models. Furthermore, it has been shown that the assumed initial distribution strongly influences the classification results referring to two proteins.

Suggested Citation

  • Brunnert, Marcus & Krahnke, Tillmann & Urfer, Wolfgang, 2001. "Secondary structure classification of amino-acid sequences using state-space modeling," Technical Reports 2001,49, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    • Handle: RePEc:zbw:sfb475:200149
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    File URL: https://www.econstor.eu/bitstream/10419/77222/2/2001-49.pdf
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    References listed on IDEAS

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    1. Brunnert, Marcus & Fischer, Paul & Urfer, Wolfgang, 2002. "Sequence-structure alignment using a statistical analysis of core models and dynamic programming," Technical Reports 2002,61, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
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