Prediction of Structurally-Determined Coiled-Coil Domains with Hidden Markov Models

  • Piero Fariselli
  • Daniele Molinini
  • Rita Casadio
  • Anders Krogh
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4414)

Abstract

The coiled-coil protein domain is a widespread structural motif known to be involved in a wealth of key interactions in cells and organisms. Coiled-coil recognition and prediction of their location in a protein sequence are important steps for modeling protein structure and function. Nowadays, thanks to the increasing number of experimentally determined protein structures, a significant number of coiled-coil protein domains is available. This enables the development of methods suited to predict the coiled-coil structural motifs starting from the protein sequence. Several methods have been developed to predict classical heptads using manually annotated coiled-coil domains. In this paper we focus on the prediction structurally-determined coiled-coil segments. We introduce a new method based on hidden Markov models that complement the existing methods and outperforms them in the task of locating structurally-defined coiled-coil segments.

Keywords

Protein structure prediction Hidden Markov models  coiled-coil domains 

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Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Piero Fariselli
    • 1
  • Daniele Molinini
    • 1
  • Rita Casadio
    • 1
  • Anders Krogh
    • 2
  1. 1.Laboratory of Biocomputing, CIRB/Department of Biology, University of Bologna, via Irnerio 42, 40126 BolognaItaly
  2. 2.The Bioinformatics Centre, Inst. of Molecular Biology and Physiology, University of Copenhagen, Universitetsparken 15, 2100 CopenhagenDenmark

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