Prediction of structurally-determined coiled-coil domains with hidden Markov models

Piero Fariselli; Daniele Molinini; Rita Casadio; Anders Krogh

Prediction of structurally-determined coiled-coil domains with hidden Markov models

Piero Fariselli^*, Daniele Molinini, Rita Casadio, Anders Krogh

^*Corresponding author for this work

Computational and RNA Biology

12 Citations (Scopus)

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 structurallydefined coiled-coil segments.

Original language	English
Title of host publication	Bioinformatics Research and Development - First International Conference, BIRD 2007 Proceedings
Number of pages	11
Volume	4414 LNBI
Publication date	27 Aug 2007
Pages	292-302
ISBN (Print)	3540712321, 9783540712329
Publication status	Published - 27 Aug 2007
Event	1st International Conference on Bioinformatics Research and Development, BIRD 2007 - Berlin, Germany Duration: 12 Mar 2007 → 14 Mar 2007

Conference

Conference	1st International Conference on Bioinformatics Research and Development, BIRD 2007
Country/Territory	Germany
City	Berlin
Period	12/03/2007 → 14/03/2007

Keywords

Coiled-coil domains
Hidden Markov models
Protein structure prediction

Cite this

Prediction of structurally-determined coiled-coil domains with hidden Markov models. / Fariselli, Piero; Molinini, Daniele; Casadio, Rita et al.
Bioinformatics Research and Development - First International Conference, BIRD 2007 Proceedings. Vol. 4414 LNBI 2007. p. 292-302.

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Fariselli, P, Molinini, D, Casadio, R & Krogh, A 2007, Prediction of structurally-determined coiled-coil domains with hidden Markov models. in Bioinformatics Research and Development - First International Conference, BIRD 2007 Proceedings. vol. 4414 LNBI, pp. 292-302, 1st International Conference on Bioinformatics Research and Development, BIRD 2007, Berlin, Germany, 12/03/2007.

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AU - Fariselli, Piero

AU - Molinini, Daniele

AU - Casadio, Rita

AU - Krogh, Anders

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Y1 - 2007/8/27

N2 - 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 structurallydefined coiled-coil segments.

AB - 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 structurallydefined coiled-coil segments.

KW - Coiled-coil domains

KW - Hidden Markov models

KW - Protein structure prediction

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M3 - Article in proceedings

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SN - 9783540712329

VL - 4414 LNBI

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BT - Bioinformatics Research and Development - First International Conference, BIRD 2007 Proceedings

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ER -

Prediction of structurally-determined coiled-coil domains with hidden Markov models

Abstract

Conference

Keywords

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