Journal Article

CCHMM_PROF: a HMM-based coiled-coil predictor with evolutionary information

Lisa Bartoli, Piero Fariselli, Anders Krogh and Rita Casadio

in Bioinformatics

Volume 25, issue 21, pages 2757-2763
Published in print November 2009 | ISSN: 1367-4803
Published online September 2009 | e-ISSN: 1460-2059 | DOI:
CCHMM_PROF: a HMM-based coiled-coil predictor with evolutionary information

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Motivation:The widespread coiled-coil structural motif in proteins is known to mediate a variety of biological interactions. Recognizing a coiled-coil containing sequence and locating its coiled-coil domains are key steps towards the determination of the protein structure and function. Different tools are available for predicting coiled-coil domains in protein sequences, including those based on position-specific score matrices and machine learning methods.

Results: In this article, we introduce a hidden Markov model (CCHMM_PROF) that exploits the information contained in multiple sequence alignments (profiles) to predict coiled-coil regions. The new method discriminates coiled-coil sequences with an accuracy of 97% and achieves a true positive rate of 79% with only 1% of false positives. Furthermore, when predicting the location of coiled-coil segments in protein sequences, the method reaches an accuracy of 80% at the residue level and a best per-segment and per-protein efficiency of 81% and 80%, respectively. The results indicate that CCHMM_PROF outperforms all the existing tools and can be adopted for large-scale genome annotation.

Availability: The dataset is available at∼lisa/coiled-coils. The predictor is freely available at


Journal Article.  4730 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology

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