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Bioinformatics. 2009 Nov 1;25(21):2757-63. doi: 10.1093/bioinformatics/btp539. Epub 2009 Sep 10.

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

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  • 1Biocomputing Group, Department of Biology, University of Bologna, 40126 Bologna, Italy.



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.


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.


The dataset is available at approximately lisa/coiled-coils. The predictor is freely available at


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