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 http://www.biocomp.unibo.it/ approximately lisa/coiled-coils. The predictor is freely available at http://gpcr.biocomp.unibo.it/cgi/predictors/cchmmprof/pred_cchmmprof.cgi. CONTACT: piero@biocomp.unibo.it.
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 http://www.biocomp.unibo.it/ approximately lisa/coiled-coils. The predictor is freely available at http://gpcr.biocomp.unibo.it/cgi/predictors/cchmmprof/pred_cchmmprof.cgi. CONTACT: piero@biocomp.unibo.it.
Authors: Aaron A Vogan; S Lorena Ament-Velásquez; Alexandra Granger-Farbos; Jesper Svedberg; Eric Bastiaans; Alfons Jm Debets; Virginie Coustou; Hélène Yvanne; Corinne Clavé; Sven J Saupe; Hanna Johannesson Journal: Elife Date: 2019-07-26 Impact factor: 8.140
Authors: Kelly N DuBois; Sam Alsford; Jennifer M Holden; Johanna Buisson; Michal Swiderski; Jean-Mathieu Bart; Alexander V Ratushny; Yakun Wan; Philippe Bastin; J David Barry; Miguel Navarro; David Horn; John D Aitchison; Michael P Rout; Mark C Field Journal: PLoS Biol Date: 2012-03-27 Impact factor: 8.029
Authors: Margaret S Sunitha; Anu G Nair; Amol Charya; Kamalakar Jadhav; Sami Mukhopadhyay; Ramanathan Sowdhamini Journal: BMC Res Notes Date: 2012-09-25