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Coiled Coil

BioDec tools for the prediction of the existence and location of potential coiled-coil domains in protein sequences.

Description:

Our tools where initially developed using a Hidden Markov Model, developed by De Lorenzi and Speed (Bioinformatics 18:617-625, 2002). The coiled-coil is a widespread protein structural motif known to have a stabilization function and to be involved in key interactions in cells and organisms. We have two programs to address the problem of coiled-coil prediction: PSCoils is a simple evolution of COILS  program.  It uses the same parameters that were developed for COILS and exploits both sequence and evolutionary information (in the form of sequence profiles).  PSCoils performs better than the other available methods in terms of per-residue, per-segment and per-protein accuracy. PSCoils performance are only lower than that obtained with CCHMM_prof.  PSCoils code (under GPL-license) is also available at http://www.plone4bio.org/trac/browser/biocomp.pscoils.  The detailed description of the method will appear in the CIBB 2009 proceedings (SIXTH INTERNATIONAL MEETING ON COMPUTATIONAL INTELLIGENCE METHODS FOR BIOINFORMATICS AND BIOSTATISTICS http://cibb09.disi.unige.it/ 15-17 October 2009 – Genova, Italy).

CCHMM_prof is a Hidden Markov Model predictor 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 accu-racy 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  detailed description of the method will appear on “CCHMM_PROF: a HMM-based Coiled-Coil Predictor with Evolutionary Information” by Lisa Bartoli, Piero Fariselli, Anders Krogh, and Rita Casadio on Bioinformatics.

License: GPL for PSCoils, BioDec for CCHMM_pred