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


BACKGROUND
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.


BACKGROUND
piero@biocomp.unibo.it.
Bartoli
Lisa
L
Biocomputing Group, Department of Biology, University of Bologna, 40126 Bologna, Italy.
Fariselli
Piero
P
Krogh
Anders
A
Casadio
Rita
R
D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D011487:Protein Conformation; D025941:Protein Interaction Mapping; D011506:Proteins / Q000737:chemistry*; D012984:Software; D013329:Structure-Activity Relationship
D011506:Proteins