 
Publication Delorenzi M. and Speed T., 2002.An HMM model for coiledcoil domains and a comparison with PSSMbased predictions. Bioinformatics, 18(4):617625, 2002. Abstract 
You can find here a short description and code for
MARCOIL For a concise but fairly precise description of the prediction method please refer to the paper and to my master thesis (this describes in detail a preliminary version of Marcoil). The webinterface is offered for ease of use when anlysing a small number of sequences. For large jobs, please download the program and run it locally. 
OPTIONS 

CoiledCoil Emission P. Matrix 
The three matrices accesible through the web interface are the one used in the paper
and trained on 9 "families" of protiens (9FAM). This is a matrix of
aminoacid probabilities derived from a large dataset of coiledcoil domains. It is unspecific, as the dataset contains all kind of domains and these differ in the number of helices, the orientation, the length and the hydrophobicity. The matrix is meant for firstpass genomic screenings. It generalises the two matrices proposed by A. Lupas and collaborators and used by the program COILS. These matrices are MTIDK, derived from 5 and MTK derived from three "families" of proteins. Those are matrices of frequency ratios and we computed from them, by using an estimate of absolute aminoacid frequencies, the other two matrices that can be used. We hope in future to offer the use of matrices that are more specific for subclasses of coiledcoil domains. 
HMM Transition P. Matrix 
The two matrices we describe in the publication can be used here.
MARCOILH gives higher posterior probabilities than MARCOILL,
but the number of true positives for a given number of false
positives is similar. See the section on the interpretation of the posterior probabilities. Alternatively you can define your own transition probabilities. In the paper we describe a parameterisation by which all the transition probabilities are computed on the basis of the 3 numbers i,r and t. Increasing i will raise all the coiledcoil probabilities, but in relative terms favor the short domains more than the long domains. Increasing t has a very similar effect. Together i and t have a smoothing effect. Larger values result in a higher sensitivity to the local "propensity" for a coiledcoil structure and can be used to identify the portion with the strongest prediction. Smaller values give a smoother profile, where the probability attributed to a position depends more on the nearby sequences. The default values of r sets a stringent requirement for the domains to respect the heptad pattern. Setting r to 0 will exclude any deviation from a perfect heptad pattern, while increasing r reduces this stringency and for r=1 the requirement for the pattern disappears completely. A moderate increase in r (maybe 10fold) can help in the identification of coiledcoil domains with irregularities in the typical pattern. An inappropriate combination of the 3 parameters can produce probabilities outside the [0, 1] range. In this case the program halts and gives an error message. 
OUTPUT 

PROBABILITY PROFILE 
The posterior probabilities that are reported and plotted depend
in a complex ways from the emission and transition probabilities.
The values that are obtained with the precomputed matrices are
loosely speaking useful estimates of the confidence level of a correct
prediction. Nonetheless, in general you need to run at least a few examples of known coiledcoils and of proteins with no coiledcoils to get a feeling for the meaning of the scale used. (The databases described in our paper are available following the link to the code) The scale is roughly comparable to that of the COILS program (but not to that of the PAIRCOIL program). COILS is more specific for parallel dimeric coiledcoils and has a scale that generally attributes lower probabilities to false as well as to true coiledcoils. If you use your own transition probabilities, than the scale changes and you will need a number of examples to calibrate the probability scale. If for example you increase considerably the i and t values, you can get deceivingly high probabilities. On the contrary as i or t approach zero, even the strongest coiledcoil domains approach probabilities of zero. 
Last modified: Feb 11, 2008 