\name{CCProfile-class} \docType{class} \alias{CCProfile-class} \alias{CCProfile} \title{Class "CCProfile"} \description{S4 class for representing coiled coil prediction results} \section{Objects from the Class}{ In principle, objects of this class can be created by calls of the form \code{new("CCProfile")}, although there is no need in doing so. Most importantly, the \code{\link[=predict,CCModel-method]{predict}} function of \code{\link{procoil}} stores its results in objects of this type. } \section{Slots}{ \describe{ \item{\code{seq}:}{Object of class \code{"character"} containing the amino acid sequence for which the prediction has been made} \item{\code{reg}:}{Object of class \code{"character"} containing the heptad register corresponding to the amino acid sequence for which the prediction has been made} \item{\code{profile}:}{Array of numerical values representing the prediction profile for the sequence under consideration. This array has the same length as the sequence.} \item{\code{b}:}{Object of class \code{"numeric"}; value \eqn{b} used in the discriminant function (see \code{\linkS4class{CCModel}} for details} \item{\code{disc}:}{Object of class \code{"numeric"} containing the discriminant function value (see \code{\linkS4class{CCModel}} for details} \item{\code{pred}:}{Object of class \code{"character"} containing the final classification. Upon a call to \code{\link[=predict,CCModel-method]{predict}}, it is either \dQuote{trimer} or \dQuote{dimer}.} } } \section{Methods}{ \describe{ \item{plot}{\code{signature(x = "CCProfile", y = "missing")}: see \code{\link[=plot,CCProfile,missing-method]{plot}}} \item{plot}{\code{signature(x = "CCProfile", y = "CCProfile")}: see \code{\link[=plot,CCProfile,CCProfile-method]{plot}}} \item{profile}{\code{signature(fitted = "CCProfile")}: see \code{\link[=profile,CCProfile-method]{profile}}} \item{show}{\code{signature(object = "CCProfile")}: see \code{\link[=show,CCProfile-method]{show}}} } } \section{Prediction profiles}{ As described in \code{\linkS4class{CCModel}}, the discriminant function of the coiled coil classifier is essentially a weighted sum of numbers of occurrences of certain patterns in the sequence under consideration, i.e. every pattern occurring in the sequence contributes a certain weight to the discriminant function. Since every such occurrence is uniquely linked to two specific residues in the sequence, every amino acid in the sequence contributes a unique weight to the discriminant function value which is nothing else but half the sum of weights of matching patterns in which this amino acid is involved. If we denote the contribution of each position \eqn{i} with \eqn{s_i(x)}{si(x)}, it follows immediately that \deqn{f(x)=b+\sum\limits_{i=1}^{L} s_i(x),}{% f(x)=b+sum over all si(x) for i=1,\dots L,} where \eqn{L} is the length of the sequence \eqn{x}. } \author{Ulrich Bodenhofer \email{bodenhofer@bioinf.jku.at}} \references{\url{http://www.bioinf.jku.at/software/procoil/} Mahrenholz, C.C., Abfalter, I.G., Bodenhofer, U., Volkmer, R., and Hochreiter, S. (2011) Complex networks govern coiled coil oligomerization - predicting and profiling by means of a machine learning approach. Mol. Cell. Proteomics. DOI: 10.1074/mcp.M110.004994} \seealso{\code{\linkS4class{CCModel}}, \code{\link[=plot,CCProfile,missing-method]{plot}}, \code{\link[=plot,CCProfile,CCProfile-method]{plot}}, \code{\link[=profile,CCProfile-method]{profile}}, \code{\link[=show,CCProfile-method]{show}}, } \examples{ showClass("CCProfile") ## predict oligomerization of GCN4 wildtype GCN4wt<-predict(PrOCoilModel, "MKQLEDKVEELLSKNYHLENEVARLKKLV", "abcdefgabcdefgabcdefgabcdefga") ## display summary of result GCN4wt ## show raw prediction profile profile(GCN4wt) ## plot profile plot(GCN4wt) } \keyword{classes}