Class "CCProfile"

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Description

S4 class for representing coiled coil prediction results

Objects from the Class

In principle, objects of this class can be created by calls of the form new("CCProfile"), although it is not advised to do so. Most importantly, the predict function of returns its results in objects of this type.

Slots

This class extends the class PredictionProfile from the kebabs package directly and therefore inherits all its slots and methods. The following slots are defined for CCProfile objects additionally:

disc:

Object of class numeric containing the discriminant function value(s) (see CCModel for details)

pred:

Object of class factor containing the final classification(s). Upon a call to predict, it is either “trimer” or “dimer”.

Prediction profiles

As described in 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 i with si(x), it follows immediately that

f(x)=b+sum over all si(x) for i=1,… L,

where L is the length of the sequence x. The values si(x) can then be understood as the contributions that the i-th residue makes to the overall classification of the sequence x, which we call prediction profile. These profiles can either be visualized as they are without taking the offset b into account or by distributing b equally over all residues. These are the so-called baselines that are included in CCProfile objects. They are computed as -b / L.

Methods

plot

signature(x="CCProfile", y="missing"): see plot

heatmap

signature(x="CCProfile", y="missing"): if the CCProfile object x contains the profiles of at least three sequences, the profiles are visualized as a heatmap. This method is inherited from the kebabs package; for details, see heatmap.

show

signature(object="CCProfile"): displays the most important information stored in the CCProfile object object, such as, the sequences, kernel parameters, baselines, profiles, and classification results.

Accessor-like methods

The CCProfile class inherits all accessors from the PredictionProfile class, such as, sequences, baselines, profiles, and the indexing operator x[i]. Additionally, the procoil package defines the following two methods:

profile

signature(fitted="CCProfile"): for compatibility with previous versions, a method profile is available, too. It extracts the profile(s) in the same way as profiles

fitted

signature(object="CCProfile"): extracts the final classifications. This function returns a factor with levels “dimer” and “trimer”. If decision.values=TRUE is specified, a numeric vector is attached to the result as an attribute "decision.values" which also contains the discriminant function values.

Author(s)

Ulrich Bodenhofer bodenhofer@bioinf.jku.at

References

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 10(5):M110.004994. DOI: 10.1074/mcp.M110.004994

Palme, J., Hochreiter, S., and Bodenhofer, U. (2015) KeBABS: an R package for kernel-based analysis of biological sequences. Bioinformatics 31(15):2574-2576. DOI: 10.1093/bioinformatics/btv176

See Also

CCModel, plot, plot, PredictionProfileAccessors,

Examples

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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)

## define four GCN4 mutations
GCN4mSeq <- c("GCN4wt"        ="MKQLEDKVEELLSKNYHLENEVARLKKLV",
              "GCN4_N16Y_L19T"="MKQLEDKVEELLSKYYHTENEVARLKKLV",
              "GCN4_E22R_K27E"="MKQLEDKVEELLSKNYHLENRVARLEKLV",
              "GCN4_V23K_K27E"="MKQLEDKVEELLSKNYHLENEKARLEKLV")
GCN4mReg <- rep("abcdefgabcdefgabcdefgabcdefga", 4)

## predict oligomerization
GCN4mut <- predict(PrOCoilModel, GCN4mSeq, GCN4mReg)

## display summary of result
GCN4mut

## display predictions
fitted(GCN4mut)

## overlay plot of two profiles 
plot(GCN4mut[c(1, 2)])

## show heatmap
heatmap(GCN4mut)