CCProfile-class {procoil} | R Documentation

## Class "CCProfile"

### 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

    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)

[Package procoil version 2.18.0 Index]