Prediction of Oligomerization of Coiled Coil Proteins

Description

The package allows for predicting whether a coiled coil sequence (amino acid sequence plus heptad register) is more likely to form a dimer or more likely to form a trimer. Additionally to the prediction itself, a prediction profile is computed which allows for determining the strengths to which the individual residues are indicative for either class. Prediction profiles can also be visualized as curves or heatmaps.

Details

The package defines two S4 classes, CCModel and CCProfile. The former's purpose is to represent a coiled coil prediction model. The default model PrOCoilModel is pre-loaded when the package is loaded. An alternative model PrOCoilModelBA is also available. Other models can be loaded with the function readCCModel. The predict function is used to predict the oligomerization of one or more coiled coil sequences (which consist of a amino acid sequences and heptad registers aligned to them). The result is stored in a CCProfile object. The resulting prediction profile can be visualized with plot.

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

Examples

## display summary of default model
PrOCoilModel

## predict oligomerization of GCN4 wildtype
GCN4wt <- predict(PrOCoilModel,
                  "MKQLEDKVEELLSKNYHLENEVARLKKLV",
                  "abcdefgabcdefgabcdefgabcdefga")

## display result
GCN4wt

## plot profile
plot(GCN4wt)

## predict oligomerization of unknown sequence (Marcoil example)
MarcoilEx <- predict(PrOCoilModel,
    "MGECDQLLVFMITSRVLVLSTLIIMDSRQVYLENLRQFAENLRQNIENVHSFLENLRADLENLRQKFPGKWYSAMPGRHG",
    "-------------------------------abcdefgabcdefgabcdefgabcdefgabcdefg--------------")

## display result
MarcoilEx

## plot profile
plot(MarcoilEx)

Results

R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(procoil)
Loading required package: kebabs
Loading required package: Biostrings
Loading required package: BiocGenerics
Loading required package: parallel

Attaching package: 'BiocGenerics'

The following objects are masked from 'package:parallel':

    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
    clusterExport, clusterMap, parApply, parCapply, parLapply,
    parLapplyLB, parRapply, parSapply, parSapplyLB

The following objects are masked from 'package:stats':

    IQR, mad, xtabs

The following objects are masked from 'package:base':

    Filter, Find, Map, Position, Reduce, anyDuplicated, append,
    as.data.frame, cbind, colnames, do.call, duplicated, eval, evalq,
    get, grep, grepl, intersect, is.unsorted, lapply, lengths, mapply,
    match, mget, order, paste, pmax, pmax.int, pmin, pmin.int, rank,
    rbind, rownames, sapply, setdiff, sort, table, tapply, union,
    unique, unsplit

Loading required package: S4Vectors
Loading required package: stats4

Attaching package: 'S4Vectors'

The following objects are masked from 'package:base':

    colMeans, colSums, expand.grid, rowMeans, rowSums

Loading required package: IRanges
Loading required package: XVector
Loading required package: kernlab

Attaching package: 'kernlab'

The following object is masked from 'package:Biostrings':

    type

> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/procoil/procoil-package.Rd_%03d_medium.png", width=480, height=480)
> ### Name: procoil-package
> ### Title: Prediction of Oligomerization of Coiled Coil Proteins
> ### Aliases: procoil-package procoil
> ### Keywords: package
> 
> ### ** Examples
> 
> ## display summary of default model
> PrOCoilModel
An object of class  "CCModel" 

Model parameters:
	coiled coil kernel with m=5 and kernel normalization
	offset b= -1.073 

Feature weights:
	  1.6363 ... L...Vd...a
	  1.5382 ... R....Eg....e
	  1.2903 ... R.Ec.e
	  1.2284 ... E..Ve..a
	  1.2040 ... I...Id...a
	     ... ... ...
	 -1.1330 ... K..La..d
	 -1.2192 ... E.Ec.e
	 -1.2290 ... L..Ld..g
	 -1.4273 ... L...Nd...a
	 -1.7811 ... N..La..d

> 
> ## predict oligomerization of GCN4 wildtype
> GCN4wt <- predict(PrOCoilModel,
+                   "MKQLEDKVEELLSKNYHLENEVARLKKLV",
+                   "abcdefgabcdefgabcdefgabcdefga")
> 
> ## display result
> GCN4wt
An object of class  "CCProfile" 

Sequence:

  A AAVector instance of length 1 
    width seq                                              
[1]    29 MKQLEDKVEELLSKNYHLENEVARLKKLV                                         

gappy pair kernel: k=1, m=5, annSpec=TRUE

Baseline:  0.03698699 

Profile:
          Pos 1        Pos 2            Pos 28       Pos 29 
[1]  0.140762197  0.024184153  ... -0.023390414  0.095688066


Predictions:
          Score  Class
[1] -0.158713692  dimer

> 
> ## plot profile
> plot(GCN4wt)
> 
> ## predict oligomerization of unknown sequence (Marcoil example)
> MarcoilEx <- predict(PrOCoilModel,
+     "MGECDQLLVFMITSRVLVLSTLIIMDSRQVYLENLRQFAENLRQNIENVHSFLENLRADLENLRQKFPGKWYSAMPGRHG",
+     "-------------------------------abcdefgabcdefgabcdefgabcdefgabcdefg--------------")
> 
> ## display result
> MarcoilEx
An object of class  "CCProfile" 

Sequence:

  A AAVector instance of length 1 
    width seq                                              
[1]    35 LENLRQFAENLRQNIENVHSFLENLRADLENLRQK                                   

gappy pair kernel: k=1, m=5, annSpec=TRUE

Baseline:  0.03064637 

Profile:
          Pos 1        Pos 2            Pos 34       Pos 35 
[1]  0.031568559 -0.017609488  ...  0.025883765  0.028083833


Predictions:
          Score  Class
[1]  0.201721022 trimer

> 
> ## plot profile
> plot(MarcoilEx)
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>