PrOCoil - Predicting the Oligomerization of Coiled Coil Proteins

We have developed an SVM-based classification method for predicting whether a given coiled coil sequence is a trimer or dimer (assuming that it is one of both). This method also allows for a deep analysis of the sequence which residues are mainly responsible for the outcome. The software is available as an R package procoil and as a simple-to-use Web application.

Important Note: the prediction models have been updated with the release of version 2.0.0 of the PrOCoil R package. The updated data sets and some information on how they have been collected are available from the PrOCoil Data Repository (v2). If you want to use the original prediction models as published by Mahrenholz et al. (2011), please follow the instructions in Section 5.5.3 of the user manual). The data sets on which the original PrOCoil models were based are still available from the PrOCoil Data Repository (v1).

Installation

The R package procoil is available from Bioconductor. The current version of the package is 2.0.2 and has been released as part of Bioconductor 3.3 on May 4, 2016. To install procoil, follow the simple standard procedure for installing Bioconductor packages, i.e. enter the following into your R session:
source("http://www.bioconductor.org/biocLite.R")
biocLite("procoil")
Please note that Bioconductor 3.3 requires an R version ≥ 3.3.0. The current development version of the package is 2.1.2.

Getting started

To use the package, enter "library(procoil)" in your R session. To get a basic introduction, enter "help(procoil)" or open the user manual by entering "vignette("procoil")".

Documentation

  1. User Manual: PDF (also contains documentation of Web interface)
  2. Reference Manual: PDF

How to cite PrOCoil

If you use PrOCoil for research that is published later, you are kindly asked to cite it as follows:
C. C. Mahrenholz, I. G. Abfalter, U. Bodenhofer, R. Volkmer, and S. Hochreiter. Complex networks govern coiled coil oligomerization - predicting and profiling by means of a machine learning approach. Mol. Cell. Proteomics 10(5):M110.004994, 2011. DOI: 10.1074/mcp.M110.004994