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. All data used for evaluating the computational approach and for training the final models are available for download.

Installation of R package procoil via Bioconductor

The R package procoil is available from Bioconductor. The current version of the package is 1.16.0 and has been released as part of Bioconductor 3.0 on October 14, 2014. To install procoil, follow the simple standard procedure for installing Bioconductor packages, i.e. enter the following into your R session:
Please note that Bioconductor 3.0 requires an R version ≥ 3.1.0.

Manual installation on older R versions

We recommend potential users to use the latest version of R and Bioconductor and to follow the installation procedure described above. The R package procoil 1.16.0, however, also works with older R versions. If you have an R version ≥ 2.12, you can still use procoil 1.16.0, but you have to download and install the package manually:
  1. Download the suitable package file from the Bioconductor 3.0 procoil page.
  2. Follow the standard procedure for installing the package file on your system.

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")".


  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