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).
The package can be installed from Bioconductor. Therefore, the the simplest way to install the package is to enter
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("procoil")
into your R session. If, for what reason ever, you prefer to install the package manually, follow the instructions in the user manual.
If you encounter any issues or if you have any question that might be of interest also for other users, before writing a private message to the package developers/maintainers, please create an issue in this repository and also consider posting on Bioconductor Support or on StackOverflow. For other matters regarding the package, please contact the package author(s).
If you use this package 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 (2011). 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.