The MACP employs machine learning algorithm for automated scoring of co-fractionation mass spectrometry (CF-MS) and then systematically map multi-protein complexes from these high-confidence protein-protein interactions (PPIs) using unsupervised learning (i.e., clustering).
Systematic mapping of multiprotein complexes formed by protein-protein interactions (PPIs) can enhance our knowledge and mechanistic basis of how proteins function in the cells. Co-fractionation coupled with mass spectrometry (CF-MS) is gaining momentum as a cost-effective strategy for charting protein assemblies under native conditions using high-resolution chromatography separation techniques (e.g., size-exclusion and ion-exchange) without the need for antibodies or tagging of individual proteins. To capture high-quality PPIs from CF-MS co-elution profile, we have developed a well standardized and fully automated CF-MS data analysis software toolkit, referred to as MACP (Macromolecular Assemblies from the Co-elution Profile) in an open-source R package, beginning with the processing of raw co-elution data to reconstruction of high-confidence PPI networks via supervised machine-learning and underlying protein complexes using unsupervised approach.
You can install the MACP from bioconductor using:
install.packages('MACP')
To install the development version in R, run:
if(!requireNamespace("devtools", quietly = TRUE)) {
install.packages("devtools")
}
devtools::install_github("BabuLab-UofR/MACP")
For a detailed introduction to MACP, see the vignette.
Check the github page for source code
This project is licensed under the MIT License - see the LICENSE.md file for more details.