Scoring Large-Scale Affinity Purification Mass Spectrometry Datasets with MiST.
High-throughput Affinity Purification Mass Spectrometry (AP-MS) experiments can identify a large number of protein interactions, but only a fraction of these interactions are biologically relevant. Here, we describe a comprehensive computational strategy to process raw AP-MS data, perform quality controls, and prioritize biologically relevant bait-prey pairs in a set of replicated AP-MS experiments with Mass spectrometry interaction STatistics (MiST). The MiST score is a linear combination of prey quantity (abundance), abundance invariability across repeated experiments (reproducibility), and prey uniqueness relative to other baits (specificity). We describe how to run the full MiST analysis pipeline in an R environment and discuss a number of configurable options that allow the lay user to convert any large-scale AP-MS data into an interpretable, biologically relevant protein-protein interaction network.
['Verschueren|Erik|E|', 'Von Dollen|John|J|', 'Cimermancic|Peter|P|', 'Gulbahce|Natali|N|', 'Sali|Andrej|A|', 'Krogan|Nevan J|NJ|']
D000465:Algorithms D002846:Chromatography, Affinity D030562:Databases, Protein D017408:Guidelines as Topic D013058:Mass Spectrometry D011786:Quality Control D013223:Statistics as Topic
affinity purification mass spectrometry protein interactions interaction networks proteomics scoring algorithms