title: Scoring Large-Scale Affinity Purification Mass Spectrometry Datasets with MiST.
abstract: 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.
authors.affiliation: Department of Cellular & Molecular Pharmacology, University of California, San Francisco, San Francisco, California.|California Institute for Quantitative Biomedical Sciences, San Francisco, California.
authors.forename: Erik
authors.initials: E
authors.lastname: Verschueren
authors.affiliation: Department of Cellular & Molecular Pharmacology, University of California, San Francisco, San Francisco, California.|California Institute for Quantitative Biomedical Sciences, San Francisco, California.
authors.forename: John
authors.initials: J
authors.lastname: Von Dollen
authors.affiliation: Department of Cellular & Molecular Pharmacology, University of California, San Francisco, San Francisco, California.|Graduate Group in Biological and Medical Informatics, University of California, San Francisco, San Francisco, California.|California Institute for Quantitative Biomedical Sciences, San Francisco, California.
authors.forename: Peter
authors.initials: P
authors.lastname: Cimermancic
authors.affiliation: Department of Cellular & Molecular Pharmacology, University of California, San Francisco, San Francisco, California.
authors.forename: Natali
authors.initials: N
authors.lastname: Gulbahce
authors.affiliation: Department of Bioengineering and Therapeutic Science, University of California, San Francisco, San Francisco, California.|Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, California.|California Institute for Quantitative Biomedical Sciences, San Francisco, California.
authors.forename: Andrej
authors.initials: A
authors.lastname: Sali
authors.affiliation: Department of Cellular & Molecular Pharmacology, University of California, San Francisco, San Francisco, California.|Gladstone Institutes, University of California, San Francisco, San Francisco, California.|California Institute for Quantitative Biomedical Sciences, San Francisco, California.
authors.forename: Nevan J
authors.initials: NJ
authors.lastname: Krogan
mesh_terms: D000465:Algorithms; D002846:Chromatography, Affinity / Q000379:methods*; D030562:Databases, Protein; D017408:Guidelines as Topic; D013058:Mass Spectrometry / Q000379:methods*; D011786:Quality Control; D013223:Statistics as Topic
keywords: affinity purification mass spectrometry; interaction networks; protein interactions; proteomics; scoring algorithms