To understand the high number of ‘false positives’ identified by AP-MS it helps to categorize interactions into 4 broad classes: (I) biologically relevant interactions (II) specific, non-biologically relevant interactions between proteins from different cellular compartments in lysed cells (III) unspecific interactions with contaminants or highly abundant proteins and (IV) nonexisting interactions caused by residual peptides from previous runs or MS identification errors. Knowing that a cellular protein is predicted to have on average 5-8 biologically relevant interactions ( Grigoriev, 2003), prioritizing these from over hundreds of proteins and thousands of spectra identified by MS is far from trivial. To this end, a number of recent studies, which used AP-MS in a high-throughput fashion, developed computational algorithms that transform such a dataset into a list of bait-prey pairs ranked according their predicted biological significance ( Jäger et al., 2011 Sowa et al., 2009 Choi et al., 2011). A high-throughput dataset containing hundreds of replicated AP-MS samples poses a clear challenge for human processing, but also presents an opportunity to mine the data collectively with computational algorithms. In recent years, due to advances in bottom-up mass-spectrometry and affinity tagging methods, this method has been applied in high throughput to chart the protein-protein interaction networks or ‘interactomes’ from entire pathways to complete eukaryotic, bacterial and even viral organisms ( Arifuzzaman et al., 2006 Jäger et al., 2011 Sowa et al., 2009). Affinity Purification Mass-Spectrometry (AP-MS) is one of the primary methods to discover the protein interactions in an unbiased manner.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |