Escherichia coli and Sf9 Contaminant Databases to Increase Efficiency of Tandem Mass Spectrometry Peptide Identification in Structural Mass Spectrometry Experiments.

Abstract

Filtering of nonspecifically binding contaminant proteins from affinity purification mass spectrometry (AP-MS) data is a well-established strategy to improve statistical confidence in identified proteins. The CRAPome (contaminant repository for affinity purification) describes the contaminating background content present in many purification strategies. However, full contaminant lists for nickel-nitrilotriacetic acid (NiNTA) and glutathione S-transferase (GST) affinity matrices are lacking. Similarly, no Spodoptera frugiperda (Sf9) contaminants are available, and only the FLAG-purified contaminants are described for Escherichia coli. For MS experiments that use recombinant protein, such as structural mass spectrometry experiments (hydrogen-deuterium exchange mass spectrometry (HDX-MS), chemical cross-linking, and radical foot-printing), failing to include these contaminants in the search database during the initial tandem MS (MS/MS) identification stage can result in complications in peptide identification. We have created contaminant FASTA databases for Sf9 and E. coli NiNTA or GST purification strategies and show that the use of these databases can effectively improve HDX-MS protein coverage, fragment count, and confidence in peptide identification. This approach provides a robust strategy toward the design of contaminant databases for any purification approach that will expand the complexity of systems able to be interrogated by HDX-MS.