Show simple item record Durbin, Kenneth en Fellers, Ryan en Ioanna, Ntai en Kelleher, Neil en Compton, Philip en 2014-01-14T07:57:20Z en 2014-01-14T07:57:20Z en 2014 en
dc.identifier.citation Forthcoming en
dc.identifier.uri en
dc.description.abstract The ability to study organisms by direct analysis of their proteomes without digestion via mass spectrometry has benefited greatly from recent advances in separation techniques, instrumentation, and bioinformatics. However, improvements to data acquisition logic have lagged in comparison. Past workflows for Top Down Proteomics (TDPs) have focused on high throughput at the expense of maximal protein coverage and characterization. This mode of data acquisition has led to enormous overlap in the identification of highly abundant proteins in subsequent LC-MS injections. Furthermore, a wealth of data is left underutilized by analyzing each newly targeted species as unique, rather than as part of a collection of fragmentation events on a distinct proteoform. Here, we present a major advance in software for acquisition of TDP data that incorporates a fully automated workflow able to detect intact masses, guide fragmentation to achieve maximal identification and characterization of intact protein species, and perform database search online to yield real-time protein identifications. On Pseudomonas aeruginosa, the software combines fragmentation events of the same precursor with previously obtained fragments to achieve improved characterization of the target form by an average of 42 orders of magnitude in confidence. When HCD fragmentation optimization was applied to intact proteins ions, there was an 18.5 order of magnitude gain in confidence. These improved metrics set the stage for increased proteome coverage and characterization of higher order organisms in the future for sharply improved control over MS instruments in a project- and lab-wide context. en
dc.language.iso en_US en
dc.relation.uri en
dc.rights Source data was created by Kelleher research group, Northwestern University, Evanston, IL 60208. This data is licensed for reuse under a Creative Commons Attribution 3.0 license. en
dc.subject Top Down Proteomics, Mass Spectrometry, LC-MS en
dc.title Autopilot: An Online Data Acquisition Control System for the Enhanced High-throughput Characterization of Intact Proteins en
dc.type Dataset en
dc.altmetrics.display true en

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