on 04-03-201707:52 AM - edited on 10-15-202112:03 PM by AnalyteGuru
Kai Scheffler, Rosa Viner, Eugen Damoc Journal of Proteomics Available online 3 April 2017 In Press, Accepted Manuscript Top-down mass spectrometry (MS) strategies allow in-depth characterization of proteins by fragmentation of the entire molecule(s) inside a mass spectrometer without requiring prior proteolytic digestion. Importantly, the fragmentation techniques on commercially available mass spectrometers have become more versatile over the past decade, with different characteristics in regards to the type and wealth of fragment ions that can be obtained while preserving labile protein post-translational modifications. Due to these and other improvements, top-down MS has become of broader interest and has started to be applied in more disciplines, such as the quality control of recombinant proteins, analysis and characterization of biopharmaceuticals, and clinical biochemistry to probe protein forms as potential disease biomarkers. This article provides a technical overview and guidance for data acquisition strategies on the Orbitrap platform for single proteins and low complexity protein mixtures. A protein standard mixture composed of six recombinant proteins is also introduced and analysis strategies are discussed in detail.
The article provides a detailed overview and guidance on how to choose from the variety of available methods for protein characterization by top-down analysis on the Orbitrap platform. Technical details are provided explaining important observations and phenomena when working with intact proteins and data from a number of different samples should serve to provide a solid understanding on how experiments were and should be setup and to set the right expectations on the outcome of these types of experiments. Additionally, a new intact protein standard sample is introduced that will help as a QC sample to check the instrument's hardware and method setup conditions as a requirement for obtaining high quality data from biologically relevant samples