The development and application of methods for the large-scale identification and quantification of proteins using mass spectrometry

The work described in this dissertation highlights the versatility of mass spectrometry-based proteomics, detailing the development and/or application of several diverse methods that enable, and improve upon, the large-scale identification and/or quantification of whole proteomes. A broad overview of mass spectrometry-based proteomics and the technological innovations that have driven the field forward are presented in Chapter 1. Chapter 2 outlines a method that utilizes NeuCode SILAC labeling and machine learning algorithms to enable product ion annotation within tandem mass spectra, facilitating the implementation of both automated database searching and de novo sequencing. Chapter 3 presents a strategy for performing multiplexed quantification in the context of data-independent acquisition. Chapter 4 describes the extension of QuantMode, a strategy that utilizes gas-phase purification to improve the quantitative accuracy of isobaric tag-based methods, to an ETD-enabled ion trap system. Chapter 5 outlines a method that improves the sampling depth of label-free experiments without the use of offline fractionation or the significant increase in analysis time. In Chapter 6, both label-free and isobaric tag-based strategies are employed to evaluate the localization and functionality of proteins, protein phosphorylation, and protein acetylation within the various tissues of the model legume Medicago truncatula .