Rapidly characterizing the three-dimensional structures of proteins and the multimeric machines they form remains one of the great challenges facing modern biological and medical sciences. Ion mobility-mass spectrometry based techniques are playing an expanding role in characterizing these functional complexes, especially in drug discovery and development workflows. Despite this expansion, ion mobility-mass spectrometry faces many challenges, especially in the context of detecting small differences in protein tertiary structure that bear functional consequences. Collision induced unfolding is an ion mobility-mass spectrometry method that enables the rapid differentiation of subtly-different protein isoforms based on their unfolding patterns and stabilities. In this review, we summarize the modern implementation of such gas-phase unfolding experiments and provide an overview of recent developments in both methods and applications.
Ion mobility−mass spectrometry (IM−MS) has become an important addition to the structural biology toolbox, but separating closely related protein conformations remain challenging. Collision-induced unfolding (CIU) has emerged as a valuable technique for distinguishing iso-crosssectional protein and protein complex ions through their distinct unfolding pathways in the gas phase. The speed and sensitivity of CIU analyses, coupled with their information-rich data sets, have resulted in the rapid growth of CIU for applications, ranging from the structural assessment of protein complexes to the characterization of biotherapeutics. This growth has occurred despite a lag in the capabilities of informatics tools available to process the complex data sets generated by CIU experiments, resulting in laborious manual analysis remaining commonplace. Here, we present CIUSuite 2, a software suite designed to enable robust, automated analysis of CIU data across the complete range of current CIU applications and to support the implementation of CIU as a true high-throughput technique. CIUSuite 2 uses statistical fitting and modeling methods to reliably quantify features of interest within CIU data sets, particularly in data with poor signal quality that cannot be interpreted with existing analysis tools. By reducing the signal-to-noise requirements for handling CIU data, we are able to demonstrate reductions in acquisition time of up to 2 orders of magnitude over current workflows. CIUSuite 2 also provides the first automated system for classifying CIU fingerprints, enabling the next generation of ligand screening and structural analysis experiments to be accomplished in a high-throughput fashion.
Glycosylation is a ubiquitous and heterogeneous post-translational modification (PTM) used to accomplish a wide variety of critical cellular tasks. Recent advances in methods for enrichment and mass spectrometric analysis of intact glycopeptides have produced large-scale, high-quality glycoproteomics datasets, but interpreting this data remains challenging. In addition to being large, complex, and heterogeneous, glycans undergo fragmentation during vibrational activation, making common PTM search strategies ineffective for their identification. We present a computational tool called MSFragger-Glyco for fast and highly sensitive identification of Nand O-linked glycopeptides using open and glycan mass offset search strategies. Reanalysis of recently published N-glycoproteomics data resulted in annotation of 83% more glycopeptidespectrum matches (glycoPSMs) than in previous results, which translated to substantial increases in the numbers of glycoproteins and glycosites that could be identified. In published O-glycoproteomics data, our method more than doubled the number of glycoPSMs annotated when searching the same peptides as the original search and resulted in up to a 6-fold increase when expanding searches to include large numbers of possible glycan compositions and other modifications. Expanded searches revealed trends in glycan composition and crosstalk with phosphorylation that remained hidden to the original search. With greatly improved spectral annotation, coupled with the fast speed of fragment ion index-based scoring, MSFragger-Glyco makes it possible to comprehensively interrogate glycoproteomics data and illuminate the many roles of glycosylation. .
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