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Ribonucleic acids (RNAs) are challenging structural biology targets, as numerous barriers exist to determining their high-resolution structures and specific biological functions. Previous results have highlighted the utility of collision-induced unfolding (CIU) to relatively rapidly assess noncoding (nc)RNA higher-order structure (HOS) information. Yet, there remain many gaps in our understanding of how these data can be related to the structures adopted by RNAs in solution as current correlations are largely qualitative. In this study, we describe significant advancements in RNA CIU. Previous RNA CIU reports reveal minimal-to-no RNA unfolding events (or features) upon being subjected to standard CIU conditions. Here, we increase the RNA CIU information through supercharging and quantitatively evaluate the improved RNA CIU data obtained to solution-phase unfolding data collected across a range of Mg2+ concentrations. Finally, we apply our supercharged CIU experiment to mitochondrial encephalopathy, lactic acidosis, and stroke-like episode (MELAS)-associated mt-tRNA leucine (Leu, UUR) (mt-tRNALeu(UUR)) species. Our data demonstrate strong quantitative correlations between gas-phase and solution-phase RNA unfolding events as a function of Mg2+ and MELAS-associated mutations. Taken together, these results indicate strong, solution-relevant relationships for CIU data collected for these RNAs. We conclude our work by discussing future work targeting RNA CIU annotation, broader biophysical characterization of disease-associated RNAs using CIU, and CIU-enabled transcriptomic analysis. 

on mobility-mass spectrometry (IM-MS) has become a technology deployed across a wide range of structural biology applications despite the challenges in characterizing closely related protein structures. Collision-induced unfolding (CIU) has emerged as a valuable technique for distinguishing closely related, iso-cross-sectional protein and protein complex ions through their distinct unfolding pathways in the gas phase. With the speed and sensitivity of CIU analyses, there has been a rapid growth of CIU-based assays, especially regarding biomolecular targets that remain challenging to assess and characterize with other structural biology tools. With information-rich CIU data, many software tools have been developed to automate laborious data analysis. However, with the recent development of new IM-MS technologies, such as cyclic IM-MS, CIU continues to evolve, necessitating improved data analysis tools to keep pace with new technologies and facilitating the automation of various data processing tasks. Here, we present CIUSuite 3, a software package that contains updated algorithms that support various IM-MS platforms and supports the automation of various data analysis tasks such as peak detection, multidimensional classification, and collision cross section (CCS) calibration. CIUSuite 3 uses local maxima searches along with peak width and prominence filters to detect peaks to automate CIU data extraction. To support both the primary CIU (CIU1) and secondary CIU (CIU2) experiments enabled by cyclic IM-MS, two-dimensional data preprocessing is deployed, which allows multidimensional classification. Our data suggest that additional dimensions in classification improve the overall accuracy of class assignments. CIUSuite 3 also supports CCS calibration for both traveling wave and drift tube IM-MS, and we demonstrate the accuracy of a new single-field CCS calibration method designed for drift tube IM-MS leveraging calibrant CIU data. Overall, CIUSuite 3 is positioned to support current and next-generation IM-MS and CIU assay development deployed in an automated format. 

Fc-fusion proteins are an emerging class of protein therapeutics that combine the properties of biological ligands with the unique properties of the fragment crystallizable (Fc) domain of an immunoglobulin G (IgG). Due to their diverse higher-order structures (HOSs), Fc-fusion proteins remain challenging characterization targets within biopharmaceutical pipelines. While high-resolution biophysical tools are available for HOS characterization, they frequently demand extended time frames and substantial quantities of purified samples, rendering them impractical for swiftly screening candidate molecules. Herein, we describe the development of ion mobility-mass spectrometry (IM-MS) and collision-induced unfolding (CIU) workflows that aim to fill this technology gap, where we focus on probing the HOS of a model Fc-Interleukin-10 (Fc-IL-10) fusion protein engineered using flexible glycine-serine linkers. We evaluate the ability of these techniques to probe the flexibility of Fc-IL-10 in the absence of bulk solvent relative to other proteins of similar size, as well as localize structural changes of low charge state Fc-IL-10 ions to specific Fc and IL-10 unfolding events during CIU. We subsequently apply these tools to probe the local effects of glycine-serine linkers on the HOS and stability of IL-10 homodimer, which is the biologically active form of IL-10. Our data reveals that Fc-IL-10 produces significantly more structural transitions during CIU and broader IM profiles when compared to a wide range of model proteins, indicative of its exceptional structural dynamism. Furthermore, we use a combination of enzymatic approaches to annotate these intricate CIU data and localize specific transitions to the unfolding of domains within Fc-IL-10. Finally, we detect a strong positive, quadratic relationship between average linker mass and fusion protein stability, suggesting a cooperative influence between glycine-serine linkers and overall fusion protein stability. This is the first reported study on the use of IM-MS and CIU to characterize HOS of Fc-fusion proteins, illustrating the practical applicability of this approach. 

One of the most important attributes of anti‐amyloid antibodies is their selective binding to oligomeric and amyloid aggregates. However, current methods of examining the binding specificities of anti‐amyloid β (Aβ) antibodies have limited ability to differentiate between complexes that form between antibodies and monomeric or oligomeric Aβ species during the dynamic Aβ aggregation process. Here, we present a high‐resolution native ion‐mobility mass spectrometry (nIM‐MS) method to investigate complexes formed between a variety of Aβ oligomers and three Aβ‐specific IgGs, namely two antibodies with relatively high conformational specificity (aducanumab and A34) and one antibody with low conformational specificity (crenezumab). We found that crenezumab primarily binds Aβ monomers, while aducanumab preferentially binds Aβ monomers and dimers and A34 preferentially binds Aβ dimers, trimers, and tetrameters. Through collision induced unfolding (CIU) analysis, our data indicate that antibody stability is increased upon Aβ binding and, surprisingly, this stabilization involves the Fc region. Together, we conclude that nIM‐MS and CIU enable the identification of Aβ antibody binding stoichiometries and provide important details regarding antibody binding mechanisms. 

We previously discovered that electron attachment to gaseous peptide anions can occur within a relatively narrow electron energy range. The resulting charge-increased radical ions undergo dissociation analogous to conventional cation electron capture/transfer dissociation (ECD/ETD), thus enabling a novel tandem mass spectrometry (MS/MS) technique that we termed negative ion electron capture dissociation (niECD). We proposed that gaseous zwitterionic structures are required for niECD with electron capture either occurring at or being directed by a positively charged site. Here, we further evaluate this zwitterion mechanism by performing niECD of peptides derivatized to alter their ability to form zwitterionic gaseous structures. Introduction of a fixed positive charge tag, a highly basic guanidino group, or a highly acidic sulfonate group to promote zwitterionic structures in singly charged anions, rescued the niECD ability of a peptide refractory to niECD in its unmodified form. We also performed a systematic study of five sets of synthetic peptides with decreasing zwitterion propensity and found that niECD efficiency decreased accordingly, further supporting the zwitterion mechanism. However, traveling-wave ion mobility-mass spectrometry experiments, performed to gain further insight into the gas-phase structures of peptides showing high niECD efficiency, exhibited an inverse correlation between the orientationally averaged collision cross sections and niECD efficiency. These results indicate that compact salt-bridged structures are also a requirement for effective niECD. 

Ribonucleic acids (RNAs) remain challenging targets for structural biology, creating barriers to understanding their vast functions in cellular biology and fully realizing their applications in biotechnology. The inherent dynamism of RNAs creates numerous obstacles in capturing their biologically relevant higher-order structures (HOSs), and as a result, many RNA functions remain unknown. In this study, we describe the development of native ion mobility–mass spectrometry and collision-induced unfolding (CIU) for the structural characterization of a variety of RNAs. We evaluate the ability of these techniques to preserve native structural features in the gas phase across a wide range of functional RNAs. Finally, we apply these tools to study the elusive mitochondrial encephalopathy, lactic acidosis, and stroke-like episodes-associated A3243G mutation. Our data demonstrate that our experimentally determined conditions preserve some solution-state memory of RNAs via the correlated complexity of CIU fingerprints and RNA HOS, the observation of predicted stability shifts in the control RNA samples, and the retention of predicted magnesium binding events in gas-phase RNA ions. Significant differences in collision cross section and stability are observed as a function of the A3243G mutation across a subset of the mitochondrial tRNA maturation pathway. We conclude by discussing the potential application of CIU for the development of RNA-based biotherapeutics and, more broadly, transcriptomic characterization.