Separation and Collision Cross Section Measurements of Protein Complexes Afforded by a Modular Drift Tube Coupled to an Orbitrap Mass Spectrometer

Fe-S cluster biosynthesis and maturation: Mass spectrometry-based methods advancing the field

Iron‑sulfur (FeS) clusters are inorganic protein cofactors that perform essential functions in many physiological processes. Spectroscopic techniques have historically been used to elucidate details of FeS cluster type, their assembly and transfer, and changes in redox and ligand binding properties. Structural probes of protein topology, complex formation, and conformational dynamics are also necessary to fully understand these FeS protein systems. Recent developments in mass spectrometry (MS) instrumentation and methods provide new tools to investigate FeS cluster and structural properties. With the unique advantage of sampling all species in a mixture, MS-based methods can be utilized as a powerful complementary approach to probe native dynamic heterogeneity, interrogate protein folding and unfolding equilibria, and provide extensive insight into protein binding partners within an entire proteome. Here, we highlight key advances in FeS protein studies made possible by MS methodology and contribute an outlook for its role in the field.

Collision Cross Section Prediction Based on Machine Learning

Ion mobility-mass spectrometry (IM-MS) is a powerful separation technique providing an additional dimension of separation to support the enhanced separation and characterization of complex components from the tissue metabolome and medicinal herbs. The integration of machine learning (ML) with IM-MS can overcome the barrier to the lack of reference standards, promoting the creation of a large number of proprietary collision cross section (CCS) databases, which help to achieve the rapid, comprehensive, and accurate characterization of the contained chemical components. In this review, advances in CCS prediction using ML in the past 2 decades are summarized. The advantages of ion mobility-mass spectrometers and the commercially available ion mobility technologies with different principles (e.g., time dispersive, confinement and selective release, and space dispersive) are introduced and compared. The general procedures involved in CCS prediction based on ML (acquisition and optimization of the independent and dependent variables, model construction and evaluation, etc.) are highlighted. In addition, quantum chemistry, molecular dynamics, and CCS theoretical calculations are also described. Finally, the applications of CCS prediction in metabolomics, natural products, foods, and the other research fields are reflected.

Expanding Orbitrap Collision Cross-Section Measurements to Native Protein Applications Through Kinetic Energy and Signal Decay Analysis

The measurement of collision cross sections (CCS, σ) offers supplemental information about sizes and conformations of ions beyond mass analysis alone. We have previously shown that CCSs can be determined directly from the time-domain transient decay of ions in an Orbitrap mass analyzer as ions oscillate around the central electrode and collide with neutral gas, thus removing them from the ion packet. Herein, we develop the modified hard collision model, thus deviating from the prior FT-MS hard sphere model, to determine CCSs as a function of center-of-mass collision energy in the Orbitrap analyzer. With this model, we aim to increase the upper mass limit of CCS measurement for native-like proteins, characterized by low charge states and presumed to be in more compact conformations. We also combine CCS measurements with collision induced unfolding and tandem mass spectrometry experiments to monitor protein unfolding and disassembly of protein complexes and measure CCSs of ejected monomers from protein complexes.

Improving Ion Mobility Mass Spectrometry of Proteins through Tristate Gating and Optimization of Multiplexing Parameters

Coupling drift tube ion mobility (IM) to Fourier transform mass spectrometry (FT-MS) affords the opportunity for gas-phase separation of ions based on size and conformation with high-resolution mass analysis. However, combining IM and FT-MS is challenging because ions exit the drift tube on a much faster time scale than the rate of mass analysis. Fourier transform (FT) and Hadamard transform multiplexing methods have been implemented to overcome the duty-cycle mismatch, offering new avenues for obtaining high-resolution, high-mass-accuracy analysis of mobility-selected ions. The gating methods used to integrate the drift tube with the FT mass analyzer discriminate against the transmission of large, low-mobility ions owing to the well-known gate depletion effect. Tristate gating strategies have been shown to increase ion transmission for drift tube IM-FT-MS systems through implementation of dual ion gating, controlling the quantity and timing of ions through the drift tube to reduce losses of slow-moving ions. Here we present an optimized set of multiplexing parameters for tristate gating ion mobility of several proteins on an Orbitrap mass spectrometer and further report parameters for increased ion transmission and mobility resolution as well as decreased experimental times from 15 min down to 30 s. On average, peak intensities in the arrival time distributions (ATDs) for ubiquitin increased 2.1× on average, while those of myoglobin increased by 1.5× with a resolving power increase on average of 11%.

Combination of continuous wavelet transform and genetic algorithm-based Otsu for efficient mass spectrometry peak detection

Mass spectrometry (MS) data is susceptible to random noises and alternating baseline, posing great challenges to spectral peak detection, especially for weak peaks and overlapping peaks. Herein, an efficient peak detection algorithm combining continuous wavelet transform (CWT) and genetic algorithm-based threshold segmentation (denoted as WSTGA) for mass spectrometry was proposed. Firstly, Mexican Hat wavelet was selected as the mother wavelet by comparing the matching degree between the difference of Gaussian (DOG) and different wavelets. Subsequently, the ridges and valleys were identified from 2D wavelet coefficient matrix. Afterward, an improved threshold segmentation method, Otsu method based on genetic algorithm, was introduced to find optimal segmentation threshold and achieve better image segmentation, overcoming the deficiency of traditional Otsu method that cannot handle long-tailed unimodal histograms. Finally, the characteristic peaks were successfully identified by utilizing the ridge-valley lines in wavelet space and original spectrum. Receiver operating characteristic (ROC) curve, area under curve (AUC) and F₁ measure are used as criterions to evaluate performance of peak detection algorithms. Compared with multi-scale peak detection (MSPD) and CWT and image segmentation (CWT-IS) methods, all the results showed that WSTGA can achieve better peak detection. More importantly, the experimental results from MALDI-TOF spectra demonstrated that WSTGA can effectively detect more weak peaks and overlapping peaks while maintaining a lower false peak detection rate than MSPD and CWT-IS methods, indicating its great advantages in characteristic peak identification.