Leveraging Parameter Dependencies in High-Field Asymmetric Waveform Ion-Mobility Spectrometry and Size Exclusion Chromatography for Proteome-wide Cross-Linking Mass Spectrometry

Abstract

Ion-mobility spectrometry shows great promise to tackle analytically challenging research questions by adding another separation dimension to liquid chromatography–mass spectrometry. The understanding of how analyte properties influence ion mobility has increased through recent studies, but no clear rationale for the design of customized experimental settings has emerged. Here, we leverage machine learning to deepen our understanding of field asymmetric waveform ion-mobility spectrometry for the analysis of cross-linked peptides. Knowing that predominantly m / z and then the size and charge state of an analyte influence the separation, we found ideal compensation voltages correlating with the size exclusion chromatography fraction number. The effect of this relationship on the analytical depth can be substantial as exploiting it allowed us to almost double unique residue pair detections in a proteome-wide cross-linking experiment. Other applications involving liquid- and gas-phase separation may also benefit from considering such parameter dependencies.

High-Field Asymmetric Waveform Ion Mobility Spectrometry Interface Enhances Parallel Reaction Monitoring on an Orbitrap Mass Spectrometer

“…In addition, PP3, peptide predicted charge when pH = 7, the Cruciani property H-bonding descriptor, and other QSAR features also contributed to the model. It is also worth noting that a recent study by Sinn et al examined the factors contributing to the FAIMS-based separation of cross-linked peptides and identified a similar collection of determinants.…”

“…Previous reports have shown that a peptide isoform’s ability to pass through the FAIMS device at a given CV value can be weakly correlated with the charge state. However, the identification of other physicochemical properties displaying a clear correlation with FAIMS transmission is only now emerging. ,,, Using a machine learning approach, we attempted to identify additional determinants that govern peptide transmission by FAIMS. We built a linear regression model to identify factors that impact peptide detectability and then used Lasso regression to minimize the number of factors in the model.…”

Ion Mobility Mass Spectrometry for the Separation and Characterization of Small Molecules

“…Because DMS uses a high-voltage dynamic electric field, the measurement of CCS is always a challenge. The calibration method based on machine learning can predict the CCS of compounds. , Ieritano et al used a machine learning-based calibration method to predict the CCS of compounds with an error of 2.6 ± 0.4%, which was very close to the inherent error of 2.2% in CCS calculation . This achievement allows DMS to realize the function of structure characterization rather than just as a filtering technology.…”

Cross-linking mass spectrometry for mapping protein complex topologies in situ

“…Finally, cross-linked peptides can be enriched in the gas phase after injection into the mass spectrometer. There have been studies showing that the physicochemical properties of cross-linked peptides allow for them to be separated from other species using the FAIMS device [ 57 , 58 ] or ion mobility cells [ 59 ] ( Figure 2 ). This removes highly abundant species that prevent cross-links from being selected for fragmentation and also reduces co-isolation events where two overlapping precursor ions are fragmented producing a messy MS2 spectrum.…”