Leveraging parameter dependencies in high-field asymmetric waveform ion-mobility spectrometry and size exclusion chromatography for proteome-wide cross-linking mass spectrometry
- 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 consideringIon-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.…
| Author details: | Ludwig R. SinnORCiD, Sven Hans-Joachim GieseORCiDGND, Marchel Stuiver, Juri RappsilberORCiDGND |
|---|---|
| DOI: | https://doi.org/10.1021/acs.analchem.1c04373 |
| ISSN: | 0003-2700 |
| ISSN: | 1520-6882 |
| Pubmed ID: | https://pubmed.ncbi.nlm.nih.gov/35276035 |
| Title of parent work (English): | Analytical chemistry : the authoritative voice of the analytical community |
| Publisher: | American Chemical Society |
| Place of publishing: | Columbus, Ohio |
| Publication type: | Article |
| Language: | English |
| Date of first publication: | 2022/03/11 |
| Publication year: | 2022 |
| Release date: | 2024/04/05 |
| Volume: | 94 |
| Issue: | 11 |
| Number of pages: | 8 |
| First page: | 4627 |
| Last Page: | 4634 |
| Funding institution: | Wellcome Trust [203149, 103139]; Deutsche Forschungsgemeinschaft (DFG,; German Research Foundation) under Germany's Excellence Strategy [EXC; 2008390540038, 392923329/GRK2473]; NVIDIA "Artificial Intelligence for; Deep Structural Proteomics; UniSysCat |
| Organizational units: | An-Institute / Hasso-Plattner-Institut für Digital Engineering gGmbH |
| DDC classification: | 5 Naturwissenschaften und Mathematik / 53 Physik / 530 Physik |
| 5 Naturwissenschaften und Mathematik / 54 Chemie / 540 Chemie und zugeordnete Wissenschaften | |
| Peer review: | Referiert |
| Publishing method: | Open Access / Hybrid Open-Access |
| License (German): |  CC-BY - Namensnennung 4.0 International |
