Optimized XL-MS workflows for heterobifunctional crosslinkers SDA and DizSEC

Posters | 2024 | Thermo Fisher Scientific | HUPOInstrumentation
LC/MS, LC/Orbitrap, LC/HRMS, LC/MS/MS, Software
Industries
Proteomics
Manufacturer
Thermo Fisher Scientific

Importance of the topic


Cross-linking mass spectrometry (XL-MS) enables detailed mapping of protein architecture and interaction networks by chemically linking amino acid residues in proximity. Optimizing workflows for photoactivatable heterobifunctional crosslinkers such as SDA and the MS-cleavable DizSEC enhances confidence in structural assignments and expands the toolkit for proteome-wide studies.

Objectives and study overview


This study aimed to refine XL-MS protocols for two classes of crosslinkers: noncleavable succinimidyl 4,4’-azipentanoate (SDA and sulfo-SDA) and the novel MS-cleavable DizSEC reagent. Key goals included maximizing identification rates of crosslinked peptides, comparing performance on monomeric and multimeric proteins, and developing an MS2-MS3 acquisition scheme to leverage DizSEC’s cleavage properties.

Methodology and instrumentation used


Standard proteins (human serum albumin, yeast enolase, dimer assemblies) were crosslinked separately with SDA, sulfo-SDA, and DizSEC under photoactivation. Peptide separation employed a Thermo Scientific Vanquish Neo UHPLC with an EASY-Spray PepMap Neo column over a 60 min gradient (6–50% acetonitrile, 0.1% formic acid). Mass spectrometry analyses were performed on Orbitrap Exploris 480 and Orbitrap Ascend platforms in data-dependent acquisition (DDA) mode. Optimization involved adjusting MS1 and MS2 parameters (resolution, normalized AGC target, maximum injection time, and collision energies). For DizSEC samples, an MS2-MS3 workflow was introduced to exploit unique fragmentation of urea bonds. Data processing utilized Proteome Discoverer 3.2 with the XlinkX node and xiSEARCH/xiFDR for crosslink identification, applying a 1% false discovery rate at the crosslinked spectrum match level.

Main results and discussion


• Comparable numbers of crosslinked peptides were detected with SDA, sulfo-SDA, and DizSEC on monomeric proteins, while SDA variants outperformed DizSEC on multimeric assemblies, likely due to linker length differences.
• Employing higher-charge-state prioritization (4+ to 6+) increased identification rates for both SDA and DizSEC crosslinks.
• The MS2-MS3 method for DizSEC reduced false positives by capturing signature fragment pairs generated by urea bond cleavage under HCD/CID.
• Venn analyses demonstrated substantial overlap between Exploris and Ascend platforms, validating method robustness across instruments.

Benefits and practical applications


Refined XL-MS workflows for SDA and DizSEC enhance structural insights in proteomics by improving crosslink coverage and identification confidence. The MS2-MS3 strategy for DizSEC offers a clearer fragmentation pattern, facilitating data analysis in complex samples. These protocols support structural biology, interaction mapping, and quality control in biopharmaceutical development.

Future trends and potential applications


Integration of MS-cleavable crosslinkers with advanced acquisition schemes and real-time search algorithms promises deeper proteome coverage. Emerging reagents with tailored spacer lengths and photoreactive chemistries will enable targeted studies of large assemblies and transient complexes in native environments. Coupling XL-MS with ion mobility and AI-driven data analysis may revolutionize in vivo structural proteomics.

Conclusion


This work presents optimized LC-MS methods for heterobifunctional crosslinkers SDA and DizSEC, including a novel MS2-MS3 approach for MS-cleavable reagents. The protocols deliver high-confidence crosslink identification across protein systems and instrument platforms, broadening the applicability of XL-MS in structural and interaction research.

References


  • Tanaka Y, Kohler JJ. Photoactivatable crosslinking sugars for capturing glycoprotein interactions. J Am Chem Soc. 2008;130(11):3278-3279.
  • Mendes ML, Fischer L, Chen ZA, et al. An integrated workflow for crosslinking mass spectrometry. Mol Syst Biol. 2019;15(9):e8994.
  • Fischer L, Rappsilber J. Quirks of error estimation in cross-linking/mass spectrometry. Anal Chem. 2017;89(7):3829-3833.
  • Faustino AM, Sharma P, Manriquez-Sandoval E, Yadav D, Fried SD. Progress toward proteome-wide photo-cross-linking to enable residue-level visualization of protein structures and networks in vivo. Anal Chem. 2023;95(28):10670-10685.
  • Lagerwaard IM, Albanese P, Jankevics A, Scheltema RA. Xlink Mapping and Analysis (XMAS)—Smooth integrative modeling in ChimeraX. bioRxiv. 2022;2022.04.21.489026.

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