Optimization of Search and Acquisition Strategies for DSSO Crosslinking Mass Spectrometry using Protein Prospector and Touchstone

Abstract

Crosslinking mass spectrometry (XL-MS) enables residue-level characterization of protein-protein interactions (PPIs) in both purified assemblies and complex biological systems. Cleavable crosslinkers such as DSSO are widely used to facilitate analysis of complex samples, yet there remains limited consensus on how best to leverage their properties in both acquisition and data analysis workflows. In part, this reflects differences in scoring, classification, and false discovery rate (FDR) estimation across analysis pipelines. Therefore, an integrated XL-MS data analysis pipeline that enables confident identification of MS-cleavable cross-linked peptides through improved scoring and rigorous FDR assessment is essential for systematically comparing and benchmarking workflows across different sample preparations. To this end, we implemented a new capability within the Protein Prospector search engine to improve MS2-based identification of MS-cleavable cross-linked peptides. Next, we developed Touchstone, a framework for re-scoring, classification, FDR-controlled summarization, and visualization of Prospector XL-MS search results. Using this approach, we evaluate the contributions of DSSO-specific fragmentation features and compare multiple acquisition strategies across datasets ranging from purified macromolecular assemblies to proteome-scale samples. We find that the primary advantage of DSSO arises from enhanced backbone fragmentation of both linked peptides, rather than from diagnostic ion formation, and that incorporating diagnostic ion pairs into MS2-based scoring or filtering does not improve identification performance. Benchmarking against established datasets shows that Prospector-Touchstone performs comparably to leading methods for non-cleavable crosslinkers and provides improved performance for DSSO analysis. We further compare acquisition strategies and find that for purified protein assemblies, MS2.stHCD characterizes more residue-pairs than ETD-based, MS3-based, and hybrid methods. In contrast, for proteome-scale XL-MS, MS3-based acquisition yields similar numbers of inter-protein PPIs with lower error rates despite reduced sampling depth. Together, these results highlight complementary strengths of MS2-and MS3-based workflows and provide practical guidance for MS-cleavable XL-MS experiments with varying sample complexities. Highlights Prospector-Touchstone improves crosslink identification and error estimation for both cleavable and non-cleavable XL-MS workflows relative to existing analysis pipelines. The cleavable crosslinking reagent DSSO enhances the identification of peptide backbone ions from both linked peptides compared to non-cleavable crosslinkers. Incorporating DSSO diagnostic ion pairs into MS2-based scoring or filtering does not improve XL-MS identification performance. For purified protein assemblies, MS2.stHCD outperforms ETD-based, MS3-based acquisition, and hybrid acquisition strategies. For proteome-scale XL-MS, MS2.stHCD identifies more residue-pairs, while MS3.CID yields higher-confidence protein-protein interactions.