Fränze Müller

Crosslinking mass spectrometry has become the method of choice for the identification of protein-protein interactions and for gaining insight into the structures of proteins in vivo. However, connecting crosslink search engine results with down-stream analysis tools, and therefore gaining biological insight from crosslink identifications, has remained a manual and cumbersome step in the analysis that often requires expert bioinformatics knowledge. Here we introduce pyXLMS, a python package and public web application which aims to simplify and streamline this intermediate step, enabling researchers even without bioinformatics knowledge to conduct in-depth crosslink analyses. In its current state pyXLMS supports input from more than seven different crosslink search engines, as well as the mzIdentML format of the HUPO Proteomics Standards Initiative. Data processing and quality control is facilitated by functionality that is directly available within pyXLMS such as aggregation, validation, annotation, filtering, and visualization. In addition, the data can easily be exported to more than ten supported down-stream analysis tools and formats. We demonstrate the applicability and benefits of pyXLMS by re-analyzing a publicly available crosslink dataset with a variety of different search engines and show how the same data analysis workflow can be applied using pyXLMS.

Crosslinking mass spectrometry has become the method of choice for the identification of protein–protein interactions and for gaining insight into the structures of proteins in vivo . However, connecting crosslink search engine results with down-stream analysis tools, and therefore gaining biological insight from crosslink identifications, has remained a manual and cumbersome step in the analysis that often requires expert bioinformatics knowledge. Here we introduce pyXLMS, a python package and public web application which aims to simplify and streamline this intermediate step, enabling researchers even without bioinformatics knowledge to conduct in-depth crosslink analyses. In its current state pyXLMS supports input from seven different crosslink search engines, as well as the mzI-dentML format of the HUPO Proteomics Standards Initiative. Down-stream analysis is facilitated by functionality that is directly available within pyXLMS such as aggregation, validation, annotation, filtering, and visualization. In addition, the data can easily be exported to more than ten supported down-stream analysis tools and formats. We demonstrate the applicability and benefits of pyXLMS by re-analyzing a publicly available crosslink dataset with a variety of different search engines and show how the same data analysis workflow can be applied using pyXLMS. pyXLMs is available via https://github.com/hgb-bin-proteomics/pyXLMS .

Crosslinking mass spectrometry is an essential tool for probing protein-protein interactions and structural organization. We here compare Orbitrap Astral and Orbitrap Eclipse instruments using Cas9 crosslinked with PhoX and DSSO under standardized chromatographic and acquisition conditions. The Astral identifies over 40% more unique residue pairs, largely due to increased MS1 sensitivity and efficient detection of low-abundance precursors. Implementation of high-field asymmetric ion mobility spectrometry further increases identifications by 30% through improved precursor filtering. On the Astral, single higher-energy collisional dissociation consistently outperforms stepped fragmentation, particularly at low sample amounts, whereas the Eclipse shows minimal dependence on fragmentation strategy. Gradient optimization experiments demonstrate that longer separations enhance identifications in purified crosslinked samples, while gains plateau in complex backgrounds, indicating the need for enrichment or isolation strategies. Column comparisons show that pore size and particle diameter affect separation efficiency, with the Aurora Ultimate column yielding sharper peaks and more crosslink identifications than PepMap. Together, these findings emphasize that instrument choice, fragmentation mode, and chromatographic design directly influence crosslinking performance. The Astral’s combination of sensitivity and scan speed supports comprehensive detection of low-abundance crosslinks, providing deeper structural coverage of protein interaction networks.

Cross-linking mass spectrometry has evolved as a powerful technique to study protein-protein interactions and to provide structural information. Low reaction efficiencies, and complex matrices lead to challenging system wide crosslink analysis. We improved and streamlined an Azide-A-DSBSO based in vivo crosslinking workflow employing two orthogonal effective enrichment steps: Affinity enrichment and size exclusion chromatography (SEC). Combined, they allow an effective enrichment of DSBSO containing peptides and remove the background of linear as well as mono-linked peptides. We found that the analysis of a single SEC fraction is effective to yield ~90% of all crosslinks, which is important whenever measurement time is limited, and sample throughput is crucial. Our workflow resulted in more than 5000 crosslinks from K562 cells and generated a comprehensive PPI network. From 393 PPI found within the nucleus, 56 are novel. We further show, that by applying DSBSO to nuclear extracts we yield more crosslinks on lower abundant proteins and showcase this on the DEAD-box RNA helicase DDX39B which is predominantly expressed in the nucleus. Our data indicates that DDX39B might be present in monomeric and dimeric forms together with DDX39A within the nuclear extracts analyzed.

The advancement of crosslinking mass spectrometry (CLMS) has significantly enhanced the ability to study protein-protein interactions and complex biological systems. This study evaluates the performance of the Orbitrap Astral and Eclipse mass spectrometers in CLMS workflows, focusing on the identification of low-abundance crosslinked peptides. The comparison employed consistent liquid chromatography setups and experimental conditions, using Cas9 crosslinked with PhoX and DSSO as quality control samples. Results demonstrated that the Astral analyzer outperformed the Eclipse, achieving over 40% more unique residue pairs (URP) due to its superior sensitivity and dynamic range, attributed to its multi-reflection time-of-flight analyzer and nearly lossless ion transmission. Additionally, the study revealed that single higher-energy collisional dissociation (HCD) fragmentation methods significantly outperformed stepped HCD methods on the Astral, while the Eclipse maintained similar performance across both approaches. Gradient optimization experiments further highlighted the impact of separation times on crosslink identifications, with longer gradients yielding higher identification rates. Collectively, this work underscores the importance of instrumentation choice, fragmentation strategies, and method optimization in maximizing CLMS performance for protein interaction studies.

Crosslinking mass spectrometry (XL-MS) is a powerful technology that recently emerged as an essential complementary tool for elucidating protein structures and mapping interactions within a protein network. Crosslinkers which are amenable to post-linking backbone cleavage simplify peptide identification, aid in 3D structure determination and enable system-wide studies of protein-protein interactions (PPIs) in cellular environments. However, state-of-the-art cleavable linkers are fraught with practical limitations, including extensive evaluation of fragmentation energies and fragmentation behavior of the crosslinker backbone. We herein introduce DiSPASO (bis(2,5-dioxopyrrolidin-1-yl) 3,3’-((5-ethynyl-1,3-phenylene)bis(methylenesulfinyl))dipropanoate) as a lysine-selective, MS-cleavable crosslinker with an alkyne handle for affinity enrichment. DiSPASO was designed and developed for efficient cell membrane permeability and crosslinking while securing low cellular perturbation. We tested DiSPASO employing three different copper-based enrichment strategies using model systems with increasing complexity (Cas9-Halo, purified ribosomes, live cells). Fluorescence microscopy in-cell crosslinking experiments revealed a rapid uptake of DiSPASO into HEK 293 cells within 5 minutes. While DiSPASO represents progress in cellular PPI analysis, its limitations and low crosslinking yield in cellular environments require careful optimisation of the crosslinker design, highlighting the complexity of developing effective XL-MS tools and the importance of continuous innovation in accurately mapping PPI networks within dynamic cellular environments.

In a community effort, we audited error control in twelve crosslinking mass spectrometry pipelines using two ground-truth designs that expose a lower bound on false identifications. Some pipelines passed, whereas for others these verifiably false identifications alone exceeded the requested false discovery rate up to 29-fold at the protein–protein interaction level. The audit also tracked improvement between software versions, supporting its use for regression testing.

The field of crosslinking mass spectrometry has seen substantial advancements over the past decades, enabling the structural analysis of proteins and protein complexes and serving as a powerful tool in protein–protein interaction studies. However, data analysis of large non-cleavable crosslink studies is still a mostly unsolved problem due to its n-squared complexity. We here introduce an algorithm for the identification of non-cleavable crosslinks implemented in our crosslinking search engine MS Annika that is based on sparse matrix multiplication and allows for proteome-wide searches on commodity hardware. We compare our algorithm to other state-of-the-art crosslinking search engines commonly used in the field and conclude that MS Annika unifies high sensitivity, accurate FDR estimation and computational performance, outperforming competing tools. Application of this algorithm enabled us to employ a proteome-wide search of C. elegans nuclei samples, where we were able to uncover previously unknown protein interactions and conclude a comprehensive structural analysis that provides a detailed view of the Box C/D complex. Moreover, our algorithm will enable researchers to conduct similar studies that were previously unfeasible.

: The field of crosslinking mass spectrometry has seen substantial advancements over the past decades, enabling the structural analysis of proteins and protein-complexes and serving as a powerful tool in protein-protein interaction studies. However, data analysis of large non-cleavable crosslink studies is still a mostly unsolved problem due to its n -squared complexity. We here introduce a novel algorithm for the identification of non-cleavable crosslinks implemented in our crosslinking search engine MS Annika that is based on sparse matrix multiplication and allows for proteome-wide searches on commodity hardware. Application of this new algorithm enabled us to employ a proteome-wide search of C. elegans nuclei samples, where we were able to uncover previously unknown protein interactions and conclude a comprehensive structural analysis that provides a detailed view of the Box C/D complex, enhancing our understanding of its assembly and functional dynamics. Our findings provide valuable insights into the intricate regulation of cellular homeostasis and immune responses, which are conserved across species, including humans. Moreover, our algorithm will enable researchers to conduct similar studies that were previously unfeasible.