Lennartsson, C. | Search bioRxiv

bioRxiv2025

ProteoBench: the community-curated platform for comparing proteomics data analysis workflows

Mass spectrometry (MS)-based proteomics is a well-established strategy for analyzing complex biological mixtures. Many MS instruments and data acquisition strategies are available, and the data they acquire differ substantially, thus requiring tailored analysis algorithms. Hence, many dedicated bioinformatics workflows are developed. These are in constant evolution, and the community lacks a centralized platform for comparing their performance. Here, we propose ProteoBench, a single platform that brings together software developers and software users to provide an ever-evolving comparison of state-of-the-art proteomics data processing tools. ProteoBench is an open-source resource that enables the community to evaluate data analysis workflows, develop benchmarking modules dedicated to specific comparisons, and discuss the best methods to compare software tools. The platform ensures that the benchmark evolves alongside advances in proteomics data analysis workflows. ProteoBench guides researchers towards the best-suited tool and parameters for their specific project and data according to their needs, and developers can test their newly developed tools or workflows privately, before adding them as public references. This community-driven effort will increase transparency and reproducibility between MS data analysis workflows, as well as facilitate the development and publication of software workflows in the field.

bioinformatics↗

bioRxiv2025

Improved peptide search for identification of SUMO and sequence-based modifications, in MaxSBM

Post-translational modifications (PTMs), such as SUMOylation and ubiquitination, regulate key cellular processes by covalently attaching to lysine residues. While mass spectrometry allows site-specific identification of PTMs, most existing search engines are optimized for small, non-fragmenting modifications and struggle to detect large, fragmenting protein-based modifiers. We refer to these as Sequence-Based Modifiers (SBMs). To overcome this limitation, we developed an SBM-specific search strategy within MaxQuant that accounts for the fragmentation behavior of SBMs during peptide identification. Using publicly available datasets, we validated our approach for SUMO2/3. Our analysis identified distinct diagnostic features and characteristic mass shifts associated with SBM fragmentation, referred to in this study as d-ions (diagnostic ions) and p-ions. By leveraging these features, our method improved the identification of SUMOylated peptides from human cell lines by 13%, SUMOylation sites in mouse embryonic cells by 18%, and in mouse adipocytes by 25%. Our search method improved spectral annotation of SBMs by up to 9% increase in the median Andromeda score. Taken together, we highlight the potential of our SBM search to enhance the discovery of protein-based modifications. HighlightsO_LIDevelopment of a MaxQuant module tailored for identifying Sequence-Based Modifiers (SBMs), including SUMO2/3 C_LIO_LIIncorporation of SBM-specific fragmentation patterns into search algorithms C_LIO_LIEnhanced biological discovery through improved PTM identification from mass spectrometry datasets C_LI In BriefHere, we introduce MaxSBM, an optimized framework for interpreting complex sequence-based modifiers (SBMs), particularly SUMO, within MaxQuant. Our approach incorporates SBM-specific d- and p-ion series into peptide scoring and annotation. By extending the theoretical spectral space to include fragments bearing partial SUMO (or other SBM) peptide remnants, MaxSBM provides a more comprehensive spectral annotation which enhances peptide scoring, resulting in increased identification rates at a higher confidence. Beyond methodological refinement, we validated MaxSBM via reanalysis of several physiological SUMO datasets, ultimately unlocking new insights via mapping of previously obscured modification sites.

bioinformatics↗