Lutz Fischer

Protein–protein interaction (PPI) information is distributed across resources that differ in organism coverage, identifier systems, evidence models, confidence scores and access mechanisms, so assembling and comparing evidence for a protein requires source-specific queries, identifier conversion and extensive post-processing. We present KlinkPPI, a web server that retrieves, compares and exports PPI evidence from STRING, BioGRID, IntAct, CORUM, HuRI and Predictomes from a single query. KlinkPPI accepts UniProtKB accessions, NCBI Gene and Ensembl identifiers and gene names, and performs taxonomy-aware mapping to a common identifier space. Users can query individual proteins across all resources available for an organism, or retrieve organism-wide interaction sets. Results are presented per source so that database-specific evidence, annotations and confidence values are retained, while an integrated view exposes coverage and agreement between resources. KlinkPPI deliberately does not merge heterogeneous confidence scores, nor collapse functional associations, complex co-membership, binary interactions and structural predictions into a single consensus network. Results can be exported in PSI-MI TAB 2.8-compatible or Apache Parquet format with user-selected evidence fields. KlinkPPI is freely available at https://rappsilberlab.org/KlinkPPI/ and the source code at https://github.com/Rappsilber-Laboratory/KlinkPPI . Graphical

Clustering is ubiquitous across science, yet a text-mining audit of 736,399 open-access articles identified as using clustering (2000-2025) reveals common practice leaves key parameters undocumented or untuned, contributing to the reproducibility crisis in science. We developed an interactive web platform featuring 11 widely adopted clustering algorithms to enable transparent clustering analysis and reporting, aligning practical use with best practices in computational research. Code availability: The browser-based clustering analysis platform RapCluster is available for download at https://github.com/lutfia95/RapCluster under MIT License and accessible at https://rappsilberlab.org/rapcluster/ The web version is capacity-limited to 8 GB of memory (ca. 12,000 candidates with 130 features). All source codes used for the analysis are included with the publication.

Mass spectrometry (MS) experiments generate rich acquisition metadata that are essential for reproducibility, data sharing, and quality control (QC). Because these metadata are typically stored only in vendor-specific formats, they often remain difficult to access. MetaXtract is a lightweight tool that extracts detailed parameters directly from Thermo Fisher raw files and exposes them in structured, tabular formats. By capturing sample information, LC-MS method settings, and scan-level metrics such as retention time, total ion current, and ion injection time, MetaXtract increases transparency and ensures that essential acquisition details accompany published data and results in easy readable form. This supports FAIR data practices by improving the findability, accessibility, interoperability, and reusability of MS datasets after converting them to other formats, thereby increasing the value of deposition in public repositories. The importance of such metadata accessibility was recently highlighted by the crosslinking mass spectrometry community in efforts to advance FAIR data principles, and it extends to MS-based omics approaches more broadly. Importantly, MetaXtract enables search-free, near real-time performance monitoring by relying on acquisition-side signals, providing actionable indicators immediately after data acquisition rather than after database searching. This also caters for laboratory or depository internal streamlined QC and troubleshooting through integration into automated pipelines. By embedding acquisition parameters into routine data handling, MetaXtract strengthens reproducibility, optimises method development, and supports large-scale applications, including machine learning and secondary data analysis. Graphical Highlights: Metadata extraction from Thermo Fisher raw files Enhanced findability, accessibility, interoperability, and reusability of deposited data Integration into workflows via GUI and command-line modes Troubleshooting support by visualizing MS1/MS2 scan details Indexed MS1/MS2 peak list export enabling machine learning workflows Availability: MetaXtract is available for free download as open-source software at https://github.com/Rappsilber-Laboratory/MetaXtract , the software is licensed under the Apache-2.0 license.

It has been shown that integrating peptide property predictions such as fragment intensity into the scoring process of peptide spectrum match can greatly increase the number of confidently identified peptides compared to using traditional scoring methods. Here, we introduce Prosit-XL, a robust and accurate fragment intensity predictor covering the cleavable (DSSO/DSBU) and non-cleavable cross-linkers (DSS/BS3), achieving high accuracy on various holdout sets with consistent performance on external datasets without fine-tuning. Due to the complex nature of false positives in XL-MS, an approach to data-driven rescoring was developed that benefits from Prosit-XL’s predictions while limiting the overestimation of the false discovery rate (FDR). After validating this approach using two ground truth datasets consisting of synthetic peptides and proteins, we applied Prosit-XL on a proteome-scale dataset, demonstrating an up to ~3.4-fold improvement in PPI discovery compared to classic approaches. Finally, Prosit-XL was used to increase the coverage and depth of a spatially resolved interactome map of intact human cytomegalovirus virions, leading to the discovery of previously unobserved interactions between human and cytomegalovirus proteins.

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.

Crosslinking mass spectrometry is a powerful tool to study protein-protein interactions under native or near-native conditions in complex mixtures. Through novel search controls, we show how biassing results towards likely correct proteins can subtly undermine error estimation of crosslinks, with significant consequences. Without adjustments to address this issue, we have misidentified an average of 260 interspecies protein-protein interactions across 16 analyses in which we synthetically mixed data of different species, misleadingly suggesting profound biological connections that do not exist. We also demonstrate how data analysis procedures can be tested and refined to restore the integrity of the decoy-false positive relationship, a crucial element for reliably identifying protein-protein interactions.

The mzIdentML data format, originally developed by the Proteomics Standards Initiative in 2011, is the open XML data standard for peptide and protein identification results coming from mass spectrometry. We present mzIdentML version 1.3.0, which introduces new functionality and support for additional use cases. First of all, a new mechanism for encoding identifications based on multiple spectra has been introduced. Furthermore, the main mzIdentML specification document can now be supplemented by extension documents which provide further guidance for encoding specific use cases for different proteomics subfields. One extension document has been added, covering additional use cases for the encoding of crosslinked peptide identifications. The ability to add extension documents facilitates keeping the mzIdentML standard up to date with advances in the proteomics field, without having to change the main specification document. The crosslinking extension document provides further explanation of the crosslinking use cases already supported in mzIdentML version 1.2.0, and provides support for encoding additional scenarios that are critical to reflect developments in the crosslinking field and facilitate its integration in structural biology. These are: (i) support for cleavable crosslinkers, (ii) support for internally linked peptides, (iii) support for noncovalently associated peptides, and (iv) improved support for encoding scores and the corresponding thresholds.

Crosslinking mass spectrometry is a powerful tool for studying protein-protein interactions under native or near native conditions in complex mixtures. By help of novel search controls, we show that measures that aim to improve the number of identifications based on heuristic considerations can undermine error estimation in non-obvious ways. The relationship between decoys and false positives is very sensitive to the injection of information that favour likely correct matches. We identify a wider challenge in crosslinking data analysis tools with maintaining the decoy-false positive relationship, and exemplify this with the filtering of results based on the information of which proteins can be observed as having reacted with the crosslinker. Without correcting for this problem, we could identify an average of 260 interspecies protein-protein interactions in 16 analyses, “solidly” suggesting groundbreaking biological connections that don’t actually exist. We also show how data analysis procedures can be tested and modified to rescue the decoy-false positive relationship. The importance of this relationship for reliably identifying protein-protein interactions cannot be overstated.

Cross-linking mass spectrometry (MS) is currently transitioning from a routine tool in structural biology to enabling structural systems biology. MS-cleavable cross-linkers could substantially reduce the associated search space expansion by allowing a MS3-based approach for identifying cross-linked peptides. However, MS2 (MS/MS)-based approaches currently outperform approaches utilizing MS3. We show here that the sensitivity and specificity of triggering MS3 have been hampered algorithmically. Our four-step MS3-trigger algorithm greatly outperformed currently employed methods and comes close to reaching the theoretical limit.

Accurately modeling the structures of proteins and their complexes using artificial intelligence is revolutionizing molecular biology. Experimental data enable a candidate‐based approach to systematically model novel protein assemblies. Here, we use a combination of in‐cell crosslinking mass spectrometry and co‐fractionation mass spectrometry (CoFrac‐MS) to identify protein–protein interactions in the model Gram‐positive bacterium Bacillus subtilis . We show that crosslinking interactions prior to cell lysis reveals protein interactions that are often lost upon cell lysis. We predict the structures of these protein interactions and others in the Subti Wiki database with AlphaFold‐Multimer and, after controlling for the false‐positive rate of the predictions, we propose novel structural models of 153 dimeric and 14 trimeric protein assemblies. Crosslinking MS data independently validates the AlphaFold predictions and scoring. We report and validate novel interactors of central cellular machineries that include the ribosome, RNA polymerase, and pyruvate dehydrogenase, assigning function to several uncharacterized proteins. Our approach uncovers protein–protein interactions inside intact cells, provides structural insight into their interaction interfaces, and is applicable to genetically intractable organisms, including pathogenic bacteria.