ArticleNature communications2026
XL-MSDigger: a deep learning-based, versatile solution for cross-linking mass spectrometry.
Article in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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7 authors.
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Abstract
Cross-linking mass spectrometry (XL-MS) technology plays an important role in protein structure and protein-protein interaction (PPI) studies. However, there are serious shortcomings in the data processing and analysis methods of XL-MS that limit its ability to perform in-depth analysis. Here, we built a deep learning-based XL-MS versatile platform, XL-MSDigger, to address the bottlenecks of XL-MS technology. The foundation of this platform is a deep learning-based multidimensional information prediction tool, Deep4D-XL, for cross-linked peptides. This tool is capable of accurately predicting retention time, collisional cross-section, and fragment ion intensity information of cross-linked peptides. Through the multidimensional information prediction, we develop rescoring algorithms and workflows for DDA and DIA analysis data, which improve the coverage of DDA-based XL-MS identification, and realize the evaluation of false discovery rate and high-reliability identification of DIA-based XL-MS analysis. In addition, XL-MSDigger enables DIA-based XL-MS analysis using predicted spectral libraries, allowing improved detection of protein-protein interactions in targeted or moderate-scale interaction analyses. We believe that the XL-MSDigger platform can be widely used as a general solution to enhance the performance of XL-MS analysis.
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