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bioRxiv · 10.64898/2026.01.08.698341

Deep Learning-Driven Fragment Ion Selection for Improved Quantification in MS based Proteomics

Vu, D. T.·Wallmann, G.·Thielert, M.·Ugur, E.·Oeller, M.·Zwiebel, M.·Ammar, C.·Mann, M.
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Abstract

Quantitative proteomics relies on accurate selection of fragment ions for quantification, yet most current algorithms apply simple strategies such as median intensity or single quality filters. Modern data-independent acquisition (DIA) searches generate rich features such as fragment ion correlations, retention time and many others that could be leveraged to assess fragment quality. We introduce QuantSelect, a novel strategy to select optimal fragments by systematically integrating these features via self-supervised deep learning. QuantSelect uses a regularized, weighted-variance loss on intensity traces normalized via our directLFQ algorithm. This allows learning a fragment quality score without ground truth labels, enabling on-the-fly training on label-free DIA datasets. Integrated within our alphaDIA pipeline, QuantSelect significantly improves quantitative accuracy and in some cases substantially corrects protein intensity estimation. Sensitivity in differential expression improved by 68% in a mixed-species benchmarking dataset and by 18% in single-cell data. QuantSelect provides a practical framework for data-driven fragment selection that improves accuracy, precision and downstream inference in DIA proteomics.

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Vu, D. T., Wallmann, G., Thielert, M., Ugur, E., Oeller, M., Zwiebel, M., Ammar, C., Mann, M.. 2026-01-08. Deep Learning-Driven Fragment Ion Selection for Improved Quantification in MS based Proteomics. https://doi.org/10.64898/2026.01.08.698341

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