Limited proteolysis coupled to mass spectrometry (LiP-MS) probes protein conformational dynamics, but interpretation of LiP-MS data is complicated by heterogeneous proteolytic cleavage patterns and missing data. Recent advances in data-independent acquisition (DIA) and machine learning-based search engines promise improved sensitivity and reproducibility, yet their performance in LiP-MS workflows remains underexplored. We systematically evaluated selected library-free DIA workflows using a rapamycin-treated human cell lysate and a yeast heat shock data set, benchmarking DIA-NN and Spectronaut for identification depth, reproducibility, and false discovery rate control. Our results show that library-free approaches achieve high sensitivity, eliminating the experimental overhead and sample requirements associated with empirical libraries. Building on these advances, we introduce a DIA-based Limited Proteolysis data Analysis pipeline (DIA-LiPA), a data analysis workflow tailored for LiP-MS data that integrates semitryptic- and tryptic-level precursor data and accounts for missingness to enable structural interpretation. Validation across multiple data sets confirmed that DIA-LiPA reproduces known structural signatures and uncovers additional regulatory patterns, providing a robust framework for mechanistic insights into protein dynamics.
Leene et al. (2026) studied this question.