Cross-linking mass spectrometry (XL-MS) has emerged as a vital technique in proteomics, enabling the study of protein-protein interactions (PPIs) and protein structural information in a high-throughput manner. XL-MS provides invaluable insights into complex biological processes that contribute to advancements in drug discovery, biomarker identification, and unraveling the complexities of cellular mechanisms.

However, the analysis of XL-MS data is nontrivial. A commonly observed phenomenon is the imbalanced peptide fragmentation efficacy in the MS spectra. One peptide often suppresses the fragmentation of the other, leading to incomplete fragmentation of the counterpart peptide. This limitation poses a significant obstacle to data interpretation and results in reduced sensitivity in dat...[ Read more ]

Cross-linking mass spectrometry (XL-MS) has emerged as a vital technique in proteomics, enabling the study of protein-protein interactions (PPIs) and protein structural information in a high-throughput manner. XL-MS provides invaluable insights into complex biological processes that contribute to advancements in drug discovery, biomarker identification, and unraveling the complexities of cellular mechanisms.

However, the analysis of XL-MS data is nontrivial. A commonly observed phenomenon is the imbalanced peptide fragmentation efficacy in the MS spectra. One peptide often suppresses the fragmentation of the other, leading to incomplete fragmentation of the counterpart peptide. This limitation poses a significant obstacle to data interpretation and results in reduced sensitivity in data analysis. To address this issue, we propose a protein feedback method that leverages peptide-protein correspondence. Our method improves the sensitivity of the standard technique by approximately 50%, without compromising precision. The advancements significantly en-hance the usability of XL-MS data.

In the second part of the thesis, we focus on addressing the challenges associated with post-translational modifications (PTMs) in XL-MS. PTMs play a crucial role in regulating biological processes and serve as indicators of specific signaling events in proteins. Understanding the functional implications of PTMs in protein networks and structures is essential for unraveling the mysteries of life. However, the scientific community lacks a tool for discovering PTMs in XL-MS due to its inherent complexity. We have developed a screening tool that utilizes tag information to facilitate PTM identification in XL-MS data. Our method accurately identifies PTMs in cross-linked peptides without the need for deciphering the complete sequence. It empowers biologists with the means to study PTMs in protein networks and structures, offering new avenues for understanding their functional significance.