Abstract
Protein-protein interactions play a vital role in nearly all cellular functions. Hence, understanding their interaction patterns and three-dimensional structural conformations can provide crucial insights about various biological processes and underlying molecular mechanisms for many disease phenotypes. Cross-linking mass spectrometry (XL-MS) has the unique capability to detect protein-protein interactions at a large scale along with spatial constraints between interaction partners. The inception of MS-cleavable cross-linkers enabled the MS2-MS3 XL-MS acquisition strategy that provides cross-link information from both MS2 and MS3 level. However, the current cross-link search algorithm available for MS2-MS3 strategy follows a "MS2-centric" approach and suffers from a high rate of mis-identified cross-links. We demonstrate the problem using two new quality assessment metrics ["fraction of mis-identifications" (FMI) and "fraction of interprotein cross-links from known interactions" (FKI)]. We then address this problem, by designing a novel "MS3-centric" approach for cross-link identification and implementing it as a search engine named MaXLinker. MaXLinker outperforms the currently popular search engine with a lower mis-identification rate, and higher sensitivity and specificity. Moreover, we performed human proteome-wide cross-linking mass spectrometry using K562 cells. Employing MaXLinker, we identified a comprehensive set of 9319 unique cross-links at 1% false discovery rate, comprising 8051 intraprotein and 1268 interprotein cross-links. Finally, we experimentally validated the quality of a large number of novel interactions identified in our study, providing a conclusive evidence for MaXLinker's robust performance.
Highlights
In the post-genomic era, one of the main goals of systems biology is to determine the functions of all the proteins of various organisms
(CSMs with at least one peptide from the S. cerevisiae search space, i.e., mis-identifications). The aim of this search is to re-assess the quality of cross-links at 1% false discovery rate (FDR), with expected fraction of incorrect CSMs involving unambiguous peptides from S. cerevisiae to be less than 1%
As the FDR filtering is typically performed at the redundant CSM level by the conventional cross-link search algorithms, we repeated the analysis at redundant CSM level and observed results consistent with what was found at the unique CSM level (Supplementary Figure 1)
Summary
In the post-genomic era, one of the main goals of systems biology is to determine the functions of all the proteins of various organisms. Generating interactome network models with high quality and coverage is a necessary step in the process of developing predictive models for protein functions at the scale of the whole cell[1]. Development of efficient MS-cleavable chemical cross-linkers such as disuccinimidyl sulfoxide (DSSO)[6] expanded the applications of XL-MS ranging from studying individual functional complexes[7] to discovering proteome-wide interactions by drastically minimizing the database search space. Liu et al[8] demonstrated the high-throughput capability of XL-MS approach with the first-ever proteome-wide XL-MS study on HeLa cell lysate. They utilized CID-ETD toggle approach and identified a set of 1822 cross-links at 1%FDR employing XlinkX, a state-of-the-art search engine for XL-MS. Rapid advancements in terms of technical capability have been reported by utilizing and combining multiple levels and types of fragmentation methods
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