Recent, rapid advances in cross-linking mass spectrometry (XL-MS) has enabled detection of novel protein-protein interactions and their structural dynamics at the proteome scale. Given the importance and scale of the novel interactions identified in these proteome-wide XL-MS studies, thorough quality assessment is critical. Almost all current XL-MS studies validate cross-links against known 3D structures of representative protein complexes. However, current structure validation approach only includes cross-links where both peptides mapped to the 3D structures.Here we provide theoretical and experimental evidence demonstrating this approach can drastically underestimate error rates for proteome-wide XL-MS datasets. Addressing current shortcomings, we propose and demonstrate a comprehensive set of four metrics, including orthogonal experimental validation to thoroughly assess quality of proteome-wide XL-MS datasets.3 Cross-linking mass spectrometry (XL-MS) is a powerful platform capable of unveiling protein interactions and capturing their structural dynamics 1 . Although XL-MS techniques were once limited to studying individual functional complexes at a time, the development of efficient MScleavable chemical cross-linkerssuch as disuccinimidyl sulfoxide (DSSO) 2 , disuccinimidyl dibutyric urea (DSBU) 3 and protein interaction reporters (PIRs) 4has broadened the applicability of XL-MS to proteome scale 5-8 . With the increased throughput of these techniques, the number of false positive cross-links and incorrect interactions derived from them can quickly add up with just one large-scale XL-MS experiment, if one is not careful. Therefore, thorough quality assessment methods have become critically important. Computationally defined false discovery rates (FDR) provide a quantitative metric allowing researchers to filter lists of cross-link identifications, until a theoretical quality level is achieved. In addition to FDR calculations, almost all proteome-wide XL-MS studies leverage available 3D structures of representative complexes to provide a means of validation and quality assessment 9, 10 . Here, we demonstrate fundamental flaws in this structure-based quality assessment approach that can drastically underestimate the error rates of large-scale XL-MS datasets.The maximum theoretical distance that a given chemical cross-linker can span (e.g. 30Ă
for DSSO 11 ) can assess agreement between existing 3D protein structures and XL-MS datasets.Particularly in small-scale studies handling intraprotein cross-links, the fraction of cross-linked residue pairs that satisfy this distance constraint may provide meaningful insights into protein flexibility or the quality of the cross-links detected. In proteome-wide XL-MS studies, researchers extend this concept and use representative, highly abundant complexes such as the ribosome and proteasome to estimate the quality of all interprotein cross-links reported. However, the majority of false positive identifications come from these interprotein crosslinks 12 . Moreover, true pos...