Cross-linking mass spectrometry discovers, evaluates, and corroborates structures and protein–protein interactions in the human cell

Abstract

Significant recent advances in structural biology, particularly in the field of cryoelectron microscopy, have dramatically expanded our ability to create structural models of proteins and protein complexes. However, many proteins remain refractory to these approaches because of their low abundance, low stability, or—in the case of complexes—simply not having yet been analyzed. Here, we demonstrate the power of using cross-linking mass spectrometry (XL-MS) for the high-throughput experimental assessment of the structures of proteins and protein complexes. This included those produced by high-resolution but in vitro experimental data, as well as in silico predictions based on amino acid sequence alone. We present the largest XL-MS dataset to date, describing 28,910 unique residue pairs captured across 4,084 unique human proteins and 2,110 unique protein–protein interactions. We show that models of proteins and their complexes predicted by AlphaFold2, and inspired and corroborated by the XL-MS data, offer opportunities to deeply mine the structural proteome and interactome and reveal mechanisms underlying protein structure and function.

Structural Host-Virus Interactome Profiling of Intact Infected Cells

“…SHVIP integrates well with AF2 to inform structural predictions at interactome scale [19][20][21] delivering residue-residue connections as corroborative evidence to purely in silico analyses. Enhancing interactome-wide AF2 modeling with SHVIP is particularly valuable from a virological point of view because (1) AF2-only predictions of dimers involving viral proteins have generally lower quality than those of host proteins 59 (Figure 4) and (2) experimental structures of host-virus protein complexes are scarce for HSV-1 and many other viruses.…”

DSBSO-Based XL-MS Analysis of Breast Cancer PDX Tissues to Delineate Protein Interaction Network in Clinical Samples

“…Next, we compared our XL-PPIs against the BioPlex and BioGrid PPI databases, as well as other published proteome-wide XL-MS data. Out of the 1,600 XL-PPIs identified from basal breast cancer, 373 were found in the selected PPI databases and 200 have been reported by previous proteome-wide XL-MS studies, − ,, with the remaining 1,027 XL-PPIs representing novel interactions (Table S5A). For the 1,411 XL-PPIs identified from luminal breast cancer, 313 were found in the PPI databases and 160 were reported in the proteome-wide XL-MS data, leaving 938 XL-PPIs as novel interactions (Table S5B).…”

“…Cross-linking mass spectrometry (XL-MS) has emerged as a powerful tool for studying PPIs both in vitro and in vivo , offering a unique capability to capture native PPIs from cellular environments. − This approach reveals endogenous PPI identities and interaction contacts concurrently to enable the differentiation between direct and indirect interactors. The cross-links formed between proximal residues of interacting proteins can further serve as distance constraints to assist in refining existing structures and elucidating architectures of native protein complexes. − ,− To advance XL-MS studies, we have previously developed a series of sulfoxide-containing MS-cleavable cross-linkers to facilitate the detection and identification of cross-linked peptides, , which have proven effective for global PPI mapping. − While various cross-linking platforms have been utilized for whole-proteome PPI mapping of cell lysates, ,− intact cells, ,− organelles , − and tissues, ,, XL-MS analysis of clinical samples has not been explored. To identify disease-specific PPIs and biomarkers from clinical samples, we have taken our previously developed XL-MS platform featuring enrichable and MS-cleavable cross-linkers, i.e., Alkyne/Azide-A-DSBSO (aka DSBSO), , for in-cell cross-linking and adapted it for XL-MS at the tissue level.…”

Proteome-Scale Recombinant Standards and a Robust High-Speed Search Engine to Advance Cross-Linking MS-Based Interactomics

“…The inter-link comparison of Scout and XlinkX PD was done separately, because the XlinkX PD output is substantially affected by post-processing settings that are not part of our standard search parameters. In particular, the FDR in XlinkX PD is substantially affected by heuristic score cutoffs, which were shown to strongly depend on dataset and search parameters 6 . A recent study suggested a minimum XlinkX score of 60 19 , whereas the XlinkX PD user guide proposes to set dynamic score cutoff based on the score of the best CSM-level decoy hits in each analysis (see Methods).…”

“…Benchmarking XL-MS search output is traditionally achieved by taking three-dimensional protein structures as the ground truth and testing how well they match the identified crosslinks 6 . However, this approach was shown to substantially underestimate the false-discovery rate in proteome-wide XL-MS studies 7 .…”