We describe an integrated workflow that robustly identifies cross-links from endogenous protein complexes in human cellular lysates. Our approach is based on the application of mass spectrometry (MS)-cleavable cross-linkers, sequential collision-induced dissociation (CID)-tandem MS (MS/MS) and electron-transfer dissociation (ETD)-MS/MS acquisitions, and a dedicated search engine, XlinkX, which allows rapid cross-link identification against a complete human proteome database. This approach allowed us to detect 2,179 unique cross-links (1,665 intraprotein cross-links at a 5% false discovery rate (FDR) and 514 interprotein cross-links at 1% FDR) in HeLa cell lysates. We validated the confidence of our cross-linking results by using a target-decoy strategy and mapping the observed cross-link distances onto existing high-resolution structures. Our data provided new structural information about many protein assemblies and captured dynamic interactions of the ribosome in contact with different elongation factors.

We describe optimized fragmentation schemes and data analysis strategies substantially enhancing the depth and accuracy in identifying protein cross-links using non-restricted whole proteome databases. These include a novel hybrid data acquisition strategy to sequence cross-links at both MS2 and MS3 level and a new algorithmic design XlinkX v2.0 for data analysis. As proof-of-concept we investigated proteome-wide protein interactions in E. coli and HeLa cell lysates, respectively, identifying 1,158 and 3,301 unique cross-links at ∼1% false discovery rate. These protein interaction repositories provide meaningful structural information on many endogenous macromolecular assemblies, as we showcase on several protein complexes involved in translation, protein folding and carbohydrate metabolism.