Cross-linking mass spectrometry discovers, evaluates, and validates the experimental and predicted structural proteome

Abstract

Significant recent advances in structural biology, particularly in the field of cryo-electron 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 analysed. Here, we demonstrate the power of combining cross-linking mass spectrometry (XL-MS) with artificial intelligence-based structure prediction to discover and experimentally substantiate models for protein and protein complex structures at proteome scale. We present the deepest 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 integrative models of complexes driven by AlphaFold Multimer and inspired and corroborated by the XL-MS data offer new opportunities to deeply mine the structural proteome and interactome and reveal new mechanisms underlying protein structure and function.

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, [25][26][27][28][29]57,58 with the remaining 1,027 XL-PPIs representing novel interactions (Table S5A).…”

Instrumentation at the Leading Edge of Proteomics

“…Native MS is also being leveraged to improve other structural biology techniques, such as soft landing of mass-selected protein complexes for further structural characterization (e.g., cryo-electron microscopy). − ,− Hydrogen–deuterium exchange and protein footprinting have also seen substantive gains due to steady improvements in instrumentation − , , At the peptide level, methods for cross-linking MS (XL-MS) have enabled large-scale structural proteomics studies owing in part to the advanced instrumentation discussed herein. ,, Proximity-dependent labeling has also emerged to help map interactions and corroborate structural studies. − As some of the more demanding studies within the field, structural proteomics at both the peptide (XL-MS) and the protein (native MS) level will likely continue to progress at rapid rates that mirror the exciting instrument developments sure to manifest in coming years.…”

Best Practices of Using AI-Based Models in Crystallography and Their Impact in Structural Biology

“…Structure predictions obtained by either AF2 or AF2-Multimer can also be used in combination with hydrogen-deuterium exchange mass spectrometry (HDX-MS) data to predict or validate models of protein–protein complexes, as HDX-MS is very efficient to identify protein regions involved in protein–protein interactions. , …”

“…Structure predictions obtained by either AF2 or AF2-Multimer can also be used in combination with hydrogendeuterium exchange mass spectrometry (HDX-MS) data to predict or validate models of protein−protein complexes, as HDX-MS is very efficient to identify protein regions involved in protein−protein interactions. 100,101 AF2 and RF should also prove very useful in combination with low-dimensionality structural methods, facilitating their interpretation, for example, with force spectroscopy, 102 CD 103 and SAXS 104−107 or FRET. Given the degeneracy associated with low-dimensionality methods, it is recommended to maximize the number of independent validations (see above).…”