Oral Presentation 28th Annual Lorne Proteomics Symposium 2023

Cross-linking mass spectrometry discovers, evaluates, and validates the experimental and predicted structural proteome (#55)

Tara K Bartolec 1 , Xabier Vázquez-Campos 1 , Alexander Norman 2 , Clement Luong 3 , Richard J Payne 2 4 , Marc R Wilkins 1 , Joel P Mackay 3 , Jason KK Low 3
  1. Systems Biology Initiative, School of Biotechnology and Biomolecular Sciences, The University of New South Wales, Randwick, NSW, Australia
  2. School of Chemistry, The University of Sydney, Sydney, NSW, Australia
  3. School of Life and Environmental Sciences,, The University of Sydney, Sydney, NSW, Australia
  4. Australian Research Council Centre of Excellence for Innovations in Peptide and Protein Science, The University of Sydney, Sydney, NSW, Australia

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.