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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.

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.

Recent successes in clinical gene therapy applications have intensified the interest in using adeno-associated viruses (AAVs) as vectors for gene delivery into human liver. An inherent intriguing characteristic of AAVs is that vector variants vary substantially in their ability to transduce hepatocytes from different species. This has historically limited the value of preclinical studies using rodent models for predicting the efficiency of AAV vectors in liver-targeted gene therapy clinical studies. In this work, we aimed to investigate the key determinants of the observed differential interspecies transduction abilities among AAV variants. We took advantage of domain swapping strategies between AAV-KP1, a newly identified variant with enhanced murine liver tropism, and AAV3b, which functions poorly in mice. The systematic in vivo comparison of AAV3b/AAV-KP1 chimeric variants allowed us to identify a threonine insertion at position 265 within variable region I (VR-I) as the key residue that confers murine hepatic transduction to human-derived clade B (AAV2-like) and clade C (AAV3b-like) variants. We propose to use this insertion to generate phylogenetically related AAV surrogates in support of toxicology and dosing studies in the murine liver model., Graphical abstract, A threonine insertion at position 265 within VR-I confers murine hepatic transduction to human-derived clade B (AAV2-like) and clade C (AAV3b-like) AAV variants. This insertion can be used to generate phylogenetically related surrogates of clinical AAVs in support of toxicology and dosing studies in the murine models of human liver.