AI Assisted Native Proteomics: Delineating Ribosomal Protein Conformations Pre- and Post-Assembly

Abstract

The simultaneous identification of proteins as well as their conformations in a biological system would greatly enhance our understanding of cellular mechanisms and disease. As an emerging technique, native proteomics analyzes proteins in their native states, facilitating the acquisition of protein stoichiometry, post-translational modifications (PTMs), and interactions with ligands. However, revealing protein conformations at the proteome scale remains a significant challenge. In this study, we propose an AI-assisted native proteomics method that integrates a protein structure prediction (PSP) module with top-down proteomics (TDP) and native mass spectrometry (nMS) to acquire both proteome identities and conformations. First, protein sequences and PTMs are obtained using the TDP method, while protein solvent-accessible surface area (SASA) is measured in parallel by nMS. These data are then input into the PSP module to acquire the most probable conformation of the protein under nMS experimental conditions. We validate the feasibility and accuracy of this method through the analysis of both a globular protein and an intrinsically disordered protein. Additionally, we apply this approach to delineate the conformations of ribosomal proteins pre- and post-assembly. Interactions of ribosomal proteins with drug molecules are also explored. By enabling proteome identification and conformation characterization from relatively small amounts of endogenous proteins, this method would bridge the gap between structural biology and conventional proteomics technologies.