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

Area of Science:

• Proteomics
• Structural Biology
• Computational Biology

Background:

• Understanding protein conformations is vital for cellular mechanisms and disease research.
• Native proteomics analyzes proteins in their native states, but characterizing proteome-wide conformations is challenging.

Purpose of the Study:

• To develop an AI-assisted native proteomics method for simultaneous protein identification and conformational analysis.
• To integrate protein structure prediction (PSP) with top-down proteomics (TDP) and native mass spectrometry (nMS).

Main Methods:

• Obtained protein sequences via TDP and measured solvent-accessible surface area using nMS.
• Integrated TDP and nMS data with a PSP module to determine protein conformations.
• Applied the method to study ribosomal protein conformations before and after assembly.

Main Results:

• Identified multiple conformational ensembles for ribosomal proteins with intrinsically disordered regions in their monomer state.
• Demonstrated that protein conformations during drug binding can differ from those within a complex.
• Highlighted the significance of characterizing 'dark' protein conformations in drug development.

Conclusions:

• The AI-assisted native proteomics method enables high-throughput proteome identification and conformational characterization.
• This approach bridges structural biology and conventional proteomics.
• Revealing diverse protein conformations is critical for drug discovery and understanding biological systems.