Elucidating Protein Quaternary Structure in Near-Native Solution: An AI-Assisted Hydrodynamic Approach.

Area of Science:

• Structural Biology
• Biophysical Chemistry
• Analytical Chemistry

Background:

• Determining protein complex structures under physiological conditions is a significant challenge.
• Existing methods often struggle with heterogeneous mixtures and near-native solution states.

Purpose of the Study:

• To develop an integrative analytical pipeline for resolving macromolecular architectures.
• To combine size exclusion chromatography, Taylor dispersion analysis, native mass spectrometry, and AI for structural determination.
• To provide a robust, experimentally grounded framework for characterizing protein assemblies.

Main Methods:

• Integrative pipeline combining size exclusion chromatography (SEC), Taylor dispersion analysis (TDA), and native mass spectrometry (nMS).
• Online TDA in a PEEK capillary to measure hydrodynamic radii via laminar-flow-induced dispersion.
• Application of experimentally derived radii as constraints in an AlphaFold 3-based structural selection process.

Main Results:

• Successfully validated the method using a six-protein model mixture, determining individual hydrodynamic radii.
• Precisely measured oligomeric hydrodynamic radii for FCGRT-B2M, streptavidin, and concanavalin A complexes.
• Determined the quaternary structures of these protein complexes using the integrated approach.

Conclusions:

• The developed pipeline accurately resolves macromolecular architectures under near-native solution conditions.
• Bridging analytical chemistry with AI-driven prediction offers a powerful strategy for structural biology.
• This method provides a robust framework for characterizing complex biological assemblies.