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Surface-induced unfolding (SIU) offers comparable reproducibility to collision-induced unfolding (CIU) for protein analysis. SIU enhances sensitivity and improves differentiation of protein subclasses, making it a valuable tool for biotherapeutic characterization.

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Area of Science:

  • Biophysical Chemistry
  • Analytical Chemistry
  • Structural Biology

Background:

  • Native ion mobility-mass spectrometry (IM-MS) with collision-induced unfolding (CIU) is key for protein characterization, providing insights into structural stability.
  • Surface-induced dissociation (SID) devices are now commercialized, enabling broader use of surface-induced unfolding (SIU) measurements.
  • Evaluating SIU's performance and reproducibility against CIU is crucial for its adoption in protein analysis.

Purpose of the Study:

  • To benchmark the reproducibility and performance of surface-induced unfolding (SIU) against collision-induced unfolding (CIU).
  • To assess SIU's capability in differentiating closely related protein subclasses and enhancing machine learning models.
  • To establish SIU as a complementary analytical method for protein characterization.

Main Methods:

  • Comparative analysis of SIU and CIU on a Waters CyclicIMS instrument using model proteins: β-lactoglobulin (β-lac), bovine serum albumin (BSA), and immunoglobulin G1 kappa (IgG1κ).
  • Reproducibility assessment using root-mean-square deviation (RMSD) across multiple charge states.
  • Application of supervised machine learning models for IgG subclass classification using SIU and CIU data.

Main Results:

  • SIU and CIU demonstrated comparable reproducibility, with average RMSD values below 4%.
  • SIU induced unfolding at lower energies, revealing subtle structural differences and unique unfolding features.
  • SIU-based machine learning models achieved high cross-validation accuracies (>90%) for classifying IgG subclasses and biotherapeutics like Adalimumab and Nivolumab, outperforming or matching CIU models.

Conclusions:

  • Surface-induced unfolding (SIU) is a reproducible and efficient alternative to collision-induced unfolding (CIU) for protein characterization.
  • SIU offers enhanced sensitivity and analytical depth, improving the differentiation of protein subclasses and closely related variants.
  • SIU integration into workflows, especially for high-throughput and machine learning applications, provides significant analytical benefits.