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The native conformation of a protein is formed by interactions between the side chains of its constituent amino acids. When the amino acids cannot form these interactions, the protein cannot fold by itself and needs chaperones. Notably, chaperones do not relay any additional information required for the folding of polypeptides; the native conformation of a protein is determined solely by its amino acid sequence. Chaperones catalyze protein folding without being a part of the folded protein.
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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.