Bringing Molecular Dynamics and Ion-Mobility Spectrometry Closer Together: Shape Correlations, Structure-Based...

Area of Science:

• Biophysics
• Computational Chemistry
• Structural Biology

Background:

• Protein unfolding provides insights into structure and energetics, observable via ion mobility mass spectrometry (IMS) by monitoring collision cross section (CCS).
• Interpreting IMS unfolding data requires accurate structural modeling, but current methods for CCS prediction and integration with molecular dynamics (MD) are computationally intensive and limited.

Purpose of the Study:

• To develop computationally inexpensive and versatile methods for predicting CCS, enabling better interpretation of protein unfolding experiments.
• To establish correlations between CCS and structural parameters like the gyration radius for monomeric and dimeric proteins.

Main Methods:

• Investigated correlations between CCS and the gyration radius for various proteins.
• Developed a simplified structural model for CCS prediction, incorporating aspects of hard-sphere and projection algorithms.
• Proposed the scaled macroscopic sphere (sMS) predictor, relying on gyration radius and chemical formula.
• Devised a strategy for switching between global and fragment-based CCS prediction for protein complexes.

Main Results:

• Found correlations between CCS and gyration radius are sensitive to protein topology and generation conditions.
• The developed sMS predictor shows applicability in describing protein unfolding and is transferable to diverse structures.
• The proposed models offer improved computational efficiency for CCS prediction compared to existing methods.

Conclusions:

• The new models and approaches facilitate the integration of structural modeling with IMS experiments, particularly for large-scale bioinformatics and on-the-fly MD biasing.
• These methods offer a computationally efficient alternative for predicting CCS, crucial for advancing protein unfolding studies.