A new approach for extracting information from protein dynamics.

Area of Science:

• Biophysics
• Network Science
• Structural Biology

Background:

• Protein structure prediction advancements shift focus to protein dynamics.
• Network science offers tools to represent and analyze protein dynamics.
• Current methods often rely on correlation measures, limiting their applicability.

Purpose of the Study:

• To apply a robust inverse approach to protein dihedral angle dynamics for network extraction.
• To avoid limitations of structural alignment in dynamic network analysis.
• To reveal biophysical insights and identify functional pathways in proteins.

Main Methods:

• Utilized the inverse approach on protein dihedral angle dynamics.
• Applied the method to internal coordinates, bypassing the need for structural alignment.
• Analyzed dynamics of FimH, Siglec-8, and SARS-CoV-2 spike protein.

Main Results:

• The inverse approach successfully identified physically interpretable and robust networks.
• Networks were relevant to allosteric pathway sites in FimH.
• Dynamical differences were detected in structurally similar proteins like Siglec-8 and SARS-CoV-2 spike protein.

Conclusions:

• The inverse approach provides a powerful method for extracting meaningful networks from protein dynamics.
• This approach yields significant biophysical insights, aiding in understanding protein function and allostery.
• The method is applicable to diverse biological systems, including immune proteins and viral structures.