Divide and correlate: mapping electronic correlations in proteins via local cut-wise reconstruction

Area of Science:

• Computational chemistry
• Biophysics
• Quantum biology

Background:

• Understanding electronic correlations is crucial for biomolecular function.
• Previous methods for analyzing these correlations in large molecules are computationally intensive.

Purpose of the Study:

• To introduce a scalable computational method for quantifying electronic correlations in large biomolecules like insulin.
• To characterize interatomic and inter-residue interactions using mutual information (MI).

Main Methods:

• A novel cut-wise strategy combining localized density functional theory (DFT) calculations on overlapping spherical regions.
• Reconstruction of global MI matrices from localized calculations.
• Validation against full-protein DFT results.

Main Results:

• The method accurately quantifies electronic correlations in insulin.
• Key biochemical features and interatomic/inter-residue interactions are characterized.
• The approach demonstrates scalability for large biomolecules.

Conclusions:

• The developed framework provides an efficient tool for quantum correlation analysis in large biomolecules.
• This method has potential applications in protein-ligand modeling, pharmacophore design, and quantum-enhanced drug discovery.