Residue-Level Affinity Decomposition via Quantum Electron Density: A Multivariable Framework Applied to HIV-1...

Area of Science:

• Computational Chemistry
• Structural Biology
• Drug Design

Background:

• Protein-ligand binding is crucial for drug development but challenging to predict.
• Understanding molecular interactions is key for rational drug design.

Purpose of the Study:

• To develop a quantum-informed, residue-level framework for protein-ligand affinity decomposition.
• To analyze noncovalent interactions between HIV-1 protease and inhibitors.

Main Methods:

• Integrated molecular dynamics, DFT, QTAIM analysis, and multivariable modeling.
• Quantified noncovalent interactions and analyzed residue-specific contributions.
• Used Hessian-based descriptors and NCI index for physical interpretation.

Main Results:

• Affinity is governed by the nature of individual contacts, not their overall number or density.
• Models distinguished stabilizing and destabilizing residue contributions, including water's influence.
• Quantum descriptors showed predictive value compared to MM-PBSA.

Conclusions:

• Developed a transferable, quantum-topological framework for mechanistic protein-ligand affinity analysis.
• The approach is applicable to diverse chemical scaffolds, aiding inhibitor optimization.
• Provides a generalizable strategy for structure-guided drug design.