More is simpler: Decomposition of ligand-binding affinity for proteins being disordered

Area of Science:

• Biochemistry
• Statistical Mechanics
• Computational Biology

Background:

• Intrinsically disordered proteins (IDPs) present unique challenges in understanding protein-ligand interactions due to their conformational flexibility.
• Statistical mechanics demonstrates that a high number of degrees of freedom can simplify analysis, a principle extended here to IDPs.

Purpose of the Study:

• To reveal how ensemble averaging of intrinsically disordered protein conformations simplifies binding affinity analysis.
• To apply this simplified model for facilitating virtual screening of IDPs.
• To provide insights into the specificity differences between IDPs and ordered proteins.

Main Methods:

• Utilizing ensemble averaging to analyze the vast conformational space of intrinsically disordered proteins.
• Decomposing binding affinity into the ligandability of the IDP and the capacity of the ligand.
• Applying the derived regularity to computational virtual screening methods.

Main Results:

• Ensemble averaging of IDP conformations simplifies binding affinity, allowing reliable decomposition into IDP ligandability and ligand capacity.
• A novel regularity in protein-ligand binding for IDPs was discovered.
• The method facilitates efficient virtual screening for IDP-targeting ligands.

Conclusions:

• The conformational ensemble of IDPs simplifies binding affinity analysis, offering a new paradigm for drug discovery.
• Specificity differences arise from deviations from this baseline binding behavior.
• This work bridges statistical mechanics principles with the study of intrinsically disordered proteins.