dMM-PBSA: A New HADDOCK Scoring Function for Protein-Peptide Docking.

Area of Science:

• Computational chemistry
• Structural biology
• Drug discovery

Background:

• Molecular docking is crucial for identifying drug candidates.
• Predicting binding affinity is a key challenge in structure-based drug design.
• Protein-peptide interactions (PPIs) are increasingly important targets.

Purpose of the Study:

• To evaluate and improve the MM-PBSA approach for protein-peptide docking.
• To develop a scoring function that ranks poses and predicts binding free energies.
• To assess the performance of MM-PBSA with APBS and DelPhi solvers for PPIs.

Main Methods:

• Systematic investigation of the MM-PBSA approach for 19 protein-peptide complexes.
• Scoring decoy structures from HADDOCK calculations using MM-PBSA.
• Dampening MM-PBSA solvation and coulombic terms to create dMM-PBSA.
• Correlation analysis between dMM-PBSA scores and experimental binding energies.

Main Results:

• MM-PBSA successfully identified poses with low binding free energy, but HADDOCK score performed better.
• The dampened MM-PBSA (dMM-PBSA) outperformed standard MM-PBSA.
• dMM-PBSA demonstrated a ranking capability similar to the HADDOCK score.
• A good correlation was observed between dMM-PBSA scores and experimental binding energies.

Conclusions:

• The dMM-PBSA scoring function shows significant promise for protein-peptide docking.
• dMM-PBSA can be effectively used alongside the HADDOCK score.
• This approach enhances the prediction of binding conformations and free energies in PPI simulations.