Evaluation of docking procedures reliability in affitins-partners interactions.

Area of Science:

• Structural biology
• Computational biophysics
• Protein engineering

Background:

• Affitins are small, stable proteins from the Sul7d family, known for DNA binding.
• Their small size and stability make them excellent candidates for protein engineering applications.
• Determining the binding geometry of engineered affitins with target proteins is crucial but experimentally challenging.

Purpose of the Study:

• To evaluate computational approaches for predicting the binding structures of affitin-protein complexes.
• To compare the effectiveness of different reranking protocols for protein-protein docking models.
• To identify the most reliable strategy for characterizing affitin-protein interactions.

Main Methods:

• Utilized ClusPro server for generating protein-protein docking models of affitins with known protein partners.
• Applied Molecular Dynamics (MD) simulations to assess the stability of docking models.
• Employed the Matrix of Local Coupling Energies (MLCE) method to predict interacting residues for comparison.
• Developed and tested two reranking protocols for docking models, individually and in consensus.

Main Results:

• Protein-protein docking generated multiple putative binding geometries for affitin complexes.
• Molecular Dynamics simulations provided insights into the stability of different binding poses.
• Comparison with MLCE predictions helped refine the accuracy of docking models.
• A consensus approach, integrating both MD stability and MLCE interaction data, proved most effective for reranking.

Conclusions:

• Computational protein-protein docking is a valuable tool for initial structural characterization of affitin complexes.
• Reranking docking models is essential for identifying plausible binding geometries.
• Combining molecular dynamics simulations and coupling energy analysis offers a robust consensus strategy for accurate prediction of affitin-protein binding structures.