A Computationally Efficient Method to Generate Plausible Conformers for Ensemble Docking and Binding Free Energy...

Area of Science:

• Computational Biology
• Structural Biology
• Biophysics

Background:

• Accurate prediction of ligand-protein interactions is crucial for drug discovery.
• Protein flexibility significantly impacts binding affinities, necessitating conformational sampling.
• Existing methods for conformational sampling can be computationally expensive.

Purpose of the Study:

• To develop a computationally efficient method for generating plausible protein conformers for ensemble docking.
• To evaluate the binding affinities of ligands to triose phosphate isomerase (TIM) using this novel approach.
• To assess the reliability and applicability of the method for diverse biological questions, including species-specific binding.

Main Methods:

• A mixed-resolution approach combining atomistic and coarse-grained models was used.
• Anisotropic Network Model identified key protein dynamics for generating conformers.
• Ensemble docking with Glide and subsequent molecular dynamics (MD) simulations were performed.
• Binding free energies were calculated using the MM-GBSA approach.

Main Results:

• The developed method efficiently generated diverse protein conformers.
• Binding free energy estimations using truncated TIM structures were comparable to intact structures.
• 100 ns MD simulations were sufficient for reliable binding affinity estimation.
• Species-specific binding dynamics were successfully highlighted for different TIM species.

Conclusions:

• The computationally efficient approach reliably estimates binding free energies for proteins and supramolecular assemblies.
• The method's ability to capture conformational flexibility enhances ligand binding prediction.
• This methodology offers a valuable tool for structure-based drug design and understanding molecular interactions.