Sampling and refinement protocols for template-based macrocycle docking: 2018 D3R Grand Challenge 4.

Area of Science:

• Computational chemistry
• Structural biology
• Drug discovery

Background:

• Accurate prediction of ligand-protein interactions is crucial for drug discovery.
• Docking flexible ligands, especially macrocycles, presents significant computational challenges.

Purpose of the Study:

• To introduce and evaluate a novel template-based computational method for docking flexible ligands to proteins.
• To assess the method's performance in a blind prediction competition for drug design.

Main Methods:

• The method integrates Monte Carlo energy minimization on the manifold with the BRIKARD algorithm for flexible ligand searching.
• It utilizes the MELD accelerator for Replica-Exchange Molecular Dynamics simulations to handle atomistic degrees of freedom.
• The approach employs a template-based strategy for efficient docking of complex molecules.

Main Results:

• The developed method demonstrated top-tier performance in the Drug Design Data Resource blind Grand Challenge.
• It achieved sub-angstrom prediction quality for most of the competition targets.
• The results validate the method's effectiveness for complex molecular docking scenarios.

Conclusions:

• The new template-based docking method is highly effective for flexible ligands, including macrocycles.
• The approach shows significant promise for accelerating drug design and discovery pipelines.
• This computational strategy offers a powerful tool for predicting ligand-protein binding with high accuracy.