Benchmarking ensemble docking methods in D3R Grand Challenge 4.

Area of Science:

• Computational drug discovery
• Molecular modeling and simulation
• Structural biology

Background:

• Drug discovery is costly and time-consuming; virtual screening aids in filtering compounds.
• Computational drug discovery benchmarks methods through grand challenges.
• Open challenges enable student participation in validating hypotheses.

Purpose of the Study:

• To predict ligand affinity rankings for Cathepsin S, a target for autoimmune diseases.
• To investigate the impact of receptor dynamics on ligand affinity predictions.
• To validate hypotheses using a community-driven competition format.

Main Methods:

• Participated in Grand Challenge 4, focusing on ligand affinity ranking.
• Employed the Relaxed Complex Scheme: molecular docking with molecular dynamics-generated receptor conformations.
• Explored advanced methods like distance-restrained docking to enhance correlation with experimental data.

Main Results:

• Cathepsin S proved to be a challenging target for standard molecular docking.
• The Relaxed Complex Scheme was utilized to incorporate receptor dynamics.
• Advanced docking techniques were explored to improve predictive accuracy.

Conclusions:

• High school students can achieve rigorous scientific validation with proper support.
• Community-driven competitions are valuable for introducing beginners to computational drug discovery.
• Incorporating receptor dynamics is crucial for accurate ligand affinity predictions.