Quantifying conformational diversity in protein-ligand ensembles for structure-based virtual screening.

Area of Science:

• Computational chemistry
• Structural biology
• Drug discovery

Background:

• Accurate protein and ligand conformational states are crucial for structure-based virtual screening (SBVS).
• Diverse geometries of proteins and ligands significantly influence binding energetics.
• Single-structure screening often fails due to protein-ligand geometric mismatch.

Purpose of the Study:

• To quantify how conformational heterogeneity and ensemble clustering affect interaction energy evaluation in SBVS.
• To compare native vs. nonnative protein-ligand pairings and the impact of apo MD and solution-sampled ensembles.
• To provide guidance on ensemble sampling scale and reduction for cost-effective SBVS.

Main Methods:

• Analysis of 79 HIV-1 protease-ligand complexes.
• Comparison of interaction energies for native complexes, nonnative pairings, protein ensembles (apo MD), and ligand ensembles (solution).
• Evaluation of the impact of ensemble reduction on recovery of favorable interaction states.

Main Results:

• Native complexes consistently showed favorable interactions; nonnative pairings were often unfavorable.
• Reduction of protein and ligand ensembles decreased the recovery of favorable interaction states.
• Simultaneous reduction of both ensembles limited complementary structural pair availability.

Conclusions:

• Protein-ligand geometric mismatch is a significant factor in SBVS failures, independent of scoring functions.
• Ensemble-based approaches are necessary for accurate SBVS, but require careful consideration of sampling scale and reduction.
• The study offers practical guidance for optimizing ensemble-based virtual screening strategies.