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Updated: Jan 16, 2026

Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis
Published on: June 20, 2025
Evaluating ligand docking methods for drugging protein-protein interfaces: insights from AlphaFold2 and molecular
Jordi Gómez Borrego1, Marc Torrent Burgas2
1The Systems Biology of Infection Laboratory, Department of Biochemistry and Molecular Biology, Biosciences Faculty, Universitat Autònoma de Barcelona, 08193, Cerdanyola del Vallès, Spain.
AlphaFold2 models perform comparably to experimental structures in protein-protein interaction docking. Structural refinement can improve outcomes, but scoring functions limit overall docking performance.
Area of Science:
- Computational biology
- Structural bioinformatics
- Drug discovery
Background:
- Protein-protein interactions (PPIs) are crucial drug targets.
- Docking protocols are vital for identifying PPI modulators.
- AlphaFold2 (AF2) and molecular dynamics (MD) are advancing structural biology.
Purpose of the Study:
- To benchmark docking protocols using AF2 models versus experimental structures for PPIs.
- To evaluate the impact of MD and AlphaFlow on docking performance.
- To identify limitations in current docking methodologies for PPIs.
Main Methods:
- Benchmarking eight docking protocols on 16 PPIs with validated modulators.
- Comparing docking results from native and AF2-predicted protein structures.
- Utilizing MD simulations and AlphaFlow for structural refinement and ensemble generation.
Main Results:
- AF2 models showed performance comparable to native structures in PPI docking.
- Local docking strategies outperformed blind docking.
- MD and AlphaFlow improved docking in some cases but introduced conformational variability.
- Overall docking performance is limited by scoring functions and methodologies.
Conclusions:
- AF2-generated structures are suitable for PPI docking protocols.
- Structural refinement can enhance docking, but limitations persist.
- Improved scoring functions and ensemble approaches are needed for effective virtual screening.
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