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Benchmarking Cross-Docking Strategies in Kinase Drug Discovery
David A Schaller1,2, Clara D Christ3, John D Chodera2
1In Silico Toxicology and Structural Bioinformatics, Institute of Physiology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Augustenburger Platz 1, 13353 Berlin, Germany.
Journal of Chemical Information and Modeling
|November 19, 2024
Summary
Accurate prediction of protein-ligand complex structures is crucial for machine learning in drug discovery. Combining docking methods and using multiple protein structures improved pose prediction accuracy for kinase inhibitors.
Area of Science:
- Computational chemistry
- Structural biology
- Machine learning in drug discovery
Background:
- Machine learning (ML) is revolutionizing drug discovery, particularly small molecule design.
- Predicting bioactivity requires accurate protein-ligand complex structures, which is a current limitation.
- Structural information can enhance ML scoring but relies on reliable complex structure prediction.
Purpose of the Study:
- To develop practical methods for generating useful kinase-inhibitor complex geometries for ML scoring.
- To create a kinase-centric docking benchmark to evaluate docking and pose selection strategies.
- To assess the recapitulation of experimentally observed binding modes in a realistic cross-docking scenario.
Main Methods:
- Assembled a benchmark dataset of 589 protein kinase structures with 423 ATP-competitive ligands.
- Evaluated various docking and pose selection strategies, including physics-based docking, shape overlap, and maximum common substructure (MCS) matching.
- Utilized the KinoML framework and OpenEye Toolkits for automated complex generation.
Main Results:
- Docking methods biased by the cocrystallized ligand (shape overlap with/without MCS) outperformed standard physics-based docking.
- Docking into multiple protein structures significantly increased the likelihood of generating accurate (low RMSD) poses.
- A combined approach (Posit) using MCS to select similar ligands and structures achieved a 70.4% success rate in reproducing binding poses.
Conclusions:
- Ligand-biased docking strategies and multi-structure docking enhance protein-ligand complex prediction accuracy.
- The Posit approach offers an efficient method for generating reliable poses for ML applications.
- Findings, though focused on kinases, are potentially transferable to other protein families for improved drug discovery pipelines.
