Re-Dock: Towards Flexible and Realistic Molecular Docking with Diffusion Bridge
(2402.11459)Abstract
Accurate prediction of protein-ligand binding structures, a task known as molecular docking is crucial for drug design but remains challenging. While deep learning has shown promise, existing methods often depend on holo-protein structures (docked, and not accessible in realistic tasks) or neglect pocket sidechain conformations, leading to limited practical utility and unrealistic conformation predictions. To fill these gaps, we introduce an under-explored task, named flexible docking to predict poses of ligand and pocket sidechains simultaneously and introduce Re-Dock, a novel diffusion bridge generative model extended to geometric manifolds. Specifically, we propose energy-to-geometry mapping inspired by the Newton-Euler equation to co-model the binding energy and conformations for reflecting the energy-constrained docking generative process. Comprehensive experiments on designed benchmark datasets including apo-dock and cross-dock demonstrate our model's superior effectiveness and efficiency over current methods.
Overview
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The Re-Dock framework uses a diffusion bridge generative model for more realistic simulation of molecular docking, accounting for ligand and pocket sidechain flexibility.
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It aims to improve drug discovery by generating better predictions of how small molecules bind to proteins, addressing the limitations of current rigid or oversimplified models.
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The framework has shown superior performance in benchmark tests, predicting flexible docking structures more accurately and efficiently than existing methods.
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Re-Dock's methodology and successful benchmarks suggest potential integration into drug discovery pipelines, promising advancements in identifying and optimizing novel therapeutics.
Re-Dock: Advancing Flexibility and Realism in Molecular Docking through a Novel Diffusion Bridge Approach
Introduction
Molecular docking, a pivotal step in drug discovery, predicts how small molecules (ligands) bind to proteins to influence their biological activity. Challenges in this domain include the accurate modeling of protein and ligand flexibility during docking—a process known as induced fit. Traditional approaches and even recent deep learning strategies often rely on rigid, pre-docked structures or oversimplify the problem, ignoring the substantial sidechain flexibility of pocket residues. Addressing these limitations, we propose the innovative Re-Dock framework, a generative model designed for flexible docking. It simulates the protein-ligand docking process more realistically by considering both ligand and pocket sidechain flexibility while incorporating an interaction-aware geometric diffusion bridge model.
Novel Contributions
The Re-Dock framework introduces several key innovations:
- It targets the flexible docking task, predicting ligand and pocket sidechain poses simultaneously under realistic constraints, which is significant for practical applications in drug discovery.
- Utilizing a diffusion bridge generative model extended to non-Euclidean manifolds, Re-Dock employs an energy-to-geometry mapping strategy inspired by mechanics principles. This approach enables the co-modeling of binding energy and conformational poses within a unified framework.
- Benchmarking on specially designed datasets shows Re-Dock's superior performance in accurately predicting flexible docking structures, outperforming current methods in both effectiveness and efficiency.
Methodology
Re-Dock’s novel diffusion bridge is founded on implicit geometric manifolds, incorporating pocket sidechain flexibility into the pose generation process. This capability is crucial for mimicking the real-world induced fit mechanism observed in protein-ligand interactions. By mapping energy to geometry using the Newton-Euler equation, Re-Dock creates a robust model capable of reflecting the energy-constrained generative process of docking. It autoregressively models sidechain distributions, ensuring high-quality pose generation. Comprehensive benchmarks, including traditional flexible re-docking, apo-dock with both crystal and predicted structures, and cross-dock scenarios, validate Re-Dock's approach.
Theoretical Underpinnings and Practical Applications
Re-Dock bridges the gap between theory and application by modeling the complex, dynamic process of molecular docking in a more accurate and pragmatic manner. It leverages knowledge from areas such as rigid body mechanics to inform its approach to drug design, underscoring the interdisciplinary nature of modern computational biology. On a practical level, Re-Dock's success in benchmark tests suggests its potential integration into drug discovery pipelines, offering a more realistic tool for the identification and optimization of novel therapeutics.
Future Directions
While Re-Dock represents a significant step forward in molecular docking, future research could focus on improving the model’s scalability and generalization to unseen proteins. The integration of additional biophysical insights into the diffusion model could further enhance its predictive accuracy. Moreover, extending the Re-Dock framework to encompass more complex biological interactions, such as protein-protein interactions, might broaden its utility across biomedical research.
Conclusion
Re-Dock introduces a groundbreaking approach to the challenge of flexible docking in molecular simulation. By harmonizing principles from mechanics with cutting-edge AI techniques, it provides a compelling solution that enhances the realism and applicability of docking predictions. As drug discovery continues to evolve, tools like Re-Dock will play a crucial role in accelerating the pace of innovation and the discovery of new therapeutic agents.
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