Dynamic prediction of protein binding sites: Integrating structural biology, molecular simulation, solvent probe mapping, and artificial intelligence

Abstract

Identifying protein binding sites is essential for mechanistic biology and structure-based drug discovery. However, single static structures often fail to capture functionally relevant sites that are shallow, transient, allosteric, or cryptic. This review focuses primarily on protein binding-site prediction and presents a unified biophysical framework emphasizing conformational dynamics, thermodynamics, and solvent-mediated recognition. We evaluated current experimental and computational approaches, with particular attention to molecular dynamics, enhanced sampling, Markov state models, and mixed-solvent molecular dynamics for identifying transient cavities and chemically favorable hotspots across conformational ensembles. Rather than relying on single-score predictions, we propose a multi-factor evidence framework that integrates pocket emergence, probe enrichment, local chemistry, reproducibility, and functional or ligand-based support. RNA binding regions and macromolecular interfaces are discussed as important extensions, where related principles of conformational heterogeneity, electrostatics, hydration, and molecular recognition also apply. Finally, we examine the emerging role of ensemble-aware artificial intelligence in integrating structural, dynamic, thermodynamic, and experimental information, and highlight the need for interpretable, uncertainty-aware, and prospectively validated prediction strategies.

Keywords

macromolecular binding sites / cryptic pockets / allosteric sites / molecular dynamics / druggability / geometric deep learning / structure-based drug design

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