Dynamics-based drug discovery by time-resolved cryo-EM

Abstract

Rational structure-based drug design (SBDD) depends on high-resolution structural models of target macromolecules or their complexes. However, the lack of atomic-level functional molecular dynamics hinders the applications of SBDD and limits their effective translation into clinically successful therapeutics. Time-resolved cryo-electron microscopy (cryo-EM) has emerged as a powerful tool in structural biology, capable of capturing high-resolution snapshots of biomolecular machines in action. Unlike molecular dynamics (MD) simulations, time-resolved cryo-EM can visualize rare intermediate states across a broader range of timescales, providing invaluable insights into drug-binding kinetics, dynamic protein-ligand interactions, and allosteric regulation. Integration of time-resolved cryo-EM with machine learning (ML) and artificial intelligence (AI) expands SBDD into a dynamics-based approach, allowing for more accurate pharmacological modeling of challenging drug targets that are beyond the reach of MD simulations. Time-resolved cryo-EM can help researchers to identify novel druggable conformations, overcome drug resistance, and reduce the time and cost of clinical translations. Despite current challenges, the future development of time-resolved cryo-EM with AI and in situ imaging strategy, such as cryo-electron tomography, holds the potential to revolutionize drug discovery by revealing in vivo molecular dynamics of drug actions at an unprecedented spatiotemporal scale.