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

Area of Science:

• Structural Biology
• Biophysics
• Drug Discovery

Background:

• Structure-based drug design (SBDD) relies on high-resolution structural models.
• Limitations in modeling molecular dynamics hinder SBDD's therapeutic translation.
• Atomic-level functional dynamics are crucial for understanding drug interactions.

Purpose of the Study:

• To highlight the potential of time-resolved cryo-electron microscopy (cryo-EM) in advancing SBDD.
• To explore the integration of cryo-EM with AI and machine learning (ML) for dynamic drug design.
• To demonstrate how cryo-EM can overcome limitations of traditional molecular dynamics simulations.

Main Methods:

• Utilizing time-resolved cryo-electron microscopy (cryo-EM) to capture dynamic biomolecular states.
• Integrating cryo-EM data with machine learning (ML) and artificial intelligence (AI) algorithms.
• Applying cryo-EM to visualize intermediate states and protein-ligand interactions.

Main Results:

• Time-resolved cryo-EM visualizes rare intermediate states and dynamic interactions.
• Integration with AI/ML enables dynamics-based SBDD for challenging targets.
• Cryo-EM provides insights into drug-binding kinetics and allosteric regulation.

Conclusions:

• Time-resolved cryo-EM combined with AI revolutionizes drug discovery by enabling dynamics-based SBDD.
• This approach can identify novel druggable conformations and overcome drug resistance.
• Future developments promise in vivo visualization of drug actions, accelerating clinical translation.