Computational methods for modeling protein-protein interactions in the AI era: Current status and future directions

Area of Science:

• Structural Biology
• Computational Biology
• Artificial Intelligence in Biochemistry

Background:

• Protein-protein interactions (PPIs) are fundamental to cellular processes.
• Accurate modeling of PPIs is crucial for understanding biological function and disease.
• Traditional computational methods have limitations in predicting complex structures.

Purpose of the Study:

• To review the current computational landscape for predicting protein complex structures.
• To highlight the impact of artificial intelligence (AI) on protein-protein interaction modeling.
• To discuss challenges and recent innovations in the field.

Main Methods:

• Survey of traditional docking approaches.
• Focus on recent advances in AI-driven methods, including deep learning and end-to-end frameworks.
• Discussion of challenges like protein flexibility and intrinsically disordered regions (IDRs).

Main Results:

• AI-driven methods, such as AlphaFold, now dominate PPI structure prediction.
• Key challenges include modeling protein flexibility, co-evolutionary signals, large assemblies, and IDRs.
• Innovations focus on improving sampling, integrating experimental data, and enhancing robustness.

Conclusions:

• AI-based tools are revolutionizing protein complex structure prediction.
• Classical methods remain relevant but AI offers transformative potential.
• Advances will deepen understanding of biomolecular interactions and accelerate therapeutic design.