Deep learning in modeling protein complex structures: From contact prediction to end-to-end approaches

Area of Science:

• Computational Biology
• Structural Biology
• Artificial Intelligence in Biology

Background:

• Protein-protein interactions are fundamental to biological processes.
• Traditional protein complex modeling relies on protein-protein docking.
• Recent AI advancements have spurred new methods for structure prediction.

Purpose of the Study:

• To review deep learning-based approaches for protein-protein complex structure modeling.
• To discuss the advantages and limitations of these AI methods.
• To explore future directions in AI-driven protein complex prediction.

Main Methods:

• Review of recent literature on deep learning for protein complex modeling.
• Analysis of inter-protein contact prediction techniques.
• Evaluation of end-to-end deep learning approaches.

Main Results:

• Deep learning significantly advances protein complex structure prediction.
• AI methods offer improved accuracy and efficiency over traditional docking.
• Key advantages include enhanced prediction capabilities and novel insights.

Conclusions:

• Deep learning is transforming protein complex modeling.
• Addressing current limitations and exploring future directions is crucial for advancing the field.
• AI holds immense potential for understanding biological mechanisms through protein interactions.