Protein-protein interaction prediction with deep learning: A comprehensive review

Area of Science:

• Bioinformatics
• Computational Biology
• Drug Discovery

Background:

• Protein-protein interactions (PPI) are crucial for biological functions, disease understanding, and therapeutic development.
• Experimental identification of PPI and protein-ligand binding is resource-intensive and time-consuming.
• Computational approaches are vital for efficient prediction in bioinformatics and drug discovery.

Purpose of the Study:

• To review recent advancements in deep learning for predicting protein functions.
• To explore deep learning applications in protein-protein interaction and binding site prediction.
• To highlight deep learning's role in protein-ligand binding and protein design.

Main Methods:

• Review of recent literature on deep learning methodologies.
• Focus on applications in protein function prediction.
• Emphasis on deep learning for predicting protein-protein interactions, binding sites, and protein design.

Main Results:

• Deep learning models show significant promise in predicting protein functions and structures.
• Computational prediction of protein-protein interactions and binding sites is becoming more accurate.
• Deep learning facilitates novel protein design and modification for desired functions.

Conclusions:

• Deep learning offers powerful tools to overcome experimental limitations in studying protein interactions.
• These methods are transforming bioinformatics and computer-aided drug discovery.
• Future research will likely focus on refining deep learning models for complex biological predictions.