Exploring Protein-Protein Docking Tools: Comprehensive Insights into Traditional and Deep-Learning Approaches

Area of Science:

• Computational Biology
• Structural Biology
• Bioinformatics

Background:

• Protein-protein interactions (PPIs) are vital for biological processes, including signaling and drug action.
• Understanding PPIs aids in elucidating protein functions and developing targeted therapies.
• Experimental determination of PPI structures is challenging, necessitating computational approaches.

Purpose of the Study:

• To provide a comprehensive review of commonly used protein-protein docking tools.
• To compare deep-learning-based and traditional computational methods for PPI analysis.
• To assist researchers in selecting appropriate docking tools for their specific studies.

Main Methods:

• Systematic review and comparative analysis of existing protein-protein docking software.
• Categorization of tools into deep-learning-based and traditional approaches.
• Evaluation of the advantages and limitations of each tool based on performance and usability.

Main Results:

• Identified key protein-protein docking tools, including both novel deep-learning methods and established traditional algorithms.
• Detailed comparison of the strengths and weaknesses of different docking strategies.
• Discussion of factors influencing tool selection, such as accuracy, speed, and data requirements.

Conclusions:

• The selection of protein-protein docking tools depends on specific research needs and available resources.
• Deep-learning methods offer promising advancements in accuracy and efficiency for PPI analysis.
• This review serves as a practical guide for researchers navigating the landscape of protein-protein docking tools.