A Review of Current Computational Tools for Peptide-Protein Docking.

Area of Science:

• Computational biochemistry
• Drug discovery
• Structural biology

Background:

• Peptide-protein interactions are crucial for biological processes and therapeutic development.
• Predicting these interactions computationally aids in designing novel peptide-based drugs.
• Existing computational tools are essential for advancing this field.

Purpose of the Study:

• To provide a comprehensive overview of current peptide-protein docking programs.
• To assist researchers in selecting appropriate docking tools for their specific needs.
• To highlight the strengths and limitations of various available software.

Main Methods:

• Extensive literature search to identify relevant peptide-protein docking software.
• Categorization of tools into dedicated programs, supporting software, and AI-driven alternatives.
• Analysis of distinct features, methodologies, strengths, and limitations of each program.

Main Results:

• Identification and description of 14 dedicated peptide-protein docking programs.
• Inclusion of small-molecule docking software capable of peptide docking.
• Exploration of emerging AI-driven docking approaches.
• Detailed comparison of the characteristics and performance of various tools.

Conclusions:

• A wide array of peptide-protein docking tools are available, catering to diverse research requirements.
• Understanding the nuances of each program is vital for effective application in drug discovery.
• AI-driven methods represent a promising frontier in enhancing docking accuracy and efficiency.