A Review of Current Computational Tools for Peptide–Protein Docking

TL;DR

This review summarizes current computational tools for predicting how peptides interact with proteins, helping researchers choose the best methods for drug discovery.

Contribution

The paper provides a comprehensive comparison of 14 peptide–protein docking programs and emerging AI-driven approaches.

Findings

The paper identifies and describes 14 dedicated peptide–protein docking programs.

It highlights the strengths and limitations of each docking tool.

Emerging AI-driven methods are explored as state-of-the-art alternatives.

Abstract

Peptide–protein docking is an increasingly important technique in computational biochemistry and drug discovery, allowing researchers to predict interactions between peptides and proteins and guiding the development of peptide‐based therapeutics. This review provides a detailed overview of the current landscape of peptide–protein docking programs, emphasizing their importance and versatility. Through an extensive literature search, we identify and describe 14 dedicated peptide–protein docking programs, along with small‐molecule docking software that supports peptide docking. Additionally, we explore state‐of‐the‐art AI‐driven alternatives that are advancing the field. By describing the distinct features, methodological approaches, strengths, and inherent limitations of each docking tool, this review aims to support researchers in navigating the wide range of available docking programs and making well‐informed choices tailored to their specific research objectives.

In this review, we compare 14 specialized peptide‐protein docking programs, adaptable small‐molecule docking tools, and emerging AI‐driven approaches. By clarifying how each method works, we offer researchers an accessible guide to selecting the most suitable tools for biochemical studies and peptide‐focused drug discovery.

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