Issue 5, 2024

Leveraging machine learning models for peptide–protein interaction prediction

Abstract

Peptides play a pivotal role in a wide range of biological activities through participating in up to 40% protein–protein interactions in cellular processes. They also demonstrate remarkable specificity and efficacy, making them promising candidates for drug development. However, predicting peptide–protein complexes by traditional computational approaches, such as docking and molecular dynamics simulations, still remains a challenge due to high computational cost, flexible nature of peptides, and limited structural information of peptide–protein complexes. In recent years, the surge of available biological data has given rise to the development of an increasing number of machine learning models for predicting peptide–protein interactions. These models offer efficient solutions to address the challenges associated with traditional computational approaches. Furthermore, they offer enhanced accuracy, robustness, and interpretability in their predictive outcomes. This review presents a comprehensive overview of machine learning and deep learning models that have emerged in recent years for the prediction of peptide–protein interactions.

Graphical abstract: Leveraging machine learning models for peptide–protein interaction prediction

Article information

Article type
Review Article
Submitted
27 Oct 2023
Accepted
07 Feb 2024
First published
13 Mar 2024
This article is Open Access
Creative Commons BY license

RSC Chem. Biol., 2024,5, 401-417

Leveraging machine learning models for peptide–protein interaction prediction

S. Yin, X. Mi and D. Shukla, RSC Chem. Biol., 2024, 5, 401 DOI: 10.1039/D3CB00208J

This article is licensed under a Creative Commons Attribution 3.0 Unported Licence. You can use material from this article in other publications without requesting further permissions from the RSC, provided that the correct acknowledgement is given.

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