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Updated: Jan 11, 2026
Construction of Cyclic Cell-Penetrating Peptides for Enhanced Penetration of Biological Barriers
Published on: September 19, 2022
Emerging landscape of bioinformatics and artificial intelligence applications in cell-penetrating peptide-based
Yu Sun1, Muqing Zhang2, Huiting Liu1
1PET Center, Chongqing University Three Gorges Hospital, Chongqing, China.
Introduction:
Peptides play diverse roles in biological processes, including drug discovery, antibacterial activity, and protein-protein interactions, making peptide prediction a crucial field. The development of bioinformatics tools has significantly enhanced our ability to study and harness peptide potential. Among these, cell-penetrating peptides (CPP) are a unique class of polypeptides capable of crossing cell membranes, facilitating the intracellular delivery of therapeutic agents such as small molecules, peptides, proteins, and nucleic acids. This ability has expanded possibilities in drug delivery, gene therapy, and molecular imaging. However, identifying and designing effective CPP remains challenging.
Areas Covered:
In recent years, various computational tools and algorithms have been developed to predict the cell-penetration potential of peptides, aiding in the discovery of novel CPP and accelerating their applications. This review provides a comprehensive overview of bioinformatics tools including artificial intelligence (AI) for peptide prediction, with a particular focus on CPP. Systematic literature search was performed from PubMed, Embase, Scopus, and the Web of Science to cover published references related to the current topic from 2011 to October 2025.
Expert Opinion:
Understanding their functions and limitations will help researchers make informed decisions and effectively utilize peptide prediction in diverse scientific and clinical applications.
