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Review
. 2023 Jan 23;28(3):1148.
doi: 10.3390/molecules28031148.

Anti-Cancer Peptides: Status and Future Prospects

Gehane Ghaly  1 , Hatem Tallima  1 , Eslam Dabbish  1 , Norhan Badr ElDin  2 , Mohamed K Abd El-Rahman  2   3 , Mahmoud A A Ibrahim  4   5 , Tamer Shoeib  1
Affiliations
Review

Anti-Cancer Peptides: Status and Future Prospects

Gehane Ghaly et al. Molecules. .

Abstract

The dramatic rise in cancer incidence, alongside treatment deficiencies, has elevated cancer to the second-leading cause of death globally. The increasing morbidity and mortality of this disease can be traced back to a number of causes, including treatment-related side effects, drug resistance, inadequate curative treatment and tumor relapse. Recently, anti-cancer bioactive peptides (ACPs) have emerged as a potential therapeutic choice within the pharmaceutical arsenal due to their high penetration, specificity and fewer side effects. In this contribution, we present a general overview of the literature concerning the conformational structures, modes of action and membrane interaction mechanisms of ACPs, as well as provide recent examples of their successful employment as targeting ligands in cancer treatment. The use of ACPs as a diagnostic tool is summarized, and their advantages in these applications are highlighted. This review expounds on the main approaches for peptide synthesis along with their reconstruction and modification needed to enhance their therapeutic effect. Computational approaches that could predict therapeutic efficacy and suggest ACP candidates for experimental studies are discussed. Future research prospects in this rapidly expanding area are also offered.

Keywords: anticancer peptides; cancer microenvironment; cancer therapy; peptide conformation; peptide mode of action.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Schematic representation of the most significant models of ACPs action. The yellow circles and the black linkers represent the hydrophilic and hydrophobic regions, respectively, of the phospholipid cell membranes.
Figure 2
Figure 2
Schematic representation showing the main difference between traditional machine and deep learning approaches.
Figure 3
Figure 3
Schematic representation showing different steps of traditional machine learning approach for building a predictor for ACPs.
Figure 4
Figure 4
Schematic representation showing different steps of deep learning approach for building a predictor for ACP.
See this image and copyright information in PMC

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