Integrating In Silico and In Vitro Approaches to Identify Natural Peptides with Selective Cytotoxicity against Cancer Cells

Abstract

Anticancer peptides (ACPs) are bioactive compounds known for their selective cytotoxicity against tumor cells via various mechanisms. Recent studies have demonstrated that in silico machine learning methods are effective in predicting peptides with anticancer activity. In this study, we collected and analyzed over a thousand experimentally verified ACPs, specifically targeting peptides derived from natural sources. We developed a precise prediction model based on their sequence and structural features, and the model's evaluation results suggest its strong predictive ability for anticancer activity. To enhance reliability, we integrated the results of this model with those from other available methods. In total, we identified 176 potential ACPs, some of which were synthesized and further evaluated using the MTT colorimetric assay. All of these putative ACPs exhibited significant anticancer effects and selective cytotoxicity against specific tumor cells. In summary, we present a strategy for identifying and characterizing natural peptides with selective cytotoxicity against cancer cells, which could serve as novel therapeutic agents. Our prediction model can effectively screen new molecules for potential anticancer activity, and the results from in vitro experiments provide compelling evidence of the candidates' anticancer effects and selective cytotoxicity.

Keywords: anticancer activity; anticancer peptide; antitumor peptide; in silico analysis; in vitro experiments; machine learning; selective cytotoxicity.

Figure 1

Flowchart depicting process for identifying natural peptides with selective cytotoxicity against cancer cells.

Figure 2

Comparison of amino acid compositions between ACPs and non-ACPs. The uppercase letters around the image represent various amino acids. The blue lines represent the amino acid compositions of ACPs, while the red lines represent those of non-ACPs.

Figure 3

Comparison of frequencies of occurrence of 20 × 20 amino acid pairs separated by k residues between ACPs and non-ACPs.

Figure 4

Comparison of compositions of secondary structure elements between ACPs and non-ACPs.

Figure 5

A comparison of the composition of amino acids and secondary structure elements at the N- and C-terminal regions between ACPs and non-ACPs. The uppercase letters in the upper part of figure represent the amino acids in the peptide sequences, with blue indicating positively charged amino acids and red indicating negatively charged amino acids. In the lower part, the uppercase letters represent the secondary structure of the peptide sequences, where H stands for alpha-helix, E stands for beta-sheet, and C stands for random coil.

Figure 6

The ROC curves of models trained by sequence and structure-based features based on the results of five-fold cross-validation experiments.

Figure 7

Functional enrichment analysis for candidate ACPs highlighting significant GO terms.

Figure 8

A validation of the anticancer activity of the putative ACPs against various cancer cell lines using the MTT colorimetric assay.