Work

Discovery of anticancer peptides from natural and generated sequences using deep learning

Year: 2024

Type: article

Abstract: Anticancer peptides (ACPs) demonstrate significant potential in clinical cancer treatment due to their ability to selectively target and kill cancer cells. In recent years, numerous artificial intelligence (AI) algorithms have been developed. However, many predictive methods lack sufficient wet lab validation, thereby constraining the progress of models and impeding the discovery of novel ACPs. This study proposes a comprehensive research strategy by introducing CNBT-ACPred, an ACP prediction model based on a three-channel deep learning architecture, supported by extensive in vitro and in vivo experiments. CNBT-ACPred achieved an accuracy of 0.9554 and a Matthews Correlation Coefficient (MCC) of 0.8602. Compared to existing excellent models, CNBT-ACPred increased accuracy by at least 5 % and improved MCC by 15 %. Predictions were conducted on over 3.8 million sequences from Uniprot, along with 100,000 sequences generated by a deep generative model, ultimately identifying 37 out of 41 candidate peptides from >30 species that exhibited effective in vitro tumor inhibitory activity. Among these, tPep14 demonstrated significant anticancer effects in two mouse xenograft models without detectable toxicity. Finally, the study revealed correlations between the amino acid composition , structure, and function of the identified ACP candidates. • CNBT-ACPred, a novel three-channel ACP prediction model, outperforms existing models in identifying anticancer peptides. • Cytotoxicity tests revealed that CNBT-ACPred achieves the accuracy over 90% for prediciting ACPs. • Peptide tPep14 from Eumenes micado , was found to be a promising anticancer peptide with high activity and low toxicity. • An integrated research pipeline for efficient ACP discovery and development was proposed.

Source: International Journal of Biological Macromolecules

DOI: 10.1016/j.ijbiomac.2024.138880