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

Jianda Yue; Tingting Li; Jiawei Xu; Zihui Chen; Yaqi Li; Songping Liang; Zhonghua Liu; Ying Wang

doi:10.1016/j.ijbiomac.2024.138880

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

Int J Biol Macromol. 2024 Dec 18:290:138880. doi: 10.1016/j.ijbiomac.2024.138880. Online ahead of print.

Authors

Jianda Yue¹, Tingting Li², Jiawei Xu³, Zihui Chen⁴, Yaqi Li⁵, Songping Liang⁶, Zhonghua Liu⁷, Ying Wang⁸

Affiliations

¹ The National and Local Joint Engineering Laboratory of Animal Peptide Drug Development, College of Life Sciences, Hunan Normal University, Changsha 410081, Hunan, China; Peptide and small molecule drug R&D plateform, Furong Laboratory, Hunan Normal University, Changsha 410081, Hunan, China; Institute of Interdisciplinary Studies, Hunan Normal University, Changsha 410081, Hunan, China. Electronic address: 201630151080@hunnu.edu.cn.
² The National and Local Joint Engineering Laboratory of Animal Peptide Drug Development, College of Life Sciences, Hunan Normal University, Changsha 410081, Hunan, China; Peptide and small molecule drug R&D plateform, Furong Laboratory, Hunan Normal University, Changsha 410081, Hunan, China; Institute of Interdisciplinary Studies, Hunan Normal University, Changsha 410081, Hunan, China. Electronic address: 202320142852@hunnu.edu.cn.
³ The National and Local Joint Engineering Laboratory of Animal Peptide Drug Development, College of Life Sciences, Hunan Normal University, Changsha 410081, Hunan, China; Peptide and small molecule drug R&D plateform, Furong Laboratory, Hunan Normal University, Changsha 410081, Hunan, China; Institute of Interdisciplinary Studies, Hunan Normal University, Changsha 410081, Hunan, China. Electronic address: 202070141023@hunnu.edu.cn.
⁴ The National and Local Joint Engineering Laboratory of Animal Peptide Drug Development, College of Life Sciences, Hunan Normal University, Changsha 410081, Hunan, China; Peptide and small molecule drug R&D plateform, Furong Laboratory, Hunan Normal University, Changsha 410081, Hunan, China; Institute of Interdisciplinary Studies, Hunan Normal University, Changsha 410081, Hunan, China.
⁵ The National and Local Joint Engineering Laboratory of Animal Peptide Drug Development, College of Life Sciences, Hunan Normal University, Changsha 410081, Hunan, China; Peptide and small molecule drug R&D plateform, Furong Laboratory, Hunan Normal University, Changsha 410081, Hunan, China; Institute of Interdisciplinary Studies, Hunan Normal University, Changsha 410081, Hunan, China. Electronic address: yqli@hunnu.edu.cn.
⁶ The National and Local Joint Engineering Laboratory of Animal Peptide Drug Development, College of Life Sciences, Hunan Normal University, Changsha 410081, Hunan, China; Peptide and small molecule drug R&D plateform, Furong Laboratory, Hunan Normal University, Changsha 410081, Hunan, China; Institute of Interdisciplinary Studies, Hunan Normal University, Changsha 410081, Hunan, China. Electronic address: liangsp@public.cs.hn.cn.
⁷ The National and Local Joint Engineering Laboratory of Animal Peptide Drug Development, College of Life Sciences, Hunan Normal University, Changsha 410081, Hunan, China; Peptide and small molecule drug R&D plateform, Furong Laboratory, Hunan Normal University, Changsha 410081, Hunan, China; Institute of Interdisciplinary Studies, Hunan Normal University, Changsha 410081, Hunan, China. Electronic address: liuzh@hunnu.edu.cn.
⁸ The National and Local Joint Engineering Laboratory of Animal Peptide Drug Development, College of Life Sciences, Hunan Normal University, Changsha 410081, Hunan, China; Peptide and small molecule drug R&D plateform, Furong Laboratory, Hunan Normal University, Changsha 410081, Hunan, China; Institute of Interdisciplinary Studies, Hunan Normal University, Changsha 410081, Hunan, China. Electronic address: wangyin@hunnu.edu.cn.

PMID: 39706427
DOI: 10.1016/j.ijbiomac.2024.138880

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.

Keywords: Anticancer peptides; Deep learning; In vivo experiments; Sequence mining; Species analysis.