ACPNet: A Deep Learning Network to Identify Anticancer Peptides by Hybrid Sequence Information.

Mingwei Sun,Sen Yang,You Zhou,Xuemei Hu

doi:10.3390/molecules27051544

Abstract

Cancer is one of the most dangerous threats to human health. One of the issues is drug resistance action, which leads to side effects after drug treatment. Numerous therapies have endeavored to relieve the drug resistance action. Recently, anticancer peptides could be a novel and promising anticancer candidate, which can inhibit tumor cell proliferation, migration, and suppress the formation of tumor blood vessels, with fewer side effects. However, it is costly, laborious and time consuming to identify anticancer peptides by biological experiments with a high throughput. Therefore, accurately identifying anti-cancer peptides becomes a key and indispensable step for anticancer peptides therapy. Although some existing computer methods have been developed to predict anticancer peptides, the accuracy still needs to be improved. Thus, in this study, we propose a deep learning-based model, called ACPNet, to distinguish anticancer peptides from non-anticancer peptides (non-ACPs). ACPNet employs three different types of peptide sequence information, peptide physicochemical properties and auto-encoding features linking the training process. ACPNet is a hybrid deep learning network, which fuses fully connected networks and recurrent neural networks. The comparison with other existing methods on ACPs82 datasets shows that ACPNet not only achieves the improvement of 1.2% Accuracy, 2.0% F1-score, and 7.2% Recall, but also gets balanced performance on the Matthews correlation coefficient. Meanwhile, ACPNet is verified on an independent dataset, with 20 proven anticancer peptides, and only one anticancer peptide is predicted as non-ACPs. The comparison and independent validation experiment indicate that ACPNet can accurately distinguish anticancer peptides from non-ACPs.

Highlights

Cancer is an enormous threat to human health, reported by the International Agency for Research on Cancer (IARC) [1], leading to rapidly increasing mortality every year, reaching 18.1 million new cases and 9.6 million cancer deaths in 2018 alone
Peptide p28 is a post-translational, multi-target anticancer agent that preferentially enters a wide variety of solid tumor cells and binds to both wild-type and mutant p53 protein, inhibiting constitutional morphogenic protein 1 (Cop1)-mediated ubiquitination and proteasomal degradation of p53, which results in increased levels of p53 and induces cell-cycle arrest at G2/M and eventual apoptosis, which results in tumor cell shrinkage and death [4]
To explore the effect of the combination of manually selected features and automatic learning features, the performances conducted by three types of combination are compared on ACPs250, and ACPs82

Summary

Introduction

Cancer is an enormous threat to human health, reported by the International Agency for Research on Cancer (IARC) [1], leading to rapidly increasing mortality every year, reaching 18.1 million new cases and 9.6 million cancer deaths in 2018 alone. Traditional methods for the treatment of cancer always meet costly and highrisk problems, accompanied by the risk of tissue death, drug resistance and other serious side effects [2]. Increasing evidence shows that some novel anticancer agents, for some peptides for instance, become novel and safe for therapy [3]. Peptide p28 is a post-translational, multi-target anticancer agent that preferentially enters a wide variety of solid tumor cells and binds to both wild-type and mutant p53 protein, inhibiting constitutional morphogenic protein 1 (Cop1)-mediated ubiquitination and proteasomal degradation of p53, which results in increased levels of p53 and induces cell-cycle arrest at G2/M and eventual apoptosis, which results in tumor cell shrinkage and death [4]. With the rapid development of data science, it is very desirable to design machine learning-based methods to identify ACPs [7]

Methods

Results

Discussion

Conclusion