Prediction of Cholecystokinin-Secretory Peptides Using Bidirectional Long Short-term Memory Model Based on Transfer Learning and Hierarchical Attention Network Mechanism

Jing Liu; Pu Chen; Hongdong Song; Pengxiao Zhang; Man Wang; Zhenliang Sun; Xiao Guan

doi:10.3390/biom13091372

Prediction of Cholecystokinin-Secretory Peptides Using Bidirectional Long Short-term Memory Model Based on Transfer Learning and Hierarchical Attention Network Mechanism

Biomolecules. 2023 Sep 11;13(9):1372. doi: 10.3390/biom13091372.

Authors

Jing Liu¹, Pu Chen¹, Hongdong Song², Pengxiao Zhang³, Man Wang³, Zhenliang Sun³, Xiao Guan²

Affiliations

¹ College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China.
² School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.
³ Joint Center for Translational Medicine, Southern Medical University Affiliated Fengxian Hospital, Shanghai 201499, China.

Abstract

Cholecystokinin (CCK) can make the human body feel full and has neurotrophic and anti-inflammatory effects. It is beneficial in treating obesity, Parkinson's disease, pancreatic cancer, and cholangiocarcinoma. Traditional biological experiments are costly and time-consuming when it comes to finding and identifying novel CCK-secretory peptides, and there is an urgent need to develop a new computational method to predict new CCK-secretory peptides. This study combines the transfer learning method with the SMILES enumeration data augmentation strategy to solve the data scarcity problem. It establishes a fusion model of the hierarchical attention network (HAN) and bidirectional long short-term memory (BiLSTM), which fully extracts peptide chain features to predict CCK-secretory peptides efficiently. The average accuracy of the proposed method in this study is 95.99%, with an AUC of 98.07%. The experimental results show that the proposed method is significantly superior to other comparative methods in accuracy and robustness. Therefore, this method is expected to be applied to the preliminary screening of CCK-secretory peptides.

Keywords: BiLSTM; CCK-secretory peptides; SMILES enumeration; cholecystokinin; hierarchical attention network; transfer learning.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Cholecystokinin*
Humans
Machine Learning*
Peptides / pharmacology
Receptors, Cholecystokinin

Substances

Cholecystokinin
Peptides
Receptors, Cholecystokinin

Grants and funding

This work was supported by the National Key Research and Development Program (2022YFF1100102), the National Natural Science Foundation of China (32172247), and the Major Project of Inner Mongolia Science and Technology Department (2021ZD0002).