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Published on: January 26, 2024
ACP-DPE: A Dual-Channel Deep Learning Model for Anticancer Peptide Prediction
Guohua Huang1,2, Yujie Cao1, Qi Dai3
1College of Information Science and Engineering, Shaoyang University, Shaoyang, China.
Abstract:
Cancer is a serious and complex disease caused by uncontrolled cell growth and is becoming one of the leading causes of death worldwide. Anticancer peptides (ACPs), as a bioactive peptide with lower toxicity, emerge as a promising means of effectively treating cancer. Identifying ACPs is challenging due to the limitation of experimental conditions. To address this, we proposed a dual-channel-based deep learning method, termed ACP-DPE, for ACP prediction. The ACP-DPE consisted of two parallel channels: one was an embedding layer followed by the bi-directional gated recurrent unit (Bi-GRU) module, and the other was an adaptive embedding layer followed by the dilated convolution module. The Bi-GRU module captured the peptide sequence dependencies, whereas the dilated convolution module characterised the local relationship of amino acids. Experimental results show that ACP-DPE achieves an accuracy of 82.81% and a sensitivity of 86.63%, surpassing the state-of-the-art method by 3.86% and 5.1%, respectively. These findings demonstrate the effectiveness of ACP-DPE for ACP prediction and highlight its potential as a valuable tool in cancer treatment research.
Insights
A new deep learning method, ACP-DPE, accurately predicts anticancer peptides (ACPs) for cancer treatment. This computational approach aids in identifying effective ACPs, offering a promising tool for cancer therapy research.
Area of Science:
- Biochemistry
- Computational Biology
- Oncology
Background:
- Cancer is a leading global cause of death due to uncontrolled cell growth.
- Anticancer peptides (ACPs) show promise for cancer treatment due to lower toxicity.
- Experimental identification of ACPs is limited by conditions and cost.
Purpose of the Study:
- To develop a computational method for predicting anticancer peptides (ACPs).
- To address the challenges in experimental identification of ACPs.
- To enhance the discovery of novel ACPs for cancer therapy.
Main Methods:
- Proposed a dual-channel deep learning model, ACP-DPE.
- Utilized a bi-directional gated recurrent unit (Bi-GRU) for sequence dependencies.
- Employed a dilated convolution module for local amino acid relationships.
Main Results:
- ACP-DPE achieved 82.81% accuracy and 86.63% sensitivity.
- The model outperformed the state-of-the-art method by 3.86% in accuracy and 5.1% in sensitivity.
- Demonstrated the computational effectiveness of the dual-channel approach.
Conclusions:
- ACP-DPE is an effective tool for anticancer peptide prediction.
- The deep learning method shows significant potential in cancer treatment research.
- Computational prediction can accelerate the discovery of therapeutic peptides.
