Abstract
The ongoing COVID-19 pandemic has caused dramatic loss of human life. There is an urgent need for safe and efficient anti-coronavirus infection drugs. Anti-coronavirus peptides (ACovPs) can inhibit coronavirus infection. With high-efficiency, low-toxicity, and broad-spectrum inhibitory effects on coronaviruses, they are promising candidates to be developed into a new type of anti-coronavirus drug. Experiment is the traditional way of ACovPs’ identification, which is less efficient and more expensive. With the accumulation of experimental data on ACovPs, computational prediction provides a cheaper and faster way to find anti-coronavirus peptides’ candidates. In this study, we ensemble several state-of-the-art machine learning methodologies to build nine classification models for the prediction of ACovPs. These models were pre-trained using deep neural networks, and the performance of our ensemble model, ACP-Dnnel, was evaluated across three datasets and independent dataset. We followed Chou's 5-step rules. (1) we constructed the benchmark datasets data1, data2, and data3 for training and testing, and introduced the independent validation dataset ACVP-M; (2) we analyzed the peptides sequence composition feature of the benchmark dataset; (3) we constructed the ACP-Dnnel model with deep convolutional neural network (DCNN) merged the bi-directional long short-term memory (BiLSTM) as the base model for pre-training to extract the features embedded in the benchmark dataset, and then, nine classification algorithms were introduced to ensemble together for classification prediction and voting together; (4) tenfold cross-validation was introduced during the training process, and the final model performance was evaluated; (5) finally, we constructed a user-friendly web server accessible to the public at http://150.158.148.228:5000/. The highest accuracy (ACC) of ACP-Dnnel reaches 97%, and the Matthew’s correlation coefficient (MCC) value exceeds 0.9. On three different datasets, its average accuracy is 96.0%. After the latest independent dataset validation, ACP-Dnnel improved at MCC, SP, and ACC values 6.2%, 7.5% and 6.3% greater, respectively. It is suggested that ACP-Dnnel can be helpful for the laboratory identification of ACovPs, speeding up the anti-coronavirus peptide drug discovery and development. We constructed the web server of anti-coronavirus peptides’ prediction and it is available at http://150.158.148.228:5000/.
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Data availability statement
All data sets used in this paper can be freely downloaded from the http://150.158.148.228:5000/Download.
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Acknowledgements
This project was supported by the Provincial Health Commission Science and Technology Foundation of Guizhou (No. gzwkj2023-590) and the Guizhou Medical University National Natural Science Foundation Cultivation Project (No. 21NSFCP40), and thanks also to all the authors involved in the project.
Funding
This work was supported by the Provincial Health Commission Science and Technology Foundation of Guizhou (No. gzwkj2023-590), Guizhou Medical University National Natural Science Foundation Cultivation Project (No. 21NSFCP40), and National Natural Science Foundation of China (No. 62071099 and No. 32160668).
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M-YL conceived and designed the study and wrote the paper. M-YL, TW, and Y-XZ configured the experimental environment. M-YL and TW finished the developmental of the webserver. And H-ML provides interpretation of the biological significance of anti-coronavirus peptides and gives guidance. Y-WZ and Z-RH participates in project discussions and provides constructive suggestions. C-CX and JH provides algorithm optimization ideas, JH participates in all research work and gives guidance. All authors have read and agreed to the published version of the manuscript.
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Liu, M., Liu, H., Wu, T. et al. ACP-Dnnel: anti-coronavirus peptides’ prediction based on deep neural network ensemble learning. Amino Acids 55, 1121–1136 (2023). https://doi.org/10.1007/s00726-023-03300-6
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DOI: https://doi.org/10.1007/s00726-023-03300-6