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© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

Anticancer peptides (ACPs) are selective and toxic to cancer cells as new anticancer drugs. Identifying new ACPs is time-consuming and expensive to evaluate all candidates’ anticancer abilities. To reduce the cost of ACP drug development, we collected the most updated ACP data to train a convolutional neural network (CNN) with a peptide sequence encoding method for initial in silico evaluation. Here we introduced PC6, a novel protein-encoding method, to convert a peptide sequence into a computational matrix, representing six physicochemical properties of each amino acid. By integrating data, encoding method, and deep learning model, we developed AI4ACP, a user-friendly web-based ACP distinguisher that can predict the anticancer property of query peptides and promote the discovery of peptides with anticancer activity. The experimental results demonstrate that AI4ACP in CNN, trained using the new ACP collection, outperforms the existing ACP predictors. The 5-fold cross-validation of AI4ACP with the new collection also showed that the model could perform at a stable level on high accuracy around 0.89 without overfitting. Using AI4ACP, users can easily accomplish an early-stage evaluation of unknown peptides and select potential candidates to test their anticancer activities quickly.

Details

Title
Peptide-Based Drug Predictions for Cancer Therapy Using Deep Learning
Author
Yih-Yun Sun 1 ; Tzu-Tang Lin 2 ; Cheng, Wen-Chih 2 ; I-Hsuan, Lu 2 ; Chung-Yen, Lin 2   VIAFID ORCID Logo  ; Shu-Hwa, Chen 3 

 Graduate Institute of Biomedical Electronics and Bioinformatics, College of Electrical Engineering and Computer Science, National Taiwan University, Taipei 106, Taiwan; [email protected]; Institute of Information Science, Academia Sinica, Taipei 115, Taiwan; [email protected] (T.-T.L.); [email protected] (W.-C.C.); [email protected] (I.-H.L.) 
 Institute of Information Science, Academia Sinica, Taipei 115, Taiwan; [email protected] (T.-T.L.); [email protected] (W.-C.C.); [email protected] (I.-H.L.) 
 Research Center of Cancer Translational Medicine, Taipei Medical University, Taipei 110, Taiwan 
First page
422
Publication year
2022
Publication date
2022
Publisher
MDPI AG
e-ISSN
14248247
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2653015124
Copyright
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.