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https://hdl.handle.net/2440/139649
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Type: | Journal article |
Title: | iAMPCN: a deep-learning approach for identifying antimicrobial peptides and their functional activities |
Author: | Xu, J. Li, F. Li, C. Guo, X. Landersdorfer, C. Shen, H.H. Peleg, A.Y. Li, J. Imoto, S. Yao, J. Akutsu, T. Song, J. |
Citation: | Briefings in Bioinformatics, 2023; 24(4):1-20 |
Publisher: | Oxford University Press |
Issue Date: | 2023 |
ISSN: | 1467-5463 1477-4054 |
Statement of Responsibility: | Jing Xu, Fuyi Li, Chen Li, Xudong Guo, Cornelia Landersdorfer, Hsin-Hui Shen, Anton Y. Peleg, Jian Li, Seiya Imoto, Jianhua Yao, Tatsuya Akutsu and Jiangning Song |
Abstract: | Antimicrobial peptides (AMPs) are short peptides that play crucial roles in diverse biological processes and have various functional activities against target organisms. Due to the abuse of chemical antibiotics and microbial pathogens' increasing resistance to antibiotics, AMPs have the potential to be alternatives to antibiotics. As such, the identification of AMPs has become a widely discussed topic. A variety of computational approaches have been developed to identify AMPs based on machine learning algorithms. However, most of them are not capable of predicting the functional activities of AMPs, and those predictors that can specify activities only focus on a few of them. In this study, we first surveyed 10 predictors that can identify AMPs and their functional activities in terms of the features they employed and the algorithms they utilized. Then, we constructed comprehensive AMP datasets and proposed a new deep learning-based framework, iAMPCN (identification of AMPs based on CNNs), to identify AMPs and their related 22 functional activities. Our experiments demonstrate that iAMPCN significantly improved the prediction performance of AMPs and their corresponding functional activities based on four types of sequence features. Benchmarking experiments on the independent test datasets showed that iAMPCN outperformed a number of state-of-the-art approaches for predicting AMPs and their functional activities. Furthermore, we analyzed the amino acid preferences of different AMP activities and evaluated the model on datasets of varying sequence redundancy thresholds. To facilitate the community-wide identification of AMPs and their corresponding functional types, we have made the source codes of iAMPCN publicly available at https://github.com/joy50706/iAMPCN/tree/master. We anticipate that iAMPCN can be explored as a valuable tool for identifying potential AMPs with specific functional activities for further experimental validation. |
Keywords: | antimicrobial peptides; bioinformatics; sequence analysis; machine learning; deep learning; functional activities |
Rights: | © The Author(s) 2023. Published by Oxford University Press. All rights reserved. |
DOI: | 10.1093/bib/bbad240 |
Grant ID: | http://purl.org/au-research/grants/nhmrc/1127948 http://purl.org/au-research/grants/nhmrc/1144652 |
Published version: | http://dx.doi.org/10.1093/bib/bbad240 |
Appears in Collections: | Medicine publications |
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