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AMP-EBiLSTM: employing novel deep learning strategies for the accurate prediction of antimicrobial peptides
Antimicrobial peptides are present ubiquitously in intra- and extra-biological environments and display considerable antibacterial and antifungal activities. Clinically, it has shown good antibacterial effect in the treatment of diabetic foot and its complications. However, the discovery and screeni...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10405519/ https://www.ncbi.nlm.nih.gov/pubmed/37554402 http://dx.doi.org/10.3389/fgene.2023.1232117 |
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author | Wang, Yuanda Wang, Liyang Li, Chengquan Pei, Yilin Liu, Xiaoxiao Tian, Yu |
author_facet | Wang, Yuanda Wang, Liyang Li, Chengquan Pei, Yilin Liu, Xiaoxiao Tian, Yu |
author_sort | Wang, Yuanda |
collection | PubMed |
description | Antimicrobial peptides are present ubiquitously in intra- and extra-biological environments and display considerable antibacterial and antifungal activities. Clinically, it has shown good antibacterial effect in the treatment of diabetic foot and its complications. However, the discovery and screening of antimicrobial peptides primarily rely on wet lab experiments, which are inefficient. This study endeavors to create a precise and efficient method of predicting antimicrobial peptides by incorporating novel machine learning technologies. We proposed a deep learning strategy named AMP-EBiLSTM to accurately predict them, and compared its performance with ensemble learning and baseline models. We utilized Binary Profile Feature (BPF) and Pseudo Amino Acid Composition (PSEAAC) for effective local sequence capture and amino acid information extraction, respectively, in deep learning and ensemble learning. Each model was cross-validated and externally tested independently. The results demonstrate that the Enhanced Bi-directional Long Short-Term Memory (EBiLSTM) deep learning model outperformed others with an accuracy of 92.39% and AUC value of 0.9771 on the test set. On the other hand, the ensemble learning models demonstrated cost-effectiveness in terms of training time on a T4 server equipped with 16 GB of GPU memory and 8 vCPUs, with training durations varying from 0 to 30 s. Therefore, the strategy we propose is expected to predict antimicrobial peptides more accurately in the future. |
format | Online Article Text |
id | pubmed-10405519 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-104055192023-08-08 AMP-EBiLSTM: employing novel deep learning strategies for the accurate prediction of antimicrobial peptides Wang, Yuanda Wang, Liyang Li, Chengquan Pei, Yilin Liu, Xiaoxiao Tian, Yu Front Genet Genetics Antimicrobial peptides are present ubiquitously in intra- and extra-biological environments and display considerable antibacterial and antifungal activities. Clinically, it has shown good antibacterial effect in the treatment of diabetic foot and its complications. However, the discovery and screening of antimicrobial peptides primarily rely on wet lab experiments, which are inefficient. This study endeavors to create a precise and efficient method of predicting antimicrobial peptides by incorporating novel machine learning technologies. We proposed a deep learning strategy named AMP-EBiLSTM to accurately predict them, and compared its performance with ensemble learning and baseline models. We utilized Binary Profile Feature (BPF) and Pseudo Amino Acid Composition (PSEAAC) for effective local sequence capture and amino acid information extraction, respectively, in deep learning and ensemble learning. Each model was cross-validated and externally tested independently. The results demonstrate that the Enhanced Bi-directional Long Short-Term Memory (EBiLSTM) deep learning model outperformed others with an accuracy of 92.39% and AUC value of 0.9771 on the test set. On the other hand, the ensemble learning models demonstrated cost-effectiveness in terms of training time on a T4 server equipped with 16 GB of GPU memory and 8 vCPUs, with training durations varying from 0 to 30 s. Therefore, the strategy we propose is expected to predict antimicrobial peptides more accurately in the future. Frontiers Media S.A. 2023-07-24 /pmc/articles/PMC10405519/ /pubmed/37554402 http://dx.doi.org/10.3389/fgene.2023.1232117 Text en Copyright © 2023 Wang, Wang, Li, Pei, Liu and Tian. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Genetics Wang, Yuanda Wang, Liyang Li, Chengquan Pei, Yilin Liu, Xiaoxiao Tian, Yu AMP-EBiLSTM: employing novel deep learning strategies for the accurate prediction of antimicrobial peptides |
title | AMP-EBiLSTM: employing novel deep learning strategies for the accurate prediction of antimicrobial peptides |
title_full | AMP-EBiLSTM: employing novel deep learning strategies for the accurate prediction of antimicrobial peptides |
title_fullStr | AMP-EBiLSTM: employing novel deep learning strategies for the accurate prediction of antimicrobial peptides |
title_full_unstemmed | AMP-EBiLSTM: employing novel deep learning strategies for the accurate prediction of antimicrobial peptides |
title_short | AMP-EBiLSTM: employing novel deep learning strategies for the accurate prediction of antimicrobial peptides |
title_sort | amp-ebilstm: employing novel deep learning strategies for the accurate prediction of antimicrobial peptides |
topic | Genetics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10405519/ https://www.ncbi.nlm.nih.gov/pubmed/37554402 http://dx.doi.org/10.3389/fgene.2023.1232117 |
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