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Identifying novel antimicrobial peptides from venom gland of spider Pardosa astrigera by deep multi-task learning
Antimicrobial peptides (AMPs) show promises as valuable compounds for developing therapeutic agents to control the worldwide health threat posed by the increasing prevalence of antibiotic-resistant bacteria. Animal venom can be a useful source for screening AMPs due to its various bioactive componen...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9449525/ https://www.ncbi.nlm.nih.gov/pubmed/36090084 http://dx.doi.org/10.3389/fmicb.2022.971503 |
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author | Lee, Byungjo Shin, Min Kyoung Yoo, Jung Sun Jang, Wonhee Sung, Jung-Suk |
author_facet | Lee, Byungjo Shin, Min Kyoung Yoo, Jung Sun Jang, Wonhee Sung, Jung-Suk |
author_sort | Lee, Byungjo |
collection | PubMed |
description | Antimicrobial peptides (AMPs) show promises as valuable compounds for developing therapeutic agents to control the worldwide health threat posed by the increasing prevalence of antibiotic-resistant bacteria. Animal venom can be a useful source for screening AMPs due to its various bioactive components. Here, the deep learning model was developed to predict species-specific antimicrobial activity. To overcome the data deficiency, a multi-task learning method was implemented, achieving F1 scores of 0.818, 0.696, 0.814, 0.787, and 0.719 for Bacillus subtilis, Escherichia coli, Pseudomonas aeruginosa, Staphylococcus aureus, and Staphylococcus epidermidis, respectively. Peptides PA-Full and PA-Win were identified from the model using different inputs of full and partial sequences, broadening the application of transcriptome data of the spider Pardosa astrigera. Two peptides exhibited strong antimicrobial activity against all five strains along with cytocompatibility. Our approach enables excavating AMPs with high potency, which can be expanded into the fields of biology to address data insufficiency. |
format | Online Article Text |
id | pubmed-9449525 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-94495252022-09-08 Identifying novel antimicrobial peptides from venom gland of spider Pardosa astrigera by deep multi-task learning Lee, Byungjo Shin, Min Kyoung Yoo, Jung Sun Jang, Wonhee Sung, Jung-Suk Front Microbiol Microbiology Antimicrobial peptides (AMPs) show promises as valuable compounds for developing therapeutic agents to control the worldwide health threat posed by the increasing prevalence of antibiotic-resistant bacteria. Animal venom can be a useful source for screening AMPs due to its various bioactive components. Here, the deep learning model was developed to predict species-specific antimicrobial activity. To overcome the data deficiency, a multi-task learning method was implemented, achieving F1 scores of 0.818, 0.696, 0.814, 0.787, and 0.719 for Bacillus subtilis, Escherichia coli, Pseudomonas aeruginosa, Staphylococcus aureus, and Staphylococcus epidermidis, respectively. Peptides PA-Full and PA-Win were identified from the model using different inputs of full and partial sequences, broadening the application of transcriptome data of the spider Pardosa astrigera. Two peptides exhibited strong antimicrobial activity against all five strains along with cytocompatibility. Our approach enables excavating AMPs with high potency, which can be expanded into the fields of biology to address data insufficiency. Frontiers Media S.A. 2022-08-24 /pmc/articles/PMC9449525/ /pubmed/36090084 http://dx.doi.org/10.3389/fmicb.2022.971503 Text en Copyright © 2022 Lee, Shin, Yoo, Jang and Sung. 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 | Microbiology Lee, Byungjo Shin, Min Kyoung Yoo, Jung Sun Jang, Wonhee Sung, Jung-Suk Identifying novel antimicrobial peptides from venom gland of spider Pardosa astrigera by deep multi-task learning |
title | Identifying novel antimicrobial peptides from venom gland of spider Pardosa astrigera by deep multi-task learning |
title_full | Identifying novel antimicrobial peptides from venom gland of spider Pardosa astrigera by deep multi-task learning |
title_fullStr | Identifying novel antimicrobial peptides from venom gland of spider Pardosa astrigera by deep multi-task learning |
title_full_unstemmed | Identifying novel antimicrobial peptides from venom gland of spider Pardosa astrigera by deep multi-task learning |
title_short | Identifying novel antimicrobial peptides from venom gland of spider Pardosa astrigera by deep multi-task learning |
title_sort | identifying novel antimicrobial peptides from venom gland of spider pardosa astrigera by deep multi-task learning |
topic | Microbiology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9449525/ https://www.ncbi.nlm.nih.gov/pubmed/36090084 http://dx.doi.org/10.3389/fmicb.2022.971503 |
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