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通过深度多任务学习从蜘蛛毒腺中鉴定新型抗菌肽。

Identifying novel antimicrobial peptides from venom gland of spider by deep multi-task learning.

作者信息

Lee Byungjo, Shin Min Kyoung, Yoo Jung Sun, Jang Wonhee, Sung Jung-Suk

机构信息

Department of Life Science, Dongguk University-Seoul, Goyang-si, South Korea.

Animal Resources Division, National Institute of Biological Resources, Incheon, South Korea.

出版信息

Front Microbiol. 2022 Aug 24;13:971503. doi: 10.3389/fmicb.2022.971503. eCollection 2022.

DOI:10.3389/fmicb.2022.971503
PMID:36090084
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9449525/
Abstract

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 1 scores of 0.818, 0.696, 0.814, 0.787, and 0.719 for , , , , and , 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 . 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.

摘要

抗菌肽（AMPs）有望成为开发治疗药物的有价值化合物，以应对全球因抗生素耐药菌患病率上升而带来的健康威胁。动物毒液因其各种生物活性成分，可作为筛选抗菌肽的有用来源。在此，开发了深度学习模型来预测物种特异性抗菌活性。为克服数据不足问题，实施了多任务学习方法，对于[具体内容缺失]、[具体内容缺失]、[具体内容缺失]、[具体内容缺失]和[具体内容缺失]，分别取得了0.818、0.696、0.814、0.787和0.719的分数。使用完整和部分序列的不同输入从模型中鉴定出肽PA - Full和PA - Win，拓宽了蜘蛛转录组数据的应用。两种肽对所有五种菌株均表现出强大的抗菌活性以及细胞相容性。我们的方法能够挖掘出高效的抗菌肽，可扩展到生物学领域以解决数据不足问题。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/eedc/9449525/b6713e16617e/fmicb-13-971503-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/eedc/9449525/3490312422a5/fmicb-13-971503-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/eedc/9449525/c63ae4a3c9d6/fmicb-13-971503-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/eedc/9449525/f79e447aab77/fmicb-13-971503-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/eedc/9449525/662f2dc959d8/fmicb-13-971503-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/eedc/9449525/b6713e16617e/fmicb-13-971503-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/eedc/9449525/3490312422a5/fmicb-13-971503-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/eedc/9449525/c63ae4a3c9d6/fmicb-13-971503-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/eedc/9449525/f79e447aab77/fmicb-13-971503-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/eedc/9449525/662f2dc959d8/fmicb-13-971503-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/eedc/9449525/b6713e16617e/fmicb-13-971503-g005.jpg

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Membrane Interactions of Latarcins: Antimicrobial Peptides from Spider Venom.
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