Mining the UniProtKB/Swiss-Prot database for antimicrobial peptides.

IF 4.5 3区 生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY
Protein Science Pub Date : 2025-04-01 DOI:10.1002/pro.70083
Chenkai Li, Darcy Sutherland, Ali Salehi, Amelia Richter, Diana Lin, Sambina Islam Aninta, Hossein Ebrahimikondori, Anat Yanai, Lauren Coombe, René L Warren, Monica Kotkoff, Linda M N Hoang, Caren C Helbing, Inanc Birol
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引用次数: 0

Abstract

The ever-growing global health threat of antibiotic resistance is compelling researchers to explore alternatives to conventional antibiotics. Antimicrobial peptides (AMPs) are emerging as a promising solution to fill this need. Naturally occurring AMPs are produced by all forms of life as part of the innate immune system. High-throughput bioinformatics tools have enabled fast and large-scale discovery of AMPs from genomic, transcriptomic, and proteomic resources of selected organisms. Public protein sequence databases, comprising over 200 million records and growing, serve as comprehensive compendia of sequences from a broad range of source organisms. Yet, large-scale in silico probing of those databases for novel AMP discovery using modern deep learning techniques has rarely been reported. In the present study, we propose an AMP mining workflow to predict novel AMPs from the UniProtKB/Swiss-Prot database using the AMP prediction tool, AMPlify, as its discovery engine. Using this workflow, we identified 8008 novel putative AMPs from all eukaryotic sequences in the database. Focusing on the practical use of AMPs as suitable antimicrobial agents with applications in the poultry industry, we prioritized 40 of those AMPs based on their similarities to known chicken AMPs in predicted structures. In our tests, 13 out of the 38 successfully synthesized peptides showed antimicrobial activity against Escherichia coli and/or Staphylococcus aureus. AMPlify and the companion scripts supporting the AMP mining workflow presented herein are publicly available at https://github.com/bcgsc/AMPlify.

挖掘UniProtKB/Swiss-Prot抗菌肽数据库。
抗生素耐药性日益增长的全球健康威胁迫使研究人员探索传统抗生素的替代品。抗菌肽(AMPs)正在成为填补这一需求的有希望的解决方案。作为先天免疫系统的一部分,所有形式的生命都会产生天然存在的amp。高通量生物信息学工具使得从选定生物体的基因组、转录组和蛋白质组资源中快速、大规模地发现amp成为可能。公共蛋白质序列数据库包含超过2亿条记录,并在不断增长,可作为广泛来源生物序列的综合纲要。然而,使用现代深度学习技术对这些数据库进行大规模的计算机探测以发现新的AMP,这方面的报道很少。在本研究中,我们提出了一个AMP挖掘工作流程,使用AMP预测工具AMPlify作为其发现引擎,从UniProtKB/Swiss-Prot数据库中预测新的AMP。使用该工作流程,我们从数据库中的所有真核生物序列中鉴定出8008个新的推定amp。着眼于抗菌肽在家禽业中作为合适抗菌剂的实际应用,我们根据这些抗菌肽在预测结构上与已知鸡肉抗菌肽的相似性,对其中40种抗菌肽进行了优先排序。在我们的实验中,38个成功合成的肽中有13个显示出对大肠杆菌和/或金黄色葡萄球菌的抗菌活性。AMPlify和支持此处提供的AMP挖掘工作流的配套脚本可在https://github.com/bcgsc/AMPlify上公开获取。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Protein Science
Protein Science 生物-生化与分子生物学
CiteScore
12.40
自引率
1.20%
发文量
246
审稿时长
1 months
期刊介绍: Protein Science, the flagship journal of The Protein Society, is a publication that focuses on advancing fundamental knowledge in the field of protein molecules. The journal welcomes original reports and review articles that contribute to our understanding of protein function, structure, folding, design, and evolution. Additionally, Protein Science encourages papers that explore the applications of protein science in various areas such as therapeutics, protein-based biomaterials, bionanotechnology, synthetic biology, and bioelectronics. The journal accepts manuscript submissions in any suitable format for review, with the requirement of converting the manuscript to journal-style format only upon acceptance for publication. Protein Science is indexed and abstracted in numerous databases, including the Agricultural & Environmental Science Database (ProQuest), Biological Science Database (ProQuest), CAS: Chemical Abstracts Service (ACS), Embase (Elsevier), Health & Medical Collection (ProQuest), Health Research Premium Collection (ProQuest), Materials Science & Engineering Database (ProQuest), MEDLINE/PubMed (NLM), Natural Science Collection (ProQuest), and SciTech Premium Collection (ProQuest).
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