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Updated: May 21, 2025

Creating and Applying a Reference to Facilitate the Discussion and Classification of Proteins in a Diverse Group

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Published on: August 16, 2017

挖掘UniProtKB/Swiss-Prot数据库中的抗菌

Chenkai Li^1,2, Darcy Sutherland^1,3,4, Ali Salehi^1,3

¹Canada's Michael Smith Genome Sciences Centre, BC Cancer Agency, Vancouver, British Columbia, Canada.

Protein science : a publication of the Protein Society

|March 18, 2025

概括

研究人员使用生物信息学发现了8008种新的抗微生物 (AMP). 13种合成的体对细菌表现出活性,为禽业提供了抗生素的替代品.

科学领域:

生物信息学是一种生物信息学.
酸科学酸科学
药物发现药物发现药物发现

背景情况:

抗生素耐药性是一个日益严重的全球健康问题,需要寻找新型抗菌剂.
抗微生物 (AMP),是天生的免疫系统的一部分,是传统抗生素有希望的替代品.
从使用深度学习的公共数据库中进行大规模的AMP发现仍未得到充分探索.

研究的目的:

开发和应用生物信息学工作流程,从大型蛋白质数据库中发现新的AMP.
为了确定在家禽行业应用的潜在的AMP.
通过实验验证新发现的AMP候选物的抗菌活性.

主要方法:

使用AMPlify预测工具开发了一个新的AMP挖矿工作流.
该工作流应用于UniProtKB/Swiss-Prot数据库,重点是真核生物序列.
假定AMP的优先级是基于与已知的AMP的结构相似性,并合成和测试了选定的.

主要成果:

工作流确定了8008个来自真核生物序列的新型假定AMP.
对于潜在的家禽申请,有40个AMP候选企业被优先考虑.
在38种合成的中,有13种表现出对大肠杆菌和/或金黄色葡萄球菌的抗菌活性.

结论:

开发的AMP挖矿工作流是有效的从大序列数据库发现新型抗菌.
已确定的AMP显示出对抗细菌感染的潜力,特别是在家禽部门.
这项研究强调了生物信息学和深度学习在加速发现新型抗菌剂方面的实用性.

关键词:

抗生素耐药性抗生素耐药性抗微生物是一种抗微生物.深度学习是一种深度学习.蛋白质序列数据库蛋白质序列数据库

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