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在植物寄生虫的效应蛋白中的特定图案的识别和表征,使用MONSTER

Giulia Calia^1,2,3, Paola Porracciolo^3,4, Yongpan Chen^3,5

¹Free University of Bolzano, Faculty of Agricultural Environmental and Food Science, Bolzano, Italy.

Communications biology

|July 11, 2024

概括

识别病原体效应因子对于作物安全至关重要. 我们开发了MONSTER,这是一个生物信息学工具,它聚合了蛋白质动机 (CLUMPs) 来优先考虑效应体候选物,改进了病原体控制策略.

科学领域:

植物病理学植物病理学
生物信息学是一种生物信息学.
分子生物学分子生物学

背景情况:

植物病原体导致大量作物损失,威胁全球粮食安全.
有效的控制需要识别和描述病原体的作用因子.
目前的效应体识别方法由于序列保存不佳而具有挑战性,并产生了众多候选者.

研究的目的:

开发一种生物信息学工具,以有效识别和优先考虑病原体效应因子候选物.
解决现有方法在处理不良序列保存和候选冗余方面的局限性.
创建一个工具,将蛋白质序列图案集成到CLUMP (图案集群) 中,并附带相关的分数.

主要方法:

开发了MONSTER,这是一个新的生物信息学工具,用于识别蛋白质序列 (CLUMPs) 的动图集群.
蒙斯特可以接受来自新工具或数据库 (Pfam,InterProScan) 的图案.
该工具通过基于氨基酸物理化学性质和发生情况的聚类和评分动机来减少动机冗余.

主要成果:

将MONSTER应用于虫效应者和植物寄生虫线虫.
在已知的线虫候选寄生体蛋白质中约60%中确定了6个CLUMP.
发现了CLUMP与蛋白质域的同时存在,这对入侵和致病性至关重要.

结论:

通过聚类动机,MONSTER有效地识别和优先考虑病原体效应因子候选物.
该工具有助于了解效应器功能和潜在目标,推进作物保护战略.
MONSTER的动机聚类和评分功能超出了效应体的表征范围,适用于任何蛋白质序列集.

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