Using machine learning to identify the functional consequences of post-translational modifications in the rice proteome

使用机器学习来识别水稻蛋白质组翻译后修饰的功能后果

• 批准号: 2438203
• 金额: --
• 依托单位: University of Liverpool
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2438203
• 关键词: Using; machine; learning; identify; functional

For much of the world's poor, rice (O. sativa) provides the majority of daily calories. Rice productivity has more than doubled in recent decades, resulting from continued breeding efforts. However, to meet the demands imposed by the projected increase in population, rice production has to continue growing rapidly, while meeting challenges imposed by a changing climate. With the recent sequencing of >3000 different varieties, there is a huge genetic resource available for identifying genetic polymorphisms associated with desirable traits e.g. tolerance to biotic or abiotic stress, yield, nutritional content etc., which in due course could be bred into a major crop variety. However, there is a great knowledge gap at present between genetic variation and functional effects, in terms of how plants respond at the cellular level to different stresses.Under normal field conditions, plants can be exposed to various biotic and abiotic environmental stresses. Plant stress tolerance and acclimation depends on significant changes in post-translational modifications (PTMs) of specific proteins to switch function rapidly. It is expected that environmental stresses, such as drought and heat will become more prevalent in the coming decades. Future successful solutions will undoubtedly involve applying next-generation technologies to improve the breeding of agro-economically important crops. This study aims to identify and quantify PTMs in rice, and in the model plant Arabidopsis thaliana, to improve our understanding of how plants respond rapidly to different types of stress. We are particularly interested to study lysine modifications that affect protein-protein interactions and turnover, such as SUMOylation and ubiquitination. We will study the evolutionary conservation of these sites, the extent to which they can be competition between different PTMs for the same site, and/or crosstalk with proximal sites. The acquired knowledge will be used to mine plant genomes for new alleles associated with desirable traits, with a long term objective of improving crop breeding efforts.

对于世界上大多数穷人来说，大米（O。Sativa）提供了大部分的每日卡路里。近几十年来，由于持续的育种努力，水稻产量增加了一倍多。然而，为了满足预计人口增长带来的需求，水稻生产必须继续快速增长，同时应对气候变化带来的挑战。随着最近对>3000个不同品种的测序，存在巨大的遗传资源可用于鉴定与期望性状（例如对生物或非生物胁迫的耐受性、产量、营养含量等）相关的遗传多态性，在适当的时候可以培育成主要的作物品种。然而，目前在植物的遗传变异和功能效应之间，在细胞水平上如何响应不同的胁迫方面，存在着很大的知识差距。在正常的田间条件下，植物可以暴露于各种生物和非生物环境胁迫。植物的抗逆性和适应性依赖于特定蛋白质翻译后修饰（PTM）的显著变化，以快速转换功能。预计干旱和高温等环境压力在未来几十年将变得更加普遍。毫无疑问，未来成功的解决方案将涉及应用下一代技术来改进农业经济重要作物的育种。本研究旨在鉴定和量化水稻和模式植物拟南芥中的PTM，以提高我们对植物如何快速响应不同类型胁迫的理解。我们特别感兴趣的是研究影响蛋白质-蛋白质相互作用和周转的赖氨酸修饰，如SUMO化和泛素化。我们将研究这些网站的进化保守性，在何种程度上，他们可以是不同的PTM之间的竞争，同一网站，和/或串扰与邻近网站。获得的知识将用于挖掘植物基因组中与理想性状相关的新等位基因，长期目标是改善作物育种工作。