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Employing AI-guided approaches to discover and engineer plant disease resistance proteins

采用人工智能引导的方法来发现和设计植物抗病蛋白

基本信息

批准号：
2898843
负责人：
金额：
--
依托单位：
Imperial College London
依托单位国家：
英国
项目类别：
Studentship
财政年份：
2023
资助国家：
英国
起止时间：
2023 至无数据
项目状态：
未结题

来源：
https://gtr.ukri.org/projects?ref=studentship-2898843
关键词：
Employing AI guided approaches discover

项目摘要

Plant diseases threaten our food supply. Pathogens can reduce crop yields by 30%, costinghundreds of billions of dollars. Losses could feed billions. Global trade, climate change, and plantpathogens' tendency to reduce disease resistance have increased plant disease outbreaks.Population is expected to reach 9 billion by 2050, and food production must increase by 70% toprevent global hunger.Here we propose an AI-guided synthetic biology approach to discover and engineer plant immunereceptors with new disease resistance gene specificities. To this end we formed a partnership withResurrect bio, to implement an AI-guided pipeline combined with our expertise in plantimmunology. We will first establish an automated machine learning pipeline to generate digitallibraries of ligand-receptor complexes from plant-pathogen genomes. Next, ligands of interest willbe screened using AF2-multimer to identify candidate receptors, which will be subsequentlyvalidated through genetic and biochemical approaches. We will then transform individualreceptors into plant species that lack them, as a proof of concept for engineering diseaseresistance. However, some pathogens encode virulent alleles of the ligands of interest, enablingthem to avoid immune recognition. We will identify such ligands from genomes of the virulentpathogen strains through genomic surveys and make necessary mutations in the correspondingreceptor interfaces to expand their resistance spectrum. Our business partners, Resurrect Bio,specialized to discover and amend defeated disease resistance traits will then employ genome-editing approaches to introduce these traits in the crops of interest

植物病害威胁着我们的食物供应。病原体可以使农作物减产30%，损失高达数十亿美元。损失可以养活数十亿人。全球贸易、气候变化和植物病原体降低抗病性的趋势增加了植物病害的爆发。预计到2050年，人口将达到90亿，粮食产量必须增加70%才能防止全球饥饿。在这里，我们提出了一种人工智能指导的合成生物学方法，以发现和设计具有新的抗病基因特异性的植物免疫受体。为此，我们与Resurrect bio建立了合作伙伴关系，结合我们在植物免疫学方面的专业知识，实施人工智能指导的管道。我们将首先建立一个自动化的机器学习管道，从植物病原体基因组中生成配体-受体复合物的digitallibrary。接下来，将使用AF 2多聚体筛选感兴趣的配体以鉴定候选受体，这些受体将通过遗传和生化方法进行验证。然后，我们将把单个受体转化为缺乏它们的植物物种，作为工程抗病性概念的证明。然而，一些病原体编码感兴趣的配体的毒性等位基因，使它们能够避免免疫识别。我们将通过基因组调查从致病病原体菌株的基因组中鉴定此类配体，并在相应的受体界面中进行必要的突变以扩大其耐药性谱。我们的商业合作伙伴Resurrect Bio专门发现和修改失败的抗病性状，然后将采用基因组编辑方法将这些性状引入感兴趣的作物中