ProtFunAI: AI based methods for functional annotation of proteins in crop genomes

ProtFunAI：基于人工智能的作物基因组蛋白质功能注释方法

• 批准号: BB/Y514044/1
• 负责人: Christine Orengo
• 金额: $ 32.43万
• 依托单位: University College London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2024
• 资助国家: 英国
• 起止时间: 2024 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FY514044%2F1
• 关键词: ProtFunAI; AI; based; methods; functional

Our project will 'build on existing links and deepen existing relationships' between the two groups pioneering the development of AI/Deep-Learning models for proteins (Rost Group, TUM) and the application of these to protein domain families (Orengo Group, UCL). It will leverage world leading expertise in protein Language Models (pLMs) in order to accelerate the scientific discovery of protein functions in the genomes of key agricultural crops important for food security. However, our approaches will be generic and rolled out to all UniProt proteins through existing collaborations.Synergies between both groups have evolved over several collaborations. Since 2019, ground-breaking results tuned the pLMs developed in the Rost Group (e.g. the ProtTrans series, incl. ProtT5, ProtTucker) with protein family and functional family data (CATH superfamilies and FunFams) generated and maintained by the Orengo Group.The partnership proposed, here, would allow researchers in the Rost and Orengo Groups to intensify exchanges through visiting each others labs and interacting more comprehensively to design more effective protocols that enhance (1) protein homologue detection (2) protein function prediction and (3) protein functional site prediction.The Orengo and Rost Groups began collaborating in 2000 when working together on protein family analysis for target identification in the NIH-funded USA Structural Genomics initiative (PSI), which ended in 2015 [21-23]. Subsequently funding from the German BMBF (Federal German Research Ministry) and DFG (German Research Foundation) supported visits of PhD and Masters students from both groups and resulted in the development of new approaches for protein function prediction [14,15]. This application seeks funds to continue these collaborations to leverage the latest advances in AI/Deep Learning. The Rost Group recently enhanced their pLMs significantly (ProstT5 [18]) and the funding would allow us to apply ProstT5 to exploit the hugely expanded CATH classification, which is currently integrating hundreds of millions of predicted protein structures from the AlphaFold portal (AFDB).The application is very timely as it will address key BBSRC strategic priorities around data intensive biology and AI and the important challenge of food security. We will apply improved function prediction methods to significantly increase the functional annotations of plant genomes. This will bring 'new knowledge about key biological principles and mechanisms using AI-based approaches' and bring 'AI in sustainable agriculture and food' and enable 'smart agriculture' by identifying genes implicated in biological systems associated with growth and stress resistance e.g. drought and antimicrobial resistance. Most genes (typically >90%) from plants valuable as crops (e.g. wheat, maize, rice, sorghum) are experimentally uncharacterized or very poorly annotated. Our methods will be state-of-the-art to accurately guide experimental validation.We will disseminate the annotations using our established web-based CATH resource accessed by over 27,000 users/month. Since CATH data is also disseminated by PDB, UniProt and InterPro the predictions will be accessible to >900,000s of users/month. We will also work closely with collaborators in the UK researching plant genomes to get feedback and solicit experimental validation where possible.The project will significantly enhance the AI/ML skills of UK based researchers in the Orengo Group, whose prior training was largely in biology. On the flip side, the more AI-focused members from the Rost group will deepen their understanding of individual proteins, organisms, and evolution. German scholars will also dive deeper into the workings of UK-based resources.

我们的项目将“建立在现有联系的基础上并加深现有关系”，这两个团体率先开发蛋白质人工智能/深度学习模型（Rost Group，TUM）并将其应用于蛋白质结构域家族（Orengo Group，UCL） 。它将利用世界领先的蛋白质语言模型（pLM）专业知识，加速对对粮食安全至关重要的关键农作物基因组中蛋白质功能的科学发现。然而，我们的方法将是通用的，并通过现有的合作推广到所有 UniProt 蛋白质。两个团队之间的协同作用已经通过多次合作而发展。自 2019 年以来，突破性的成果利用 Orengo 集团生成和维护的蛋白质家族和功能家族数据（CATH 超家族和 FunFams）调整了 Rost 集团开发的 pLM（例如 ProtTrans 系列，包括 ProtT5、ProtTucker）。这里提出的建议将允许罗斯特和奥伦戈小组的研究人员通过访问彼此的实验室和更全面的互动来加强交流，以设计更有效的方案增强 (1) 蛋白质同源物检测 (2) 蛋白质功能预测和 (3) 蛋白质功能位点预测。Orengo 和 Rost 小组于 2000 年开始合作，当时在 NIH 资助的美国结构基因组学计划中共同致力于蛋白质家族分析以识别目标(PSI)，于 2015 年结束[21-23]。随后，德国 BMBF（德国联邦研究部）和 DFG（德国研究基金会）的资助支持了这两个小组的博士生和硕士生的访问，并导致了蛋白质功能预测新方法的开发[14,15]。该申请寻求资金来继续这些合作，以利用人工智能/深度学习的最新进展。 Rost Group 最近显着增强了他们的 pLM（ProstT5 [18]），这笔资金将使我们能够应用 ProstT5 来利用大幅扩展的 CATH 分类，该分类目前正在整合来自 AlphaFold 门户 (AFDB) 的数亿个预测蛋白质结构。该申请非常及时，因为它将解决 BBSRC 围绕数据密集型生物学和人工智能的关键战略重点以及粮食安全的重要挑战。我们将应用改进的功能预测方法来显着增加植物基因组的功能注释。这将带来“关于使用基于人工智能的方法的关键生物学原理和机制的新知识”，并将“人工智能引入可持续农业和食品”，并通过识别与生长和抗逆性相关的生物系统中涉及的基因来实现“智能农业”。干旱和抗菌素耐药性。来自具有农作物价值的植物（例如小麦、玉米、水稻、高粱）的大多数基因（通常> 90％）在实验上未经表征或注释非常少。我们的方法将采用最先进的方法来准确指导实验验证。我们将使用我们已建立的基于网络的 CATH 资源（每月有超过 27,000 名用户访问）来传播注释。由于 CATH 数据也由 PDB、UniProt 和 InterPro 传播，每月将有超过 900,000 名用户可以访问预测。我们还将与英国研究植物基因组的合作者密切合作，尽可能获得反馈并征求实验验证。该项目将显着提高 Orengo Group 英国研究人员的 AI/ML 技能，他们之前的培训主要是生物学。另一方面，Rost 小组中更关注人工智能的成员将加深对个体蛋白质、有机体和进化的理解。德国学者还将更深入地研究英国资源的运作情况。