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

我们的项目将“建立在现有的联系和深化现有的关系”，这两个小组将率先开发蛋白质的人工智能/深度学习模型（Rost Group， TUM），并将这些模型应用于蛋白质结构域家族（Orengo Group， UCL）。它将利用蛋白质语言模型（pLMs）方面的世界领先专业知识，加速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。在几次合作中，两组之间的协同作用逐渐形成。自2019年以来，Rost Group开发的plm（例如ProtTrans系列，包括ProtT5， ProtTucker）与Orengo Group生成和维护的蛋白质家族和功能家族数据（CATH超家族和FunFams）进行了突破性的调整。在这里提出的合作伙伴关系将允许Rost和Orengo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，以增强(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，当时他们在美国国立卫生研究院资助的美国结构基因组学计划（PSI）中共同研究蛋白质家族分析以确定目标，该计划于2015年结束[21-23]。随后，德国联邦研究部（BMBF）和德国研究基金会（DFG）资助了两组博士生和硕士生的访问，并开发了蛋白质功能预测的新方法[14,15]。该应用程序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集团最近显著增强了他们的pLMs (ProstT5 b[18])，这笔资金将允许我们应用ProstT5来开发巨大扩展的CATH分类，该分类目前正在整合来自AlphaFold门户网站（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以及粮食安全的重要挑战的关键战略重点。我们将应用改进的功能预测方法来显著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粮食”，并通过识别与生长和抗旱性（如干旱和抗菌素耐药性）相关的生物系统中涉及的基因，实现“智能农业”。来自有作物价值的植物（如小麦、玉米、水稻、高粱）的大多数基因（通常为90%）在实验上未被表征或注释很差。我们的方法将是最先进的，以准确地指导实验验证。我们将使用我们建立的基于web的CATH资源传播注释，每月有超过27,000名用户访问。由于CATH数据也由PDB、UniProt和InterPro传播，因此预测结果将可供每月50万至90万用户访问。我们还将与英国的合作者密切合作，研究植物基因组，以获得反馈，并在可能的情况下征求实验验证。该项目将大大提高Orengo集团英国研究人员的人工智能/机器学习技能，他们之前的培训主要是生物学。另一方面，罗斯特小组中更关注人工智能的成员将加深他们对单个蛋白质、生物体和进化的理解。德国学者还将深入研究英国资源的运作。

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.