UniProt - Protein sequence and function embeddings for AI/Machine Learning readiness

项目摘要 用于AI/机器学习的Uniprot蛋白质序列和功能嵌入 2022年审核申请 人工智能和机器学习（AI/ML）具有推动生物医学研究的潜力。的 UniProt父母补助金（U24 HG 007822）的补充申请的总体目标是（i）支持 AI/ML社区通过提供蛋白质序列嵌入，并利用这些嵌入 UniRef生产中准确和快速的蛋白质聚类，（ii）探索嵌入UniProt的方法 功能注释数据，以及（iii）与AI/ML社区合作，以推进UniProt的AI/ML准备 数据UniProt自成立以来一直是提供蛋白质序列和注释数据的领导者 2002年UniProt为生物医学领域的数百个AI/ML应用提供金标准训练数据 research.蛋白质序列嵌入显示出巨大的前景，蛋白质聚类和结构， 功能分析与预测通过提供UniProt蛋白质序列嵌入，我们将增加 序列嵌入的可访问性，减少社区中的重复工作，并建立标准 这有助于评估和比较各种模式。我们将测试不同的嵌入方法， 根据我们最近对用户社区的调查，最广泛采用的方法。我们还将 研究在UniRef生产中使用序列嵌入来加速序列聚类。同样地， 在UniProt中有许多功能注释，它们可以嵌入AI/ML模型中使用。 作为一个测试案例，我们将探索为酶嵌入Rhea生化反应注释， 运输机除了传播嵌入，我们还将开发可视化方法， 将其与现有的酶分类系统进行比较。最后，为了确保我们的工作符合 社区的需求，我们将组织一个工作坊，与社区合作， 嵌入的应用。我们将邀请各种利益相关者，包括参与 嵌入调查，参与者的金属结合位点预测挑战目前正在进行的一部分， 以及NIH的代表。总的来说，本提案中的工作将加强 UniProt已准备好用于AI/ML，并将AI/ML方法集成到UniProt生产中。

PROJECT SUMMARY UNIPROT PROTEIN SEQUENCE AND FUNCTION EMBEDDINGS FOR AI/MACHINE LEARNING READINESS - SUPPLEMENT REQUEST 2022 Artificial intelligence and machine learning (AI/ML) has the potential to advance biomedical research. The overall goals of this Supplement application for the UniProt parent grant (U24HG007822) are to (i) support the AI/ML community by providing protein sequence embeddings and make use of these embeddings for accurate and fast protein clustering in UniRef production, (ii) explore methods of embedding UniProt functional annotation data, and (iii) engage with the AI/ML community to advance AI/ML readiness of UniProt data. UniProt has been a leader in the provision of protein sequence and annotation data since its inception in 2002. UniProt provides gold standard training data for hundreds of AI/ML applications in biomedical research. Protein sequence embeddings show enormous promise for protein clustering and structural and functional analysis and prediction. By providing UniProt protein sequence embeddings, we will increase the accessibility of sequence embeddings, reduce duplication of effort in the community, and establish a standard that can facilitate evaluation and comparison of models. We will test different embedding methods, focusing on the most widely adopted methods as determined by our recent survey of the user community. We will also investigate using sequence embeddings to speed up sequence clustering in UniRef production. Similarly, there are many functional annotations in UniProt that are amenable to embedding for use in AI/ML models. As a test case, we will explore embedding of Rhea biochemical reaction annotations for enzymes and transporters. In addition to disseminating the embeddings, we will develop methods to visualize them and compare them to existing enzyme classification systems. Finally, to ensure that our work aligns with community needs, we will organise a workshop to work with the community on their use cases and applications for embeddings. We will invite various stakeholders, including researchers that participated in the embeddings survey, participants of the metal binding site prediction challenge currently underway as part of the parent grant, as well as NIH representatives. Overall, the work in this proposal will enhance the readiness of UniProt for use in AI/ML and will integrate AI/ML methods into UniProt production.