Improving accuracy, coverage, and sustainability of functional protein annotation in InterPro, Pfam and FunFam using Deep Learning methods

使用深度学习方法提高 InterPro、Pfam 和 FunFam 中功能蛋白注释的准确性、覆盖范围和可持续性

• 批准号: BB/X018660/1
• 负责人: Alex Bateman
• 金额: $ 95.75万
• 依托单位: European Bioinformatics Institute
• 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%2FX018660%2F1
• 关键词: Improving; accuracy; coverage; sustainability; functional

Proteins are macromolecules responsible for biological processes in the cell. At their most basic level, they consist of a sequence of amino acids, determined by the sequence of nucleotides (the ATGC building blocks of life) in a gene. Proteins usually fold into three-dimensional structures, allowing them to interact with other molecules and perform their functions. Recent advances in sequencing technologies have led to a substantial accumulation of protein data, and our capacity of generating new protein sequences has surpassed our ability to fully understand their functions. Therefore, it is crucial to develop computational methods that identify sequence or structural similarities between characterised and uncharacterised proteins to transfer functional information from the former to the latter.InterPro, Pfam and FunFam are world-leading, UK-based resources that group similar protein sequences together, forming protein families. Pfam is a collection of protein domain families containing functional annotations. FunFam focuses on protein structural domains that share a common function. InterPro merges information from 13 expert protein databases, including Pfam and FunFam, into a single searchable resource, and further annotates protein families.In the past few years, Artificial Intelligence methods have been successfully applied to several biological applications. For instance, DeepMind's AlphaFold has revolutionised the prediction of how protein sequences fold into three-dimensional structures. Several promising tools are being developed by our collaborators to better identify protein families using Deep Learning (DL). These methods outperform current state-of-the-art approaches in terms of accuracy, coverage and computing efficiency, thus making them more environmentally sustainable.In this ambitious project, we will improve the efficiency, accuracy, and sustainability of InterPro, Pfam and FunFam. This will be accomplished by reducing the technical debt of Pfam, established almost three decades ago, adopting DL approaches to enhance the classification of protein sequences into families, and significantly reducing the carbon footprint of sequence annotation. Finally, we will improve the annotation of agriculturally important plant pathogens, resulting in the creation of hundreds of additional InterPro and Pfam entries.

蛋白质是负责细胞生物过程的大分子。在最基本的水平上，它们由一系列氨基酸组成，该氨基酸由基因中的核苷酸序列（ATGC构建块）确定。蛋白质通常折叠成三维结构，使其与其他分子相互作用并执行其功能。测序技术的最新进展导致了蛋白质数据的大量积累，我们产生新蛋白质序列的能力超过了我们完全理解其功能的能力。因此，开发计算方法至关重要，以识别特征性和未表征蛋白质之间的序列或结构相似性，以将功能信息从前者传递到后者。InterPro，PFAM和Funfam是基于世界领先的英国资源，这些资源是将相似的蛋白质序列组合在一起的，形成蛋白质家族。 PFAM是包含功能注释的蛋白质结构域家族的集合。 Funfam专注于具有共同功能的蛋白质结构域。 Interpro将包括PFAM和Funfam在内的13个专家蛋白质数据库中的信息合并为一个可搜索的资源，并进一步注释蛋白质家族。在过去的几年中，人工智能方法已成功地应用于几种生物应用。例如，DeepMind的Alphafold彻底改变了蛋白质序列如何折叠成三维结构的预测。我们的合作者正在开发几种有希望的工具，以更好地使用深度学习（DL）识别蛋白质家族。这些方法在准确性，覆盖效率和计算效率方面的表现优于当前最新方法，从而使它们在环境方面更可持续。在这个雄心勃勃的项目中，我们将提高Interpro，PFAM和Funfam的效率，准确性和可持续性。这将通过减少近三十年前建立的PFAM的技术债务来实现，并采用DL方法来增强蛋白质序列分类为家族，并大大减少序列注释的碳足迹。最后，我们将改善农业重要的植物病原体的注释，从而产生数百个额外的Interpro和PFAM条目。