Transforming the Structural Landscape of CATH to Aid Variant Analyses in Human and Agricultural Organisms and their Pathogens

蛋白质是自然界的分子机器，参与生命系统的大多数生化过程。蛋白质的突变可以影响它们的稳定性和/或形状或化学性质，改变它们的功能。了解蛋白质的三维结构对于理解这些突变是否以及如何产生这种影响非常有帮助。蛋白质通常由多个“结构域”（重要的功能模块）组成，每个结构域都有一个独特的球形。我们的CATH分类根据进化祖先对域进行分组。近亲基因之所以被识别出来，是因为它们的核心结构相似，而且通常具有共同的功能特征，尽管核心外的变异可以改变功能。因此，如果亲属具有高度相似的结构和功能，我们就将其分类为功能家族。确定蛋白质结构的实验技术具有挑战性<1%的已知蛋白质具有实验结构。然而，用于预测结构的人工智能技术已经得到了极大的改进。最好的方法是利用数以百万计的蛋白质序列（一维分子链（残基））的信息来预测蛋白质如何在3D中折叠。通过对不同环境进行采样而获得的序列数据（目前已知的100亿个序列）的大量增加，使新方法（DeepMind的AlphaFold2）能够预测与实验结构一样好的模型结构。到2022年，DeepMind将提供约1.38亿个蛋白质结构，是目前蛋白质结构的200倍。我们将通过引入这些庞大的3D数据来改变CATH进化分类中的知识，我们还将引入预测结构所涉及的序列。这个更大的序列数据将揭示进化上的保守位点极有可能与功能有关。为了处理如此庞大的数据，我们将构建强大的新方法。我们最近的试验使用了一种新的方法（CATHe），正确地将结构域序列分配到它们的进化家族中~90%的时间。当我们有一个领域的AlphaFold2结构时，我们将应用精确的结构比较来验证分类。一个主要目标将是使用这种新的3D数据和更准确地预测功能位点，以了解病原体（例如SARS-CoV-2）的突变如何导致毒性或传播增加。我们将通过我们的CATH-FunVar平台来检测突变在蛋白质结构上的位置。靠近功能位点意味着突变可能损害或增强功能。我们已经开始使用FunVar来分析SARS-CoV2中值得关注的变体。我们将把它扩展到与人类健康和福祉有关的其他生物体和病原体，例如小麦和水稻等对粮食安全至关重要的作物，在这些作物中，变异影响的知识可以指导选择和设计更耐寒或生长更快的品种。为了改进FunVar，我们将提高我们预测功能家族的准确性和检测其中的保守功能位点。为了做到这一点，我们将利用大量的结构和序列数据，并调整我们的新人工智能方法，使它们在这项具有挑战性的任务中更加强大。我们将构建工具来分析这些家族中的结构-功能关系，并开发强大的新可视化来显示这些见解。由于我们需要处理进入CATH的大量扩展数据和处理它的许多新方法，并且由于一些新数据现在以我们的计算机程序无法读取的方式捕获，我们将完全重新设计CATH中用于分类域的现有管道。我们已经建立了初步的管道，将超过25万个AlphaFold2模型带入CATH。这个项目将使我们能够使这些方法更加稳健，然后应用它们来引入至少100倍的模型来扩展FunVar，并确定可能影响人类健康和粮食安全的变异的影响。

Proteins are Nature's molecular machines involved in most biochemical processes in living systems. Mutations in proteins can affect their stability and/or shape or chemical properties, altering their function. Knowing the 3D structure of the protein can be extremely helpful in understanding whether and how these mutations have this effect. Proteins are typically made up of multiple 'domains' - important functional modules - each associated with a distinct globular shape. Our CATH classification groups domains according to evolutionary ancestry. Relatives are recognised because they have similar structures in their core and often functional features in common, though variations outside the core can modify function. We therefore sub-classify relatives into functional families if they have highly similar structures and functions. Experimental techniques for determining protein structures are challenging <1% of known proteins have experimental structures. However, AI technologies for predicting structures have been improving immensely. The best use information from millions of protein sequences (1D strings of molecules (residues)) to predict how proteins will fold up in 3D. The massive increase in sequence data (> one billion sequences now known) obtained by sampling diverse environments have empowered new methods (DeepMind's AlphaFold2) to predict model structures that are as good as experimental structures. DeepMind will provide ~138 million protein structures in 2022, ~200 times more than exists now. We will transform knowledge in our CATH evolutionary classification by bringing in this vast 3D data - and we will also bring in the sequences involved in predicting the structures. This even vaster sequence data will reveal evolutionary conserved sites highly likely to be linked to function. To handle this massive amount of data we will build powerful new methods. Our recent trials using a new approach (CATHe) correctly assigned domain sequences to their evolutionary family ~90% of the time. Where we have an AlphaFold2 structure for the domain we will apply accurate structure comparisons to validate the classification. A major aim will be use this new 3D data and more accurately predicted functional sites to understand how mutations in pathogens (e.g. SARS-CoV-2) can lead to increased virulence or transmission. We'll do this through our CATH-FunVar platform which examines where mutations lie on the protein structure. Proximity to functional sites means the mutation may damage or enhance the function. We have started using FunVar to analyse variants of concern in SARS-CoV2. We will extend it to other organisms and pathogens linked to human health and well-being e.g. crops like wheat and rice that are essential for food security and where knowledge of variant impacts can guide selection and engineering of more hardy or faster growing varieties. To improve FunVar we will improve the accuracy of our predicted functional families and detection of conserved functional sites in them. To do this we will exploit the vast structure and sequence data and adapt our new AI methods to make them even more powerful for this challenging task. We will build tools to analyse structure - function relationships in these families and develop powerful new visualisations for displaying these insights. Since we'll need to handle massive expansions in the data coming into CATH and lots of new methods for processing it - and since some new data is now captured in a way our computer programs can't read - we will completely re-engineer existing pipelines for classifying domains in CATH. We have already built preliminary pipelines that brought over a quarter of a million AlphaFold2 models into CATH. This project will allow us to make these methods more robust and then apply them to bring in at least 100 fold more models to expand FunVar and determine the impacts of variants that could impact on human health and food security.