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，参与了生命系统中的大多数生化过程。蛋白质的突变会影响它们的稳定性和/或形状或化学性质，从而改变它们的功能。了解蛋白质的3D结构对于了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，每个结构域都与不同的球状结构相关联。我们的CATH分类根据进化祖先对域进行分组。近亲之所以被认出，是因为它们在核心中具有相似的结构，而且往往具有共同的功能特征，尽管核心之外的变化可能会改变功能。因此，如果亲属具有高度相似的结构和功能，我们将其细分为功能家族。确定蛋白质结构的实验技术是具有挑战性的&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一直在极大地改进。最好的利用来自数百万蛋白质序列(一维分子(残基)串)的信息来预测蛋白质将如何在3D中折叠。通过对不同环境进行采样而获得的大量序列数据(现已知的10亿个序列)使新的方法(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，是目前的约200倍。我们将通过引入这些庞大的3D数据来转化我们的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。这种更广泛的序列数据将揭示进化上高度保守的位置，很可能与功能有关。为了处理如此海量的数据，我们将建立强大的新方法。我们最近的试验使用一种新的方法(CATH)，大约90%的时间正确地将结构域序列分配给它们的进化家族。在我们具有域的AlphaFold2结构的情况下，我们将应用准确的结构比较来验证分类。一个主要目标将是使用这些新的3D数据和更准确的预测功能位点来了解病原体(例如SARS-CoV-2)的突变如何导致毒力或传播能力增强。我们将通过我们的Cath-FunVar平台来完成这项工作，该平台可以检查蛋白质结构上的突变位置。接近功能位点意味着突变可能会损害或增强功能。我们已经开始使用FunVar来分析SARS-CoV2中令人担忧的变种。我们将把它扩展到与人类健康和福祉有关的其他生物和病原体，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培育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的功能家族的准确性，以及对其中保守功能位点的检测。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变得更加强大。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展示这些见解。由于我们将需要处理进入Cath的数据的大规模扩展以及许多处理这些数据的新方法--而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被捕获--我们将完全重新设计用于在Cath中对域进行分类的现有管道。我们已经建立了初步的管道，将超过25万个AlphaFold2型号引入Cath。这个项目将使我们能够使这些方法更强大，然后应用它们来引入至少100倍的模型，以扩展FunVar，并确定可能影响人类健康和食品安全的变异的影响。

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.