An Integrated CATH Resource for the Postgenomic Era

全球基因组计划的成功为我们提供了包括人类和小鼠在内的300多个物种的蛋白质序列。现在的挑战是预测这些蛋白质的功能，以及它们如何相互作用，以提供在自然界中观察到的各种生物库。蛋白质的三维结构比它的序列更难确定，这解释了为什么已知的结构少于25，000个，而非冗余序列约为250万个。然而，结构数据通常可以更深刻地了解蛋白质作用和相互作用的机制。此外，由于结构比序列更保守，我们可以检测到更远的关系，从而更清楚地了解蛋白质如何进化。存在许多结构分类，通过它们的结构相似性对蛋白质进行分组，并且对于理解亲属的序列和结构的变化如何改变功能特别有价值。由于我们无法在实验上预测所有蛋白质，因此能够准确预测相关蛋白质的功能对于理解生物系统以及确定疾病的原因和治疗方法至关重要。CATH分类是这些结构性家族资源中使用最广泛和最全面的分类之一。自1993年建立以来，它已经扩大了12倍，现在生物学家每月通过网络访问近100万次。唯一的另一种资源是SCOP，它对类似数量的蛋白质结构进行了分类。这两种资源采用不同的方法，SCOP主要依靠人工检查来识别远程结构相似性，而CATH则采用自动算法和人工检查来验证最困难的情况。这种经过仔细验证的自动化方法的使用将确保CATH能够科普未来十年预期的大量数据。世界范围内的结构基因组学倡议目前正在解决蛋白质家族的结构，没有结构信息的存在。虽然这些举措非常受欢迎，因为它们扩展了我们对蛋白质结构的了解，但它们需要更快，更灵敏的CATH自动方法，以及更大程度的手动验证。在这个项目中，我们将开发更有效的方法来分类这些结构，以跟上结构基因组学的步伐。由于很少有蛋白质具有已知的结构，CATH将带来更广泛的好处，生物界，如果结构数据可以预测数以百万计的序列尚未结构特征。我们已经开发了非常强大的技术来预测哪些基因组序列可以分配给CATH结构家族。国际比赛表明，这些是世界上表现最好的。使用这些技术，我们可以预测某些生物中高达80%的蛋白质的结构。因此，在这个项目中，我们建议开发一个综合资源，将结构家族的信息与基因组中所有序列的结构预测相结合。我们也有方法整合蛋白质的任何可用功能信息。此外，我们的内部建模技术可以为许多这些序列提供合理的3D模型，这将有助于生物学家了解蛋白质的功能特性，并确定它们参与的功能网络。我们计划的集成CATH资源将为生物学家提供任何感兴趣蛋白质的结构数据，结合全面的功能数据和高度直观的网页，帮助他们在所有可用功能数据的背景下查看结构。通过以这种方式整合数据，这种资源最终将丰富我们对生物系统的理解。

The success of the worldwide genome initiatives has given us the protein sequences for more than 300 species including human and mouse. The challenge now is to predict the functions of these proteins and how they interact with each other to give the diverse biological repertoires observed in nature. The three dimensional structure of a protein is much harder to determine than its sequence explaining why fewer than 25,000 structures are known compared with ~2.5 million non-redundant sequences. However, structural data often gives more profound insights into the mechanisms by which proteins act and interact. Also, because structure is more conserved than sequence we can detect more distant relationships giving clearer insights into how proteins evolve. A number of structural classifications exist to group proteins by their structural similarity and are particularly valuable for understanding how changes in the sequences and structures of relatives can modify functions. Since we cannot experimentally characterise all proteins, being able to accurately predict functions from related proteins is essential for understanding biological systems and determining the causes of and remedies for disease. The CATH classification is one of the most widely used and comprehensive of these structural family resources. It has expanded 12-fold since it was established in 1993 and is now accessed by biologists nearly 1 million times per month over the web. The only other resource of this kind is SCOP, which classifies a similar number of protein structures. The two resources employ different approaches, SCOP relying largely on manual inspection for the identification of remote structural similarities whilst CATH applies automated algorithms and manual inspection to validate only the hardest cases. This use of carefully validated automated approaches will ensure that CATH can cope with the massive flood of data expected over the next decade. The worldwide structural genomics initiatives are currently solving the structures for protein families for which no structural information exits. Although these initiatives are very welcome because they are expanding our knowledge of protein structures, they are necessitating faster and much more sensitive automatic methods for CATH, as well as a greater degree of manual validation. In this project we will develop much more efficient ways of classifying these structures to keep pace with the structural genomics initiatives. Since very few proteins have known structures, CATH will bring much wider benefits to the biological community if structural data can be predicted for the millions of sequences not yet structurally characterised. We have already developed very robust technologies for predicting which genome sequences can be assigned to CATH structural families. International competetions have shown these to be amongst the best performing in the world. Using these techniques we can predict structures for up to 80% of proteins in some organisms. In this project, we therefore propose to develop an integrated resource that combines information on structural families with structural predictions for all sequences in the genomes. We also have methods to integrate any available functional information for the proteins. Furthermore, our in-house modelling techniques can provide reasonable 3D models for many of these sequences which will help biologists in understanding the functional properties of the proteins and in determining the functional networks in which they participate. The integrated CATH resource we plan will present biologists with structural data for any protein of interest, combined with comprehensive functional data and highly intuitive web pages that help them to view the structures in the context of all the available functional data. By integrating data in this way this resource will ultimately enrich our understanding of biological systems.