Keeping pace with protein sequence annotation; consolidating and enhancing Pfam and InterPro's methodologies for functional prediction

过去几年开发的新技术大大增加了实验室可能产生的生物序列信息量。因此，现在有非常大的和不断增长的数量的序列数据进入公共数据库。这些序列中的绝大多数还没有被科学家研究过，也没有任何实验信息表明它们的功能可能是什么。Pfam和InterPro资源有助于填补这一空白，使用概率模型通过检查蛋白质的氨基酸序列来预测蛋白质的功能。Pfam可以说是最知名的，也是最大的此类模型生产商之一。与此同时，InterPro并不直接生成模型，而是从Pfam和其他10个补充数据库中获取模型，将它们整合在一起并添加功能信息。InterPro会定期与UniProt Knowledgebase（蛋白质序列公共知识库）的全部内容进行比对，以便其功能预测能够被转移。为了InterPro和Pfam能够继续覆盖不断增长的序列数量并保持预测的准确性，需要构建和整合新的模型，需要检查现有的模型，并评估它们匹配的蛋白质。该项目的目的之一是支持这一努力。另一个目的是研究Pfam或InterPro目前未使用的其他预测方法，这些方法可以识别蛋白质序列中负责蛋白质功能的单个氨基酸。我们将在资源中添加此功能，并使用它来使其预测更加准确。这将反过来提高UniProt知识库中与大量蛋白质相关的信息的质量。以这种方式添加资源将需要更改某些底层软件。与此同时，我们将更新InterPro和Pfam网站，以便用户可以轻松地看到新的和改进的数据，并了解其含义。最后，我们将准备和组织培训材料和课程，向新用户介绍资源，并向现有用户介绍新功能和更新功能。

New technologies, developed in the last few years, have greatly increased the amount of biological sequence information that it is possible for laboratories to produce. As a result, there is now a very large and ever-growing amount of sequence data entering public databases. The overwhelming majority of these sequences have not been examined by scientists, nor is there any experimental information to suggest what their function might be. The Pfam and InterPro resources help plug this gap, using probabilistic models to predict the function of proteins by examining their amino acid sequences. Pfam is arguably the most well-known and one of the largest producers of such models. InterPro, meanwhile, does not produce models directly, but takes them from Pfam and 10 other complementary databases, integrating them together and adding functional information. InterPro is regularly run against the full contents of the main public repository for protein sequences, the UniProt Knowledgebase, so that its functional predictions can be transferred.In order that InterPro and Pfam can continue to cover the growing number of sequences and remain accurate in their predictions, new models need to be made and integrated, existing models need to be checked and the proteins that they match evaluated. One aim of the project is to support this effort. Another aim is to look at other prediction methods, not currently used by either Pfam or InterPro, that identify the individual amino acids in a protein sequence that are responsible for the protein's functions. We will add this functionality to the resources and use it to make their predictions more accurate. This will in turn improve the quality of information associated with large numbers of proteins in the UniProt Knowledgebase. Adding to the resources in this way will require changes to some of the underlying software. At the same time, we will update the InterPro and Pfam web sites, so that users can easily see the new and improved data, and understand what it means. Finally, we will prepare and organise training materials and courses to introduce new users to the resources and educate existing users about the new and updated features.