Exploiting High Performance Computing to Provide Functional Annotations via CATH-Gene3D

在过去的十年里，人们一直在努力确定不同生物的蛋白质组成，包括人类和来自所有生命王国的其他模式生物。目前已经完成了1000多种生物的测序，确定了近1000万个蛋白质序列。2000年，人类基因组完成，最新的估计表明它包含了23000到25000个蛋白质编码基因。确定所有这些蛋白质的功能特性是困难的，昂贵的和耗时的，对于包括人类在内的许多生物来说，只有不到15%的蛋白质被直接实验表征以确定其功能。因此，生物信息学小组的主要活动和挑战是需要设计计算方法来推断蛋白质的功能。大多数预测方法的前提是，不同物种的蛋白质彼此相关（同源物），因为它们是从共同的祖先蛋白质进化而来的。这些同源蛋白通常具有相似的功能特性，在进化过程中被保存下来。因此，许多方法在蛋白质序列中寻找相似性，表明一种进化关系，然后允许功能信息被遗传。换句话说，例如，在苍蝇身上实验表征的蛋白质，可以用来给在人类身上发现的一种进化相关蛋白质分配功能特性。这些方法面临的主要挑战是基因复制发生在所有生物体的整个进化过程中。因此，除了源自祖先蛋白质的蛋白质的原始副本外，还可能存在其他副本，这些副本可能已经进化出轻微修改的功能，以扩大生物体的功能库，从而提高其存活率。我们已经开发了一个资源（cat - gene3d），它根据蛋白质的3D结构（如果有）和序列的相似性将蛋白质分组到进化家族中。目前，超过2200个家族在cat - gene3d中被分类，占大多数蛋白质结构域序列。其中一些家族含有非常多的序列，因为蛋白质在生物体中高度复制。这些家族由于其亲属的功能经常发生分化，对功能预测方法提出了挑战。我们设计了一种新的方法（GeMMA），它使用一种复杂的方法来比较一系列的进化序列，将它们分组到蛋白质亚家族中，这些亚家族很可能具有相同的功能特性。虽然GeMMA已经被证明在亲属之间传递功能信息是准确的，但在cats - gene3d中，对于非常大的家族来说，可能需要很长时间。因此，为了加快速度，该项目将修改GeMMA协议，以便我们可以在广泛的公共可用的HPC资源上运行它。我们还将开发高度直观的网页，使GeMMA亚家族提供的信息对生物界来说非常容易访问。该网站还允许生物学家提交未知功能的查询蛋白，然后根据GeMMA亚家族进行搜索，以预测推测的功能。cats - gene3d已经被生物学家广泛使用，这种新的功能亚分类将通过为他们正在研究的新蛋白质提供更精确的功能注释，使这些资源对这些研究人员更有价值。

Over the last ten years there have been intense efforts to determine the protein compositions of different organisms, including human and other model organisms from all kingdoms of life. Currently more than 1,000 organisms have been completely sequenced and nearly 10 million protein sequences determined. In 2000 the human genome was completed and the latest estimates say it contains between 23,000 and 25,000 protein-coding genes. It is difficult, expensive and time-consuming to determine the functional properties of all these proteins and for many organisms, including human, fewer than 15% of the proteins have been directly experimentally characterised to determine their function. Therefore, a major activity and challenge for bioinformatics groups has been the need to devise computational methods for inferring the functions of proteins. Most predictive methods exploit the premise that proteins in different species are related to each other (homologues) as they have evolved from a common ancestral protein. These homologous proteins frequently share similar functional properties, conserved during evolution. Therefore, many methods search for similarities in the sequences of proteins, indicative of an evolutionary relationship, which then allows functional information to be inherited. In other words, a protein that has been experimentally characterised in fly, for example, can be used to assign functional properties to an evolutionary related protein identified in human. The main challenge faced by these approaches is the fact that gene duplication occurs in all organisms throughout evolution. Therefore, as well as the original copy of a protein, derived from an ancestral protein, there can be additional copies which may have evolved slightly modified functions to expand the functional repertoire of the organism, thereby enhancing its survival. We have developed a resource (CATH-Gene3D) which groups proteins into evolutionary families on the basis of similarities in their 3D structures (where available) and their sequences. Currently, more than 2,200 families are classified in CATH-Gene3D accounting for the majority of protein domain sequences. Some of these families contain very many sequences as the proteins have been highly duplicated in organisms. These families pose a challenge to function prediction methods as the functions of the relatives have frequently diverged. We have designed a new method (GeMMA) which uses a sophisticated approach for comparing sets of evolutionary sequences to group them into subfamilies of proteins, which are very likely to share functional properties. Whilst GeMMA has been shown to be accurate in transferring functional information between relatives it can take a long time to run for the very large families in CATH-Gene3D. Therefore, to speed it up, this project will modify the GeMMA protocol so that we can run it on a wide range of publicly available HPC resources. We will also develop highly intuitive web pages to make the information provided by the GeMMA subfamilies very accessible for the biology community. This web site will also allow biologists to submit a query protein of unknown function which will then be searched against the GeMMA subfamilies to predict a putative function. CATH-Gene3D is already widely used by biologists and this new functional sub-classification will make the resource even more valuable to these researchers by providing more precise functional annotations for the novel proteins they are studying.