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

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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.