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

利用高性能计算通过 CATH-Gene3D 提供功能注释

• 批准号: BB/H02364X/1
• 负责人: Christine Orengo
• 金额: $ 13.88万
• 依托单位: University College London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2010
• 资助国家: 英国
• 起止时间: 2010 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FH02364X%2F1
• 关键词: Exploiting; Performance; Computing; Provide; Functional

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.

在过去的十年里，在确定不同生物体的蛋白质组成方面进行了密集的努力，包括来自所有生命王国的人类和其他模式生物体。目前，已对1000多种生物进行了完整测序，并确定了近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23,000到25,000个蛋白质编码基因。要确定所有这些蛋白质的功能特性是困难、昂贵和耗时的，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，生物信息学小组的一个主要活动和挑战是需要设计出推断蛋白质功能的计算方法。大多数预测方法利用的前提是不同物种中的蛋白质彼此相关(同源)，因为它们是从共同的祖先蛋白质进化而来的。这些同源蛋白经常具有相似的功能特性，在进化过程中保持保守。因此，许多方法在蛋白质序列中寻找相似性，以指示一种进化关系，然后允许功能信息被遗传。换句话说，例如，一种在苍蝇身上实验确定的蛋白质可以用来赋予在人类中识别的进化相关蛋白质的功能特性。这些方法面临的主要挑战是，在整个进化过程中，基因复制发生在所有生物体中。因此，除了源自祖先蛋白质的蛋白质的原始拷贝外，还可以有额外的拷贝，这些拷贝可能已经进化出略微改变的功能，以扩大有机体的功能，从而提高其存活率。我们已经开发了一个资源(Cath-Gene3D)，它根据蛋白质的3D结构(如果有)及其序列的相似性将蛋白质分组为进化家族。目前，在Cath-Gene3D中分类的蛋白质家族超过2200个，占蛋白质结构域序列的大部分。其中一些家族包含非常多的序列，因为蛋白质在生物体中高度复制。这些家族对功能预测方法提出了挑战，因为亲属的功能经常不同。我们设计了一种新的方法(GEMA)，它使用一种复杂的方法来比较进化序列集，将它们分组为蛋白质亚家族，这些蛋白质很可能具有共同的功能特性。虽然Gema已被证明在亲属之间传递功能信息是准确的，但对于Cath-Gene3D中的非常大的家庭来说，它可能需要很长时间才能运行。因此，为了加快速度，本项目将修改Gema协议，以便我们可以在广泛的公开可用的HPC资源上运行它。我们还将开发非常直观的网页，使生物界能够很容易地获取由杰玛子家族提供的信息。该网站还将允许生物学家提交未知功能的查询蛋白质，然后根据GEMA亚家族进行搜索，以预测推测的功能。Cath-Gene3D已经被生物学家广泛使用，这种新的功能分类将通过为他们正在研究的新蛋白质提供更准确的功能注释，使资源对这些研究人员更有价值。

项目成果

GeMMA: functional subfamily classification within superfamilies of predicted protein structural domains.

• DOI: 10.1093/nar/gkp1049
• 发表时间: 2010-01
• 期刊: Nucleic acids research
• 影响因子: 14.9
• 作者: Lee DA;Rentzsch R;Orengo C
• 通讯作者: Orengo C

A large-scale evaluation of computational protein function prediction.

计算蛋白质功能预测的大规模评估

• DOI: 10.1038/nmeth.2340
• 发表时间: 2013-03
• 期刊: NATURE METHODS
• 影响因子: 48
• 作者: Radivojac, Predrag;Clark, Wyatt T.;Oron, Tal Ronnen;Schnoes, Alexandra M.;Wittkop, Tobias;Sokolov, Artem;Graim, Kiley;Funk, Christopher;Verspoor, Karin;Ben-Hur, Asa;Pandey, Gaurav;Yunes, Jeffrey M.;Talwalkar, Ameet S.;Repo, Susanna;Souza, Michael L.;Piovesan, Damiano;Casadio, Rita;Wang, Zheng;Cheng, Jianlin;Fang, Hai;Goughl, Julian;Koskinen, Patrik;Toronen, Petri;Nokso-Koivisto, Jussi;Holm, Liisa;Cozzetto, Domenico;Buchan, Daniel W. A.;Bryson, Kevin;Jones, David T.;Limaye, Bhakti;Inamdar, Harshal;Datta, Avik;Manjari, Sunitha K.;Joshi, Rajendra;Chitale, Meghana;Kihara, Daisuke;Lisewski, Andreas M.;Erdin, Serkan;Venner, Eric;Lichtarge, Olivier;Rentzsch, Robert;Yang, Haixuan;Romero, Alfonso E.;Bhat, Prajwal;Paccanaro, Alberto;Hamp, Tobias;Kassner, Rebecca;Seemayer, Stefan;Vicedo, Esmeralda;Schaefer, Christian;Achten, Dominik;Auer, Florian;Boehm, Ariane;Braun, Tatjana;Hecht, Maximilian;Heron, Mark;Hoenigschmid, Peter;Hopf, Thomas A.;Kaufmann, Stefanie;Kiening, Michael;Krompass, Denis;Landerer, Cedric;Mahlich, Yannick;Roos, Manfred;Bjorne, Jari;Salakoski, Tapio;Wong, Andrew;Shatkay, Hagit;Gatzmann, Fanny;Sommer, Ingolf;Wass, Mark N.;Sternberg, Michael J. E.;Skunca, Nives;Supek, Fran;Bosnjak, Matko;Panov, Pance;Dzeroski, Saso;Smuc, Tomislav;Kourmpetis, Yiannis A. I.;van Dijk, Aalt D. J.;ter Braak, Cajo J. F.;Zhou, Yuanpeng;Gong, Qingtian;Dong, Xinran;Tian, Weidong;Falda, Marco;Fontana, Paolo;Lavezzo, Enrico;Di Camillo, Barbara;Toppo, Stefano;Lan, Liang;Djuric, Nemanja;Guo, Yuhong;Vucetic, Slobodan;Bairoch, Amos;Linial, Michal;Babbitt, Patricia C.;Brenner, Steven E.;Orengo, Christine;Rost, Burkhard;Mooney, Sean D.;Friedberg, Iddo
• 通讯作者: Friedberg, Iddo

Protein function prediction using domain families.

• DOI: 10.1186/1471-2105-14-s3-s5
• 发表时间: 2013
• 期刊: BMC bioinformatics
• 影响因子: 3
• 作者: Rentzsch R;Orengo CA
• 通讯作者: Orengo CA