GENOME-3D: a UK network providing structure-based annotations for genotype to phenotype studies

GENOME-3D：英国网络，为基因型到表型研究提供基于结构的注释

• 批准号: BB/I025271/1
• 负责人: Michael Sternberg
• 金额: $ 11.14万
• 依托单位: Imperial College London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2011
• 资助国家: 英国
• 起止时间: 2011 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FI025271%2F1
• 关键词: GENOME; 3D; UK; network; providing

The 3D structures of proteins are essential to fully characterise the sites mediating their molecular functions and their interactions with other proteins. However, whilst revolutionary technologies have enabled the sequencing of thousands of complete genomes, it is more challenging to determine the 3D structures of the proteins. Although the sequence repositories now contain >10 million protein sequences, less than 70,000 protein structures have been determined. Fortunately, in parallel with developments in sequencing technologies, powerful computational methods have emerged to predict the structure of a protein from its sequence. Currently these methods provide putative structures for ~80% of domain sequences from completed genomes, although the accuracy of this data varies from reasonably precise when structures are modelled using templates based on close relatives, through to quite approximate for models based on remote relatives and where proteins have no structurally characterised relatives. This project will bring together 6 internationally renowned UK groups involved in (1) classifying protein domains into evolutionary families (as this facilitates structure and function prediction) and/or (2) protein structure prediction. As regards the first activity - classification of protein structures - the two groups involved (SCOP,CATH) are the only groups, worldwide, providing this data. However, each applies somewhat different methodologies to make their assignments. Collaboration between these groups, in GENOME-3D, will involve comparison of domain structures and family classifications leading to refinements of assignments and/or confidence levels where the methods disagree. Since manual curation of the data is essential and since the rate at which the structures are determined is increasing, collaborations will speed up classification by allowing the groups to share information on the more challenging assignments and to discuss outcomes. For the second activity, structure prediction, the groups involved use technologies that vary in their sensitivity and in their ability to handle large numbers of sequences. Whilst SUPERFAMILY (based on SCOP) and Gene3D (based on CATH) provide greater coverage they are less likely to recognise very remote homologues, where methods such as GenTHREADER, Phyre, Fugue perform better. For each sequence, we will combine predictions from these different resources and assign confidence for each residue position in a query sequence based on the number of methods that agree in their structural prediction. We will provide pre-calculated assignments and also allow dynamic queries on the methods. We will also build 3D models for the sequences with residue positions highlighted according to agreement between the methods. We will develop computational platforms that integrate the information provided by each resource. To distribute this data to the biological and medical community we will build a dedicated web site. We will also establish web servers that link the methods ie run all the methods on query sequences and then report consensus assignments and highlight differences. In addition the consensus classification and annotation data will also be provided via two major international sites - the PDBe and InterPro. The sequence repositories are expanding at phenomenal rates as metagenomics and next gen sequencing initiatives bring in sequences from diverse microbial environments and report sequence variants occurring across different human populations or associated with different disease phenotypes. Structural data will enhance the insights available from this data. For example, known or predicted structures can reveal whether residue mutations oc

蛋白质的3D结构对于完全确定介导其分子功能及其与其他蛋白质相互作用的位点至关重要。然而，虽然革命性的技术已经使成千上万的完整基因组测序成为可能，但确定蛋白质的3D结构更具挑战性。虽然序列库现在包含超过1000万个蛋白质序列，但已经确定的蛋白质结构不到70，000个。幸运的是，随着测序技术的发展，强大的计算方法已经出现，可以从蛋白质的序列中预测其结构。目前，这些方法提供了来自完整基因组的约80%的结构域序列的推定结构，尽管该数据的准确性从使用基于近亲的模板建模结构时的合理精确到基于远亲的模型的相当近似而变化，并且蛋白质没有结构特征的亲属。该项目将汇集6个国际知名的英国小组，涉及（1）将蛋白质结构域分类为进化家族（因为这有助于结构和功能预测）和/或（2）蛋白质结构预测。关于第一项活动--蛋白质结构分类--所涉及的两个小组（SCOP、CATH）是全球唯一提供该数据的小组。然而，每一个应用稍微不同的方法来进行分配。在GENOME-3D中，这些小组之间的合作将涉及比较域结构和家族分类，从而在方法不一致的情况下改进分配和/或置信水平。由于人工管理数据至关重要，而且确定结构的速度正在增加，因此合作将加快分类速度，使各小组能够分享关于更具挑战性的任务的信息，并讨论结果。对于第二个活动，结构预测，参与的小组使用的技术在其敏感性和处理大量序列的能力方面各不相同。虽然SUPERFAMILY（基于SCOP）和Gene 3D（基于CATH）提供了更大的覆盖范围，但它们不太可能识别非常遥远的同源物，其中GenthREADER，Phyre，Fugue等方法表现更好。对于每个序列，我们将结合来自这些不同资源的联合收割机预测，并基于在其结构预测中一致的方法的数量为查询序列中的每个残基位置分配置信度。我们将提供预先计算的分配，并允许对方法进行动态查询。我们还将根据方法之间的一致性为具有突出显示的残基位置的序列构建3D模型。我们将开发整合每个资源提供的信息的计算平台。为了将这些数据分发给生物和医学界，我们将建立一个专门的网站。我们还将建立链接方法的Web服务器，即在查询序列上运行所有方法，然后报告共识分配并突出显示差异。此外，共识分类和注释数据也将通过两个主要的国际网站-PDBe和InterPro提供。随着宏基因组学和下一代测序计划从不同的微生物环境中引入序列，并报告发生在不同人群中或与不同疾病表型相关的序列变异，序列库正在以惊人的速度扩展。结构数据将增强从这些数据中获得的见解。例如，已知的或预测的结构可以揭示残基突变是否与基因突变有关。

项目成果

An expanded evaluation of protein function prediction methods shows an improvement in accuracy.

• DOI: 10.1186/s13059-016-1037-6
• 发表时间: 2016-09-07
• 期刊: Genome biology
• 影响因子: 12.3
• 作者: Jiang Y;Oron TR;Clark WT;Bankapur AR;D'Andrea D;Lepore R;Funk CS;Kahanda I;Verspoor KM;Ben-Hur A;Koo da CE;Penfold-Brown D;Shasha D;Youngs N;Bonneau R;Lin A;Sahraeian SM;Martelli PL;Profiti G;Casadio R;Cao R;Zhong Z;Cheng J;Altenhoff A;Skunca N;Dessimoz C;Dogan T;Hakala K;Kaewphan S;Mehryary F;Salakoski T;Ginter F;Fang H;Smithers B;Oates M;Gough J;Törönen P;Koskinen P;Holm L;Chen CT;Hsu WL;Bryson K;Cozzetto D;Minneci F;Jones DT;Chapman S;Bkc D;Khan IK;Kihara D;Ofer D;Rappoport N;Stern A;Cibrian-Uhalte E;Denny P;Foulger RE;Hieta R;Legge D;Lovering RC;Magrane M;Melidoni AN;Mutowo-Meullenet P;Pichler K;Shypitsyna A;Li B;Zakeri P;ElShal S;Tranchevent LC;Das S;Dawson NL;Lee D;Lees JG;Sillitoe I;Bhat P;Nepusz T;Romero AE;Sasidharan R;Yang H;Paccanaro A;Gillis J;Sedeño-Cortés AE;Pavlidis P;Feng S;Cejuela JM;Goldberg T;Hamp T;Richter L;Salamov A;Gabaldon T;Marcet-Houben M;Supek F;Gong Q;Ning W;Zhou Y;Tian W;Falda M;Fontana P;Lavezzo E;Toppo S;Ferrari C;Giollo M;Piovesan D;Tosatto SC;Del Pozo A;Fernández JM;Maietta P;Valencia A;Tress ML;Benso A;Di Carlo S;Politano G;Savino A;Rehman HU;Re M;Mesiti M;Valentini G;Bargsten JW;van Dijk AD;Gemovic B;Glisic S;Perovic V;Veljkovic V;Veljkovic N;Almeida-E-Silva DC;Vencio RZ;Sharan M;Vogel J;Kansakar L;Zhang S;Vucetic S;Wang Z;Sternberg MJ;Wass MN;Huntley RP;Martin MJ;O'Donovan C;Robinson PN;Moreau Y;Tramontano A;Babbitt PC;Brenner SE;Linial M;Orengo CA;Rost B;Greene CS;Mooney SD;Friedberg I;Radivojac P
• 通讯作者: Radivojac P

Genome3D: a UK collaborative project to annotate genomic sequences with predicted 3D structures based on SCOP and CATH domains.

• DOI: 10.1093/nar/gks1266
• 发表时间: 2013-01
• 期刊: Nucleic acids research
• 影响因子: 14.9
• 作者: Lewis TE;Sillitoe I;Andreeva A;Blundell TL;Buchan DW;Chothia C;Cuff A;Dana JM;Filippis I;Gough J;Hunter S;Jones DT;Kelley LA;Kleywegt GJ;Minneci F;Mitchell A;Murzin AG;Ochoa-Montaño B;Rackham OJ;Smith J;Sternberg MJ;Velankar S;Yeats C;Orengo C
• 通讯作者: Orengo C

Genome3D: exploiting structure to help users understand their sequences.

• DOI: 10.1093/nar/gku973
• 发表时间: 2015-01
• 期刊: Nucleic acids research
• 影响因子: 14.9
• 作者: Lewis TE;Sillitoe I;Andreeva A;Blundell TL;Buchan DW;Chothia C;Cozzetto D;Dana JM;Filippis I;Gough J;Jones DT;Kelley LA;Kleywegt GJ;Minneci F;Mistry J;Murzin AG;Ochoa-Montaño B;Oates ME;Punta M;Rackham OJ;Stahlhacke J;Sternberg MJ;Velankar S;Orengo C
• 通讯作者: Orengo C