DockIt: Development and launch of a crowd-sourced serious-games platform for protein docking for use by the public and the scientific community.
DockIt: Development and launch of a crowd-sourced serious-games platform for protein docking for use by the public and the scientific community.
批准号:
BB/L005247/1
负责人:
Michael Sternberg
金额:
$51.24万
依托单位:
依托单位国家:
英国
项目类别:
Research Grant
财政年份:
2013
资助国家:
英国
项目状态:
已结题
起止时间:
2013 至 --
中文摘要
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英文摘要
AIM - The aim of this grant is to develop a serious and enjoyable computer game (DockIt) used by numerous players (a crowd) to model how two large biological molecules (proteins) fit together and thereby perform their function. PROTEIN DOCKING: AN UNSOLVED BIOLOGICAL PROBLEM - Proteins are large biological molecules, generally with at least 1,000 atoms, which perform many biological functions. The three-dimensional structure of a protein is complex. Central to its biological activity is that often it docks to another protein to form a complex. The structure of the complex is dictated by how the atoms in the molecules interact with each other. The shape of the complex helps in understanding the mechanism of action of proteins and this knowledge can have major benefits in food security, industrial biotechnology and the design of novel pharmaceuticals. Computational approaches are used to predict the shape of a complex given knowledge of the structures of the unbound components. However the programs have only limited success. Research in other areas of science, including modelling the shape of proteins, has shown that individuals using human judgement can sometimes find solutions to problems that cannot yet be solved automatically. In particular finding consensus solutions from a crowd of players can yield successful results. This approach is known as crowd sourcing. Accordingly, we propose to develop a computer game DockIt where the players will manipulate the two protein structures and predict a complex. This is similar to solving a three-dimensional jigsaw puzzle. The docking will start with a set of possible complexes generated by state of the art programs and thus successful plays can yield predicted structures superior to those automatically generated.THE DOCKIT GAME - We will design DockIt so that it is fun to play by exploiting the latest technology and design from the casual games industry. We will develop DockIt for a range of platforms including tablets, smartphones and PCs thereby ensuring wide take-up. A user will be able to adjust the relative position of the two proteins and to change the shape of each protein. The approaches to alter the proteins will be encoded as a set of recipes which the user can employ. We will provide a scoring function to feedback to the players an estimate of the quality of the predicted structure.RELEASE OF DOCKIT - We will release DockIt for use by the academic community and to the general public. Our strategy to publicise DockIt is to work with our partners, collaborators and industry contacts to promote DockIt via their extensive player databases, for example by inclusion in newsletters. The docking targets will include those for which the solution is known and those where DockIt is used to solve a real-world biological problem. Players will download a possible complex from the host server and then change its shape to predict the best complex which will then be returned to the server. Results from different players will be pooled and we will identify the best prediction. Players will be supported by the presence of a tutorial which will provide progressive steps to learn the game. There will be E-mail support and a social network group will be established (e.g. Google+, FaceBook, LinkedIn and Twitter).DISSEMINATION - DockIt will be disseminated to the scientific community and the general public. We will make presentations at scientific conferences and publish technical papers. We will issue a press release aimed at the games together with popular science magazines. We will also target TV, Radio, the educational press and trade bodies. We envisage that DockIt could be used at schools and universities in teaching.
期刊论文(4)
专著(0)
科研奖励(0)
会议论文
DOI:
10.1016/j.jmb.2016.02.001
发表时间:
2016-02
期刊:
Journal of molecular biology
影响因子:
5.6
作者:
[M. Sternberg;M. Ostankovitch]
通讯作者:
M. Sternberg;M. Ostankovitch
DOI:
10.1186/s13073-015-0212-9
发表时间:
2015-09-01
期刊:
Genome medicine
影响因子:
12.3
作者:
[Cornish AJ, Filippis I, David A, Sternberg MJ]
通讯作者:
Sternberg MJ
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
21-BBSRC/NSF-BIO: Modeling of protein interactions to predict phenotypic effects of genetic mutations
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批准号:BB/X01830X/1
-
项目类别:Research Grant
-
资助金额:$70.26万
-
财政年份:2023
-
负责人:Michael Sternberg
-
依托单位:
Enhancing the Phyre protein modelling resource: prediction of ligand binding and the impact of missense variants
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批准号:BB/V018558/1
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项目类别:Research Grant
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资助金额:$63.68万
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财政年份:2022
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负责人:Michael Sternberg
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依托单位:
18-BBSRC-NSF/BIO - Structural modeling of interactome to assess phenotypic effects of genetic variation
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批准号:BB/T010487/1
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项目类别:Research Grant
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资助金额:$63.69万
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财政年份:2020
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负责人:Michael Sternberg
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依托单位:
FunPDBe - Community driven enrichment of PDB data with structural and functional annotations
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批准号:BB/P023959/1
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项目类别:Research Grant
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资助金额:$15.73万
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财政年份:2019
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负责人:Michael Sternberg
-
依托单位:
Development and marketing of protein docking games for the educational sector
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批准号:BB/R01955X/1
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项目类别:Research Grant
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资助金额:$25.59万
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财政年份:2018
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负责人:Michael Sternberg
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依托单位:
EzMol and BioBlox: Assessing the commercial opportunities and societal benefits of protein modelling resources in industry, schools and museums
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批准号:BB/R005958/1
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项目类别:Research Grant
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资助金额:$1.2万
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财政年份:2017
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负责人:Michael Sternberg
-
依托单位:
Modeling protein interactions to interpret genetic variation
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批准号:BB/P011705/1
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项目类别:Research Grant
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资助金额:$58.37万
-
财政年份:2016
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负责人:Michael Sternberg
-
依托单位:
Enhancing the Phyre2 protein modelling portal for the community
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批准号:BB/M011526/1
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项目类别:Research Grant
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资助金额:$78.34万
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财政年份:2015
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负责人:Michael Sternberg
-
依托单位:
Maintaining and extending PHYRE2 to deliver an internationally-recognised resource for protein model
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批准号:BB/J019240/1
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项目类别:Research Grant
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资助金额:$45.2万
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财政年份:2012
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负责人:Michael Sternberg
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依托单位:
GENOME-3D: a UK network providing structure-based annotations for genotype to phenotype studies
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批准号:BB/I025271/1
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项目类别:Research Grant
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资助金额:$11.14万
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财政年份:2011
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负责人:Michael Sternberg
-
依托单位:
A Community Resource for the Prediction of Protein Structure: PHYRE
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批准号:BB/G022569/1
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项目类别:Research Grant
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资助金额:$40.26万
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财政年份:2009
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负责人:Michael Sternberg
-
依托单位:
Integration of enhanced protein function prediction with experimental studies of fertilisation in Plasmodium - a wet/dry study
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批准号:BB/F020481/1
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项目类别:Research Grant
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资助金额:$80.41万
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财政年份:2008
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负责人:Michael Sternberg
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依托单位:
A novel and rapid approach to predict protein structure
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批准号:BB/G003912/1
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项目类别:Research Grant
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资助金额:$41.06万
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财政年份:2008
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负责人:Michael Sternberg
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依托单位:
Protein Function Prediction using Machine Learning by an Enhanced Novel Support Vector Logic-based Approach
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批准号:BB/E000940/1
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项目类别:Research Grant
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资助金额:$87.09万
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财政年份:2006
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负责人:Michael Sternberg
-
依托单位:
国内基金
海外基金
水稻边界发育缺陷突变体abnormal boundary development(abd)的基因克隆与功能分析
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批准号:32070202
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项目类别:面上项目
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资助金额:58.0万元
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批准年份:2020
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负责人:汪泉
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依托单位:
Development of a Linear Stochastic Model for Wind Field Reconstruction from Limited Measurement Data
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批准号:--
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项目类别:--
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资助金额:40万元
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批准年份:2020
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负责人:Vikrant Gupta
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依托单位: