DockIt: Development and launch of a crowd-sourced serious-games platform for protein docking for use by the public and the scientific community.

这项资助的目的是开发一个严肃而有趣的电脑游戏（DockIt），供众多玩家（人群）使用，以模拟两个大的生物分子（蛋白质）如何结合在一起，从而发挥其功能。蛋白质对接：蛋白质是大的生物分子，通常至少有1,000个原子，执行许多生物功能。蛋白质的三维结构是复杂的。其生物活性的核心是它经常与另一种蛋白质对接形成复合物。复合物的结构取决于分子中的原子如何相互作用。复合物的形状有助于理解蛋白质的作用机制，这一知识可以在食品安全，工业生物技术和新型药物设计方面产生重大利益。计算方法被用来预测一个复杂的给定的知识的结构的未绑定组件的形状。然而，这些计划只取得了有限的成功。在其他科学领域的研究，包括蛋白质形状的建模，已经表明，使用人类判断的个人有时可以找到解决问题的方法，这些问题还不能自动解决。特别是从一群参与者中找到共识解决方案可以产生成功的结果。这种方法被称为众包。因此，我们建议开发一个计算机游戏DockIt，玩家将操纵两个蛋白质结构并预测一个复合物。这类似于解决三维拼图游戏。对接将开始与一组可能的复合体产生的最先进的程序，从而成功的发挥可以产生预测结构上级的自动生成。DOCKIT游戏-我们将设计DockIt，使它是有趣的发挥利用最新的技术和设计的休闲游戏行业。我们将为包括平板电脑、智能手机和PC在内的一系列平台开发DockIt，从而确保广泛采用。用户将能够调整两种蛋白质的相对位置并改变每种蛋白质的形状。改变蛋白质的方法将被编码为一组用户可以使用的食谱。我们将提供一个评分功能来反馈给玩家对预测结构质量的估计。DOCKIT的发布-我们将发布DockIt供学术界和公众使用。我们宣传DockIt的策略是与我们的合作伙伴，合作者和行业联系人合作，通过他们广泛的玩家数据库推广DockIt，例如通过包含在时事通讯中。对接目标将包括那些已知解决方案的目标，以及那些使用DockIt解决现实世界生物问题的目标。玩家将从主机服务器下载一个可能的复合体，然后改变其形状以预测最佳复合体，然后将其返回到服务器。不同参与者的结果将被汇总，我们将确定最佳预测。玩家将得到一个教程的支持，该教程将提供渐进的步骤来学习游戏。将提供电子邮件支持，并将建立一个社交网络群（例如Google+、FaceBook、LinkedIn和Twitter）。我们将在科学会议上发表演讲并发表技术论文。我们将与科普杂志一起发布针对奥运会的新闻稿。我们还将针对电视、广播、教育报刊和贸易机构。我们设想DockIt可以在学校和大学的教学中使用。

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.