Predicting protein structures with a multiplayer online game.
Predicting protein structures with a multiplayer online game.
复制标题
作者：
人们在玩电脑游戏时会花费大量的精力来解决问题。简单的图像和文本识别任务已经成功地通过游戏众包，但目前尚不清楚更复杂的科学问题是否可以通过人工指导的计算来解决。蛋白质结构预测就是这样一个问题：由于搜索空间非常大，定位蛋白质的生物相关天然构象是一个艰巨的计算挑战。在这里，我们描述Foldit，一个多人在线游戏，从事非科学家在解决困难的预测问题。Foldit玩家使用直接操作工具和Rosetta结构预测方法的用户友好版本的算法与蛋白质结构进行交互，同时他们竞争和合作以优化计算的能量。我们表明，顶级Foldit球员擅长解决具有挑战性的结构优化问题，其中大量的骨架重排是必要的，以实现埋葬的疏水残基。玩家合作开发了各种各样的新策略和算法;与计算方法不同，他们不仅探索构象空间，还探索可能的搜索策略空间。通过交互式多人游戏将人类视觉解决问题和策略开发能力与传统计算算法相结合，是解决计算有限的科学问题的一种强大的新方法。
People exert significant amounts of problem solving effort playing computer games. Simple image- and text-recognition tasks have been successfully crowd-sourced through games, but it is not clear if more complex scientific problems can be similarly solved with human-directed computing. Protein structure prediction is one such problem: locating the biologically relevant native conformation of a protein is a formidable computational challenge given the very large size of the search space. Here we describe Foldit, a multiplayer online game that engages non-scientists in solving hard prediction problems. Foldit players interact with protein structures using direct manipulation tools and user-friendly versions of algorithms from the Rosetta structure prediction methodology, while they compete and collaborate to optimize the computed energy. We show that top Foldit players excel at solving challenging structure refinement problems in which substantial backbone rearrangements are necessary to achieve burial of hydrophobic residues. Players working collaboratively develop a rich assortment of new strategies and algorithms; unlike computational approaches, they explore not only conformational space but also the space of possible search strategies. The integration of human visual problem-solving and strategy development capabilities with traditional computational algorithms through interactive multiplayer games is a powerful new approach to solving computationally-limited scientific problems.
登录
查看更多内容
DOI：
10.1089/cpb.2006.9.772
发表时间：
2006-12-01
期刊：
CYBERPSYCHOLOGY & BEHAVIOR
影响因子：
--
作者：
Yee, Nick
通讯作者：
Yee, Nick
影响因子：
64.8
作者：
Qian, Bin;Raman, Srivatsan;Das, Rhiju;Bradley, Philip;McCoy, Airlie J;Read, Randy J;Baker, David
通讯作者：
Baker, David
影响因子：
56.9
作者：
Bradley, P;Misura, KMS;Baker, D
通讯作者：
Baker, D
影响因子：
56.9
作者：
ANFINSEN, CB
通讯作者：
ANFINSEN, CB