HCC-Small: Protein Design Through Massively Distributed Video Games

在过去的几十年里，生物化学家们发现，几乎所有基本的分子设计问题都是关于复杂分子的几何关系。最近已经开发了这种算法的大规模并行和分布式版本，希望用剪切蛮力找到这样的蛋白质。例如，实现屏幕保护程序，从全球PC用户那里收集空闲的CPU时间，以提供足够的计算能力来解决蛋白质预测问题。到目前为止，所有这些尝试都产生了非常有限的改进。这个项目采取了一种完全不同的方法：分子折叠问题是一个大规模分布的3D益智游戏，鼓励人们与计算机一起寻找解决当前开放问题的方法，包括治疗癌症，艾滋病和发现新的生物燃料。基本思想是采用用户辅助优化蛋白质设计，并将其表述为一个由数千人参与的竞争性游戏。这个项目的意图是，人们会玩这个游戏，因为它很有趣（它看起来像一个有趣的3d拼图，而不是像一些生物化学教科书），它是上瘾的，它是竞争性的，它是合作的（玩家可以在小组中一起解决问题），有影响力（玩家希望获得治疗癌症的药物的功劳）。这个提议的影响在于它解决了一些基本领域：1）如何最好地开发游戏，以最大限度地提高人类发现新蛋白质的能力，超越目前仅使用计算方法的可能性; 2）确定成功的分子设计游戏的指导原则; 3）如何最好地将游戏开发原则推广到尽可能广泛的生物化学问题; 4）揭示人们从游戏中学到了什么，以及如何将这些策略“提炼”为开发更强大的自动化方法。

In the past few decades biochemists have discovered that practically all fundamental molecular design problems are about the geometric relationship of complex molecules. Massively parallel and distributed versions of such algorithms have been recently developed in hope of finding such proteins with shear brute force. For example, implementing screensavers that harvest idle CPU time from PC users worldwide to provide sufficient computing power to solve the protein prediction problems. So far, all these attempts have produced very modest improvements. This project takes a radically different approach: the molecular folding problem is cast as a massively distributed 3D puzzle game, and encourages people to work together with computers to find the solutions to current open problems including cures for cancer, AIDS, and discovery of novel biofuels.The fundamental idea is to employ user-assisted optimization for protein design, and formulate and present it as a competitive game played by thousands of people. The intention in this project is that people will play the game beacause it is fun (it looks like a fun 3d puzzle and not like some biochemistry textbook), it is addicting, it is competitive, it is collaborative (players can work together in groups to solve a problem), has impact (players want to get credit for the drug that cures cancer). The impact of this proposal is in its addressing of a number of fundamental areas: 1) how to best develop games to maximize human ability to discover novel proteins beyond what is currently possible with computation-only approaches; 2) determining the guiding principles of a successful molecular design game; 3) how to best generalize game-development principles to the widest possible range of biochemical problems; 4) revealing what is learned from the way people play the game, and how these strategies could be "distilled" towards developing stronger automated approaches.