Jorda Julien, Sawaya Michael R, Yeates Todd O
UCLA-DOE, Institute for Genomics and Proteomics, 611 Charles Young Drive East, Los Angeles, CA 90095, USA.
Acta Crystallogr D Biol Crystallogr. 2014 Jun;70(Pt 6):1538-48. doi: 10.1107/S1399004714006427. Epub 2014 May 23.
The human mind innately excels at some complex tasks that are difficult to solve using computers alone. For complex problems amenable to parallelization, strategies can be developed to exploit human intelligence in a collective form: such approaches are sometimes referred to as crowdsourcing'. Here, a first attempt at a crowdsourced approach for low-resolution ab initio phasing in macromolecular crystallography is proposed. A collaborative online game named CrowdPhase was designed, which relies on a human-powered genetic algorithm, where players control the selection mechanism during the evolutionary process. The algorithm starts from a population of individuals', each with a random genetic makeup, in this case a map prepared from a random set of phases, and tries to cause the population to evolve towards individuals with better phases based on Darwinian survival of the fittest. Players apply their pattern-recognition capabilities to evaluate the electron-density maps generated from these sets of phases and to select the fittest individuals. A user-friendly interface, a training stage and a competitive scoring system foster a network of well trained players who can guide the genetic algorithm towards better solutions from generation to generation via gameplay. CrowdPhase was applied to two synthetic low-resolution phasing puzzles and it was shown that players could successfully obtain phase sets in the 30° phase error range and corresponding molecular envelopes showing agreement with the low-resolution models. The successful preliminary studies suggest that with further development the crowdsourcing approach could fill a gap in current crystallographic methods by making it possible to extract meaningful information in cases where limited resolution might otherwise prevent initial phasing.
人类思维天生擅长一些复杂任务，而仅用计算机难以解决这些任务。对于适合并行化的复杂问题，可以开发策略以集体形式利用人类智能：此类方法有时被称为“众包”。在此，提出了一种用于大分子晶体学中低分辨率从头相位确定的众包方法的首次尝试。设计了一款名为CrowdPhase的在线协作游戏，它依赖于一种人工驱动的遗传算法，玩家在进化过程中控制选择机制。该算法从一群“个体”开始，每个个体都有随机的基因组成，在这种情况下是由一组随机相位制备的图谱，并试图使群体基于达尔文的适者生存原则朝着具有更好相位的个体进化。玩家运用他们的模式识别能力来评估从这些相位集生成的电子密度图，并选择最适合的个体。一个用户友好的界面、一个训练阶段和一个竞争性评分系统培养了一个训练有素的玩家网络，他们可以通过游戏玩法一代又一代地引导遗传算法找到更好的解决方案。CrowdPhase被应用于两个合成的低分辨率相位确定谜题，结果表明玩家能够成功获得相位误差在30°范围内的相位集以及与低分辨率模型相符的相应分子包络。这些成功的初步研究表明，随着进一步发展，众包方法有可能填补当前晶体学方法的空白，因为在有限分辨率可能会阻碍初始相位确定的情况下，它能够提取有意义的信息。
