RNA design rules from a massive open laboratory
RNA design rules from a massive open laboratory
复制标题
DOI：
10.1073/pnas.1313039111
复制
发表时间：
2014-02-11
影响因子：
11.1
通讯作者：
Das, Rhiju
中科院分区：
文献类型：
--
作者：
Lee, Jeehyung;Kladwang, Wipapat;Das, Rhiju
自组装 RNA 分子为合理询问和控制生命系统提供了引人注目的底物。然而，计算机模型的不完善——即使是在二级结构水平上——也阻碍了合成后能正常发挥作用的新RNA的设计。在这里，我们提出了一种解决此类实证问题的独特且可能通用的方法：大规模开放实验室。 EteRNA 项目通过在线界面将 37,000 名爱好者连接到 RNA 设计难题。独特的是，EteRNA 参与者不仅可以操纵模拟分子，还可以控制用于高通量 RNA 合成和结构图谱的远程实验管道。我们在此表明，EteRNA 社区利用数十个连续湿实验室反馈循环来学习解决自动化方法失败的体外 RNA 设计问题的策略。最重要的策略——包括一些以前未被识别的负面设计规则——被机器学习提炼成一种算法，EteRNABot。在严格的 1 年测试阶段，EteRNA 社区和 EteRNABot 在十几个 RNA 二级结构设计测试中都显着优于先前的算法，包括为小分子传感器创建类似树状聚合物的结构和支架。这些结果表明，在线社区可以进行大规模实验、假设生成和算法设计，以创造实证科学的实际进展。
Self-assembling RNA molecules present compelling substrates for the rational interrogation and control of living systems. However, imperfect in silico models-even at the secondary structure level-hinder the design of new RNAs that function properly when synthesized. Here, we present a unique and potentially general approach to such empirical problems: the Massive Open Laboratory. The EteRNA project connects 37,000 enthusiasts to RNA design puzzles through an online interface. Uniquely, EteRNA participants not only manipulate simulated molecules but also control a remote experimental pipeline for high-throughput RNA synthesis and structure mapping. We show herein that the EteRNA community leveraged dozens of cycles of continuous wet laboratory feedback to learn strategies for solving in vitro RNA design problems on which automated methods fail. The top strategies-including several previously unrecognized negative design rules-were distilled by machine learning into an algorithm, EteRNABot. Over a rigorous 1-y testing phase, both the EteRNA community and EteRNABot significantly outperformed prior algorithms in a dozen RNA secondary structure design tests, including the creation of dendrimer-like structures and scaffolds for small molecule sensors. These results show that an online community can carry out large-scale experiments, hypothesis generation, and algorithm design to create practical advances in empirical science.