Control of RNA function by conformational design

在过去的二十年里，RNA已经成为研究的主要焦点。核酶、核开关和参与大量细胞过程的小非编码RNA的发现引起了人们对RNA结构和构象如何与其功能联系以及RNA功能如何通过构象设计来控制的理解的极大兴趣。因此，更复杂的设计目标显然不适合人工顺序设计，而需要使用基于计算机的优化方法。另一方面，由于能量模型中即使很小的误差也会显著改变不同构象之间的平衡，因此计算RNA结构预测通常不够准确，无法立即产生具有功能的RNA分子。然而，计算模型能够非常有效地将搜索空间减少到可以通过实验探测的少量候选对象。通过将计算机辅助预测与湿实验室实验相结合，我们旨在开发设计功能性RNA分子的协议和软件工具。特别是，将评估三个系统：(i)自剪接和（ii）源自发夹核酶的自复制RNA，以及（iii）自诱导RNA开关。各自系统的设计将建立在先前的实验工作的基础上，并将通过RNA折叠动力学，RNA复合物形成和构象分布的计算预测作为成功解决更复杂设计问题的重要工具，强烈指导和/或优化。反过来，理论预测的实验测试将使我们能够识别计算模型的缺点。反复的建模和测试将用于迭代地完善模型设置和参数。

Over the past two decades RNA has become a major focus of research. The discovery of ribozymes, riboswitches and small noncoding RNAs being involved in a large number of cellular processes has led to much interest in the understanding of how RNA structure and conformation are linked to its function and how RNA function can be controlled by conformational design. Accordingly, it has become evident that more complex design targets are not amenable to manual sequence design, but require the use of computer based optimization methods. On the other hand, since even small errors in the energy model can significantly shift the balance between alternative conformations, computational RNA structure prediction is often not accurate enough to yield immediately functional RNA molecules. The computational models are, however, very efficiently able to reduce the search space to a small number of candidates that can be probed experimentally. By combining computer-aided prediction with wet lab experiments, we aim at developing protocols and software tools for the design of functional RNA molecules. In particular, three systems will be evaluated: (i) self-splicing and (ii) self-replicating RNAs derived from the hairpin ribozyme, and (iii) self-induced RNA switches. Design of the respective systems will build up on previous experimental work, and will be strongly guided and/or optimized by computational prediction of RNA folding kinetics, RNA complex formation and conformational distribution as an essential tool to successfully solve more complex design problems. In turn, experimental testing of the theoretical predictions will allow us to identify shortcomings of the computational models. Repeated rounds of modelling and testing will be used to iteratively refine model setup and parameters.