Prediction of RNA-RNA Interactions by Kinetic Modelling

生物体的转录组的大部分不由蛋白质编码RNA组成，而是代表所谓的非编码RNA（ncRNA），它们中的大多数充当调控元件。这些ncRNA分子中的许多需要与蛋白质编码信使RNA或其他ncRNA相互作用以实现其调节功能。对这些RNA-RNA相互作用的详细了解有助于深入了解生物体的调控网络。由于实验研究的复杂性和昂贵性，迫切需要计算预测方法。目前的方法通常局限于简单的相互作用类型，并且显示出较低的预测精度。在本项目中，我们将识别限制已知相互作用的空间和拓扑特征，以提高RNA-RNA相互作用预测模型的预测质量。此外，我们将定义新的模型，涵盖更一般的交互模式，多站点交互。识别的特征将被整合为硬约束以避免不切实际的结构，并通过伪能量项更好地指导应用的优化方法。这将伴随着对RNA-RNA相互作用动力学的广泛研究，因为它能够指导相互作用形成不可预测的次优结构。因此，新的能源景观模型将被定义和实施，允许一个详细的，但仍然计算可访问的能源景观为基础的动力学研究。这包括开发新的方法来有效地探索大的能量景观以及计算转换模型和动力学。这两个研究领域在新的RNA-RNA相互作用预测管道中加入，该管道将尊重新的结构约束以及解释定义相互作用形成的动力学效应。我们将应用我们的新管道来研究ncRNA的相互作用网络，筛选调节ncRNA的靶点，并增加对某些结合途径的机制细节的了解。其中一个应用方向将是研究反义RNA相互作用的机制细节。反义RNA广泛存在于原核生物和真核生物的转录组中，但对它们的调控作用却知之甚少。一个特别有趣的主题是确定反义转录本在多大程度上实际上与其各自的正义转录本形成双链体以及可能的调控机制。我们的计算研究将得到湿实验室实验的支持。除了新的见解，该项目旨在开发一个大型工具集，以促进该领域的进一步研究。目标结果不仅支持RNA-RNA相互作用研究，而且还使单分子结构预测以及其他系统大能量景观的动力学研究取得进展。

The majority of an organism's transcriptome does not consist of protein encoding RNA but represents so called non-coding RNA (ncRNA), most of them acting as regulatory elements. Many of these ncRNA molecules require interactions with protein-coding messenger RNAs or other ncRNAs to fulfill their regulatory functions. A detailed understanding of these RNA-RNA interactions gives insight into the regulatory networks of organisms.Due to the complexity and expense for experimental studies, there is a strong need for computational prediction approaches. Current methods are usually restricted to simple interaction types and show a low prediction accuracy.Within this project we will identify steric and topological features constraining known interactions to increase prediction quality of RNA-RNA interaction prediction models. Furthermore, we will define new models covering more general interaction patterns up to multi-site interactions. Identified features will be integrated both as hard constraints to avoid unrealistic structures and via pseudo energy terms for a better guiding of the applied optimization methods.This will be accompanied with an extensive research of the kinetics of RNA-RNA interactions, since it is able to guide the interaction formation to non-predictable, suboptimal structures. Therefore, new energy landscape models will be defined and implemented that allow for a detailed but still computationally accessible study of the energy landscape based kinetics. This includes the development of new methods for an efficient exploration of large energy landscapes as well as for the computation of transition model and kinetics.These two research fields are joined within new RNA-RNA interaction prediction pipelines that will respect the new structure constraints as well as account for kinetic effects defining the interaction formation. We will apply our new pipelines to investigate interaction networks for ncRNAs, to screen for targets of regulating ncRNAs, and to increase knowledge of the mechanistic details of certain binding pathways.One direction of application will be the investigation of the mechanistic details of anti-sense RNA interactions. Anti-sense RNAs are found in great extent in the transcriptome of pro- and eukaryotes while there is only limited knowledge about their regulatory impact. An especially interesting topic is to determine to what extent anti-sense transcripts actually form duplexes with their respective sense transcripts and what regulatory mechanisms are possible. Our computational studies will be supported by wet-lab experiments.Beside new insights, the project aims at the development of a large tool set to promote further research in this field. The targeted results do not only support RNA-RNA interaction studies but also enable progress for structure prediction of single molecules as well as kinetics studies of large energy landscapes of other systems.