Statistical mechanics of RNA folding

 描述(申请人提供)：测序技术的进步导致了RNA序列信息量的快速增长，同时，在已知序列的数量和已知结构的数量之间产生了越来越大的差距。此外，非编码RNA数量的急剧增加及其功能的发现比以往任何时候都更需要对RNA结构的清楚了解。然而，RNA结构的实验测定非常耗时，跟不上日益增长的需求的步伐。这导致了开发准确的计算模型来预测RNA3D结构的迫切需求。在过去的十年里，RNA结构预测取得了显著的进展。然而，RNA结构预测的进一步进展受到两个主要障碍的阻碍：(A)无法预测远程三级接触和(B)缺乏结构模板。在我们之前非常成功的粗粒度RNA折叠模型(Vold模型)的基础上，我们提出了一种新的方法来应对这些挑战，并开发了一种新的RNA结构预测模型。我们在这个提议中有三个主要目标：(A)系统地发展一种计算三级折叠熵和自由能的方法。(B)开发一种基于自由能量的方法来根据序列预测碱基对和三级接触。(C)开发一个三维全原子模型，并根据实验确定的结构系统地测试和改进该模型。我们还将继续改进和传播我们的Vold网络服务器，以便最好地为科学界服务。我们提出的方法将基于物理的三级折叠的熵和自由能建模与基于知识的评分函数训练相结合。该方法的主要优点是它基于完整的构象集合而不是随机抽样的构象，并且评分函数不仅考虑了自然褶皱，而且还考虑了非自然相互作用的影响。其他优点包括使用可以处理镁离子的静电模型，以及在结构模型的选择中包含水化能。初步测试显示了非常有希望的结果，这表明了我们方法的可行性。此外，通过与生物化学家和基于RNA的癌症生物学家的合作，我们将不断测试、完善和验证该模型，并将该模型应用于解决基于RNA的治疗设计等具有生物学意义的及时问题。

 DESCRIPTION (provided by applicant): Advances in sequencing technology lead to rapidly growing amount of RNA sequence information and in the mean- time, create an increasing gap between the number of known sequences and the number of known structures. Moreover, the dramatic increase in the amount of non-coding RNAs and the discovery of their functions require more than ever a clear understanding of RNA structures. However, experimental determination of RNA structure is time consuming and cannot keep up the pace with ever-increasing demand. This causes a pressing demand to develop accurate computational models to predict RNA 3D structures. In the past ten years, remarkable progress has been achieved on RNA structure predictions. Further advances of RNA structure prediction, however, are blocked by two main hurdles: (a) the inability to predict long-range tertiary contacts and (b) lack of structural templates. Building upon our previous highly successful coarse-grained RNA folding model (Vfold model), we propose a new approach to tackle these challenges and to develop a new RNA structure prediction model. We have three major aims in this proposal: (a) To systematically develop a method to calculate tertiary folding entropy and free energy. (b) To develop a free energy-based approach to predict the base pairs and the tertiary contacts from the sequence. (c) To develop a three-dimensional all-atom model and to systematically test and refine the model based on the experimentally determined structures. We will also continue the improvement and dissemination of our Vfold web server to best serve the scientific community. Our proposed method will integrate a physics-based modeling of entropy and free energy of tertiary folds with knowledge-based training of scoring functions. Key advantages of the approach are that it is based on the complete conformation ensemble instead of randomly sampled conformations and that the scoring function accounts for not only the native folds but also the effect from the nonnative interactions. Other advantages include the use of an electrostatic model that can treat Mg2+ ions and the inclusion of hydration energy in the selection of structural models. The preliminary tests show very promising results, suggesting the feasibility of our approach. Furthermore, through collaborations with biochemists and RNA-based cancer biologists, we will continuously test, refine and validate the model, and apply the model to solve biologically significant and timely problems such as RNA-based therapeutic design.