Statistical mechanics of RNA folding

 描述（由申请人提供）：测序技术的进步导致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结构的清晰理解。然而，RNA结构的实验测定非常耗时，无法跟上不断增长的需求。这就迫切需要开发精确的计算模型来预测RNA的3D结构。 在过去的十年中，RNA结构预测取得了显著的进展。然而，RNA结构预测的进一步发展受到两个主要障碍的阻碍：（a）无法预测长距离三级接触和（B）缺乏结构模板。基于我们以前非常成功的粗粒度RNA折叠模型（Vfold模型），我们提出了一种新的方法来解决这些挑战，并开发一个新的RNA结构预测模型。本研究的主要目的有三：（1）系统地发展三级折叠熵和自由能的计算方法。(b)建立一种基于自由能的方法来预测序列中的碱基对和三级接触。(c)建立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模型。我们还将继续改进和传播我们的Vfold网络服务器，以更好地为科学界服务。 我们提出的方法将集成基于物理的建模熵和自由能的第三次折叠与知识为基础的训练评分功能。该方法的主要优点是，它是基于完整的构象合奏，而不是随机抽样的构象和评分功能不仅占原生折叠，但也从非原生相互作用的影响。其他优点包括使用可以处理Mg2+离子的静电模型，以及在结构模型的选择中包含水合能。初步的测试结果表明，非常有希望的结果，表明我们的方法的可行性。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，改进和验证模型，并应用该模型解决生物学上重要和及时的问题，例如基于RNA的治疗设计。

 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.