From RNA Sequence to 3D structure, Accurate Prediction Through Backbone K-Trees

从RNA序列到3D结构，通过骨干K树进行准确预测

• 批准号: 9119609
• 负责人: LIMING CAI
• 金额: $ 21.39万
• 依托单位: UNIVERSITY OF GEORGIA
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-08-01 至 2018-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9119609
• 关键词: Address; Adopted; Algorithms; Area; Biochemical; Biology; Cell physiology; Complex; Development; Foundations; Graph; Health; Human Biology; Investigation; Life; Medicine; Methods; Modeling; Molecular; Molecular Conformation; Nucleotides; Organism; Performance; RNA; RNA Sequences; Research; Role; Sampling; Scientist; Small RNA; Specific qualifier value; Structure; Techniques; Theoretical model; Trees; Untranslated RNA; Vertebral column; computerized tools; graph theory; improved; novel; prediction algorithm; research study; theories; three dimensional structure; tool

 DESCRIPTION (provided by applicant): This proposed project is a non-conventional framework for RNA 3D structure prediction from sequences. It solves the 3D structure prediction problem with a novel graph-theoretic model of backbone k-tree that has the potential to markedly reduce the molecular 3D conformation space. Specific objectives of this research are: (1) to develop the proposed structure prediction framework into deliverable tools with the desired accuracy and the ability to scale to large RNA molecules; (2) to establish algorithmic graph theory for backbone k-tree optimization serving as the foundation for the prediction algorithm development; and (3) to validate and improve the prediction method by incorporating structural studies and biochemical experiments with selected model non-coding RNAs. Computational prediction of RNA 3D structure is critical for understanding cellular functions of non-coding RNAs. However, identifying native structures from RNA sequences has proven to be a significant challenge due to the difficulty of searching the immense space of 3D conformations even for a small RNA molecule. Prediction accuracy can be compromised by non-optimal, sampling techniques often adopted for computational feasibility; in particular, existing methods have yet to deliver the desired performance for RNA sequences of more than 50 nucleotides. This project proposes to directly address this challenging issue with a novel backbone k-tree model for nucleotide interaction relationships that specify the 3D conformation. The model significantly reduces the search space of conformations and potentially enables efficient algorithms for 3D structure prediction. To bring this capability to fruition, this research will undertake a full investigation of the unexplored algorithmic graph theory of backbone k-trees. A thorough understanding of such a theory will not only benefit the RNA 3D structure prediction but also offer viable solutions to other challenging problems such as RNA-RNA complex prediction. Accurate and scalable tools for 3D structure prediction will permit effective structure elucidation of newly discovered RNAs.

 描述(由申请人提供)：该提议的项目是从序列中预测RNA3D结构的非常规框架。它用一种新的主干k-树图论模型解决了三维结构预测问题，该模型有可能显着减少分子的三维构象空间。本研究的具体目标是：(1)将所提出的结构预测框架开发成具有所需精度和大RNA分子规模的可交付工具；(2)建立主干k-树优化的算法图理论，作为预测算法开发的基础；以及(3)通过将结构研究和生化实验与选定的模型非编码RNA相结合来验证和改进预测方法。RNA三维结构的计算预测对于理解非编码RNA的细胞功能至关重要。然而，从RNA序列中识别天然结构已经被证明是一个巨大的挑战，因为即使是一个小的RNA分子也很难搜索巨大的3D构象空间。预测精度可能会受到通常为计算可行性而采用的非最佳抽样技术的影响；特别是，现有方法尚未为超过50个核苷酸的RNA序列提供所需的性能。这个项目建议用一个新的核苷相互作用关系的主干k-树模型来直接解决这个具有挑战性的问题，该模型指定了3D构象。该模型显著减少了构象的搜索空间，并潜在地为三维结构预测提供了有效的算法。为了实现这一能力，本研究将对尚未探索的主干k-树的算法图理论进行充分的研究。对这一理论的深入理解不仅有利于RNA三维结构预测，还将为RNA-RNA复合体预测等其他具有挑战性的问题提供可行的解决方案。用于3D结构预测的准确和可扩展的工具将使有效的结构成为可能 阐明新发现的RNA。