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: 9037760
• 负责人: LIMING CAI
• 金额: $ 21.29万
• 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/9037760
• 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; Solutions; Specific qualifier value; Structure; Techniques; Theoretical model; Trees; Untranslated RNA; Vertebral column; computerized tools; improved; novel; public health relevance; research study; theories; three dimensional structure; tool; transcriptome sequencing

 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）：该拟议项目是一个非传统的框架，用于从序列预测RNA 3D结构。它解决了三维结构预测问题的一种新的图论模型的骨干k树，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发展成具有所需精度和扩展到大RNA分子的能力的可交付工具：（2）建立用于骨干k树优化的算法图论，作为预测算法开发的基础;以及（3）通过将结构研究和生物化学实验与选定的模型非编码RNA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被证明是一个重大的挑战，因为即使对于小RNA分子，也难以搜索3D构象的巨大空间。为了计算可行性而通常采用的非最佳采样技术可能会损害预测准确性;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。该项目提出直接解决这个具有挑战性的问题，一种新的骨干k树模型的核苷酸相互作用关系，指定的3D构象。该模型显着减少了构象的搜索空间，并有可能实现有效的算法进行三维结构预测。为了实现这种能力，本研究将对骨干k树的未开发算法图论进行全面调查。对这一理论的深入理解不仅有利于RNA的三维结构预测，而且为其他具有挑战性的问题，如RNA-RNA复合物预测提供了可行的解决方案。用于3D结构预测的精确和可扩展的工具将允许有效的结构 新发现的RNA。