Computational modeling of sequence-dependent DNA curvature using hydrodynamics

使用流体动力学对序列依赖性 DNA 曲率进行计算建模

• 批准号: 0706951
• 负责人: Oscar Gonzalez
• 金额: $ 16.69万
• 依托单位: University of Texas at Austin
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-06-15 至 2011-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0706951&HistoricalAwards=false
• 关键词: Computational; modeling; sequence; dependent; DNA

Gonzalez0706951 The investigator develops a computational method forestimating local, sequence-dependent curvature parameters for DNAfrom hydrodynamic data on short, relatively stiff fragments. Themethod consists of minimizing a least-squares functional whichquantifies the difference between theoretical and experimentalsedimentation speeds for a given collection of fragments, and itsnumerical implementation requires the repeated solution of anexterior Stokes-type problem around various slender,three-dimensional domains. The research effort is centered onthree areas: (1) the study of the theoretical sedimentation speedof a stiff polymer in a Stokes-type fluid with thermalfluctuations in different asymptotic limits, (2) the design andanalysis of fast, provably convergent boundary element methodsfor computing the theoretical sedimentation speed based on thenovel idea of a kernel-adapted Nystrom method, and (3) the designand analysis of fast, reliable methods for minimizing aleast-squares sedimentation functional over a space ofsequence-dependent curvature parameters. The sequence-dependent curvature and flexibility of DNA iscritical for its packaging into the cell, recognition by othermolecules, and conformational changes during biochemicallyimportant processes. However, few experimental methods areavailable for probing these properties at the basepair level. The objective of this project is to exploit the stiffness of DNAat short length scales and develop a method for estimating local,sequence-dependent curvature parameters from hydrodynamic data onshort fragments. The method is based on the observation thatsmall, relatively stiff fragments of different shape typicallytravel at different speeds when forced through a viscous fluid orthrough a dilute gel. The investigator seeks to develop asufficiently refined mathematical relation between shape andspeed and further use this relation to develop a computationalmethod to estimate shape parameters from measurements of speed. Results from this research lead to new tools for quantifying theintrinsic sequence-dependent curvature of DNA, which in turn maylead to an enhanced understanding of important genomic regionsand of how DNA interacts with other molecules.

研究者开发了一种计算方法，从短的、相对坚硬的片段的流体动力学数据中预测dna的局部、序列相关的曲率参数。该方法包括最小化最小二乘函数，该函数量化了给定碎片集合的理论和实验沉降速度之间的差异，并且其数值实现需要围绕各种细长的三维域重复解决外部stokes型问题。研究工作主要集中在三个方面：(1)研究了具有不同渐近极限的热波动的stokes型流体中刚性聚合物的理论沉降速度；(2)基于核适应Nystrom方法的新思想设计和分析了计算理论沉降速度的快速、可证明收敛的边界元方法；在序列相关曲率参数空间上最小化近方沉降函数的可靠方法。DNA的序列依赖性曲率和灵活性对于其包装进入细胞，被其他分子识别以及在重要的生化过程中构象变化至关重要。然而，很少有实验方法可以在碱基对水平上探测这些特性。该项目的目标是利用DNAat短长度尺度的刚度，并开发一种从短片段的水动力数据估计局部序列相关曲率参数的方法。这种方法是基于这样一种观察，即不同形状的相对坚硬的小碎片在被迫通过粘性流体或稀释凝胶时，通常以不同的速度移动。研究者试图在形状和速度之间建立足够精确的数学关系，并进一步利用这种关系来开发一种计算方法，从速度的测量中估计形状参数。这项研究的结果导致了量化DNA固有序列依赖曲率的新工具，这反过来可能导致对重要基因组区域以及DNA如何与其他分子相互作用的更好理解。