DYNAMIC STRUCTURE OF BIOMOLECULES IN SOLUTION

许多生物分子在溶液中是灵活的。这可以是 通过核磁共振(NMR)方法进行评估。事实上，J 标量耦合常数和核Overhauser效应(NOE)数据为 适用于不同类型的平均化，当在 解决方案。在一种情况下，可能不可能符合所有观察到情况 具有单一分子构象的核磁共振参数。我们有 开发了基于二次函数的概率分布程序PDQPRO 编程)，其确定最优概率分布 通过找到最佳匹配来获取预定义的潜在异构体集合 计算的核磁共振参数与实验的核磁共振参数之间的关系。 这种方法需要一种独立的采样方法来产生 一组潜在的异构体。分析了一些灵活的测试方法 用模拟的核磁共振数据证明了PDQPRO能够 识别正确的异构体和 计算正确的概率，前提是那些正确的概率 在潜在的结构池中存在构象。目前 我们正在调查PDQPRO与 各种取样方法。特别是，我们使用了MDtar 方法由Torda等人开发。要为对象生成MD轨迹 灵活的RNA环。MDTAR轨迹作为一个整体解释了 现有的核磁共振数据；然而，MD轨迹不太适合 由于其规模较大，因此需要进行全面的结构分析。应用程序 到这个轨迹的PDQPRO减少了一个数量级 解释实验数据所需的异构体数量。这个 计算机图形学实验室资源是我们的项目所必需的 用于结构集合的图形表示，这是一种 非同小可的问题。特别是，我们使用了许多 Eric Pettersen为此特殊目的编写的MidasPlus代表 还有大卫·科纳丁。

Many biological molecules are flexible in solution. This can be assessed by nuclear magnetic resonance (NMR) methods. Indeed, J scalar coupling constants and nuclear Overhauser effect (NOE) data are subject to different types of averaging when conformers are present in solution. In suc a case, it may not be possible to fit all observed NMR parameters with a single molecular conformation. We have developed a program, PDQPRO (Probability Distribution by Quadratic PROgramming), which determines the optimal probability distribution for a predefined pool of potential conformers by finding the best fit between calculated and experimental NMR parameters. Such an approach requires an independent sampling method producing a set of potential conformers. Analysis of a number of flexible test molecules with simulated NMR data has demonstrated that PDQPRO is able to recognize the correct conformers and calculate the correct probabilities, provided that those correct conformers are present in the pool of potential structures. Presently we are investigating the performance of PDQPRO in combination with various sampling methods. In particular, we have used the MDtar method developed by Torda et al. to generate an MD trajectory for a flexible RNA loop. The MDtar trajectory as a whole explained the existing NMR data; however, MD trajectories are not very suitable for a comprehensive structural analysis due to its size. The application of the PDQPRO to this trajectory reduced by an order of magnitude the number of conformers required to explain the experimental data. The Computer Graphics Laboratory resources are necessary for our project for graphical representation of structural ensembles, which is a non-trivial problem. In particular, we are using a number of MidasPlus delegates written for this special purpose by Eric Pettersen and David Konerding.