Bayesian inference applied to macromolecular structure determination
Bayesian inference applied to macromolecular structure determination
复制标题
DOI：
10.1103/physreve.72.031912
复制
发表时间：
2005-09-01
影响因子：
2.4
通讯作者：
Rieping, W
中科院分区：
文献类型：
--
作者：
Habeck, M;Nilges, M;Rieping, W
由实验数据确定大分子结构是一个不适定的反问题。然而，结构确定的常规技术尝试通过最小化目标函数来反演数据。如果数据稀疏、有噪声、异构或难以从理论上描述，则这种方法会导致问题。我们建议在这里查看生物分子结构的测定作为一个推理，而不是一个反演问题。概率论提供了一个一致的形式主义来解决任何结构确定问题：我们使用贝叶斯定理来推导原子坐标和所有其他未知数的概率分布。这种分布代表了数据中包含的完整信息，可以通过马尔可夫链蒙特卡罗抽样技术进行数值分析。我们应用我们的方法从核磁共振实验获得的数据，并讨论了理论参数的估计。
The determination of macromolecular structures from experimental data is an ill-posed inverse problem. Nevertheless, conventional techniques to structure determination attempt an inversion of the data by minimization of a target function. This approach leads to problems if the data are sparse, noisy, heterogeneous, or difficult to describe theoretically. We propose here to view biomolecular structure determination as an inference rather than an inversion problem. Probability theory then offers a consistent formalism to solve any structure determination problem: We use Bayes' theorem to derive a probability distribution for the atomic coordinates and all additional unknowns. This distribution represents the complete information contained in the data and can be analyzed numerically by Markov chain Monte Carlo sampling techniques. We apply our method to data obtained from a nuclear magnetic resonance experiment and discuss the estimation of theory parameters.