Inferential structure determination
Inferential structure determination
复制标题
DOI：
10.1126/science.1110428
复制
发表时间：
2005-07-08
期刊：
影响因子：
56.9
通讯作者：
Nilges, M
中科院分区：
文献类型：
--
作者：
Rieping, W;Habeck, M;Nilges, M
由核磁共振数据计算出的大分子结构并不完全由实验数据决定，而是取决于数据处理和参数设置的主观选择。这使得客观判断结构的精度变得困难。我们使用贝叶斯推理来推导表示未知结构及其精度的概率分布。该概率分布还确定了其他未知数，例如以前必须凭经验选择的理论参数。我们通过使用马尔可夫链、蒙特卡罗技术来实现这种方法。我们的方法提供了客观的品质因数并提高了结构质量。
Macromolecular structures calculated from nuclear magnetic resonance data are not fully determined by experimental data but depend on subjective choices in data treatment and parameter settings. This makes it difficult to objectively judge the precision of the structures. We used Bayesian inference to derive a probability distribution that represents the unknown structure and its precision. This probability distribution also determines additional unknowns, such as theory parameters, that previously had to be chosen empirically. We implemented this approach by using Markov chain, Monte Carlo techniques. Our method provides an objective figure of merit and improves structural quality.