Rieping Wolfgang, Nilges Michael, Habeck Michael
Department of Biochemistry, University of Cambridge, 80 Tennis Court Road, Cambridge CB2 1GA, UK.
Bioinformatics. 2008 Apr 15;24(8):1104-5. doi: 10.1093/bioinformatics/btn062. Epub 2008 Feb 28.
The conventional approach to calculating biomolecular structures from nuclear magnetic resonance (NMR) data is often viewed as subjective due to its dependence on rules of thumb for deriving geometric constraints and suitable values for theory parameters from noisy experimental data. As a result, it can be difficult to judge the precision of an NMR structure in an objective manner. The inferential structure determination (ISD) framework, which has been introduced recently, addresses this problem by using Bayesian inference to derive a probability distribution that represents both the unknown structure and its uncertainty. It also determines additional unknowns, such as theory parameters, that normally need to be chosen empirically. Here we give an overview of the ISD software package, which implements this methodology.
从核磁共振（NMR）数据计算生物分子结构的传统方法通常被视为主观的，因为它依赖于经验法则来从嘈杂的实验数据中推导几何约束和理论参数的合适值。因此，很难客观地判断NMR结构的精度。最近引入的推理结构确定（ISD）框架通过使用贝叶斯推理来推导表示未知结构及其不确定性的概率分布，解决了这个问题。它还确定了通常需要凭经验选择的其他未知量，例如理论参数。在这里，我们概述了实现这种方法的ISD软件包。
