Determining protein structures by combining semireliable data with atomistic physical models by Bayesian inference
Determining protein structures by combining semireliable data with atomistic physical models by Bayesian inference
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DOI：
10.1073/pnas.1506788112
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发表时间：
2015-06-02
影响因子：
11.1
通讯作者：
Dill, Ken A.
中科院分区：
文献类型：
--
作者：
MacCallum, Justin L.;Perez, Alberto;Dill, Ken A.
现在有超过10万种蛋白质结构在原子细节上为人所知。然而，目前还不知道更多的蛋白质，特别是在大型或复杂的蛋白质中。通常，实验信息只是半可靠的，因为它在重要方面是不确定的、有限的或令人困惑的。有些实验提供的信息稀少，有些提供的信息含糊不清或不明确，还有一些提供不确定的信息--有些是对的，有些是错的，但我们不知道是哪一个。我们描述了一种称为有限数据建模(MELD)的方法，该方法可以在基于物理的贝叶斯框架中利用这些有问题的信息来改进结构确定。我们将MELD应用于8个已知结构的蛋白质，这些蛋白质的结构数据存在问题，包括一个稀疏的核磁共振数据集，两个模棱两可的EPR数据集，以及四个来自序列进化数据的不确定数据集。MELD提供了极好的结构，表明它有望在只有半可靠数据可用的实验生物分子结构确定中发挥作用。
More than 100,000 protein structures are now known at atomic detail. However, far more are not yet known, particularly among large or complex proteins. Often, experimental information is only semireliable because it is uncertain, limited, or confusing in important ways. Some experiments give sparse information, some give ambiguous or nonspecific information, and others give uncertain information-where some is right, some is wrong, but we don't know which. We describe a method called Modeling Employing Limited Data (MELD) that can harness such problematic information in a physics-based, Bayesian framework for improved structure determination. We apply MELD to eight proteins of known structure for which such problematic structural data are available, including a sparse NMR dataset, two ambiguous EPR datasets, and four uncertain datasets taken from sequence evolution data. MELD gives excellent structures, indicating its promise for experimental biomolecule structure determination where only semireliable data are available.