Protein homology model refinement by large-scale energy optimization
Protein homology model refinement by large-scale energy optimization
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DOI：
10.1073/pnas.1719115115
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发表时间：
2018-03-20
影响因子：
11.1
通讯作者：
Baker, David
中科院分区：
文献类型：
--
作者：
Park, Hahnbeom;Ovchinnikov, Sergey;Baker, David
蛋白质折叠到它们的最低自由能结构，因此提高部分不正确的蛋白质结构模型的准确性的最直接方法是搜索附近能量最低的结构。这种直接的方法几乎没有成功，原因有二：第一，能量函数的不准确会导致错误的能量最小值，导致模型退化而不是改进;第二，即使有一个准确的能量函数，搜索问题也是艰巨的，因为能量只在全局最小值附近显著下降，并且有非常大的自由度。在这里，我们描述了一种基于大规模能量优化的细化方法，该方法结合了搜索和能量函数准确性的进步，可以大大提高低分辨率同源模型的准确性。该方法将84个不同蛋白质家族中的50个的低分辨率同源模型细化为正确的折叠，并在最近的盲结构预测实验中生成改进的模型。这些改进的基础分析揭示了构象采样技术和能量函数的改进。
Proteins fold to their lowest free-energy structures, and hence the most straightforward way to increase the accuracy of a partially incorrect protein structure model is to search for the lowest-energy nearby structure. This direct approach has met with little success for two reasons: first, energy function inaccuracies can lead to false energy minima, resulting in model degradation rather than improvement; and second, even with an accurate energy function, the search problem is formidable because the energy only drops considerably in the immediate vicinity of the global minimum, and there are a very large number of degrees of freedom. Here we describe a large-scale energy optimization-based refinement method that incorporates advances in both search and energy function accuracy that can substantially improve the accuracy of low-resolution homology models. The method refined low-resolution homology models into correct folds for 50 of 84 diverse protein families and generated improved models in recent blind structure prediction experiments. Analyses of the basis for these improvements reveal contributions from both the improvements in conformational sampling techniques and the energy function.