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.

蛋白质折叠成其最低的自由能结构，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寻找最低能量的附近结构。这种直接方法收效甚微，原因有两个：第一，能量函数的不精确会导致虚假的最小能量，从而导致模型退化而不是改进。第二，即使具有精确的能量函数，搜索问题也很艰巨，因为能量仅在全局最小值的附近显着下降，并且自由度非常大。在这里，我们描述了一种基于能量优化的大规模优化方法，该方法融合了搜索和能量函数精度方面的先进技术，可以大大提高低分辨率同源模型的精度。该方法将低分辨率同源性模型细化为84个不同蛋白质家族中50个的正确折叠，并在最近的盲结构预测实验中生成了改进的模型。对这些改进的基础的分析表明，构象采样技术和能量函数的改进都有贡献。