Summa Christopher M, Levitt Michael
Department of Structural Biology, Stanford University School of Medicine, Stanford, CA 94305-5126, USA.
Proc Natl Acad Sci U S A. 2007 Feb 27;104(9):3177-82. doi: 10.1073/pnas.0611593104. Epub 2007 Feb 20.
One of the greatest shortcomings of macromolecular energy minimization and molecular dynamics techniques is that they generally do not preserve the native structure of proteins as observed by x-ray crystallography. This deformation of the native structure means that these methods are not generally used to refine structures produced by homology-modeling techniques. Here, we use a database of 75 proteins to test the ability of a variety of popular molecular mechanics force fields to maintain the native structure. Minimization from the native structure is a weak test of potential energy functions: It is complemented by a much stronger test in which the same methods are compared for their ability to attract a near-native decoy protein structure toward the native structure. We use a powerfully convergent energy-minimization method and show that, of the traditional molecular mechanics potentials tested, only one showed a modest net improvement over a large data set of structurally diverse proteins. A smooth, differentiable knowledge-based pairwise atomic potential performs better on this test than traditional potential functions. This work is expected to have important implications for protein structure refinement, homology modeling, and structure prediction.
大分子能量最小化和分子动力学技术最大的缺点之一是，它们通常无法保持通过X射线晶体学观察到的蛋白质天然结构。天然结构的这种变形意味着这些方法通常不用于优化同源建模技术产生的结构。在此，我们使用一个包含75种蛋白质的数据库来测试各种流行分子力学力场维持天然结构的能力。从天然结构开始进行最小化是对势能函数的一种较弱测试：通过一项更强的测试对其进行补充，在该测试中比较相同方法将接近天然的诱饵蛋白结构吸引至天然结构的能力。我们使用一种强大的收敛能量最小化方法，并表明，在所测试的传统分子力学势中，只有一种在一大组结构多样的蛋白质数据集上显示出适度的净改善。一种平滑、可微的基于知识的成对原子势在该测试中的表现优于传统势函数。这项工作预计将对蛋白质结构优化、同源建模和结构预测产生重要影响。
