• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Proceedings of the National Academy of Sciences of the United States of America >Near-native structure refinement using in vacuo energy minimization
【24h】

Near-native structure refinement using in vacuo energy minimization

机译：使用真空能量最小化进行近乎本地的结构优化

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

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.
机译：大分子能量最小化和分子动力学技术的最大缺点之一是，它们通常不保留通过X射线晶体学观察到的蛋白质的天然结构。天然结构的这种变形意味着这些方法通常不用于精炼由同源建模技术产生的结构。在这里，我们使用包含75种蛋白质的数据库来测试各种流行的分子力学力场维持天然结构的能力。天然结构的最小化是对潜在能量功能的较弱测试：它被更强大的测试所补充，在该测试中，比较了相同方法向天然结构吸引近天然诱饵蛋白质结构的能力。我们使用了一种功能强大的能量最小化方法，结果表明，在测试的传统分子力学潜能中，只有一个方法对大量结构多样的蛋白质数据显示出适度的净改进。与传统的势函数相比，在该测试上，基于知识的平滑，可微分的成对原子势表现更好。预期这项工作对蛋白质结构的细化，同源性建模和结构预测具有重要意义。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号