...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Physical biology >Statistical mechanical properties of sequence space determine the efficiency of the various algorithms to predict interaction energies and native contacts from protein coevolution
【24h】

Statistical mechanical properties of sequence space determine the efficiency of the various algorithms to predict interaction energies and native contacts from protein coevolution

机译：序列空间的统计机械性能决定了各种算法的效率，以预测蛋白共存的相互作用能量和天然触点

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

Studying evolutionary correlations in alignments of homologous sequences by means of an inverse Potts model has proven useful to obtain residue-residue contact energies and to predict contacts in proteins. The quality of the results depend much on several choices of the detailed model and on the algorithms used. We built, in a very controlled way, synthetic alignments with statistical properties similar to those of real proteins, and used them to assess the performance of different inversion algorithms and of their variants. Realistic synthetic alignments display typical features of lowtemperature phases of disordered systems, a feature that affects the inversion algorithms. We showed that a Boltzmann-learning algorithm is computationally feasible and performs well in predicting the energy of native contacts. However, all algorithms, when applied to alignments of realistic size, suffer of false positives quite equally, making the quality of the prediction of native contacts with the different algorithm much system-dependent.
机译：通过反向Potts模型研究在同源序列的对准中的进化相关性已经证明是有用的可用于获得残留残基接触能量并预测蛋白质中的接触。结果的质量取决于详细模型的几种选择和使用的算法。以一种非常受控的方式建立了与实际蛋白质类似的统计特性的合成对齐，并使用它们来评估不同反转算法和其变体的性能。现实的合成校准显示无序系统的低温阶段的典型特征，这是影响反演算法的特征。我们表明，Boltzmann学习算法是在计算上可行的并且在预测本机触点的能量时表现良好。然而，当应用于现实尺寸的对齐时，所有算法都非常平等地遭受误报，使得具有不同算法的本机触点预测的质量多得多。

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