Statistical mechanics-based method to extract atomic distance-dependent potentials from protein structures
Statistical mechanics-based method to extract atomic distance-dependent potentials from protein structures
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DOI：
10.1002/prot.23086
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发表时间：
2011-09-01
影响因子：
2.9
通讯作者：
Zou, Xiaoqin
中科院分区：
文献类型：
--
作者：
Huang, Sheng-You;Zou, Xiaoqin
在这项研究中，我们已经开发了一种基于统计力学的迭代方法，从已知的，非冗余的蛋白质结构中提取统计原子相互作用势。我们的方法绕过了长期存在的参考状态问题，在传统的基于知识的评分函数，通过使用快速迭代，通过物理，全局收敛函数。与其他参数优化方法不同，这种基于物理的方法的快速收敛保证了导出距离相关的全原子统计势以保持评分精度的可行性。衍生的潜力，被称为ITScore/Pro，已使用三个不同的基准验证：高分辨率诱饵集，琥珀基准诱饵集，和CASP 8诱饵集。业绩有了显著改善。最后，我们的模型的潜力和潜力的知识为基础的评分功能与随机参考状态之间的比较揭示了我们的评分功能，这可以提供有用的洞察到其他物理评分功能的发展更好的性能的原因。在这项研究中开发的潜力是普遍适用于蛋白质结构预测的结构选择。
In this study, we have developed a statistical mechanics-based iterative method to extract statistical atomic interaction potentials from known, nonredundant protein structures. Our method circumvents the long-standing reference state problem in deriving traditional knowledge-based scoring functions, by using rapid iterations through a physical, global convergence function. The rapid convergence of this physics-based method, unlike other parameter optimization methods, warrants the feasibility of deriving distance-dependent, all-atom statistical potentials to keep the scoring accuracy. The derived potentials, referred to as ITScore/Pro, have been validated using three diverse benchmarks: the high-resolution decoy set, the AMBER benchmark decoy set, and the CASP8 decoy set. Significant improvement in performance has been achieved. Finally, comparisons between the potentials of our model and potentials of a knowledge-based scoring function with a randomized reference state have revealed the reason for the better performance of our scoring function, which could provide useful insight into the development of other physical scoring functions. The potentials developed in this study are generally applicable for structural selection in protein structure prediction.