Zhu Jiang, Fan Hao, Periole Xavier, Honig Barry, Mark Alan E
Howard Hughes Medical Institute and Columbia University, Center for Computational Biology and Bioinformatics, Department of Biochemistry and Molecular Biophysics, Columbia University, New York, USA.
Proteins. 2008 Sep;72(4):1171-88. doi: 10.1002/prot.22005.
A protocol is presented for the global refinement of homology models of proteins. It combines the advantages of temperature-based replica-exchange molecular dynamics (REMD) for conformational sampling and the use of statistical potentials for model selection. The protocol was tested using 21 models. Of these 14 were models of 10 small proteins for which high-resolution crystal structures were available, the remainder were targets of the recent CASPR exercise. It was found that REMD in combination with currently available force fields could sample near-native conformational states starting from high-quality homology models. Conformations in which the backbone RMSD of secondary structure elements (SSE-RMSD) was lower than the starting value by 0.5-1.0 A were found for 15 out of the 21 cases (average 0.82 A). Furthermore, when a simple scoring function consisting of two statistical potentials was used to rank the structures, one or more structures with SSE-RMSD of at least 0.2 A lower than the starting value was found among the five best ranked structures in 11 out of the 21 cases. The average improvement in SSE-RMSD for the best models was 0.42 A. However, none of the scoring functions tested identified the structures with the lowest SSE-RMSD as the best models although all identified the native conformation as the one with lowest energy. This suggests that while the proposed protocol proved effective for the refinement of high-quality models of small proteins scoring functions remain one of the major limiting factors in structure refinement. This and other aspects by which the methodology could be further improved are discussed.
本文提出了一种用于蛋白质同源模型全局优化的方案。该方案结合了基于温度的复制交换分子动力学（REMD）在构象采样方面的优势以及使用统计势进行模型选择的方法。使用21个模型对该方案进行了测试。其中14个是10种小蛋白质的模型，这些蛋白质有高分辨率晶体结构可供参考，其余的是近期CASPR练习的目标。研究发现，REMD与当前可用的力场相结合，可以从高质量的同源模型开始采样接近天然的构象状态。在21个案例中的15个案例（平均为0.82 Å）中，发现二级结构元件的主链均方根偏差（SSE-RMSD）比起始值低0.5-1.0 Å的构象。此外，当使用由两个统计势组成的简单评分函数对结构进行排名时，在21个案例中的11个案例的五个排名最佳的结构中，发现了一个或多个SSE-RMSD比起始值至少低0.2 Å的结构。最佳模型的SSE-RMSD平均改善为0.42 Å。然而，尽管所有测试的评分函数都将天然构象识别为能量最低的构象，但没有一个评分函数将SSE-RMSD最低的结构识别为最佳模型。这表明，虽然所提出的方案被证明对小蛋白质的高质量模型的优化有效，但评分函数仍然是结构优化中的主要限制因素之一。本文讨论了该方法可以进一步改进的这一方面和其他方面。
