Department of Pharmaceutical Chemistry, University of California at San Francisco, San Francisco, California 94158-2330, USA.
Proteins. 2012 Jul;80(7):1744-54. doi: 10.1002/prot.24068. Epub 2012 Apr 13.
The efficiency of using a variant of Hamiltonian replica-exchange molecular dynamics (Chaperone H-replica-exchange molecular dynamics [CH-REMD]) for the refinement of protein structural models generated de novo is investigated. In CH-REMD, the interaction between the protein and its environment, specifically, the electrostatic interaction between the protein and the solvating water, is varied leading to cycles of partial unfolding and refolding mimicking some aspects of folding chaperones. In 10 of the 15 cases examined, the CH-REMD approach sampled structures in which the root-mean-square deviation (RMSD) of secondary structure elements (SSE-RMSD) with respect to the experimental structure was more than 1.0 Å lower than the initial de novo model. In 14 of the 15 cases, the improvement was more than 0.5 Å. The ability of three different statistical potentials to identify near-native conformations was also examined. Little correlation between the SSE-RMSD of the sampled structures with respect to the experimental structure and any of the scoring functions tested was found. The most effective scoring function tested was the DFIRE potential. Using the DFIRE potential, the SSE-RMSD of the best scoring structures was on average 0.3 Å lower than the initial model. Overall the work demonstrates that targeted enhanced-sampling techniques such as CH-REMD can lead to the systematic refinement of protein structural models generated de novo but that improved potentials for the identification of near-native structures are still needed.
研究了使用哈密顿复制交换分子动力学的变体(伴侣分子辅助复制交换分子动力学 [CH-REMD])来改进从头生成的蛋白质结构模型的效率。在 CH-REMD 中,蛋白质与其环境之间的相互作用,特别是蛋白质与溶剂水之间的静电相互作用,会发生变化,从而模拟折叠伴侣的某些方面进行部分展开和折叠的循环。在所检查的 15 个案例中的 10 个案例中,CH-REMD 方法采样的结构中,相对于实验结构的二级结构元素(SSE-RMSD)的均方根偏差(RMSD)比初始从头模型低 1.0 Å 以上。在 15 个案例中的 14 个案例中,改进超过 0.5 Å。还检查了三种不同统计势能识别近天然构象的能力。采样结构相对于实验结构的 SSE-RMSD 与测试的任何评分函数之间几乎没有相关性。测试的最有效评分函数是 DFIRE 势能。使用 DFIRE 势能,评分最高的结构的 SSE-RMSD 平均比初始模型低 0.3 Å。总体而言,这项工作表明,有针对性的增强采样技术,如 CH-REMD,可以系统地改进从头生成的蛋白质结构模型,但仍需要改进识别近天然结构的势能。