Center for Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA.
Structure. 2011 Dec 7;19(12):1784-95. doi: 10.1016/j.str.2011.09.022.
One of critical difficulties of molecular dynamics (MD) simulations in protein structure refinement is that the physics-based energy landscape lacks a middle-range funnel to guide nonnative conformations toward near-native states. We propose to use the target model as a probe to identify fragmental analogs from PDB. The distance maps are then used to reshape the MD energy funnel. The protocol was tested on 181 benchmarking and 26 CASP targets. It was found that structure models of correct folds with TM-score >0.5 can be often pulled closer to native with higher GDT-HA score, but improvement for the models of incorrect folds (TM-score <0.5) are much less pronounced. These data indicate that template-based fragmental distance maps essentially reshaped the MD energy landscape from golf-course-like to funnel-like ones in the successfully refined targets with a radius of TM-score ∼0.5. These results demonstrate a new avenue to improve high-resolution structures by combining knowledge-based template information with physics-based MD simulations.
在蛋白质结构精修的分子动力学 (MD) 模拟中,一个关键的困难是基于物理的能量景观缺乏中间范围的漏斗,无法将非天然构象引导到近天然状态。我们建议使用目标模型作为探针从 PDB 中识别片段类似物。然后使用距离图来重塑 MD 能量漏斗。该方案在 181 个基准测试和 26 个 CASP 目标上进行了测试。结果发现,具有 TM 分数>0.5 的正确折叠结构模型通常可以通过更高的 GDT-HA 分数更接近天然结构,但对于错误折叠(TM 分数<0.5)的模型的改进则不那么明显。这些数据表明,基于模板的片段距离图实质上重塑了 MD 能量景观,使成功精修的目标的能量景观从高尔夫球场样变为漏斗样,TM 分数的半径约为 0.5。这些结果表明了一种通过将基于知识的模板信息与基于物理的 MD 模拟相结合来改进高分辨率结构的新途径。