High-accuracy refinement using Rosetta in CASP13
High-accuracy refinement using Rosetta in CASP13
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DOI：
10.1002/prot.25784
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发表时间：
2019-08-05
影响因子：
2.9
通讯作者：
Baker, David
中科院分区：
文献类型：
--
作者：
Park, Hahnbeom;Lee, Gyu Rie;Baker, David
由于蛋白质通常折叠到其最低的自由能状态，原则上，能量导向的精化应该能够系统地提高使用同源结构或共同进化衍生信息生成的蛋白质结构模型的质量。然而，由于搜索空间的高维性，降低近原生模型质量的方法远远多于改进它的方法，因此，改进方法对能量函数误差非常敏感。在蛋白质结构预测技术的第13次关键评估（CASP13）中，我们试图在初始模型附近使用约束来彻底搜索低能态，以避免偏离太远。该方法相当成功地改进了初始模型中大部分不正确的区域，以及开始时更接近正确结构的核心区域。对5个目标获得了GDT-HA大于70的模型，其中一个目标实现了0.5个主干均方根偏差（RMSD）的精度。当前的一个重要挑战是提高精炼低聚物和较大蛋白质的性能，这方面的搜索问题仍然非常困难。
Because proteins generally fold to their lowest free energy states, energy-guided refinement in principle should be able to systematically improve the quality of protein structure models generated using homologous structure or co-evolution derived information. However, because of the high dimensionality of the search space, there are far more ways to degrade the quality of a near native model than to improve it, and hence, refinement methods are very sensitive to energy function errors. In the 13th Critial Assessment of techniques for protein Structure Prediction (CASP13), we sought to carry out a thorough search for low energy states in the neighborhood of a starting model using restraints to avoid straying too far. The approach was reasonably successful in improving both regions largely incorrect in the starting models as well as core regions that started out closer to the correct structure. Models with GDT-HA over 70 were obtained for five targets and for one of those, an accuracy of 0.5 a backbone root-mean-square deviation (RMSD) was achieved. An important current challenge is to improve performance in refining oligomers and larger proteins, for which the search problem remains extremely difficult.