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首页> 外文期刊>PLoS One >Applying an Empirical Hydropathic Forcefield in Refinement May Improve Low-Resolution Protein X-Ray Crystal Structures
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Applying an Empirical Hydropathic Forcefield in Refinement May Improve Low-Resolution Protein X-Ray Crystal Structures

机译:在细化中应用经验的亲水力场可能会改善低分辨率蛋白质X射线晶体结构

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Background The quality of X-ray crystallographic models for biomacromolecules refined from data obtained at high-resolution is assured by the data itself. However, at low-resolution, 3.0 Å, additional information is supplied by a forcefield coupled with an associated refinement protocol. These resulting structures are often of lower quality and thus unsuitable for downstream activities like structure-based drug discovery. Methodology An X-ray crystallography refinement protocol that enhances standard methodology by incorporating energy terms from the HINT (Hydropathic INTeractions) empirical forcefield is described. This protocol was tested by refining synthetic low-resolution structural data derived from 25 diverse high-resolution structures, and referencing the resulting models to these structures. The models were also evaluated with global structural quality metrics, e.g., Ramachandran score and MolProbity clashscore. Three additional structures, for which only low-resolution data are available, were also re-refined with this methodology. Results The enhanced refinement protocol is most beneficial for reflection data at resolutions of 3.0 Å or worse. At the low-resolution limit, ≥4.0 Å, the new protocol generated models with Cα positions that have RMSDs that are 0.18 Å more similar to the reference high-resolution structure, Ramachandran scores improved by 13%, and clashscores improved by 51%, all in comparison to models generated with the standard refinement protocol. The hydropathic forcefield terms are at least as effective as Coulombic electrostatic terms in maintaining polar interaction networks, and significantly more effective in maintaining hydrophobic networks, as synthetic resolution is decremented. Even at resolutions ≥4.0 Å, these latter networks are generally native-like, as measured with a hydropathic interactions scoring tool.
机译:背景技术数据本身确保了从高分辨率获得的数据中提炼出的生物大分子X射线晶体学模型的质量。但是,在> 3.0Å的低分辨率下,力场会提供附加信息,并附带相关的优化协议。这些产生的结构通常质量较低,因此不适合进行下游活动,例如基于结构的药物发现。方法学描述了一种X射线晶体学细化方案,该方案通过结合来自HINT(水动力INTeractions)经验力场的能量项来增强标准方法学。通过细化来自25个不同高分辨率结构的合成低分辨率结构数据,并将生成的模型引用到这些结构中,对该协议进行了测试。还使用全球结构质量指标(例如Ramachandran得分和MolProbity冲突分数)对模型进行了评估。使用此方法还改进了三个其他结构,这些结构仅提供低分辨率数据。结果增强的细化方案对于分辨率为3.0或更差的反射数据最有利。在低分辨率限制(≥4.0Å)下,新协议生成的Cα位置模型的RMSD与参考高分辨率结构相似,比RMSD高0.18Å,Ramachandran得分提高了13%,clashscores提高了51%,所有这些都与使用标准优化协议生成的模型相比。亲水力场项在维持极性相互作用网络方面至少与库仑静电项有效,而随着合成分辨率的降低,在维持疏水网络方面显着更有效。甚至在分辨率≥4.0Å的情况下,使用亲水相互作用评分工具测得的结果,后一种网络通常也很像原生网络。

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