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Protein structure prediction guided by cross-linking restraints - A systematic evaluation of the impact of the cross-linking spacer length

机译:由交联限制引导的蛋白质结构预测 - a.   系统评估交联间隔物长度的影响

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摘要

Recent development of high-resolution mass spectrometry (MS) instrumentsenables chemical cross-linking (XL) to become a high-throughput method forobtaining structural information about proteins. Restraints derived from XL-MSexperiments have been used successfully for structure refinement andprotein-protein docking. However, one formidable question is under whichcircumstances XL-MS data might be sufficient to determine a protein's tertiarystructure de novo? Answering this question will not only include understandingthe impact of XL-MS data on sampling and scoring within a de novo proteinstructure prediction algorithm, it must also determine an optimal cross-linkertype and length for protein structure determination. While a longercross-linker will yield more restraints, the value of each restraint forprotein structure prediction decreases as the restraint is consistent with alarger conformational space. In this study, the number of cross-links and their discriminative power wassystematically analyzed in silico on a set of 2,055 non-redundant protein foldsconsidering Lys-Lys, Lys-Asp, Lys-Glu, Cys-Cys, and Arg-Arg reactivecross-linkers between 1 {\AA} and 60 {\AA}. Depending on the protein size aheuristic was developed that determines the optimal cross-linker length. Next,simulated restraints of variable length were used to de novo predict thetertiary structure of fifteen proteins using the BCL::Fold algorithm. Theresults demonstrate that a distinct cross-linker length exists for whichinformation content for de novo protein structure prediction is maximized. Thesampling accuracy improves on average by 1.0 {\AA} and up to 2.2 {\AA} in themost prominent example. XL-MS restraints enable consistently an improvedselection of native-like models with an average enrichment of 2.1.
机译:高分辨率质谱(MS)仪器的最新发展使化学交联(XL)成为获取有关蛋白质结构信息的高通量方法。源自XL-MS实验的约束已成功用于结构优化和蛋白质-蛋白质对接。但是,一个巨大的问题是,在哪种情况下XL-MS数据可能足以确定从头开始的蛋白质三级结构?回答这个问题将不仅包括了解XL-MS数据对从头蛋白质结构预测算法中采样和评分的影响,而且还必须确定用于确定蛋白质结构的最佳交联剂类型和长度。虽然更长的交联剂会产生更多的约束,但是每个约束的蛋白质结构预测值会降低,因为该约束与更大的构象空间一致。在这项研究中,系统地在计算机上对包括Lys-Lys,Lys-Asp,Lys-Glu,Cys-Cys和Arg-Arg反应性交联的2055种非冗余蛋白质折叠进行了系统分析,分析了交联的数量及其区分力。 1 {\ AA}和60 {\ AA}之间的链接。根据蛋白质的大小,开发了确定最佳交联剂长度的启发式方法。接下来,使用BCL :: Fold算法,使用可变长度的模拟约束从头开始预测15种蛋白质的三级结构。结果表明存在独特的交联剂长度,对于该长度,从头蛋白质结构预测的信息含量最大。在最突出的示例中,采样精度平均提高了1.0 {\ AA},最高提高了2.2 {\ AA}。 XL-MS限制条件能够始终如一地改善平均水平为2.1的原生模型的选择。

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