Reassessing random-coil statistics in unfolded proteins
Reassessing random-coil statistics in unfolded proteins
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DOI：
10.1073/pnas.0404236101
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发表时间：
2004-08-24
影响因子：
11.1
通讯作者：
Rose, GD
中科院分区：
文献类型：
--
作者：
Fitzkee, NC;Rose, GD
自20世纪50年代以来，高斯分布的无规卷曲一直是变性蛋白质的主要模型，长期以来，它一直被解释为蛋白质在6 M氯化胍中是无特征的统计卷曲。在这里，我们证明随机卷曲统计数据并不是无特征聚合物的独特特征。无规卷曲模型确实成功地预测了变性蛋白质的实验确定的卷曲尺寸。然而，其他同样令人信服的实验表明，变性蛋白质偏向于特定的构象，与随机卷曲模型明显冲突。我们试图通过引入一个人为的反例来解决这个悖论，在这个反例中，大部分天然蛋白质集合仍然表现出随机卷曲的特征。具体地说，已知结构的蛋白质被用来产生无序构象，通过随机改变约8%的残基的骨架扭转角;其余约92%的残基保持固定在其天然构象。这些无序结构的合奏产生的33种蛋白质通过使用扭转角蒙特卡罗算法与硬球空间位阻，批量统计，然后计算每个合奏。尽管这种极端程度的强加的内部结构，这些合奏有端到端的距离和平均回转半径，同意以及在所有的随机线圈的预期，但在两种情况下。
The Gaussian-distributed random coil has been the dominant model for denatured proteins since the 1950s, and it has long been interpreted to mean that proteins are featureless, statistical coils in 6 M guanidinium chloride. Here, we demonstrate that random-coil statistics are not a unique signature of featureless polymers. The random-coil model does predict the experimentally determined coil dimensions of denatured proteins successfully. Yet, other equally convincing experiments have shown that denatured proteins are biased toward specific conformations, in apparent conflict with the random-coil model. We seek to resolve this paradox by introducing a contrived counterexample in which largely native protein ensembles nevertheless exhibit random-coil characteristics. Specifically, proteins of known structure were used to generate disordered conformers by varying backbone torsion angles at random for approximate to8% of the residues; the remaining approximate to92% of the residues remained fixed in their native conformation. Ensembles of these disordered structures were generated for 33 proteins by using a torsion-angle Monte Carlo algorithm with hard-sphere sterics; bulk statistics were then calculated for each ensemble. Despite this extreme degree of imposed internal structure, these ensembles have end-to-end distances and mean radii of gyration that agree well with random-coil expectations in all but two cases.