De novo protein structure determination using sparse NMR data
De novo protein structure determination using sparse NMR data
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DOI：
10.1023/a:1026744431105
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发表时间：
2000-12-01
影响因子：
2.7
通讯作者：
Baker, D
中科院分区：
文献类型：
--
作者：
Bowers, PM;Strauss, CEM;Baker, D
我们描述了一种利用有限的核磁共振数据结合从头算蛋白质结构预测方法Rosetta生成中等到高分辨率蛋白质结构的方法。根据序列相似性和化学位移和NOE数据的一致性，从已知结构的蛋白质中选择肽片段。通过最小化有利于疏水埋藏、链配对和满足NOE约束的能量函数，从这些片段建立模型。使用该程序生成的模型在某些情况下比已发表的核磁共振溶液结构更接近相应的x射线结构，每个残留物具有类似的NOE约束。该方法只需要在核磁共振结构确定的初始阶段可用的稀疏约束，因此有望提高蛋白质溶液结构确定的速度。
We describe a method for generating moderate to high-resolution protein structures using limited NMR data combined with the ab initio protein structure prediction method Rosetta. Peptide fragments are selected from proteins of known structure based on sequence similarity and consistency with chemical shift and NOE data. Models are built from these fragments by minimizing an energy function that favors hydrophobic burial, strand pairing, and satisfaction of NOE constraints. Models generated using this procedure with similar to1 NOE constraint per residue are in some cases closer to the corresponding X-ray structures than the published NMR solution structures. The method requires only the sparse constraints available during initial stages of NMR structure determination, and thus holds promise for increasing the speed with which protein solution structures can be determined.