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利用稀疏核磁共振数据从头确定蛋白质结构

De novo protein structure determination using sparse NMR data.

作者信息

Bowers P M, Strauss C E, Baker D

机构信息

Department of Biochemistry, University of Washington School of Medicine, Seattle 98195, USA.

出版信息

J Biomol NMR. 2000 Dec;18(4):311-8. doi: 10.1023/a:1026744431105.

Abstract

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 approximately 1 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.

摘要

我们描述了一种使用有限的核磁共振(NMR)数据结合从头算蛋白质结构预测方法Rosetta来生成中等至高分辨率蛋白质结构的方法。基于序列相似性以及与化学位移和核Overhauser效应(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,从这些片段构建模型。在某些情况下,使用该程序生成的每个残基约有1个NOE约束的模型比已发表的NMR溶液结构更接近相应的X射线结构。该方法仅需要NMR结构测定初始阶段可用的稀疏约束,因此有望提高确定蛋白质溶液结构的速度。

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