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13Cα化学位移在蛋白质结构测定中的应用。

Use of 13Calpha chemical shifts in protein structure determination.

作者信息

Vila Jorge A, Ripoll Daniel R, Scheraga Harold A

机构信息

Baker Laboratory of Chemistry and Chemical Biology, Cornell University, Ithaca, New York 14853-1301, USA.

出版信息

J Phys Chem B. 2007 Jun 14;111(23):6577-85. doi: 10.1021/jp0683871. Epub 2007 May 22.

Abstract

A physics-based method aimed at determining protein structures by using NOE-derived distances together with observed and computed 13C chemical shifts is proposed. The approach makes use of 13Calpha chemical shifts, computed at the density functional level of theory, to obtain torsional constraints for all backbone and side-chain torsional angles without making a priori use of the occupancy of any region of the Ramachandran map by the amino acid residues. The torsional constraints are not fixed but are changed dynamically in each step of the procedure, following an iterative self-consistent approach intended to identify a set of conformations for which the computed 13Calpha chemical shifts match the experimental ones. A test is carried out on a 76-amino acid, all-alpha-helical protein; namely, the Bacillus subtilis acyl carrier protein. It is shown that, starting from randomly generated conformations, the final protein models are more accurate than an existing NMR-derived structure model of this protein, in terms of both the agreement between predicted and observed 13Calpha chemical shifts and some stereochemical quality indicators, and of similar accuracy as one of the protein models solved at a high level of resolution. The results provide evidence that this methodology can be used not only for structure determination but also for additional protein structure refinement of NMR-derived models deposited in the Protein Data Bank.

摘要

提出了一种基于物理学的方法,该方法旨在通过使用源自核Overhauser效应(NOE)的距离以及观测到的和计算出的13C化学位移来确定蛋白质结构。该方法利用在密度泛函理论水平上计算得到的13Cα化学位移,以获得所有主链和侧链扭转角的扭转约束,而无需事先利用氨基酸残基在拉氏图任何区域的占有率。扭转约束不是固定的,而是在该过程的每个步骤中动态变化,遵循一种迭代自洽方法,旨在识别一组构象,对于这些构象,计算得到的13Cα化学位移与实验值相匹配。对一种由76个氨基酸组成的全α螺旋蛋白,即枯草芽孢杆菌酰基载体蛋白进行了测试。结果表明,从随机生成的构象开始,最终的蛋白质模型在预测和观测到的13Cα化学位移之间的一致性以及一些立体化学质量指标方面,比该蛋白质现有的基于核磁共振(NMR)的结构模型更准确,并且与在高分辨率下解析的一种蛋白质模型具有相似的准确性。这些结果证明,这种方法不仅可用于结构测定,还可用于对蛋白质数据库中基于NMR的模型进行额外的蛋白质结构优化。

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本文引用的文献

1
Predicting 13Calpha chemical shifts for validation of protein structures.预测13Cα化学位移以验证蛋白质结构。
J Biomol NMR. 2007 Jul;38(3):221-35. doi: 10.1007/s10858-007-9162-x. Epub 2007 Jun 9.

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