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蛋白质中协同相互作用的结构分布:天然态系综分析

The structural distribution of cooperative interactions in proteins: analysis of the native state ensemble.

作者信息

Hilser V J, Dowdy D, Oas T G, Freire E

机构信息

Department of Biology and Biocalorimetry Center, The Johns Hopkins University, Baltimore, MD 21218, USA.

出版信息

Proc Natl Acad Sci U S A. 1998 Aug 18;95(17):9903-8. doi: 10.1073/pnas.95.17.9903.

Abstract

Cooperative interactions link the behavior of different amino acid residues within a protein molecule. As a result, the effects of chemical or physical perturbations to any given residue are propagated to other residues by an intricate network of interactions. Very often, amino acids "sense" the effects of perturbations occurring at very distant locations in the protein molecule. In these studies, we have investigated by computer simulation the structural distribution of those interactions. We show here that cooperative interactions are not intrinsically bi-directional and that different residues play different roles within the intricate network of interactions existing in a protein. The effect of a perturbation to residue j on residue k is not necessarily equal to the effect of the same perturbation to residue k on residue j. In this paper, we introduce a computer algorithm aimed at mapping the network of cooperative interactions within a protein. This algorithm exhaustively performs single site thermodynamic mutations to each residue in the protein and examines the effects of those mutations on the distribution of conformational states. The algorithm has been applied to three different proteins (lambda repressor fragment 6-85, chymotrypsin inhibitor 2, and barnase). This algorithm accounts well for the observed behavior of these proteins.

摘要

协同相互作用将蛋白质分子内不同氨基酸残基的行为联系起来。因此,对任何给定残基的化学或物理扰动的影响会通过一个复杂的相互作用网络传播到其他残基。氨基酸常常能“感知”蛋白质分子中非常远处发生的扰动的影响。在这些研究中,我们通过计算机模拟研究了那些相互作用的结构分布。我们在此表明,协同相互作用本质上并非双向的,并且在蛋白质中存在的复杂相互作用网络内不同残基发挥着不同的作用。对残基j的扰动对残基k的影响不一定等于对残基k的相同扰动对残基j的影响。在本文中,我们引入了一种旨在绘制蛋白质内协同相互作用网络的计算机算法。该算法对蛋白质中的每个残基详尽地进行单一位点热力学突变,并检查这些突变对构象状态分布的影响。该算法已应用于三种不同的蛋白质(λ阻遏物片段6 - 85、胰凝乳蛋白酶抑制剂2和芽孢杆菌RNA酶)。该算法很好地解释了这些蛋白质的观察到的行为。

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