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一项高斯网络模型研究表明,蛋白激酶的非活性状态比活性状态具有更高的结构波动。

A Gaussian network model study suggests that structural fluctuations are higher for inactive states than active states of protein kinases.

作者信息

Kalaivani Raju, Srinivasan Narayanaswamy

机构信息

Molecular Biophysics Unit, Indian Institute of Science, Lab no. 103, Bangalore 560012, Karnataka, India.

出版信息

Mol Biosyst. 2015 Apr;11(4):1079-95. doi: 10.1039/c4mb00675e.

DOI:10.1039/c4mb00675e
PMID:25635939
Abstract

We performed Gaussian network model based normal mode analysis of 3-dimensional structures of multiple active and inactive forms of protein kinases. In 14 different kinases, a more number of residues (1095) show higher structural fluctuations in inactive states than those in active states (525), suggesting that, in general, mobility of inactive states is higher than active states. This statistically significant difference is consistent with higher crystallographic B-factors and conformational energies for inactive than active states, suggesting lower stability of inactive forms. Only a small number of inactive conformations with the DFG motif in the "in" state were found to have fluctuation magnitudes comparable to the active conformation. Therefore our study reports for the first time, intrinsic higher structural fluctuation for almost all inactive conformations compared to the active forms. Regions with higher fluctuations in the inactive states are often localized to the αC-helix, αG-helix and activation loop which are involved in the regulation and/or in structural transitions between active and inactive states. Further analysis of 476 kinase structures involved in interactions with another domain/protein showed that many of the regions with higher inactive-state fluctuation correspond to contact interfaces. We also performed extensive GNM analysis of (i) insulin receptor kinase bound to another protein and (ii) holo and apo forms of active and inactive conformations followed by multi-factor analysis of variance. We conclude that binding of small molecules or other domains/proteins reduce the extent of fluctuation irrespective of active or inactive forms. Finally, we show that the perceived fluctuations serve as a useful input to predict the functional state of a kinase.

摘要

我们对多种活性和非活性形式的蛋白激酶的三维结构进行了基于高斯网络模型的正常模式分析。在14种不同的激酶中,与活性状态(525个)相比,更多数量的残基(1095个)在非活性状态下表现出更高的结构波动,这表明一般来说,非活性状态的流动性高于活性状态。这种具有统计学意义的差异与非活性状态比活性状态更高的晶体学B因子和构象能量一致,表明非活性形式的稳定性较低。仅发现少数处于“in”状态且具有DFG模体的非活性构象的波动幅度与活性构象相当。因此,我们的研究首次报道了几乎所有非活性构象相对于活性形式具有更高的固有结构波动。非活性状态下波动较高的区域通常位于αC螺旋、αG螺旋和激活环,这些区域参与活性和非活性状态之间的调节和/或结构转变。对涉及与另一个结构域/蛋白质相互作用的476个激酶结构的进一步分析表明,许多非活性状态波动较高的区域对应于接触界面。我们还对(i)与另一种蛋白质结合的胰岛素受体激酶以及(ii)活性和非活性构象的全酶和脱辅基形式进行了广泛的高斯网络模型分析,随后进行了多因素方差分析。我们得出结论,小分子或其他结构域/蛋白质的结合会降低波动程度,而与活性或非活性形式无关。最后,我们表明观察到的波动可作为预测激酶功能状态的有用输入。

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引用本文的文献

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Long-range molecular dynamics show that inactive forms of Protein Kinase A are more dynamic than active forms.长程分子动力学表明,蛋白激酶 A 的非活性形式比活性形式更具动态性。
Protein Sci. 2019 Mar;28(3):543-560. doi: 10.1002/pro.3556. Epub 2018 Dec 30.
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Combining protein sequence, structure, and dynamics: A novel approach for functional evolution analysis of PAS domain superfamily.结合蛋白质序列、结构和动力学:PAS 结构域超家族功能进化分析的新方法。
Protein Sci. 2018 Feb;27(2):421-430. doi: 10.1002/pro.3329. Epub 2017 Nov 2.
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From residue coevolution to protein conformational ensembles and functional dynamics.
从残基协同进化到蛋白质构象集合与功能动力学。
Proc Natl Acad Sci U S A. 2015 Nov 3;112(44):13567-72. doi: 10.1073/pnas.1508584112. Epub 2015 Oct 20.