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PDZ 能量网络中的变构信号:嵌入错误分析。

Allosteric Signaling in PDZ Energetic Networks: Embedding Error Analysis.

机构信息

Department of Computer Science, Wesleyan University, Middletown, Connecticut06457, United States.

College of Integrative Sciences, Wesleyan University, Middletown, Connecticut06457, United States.

出版信息

J Phys Chem B. 2023 Jan 26;127(3):623-633. doi: 10.1021/acs.jpcb.2c06546. Epub 2023 Jan 10.

DOI:10.1021/acs.jpcb.2c06546
PMID:36626697
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9884075/
Abstract

Allosteric signaling in proteins has been known for some half a century, yet how the signal traverses the protein remains an active area of research. Recently, the importance of electrostatics to achieve long-range signaling has become increasingly appreciated. Our laboratory has been working on developing network approaches to capture such interactions. In this study, we turn our attention to the well-studied allosteric model protein, PDZ. We study the allosteric dynamics on a per-residue basis in key constructs involving the PDZ domain, its allosteric effector, and its peptide ligand. We utilize molecular dynamics trajectories to create the networks for the constructs to explore the allosteric effect by plotting the heat kernel results onto axes defined by principal components. We introduce a new metric to quantitate the volume sampled by a residue in the latent space. We relate our findings to PDZ and the greater field of allostery.

摘要

蛋白质的变构信号已经为人所知大约半个世纪了,但信号如何在蛋白质中传播仍然是一个活跃的研究领域。最近,静电作用对于实现远程信号传递的重要性越来越受到重视。我们的实验室一直在致力于开发网络方法来捕捉这种相互作用。在这项研究中,我们将注意力转向了研究充分的变构模型蛋白 PDZ。我们在涉及 PDZ 结构域、其变构效应物和肽配体的关键结构上,对每个残基的变构动力学进行了研究。我们利用分子动力学轨迹为这些结构创建网络,通过将热核结果绘制到由主成分定义的轴上来探索变构效应。我们引入了一种新的度量来量化残基在潜在空间中采样的体积。我们将我们的发现与 PDZ 和更广泛的变构领域联系起来。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4a53/9884075/443c5d17501a/jp2c06546_0007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4a53/9884075/5ab6dfd6fb29/jp2c06546_0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4a53/9884075/dc2f1746635d/jp2c06546_0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4a53/9884075/c5fe8f575f1a/jp2c06546_0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4a53/9884075/2531dd6fd076/jp2c06546_0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4a53/9884075/fbc490f06df3/jp2c06546_0006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4a53/9884075/443c5d17501a/jp2c06546_0007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4a53/9884075/5ab6dfd6fb29/jp2c06546_0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4a53/9884075/dc2f1746635d/jp2c06546_0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4a53/9884075/c5fe8f575f1a/jp2c06546_0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4a53/9884075/2531dd6fd076/jp2c06546_0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4a53/9884075/fbc490f06df3/jp2c06546_0006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4a53/9884075/443c5d17501a/jp2c06546_0007.jpg

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Allosteric Regulation at the Crossroads of New Technologies: Multiscale Modeling, Networks, and Machine Learning.
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