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从晶体学多构象体模型集合中映射相关蛋白质结构中的变构重排。

Mapping allosteric rewiring in related protein structures from collections of crystallographic multiconformer models.

作者信息

Raju Akshay, Sharma Shivani, Riley Blake T, Djuraev Shakhriyor, Tan Yingxian, Kim Minyoung, Mahmud Toufique, Keedy Daniel A

机构信息

Structural Biology Initiative, CUNY Advanced Science Research Center, New York, NY 10031.

PhD Program in Biology, CUNY Graduate Center, New York, NY 10016.

出版信息

bioRxiv. 2025 May 27:2025.05.23.655529. doi: 10.1101/2025.05.23.655529.

Abstract

How do related proteins with a common fold perform diverse biological functions? Although the average structure may be similar, structural excursions from this average may differ, giving rise to allosteric rewiring that enables differential activity and regulation. However, this idea has been difficult to test in detail. Here we used the qFit algorithm to model "hidden" alternate conformations from electron density maps for an entire protein family, the Protein Tyrosine Phosphatases (PTPs), spanning 26 enzymes and 221 structures. To interrogate these multiconformer models, we developed a new algorithm, Residue Interaction Networks From Alternate conformations In RElated structures (RINFAIRE), that calculates networks of interactions between flexible residues and quantitatively compares them. We show that PTPs share a common allosteric network which rewires dynamically in response to catalytic loop motions or active-site vs. allosteric ligand binding, but also that individual PTPs have unique allosteric signatures. As experimental validation, we show that targeted mutations at residues with varying sequence conservation but high network connectivity modulate enzyme catalysis, including a surprising enhancement of activity. Overall, our work provides new tools for understanding how evolution has recycled modular macromolecular building blocks to diversify biological function. RINFAIRE is available at https://github.com/keedylab/rinfaire.

摘要

具有共同折叠结构的相关蛋白质是如何执行多种生物学功能的?尽管平均结构可能相似,但偏离该平均结构的结构变化可能不同,从而产生变构重排,实现不同的活性和调控。然而,这一观点很难进行详细验证。在这里,我们使用qFit算法从一个完整的蛋白质家族——蛋白酪氨酸磷酸酶(PTPs)的电子密度图中模拟“隐藏”的替代构象,该家族涵盖26种酶和221个结构。为了研究这些多构象模型,我们开发了一种新算法——相关结构中替代构象的残基相互作用网络(RINFAIRE),该算法可以计算柔性残基之间的相互作用网络,并对它们进行定量比较。我们发现,PTPs共享一个共同的变构网络,该网络会根据催化环运动或活性位点与变构配体结合而动态重排,但各个PTPs也具有独特的变构特征。作为实验验证,我们表明,在序列保守性不同但网络连接性高的残基处进行靶向突变会调节酶的催化作用,包括活性的惊人增强。总体而言,我们的工作为理解进化如何循环利用模块化大分子构建块以实现生物功能多样化提供了新工具。RINFAIRE可在https://github.com/keedylab/rinfaire获取。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c5a8/12154631/0def44c402ae/nihpp-2025.05.23.655529v1-f0001.jpg

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