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动力学转变网络模型揭示蛋白质二聚体形成机制的多样性。

A Kinetic Transition Network Model Reveals the Diversity of Protein Dimer Formation Mechanisms.

机构信息

Systems Biology of Reproduction Research Group, Institute of Enzymology, HUN-REN Research Centre for Natural Sciences, 1117 Budapest, Hungary.

Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, 1083 Budapest, Hungary.

出版信息

Biomolecules. 2023 Nov 26;13(12):1708. doi: 10.3390/biom13121708.

DOI:10.3390/biom13121708
PMID:38136580
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10741920/
Abstract

Protein homodimers have been classified as three-state or two-state dimers depending on whether a folded monomer forms before association, but the details of the folding-binding mechanisms are poorly understood. Kinetic transition networks of conformational states have provided insight into the folding mechanisms of monomeric proteins, but extending such a network to two protein chains is challenging as all the relative positions and orientations of the chains need to be included, greatly increasing the number of degrees of freedom. Here, we present a simplification of the problem by grouping all states of the two chains into two layers: a dissociated and an associated layer. We combined our two-layer approach with the Wako-Saito-Muñoz-Eaton method and used Transition Path Theory to investigate the dimer formation kinetics of eight homodimers. The analysis reveals a remarkable diversity of dimer formation mechanisms. Induced folding, conformational selection, and rigid docking are often simultaneously at work, and their contribution depends on the protein concentration. Pre-folded structural elements are always present at the moment of association, and asymmetric binding mechanisms are common. Our two-layer network approach can be combined with various methods that generate discrete states, yielding new insights into the kinetics and pathways of flexible binding processes.

摘要

蛋白质同源二聚体根据折叠单体在缔合之前是否形成,被分类为三态或两态二聚体,但折叠结合机制的细节仍知之甚少。构象态的动力学转变网络为单体蛋白的折叠机制提供了深入的了解,但将这样的网络扩展到两条蛋白质链是具有挑战性的,因为需要包括所有链的相对位置和取向,这大大增加了自由度的数量。在这里,我们通过将两条链的所有状态分为两层来简化这个问题:解离层和缔合层。我们将两层方法与 Wako-Saito-Muñoz-Eaton 方法相结合,并使用过渡路径理论研究了八个同源二聚体的二聚体形成动力学。分析揭示了二聚体形成机制的显著多样性。诱导折叠、构象选择和刚性对接通常同时起作用,它们的贡献取决于蛋白质浓度。在缔合的时刻,总是存在预先折叠的结构元件,并且不对称结合机制很常见。我们的两层网络方法可以与生成离散状态的各种方法相结合,为柔性结合过程的动力学和途径提供新的见解。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6b76/10741920/892ae8e8effc/biomolecules-13-01708-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6b76/10741920/ff1a6fc65ff3/biomolecules-13-01708-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6b76/10741920/81fd4e8352b1/biomolecules-13-01708-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6b76/10741920/69be09e5edd4/biomolecules-13-01708-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6b76/10741920/76a3cc44693d/biomolecules-13-01708-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6b76/10741920/35a47e31c53c/biomolecules-13-01708-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6b76/10741920/892ae8e8effc/biomolecules-13-01708-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6b76/10741920/ff1a6fc65ff3/biomolecules-13-01708-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6b76/10741920/81fd4e8352b1/biomolecules-13-01708-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6b76/10741920/69be09e5edd4/biomolecules-13-01708-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6b76/10741920/76a3cc44693d/biomolecules-13-01708-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6b76/10741920/35a47e31c53c/biomolecules-13-01708-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6b76/10741920/892ae8e8effc/biomolecules-13-01708-g006.jpg

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本文引用的文献

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The Wako-Saitô-Muñoz-Eaton Model for Predicting Protein Folding and Dynamics.Wako-Saitô-Muñoz-Eaton 模型用于预测蛋白质折叠和动力学。
Molecules. 2022 Jul 12;27(14):4460. doi: 10.3390/molecules27144460.
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A series of PDB-related databanks for everyday needs.一系列满足日常需求的与蛋白质数据银行(PDB)相关的数据库。
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