Molecular Origins of Force-Dependent Protein Complex Stabilization during Bacterial Infections
Molecular Origins of Force-Dependent Protein Complex Stabilization during Bacterial Infections
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DOI：
10.1021/jacs.2c07674
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发表时间：
2022-12-01
影响因子：
15
通讯作者：
Bernardi, Rafael C.
中科院分区：
文献类型：
--
作者：
Melo, Marcelo C. R.;Gomes, Diego E. B.;Bernardi, Rafael C.
根据生化和机械因素的不同，蛋白质复合体的解离途径会有很大的不同。在机械应力作用下，络合物可能通过不同于简单热解离的机制解离，导致剪切力和热解离作用下的解离速率不同。这是生物力学中研究的一个众所周知的现象，其分子和原子的细节仍然难以捉摸。一个特别有趣的例子是细菌粘附素与其人类多肽靶标形成的复合体。这些蛋白质相互作用具有相当于共价键的力弹性，比广泛使用的链霉亲和素：生物素复合体强一个数量级，而具有普通的亲和力，远低于链霉亲和素：生物素。在此，我们用分子动力学模拟的方法研究了粘附素/多肽复合体的解离机理。我们展示了表皮葡萄球菌粘附素SdrG如何使用捕获键机制来增加复合体的稳定性，同时增加机械应力。虽然在低力区域允许热解离，但在高力区域出现了完全不同的机械解离路径，揭示了一种不依赖于肽的氨基酸序列的复杂机制。使用动态网络分析方法，我们确定了描述该复合体机理的关键氨基酸接触，揭示了阻碍热解离并建立机械解离路径的动力学差异。然后，我们使用它们的动力学来验证所选氨基酸接触的信息含量，通过机器学习模型成功地预测了该复合体的破裂力。
The unbinding pathway of a protein complex can vary significantly depending on biochemical and mechanical factors. Under mechanical stress, a complex may dissociate through a mechanism different from that used in simple thermal dissociation, leading to different dissociation rates under shear force and thermal dissociation. This is a well-known phenomenon studied in biomechanics whose molecular and atomic details are still elusive. A particularly interesting case is the complex formed by bacterial adhesins with their human peptide target. These protein interactions have a force resilience equivalent to those of covalent bonds, an order of magnitude stronger than the widely used streptavidin:biotin complex, while having an ordinary affinity, much lower than that of streptavidin:biotin. Here, in an in silico single-molecule force spectroscopy approach, we use molecular dynamics simulations to investigate the dissociation mechanism of adhesin/peptide complexes. We show how the Staphylococcus epidermidis adhesin SdrG uses a catch-bond mechanism to increase complex stability with increasing mechanical stress. While allowing for thermal dissociation in a low-force regime, an entirely different mechanical dissociation path emerges in a high-force regime, revealing an intricate mechanism that does not depend on the peptide's amino acid sequence. Using a dynamic network analysis approach, we identified key amino acid contacts that describe the mechanics of this complex, revealing differences in dynamics that hinder thermal dissociation and establish the mechanical dissociation path. We then validate the information content of the selected amino acid contacts using their dynamics to successfully predict the rupture forces for this complex through a machine learning model.