A Predictive Model of Antibody Binding in the Presence of IgG-Interacting Bacterial Surface Proteins.
A Predictive Model of Antibody Binding in the Presence of IgG-Interacting Bacterial Surface Proteins.
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DOI：
10.3389/fimmu.2021.629103
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发表时间：
2021
影响因子：
7.3
通讯作者：
Nordenfelt P
中科院分区：
文献类型：
--
作者：
Kumra Ahnlide V;de Neergaard T;Sundwall M;Ambjörnsson T;Nordenfelt P
许多细菌可以干扰抗体与其表面结合的方式。这种细菌抗体的靶向性使得预测细菌相关抗体的免疫功能具有挑战性。A组链球菌(GAS)的M蛋白和类M蛋白具有IgGFc结合区，它们用来根据宿主环境逆转Ig G结合方向。解开这些结合特征背后的机制，可以确定结合的免疫球蛋白在哪些条件下可以驱动有效的免疫反应。在这里，我们开发了一个生物物理模型来描述这些复杂的蛋白质-抗体相互作用。我们展示了如何将该模型作为一种工具，通过在计算机模拟中执行并将该数据与实验测量相关联，来研究不同的免疫球蛋白样本与M蛋白的结合行为。除了用于机械理解之外，该模型还可能被用作帮助开发抗体治疗的工具。我们通过模拟血清中的抗体与GAS的结合如何随着特定数量的单抗或混合抗体的加入而改变来说明这一点。吞噬实验将这种改变的抗体结合与生理功能联系起来，并证明了用我们的模型预测免疫球蛋白治疗的效果是可能的。我们的研究从机理上理解了细菌抗体靶向，并提供了一种工具来预测在细菌存在的情况下抗体治疗的效果，这些抗体具有调节表面蛋白的免疫球蛋白。
Many bacteria can interfere with how antibodies bind to their surfaces. This bacterial antibody targeting makes it challenging to predict the immunological function of bacteria-associated antibodies. The M and M-like proteins of group A streptococci (GAS) exhibit IgGFc-binding regions, which they use to reverse IgG binding orientation depending on the host environment. Unraveling the mechanism behind these binding characteristics may identify conditions under which bound IgG can drive an efficient immune response. Here, we have developed a biophysical model for describing these complex protein-antibody interactions. We show how the model can be used as a tool for studying the binding behavior of various IgG samples to M protein by performing in silico simulations and correlating this data with experimental measurements. Besides its use for mechanistic understanding, this model could potentially be used as a tool to aid in the development of antibody treatments. We illustrate this by simulating how IgG binding to GAS in serum is altered as specified amounts of monoclonal or pooled IgG is added. Phagocytosis experiments link this altered antibody binding to a physiological function and demonstrate that it is possible to predict the effect of an IgG treatment with our model. Our study gives a mechanistic understanding of bacterial antibody targeting and provides a tool for predicting the effect of antibody treatments in the presence of bacteria with IgG-modulating surface proteins.
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影响因子：
14.9
作者：
Nilsson AN;Emilsson G;Nyberg LK;Noble C;Stadler LS;Fritzsche J;Moore ER;Tegenfeldt JO;Ambjörnsson T;Westerlund F
通讯作者：
Westerlund F
影响因子：
64.8
作者：
Macheboeuf, Pauline;Buffalo, Cosmo;Fu, Chi-yu;Zinkernagel, Annelies S.;Cole, Jason N.;Johnson, John E.;Nizet, Victor;Ghosh, Partho
通讯作者：
Ghosh, Partho
抗体 - 抗原络合物的计算对接，流感血凝素所说明的机会和陷阱。
影响因子：
5.6
作者：
Pedotti M;Simonelli L;Livoti E;Varani L
通讯作者：
Varani L
影响因子：
11.8
作者：
Carapetis, Jonathan R.;Jacoby, Peter;Andrews, Ross
通讯作者：
Andrews, Ross
影响因子：
11.8
作者：
Darenberg, J;Ihendyane, N;Norrby-Teglund, A
通讯作者：
Norrby-Teglund, A