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通过整合建模和突变预测阐明PTEN构象动力学和磷酸酶调节。

Elucidating PTEN conformational dynamics and phosphatase regulation via integrative modeling and mutation prediction.

作者信息

Dawson Jennifer Erin, Smith Iris Nira, Tushar Ann Marie, Eng Charis

机构信息

Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, 9500 Euclid Avenue, NE-50, Cleveland, OH 44195, USA.

Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, 9500 Euclid Avenue, NE-50, Cleveland, OH 44195, USA.

出版信息

Structure. 2025 Jun 30. doi: 10.1016/j.str.2025.06.002.

DOI:10.1016/j.str.2025.06.002
PMID:40614725
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12233165/
Abstract

PTEN (Phosphatase and TENsin homolog deleted on chromosome ten) is a major tumor suppressor gene that is frequently mutated or lost under cancerous conditions. PTEN is a dual-specificity phosphatase that negatively regulates the PI3K/AKT/mTOR signaling pathway at the plasma membrane (PM). Its functional regulation and cellular localization are known to be conformationally driven. Access to the PM is phosphoregulated by open and closed PTEN forms. However, clarifying the underlying structural mechanisms is still an open avenue of research. Here, we apply an integrative structural modeling approach, combining coarse-grained and all-atom molecular dynamics with experimental crosslinking mass spectrometry. Conformational exchange between an "eased" form and a "strained" form brings the protein's phosphatase and C2 domains closer together, blocking the catalytic site, and affecting the loops involved in PM binding. Our full-length PTEN models, AlphaMissense, and RaSP were used to better predict the consequences of PTEN mutations.

摘要

PTEN(第10号染色体缺失的磷酸酶和张力蛋白同源物)是一种主要的肿瘤抑制基因,在癌症状态下经常发生突变或缺失。PTEN是一种双特异性磷酸酶,在质膜(PM)上对PI3K/AKT/mTOR信号通路进行负调控。已知其功能调节和细胞定位受构象驱动。PTEN通过开放和闭合形式对进入质膜进行磷酸化调节。然而,阐明其潜在的结构机制仍是一个有待探索的研究方向。在此,我们应用一种综合结构建模方法,将粗粒度和全原子分子动力学与实验交联质谱相结合。“松弛”形式和“紧张”形式之间的构象交换使蛋白质的磷酸酶和C2结构域靠得更近,阻断催化位点,并影响参与质膜结合的环。我们的全长PTEN模型、AlphaMissense和RaSP被用于更好地预测PTEN突变的后果。

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本文引用的文献

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Accurate structure prediction of biomolecular interactions with AlphaFold 3.利用 AlphaFold 3 进行生物分子相互作用的精确结构预测。
Nature. 2024 Jun;630(8016):493-500. doi: 10.1038/s41586-024-07487-w. Epub 2024 May 8.
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Accurate proteome-wide missense variant effect prediction with AlphaMissense.使用 AlphaMissense 进行精确的全蛋白质错义变异效应预测。
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Rapid protein stability prediction using deep learning representations.利用深度学习表示进行快速蛋白质稳定性预测。
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Comparative Protein Structural Network Analysis Reveals C-Terminal Tail Phosphorylation Structural Communication Fingerprint in -Associated Mutations in Autism and Cancer.比较蛋白质结构网络分析揭示了自闭症和癌症中与相关的突变的 C 端尾部磷酸化结构通讯指纹。
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Structural and Dynamic Effects of PTEN C-Terminal Tail Phosphorylation.PTEN C 端尾部磷酸化的结构和动态效应。
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8
ColabFold: making protein folding accessible to all.ColabFold:让蛋白质折叠变得人人可用。
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9
Dali server: structural unification of protein families.达尔服务器:蛋白质家族的结构统一。
Nucleic Acids Res. 2022 Jul 5;50(W1):W210-W215. doi: 10.1093/nar/gkac387.
10
Shape shifting: The multiple conformational substates of the PTEN N-terminal PIP -binding domain.形态转变:PTEN N 端 PIP 结合域的多种构象亚基态。
Protein Sci. 2022 May;31(5):e4308. doi: 10.1002/pro.4308.