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基于结构的Ras效应物结合亲和力预测及“分支调节”界面突变设计。

Structure-based prediction of Ras-effector binding affinities and design of "branchegetic" interface mutations.

作者信息

Junk Philipp, Kiel Christina

机构信息

Systems Biology Ireland, School of Medicine, University College Dublin, Dublin 4, Ireland; UCD Charles Institute of Dermatology, School of Medicine, University College Dublin, Dublin 4, Ireland.

Systems Biology Ireland, School of Medicine, University College Dublin, Dublin 4, Ireland; UCD Charles Institute of Dermatology, School of Medicine, University College Dublin, Dublin 4, Ireland; Department of Molecular Medicine, University of Pavia, 27100 Pavia, Italy.

出版信息

Structure. 2023 Jul 6;31(7):870-883.e5. doi: 10.1016/j.str.2023.04.007. Epub 2023 May 10.

DOI:10.1016/j.str.2023.04.007
PMID:37167973
Abstract

Ras is a central cellular hub protein controlling multiple cell fates. How Ras interacts with a variety of potential effector proteins is relatively unexplored, with only some key effectors characterized in great detail. Here, we have used homology modeling based on X-ray and AlphaFold2 templates to build structural models for 54 Ras-effector complexes. These models were used to estimate binding affinities using a supervised learning regressor. Furthermore, we systematically introduced Ras "branch-pruning" (or branchegetic) mutations to identify 200 interface mutations that affect the binding energy with at least one of the model structures. The impacts of these branchegetic mutants were integrated into a mathematical model to assess the potential for rewiring interactions at the Ras hub on a systems level. These findings have provided a quantitative understanding of Ras-effector interfaces and their impact on systems properties of a key cellular hub.

摘要

Ras是一种控制多种细胞命运的核心细胞枢纽蛋白。Ras如何与各种潜在的效应蛋白相互作用相对未被探索,仅有一些关键效应蛋白得到了详细表征。在此,我们基于X射线和AlphaFold2模板使用同源建模来构建54种Ras-效应蛋白复合物的结构模型。这些模型被用于通过监督学习回归器估计结合亲和力。此外,我们系统性地引入Ras“分支修剪”(或分支调控)突变以鉴定出200个影响与至少一种模型结构结合能的界面突变。这些分支调控突变体的影响被整合到一个数学模型中,以在系统水平上评估Ras枢纽处相互作用重新布线的潜力。这些发现提供了对Ras-效应蛋白界面及其对关键细胞枢纽系统特性影响的定量理解。

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