Structure-based prediction of Wnt binding affinities for Frizzled-type cysteine-rich domains

基于结构的Wnt与Frizzled型富含半胱氨酸结构域结合亲和力的预测

阅读:1

Abstract

Wnt signaling pathways are of significant interest in development and oncogenesis. The first step in these pathways typically involves the binding of a Wnt protein to the cysteine-rich domain (CRD) of a Frizzled receptor. Wnt-Frizzled interactions can be antagonized by secreted Frizzled-related proteins (SFRPs), which also contain a Frizzled-like CRD. The large number of Wnts, Frizzleds, and SFRPs, as well as the hydrophobic nature of Wnt, poses challenges to laboratory-based investigations of interactions involving Wnt. Here, utilizing structural knowledge of a representative Wnt-Frizzled CRD interaction, as well as experimentally determined binding affinities for a selection of Wnt-Frizzled CRD interactions, we generated homology models of Wnt-Frizzled CRD interactions and developed a quantitative structure-activity relationship for predicting their binding affinities. The derived model incorporates a small selection of terms derived from scoring functions used in protein-protein docking, as well as an energetic term considering the contribution made by the lipid of Wnt to the Wnt-Frizzled binding affinity. Validation with an external test set suggests that the model can accurately predict binding affinity for 75% of cases and that the error associated with the predictions is comparable with the experimental error. The model was applied to predict the binding affinities of the full range of mouse and human Wnt-Frizzled and Wnt-SFRP interactions, indicating trends in Wnt binding affinity for Frizzled and SFRP CRDs. The comprehensive predictions made in this study provide the basis for laboratory-based studies of previously unexplored Wnt-Frizzled and Wnt-SFRP interactions, which, in turn, may reveal further Wnt signaling pathways.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。