RPI-Bind: a structure-based method for accurate identification of RNA-protein binding sites.
RPI-Bind: a structure-based method for accurate identification of RNA-protein binding sites.
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RPI-Bind：一种基于结构的方法，用于准确鉴定 RNA-蛋白质结合
DOI：
10.1038/s41598-017-00795-4
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发表时间：
2017-04-04
影响因子：
4.6
通讯作者：
Zhou X
中科院分区：
文献类型：
--
作者：
Luo J;Liu L;Venkateswaran S;Song Q;Zhou X
RNA和蛋白质相互作用在多种生物过程中发挥着至关重要的作用，而这些相互作用很大程度上受到蛋白质和RNA分子的结构和序列的影响。在这项研究中，我们首先对 RNA-蛋白质相互作用复合物进行了分析，并确定了序列和结构的界面特性，这揭示了结合位点的多样性。根据观察结果，我们建立了一个三步预测模型，即 RPI-Bind，用于使用蛋白质和 RNA 的序列和结构来识别 RNA-蛋白质结合区域。这三个步骤包括 1) 预测蛋白质上的 RNA 结合区域，2) 预测 RNA 上的蛋白质结合区域，以及 3) 同时预测 RNA 和蛋白质上的相互作用区域，以及步骤 1) 和 2) 的结果。与大多数仅使用序列的现有方法相比，我们的模型显着提高了三个步骤中每个步骤的预测精度。特别是，我们的模型在第 3 步的性能优于 catRAPID > 20%。所有这些结果都表明结构在 RNA-蛋白质相互作用中的重要性，并表明 RPI-Bind 模型是研究 RNA-蛋白质相互作用的强大理论框架。
RNA and protein interactions play crucial roles in multiple biological processes, while these interactions are significantly influenced by the structures and sequences of protein and RNA molecules. In this study, we first performed an analysis of RNA-protein interacting complexes, and identified interface properties of sequences and structures, which reveal the diverse nature of the binding sites. With the observations, we built a three-step prediction model, namely RPI-Bind, for the identification of RNA-protein binding regions using the sequences and structures of both proteins and RNAs. The three steps include 1) the prediction of RNA binding regions on protein, 2) the prediction of protein binding regions on RNA, and 3) the prediction of interacting regions on both RNA and protein simultaneously, with the results from steps 1) and 2). Compared with existing methods, most of which employ only sequences, our model significantly improves the prediction accuracy at each of the three steps. Especially, our model outperforms the catRAPID by >20% at the 3rd step. All of these results indicate the importance of structures in RNA-protein interactions, and suggest that the RPI-Bind model is a powerful theoretical framework for studying RNA-protein interactions.
影响因子：
3.7
作者：
Liu L;Chen SJ
通讯作者：
Chen SJ