Zheng Suxin, Robertson Timothy A, Varani Gabriele
Department of Chemistry, University of Washington, Seattle, WA 98195, USA.
FEBS J. 2007 Dec;274(24):6378-91. doi: 10.1111/j.1742-4658.2007.06155.x. Epub 2007 Nov 12.
RNA-protein interactions are fundamental to gene expression. Thus, the molecular basis for the sequence dependence of protein-RNA recognition has been extensively studied experimentally. However, there have been very few computational studies of this problem, and no sustained attempt has been made towards using computational methods to predict or alter the sequence-specificity of these proteins. In the present study, we provide a distance-dependent statistical potential function derived from our previous work on protein-DNA interactions. This potential function discriminates native structures from decoys, successfully predicts the native sequences recognized by sequence-specific RNA-binding proteins, and recapitulates experimentally determined relative changes in binding energy due to mutations of individual amino acids at protein-RNA interfaces. Thus, this work demonstrates that statistical models allow the quantitative analysis of protein-RNA recognition based on their structure and can be applied to modeling protein-RNA interfaces for prediction and design purposes.
RNA与蛋白质的相互作用是基因表达的基础。因此，蛋白质-RNA识别序列依赖性的分子基础已得到广泛的实验研究。然而，针对这个问题的计算研究非常少，并且尚未持续尝试使用计算方法来预测或改变这些蛋白质的序列特异性。在本研究中，我们提供了一种基于距离的统计势函数，该函数源自我们先前关于蛋白质-DNA相互作用的工作。此势函数能够区分天然结构与诱饵结构，成功预测序列特异性RNA结合蛋白识别的天然序列，并概括了由于蛋白质-RNA界面单个氨基酸突变导致的结合能实验测定的相对变化。因此，这项工作表明统计模型能够基于蛋白质-RNA的结构对其识别进行定量分析，并且可应用于为预测和设计目的对蛋白质-RNA界面进行建模。
