tailieunhanh - Báo cáo khoa học: A knowledge-based potential function predicts the specificity and relative binding energy of RNA-binding proteins

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. | ễFEBS Journal A knowledge-based potential function predicts the specificity and relative binding energy of RNA-binding proteins Suxin Zheng1 Timothy A. Robertson2 and Gabriele Varani1 2 1 Department of Chemistry University of Washington Seattle WA USA 2 Department of Biochemistry University of Washington Seattle WA USA Keywords distance-dependent potential protein-RNA interaction RRM recognition statistical potential Correspondence G. Varani Department of Chemistry and Department of Biochemistry University of Washington Seattle WA 98195 USA Fax 1 206 685 8665 Tel 1 206 543 7113 E-mail varani@ These authors contributed equally to this work Received 25 July 2007 revised 22 September 2007 accepted 19 October 2007 doi 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. The sequence-specific recognition of RNA by proteins plays a fundamental role in gene expression by directing different cellular RNAs to .

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