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A robust model for quantitative prediction of the silencing efficacy of wild-type and A-to-I edited miRNAs

机译:一种鲁棒模型,用于定量预测野生型和A-to-I编辑miRNA的沉默效果

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摘要

MicroRNAs (miRNAs) are small non-coding RNAs that play essential roles in the regulation of gene function by a mechanism known as RNA silencing. In a previous study, we revealed that miRNA-mediated silencing efficacy is correlated with the combinatorial thermodynamic properties of the miRNA seed-target mRNA duplex and the 5 '-terminus of the miRNA duplex, which can be predicted using 'miScore'. In this study, a robust refined-miScore was developed by integrating the thermodynamic properties of various miRNA secondary structures and the latest thermodynamic parameters of wobble base-pairing, including newly established parameters for I:C base pairing. Through repeated random sampling and machine learning, refined-miScore models calculated with either melting temperature (T-m) or free energy change (Delta G) values were successfully built and validated in both wild-type and adenosine-to-inosine edited miRNAs. In addition to the previously reported contribution of the seed-target duplex and 5 '-terminus region, the refined-miScore suggests that the central and 3 '-terminus regions of the miRNA duplex also play a role in the thermodynamic regulation of miRNA-mediated silencing efficacy.
机译:MicroRNA(miRNA)是小的非编码RNA,其在基因功能调节中起主要作用的基本作用通过称为RNA沉默的机制。在先前的研究中,我们揭示了miRNA介导的沉默疗效与miRNA种子靶mRNA双链体的组合热力学性质和miRNA双链体的5'次,这可以使用“错误”来预测。在这项研究中,通过整合各种miRNA二次结构的热力学性能和摆动碱基配对的最新热力学参数的热力学性能来开发强大的精制错误,包括新建立的I:C碱基配对。通过重复的随机取样和机器学习,用熔化温度(T-M)或自由能量变化(Delta G)值计算并验证野生型和腺苷对尼古斯的MiRNA。除了先前报告的种子目标双链体和5' - 重量区域的贡献之外,细化的错误表明,miRNA双工的中央和3' - 3'次区域也在miRNA介导的热力学调节中起作用沉默的疗效。

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