基于自由能距离解码器的蛋白质翻译扩充分组码模型

M. Bataineh, Lun Huang, Alicia Fuente Acedo, G. Atkin, N. Menhart
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引用次数: 0

摘要

基因序列的信息分析揭示了遗传过程与通信工程领域中使用的信息处理系统之间存在着重要的相似之处。通过分析基因表达过程中涉及的关键要素,我们开发了几种基于通信和编码理论的翻译过程模型[1-5]。先前的一项研究探讨了使用基于编码理论的模型定量描述原核生物翻译起始过程中核糖体的行为[1]。本文研究了一种带有修正准则和假设的增强型分组码模型。我们还使用了几个最小距离解码器来验证基于核糖体和mRNA序列之间结合的自由能所提出的改进模型。在形成所研究的模型时考虑的关键生物学因素是:16S rRNA分子3'端的最后13个碱基,细菌核糖体结合位点的共同特征(如Shine-Dalgarno序列的存在和位置),rRNA- mrna相互作用所涉及的能量,以及RNA/DNA碱基配对原则。该模型在五种不同的大肠杆菌基因组上进行了测试。所得结果证明了该模型在明确区分四种不同的基因预测测试组方面的有效性和意义。其中两个是基于著名的基因搜索软件(如GeneMark[2]和Glimmer[3])。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An augmented block code model for protein translation using free energy based distance decoders
Informational analysis of genetic sequences has revealed the existence of significant analogies between the genetic process and information processing systems used in the field of communications engineering. By analyzing key elements involved in the process of gene expression, we have developed several communications and coding theory based models for the process of translation [1–5]. A previous research investigated the use of coding theory based models that quantitatively describe the behavior of the ribosome during translation initiation in prokaryotic organisms [1]. In this paper we have investigated an augmented block code model with modified criteria and assumptions. We have also employed several minimum distance decoders to verify the proposed modified model based on the free energies involved in the binding between the ribosome and the mRNA sequence. The key biological elements considered in forming the investigated model are: the last 13 bases of the 3' end of the 16S rRNA molecule, the common features of bacterial ribosomal binding sites (such as the existence and location of the Shine-Dalgarno sequence), the energies involved in the rRNA-mRNA interaction, and RNA/DNA base-pairing principles. The model was tested on five different E. coli bacterial genomes. The obtained results prove the validity and significance of the model in clearly distinguishing four different test groups of gene predictions. Two of them are based on well known gene finder softwares (e.g. GeneMark [2] and Glimmer [3]).
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