Is there a bias in the codon frequency corresponding to homo-repeats found in human proteins?

IF 1.9 4区 生物学 Q2 BIOLOGY Biosystems Pub Date : 2024-10-21 DOI:10.1016/j.biosystems.2024.105357
Nikita V. Dovidchenko , Mikhail Yu. Lobanov , Oxana V. Galzitskaya
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Abstract

It is well known that there is a codon usage bias in genomes, that is, some codons are observed more often than others. Codons implicated in the homo-repeats regions in human proteins are no exception. In this work, we analyzed the codon usage bias for all amino acid residues in homo-repeats larger than 4 in 3753 human proteins from 20447 protein sequences from the canonically reviewed human proteome. We have discovered that almost all homo-repeats in the human proteome, most of which encode Ala, Glu, Gly, Leu, Pro, and Ser (∼80% of all homo-repeats), have a codon usage bias, i.e. are mainly encoded by one codon. Moreover, there is a strong shift in homo-repeats in favor of the content of GC rich codons. Homo-repeats with Ala, Glu, Gly, Leu, Pro, and Ser predominate in the PDB, which has both ordered and disordered status. Examining the distribution of splicing sites, we found that about 15% of homo-repeats either contain or are located within 10 nucleotides of the splicing site, and Glu and Leu predominate in these homo-repeats. Our data is important for future study of the functions of homo-repeats, protein-protein interactions, and evolutionary fitness.
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人类蛋白质中的同源重复密码子频率是否存在偏差?
众所周知,基因组中存在密码子使用偏差,即某些密码子比其他密码子更常见。涉及人类蛋白质中同源重复区域的密码子也不例外。在这项工作中,我们分析了来自规范审查的人类蛋白质组 20447 个蛋白质序列中 3753 个人类蛋白质中大于 4 的同源重复序列中所有氨基酸残基的密码子使用偏倚。我们发现,人类蛋白质组中几乎所有的同源重复序列(其中大部分编码 Ala、Glu、Gly、Leu、Pro 和 Ser,占所有同源重复序列的 80%)都存在密码子使用偏差,即主要由一个密码子编码。此外,在同源重复序列中,富含 GC 的密码子的含量有很大的变化。在具有有序和无序状态的 PDB 中,Ala、Glu、Gly、Leu、Pro 和 Ser 的同源重复占主导地位。通过研究剪接位点的分布,我们发现约有 15%的同源重复序列包含剪接位点或位于剪接位点 10 个核苷酸以内,而在这些同源重复序列中,Glu 和 Leu 占主导地位。我们的数据对今后研究同源重复序列的功能、蛋白质与蛋白质之间的相互作用以及进化适应性具有重要意义。
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来源期刊
Biosystems
Biosystems 生物-生物学
CiteScore
3.70
自引率
18.80%
发文量
129
审稿时长
34 days
期刊介绍: BioSystems encourages experimental, computational, and theoretical articles that link biology, evolutionary thinking, and the information processing sciences. The link areas form a circle that encompasses the fundamental nature of biological information processing, computational modeling of complex biological systems, evolutionary models of computation, the application of biological principles to the design of novel computing systems, and the use of biomolecular materials to synthesize artificial systems that capture essential principles of natural biological information processing.
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