New Perspectives on Reverse Translation: Brief History and Updates

Q2 Multidisciplinary
Adhityo Wicaksono, V. D. Kharisma, A. A. Parikesit
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引用次数: 5

Abstract

Since the 1950s, reverse translation has been an enigmatic part of Crick’s central dogma of molecular biology. It might be described as the possibility to back-translate information from proteins to nucleic acids (or codons). A few studies have attempted to theorize and/or conduct in vitro experiments to test the likelihood of reverse translation, with ideas often involving the creation of peptide recognition sites that bridge the peptide and the codon. However, due to many constraints including an asymmetrical informational transfer, the stability of protein-peptide bonds, the structural non-uniformity of protein R-groups, and the informational loss in post-translational protein modifications, this concept requires follow-up studies. On the other hand, current bioinformatic tools that rely on computational programs and biological databases represent a growing branch of biology. Bioinformatics-based reverse translationcan utilize codon usage tables to predict codons from their peptide counterparts. In addition, the development of machine learning tools may allow for the exploration of biological reverse translation in vitro. Thus, while in vivo reverse translation appears to be nearly impossible (due to biological complexity), related biological and bioinformatics studies might be useful to understand better thecentral dogma’s informational transfer and to develop more complex biological machinery.
逆向翻译的新视角:简史与最新进展
自20世纪50年代以来,反向翻译一直是克里克分子生物学核心教条中神秘的一部分。它可以被描述为将信息从蛋白质反翻译为核酸(或密码子)的可能性。一些研究试图建立理论和/或进行体外实验,以测试反向翻译的可能性,其想法通常涉及创建连接肽和密码子的肽识别位点。然而,由于许多限制因素,包括不对称的信息传递、蛋白质肽键的稳定性、蛋白质R-基团的结构不均匀性以及翻译后蛋白质修饰中的信息损失,这一概念需要后续研究。另一方面,目前依赖于计算程序和生物数据库的生物信息学工具代表了生物学的一个不断发展的分支。基于生物信息学的反向翻译可以利用密码子使用表来预测来自肽对应物的密码子。此外,机器学习工具的开发可能有助于体外生物反向翻译的探索。因此,尽管体内反向翻译似乎几乎是不可能的(由于生物学的复杂性),但相关的生物学和生物信息学研究可能有助于更好地理解中心法则的信息传递,并开发更复杂的生物机制。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Universitas Scientiarum
Universitas Scientiarum Multidisciplinary-Multidisciplinary
CiteScore
1.20
自引率
0.00%
发文量
9
审稿时长
15 weeks
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