Branciamore Sergio, Gogoshin Grigoriy, Di Giulio Massimo, Rodin Andrei S
Department of Diabetes Complications and Metabolism, Diabetes and Metabolism Research Institute, City of Hope, Duarte, 91010 CA, USA.
Early Evolution of Life Laboratory, Institute of Biosciences and Bioresources, CNR, 80131 Naples, Italy.
Life (Basel). 2018 Feb 8;8(1):5. doi: 10.3390/life8010005.
The identity/recognition of tRNAs, in the context of aminoacyl tRNA synthetases (and other molecules), is a complex phenomenon that has major implications ranging from the origins and evolution of translation machinery and genetic code to the evolution and speciation of tRNAs themselves to human mitochondrial diseases to artificial genetic code engineering. Deciphering it via laboratory experiments, however, is difficult and necessarily time- and resource-consuming. In this study, we propose a mathematically rigorous two-pronged in silico approach to identifying and classifying tRNA positions important for tRNA identity/recognition, rooted in machine learning and information-theoretic methodology. We apply Bayesian Network modeling to elucidate the structure of intra-tRNA-molecule relationships, and distribution divergence analysis to identify meaningful inter-molecule differences between various tRNA subclasses. We illustrate the complementary application of these two approaches using tRNA examples across the three domains of life, and identify and discuss important (informative) positions therein. In summary, we deliver to the tRNA research community a novel, comprehensive methodology for identifying the specific elements of interest in various tRNA molecules, which can be followed up by the corresponding experimental work and/or high-resolution position-specific statistical analyses.
在氨酰tRNA合成酶（及其他分子）的背景下，tRNA的身份识别是一种复杂现象，其影响深远，涵盖从翻译机制和遗传密码的起源与进化，到tRNA自身的进化与物种形成，再到人类线粒体疾病以及人工遗传密码工程等诸多方面。然而，通过实验室实验来破解这一现象既困难又必然耗费时间和资源。在本研究中，我们提出一种数学严谨的双管齐下的计算机模拟方法，用于识别和分类对tRNA身份识别重要的tRNA位置，该方法基于机器学习和信息论方法。我们应用贝叶斯网络建模来阐明tRNA分子内部关系的结构，并运用分布差异分析来识别不同tRNA亚类之间有意义的分子间差异。我们通过生命三域中的tRNA实例来说明这两种方法的互补应用，并识别和讨论其中重要的（信息丰富的）位置。总之，我们为tRNA研究群体提供了一种新颖、全面的方法，用于识别各种tRNA分子中感兴趣的特定元件，后续可开展相应的实验工作和/或高分辨率的位置特异性统计分析。
