Next Article in Journal
The Prevailing Catalytic Role of Meteorites in Formamide Prebiotic Processes
Previous Article in Journal
The Diverging Routes of BORIS and CTCF: An Interactomic and Phylogenomic Analysis
Previous Article in Special Issue
Frozen Accident Pushing 50: Stereochemistry, Expansion, and Chance in the Evolution of the Genetic Code
Article Menu

Export Article

Open AccessArticle

Intrinsic Properties of tRNA Molecules as Deciphered via Bayesian Network and Distribution Divergence Analysis

1
Department of Diabetes Complications and Metabolism, Diabetes and Metabolism Research Institute, City of Hope, Duarte, 91010 CA, USA
2
Early Evolution of Life Laboratory, Institute of Biosciences and Bioresources, CNR, 80131 Naples, Italy
*
Authors to whom correspondence should be addressed.
Received: 9 December 2017 / Revised: 22 January 2018 / Accepted: 23 January 2018 / Published: 8 February 2018
Full-Text   |   PDF [607 KB, uploaded 8 February 2018]   |  

Abstract

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. View Full-Text
Keywords: tRNA identity; tRNA recognition; operational code; bayesian networks; information theory; distribution divergence tRNA identity; tRNA recognition; operational code; bayesian networks; information theory; distribution divergence
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Supplementary material

SciFeed

Share & Cite This Article

MDPI and ACS Style

Branciamore, S.; Gogoshin, G.; Di Giulio, M.; Rodin, A.S. Intrinsic Properties of tRNA Molecules as Deciphered via Bayesian Network and Distribution Divergence Analysis. Life 2018, 8, 5.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Life EISSN 2075-1729 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top