FND-Genetic code

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Abstract

The genetic code is conveniently available as a named character vector, via the Biostrings package. We access the code, review syntax of how to work with it, and discuss some of its properties.


 


This unit ...

Prerequisites

You need the following preparation before beginning this unit. If you are not familiar with this material from courses you took previously, you need to prepare yourself from other information sources:

  • Biomolecules: The molecules of life; nucleic acids and amino acids; the genetic code; protein folding; post-translational modifications and protein biochemistry; membrane proteins; biological function.
  • The Central Dogma: Regulation of transcription and translation; protein biosynthesis and degradation; quality control.

You need to complete the following units before beginning this one:


 


Objectives

This unit will ...

  • explore the syntax and contents of the named charcater vector that stores the standard genetic code for the Biostrings package;
  • note the existence of alternative codes;
  • introduce an alternative representation as a 3D array.


 


Outcomes

After working through this unit you ...

  • can fetch and use the genetic code from the Biostrings package;
  • solve a variety of tasks concerned with the analysis of the code and its display.


 


Deliverables

  • Time management: Before you begin, estimate how long it will take you to complete this unit. Then, record in your course journal: the number of hours you estimated, the number of hours you worked on the unit, and the amount of time that passed between start and completion of this unit.
  • Journal: Document your progress in your Course Journal. Some tasks may ask you to include specific items in your journal. Don't overlook these.
  • Insights: If you find something particularly noteworthy about this unit, make a note in your insights! page.


 


Evaluation

Evaluation: NA

This unit is not evaluated for course marks.


 


Contents

Task:

 
  • Open RStudio and load the ABC-units R project. If you have loaded it before, choose File → Recent projects → ABC-Units. If you have not loaded it before, follow the instructions in the RPR-Introduction unit.
  • Choose Tools → Version Control → Pull Branches to fetch the most recent version of the project from its GitHub repository with all changes and bug fixes included.
  • Type init() if requested.
  • Open the file FND-Genetic_code.R and follow the instructions.


 

Note: take care that you understand all of the code in the script. Evaluation in this course is cumulative and you may be asked to explain any part of code.


 


 


Further reading, links and resources

Ohama et al. (1993) Non-universal decoding of the leucine codon CUG in several Candida species. Nucleic Acids Res 21:4039-45. (pmid: 8371978)

[ PubMed ] [ DOI ] It has been reported that CUG, a universal leucine codon, is read as serine in an asporogenic yeast, Candida cylindracea. The distribution of this non-universal genetic code in various yeast species was studied using an in vitro translation assay system with a synthetic messenger RNA containing CUG codons in-frame. It was found that CUG is used as a serine codon in six out of the fourteen species examined, while it is used for leucine in the remaining eight. The tRNA species responsible for the translation of codon CUG as serine was detected in all the six species in which CUG is translated as serine. The grouping according to the CUG codon assignments in these yeast species shows a good correlation with physiological classification by the chain lengths of the isoprenoid moiety of ubiquinone and the cell-wall sugar contained in the yeasts. The six Candida species examined in which CUG is used as serine belong to one distinct group in Hemiascomycetes.

Santos et al. (2011) The genetic code of the fungal CTG clade. C R Biol 334:607-11. (pmid: 21819941)

[ PubMed ] [ DOI ] Genetic code alterations discovered over the last 40 years in bacteria and eukaryotes invalidate the hypothesis that the code is universal and frozen. Mitochondria of various yeast species translate the UGA stop codon as tryptophan (Trp) and leucine (Leu) CUN codons (N = any nucleotide) as threonine (Thr) and fungal CTG clade species reassigned Leu CUG codons to serine and translate them ambiguously in their cytoplasms. This unique sense-to-sense genetic code alteration is mediated by a Ser-tRNA containing a Leu 5'-CAG-3'anticodon (ser-tRNA(CAG)), which is recognized and charged with Ser (~97%) by the seryl-tRNA synthetase (SerRS) and with Leu (~3%) by the leucyl-tRNA synthetase (LeuRS). This unusual tRNA appeared 272 ± 25 million years ago and had a profound impact on the evolution of the CTG clade species. Here, we review the most recent results and concepts arising from the study of this codon reassignment and we highlight how its study is changing our views of the evolution of the genetic code.

Fimmel & Strüngmann (2018) Mathematical fundamentals for the noise immunity of the genetic code. BioSystems 164:186-198. (pmid: 28918301)

[ PubMed ] [ DOI ] Symmetry is one of the essential and most visible patterns that can be seen in nature. Starting from the left-right symmetry of the human body, all types of symmetry can be found in crystals, plants, animals and nature as a whole. Similarly, principals of symmetry are also some of the fundamental and most useful tools in modern mathematical natural science that play a major role in theory and applications. As a consequence, it is not surprising that the desire to understand the origin of life, based on the genetic code, forces us to involve symmetry as a mathematical concept. The genetic code can be seen as a key to biological self-organisation. All living organisms have the same molecular bases - an alphabet consisting of four letters (nitrogenous bases): adenine, cytosine, guanine, and thymine. Linearly ordered sequences of these bases contain the genetic information for synthesis of proteins in all forms of life. Thus, one of the most fascinating riddles of nature is to explain why the genetic code is as it is. Genetic coding possesses noise immunity which is the fundamental feature that allows to pass on the genetic information from parents to their descendants. Hence, since the time of the discovery of the genetic code, scientists have tried to explain the noise immunity of the genetic information. In this chapter we will discuss recent results in mathematical modelling of the genetic code with respect to noise immunity, in particular error-detection and error-correction. We will focus on two central properties: Degeneracy and frameshift correction. DEGENERACY: Different amino acids are encoded by different quantities of codons and a connection between this degeneracy and the noise immunity of genetic information is a long standing hypothesis. Biological implications of the degeneracy have been intensively studied and whether the natural code is a frozen accident or a highly optimised product of evolution is still controversially discussed. Symmetries in the structure of degeneracy of the genetic code are essential and give evidence of substantial advantages of the natural code over other possible ones. In the present chapter we will present a recent approach to explain the degeneracy of the genetic code by algorithmic methods from bioinformatics, and discuss its biological consequences. FRAMESHIFT CORRECTION: The biologists recognised this problem immediately after the detection of the non-overlapping structure of the genetic code, i.e., coding sequences are to be read in a unique way determined by their reading frame. But how does the reading head of the ribosome recognises an error in the grouping of codons, caused by e.g. insertion or deletion of a base, that can be fatal during the translation process and may result in nonfunctional proteins? In this chapter we will discuss possible solutions to the frameshift problem with a focus on the theory of so-called circular codes that were discovered in large gene populations of prokaryotes and eukaryotes in the early 90s. Circular codes allow to detect a frameshift of one or two positions and recently a beautiful theory of such codes has been developed using statistics, group theory and graph theory.

The Genetic Code (Wikipedia)
Genetic Code Data (NCBI)


 


Notes


 


Self-evaluation

 



 



 

If in doubt, ask! If anything about this learning unit is not clear to you, do not proceed blindly but ask for clarification. Post your question on the course mailing list: others are likely to have similar problems. Or send an email to your instructor.


 

About ...
 
Author:

Boris Steipe <boris.steipe@utoronto.ca>

Created:

2017-08-05

Modified:

2017-09-28

Version:

1.0

Version history:

  • 1.0 First live version
  • 0.1 First stub

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