RPR-Genetic code optimality

Optimality of the Genetic Code: an R Exploration

 

Keywords:  Simulating genetic code optimality

 

 

 

Abstract

This unit explores R code to test the idea that the genetic code is not random.

 

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.
• Evolution: Theory of evolution; variation, neutral drift and selection.

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

• BIN-Sequence (Sequence)

 

Objectives

This unit will ...

• ... introduce the concept of estimating evolutionary pressure on the genetic code by quantifying the effect of mutations;
• ... demonstrate how a computational experiment is conducted;
• ... teach some programming techniques for working with sequences and sequence variations;

 

Outcomes

After working through this unit you ...

• ... are familar with the concept of an optimized genetic code;
• ... can set up a computational experiment;
• ... can write code to mutate and translate sequences.

 

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 RPR-Genetic_code_optimality.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

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

[ PubMed ] [ DOI ] Abstract 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.

Koonin & Novozhilov (2017) Origin and Evolution of the Universal Genetic Code. Annu Rev Genet 51:45-62. (pmid: 28853922)

[ PubMed ] [ DOI ] Abstract The standard genetic code (SGC) is virtually universal among extant life forms. Although many deviations from the universal code exist, particularly in organelles and prokaryotes with small genomes, they are limited in scope and obviously secondary. The universality of the code likely results from the combination of a frozen accident, i.e., the deleterious effect of codon reassignment in the SGC, and the inhibitory effect of changes in the code on horizontal gene transfer. The structure of the SGC is nonrandom and ensures high robustness of the code to mutational and translational errors. However, this error minimization is most likely a by-product of the primordial code expansion driven by the diversification of the repertoire of protein amino acids, rather than a direct result of selection. Phylogenetic analysis of translation system components, in particular aminoacyl-tRNA synthetases, shows that, at a stage of evolution when the translation system had already attained high fidelity, the correspondence between amino acids and cognate codons was determined by recognition of amino acids by RNA molecules, i.e., proto-tRNAs. We propose an experimentally testable scenario for the evolution of the code that combines recognition of amino acids by unique sites on proto-tRNAs (distinct from the anticodons), expansion of the code via proto-tRNA duplication, and frozen accident.

 

Notes

 

Self-evaluation

 

 

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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.

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About ...
 
Author:

Boris Steipe <boris.steipe@utoronto.ca>

Created:

2017-08-05

Modified:

2017-08-05

Version:

1.0

Version history:

• 1.0 New material
• 0.1 First stub

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