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Related Concept Videos

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Gene Evolution - Fast or Slow?

The genomes of eukaryotes are punctuated by long stretches of sequence which do not code for proteins or RNAs. Although some of these regions do contain crucial regulatory sequences, the vast majority of this DNA serves no known function. Typically, these regions of the genome are the ones in which the fastest change, in evolutionary terms, is observed, because there is typically little to no selection pressure acting on these regions to preserve their sequences.
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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
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Related Experiment Video

Updated: Jun 4, 2026

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
10:58

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Published on: July 25, 2013

Simulated evolution applied to study the genetic code optimality using a model of codon reassignments.

José Santos1, Angel Monteagudo

  • 1Department of Computer Science, University of A Coruña, Campus de Elviña s/n, 15071 A Coruña, Spain. santos@udc.es

BMC Bioinformatics
|February 23, 2011
PubMed
Summary
This summary is machine-generated.

The canonical genetic code is not optimal, but evolutionary computing suggests it

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Following the Dynamics of Structural Variants in Experimentally Evolved Populations
04:52

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

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Area of Science:

  • Genetics
  • Evolutionary Biology
  • Bioinformatics

Background:

  • The canonical genetic code's universality and optimization are debated.
  • Two main theories, statistical and engineering approaches, exist to measure code optimization.
  • Optimization considers harmful consequences of point mutations replacing amino acids.

Purpose of the Study:

  • To investigate the optimization level of the canonical genetic code using evolutionary computing.
  • To compare the canonical code's fitness against hypothetical, better-adapted codes.
  • To provide a new perspective on the statistical vs. engineering approach debate.

Main Methods:

  • Employed a genetic algorithm to search for hypothetical, optimized genetic codes.
  • Utilized two models: one reflecting known codon reassignments and a standard translation table.
  • Assessed the difficulty in finding alternative codes to situate the canonical code in the fitness landscape.

Main Results:

  • Simulated evolution indicates the canonical genetic code is far from optimal.
  • The canonical code's efficiency improves when mistranslations are considered.
  • The evolutionary algorithm faced greater difficulty optimizing codes using a realistic codon reassignment model.

Conclusions:

  • The canonical genetic code is not fully optimized, aligning with the engineering approach.
  • The statistical approach's arguments for extreme efficiency are insufficient.
  • Optimal codes exhibit patterns similar to the standard genetic code when mistranslations are factored in.