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Benjamin Good March 17, 2008

The Optimality of the Genetic Code. Benjamin Good March 17, 2008. The Genetic Code. Sequence constructed from 4 “letters” known as nucleotides or bases, denoted “A”, “G”, “C”, “U” / ”T” These letters form fixed length “words” known as codons.

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Benjamin Good March 17, 2008

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  1. The Optimality of the Genetic Code Benjamin Good March 17, 2008

  2. The Genetic Code • Sequence constructed from 4 “letters” known as nucleotides or bases, denoted “A”, “G”, “C”, “U” / ”T” • These letters form fixed length “words” known as codons. • Groups of codons form “sentences” which encode proteins.  Codon

  3. The Genetic Code • A given codon can either stand for a specific amino acid or act as a “start/stop codon”, which signals either the beginning or end of a protein’s code respectively. • There are 4*4*4=64 different codons but only 20 amino acids to code for, making a total of 21 different possible meanings for a given codon (including start/stop). • How are codons distributed among the 21 different categories?

  4. The Genetic Code • The “Canonical Code” • But why this arrangement and not another? • Crick: Canonical code is a frozen artifact of a code that was “good enough” to work

  5. Why the canonical code? • An alternative is that the canonical code itself evolved to optimize for some selected trait. • Noting the connection between similar codons and similar amino acids, several researchers hypothesized that the canonical code evolved to optimize against copying/transcription errors.

  6. The Polar Requirement • Woese and Alf-Steinberger came up with a measure for error susceptibility in genetic code based on hydrophobicity. • A given codon is subject to a single mutation. The polar difference between the new amino acid and the old one is calculated. • The “error” resulting from the mutation is taken as the distance squared (mean squared distance).

  7. How Optimal is the Canonical Code? • Unfortunately, Alf-Steinberger’s results have not been reproducible. • The first reproducible “test” of the polar requirement was published by Haig and Hurst in 1991. • Using this method, they calculated the total error for a large sample of possible code assignments. • Out of 10,000, only twoother codes had lowererror values than thecanonical code!

  8. One in a million? • Freeland and Hurst built upon H&H’s model to introduce more realistic assumptions. • Two types of code errors possible: transition andtransversion. • Introduced weighting fortwo types of errors because they are not equally probable in nature. • Also introduced bias towardsmistranslation rather thanmutation (higher rates oferrors in 1st and 3rd slots)

  9. One in a million? Weighted errors make the canonical code even more optimized relativeto the rest. Peak efficiency Around w = 3

  10. One in a million? Out of a sample of 1,000,000 random codes, only 1 had a lower error value than the CC! It was relatively far away in search space, but behaved similarly to CC.

  11. Beyond the Polar Requirement • In the paper we read for class, Freeland and Hurst question previous studies (including their own). • Is the polar requirement a biased measurement? • Is using the (W)MSD a biased measurement? • Some biosynthetic acids might be tied to particular codons, so code space could be artificially symmetric. Proposed a new measurement based on PAM matrices, which measure the “similarity” of two amino acids on a functional level.

  12. Beyond the Polar Requirement General error metric: ei is the physical error resulting from substitution i A code’s total error = αi is the number oftransition errors leadingto substitution i.i.e. U ↔C,A↔G PAM matrix βi is the number oftransversion errors leadingto substitution i.i.e. U,C ↔ A,G Polar requirement

  13. Beyond the Polar Requirement • Results: PAM Matrix Polar Requirement Far from overturning the adaptive hypothesis, this new study showed the canonical code to be even more optimized than previously thought!

  14. Other optimizations… • Studies of the assignment of stop codons found that the canonical code is highly optimized against frameshift and nonsense mutations. (S. Naumenko et al., 2008) • Furthermore, these same optimizations against frame shift errors allow the CC to be more efficient at encoding parallel information on top of a protein coding sequence. (Itzkovitz and Alon, 2007)

  15. Is the canonical code optimized? • YES! • But many aspects are still unclear – e.g. a mechanism for code selection. • Conditions in precanonical times are still relatively unknown and the canonical code seems to be universally adhered to in modern organisms.

  16. The End

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