The Optimality of the Genetic Code

General Academics Science, Technology and Medicine
1

The purpose of this thread is to discuss recent findings on the optimality of the genetic code.
Selected articles in the OP include the following:

[]Early Fixation of an Optimal Genetic Code
[]Evolution of the genetic code: partial optimization of a random code for robustness to translation error in a rugged fitness landscape
[]The genetic code is nearly optimal for allowing additional information within protein-coding sequences
[]An extension of the coevolution theory of the origin of the genetic code
[]Can the genetic code be mathematically described?
[]On the Hypercube Structure of the Genetic Code
[]Topological structure of the triplet genetic code
[]A Neutral Origin for Error Minimization in the Genetic Code.
[]Does codon bias have an evolutionary origin?
[]A chemical toolkit for proteins — an expanded genetic code
[/list]
Feel free to add more interesting articles as the discussion continues.
Article 1
Thus, to begin, in the first article it was determined by the researchers that:

[QUOTE]The Best of All Possible Codes?
When the error value of the standard code is compared with the lowest error value of any code found in an extensive search of parameter space, results are somewhat more variable. Estimates based on PAM data for the restricted set of codes indicate that the canonical code achieves between 96% and 100% optimization relative to the best possible code configuration (fig. 2c ). If our definition of biosynthetic restrictions are a good approximation of the possible variation from which the canonical code emerged, then it appears at or very close to a global optimum for error minimization: the best of all possible codes.
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No better codes out of a million biosynthetically restricted codes.
This conclusion might be misleading though (addressed here), as the paper states that the tested codes were from a biosynthetically restricted set based on the current hypothesis of the evolution of the genetic code from pre-biotic scenarios. When not viewed from this point of view, other, more optimized codes are possible.

The next article (nr 2) shows that:

[QUOTE]Thus, the standard genetic code appears to be a point on an evolutionary trajectory from a random point (code) about half the way to the summit of the local peak. The fitness landscape of code evolution appears to be extremely rugged, containing numerous peaks with a broad distribution of heights, and the standard code is relatively unremarkable, being located on the slope of a moderate-height peak.
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Thus showing in that analysis which include all possible codes (not only biosynthetically restricted codes) that the genetic code is partially optimal with regards to error minimization. It should be noted though that analysis only included a subset of the possible optimality feature of the code.

From article 3
The analysis above did not include other nearly optimal features of the genetic code including:
A) The actual code is far better than other possible codes in minimizing the number of amino acids incorporated until translation is interrupted after a frameshift error occurred.
B) The code is highly optimal for encoding arbitrary additional information, i.e., information other than the amino acid sequence in protein-coding sequences.

Thus, two more features for which the code is close to being optimal. What is interesting about these two optimal features is that they may facilitate evolution i.e. the code is primed for the future by being optimal in allowing future incorporation of additional information.

2

In article nr.4
The coevolution theory of the origin of the genetic code is discussed. The theory suggests that the genetic code is an imprint of the biosynthetic (biosynthetically restricted) relationships between amino acids.
A few interesting observations can be made:
Firstly, from the article.

[QUOTE]As will become clear in the following, I maintain that these amino acid-pre-tRNAs came directly from the biosynthetic pathways of the first six amino acids evolving along the biosynthetic pathways of energetic metabolism and that they were the first amino acids to be codified on these still evolving mRNAs.
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It should be noted that other exotic amino acids are also used by a few other codes (derived form the original). E.g. Selenocysteine and pyrrolysine are encoded for in many archaea and vertebrates. Archaea, however seem to be the most primitive organisms, thus these encoded amino acids must have been fixated early on.
Thus an interesting question can be applied to an “evolving” code as posited in the above quote:
Are these “still evolving” mRNAs, still evolving? Or did it hit an inevitable global optimum?

Secondly, from the article:

[QUOTE]While Wong [9] highlighted the precursor-product relationships between amino acids and their crucial role in defining the organisation of the genetic code, Miseta [10] clearly identified that the non-amino acid molecules that were precursors of amino acids might have been able to play an important role in organising the genetic code. Miseta [10] suggested the idea of an intimate relationship between molecules, the intermediates of glucose degradation, as precursors of precursor amino acids, and the organisation of the genetic code. This observation is also analysed by Taylor and Coates [11] who showed the relationship between the glycolytic pathway, the citric acid cycle, the biosyntheses of amino acids and the genetic code (Fig. 1) and, in particular, they point out that (i) all the amino acids that are members of a biosynthetic family tend to have codons with the same first base (Fig. 1) and (ii) that the five amino acids codified by GNN codons are found in four biosynthetic pathways close to or at the beginning of the pathway head (Fig. 1)[11]. More recently, Davis [12,13] has provided evidence that tRNAs descending from a common ancestor were adaptors of amino acids synthesised by a common precursor and he also discusses the biosynthetic families of amino acids, suggesting their importance in genetic code origin.
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Is it correct to assume that in the presence of the precursors of the standard genetic code (e.g. intermediates of glucose degradation and the citric acid cycle), the intimate relationship between these molecules resulted in the inevitable organization of the genetic code (global optimum of the system)?

Articles 5-7
These articles discuss fascinating mathematical representation of the genetic code. For example, in article 6 a representation of the genetic code as a six–dimensional Boolean hypercube is proposed.
Abstract:

[QUOTE]It is assumed here that this structure is the result of the hierarchical order of the interaction energies of the bases in codon–anticodon recognition. The proposed structure demonstrates that in the genetic code there is a balance between conservatism and innovation. Comparing aligned positions in homologous protein sequences two different behaviors are found:
a)There are sites in which the different amino acids present may be explained by one or two “attractor nodes” (coding for the dominating amino acid(s)) and their one–bit neighbors in the codon hypercube, and
b) There are sites in which the amino acids present correspond to codons located in closed paths in the hypercube. The structure of the code facilitates evolution: the variation found at the variable positions of proteins do not corresponds to random jumps at the codon level, but to well defined regions of the hypercube.
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3

Article 8:
In this article it once again discusses the optimality of the code and a few fascinating conclusions were made. For example:

[QUOTE]The genetic code has the remarkable property of error minimization, whereby the arrangement of amino acids to codons is highly efficient at reducing the deleterious effects of random point mutations and transcriptional and translational errors. Whether this property has been explicitly selected for is unclear. Here, three scenarios of genetic code evolution are examined, and their effects on error minimization assessed. First, a simple model of random stepwise addition of physicochemically similar amino acids to the code is demonstrated to result in substantial error minimization. Second, a model of random addition of physicochemically similar amino acids in a codon expansion scheme derived from the Ambiguity Reduction Model results in improved error minimization over the first model. Finally, a recently introduced 213 Model of genetic code evolution is examined by the random addition of physicochemically similar amino acids to a primordial core of four amino acids. Under certain conditions, 22% of the resulting codes produced according to the latter model possess equivalent or superior error minimization to the standard genetic code. These analyses demonstrate that a substantial proportion of error minimization is likely to have arisen neutrally, simply as a consequence of code expansion, facilitated by duplication of the genes encoding adaptor molecules and charging enzymes. This implies that selection is at best only partly responsible for the property of error minimization. These results caution against assuming that selection is responsible for every beneficial trait observed in living organisms.
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Also form the article:

[QUOTE]The SGC (Standard Genetic Code) has an EM (Error Minimization) value (see Methods for calculation) of 60.7. Ten thousand random codes have an average EM value of 74.5, and only 0.03% of these have equal or greater optimality than the SGC. These calculations once again illustrate the remarkable ‘optimization’ of the genetic code for EM.
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Thus, an important point is raised:

[QUOTE]The point should be made that explicit selection for EM seems to necessitate both the occurrence of codon reassignments and group selection to generate and select alternate codes. The proposal that explicit selection for the EM did not occur, and that EM arose neutrally from the addition of similar amino acids to similar codons, may be termed the ‘Nonadaptive Code’ Hypothesis, in contrast to the Adaptive Code Hypothesis. Finally, on a fundamental level, as a result of the analyses presented here, the presence of EM in the SGC may be used as evidence that enzymes, whether partially proteinaceous, RNA based, or based on some other macromolecule, were already extant during the evolution of the SGC.
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The article cautions on blithely using natural selection as an explanation for the features of the genetic code.

Article 9:
In this article, the functional integrity and how the architecture of the code relates to it is discussed.
From the article:

[QUOTE]The results put the concept of “codon bias” into a novel perspective. The internal connectivity of codons indicates that all synonymous codons might be integrated parts of the Genetic Code with equal importance in maintaining its functional integrity.
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Thus, the properties of the code allow it to maintain its own functional integrity.
Also form the article:

[QUOTE]The cumulative Codon Usage Frequency of any codon is strongly dependent on the cumulative Codon Usage Frequency of other codons belonging to the same species. The rules of this codon dependency are the same for all species and reflect WC base pair complementarity. This internal connectivity of codons indicates that all synonymous codons are integrated parts of the Genetic Code with equal importance in maintaining its functional integrity. The so-called codon bias is a bias caused by the protein-centric view of the genome.
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The maintenance of the integrity of the code is not dependent on selection, but dependent on internal variables (feedback system) for maintaining functional integrity. Again, showing another form of optimality.
Finally, in article 10:
Fascinating research was conducted whereby a sundry of different unnatural amino acids with novel three and four base codons have been selectively incorporated (engineered) into proteins yielding viable organisms.

An intriguing question arises from this research. It is easy to imagine these to arise through chance and selection (e.g. amino acids with photoaffinity) and then be incorporated into the standard code. Yet the code seems to remain stagnant. For billions of year after fixation, little evolution happened in the code. Why?
Did it arrive at a global optimum in a pre-existing fitness landscape, with a pre-existing fitness function?

The genetic code sure is interesting. Whatever the explanation for the origins of the code, whether intentional agency, only RV+NS, self-organization or a combination of these, the fact that these processes converged on a reasonably optimal code that is able to facilitate evolution makes it look like it was an inevitable result from the system. The system seems to be rigged and biased towards certain outcomes similar to the evolution of life.

4

Bollenbach et al. (2007) briefly describes a few of the optimal features (some described above) of the genetic code:
Evolution and multilevel optimization of the genetic code

[QUOTE]They (Itzkovitz and Alon) compared the actual genetic code with an ensemble of all other codes that are equally optimized with respect to mistranslation or mutation (for more on this statistical approach, see also Alff-Steinberger 1969; Haig and Hurst 1991; Freeland and Hurst 1998). Assuming that the usage frequencies of the different amino acids are fixed, while their codon assignments vary in the ensemble, they find that the actual code is far better than other possible codes in minimizing the number of amino acids incorporated until translation is interrupted after a frameshift error occurred. This new observation by Itzkovitz and Alon could therefore be seen as reviving the basis for Crick’s theory of a comma-less code, modified by the constraints imposed on the code by the need to be robust to other kinds of translation errors and mutations. Another possible interpretation of their result is that the amino acid usage has adjusted to reduce the effects of frameshift errors; alternative genetic codes would have had a different amino acid usage coadapted to them. It has been shown previously that amino acid usage is rather malleable, and, for example, influenced by GC content (Knight et al. 2001b).
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They briefly proceed to mention how it could have evolved:

[QUOTE] (1) the code has evolved under selection pressure to optimize certain functions such as minimization of the impact of mutations (Sonneborn 1965) or translation errors (Woese 1965a); Random mutation is a source of variability, yet selection pressure is believed to have selected for a system to put constraints on variability. Why?
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[QUOTE] (2) the number of amino acids in the code has increased over evolutionary time according to evolution of the pathways for amino acid biosynthesis (Wong 1975)
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Intriguing questions can arise from the above suggestions.

  1. Why was selection so strong in removing the other variants with fewer codons?
  2. Is there evidence of organisms using only 5, 6, 9, 13, 18 etc. amino acid codons? And why isn’t the code expanding to incorporate other codons when it is not even difficult to envision it happening, as it can contribute to fitness AND variety (See post #2, article #10).

The authors point this out:

[QUOTE]The discovery of variant codes (Barrell et al. 1979; Fox 1987; Knight et al. 2001a) made the connection between evolvability and universality even more puzzling. On one hand, they prove that the genetic codes can evolve; on the other hand, if they could easily evolve, why are all variations minor? It was recently proposed that extensive horizontal gene transfer during early evolution can account for both evolution toward optimality and the near universality of the genetic code (Vetsigian et al. 2006).
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Part of the answer lies in the code’s inherent capability of maintaining its own functional integrity that is independent of natural selection (post #article #9). Also, it is cautioned against blithely invoking natural selection as an explanation for the properties of the code (article #8).

The authors conclude:

[QUOTE]As we learn more about the functions of the genetic code, it becomes ever clearer that the degeneracy in the genetic code is not exploited in such a way as to optimize one function, but rather to optimize a combination of several different functions simultaneously. Looking deeper into the structure of the code, we wonder what other remarkable properties it may bear. While our understanding of the genetic code has increased substantially over the last decades, it seems that exciting discoveries are waiting to be made.
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Irrespective of its origin, the code seems to be optimized for evolution and maintain its own functional integrity.

5

Optimality of the genetic code with respect to protein stability and amino-acid frequencies

Background

The genetic code is known to be efficient in limiting the effect of mistranslation errors. A misread codon often codes for the same amino acid or one with similar biochemical properties, so the structure and function of the coded protein remain relatively unaltered. Previous studies have attempted to address this question quantitatively, by estimating the fraction of randomly generated codes that do better than the genetic code in respect of overall robustness. We extended these results by investigating the role of amino-acid frequencies in the optimality of the genetic code.
Results

We found that taking the amino-acid frequency into account decreases the fraction of random codes that beat the natural code. This effect is particularly pronounced when more refined measures of the amino-acid substitution cost are used than hydrophobicity. To show this, we devised a new cost function by evaluating in silico the change in folding free energy caused by all possible point mutations in a set of protein structures. With this function, which measures protein stability while being unrelated to the code’s structure, we estimated that around two random codes in a billion (10^9) are fitter than the natural code. When alternative codes are restricted to those that interchange biosynthetically related amino acids, the genetic code appears even more optimal.
Conclusions

These results lead us to discuss the role of amino-acid frequencies and other parameters in the genetic code’s evolution, in an attempt to propose a tentative picture of primitive life.

So… with regards to minimizing the consequences of translation errors on the 3D structure and stability of proteins, the natural genetic code is VERY optimal…

Only two random codes in a billion are fitter than the natural code.

6

A strange smell of ID hangs around this thread, not so?

7

In article 5, the question is asked:
Can the genetic code be mathematically described?

A few intriguing properties arose from the investigation. Including:
Parity coding
Palindromic symmetry
Binary coding
Error-correction mechanism based on parity checking

The author conclude:

[QUOTE]It remains striking, however, that different fundamental properties of the genetic code, such as degeneracy distribution, and also unexpected hidden properties, such as the palindromic symmetry and the parity marking of triplets presented here, reflect a strong mathematical order which is accurately described by means of one of the most elementary operations at the root of mathematics: number representation.
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8

Let’s just summarize how optimal the genetic code is:
1) No better codes out of a million biosynthetically restricted codes. (Freeland et al., 2000)
2) The actual code is far better than other possible codes in minimizing the number of amino acids incorporated until translation is interrupted after a frameshift error occurred. (Itzkovitz and Alon, 2007)
3) The code is highly optimal for encoding arbitrary additional information, i.e., information other than the amino acid sequence in protein-coding sequences. (Itzkovitz and Alon, 2007)
4) The genetic code is generally regarded as the biological element least capable of evolving (Vetsigian et al.,2006)
5) Out of all possible codes (not only biosynthetically restricted codes), the genetic code is partially optimal with regards to error minimization. The analysis only included a subset of the possible “optimality features” of the code.(Novozhilov et al., 2007)
6) Massey (2008) found that ten thousand random codes have an average Error Minimization value of 74.5, and only 0.03% of these have equal or greater optimality than the Standard Genetic Code. He suggests that selection is at best only partly responsible for the property of error minimization.
7) Biro (2008) have shown that the properties of the code allow it to maintain its own functional integrity.
8 ) The code has the following mathematical properties (Gonzalez, 2004):
Parity coding
Palindromic symmetry
Binary coding
Error-correction mechanism based on parity checking
9) Gilis et al., (2001) have shown that with regards to minimizing the consequences of translation errors on the 3D structure and stability of proteins, only two random codes in a billion are fitter than the natural code.

Now read this:
Collective evolution and the genetic code
Abstract:

A dynamical theory for the evolution of the genetic code is presented, which accounts for its universality and optimality. The central concept is that a variety of collective, but non-Darwinian, mechanisms likely to be present in early communal life generically lead to refinement and selection of innovation-sharing protocols, such as the genetic code. Our proposal is illustrated by using a simplified computer model and placed within the context of a sequence of transitions that early life may have made, before the emergence of vertical descent.
They propose the "Freezing Before Diversification" hypothesis whereby the universal optimal properties of the code was present in the Last Universal Communal Organisms. Other explanations were considered likely to be problematic.

Guess since the emergence of life, life had a genetic code that maintains its own functional integrity and is optimized for evolution as evidenced by the various optimal features of the code (see above).

9

How does this prove your god?

10

Mmm, looks like cut and paste is too easy these days. Let me try:
Please guys, this is not productive at all. Irreverend, you are quadruple posting and Mechanist you are mass cross posting without making any useful arguments. Please don’t reply to this post - start a new thread or PM/email privately.

11

split to http://forum.skeptic.za.org/flame-wars/re-the-optimality-of-the-genetic-code/

12

Artem S Novozhilov, Yuri I Wolf, and Eugene V Koonin Evolution of the genetic code: partial optimization of a random code for robustness to translation error in a rugged fitness landscape, Biol Direct. 2007; 2: 24.

Instead of analyzing just the restricted code set, they analyzed the full code set and found some interesting results

  1. The code fitness landscape is extremely rugged such that almost any random initial point (code) tends to its own local optimum (fitness peak).

  2. The standard genetic code shows a level of optimization for robustness to errors of translation that can be achieved easily and exceeded by minimization procedure starting from almost any random code.

  3. On average, optimization of random codes yielded evolutionary trajectories that converged at the same level of robustness as the optimization path of the standard code; however, the standard code required considerably fewer steps to reach that level than an average random code.

  4. When evolutionary trajectories start from random codes whose fitness is comparable to the fitness of the standard code, they typically reach much higher level of optimization than that achieved by optimization of the standard code as an initial condition, and the same holds true for the minimization percentage. Thus, the standard code is much closer to its local minimum (fitness peak) than most of the random codes with similar levels of robustness (Fig. 9).

(number) 2. is the clincher which shows that a genetic code from almost any initial condition could evolve to reach an optimum as good or better than the standard code.

So much for ‘best of all possible codes’

source

Given this, what does it mean: " Optimality of the Genetic Code"? What is the OP’s pupose with this thread?

13

Yes. Rather include all the findings and not just 4 ;).

1. The code fitness landscape is extremely rugged such that almost any random initial point (code) tends to its own local optimum (fitness peak).

  1. The standard genetic code shows a level of optimization for robustness to errors of translation that can be achieved easily and exceeded by minimization procedure starting from almost any random code.

  2. On average, optimization of random codes yielded evolutionary trajectories that converged at the same level of robustness as the optimization path of the standard code; however, the standard code required considerably fewer steps to reach that level than an average random code.

  3. When evolutionary trajectories start from random codes whose fitness is comparable to the fitness of the standard code, they typically reach much higher level of optimization than that achieved by optimization of the standard code as an initial condition, and the same holds true for the minimization percentage. Thus, the standard code is much closer to its local minimum (fitness peak) than most of the random codes with similar levels of robustness (Fig. 9).

  4. Principal component analysis of the between amino acids distance vectors indicates that the standard code is very different from the sets r (all random codes) and O (highly optimized codes produced by error cost minimization for random codes that are better than the standard code), and more similar to the codes from o (optimized random codes) and R (the robust subset of random codes). More importantly, the optimized code produced by minimization of the standard code is much closer to the set of optimized random codes (o) than to any other of the analyzed sets of codes.

  5. In this fitness landscape, it takes only 15–30 evolutionary steps (codon series swaps) for a typical code to reach the nearest local peak. Notably, the average number of steps that are required for a random code to reach the peak minus the number of steps necessary for the standard code to reach its own peak takes a random code to the same level of robustness as that of the standard code.

(number) 2. is the clincher which shows that a genetic code from almost any initial condition could evolve to reach an optimum as good or better than the standard code.
The clincher? For what? That the standard is not optimal for error minimization? That is nothing new though. Bare in mind they used one parameter... Error minimization. With regards to error minimization only, the fitness landscape looks like this:

http://www.pubmedcentral.nih.gov/picrender.fcgi?artid=2211284&blobname=1745-6150-2-24-9.jpg

Evolution of codes in a rugged fitness landscape (a cartoon illustration).r1, r2 ∈ r: random codes with the same block structure as the standard code. o1, o2 ∈ o: codes obtained from r1, r2 ∈ r: after optimization. R1, R2 ∈ R: random codes with fitness values greater than the fitness of the standard code. O1, O2 ∈ O: codes obtained from R1, R2 ∈ R: after optimization.

The fitness landscape for other parameters might (probably does) look different.

You must be refering to article 1 which used a set of codes that were biosynthetically restricted based on the current hypothesis of the evolution of the genetic code from pre-biotic scenarios. With regards to that aspect, the SGC is “very close to a global optimum for error minimization: the best of all possible codes.”

From the OP:
The purpose of this thread is to discuss recent findings on the optimality of the genetic code.

The article you cited is mentioned in the OP (under article 1).

14

I know. That is where I found it. So now I ask again, given that The standard genetic code shows a level of optimization for robustness to errors of translation that can be achieved easily and exceeded by minimization procedure starting from almost any random code; what does it mean to talk about the “optimal code”? It seems redundant to say the least.

15

Err… There is more than 1 parameter to discuss ;). And we are discussing the optimality of the code, not the “optimal code”.

16

Okay, it is pretty optimal. Any other code would also have been optimal or even more optimal that the one we do have. Now what?

17

You are only taking one parameter into consideration… Error minimization.

18

So for which paramter(s) is this code and only this code optimal?

19

There are many parameters and you are welcome to scour the literature (including the OP) for an answer. Ideally you want to know the optimality of the code by considering all the known parameters (not just 1). At present this is unknown mainly because the actual primordial evolution is uncertain and there is no real understanding of the code origin (yet).

But, the code is pretty optimal for a variety of parameters and it is pretty stable (read here).

20

I’m not interested in doing that. And that you think forums are geared towards that sort of thing is problematic.

Ideally you want to know the optimality of the code by considering all the known parameters (not just 1). At present this is unknown mainly because the actual primordial evolution is uncertain and there is no real understanding of the code origin (yet).
Ideally I want to understand what it means to talk about an optimal code and the reason for this thread.
But, the code is pretty optimal for a variety of parameters and it is pretty stable (read [url=http://forum.skeptic.za.org/science-and-technology/the-optimality-of-the-genetic-code/msg5683/#msg5683]here[/url]).
Yeah and other codes would have been as well? At least for one of the many parameters required. What does that mean?