# Evolution of the Redundancy of the Genetic Code

We are searching data for your request:

Forums and discussions:
Manuals and reference books:
Data from registers:
Wait the end of the search in all databases.
Upon completion, a link will appear to access the found materials.

In short

Looking at the genetic code, it appears that most redundancy is on the third letter rather than on the first or the second letter of the codon. Why has it evolved this way?

Longer version

In order to compare the relative redundancy accounted by each letter of the codon, let's assume that that every codon occurs at equal frequency. It is probably wrong but useful for the sake of the calculations. Using observed frequencies of codon usage in a given population would change the following probabilities but the question of why some positions in the codon has more redundancy than some others still hold.

A substitution of the first letter of the codon has a probability of \$frac{1}{2048}≈0.00005\$ (Stop codon) to be synonymous. A substitution of the second letter of the codon has a probability \$frac{3}{256}≈0.012\$ (nucleobases U and G) to be synonymous. A substitution of the third letter has a probability of exactly \$frac{2}{3}\$ to be synonymous.

Probability of a substitution to be synonymous given that it occurred on the…

• First letter: \$frac{1}{2048}≈0.00005\$
• Second letter: \$frac{3}{256}≈0.012\$
• Third letter: \$frac{2}{3}\$

Why are there more redundancy on the third position than on the second (which has more redundancy than the first position) in the codon?

To fully comprehend the concept of wobble base-pairing we need to consider the nucleotide sequences of the anti-codons in the tRNAs that have to "read" the genetic code when the mRNA is being translated on the ribosome. The nucleotide in the anti-codon's wobble position is, for example, often inosine. Under the rules for wobble base-pairing an Inosine can potentially base-pair with three other nucleotides.

In real terms that means a cell can use less than 63 unique tRNA genes to decode mRNAs carrying messages made of the 63 different "words" (codons).

In an active cell the Ribosome's A-site, where the charged tRNA binds, is occupied by the incorrect tRNA most of the time (based on the law of mass action where any charged tRNA can randomly diffuse into the binding site). With tRNAs that can recognize multiple codons (which is what the wobble hypothesis gets us), any given protein can be translated faster (assuming that correct charged tRNAs are limiting for the polypeptide polymerization reaction).

So those are the practical ramifications of the table that you presented, but the explanation, as for most why based questions about biological evolution, is a retrofit. Natural selection can only work with the materials at hand, and so we can infer that during the period when this genetic code was finalized that the organisms who used it were more successful than the others. And the current code is based on whatever the previous one looked like. "Descent by modification" is the original description.

[whoops, sorry about the pedantic voice on the "how selection works" bit, I just looked at your profile and realized you can likely teach me on this subject]

## On the Evolution of Redundancy in Genetic Codes

We simulate a deterministic population genetic model for the coevolution of genetic codes and protein-coding genes. We use very simple assumptions about translation, mutation, and protein fitness to calculate mutation-selection equilibria of codon frequencies and fitness in a large asexual population with a given genetic code. We then compute the fitnesses of altered genetic codes that compete to invade the population by translating its genes with higher fitness. Codes and genes coevolve in a succession of stages, alternating between genetic equilibration and code invasion, from an initial wholly ambiguous coding state to a diversified frozen coding state. Our simulations almost always resulted in partially redundant frozen genetic codes. Also, the range of simulated physicochemical properties among encoded amino acids in frozen codes was always less than maximal. These results did not require the assumption of historical constraints on the number and type of amino acids available to codes nor on the complexity of proteins, stereochemical constraints on the translational apparatus, nor mechanistic constraints on genetic code change. Both the extent and timing of amino-acid diversification in genetic codes were strongly affected by the message mutation rate and strength of missense selection. Our results suggest that various omnipresent phenomena that distribute codons over sites with different selective requirements—such as the persistence of nonsynonymous mutations at equilibrium, the positive selection of the same codon in different types of sites, and translational ambiguity—predispose the evolution of redundancy and of reduced amino acid diversity in genetic codes.

This is a preview of subscription content, access via your institution.

## The Institute for Creation Research

&ldquoNewspeak&rdquo was the language developed by the fictional totalitarian regime Oceania in George Orwell&rsquos classic novel 1984. The regime redefined words and slogans as a means of thought control over its citizens. Often, Newspeak words meant the exact opposite of the &ldquoOldspeak&rdquo vocabulary. Citizens&rsquo thinking eventually became characterized by contradictory beliefs that were embraced simultaneously, a practice known as &ldquodoublethink.&rdquo For instance, the Ministry of Truth produced Newspeak and fabricated history-altering propaganda. And in the Ministry of Love, people were tortured for committing &ldquothoughtcrimes&rdquo such as individualism and independent thinking. Orwell&rsquos novel cautions us against being fooled by cunning misapplication of words or deceptive redefinitions.

The answer to this question may seem obvious. Obvious, that is, if you think in Oldspeak, but elusive if you think in Evolutionspeak. When evolutionists claim that a biological feature is degenerate, do they mean that it is degraded, superfluous, redundant, or a defining characteristic of biological complexity? Apparently, any of these, depending on the evolutionist and the particular conclusions he or she is trying to draw.

A research paper from the 1970s described the genetic code as &ldquoa universal, highly degenerate, three-letter code&rdquo. 1 For reference, a three-letter code, also called a codon, is a group of three bases of DNA that specify a single amino-acid building block for a protein. DNA bases are also referred to as nucleotides.

A more recent paper exploring the evolution of genes states, &ldquoBecause there are much more different codons than coded amino acids, the genetic code is called degenerate. Since the discovery of the genetic code&helliphow it is degenerated is one of the most fascinating problems of genetics.&rdquo This fascinating problem has evolutionary implications: &ldquoThe hypotheses trying to explain the evolution of the genetic code can be divided into two groups [mechanistic and random].&rdquo 2 The authors advocate the random hypothesis and describe how the alleged evolution and degeneracy of the genetic code developed together.

What might a biology undergraduate student learning about DNA make of these characterizations of genetic degeneracy? Evolutionists believe that genetic variety is mindlessly fractionated between organisms in a deadly struggle for life. Destruction associated with a struggle for life may fit the primary definition of &ldquodegenerate&rdquo in dictionaries like Merriam-Webster:

1 a: having declined or become less specialized (as in nature, character, structure, or function) from an ancestral or former state b: having sunk to a condition below that which is normal to a type especially: having sunk to a lower and usually corrupt and vicious state c: degraded. 3

The student could, therefore, readily interpret genomic degeneracy according to its common meaning&mdashdegradation.

But the student may be pushed to another evolutionary meaning of degenerate. Because there are more codons than coded amino acids, degeneracy might support evolution if some of the codons (or one of the three nucleotides making a codon) were superfluous. This understanding aligns with reports claiming that one in 200 human genes is &ldquononsense.&rdquo A lead researcher stated, &ldquoOur study suggests that overall, gene loss has not been a major evolutionary force: our genome does not seem to be in a hurry to get rid of these &lsquosuperfluous&rsquo genes.&rdquo 4 Since living things supposedly evolve through the inefficient survival-of-the-fittest process, degeneracy could result from DNA being &ldquocobbled together&rdquo 5 through &ldquoevolution as a &lsquotinkerer,&rsquo building new machines from salvaged parts.&rdquo 6 Alleged unessential leftovers junking up the DNA are therefore declared to be evidence of evolution&hellipand certainly not indicators of good design.

As the student gathers yet more background information on the evolution of the genetic code, he may be surprised that in yet another report degeneracy does not mean superfluous, or functionally redundant, or degraded, but actually &ldquodegeneracy is a ubiquitous biological property&rdquo that researchers argue &ldquois a feature of complexity at genetic, cellular, system, and population levels.&rdquo 7 This novel evolutionary-based usage affirms,

Degeneracy, the ability of elements that are structurally different to perform the same function or yield the same output&hellipis both necessary for, and an inevitable outcome of, natural selection. 7

It seems that evolutionists can invoke degeneracy to bolster their favored concept.

To find a way through all of the Evolutionspeak on genomic degeneracy, the student can turn to actual studies of function for the three nucleotides in a codon. These studies point to another major evolutionary blunder since all of these possible evolutionary understandings of degeneracy are not supported by the science.

Degeneracy, in Evolutionspeak, Is Stunningly Wrong

A detailed literature review in 2014 found that even if different codons prescribed the same amino acid in a protein, the codon differences still mattered in how the protein was made. The final folding shape of proteins is vital to their function. David D&rsquoOnofrio and David Abel documented that the DNA and its corresponding RNA sequence carried information not only for the proper amino acid sequence but also to control the timing of its folding. They &ldquodemonstrate that this TP [translational pausing] code is programmed into the supposedly degenerate redundancy of the codon table.&rdquo 8 What this means is that the code of differing codons, even if they specify the same amino acid, still supplies important information, information that &ldquopurposely slows or speeds up the translation-decoding process&hellip.Variable translation rates help prescribe functional folding of the nascent protein. Redundancy of the codon to amino acid mapping, therefore, is anything but superfluous or degenerate.&rdquo 8

A recent experiment again shows that the specific nucleotides in a codon do matter. Mutations to a codon that do not change the protein-coding sequence are called synonymous. The consensus view was, &ldquoUntil recently, most biologists believed that so-called silent mutations, created by &lsquosynonymous&rsquo DNA changes&mdashthose that do not affect the protein-coding sequence&mdashhad very weak effects on the evolution of organisms.&rdquo But this long-term experiment with bacteria found &ldquothat single highly beneficial synonymous mutations can allow organisms to rapidly evolve and adapt to their environment.&rdquo 9 Another &ldquointeresting phenomenon&rdquo was that bacteria with different codons initially, when faced with the same challenges, seemed to converge on the same changes. Researchers found that &ldquofurthermore, these mutations occurred at single points within the gene, were highly beneficial, and they seemed to recur in multiple experiments.&rdquo 9

A nucleotide order that specifies rapid, repeatable, and useful adjustments to changed conditions does not sound like the serendipitous side effect of random &ldquosilent mutations&rdquo but rather speaks loudly of the designed outcome of intentional planning. The genomic code is not degraded or superfluous. It is also clear that structurally different elements that specify a common element do not necessarily yield the same output. Observations like these prompted D&rsquoOnofrio and Abel to conclude, &ldquoThe functionality of condonic [sic] redundancy denies the ill-advised label of &lsquodegeneracy.&rsquo&rdquo 10

Corollaries to the &ldquoDegenerate Genome&rdquo Blunder

Commenting on wide-ranging ramifications of D&rsquoOnofrio and Abel&rsquos work, Casey Luskin made an insightful observation regarding how it relates to the conclusions of many other evolutionary studies. He observes,

Seeking to infer the activity of natural selection, evolutionary biologists statistically analyze the frequency of synonymous (thought to be functionally unimportant) and nonsynonymous (thought to be functionally important) codons in a gene&hellip.As the thinking goes, if synonymous codons are functionally unimportant, then three conclusions may follow: a bias toward synonymous codons implies purifying selection in the gene, a bias towards nonsynonymous codons implies positive selection, and an equal balance implies neutral evolution (no selection). But if synonymous codons can have important functional meaning, then the whole methodology goes out the window, and hundreds of studies that used these methods to infer &ldquoselection&rdquo during the supposed &ldquoevolution of genes&rdquo could be wrong. 11

Aside from the science showing that the genome is not &ldquodegenerate&rdquo in any evolutionary sense, there is another&mdashmore important&mdashlesson made evident by this blunder. It pertains to Evolutionspeak within evolutionary literature. This lesson flows from the ambiguous usage of words coupled with novel definitions that essentially oppose their primary meaning. That is conceptually misleading, and it&rsquos possible that this Evolutionspeak could produce the same effect as Newspeak. Orwell was concerned about misleading definitions used by powerful institutions to impose big lies on those under their control. He illustrated this in 1984 with the Party&rsquos oft-repeated mantra &ldquowar is peace, ignorance is strength, freedom is slavery.&rdquo

In scientific literature, metaphors, analogies, and anthropomorphisms abound. Some are useful in bringing clarity. However, cross-definitions, false analogies, or applying a word to something that its definition could never support can be misleading. This practice is highly detrimental to science, which is structured on precise language and clarity. We must be on guard to make sure Evolutionspeak doesn&rsquot creep into and warp our own way of thinking about science.

The Genetic Code Is a Design Marvel

As noted above, repeatability is found in synonymous changes enabling bacteria to consistently overcome challenges. Repeatability is not a hallmark of chance outcomes but is suggestive that this result is due to designed mechanisms.

It is also telling how Gerald Edelman and Joseph Gally recognize that backup, or functionally redundant, systems are indicative of design. However, their worldview not only precludes any consideration of that conclusion but also shapes their choice of vocabulary in conveying their thoughts to others&mdashi.e., Evolutionspeak. They astutely note:

The contrast between degeneracy and redundancy at the structural level is sharpened by comparing design and selection in engineering and evolution, respectively. In engineering systems, logic prevails, and, for fail-safe operation, redundancy is built into design. This is not the case for biological systems. Indeed, not the least of Darwin&rsquos achievements was to lay the argument by design to rest. 12

Thus, they believe &ldquothe term &lsquodegeneracy&rsquo is more apt than &lsquofunctional redundancy.&rsquo&rdquo 12

When humans can identify the true source of fail-safe redundancy, it always is an indicator of good design and a good designer. Given that, redundancy of a code embedded in another code reveals great design. D&rsquoOnofrio explains, &ldquoRedundancy in the primary genetic code allows for additional independent codes&hellip.We have shown a secondary code superimposed upon the primary codonic prescription of amino acid sequence in proteins.&rdquo 13

Geneticist Dr. Jeffrey Tomkins of the Institute for Creation Research summarizes that &ldquowe are only beginning to decipher the true complexity of these different genetic languages,&rdquo but we do know that &ldquofor the genome to function in all its complexity, many different codes and languages are used, and they all mesh and work interactively with one another&hellip.These highly complex language systems speak directly to a Creator of infinite wisdom and capabilities.&rdquo 14 How true. For by the Lord Jesus &ldquoall things were created that are in heaven and that are on earth, visible and invisible&rdquo (Colossians 1:16).

## Genetic Code Complexity Just Tripled

A codon is a triplet of three nucleotides in DNA. Genes are read in these triplet codons, each one standing for an amino acid or a “punctuation” mark as the gene gets translated (61 of the 64 possible triplets actually code for amino acids the others work as “start” and “stop” codons). This much we’ve known since the 1960s. Now, however, two scientists from the University of Utah want to complicate matters further.

An article at Phys.org explains:

The so-called central dogma of molecular biology states the process for turning genetic information into proteins that cells can use. “DNA makes RNA,” the dogma says, “and RNA makes protein.” Each protein is made of a series of amino acids, and each amino acid is coded for by sets of “triplets,” which are sets of three informational DNA units, in the genetic code.

University of Utah biologists now suggest that connecting amino acids to make proteins in ribosomes, the cell’s protein factories, may in fact be influenced by sets of three triplets – a “triplet of triplets” that provide crucial context for the ribosome. [Emphasis added.]

It sounds like a wild idea, but it was just published in the Proceedings of the National Academy of Sciences. What could be the impact of this “Case for the genetic code as a triplet of triplets”?

Neo-Darwinian evolution is supposed to work by mutating DNA codons, either making them “synonymous” with the prior codon (i.e., yielding the same amino acid), or “non-synonymous” (i.e., putting a different amino acid in its place, potentially affecting the resulting protein). If codons could be treated as independent entities acted on by natural selection, Darwinians at least understood the challenge before them. If these researchers are correct, the stakes just skyrocketed.

We had already learned that synonymous codons are not truly identical, even if the same amino acid gets into the final protein. Like English synonyms with different shades of meaning, synonymous codons can affect the genetic language. They can do this by affecting the rate of transcription, the modifications needed on the transfer RNA (tRNA), or the rate of translation (see “A Genetic Snooze Button” here). That finding provided a reason for the redundancy in the genetic code, where one amino acid can be represented by one to six different triplet codons. This new paper agrees:

Synonymous changes will lead to differences in translation rates that, especially when different tRNAs are used, have different binding efficiencies, abundances, and charging rates and result in differential mRNA stabilities. In addition, the same tRNA reads different codons with different efficiencies, as was determined in an in vivo translational speedometer assay system.

Finding this extra functionality in this redundancy in the genetic code (called “degeneracy”) was interesting enough, but now evolutionists are faced with a new challenge. The authors of the PNAS paper, Hughes and Chevance, describe what drove them to examine the context for each triplet codon. They were playing with the genes for a component of the bacterial flagellum named FlgM when they noticed something interesting:

Changing the codon on one side of the defective codon resulted in a 10-fold increase in FlgM protein activity. Changing the codon on the other side resulted in a 20-fold decrease. And the two changes together produced a 35-fold increase. “We realized that these two codons, although separated by a codon, were talking to each other,” Hughes says. “The effective code might be a triplet of triplets.”

To understand the genetic code, if this is true, geneticists are going to have to understand the context of each triplet. Each codon could be affected by its flanking codons, creating vastly different outcomes in terms of gene activity.

We conclude that codon recognition is initiated by codon–anticodon hydrogen bonding between the first and second bases of the translated codon followed by sensing the correct fit at the wobble base and base-stacking interactions contributed by the preceding two codons and bound tRNAs.

The “triplet of triplets” problem helps explain why you can’t easily get the same expression pattern by substituting a plant or animal protein in a bacterium, a lab procedure called heterologous expression. One doesn’t just tinker with a particular codon and expect to get the same result in a different organism that has a different expression context. The particular codon used affects downstream factors, including tRNA modifications, which the authors say are extensive in every organism.

Modification of tRNA species in E. coli also has been shown to vary with the growth phase of the cell. Specific codon-context effects could represent translation domains of life based on tRNA modifications.

We begin to see what this could do to neo-Darwinian theory. The authors don’t go into detail, but they issue an ominous warning in the paper’s final paragraph:

The difficulty for natural selection would be in finding codon optimization for a given gene. If the speed through a codon is dependent on the 5′ and 3′ flanking codons, and the flanking codons are dependent on their 5′ and 3′ flanking codons, then selection pressure on a single codon is exerted over five successive codons, which represent 61 5 or 844,596,301 codon combinations. If modified tRNAs interact with bases in a codon context-dependent manner that differs among species depending on differences in tRNA modifications, ribosome sequences, and ribosomal and translation factor proteins, it is easy to understand why many genes are poorly expressed in heterologous expression systems in which codon use is the primary factor in the design of coding sequences for foreign protein expression. The potential impact of differences in tRNA modifications represents a significant challenge in designing genes for maximal expression whether by natural selection or in the laboratory.

The paragraph on the “significance” of the hypothesis states the challenge succinctly:

“Data presented here support a model in which the evolutionary selection pressure on a single codon is over five successive codons, including synonymous codons.

The more that natural selection has to “think” about (if you’ll pardon the expression), the less able it will be to get things right. More accurately, it’s going to take a lot more of what David Berlinski calls “sheer dumb luck” to find a beneficial change. If there are 844,596,301 codon combinations to worry about, it’s like having to get many more numbers right in Powerball than you thought when you bought your lottery ticket. This is what they imply:

The tRNA modifications vary throughout the three kingdoms of life and could affect codon–anticodon pairing. The differences in tRNA modifications could account for differences in synonymous codon biases and for the effects of codon context (the ability to translate specific triplet bases relative to specific neighboring codons) on translation among different species. Here, using in vivo genetic systems of Salmonella, we demonstrate that the translation of a specific codon depends on the nature of the codons flanking both the 5′ and 3′ sides of the translated codon, thus generating higher-order genetic codes for proteins that can include codon pairs and codon triplets.

It will be interesting to see how this hypothesis plays out. One immediate impact will be on research concerning genetic diseases. The triplet-of-triplets coding scheme might explain why mouse models of disease treatments don’t always translate well into human trials: the context is different.

## Molecular switches

One of the most intriguing examples of genetic redundancy is found in the SRC gene family. This family comprises a group of eight genes that code for eight distinct proteins all with a function that is technically known as tyrosine kinase. SRC proteins attach phosphate groups to other proteins that contain the amino acid tyrosine in a specific amino acid context. The result of this attachment is that the protein becomes activated it is switched on, and can hence pass down information in a signalling cascade. Four closely related members of the family are named SRC, YES, FYN and FGR, and the other related members are known as BLK, HCK, LCK and LYN. Both families are so-called &lsquonuclear receptors&rsquo, and transmit signals from the exterior of the cell to the nucleus, the operation centre where the information present in the genes is transcribed into messenger RNA. The proteins of the SRC gene family operate as molecular switches that regulate growth and differentiation of cells. When a cell is triggered to proliferate, tyrosine kinase proteins are transiently switched on, and then immediately switched off.

The SRC gene family is among the most notorious genes known to man, since they cause cancer as a consequence of single point mutations. A point mutation is a change in a DNA sequence that alters only one single nucleotide&mdasha DNA letter&mdashof the entire gene. When the point mutation is not on a silent position, it will cause the organism&rsquos protein-making machines to incorporate a wrong amino acid. The consequence of the point mutation is that the organism now produces a protein that cannot be switched off. Mutated SRC genes are of particular danger because they will permanently activate signalling cascades that induce cell proliferation: the signal that tells cells to divide is permanently switched on. The result is uncontrolled proliferation of cells&mdashcancer. The growth-promoting point mutations cannot be overcome by allelic compensation because a normal protein cannot help to switch off the mutated protein.

Despite the SRC protein being expressed in many tissues and cell types, mice in which the SRC gene has been knocked out are still viable. The only obvious characteristic of the knockout is the absence of two front teeth due to osteoporosis. In contrast, there are essentially no point mutations allowed in the SRC protein without severe phenotypic consequences. Amino acid changing point mutations in most, presumably all, of the SRC genes can lead to uncontrolled cellular replication. 1 Knockout mice models have been generated to reveal the functions of all the members of the SRC gene family. Four out of eight knockouts did not have a detectable phenotype. Despite their cancer-inducing properties, half of the SRC genes appear to be redundant. Standard evolutionary theory tells us that redundant gene family members originated through gene duplications. Duplicated genes are truly redundant and as such they are expected to reduce to a single functional copy over time through the accumulation of mutations that damage the duplicated genes. Such mutations can be frame-shift mutations that introduce premature stop signals, which are recognized by the cellular translation-machines to terminate protein synthesis. The existence of the SRC gene family has been explained as follows:

This scenario implies that the SRC genes are destined to reside in the genome forever. Point mutations that immediately kill raise an intriguing origin question. If the SRC genes are really so potently harmful that point mutations induce cancer, how could this extended gene family come into existence through gene duplication and diversify through mutations in the first place? After the first duplication, neither of the genes is allowed to change because it will invoke a lethal phenotype and kill the organism through cancer. Amino acid changing mutations in the SRC genes will permanently be selected against. The same holds true for the third, fourth and additional gene duplication. New gene copies are only allowed to mutate at neutral sites that do not replace amino acid in the protein. Otherwise the organism will die from tumours. Because of this &lsquopurifying&rsquo selection mechanism, the duplicates should remain as they are. Yet the proteins of the SRC family are distinctly different, only sharing 60&ndash80% of their sequences.

Reviewer 1: David Ardell, The Linnaeus Centre for Bioinformatics, Uppsala University

With this manuscript Novozhilov et al. bravely enter the contentious field of modelling the evolution of the genetic code. Novozhilov et al. have contributed some original approaches, concepts and techniques to the field in this work. Although the details of the method are omitted, they convincingly use linear programming to address the question of which amino acid cost matrix would minimize the cost of mistranslation assuming a fixed pattern of translational misreading error. Having solved this problem they then apply their solution to estimate which codon assignments are most deleterious in the standard code. Their conclusion that the placement of arginine is exceedingly maladaptive echoes several earlier uncited works, although their have reached this conclusion by original means. The uncited earlier works include Tolstrup et al. (1994) JMB 243:816, who showed that an artificial neural network trained to learn the standard code segregates amino acids in its internal representation in groups that perfectly correspond to a measure of their hydrophilicity with the exception of arginine. Tolstrup et al. themselves cite earlier work indicating the misfitting assignment of arginine, including Swanson(1984) Bull. Math. Biol. 46:187, Taylor and Coates, and T.H. Jukes (1973) Nature 246:22 who discussed evidence that arginine was a late addition to the genetic code. Finally, arginine is the only amino acid for which strong evidence has been made of a stereochemical association of an amino acid with its codons (Knight and Landweber(2000) RNA 6(4):499).

Authors' response: We appreciate Ardell pointing out this earlier work concerning arginine's position cited in the revision.

I also appreciated the application of principal components analysis on representations of genetic codes based on the "codon distances" of amino acids. This is a nice way to measure genetic code similarity in the face of the large equivalence classes of genetic codes under the cost metric that they used (for instance, swapping the two purines or the two pyrimidines in either the first or second codon positions or both, while holding the amino acid assignments fixed, will yield codes with the same cost).

Finally, in terms of incremental improvements to the field, the authors promote an original model for the space of possible genetic codes, and an original mechanism of genetic code change. Their conclusion that the genetic code is a partially optimized random code, is appealing and not controversial to me, although I am quite sure it will continue to be controversial (and perhaps ignored) by others.

However, I take issue with some assumptions and lines of reasoning in this work, which I now outline in decreasing order of relevance to its overall impact:

1. Have the authors meaningfully analyzed a fitness landscape and plausible evolutionary trajectories of genetic codes? This would require adequate measures of 1) fitness and 2) a mechanism of evolutionary change. The authors repeatedly confuse the distinction between "cost" and "fitness" throughout this paper, despite pointing out the distinction themselves at one point in the paper. They also rightly conclude that a true treatment of fitness requires consideration of the population of genes that the genetic code is translating. The important point they neglect is that these genes will also influence how genetic codes can change. Because of this evolutionary constraint that genes place on genetic codes, the likelihood of a swap of amino acids or anticodons between two alloacceptor tRNAs is virtually nil, especially after translation evolved to be accurate enough, so that 20 amino acids could be translated consistently. This precondition is necessary for such a swap (as modelled) to even be meaningful. Perhaps that is why we see no evidence of such radical variation in genetic codes on Earth today. The fact that fitness is not adequately measured in this work and the way that codes change is misrepresented, leaving the basis of their conclusions in doubt. To this I may add that the ruggedness of the cost landscape that they describe is an inevitable consequence, at least in part, of the aforementioned symmetries in the cost metric that they used (leading to equivalence classes of codes as mentioned previously).

On the other hand, the authors' postulated mechanism of swaps of amino acids between pairs of codon blocks is adequate to show that the standard code is sub-optimal, although this has been shown before by others.

Authors' response: There are two distinct points in this comment. One is the alleged confusion between costs and fitness. On this count, we plead not guilty. The cost is defined unequivocally, and the inverse relationship between this cost and fitness is explained right after this definition. In the rest of the text, we speak of reduced cost in the more technical sense and of increased fitness where it comes to a more biologically oriented discussion. We believe this creates clarity rather than confusion.

The second point is that the model of code evolution might not be realistic. Here, we plead guilty. The model is deliberately oversimplified to allow straightforward conclusions on the relationships between the standard code and various random codes, and we emphasize this in the revised Discussion.

2. Is the block structure of the standard genetic code inevitable? There are two components to the block structure: the number of codons assigned to each amino acid and the clustering of redundant codons by the third codon positions. Certainly the "wobble" rules in the third codon position might reasonably be assumed invariant throughout the history of the code. But different tRNA isoacceptors may be altered in their reading capacity through mutations and modifications of their first anticodon bases, i.e. changed in which wobble rule they use. Furthermore, extant altered genetic codes vary in the number of codons assigned to different amino acids. Our own earlier claim (Ardell and Sella, 2001) not withstanding, there is clear evidence in extant life that certain amino acids have most likely inherited or invaded codons from others, thus neither the block structure of the genetic code nor its amino acid expressivity has been invariant throughout its evolution.

Authors' response: In the revised version of the manuscript, we added a caveat emptor (in the discussion section) where we emphasize that we, essentially, explore a toy model of the code's evolution that ignores the expansion of the number of amino acids and involves only codon series swaps. The gist of this paper is the determination of the place of the standard code in the code space, in relation to various classes of random codes, and we believe that, in this respect, the model we employ is adequate.

3. Using the code itself to decide among different measures of the cost of amino acid replacements, or to infer the nature of translational error, without other evidence, is fallacious. Especially considering the author's own conclusions that the code is non-optimal.

Authors' response: We do not actually use the code itself to decide among cost measures. It is another matter that some such measures (e.g., PAM or Blosum matrices) are themselves dependent on the code and therefore hardly appropriate. We do not believe there is anything fallacious in this.

4. Even though it is widely used, the cited experimental justification for the translational misreading probability scheme in equation 2 is weak, especially in that translational misreading is more transition-biased in misreading of the second codon position than in the first. The data are extremely limited on these points! The cited references are: Friedman and Weinstein, 1966, Woese, 1965 and Parker 1989. In the first reference, only the data translating poly-U are directly interpretable (the poly-UG data has as its highest incorporation Phenylalanine, demonstrative that the mRNA was a random copolymer). Their data (Table 2, page 990) does show a transition-bias of misreading of this one codon as the overall rate of error is increased. But there is no evidence that this bias is greater in the second codon position than the first codon position. In contrast, the data reviewed by Woese (1965) for poly-U show no sign of transition biased misreading in the first codon position at all, but a sign of it in the second codon position. Therefore, the data from these two sources are inconsistent. Furthermore, they arefor only one codon, and Woese writes that the pattern of misreadingof other codons that could be assayed at that time was very different. Importantly, even very recent studies of translational misreading either experimentally in vitro (E. Bouakaz et al. http://publications.uu.se/abstract.xsql?dbid=6324) or using molecular dynamics simulations (Almlöf et al. (2007) Biochemistry 46:200) center only on the UUU or UUC codons. All authors agree that more studies are necessary with other codons to generalize conclusions. Parker's review of in vivo misreading rates (Table 1, page 277) in no way allow the reader to draw general conclusions regarding the form of translational misreading errors in the different codon positions, other than the general position effect.

On the other hand Kramer and Farabaugh's recent work (cited here in this paper) do demonstrate a greater transition-bias in misreading in position 2 than position 1, in vivo, of all possible one-mutant neighbors to the lysine codon AAA. Nonetheless, this raises the following two questions for me: 1) what translation system under which conditions is the best experimental model for the primordial translation systems under which the genetic code evolved? and 2) Are the highly evolved translation systems studied today biased by actually having co-adapted to the genetic code, so that error frequencies are greatest where costs of errors are weakest?

Authors' response: In the revised manuscript, we are more cautious about the differences in the transition bias between codon positions. The questions asked by Ardell are interesting and relevant. Like he, we currently have no definitive answers.

As a general point, this paper would benefit very much from separating materials and methods from the results for clarity. In many turns the paper is well written, but in other ways combining M&M and Results makes the paper badly organized and forces the reader to piece together important details of how the work was done from scattered sections of the paper. For example, only incidentally can the reader learn how many different genetic codes were actually analyzed in evaluation of the 4 sets o, r, O and R.

Authors' response: We disagree regarding the amalgamation of M&M and Results. We initially attempted to write the paper in a more traditional manner but found that, in this case, the main methodological approaches were virtually inseparable from the results. The numbers of evaluated codes are now indicated explicitly.

(p. 9 and elsewhere): Although we (Ardell, 1998, Sella and Ardell,2001, Ardell and Sella 2001, 2002) have shown that 1) mathematical forms such as your eq. 1 are minimized by pairing large terms of p (.|.) with d(. ), and that 2) codes that imply such pairings are indeed more fit in certain population genetic models, it is only inviting confusions and misunderstanding for the authors' to use the term "fitness" to describe the quantity being optimized. This point is correctly touched on in the paper, but then treated misleadingly elsewhere. May I suggest to call it what it is, which is "cost"?

Authors' response: already addressed above. In general, we do not see conflation of costs and fitness.

Please detail, regarding software used, how the linear programming problem was solved for reproducibility. Why not provide source code in supplementary methods?

Authors' response: The linear programming problem was solved with a standard routine LPSolve presented in Optimization package of Maple 9.5.

Reviewer 2: Allan Drummond, Harvard University (nominated by Laura Landweber)

Review of "Evolution of the genetic code: partial optimization of a random code for translational robustness in a rugged fitness landscape", submitted to Biology Direct.

The logical development and main results of the paper are as follows. First, a cost function for genetic codes is specified, and its terms explained (including choices for a distance measure between amino acids). A framework for generating alternative codes is introduced, with a set of assumptions to winnow the search space by roughly 66 orders of magnitude to a tractable set, most importantly the assumption that the block structure of the standard code is a mechanistic consequence of the translational apparatus and therefore non-blocked codes may be safely set aside. The standard code is compared with alternative codes and found to outperform the vast majority of them given a few variants of the assumed cost function. Improvement opportunities for the genetic code are identified by an attempt to minimize the cost function via changes to the distance measure. A greedy minimization algorithm is introduced to search locally for improved variants of an initial code via swaps of codon families. Using this algorithm, the question of whether the standard code should be considered optimized for error minimization is addressed: optimized versions of the standard code and random blocked codes are obtained, and it is found that the standard code's cost, and that of its optimized version, can be matched or beaten by optimized versions of many random blocked codes. The paper's major conclusion is that the standard code is rather unremarkable in its error minimization when compared with other blocked codes.

Overall, I find the subject exciting, the approaches as daring as would be expected from this leading group, and the conclusions interesting. The authors make major assumptions with which I'm not entirely happy, with the justification that they are necessary to make progress. My concern is that unless the assumptions are good, progress is not actually being made, and the topic is better left alone.

I suggest that some assumptions be clarified and buttressed with evidence where they conflict with compelling alternative arguments. The results derived from the greedy minimization algorithm should be substantially revised, as several important claims about this algorithm's output (e.g., that it finds shortest paths) are incorrect. Finally, long-standing questions about the inferences one can draw about evolutionary trajectories, possible or actual, from the output of analytical or computational optimization algorithms should be addressed.

To begin, the simplifying assumptions made to render the search for better codes tractable bear closer examination. In particular, the limitation of searches to codes having the block structure of the universal code is defended, and then used, in a novel way. Given the goal of interpreting simulated trajectories of code modification as informative about the actual process of code evolution (so that, for example, the concept of "close to a fitness peak" and the data in Figures 7 and 8 have meaning in biology as well as the simulation), the authors must establish that the simplifications are biologically reasonable. The burden is heavier here than on other works that make similar assumptions (e.g. the works of Freeland and colleagues) but in which no claims about evolutionary trajectories or the mode of evolutionary exploration are made.

The major assumption leading to the reduction in the search space is that ". the block structure of the code is a direct, mechanistic consequence of the mode of interaction between the ribosome, mRNA, and the cognate tRNA [50]". The premise is worded in a way suggesting that biophysics alone suffice to impose the observed block structure, without invoking selective pressure against mistranslation. This is to my knowledge a completely novel and exciting idea, and substantial evidence should be presented to support it. I was unable to connect the contents of [50] (Spirin, RNA Biol. 2004) with this premise, and would be helped by exposition on what is being assumed and what is known.

By contrast, the authors might mean the alternative where the block structure of the code arises both from the mode of interaction (e.g., third-position binding contributes most weakly to discrimination, and codon-anticodon mismatches involving transitions are more stable than those involving transversions) and selective pressure for error minimization, which jointly favored a code structure in which third-position transition errors are largely synonymous – a blocked code. That natural selection favors translational error minimization seems obvious the question at issue is whether the structure of the genetic code contributes alongside other adaptations such as ribosomal structure, kinetic proofreading, synthetase editing activity, biased codon usage for translational accuracy (Akashi Genetics 1994), biased codon usage for error minimization (Archetti JME 2004), tolerance of proteins to mistranslation, etc. If this weaker but plausible assumption is what the authors mean, then it becomes less clear how unblocked codes can be eliminated from consideration in evolutionary pathways, since they are merely assumed to be less fit (as are most codes in the reduced landscape), not unviable, and there are overwhelmingly (

10^66-fold) more of them in the space of all codes, such that selection must work hard to eliminate them.

Indeed, there are extant codes that have a more consistent block structure than the standard genetic code, such as the vertebrate mitochondrial code in which there are no single-codon families (unlike Trp and Met in the standard code). Such block structures are apparently consistent with the mechanism of translation, but are not considered in the present study. I recommend that in the manuscript the authors more muscularly defend the omission of unblocked and differently-blocked codes from evolutionary trajectories.

Authors' response: Indeed, what we mean is that the first two bases contribute substantially more to the recognition of a cognate tRNA than the third, wobble base. This was made explicit in the revision, and the references in support of these differential contributions of bases are cited ([49–51]). Drummond's point is well taken in that this does not render codes with different block structures impossible "in principle". However, it does make them improbable, and given that for any simulation to run to completion, a relatively small domain of the code space needs to be chosen, fixing the block structure seemed like the best choice. We explain all this in the revision. This may not amount to a "more muscular" defense suggested by Drummond but this is how things are.

The latter half of the work is mainly concerned with how optimized the standard genetic code is. Given that evolution is a stochastic process, the natural way to think about optimization is to ask what proportion of mutations increase versus decrease the score – a truly optimal code will have zero improvement mutations, and a highly optimized code will be improved by only a tiny fraction of the many possible mutations. Many workers have estimated this proportion by locating the standard code's score relative to a sampled distribution of alternative scores. The authors have taken another approach, using the number of "greedy" codon-family swaps separating a given code from a local optimum to measure how optimized it is. The use of distances between a given code's score and an optimum – here, the minimization percentage, MP – to ascertain the strength of selection has been criticized (Freeland, Knight and Landweber, TiBS 2000), essentially because it improperly treats these distances as a linear measure. The present work compares MP's between codes and is subject to the same criticism. The problem is exacerbated here because the MP is computed relative to each code's local greedy minimum. If, for example, the standard code has an MP of 0.93 and a competing code an MP of 0.8, one cannot conclude that the standard code is more optimized in the sense of having fewer mutations which improve it. That is, the difference between MPs is not equivalent to the difference in optimization level. An alternative is that the codes in the standard code's neighborhood generally score well, so that obtaining a high MP is easy and many mutations would improve the standard code, making it poorly optimized, while the competing code's neighborhood is filled with poor-scoring codes, and it is heavily optimized with few better-scoring codes to move to. Rugged landscapes of the sort explored here are more likely to have such features. The authors should directly address the criticisms regarding MP and search-derived versus stochastically derived measures of optimization, and should sample the local landscape around each code to address the concerns about level of optimization.

Authors' response: In order to compare codes, we employed both a statistical approach and an optimization approach. As a measure of the distance between codes, we used not only the number of codon swaps but also the difference in the error cost values as can be seen in Figs. 3, 4, 5, 7and 8. It is not clear why we cannot use MP in the context of the present study. The criticisms of Freeland et al. 2000 addressed the conclusion that, considering the low MP value of the standard code, the code could not evolve under selective pressure to reduce the effect of translation errors. We do not argue with that critique. We use MP only to compare different random codes with the standard code under exactly specified rules for fitness landscape search.

The mutation-selection balance/non-linear adaptation argument should be considered in the Conclusion where the authors ask, "Why did the code's evolution stop where it stopped?" An answer I glean from much of the error-minimization literature cited in the present work is that it might be wildly improbable for selection to push the level of error-minimization any higher, given countervailing pressure from mutation. If the genetic code is "one in a million" in the sense favored by Freeland and Hurst (JME 1998), that is a high level of optimization by most standards. (A demonstration that many mutations improve that one-in-a-million code would be compelling contrary evidence.) Algorithmic optimization of the sort carried out here is blind to such statistical features – in greedy minimization, the first optimization step is as easy as the last step, because all possible alternatives must be evaluated each time, whereas in a blind sampling-based process such as evolution, the farther uphill one climbs, the more improbable improvement becomes and the less likely it is to persist once attained. This is the essence of Freeland et al. TiBS (2000)'s criticism.

In the same vein, there are several standard evolutionary hypotheses which seem to be missing for why the present genetic code should not be optimal:

- Error minimization was not the sole target of selection. If any other traits were under substantial selection in primordial genomes, and these traits were not perfectly congruent with error minimization, then an evolutionary process favoring increased fitness would yield a sub-optimal genetic code.

- The effective population size was not infinite. Natural selection cannot distinguish fitness differences smaller than the reciprocal of the effective population size. As a consequence, any mutations (to tRNAs, synthetases, release factors, ribosomal components, etc.) which improve the error minimization of the genetic code, but confer a selective advantage below this threshold, would not be expected to reach fixation except by drift. One could in principle estimate how many codes have such a property, and thereby estimate how much optimality would be "left on the table" simply because of the nature of the evolutionary process.

- Mutation-selection balance was achieved. Suppose that error minimization has a bell-shaped distribution, and high levels of error minimization are selectively advantageous, but not infinitely so. The higher error minimization is pushed by selection, the more strongly it is opposed by an increasing proportion of deleterious mutations, until equilibrium – likely at a sub-optimal level of EM – is reached. (Mutation-selection balance is a more mainstream term for the "non-linear adaptation" argument touched on above and briefly by the authors in the Introduction.)

If any of these three standard hypotheses have merit, then the genetic code is expected to be sub-optimal with respect to its robustness to mistranslation. The present work should address these hypotheses in their Discussion, where presently the "balance of two forces" argument addressing the same point is made.

Authors' response: We appreciate these interesting comments. However, in our opinion, this demand puts the plank unrealistically high for any analysis of the evolution of the code. We do not know this "expected non-optimality".

The authors measure the stepwise distance from a code to a local peak of the landscape, thereby ascribing significance to this peak. A serious concern is that the algorithm by which this greedy peak is found sheds no light on what general significance the peak possesses. Let us assume that the greedy peak is found to be N steps away from the starting point. The greedy peak is not guaranteed to be a) the closest peak, b) the tallest peak within N steps, or c) the closest or tallest peak approachable using exclusively uphill steps. In a rugged fitness landscape, there is additionally no guarantee that the height of or distance to the greedy peak are informative about the height of or distance to these peaks.

Further, the authors state, "Using this algorithm we can find the shortest evolutionary trajectory from a given starting code to its local minimum of the error cost function (i.e. to a local fitness peak)." This statement is incorrect. Greedy paths will not in general be shortest paths. This can be seen most clearly in Figures 7 and 8, which plot minimization paths of >26 steps, and concluding point #6 which states that a typical code can reach its local peak in 15–30 steps. Given the algorithm used (where a set of mutually accessible codes is uniquely specified by the position of 14 four-codon and 14 two-codon blocks), any code can be changed into any other accessible code in (14-1) + (14-1) = 26 swaps. (The problem is equivalent to a list-reordering problem, and a list of n items can be put in any specified order in n-1 swaps or fewer). It is impossible for a shortest path in the described model to be longer than 26 steps.

As a consequence, Figures 7 and 8 and the accompanying text should be revised. The aim of the experiment is to determine how far various codes are from the local minimum. If "how far" is the shortest-path difference to the closest local minimum, then the data should be retaken using a suitable approach such as dynamic programming. If "how far" is meant in an evolutionary sense, then neither the greedy path nor the shortest path are expected to be representative of evolutionary trajectories, which are blind and therefore subject to entropic constraints as well (cf. the arguments of Freeland and colleagues and the mutation-selection balance comment above). The greedy algorithm is a dubious choice for evolutionary studies, since, for example, the probability of an evolving population moving from one code to the next in any evolutionary model should be a function of the probability of occurrence of the proper mutation and the probability of subsequent fixation, and the greedy algorithm ignores the probability of occurrence altogether. It is easy to imagine deterministic algorithms of equivalent computational complexity which do not ignore such statistics – when all alternatives are being assessed, as in the greedy algorithm, the population mean, median, and so on are deterministic.

Authors response: It is true that, allowing any codon swaps, we can reach any code in (14-1) + (14-1) = 26 steps (not 20). If we knew the final state (the global minimum) this would be an easy problem. Without such knowledge, theoretically, it is possible to find the global minimum using dynamic programming but, practically, this problem is not solvable due to immense number of possible codes. If we allow only swaps that yield the largest fitness increase, then, we find the closest peak (we define a peak as a state from which no codon swaps yield fitness increase) and the tallest peak (because, under the given algorithm only one peak can be reached from any starting point) for this algorithm. We should note that this is, to the best of our understanding, the most reasonable deterministic algorithm of evolution imaginable. It would be a different approach if we added some kind of stochasticity in the landscape search. We decided to use the aforementioned, simple, and therefore, tractable, deterministic, greedy algorithm. In the revised manuscript, we clarified this point in the description of the search algorithm by making it explicit that the algorithm finds an optimization path in which each step involves the maximum possible increase of the code robustness, and added a statement on caveats in the Discussion.

The validity of a fitness landscape, whether quantitative or illustrative (Fig. 9), derives from that of its metric (distance measure) and fitness function (height). The distance metric chosen here is swaps. That is to say, one step across the landscape equals one exchange in the meaning of two families of codons which encode different amino acids. The authors assert that it is likely the genetic code evolved, at least in part, by such swaps. Woese (BioScience 20(8):471–480 + 485 [1970]) considered several mechanisms for the evolution of the code. Like the authors, he noted that codon reassignments were almost certain to be strongly deleterious, but instead argued that this stumbling block favored an alternate class of evolutionary paths, namely refinement of ancestral stochastic overlapping codon – amino-acid assignments into more precisely delineated families. Evidence addressing Woese's argument, specifically in support the importance of swaps, should be provided early in the manuscript, as the plausibility of the conclusions depend on the acceptance of this premise.

Authors' response: Again, this work is not an attempt to reconstruct a truly realistic scenario for the evolution of the code but rather to determine the status of the standard code in the code space, compared to various sets of random codes, and delineate possible evolutionary links between the standard code and different random codes. This is clarified in the revision.

The authors note that 9 or 11 swaps is required to take the standard code to its greedy minimum, and refer to this distance as "relatively small" this interpretation should be justified. As implied above, 9–11 mutations suffice to move any code halfway across the entire vast space of all blocked codes (but recall that the greedy minimum is not necessarily the closest or deepest local minimum). As these swaps are macromutations – several physical mutations would likely be required to swap two four-codon families – the distance would certainly be even larger in reality. I suggest providing the above calculation of the maximum shortest-path length to put whatever distance is found upon revision in perspective.

Authors' response: "Relatively small" means, literally, relatively small with respect to other random codes. It does not seem to us that additional justification is necessary.

The term "translational robustness" has previously been used to refer to the robustness of individual proteins to mistranslation (Drummond et al. PNAS 2005 Koonin and Wolf, Curr. Op. Biotech. 2006). Here, it is being applied for the first time to the genetic code to denote the idea that certain codes have error spectra which lead to disrupted protein fold or function less than others. These are different phenomena – in the biophysics of how robustness might be obtained and modified, and the scope of consequences if it is altered, among other aspects – and using the same term risks inducing the opposite impression. The phenomenon under consideration has been the subject of many previous works, so the field's common use of "error minimization" might be considered. If a new term is sought, "error robustness" would incorporate the robustness concept while carefully distinguishing it from previous work. If the term must be kept, a short description of how its use here differs from the earlier definition would help to minimize confusion.

Authors' response: We agree, the terminology here deserves more attention. "Translational robustness" could be ambiguous but "error minimization" is not a good phrase either because the structure of the code does not minimize errors, it minimizes their effect. So we went through the manuscript and made changes, speaking of "robustness to translation errors" or, where no ambiguity is perceived, simply, of "robustness".

Reviewer 3: Rob Knight, University of Colorado, Boulder

In this manuscript, Novozhilov et al. provide a more detailed exploration of the level of optimality of the genetic code and the evolutionary trajectory of optimization than has previously been available. Specifically, they use a standard approach to measuring the "cost" of a genetic code in terms of the weighted frequency of errors of different severity, and measure the trajectory of codes using a hill-climbing optimization algorithm. They recapture the uncontroversial result that the genetic code is much better at minimizing errors than a random genetic code (as has been shown by many authors), but is at neither a local nor global optimum (as has also been shown previously). However, the results go beyond what has previously been done by comparing the evolutionary trajectory of the standard genetic code to the trajectories of other, random codes to get an estimate of what the overall process should look like.

I believe that the authors overstate their result that the standard genetic code is "not special". Their own results show that it is difficult to explain except as the result of an optimization process: the argument that the standard genetic code is a global optimum is not to my knowledge taken seriously in the field, so the results cannot be seen as overturning it (see discussion between Steve Freeland, Massimo Di Giulio and myself in TiBS in 2000, which is cited appropriately in the paper). Rather, they show that, like most other features of organisms, the genetic code is optimized but not optimal, and probably reflects a range of constraints beyond the specific feature being examined. The manuscript could also benefit from being shortened substantially, as it appears to be relatively long in relation to its news value.

Authors' response: When we say that the standard code is "not special", we mean that it very well could have evolved by partial optimization of a purely random code of the same block structure. Various edits have been made to clarify this but the gist remains. In a way, this is a shift from a half-full glass touted in several previous studies which emphasized that the standard code was "one in a million" or even better than that, to a half-empty glass whereby the standard code appears rather trivial when the entire landscape is considered.

It is interesting that many of the non-canonical genetic codes in fact do have different block structure or amino acid counts than the canonical genetic codes: indeed, this seems to be the main way that the genetic code is currently evolving (see Knight et al. 2001 Nature Reviews Genetics for a discussion, and Caporaso et al. 2005 J Mol Evol for some simulations involving alternative block structures – the authors might consider citing this latter paper in the discussion of alternative models for code evolution). It might be worth relaxing some of these assumptions in the simulations to model more accurately how we think the code is changing today, although this is perhaps beyond the scope of the present manuscript. We did some work on the optimality of the non-canonical codes in Freeland et al. Mol Biol Evol.

Authors' response: The specifics of the deviant codes in modern organisms, indeed, seem to be beyond the scope of the paper. It is hard to be sure that these in any ways recapitulate the original evolution of the code.

I think the statement "the standard code is unremarkable" is misleading, for the reasons mentioned above. It is still far better at minimizing errors than the vast majority of codes: perhaps the authors could restate what they mean more clearly.

Authors' response: Restated and qualified in the revision.

I still disagree that the earlier paper cited, by the same authors, adequately addresses the evidence in favor of a stereochemical effecton the modern code structure. The Caporaso et al. 2005 JME paper shows that there is plenty of "room" for adaptation even if substantial parts of the code were fixed by stereochemistry, for example. Similarly, the present paper does not really discuss the evidence supporting coevolutionary models, although it could be argued that both lines of evidence are outside the scope of this work.

Authors' response: In this context, the previous paper by the same authors (YIW and EVK) is cited as a succinct review of the evidence in support (or lack thereof) of the stereochemical hypothesis. The main conclusion is that there is no compelling evidence in favor of that hypothesis, and we stand by that. As for the being plenty of room for adaptation, we do not seem to be in dispute on this.

The discussion of the circularity of using PAM matrices should cite Di Giulio's 2001 J Theor Biol paper on the topic, and I also show in my PhD thesis that even very small contamination of a substitution matrix with the genetic code matrix can lead to artifactual statistical significance of the optimality of the genetic code.

Authors' response: The Di Giulio reference was added [48]. Unfortunately, at this time, we do not have access to the thesis.

The assertion that "using this algorithm we can find the shortest evolutionary trajectory from a given starting code to its local minimum of the error cost function" is not true – the shortest trajectory might involve a transition to a worse solution to get to a better one. The algorithm employed can only find the shortest continuously improving path, which might be much longer than a direct route. This point should be clarified in the manuscript.

Authors' response: Indeed, this point has been clarified – see the response to Drummond's comments above.

## References

Andrianantoandro, E. et al. Synthetic biology: new engineering rules for an emerging discipline. Mol. Syst. Biol. 2, 2006.0028 (2006).

Grozinger, L. et al. Pathways to cellular supremacy in biocomputing. Nat. Commun. 10, 1–11 (2019).

Dobzhansky, T. Nothing in biology makes sense except in the light of evolution. Am. Biol. Teach. 35, 125–129 (1973).

Renda, B. A., Hammerling, M. J. & Barrick, J. E. Engineering reduced evolutionary potential for synthetic biology. Mol. BioSyst. 10, 1668–1678 (2014).

Ellis, T. Predicting how evolution will beat us. Microb. Biotechnol. 12, 41–43 (2019).

Fernandez-Rodriguez, J., Yang, L., Gorochowski, T. E., Gordon, D. B. & Voigt, C. A. Memory and combinatorial logic based on DNA inversions: dynamics and evolutionary stability. ACS Synth. Biol. 4, 1361–1372 (2015).

Yokobayashi, Y., Weiss, R. & Arnold, F. H. Directed evolution of a genetic circuit. Proc. Natl Acad. Sci. USA 99, 16587–16591 (2002).

Giver, L., Gershenson, A., Freskgard, P.-O. & Arnold, F. H. Directed evolution of a thermostable esterase. Proc. Natl Acad. Sci. USA 95, 12809–12813 (1998). A landmark work on the directed evolution of proteins – here used to improve the thermal stability of an enzyme.

Boder, E. T., Midelfort, K. S. & Wittrup, K. D. Directed evolution of antibody fragments with monovalent femtomolar antigen-binding affinity. Proc. Natl Acad. Sci. USA 97, 10701–10705 (2000).

Esvelt, K. M., Carlson, J. C. & Liu, D. R. A system for the continuous directed evolution of biomolecules. Nature 472, 499–503 (2011).

Guntas, G., Mansell, T. J., Kim, J. R. & Ostermeier, M. Directed evolution of protein switches and their application to the creation of ligand-binding proteins. Proc. Natl Acad. Sci. USA 102, 11224–11229 (2005).

Wang, H. H. et al. Programming cells by multiplex genome engineering and accelerated evolution. Nature 460, 894–898 (2009).

Anderson, J. et al. Engineering and ethical perspectives in synthetic biology. Rigorous, robust and predictable designs, public engagement and a modern ethical framework are vital to the continued success of synthetic biology. EMBO Rep. 13, 584–590 (2012).

Wright, O., Stan, G.-B. & Ellis, T. Building-in biosafety for synthetic biology. Microbiology (Reading) 159, 1221–1235 (2013).

Chan, C., Lee, J., Cameron, E., Bashor, C. & Collins, J. ‘Deadman’ and ‘Passcode’ microbial kill switches for bacterial containment. Nat. Chem. Biol. 12, 82–86 (2015).

Mandell, D. J. et al. Biocontainment of genetically modified organisms by synthetic protein design. Nature 518, 55–60 (2015).

Winston, M. L. The biology and management of Africanized honey bees. Annu. Rev. Entomol. 37, 173–193 (1992).

Oye, K. A. et al. Regulating gene drives. Science 345, 626–628 (2014).

Pigliucci, M. Are ecology and evolutionary biology “soft” sciences? Ann. Zool. Fennici 39, 87–98 (2002).

Bartley, B. A., Kim, K., Medley, J. K. & Sauro, H. M. Synthetic biology: engineering living systems from biophysical principles. Biophys. J. 112, 1050–1058 (2017).

Kirschner, M. & Gerhart, J. Evolvability. Proc. Natl Acad. Sci. USA 95, 8420–8427 (1998).

Mayo, A. E., Setty, Y., Shavit, S., Zaslaver, A. & Alon, U. Plasticity of the cis-regulatory input function of a gene. PLoS Biol. 4, 555–561 (2006). This work experimentally demonstrates specific evolvability by showing that mutations in a gene regulatory region can change its function without destroying it.

Wagner, A. Robustness and evolvability: a paradox resolved. Proc. R. Soc. B Biol. Sci. 275, 91–100 (2008).

Wright, S. The Roles of Mutation, Inbreeding, Crossbreeding, and Selection in Evolution. Vol. 1, 355–366 (na, 1932).

Cano, A. V. & Payne, J. L. Mutation bias interacts with composition bias to influence adaptive evolution. PLoS Comput. Biol. 16, e1008296 (2020).

Stoltzfus, A. & Norris, R. W. On the causes of evolutionary transition:transversion bias. Mol. Biol. Evol. 33, 595–602 (2016).

Jones, P. A., Rideout, W. M., Shen, J. C., Spruck, C. H. & Tsai, Y. C. Methylation, mutation and cancer. Bioessays 14, 33–36 (1992).

Zhu, Y. O., Siegal, M. L., Hall, D. W. & Petrov, D. A. Precise estimates of mutation rate and spectrum in yeast. Proc. Natl Acad. Sci. USA 111, E2310–E2318 (2014).

Drake, J., Charlseworth, B., Charlseworth, D. & Crow, J. Rates of spontaneous mutation. Genetics 148, 1667–1686 (1998).

Chaitin, G. Proving Darwin: Making Biology Mathematical (Vintage, 2013).

Levinson, G. & Gutman, G. A. Slipped-strand mispairing: a major mechanism for DNA sequence evolution. Mol. Biol. Evol. 4, 203–221 (1987).

Vos, M. Why do bacteria engage in homologous recombination? Trends Microbiol. 17, 226–232 (2009).

Frost, L. S., Leplae, R., Summers, A. O. & Toussaint, A. Mobile genetic elements: the agents of open source evolution. Nat. Rev. Microbiol. 3, 722–732 (2005).

Eigen, M. On the nature of virus quasispecies. Trends Microbiol. 4, 216–218 (1996).

Canton, B., Labno, A. & Endy, D. Refinement and standardization of synthetic biological parts and devices. Nat. Biotechnol. 26, 787 (2008).

Jack, B. R. et al. Predicting the genetic stability of engineered DNA sequences with the EFM calculator. ACS Synth. Biol. 4, 939–943 (2015).

Sleight, S. C. & Sauro, H. M. Visualization of evolutionary stability dynamics and competitive fitness of Escherichia coli engineered with randomized multigene circuits. ACS Synth. Biol. 2, 519–528 (2013). This work experimentally uncovered design principles for improving evolutionarily stability in synthetic genetic circuits in vivo.

Hossain, A. et al. Automated design of thousands of nonrepetitive parts for engineering stable genetic systems. Nat. Biotechnol. 38, 1466–1475 (2020).

Geng, P., Leonard, S. P., Mishler, D. M. & Barrick, J. E. Synthetic genome defenses against selfish DNA elements stabilize engineered bacteria against evolutionary failure. ACS Synth. Biol. 8, 521–531 (2019).

Csörgő, B., Fehér, T., Tímár, E., Blattner, F. R. & Pósfai, G. Low-mutation-rate, reduced-genome Escherichia coli: an improved host for faithful maintenance of engineered genetic constructs. Microb. Cell Factories 11, 11 (2012). This work is an example of engineering the host organism’s genome to reduce global mutation rates.

Ravikumar, A., Arzumanyan, G. A., Obadi, M. K. A., Javanpour, A. A. & Liu, C. C. Scalable, continuous evolution of genes at mutation rates above genomic error thresholds. Cell 175, 1946–1957 (2018). An orthogonal plasmid mutation system for directed evolution at elevated error rates.

Dymond, J. & Boeke, J. The Saccharomyces cerevisiae SCRaMbLE system and genome minimization. Bioeng. Bugs 3, 168–171 (2012). An inducible evolution system based on large-scale genomic shuffling in the synthetic yeast project Sc2.0.

Koonin, E. V., Makarova, K. S. & Aravind, L. Horizontal gene transfer in prokaryotes: quantification and classification. Annu. Rev. Microbiol. 55, 709–742 (2001).

Ahnert, S. E. Structural properties of genotype– phenotype maps. J. R. Soc. Interface 14, 20170275 (2017).

Vogt, G. Stochastic developmental variation, an epigenetic source of phenotypic diversity with far-reaching biological consequences. J. Biosci. 40, 159–204 (2015).

Strogatz, S. Nonlinear Dynamics and Chaos: With Applications to Physics, Biology, Chemistry, and Engineering (Westview, 2014).

Ferrada, E. & Wagner, A. A comparison of genotype-phenotype maps for RNA and proteins. Biophys. J. 102, 1916–1925 (2012).

Aguilar-Rodríguez, J., Payne, J. L. & Wagner, A. A thousand empirical adaptive landscapes and their navigability. Nat. Ecol. Evol. 1, 0045 (2017).

Savageau, M. A., Coelho, P. M. B. M., Fasani, R. A., Tolla, D. A. & Salvador, A. Phenotypes and tolerances in the design space of biochemical systems. Proc. Natl Acad. Sci. USA 106, 6435–6440 (2009).

Schaerli, Y. et al. Synthetic circuits reveal how mechanisms of gene regulatory networks constrain evolution. Mol. Syst. Biol. 14, 1–18 (2018). This work experimentally demonstrates how genetic circuits with identical phenotypes can differ in their phenotype landscapes.

Pines, G., Winkler, J. D., Pines, A. & Gill, R. T. Refactoring the genetic code for increased evolvability. mBio 8, e01654–17.mBio.01654-17 (2017).

Schaper, S. & Louis, A. A. The arrival of the frequent: how bias in genotype-phenotype maps can steer populations to local optima. PLoS ONE 9, e86635 (2014).

Li, H., Helling, R., Tang, C. & Wingreen, N. Emergence of preferred structures In a simple model of protein folding. Science 273, 666–669 (1996).

Carroll, S. In Endless Forms Most Beautiful 29–36 (Weidenfeld & Nicolson, 2006).

Zhang, J. Evolution by gene duplication: an update. Trends Ecol. Evol. 18, 292–298 (2003).

Albert, R., Jeong, H. & Barabási, A.-L. Error and attack tolerance of complex networks nature. Nature 406, 268–382 (2001).

Tononi, G., Sporns, O. & Edelman, G. M. Measures of degeneracy and redundancy in biological networks. Proc. Natl Acad. Sci. USA 96, 3257–3262 (1999).

Lipson, H. Principles of modularity, regularity, and hierarchy for scalable systems. J. Biol. Phys. Chem. 2007, 125–128 (2007).

Wagner, G., Pavlicev, M. & Cheverud, J. The road to modularity. Focus Evo-Devo 8, 921–931 (2007).

Simon, H. A. In Facets of Systems Science (ed. Klir, G. J.) 457–476 (Springer, 1991).

Kashtan, N. & Alon, U. Spontaneous evolution of modularity and network motifs. PNAS 102, 13773–13778 (2005). This work uses computational models to show how evolvable modular architectures can evolve in response to modularly varying selection pressures.

Kaneko, K. Evolution of robustness to noise and mutation in gene expression dynamics. PLoS ONE 2, e434 (2007).

Bloom, J. D., Labthavikul, S. T., Otey, C. R. & Arnold, F. H. Protein stability promotes evolvability. Proc. Natl Acad. Sci. USA 103, 5869–5874 (2006).

Afroz, T. & Beisel, C. L. Understanding and exploiting feedback in synthetic biology. Chem. Eng. Sci. 103, 79–90 (2013).

Ceroni, F. et al. Burden-driven feedback control of gene expression. Nat. Methods 15, 387–393 (2018).

Kelly, C. L. et al. Synthetic negative feedback circuits using engineered small RNAs. Nucleic Acids Res. 46, 9875–9889 (2018).

Bartoli, V., Meaker, G. A., di Bernardo, M. & Gorochowski, T. E. Tunable genetic devices through simultaneous control of transcription and translation. Nat. Commun. 11, 2095 (2020).

Greco, F. V., Pandi, A., Erb, T. J., Grierson, C. S. & Gorochowski, T. E. Harnessing the central dogma for stringent multi-level control of gene expression. Nat. Commun. 12, 1738 (2021).

Aoki, S. K. et al. A universal biomolecular integral feedback controller for robust perfect adaptation. Nature 570, 533–537 (2019).

Brakefield, P. M. Evo-devo and constraints on selection. Trends Ecol. Evol. 21, 362–368 (2006).

Payne, J. L., Khalid, F. & Wagner, A. RNA-mediated gene regulation is less evolvable than transcriptional regulation. Proc. Natl Acad. Sci. USA 115, E3481–E3490 (2018).

Gerthart, J. & Kirschner, M. The theory of facilitated variation | PNAS. Proc. Natl Acad. Sci. USA 104, 8582–8589 (2007).

Kim, H., Muñoz, S., Osuna, P. & Gershenson, C. Antifragility predicts the robustness and evolvability of biological networks through multi-class classification with a convolutional neural network. Entropy 22, 986 (2020).

Noman, N., Monjo, T., Moscato, P. & Iba, H. Evolving Robust Gene Regulatory Networks. PLoS One 10, e0116258 (2015).

Kauffman, S. A. In The Origins of Order: Self-Organization and Selection in Evolution 33–120 (Oxford Univ., USA, 1993). A seminal work on NK fitness landscapes that illustrates how the statistical properties of fitness landscapes can constrain evolution.

Dekel, E. & Alon, U. Optimality and evolutionary tuning of the expression level of a protein. Nature 436, 588–592 (2005).

Wannier, T. M. et al. Adaptive evolution of genomically recoded Escherichia coli. Proc. Natl Acad. Sci. USA 115, 3090–3095 (2018).

Springman, R., Molineux, I. J., Duong, C., Bull, R. J. & Bull, J. J. Evolutionary stability of a refactored phage genome. ACS Synth. Biol. 1, 425–430 (2012).

Kurokawa, M., Seno, S., Matsuda, H. & Ying, B.-W. Correlation between genome reduction and bacterial growth. DNA Res. 23, 517–525 (2016).

Martínez-García, E., Nikel, P. I., Aparicio, T. & de Lorenzo, V. Pseudomonas 2.0: genetic upgrading of P. putida KT2440 as an enhanced host for heterologous gene expression. Micro. Cell Fact. 13, 159 (2014).

Stieber, D., Gabant, P. & Szpirer, C. Y. The art of selective killing: plasmid toxin/antitoxin systems and their technological applications. BioTechniques 45, 344–346 (2008).

Umenhoffer, K. et al. Genome-wide abolishment of mobile genetic elements using genome shuffling and CRISPR/Cas-assisted MAGE allows the efficient stabilization of a bacterial chassis. ACS Synth. Biol. 6, 1471–1483 (2017).

Reuter, J. A., Spacek, D. V. & Snyder, M. P. High-throughput sequencing technologies. Mol. Cell 58, 586–597 (2015).

Sharon, E. et al. Inferring gene regulatory logic from high-throughput measurements of thousands of systematically designed promoters. Nat. Biotechnol. 30, 521–530 (2012).

Gilliot, P.-A. & Gorochowski, T. E. Sequencing enabling design and learning in synthetic biology. Curr. Opin. Chem. Biol. 58, 54–62 (2020).

Sarkisyan, K. S. et al. Local fitness landscape of the green fluorescent protein. Nature 533, 397–401 (2016). The first high-throughput experimental characterisation of the (partial) fitness landscape of a protein.

Nevozhay, D., Adams, R. M., Itallie, E. V., Bennett, M. R. & Balázsi, G. Mapping the environmental fitness landscape of a synthetic gene circuit. PLOS Comput. Biol. 8, e1002480 (2012).

Cira, N. J., Pearce, M. T. & Quake, S. R. Neutral and selective dynamics in a synthetic microbial community. Proc. Natl Acad. Sci. USA 115, E9842–E9848 (2018).

van Opijnen, T., Bodi, K. L. & Camilli, A. Tn-seq: high-throughput parallel sequencing for fitness and genetic interaction studies in microorganisms. Nat. Methods 6, 767–772 (2009).

Gorochowski, T. E. & Ellis, T. Designing efficient translation. Nat. Biotechnol. 36, 934–935 (2018).

Pepperkok, R. & Ellenberg, J. High-throughput fluorescence microscopy for systems biology. Nat. Rev. Mol. Cell Biol. 7, 690–696 (2006).

Goda, K. et al. High-throughput single-microparticle imaging flow analyzer. Proc. Natl Acad. Sci. USA 109, 11630–11635 (2012).

Marucci, L. et al. Computer-aided whole-cell design: taking a holistic approach by integrating synthetic with systems biology. Front. Bioeng. Biotechnol. 8, 942 (2020).

Nielsen, A. A. K. et al. Genetic circuit design automation. Science 352, aac7341–aac7341 (2016).

du Plessis, L., Leventhal, G. E. & Bonhoeffer, S. How good are statistical models at approximating complex fitness landscapes? Mol. Biol. Evol. 33, 2454–2468 (2016).

Palmer, A. C. et al. Delayed commitment to evolutionary fate in antibiotic resistance fitness landscapes. Nat. Commun. 6, 7385 (2015).

Henningsson, R., Moratorio, G., Bordería, A. V., Vignuzzi, M. & Fontes, M. DISSEQT—DIStribution-based modeling of SEQuence space Time dynamics†. Virus Evol. 5, 1–14 (2019).

De Visser, J. A. G. M. & Krug, J. Empirical fitness landscapes and the predictability of evolution. Nat. Rev. Genet. 15, 480–490 (2014).

Beal, J. et al. The long journey towards standards for engineering biosystems. EMBO Rep. 21, e50521 (2020).

Schreiber, F. et al. Specifications of standards in systems and synthetic biology: status and developments in 2020. J. Integr. Bioinform. 17, 20200022 (2020).

Mozhaev, V. V. & Martinek, K. Structure-stability relationships in proteins: new approaches to stabilizing enzymes. Enzym. Microb. Technol. 6, 50–59 (1984).

Archetti, M. Genetic robustness and selection at the protein level for synonymous codons. J. Evolut. Biol. 19, 353–365 (2006).

McDonald, J. I. et al. Reprogrammable CRISPR/Cas9-based system for inducing site-specific DNA methylation. Biol. Open 5, 866–874 (2016).

Nivina, A. et al. Structure-specific DNA recombination sites: Design, validation, and machine learning–based refinement. Sci. Adv. 6, eaay2922 (2020).

Romanini, D. W., Peralta-Yahya, P., Mondol, V. & Cornish, V. W. A heritable recombination system for synthetic Darwinian evolution in yeast. ACS Synth. Biol. 1, 602–609 (2012).

Reis, A. C. et al. Simultaneous repression of multiple bacterial genes using nonrepetitive extra-long sgRNA arrays. Nat. Biotechnol. 37, 1294–1301 (2019).

Umenhoffer, K. et al. Reduced evolvability of Escherichia coli MDS42, an IS-less cellular chassis for molecular and synthetic biology applications. Microb. Cell Factories 9, 38 (2010).

Nyerges, Á. et al. CRISPR-interference-based modulation of mobile genetic elements in bacteria. Synth. Biol. 4, ysz008 (2019).

Le Breton, Y., Mohapatra, N. P. & Haldenwang, W. G. In vivo random mutagenesis of Bacillus subtilis by use of TnYLB-1, a mariner-based transposon. Appl. Environ. Microbiol. 72, 327–333 (2006).

Greener, A., Callahan, M. & Jerpseth, B. An efficient random mutagenesis technique using an E. coli mutator strain. Methods Mol. Biol. 57, 375–385 (1996).

Badran, A. H. & Liu, D. R. Development of potent in vivo mutagenesis plasmids with broad mutational spectra. Nat. Commun. 6, 8425 (2015).

Halperin, S. O. et al. CRISPR-guided DNA polymerases enable diversification of all nucleotides in a tunable window. Nature 560, 248–252 (2018).

Camps, M., Naukkarinen, J., Johnson, B. P. & Loeb, L. A. Targeted gene evolution in Escherichia coli using a highly error-prone DNA polymerase I. Proc. Natl Acad. Sci. USA 100, 9727–9732 (2003).

Vojta, A. et al. Repurposing the CRISPR-Cas9 system for targeted DNA methylation. Nucleic Acids Res. 44, 5615–5628 (2016).

Hess, G., Frésard, L., Han, K., Lee, C. & Bassik, M. Directed evolution using dCas9-targeted somatic hypermutation in mammalian cells. Nat. Methods 13, 1036–1042 (2016).

Tyo, K. E. J., Ajikumar, P. K. & Stephanopoulos, G. Stabilized gene duplication enables long-term selection-free heterologous pathway expression. Nat. Biotechnol. 27, 760–765 (2009).

Albert, R. Scale-free networks in cell biology. J. Cell Sci. 118, 4947–4957 (2005).

Park, Y., Espah Borujeni, A., Gorochowski, T. E., Shin, J. & Voigt, C. A. Precision design of stable genetic circuits carried in highly-insulated E. coli genomic landing pads. Mol. Syst. Biol. 16, e9584 (2020).

Meyer, A. J., Ellefson, J. W. & Ellington, A. D. Directed evolution of a panel of orthogonal T7 RNA polymerase variants for in vivo or in vitro synthetic circuitry. ACS Synth. Biol. 4, 1070–1076 (2015).

Kylilis, N., Tuza, Z. A., Stan, G.-B. & Polizzi, K. M. Tools for engineering coordinated system behaviour in synthetic microbial consortia. Nat. Commun. 9, 2677 (2018).

Wei, S.-P. et al. Formation and functionalization of membraneless compartments in Escherichia coli. Nat. Chem. Biol. 16, 1143–1148 (2020).

Xiang, N. et al. Using synthetic biology to overcome barriers to stable expression of nitrogenase in eukaryotic organelles. Proc. Natl Acad. Sci. USA 117, 16537–16545 (2020).

Richardson, S. M. et al. Design of a synthetic yeast genome. Science 355, 1040–1044 (2017).

Steel, H. & Papachristodoulou, A. Low-burden biological feedback controllers for near-perfect adaptation. ACS Synth. Biol. 8, 2212–2219 (2019).

Gorochowski, T. E., Avcilar-Kucukgoze, I., Bovenberg, R. A. L., Roubos, J. A. & Ignatova, Z. A minimal model of ribosome allocation dynamics captures trade-offs in expression between endogenous and synthetic genes. ACS Synth. Biol. 5, 710–720 (2016).

Gorochowski, T. E., Van Den Berg, E., Kerkman, R., Roubos, J. A. & Bovenberg, R. A. L. Using synthetic biological parts and microbioreactors to explore the protein expression characteristics of Escherichia coli. ACS Synth. Biol. 3, 129–139 (2014).

Mittal, P., Brindle, J., Stephen, J., Plotkin, J. B. & Kudla, G. Codon usage influences fitness through RNA toxicity. Proc. Natl Acad. Sci. USA 115, 8639–8644 (2018).

Abil, Z., Ellefson, J. W., Gollihar, J. D., Watkins, E. & Ellington, A. D. Compartmentalized partnered replication for the directed evolution of genetic parts and circuits. Nat. Protoc. 12, 2493–2512 (2017).

Yang, G. & Withers, S. G. Ultrahigh-throughput FACS-based screening for directed enzyme evolution. ChemBioChem 10, 2704–2715 (2009).

Smith, G. P. & Petrenko, V. A. Phage display. Chem. Rev. 97, 391–410 (1997).

## Function of Genetic Code

The genetic code allows cells to contain a mind-boggling amount of information.

Consider this: a microscopic fertilized egg cell, following the instructions contained in its genetic code, can produce a human or elephant which even has similar personality and behaviors to those of its parents. There is a lot of information in there!

The development of the genetic code was vital because it allowed living things to reliably produce products necessary for their survival – and pass instructions for how to do the same onto the next generation.

When a cell seeks to reproduce, one of the first things it does is make a copy of its DNA. This is the “S” phase of the cell cycle, which stands for the “Synthesis” of a new copy of the cell’s DNA.

The information encoded in DNA is preserved by the specific pairing of DNA bases with each other. Adenine will only bond with Thymine, Cytosine with Guanine, etc..

That means that when a cell wants to copy its DNA, all it has to do is part the two strands of the double helix and line up the nucleotides that the bases of the existing DNA “want” to pair with.

This specific base pairing ensures that the new partner strand will contain the same sequence of base pairs – the same “code” – as the old partner strand. Each resulting double helix contains one strand of old DNA paired with one strand of new DNA.

These new double helixes will be inherited by two daughter cells. When it’s time for those daughter cells to reproduce, each strand of these new double helices, act as templates for a new double helix!

When the time comes for a cell to “read” the instructions contained within its DNA, it uses the same principle of specific pair bonding. RNA is very similar to DNA, and each RNA base bonds specifically to one DNA base. Uracil binds to Adenine, Cytosine to Guanine, etc..

This means that, just like DNA replication, the information in DNA is accurately transferred to RNA as long as the resulting RNA strand is composed of the bases that bind specifically with the bases in the DNA.

Sometimes, the RNA strand itself can be the end product. Structures made of RNA perform important functions in ourselves, including assembling proteins, regulating gene expression, and catalyzing the formation of proteins.

In fact, some scientists think that the first life on Earth might have been composed mainly of RNA. This is because RNA can store information in its base pairs just like DNA, but can also perform some enzymatic and regulatory functions.

In most cases, however, the RNA goes on to be transcribed into a protein. Using the amino acid “building blocks of life,” our cells can build almost protein machines for almost any purpose, from muscle fibers to neurotransmitters to digestive enzymes.

In protein transcription, the RNA codons that were transcribed from the DNA are “read” by a ribosome. The ribosome finds the appropriate transfer RNA (tRNA) with “anti-codons” that are complimentary to the codons in the messenger RNA (mRNA) that has been transcribed from the DNA.

Ribosomes catalyze the formation of peptide bonds between the amino acids as they “read” each codon in the mRNA. At the end of the process, you have a string of amino acids as specified by the DNA – that is, a protein.

Other building blocks of life, such as sugars and lipids, are in turn created by proteins. In this way the information contained in the DNA is transformed into all of the materials of life, using the genetic code!

Aita, T., Urata, S. and Husimi, Y.: 2000, From Amino Acid Landscape to Protein Landscape: Analysis of Genetic Codes in Terms of Fitness Landscape, J. Mol. Evol. 50, 313-323.

Alff-Steinberger, C.: 1969, The Genetic Code and Error Transmission, Proc. Natl. Acad. Sci. USA 64, 584-591.

Amirnovin, R.: 1997, An Analysis of the Metabolic Theory of the Origin of the Genetic Code, J. Mol. Evol. 44, 473-476.

Amirnovin, R. and Miller, S. L.: 1999, Response, J. Mol. Evol. 48, 253-255.

Ardell, D. H.: 1998, On error Minimization in a Sequential Origin of the Standard Genetic Code, J. Mol. Evol. 47, 1-13.

Ardell, D. and Sella, G.: 2001, On the Evolution of Redundancy in Genetic Codes, J. Mol. Evol. 53, 269-281.

Barrell, B. G., Bankier, A. T. and Drouin, J.: 1979, A Different Genetic Code in Human Mitochondria, Nature 282, 189-194.

Bashford, J. D., Tsohantjis, I. and Jarvis, P. D.: 1998, A Supersymmetric Model for the Evolution of the Genetic Code, Proc. Natl. Acad. Sci. USA 95, 987-992.

Baumann, U. and Oro, J.: 1993, Three Stages in the Evolution of the Genetic Code, Biosystems 29, 133-141.

Crick, F. H. C.: 1966, Codon-Anticodon Pairing: TheWobble Hypothesis, J. Mol. Biol. 19, 548-555.

Crick, F. H. C.: 1968, The Origin of the Genetic Code, J. Mol. Biol. 38, 367-379.

Crick, F. H. C., Griffith, J. S. and Orgel, L. E.: 1957, Codes Without Commas, Proc. Natl. Acad. Sci. USA 43, 416-421.

Cullman, G. and Labouygues, J.: 1983, Noise Immunity of the Genetic Code, Bio Systems 16, 9-29.

Cullman, G. and Labouygues, J.: 1987, The Logic of the Genetic Code, Math. Model 8, 643-646.

Davies, J., Gilbert, W. and Gorini, L.: 1964, Streptomycin, Suppression and the Code, Proc. Natl. Acad. Sci. USA 51, 883-890.

Davis, B. K.: 1999, Evolution of the Genetic Code, Progr. Biophys. Molec. Biol. 72, 157-243.

Davydov, O.: 1996, Internal Logic of the Genetic Encoding: End-atom Rules of Doublet Composition, ISSOL Newsletter 23, 12.

Davydov, O. V.: 1998, Amino Acid Contribution to the Genetic Code Structure: End-atom Chemical Rules of Doublet Composition, J. Theor. Biol. 193, 679-690.

Di Giulio, M.: 1989, The Extension Reached by the Minimisation of Polarity Distances During the Evolution of the Genetic Code, J. Mol. Evol. 29, 288-293.

Di Giulio, M.: 1991, On the Relationships Between the Genetic Code Co-evolution Hypothesis and the Physiochemical Hypothesis, Z. Naturforsch 46c, 305-312.

Di Giulio, M.: 1994, The Phylogeny of tRNAs Seems to Confirm the Coevolution of the Origin of the Genetic Code, Orig. Life Evol. Biosph. 25, 549-564.

Di Giulio, M.: 1997, On the Origin of the Genetic Code, J. Theor. Biol. 187, 573-581.

Di Giulio, M.: 1998, The Historical Factor: The Biosynthetic Relationships Between Amino Acids and their Physiochemical Properties in the Origin of the Genetic Code, J. Mol. Evol. 46, 615-621.

Di Giulio, M.: 1999a, The Coevolution Theory of the Origin of the Genetic Code, J. Molec. Evol. 48, 253-254.

Di Giulio, M.: 1999b, The RNAWorld, the Genetic Code and the tRNA Molecule, Trends Genet. 15, 223-229.

Di Giulio, M.: 2000, Genetic Code Origin and the Strength of Natural Selection, J. Theor. Biol. 205, 659-661.

Di Giulio, M.: 2001a, A Blind Empiricism Against the Coevolution Theory of the Origin of the Genetic Code, J. Mol. Evol. 53, 724-732.

Di Giulio, M.: 2001b, The Origin of the Genetic Code cannot be Studied using Measurements Based on the PAM Matrix Because this Matrix Reflects the Code itself, Making any such Analyses Tautologous, J. Theor. Biol. 208, 141-144.

Di Giulio, M., Capobianco, M. R. and Medugno M.: 1994, On the Optimisation of the Physiochemcial Distances Between Amino Acids in the Evolution of the Genetic Code, J. Theor. Biol. 168, 43-51.

Di Giulio, M. and Medugno, M.: 1999, Physicochemical Optimization in the Genetic Code Origin as the Number of Codified Amino Acids Increases, J. Molec. Evol. 49, 1-10.

Di Giulio, M. and Medugno, M.: 2000, The Robust Statistical Bases of the Coevolution Theory of Genetic Code Origin, J. Molec. Evol. 50, 258-263.

Di Giulio, M. and Medugno, M.: 2001, The Level and Landscape of Optimization in the Origin of the Genetic Code, J. Molec. Evol. 52, 372-382.

Dillon, L. S.: 1973, The Origins of the Genetic Code, The Botan. Rev. 39, 301-345.

Eigen, M.: 1971, Self-organization of Matter and the Evolution of Biological Macromolecules, Naturwissenschaften 58, 465-522.

Eigen, M. and Schuster, P.: 1979, The Hypercycle: A Principle of Natural Self-organisation, Springer, New York.

Ellington, A. D., Khrapov, M. and Shaw, C. A: 2000, The Scene of a Frozen Accident, RNA 6, 485-498.

Epstein, C. J.: 1966, Role of the Amino-acid 'Code' and of Selection for Conformation in the Evolution of Proteins, Nature 210, 25-28.

Eschenmoser, A.: 1999, Chemical Etiology of Nucleic Acid Structure, Science 284, 2118-2124.

Figureau, A.: 1987, Information Theory and the Genetic Code, Orig Life 17, 439-449.

Figureau, A.: 1989, Optimization and the Genetic Code, Orig. Life Evol. Biosph. 19, 57-67.

Figureau, A. and Pouzet, M.: 1984, Genetic Code and Optimal Resistance to the Effect of Mutations, Orig. Life Evol. Biosph. 14, 579-588.

Fisher, R. A.: 1930, The Genetical Theory of Natural Selection, Oxford University Press, Oxford.

Fitch, W. M.: 1966a, An Improved Method for Testing for Evolutionary Homology, J. Mol. Biol. 16, 9-16.

Fitch, W. M.: 1966b, The Relation Between Frequencies of Amino Acids and Ordered Trinucleotides, J. Mol. Biol. 16, 1-8.

Fitch, W. M. and Upper, K.: 1987, The Phylogeny of tRNA Sequences Provides Evidence for Ambiguity Reduction in the Origin of the Genetic Code, Cold Spring Harbour Symp. Quant. Biol. 52, 759-767.

Freeland, S. J.: 2002, The Darwinian Code: An Adaptation for Adapting, J. Gen. Progr. Evolv. Machines 3, 113-127.

Freeland, S. J. and Hurst, L. D.: 1998a, The Genetic Code is One in a Million, J. Mol. Evol. 47, 238-248.

Freeland, S. J. and Hurst, L. D.: 1998b, Load Minimisation of the Code: History does not Explain the Pattern, Proc. Roy. Soc. Lond. B 265, 2111-2119.

Freeland, S. J., Knight, R. D. and Landweber, L. F.: 2000a, Measuring Adaptation within the Genetic Code, Trends Biochem. Sci. 25, 44-45.

Freeland, S. J., Knight, R. D., Landweber L. F. and Hurst, L. D.: 2000b, Early Fixation of an Optimal Genetic Code, Mol. Biol. Evol. 17, 511-518.

Friedman, S. M. and Weintstein, I. B.: 1964, Lack of Fidelity in the Translation of Ribopolynucleotides, Proc. Natl. Acad. Sci. USA 52, 988-996.

Frisch, L. (ed.): 1966, 'The Genetic Code', Cold Spring Harbor Symposia on Quantitative Biology, pp. 1-747.

Gamow, G.: 1954, Possible Relation Between Deoxyribonucleic Acid and Protein Structures, Nature 173, 318.

Gamow, G. and Ycas, M.: 1955, Statistical Correlation of Protein and Ribonucleic Acid Composition, Proc. Natl. Acad. Sci. USA 41, 1011-1019.

Gesteland, R. F. and Atkins, J. F.: 1993, The RNA World, Cold Spring Harbour, Cold Spring Harbour Laboratory Press, New York.

Gesteland, R. F., Cech, T. and Atkins J. F. (eds): 1999, The RNA World, Cold Spring Harbor Monograph Series, Cold Spring Harbor Laboratory, New York.

Gilis, D., Massar, S. and Rooman M.: 2001, Optimality of the Genetic Code with Respect to Protein Stability and Amino-acid Frequencies, Genome Biol. 2, RESEARCH0049.

Goldberg, A. L. and R. E. Wittes: 1966, Genetic Code: Aspects of Organisation, Science 153, 420-424.

Goldman, N.: 1993, Further Results on error Minimization in the Genetic Code, J. Mol. Evol. 37, 662-664.

Grivell, L. A.: 1986, Deciphering Divergent Codes, Nature 324, 109-110.

Haig, D. and Hurst, L. D.: 1991, A Quantitative Measure of Error Minimisation within the Genetic Code, J. Mol. Evol. 33, 412-417.

Haig, D. and Hurst, L. D.: 1999, A Quantitative Measure of Error Minimization in the Genetic Code, J. Mol. Evol. 49, 708.

Hartman, H.: 1975, Speculations on the Evolution of the Genetic Code, Orig. Life. 6(3), 423-427.

Hartman, H.: 1978, Speculations on the Evolution of the Genetic Code. II, Orig. Life 9, 133-136.

Hartman, H.: 1984, Speculations on the Evolution of the Genetic Code III: The Evolution of t-RNA, Orig. Life 14, 643-648.

Hartman, H.: 1995a, Speculations on the Evolution of the Genetic Code IV. The Evolution of the Aminoacyl-tRNA Synthetases, Orig. Life Evol. Biosph. 25, 265-269.

Hartman, H.: 1995b, Speculations on the Origin of the Genetic Code, J. Mol. Evol. 40, 541-544.

Hasegawa, M. and Miyata, T.: 1980, On the Asymmetry of the Amino Acid Code Table, Orig. Life 10, 265-270.

Hayes, B.: 1998, The Invention of the Genetic Code, Amer. Scientist 86, 8-14.

Illangasekare, M. and Yarus, M.: 2002, Phenylalanine-binding RNAs and Genetic Code Evolution, J. Mol. Evol. 54, 298-311.

Jimenez-Sanchez: 1995, On the Origin and Evolution of the Genetic Code, J. Mol. Evol. 41, 712-716.

Judson, O. and Haydon, D.: 1999, The Genetic Code: What is it Good for? An Analysis of the Effects of Selection Pressures on Genetic Codes, J. Mol. Evol. 49, 539-550.

Jukes, T. H.: 1981, Amino Acids Codes in Mitochondria as Possible Clues to Primitive Codes, J. Mol. Evol. 18, 15-17.

Kargupta, H.: 2001, A Striking Property of Genetic Code-like Transformations, Compl. Syst. 13, 1-32.

Kauffman, S. A.: 1993, The Origins of Order: Self Organisation and Selection in Evolution, Oxford University Press, New York.

King, J. L. and Jukes, T. H.: 1969, Non-Darwinian Evolution, Science 164, 788-798.

Knight, R. D., Freeland, S. J. and Landweber, L. F.: 1999, Selection, History and Chemistry: The Three Faces of the Genetic Code, Trends Biochem. Sci. 24, 241-247.

Knight, R. D., Freeland, S. J. and Landweber L. F.: 2001a, Rewiring the Keyboard: Evolvability of the Genetic Code, Nat. Rev. Genet. 2, 49-58.

Knight, R. D., Landweber, L. F. and Yarus, M.: 2001b, How Mitochondria Redefine the Code, J. Mol. Evol. 53, 299-313.

Knight, R. D., Freeland, S. J. and Landweber L. F.: 2001c, A Simple Model Based on Mutation and Selection Explains Trends in Codon and Amino-acid Usage and GC Composition within and Across Genomes. Genome Biol. 2001 2(4), RESEARCH0010.

Knight, R. D. and Landweber, L. F: 1998, Rhyme or Reason: RNA-arginine Interactions and the Genetic Code, Chem. Biol. 5, R215-R220.

Knight, R. D. and Landweber, L. F: 2000a, The Early Evolution of the Genetic Code, Cell 101, 569-572.

Knight, R. D. and Landweber, L. F: 2000b, Guilt by Association: The Arginine Case Revisited, RNA 6, 499-510.

Lehman, N. and Jukes, T. H.: 1988, Genetic Code Development by Stop Codon Takeover, J. Theor. Biol. 135, 203-214.

Luo, L. F.: 1988, The Degeneracy Rule of Genetic Code, Orig. Life 18, 65-70.

Luo, L. F.: 1989, The Distribution of Amino Acids in Genetic Code, Orig. Life 19, 621-631.

Luo, L. F. and Li, X.: 2002, Coding Rules for Amino Acids in the Genetic Code: The Genetic Code is a Minimal Code of Mutational Deterioration, Orig. Life Evol. Biosph. 32, 621-631.

Maeshiro, T. and Kimura, M.: 1998, The Role of Robustness and Changeability on the Origin and Evolution of Genetic Codes, Proc. Natl. Acad. Sci. USA 95, 5088-5093.

Osawa, S.: 1995, Evolution of the Genetic Code, Oxford University Press, Oxford.

Osawa, S. and Jukes, T. H.: 1989, Codon Reassignment (Codon Capture) in Evolution, J. Mol. Evol. 21, 271-278.

Pace, C., Shirley, B., McNutt, M., and Gajiwala, K.: 1996, Forces Contributing to the Conformational Stability of Proteins, FASEB J. 10, 75-83.

Parker, J.: 1989, Errors and Alternatives in Reading the Universal Genetic Code, Microbiol. Rev. 55, 273-298.

Petrov, D. and Hartl, D.: 1999, Patterns of Substitution in Drosophila and Mammalian Genomes, Proc. Natl. Acad. Sci. USA 96, 1475-1479.

Piccirilli, J. A., Krauch, T., Moroney S. E. and Benner S. A.: 1990, Enzymatic Incorporation of a New Base Pair into DNA and RNA Extends the Genetic Alphabet, Nature 343, 33-37.

Ronneberg, T. A., Landweber, L. F. and Freeland, S. J.: 2000, Testing a Biosynthetic Theory of the Genetic Code: Fact or Artifact? Proc. Natl. Acad. Sci. USA 97, 13690-13695.

Sella, G. and Ardell, D.: 2002, The Impact of Message Mutation on the Fitness of a Genetic Code, J. Mol. Evol. 54, 638-651.

Shepherd, J. C.: 1981, Periodic Correlations in DNA Sequences and Evidence Suggesting their Evolutionary Origin in a Comma-less Genetic Code, J. Mol. Evol. 17, 94-102.

Sonneborn, T. M.: 1965, Degeneracy in the Genetic Code: Extent, Nature and Genetic Implications, Evolving Genes and Proteins, V. Bryson and H. J. Vogel (eds), Academic Press, New York and London.

Sowerby, S. J. and Heckl, W. M.: 1998, The Role of Self-assembled Monolayers of the Purine and Pyrimidine Bases in the Emergence of Life, Orig. Life Evol. Biosph. 28, 283-310.

Stahl, G., McCarty, G. and Farabaugh P. J.: 2002, Ribosome Structure: Revisiting the Connection Between Translational Accuracy and Unconventional Decoding, Trends Biochem. Sci. 27, 178-183.

Swanson, R.: 1984, A Unifying Concept for the Amino Acid Code, Bull. Math. Biol. 46, 187-203.

Szathmáry, E.: 1991a, Codon Swapping as a Possible Evolutionary Mechanism, J. Mol. Evol. 32, 178-182.

Szathmáry, E.: 1991b, Four Letters in the Genetic Alphabet: A Frozen Evolutionary Optimum? Proc. Roy. Soc. Lond. B 245, 91-99.

Szathmáry, E.: 1992, What is the Optimum Size for the Evolutionary Alphabet? Proc. Natl. Acad. Sci. USA 89, 2614-2618.

Szathmáry, E.: 1999, The Origin of the Genetic Code, Trends Genetics 15, 223-229.

Szathmáry, E. and Maynard Smith, J.: 1995, The Major Transitions in Evolution, W. H. Freeman, Oxford and New York.

Szathmáry, E. and Zintzaras, E.: 1992, A Statistical Test of Hypotheses on the Organization and Origin of the Genetic Code, J. Mol. Evol. 35, 185-189.

Tomii, K. and Kanehisa, M.: 1996, Analysis of Amino Acid Indices and Mutation Matrices for Sequence Comparison and Structure Prediction of Proteins, Protein Eng. 9, 27-36.

Topal, M. D. and Fresco, J. R.: 1976, Complementary Base Pairing and the Origin of Substitution Mutations, Nature 263, 285-289.

Trifonov, E. and Bettecken, T.: 1997, Sequence Fossils, Triplet Expansion, and Reconstruction of Earliest Codons, Gene 205, 1-6.

Trifonov, E. N.: 2000, Consensus Temporal Order of Amino Acids and Evolution of the Triplet Code, Gene 261, 139-151.

Volkenstein, M. V.: 1965, Coding of Polar and Non-polar Amino Acids, Nature 207, 294-295.

Wakeley, J.: 1994, Substitution-rate Variation Among Sites and the Estimation of Transition Bias, Mol. Biol. Evol. 11, 436-442.

Weber, A. L. and Miller, S. L.: 1981, Reasons for the Occurrence of the Twenty Coded Protein Amino Acids, J. Mol. Evol. 17, 273-284.

Woese, C. R.: 1965, On the Evolution of the Genetic Code, Proc. Natl. Acad. Sci. USA 54, 1546-1552.

Woese, C. R.: 1973, Evolution of the Genetic Code, Naturwissenschaften 60, 447-459.

Woese, C. R., Dugre, D. H., Saxinger W. C. and Dugre S. A.: 1966, On the Fundamental Nature and Evolution of the Genetic Code, Cold Spring Harbour Symp. Quant. Biol. 31, 723-736.

Wong, J. T.-F.: 1975, A Co-evolution Theory of the Genetic Code, Proc. Natl. Acad. Sci. USA 72, 1909-1912.

Wong, J. T.-F.: 1976, The Evolution of a Universal Genetic Code, Proc. Natl. Acad. Sci. USA 73, 2336-2340.

Wong, J. T.-F.: 1980, Role of Minimisation of Chemical Distances Between Amino Acids in the Evolution of the Genetic Code, Proc. Natl. Acad. Sci. USA 77, 1083-1086.

Wong, J. T.-F.: 1981, Co-evolution of the Genetic Code and Amino Acid Biosynthesis, Trends Biochem. Sci. 6, 33-35.

Wong, J. T.-F.: 1988, Evolution of the Genetic Code, Microbiol. Sci. 5, 174-181.

Wong, J. T.-F. and P. M. Bronskill: 1979, Inadequacy of Pre-biotic Synthesis as the Origin of Proteinaceous Amino Acids, J. Mol. Evol. 13, 115-125.

Yarus, M.: 2000, RNA-ligand Chemistry: A Testable Source for the Genetic Code, RNA 6 475-484.

Zuckerkandl, E. and Pauling, L.: 1965, Evolutionary Divergence and Convergence in Proteins. Evolving Genes and Proteins, V. Bryson and H. J. Vogel (eds), Academic Press, New York and London.