Optimal features in the genetic code

Optimal features in the genetic code

http://reasonandscience.heavenforum.org/t1501-optimal-features-in-the-genetic-code

Information and function in a biological system
Literature from those who posture in favor of creation abounds with examples of the tremendous odds against chance producing a meaningful code. For instance, the estimated number of elementary particles in the universe is 10^80. The most rapid events occur at an amazing 10^45 per second. Thirty billion years contains only 10^18 seconds. By totaling those, we find that the maximum elementary particle events in 30 billion years could only be 10^143. Yet, the simplest known free-living organism, Mycoplasma genitalium, has 470 genes that code for 470 proteins that average 347 amino acids in length. The odds against just one specified protein of that length are 1:10^451.
http://www.doesgodexist.org/NovDec09/Information-Function.html

If amino acids were randomly assigned to triplet codons, then there would be 1.5 x 10^84 possible genetic codes to choose from
http://en.wikipedia.org/wiki/Genetic_code

Origin and evolution of the genetic code: the universal enigma
In our opinion, despite extensive and, in many cases, elaborate attempts to model code optimization, ingenious theorizing along the lines of the coevolution theory, and considerable experimentation, very little definitive progress has been made. Summarizing the state of the art in the study of the code evolution, we cannot escape considerable skepticism. It seems that the two-pronged fundamental question: “why is the genetic code the way it is and how did it come to be?”, that was asked over 50 years ago, at the dawn of molecular biology, might remain pertinent even in another 50 years. Our consolation is that we cannot think of a more fundamental problem in biology.
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3293468/

The genetic code is one in a million
if we employ weightings to allow for biases in translation, then only 1 in every million random alternative codes generated is more efficient than the natural code. We thus conclude not only that the natural genetic code is extremely efficient at minimizing the effects of errors, but also that its structure reflects biases in these errors, as might be expected were the code the product of selection.
http://www.ncbi.nlm.nih.gov/pubmed/9732450

The genetic code is nearly optimal for allowing additional information within protein-coding sequences
DNA sequences that code for proteins need to convey, in addition to the protein-coding information, several different signals at the same time. These “parallel codes” include binding sequences for regulatory and structural proteins, signals for splicing, and RNA secondary structure. Here, we show that the universal genetic code can efficiently carry arbitrary parallel codes much better than the vast majority of other possible genetic codes. This property is related to the identity of the stop codons. We find that the ability to support parallel codes is strongly tied to another useful property of the genetic code—minimization of the effects of frame-shift translation errors. Whereas many of the known regulatory codes reside in nontranslated regions of the genome, the present findings suggest that protein-coding regions can readily carry abundant additional information.
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1832087/?report=classic

Determination of the Core of a Minimal Bacterial Gene Set
Based on the conjoint analysis of several computational and experimental strategies designed to define the minimal set of protein-coding genes that are necessary to maintain a functional bacterial cell, we propose a minimal gene set composed of 206 genes ( which code for 13 protein complexes ) Such a gene set will be able to sustain the main vital functions of a hypothetical simplest bacterial cell with the following features. These protein complexes could not emerge through evolution ( muations and natural selection ) , because evolution depends on the dna replication, which requires precisely these original genes and proteins ( chicken and egg prolem ). So the only mechanism left is chance, and physical necessity.
http://mmbr.asm.org/content/68/3/518.full.pdf

On the origin of the translation system and the genetic code in the RNA world by means of natural selection, exaptation, and subfunctionalization
The origin of the translation system is, arguably, the central and the hardest problem in the study of the origin of life, and one of the hardest in all evolutionary biology. The problem has a clear catch-22 aspect: high translation fidelity hardly can be achieved without a complex, highly evolved set of RNAs and proteins but an elaborate protein machinery could not evolve without an accurate translation system. The origin of the genetic code and whether it evolved on the basis of a stereochemical correspondence between amino acids and their cognate codons (or anticodons), through selectional optimization of the code vocabulary, as a "frozen accident" or via a combination of all these routes is another wide open problem despite extensive theoretical and experimental studies.
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1894784/





However, the genetic code used by all known forms of life is nearly universal with few minor variations.

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1832087/?report=classic

Abstract

DNA sequences that code for proteins need to convey, in addition to the protein-coding information, several different signals at the same time. These “parallel codes” include binding sequences for regulatory and structural proteins, signals for splicing, and RNA secondary structure. Here, we show that the universal genetic code can efficiently carry arbitrary parallel codes much better than the vast majority of other possible genetic codes. This property is related to the identity of the stop codons. We find that the ability to support parallel codes is strongly tied to another useful property of the genetic code—minimization of the effects of frame-shift translation errors. Whereas many of the known regulatory codes reside in nontranslated regions of the genome, the present findings suggest that protein-coding regions can readily carry abundant additional information.

http://www.arn.org/blogs/index.php/literature/2007/03/01/optimality_features_in_the_genetic_code

"The genetic code is the mapping of 64 three-letter codons to 20 amino-acids and a stop signal." It stands out among possible competing codes in several ways. "First, the assignment of amino acids to codons appears to be optimal for minimizing the effect of translational misread errors." Errors in misreading a codon tend to have minimal effects on the translated protein. "Second, amino acids with simple chemical structure tend to have more codons assigned to them", as "they are required more often in protein assembly". But the researchers main interest in the paper below was the ability of the genetic code to carry parallel messages. The list is already impressive (and there is no reason why it should not be extended with research) - binding sequences of regulatory proteins that bind within coding regions, splicing signals that include specific 6-8 bp sequences within coding regions and mRNA secondary structure signals - all higher-order codes that ride over the protein forming code. "They found that the real genetic code could accommodate more arbitrary motifs in coding sequence than almost any of the other possibilities - it has a higher information content. One reason for the real genetic code's superiority is the fact that its stop codons, when frame-shifted, tend to form common codons, whereas in other codes frame-shifted stop codons form rarer codons or even other stop codons."
The authors appeal to selection to explain why the genetic code is optimal. The implication of this approach is that the selection had to take place before the Last Common Ancestor emerged on Earth. All this complexity had to be fine tuned in a single celled organism that predated all subsequent diversity. An information-based approach linked to Intelligent Agency deserves a fair hearing when seeking an explanation for optimal design.

The genetic code is nearly optimal for allowing additional information within protein-coding sequences
Shalev Itzkovitz and Uri Alon
Genome Research, 2007 17: 405-412. doi 10.1101/gr.5987307(Open Access)

DNA sequences that code for proteins need to convey, in addition to the protein-coding information, several different signals at the same time. These "parallel codes" include binding sequences for regulatory and structural proteins, signals for splicing, and RNA secondary structure. Here, we show that the universal genetic code can efficiently carry arbitrary parallel codes much better than the vast majority of other possible genetic codes. This property is related to the identity of the stop codons. We find that the ability to support parallel codes is strongly tied to another useful property of the genetic code—minimization of the effects of frame-shift translation errors. Whereas many of the known regulatory codes reside in nontranslated regions of the genome, the present findings suggest that protein-coding regions can readily carry abundant additional information.

See also:
Goymer, P., Evolution: The genetic code sees off rivals, Nature Reviews Genetics 8, 168-169 (March 2007) | doi:10.1038/nrg2076

There are many possible three-letter genetic codes that could adequately encode protein sequences, but what about the need to encode higher-order information on binding and splicing sites? New research shows that the actual genetic code is better than potential alternatives at encoding such information at the same time as encoding protein.

Evolution and multilevel optimization of the genetic code
Tobias Bollenbach, Kalin Vetsigian, and Roy Kishony
Genome Research, 2007 17: 401-404. doi 10.1101/gr.6144007

Abstract: The discovery of the genetic code was one of the most important advances of modern biology. But there is more to a DNA code than protein sequence; DNA carries signals for splicing, localization, folding, and regulation that are often embedded within the protein-coding sequence. In this issue, Itzkovitz and Alon show that the specific 64-to-20 mapping found in the genetic code may have been optimized for permitting protein-coding regions to carry this extra information and suggest that this property may have evolved as a side benefit of selection to minimize the negative effects of frameshift errors.

Last paragraph: "As we learn more about the functions of the genetic code, it becomes ever clearer that the degeneracy in the genetic code is not exploited in such a way as to optimize one function, but rather to optimize a combination of several different functions simultaneously. Looking deeper into the structure of the code, we wonder what other remarkable properties it may bear. While our understanding of the genetic code has increased substantially over the last decades, it seems that exciting discoveries are waiting to be made."

http://mbe.oxfordjournals.org/content/17/4/511.long

The evolutionary forces that produced the canonical genetic code before the last universal ancestor remain obscure.

Here, we show that if theoretically possible code structures are limited to reflect plausible biological constraints, and amino acid similarity is quantified using empirical data of substitution frequencies, the canonical code is at or very close to a global optimum for error minimization across plausible parameter space. This result is robust to variation in the methods and assumptions of the analysis. Although significantly better codes do exist under some assumptions, they are extremely rare and thus consistent with reports of an adaptive code: previous analyses which suggest otherwise derive from a misleading metric. However, all extant, naturally occurring, secondarily derived, nonstandard genetic codes do appear less adaptive. The arrangement of amino acid assignments to the codons of the standard genetic code appears to be a direct product of natural selection for a system that minimizes the phenotypic impact of genetic error.

While the evidence for an adaptive code is clear, the process by which the code achieved this optimization requires further attention.

The genetic code is nearly optimal for allowing additional information within protein-coding sequences

DNA sequences that code for proteins need to convey, in addition to the protein-coding information, several different signals at the same time. These “parallel codes” include binding sequences for regulatory and structural proteins, signals for splicing, and RNA secondary structure. Here, we show that the universal genetic code can efficiently carry arbitrary parallel codes much better than the vast majority of other possible genetic codes. This property is related to the identity of the stop codons. We find that the ability to support parallel codes is strongly tied to another useful property of the genetic code—minimization of the effects of frame-shift translation errors. Whereas many of the known regulatory codes reside in nontranslated regions of the genome, the present findings suggest that protein-coding regions can readily carry abundant additional information.

The genetic code is the mapping of 64 three-letter codons to 20 amino-acids and a stop signal (Woese 1965; Crick 1968; Knight et al. 2001). The genetic code has been shown to be nonrandom in at least two ways: first, the assignment of amino acids to codons appears to be optimal for minimizing the effect of translational misread errors. This optimality is achieved by mapping close codons (codons that differ by one letter) to either the same amino acids or to chemically related ones (Woese 1965). This feature has been attributed to an adaptive selection of a code, so that errors that misread a codon by one letter would result in minimal effects on the translated protein (Freeland and Hurst 1998; Freeland et al. 2000; Gilis et al. 2001; Wagner 2005b). Second, amino acids with simple chemical structure tend to have more codons assigned to them (Hasegawa and Miyata 1980; Dufton 1997; Di Giulio 2005).

There exist a large number of alternative genetic codes that are equivalent to the real code in these two prominent features (Fig. 1). Here we ask whether the real code stands out among these alternative codes as being  optimal for other properties.

Robustness to translational frame-shift errors

How did such near optimality for parallel codes evolve? One possibility is that the ability to include parallel codes within protein-coding sequences conferred a selection advantage during the early evolution of the genetic code.

The Genetic Code: proof of intelligent design

Whenever the inference of intelligent design is brought up on this wonderful forum for debate, the usual answer is "there is no evidence for design and your invisible magic man!" I don't take this argument seriously because it is an emotional one, born out of ignorance and personal incredulity. All I will say is look no further than the universal genetic code which is the foundation of life itself. Without it, no cellular organism could even begin to produce the proteins and enzymes that are necessary for its survival. it is essentially a map, that assigns trinucleotide "codons" to 20 amino acids and a STOP site. The enzymes that decode the mRNA transcript will identify each codon with its corresponding amino acid and so allow protein translation to take place. These amino acids constitute a chemical "alphabet" out of which peptide sequences are built. 1

Naturalists have tried to speculate on how this code could have come about purely through the laws of physics and chemistry but have so far failed. But let us avoid any unnecessary speculation and try and determine one thing: is there a strong inference for its purposeful and intelligent design or not? Is it an optimal code or really quite arbitrary in nature?

If amino acids were randomly assigned to triplet codons, then there would be 1.5 x 10^84 possible genetic codes to choose from



However, the genetic code used by all known forms of life is nearly universal with few minor variations.

The genetic code is called redundant or degenerate because there are 4^3 =64 codons for 21 amino acids and a stop site that marks the end of translation. This is an inevitable consequence of having a code with triplets and 4 bases . As such, changing the third base pair of a codon need not result in a change in the amino acid: A good thing for many of us.

As there are 64 codons representing 21 sites, it so happens that there are 1.51 * 10^84 theoretical general codes! But only one exists - it t is universal, albeit with some variation, which indicates it has been around since the time life first began.

Here are some reasons to suppose that the code is optimal and thus the best out of all possible codes:

1) It is a digital and quaternary code. The most widely used digital code we use is binary because we communicate data electronically and there are only two voltage levels (high and low). The genetic code just used 4 symbols (A.C,G,T) instead of 2 binary digits (0,1) to represent information. This is also more efficient as it means less physical space is necessary.

2) The frequency of codons to amino acids accurately reflects the frequency of amino acids in protein sequence with the exception of arginine which is a special case that I won't go into detail here (but note that arginine is over-represented in many important motifs such as the homeobox).

2) It is fault-tolerant. This is partly derived from the fact that most of the 64 codons are synonymous, as explained above, but also due to the fact that a single base pair changes in the 1st and 2nd letters often result in chemically similar amino acids. For example, CTT (leucine) becomes isoleucine when the "C" is substituted for "A". The code therefore dictates the "appropriate distance" between amino acids.

3) It facilitates adaptation. This is because many alkalines and acids are clustered together in the table. Often, a change in the pH level is needed in response to an environmental stimulus. Thus, going from the alkaline lysine (AAA/AAG) to glutamic acid (GAA/GAG) requires only one substitution.

4) It is extremely elegant in that each amino acid is represented by the first two base pairs: the third letter can often be changed but this won't change the amino acid residue.

5) It fully accounts for the transition-transversion bias. Due to molecular mechanisms, the former are twice as likely to occur as the latter. The code recognizes this by ensuring that transitions in the third base pair, but also in the first two, end up with not radically different residues.

6) Just as in asynchronous serial communication, where you have start and stop bits bounding the data frame, so the genetic code incorporates start and stop sites that demarcate the open reading frame (the translated sequence).

Therefore, it is clear that the genetic represents a unique design. So when you look at a table of the genetic code, you are actually staring into the super-intelligent mind of your Creator. It is a religious experience born out of scientific reality.

1) http://www.rationalskepticism.org/creationism/the-genetic-code-proof-of-intelligent-design-t25736.html