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PubMed2001

Topological nature of the genetic code.

A model for topological coding of proteins is proposed. The model is based on the capacity of hydrogen bonds (property of connectivity) to fix conformations of protein molecules. The protein chain is modeled by an n -arc graph with the following elements: vertices (alpha -carbon atoms), structural edges (peptide bonds) and connectivity edges (virtual edges connecting non-adjacent atoms). It was shown that 64 conformations of the 4-arc graph can be described in the binary system by matrices of six variables which form a supermatrix containing four blocks. On the basis of correspondences between the pairs of variables in matrices and four letters of the genetic code matrices and supermatrix are converted, respectively, into the triplets and the table of the genetic code. An algorithm admitting computer programming is proposed for coding the n -arc graph and protein chain. Connectivity operators (polar amino acids) are assigned to blocks of triplets coding for cyclic conformations (G, A-in the second position), while anti-connectivity operators (non-polar amino acids) correspond to blocks of triplets coding for open conformations (C, U-in the second position). Amino acids coded by triplets differing by the first base have different structures. The third base for C, U and G, A is degenerated. Properties of the real genetic code are in full agreement with the model. The model provides an insight into the topological nature of the genetic code and can be used for development of algorithms for the prediction of the protein structure.

Algorithms↗

PubMed1995

Speculations on the origin of the genetic code.

The most primitive code is assumed to be a GC code: GG coding for glycine, CC coding for proline, GC coding for alanine, CG coding for "arginine." The genetic code is assumed to have originated with the coupling of glycine to its anticodon CC mediated by a copper-montmorillonite. The polymerization of polyproline followed when it was coupled to its anticodon GG. In this case the aminoacyl-tRNA synthetase was a copper-montmorillonite. The first membrane is considered to be a beta sheet formed from polyglycine. As the code grew more complicated, the alternative hydrophobic-hydrophilic polypeptide (alanine-"arginine") was coded for by the alternating CG copolymer. This alternating polypeptide (ala-"arg") began to function as both a primitive membrane and as an aminoacyl-tRNA synthetase. The evolution of protein structure is tightly coupled to the evolution of the membrane. The alpha helix was evolved as lipids became part of the structure of biological membranes. The membrane finally became the fluid mosaic structure that is now universal.

Amino Acids↗

PubMed1995

Evolution of the aminoacyl-tRNA synthetases and the origin of the genetic code.

The aminoacyl-tRNA synthetases exist as two enzyme families which were apparently generated by divergent evolution from two primordial synthetases. The two classes of enzymes exhibit intriguing familial relationships, in that they are distributed nonrandomly within the codon-amino acid matrix of the genetic code. For example, all XCX codons code for amino acids handled by class II synthetases, and all but one of the XUX codons code for amino acids handled by class I synthetases. One interpretation of these patterns is that the synthetases coevolved with the genetic code. The more likely explanation, however, is that the synthetases evolved in the context of an already-established genetic code--a code which developed earlier in an RNA world. The rules which governed the development of the genetic code, and led to certain patterns in the coding catalog between codons and amino acids, would also have governed the subsequent evolution of the synthetases in the context of a fixed code, leading to patterns in synthetase distribution such as those observed. These rules are (1) conservative evolution of amino acid and adapter binding sites and (2) minimization of the disruptive effects on protein structure caused by codon meaning changes.

Amino Acids↗

PubMed1995

On the origin and evolution of the genetic code.

Two ideas have essentially been used to explain the origin of the genetic code: Crick's frozen accident and Woese's amino acid-codon specific chemical interaction. Whatever the origin and codon-amino acid correlation, it is difficult to imagine the sudden appearance of the genetic code in its present form of 64 codons coding for 20 amino acids without appealing to some evolutionary process. On the contrary, it is more reasonable to assume that it evolved from a much simpler initial state in which a few triplets were coding for each of a small number of amino acids. Analysis of genetic code through information theory and the metabolism of pyrimidine biosynthesis provide evidence that suggests that the genetic code could have begun in an RNA world with the two letters A and U grouped in eight triplets coding for seven amino acids and one stop signal. This code could have progressively evolved by making gradual use of letters G and C to end with 64 triplets coding for 20 amino acids and three stop signals. According to proposed evidence, DNA could have appeared after the four-letter structure was already achieved. In the newborn DNA world, T substituted U to get higher physicochemical and genetic stability.

Animals↗

PubMed1997

An analysis of the metabolic theory of the origin of the genetic code.

A computer program was used to test Wong's coevolution theory of the genetic code. The codon correlations between the codons of biosynthetically related amino acids in the universal genetic code and in randomly generated genetic codes were compared. It was determined that many codon correlations are also present within random genetic codes and that among the random codes there are always several which have many more correlations than that found in the universal code. Although the number of correlations depends on the choice of biosynthetically related amino acids, the probability of choosing a random genetic code with the same or greater number of codon correlations as the universal genetic code was found to vary from 0.1% to 34% (with respect to a fairly complete listing of related amino acids). Thus, Wong's theory that the genetic code arose by coevolution with the biosynthetic pathways of amino acids, based on codon correlations between biosynthetically related amino acids, is statistical in nature.

Amino Acids↗

PubMed1998

The historical factor: the biosynthetic relationships between amino acids and their physicochemical properties in the origin of the genetic code.

Two forces are in general, hypothesized to have influenced the origin of the organization of the genetic code: the physicochemical properties of amino acids and their biosynthetic relationships. In view of this, we have considered a model incorporating these two forces. In particular, we have studied the optimization level of the physicochemical properties of amino acids in the set of amino acid permutation codes that respects the biosynthetic relationships between amino acids. Where the properties of amino acids are represented by polarity and molecular volume we obtain indetermination percentages in the organization of the genetic code of approximately 40%. This indicates that the contingent factor played a significant role in structuring the genetic code. Furthermore, this result is in agreement with the genetic code coevolution hypothesis, which attributes a merely ancillary role to the properties of amino acids while it suggests that it was their biosynthetic relationships that organized the code. Furthermore, this result does not favor the stereochemical models proposed to explain the origin of the genetic code. On the other hand, where the properties of amino acids are represented by polarity alone, we obtain an indetermination percentage of at least 21.5%. This might suggest that the polarity distances played an important role and would therefore provide evidence in favor of the physicochemical hypothesis of genetic code origin. Although, overall, the analysis might have given stronger support to the latter hypothesis, this did not actually occur. The results are therefore discussed in the context of the different theories proposed to explain the origin of the genetic code.

Algorithms↗

PubMed2004

A new classification scheme of the genetic code.

Since the early days of the discovery of the genetic code nonrandom patterns have been searched for in the code in the hope of providing information about its origin and early evolution. Here we present a new classification scheme of the genetic code that is based on a binary representation of the purines and pyrimidines. This scheme reveals known patterns more clearly than the common one, for instance, the classification of strong, mixed, and weak codons as well as the ordering of codon families. Furthermore, new patterns have been found that have not been described before: Nearly all quantitative amino acid properties, such as Woese's polarity and the specific volume, show a perfect correlation to Lagerkvist's codon-anticodon binding strength. Our new scheme leads to new ideas about the evolution of the genetic code. It is hypothesized that it started with a binary doublet code and developed via a quaternary doublet code into the contemporary triplet code. Furthermore, arguments are presented against suggestions that a "simpler" code, where only the midbase was informational, was at the origin of the genetic code.

Amino Acids↗

PubMed2006

An analytical model of gene evolution with six mutation parameters: an application to archaeal circular codes.

We develop here an analytical evolutionary model based on a trinucleotide mutation matrix 64 x 64 with six substitution parameters associated with the transitions and transversions in the three trinucleotide sites. It generalizes the previous models based on the nucleotide mutation matrices 4 x 4 and the trinucleotide mutation matrix 64 x 64 with three parameters. It determines at some time t the exact occurrence probabilities of trinucleotides mutating randomly according to six substitution parameters. An application of this model allows an evolutionary study of the common circular code COM and the 15 archaeal circular codes X which have been recently identified in several archaeal genomes. The main property of a circular code is the retrieval of the reading frames in genes, both locally, i.e. anywhere in genes and in particular without a start codon, and automatically with a window of a few nucleotides. In genes, the circular code is superimposed on the traditional genetic one. Very unexpectedly, the evolutionary model demonstrates that the archaeal circular codes can derive from the common circular code subjected to random substitutions with particular values for six substitutions parameters. It has a strong correlation with the statistical observations of three archaeal codes in actual genes. Furthermore, the properties of these substitution rates allow proposal of an evolutionary classification of the 15 archaeal codes into three main classes according to this model. In almost all the cases, they agree with the actual degeneracy of the genetic code with substitutions more frequent in the third trinucleotide site and with transitions more frequent that transversions in any trinucleotide site.

Archaea↗

PubMed1994

Correlation approach to identify coding regions in DNA sequences.

Recently, it was observed that noncoding regions of DNA sequences possess long-range power-law correlations, whereas coding regions typically display only short-range correlations. We develop an algorithm based on this finding that enables investigators to perform a statistical analysis on long DNA sequences to locate possible coding regions. The algorithm is particularly successful in predicting the location of lengthy coding regions. For example, for the complete genome of yeast chromosome III (315,344 nucleotides), at least 82% of the predictions correspond to putative coding regions; the algorithm correctly identified all coding regions larger than 3000 nucleotides, 92% of coding regions between 2000 and 3000 nucleotides long, and 79% of coding regions between 1000 and 2000 nucleotides. The predictive ability of this new algorithm supports the claim that there is a fundamental difference in the correlation property between coding and noncoding sequences. This algorithm, which is not species-dependent, can be implemented with other techniques for rapidly and accurately locating relatively long coding regions in genomic sequences.

Algorithms↗

PubMed2001

The molecular basis of nuclear genetic code change in ciliates.

BACKGROUND: The nuclear genetic code has changed in several lineages of ciliates. These changes, UAR to glutamine and UGA to cysteine, imply that eukaryotic release factor 1 (eRF1), the protein that recognizes stop codons and terminates translation, changes specificity. Here we test whether changes in eRF1 drive genetic code evolution. RESULTS: Database sequence analysis reveals numerous genetic code alterations in ciliates, including UGA --> tryptophan in Blepharisma americanum and the distantly related Colpoda. We sequenced eRF1 from four ciliates: B. americanum, a heterotrich that independently derived the same eRF1 specificity as Euplotes, and three spirotrichs, Stylonychia lemnae, S. mytilus, and Oxytricha trifallax, that independently derived the same genetic code as Tetrahymena (UAR --> glutamine). Distantly related ciliates with similar codes show characteristic changes in eRF1. We used a sliding window analysis to test associations between changes in specific eRF1 residues and changes in the genetic code. The regions of eRF1 that display convergent substitutions are identical to those identified in a recently reported nonsense suppression mutant screen in yeast. CONCLUSIONS: Genetic code change by stop codon reassignment is surprisingly frequent in ciliates, with UGA --> tryptophan occurring twice independently. This is the first description of this code, previously found only in bacteria and mitochondria, in a eukaryotic nuclear genome. eRF1 has evolved strikingly convergently in lineages with variant genetic codes. The strong concordance with biochemical data indicates that our methodology may be generally useful for detecting molecular determinants of biochemical changes in evolution.

Amino Acid Sequence↗

PubMed2003

What can we learn by looking for the first code of professional ethics?

The first code of professional ethics must: (1) be a code of ethics; (2) apply to members of a profession; (3) apply to all members of that profession; and (4) apply only to members of that profession. The value of these criteria depends on how we define "code", "ethics", and "profession", terms the literature on professions has defined in many ways. This paper applies one set of definitions of "code", "ethics", and "profession" to a part of what we now know of the history of professions, thereby illustrating how the choice of definition can alter substantially both our answer to the question of which came first and (more importantly) our understanding of professional codes (and the professions that adopt them). Because most who write on codes of professional ethics seem to take for granted that physicians produced the first professional code, whether the Hippocratic Oath, Percival's Medical Ethics, the 1847 Code of Ethics of the American Medical Association (AMA), or some other document, I focus my discussion on these codes.

American Medical Association↗

PubMed1987

Evolution of anticodons: variations in the genetic code.

Clues to evolution of the genetic code can be found by comparing usage of anticodons in various organisms and organelles. GC content of DNA varies, as a result of directional mutation pressure (AT/GC pressure), especially in bacteria. Low GC in Mycoplasma is accompanied by use of UGA for tryptophan and, in ciliated protozoa, by use of UAA and UAG for glutamine. These are examples of "stop codon capture," which has been preceded by duplication of tRNA genes followed by nucleotide substitutions in their sequences, including mutational changes in their anticodons. Evolutionary changes in the code may have resulted from disappearance of codons and anticodons resulting from GC pressure and from their reappearance when the direction of the pressure was reversed. In this manner, codon UGA and anticodon UCA for tryptophan could have disappeared under GC pressure and reappeared in Mycoplasma under AT pressure. Stop codon UGA may have been the third of the three stop codons to appear, originating from mutations in UAA. Changes in the code are adaptive and nondeleterious. We propose that the number of anticodons has increased and that evolution continued until three existing forms of the universal code were produced: eukaryotic, eubacterial, and the code for halobacteria and methanococci. These three codes are distinguished from each other by their anticodon pattern. The eukaryotic code contains eight INN (ANN) anticodons that have replaced GNN anticodons as a result of AT pressure. Mitochondrial and chloroplast codes have evolved from the eubacterial code through genomic economization and AT pressure, leading to losses of GNN and CNN anticodons.(ABSTRACT TRUNCATED AT 250 WORDS)

Amino Acids↗

PubMed2006

Parallel evolution of the genetic code in arthropod mitochondrial genomes.

The genetic code provides the translation table necessary to transform the information contained in DNA into the language of proteins. In this table, a correspondence between each codon and each amino acid is established: tRNA is the main adaptor that links the two. Although the genetic code is nearly universal, several variants of this code have been described in a wide range of nuclear and organellar systems, especially in metazoan mitochondria. These variants are generally found by searching for conserved positions that consistently code for a specific alternative amino acid in a new species. We have devised an accurate computational method to automate these comparisons, and have tested it with 626 metazoan mitochondrial genomes. Our results indicate that several arthropods have a new genetic code and translate the codon AGG as lysine instead of serine (as in the invertebrate mitochondrial genetic code) or arginine (as in the standard genetic code). We have investigated the evolution of the genetic code in the arthropods and found several events of parallel evolution in which the AGG codon was reassigned between serine and lysine. Our analyses also revealed correlated evolution between the arthropod genetic codes and the tRNA-Lys/-Ser, which show specific point mutations at the anticodons. These rather simple mutations, together with a low usage of the AGG codon, might explain the recurrence of the AGG reassignments.

Animals↗

PubMed2001

Factors that explain the power line configuration wiring code-childhood leukemia association: what would they look like?

We explore the question of how to characterize a factor that would explain the patterns of wire code-childhood leukemia association observed in three major United States case-control studies. In positive studies, such a factor needs to be more prevalent in "high" wire codes than "low" whereas in negative studies, the factor should not be correlated to wire code. These concepts are quantified and we use these results to characterize potential factors that might explain the wire code-childhood leukemia association. We then re-evaluate findings from a survey of correlates of wire code in Connecticut with respect to potential for explaining the wire code-leukemia association. Under the presumption that the Connecticut correlations apply to the populations in which a wire code-leukemia association has been observed, only age and type of home are correlated enough to be potential explanatory factors. In particular, it appears very unlikely that traffic density can explain a significant portion of the wire code association. We suggest that correlation studies can be useful for identifying potential explanatory factors, but they must be done in regions where a wire code-childhood leukemia association has been observed.

Biometry↗

PubMed1993

Repeatability of measurements of residential magnetic fields and wire codes.

Several epidemiological studies have been based on wire codes (i.e., categories of electrical wiring configurations near residences) or on in-home spot measurements of magnetic flux density (MFD) as surrogates for short- and long-term exposure of children and adults to residential magnetic fields. We used wire code and MFD measurements that were made in 81 Colorado homes in 1985 and again in 1990 to assess their repeatability over periods of 0-24 h and 5 years. These homes, a subset of those lived in by subjects from the case-control study of Savitz et al. [Am J Epidemiol 128:21-38, 1988], were divided into four approximately equally sized groups that were differentiated by wire code and by case-control status. Eight homes were assigned wire codes in 1990 that differed from the 1985 coding of Savitz and colleagues [1988]; of these, seven were coded as high-current configuration (HCC) and one as low-current configuration (LCC) in 1985. Overall, 37 homes were coded as HCC in 1990 compared with 38 homes in 1985. Coding differences were due to differing distance measurements (four homes), differing "thick" vs. "thin" categorization of primary-distribution line-conductor sizes (two homes), differing "first-span" vs. "second-span" categorization of secondary wires (one home), and physical changes in proximate electrical wiring (one home). Coefficients of correlation between MFD spot measurements that were separated in time by 0-24 h range between 0.70 and 0.90. The coefficient between spot measurements made in 1985 and then again in 1990 is 0.70. These coefficients are similar for HCC and LCC homes and do not depend on whether residential appliances were turned on or off. The data show (at least for the portion of Colorado studied) that residential wire code and, more surprisingly, spot MFD measurements, are fairly reliable over 0-24-h and 5-year periods.

Adult↗

PubMed2000

Coding neuroradiology reports for the Northern Manhattan Stroke Study: a comparison of natural language processing and manual review.

Automated systems using natural language processing may greatly speed chart review tasks for clinical research, but their accuracy in this setting is unknown. The objective of this study was to compare the accuracy of automated and manual coding in the data acquisition tasks of an ongoing clinical research study, the Northern Manhattan Stroke Study(NOMASS). We identified 471 neuroradiology reports of brain images used in the NOMASS study. Using both automated and manual coding, we completed a standardized NOMASS imaging form with the information contained in these reports. We then generated ROC curves for both manual and automated coding by comparing our results to the original NOMASS data, where study in investigators directly coded their interpretations of brain images. The areas under the ROC curves for both manual and automated coding were the main outcome measure. The overall predictive value of the automated system (ROC area 0.85, 95% CI 0.84-0.87) was not statistically different from the predictive value of the manual coding (ROC area 0.87, 95% CI 0.83-0.91). Measured in terms of accuracy, the automated system performed slightly worse than manual coding. The overall accuracy of the automated system was 84% (CI 83-85%). The overall accuracy of manual coding was 86% (CI 84-88%). The difference in accuracy between the two methods was small but statistically significant (P = 0.026). Errors in manual coding appeared to be due to differences between neurologists' and nueroradiologists' interpretation, different use of detailed anatomic terms, and lack of clinical information. Automated systems can use natural language processing to rapidly perform complex data acquisition tasks. Although there is a small decrease in the accuracy of the data as compared to traditional methods, automated systems may greatly expand the power of chart review in clinical research design and implementation.

Brain↗

PubMed1997

Another neural code?

This paper presents the conjecture that functional integration may be mediated by the mutual induction and maintenance of stereotyped spatiotemporal patterns of activity (i.e., transients) in different neuronal populations. In contradistinction to temporal and rate coding models of neuronal interactions, transient coding considers that transactions among neuronal systems use transient dynamics that are distributed in a structured way over both space and time. In contrast to synchronization models, transient coding does not depend on interactions at the same frequencies, in different parts of the brain, but involves covariations among different frequencies and can therefore be considered a more general form of coding. Using an analysis of the correlations among the spectral density of neuromagnetic signals, measured at different cortical regions, this hypothesis was confirmed. For example high (gamma)-frequency oscillations in the prefrontal cortex are associated with low (20 Hz)-frequency oscillations in the parietal cortex. The results are consistent with transient coding and suggest that transient dynamics endure for at least 40-200 ms. Transient coding means that correlations (rate coding) and coherence (synchrony) are neither complete nor sufficient characterizations of neuronal interactions. Although temporal coding, rate coding, and synchrony are important aspects of neuronal interactions, the results speak to further integrative neuronal mechanisms of a more general nature.

Models, Neurological↗

PubMed1990

Potential genetic functions of tandem repeated DNA sequence blocks in the human genome are based on a highly conserved "chromatin folding code".

This review is based on a thorough description of the structure and sequence organization of tandemly organized repetitive DNA sequence families in the human genome; it is aimed at revealing the locus-specific sequence organization of tandemly repetitive sequence structures as a highly conserved DNA sequence code. These repetitive so-called "super-structures" or "higher-order" structures are able to attract specific nuclear proteins. I shall define this code therefore as a "chromatin folding code". Since locus-specific superstructures of tandemly repetitive sequence units are present not only in the chromosome centromere or telomere region but also on the arms of the chromosomes, I assume that their chromatin folding code may contribute to, or even organize, the folding pathway of the chromatin chain in the nucleus. The "chromatin folding code" is based on its specific "chromatin code", which describes the sequence dependence of the helical pathway of the DNA primary sequence (i.e., secondary structure) entrapping the histone octamers in preferential positions. There is no periodicity in the distribution of the nucleosomes along the DNA chain. The folding pathway of the nucleosomal chromatin chain is however still flexible and determined by e.g., the length of the DNA chain between the nucleosomes. The fixation and stabilization of the chromatin chain in the space of the nucleus (i.e., its "functional state") may be mediated by additionally unique DNA protein interactions that are dictated by the "chromatin folding code". The unique DNA-protein interactions around the centromeres of human chromosomes are revealed for example by their "C-banding". I wish to stress that it is not my aim to relate each block of repetitive DNA sequences to a specific "chromatin folding code", but I shall demonstrate that there is an inherent potential for tandem repeated sequence units to develop a locus-specific repetitive higher order structure; this potential may create a specific chromatin folding code whenever a selection force exists at the position of this repetitive DNA structure in the genome.

Chromatin↗