Biomedical subjects

Hamed Shateri Najafabadi

Publications and source records attributed to Hamed Shateri Najafabadi.

10 recordsLinked to original sources

PubMed2006

Error minimization explains the codon usage of highly expressed genes in Escherichia coli.

Different organisms use synonymous codons with different preferences. Several measures have been introduced to compute the extent of codon usage bias within a gene or genome, among which the codon adaptation index (CAI) has been shown to be well correlated with mRNA levels of Escherichia coli. In this work an error adaptation index (eAI) is introduced, which estimates the level at which a gene can tolerate the effects of mistranslations. It is shown that the eAI has a strong correlation with CAI, as well as with mRNA levels, which suggests that the codons of highly expressed genes are selected so that mistranslation would have the minimum possible effect on the structure and function of the related proteins.

Base Composition↗

PubMed2006

The case for an error minimizing set of coding amino acids.

The fidelity of the translation machinery largely depends on the accuracy by which the tRNAs within the living cells are charged. Aminoacyl-tRNA synthetases (aaRSs) attach amino acids to their cognate tRNAs ensuring the fidelity of translation in coding sequences. Based on the sequence analysis and catalytic domain structure, these enzymes are classified into two major groups of 10 enzymes each. In this study, we have generally tackled the role of aaRSs in decreasing the effects of mistranslations and consequently the evolution of the translation machinery. To this end, a fitness function was introduced in order to measure the accuracy by which each tRNA is charged with its cognate amino acid. Our results suggest that the aaRSs are very well optimized in "load minimization" based on their classes and their mechanisms in distinguishing the correct amino acids. Besides, our results support the idea that from an evolutionary point, a selectional pressure on the translational fidelity seems to be responsible in the occurrence of the 20 coding amino acids.

Amino Acids↗

PubMed2005

On the coevolution of genes and genetic code.

The canonical genetic code acts efficiently in minimizing the effects of mistranslations and point mutations. In the work presented we have also considered the effects of single nucleotide insertions and deletions on the optimality of the genetic code. Our results suggest that the canonical genetic code compensates for the ins/del mutations as well as mistranslations and point mutations. On the other hand, we highlighted the point that ins/del mutations have a lesser impact on the selected genes of Saccharomyces cerevisiae compared to randomly generated ones. We hypothesized that the codon usage preferences in S. cerevisiae genes are responsible for the higher efficiency of translation machinery in this organism. Our results support the conjecture that codon usage preferences render the genetic code more effective in minimizing the effects of ins/del mutations.

Animals↗

PubMed2005

On the optimality of the genetic code, with the consideration of coevolution theory by comparison of prominent cost measure matrices.

Statistical and biochemical studies have revealed non-random patterns in codon assignments. The canonical genetic code is known to be highly efficient in minimizing the effects of mistranslation errors and point mutations, since it is known that when an amino acid is converted to another due to error, the biochemical properties of the resulted amino acid are usually very similar to those of the original one. In this study, using altered forms of the fitness functions used in the prior studies, we have optimized the parameters involved in the calculation of the error minimizing property of the genetic code so that the genetic code outscores the random codes as much as possible. This work also compares two prominent matrices, the Mutation Matrix and Point Accepted Mutations 74-100 (PAM(74-100)). It has been resulted that the hypothetical properties of the coevolution theory of the genetic code are already considered in PAM(74-100), giving more evidence on the existence of bias towards the genetic code in this matrix. Furthermore, our results indicate that PAM(74-100) is biased towards the single base mistranslation occurrences in second codon position as well as the frequency of amino acids. Thus PAM(74-100) is not a suitable substitution matrix for the studies conducted on the evolution of the genetic code.

Animals↗

PubMed2005

Applying a neural network to predict the thermodynamic parameters for an expanded nearest-neighbor model.

Predicting the secondary and tertiary structure of RNAs largely depends on our capabilities in estimating the thermodynamics of RNA duplexes. In this work, an expanded nearest-neighbor model, designated INN-48, is established. The thermodynamic parameters of this model are predicted using both multiple linear regression analysis and neural network analysis. It is suggested that due to the increase in the number of parameters and the insufficiency of the existing data, neural network analysis results in more reliable predictions. Furthermore, it is suggested that INN-48 can be used to estimate the thermodynamics of RNA duplex formation for longer sequences, whereas INN-HB, the previous model on which INN-48 is based, can be used for short sequences.

Animals↗

PubMed2005

Optimality of codon usage in Escherichia coli due to load minimization.

The canonical genetic code is known to be highly efficient in minimizing the effects of mistranslational errors and point mutations, an ability which in term is designated "load minimization". One parameter involved in calculating the load minimizing property of the genetic code is codon usage. In most bacteria, synonymous codons are not used with equal frequencies. Different factors have been proposed to contribute to codon usage preference. It has been shown that the codon preference is correlated with the composition of the tRNA pool. Selection for translational efficiency and translational accuracy both result in such a correlation. In this work, it is shown that codon usage bias in Escherichia coli works so as to minimize the consequences of translational errors, i.e. optimized for load minimization.

Codon↗

PubMed2005

Designing a neural network for the constraint optimization of the fitness functions devised based on the load minimization of the genetic code.

Nonrandom patterns in codon assignments are supported by many statistical and biochemical studies in the last two decades. The canonical genetic code is known to be highly efficient in minimizing the effects of mistranslational errors and point mutations, an ability, which in term is designated "load minimization". Prior studies have included many attempts at quantitative estimation of the fraction of randomly generated codes, which in terms of load minimization, score higher than the canonical genetic code. In this study, a neural network, which estimates a highly optimized genetic code in a relatively short period of time has been devised. Several fitness functions were used throughout this text. Meanwhile, we have made use of two cost measure matrices, PAM74-100 and mutation matrix.

Algorithms↗

PubMed2005

The impact of including tRNA content on the optimality of the genetic code.

Statistical and biochemical studies have revealed nonrandom patterns in codon assignments. The canonical genetic code is known to be highly efficient in minimizing the effects of mistranslational errors and point mutations, since it is known that, when an amino acid is converted to another due to error, the biochemical properties of the resulted amino acid are usually very similar to those of the original one. In this study, we have taken into consideration both relative frequencies of amino acids and relative gene copy frequencies of tRNAs in genomic sequences in order to introduce a fitness function which models the mistranslational probabilities more accurately in modern organisms. The relative gene copy frequencies of tRNAs are used as estimates of the tRNA content. We also altered the rule previously used for the calculation of the probabilities of single base mutation occurrences. Our model signifies higher optimality of the genetic code towards load minimization and suggests the presence of a coevolution of tRNA frequency and the genetic code.

Amino Acids↗

PubMed2004

Correspondence regarding Bharanidharan et al., "Correlations between nucleotide frequencies and amino acid composition in 115 bacterial species".

Bharanidharan et al. [Biochem. Biophys. Res. Commun. 315 (2004) 1097-1103] claimed that the frequencies of most amino acids are determined by the dinucleotide composition of the genome. Here, regarding a methodological problem in their work, it is suggested that the standard deviations of amino acid frequencies should be determined to indicate how significant a certain deviation from the predicted frequency is. Furthermore, using a different method that is expected to be more reliable, we suggest that the dinucleotide composition cannot explain the observed frequencies of most amino acids, and the deviations of amino acid frequencies from what dinucleotide composition predicts are larger than to be expected by chance.

Amino Acids↗

PubMed2004

How reliable re-adjustment is: correspondence regarding A. Fuglsang, "The 'effective number of codons' revisited".

A. Fuglsang [Biochem. Biophys. Res. Commun. 317 (2004) 957-964] suggested that effective number of codons for individual amino acids (Nc-values) should be re-adjusted to the number of synonymous codons of those amino acids, in order to prevent the overestimation of the effective number of codons. Here, it is shown that re-adjustment at the level of individual amino acids results in loss of considerable amounts of information. Furthermore, we have shown that theoretical Nc-values are functions of GC3s (and GC1s); as a result, when an amino acid Nc-value exceeds the related theoretical Nc-value, the implication of re-adjustment depends on the GC composition of the gene.

Amino Acids↗