MOTIVATION: We propose representing amino acids by bit-patterns so they may be used in a filter algorithm for similarity searches over protein databases, to rapidly eliminate non-homologous regions of database sequences. The filter algorithm would be based on dynamic programming optimization. It would have the advantage over previous filter algorithms that its substitution scoring function distinguishes between conservative and non-conservative amino acid substitutions. RESULTS: Simulated annealing was used to search for the best five-bit or three-bit patterns to represent amino acids, where similar amino acids were given similar bit-patterns. The similarity between amino acids was estimated from the BLOSUM45 matrix. Representing amino acids by these five-bit and three-bit patterns, the Escherichia coli PhoE precursor and the bacteriophage PA2 LC precursor were aligned. The alignments were nearly the same as that obtained when BLOSUM45 was used to score substitutions. AVAILABILITY: The C code of the optimization algorithm for searching for the optimal bit-pattern representation of amino acids is available from the authors upon request.

Algorithms↗

PubMed2000

Lab rats, rejoice!

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Animal Experimentation↗

PubMed2000

Relationship of codon bias to mRNA concentration and protein length in Saccharomyces cerevisiae.

In 1982, Ikemura reported a strikingly unequal usage of different synonymous codons, in five Saccharomyces cerevisiae nuclear genes having high protein levels. To study this trend in detail, we examined data from three independent studies that used oligonucleotide arrays or SAGE to estimate mRNA concentrations for nearly all genes in the genome. Correlation coefficients were calculated for the relationship of mRNA concentration to four commonly used measures of synonymous codon usage bias: the codon adaptation index (CAI), the codon bias index (CBI), the frequency of optimal codons (F(op)), and the effective number of codons (N(c)). mRNA concentration was best approximated as an exponential function of each of these four measures. Of the four, the CAI was the most strongly correlated with mRNA concentration (r(s)=0.62+/-0.01, n=2525, p<10(-17)). When we controlled for CAI, mRNA concentration and protein length were negatively correlated (partial r(s)=-0.23+/-0.01, n=4765, p<10(-17)). This may result from selection to reduce the size of abundant proteins to minimize transcriptional and translational costs. When we controlled for mRNA concentration, protein length and CAI were positively correlated (partial r(s)=0.16+/-0.01, n=4765, p<10(-17)). This may reflect more effective selection in longer genes against missense errors during translation. The correlation coefficients between the mRNA levels of individual genes, as measured by different investigators and methods, were low, in the range r(s)=0.39-0.68.

Codon↗

PubMed2000