Splitting the BLOSUM Score into Numbers of Biological Significance

Mathematical tools developed in the context of Shannon information theory were used to analyze the meaning of the BLOSUM score, which was split into three components termed as the BLOSUM spectrum (or BLOSpectrum). These relate respectively to the sequence convergence (the stochastic similarity of the two protein sequences), to the background frequency divergence (typicality of the amino acid probability distribution in each sequence), and to the target frequency divergence (compliance of the amino acid variations between the two sequences to the protein model implicit in the BLOCKS database). This treatment sharpens the protein sequence comparison, providing a rationale for the biological significance of the obtained score, and helps to identify weakly related sequences. Moreover, the BLOSpectrum can guide the choice of the most appropriate scoring matrix, tailoring it to the evolutionary divergence associated with the two sequences, or indicate if a compositionally adjusted matrix could perform better.[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29]     

Analysis of Free Energy Signals Arising from Nucleotide Hybridization Between rRNA and mRNA Sequences during Translation in Eubacteria

A decoding algorithm is tested that mechanistically models the progressive alignments that arise as the mRNA moves past the rRNA tail during translation elongation. Each of these alignments provides an opportunity for hybridization between the single-stranded, -terminal nucleotides of the 16S rRNA and the spatially accessible window of mRNA sequence, from which a free energy value can be calculated. Using this algorithm we show that a periodic, energetic pattern of frequency 1/3 is revealed. This periodic signal exists in the majority of coding regions of eubacterial genes, but not in the non-coding regions encoding the 16S and 23S rRNAs. Signal analysis reveals that the population of coding regions of each bacterial species has a mean phase that is correlated in a statistically significant way with species () content. These results suggest that the periodic signal could function as a synchronization signal for the maintenance of reading frame and that codon usage provides a mechanism for manipulation of signal phase.[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32]     

Algorithms for Finding Small Attractors in Boolean Networks

A Boolean network is a model used to study the interactions between different genes in genetic regulatory networks. In this paper, we present several algorithms using gene ordering and feedback vertex sets to identify singleton attractors and small attractors in Boolean networks. We analyze the average case time complexities of some of the proposed algorithms. For instance, it is shown that the outdegree-based ordering algorithm for finding singleton attractors works in time for , which is much faster than the naive time algorithm, where is the number of genes and is the maximum indegree. We performed extensive computational experiments on these algorithms, which resulted in good agreement with theoretical results. In contrast, we give a simple and complete proof for showing that finding an attractor with the shortest period is NP-hard.[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32]     

Sequence Diversity of the oprI Gene,Coding for Major Outer Membrane Lipoprotein I,among rRNA Group I Pseudomonads

The sequence of oprI, the gene coding for the major outer membrane lipoprotein I, was determined by PCR sequencing for representatives of 17 species of rRNA group I pseudomonads, with a special emphasis on Pseudomonas aeruginosa and Pseudomonas fluorescens. Within the P. aeruginosa species, oprI sequences for 25 independent isolates were found to be identical, except for one silent substitution at position 96. The oprI sequences diverged more for the other rRNA group I pseudomonads (85 to 91% similarity with P. aeruginosa oprI). An accumulation of silent and also (but to a much lesser extent) nonsilent substitutions in the different sequences was found. A clustering according to the respective presence and/or positions of the HaeIII, PvuII, and SphI sites could also be obtained. A sequence cluster analysis showed a rather widespread distribution of P. fluorescens isolates. All other rRNA group I pseudomonads clustered in a manner that was in agreement with other studies, showing that the oprI gene can be useful as a complementary phylogenetic marker for classification of rRNA group I pseudomonads.Pseudomonads are increasingly being recognized as important microorganisms in our biosphere, and Pseudomonas aeruginosa and Pseudomonas fluorescens are two important representatives of this genus. As a typical opportunist, P. aeruginosa is more and more involved in a variety of often fatal nosocomial infections, in which it accounts for more than 11% of all isolates recovered (29). In cystic fibrosis, one of the most common autosomal recessive genetic diseases, it is a characteristic pathogen responsible for most of the cases of morbidity and mortality (16, 38). In general, fluorescent pseudomonads, including P. aeruginosa, Pseudomonas putida, P. fluorescens, and other species, are frequently found as rhizosphere microorganisms, in some cases promoting plant growth (11, 19, 20).P. fluorescens and P. aeruginosa are also found as inherent flora of mineral water (14, 39). Identification of fluorescent pseudomonads is often tedious and not reliable. Indeed, the present taxonomy of this group is far from clear at the finer taxonomic level, as polyphasic investigations have demonstrated (4, 13, 18, 26). Ribosomal RNAs have been applied as molecular markers with great success to unravel the rough phylogenetic structure which, at the finer level, is not always in complete agreement with the genotypic and phenotypic similarities deduced from other parts of the genome. Horizontal gene transfer, chromosomal mutation hot spots, and internal genomic rearrangements are probably the bases of these discrepancies at the species and subspecies levels. These arguments, together with the importance of discriminating phenotypic tests in routine identifications, support a polyphasic approach in bacterial taxonomy (2, 8–10, 13, 37, 40). Additional phylogenetic information requires the identification of molecules, like the recA or the gyrB genes, that are widely distributed, large enough to contain a substantial amount of information, and conserved to an appropriate degree (24, 46). In the phylogenetic tree published by Woese (43), species with the same generic name were allocated in phylogenetically distant groups. This was the case for the “genus” Pseudomonas, which is known to be a dump of assemblages of distantly related species (3, 8–10, 17). Taxonomic rearrangements of the genus Pseudomonas sensu stricto resulted in the splitting of the genus and as a logical consequence, the present genus Pseudomonas is restricted to the rRNA group I organisms, with P. aeruginosa as the type species in this group (27, 28, 37, 42, 44, 45).The oprI gene, coding for the outer membrane lipoprotein I of P. aeruginosa (5), was found to be conserved among the fluorescent pseudomonads and was considered to be a possible phylogenetic marker (6, 31). In this study, we tested whether the oprI gene could be a useful detection and identification target molecule as well as a complementary phylogenetic marker for rRNA group I pseudomonads. Also, we examined to what extent the sequence variation of the oprI gene reflects the species diversity in P. aeruginosa and P. fluorescens.