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Briantseva IA Gorlenko VM Turova TP Kuznetsov BB Lysenko AM Bykova SA Gal'chenko VF Mitiushina LL Osipov GA 《Mikrobiologiia》2000,69(3):396-406
Two new species of heliobacteria isolated from cyanobacterial mats of two alkaline sulfidic hot springs are formally described. Strains BR4 and BG29 are assigned to anoxygenic phototrophic bacteria of the family Heliobacteriaceae, since they possess the unique properties of this taxon: strict anaerobiosis, formation of bacteriochlorophyll g, the lack of extensive intracytoplasmic membranes and chlorosomes, an unusual cell wall structure, and phylogenetic relatedness to the low G + C gram-positive eubacteria. Based on the 16S rDNA sequence similarity, strains BR4 and BG29 are assigned to the genus Heliobacterium and described as two new species of this genus: Heliobacterium sulfidophilum sp. nov. and Heliobacterium undosum sp. nov. The G + C content of the DNA is 51.3 mol % in Hbt. sulfidophilum and 57.2-57.7 mol % in Hbt. undosum. The cells of Hbt. sulfidophilum are rods, and the cells of Hbt. undosum are slightly twisted spirilla or short rods. Both new bacteria are motile by peritrichous flagella. Hbt. sulfidophilum produces endospores. The new bacteria are strict anaerobes growing photoheterotrophically on a limited range of organic compounds. In the dark, they can switch from photosynthesis to the slow fermentation of pyruvate. Biotin is required as a growth factor. Both species are highly tolerant to sulfide (up to 2 mM at pH 7.5) and oxidize it photoheterotrophically to elemental sulfur; photoautotrophic growth was not observed. The temperature optimal for growth of Hbt. sulfidophilum and Hbt. undosum is 30-35 degrees C, and the optimal pH is 7-8. 相似文献
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Distance-based methods are popular for reconstructing evolutionary trees thanks to their speed and generality. A number of methods exist for estimating distances from sequence alignments, which often involves some sort of correction for multiple substitutions. The problem is to accurately estimate the number of true substitutions given an observed alignment. So far, the most accurate protein distance estimators have looked for the optimal matrix in a series of transition probability matrices, e.g. the Dayhoff series. The evolutionary distance between two aligned sequences is here estimated as the evolutionary distance of the optimal matrix. The optimal matrix can be found either by an iterative search for the Maximum Likelihood matrix, or by integration to find the Expected Distance. As a consequence, these methods are more complex to implement and computationally heavier than correction-based methods. Another problem is that the result may vary substantially depending on the evolutionary model used for the matrices. An ideal distance estimator should produce consistent and accurate distances independent of the evolutionary model used. 相似文献7.
Background
Profile hidden Markov model (HMM) techniques are among the most powerful methods for protein homology detection. Yet, the critical features for successful modelling are not fully known. In the present work we approached this by using two of the most popular HMM packages: SAM and HMMER. The programs' abilities to build models and score sequences were compared on a SCOP/Pfam based test set. The comparison was done separately for local and global HMM scoring. 相似文献8.
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