Molecular investigation of the microbial populations of the pink sugarcane mealybug, Saccharicoccus sacchari

In an attempt to better understand the microbial diversity and endosymbiotic microbiota of the pink sugarcane mealybug (PSMB) Saccharicoccus sacchari Cockerell (Homoptera: Pseudococcidae), culture-independent approaches, namely PCR, a 16S rDNA clone library, and temperature gradient gel electrophoresis (TGGE) were used. Previous work has indicated that the acetic acid bacteria Gluconacetobacter sacchari, Gluconacetobacter diazotrophicus, and Gluconacetobacter liquefaciens represent only a small proportion of the microbial community of the PSMB. These findings were supported in this study by TGGE, where no bands representing G. sacchari, G. diazotrophicus, and G. liquefaciens on the acrylamide gel could be observed following electrophoresis, and by a 16S rDNA clone library study, where no clones with the sequence of an acetic acid bacterium were found. Instead, TGGE revealed that the mealybug microbial community was dominated by beta- and gamma-Proteobacteria. The dominant band in TGGE gels found in a majority of the mealybug samples was most similar, according to BLAST analysis, to the beta-symbiont of the craw mealybug Antonina crawii and to Candidatus Tremblaya princeps, an endosymbiont from the mealybug Paracoccus nothofagicola. The sequences of other dominant bands were identified as gamma-Proteobacteria, and were most closely related to uncultured bacterial clones obtained from soil samples. Mealybugs collected from different areas in Queensland, Australia, were found to produce similar TGGE profiles, although there were a few exceptions. A 16S rDNA clone library based on DNA extracted from a mealybug collected from sugarcane in the Burdekin region in Queensland, Australia, indicated very low levels of diversity among mealybug microbial populations. All sequenced clones were most closely related to the same members of the gamma-Proteobacteria, according to BLAST analysis.

Better prediction of protein contact number using a support vector regression analysis of amino acid sequence

Background: Protein tertiary structure can be partly characterized via each amino acid's contact number measuring how residues are spatially arranged. The contact number of a residue in a folded protein is a measure of its exposure to the local environment, and is defined as the number of C-beta atoms in other residues within a sphere around the C-beta atom of the residue of interest. Contact number is partly conserved between protein folds and thus is useful for protein fold and structure prediction. In turn, each residue's contact number can be partially predicted from primary amino acid sequence, assisting tertiary fold analysis from sequence data. In this study, we provide a more accurate contact number prediction method from protein primary sequence. Results: We predict contact number from protein sequence using a novel support vector regression algorithm. Using protein local sequences with multiple sequence alignments (PSI-BLAST profiles), we demonstrate a correlation coefficient between predicted and observed contact numbers of 0.70, which outperforms previously achieved accuracies. Including additional information about sequence weight and amino acid composition further improves prediction accuracies significantly with the correlation coefficient reaching 0.73. If residues are classified as being either contacted or non-contacted, the prediction accuracies are all greater than 77%, regardless of the choice of classification thresholds. Conclusion: The successful application of support vector regression to the prediction of protein contact number reported here, together with previous applications of this approach to the prediction of protein accessible surface area and B-factor profile, suggests that a support vector regression approach may be very useful for determining the structure-function relation between primary sequence and higher order consecutive protein structural and functional properties.