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Identification and characterization of Streptomyces ghanaensis ATCC14672 integration sites for three actinophage-based plasmids.

ABSTRACT: Streptomyces ghanaensis produces the antibiotic moenomycin A, which is the only known direct inhibitor of bacterial peptidoglycan glycosyltransferases (transglycosylases). Recent progress in understanding moenomycin biosynthesis opens the door to the generation of novel moenomycins via biocombinatorial approaches. To realize the promise of such an approach, one needs better knowledge of the S. ghanaensis genome and diverse genetic tools for stable expression of recombinant constructs in this strain. In this respect, we report the intergeneric Escherichia coli-S. ghanaensis conjugal transfer of plasmids pRT801 and pSOK804 based on the actinophage BT1 and VWB integrase systems, respectively. The attB sites for these two plasmids and for pSET152 were characterized. In particular, sequencing revealed that a putative Arg-tRNA gene serves as an integration site for both phage VWB and pSAM2-like actinomycete integrative and conjugative element recently suggested to be widespread and functional in actinomycetes. The stability of the studied plasmids and their neutrality with respect to antibiotic production warrant their use for manipulations of S. ghanaensis genome.

SUBMITTER: Ostash B

PROVIDER: S-EPMC2699751 | biostudies-literature | 2009 May

REPOSITORIES: biostudies-literature

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Identification and characterization of Streptomyces ghanaensis ATCC14672 integration sites for three actinophage-based plasmids.

Ostash Bohdan B Makitrinskyy Roman R Walker Suzanne S Fedorenko Victor V

Plasmid 20090122 3

Streptomyces ghanaensis produces the antibiotic moenomycin A, which is the only known direct inhibitor of bacterial peptidoglycan glycosyltransferases (transglycosylases). Recent progress in understanding moenomycin biosynthesis opens the door to the generation of novel moenomycins via biocombinatorial approaches. To realize the promise of such an approach, one needs better knowledge of the S. ghanaensis genome and diverse genetic tools for stable expression of recombinant constructs in this str ...[more]

PMID: 19167423

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Project description:BackgroundProtein phosphoglycerylation, the addition of a 1,3-bisphosphoglyceric acid (1,3-BPG) to a lysine residue of a protein and thus to form a 3-phosphoglyceryl-lysine, is a reversible and non-enzymatic post-translational modification (PTM) and plays a regulatory role in glucose metabolism and glycolytic process. As the number of experimentally verified phosphoglycerylated sites has increased significantly, statistical or machine learning methods are imperative for investigating the characteristics of phosphoglycerylation sites. Currently, research into phosphoglycerylation is very limited, and only a few resources are available for the computational identification of phosphoglycerylation sites.ResultWe present a bioinformatics investigation of phosphoglycerylation sites based on sequence-based features. The TwoSampleLogo analysis reveals that the regions surrounding the phosphoglycerylation sites contain a high relatively of positively charged amino acids, especially in the upstream flanking region. Additionally, the non-polar and aliphatic amino acids are more abundant surrounding phosphoglycerylated lysine following the results of PTM-Logo, which may play a functional role in discriminating between phosphoglycerylation and non-phosphoglycerylation sites. Many types of features were adopted to build the prediction model on the training dataset, including amino acid composition, amino acid pair composition, positional weighted matrix and position-specific scoring matrix. Further, to improve the predictive power, numerous top features ranked by F-score were considered as the final combination for classification, and thus the predictive models were trained using DT, RF and SVM classifiers. Evaluation by five-fold cross-validation showed that the selected features was most effective in discriminating between phosphoglycerylated and non-phosphoglycerylated sites.ConclusionThe SVM model trained with the selected sequence-based features performed well, with a sensitivity of 77.5%, a specificity of 73.6%, an accuracy of 74.9%, and a Matthews Correlation Coefficient value of 0.49. Furthermore, the model also consistently provides the effective performance in independent testing set, yielding sensitivity of 75.7% and specificity of 64.9%. Finally, the model has been implemented as a web-based system, namely iDPGK, which is now freely available at http://mer.hc.mmh.org.tw/iDPGK/ .

Dataset Information

Identification and characterization of Streptomyces ghanaensis ATCC14672 integration sites for three actinophage-based plasmids.

Publications

Identification and characterization of Streptomyces ghanaensis ATCC14672 integration sites for three actinophage-based plasmids.

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