ASMPKS: an analysis system for modular polyketide synthases

Tae, Hongseok; Kong, Eun Bae; Park, Kiejung

doi:10.1186/1471-2105-8-327

Cited by 59 publications

(39 citation statements)

References 31 publications

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“…Oftentimes, the distribution of the ion of interest is interfacial and the producer is ambiguous. By employing analytical and biological approaches, such as microbial IMS, IMS of multiple time points (67), traditional solvent extraction, purification, and structural determination methods, such as tandem MS and nuclear magnetic resonance (NMR) (18,30,33,67,68), genetics and microbiology, 16S rRNA sequencing (54), established MALDI-TOF protocols (12,49,53), genome mining approaches (7,13,15,45,63) and predictive programs (2,3,16,33, 40,48,55,62,65,68), peptidogenomics (31), and literature and database searches (23, 52) (AntiMarin database, Dictionary of Natural Products, the National Institute of Standards and Technology [NIST] databases, and the SciFinder database), one can typically annotate microbial IMS data. Two main strategies to confirm the producing microbe and the resultant phenotype are genetic knockout and complementation studies or assays with purified compound, in combination with more IMS.…”

Section: Challenges In Microbial Imsmentioning

confidence: 99%

Primer on Agar-Based Microbial Imaging Mass Spectrometry

et al. 2012

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Matrix-assisted laser desorption ionization–time of flight (MALDI-TOF) imaging mass spectrometry (IMS) applied directly to microbes on agar-based medium captures global information about microbial molecules, allowing for direct correlation of chemotypes to phenotypes. This tool was developed to investigate metabolic exchange factors of intraspecies, interspecies, and polymicrobial interactions. Based on our experience of the thousands of images we have generated in the laboratory, we present five steps of microbial IMS: culturing, matrix application, dehydration of the sample, data acquisition, and data analysis/interpretation. We also address the common challenges encountered during sample preparation, matrix selection and application, and sample adherence to the MALDI target plate. With the practical guidelines described herein, microbial IMS use can be extended to bio-based agricultural, biofuel, diagnostic, and therapeutic discovery applications.

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Section: Challenges In Microbial Imsmentioning

confidence: 99%

Primer on Agar-Based Microbial Imaging Mass Spectrometry

et al. 2012

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“…Each gene was functionally classified by assigning a "clusters of orthologous groups" (COG) number and corresponding COG category (Tatusov et al 2003) together with gene ontology numbers based on the best BLASTP results versus COG results. The motifs and domains of potential protein-coding sequences (CDSs) involved in secondary metabolites were documented based on intensive searches against publicly available databases and by using their application tools, including Pfam, PROSITE, NRPS-PKS (Ansari et al 2004), ASMPKS (Tae et al 2007), NORINE (Caboche et al 2008), and antiSMASH (Medema et al 2011). The tRNA and transfer-mRNA genes were predicted using the tRNAscan-SE (Lowe and Eddy 1997) and ARAGORN (Laslett and Canback 2004) programs, respectively.…”

Section: Genome Annotation and Analysismentioning

confidence: 99%

Characterization and analysis of an industrial strain ofStreptomyces bingchenggensisby genome sequencing and gene microarray

Wang

Zhang

Yan

et al. 2013

Genome

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Streptomyces bingchenggensis is a soil bacterium that produces milbemycins, a family of macrolide antibiotics that are commercially important in crop protection and veterinary medicine. In addition, S. bingchenggensis produces many other natural products including the polyether nanchangmycin and novel cyclic pentapeptides. To identify the gene clusters involved in the biosynthesis of these compounds, and better clarify the biochemical pathways of these gene clusters, the whole genome of S. bingchenggensis was sequenced, and the transcriptome profile was subsequently investigated by microarray. In comparison with other sequenced genomes in Streptomyces, S. bingchenggensis has the largest linear chromosome consisting of 11 936 683 base pairs (bp), with an average GC content of 70.8%. The 10 023 predicted protein-coding sequences include at least 47 gene clusters correlated with the biosynthesis of known or predicted secondary metabolites. Transcriptional analysis demonstrated an extremely high expression level of the milbemycin gene cluster during the entire growth period and a moderately high expression level of the nanchangmycin gene cluster during the initial hours that subsequently decreased. However, other gene clusters appear to be silent. The genome-wide analysis of the secondary metabolite gene clusters in S. bingchenggensis, coupled with transcriptional analysis, will facilitate the rational development of high milbemycins-producing strains as well as the discovery of new natural products.

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“…Additional domains participate in the modification of the carbonyl group; these include ketoreductase (KR), dehydratase (DH) and enoyl reductase (ER) domains. A thioesterase (TE) domain catalyzes the release of the polyketide product from the last PKS participating in chain elongation [9][10][11].…”

Section: Inroductionmentioning

confidence: 99%

Computational Prediction of Properties and Analysis of Molecular Phylogenetics of Polyketide Synthases in Three Species of Actinomycetes

Moosawi¹,

Mohabatkar²,

Mohsenzadeh

2010

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Polyketides are secondary metabolites of microorganisms synthesized by serialized reactions of a set of enzymes called polyketide synthases (PKS). As many infectious microorganisms are acquiring tolerance to antibiotics, the need for novel medicines is increasing. Recently, various methods are being used for drug discovery, including gene manipulation for biosynthesis of antibiotics such as polyketides. Due to their importance as drugs, the volume of data on polyketides is rapidly increasing. In the present paper, by using SEARCHPKS and ASMPKS servers, domain identification, and domain organization, substrate specificity of AT domain, domain assembly and chemical moiety of three Actinomycetes i.e., Mycobacterium abscessus, Micromonospora chalcea and Streptomyces achromogenes are analyzed. So far, no secondary metabolite of any of these bacteria is known. Here, it was demonstrated that KR1, KR2 and KR3 domains from M. chalcea, KR5 domain from M. abscessus and KR6 domain from S. achromogenes could be assigned as B1-type, while KR4 domain from M. abscessus and KR7 domain from S. achromogenes could be assigned as A1 type. Substrate specificity of AT1 and AT2 of M. abscessus predicted to be malonate. AT2 domain of PKS protein from S. achromogenes also selected as malonate. Methylmalonate substrate was predicted for AT1 and AT3 domains. All of the AT domains in modules of PKS protein of M. chalcea were predicted to be specific for methylmalonate. Analysis of folding rate of these proteins showed that the logarithm of (kf) decreased in proportion to protein chain length. We have also performed a comprehensive phylogenetics analysis of AT and DH domains with FabA, FabZ and dehydratase proteins of various bacteria and secondary metabolites. The phylogenetic tree derived from these sequences reflects the long joint evolution process.

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ASMPKS: an analysis system for modular polyketide synthases

Cited by 59 publications

References 31 publications

Primer on Agar-Based Microbial Imaging Mass Spectrometry

Primer on Agar-Based Microbial Imaging Mass Spectrometry

Characterization and analysis of an industrial strain ofStreptomyces bingchenggensisby genome sequencing and gene microarray

Computational Prediction of Properties and Analysis of Molecular Phylogenetics of Polyketide Synthases in Three Species of Actinomycetes

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