GNPS M1152/R10 alpha, beta, gamma 20170912
Ontology highlight
ABSTRACT:
INSTRUMENT(S): 6310 Ion Trap LC/MS
ORGANISM(S): Streptomyces Coelicolor (ncbitaxon:1902)
SUBMITTER: 
Bradley S. Moore
PROVIDER: MSV000081530 | GNPS |
REPOSITORIES: GNPS
Similar Datasets
Project description:Transcriptional profiling of S. coelicolor cells treated with sub-inhibitory or inhibitory concentrations of ciprofloxacin in comparision to untreated control when cultured in R5 media. Two-condition experiment, Control Vs CIP treatment.
2019-06-01 | GSE115753 | GEO
Project description:We used transcriptome profiling by RNAseq to identify the gene expression signatures elucidated in S. coelicolor in response to the three different glycopeptide compounds that share high degree of structural similarities and the same primary mode of action: dalbavancin, vancomycin and chlorobiphenyl-vancomycin.
2021-02-15 | E-MTAB-9846 | biostudies-arrayexpress
Project description:Biogas obtainment and other compounds of biotechnological interest from citrus/orange processing residues
| PRJNA526842 | ENA
Project description:We analyzed the total proteome of CD4+ and CD8+ T cells isolated from human peripheral blood mononuclear cells (PBMC), and cultured to perform a CRISPR/CAS9 edition of their genome, in order to introduce an OST sequence at the C-terminus of proteins of interest (SLP76 or ZAP70, n=3 biological replicates in each case). Control T cells , isolated and cultured in the same way, but not modified by CRISPR/CAS9, were also analyzed (WT, n=3 or 6 biological replicates).
2022-01-10 | PXD025174 | Pride
Project description:Differential expression data set of S. coelicolor M145 and a abrC3 mutant cultured in NB. RNA samples were collected at 36, 48 and 60 hours following cDNA synthesis and labeling. The hybridization was conducted using DNA 104K microarrays (Surrey) using gDNA from S. coelicolor M145 as reference.
2014-01-14 | E-MTAB-1453 | biostudies-arrayexpress
Project description:Differential expression data set of S. coelicolor M145, tdd8 deletion mutant and overexpressed tdd8 mutant cultured in R5. RNA samples were collected at the visual onset Red antibiotic production following by cDNA synthesis and labeling. The hybridization was conducted using DNA 104K microarrays (Surrey) using gDNA from S. coelicolor M145 as reference.
2015-06-08 | E-MTAB-2644 | biostudies-arrayexpress
Project description:Streptomyces coelicolor is a model organism for the study of Streptomyces, a genus of Gram-positive bacteria that undergoes a complex life cycle and produces an enormous repertoire of bioactive metabolites and extracellular enzymes. This study investigated the production and characterization of membrane vesicles (MVs) in liquid cultures of S. coelicolor M145 from a structural and biochemical point of view using microscopic, physical and -omics analyzes. Two main populations of MVs, F3 and F4 MVs, with different size and cargo were isolated and purified. S. coelicolor MV cargo is complex and contains many “messages” such as proteins and metabolites. A total of 166 proteins involved in cell metabolism and differentiation, molecular processing and transport was identified in MVs. In particular, a subset of these proteins was protected from the degradation after proteinase K treatment, indicating their localization inside the vesicles. Vesicle proteome includes many stress response proteins which also play an important role in S. coelicolor morpho- physiological differentiation. Moreover, MVs packaged with an array of metabolites such as antibiotics, vitamins, amino acids and components of carbon metabolism. The analysis of S. coelicolor MV cargo will provide informations to elucidate their biogenesis and functions
2021-11-02 | PXD024085 | Pride
Project description:Streptomyces coelicolor A3(2) strain:M145 Targeted loci cultured
| PRJNA933392 | ENA
Project description:Protein secondary structure elements (PSSEs) such as α-helices, β-strands, and turns are the primary building blocks of the tertiary protein structure. Our primary interest here is to reveal the characteristics of the nanoenvironment formed by both PSSEs and their surrounding amino acid residues (AARs), which might contribute to the general understanding of how proteins fold. The characteristics of such nanoenvironments must be specific to each secondary structure element, and we have set our goal here to gather the fullest possible description of the α-helical nanoenvironment. In general, this postulate (the existence of specific nanoenvironments for specific protein substructures/neighbourhoods/regions with distinct functionality) was already successfully explored and confirmed for some protein regions, such as protein-protein interfaces and enzyme catalytic sites. Consequently, PSSEs were the obvious next choice for additional work for further evidence showing that specific nanoenvironments (having characteristics fully describable by means of structural and physical chemical descriptors) do exist for the corresponding and determined intraprotein regions. The nanoenvironment of α-helices (nEoαH) is defined as any region of the protein where this secondary structure element type is detected. The nEoαH, therefore, includes not only the α-helix amino acid residues but also the residues immediately around the α-helix. The hypothesis that motivated this work is that it might in fact be possible to detect a postulated "signal" or "signature" that distinguishes the specific location of α-helices. This "signal" must be discernible by tracking differences in the values of physical, chemical, physicochemical, structural and geometric descriptors immediately before (or after) the PSSE from those in the region along the α-helices. The search for this specific nanoenvironment "signal" was made possible by aligning previously selected α-helices of equal length. Afterward, we calculated the average value, standard deviation and mean square error at each aligned residue position for each selected descriptor. We applied Student's t-test, the Kolmogorov-Smirnov test and MANOVA statistical tests to the dataset constructed as described above, and the results confirmed that the hypothesized "signal"/"signature" is both existing/identifiable and capable of distinguishing the presence of an α-helix inside the specific nanoenvironment, contextualized as a specific region within the whole protein. However, such conclusion might rarely be reached if only one descriptor is considered at a time. A more accurate signal with broader coverage is achieved only if one applies multivariate analysis, which means that several descriptors (usually approximately 10 descriptors) should be considered at the same time. To a limited extent (up to a maximum of 15% of cases), such conclusion is also possible with only a single descriptor, and the conclusion is also possible in general for up to 50-80% of cases when no less than 5 nonlinear descriptors are selected and considered. Using all the descriptors considered in this work, provided all assumptions about data characteristics for this analysis are met, multivariate analysis regularly reached a coverage and accuracy above 90%. Understanding how secondary structure elements are formed and maintained within a protein structure could enable a more detailed understanding of how proteins reach their final 3D structure and consequently, their function. Likewise, this knowledge may also improve the tools used to determine how good a structure is by means of comparing the "signal" around a selected PSSE with the one obtained from the best (resolution and quality wise) protein structures available.
| S-EPMC6039001 | biostudies-literature
Project description:Molecular networking has become a key method to visualize and annotate the chemical space in non-targeted mass spectrometry data. We present feature-based molecular networking (FBMN) as an analysis method in the Global Natural Products Social Molecular Networking (GNPS) infrastructure that builds on chromatographic feature detection and alignment tools. FBMN enables quantitative analysis and resolution of isomers, including from ion mobility spectrometry.
| S-EPMC7885687 | biostudies-literature