Gobichelin A and B: Mixed-Ligand Siderophores Discovered Using Proteomics.
Ontology highlight
ABSTRACT:
SUBMITTER: Chen Y
PROVIDER: S-EPMC3547389 | biostudies-other | 2013 Jan
REPOSITORIES: biostudies-other
Publications
Gobichelin A and B: Mixed-Ligand Siderophores Discovered Using Proteomics.
Chen Yunqiu Y Unger Michelle M Ntai Ioanna I McClure Ryan A RA Albright Jessica C JC Thomson Regan J RJ Kelleher Neil L NL
MedChemComm 20120924 1
"Omic" strategies have been increasingly applied to natural product discovery processes, with (meta-)genome sequencing and mining implemented in many laboratories to date. Using the proteomics-based discovery platform called PrISM (Proteomic Investigation of Secondary Metabolism), we discovered two new siderophores gobichelin A and B from Streptomyces sp. NRRL F-4415, a strain without a sequenced genome. Using the proteomics information as a guide, the 37 kb gene cluster responsible for producti ...[more]
Similar Datasets
Project description:Pancreatic cancer is a lethal disease where specific early detection biomarkers would be very valuable to improve outcomes in patients. Many previous studies have compared biosamples from pancreatic cancer patients with healthy controls to find potential biomarkers. However, a range of related disease conditions can influence the performance of these putative biomarkers, including pancreatitis and diabetes. In this study, quantitative proteomics methods were applied to discover potential serum glycoprotein biomarkers that distinguish pancreatic cancer from other pancreas related conditions (diabetes, cyst, chronic pancreatitis, obstructive jaundice) and healthy controls. Aleuria aurantia lectin (AAL) was used to extract fucosylated glycoproteins and then both TMT protein-level labeling and label-free quantitative analysis were performed to analyze glycoprotein differences from 179 serum samples across the six different conditions. A total of 243 and 354 serum proteins were identified and quantified by label-free and TMT protein-level quantitative strategies, respectively. Nineteen and 25 proteins were found to show significant differences in samples between the pancreatic cancer and other conditions using the label-free and TMT strategies, respectively, with 7 proteins considered significant in both methods. Significantly different glycoproteins were further validated by lectin-ELISA and ELISA assays. Four candidates were identified as potential markers with profiles found to be highly complementary with CA 19-9 (p < 0.001). Obstructive jaundice (OJ) was found to have a significant impact on the performance of every marker protein, including CA 19-9. The combination of α-1-antichymotrypsin (AACT), thrombospondin-1 (THBS1), and haptoglobin (HPT) outperformed CA 19-9 in distinguishing pancreatic cancer from normal controls (AUC = 0.95), diabetes (AUC = 0.89), cyst (AUC = 0.82), and chronic pancreatitis (AUC = 0.90). A marker panel of AACT, THBS1, HPT, and CA 19-9 showed a high diagnostic potential in distinguishing pancreatic cancer from other conditions with OJ (AUC = 0.92) or without OJ (AUC = 0.95).
| S-EPMC3993962 | biostudies-literature
Project description:The increasing number of RNA crystal structures enables a structure-based approach to the discovery of new RNA-binding ligands. To develop the poorly explored area of RNA-ligand docking, we have conducted a virtual screening exercise for a purine riboswitch to probe the strengths and weaknesses of RNA-ligand docking. Using a standard protein-ligand docking program with only minor modifications, four new ligands with binding affinities in the micromolar range were identified, including two compounds based on molecular scaffolds not resembling known ligands. RNA-ligand docking performed comparably to protein-ligand docking indicating that this approach is a promising option to explore the wealth of RNA structures for structure-based ligand design.
| S-EPMC3119931 | biostudies-literature
Project description:Chlamydia pneumonia, a species of the family Chlamydiacea, is a leading cause of pneumonia. Failure to eradicate C. pneumoniae can lead to chronic infection, which is why it is also considered responsible for chronic inflammatory disorders such as asthma, arthritis, etc. There is an urgent need to tackle the major concerns arising due to persistent infections caused by C. pneumoniae as no FDA-approved drug is available against this chronic infection. In the present study, an approach named subtractive proteomics was employed to the core proteomes of five strains of C. pneumonia using various bioinformatic tools, servers, and software. However, 958 non-redundant proteins were predicted from the 4754 core proteins of the core proteome. BLASTp was used to analyze the non-redundant genes against the proteome of humans, and the number of potential genes was reduced to 681. Furthermore, based on subcellular localization prediction, 313 proteins with cytoplasmic localization were selected for metabolic pathway analysis. Upon subsequent analysis, only three cytoplasmic proteins, namely 30S ribosomal protein S4, 4-hydroxybenzoate decarboxylase subunit C, and oligopeptide binding protein, were identified, which have the potential to be novel drug target candidates. The Swiss Model server was used to predict the target proteins’ three-dimensional (3D) structure. The molecular docking technique was employed using MOE software for the virtual screening of a library of 15,000 phytochemicals against the interacting residues of the target proteins. Molecular docking experiments were also evaluated using molecular dynamics simulations and the widely used MM-GBSA and MM-PBSA binding free energy techniques. The findings revealed a promising candidate as a novel target against C. pneumonia infections.
| S-EPMC9223490 | biostudies-literature
Project description:Shotgun proteomics protocols are widely used for the identification and/or quantitation of proteins in complex biological samples. Described here is a shotgun proteomics protocol that can be used to identify the protein targets of biologically relevant ligands in complex protein mixtures. The protocol combines a quantitative proteomics platform with a covalent modification strategy, termed Stability of Proteins from Rates of Oxidation (SPROX), which utilizes the denaturant dependence of hydrogen peroxide-mediated oxidation of methionine side chains in proteins to assess the thermodynamic properties of proteins and protein-ligand complexes. The quantitative proteomics platform involves the use of isobaric mass tags and a methionine-containing peptide enhancement strategy. The protocol is evaluated in a ligand binding experiment designed to identify the proteins in a yeast cell lysate that bind the well-known enzyme cofactor, ?-nicotinamide adenine dinucleotide (NAD+). The protocol is also used to investigate the protein targets of resveratrol, a biologically active ligand with less well-understood protein targets. A known protein target of resveratrol, cytosolic aldehyde dehydrogenase, was identified in addition to six other potential new proteins targets including four that are associated with the protein translation machinery, which has previously been implicated as a target of resveratrol.
| S-EPMC3208786 | biostudies-literature
Project description:Image data are increasingly encountered and are of growing importance in many areas of science. Much of these data are quantitative image data, which are characterized by intensities that represent some measurement of interest in the scanned images. The data typically consist of multiple images on the same domain and the goal of the research is to combine the quantitative information across images to make inference about populations or interventions. In this paper, we present a unified analysis framework for the analysis of quantitative image data using a Bayesian functional mixed model approach. This framework is flexible enough to handle complex, irregular images with many local features, and can model the simultaneous effects of multiple factors on the image intensities and account for the correlation between images induced by the design. We introduce a general isomorphic modeling approach to fitting the functional mixed model, of which the wavelet-based functional mixed model is one special case. With suitable modeling choices, this approach leads to efficient calculations and can result in flexible modeling and adaptive smoothing of the salient features in the data. The proposed method has the following advantages: it can be run automatically, it produces inferential plots indicating which regions of the image are associated with each factor, it simultaneously considers the practical and statistical significance of findings, and it controls the false discovery rate. Although the method we present is general and can be applied to quantitative image data from any application, in this paper we focus on image-based proteomic data. We apply our method to an animal study investigating the effects of opiate addiction on the brain proteome. Our image-based functional mixed model approach finds results that are missed with conventional spot-based analysis approaches. In particular, we find that the significant regions of the image identified by the proposed method frequently correspond to subregions of visible spots that may represent post-translational modifications or co-migrating proteins that cannot be visually resolved from adjacent, more abundant proteins on the gel image. Thus, it is possible that this image-based approach may actually improve the realized resolution of the gel, revealing differentially expressed proteins that would not have even been detected as spots by modern spot-based analyses.
| S-EPMC3298181 | biostudies-literature
Project description:In quantitative proteomics, mass tag labeling techniques have been widely adopted in mass spectrometry experiments. These techniques allow peptides (short amino acid sequences) and proteins from multiple samples of a batch being detected and quantified in a single experiment, and as such greatly improve the efficiency of protein profiling. However, the batch-processing of samples also results in severe batch effects and non-ignorable missing data occurring at the batch level. Motivated by the breast cancer proteomic data from the Clinical Proteomic Tumor Analysis Consortium, in this work, we developed two tailored multivariate MIxed-effects SElection models (mvMISE) to jointly analyze multiple correlated peptides/proteins in labeled proteomics data, considering the batch effects and the non-ignorable missingness. By taking a multivariate approach, we can borrow information across multiple peptides of the same protein or multiple proteins from the same biological pathway, and thus achieve better statistical efficiency and biological interpretation. These two different models account for different correlation structures among a group of peptides or proteins. Specifically, to model multiple peptides from the same protein, we employed a factor-analytic random effects structure to characterize the high and similar correlations among peptides. To model biological dependence among multiple proteins in a functional pathway, we introduced a graphical lasso penalty on the error precision matrix, and implemented an efficient algorithm based on the alternating direction method of multipliers. Simulations demonstrated the advantages of the proposed models. Applying the proposed methods to the motivating data set, we identified phosphoproteins and biological pathways that showed different activity patterns in triple negative breast tumors versus other breast tumors. The proposed methods can also be applied to other high-dimensional multivariate analyses based on clustered data with or without non-ignorable missingness.
| S-EPMC6797056 | biostudies-literature
Project description:Identifying targets of biologically active small molecules is an essential but still challenging task in drug research and chemical genetics. Energetics-based target identification is an approach that utilizes the change in the conformational stabilities of proteins upon ligand binding in order to identify target proteins. Different from traditional affinity-based capture approaches, energetics-based methods do not require any labeling or immobilization of the test molecule. Here, we report a surprisingly simple version of energetics-based target identification, which only requires ion exchange chromatography, SDS PAGE, and minimal use of mass spectrometry. The complexity of a proteome is reduced through fractionation by ion exchange chromatography. Urea-induced unfolding of proteins in each fraction is then monitored by the significant increase in proteolytic susceptibility upon unfolding in the presence and the absence of a ligand. Proteins showing a different degree of unfolding with the ligand are identified by SDS PAGE followed by mass spectrometry. Using this approach, we identified ATP-binding proteins in the Escherichia coli proteome. In addition to known ATP-binding proteins, we also identified a number of proteins that were not previously known to interact with ATP. To validate one such finding, we cloned and purified phosphoglyceromutase, which was not previously known to bind ATP, and confirmed that ATP indeed stabilizes this protein. The combination of fractionation and pulse proteolysis offers an opportunity to investigate protein-drug or protein-metabolite interactions on a proteomic scale with minimal instrumentation and without modification of a molecule of interest.
| S-EPMC3631357 | biostudies-literature
Project description:A total of 12 WW domains from Saccharomyces cerevisiae were expressed and purified as fusion proteins to either GST or MBP. The fusion proteins were chemically biotinylated and applied to duplicate protein microarrays. Data processing revealed a total of 587 interactions between the domains and 207 proteins. Most of these interactions have not been previously observed. Keywords: Protein microarray analysis of yeast WW domains
2006-02-08 | GSE3758 | GEO
Project description:Regulation of gene expression at the transcriptional level is achieved by complex interactions of transcription factors operating at their target genes. Dissecting the specific combination of factors that bind each target is a significant challenge. Here, we describe in detail the Allele Binding Cooperativity test, which uses variation in transcription factor binding among individuals to discover combinations of factors and their targets. We developed the ALPHABIT (a large-scale process to hunt for allele binding interacting transcription factors) pipeline, which includes statistical analysis of binding sites followed by experimental validation, and demonstrate that this method predicts transcription factors that associate with NF?B. Our method successfully identifies factors that have been known to work with NF?B (E2A, STAT1, IRF2), but whose global coassociation and sites of cooperative action were not known. In addition, we identify a unique coassociation (EBF1) that had not been reported previously. We present a general approach for discovering combinatorial models of regulation and advance our understanding of the genetic basis of variation in transcription factor binding.
| S-EPMC3156166 | biostudies-literature
Project description:Fundamental algorithms such as sorting or hashing are used trillions of times on any given day1. As demand for computation grows, it has become critical for these algorithms to be as performant as possible. Whereas remarkable progress has been achieved in the past2, making further improvements on the efficiency of these routines has proved challenging for both human scientists and computational approaches. Here we show how artificial intelligence can go beyond the current state of the art by discovering hitherto unknown routines. To realize this, we formulated the task of finding a better sorting routine as a single-player game. We then trained a new deep reinforcement learning agent, AlphaDev, to play this game. AlphaDev discovered small sorting algorithms from scratch that outperformed previously known human benchmarks. These algorithms have been integrated into the LLVM standard C++ sort library3. This change to this part of the sort library represents the replacement of a component with an algorithm that has been automatically discovered using reinforcement learning. We also present results in extra domains, showcasing the generality of the approach.
| S-EPMC10247365 | biostudies-literature