Gene/Protein Disease Symptom Drug Enzyme Compound
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Query: EC:2.7.7.6 (RNA polymerase)
 34,946 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

Affinity purification combined with tandem mass spectrometry (AP-MS/MS) is a well-established method used to discover interaction partners for a given protein of interest. Because most AP-MS/MS approaches are performed using the soluble fraction of whole cell extracts (WCE), information about the cellular compartments where the interactions occur is lost. More importantly, classical AP-MS/MS often fails to identify interactions that take place in the nonsoluble fraction of the cell, for example, on the chromatin or membranes; consequently, protein complexes that are less soluble are underrepresented. In this paper, we introduce a method called multiple cell compartment AP-MS/MS (MCC-AP-MS/MS), which identifies the interactions of a protein independently in three fractions of the cell: the cytoplasm, the nucleoplasm, and the chromatin. We show that this fractionation improves the sensitivity of the method when compared to the classical affinity purification procedure using soluble WCE while keeping a very high specificity. Using three proteins known to localize in various cell compartments as baits, the CDK9 subunit of transcription elongation factor P-TEFb, the RNA polymerase II (RNAP II)-associated protein 4 (RPAP4), and the largest subunit of RNAP II, POLR2A, we show that MCC-AP-MS/MS reproducibly yields fraction-specific interactions. Finally, we demonstrate that this improvement in sensitivity leads to the discovery of novel interactions of RNAP II carboxyl-terminal domain (CTD) interacting domain (CID) proteins with POLR2A.
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PMID:Discovery of cell compartment specific protein-protein interactions using affinity purification combined with tandem mass spectrometry. 2315 68

The objective of this study was to characterize native Lactobacillus fermentum isolates for their probiotic attributes. Accordingly, 12 L. fermentum isolates selected from indigenous fermented dairy products and infant fecal samples were evaluated for their probiotic properties by in vitro and PCR methods. The cultures exhibited high tolerance to acid and bile as well as survival in simulated transit fluids (above 70 %). Cell surface hydrophobicity was in the range of 0.55-57.69 % for xylene and 0.45-77.12 % for hexadecane, whereas auto-aggregation ranged between 9 and 62 %. Isolates exhibited efficient binding to mucin and fibronectin, bile salt hydrolase activity, cholesterol assimilation (49-76 %), and radical scavenging activity (37-77 %). The isolates demonstrated antibacterial activity against Listeria monocytogenes Scott A and Micrococcus luteus ATCC 9341. Molecular fingerprinting and identification of the isolates were achieved by PCR with GTG5 as well as 16S rRNA, phenylalanyl-tRNA synthetase alpha subunit (pheS), and RNA polymerase alpha subunit (rpoA) genes. This revealed the genomic diversity of the isolates from the two sources. Gene-specific amplification of probiotic marker genes was attained by PCR-based methods, and resultant products were sequenced. Multiple sequence alignment of the probiotic marker genes using bioinformatics revealed similarity to completely sequenced genomes of L. fermentum CECT 5716 and IFO 3956 with a few variations in mucin-binding protein gene sequences. Isolates designated as L. fermentum MCC 2759 and L. fermentum MCC 2760 showed the best probiotic attributes with high survival in simulated gastrointestinal fluids, in vitro adhesion, cholesterol reduction, and high antioxidative potential. Thus, these cultures could be potential probiotic candidates for application as functional foods.
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PMID:Probiotic attributes of Lactobacillus fermentum isolated from human feces and dairy products. 2600 4

Rifampicin resistance is a major therapeutic challenge, particularly in tuberculosis, leprosy, P. aeruginosa and S. aureus infections, where it develops via missense mutations in gene rpoB. Previously we have highlighted that these mutations reduce protein affinities within the RNA polymerase complex, subsequently reducing nucleic acid affinity. Here, we have used these insights to develop a computational rifampicin resistance predictor capable of identifying resistant mutations even outside the well-defined rifampicin resistance determining region (RRDR), using clinical M. tuberculosis sequencing information. Our tool successfully identified up to 90.9% of M. tuberculosis rpoB variants correctly, with sensitivity of 92.2%, specificity of 83.6% and MCC of 0.69, outperforming the current gold-standard GeneXpert-MTB/RIF. We show our model can be translated to other clinically relevant organisms: M. leprae, P. aeruginosa and S. aureus, despite weak sequence identity. Our method was implemented as an interactive tool, SUSPECT-RIF (StrUctural Susceptibility PrEdiCTion for RIFampicin), freely available at https://biosig.unimelb.edu.au/suspect_rif/ .
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PMID:Prediction of rifampicin resistance beyond the RRDR using structure-based machine learning approaches. 3309 32