6 resultados para Identification of a putative gene

em Aston University Research Archive


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Homology modelling was used to generate three-dimensional structures of the nucleotide-binding domains (NBDs) of human ABCB1 and ABCG2. Interactions between a series of steroidal ligands and transporter NBDs were investigated using an in silico docking approach. C-terminal ABCB1 NBD (ABCB1 NBD2) was predicted to bind steroids within a cavity formed partly by the P-Loop, Tyr1044 and Ile1050. The P-Loop within ABCG2 NBD was also predicted to be involved in steroid binding. No overlap between ATP- and RU-486-binding sites was predicted in either NBD, though overlaps between ATP- and steroid-binding sites were predicted in the vicinity of the P-Loop in both nucleotide-binding domains.

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Nesfatin-1 is a recently identified anorexigenic peptide derived from its precursor protein, nonesterified fatty acid/nucleobindin 2 (NUCB2). Although the hypothalamus is pivotal for the maintenance of energy homeostasis, adipose tissue plays an important role in the integration of metabolic activity and energy balance by communicating with peripheral organs and the brain via adipokines. Currently no data exist on nesfatin-1 expression, regulation, and secretion in adipose tissue. We therefore investigated NUCB2/nesfatin-1 gene and protein expression in human and murine adipose tissue depots. Additionally, the effects of insulin, dexamethasone, and inflammatory cytokines and the impact of food deprivation and obesity on nesfatin-1 expression were studied by quantitative RT-PCR and Western blotting. We present data showing NUCB2 mRNA (P < 0.001), nesfatin-1 intracellular protein (P < 0.001), and secretion (P < 0.01) were significantly higher in sc adipose tissue compared with other depots. Also, nesfatin-1 protein expression was significantly increased in high-fat-fed mice (P < 0.01) and reduced under food deprivation (P < 0.01) compared with controls. Stimulation of sc adipose tissue explants with inflammatory cytokines (TNFa and IL-6), insulin, and dexamethasone resulted in a marked increase in intracellular nesfatin-1 levels. Furthermore, we present evidence that the secretion of nesfatin-1 into the culture media was dramatically increased during the differentiation of 3T3-L1 preadipocytes into adipocytes (P < 0.001) and after treatments with TNF-a, IL-6, insulin, and dexamethasone (P < 0.01). In addition, circulating nesfatin-1 levels were higher in high-fat-fed mice (P < 0.05) and showed positive correlation with body mass index in human. We report that nesfatin-1 is a novel depot specific adipokine preferentially produced by sc tissue, with obesity- and food deprivation-regulated expression.

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Six independent studies have identified linkage to chromosome 18 for developmental dyslexia or general reading ability. Until now, no candidate genes have been identified to explain this linkage. Here, we set out to identify the gene(s) conferring susceptibility by a two stage strategy of linkage and association analysis. Methodology/Principal Findings: Linkage analysis: 264 UK families and 155 US families each containing at least one child diagnosed with dyslexia were genotyped with a dense set of microsatellite markers on chromosome 18. Association analysis: Using a discovery sample of 187 UK families, nearly 3000 SNPs were genotyped across the chromosome 18 dyslexia susceptibility candidate region. Following association analysis, the top ranking SNPs were then genotyped in the remaining samples. The linkage analysis revealed a broad signal that spans approximately 40 Mb from 18p11.2 to 18q12.2. Following the association analysis and subsequent replication attempts, we observed consistent association with the same SNPs in three genes; melanocortin 5 receptor (MC5R), dymeclin (DYM) and neural precursor cell expressed, developmentally down-regulated 4-like (NEDD4L). Conclusions: Along with already published biological evidence, MC5R, DYM and NEDD4L make attractive candidates for dyslexia susceptibility genes. However, further replication and functional studies are still required.

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We have successfully linked protein library screening directly with the identification of active proteins, without the need for individual purification, display technologies or physical linkage between the protein and its encoding sequence. By using 'MAX' randomization we have rapidly constructed 60 overlapping gene libraries that encode zinc finger proteins, randomized variously at the three principal DNA-contacting residues. Expression and screening of the libraries against five possible target DNA sequences generated data points covering a potential 40,000 individual interactions. Comparative analysis of the resulting data enabled direct identification of active proteins. Accuracy of this library analysis methodology was confirmed by both in vitro and in vivo analyses of identified proteins to yield novel zinc finger proteins that bind to their target sequences with high affinity, as indicated by low nanomolar apparent dissociation constants.

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Human leukocyte antigen (HLA)-DM is a critical participant in antigen presentation that catalyzes the dissociation of the Class II-associated Invariant chain-derived Peptide (CLIP) from the major histocompatibility complex (MHC) Class II molecules. There is competition amongst peptides for access to an MHC Class II groove and it has been hypothesised that DM functions as a 'peptide editor' that catalyzes the replacement of one peptide for another within the groove. It is established that the DM catalyst interacts directly with the MHC Class II but the precise location of the interface is unknown. Here, we combine previously described mutational data with molecular docking and energy minimisation simulations to identify a putative interaction site of >4000A2 which agrees with known point mutational data for both the DR and DM molecule. The docked structure is validated by comparison with experimental data and previously determined properties of protein-protein interfaces. A possible dissociation mechanism is suggested by the presence of an acidic cluster near the N terminus of the bound peptide.

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DNA-binding proteins are crucial for various cellular processes, such as recognition of specific nucleotide, regulation of transcription, and regulation of gene expression. Developing an effective model for identifying DNA-binding proteins is an urgent research problem. Up to now, many methods have been proposed, but most of them focus on only one classifier and cannot make full use of the large number of negative samples to improve predicting performance. This study proposed a predictor called enDNA-Prot for DNA-binding protein identification by employing the ensemble learning technique. Experiential results showed that enDNA-Prot was comparable with DNA-Prot and outperformed DNAbinder and iDNA-Prot with performance improvement in the range of 3.97-9.52% in ACC and 0.08-0.19 in MCC. Furthermore, when the benchmark dataset was expanded with negative samples, the performance of enDNA-Prot outperformed the three existing methods by 2.83-16.63% in terms of ACC and 0.02-0.16 in terms of MCC. It indicated that enDNA-Prot is an effective method for DNA-binding protein identification and expanding training dataset with negative samples can improve its performance. For the convenience of the vast majority of experimental scientists, we developed a user-friendly web-server for enDNA-Prot which is freely accessible to the public. © 2014 Ruifeng Xu et al.