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The cytomegalovirus (CMV) genome encodes four clusters of genes expressed immediately after infection — i.e.: UL36-38, UL122-123, TRS1-IRS1, and US3. The general function of these genes is associated with inhibition of cellular mechanisms of antiviral response. Although several biological processes have been mapped onto specific gene products, the knowledge of the molecular mechanism of their activity remains fragmentary. Here, we report the application of protein structure prediction methods in assigning the function to a glycosylated domain encoded by UL37 of CMV (gpUL37, UL37x3). The discerned similarity clearly points out that this domain represents a novel type of a major histocompatibility complex (MHC)-like protein, and consequently may play a central role in an additional mechanism of escape from antiviral response.
ICP4 is an important factor regulating the life cycle of HSV1. This conserved protein has several molecular functions, including activation of expression of viral late gene transcripts and inhibition of immediate early genes. Although ICP4 and its Alphaherpesvirinae homologs (eg.: IE62 of VZV) have been subjects of various molecular studies, a complete view of their molecular function is lacking. Here we present the results of fold recognition and molecular modelling of ICP4 functional domains. The performed state-of-the-art bioinformatic fold recognition analysis identified a dual helix-turn-helix motif as a binding module of repressor activities (so called region 2 domain). The mapping of distant homology identified that a segment responsible for activation of late gene promoters (region 4) exhibits folding of uracil DNA glycosylase (UDG), but seems to be a non-functional homolog of UDG. Potential implications of the results are discussed.
Colorectal cancer, one of the most challenging malignancies, still has a limited number of recognized prognostic and predictive markers indicating appropriate treatment. MACC1 (metastasis-associated in colon cancer-1), a novel regulator of tumor growth and metastasis has recently been identified as an important prognostic factor of metastatic disease in colorectal cancer. The mechanism of MACC1 activity remains undetermined. Here we apply a combination of fold recognition and homology modeling algorithms to draft MACC1 function. The applied methods revealed that the MACC1 protein consists of four domains: ZU5, SH3, and two C-terminal death domains (DD). Previously a similar domain architecture (ZU5-DD) was observed in other proteins, involved mainly in signal transduction and apoptosis regulation. Based on the specific aspects of the closest homologues' biology functional hypotheses on MACC1 are proposed. A broad range of bioinformatic analyzes indicates that MACC1, besides its involvement in signal transduction from the MET receptor, links MET signaling and apoptosis.
We present here a neural network-based method for detection of signal peptides (abbreviation used: SP) in proteins. The method is trained on sequences of known signal peptides extracted from the Swiss-Prot protein database and is able to work separately on prokaryotic and eukaryotic proteins. A query protein is dissected into overlapping short sequence fragments, and then each fragment is analyzed with respect to the probability of it being a signal peptide and containing a cleavage site. While the accuracy of the method is comparable to that of other existing prediction tools, it provides a significantly higher speed and portability. The accuracy of cleavage site prediction reaches 73% on heterogeneous source data that contains both prokaryotic and eukaryotic sequences while the accuracy of discrimination between signal peptides and non-signal peptides is above 93% for any source dataset. As a consequence, the method can be easily applied to genome-wide datasets. The software can be downloaded freely from http://rpsp.bioinfo. pl/RPSP.tar.gz.
We present here a simple method for fast and accurate comparison of proteins us­ing their structures. The algorithm is based on structural alignment of segments of Ca chains (with size of 99 or 199 residues). The method is optimized in terms of speed and accuracy. We test it on 97 representative proteins with the similarity mea­sure based on the SCOP classification. We compare our algorithm with the LGscore2 automatic method. Our method has the same accuracy as the LGscore2 algorithm with much faster processing of the whole test set, which is promising. A second test is done using the ToolShop structure prediction evaluation program and shows that our tool is on average slightly less sensitive than the DALI server. Both algorithms give a similar number of correct models, however, the final alignment quality is better in the case of DALI. Our method was implemented under the name 3D-Hit as a web server at http://3dhit.bioinfo.pl/ free for academic use, with a weekly updated database containing a set of 5000 structures from the Protein Data Bank with non-homologous sequences.
Gene promoters contain several classes of functional sequence elements (cis elements) recognized by protein agents, e.g. transcription factors and essential components of the transcription machinery. Here we describe a common DNA regulatory element (tandem TCTCGCGAGA motif) of human TATA-less promoters. A combination of bioinformatic and experimental methodology suggests that the element can be critical for expression of genes involved in enhanced protein synthesis and the G1/S transition in the cell cycle. The motif was identified in a substantial fraction of promoters of cell cycle genes, like cyclins (CCNC, CCNG1), as well as transcription regulators (TAF7, TAF13, KLF7, NCOA2), chromatin structure modulators (HDAC2, TAF6L), translation initiation factors (EIF5, EIF2S1, EIF4G2, EIF3S8, EIF4) and previously reported 18 ribosomal protein genes. Since the motif can define a subset of promoters with a distinct mechanism of activation involved in regulation of expression of about 5% of human genes, further investigation of this regulatory element is an emerging task.
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