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Acid Protein

The Experts below are selected from a list of 285 Experts worldwide ranked by ideXlab platform

Michel Fevre – 1st expert on this subject based on the ideXlab platform

  • the glucose repressor cre1 from sclerotinia sclerotiorum is functionally related to crea from aspergillus nidulans but not to the mig Proteins from saccharomyces cerevisiae
    FEBS Letters, 1999
    Co-Authors: Geraldine Vautard, Pascale Cotton, Michel Fevre

    Abstract:

    We isolated the putative glucose repressor gene cre1 from the phytopathogenic fungus Sclerotinia sclerotiorum. cre1 encodes a 429 amino Acid Protein 59% similar to the carbon catabolite repressor CREA from Aspergillus nidulans. In addition to the overall amino Acid sequence relatedness between CRE1 and CREA Proteins, cre1 can functionally complement the A. nidulans creAd30 mutation as assessed by repression of the alcohol dehydrogenase I gene expression. The CRE1 region carrying the two zinc fingers is also very similar to the DNA binding domains of the Saccharomyces cerevisiae glucose repressors Mig1p and Mig2p. Despite the presence in the CRE1 Protein of several motifs involved in the regulation of Mig1p activity, cre1 cannot complement mig deficiencies in S. cerevisiae. These data suggest that glucose repression pathways may have evolved differently in yeasts and filamentous fungi.

Jeffrey S Simske – 2nd expert on this subject based on the ideXlab platform

  • genomic structure cdna sequence and expression of gly96 a growth factor inducible immediate early gene encoding a short lived glycosylated Protein
    Oncogene, 1993
    Co-Authors: Catherine H Charles, Jeffrey S Simske, Jeong Kyo Yoon

    Abstract:

    : We report the cDNA sequence and genomic structure of gly96, an immediate early gene inducible by serum growth factors in mouse fibroblasts. It encodes a 153-amino Acid Protein that does not share significant sequence similarity with any known Protein. In the adult mouse, gly96 is expressed predominantly in the lung, testes and the uterus. We have identified the Gly96 Protein in Balb/c 3T3 cells using affinity-purified antibodies recognizing the Gly96 polypeptide. We show that Gly96 is glycosylated and has a short half-life in serum stimulated fibroblasts.

Ajit Kumar – 3rd expert on this subject based on the ideXlab platform

  • Information theory and signal processing methodology to identify nucleic AcidProtein binding sequences in RNA-Protein interactions
    2019 53rd Annual Conference on Information Sciences and Systems (CISS), 2019
    Co-Authors: Harry Shaw, Nagarajan Pattabiraman, Deborah Preston, Tatiana Ammosova, Yuri Obukhov, Sergei Nekhai, Ajit Kumar

    Abstract:

    RNA binding Proteins are known to modulate an impressive array of cellular processes. Recent studies have focused on a variety of techniques to analyze RNA-Protein (RBP) complex formation including NMR, X-ray crystallography, and mass spectrometry. To explore the factors that regulate RBP formation, we developed a computational method as a step prior to biochemical validation of RBP by mass spectrometry. Here we describe a methodology to predict the sequences involved in RNA-Protein complex formation including transient interactions. The approach is based on an information entropy-based algorithm calibrated against known ΔG and binding probabilities for RNA nucleotides-amino Acid residues. The method is then used to predict binding sites of specific RNA associated Proteins identified by mass spectroscopy of RNA associated Proteins. The estimates of specific nucleotide peptide interactions was based on the Gibbs free energy of nucleotide-peptide fragments in a given RBP complex, and a dynamic model that uses multiple binding sites within a nucleotide-peptide fragment to quantify the binding affinity of weak and transient RNA-Protein interactions. A concept originally described by Claude Shannon is now being used to foster a new paradigm for assisting in the search for specific RNA-Protein binding sites. In this paper we will detail the following information, in order: 1. An information-theoretic based approach to modelling RNA-Protein interactions down to specific RNA-Protein complex motifs based upon information entropy; 2. The theory applied to a calibration dataset of known RNA-Protein interactions to predict the RNA-Protein binding motifs; 3. A prediction of RNA-Protein binding motifs on a set of co-immunoprecipitation assays.

  • information theory and signal processing methodology to identify nucleic Acid Protein binding sequences in rna Protein interactions
    Conference on Information Sciences and Systems, 2019
    Co-Authors: Harry Shaw, Nagarajan Pattabiraman, Deborah Preston, Tatiana Ammosova, Yuri Obukhov, Sergei Nekhai, Ajit Kumar

    Abstract:

    RNA binding Proteins are known to modulate an impressive array of cellular processes. Recent studies have focused on a variety of techniques to analyze RNA-Protein (RBP) complex formation including NMR, X-ray crystallography, and mass spectrometry. To explore the factors that regulate RBP formation, we developed a computational method as a step prior to biochemical validation of RBP by mass spectrometry. Here we describe a methodology to predict the sequences involved in RNA-Protein complex formation including transient interactions. The approach is based on an information entropy-based algorithm calibrated against known $\Delta \mathrm {G}$ and binding probabilities for RNA nucleotides-amino Acid residues. The method is then used to predict binding sites of specific RNA associated Proteins identified by mass spectroscopy of RNA associated Proteins. The estimates of specific nucleotide peptide interactions was based on the Gibbs free energy of nucleotide-peptide fragments in a given RBP complex, and a dynamic model that uses multiple binding sites within a nucleotide-peptide fragment to quantify the binding affinity of weak and transient RNA-Protein interactions. A concept originally described by Claude Shannon is now being used to foster a new paradigm for assisting in the search for specific RNA-Protein binding sites. In this paper we will detail the following information, in order: 1. An information-theoretic based approach to modelling RNA-Protein interactions down to specific RNA-Protein complex motifs based upon information entropy; 2. The theory applied to a calibration dataset of known RNA-Protein interactions to predict the RNA-Protein binding motifs; 3. A prediction of RNA-Protein binding motifs on a set of co-immunoprecipitation assays.