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      Ranatuerin 1T: an antimicrobial peptide isolated from the skin of the frog, Rana temporaria

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      Peptides
      Elsevier BV

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          Abstract

          A peptide, termed ranatuerin 1T, with growth-inhibiting activity toward Staphylococcus aureus, was isolated from an extract of the skin of the European brown frog, Rana temporaria. The primary structure of the peptide was established as: GLLSGLKKVG10 KHVAKNVAVS20LMDSLKCKIS30GDC. In common with other anti-microbial peptides from Ranid frogs, (e.g., ranalexin, ranatuerins, gaegurins, brevinins, esculetins, rugosins), ranatuerin IT contains an intramolecular disulfide bridge forming a heptapeptide ring but there is little structural similarity outside this cyclic region. The minimum inhibitory concentration (MIC) of ranatuerin 1T was 120 microM against the Gram-positive bacterium S. aureus and 40 microM against the Gram-negative bacterium Escherichia coli, but the peptide was not active against the yeast Candida albicans.

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          Most cited references18

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          Analysis of the accuracy and implications of simple methods for predicting the secondary structure of globular proteins.

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            Magainins, a class of antimicrobial peptides from Xenopus skin: isolation, characterization of two active forms, and partial cDNA sequence of a precursor.

            M. Zasloff (1987)
            A family of peptides with broad-spectrum antimicrobial activity has been isolated from the skin of the African clawed frog Xenopus laevis. It consists of two closely related peptides that are each 23 amino acids and differ by two substitutions. These peptides are water soluble, nonhemolytic at their effective antimicrobial concentrations, and potentially amphiphilic. At low concentrations they inhibit growth of numerous species of bacteria and fungi and induce osmotic lysis of protozoa. The sequence of a partial cDNA of the precursor reveals that both peptides derive from a common larger protein. These peptides appear to represent a previously unrecognized class of vertebrate antimicrobial activities.
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              Predicting the secondary structure of globular proteins using neural network models.

              We present a new method for predicting the secondary structure of globular proteins based on non-linear neural network models. Network models learn from existing protein structures how to predict the secondary structure of local sequences of amino acids. The average success rate of our method on a testing set of proteins non-homologous with the corresponding training set was 64.3% on three types of secondary structure (alpha-helix, beta-sheet, and coil), with correlation coefficients of C alpha = 0.41, C beta = 0.31 and Ccoil = 0.41. These quality indices are all higher than those of previous methods. The prediction accuracy for the first 25 residues of the N-terminal sequence was significantly better. We conclude from computational experiments on real and artificial structures that no method based solely on local information in the protein sequence is likely to produce significantly better results for non-homologous proteins. The performance of our method of homologous proteins is much better than for non-homologous proteins, but is not as good as simply assuming that homologous sequences have identical structures.
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                Author and article information

                Journal
                Peptides
                Peptides
                Elsevier BV
                01969781
                February 1999
                February 1999
                : 20
                : 2
                : 159-163
                Article
                10.1016/S0196-9781(98)00174-0
                10422869
                731a186a-f233-47d6-9430-0e38fff734de
                © 1999

                http://www.elsevier.com/tdm/userlicense/1.0/

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