971 resultados para Peptide secondary structure


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The sequences of the 16S rRNA genes from 38 strains of the family Thermaceae were compared by alignment analysis. The genus-specific and species-specific base substitutions or base deletions (signature positions) were found in three hypervariable regions (in the helices 6, 10 and 17). The differentiation of secondary structures of the high variable regions in the 5' end (38-497) containing several signature positions further supported the concept. Based on the comparisons of the secondary structures in the segments of 16S rRNAs, a key to the species of the family Thermaceae was proposed. (C) 2003 Published by Elsevier Science B.V. on behalf of the Federation of European Microbiological Societies.

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Based on the statistical analysis of 119 human and 92 E. coli proteins it was found that for both human and E. coli, the mRNA sequences consisting of tri-codon and tetra-codon with high translation speed preferably code for alpha helices more than for coils. For beta strand, the preference/ avoidance oscillates with the translation speed. Moreover, the non-homogeneous usages of tri-codon and tetra-codon with different translation speeds in a given secondary structure have also been found. These results cannot be simply explained by the effect of stochastic fluctuation.

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Anew integrated sequence-structure database, called IADE (Integrated ASTRAL-DSSP-EMBL), incorporating matching mRNA sequence, amino acid sequence, and protein secondary structural data, is constructed. It includes 648 protein domains. Based on the IADE database, we studied the relation between RNA stem-loop frequencies and protein secondary structure. It was found that the alpha-helices and beta-strands on proteins tend to be preferably "coded" by mRNA stem region, while the coils on proteins tend to be preferably "coded" by mRNA loop region. These tendencies are more obvious if we observe the structural words (SWs). An SW is defined by a four-amino-acid-fragment that shows the pronounced secondary structural (alpha-helix or beta-strand) propensity. It is demonstrated that the deduced correlation between protein and mRNA structure can hardly be explained as the stochastic fluctuation effect. (C) 2003 Wiley Periodicals, Inc.

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The mitochondrial 16S ribosomal RNA (rRNA) gene sequences from 93 cyprinid fishes were examined to reconstruct the phylogenetic relationships within the diverse and economically important subfamily Cyprininae. Within the subfamily a biased nucleotide composition (A > T, C > G) was observed in the loop regions of the gene, and in stem regions apparent selective pressures of base pairing showed a bias in favor of G over C and T over A. The bias may be associated with transition-transversion bias. Rates of nucleotide substitution were lower in stems than in loops. Analysis of compensatory substitutions across these taxa demonstrates 68% covariation in the gene and a logical weighting factor to account for dependence in mutations for phylogenetic inference should be 0.66. Comparisons of varied stem-loop weighting schemes indicate that the down-weightings for stem regions could improve the phylogenetic analysis and the degree of non-independence of stem substitutions was not as important as expected. Bayesian inference under four models of nucleotide substitution indicated that likelihood-based phylogenetic analyses were more effective in improving the phylogenetic performance than was weighted parsimony analysis. In Bayesian analyses, the resolution of phylogenies under the 16-state models for paired regions, incorporating GTR + G + I models for unpaired regions was better than those under other models. The subfamily Cyprininae was resolved as a monophyletic group, as well as tribe Labein and several genera. However, the monophyly of the currently recognized tribes, such as Schizothoracin, Barbin, Cyprinion + Onychostoma lineages, and some genera was rejected. Furthermore, comparisons of the parsimony and Bayesian analyses and results of variable length bootstrap analysis indicates that the mitochondrial 16S rRNA gene should contain important character variation to recover well-supported phylogeny of cyprinid taxa whose divergences occurred within the recent 8 MY, but could not provide resolution power for deep phylogenies spanning 10-19 MYA. (c) 2008 Published by Elsevier Inc.

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The king cobra neuotoxin CM-11 is a small protein with 72 amino acid residues. After its complete assignments of H-1-NMR resonance's were obtained using various 2D-NMR technologies, including of DQF-COSY, clean-TOCSY AND NOESY, the secondary structure was analysed by studying the various NOEs extracted from the NOESY spectra and the distribution of chemical shifts. The secondary structure was finally determined by MCD as follows: a triple-strand antiparallel beta sheet with I20-W36, R37-A43 and V53--S59 as its beta strands, a short alpha helix formed by W30-G35 and four turns formed by P7-K10, C14-G17, K50-V53 and D61-N64.

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The hybridization kinetics for a series of designed 25mer probe�target pairs having varying degrees of secondary structure have been measured by UV absorbance and surface plasmon resonance (SPR) spectroscopy in solution and on the surface, respectively. Kinetic rate constants derived from the resultant data decrease with increasing probe and target secondary structure similarly in both solution and surface environments. Specifically, addition of three intramolecular base pairs in the probe and target structure slow hybridization by a factor of two. For individual strands containing four or more intramolecular base pairs, hybridization cannot be described by a traditional two-state model in solution-phase nor on the surface. Surface hybridization rates are also 20- to 40-fold slower than solution-phase rates for identical sequences and conditions. These quantitative findings may have implications for the design of better biosensors, particularly those using probes with deliberate secondary structure.

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The classification of protein structures is an important and still outstanding problem. The purpose of this paper is threefold. First, we utilize a relation between the Tutte and homfly polynomial to show that the Alexander-Conway polynomial can be algorithmically computed for a given planar graph. Second, as special cases of planar graphs, we use polymer graphs of protein structures. More precisely, we use three building blocks of the three-dimensional protein structure-alpha-helix, antiparallel beta-sheet, and parallel beta-sheet-and calculate, for their corresponding polymer graphs, the Tutte polynomials analytically by providing recurrence equations for all three secondary structure elements. Third, we present numerical results comparing the results from our analytical calculations with the numerical results of our algorithm-not only to test consistency, but also to demonstrate that all assigned polynomials are unique labels of the secondary structure elements. This paves the way for an automatic classification of protein structures.

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The self-assembly in aqueous solution of hybrid block copolymers consisting of amphiphilic β-strand peptide sequences flanked by one or two PEG chains was investigated by means of circular dichroism spectroscopy, small-angle X-ray scattering, and transmission electron microscopy. In comparison with the native peptide sequence, it was found that the peptide secondary structure was stabilized against pH variation in the di-and tri-block copolymers with PEG. Small-angle X-ray scattering indicated the presence of fibrillar structures, the dimensions of which are comparable to the estimated width of a β-strand (with terminal PEG chains in the case of the copolymers). Transmission electron microscopy on selectively stained and dried specimens shows directly the presence of fibrils. It is proposed that these fibrils result from the hierarchical self-assembly of peptide β-strands into helical tapes, which then stack into fibrils.

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Motivation: A new method that uses support vector machines (SVMs) to predict protein secondary structure is described and evaluated. The study is designed to develop a reliable prediction method using an alternative technique and to investigate the applicability of SVMs to this type of bioinformatics problem. Methods: Binary SVMs are trained to discriminate between two structural classes. The binary classifiers are combined in several ways to predict multi-class secondary structure. Results: The average three-state prediction accuracy per protein (Q3) is estimated by cross-validation to be 77.07 ± 0.26% with a segment overlap (Sov) score of 73.32 ± 0.39%. The SVM performs similarly to the 'state-of-the-art' PSIPRED prediction method on a non-homologous test set of 121 proteins despite being trained on substantially fewer examples. A simple consensus of the SVM, PSIPRED and PROFsec achieves significantly higher prediction accuracy than the individual methods. Availability: The SVM classifier is available from the authors. Work is in progress to make the method available on-line and to integrate the SVM predictions into the PSIPRED server.

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If secondary structure predictions are to be incorporated into fold recognition methods, an assessment of the effect of specific types of errors in predicted secondary structures on the sensitivity of fold recognition should be carried out. Here, we present a systematic comparison of different secondary structure prediction methods by measuring frequencies of specific types of error. We carry out an evaluation of the effect of specific types of error on secondary structure element alignment (SSEA), a baseline fold recognition method. The results of this evaluation indicate that missing out whole helix or strand elements, or predicting the wrong type of element, is more detrimental than predicting the wrong lengths of elements or overpredicting helix or strand. We also suggest that SSEA scoring is an effective method for assessing accuracy of secondary structure prediction and perhaps may also provide a more appropriate assessment of the “usefulness” and quality of predicted secondary structure, if secondary structure alignments are to be used in fold recognition.

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The elucidation of the domain content of a given protein sequence in the absence of determined structure or significant sequence homology to known domains is an important problem in structural biology. Here we address how successfully the delineation of continuous domains can be accomplished in the absence of sequence homology using simple baseline methods, an existing prediction algorithm (Domain Guess by Size), and a newly developed method (DomSSEA). The study was undertaken with a view to measuring the usefulness of these prediction methods in terms of their application to fully automatic domain assignment. Thus, the sensitivity of each domain assignment method was measured by calculating the number of correctly assigned top scoring predictions. We have implemented a new continuous domain identification method using the alignment of predicted secondary structures of target sequences against observed secondary structures of chains with known domain boundaries as assigned by Class Architecture Topology Homology (CATH). Taking top predictions only, the success rate of the method in correctly assigning domain number to the representative chain set is 73.3%. The top prediction for domain number and location of domain boundaries was correct for 24% of the multidomain set (±20 residues). These results have been put into context in relation to the results obtained from the other prediction methods assessed

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The influence of a non-ionic polymeric surfactant on the self-assembly of a peptide amphiphile (PA) that forms nanotapes is investigated using a combination of microscopic, scattering and spectroscopic techniques. Mixtures of Pluronic copolymer P123 with the PA C16-KTTKS in aqueous solution were studied at a fixed concentration of the PA at which it is known to self-assemble into extended nanotapes, but varying P123 concentration. We find that P123 can disrupt the formation of C16- KTTKS nanotapes, leading instead to cylindrical nanofibril structures. The spherical micelles formed by P123 at room temperature are disrupted in the presence of the PA. There is a loss of cloudiness in the solutions as the large nanotape aggregates formed by C16-KTTKS are broken up, by P123 solubilization. At least locally, b-sheet structure is retained, as confirmed by XRD and FTIR spectroscopy, even for solutions containing 20 wt% P123. This indicates, unexpectedly, that peptide secondary structure can be retained in solutions with high concentration of non-ionic surfactant. Selfassembly in this system exhibits slow kinetics towards equilibrium, the initial self-assembly being dependent on the order of mixing. Heating above the lipid chain melting temperature assists in disrupting trapped non-equilibrium states.

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The complete sequences of the dsrA and dsrB genes coding for the α− and β−subunits, respectively, of the sulphite reductase enzyme in Desulfovibrio desulfuricans were determined. Analyses of the amino acid sequences indicated a number of serohaem/Fe4S4 binding consensus sequences whilst predictive secondary structure analysis revealed a similar pattern of α−helix and β−strand structures between the two subunits which was indicative of gene duplication.

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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

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The proline-rich N-terminal domain of gamma-zein has been reported in relevant process, which include its ability to cross the cell membranes. Evidences indicate that synthetic hexapeptide (PPPVHL), naturally found in N-terminal portion of gamma-zein, can adopt the polyproline II (PPII) conformation in aqueous solution. The secondary structure of gamma-zein in maize protein bodies had been analyzed by solid state Fourier transform infrared and nuclear magnetic resonance spectroscopies. However, it was not possible to measure PPII content in physiological environment since the beta-sheet and PPII signals overlap in both solid state techniques. Here, the secondary structure of gamma-zein has been analyzed by circular dichroism in SDS aqueous solution with and without ditiothreitol (DTT), and in 60% of 2-propanol and water with DTT The results show that gamma-zein has high helical content in all solutions. The PPII conformation was present at about 7% only in water/DTT solution. (c) 2007 Wiley Periodicals, Inc.