57 resultados para secondary structure analysis

em CentAUR: Central Archive University of Reading - UK


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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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A novel bis(glycinato) copper(II) paradodecatungstate Na-8[{Cu(gly)(2)}(2)]-{H-2(H2W12O42)}] center dot 24H(2)O (1) has been synthesized under hydrothermal conditions. The crystal structure of 1 reveals an infinite one-dimensional chain along the [100] direction and is built from paradodecatungstate (H2W12O42)(10-) clusters joined through [Cu(gly)(2)] moieties. Parallel chains are interlinked by NaO6 octahedra to generate a two-dimensional network.

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Two different ways of performing low-energy electron diffraction (LEED) structure determinations for the p(2 x 2) structure of oxygen on Ni {111} are compared: a conventional LEED-IV structure analysis using integer and fractional-order IV-curves collected at normal incidence and an analysis using only integer-order IV-curves collected at three different angles of incidence. A clear discrimination between different adsorption sites can be achieved by the latter approach as well as the first and the best fit structures of both analyses are within each other's error bars (all less than 0.1 angstrom). The conventional analysis is more sensitive to the adsorbate coordinates and lateral parameters of the substrate atoms whereas the integer-order-based analysis is more sensitive to the vertical coordinates of substrate atoms. Adsorbate-related contributions to the intensities of integer-order diffraction spots are independent of the state of long-range order in the adsorbate layer. These results show, therefore, that for lattice-gas disordered adsorbate layers, for which only integer-order spots are observed, similar accuracy and reliability can be achieved as for ordered adsorbate layers, provided the data set is large enough.

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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 self-assembly and hydrogelation properties of two Fmoc-tripeptides [Fmoc = N-(fluorenyl-9-methoxycarbonyl)] are investigated, in borate buffer and other basic solutions. A remarkable difference in self-assembly properties is observed comparing Fmoc-VLK(Boc) with Fmoc-K(Boc)LV, both containing K protected by N(epsilon)-tert-butyloxycarbonate (Boc). In borate buffer, the former peptide forms highly anisotropic fibrils which show local alignment, and the hydrogels show flow-aligning properties. In contrast, Fmoc-K(Boc)LV forms highly branched fibrils that produce isotropic hydrogels with a much higher modulus (G' > 10(4) Pa), and lower concentration for hydrogel formation. The distinct self-assembled structures are ascribed to conformational differences, as revealed by secondary structure probes (CD, FTIR, Raman spectroscopy) and X-ray diffraction. Fmoc-VLK(Boc) forms well-defined beta-sheets with a cross-beta X-ray diffraction pattern, whereas Fmoc-KLV(Boc) forms unoriented assemblies with multiple stacked sheets. Interchange of the K and V residues when inverting the tripeptide sequence thus leads to substantial differences in self-assembled structures, suggesting a promising approach to control hydrogel properties.

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The structure of the mixed p(3x3)-(3OH+3H(2)O) phase on Pt{111} has been investigated by low-energy electron diffraction-IV structure analysis. The OH+H2O overlayer consists of hexagonal rings of coplanar oxygen atoms interlinked by hydrogen bonds. Lateral shifts of the O atoms away from atop sites result in different O-O separations and hexagons with only large separations (2.81 and 3.02 angstrom) linked by hexagons with alternating separations of 2.49 and 2.81/3.02 A. This unusual pattern is consistent with a hydrogen-bonded network in which water is adsorbed in cyclic rings separated by OH in a p(3x3) structure. The topmost two layers of the Pt atoms relax inwards with respect to the clean surface and both show vertical buckling of up to 0.06 angstrom. In addition, significant shifts away from the lateral bulk positions have been found for the second layer of Pt atoms. (C) 2005 American Institute of Physics.

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The surface geometries of the p (root7- x root7)R19degrees-(4CO) and c(2 x 4)-(2CO) layers on Ni {111} and the clean Ni {111} surface were determined by low energy electron diffraction structure analysis. For the clean surface small but significant contractions of d(12) and d(23) (both 2.02 Angstrom) were found with respect to the bulk interlayer distance (2.03 Angstrom). In the c(2 x 4)-(2CO) structure these distances are expanded, with values of d(12) = 2.08 Angstrom and d(23) = 2.06 Angstrom and buckling of 0.08 and 0.02 Angstrom, respectively, in the first and second layer. CO resides near hcp and fcc hollow sites with relatively large lateral shifts away from the ideal positions leading to unequal C-Ni bond lengths between 1.76 and 1.99 Angstrom. For the p(root7- x root7-)R19'-(4CO) layer two best fit geometries were found, which agree in most of their atomic positions, except for one out of four CO molecules, which is either near atop or between bridge and atop. The remaining three molecules reside near hcp and fcc sites, again with large lateral deviations from their ideal positions. The average C Ni bond length for these molecules is, however, the same as for CO on hollow sites at low coverage. The average CNi bond length at hollow sites, the interlayer distances, and buckling in the first Ni layer are similar to the c(2 x 4)(2CO) geometry, only the buckling in the second layer (0.08 Angstrom) is significantly larger. Lateral and vertical shifts of the Ni atoms in the first layer lead to unsymmetric environments for the CO molecules, which can be regarded as an imprint of the chiral p(root7- x root7-)R19degrees lattice geometry onto the substrate.

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The species [{Sn(C2H2iPr3-2,4,6)2}3] has been obtained in a simple, essentially quantitative, synthesis from SnCl2 and ArLi in diethyl ether at low temperature. The crystal structure analysis confirms the trimeric nature of the molecular units but reveals some unusual features. The crystal contains the unusual feature of an asymmetric unit that consists of three units of [{SnAr2}3] in P21/c; the molecular unit is a scalene triangle, showing high consistency between the three molecules, in contrast to analogous trimeric species of silicon or germanium. The SnSn bonds are lengthened (average value 2.942 Å) owing to steric crowding.

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The clusters [Fe3(CO)11(RCN)] (1: R = Me, C3H5, C6H5, or C6H4-2-Me) have been prepared at low temperature from [Fe3(CO)12] and RCN in the presence of Me3NO. Compounds 1 react essentially quantitatively with a wide range of two-electron donors, L, (viz.: CO, PPh3, P(OMe)3, PPh2H, PPh2Me, PF3, CyNC (Cy = cyclohexyl), P(OEt)3, SbPh3, PBu3, AsPh3, or SnR2 (R = CH(SiMe3)2)) to give [Fe3(CO)11L] (2). In some cases (2), on treatment with Me3NO and then L′ (L′ = a second two-electron donor) yields [Fe3(CO)10LL′] in high yield. The crystal and molecular structures of 1 (L = NCC6H4Me-2) have been determined by a full single crystal structure analysis, and shown to have an axial nitrile coordinated at the unique iron atom, with two CO groups bridging the other two metal atoms.

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A number of state-of-the-art protein structure prediction servers have been developed by researchers working in the Bioinformatics Unit at University College London. The popular PSIPRED server allows users to perform secondary structure prediction, transmembrane topology prediction and protein fold recognition. More recent servers include DISOPRED for the prediction of protein dynamic disorder and DomPred for domain boundary prediction.

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What constitutes a baseline level of success for protein fold recognition methods? As fold recognition benchmarks are often presented without any thought to the results that might be expected from a purely random set of predictions, an analysis of fold recognition baselines is long overdue. Given varying amounts of basic information about a protein—ranging from the length of the sequence to a knowledge of its secondary structure—to what extent can the fold be determined by intelligent guesswork? Can simple methods that make use of secondary structure information assign folds more accurately than purely random methods and could these methods be used to construct viable hierarchical classifications?

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The PSIPRED protein structure prediction server allows users to submit a protein sequence, perform a prediction of their choice and receive the results of the prediction both textually via e-mail and graphically via the web. The user may select one of three prediction methods to apply to their sequence: PSIPRED, a highly accurate secondary structure prediction method; MEMSAT 2, a new version of a widely used transmembrane topology prediction method; or GenTHREADER, a sequence profile based fold recognition method.

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A penta-nuclear. star-shaped hetero-metallic compound containing a unique Ni4KO8 core has been synthesized. The X-ray single crystal structure analysis reveals that in the complex, [K(Ni(LH)(2))(4)(OH2)(8)](Br)(ClO4)(8)center dot 11H(2)O (LH=(CH3)(2)HN+(CH2)(3)N=CHC6H4O-) the eight coordinate central K+ ion is encapsulated by four terminal [Ni(LH)(2)](2+) units through the double water bridges between K+ and each Ni(II) ions.