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Secondary Structure Prediction (secondary + structure_prediction)
Selected AbstractsThe optimization of protein secondary structure determination with infrared and circular dichroism spectraFEBS JOURNAL, Issue 14 2004Keith A. Oberg We have used the circular dichroism and infrared spectra of a specially designed 50 protein database [Oberg, K.A., Ruysschaert, J.M. & Goormaghtigh, E. (2003) Protein Sci. 12, 2015,2031] in order to optimize the accuracy of spectroscopic protein secondary structure determination using multivariate statistical analysis methods. The results demonstrate that when the proteins are carefully selected for the diversity in their structure, no smaller subset of the database contains the necessary information to describe the entire set. One conclusion of the paper is therefore that large protein databases, observing stringent selection criteria, are necessary for the prediction of unknown proteins. A second important conclusion is that only the comparison of analyses run on circular dichroism and infrared spectra independently is able to identify failed solutions in the absence of known structure. Interestingly, it was also found in the course of this study that the amide II band has high information content and could be used alone for secondary structure prediction in place of amide I. [source] HATODAS II , heavy-atom database system with potentiality scoringJOURNAL OF APPLIED CRYSTALLOGRAPHY, Issue 3 2009Michihiro Sugahara HATODAS II is the second version of HATODAS (the Heavy-Atom Database System), which suggests potential heavy-atom reagents for the derivatization of protein crystals. The present expanded database contains 3103 heavy-atom binding sites, which is four times more than the previous version. HATODAS II has three new criteria to evaluate the feasibility of the search results: (1) potentiality scoring for the predicted heavy-atom reagents, (2) exclusion of the disordered amino acid residues based on the secondary structure prediction and (3) consideration of the solvent accessibility of amino acid residues from a homology model. In the point mutation option, HATODAS II suggests possible mutation sites into reactive amino acid residues such as Met, Cys and His, on the basis of multiple sequence alignments of homologous proteins. These new features allow the user to make a well informed decision as to the possible heavy-atom derivatization experiments of protein crystals. [source] The implications of higher (or lower) success in secondary structure prediction of chain fragments,PROTEIN SCIENCE, Issue 8 2005Chung-Jung Tsai No abstract is available for this article. [source] Cascaded multiple classifiers for secondary structure predictionPROTEIN SCIENCE, Issue 6 2000Mohammed Ouali Abstract We describe a new classifier for protein secondary structure prediction that is formed by cascading together different types of classifiers using neural networks and linear discrimination. The new classifier achieves an accuracy of 76.7% (assessed by a rigorous full Jack-knife procedure) on a new nonredundant dataset of 496 nonhomologous sequences (obtained from G.J. Barton and JA. Cuff). This database was especially designed to train and test protein secondary structure prediction methods, and it uses a more stringent definition of homologous sequence than in previous studies. We show that it is possible to design classifiers that can highly discriminate the three classes (H, E, C) with an accuracy of up to 78% for ,-strands, using only a local window and resampling techniques. This indicates that the importance of long-range interactions for the prediction of ,-strands has been probably previously overestimated. [source] Neuro-fuzzy structural classification of proteins for improved protein secondary structure predictionPROTEINS: STRUCTURE, FUNCTION AND BIOINFORMATICS, Issue 8 2003Joachim A. Hering Abstract Fourier transform infrared (FTIR) spectroscopy is a very flexible technique for characterization of protein secondary structure. Measurements can be carried out rapidly in a number of different environments based on only small quantities of proteins. For this technique to become more widely used for protein secondary structure characterization, however, further developments in methods to accurately quantify protein secondary structure are necessary. Here we propose a structural classification of proteins (SCOP) class specialized neural networks architecture combining an adaptive neuro-fuzzy inference system (ANFIS) with SCOP class specialized backpropagation neural networks for improved protein secondary structure prediction. Our study shows that proteins can be accurately classified into two main classes "all alpha proteins" and "all beta proteins" merely based on the amide I band maximum position of their FTIR spectra. ANFIS is employed to perform the classification task to demonstrate the potential of this architecture with moderately complex problems. Based on studies using a reference set of 17 proteins and an evaluation set of 4 proteins, improved predictions were achieved compared to a conventional neural network approach, where structure specialized neural networks are trained based on protein spectra of both "all alpha" and "all beta" proteins. The standard errors of prediction (SEPs) in % structure were improved by 4.05% for helix structure, by 5.91% for sheet structure, by 2.68% for turn structure, and by 2.15% for bend structure. For other structure, an increase of SEP by 2.43% was observed. Those results were confirmed by a "leave-one-out" run with the combined set of 21 FTIR spectra of proteins. [source] Efficient expression and purification of human aglycosylated Fc, receptors in Escherichia coli,BIOTECHNOLOGY & BIOENGINEERING, Issue 1 2010Sang Taek Jung Abstract Effector Fc gamma receptors (Fc,Rs) are expressed on the surface of a variety of cells of hematopoietic lineage and serve as a bridge between adaptive and innate immune responses. The interaction between immune complexes, formed by IgG class antibodies that are crosslinked with antigen, and Fc,Rs triggers signaling cascades that result in numerous cellular responses including the activation or donwregulation of cytotoxic responses, cytokine release, and antibody synthesis. Here, the extracellular domains of the human type I transmembrane Fc,Rs were expressed in Escherichia coli and their interactions to subclass IgGs (IgG1, IgG2, IgG3, and IgG4) antibodies were analyzed. Expression using fully synthetic E. coli codon optimized Fc,R genes and optimization of sequences for N-terminal translation initiation region through mRNA secondary structure prediction enabled us to achieve high yield of purified, bacterially expressed receptors, including Fc,RI and Fc,RIIIa which have not been successfully expressed in bacteria until now. The aglycosylated Fc,Rs showed similar IgG subclass binding selectivity compared to the respective glycosylated Fc,Rs expressed in mammalian cells. Biotechnol. Bioeng. 2010;107: 21,30. © 2010 Wiley Periodicals, Inc. [source] Sequence and structure relatedness of matrix protein of human respiratory syncytial virus with matrix proteins of other negative-sense RNA virusesCLINICAL MICROBIOLOGY AND INFECTION, Issue 10 2004K. Latiff Abstract Matrix proteins of viruses within the order Mononegavirales have similar functions and play important roles in virus assembly. Protein sequence alignment, phylogenetic tree derivation, hydropathy profiles and secondary structure prediction were performed on selected matrix protein sequences, using human respiratory syncytial virus matrix protein as the reference. No general conservation of primary, secondary or tertiary structure was found, except for a broad similarity in the hydropathy pattern correlating with the fact that all the proteins studied are membrane-associated. Interestingly, the matrix proteins of Ebola virus and human respiratory syncytial virus shared secondary structure homology. [source] N -Terminal domain of HTLV-I integrase.JOURNAL OF PEPTIDE SCIENCE, Issue 11 2001Complexation, conformational studies of the zinc finger Abstract The HTLV-I integrase N -terminal domain [50-residue peptide (IN50)], and a 35-residue truncated peptide formed by residues 9,43 (IN35) have been synthesized by solid-phase peptide synthesis. Formation of the 50-residue zinc finger type structure through a HHCC motif has been proved by UV-visible absorption spectroscopy. Its stability was demonstrated by an original method using RP-HPLC. Similar experiments performed on the 35-residue peptide showed that the truncation does not prevent zinc complex formation but rather that it significantly influences its stability. As evidenced by CD spectroscopy, the 50-residue zinc finger is unordered in aqueous solution but adopts a partially helical conformation when trifluoroethanol is added. These results are in agreement with our secondary structure predictions and demonstrate that the HTLV-I integrase N -terminal domain is likely to be composed of an helical region (residues 28,42) and a ,-strand (residues 20,23), associated with a HHCC zinc-binding motif. Size-exclusion chromatography showed that the structured zinc finger dimerizes through the helical region. Copyright © 2000 European Peptide Society and John Wiley & Sons, Ltd. [source] NarE: a novel ADP-ribosyltransferase from Neisseria meningitidisMOLECULAR MICROBIOLOGY, Issue 3 2003Vega Masignani Summary Mono ADP-ribosyltransferases (ADPRTs) are a class of functionally conserved enzymes present in prokaryotic and eukaryotic organisms. In bacteria, these enzymes often act as potent toxins and play an important role in pathogenesis. Here we report a profile-based computational approach that, assisted by secondary structure predictions, has allowed the identification of a previously undiscovered ADP-ribosyltransferase in Neisseria meningitidis (NarE). NarE shows structural homologies with E. coli heat-labile enterotoxin (LT) and cholera toxin (CT) and possesses ADP-ribosylating and NAD-glycohydrolase activities. As in the case of LT and CT, NarE catalyses the transfer of the ADP-ribose moiety to arginine residues. Despite the absence of a signal peptide, the protein is efficiently exported into the periplasm of Neisseria. The narE gene is present in 25 out of 43 strains analysed, is always present in ET-5 and Lineage 3 but absent in ET-37 and Cluster A4 hypervirulent lineages. When present, the gene is 100% conserved in sequence and is inserted upstream of and co-transcribed with the lipoamide dehydrogenase E3 gene. Possible roles in the pathogenesis of N. meningitidis are discussed. [source] Structural Models and Binding Site Prediction of the C-terminal Domain of Human Hsp90: A New Target for Anticancer DrugsCHEMICAL BIOLOGY & DRUG DESIGN, Issue 5 2008Miriam Sgobba Heat shock protein 90 is a valuable target for anticancer drugs because of its role in the activation and stabilization of multiple oncogenic signalling proteins. While several compounds inhibit heat shock protein 90 by binding the N-terminal domain, recent studies have proved that the C-terminal domain is important for dimerization of the chaperone and contains an additional binding site for inhibitors. Heat shock protein 90 inhibition achieved with molecules binding to the C-terminal domain provides an additional and novel opportunity to design and develop drugs. Therefore, for the first time, we have investigated the structure and the dynamic behaviour of the C-terminal domain of human heat shock protein 90 with and without the small-middle domain, using homology modelling and molecular dynamics simulations. In addition, secondary structure predictions and peptide folding simulations proved useful to investigate a putative additional ,-helix located between H18 and ,20 of the C-terminal domain. Finally, we used the structural information to infer the location of the binding site located in the C-terminal domain by using a number of computational tools. The predicted pocket is formed by two grooves located between helix H18, the loop downstream of H18 and the loop connecting helices H20 and H21 of each monomer of the C-terminal domain, with only two amino acids contributing from each middle domain. [source] | |||||||||||||