6 resultados para GENTHREADER


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Motivation: In order to enhance genome annotation, the fully automatic fold recognition method GenTHREADER has been improved and benchmarked. The previous version of GenTHREADER consisted of a simple neural network which was trained to combine sequence alignment score, length information and energy potentials derived from threading into a single score representing the relationship between two proteins, as designated by CATH. The improved version incorporates PSI-BLAST searches, which have been jumpstarted with structural alignment profiles from FSSP, and now also makes use of PSIPRED predicted secondary structure and bi-directional scoring in order to calculate the final alignment score. Pairwise potentials and solvation potentials are calculated from the given sequence alignment which are then used as inputs to a multi-layer, feed-forward neural network, along with the alignment score, alignment length and sequence length. The neural network has also been expanded to accommodate the secondary structure element alignment (SSEA) score as an extra input and it is now trained to learn the FSSP Z-score as a measurement of similarity between two proteins. Results: The improvements made to GenTHREADER increase the number of remote homologues that can be detected with a low error rate, implying higher reliability of score, whilst also increasing the quality of the models produced. We find that up to five times as many true positives can be detected with low error rate per query. Total MaxSub score is doubled at low false positive rates using the improved method.

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Currently, the Genomic Threading Database (GTD) contains structural assignments for the proteins encoded within the genomes of nine eukaryotes and 101 prokaryotes. Structural annotations are carried out using a modified version of GenTHREADER, a reliable fold recognition method. The Gen THREADER annotation jobs are distributed across multiple clusters of processors using grid technology and the predictions are deposited in a relational database accessible via a web interface at http://bioinf.cs.ucl.ac.uk/GTD. Using this system, up to 84% of proteins encoded within a genome can be confidently assigned to known folds with 72% of the residues aligned. On average in the GTD, 64% of proteins encoded within a genome are confidently assigned to known folds and 58% of the residues are aligned to structures.

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The Genomic Threading Database currently contains structural annotations for the genomes of over 100 recently sequenced organisms. Annotations are carried out by using our modified GenTHREADER software and through implementing grid technology.

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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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The Human Respiratory Syncytial Virus (HRSV) fusion protein (F) was expressed in Escherichia call BL21A using the pET28a vector at 37 degrees C. The protein was purified from the soluble fraction using affinity resin. The structural quality of the recombinant fusion protein and the estimation of its secondary structure were obtained by circular dichroism. Structural models of the fusion protein presented 46% of the helices in agreement with the spectra by circular dichroism analysis. There are only few studies that succeeded in expressing the HRSV fusion protein in bacteria. This is a report on human fusion protein expression in E. call and structure analysis, representing a step forward in the development of fusion protein F inhibitors and the production of antibodies. (c) 2008 Elsevier Inc. All rights reserved.

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Wurst is a protein threading program with an emphasis on high quality sequence to structure alignments (http://www.zbh.uni-hamburg.de/wurst). Submitted sequences are aligned to each of about 3000 templates with a conventional dynamic programming algorithm, but using a score function with sophisticated structure and sequence terms. The structure terms are a log-odds probability of sequence to structure fragment compatibility, obtained from a Bayesian classification procedure. A simplex optimization was used to optimize the sequence-based terms for the goal of alignment and model quality and to balance the sequence and structural contributions against each other. Both sequence and structural terms operate with sequence profiles.