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  • 1
    Language: English
    In: Journal of Global Optimization, 2003, Vol.26(3), pp.321-333
    Description: Nuclear magnetic resonance (NMR) structure modeling usually produces a sparse set of inter-atomic distances in protein. In order to calculate the three-dimensional structure of protein, current approaches need to estimate all other “missing” distances to build a full set of distances. However, the estimation step is costly and prone to introducing errors. In this report, we describe a geometric build-up algorithm for solving protein structure by using only a sparse set of inter-atomic distances. Such a sparse set of distances can be obtained by combining NMR data with our knowledge on certain bond lengths and bond angles. It can also include confident estimations on some “missing” distances. Our algorithm utilizes a simple geometric relationship between coordinates and distances. The coordinates for each atom are calculated by using the coordinates of previously determined atoms and their distances. We have implemented the algorithm and tested it on several proteins. Our results showed that our algorithm successfully determined the protein structures with sparse sets of distances. Therefore, our algorithm reduces the need of estimating the “missing” distances and promises a more efficient approach to NMR structure modeling.
    Keywords: Molecular distance geometry ; Protein structure determination ; Numerical linear algebra and optimization
    ISSN: 0925-5001
    E-ISSN: 1573-2916
    Source: Springer Science & Business Media B.V.
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  • 2
    Language: English
    In: Nucleic acids research, 01 July 2003, Vol.31(13), pp.3597-600
    Description: The GeneSeqer@PlantGDB Web server (http://www.plantgdb.org/cgi-bin/GeneSeqer.cgi) provides a gene structure prediction tool tailored for applications to plant genomic sequences. Predictions are based on spliced alignment with source-native ESTs and full-length cDNAs or non-native probes derived from putative homologous genes. The tool is illustrated with applications to refinement of current gene structure annotation and de novo annotation of draft genomic sequences. The service should facilitate expert annotation as a community effort by providing convenient access to all public plant sequences via the PlantGDB database, a simple four-step protocol for spliced alignment and visually appealing displays of the predicted gene structures in addition to detailed sequence alignments.
    Keywords: Genome, Plant ; Software ; Sequence Analysis, DNA -- Methods
    E-ISSN: 1362-4962
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  • 3
    Language: English
    In: Nucleic acids research, 01 January 2003, Vol.31(1), pp.244-7
    Description: Zea mays DataBase (ZmDB) seeks to provide a comprehensive view of maize (corn) genetics by linking genomic sequence data with gene expression analysis and phenotypes of mutant plants. ZmDB originated in 1999 as the Web portal for a large project of maize gene discovery, sequencing and phenotypic analysis using a transposon tagging strategy and expressed sequence tag (EST) sequencing. Recently, ZmDB has broadened its scope to include all public maize ESTs, genome survey sequences (GSSs), and protein sequences. More than 170 000 ESTs are currently clustered into approximately 20 000 contigs and about an equal number of apparent singlets. These clusters are continuously updated and annotated with respect to potential encoded protein products. More than 100 000 GSSs are similarly assembled and annotated by spliced alignment with EST and protein sequences. The ZmDB interface provides quick access to analytical tools for further sequence analysis. Every sequence record is linked to several display options and similarity search tools, including services for multiple sequence alignment, protein domain determination and spliced alignment. Furthermore, ZmDB provides web-based ordering of materials generated in the project, including ESTs, ordered collections of genomic sequences tagged with the RescueMu transposon and microarrays of amplified ESTs. ZmDB can be accessed at http://zmdb.iastate.edu/.
    Keywords: Databases, Genetic ; Genome, Plant ; Zea Mays -- Genetics
    E-ISSN: 1362-4962
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