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  • 1
    Online Resource
    Online Resource
    A Novel Family of Divergent Seven-Transmembrane Proteins
    Elsevier BV ; 1999
    In:  Neuron Vol. 22, No. 2 ( 1999-02), p. 327-338
    Details
    In: Neuron, Elsevier BV, Vol. 22, No. 2 ( 1999-02), p. 327-338
    Type of Medium: Online Resource
    ISSN: 0896-6273
    URL: Article
    DOI: 10.1016/S0896-6273(00)81093-4
    Language: English
    Publisher: Elsevier BV
    Publication Date: 1999
    detail.hit.zdb_id: 2001944-0
    SSG: 12
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  • 2
    Online Resource
    Online Resource
    Candidate Taste Receptors in Drosophila
    American Association for the Advancement of Science (AAAS) ; 2000
    In:  Science Vol. 287, No. 5459 ( 2000-03-10), p. 1830-1834
    Details
    In: Science, American Association for the Advancement of Science (AAAS), Vol. 287, No. 5459 ( 2000-03-10), p. 1830-1834
    Abstract: Little is known about the molecular mechanisms of taste perception in animals, particularly the initial events of taste signaling. A large and diverse family of seven transmembrane domain proteins was identified from the Drosophila genome database with a computer algorithm that identifies proteins on the basis of structure. Eighteen of 19 genes examined were expressed in the Drosophila labellum, a gustatory organ of the proboscis. Expression was not detected in a variety of other tissues. The genes were not expressed in the labellum of a Drosophila mutant, pox-neuro 70 , in which taste neurons are eliminated. Tissue specificity of expression of these genes, along with their structural similarity, supports the possibility that the family encodes a large and divergent family of taste receptors.
    Type of Medium: Online Resource
    ISSN: 0036-8075 , 1095-9203
    URL: Article
    DOI: 10.1126/science.287.5459.1830
    RVK:
    TA 1000
    RVK:
    WA 15000
    Language: English
    Publisher: American Association for the Advancement of Science (AAAS)
    Publication Date: 2000
    detail.hit.zdb_id: 128410-1
    detail.hit.zdb_id: 2066996-3
    detail.hit.zdb_id: 2060783-0
    SSG: 11
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  • 3
    Online Resource
    Online Resource
    Identification of novel multi-transmembrane proteins from genomic databases using quasi-periodic structural properties
    Oxford University Press (OUP) ; 2000
    In:  Bioinformatics Vol. 16, No. 9 ( 2000-09-01), p. 767-775
    Details
    In: Bioinformatics, Oxford University Press (OUP), Vol. 16, No. 9 ( 2000-09-01), p. 767-775
    Abstract: Motivation: Identification of novel G protein-coupled receptors and other multi-transmembrane proteins from genomic databases using structural features. Results: Here we describe a new algorithm for identifying multi-transmembrane proteins from genomic databases with a specific application to identifying G protein-coupled receptors (GPCRs) that we call quasi-periodic feature classifier (QFC). The QFC algorithm uses concise statistical variables as the ‘feature space’ to characterize the quasi-periodic physico-chemical properties of multi-transmembrane proteins. For the case of identifying GPCRs, the variables are then used in a non-parametric linear discriminant function to separate GPCRs from non-GPCRs. The algorithm runs in time linearly proportional to the number of sequences, and performance on a test dataset shows 96% positive identification of known GPCRs. The QFC algorithm also works well with short random segments of proteins and it positively identified GPCRs at a level greater than 90% even with segments as short as 100 amino acids. The primary advantage of the algorithm is that it does not directly use primary sequence patterns which may be subject to sampling bias. The utility of the new algorithm has been demonstrated by the isolation from the Drosophila genome project database of a novel class of seven-transmembrane proteins which were shown to be the elusive olfactory receptor genes of Drosophila. Availability: C++/Perl available from http://jkim.eeb.yale.edu/index.html Contact: Junhyong Kim, Dept. of Ecology and Evolutionary Biology, Yale University, P.O. Box 208106, New Haven, CT 06520-8106; junhyong.kim@yale.edu Supplementary information: Test dataset and training dataset are available from http://jkim.eeb.yale.edu/index.html **** To whom correspondence should be addressed.
    Type of Medium: Online Resource
    ISSN: 1367-4811 , 1367-4803
    URL: Article
    DOI: 10.1093/bioinformatics/16.9.767
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2000
    detail.hit.zdb_id: 1468345-3
    SSG: 12
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