Sequence-structure homology recognition by iterative alignment refinement and comparative modeling

M. G. Williams, H. Shirai, J. Shi, H. G. Nagendra, J. Mueller, K. Mizuguchi, R. N. Miguel, S. C. Lovell, C. A. Innis, C. M. Deane, L. Chen, N. Campillo, D. F. Burke, T. L. Blundell, P. I W De Bakker

    Research output: Contribution to journalArticlepeer-review

    Abstract

    Our approach to fold recognition for the fourth critical assessment of techniques for protein structure prediction (CASP4) experiment involved the use of the FUGUE sequence-structure homology recognition program (http://www-cryst. bioc.cam.ac.uk/fugue), followed by model building. We treat models as hypotheses and examine these to determine whether they explain the available data. Our method depends heavily on environment-specific substitution tables derived from our database of structural alignments of homologous proteins (HOMSTRAD, http://www-cryst.bioc.cam.ac.uk/homstrad/). FUGUE uses these tables to incorporate structural information into profiles created from HOMSTRAD alignments that are matched against a profile created for the target from multiple sequence alignment. In addition, environment-specific substitution tables are used throughout the modeling procedure and as part of the model evaluation. Annotation of sequence alignments with JOY, to reflect local structural features, proved valuable, both for modifying hypotheses, and for rejecting predictions when the expected pattern of conservation is not observed. Our stringency in rejecting incorrect predictions led us to submit a relatively small number of models, including only a low number of false positives, resulting in a high average score. © 2002 Wiley-Liss, Inc.
    Original languageEnglish
    Pages (from-to)92-97
    Number of pages5
    JournalProteins: Structure, Function and Bioinformatics
    Volume45
    Issue number5
    DOIs
    Publication statusPublished - 2001

    Keywords

    • Alignment databases
    • Environment-specific substitution tables
    • Homology recognition
    • Model evaluation
    • Protein structure
    • Structure prediction

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