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      Connecting protein structure with predictions of regulatory sites.

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          Abstract

          A common task posed by microarray experiments is to infer the binding site preferences for a known transcription factor from a collection of genes that it regulates and to ascertain whether the factor acts alone or in a complex. The converse problem can also be posed: Given a collection of binding sites, can the regulatory factor or complex of factors be inferred? Both tasks are substantially facilitated by using relatively simple homology models for protein-DNA interactions, as well as the rapidly expanding protein structure database. For budding yeast, we are able to construct reliable structural models for 67 transcription factors and with them redetermine factor binding sites by using a Bayesian Gibbs sampling algorithm and an extensive protein localization data set. For 49 factors in common with a prior analysis of this data set (based largely on phylogenetic conservation), we find that half of the previously predicted binding motifs are in need of some revision. We also solve the inverse problem of ascertaining the factors from the binding sites by assigning a correct protein fold to 25 of the 49 cases from a previous study. Our approach is easily extended to other organisms, including higher eukaryotes. Our study highlights the utility of enlarging current structural genomics projects that exhaustively sample fold structure space to include all factors with significantly different DNA-binding specificities.

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          Author and article information

          Journal
          Proc Natl Acad Sci U S A
          Proceedings of the National Academy of Sciences of the United States of America
          Proceedings of the National Academy of Sciences
          0027-8424
          0027-8424
          Apr 24 2007
          : 104
          : 17
          Affiliations
          [1 ] Center for Studies in Physics and Biology, The Rockefeller University, 1230 York Avenue, New York, NY 10021, USA. morozov@edsb.rockefeller.edu
          Article
          0701356104
          10.1073/pnas.0701356104
          1855371
          17438293
          e80c6cb8-0ae6-4756-a286-c67ed877f684
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