http://precedings.nature.com/users/5bef8c118935f9b0f61b6168fef12439/ Documents posted by Leonid Mirny Nature Publishing Group en Nature Precedings http://precedings.nature.com/images/header_logo.gif http://precedings.nature.com http://precedings.nature.com/documents/2796/version/1 Cooperative binding of transcription factors (TFs) to cis-regulatory regions (CRRs) is essential for precision of gene expression in development and other processes. The classical model of cooperativity requires direct interactions between TFs, thus constraining the arrangement of TFs sites in a CRR. On the contrary, genomic and functional studies demonstrate a great deal of flexibility in such arrangements with variable distances, numbers of sites, and identities of the involved TFs. Such flexibility is inconsistent with the cooperativity by direct interactions between TFs. Here we demonstrate that strong cooperativity among non-interacting TFs can be achieved by their competition with nucleosomes. We find that the mechanism of nucleosome-mediated cooperativity is mathematically identical to the Monod-Wyman-Changeux (MWC) model of cooperativity in hemoglobin. This surprising parallel provides deep insights, with parallels between heterotropic regulation of hemoglobin (e.g. Bohr effect) and roles of nucleosome-positioning sequences and chromatin modifications in gene regulation. Characterized mechanism is consistent with numerous experimental results, allows substantial flexibility in and modularity of CRRs, and provides a rationale for a broad range of genomic and evolutionary observations. Striking parallels between cooperativity in hemoglobin and in transcription regulation point at a new design principle that may be used in range of biological systems. http://precedings.nature.com/documents/2796/version/1 Wed, 21 Jan 2009 20:17:12 UTC Nucleosome-mediated cooperativity between transcription factors hdl:10101/npre.2009.2796.1 2009-01-21 Leonid A. Mirny Nature Precedings 2009-01-21T20:17:12Z Manuscript Developmental Biology Genetics & Genomics Molecular Cell Biology Bioinformatics http://creativecommons.org/licenses/by/3.0/ http://precedings.nature.com/documents/2688/version/2 To regulate a particular gene, a transcription factor (TF) needs to bind a specific genome location. How is this genome address specified amid the presence of ~106-109 decoy sites? Our analysis of 319 known TF binding motifs clearly demonstrates that prokaryotes and eukaryotes use strikingly different strategies to target TFs to specific genome locations; eukaryotic TFs exhibit widespread nonfunctional binding and require clustering of sites in regulatory regions for specificity. http://precedings.nature.com/documents/2688/version/2 Sun, 28 Dec 2008 18:00:29 UTC Fundamentally different strategies for transcriptional regulation are revealed by analysis of binding motifs hdl:10101/npre.2008.2688.2 2009-01-06 Leonid Mirny Nature Precedings 2008-12-28T18:00:29Z Manuscript Genetics & Genomics Molecular Cell Biology Bioinformatics Evolutionary Biology http://creativecommons.org/licenses/by/3.0/ http://precedings.nature.com/documents/1881/version/1 Peptide recognition modules (PRMs) are used throughout biology to mediate protein-protein interactions, and many PRMs are members of large protein domain families. Members of these families are often quite similar to each other, but each domain recognizes a distinct set of peptides, raising the question of how peptide recognition specificity is achieved using similar protein domains. The analysis of individual protein complex structures often gives answers that are not easily applicable to other members of the same PRM family. Bioinformatics-based approaches, one the other hand, may be difficult to interpret physically. Here we integrate structural information with a large, quantitative data set of SH2-peptide interactions to study the physical origin of domain-peptide specificity. We develop an energy model, inspired by protein folding, based on interactions between the amino acid positions in the domain and peptide. We use this model to successfully predict which SH2 domains and peptides interact and uncover the positions in each that are important for specificity. The energy model is general enough that it can be applied to other members of the SH2 family or to new peptides, and the cross-validation results suggest that these energy calculations will be useful for predicting binding interactions. It can also be adapted to study other PRM families, predict optimal peptides for a given SH2 domain, or study other biological interactions, e.g. protein-DNA interactions. http://precedings.nature.com/documents/1881/version/1 Mon, 12 May 2008 22:34:17 UTC An optimized energy potential can predict SH2 domain-peptide interactions hdl:10101/npre.2008.1881.1 2008-05-20 Zeba Wunderlich Nature Precedings 2008-05-12T22:34:17Z Manuscript Molecular Cell Biology Bioinformatics http://creativecommons.org/licenses/by/3.0/