Dataset Information

Molecular Dynamics of "Fuzzy" Transcriptional Activator-Coactivator Interactions.

ABSTRACT: Transcriptional activation domains (ADs) are generally thought to be intrinsically unstructured, but capable of adopting limited secondary structure upon interaction with a coactivator surface. The indeterminate nature of this interface made it hitherto difficult to study structure/function relationships of such contacts. Here we used atomistic accelerated molecular dynamics (aMD) simulations to study the conformational changes of the GCN4 AD and variants thereof, either free in solution, or bound to the GAL11 coactivator surface. We show that the AD-coactivator interactions are highly dynamic while obeying distinct rules. The data provide insights into the constant and variable aspects of orientation of ADs relative to the coactivator, changes in secondary structure and energetic contributions stabilizing the various conformers at different time points. We also demonstrate that a prediction of ?-helical propensity correlates directly with the experimentally measured transactivation potential of a large set of mutagenized ADs. The link between ?-helical propensity and the stimulatory activity of ADs has fundamental practical and theoretical implications concerning the recruitment of ADs to coactivators.

SUBMITTER: Scholes NS

PROVIDER: S-EPMC4866707 | biostudies-literature | 2016 May

REPOSITORIES: biostudies-literature

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Molecular Dynamics of "Fuzzy" Transcriptional Activator-Coactivator Interactions.

Scholes Natalie S NS Weinzierl Robert O J RO

PLoS computational biology 20160513 5

Transcriptional activation domains (ADs) are generally thought to be intrinsically unstructured, but capable of adopting limited secondary structure upon interaction with a coactivator surface. The indeterminate nature of this interface made it hitherto difficult to study structure/function relationships of such contacts. Here we used atomistic accelerated molecular dynamics (aMD) simulations to study the conformational changes of the GCN4 AD and variants thereof, either free in solution, or bou ...[more]

PMID: 27175900

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Project description:BackgroundTo date, only a limited number of transcriptional regulatory interactions have been uncovered. In a pilot study integrating sequence data with microarray data, a position weight matrix (PWM) performed poorly in inferring transcriptional interactions (TIs), which represent physical interactions between transcription factors (TF) and upstream sequences of target genes. Inferring a TI means that the promoter sequence of a target is inferred to match the consensus sequence motifs of a potential TF, and their interaction type such as AT or RT is also predicted. Thus, a robust PWM (rPWM) was developed to search for consensus sequence motifs. In addition to rPWM, one feature extracted from ChIP-chip data was incorporated to identify potential TIs under specific conditions. An interaction type classifier was assembled to predict activation/repression of potential TIs using microarray data. This approach, combining an adaptive (learning) fuzzy inference system and an interaction type classifier to predict transcriptional regulatory networks, was named AdaFuzzy.ResultsAdaFuzzy was applied to predict TIs using real genomics data from Saccharomyces cerevisiae. Following one of the latest advances in predicting TIs, constrained probabilistic sparse matrix factorization (cPSMF), and using 19 transcription factors (TFs), we compared AdaFuzzy to four well-known approaches using over-representation analysis and gene set enrichment analysis. AdaFuzzy outperformed these four algorithms. Furthermore, AdaFuzzy was shown to perform comparably to 'ChIP-experimental method' in inferring TIs identified by two sets of large scale ChIP-chip data, respectively. AdaFuzzy was also able to classify all predicted TIs into one or more of the four promoter architectures. The results coincided with known promoter architectures in yeast and provided insights into transcriptional regulatory mechanisms.ConclusionAdaFuzzy successfully integrates multiple types of data (sequence, ChIP, and microarray) to predict transcriptional regulatory networks. The validated success in the prediction results implies that AdaFuzzy can be applied to uncover TIs in yeast.

Project description:Binding free energies are calculated for the phosphorylated and unphosphorylated complexes between the kinase inducible domain (KID) of the DNA transcriptional activator cAMP response element binding (CREB) protein and the KIX domain of its coactivator, CREB-binding protein (CBP). To our knowledge, this is the first application of a method based on a potential of mean force (PMF) with restraining potentials to compute the binding free energy of protein-protein complexes. The KID:KIX complexes are chosen here because of their biological relevance to the DNA transcription process and their relatively small size (81 residues for the KIX domain of CBP, and 28 residues for KID). The results for pKID:KIX and KID:KIX are -9.55 and -4.96 kcal/mol, respectively, in good agreement with experimental estimates (-8.8 and -5.8 kcal/mol, respectively). A comparison between specific contributions to protein-protein binding for the phosphorylated and unphosphorylated complexes reveals a dual role for the phosphorylation of KID at Ser-133 in effecting a more favorable free energy of the bound system: 1), stabilization of the unbound conformation of phosphorylated KID due to favorable intramolecular interactions of the phosphate group of Ser-133 with the charged groups of an arginine-rich region spanning both ?-helices, which lowers the configurational entropy; and 2), more favorable intermolecular electrostatic interactions between pSer-133 and Arg-131 of KID, and Lys-662, Tyr-658, and Glu-666 of KIX. Charge reduction through ligand phosphorylation emerges as a possible mechanism for controlling the unbound state conformation of KID and, ultimately, gene expression. This work also demonstrates that the PMF-based method with restraining potentials provides an added benefit in that important elements of the binding pathway are evidenced. Furthermore, the practicality of the PMF-based method for larger systems is validated by agreement with experiment. In addition, we provide a somewhat differently structured exposition of the PMF-based method with restraining potentials and outline its generalization to systems in which both protein and ligand may adopt unbound conformations that are different from those of the bound state.

Project description:During early fasting, increases in skeletal muscle proteolysis liberate free amino acids for hepatic gluconeogenesis in response to pancreatic glucagon. Hepatic glucose output diminishes during the late protein-sparing phase of fasting, when ketone body production by the liver supplies compensatory fuel for glucose-dependent tissues. Glucagon stimulates the gluconeogenic program by triggering the dephosphorylation and nuclear translocation of the CREB regulated transcription coactivator 2 (CRTC2; also known as TORC2), while parallel decreases in insulin signalling augment gluconeogenic gene expression through the dephosphorylation and nuclear shuttling of forkhead box O1 (FOXO1). Here we show that a fasting-inducible switch, consisting of the histone acetyltransferase p300 and the nutrient-sensing deacetylase sirtuin 1 (SIRT1), maintains energy balance in mice through the sequential induction of CRTC2 and FOXO1. After glucagon induction, CRTC2 stimulated gluconeogenic gene expression by an association with p300, which we show here is also activated by dephosphorylation at Ser 89 during fasting. In turn, p300 increased hepatic CRTC2 activity by acetylating it at Lys 628, a site that also targets CRTC2 for degradation after its ubiquitination by the E3 ligase constitutive photomorphogenic protein (COP1). Glucagon effects were attenuated during late fasting, when CRTC2 was downregulated owing to SIRT1-mediated deacetylation and when FOXO1 supported expression of the gluconeogenic program. Disrupting SIRT1 activity, by liver-specific knockout of the Sirt1 gene or by administration of a SIRT1 antagonist, increased CRTC2 activity and glucose output, whereas exposure to SIRT1 agonists reduced them. In view of the reciprocal activation of FOXO1 and its coactivator peroxisome proliferator-activated receptor-gamma coactivator-1alpha (PGC-1alpha, encoded by Ppargc1a) by SIRT1 activators, our results illustrate how the exchange of two gluconeogenic regulators during fasting maintains energy balance.

Dataset Information

Molecular Dynamics of "Fuzzy" Transcriptional Activator-Coactivator Interactions.

Publications

Molecular Dynamics of "Fuzzy" Transcriptional Activator-Coactivator Interactions.

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