Identifying Ipr1 binding sites in RAW264.7 cells using ChIP-seq
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ABSTRACT:
ORGANISM(S): Mus musculus
PROVIDER: GSE121548 | GEO | 2018/10/22
REPOSITORIES: GEO
Similar Datasets
Project description:Identifying Ipr1 binding sites in RAW264.7 cells using ChIP-seq
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Project description:In order to identify the locations of metal ions in the binding sites of proteins, we have developed a method named the MADE (MAcromolecular DEnsity and Structure Analysis) approach. The MADE approach represents an evolution of our previous toolset, the ProBiS H2O (MD) methodology, for the identification of conserved water molecules. Our method uses experimental structures of proteins homologous to a query, which are subsequently superimposed upon it. Areas with a particular species present in a similar location among many homologous protein structures are identified using a clustering algorithm. Dense clusters likely represent positions containing species important to the query protein structure or function. We analyze well-characterized apo protein structures and show that the MADE approach can identify clusters corresponding to the expected positions of metal ions in their binding sites. The greatest advantage of our method lies in its generality. It can in principle be applied to any species found in protein records; it is not only limited to metal ions. We additionally demonstrate that the MADE approach can be successfully applied to predict the location of cofactors in computer-modeled structures, e.g., via AlphaFold. We also conduct a careful protein superposition method comparison and find our methodology robust and the results largely independent of the selected protein superposition algorithm. We postulate that with increasing structural data availability, additional applications of the MADE approach will be possible such as non-protein systems, water network identification, protein binding site elaboration, and analysis of binding events, all in a dynamic manner. We have implemented the MADE approach as a plugin for the PyMOL molecular visualization tool. The MADE plugin is available free of charge at https://gitlab.com/Jukic/made_software.
| S-EPMC10466382 | biostudies-literature
Project description:We present a method to identify small molecule ligand binding sites and poses within a given protein crystal structure using GPU-accelerated Hamiltonian replica exchange molecular dynamics simulations. The Hamiltonians used vary from the physical end state of protein interacting with the ligand to an unphysical end state where the ligand does not interact with the protein. As replicas explore the space of Hamiltonians interpolating between these states, the ligand can rapidly escape local minima and explore potential binding sites. Geometric restraints keep the ligands from leaving the vicinity of the protein and an alchemical pathway designed to increase phase space overlap between intermediates ensures good mixing. Because of the rigorous statistical mechanical nature of the Hamiltonian exchange framework, we can also extract binding free energy estimates for all putative binding sites. We present results of this methodology applied to the T4 lysozyme L99A model system for three known ligands and one non-binder as a control, using an implicit solvent. We find that our methodology identifies known crystallographic binding sites consistently and accurately for the small number of ligands considered here and gives free energies consistent with experiment. We are also able to analyze the contribution of individual binding sites to the overall binding affinity. Our methodology points to near term potential applications in early-stage structure-guided drug discovery.
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Project description:Heterochromatin protein HP1 is thought to play key role in chromatin structure and gene regulation. We performed a genome-wide mapping of HP1 target genes in the non-polytenic Drosophila Kc cells by using DamID. This approach is based on the ability of a chromatin protein fused to Escherichia coli DNA adenine methyltransferase (Dam) to methylate the native binding site of the chromatin protein. Dam-fusion proteins are expressed at very low levels to avoid mistargeting. Subsequently, methylated DNA fragments are isolated, labeled (using Cy3 or Cy5) and hybridized to a microarray. Methylated DNA fragments from cells transfected with Dam alone served as reference. Genomic binding sites of the protein can then be identified based on the targeted methylation pattern. For detailed background information on DamID, see: van Steensel, B., Delrow, J. & Henikoff, S. Chromatin profiling using targeted DNA adenine methyltransferase. Nat Genet 27, 304-8 (2001); van Steensel, B. & Henikoff, S. Identification of in vivo DNA targets of chromatin proteins using tethered dam methyltransferase. Nat Biotechnol 18, 424-8 (2000). We performed three independent replicates. We used for this study a cDNA array developed by the GeneCore facility in EMBL (Heidelberg, Germany), covering the DGC1 and DGC2 cDNA libraries from the Berkeley Drosophila Genome Project, which represents more than 70% of the coding Drosophila genome.
2007-07-05 | E-MEXP-864 | biostudies-arrayexpress
Project description:Here we describe a new method to identify calcium-binding sites in proteins using high-resolution liquid chromatography-mass spectrometry in concert with calcium-directed collision-induced dissociations. Our method does not require any modifications to the liquid chromatography-mass spectrometry apparatus, uses standard digestion protocols, and can be applied to existing high-resolution MS data files. In contrast to NMR, our method is applicable to very small amounts of complex protein mixtures (femtomole level). Calcium-bound peptides can be identified using three criteria: (1) the calculated exact mass of the calcium containing peptide; (2) specific dissociations of the calcium-containing peptide from threonine and serine residues; and (3) the very similar retention times of the calcium-containing peptide and the free peptide.
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Project description:UnlabelledRetroviral integration has been implicated in several biomedical applications, including identification of cancer-associated genes and malignant transformation in gene therapy clinical trials. We introduce an efficient and scalable method for fast identification of viral vector integration sites from long read high-throughput sequencing. Individual sequence reads are masked to remove non-genomic sequence, aligned to the host genome and assembled into contiguous fragments used to pinpoint the position of integration.Availability and implementationThe method is implemented in a publicly accessible web server platform, SeqMap 2.0, containing analysis tools and both private and shared lab workspaces that facilitate collaboration among researchers. Available at http://seqmap.compbio.iupui.edu/.
| S-EPMC3042184 | biostudies-literature
Project description:We introduce a method (MONKEY) to identify conserved transcription-factor binding sites in multispecies alignments. MONKEY employs probabilistic models of factor specificity and binding-site evolution, on which basis we compute the likelihood that putative sites are conserved and assign statistical significance to each hit. Using genomes from the genus Saccharomyces, we illustrate how the significance of real sites increases with evolutionary distance and explore the relationship between conservation and function.
| S-EPMC545801 | biostudies-other
Project description:MotivationAccurate knowledge of the genome-wide binding of transcription factors in a particular cell type or under a particular condition is necessary for understanding transcriptional regulation. Using epigenetic data such as histone modification and DNase I, accessibility data has been shown to improve motif-based in silico methods for predicting such binding, but this approach has not yet been fully explored.ResultsWe describe a probabilistic method for combining one or more tracks of epigenetic data with a standard DNA sequence motif model to improve our ability to identify active transcription factor binding sites (TFBSs). We convert each data type into a position-specific probabilistic prior and combine these priors with a traditional probabilistic motif model to compute a log-posterior odds score. Our experiments, using histone modifications H3K4me1, H3K4me3, H3K9ac and H3K27ac, as well as DNase I sensitivity, show conclusively that the log-posterior odds score consistently outperforms a simple binary filter based on the same data. We also show that our approach performs competitively with a more complex method, CENTIPEDE, and suggest that the relative simplicity of the log-posterior odds scoring method makes it an appealing and very general method for identifying functional TFBSs on the basis of DNA and epigenetic evidence.Availability and implementationFIMO, part of the MEME Suite software toolkit, now supports log-posterior odds scoring using position-specific priors for motif search. A web server and source code are available at http://meme.nbcr.net. Utilities for creating priors are at http://research.imb.uq.edu.au/t.bailey/SD/Cuellar2011.Contactt.bailey@uq.edu.auSupplementary informationSupplementary data are available at Bioinformatics online.
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Project description:The goal of this work was to determine where Lsr2 binds within the S. venezuelae chromosome, and to differentiate between direct versus indirect effects by comparing our ChIP-seq results with RNA-seq results
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Project description:The aim of this work was to determine where SigG1 binds within the S. tsukubaensis chromosome
2020-12-21 | GSE144907 | GEO