37 research outputs found

    Computational Reconstruction of Multidomain Proteins Using Atomic Force Microscopy Data

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    SummaryClassical structural biology techniques face a great challenge to determine the structure at the atomic level of large and flexible macromolecules. We present a novel methodology that combines high-resolution AFM topographic images with atomic coordinates of proteins to assemble very large macromolecules or particles. Our method uses a two-step protocol: atomic coordinates of individual domains are docked beneath the molecular surface of the large macromolecule, and then each domain is assembled using a combinatorial search. The protocol was validated on three test cases: a simulated system of antibody structures; and two experimentally based test cases: Tobacco mosaic virus, a rod-shaped virus; and Aquaporin Z, a bacterial membrane protein. We have shown that AFM-intermediate resolution topography and partial surface data are useful constraints for building macromolecular assemblies. The protocol is applicable to multicomponent structures connected in the polypeptide chain or as disjoint molecules. The approach effectively increases the resolution of AFM beyond topographical information down to atomic-detail structures

    Accurate Prediction of Inducible Transcription Factor Binding Intensities In Vivo

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    DNA sequence and local chromatin landscape act jointly to determine transcription factor (TF) binding intensity profiles. To disentangle these influences, we developed an experimental approach, called protein/DNA binding followed by high-throughput sequencing (PB–seq), that allows the binding energy landscape to be characterized genome-wide in the absence of chromatin. We applied our methods to the Drosophila Heat Shock Factor (HSF), which inducibly binds a target DNA sequence element (HSE) following heat shock stress. PB–seq involves incubating sheared naked genomic DNA with recombinant HSF, partitioning the HSF–bound and HSF–free DNA, and then detecting HSF–bound DNA by high-throughput sequencing. We compared PB–seq binding profiles with ones observed in vivo by ChIP–seq and developed statistical models to predict the observed departures from idealized binding patterns based on covariates describing the local chromatin environment. We found that DNase I hypersensitivity and tetra-acetylation of H4 were the most influential covariates in predicting changes in HSF binding affinity. We also investigated the extent to which DNA accessibility, as measured by digital DNase I footprinting data, could be predicted from MNase–seq data and the ChIP–chip profiles for many histone modifications and TFs, and found GAGA element associated factor (GAF), tetra-acetylation of H4, and H4K16 acetylation to be the most predictive covariates. Lastly, we generated an unbiased model of HSF binding sequences, which revealed distinct biophysical properties of the HSF/HSE interaction and a previously unrecognized substructure within the HSE. These findings provide new insights into the interplay between the genomic sequence and the chromatin landscape in determining transcription factor binding intensity

    Conference highlights of the 15th international conference on human retrovirology: HTLV and related retroviruses, 4-8 june 2011, Leuven, Gembloux, Belgium

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    The June 2011 15th International Conference on Human Retrovirology: HTLV and Related Viruses marks approximately 30 years since the discovery of HTLV-1. As anticipated, a large number of abstracts were submitted and presented by scientists, new and old to the field of retrovirology, from all five continents. The aim of this review is to distribute the scientific highlights of the presentations as analysed and represented by experts in specific fields of epidemiology, clinical research, immunology, animal models, molecular and cellular biology, and virology

    Parsimonious Higher-Order Hidden Markov Models for Improved Array-CGH Analysis with Applications to Arabidopsis thaliana

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    Array-based comparative genomic hybridization (Array-CGH) is an important technology in molecular biology for the detection of DNA copy number polymorphisms between closely related genomes. Hidden Markov Models (HMMs) are popular tools for the analysis of Array-CGH data, but current methods are only based on first-order HMMs having constrained abilities to model spatial dependencies between measurements of closely adjacent chromosomal regions. Here, we develop parsimonious higher-order HMMs enabling the interpolation between a mixture model ignoring spatial dependencies and a higher-order HMM exhaustively modeling spatial dependencies. We apply parsimonious higher-order HMMs to the analysis of Array-CGH data of the accessions C24 and Col-0 of the model plant Arabidopsis thaliana. We compare these models against first-order HMMs and other existing methods using a reference of known deletions and sequence deviations. We find that parsimonious higher-order HMMs clearly improve the identification of these polymorphisms. Moreover, we perform a functional analysis of identified polymorphisms revealing novel details of genomic differences between C24 and Col-0. Additional model evaluations are done on widely considered Array-CGH data of human cell lines indicating that parsimonious HMMs are also well-suited for the analysis of non-plant specific data. All these results indicate that parsimonious higher-order HMMs are useful for Array-CGH analyses. An implementation of parsimonious higher-order HMMs is available as part of the open source Java library Jstacs (www.jstacs.de/index.php/PHHMM)

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    Combining H/D Exchange Mass Spectrometry and Computational Docking To Derive the Structure of Protein–Protein Complexes

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    Protein–protein interactions are essential for biological function, but structures of protein–protein complexes are difficult to obtain experimentally. To derive the protein complex of the DNA-repair enzyme human uracil-DNA-glycosylase (hUNG) with its protein inhibitor (UGI), we combined rigid-body computational docking with hydrogen/deuterium exchange mass spectrometry (DXMS). Computational docking of the unbound protein structures provides a list of possible three-dimensional models of the complex; DXMS identifies solvent-protected protein residues. DXMS showed that unbound hUNG is compactly folded, but unbound UGI is loosely packed. An increased level of solvent protection of hUNG in the complex was localized to four regions on the same face. The decrease in the number of incorporated deuterons was quantitatively interpreted as the minimum number of main-chain hUNG amides buried in the protein–protein interface. The level of deuteration of complexed UGI decreased throughout the protein chain, indicating both tighter packing and direct solvent protection by hUNG. Three UGI regions showing the greatest decreases were best interpreted leniently, requiring just one main-chain amide from each in the interface. Applying the DXMS constraints as filters to a list of docked complexes gave the correct complex as the largest favorable energy cluster. Thus, identification of approximate protein interfaces was sufficient to distinguish the protein complex. Surprisingly, incorporating the DXMS data as added favorable potentials in the docking calculation was less effective in finding the correct complex. The filtering method has greater flexibility, with the capability to test each constraint and enforce simultaneous contact by multiple regions, but with the caveat that the list from the unbiased docking must include correct complexes

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