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Search Results: 1 - 10 of 601961 matches for " Steven J. M. Jones "
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A Computational Approach to Finding Novel Targets for Existing Drugs
Yvonne Y. Li ,Jianghong An,Steven J. M. Jones
PLOS Computational Biology , 2011, DOI: 10.1371/journal.pcbi.1002139
Abstract: Repositioning existing drugs for new therapeutic uses is an efficient approach to drug discovery. We have developed a computational drug repositioning pipeline to perform large-scale molecular docking of small molecule drugs against protein drug targets, in order to map the drug-target interaction space and find novel interactions. Our method emphasizes removing false positive interaction predictions using criteria from known interaction docking, consensus scoring, and specificity. In all, our database contains 252 human protein drug targets that we classify as reliable-for-docking as well as 4621 approved and experimental small molecule drugs from DrugBank. These were cross-docked, then filtered through stringent scoring criteria to select top drug-target interactions. In particular, we used MAPK14 and the kinase inhibitor BIM-8 as examples where our stringent thresholds enriched the predicted drug-target interactions with known interactions up to 20 times compared to standard score thresholds. We validated nilotinib as a potent MAPK14 inhibitor in vitro (IC50 40 nM), suggesting a potential use for this drug in treating inflammatory diseases. The published literature indicated experimental evidence for 31 of the top predicted interactions, highlighting the promising nature of our approach. Novel interactions discovered may lead to the drug being repositioned as a therapeutic treatment for its off-target's associated disease, added insight into the drug's mechanism of action, and added insight into the drug's side effects.
Perturbation of Interaction Networks for Application to Cancer Therapy
Adrian P. Quayle,Asim S. Siddiqui,Steven J. M. Jones
Cancer Informatics , 2007,
Abstract: We present a computational approach for studying the effect of potential drug combinations on the protein networks associated with tumor cells. The majority of therapeutics are designed to target single proteins, yet most diseased states are characterized by a combination of many interacting genes and proteins. Using the topology of protein-protein interaction networks, our methods can explicitly model the possible synergistic effect of targeting multiple proteins using drug combinations in different cancer types. The methodology can be conceptually split into two distinct stages. Firstly, we integrate protein interaction and gene expression data to develop network representations of different tissue types and cancer types. Secondly, we model network perturbations to search for target combinations which cause significant damage to a relevant cancer network but only minimal damage to an equivalent normal network. We have developed sets of predicted target and drug combinations for multiple cancer types, which are validated using known cancer and drug associations, and are currently in experimental testing for prostate cancer. Our methods also revealed significant bias in curated interaction data sources towards targets with associations compared with high-throughput data sources from model organisms. The approach developed can potentially be applied to many other diseased cell types.
Theoretical Investigation of the D83V Mutation within the Myocyte-Specific Enhancer Factor-2 Beta and Its Role in Cancer
Oleksandr Yakovenko,Ryan Morin,Ganna Vashchenko,Steven J. M. Jones
Journal of Theoretical Chemistry , 2013, DOI: 10.1155/2013/313419
Abstract: The D83V mutation in the myocyte-specific enhancer factor-2 beta (MEF2B) gene is frequently observed in lymphomas. Surprisingly, this apparent gain-of-function mutation is within a protein that is involved in the promotion of apoptosis in B cells. To investigate the oncogenic effects of this alteration and explain its predominance over other known loss-of-function mutations of MEF2B, we propose a hypothesis that this mutation influences the dynamic folding of the C-terminal loop of the N-terminal domain of MEF2B. According to our hypothesis, the mutation allows MEF2B to bind promiscuously to a wider variety of gene promoters. A large set of molecular dynamic simulations (MD) was conducted to investigate the effects of D83V mutation in silico and support the hypothesis. 1. Introduction The myocyte-specific enhancer factor-2 (MEF2) family of transcription factors plays an essential role in myogenesis [1, 2] and the regulation of the proapoptotic gene Nur77 [3–6]. This family is composed of four members, MEF2A, -B, -C, and -D, which normally function either as homo- or heterodimers [7]. All of these factors recognize the DNA consensus sequence C/TTA(A/T)4TAG/A, which is present within many gene promoters (see [8, 9] and references therein), and consist of a MADS-box containing a N-terminal domain responsible for DNA recognition and dimerization and an as yet uncharacterized C-terminal domain [10]. The sequence differences between the N-terminal domain of MEF2 forms are mainly within the MEF2S subdomain (residues 58–95) of the N-terminal domain which is responsible for the determining of DNA binding specificity [11–18]. Recently, MEF2B was found to be altered in many B-cell-derived lymphomas [19]. Morin et al. [19] and Youn and Liu [20] proposed a model of MEF2B involvement in the development of lymphomas through dependent signaling downstream of the B-cell receptor. In normal germinal centre B-cells MEF2B binds to the promoter of Nur77, a proapoptotic gene that plays a crucial role in TCR-mediated apoptosis [21–23]. However, MEF2B is kept transcriptionally inactive by the suppressor Cabin1 which binds to its N-terminal domain preventing the transcription of Nur77. This repression is calcium-dependent [3, 4]: when enters the cytoplasm in response to B-cell receptor activation, it folds the intrinsically unstructured Calmodulin, which becomes a more preferred partner of Cabin1. Calmodulin in complex with releases Cabin1 from its complex with MEF2B, allowing MEF2B to initiate Nur77 transcription and recruit other activating factors such as HDAC7, HDAC9, and
The dynamics and excitation of torsional waves in geodynamo simulations
Robert J. Teed,Chris A. Jones,Steven M. Tobias
Physics , 2013, DOI: 10.1093/gji/ggt432
Abstract: The predominant force balance in rapidly rotating planetary cores is between Coriolis, pressure, buoyancy and Lorentz forces. This magnetostrophic balance leads to a Taylor state where the spatially averaged azimuthal Lorentz force is compelled to vanish on cylinders aligned with the rotation axis. Any deviation from this state leads to a torsional oscillation, signatures of which have been observed in the Earth's secular variation and are thought to influence length of day variations via angular momentum conservation. In order to investigate the dynamics of torsional oscillations, we perform several three-dimensional dynamo simulations in a spherical shell. We find torsional oscillations, identified by their propagation at the correct Alfv\'{e}n speed, in many of our simulations. We find that the frequency, location and direction of propagation of the waves are influenced by the choice of parameters. Torsional waves are observed within the tangent cylinder and also have the ability to pass through it. Several of our simulations display waves with core travel times of 4 to 6 years. We calculate the driving terms for these waves and find that both the Reynolds force and ageostrophic convection acting through the Lorentz force are important in driving torsional oscillations.
KiWi: A Scalable Subspace Clustering Algorithm for Gene Expression Analysis
Obi L. Griffith,Byron J. Gao,Mikhail Bilenky,Yuliya Prichyna,Martin Ester,Steven J. M. Jones
Computer Science , 2009,
Abstract: Subspace clustering has gained increasing popularity in the analysis of gene expression data. Among subspace cluster models, the recently introduced order-preserving sub-matrix (OPSM) has demonstrated high promise. An OPSM, essentially a pattern-based subspace cluster, is a subset of rows and columns in a data matrix for which all the rows induce the same linear ordering of columns. Existing OPSM discovery methods do not scale well to increasingly large expression datasets. In particular, twig clusters having few genes and many experiments incur explosive computational costs and are completely pruned off by existing methods. However, it is of particular interest to determine small groups of genes that are tightly coregulated across many conditions. In this paper, we present KiWi, an OPSM subspace clustering algorithm that is scalable to massive datasets, capable of discovering twig clusters and identifying negative as well as positive correlations. We extensively validate KiWi using relevant biological datasets and show that KiWi correctly assigns redundant probes to the same cluster, groups experiments with common clinical annotations, differentiates real promoter sequences from negative control sequences, and shows good association with cis-regulatory motif predictions.
A Fluorescent Aerogel for Capture and Identification of Interplanetary and Interstellar Dust
Gerardo Dominguez,Andrew J. Westphal,Mark L. F. Phillips,Steven M. Jones
Physics , 2003, DOI: 10.1086/375549
Abstract: Contemporary interstellar dust has never been analyzed in the laboratory, despite its obvious astronomical importance and its potential as a probe of stellar nucleosynthesis and galactic chemical evolution. Here we report the discovery of a novel fluorescent aerogel which is capable of capturing hypervelocity dust grains and passively recording their kinetic energies. An array of these "calorimetric" aerogel collectors in low earth orbit would lead to the capture and identification of large numbers of interstellar dust grains.
New class of compounds - variators - are reprogramming substrate specificity of H4K12Ac, H4K16Ac and H4K20Ac epigenetic marks reading bromodomain of BPTF protein
Oleksandr Ya Yakovenko,Sreeja Leelakumari,Ganna Vashchenko,Albert Badiong,Steven J. M. Jones
Quantitative Biology , 2015,
Abstract: Previously reported [http://arxiv.org/abs/1506.06433] reprogramming of substrate specificity of H3K4Me3 epigenetic marks reading PHD domain of BPTF protein illustrates therapeutic potential of a new class of non-inhibitor small organic compounds - variators. Here we address the question about reproducibility of rational design of variators by reprogramming of the second epigenetic marks reading domain of BPTF protein - bromodomain. Bromodomain of BPTF binds to epigenetic marks in form of acetylated lysine of histone H4 (H4K12Ac, H4K16Ac and H4K20Ac), which physicochemical properties and binding mode differs considerably from those of methylated H3K4 marks. Thus, detailed description of computational approach for reprogramming of bromodomain substrate specificity illustrates both general and target specific attributes of computer aided variators design.
New class of compounds - variators - are reprogramming substrate specificity of H3K4me3 epigenetic marks reading PHD domain of BPTF protein
Oleksandr Ya Yakovenko,Sreeja Leelakumari,Ganna Vashchenko,Pierre Cheung,Albert Badiong,Steven J. M. Jones
Quantitative Biology , 2015,
Abstract: In lymphoma, mutations in genes of histone modifying proteins are frequently observed. Notably, somatic mutations in the activatory histone modification writing protein MLL2 and the repressive modification writer EZH2 are the most frequent. Gain of function mutations are typically detected in EZH2 whilst MLL2 mutations are usually observed as conferring a homozygous loss of function. The gain-of-function mutations in EZH2 provide an obvious target for the development of inhibitors with therapeutic potential. To counter the loss of functional MLL2 protein, we computationally predicted compounds that are able to modulate the reader of the corresponding modifications, BPTF, to recognize other forms of the histone H3 lysine 4, instead of the tri-methylated form normally produced by MLL2. By forming a synthetic triple-complex of a compound, the histone H3 tail and BPTF we potentially circumvent the requirement for functional MLL2 methyl-transferase through the modulation of BPTF activity. Here we show a proof-of-principle that special compounds, named variators, can reprogram selectivity of protein binding and thus create artificial regulatory pathways which can have a potential therapeutic role. A therapeutic role of BPTF variators may extend to other diseases that involve loss of MLL2 function, such as Kabuki syndrome or the aberrant functioning of H3K4 modification as observed in Huntington disease and in memory formation.
The nature of the dwarf population in Abell 868
Peter J. Boyce,Steven Phillipps,J. Bryn Jones,Simon P. Driver,Rodney M. Smith,Warrick J. Couch
Physics , 2001, DOI: 10.1046/j.1365-8711.2001.04862.x
Abstract: We present the results of a study of the morphology of the dwarf galaxy population in Abell 868, a rich, intermediate redshift (z=0.154) cluster which has a galaxy luminosity function with a steep faint-end slope (alpha=-1.26 +/- 0.05). A statistical background subtraction method is employed to study the B-R colour distribution of the cluster galaxies. This distribution suggests that the galaxies contributing to the faint-end of the measured cluster LF can be split into three populations: dIrrs with B-R<1.4; dEs with 1.42.5. The remvoal of the contribution of the background gEs from the counts only marginally lessens the faint-end slope (alpha=-1.22 +/- 0.16). However, the removal of the contribution of the dIrrs from the counts produces a flat LF (alpha=-0.91 +/- 0.16). The dEs and the dIrrs have similar spatial distributions within the cluster except that the dIrrs appear to be totally absent within a central projected radius of about 0.2 Mpc (Ho=75 km/s /Mpc). The number density of both dEs and dIrrs appear to fall off beyond a projected radius of about 0.35 Mpc. We suggest that the dE and dIrr populations of A868 have been associated with the cluster for similar timescales but that evolutionary processes such as `galaxy harassment' tend to fade the dIrr galaxies while having much less effect on the dE galaxies. The harassement would be expected to have the greatest effect on dwarfs residing in the central parts of the cluster.
Color Dispersion and Milky Way Reddening Among Type Ia Supernovae
Daniel M. Scolnic,Adam G. Riess,Ryan J. Foley,Armin Rest,Steven A. Rodney,Dillon J. Brout,David O. Jones
Physics , 2013, DOI: 10.1088/0004-637X/780/1/37
Abstract: Past analyses of Type Ia Supernovae (SNe Ia) have identified an irreducible scatter of 5-10% in distance widely attributed to an intrinsic dispersion in luminosity. Another, equally valid, source of this scatter is intrinsic dispersion in color. Misidentification of the true source of this scatter can bias both the retrieved color-luminosity relation and cosmological parameter measurements. The size of this bias depends on the magnitude of the intrinsic color dispersion relative to the distribution of colors that correlate with distance. We produce a realistic simulation of a misattribution of intrinsic scatter, and find a negative bias in the recovered color-luminosity relation, beta, of dbeta -1.0 (~33%) and a positive bias in the equation of state parameter, w, of dw +0.04 (~4%). We re-analyze current published data sets with the assumptions that the distance scatter is predominantly the result of color. Unlike previous analyses, we find that the data are consistent with a Milky Way reddening law R_V=3.1, and that a Milky Way dust model better predicts the asymmetric color-luminosity trends than the conventional luminosity scatter hypothesis. We also determine that accounting for color variation reduces the correlation between various Host galaxy properties and Hubble residuals by ~20%.
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