Browsing by Subject "Protein Interaction Mapping"

Now showing 1 - 6 of 6
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    A nucleosome-guided map of transcription factor binding sites in yeast.
    (PLoS Comput Biol, 2007-11) Narlikar, Leelavati; Gordân, Raluca; Hartemink, Alexander J
    Finding functional DNA binding sites of transcription factors (TFs) throughout the genome is a crucial step in understanding transcriptional regulation. Unfortunately, these binding sites are typically short and degenerate, posing a significant statistical challenge: many more matches to known TF motifs occur in the genome than are actually functional. However, information about chromatin structure may help to identify the functional sites. In particular, it has been shown that active regulatory regions are usually depleted of nucleosomes, thereby enabling TFs to bind DNA in those regions. Here, we describe a novel motif discovery algorithm that employs an informative prior over DNA sequence positions based on a discriminative view of nucleosome occupancy. When a Gibbs sampling algorithm is applied to yeast sequence-sets identified by ChIP-chip, the correct motif is found in 52% more cases with our informative prior than with the commonly used uniform prior. This is the first demonstration that nucleosome occupancy information can be used to improve motif discovery. The improvement is dramatic, even though we are using only a statistical model to predict nucleosome occupancy; we expect our results to improve further as high-resolution genome-wide experimental nucleosome occupancy data becomes increasingly available.
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    Bayesian inference for genomic data integration reduces misclassification rate in predicting protein-protein interactions.
    (PLoS Comput Biol, 2011-07) Xing, Chuanhua; Dunson, David B
    Protein-protein interactions (PPIs) are essential to most fundamental cellular processes. There has been increasing interest in reconstructing PPIs networks. However, several critical difficulties exist in obtaining reliable predictions. Noticeably, false positive rates can be as high as >80%. Error correction from each generating source can be both time-consuming and inefficient due to the difficulty of covering the errors from multiple levels of data processing procedures within a single test. We propose a novel Bayesian integration method, deemed nonparametric Bayes ensemble learning (NBEL), to lower the misclassification rate (both false positives and negatives) through automatically up-weighting data sources that are most informative, while down-weighting less informative and biased sources. Extensive studies indicate that NBEL is significantly more robust than the classic naïve Bayes to unreliable, error-prone and contaminated data. On a large human data set our NBEL approach predicts many more PPIs than naïve Bayes. This suggests that previous studies may have large numbers of not only false positives but also false negatives. The validation on two human PPIs datasets having high quality supports our observations. Our experiments demonstrate that it is feasible to predict high-throughput PPIs computationally with substantially reduced false positives and false negatives. The ability of predicting large numbers of PPIs both reliably and automatically may inspire people to use computational approaches to correct data errors in general, and may speed up PPIs prediction with high quality. Such a reliable prediction may provide a solid platform to other studies such as protein functions prediction and roles of PPIs in disease susceptibility.
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    Domain-oriented edge-based alignment of protein interaction networks.
    (Bioinformatics, 2009-06-15) Guo, Xin; Hartemink, Alexander J
    MOTIVATION: Recent advances in high-throughput experimental techniques have yielded a large amount of data on protein-protein interactions (PPIs). Since these interactions can be organized into networks, and since separate PPI networks can be constructed for different species, a natural research direction is the comparative analysis of such networks across species in order to detect conserved functional modules. This is the task of network alignment. RESULTS: Most conventional network alignment algorithms adopt a node-then-edge-alignment paradigm: they first identify homologous proteins across networks and then consider interactions among them to construct network alignments. In this study, we propose an alternative direct-edge-alignment paradigm. Specifically, instead of explicit identification of homologous proteins, we directly infer plausibly alignable PPIs across species by comparing conservation of their constituent domain interactions. We apply our approach to detect conserved protein complexes in yeast-fly and yeast-worm PPI networks, and show that our approach outperforms two recent approaches in most alignment performance metrics. AVAILABILITY: Supplementary material and source code can be found at http://www.cs.duke.edu/ approximately amink/.
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    DOMINE: a comprehensive collection of known and predicted domain-domain interactions.
    (Nucleic acids research, 2011-01) Yellaboina, Sailu; Tasneem, Asba; Zaykin, Dmitri V; Raghavachari, Balaji; Jothi, Raja
    DOMINE is a comprehensive collection of known and predicted domain-domain interactions (DDIs) compiled from 15 different sources. The updated DOMINE includes 2285 new domain-domain interactions (DDIs) inferred from experimentally characterized high-resolution three-dimensional structures, and about 3500 novel predictions by five computational approaches published over the last 3 years. These additions bring the total number of unique DDIs in the updated version to 26,219 among 5140 unique Pfam domains, a 23% increase compared to 20,513 unique DDIs among 4346 unique domains in the previous version. The updated version now contains 6634 known DDIs, and features a new classification scheme to assign confidence levels to predicted DDIs. DOMINE will serve as a valuable resource to those studying protein and domain interactions. Most importantly, DOMINE will not only serve as an excellent reference to bench scientists testing for new interactions but also to bioinformaticans seeking to predict novel protein-protein interactions based on the DDIs. The contents of the DOMINE are available at http://domine.utdallas.edu.
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    Loss of tumor suppressor IGFBP4 drives epigenetic reprogramming in hepatic carcinogenesis.
    (Nucleic acids research, 2018-09) Lee, Ying-Ying; Mok, Myth Ts; Kang, Wei; Yang, Weiqin; Tang, Wenshu; Wu, Feng; Xu, Liangliang; Yan, Mingfei; Yu, Zhuo; Lee, Sau-Dan; Tong, Joanna HM; Cheung, Yue-Sun; Lai, Paul BS; Yu, Dae-Yeul; Wang, Qianben; Wong, Grace LH; Chan, Andrew M; Yip, Kevin Y; To, Ka-Fai; Cheng, Alfred SL
    Genomic sequencing of hepatocellular carcinoma (HCC) uncovers a paucity of actionable mutations, underscoring the necessity to exploit epigenetic vulnerabilities for therapeutics. In HCC, EZH2-mediated H3K27me3 represents a major oncogenic chromatin modification, but how it modulates the therapeutic vulnerability of signaling pathways remains unknown. Here, we show EZH2 acts antagonistically to AKT signaling in maintaining H3K27 methylome through epigenetic silencing of IGFBP4. ChIP-seq revealed enrichment of Ezh2/H3K27me3 at silenced loci in HBx-transgenic mouse-derived HCCs, including Igfbp4 whose down-regulation significantly correlated with EZH2 overexpression and poor survivals of HCC patients. Functional characterizations demonstrated potent growth- and invasion-suppressive functions of IGFBP4, which was associated with transcriptomic alterations leading to deregulation of multiple signaling pathways. Mechanistically, IGFBP4 stimulated AKT/EZH2 phosphorylation to abrogate H3K27me3-mediated silencing, forming a reciprocal feedback loop that suppressed core transcription factor networks (FOXA1/HNF1A/HNF4A/KLF9/NR1H4) for normal liver homeostasis. Consequently, the in vivo tumorigenicity of IGFBP4-silenced HCC cells was vulnerable to pharmacological inhibition of EZH2, but not AKT. Our study unveils chromatin regulation of a novel liver tumor suppressor IGFBP4, which constitutes an AKT-EZH2 reciprocal loop in driving H3K27me3-mediated epigenetic reprogramming. Defining the aberrant chromatin landscape of HCC sheds light into the mechanistic basis of effective EZH2-targeted inhibition.
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    Phosphoproteomic profiling of human myocardial tissues distinguishes ischemic from non-ischemic end stage heart failure.
    (PLoS One, 2014) Schechter, Matthew A; Hsieh, Michael KH; Njoroge, Linda W; Thompson, J Will; Soderblom, Erik J; Feger, Bryan J; Troupes, Constantine D; Hershberger, Kathleen A; Ilkayeva, Olga R; Nagel, Whitney L; Landinez, Gina P; Shah, Kishan M; Burns, Virginia A; Santacruz, Lucia; Hirschey, Matthew D; Foster, Matthew W; Milano, Carmelo A; Moseley, M Arthur; Piacentino, Valentino; Bowles, Dawn E
    The molecular differences between ischemic (IF) and non-ischemic (NIF) heart failure are poorly defined. A better understanding of the molecular differences between these two heart failure etiologies may lead to the development of more effective heart failure therapeutics. In this study extensive proteomic and phosphoproteomic profiles of myocardial tissue from patients diagnosed with IF or NIF were assembled and compared. Proteins extracted from left ventricular sections were proteolyzed and phosphopeptides were enriched using titanium dioxide resin. Gel- and label-free nanoscale capillary liquid chromatography coupled to high resolution accuracy mass tandem mass spectrometry allowed for the quantification of 4,436 peptides (corresponding to 450 proteins) and 823 phosphopeptides (corresponding to 400 proteins) from the unenriched and phospho-enriched fractions, respectively. Protein abundance did not distinguish NIF from IF. In contrast, 37 peptides (corresponding to 26 proteins) exhibited a ≥ 2-fold alteration in phosphorylation state (p<0.05) when comparing IF and NIF. The degree of protein phosphorylation at these 37 sites was specifically dependent upon the heart failure etiology examined. Proteins exhibiting phosphorylation alterations were grouped into functional categories: transcriptional activation/RNA processing; cytoskeleton structure/function; molecular chaperones; cell adhesion/signaling; apoptosis; and energetic/metabolism. Phosphoproteomic analysis demonstrated profound post-translational differences in proteins that are involved in multiple cellular processes between different heart failure phenotypes. Understanding the roles these phosphorylation alterations play in the development of NIF and IF has the potential to generate etiology-specific heart failure therapeutics, which could be more effective than current therapeutics in addressing the growing concern of heart failure.