Top PDF Computational analysis in vivo p53 binding sites in the context of chromatin and repeat regions

Computational analysis in vivo p53 binding sites in the context of chromatin and repeat regions

Computational analysis in vivo p53 binding sites in the context of chromatin and repeat regions

P53 plays an important role in regulating cell cycles and maintaining genomic stability. The transcription regulation can be influenced by many factors. Leroy et al. [42] reported that more than half of human cancers carry TP53 gene mutations and these relate with p53 REs. Posttranslational Modification can impact the p53 function in response to genotoxic or nongenotoxic stresses [43]. Some family members like p63 and p73 can also influence p53 activities [44]. All these factors active in the p53 chromatin context because p53 plays a role in transcription regulation and interactions with DNA. In this study, different p53 chromosome distributions and organizations between normal and cancer cell lines were analyzed using ~120,000 ChIP fragments. As our datasets are comprehensive and cover most published p53 DNA binding sites in vivo, the normal and cancer cell lines are under the same treatments (Table 1). For example, both of them have treatments like 5-FU, Nutlin 3a, DXR. Actually these datasets come from different experiments, labs and researches. From our results in “Concordance between P53 Binding Sites and ChIP Fragments” and “Core Binding Sites”, there were consistently large overlapping areas. So the chromatin context analyses in our research has statistic significance.
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In vivo chromatin organization on native yeast telomeric regions is independent of a cis-telomere loopback conformation

In vivo chromatin organization on native yeast telomeric regions is independent of a cis-telomere loopback conformation

on a terminal TG repeat tract, such as on telomeres TEL03L and TEL06R, or abut to the distal end of a Y’-ele- ment. We thus wondered whether the chromatin organi- zation on an X-telomere junction was different of that on an X–Y’ junction. Thus, we analyzed the ChEC pattern of two X–Y’ junctions, those in TEL05R and TEL16R. Whereas some X–Y’ junctions show TG repeats between the X element and Y’ [2], we confirmed by sequencing that, as expected from the available data on published databases, these two X–Y’ junctions do not have TG repeats between the X and Y’ element. However, these X–Y’ junctions show very distinct differences in size, spacing between the X-ACS and the X-Abf1 sites and in the number of potential Tbf1- and Reb1-binding sites (Additional file  1: Fig. S5a). The X element on TEL05R shows an organization and features similar to most X–Y’ areas with a 221-bp spacing between the X-ACS and the Abf1 sites and ends distally in a relatively high density of potential Tbf1- and Reb1-binding sites (Additional file 1: Fig. S5a). Note that overall, the X(TEL05R) is also simi- lar to the X(TEL03L) previously analyzed in the X-only context (Additional file 1: Fig. S3a). To analyze the X–Y’ region of TEL05R by the ChEC method, we used a probe complementary to a subtelomeric region located at 1281 bp from the start of the X element (Fig. 5a). The analyzed fragment (AF) corresponding to this probe is obtained by genomic DNA digestion with PvuI and in addition to the X element, encompasses the first 4467 bp of a Y’(long) element (Fig.  5a). Upstream of the X ele- ment start we identified three MNase-sensitive sites (S-I, S-II and S-III on Fig. 5b, c). Moreover, we identified four MNase-sensitive sites on the X(TEL05R) element that are cut by all MN-fused proteins tested: GBD-MN, NLS- MN, H2A-MN and MN-Rap1 (Fig. 5b, c). As previously observed for the X(TEL03L)-TRF, two MNase-sensitive sites flank the X(TEL05R)-ACS located at 1325 bp from the PvuI site, generating a very short protected area of about 85 bp only (X-I to X-II in data summary in Fig. 5d).
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Site specific chromatin immunoprecipitation: a selective method to individually analyze neighboring transcription factor binding sites in vivo

Site specific chromatin immunoprecipitation: a selective method to individually analyze neighboring transcription factor binding sites in vivo

In contrast, the chromatin immunoprecipitation (ChIP) offers a distinct advantage over EMSA and in vivo footprinting, since the ChIP technique not only specifies which nucleotides are bound, but also identifies the interacting protein(s) in the context of in vivo sam- ples [7]. In this context we use the term in vivo to refer to any experiments performed on living cells weather within or outside a whole organism (sometimes referred to as ex vivo). Specific modifications of the ChIP assay exist to enable the analysis of mammalian tissues, thereby allowing the detection of differences in the interaction of transcription factors and promoter regions of genes in normal and neoplastic tissues [8,9]. How- ever, the standard ChIP has its limitations. The applied fragmentation techniques (sonication or enzymatic DNA restriction by MNase digestion) are unspecific. The indi- vidual analysis of neighboring TFBSs is therefore lim- ited, since the standard ChIP technique does not provide a DNA cleavage in specific positions flanking a sole binding motif (see Figure 1). Approaches using restriction enzyme digestion instead of the standard methods to fragment chromatin in ChIP in order to restrict analysis to particular gene regions or transcrip- tion factor binding sites has been previously described [10,11]. Nevertheless, the enzyme-based DNA
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SATB1-Binding Sequences and Alu-Like Motifs Define a Unique Chromatin Context in the Vicinity of Human Immunodeficiency Virus Type 1 Integration Sites

SATB1-Binding Sequences and Alu-Like Motifs Define a Unique Chromatin Context in the Vicinity of Human Immunodeficiency Virus Type 1 Integration Sites

The Alu repeats constitute about 5 to 10% of the human genome (4). Alu elements affect the genome in several ways, causing insertion mutations, recombination between elements, gene conversion, and alterations in gene expression (4). The Alu repeats have been implicated in transcription and tran- scription control (30, 34). In support of this, Alu repeats have been shown to be enriched in histone H3 lysine 9 methylation (30). Alu elements are each a dimer of similar, but not iden- tical, fragments with a total size of about 300 bp and originate from the 7SL RNA gene. Each element contains a bipartite promoter for RNA polymerase III, a poly(A) tract located between the monomers, a 3 ⬘ -terminal poly(A) tract, and nu- merous CpG islands and is flanked by short direct repeats. The chromatin context of the Alu repeats is important for their function (38), and the Alu elements themselves can play a role in chromosomal rearrangement (37). Interestingly, analysis of HIV-1 proviral integrations in isolates derived both from inte- grations in infected individuals and from cultured cells re- vealed a significant propensity of HIV-1 to integrate at or near the Alu repeats (50). Additionally, genome-wide analysis of HIV integration sites by the Bushman group found 15.9% of the in vivo integration sites to be in Alu repeats (47). There- fore, it was not very surprising that our analysis picked up Alu-like motifs in the sequences flanking HIV-1 integration sites. Additionally, mapping of genomic positions of integra- tion sites revealed that HIV-1 preferentially integrates within the transcribed and GC-rich regions of the human genome (19). The high GC content of Alu repeats may therefore con- stitute another feature facilitating their preferential targeting by the PIC.
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Inhibition of p53 expression modifies the specificity of chromatin binding by the androgen receptor

Inhibition of p53 expression modifies the specificity of chromatin binding by the androgen receptor

The androgen receptor (AR) is known to play a critical role in prostate cancer (PC). p53 likely also plays a role given that p53 mutations are commonly found in advanced PC, and loss of wild-type protein function contributes to the phenotype of castration- resistant prostate cancer (CRPC). Nevertheless, the extent of the contribution of p53 dysfunction to PC remains unclear. Here we analyze the effects of p53 inhibition in PC cells and show that it has significant consequences for both the interaction between AR, and chromatin and the proliferative capacity of these cells. Inhibition of p53 expression enabled LNCaP cells to proliferate independently of androgens. Moreover, it modified the genome-wide binding pattern of AR. ChIP-sequnce analyis (ChIP-seq) revealed that fewer AR-binding sites were present in the context of p53 inhibition, suggesting that wild-type p53 is required for stable binding of AR to certain chromatin regions. Further analysis revealed that a lower AR occupancy was accompanied by a reduction in FoxA1 binding at regulatory regions of AR-dependent genes. Our study also identifies a pool of genes that may be transcriptionally regulated by AR only in the absence of p53, and that may contribute to the CRPC phenotype. Overall, our results point to p53 playing an important role in regulating AR activity across the genome.
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The invariant arginine within the chromatin binding motif regulates both nucleolar localization and chromatin binding of Foamy virus Gag

The invariant arginine within the chromatin binding motif regulates both nucleolar localization and chromatin binding of Foamy virus Gag

specificities in the replication strategy of FVs set them apart from orthoretroviruses. These include the fact that reverse transcription occurs during viral particle produc- tion [5, 6], and that Pol is expressed independently of Gag from a specific spliced transcript [7, 8]. The struc- tural organization and maturation profile of FV Gag are also peculiar. FV Gag lacks the major homology region (MHR) and the Cys-His zinc-finger motifs that are hall- marks of orthoretroviral Gag proteins. Moreover, FV Gag is not processed into the matrix (MA), capsid (CA) and nucleocapsid (NC) mature products like its orthoretro- viral counterparts, but rather undergoes a single cleav- age event that removes a 4 kDa C-terminal peptide ([9], reviewed in [10]). This feature is shared by the Gag pro- teins of the Drosophila retrovirus Gypsy [11] and the Ty1 retrotransposon of S. cerevisiae [12]. Recent studies showed that PFV Gag N-terminal domain (NTD, amino acids (aa) 1–180) is entirely unrelated to its orthoretrovi- ral counterpart [13]. They also confirmed that the NTD, which harbors the cytoplasmic targeting and retention signal (CTRS) and the self-dimerization domain, plays a role similar to orthoretroviral CA in viral capsid assem- bly [13]. In contrast the central conserved region of PFV Gag (aa 300–477), which is involved in the formation of higher-order multimers, shares a conformation analo- gous to that of orthoretroviral CA, suggesting evolution from a common ancestral protein [14]. In the absence of structural data, functional studies indicate that the C-terminal domain (CTD, aa 400–648) of FV Gag plays a role related to that of orthoretroviral NC in genome packaging [10, 15]. This domain is enriched in glycine and arginine residues that in primate FV Gag proteins are clustered in three regions named GR boxes (GRI-III) [15]. GRI binds nucleic acids in vitro and was proposed to be responsible of the incorporation of both the gRNA and Pol into virions [16–18]. The GRII box shows the high- est conservation throughout evolution and is involved in the accumulation of PFV Gag in the nucleus [15]. The determinant for nuclear localization within GRII maps to a 13-aa chromatin binding sequence (CBS, aa 534–546) that recognizes the H2A/H2B core histones. This inter- action tethers the pre-integration complex (PIC) to host cell chromatin prior to viral integration [19–21]. The role of GRIII in FV replication is enigmatic but likely related to that of GRI, since the two motifs can functionally com- plement each other [22]. Although the GR boxes were ini- tially viewed as independent entities playing both specific and redundant functions, a recent study rather indicates that the positively charged residues within the CTD, not the GR boxes individually, mediate gRNA packaging and Pol encapsidation [23].
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CpG binding protein (CFP1) occupies open chromatin regions of active genes, including enhancers and non-CpG islands

CpG binding protein (CFP1) occupies open chromatin regions of active genes, including enhancers and non-CpG islands

Blue boxes, known regulatory regions; green box, CGI. Fig. S6: Distribu- tion of TrxG components in erythroid cells. Green indicates CGI and blue indicates other putative regulatory regions. All loci transcribed right to left. Pileups are shown scaled to 1x genome coverage, with full scale 0–50x depth. (A) Housekeeping genes ACTB, left (chr7), and LUC7L, right (chr16). (B) β-globin locus (chr11), (C) Non-expressed RHBDF1 locus (chr16). Fig. S7: Overlap of TrxG subunit ChIP peaks in a high-confidence subset of regions. SET1A complexes are represented by CFP1-SET1A colocalisation. MLL1/2 complexes are represented by Menin, and MLL3/4 complexes are represented by UTX, respectively. HCF1 is found in SET1A/B and MLL1/2 complexes, and RBBP5 is a member of SET1A/B and MLL1/2/3/4 complexes. Red outline (4220 peaks) shows strong colocalisation of Menin and CFP1-SET1A, accounting for the vast majority (99.5%) of 4242 CFP1-SET1A and half (50.0%) of 8432 Menin peak regions. Majority (87.0%, 2089/2400 peaks) of HCF1 (blue region) is accounted for by approximately half (49.5%, 2089/4220) of regions of Menin-SET1A-CFP1 colocalisation. Regions where either SET1A-CFP1 or Menin or both are colocalised with HCF1 (blue dashed line) accounts for nearly all (99.6%, 2390/2400) HCF1 regions, suggesting that HCF1 bound to DNA is primarily present as part of SET1A/B or MLL1/2 complexes. Fig. S8: Chromatin accessibility in TSSs and enhancers in erythroid cells as measured by ATAC-seq and DNase- seq. 1x-normalised, input-subtracted signals from ATAC-seq and DNase were averaged in a 2-kb window about TSSs and putative enhancers. Z-score transformed values for ATAC-seq and DNase-seq at a given locus were averaged. Fig. S9: Relationship of CFP1 signal to three predictive factors in top-decile open chromatin regions. A linear combination of CpG density and SET1A and H3K4me3 ChIP signals explains a substantial frac- tion of variation in CFP1 ChIP signal. Table S1: Bias of CFP1 for CGI TSSs in cell types and gene classes. Table S2: Bias of CFP1 for housekeeping gene TSSs. Table S3: Motifs associated with CFP1 peaks. Table S4: Dependence of CFP1 ChIP signal in erythroid cells on covariates putatively associated with its binding. Table S5: Analysis of variance of CFP1 signal in top-decile open chromatin regions surrounding TSSs and putative enhancers.
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Recidivism in context: A meta-analysis of neighborhood concentrated disadvantage and repeat offending

Recidivism in context: A meta-analysis of neighborhood concentrated disadvantage and repeat offending

As for study features, we used three-level meta-regression models to test effect variation by geographic unit, race-inclusive measure of concentrated disadvantage (i.e., whether race was included in the measure), potential over-control (i.e., total number of control variables adjusted in analyses), and the definition of recidivism (i.e., evidence and offense type). Of these predictors, using a race inclusive measure of concentrated disadvantage (log OR = -0.01, p = 0.62) and potential over-control (log OR = 0.003, p = 0.14) were not statistically significant predictors of the variation between studies (k = 32). Geographic unit approached statistical significance – studies that measured concentrated disadvantage in small to moderate units of analysis (i.e., block group, tract, or zip code) had, on average, stronger associations between concentrated disadvantage and recidivism than large units of analysis (i.e., county, region), but this effect bordered on significance at the .05 level (log OR = 0.08, p = 0.05, k =32). viii
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Temporal Binding and Segmentation in Visual Search: A Computational Neuroscience Analysis

Temporal Binding and Segmentation in Visual Search: A Computational Neuroscience Analysis

The data indicate that, when temporal binding was in- troduced into sSoTS, the conditions more closely matched those found in human search (Kunar et al., 2003a). The single feature and standard preview search conditions differed in overall RTs but not in terms of search efficiency, and both were more efficient than the conjunction baseline. This agrees with human search data, where single feature and preview search conditions, though they differ in RTs, are both more efficient than the conjunction baseline ( Watson & Humphreys, 1997). The introduction of temporal binding did not disrupt the advantages from reduced competition either when one set of distractors was omitted (the single feature base- line) or when distractors were suppressed (by adaptation and top – down suppression, in the preview condition). However, there was a disruption to search in the preview gap condition, when an interval was introduced between the preview and the search display and all the items in the search display onset together. Although there was no difference in overall RTs, there was a big difference in the slopes; the slope for standard preview was 13.23 msec/item, and the slope for the preview gap con- dition was 60.93 msec/item. This result comes about because re-presenting all the search items again, after the interval, creates a set of common new onsets, coun- teracting effects of adaptation and top – down inhibition, which otherwise bias search against the old items. The preview benefit decreases. Figure 5 shows activation in the original sSoTS and in b-sSoTS in the preview gap con- dition. Note that the gain in activity for the target location
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Prediction of DNA-Binding Proteins and their Binding Sites

Prediction of DNA-Binding Proteins and their Binding Sites

With the exponential growth of proteomic data and the enormous complexities involved in their modeling, bioinformatics becomes essential for the management and mining of biological data in modern biology, medicine and drug discovery. The development of computational tools to solve the problem requires the expertise from many fields of computer science, like i) Data science for mining, collection and preparation of data, ii) Scientific Computing to extract useful knowledge from large sets of data and mathematically quantify the knowledge as characteristics features, iii) Machine Learning to develop novel algorithms to model the data using features, and iv) Statistical and Probabilistic Analysis to empirically evaluate the model by comparative analysis and visualize the outputs. These methods have been utilized throughout the course of the thesis to develop novel tools for the prediction of DNA-binding proteins and their binding sites using sequence information only.
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Large-Scale Analysis of Protein-Ligand Binding Sites using the Binding MOAD Database.

Large-Scale Analysis of Protein-Ligand Binding Sites using the Binding MOAD Database.

Figure VI-5A shows the initial global NW sequence alignment using default parameters (BLOSUM50), and the resulting standard and weighted superpositions are provided in Figure VI-5B and C. The final alignment generated by SE is shown in Figure VI-5D. Any residue pairs that received a weight of 0.5 or greater from the wRMSD calculation are noted with an asterisk. The underlined region of the sequence alignment in Figure VI-5A and D corresponds to a flexible domain between the proteins; as would be expected, none of these residues were significantly weighted to contribute to the superposition. The black boxes in Figure VI-5A indicate two regions of incorrect atom pairing. The first is due to an erroneous gap placement (in 1IPA) and corresponds to the residues of the denoted H8 α-helix in Figure VI-5B and C. The residues of the α-helix were not aligned properly, and hence, the appropriate Cα atoms were not paired together. However, after the weighted superposition, they are brought into close spatial proximity, and the final sequence alignment obtained by SE eliminates the gap to produce a correct pairing as evidenced by the high weights (Figure VI-5C). The β-sheet, noted in Figure VI-5B and C, is also a misalignment that is overcome by the wRMSD superposition. This initial error is caused by the default behavior of the Biopython parser [228], used to pull sequence information from the coordinates in the PDB files, which omits a modified methionine residue. While this is easily rectified programmatically, we allow the omission to serve as an example of parser error. Some parsers ignore non-standard amino acids (listed as HETATMs), and in the 1GZ0 structure, the methionines have been replaced with selenomethionine to aid in solving the structure. Once again, the structural superposition overrides the ambiguity, and the final alignment correctly pairs the beta sheet residues (with selenomethionine present this time due to the smarter parsing inherent to SE).
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DisBind: A database of classified functional binding sites in disordered and structured regions of intrinsically disordered proteins

DisBind: A database of classified functional binding sites in disordered and structured regions of intrinsically disordered proteins

Here we have compiled a database, DisBind (Disorder Binding sites), which is dedicated to classification of func- tional binding sites of IDPs and proteins with both intrin- sically disordered and structured regions from the DisProt database, regardless if IDPs have or do not have experimen- tally determined structures by induced folding. Residue- level binding sites are important first step for understanding the functional impacts of genetic variants in coding regions of human and other genomes, considering that a significant portion of eukaryotic genomes code for intrinsically disor- dered regions in proteins [17]. We categorize binding sites into eight categories according to their binding partners: DNA, RNA, proteins, cofactor/heme, metal ions, substrate/ ligand, ATP/GTP, and others. Although some categories only have a few sites, we include them in the database for completeness. This database provides a classification of functional binding sites in IDPs annotated according to ex- perimentally supported evidences. As a comparison, IDEAL does not contain binding sites from metals and ligands. DisProt does not contain binding site information. For completeness, both structured and disordered regions of an intrinsically disordered protein are annotated. Most disor- dered regions with annotated binding sites do not have known structures. Some disordered regions, however, have experimentally-determined structures when they are in complex with their interaction partners (binding induced folding or conformational selections). For those special cases, we annotated secondary structure motifs involved in binding regions which can provide a basis for initial under- standing of binding mechanisms.
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Variable role of the long terminal repeat Sp1-binding sites in human immunodeficiency virus replication in T lymphocytes.

Variable role of the long terminal repeat Sp1-binding sites in human immunodeficiency virus replication in T lymphocytes.

1414 Downloaded from http://jvi.asm.org/ on November 10, 2019 by guest The long terminal repeat LTR of the human immunodeficiency virus HIV contains three binding sites for the transcrip[r]

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VISPA: a computational pipeline for the identification and analysis of genomic vector integration sites

VISPA: a computational pipeline for the identification and analysis of genomic vector integration sites

The percentage of reads with correct barcodes ranged from 96.61% to 99.23% of the total (Figure 5A). On the other hand, about 30% of the sequencing reads was ex- cluded after trimming (Figure 5B) due to the absence of a valid LTR (12% on average) or because they were too short to be mapped on the reference genome (15% on average). The decrease in number of reads associated to the first three filtering steps was comparable in all pa- tients (Figure 5A, B). As shown in Figure 5C, the trim- ming step modifies the distribution of the length of sequencing reads by introducing a shift towards smaller sizes and a slight change in its profile. After the align- ment to the reference genome (Figure 5D) about 40% of the initial reads was excluded from further analysis be- cause: (1) lacking a valid match on the reference gen- ome (6% on average); or (2) because these reads were vector-only sequences (21% on average); or (3) repetitive elements that could not be univocally mapped to the reference genome (13% on average). Reads left after quality-based filtering (the R dataset) were, on average, 90.87% of U reads, with a standard deviation of less than 1%. The sequence length distribution profile of the uni- vocally mapped reads (the R dataset) was similar for all patients of both clinical studies (Figure 6A). Finally by applying the sliding window approach described above, we identified all IS reads that fall in the same 3 bp inter- val as belonging to the same integration event (the IS merging step), the number of such reads can be seen as a measure of the ‘signal power’ of the integration (Figure 6B). To evaluate the precision of this approach, we computed the percentage of IS positions (starting covered bases) hit by an IS read within each window: as shown in Figure 6B, over 60% of IS bases fall in the first position (blue bar), while for other bases the percentage decreases as the distance from the IS increases.
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Widespread RNA binding by chromatin associated proteins

Widespread RNA binding by chromatin associated proteins

We re-suspended frozen pellets in 1 mL of RIPA lysis buffer (50 mM Tris (pH 8), 150 mM KCl, 0.1 % SDS, 1 % Triton-X, 5 mM EDTA, 0.5 % sodium deoxycholate, 0.5 mM DTT (add fresh) + protease inhibitor cocktail (Thermo Scientific, PI-87785) + 100 U/ml RNaseOUT™ (Life Technologies, 10777–019)). We incubated cells at 4 °C for 10 minutes before lysing on a Branson® digital sonifier using 10 % amplitude for 0.7 seconds on and 1.3 seconds off at 30 second intervals for a total of 90 seconds. We used chilled tube holders and swapped them out between shearing runs to reduce temperature elevation. After lysis, we spun the lysate at 4 °C max speed for 10 minutes. We collected supernatant and di- luted by adding equal volume of fRIP binding/wash buf- fer (150 mM KCl, 25 mM Tris (pH 7.5), 5 mM EDTA, 0.5 % NP-40, 0.5 mM DTT (add fresh), 1× PIC (add fresh), 100 U/mL RNaseOUT (add fresh)). At this point, we removed 50 μl of lysate for input sample and stored at −20 °C for later RNA purification and library construction. After dilution, we clarified the lysate by passage through a 0.45 μM syringe filter. We then “pre- cleared” filtered lysate by incubating with Dynabeads® Protein G (Life Technologies catalog #10004D) at a con- centration of 25 μl of beads per 5 million cells for 30 mi- nutes at 4 °C with slow rotation. We flash froze pre- cleared lysate in 1 mL aliquots of ~5 million cells and
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Identification of factor-binding sites in the duck hepatitis B virus enhancer and in vivo effects of enhancer mutations.

Identification of factor-binding sites in the duck hepatitis B virus enhancer and in vivo effects of enhancer mutations.

Though mutations at the HNF1 site and the two HNF3 sites seemed to make the virus enhancer slightly defective in the LMH liver cell line, as expected from the assays with the hGH reporte[r]

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CaMELS : In silicoprediction of calmodulin binding proteins and their binding sites

CaMELS : In silicoprediction of calmodulin binding proteins and their binding sites

and therefore use CaM as a signal transducer and calcium sensor. 5, 6 Due to the involvement of CaM in different important biological processes, identification of proteins that bind CaM and the location of CaM binding sites within a protein can help biologists in elucidating underlying biological processes at the molecular level. Due to Ca 2+ dependent binding and the large sequence diversity of its targets, identifying CaM interactions and binding sites in the wet lab is very costly and time consuming. 7 Therefore, there is an utmost need for computational techniques to support wet-lab experiments by predicting CaM binding proteins and their binding sites. This work presents a highly accurate in-silico CaM binding site and interaction prediction method that relies only on protein sequences.
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Activation of the ATR Pathway by Human Immunodeficiency Virus Type 1 Vpr Involves Its Direct Binding to Chromatin In Vivo

Activation of the ATR Pathway by Human Immunodeficiency Virus Type 1 Vpr Involves Its Direct Binding to Chromatin In Vivo

Human immunodeficiency virus type 1 (HIV-1) is the caus- ative agent of AIDS, which is characterized by continual loss of CD4 ⫹ T lymphocytes and enhanced susceptibility to opportu- nistic infections and malignancies. To achieve optimal replica- tive efficiency, HIV-1 manipulates host cell processes such as gene regulation, chromatin remodeling, signal transduction, and regulations of major histocompatibility complex class 1 surface expression, cell cycle, and apoptosis, as well as over- coming host antiviral mechanisms and targeting bystander cells (50, 54). These multiple activities of HIV are mediated by the specific interactions of viral proteins with various cellular com- ponents. As a complex retrovirus, HIV-1 encodes not only the essential structural proteins, Gag, Pol, and Env, but also sev- eral regulatory (Tat and Rev) and accessory (Vpr, Vif, Vpu, and Nef) proteins. These accessory proteins, while initially thought to be dispensable for infection, have now been shown to be important for HIV infectivity and pathogenesis in vivo (8, 16, 19, 20, 50). Among them, Vpr (viral protein R) (51) is unique in that it is incorporated in the HIV-1 virion at a high copy number (10), suggesting that it may play a significant role in the early stage of infection.
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A Computational Approach to Binding Theory

A Computational Approach to Binding Theory

A Computational Approach to Binding Theory A Computational Approach to Binding Theory* Alessandra Giorgi, Fabio Pianesi, Giorgio Satta** Istituto per la Ricerca Scientifica e Tecnologica, 38050 Povo ([.]

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Mutational analysis of the 18-base-pair inverted repeat element at the bovine papillomavirus origin of replication: identification of critical sequences for E1 binding and in vivo replication.

Mutational analysis of the 18-base-pair inverted repeat element at the bovine papillomavirus origin of replication: identification of critical sequences for E1 binding and in vivo replication.

the properties of identical mutations at equivalent positions in each half of the 18-bp region were compared (on the basis of data in Fig. 4). There were five positions where the same mutation was available in each half site: 7941A/11T, 7942G/ 10C, 7943T/9A, 7944C/8G, and 7945A/7T. With one exception (mutation 7945A versus 7T), equivalent mutations in each half of the IR element exhibited very similar degrees of E1 binding. While the significance of the one exception is unclear, the overall symmetrical trend suggests that the two half sites are functionally equivalent with respect to E1 binding. Likewise, at four of the five positions where identical mutations were avail- able in both half sites, levels of replication were very similar, implying an overall functional symmetry of the half sites. Only at one pair of mutations, 7942G/10C, was there a disparity in replication, with 7942G replicating poorly while 10C replicated at near wild-type levels. The significance of the disparity be- tween 7942G and 10C is unknown but may reflect the intrinsic asymmetry in the organization of the origin region. The E1 binding site is flanked by an AT-rich region on the 5 9 side and an E2 binding site on the 3 9 side. The difference in replication for 7942G and 10C may reflect how E1 bound to the 5 9 and 3 9 halves of the IR element interacts with the adjacent elements. Given the apparent functional equivalency of the 5 9 and 3 9 halves of the IR for binding and replication, the mutational data for each half site were combined for further analysis. This allowed the evaluation of the effect on binding (data not shown) or replication (Fig. 7) of two or more nucleotide changes at each position in the half site. The effect of a muta- tion on replication depended on both the position of the mu- tation and the specific nucleotide change. Three of nine posi- tions in the half site replicated well with any of three nucleotides (positions 7940, 7945, and 3), while only one po- sition failed to replicate well with either available mutation (position 7944). The other five positions retained significant replication with certain nucleotide changes but not with others. On the basis of these levels of replication with different avail- able mutations, a consensus half site sequence of nucleotides critical for replication was derived. A similar analysis of the effect of mutations on E1 binding was performed (not shown), and this analysis defined the consensus half site sequence of nucleotides (Fig. 7) required for efficient E1-DNA interaction. The consensus for replication function was very similar to the
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