Prediction of the functional class of metal-binding proteins from sequence derived physicochemical properties by support vector machine approach

Metal-binding proteins play important roles in structural stability, signaling, regulation, transport, immune response, metabolism control, and metal homeostasis. Because of their functional and sequence diversity, it is desirable to explore additional methods for predicting metal-binding proteins irrespective of sequence similarity. This work explores support vector machines (SVM) as such a method. SVM prediction systems were developed by using 53,333 metal-binding and 147,347 non-metal-binding proteins, and evaluated by an independent set of 31,448 metal-binding and 79,051 non-metal-binding proteins. The computed prediction accuracy is 86.3%, 81.6%, 83.5%, 94.0%, 81.2%, 85.4%, 77.6%, 90.4%, 90.9%, 74.9% and 78.1% for calcium-binding, cobalt-binding, copper-binding, iron-binding, magnesium-binding, manganese-binding, nickel-binding, potassium-binding, sodium-binding, zinc-binding, and all metal-binding proteins respectively. The accuracy for the non-member proteins of each class is 88.2%, 99.9%, 98.1%, 91.4%, 87.9%, 94.5%, 99.2%, 99.9%, 99.9%, 98.0%, and 88.0% respectively. Comparable accuracies were obtained by using a different SVM kernel function. Our method predicts 67% of the 87 metal-binding proteins non-homologous to any protein in the Swissprot database and 85.3% of the 333 proteins of known metal-binding domains as metal-binding. These suggest the usefulness of SVM for facilitating the prediction of metal-binding proteins. Our software can be accessed at the SVMProt server

Improving "color rendering" of LED lighting for the growth of lettuce

Light plays a vital role on the growth and development of plant. On the base of white light with high color rendering to the benefit of human survival and life, we proposed to improve “color rendering” of LED lighting for accelerating the growth of lettuce. Seven spectral LED lights were adopted to irradiate the lettuces under 150 μmol·m−2·s−1 for a 16 hd−1 photoperiod. The leaf area and number profiles, plant biomass, and photosynthetic rate under the as-prepared LED light treatments were investigated. We let the absorption spectrum of fresh leaf be the emission spectrum of ideal light and then evaluate the “color rendering” of as-prepared LED lights by the Pearson product-moment correlation coefficient and CIE chromaticity coordinates. Under the irradiation of red-yellow-blue light with high correlation coefficient of 0.587, the dry weights and leaf growth rate are 2-3 times as high as the sharp red-blue light. The optimized LED light for lettuce growth can be presumed to be limited to the angle (about 75°) between the vectors passed through the ideal light in the CIE chromaticity coordinates. These findings open up a new idea to assess and find the optimized LED light for plant growth

PRED_PPI: a server for predicting protein-protein interactions based on sequence data with probability assignment

<p>Abstract</p> <p>Background</p> <p>Protein-protein interactions (PPIs) are crucial for almost all cellular processes, including metabolic cycles, DNA transcription and replication, and signaling cascades. Given the importance of PPIs, several methods have been developed to detect them. Since the experimental methods are time-consuming and expensive, developing computational methods for effectively identifying PPIs is of great practical significance.</p> <p>Findings</p> <p>Most previous methods were developed for predicting PPIs in only one species, and do not account for probability estimations. In this work, a relatively comprehensive prediction system was developed, based on a support vector machine (SVM), for predicting PPIs in five organisms, specifically humans, yeast, <it>Drosophila</it>, <it>Escherichia coli</it>, and <it>Caenorhabditis elegans</it>. This PPI predictor includes the probability of its prediction in the output, so it can be used to assess the confidence of each SVM prediction by the probability assignment. Using a probability of 0.5 as the threshold for assigning class labels, the method had an average accuracy for detecting protein interactions of 90.67% for humans, 88.99% for yeast, 90.09% for <it>Drosophila</it>, 92.73% for <it>E. coli</it>, and 97.51% for <it>C. elegans</it>. Moreover, among the correctly predicted pairs, more than 80% were predicted with a high probability of ≥0.8, indicating that this tool could predict novel PPIs with high confidence.</p> <p>Conclusions</p> <p>Based on this work, a web-based system, Pred_PPI, was constructed for predicting PPIs from the five organisms. Users can predict novel PPIs and obtain a probability value about the prediction using this tool. Pred_PPI is freely available at <url>http://cic.scu.edu.cn/bioinformatics/predict_ppi/default.html</url>.</p

Oxide Heterostructures from a Realistic Many-Body Perspective

Oxide heterostructures are a new class of materials by design, that open the possibility for engineering challenging electronic properties, in particular correlation effects beyond an effective single-particle description. This short review tries to highlight some of the demanding aspects and questions, motivated by the goal to describe the encountered physics from first principles. The state-of-the-art methodology to approach realistic many-body effects in strongly correlated oxides, the combination of density functional theory with dynamical mean-field theory, will be briefly introduced. Discussed examples deal with prominent Mott-band- and band-band-insulating type of oxide heterostructures, where different electronic characteristics may be stabilized within a single architectured oxide material.Comment: 19 pages, 9 figure

Telomere disruption results in non-random formation of de novo dicentric chromosomes involving acrocentric human chromosomes

Copyright: © 2010 Stimpson et al.Genome rearrangement often produces chromosomes with two centromeres (dicentrics) that are inherently unstable because of bridge formation and breakage during cell division. However, mammalian dicentrics, and particularly those in humans, can be quite stable, usually because one centromere is functionally silenced. Molecular mechanisms of centromere inactivation are poorly understood since there are few systems to experimentally create dicentric human chromosomes. Here, we describe a human cell culture model that enriches for de novo dicentrics. We demonstrate that transient disruption of human telomere structure non-randomly produces dicentric fusions involving acrocentric chromosomes. The induced dicentrics vary in structure near fusion breakpoints and like naturally-occurring dicentrics, exhibit various inter-centromeric distances. Many functional dicentrics persist for months after formation. Even those with distantly spaced centromeres remain functionally dicentric for 20 cell generations. Other dicentrics within the population reflect centromere inactivation. In some cases, centromere inactivation occurs by an apparently epigenetic mechanism. In other dicentrics, the size of the alpha-satellite DNA array associated with CENP-A is reduced compared to the same array before dicentric formation. Extrachromosomal fragments that contained CENP-A often appear in the same cells as dicentrics. Some of these fragments are derived from the same alpha-satellite DNA array as inactivated centromeres. Our results indicate that dicentric human chromosomes undergo alternative fates after formation. Many retain two active centromeres and are stable through multiple cell divisions. Others undergo centromere inactivation. This event occurs within a broad temporal window and can involve deletion of chromatin that marks the locus as a site for CENP-A maintenance/replenishment.This work was supported by the Tumorzentrum Heidelberg/Mannheim grant (D.10026941)and by March of Dimes Research Foundation grant #1-FY06-377 and NIH R01 GM069514

LRP16 Integrates into NF-κB Transcriptional Complex and Is Required for Its Functional Activation

BACKGROUND: Nuclear factor κB (NF-κB)-mediated pathways have been widely implicated in cell survival, development and tumor progression. Although the molecular events of determining NF-κB translocation from cytoplasm to nucleus have been extensively documented, the regulatory mechanisms of NF-κB activity inside the nucleus are still poorly understood. Being a special member of macro domain proteins, LRP16 was previously identified as a coactivator of both estrogen receptor and androgen receptor, and as an interactor of NF-κB coactivator UXT. Here, we investigated the regulatory role of LRP16 on NF-κB activation. METHODOLOGY: GST pull-down and coimmunoprecipitation (CoIP) assays assessed protein-protein interactions. The functional activity of NF-κB was assessed by luciferase assays, changes in expression of its target genes, and its DNA binding ability. Annexin V staining and flow cytometry analysis were used to evaluate cell apoptosis. Immunohistochemical staining of LRP16 and enzyme-linked immunosorbent assay-based evaluation of active NF-κB were performed on primary human gastric carcinoma samples. RESULTS: We demonstrate that LRP16 integrates into NF-κB transcriptional complex through associating with its p65 component. RNA interference knockdown of the endogenous LRP16 in cells leads to impaired NF-κB activity and significantly attenuated NF-κB-dependent gene expression. Mechanistic analysis revealed that knockdown of LRP16 did not affect tumor necrosis factor α (TNF-α)-induced nuclear translocation of NF-κB, but blunted the formation or stabilization of functional NF-κB/p300/CREB-binding protein transcription complex in the nucleus. In addition, knockdown of LRP16 also sensitizes cells to apoptosis induced by TNF-α. Finally, a positive link between LRP16 expression intensity in nuclei of tumor cells and NF-κB activity was preliminarily established in human gastric carcinoma specimens. CONCLUSIONS: Our findings not only indicate that LRP16 is a crucial regulator for NF-κB activation inside the nucleus, but also suggest that LRP16 may be an important contributor to the aberrant activation of NF-κB in tumors

Assessing the Quality of Reports about Randomized Controlled Trials of Acupuncture Treatment on Diabetic Peripheral Neuropathy

BACKGROUND: To evaluate the reports' qualities which are about randomized controlled trials (RCTs) of acupuncture treatment on Diabetic Peripheral Neuropathy (DPN). METHODOLOGY/PRINCIPAL FINDINGS: Eight databases including The Cochrane Library(1993-Sept.,2011), PubMed (1980-Sept., 2011), EMbase (1980-Sept.,2011), SCI Expanded (1998-Sept.,2011), China Biomedicine Database Disc (CBMdisc, 1978-Sept., 2011), China National Knowledge Infrastructure (CNKI, 1979-Sept., 2011 ), VIP (a full text issues database of China, 1989-Sept., 2011), Wan Fang (another full text issues database of China 1998-Sept., 2011) were searched systematically. Hand search for further references was conducted. Language was limited to Chinese and English. We identified 75 RCTs that used acupuncture as an intervention and assessed the quality of these reports with the Consolidated Standards for Reporting of Trials statement 2010 (CONSORT2010) and Standards for Reporting Interventions Controlled Trials of Acupuncture 2010(STRICTA2010). 24 articles (32%) applied the method of random allocation of sequences. No article gave the description of the mechanism of allocation concealment, no experiment applied the method of blinding. Only one article (1.47%) could be identified directly from its title as about the Randomized Controlled Trials, and only 4 articles gave description of the experimental design. No article mentioned the number of cases lost or eliminated. During one experiment, acupuncture syncope led to temporal interruption of the therapy. Two articles (2.94%) recorded the number of needles, and 8 articles (11.76%) mentioned the depth of needle insertion. None of articles reported the base of calculation of sample size, or has any analysis about the metaphase of an experiment or an explanation of its interruption. One (1.47%) mentioned intentional analysis (ITT). CONCLUSIONS/SIGNIFICANCE: The quality of the reports on RCTs of acupuncture for Diabetic Peripheral Neuropathy is moderate to low. The CONSORT2010 and STRICTA2010 should be used to standardize the reporting of RCTs of acupuncture in future

Suppression of Estrogen Receptor Transcriptional Activity by Connective Tissue Growth Factor

Secreted growth factors have been shown to stimulate the transcriptional activity of estrogen receptors (ER) that are responsible for many biological processes. However, whether these growth factors physically interact with ER remains unclear. Here, we show for the first time that connective tissue growth factor (CTGF) physically and functionally associates with ER. CTGF interacted with ER both in vitro and in vivo. CTGF interacted with ER DNA-binding domain. ER interaction region in CTGF was mapped to the thrombospondin type I repeat, a cell attachment motif. Overexpression of CTGF inhibited ER transcriptional activity as well as the expression of estrogen-responsive genes, including pS2 and cathepsin D. Reduction of endogenous CTGF with CTGF small interfering RNA enhanced ER transcriptional activity. The interaction between CTGF and ER is required for the repression of estrogen-responsive transcription by CTGF. Moreover, CTGF reduced ER protein expression, whereas the CTGF mutant that did not repress ER transcriptional activity also did not alter ER protein levels. The results suggested the transcriptional regulation of estrogen signaling through interaction between CTGF and ER, and thus may provide a novel mechanism by which cross-talk between secreted growth factor and ER signaling pathways occurs

Rice_Phospho 1.0: a new rice-specific SVM predictor for protein phosphorylation sites

Experimentally-determined or computationally-predicted protein phosphorylation sites for distinctive species are becoming increasingly common. In this paper, we compare the predictive performance of a novel classification algorithm with different encoding schemes to develop a rice-specific protein phosphorylation site predictor. Our results imply that the combination of Amino acid occurrence Frequency with Composition of K-Spaced Amino Acid Pairs (AF-CKSAAP) provides the best description of relevant sequence features that surround a phosphorylation site. A support vector machine (SVM) using AF-CKSAAP achieves the best performance in classifying rice protein phophorylation sites when compared to the other algorithms. We have used SVM with AF-CKSAAP to construct a rice-specific protein phosphorylation sites predictor, Rice-Phospho 1.0 (http://bioinformatics.fafu.edu.cn/rice-phospho1.0). We measure the Accuracy (ACC) and Matthews Correlation Coefficient (MCC) of Rice-Phospho 1.0 to be 82.0% and 0.64, significantly higher than those measures for other predictors such as Scansite, Musite, PlantPhos and PhosphoRice. Rice-Phospho 1.0 also successfully predicted the experimentally identified phosphorylation sites in LOC-Os03g51600.1, a protein sequence which did not appear in the training dataset. In summary, Rice-phospho 1.0 outputs reliable predictions of protein phosphorylation sites in rice, and will serve as a useful tool to the community