Gene Function Classification Using Bayesian Models with Hierarchy-Based Priors

We investigate the application of hierarchical classification schemes to the annotation of gene function based on several characteristics of protein sequences including phylogenic descriptors, sequence based attributes, and predicted secondary structure. We discuss three Bayesian models and compare their performance in terms of predictive accuracy. These models are the ordinary multinomial logit (MNL) model, a hierarchical model based on a set of nested MNL models, and a MNL model with a prior that introduces correlations between the parameters for classes that are nearby in the hierarchy. We also provide a new scheme for combining different sources of information. We use these models to predict the functional class of Open Reading Frames (ORFs) from the E. coli genome. The results from all three models show substantial improvement over previous methods, which were based on the C5 algorithm. The MNL model using a prior based on the hierarchy outperforms both the non-hierarchical MNL model and the nested MNL model. In contrast to previous attempts at combining these sources of information, our approach results in a higher accuracy rate when compared to models that use each data source alone. Together, these results show that gene function can be predicted with higher accuracy than previously achieved, using Bayesian models that incorporate suitable prior information

Virtual screening for inhibitors of the human TSLP:TSLPR interaction

The pro-inflammatory cytokine thymic stromal lymphopoietin (TSLP) plays a pivotal role in the pathophysiology of various allergy disorders that are mediated by type 2 helper T cell (Th2) responses, such as asthma and atopic dermatitis. TSLP forms a ternary complex with the TSLP receptor (TSLPR) and the interleukin-7-receptor subunit alpha (IL-7Ra), thereby activating a signaling cascade that culminates in the release of pro-inflammatory mediators. In this study, we conducted an in silico characterization of the TSLP: TSLPR complex to investigate the drugability of this complex. Two commercially available fragment libraries were screened computationally for possible inhibitors and a selection of fragments was subsequently tested in vitro. The screening setup consisted of two orthogonal assays measuring TSLP binding to TSLPR: a BLI-based assay and a biochemical assay based on a TSLP: alkaline phosphatase fusion protein. Four fragments pertaining to diverse chemical classes were identified to reduce TSLP: TSLPR complex formation to less than 75% in millimolar concentrations. We have used unbiased molecular dynamics simulations to develop a Markov state model that characterized the binding pathway of the most interesting compound. This work provides a proof-ofprinciple for use of fragments in the inhibition of TSLP: TSLPR complexation

Human Bocavirus NS1 and NS1-70 Proteins Inhibit TNF-α-Mediated Activation of NF-κB by targeting p65.

Human bocavirus (HBoV), a parvovirus, is a single-stranded DNA etiologic agent causing lower respiratory tract infections in young children worldwide. Nuclear factor kappa B (NF-κB) transcription factors play crucial roles in clearance of invading viruses through activation of many physiological processes. Previous investigation showed that HBoV infection could significantly upregulate the level of TNF-α which is a strong NF-κB stimulator. Here we investigated whether HBoV proteins modulate TNF-α-mediated activation of the NF-κB signaling pathway. We showed that HBoV NS1 and NS1-70 proteins blocked NF-κB activation in response to TNF-α. Overexpression of TNF receptor-associated factor 2 (TRAF2)-, IκB kinase alpha (IKKα)-, IκB kinase beta (IKKβ)-, constitutively active mutant of IKKβ (IKKβ SS/EE)-, or p65-induced NF-κB activation was inhibited by NS1 and NS1-70. Furthermore, NS1 and NS1-70 didn't interfere with TNF-α-mediated IκBα phosphorylation and degradation, nor p65 nuclear translocation. Coimmunoprecipitation assays confirmed the interaction of both NS1 and NS1-70 with p65. Of note, NS1 but not NS1-70 inhibited TNF-α-mediated p65 phosphorylation at ser536. Our findings together indicate that HBoV NS1 and NS1-70 inhibit NF-κB activation. This is the first time that HBoV has been shown to inhibit NF-κB activation, revealing a potential immune-evasion mechanism that is likely important for HBoV pathogenesis

Scoring Protein Relationships in Functional Interaction Networks Predicted from Sequence Data

The abundance of diverse biological data from various sources constitutes a rich source of knowledge, which has the power to advance our understanding of organisms. This requires computational methods in order to integrate and exploit these data effectively and elucidate local and genome wide functional connections between protein pairs, thus enabling functional inferences for uncharacterized proteins. These biological data are primarily in the form of sequences, which determine functions, although functional properties of a protein can often be predicted from just the domains it contains. Thus, protein sequences and domains can be used to predict protein pair-wise functional relationships, and thus contribute to the function prediction process of uncharacterized proteins in order to ensure that knowledge is gained from sequencing efforts. In this work, we introduce information-theoretic based approaches to score protein-protein functional interaction pairs predicted from protein sequence similarity and conserved protein signature matches. The proposed schemes are effective for data-driven scoring of connections between protein pairs. We applied these schemes to the Mycobacterium tuberculosis proteome to produce a homology-based functional network of the organism with a high confidence and coverage. We use the network for predicting functions of uncharacterised proteins

The Personal Sequence Database: a suite of tools to create and maintain web-accessible sequence databases

Background: Large molecular sequence databases are fundamental resources for modern\ud bioscientists. Whether for project-specific purposes or sharing data with colleagues, it is often\ud advantageous to maintain smaller sequence databases. However, this is usually not an easy task for\ud the average bench scientist.\ud \ud Results: We present the Personal Sequence Database (PSD), a suite of tools to create and\ud maintain small- to medium-sized web-accessible sequence databases. All interactions with PSD\ud tools occur via the internet with a web browser. Users may define sequence groups within their\ud database that can be maintained privately or published to the web for public use. A sequence group\ud can be downloaded, browsed, searched by keyword or searched for sequence similarities using\ud BLAST. Publishing a sequence group extends these capabilities to colleagues and collaborators. In\ud addition to being able to manage their own sequence databases, users can enroll sequences in\ud BLASTAgent, a BLAST hit tracking system, to monitor NCBI databases for new entries displaying\ud a specified level of nucleotide or amino acid similarity.\ud \ud Conclusion: The PSD offers a valuable set of resources unavailable elsewhere. In addition to\ud managing sequence data and BLAST search results, it facilitates data sharing with colleagues,\ud collaborators and public users. The PSD is hosted by the authors and is available at http://\ud bioinfo.cgrb.oregonstate.edu/psd/

Copy Number Variation in CNP267 Region May Be Associated with Hip Bone Size

Osteoporotic hip fracture (HF) is a serious global public health problem associated with high morbidity and mortality. Hip bone size (BS) has been identified as one of key measurable risk factors for HF, independent of bone mineral density (BMD). Hip BS is highly genetically determined, but genetic factors underlying BS variation are still poorly defined. Here, we performed an initial genome-wide copy number variation (CNV) association analysis for hip BS in 1,627 Chinese Han subjects using Affymetrix GeneChip Human Mapping SNP 6.0 Array and a follow-up replicate study in 2,286 unrelated US Caucasians sample. We found that a copy number polymorphism (CNP267) located at chromosome 2q12.2 was significantly associated with hip BS in both initial Chinese and replicate Caucasian samples with p values of 4.73E-03 and 5.66E-03, respectively. An important candidate gene, four and a half LIM domains 2 (FHL2), was detected at the downstream of CNP267, which plays important roles in bone metabolism by binding to several bone formation regulator, such as insulin-like growth factor-binding protein 5 (IGFBP-5) and androgen receptor (AR). Our findings suggest that CNP267 region may be associated with hip BS which might influence the FHL2 gene downstream

Experimental and theoretical investigation of ligand effects on the synthesis of ZnO nanoparticles

ZnO nanoparticles with highly controllable particle sizes(less than 10 nm) were synthesized using organic capping ligands in Zn(Ac)2 ethanolic solution. The molecular structure of the ligands was found to have significant influence on the particle size. The multi-functional molecule tris(hydroxymethyl)-aminomethane (THMA) favoured smaller particle distributions compared with ligands possessing long hydrocarbon chains that are more frequently employed. The adsorption of capping ligands on ZnnOn crystal nuclei (where n = 4 or 18 molecular clusters of(0001) ZnO surfaces) was modelled by ab initio methods at the density functional theory (DFT) level. For the molecules examined, chemisorption proceeded via the formation of Zn...O, Zn...N, or Zn...S chemical bonds between the ligands and active Zn2+ sites on ZnO surfaces. The DFT results indicated that THMA binds more strongly to the ZnO surface than other ligands, suggesting that this molecule is very effective at stabilizing ZnO nanoparticle surfaces. This study, therefore, provides new insight into the correlation between the molecular structure of capping ligands and the morphology of metal oxide nanostructures formed in their presence

Discovering patterns in drug-protein interactions based on their fingerprints

<p>Abstract</p> <p>Background</p> <p>The discovering of interesting patterns in drug-protein interaction data at molecular level can reveal hidden relationship among drugs and proteins and can therefore be of paramount importance for such application as drug design. To discover such patterns, we propose here a computational approach to analyze the molecular data of drugs and proteins that are known to have interactions with each other. Specifically, we propose to use a data mining technique called <it>Drug-Protein Interaction Analysis </it>(<it>D-PIA</it>) to determine if there are any commonalities in the fingerprints of the substructures of interacting drug and protein molecules and if so, whether or not any patterns can be generalized from them.</p> <p>Method</p> <p>Given a database of drug-protein interactions, <it>D-PIA </it>performs its tasks in several steps. First, for each drug in the database, the fingerprints of its molecular substructures are first obtained. Second, for each protein in the database, the fingerprints of its protein domains are obtained. Third, based on known interactions between drugs and proteins, an interdependency measure between the fingerprint of each drug substructure and protein domain is then computed. Fourth, based on the interdependency measure, drug substructures and protein domains that are significantly interdependent are identified. Fifth, the existence of interaction relationship between a previously unknown drug-protein pairs is then predicted based on their constituent substructures that are significantly interdependent.</p> <p>Results</p> <p>To evaluate the effectiveness of <it>D-PIA</it>, we have tested it with real drug-protein interaction data. <it>D-PIA </it>has been tested with real drug-protein interaction data including enzymes, ion channels, and protein-coupled receptors. Experimental results show that there are indeed patterns that one can discover in the interdependency relationship between drug substructures and protein domains of interacting drugs and proteins. Based on these relationships, a testing set of drug-protein data are used to see if <it>D-PIA </it>can correctly predict the existence of interaction between drug-protein pairs. The results show that the prediction accuracy can be very high. An AUC score of a ROC plot could reach as high as 75% which shows the effectiveness of this classifier.</p> <p>Conclusions</p> <p><it>D-PIA </it>has the advantage that it is able to perform its tasks effectively based on the fingerprints of drug and protein molecules without requiring any 3D information about their structures and <it>D-PIA </it>is therefore very fast to compute. <it>D-PIA </it>has been tested with real drug-protein interaction data and experimental results show that it can be very useful for predicting previously unknown drug-protein as well as protein-ligand interactions. It can also be used to tackle problems such as ligand specificity which is related directly and indirectly to drug design and discovery.</p

Therapeutic effects of pyrrolidine dithiocarbamate on acute lung injury in rabbits

<p>Abstract</p> <p>Background</p> <p>Acute lung injury (ALI) and acute respiratory distress syndrome (ARDS) is an early characteristic of multiple organ dysfunction, responsible for high mortality and poor prognosis in patients. The present study aims to evaluate therapeutic effects and mechanisms of pyrrolidine dithiocarbamate (PDTC) on ALI.</p> <p>Methods</p> <p>Alveolar-arterial oxygen difference, lung tissue edema and compromise, NF-κB activation in polymorphonuclear neutrophil (PMN), and systemic levels of tumor necrosis factor-alpha (TNFa) and intercellular adhesion molecule-1 (ICAM-1) in rabbits induced by the intravenous administration of lipopolysaccharide (LPS) and treated with PDTC. Production of TNFa and IL-8, activation of Cathepsin G, and PMNs adhesion were also measured.</p> <p>Results</p> <p>The intravenous administration of PDTC had partial therapeutic effects on endotoxemia-induced lung tissue edema and damage, neutrophil influx to the lung, alveolar-capillary barrier dysfunction, and high systemic levels of TNFa and ICAM-1 as well as over-activation of NF-κB. PDTC could directly and partially inhibit LPS-induced TNFa hyper-production and over-activities of Cathepsin G. Such inhibitory effects of PDTC were related to the various stimuli and enhanced through combination with PI3K inhibitor.</p> <p>Conclusion</p> <p>NF-κB signal pathway could be one of targeting molecules and the combination with other signal pathway inhibitors may be an alternative of therapeutic strategies for ALI/ARDS.</p