Novel Inhibitor Design for Hemagglutinin against H1N1 Influenza Virus by Core Hopping Method

The worldwide spread of H1N1 avian influenza and the increasing reports about its resistance to the current drugs have made a high priority for developing new anti-influenza drugs. Owing to its unique function in assisting viruses to bind the cellular surface, a key step for them to subsequently penetrate into the infected cell, hemagglutinin (HA) has become one of the main targets for drug design against influenza virus. To develop potent HA inhibitors, the ZINC fragment database was searched for finding the optimal compound with the core hopping technique. As a result, the Neo6 compound was obtained. It has been shown through the subsequent molecular docking studies and molecular dynamic simulations that Neo6 not only assumes more favorable conformation at the binding pocket of HA but also has stronger binding interaction with its receptor. Accordingly, Neo6 may become a promising candidate for developing new and more powerful drugs for treating influenza. Or at the very least, the findings reported here may provide useful insights to stimulate new strategy in this area

Prediction of Protein Domain with mRMR Feature Selection and Analysis

The domains are the structural and functional units of proteins. With the avalanche of protein sequences generated in the postgenomic age, it is highly desired to develop effective methods for predicting the protein domains according to the sequences information alone, so as to facilitate the structure prediction of proteins and speed up their functional annotation. However, although many efforts have been made in this regard, prediction of protein domains from the sequence information still remains a challenging and elusive problem. Here, a new method was developed by combing the techniques of RF (random forest), mRMR (maximum relevance minimum redundancy), and IFS (incremental feature selection), as well as by incorporating the features of physicochemical and biochemical properties, sequence conservation, residual disorder, secondary structure, and solvent accessibility. The overall success rate achieved by the new method on an independent dataset was around 73%, which was about 28–40% higher than those by the existing method on the same benchmark dataset. Furthermore, it was revealed by an in-depth analysis that the features of evolution, codon diversity, electrostatic charge, and disorder played more important roles than the others in predicting protein domains, quite consistent with experimental observations. It is anticipated that the new method may become a high-throughput tool in annotating protein domains, or may, at the very least, play a complementary role to the existing domain prediction methods, and that the findings about the key features with high impacts to the domain prediction might provide useful insights or clues for further experimental investigations in this area. Finally, it has not escaped our notice that the current approach can also be utilized to study protein signal peptides, B-cell epitopes, HIV protease cleavage sites, among many other important topics in protein science and biomedicine

Shedding light on the elusive role of endothelial cells in cytomegalovirus dissemination.

Cytomegalovirus (CMV) is frequently transmitted by solid organ transplantation and is associated with graft failure. By forming the boundary between circulation and organ parenchyma, endothelial cells (EC) are suited for bidirectional virus spread from and to the transplant. We applied Cre/loxP-mediated green-fluorescence-tagging of EC-derived murine CMV (MCMV) to quantify the role of infected EC in transplantation-associated CMV dissemination in the mouse model. Both EC- and non-EC-derived virus originating from infected Tie2-cre(+) heart and kidney transplants were readily transmitted to MCMV-naïve recipients by primary viremia. In contrast, when a Tie2-cre(+) transplant was infected by primary viremia in an infected recipient, the recombined EC-derived virus poorly spread to recipient tissues. Similarly, in reverse direction, EC-derived virus from infected Tie2-cre(+) recipient tissues poorly spread to the transplant. These data contradict any privileged role of EC in CMV dissemination and challenge an indiscriminate applicability of the primary and secondary viremia concept of virus dissemination

Targeted hepatitis C antibody testing interventions: a systematic review and meta-analysis

Testing for hepatitis C virus (HCV) infection may reduce the risk of liver-related morbidity, by facilitating earlier access to treatment and care. This review investigated the effectiveness of targeted testing interventions on HCV case detection, treatment uptake, and prevention of liver-related morbidity. A literature search identified studies published up to 2013 that compared a targeted HCV testing intervention (targeting individuals or groups at increased risk of HCV) with no targeted intervention, and results were synthesised using meta-analysis. Exposure to a targeted testing intervention, compared to no targeted intervention, was associated with increased cases detected [number of studies (n) = 14; pooled relative risk (RR) 1.7, 95 % CI 1.3, 2.2] and patients commencing therapy (n = 4; RR 3.3, 95 % CI 1.1, 10.0). Practitioner-based interventions increased test uptake and cases detected (n = 12; RR 3.5, 95 % CI 2.5, 4.8; and n = 10; RR 2.2, 95 % CI 1.4, 3.5, respectively), whereas media/information-based interventions were less effective (n = 4; RR 1.5, 95 % CI 0.7, 3.0; and n = 4; RR 1.3, 95 % CI 1.0, 1.6, respectively). This meta-analysis provides for the first time a quantitative assessment of targeted HCV testing interventions, demonstrating that these strategies were effective in diagnosing cases and increasing treatment uptake. Strategies involving practitioner-based interventions yielded the most favourable outcomes. It is recommended that testing should be targeted at and offered to individuals who are part of a population with high HCV prevalence, or who have a history of HCV risk behaviour

Semi-automatic conversion of BioProp semantic annotation to PASBio annotation

<p>Abstract</p> <p>Background</p> <p>Semantic role labeling (SRL) is an important text analysis technique. In SRL, sentences are represented by one or more predicate-argument structures (PAS). Each PAS is composed of a predicate (verb) and several arguments (noun phrases, adverbial phrases, etc.) with different semantic roles, including main arguments (agent or patient) as well as adjunct arguments (time, manner, or location). PropBank is the most widely used PAS corpus and annotation format in the newswire domain. In the biomedical field, however, more detailed and restrictive PAS annotation formats such as PASBio are popular. Unfortunately, due to the lack of an annotated PASBio corpus, no publicly available machine-learning (ML) based SRL systems based on PASBio have been developed. In previous work, we constructed a biomedical corpus based on the PropBank standard called BioProp, on which we developed an ML-based SRL system, BIOSMILE. In this paper, we aim to build a system to convert BIOSMILE's BioProp annotation output to PASBio annotation. Our system consists of BIOSMILE in combination with a BioProp-PASBio rule-based converter, and an additional semi-automatic rule generator.</p> <p>Results</p> <p>Our first experiment evaluated our rule-based converter's performance independently from BIOSMILE performance. The converter achieved an F-score of 85.29%. The second experiment evaluated combined system (BIOSMILE + rule-based converter). The system achieved an F-score of 69.08% for PASBio's 29 verbs.</p> <p>Conclusion</p> <p>Our approach allows PAS conversion between BioProp and PASBio annotation using BIOSMILE alongside our newly developed semi-automatic rule generator and rule-based converter. Our system can match the performance of other state-of-the-art domain-specific ML-based SRL systems and can be easily customized for PASBio application development.</p

Multi-objective optimization for optimum tolerance synthesis with process and machine selection using a genetic algorithm

This paper presents a new approach to the tolerance synthesis of the component parts of assemblies by simultaneously optimizing three manufacturing parameters: manufacturing cost, including tolerance cost and quality loss cost; machining time; and machine overhead/idle time cost. A methodology has been developed using the Genetic Algorithm (GA) technique to solve this multi-objective optimization problem. The effectiveness of the proposed methodology has been demonstrated by solving a wheel mounting assembly problem consisting of five components, two subassemblies, two critical dimensions, two functional tolerances, and eight operations. Significant cost saving can be achieved by employing this methodology

Two Birds with One Stone? Possible Dual-Targeting H1N1 Inhibitors from Traditional Chinese Medicine

The H1N1 influenza pandemic of 2009 has claimed over 18,000 lives. During this pandemic, development of drug resistance further complicated efforts to control and treat the widespread illness. This research utilizes traditional Chinese medicine Database@Taiwan (TCM Database@Taiwan) to screen for compounds that simultaneously target H1 and N1 to overcome current difficulties with virus mutations. The top three candidates were de novo derivatives of xylopine and rosmaricine. Bioactivity of the de novo derivatives against N1 were validated by multiple machine learning prediction models. Ability of the de novo compounds to maintain CoMFA/CoMSIA contour and form key interactions implied bioactivity within H1 as well. Addition of a pyridinium fragment was critical to form stable interactions in H1 and N1 as supported by molecular dynamics (MD) simulation. Results from MD, hydrophobic interactions, and torsion angles are consistent and support the findings of docking. Multiple anchors and lack of binding to residues prone to mutation suggest that the TCM de novo derivatives may be resistant to drug resistance and are advantageous over conventional H1N1 treatments such as oseltamivir. These results suggest that the TCM de novo derivatives may be suitable candidates of dual-targeting drugs for influenza.National Science Council of Taiwan (NSC 99-2221-E-039-013-)Committee on Chinese Medicine and Pharmacy (CCMP100-RD-030)China Medical University and Asia University (CMU98-TCM)China Medical University and Asia University (CMU99-TCM)China Medical University and Asia University (CMU99-S-02)China Medical University and Asia University (CMU99-ASIA-25)China Medical University and Asia University (CMU99-ASIA-26)China Medical University and Asia University (CMU99-ASIA-27)China Medical University and Asia University (CMU99-ASIA-28)Taiwan Department of Health. Clinical Trial and Research Center of Excellence (DOH100-TD-B-111-004)Taiwan Department of Health. Cancer Research Center of Excellence (DOH100-TD-C-111-005

Prediction of peptide and protein propensity for amyloid formation

Understanding which peptides and proteins have the potential to undergo amyloid formation and what driving forces are responsible for amyloid-like fiber formation and stabilization remains limited. This is mainly because proteins that can undergo structural changes, which lead to amyloid formation, are quite diverse and share no obvious sequence or structural homology, despite the structural similarity found in the fibrils. To address these issues, a novel approach based on recursive feature selection and feed-forward neural networks was undertaken to identify key features highly correlated with the self-assembly problem. This approach allowed the identification of seven physicochemical and biochemical properties of the amino acids highly associated with the self-assembly of peptides and proteins into amyloid-like fibrils (normalized frequency of β-sheet, normalized frequency of β-sheet from LG, weights for β-sheet at the window position of 1, isoelectric point, atom-based hydrophobic moment, helix termination parameter at position j+1 and ΔGº values for peptides extrapolated in 0 M urea). Moreover, these features enabled the development of a new predictor (available at http://cran.r-project.org/web/packages/appnn/index.html) capable of accurately and reliably predicting the amyloidogenic propensity from the polypeptide sequence alone with a prediction accuracy of 84.9 % against an external validation dataset of sequences with experimental in vitro, evidence of amyloid formation

ngs_backbone: a pipeline for read cleaning, mapping and SNP calling using Next Generation Sequence

Background: The possibilities offered by next generation sequencing (NGS) platforms are revolutionizing biotechnological laboratories. Moreover, the combination of NGS sequencing and affordable high-throughput genotyping technologies is facilitating the rapid discovery and use of SNPs in non-model species. However, this abundance of sequences and polymorphisms creates new software needs. To fulfill these needs, we have developed a powerful, yet easy-to-use application. Results: The ngs_backbone software is a parallel pipeline capable of analyzing Sanger, 454, Illumina and SOLiD (Sequencing by Oligonucleotide Ligation and Detection) sequence reads. Its main supported analyses are: read cleaning, transcriptome assembly and annotation, read mapping and single nucleotide polymorphism (SNP) calling and selection. In order to build a truly useful tool, the software development was paired with a laboratory experiment. All public tomato Sanger EST reads plus 14.2 million Illumina reads were employed to test the tool and predict polymorphism in tomato. The cleaned reads were mapped to the SGN tomato transcriptome obtaining a coverage of 4.2 for Sanger and 8.5 for Illumina. 23,360 single nucleotide variations (SNVs) were predicted. A total of 76 SNVs were experimentally validated, and 85% were found to be real. Conclusions: ngs_backbone is a new software package capable of analyzing sequences produced by NGS technologies and predicting SNVs with great accuracy. In our tomato example, we created a highly polymorphic collection of SNVs that will be a useful resource for tomato researchers and breeders. The software developed along with its documentation is freely available under the AGPL license and can be downloaded from http://bioinf. comav.upv.es/ngs_backbone/ or http://github.com/JoseBlanca/franklin.Blanca Postigo, JM.; Pascual Bañuls, L.; Ziarsolo Areitioaurtena, P.; Nuez Viñals, F.; Cañizares Sales, J. (2011). Ngs_backbone: a pipeline for read cleaning, mapping and SNP calling using Next Generation Sequence. BMC Genomics. 12:1-8. doi:10.1186/1471-2164-12-285S1812Metzker ML: Sequencing technologies - the next generation. Nature Reviews Genetics. 2010, 11 (1): 31-46. 10.1038/nrg2626.454 sequencing. [ http://www.454.com/ ]Illumina Inc. [ http://www.illumina.com/ ]Flicek P, Birney E: Sense from sequence reads: methods for alignment and assembly (vol 6, pg S6, 2009). Nature Methods. 2010, 7 (6): 479-479.Chevreux B, Pfisterer T, Drescher B, Driesel AJ, Muller WEG, Wetter T, Suhai S: Using the miraEST assembler for reliable and automated mRNA transcript assembly and SNP detection in sequenced ESTs. Genome Research. 2004, 14 (6): 1147-1159. 10.1101/gr.1917404.Li H, Durbin R: Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics. 2009, 25 (14): 1754-1760. 10.1093/bioinformatics/btp324.Langmead B, Trapnell C, Pop M, Salzberg SL: Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biology. 2009, 10 (3):Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G, Abecasis G, Durbin R, Genome Project Data P: The Sequence Alignment/Map format and SAMtools. Bioinformatics. 2009, 25 (16): 2078-2079. 10.1093/bioinformatics/btp352.1000 Genomes. A deep Catalog of Human Genetic Variation. [ http://1000genomes.org/wiki/doku.php?id=1000_genomes:analysis:vcf4.0 ]The seqanswers internet forum. [ http://seqanswers.com/ ]Blankenberg D, Taylor J, Schenck I, He JB, Zhang Y, Ghent M, Veeraraghavan N, Albert I, Miller W, Makova KD, Ross CH, Nekrutenko A: A framework for collaborative analysis of ENCODE data: Making large-scale analyses biologist-friendly. Genome Research. 2007, 17 (6): 960-964. 10.1101/gr.5578007.CloVR Automated Sequence Analysis from Your Desktop. [ http://clovr.org/ ]Papanicolaou A, Stierli R, Ffrench-Constant RH, Heckel DG: Next generation transcriptomes for next generation genomes using est2assembly. Bmc Bioinformatics. 2009, 10:Applied Biosystems by life technologies. [ http://www.appliedbiosystems.com/absite/us/en/home/applications-technologies/solid-next-generation-sequencing.html ]Wall PK, Leebens-Mack J, Chanderbali AS, Barakat A, Wolcott E, Liang HY, Landherr L, Tomsho LP, Hu Y, Carlson JE, Ma H, Schuster SC, Soltis DE, Soltis PS, Altman N, dePamphilis CW: Comparison of next generation sequencing technologies for transcriptome characterization. Bmc Genomics. 2009, 10:Murchison EP, Tovar C, Hsu A, Bender HS, Kheradpour P, Rebbeck CA, Obendorf D, Conlan C, Bahlo M, Blizzard CA, Pyecroft S, Kreiss A, Kellis M, Stark A, Harkins TT, Marshall Graves JA, Woods GM, Hanon GJ, Papenfuss AT: The Tasmanian Devil Transcriptome Reveals Schwann Cell Origins of a Clonally Transmissible Cancer. Science. 2010, 327 (5961): 84-87. 10.1126/science.1180616.Parchman TL, Geist KS, Grahnen JA, Benkman CW, Buerkle CA: Transcriptome sequencing in an ecologically important tree species: assembly, annotation, and marker discovery. Bmc Genomics. 2010, 11:Babik W, Stuglik M, Qi W, Kuenzli M, Kuduk K, Koteja P, Radwan J: Heart transcriptome of the bank vole (Myodes glareolus): towards understanding the evolutionary variation in metabolic rate. BMC Genomics. 2010, 11: 390-10.1186/1471-2164-11-390.Miller JC, Tanksley SD: RFLP analysis of phylogenetic-relationships and genetic-variation in the genus Lycopersicon. Theoretical and Applied Genetics. 1990, 80 (4): 437-448.Williams CE, Stclair DA: Phenetic relationships and levels of variability detected by restriction-fragment-length-polymorphism and random amplified polymorphic DNA analysis of cultivated and wild accessions of Lycopersicon-esculentum. Genome. 1993, 36 (3): 619-630. 10.1139/g93-083.Rick CM: Tomato, Lycopersicon esculentum (Solanaceae). Evolution of crop plants. Edited by: Simmonds NW. 1976, London: Longman Group, 268-273.Labate JA, Baldo AM: Tomato SNP discovery by EST mining and resequencing. Molecular Breeding. 2005, 16 (4): 343-349. 10.1007/s11032-005-1911-5.Yano K, Watanabe M, Yamamoto N, Maeda F, Tsugane T, Shibata D: Expressed sequence tags (EST) database of a miniature tomato cultivar, Micro-Tom. Plant and Cell Physiology. 2005, 46: S139-S139.Jimenez-Gomez JM, Maloof JN: Sequence diversity in three tomato species: SNPs, markers, and molecular evolution. Bmc Plant Biology. 2009, 9:Yang WC, Bai XD, Kabelka E, Eaton C, Kamoun S, van der Knaap E, Francis D: Discovery of single nucleotide polymorphisms in Lycopersicon esculentum by computer aided analysis of expressed sequence tags. Molecular Breeding. 2004, 14 (1): 21-34.Van Deynze A, Stoffel K, Buell CR, Kozik A, Liu J, van der Knaap E, Francis D: Diversity in conserved genes in tomato. Bmc Genomics. 2007, 8:Sim SC, Robbins MD, Chilcott C, Zhu T, Francis DM: Oligonucleotide array discovery of polymorphisms in cultivated tomato (Solanum lycopersicum L.) reveals patterns of SNP variation associated with breeding. Bmc Genomics. 2009, 10:Bioinformatics at COMAV. [ http://bioinf.comav.upv.es/ngs_backbone/index.html ]Broad institute. [ http://www.broadinstitute.org/igv ]Bioinformatics at COMAV. [ http://bioinf.comav.upv.es/ngs_backbone/install.html ]Github social coding. [ http://github.com/JoseBlanca/franklin ]Chou HH, Holmes MH: DNA sequence quality trimming and vector removal. Bioinformatics. 2001, 17 (12): 1093-1104. 10.1093/bioinformatics/17.12.1093.Picard. [ http://picard.sourceforge.net/index.shtml ]McKenna A, Hanna M, Banks E, Sivachenko A, Citulskis K, Kernytsky A, Garimella K, Altshuler D, Gabriel S, Daly M, DePristo MA: The Genome Analysis Toolkit: A MapReduce framework for analyzing next-generation DNA sequencing data. Genome Research. 2010, 20: 1297-1303. 10.1101/gr.107524.110.Sol Genomics Network. [ ftp://ftp.solgenomics.net/ ]NCBI Genbank. [ http://www.ncbi.nlm.nih.gov/genbank/ ]Gundry CN, Vandersteen JG, Reed GH, Pryor RJ, Chen J, Wittwer CT: Amplicon melting analysis with labeled primers: A closed-tube method for differentiating homozygotes and heterozygotes. Clinical Chemistry. 2003, 49 (3): 396-406. 10.1373/49.3.396

Completeness and timeliness of tuberculosis notification in Taiwan

Tuberculosis (TB) is a notifiable disease by the Communicable Disease Control Law in Taiwan. Several measures have been undertaken to improve reporting of TB but the completeness and timeliness of TB notification in Taiwan has not yet been systemically evaluated