WorldWideScience

Sample records for structure predicts serotype

  1. Dengue serotype immune-interactions and their consequences for vaccine impact predictions

    José Lourenço

    2016-09-01

    Full Text Available Dengue is one of the most important and wide-spread viral infections affecting human populations. The last few decades have seen a dramatic increase in the global burden of dengue, with the virus now being endemic or near-endemic in over 100 countries world-wide. A recombinant tetravalent vaccine candidate (CYD-TDV has recently completed Phase III clinical efficacy trials in South East Asia and Latin America and has been licensed for use in several countries. The trial results showed moderate-to-high efficacies in protection against clinical symptoms and hospitalisation but with so far unknown effects on transmission and infections per se. Model-based predictions about the vaccine's short- or long-term impact on the burden of dengue are therefore subject to a considerable degree of uncertainty. Furthermore, different immune interactions between dengue's serotypes have frequently been evoked by modelling studies to underlie dengue's oscillatory dynamics in disease incidence and serotype prevalence. Here we show how model assumptions regarding immune interactions in the form of antibody-dependent enhancement, temporary cross-immunity and the number of infections required to develop full immunity can significantly affect the predicted outcome of a dengue vaccination campaign. Our results thus re-emphasise the important gap in our current knowledge concerning the effects of previous exposure on subsequent dengue infections and further suggest that intervention impact studies should be critically evaluated by their underlying assumptions about serotype immune-interactions.

  2. Serotype-specific Differences in Dengue Virus Non-structural Protein 5 Nuclear Localization*

    Hannemann, Holger; Sung, Po-Yu; Chiu, Han-Chen; Yousuf, Amjad; Bird, Jim; Lim, Siew Pheng; Davidson, Andrew D.

    2013-01-01

    The four serotypes of dengue virus (DENV-1 to -4) cause the most important arthropod-borne viral disease of humans. DENV non-structural protein 5 (NS5) contains enzymatic activities required for capping and replication of the viral RNA genome that occurs in the host cytoplasm. However, previous studies have shown that DENV-2 NS5 accumulates in the nucleus during infection. In this study, we examined the nuclear localization of NS5 for all four DENV serotypes. We demonstrate for the first time that there are serotypic differences in NS5 nuclear localization. Whereas the DENV-2 and -3 proteins accumulate in the nucleus, DENV-1 and -4 NS5 are predominantly if not exclusively localized to the cytoplasm. Comparative studies on the DENV-2 and -4 NS5 proteins revealed that the difference in DENV-4 NS5 nuclear localization was not due to rapid nuclear export but rather the lack of a functional nuclear localization sequence. Interaction studies using DENV-2 and -4 NS5 and human importin-α isoforms failed to identify an interaction that supported the differential nuclear localization of NS5. siRNA knockdown of the human importin-α isoform KPNA2, corresponding to the murine importin-α isoform previously shown to bind to DENV-2 NS5, did not substantially affect DENV-2 NS5 nuclear localization, whereas knockdown of importin-β did. The serotypic differences in NS5 nuclear localization did not correlate with differences in IL-8 gene expression. The results show that NS5 nuclear localization is not strictly required for virus replication but is more likely to have an auxiliary function in the life cycle of specific DENV serotypes. PMID:23770669

  3. Serotype-specific differences in dengue virus non-structural protein 5 nuclear localization.

    Hannemann, Holger; Sung, Po-Yu; Chiu, Han-Chen; Yousuf, Amjad; Bird, Jim; Lim, Siew Pheng; Davidson, Andrew D

    2013-08-02

    The four serotypes of dengue virus (DENV-1 to -4) cause the most important arthropod-borne viral disease of humans. DENV non-structural protein 5 (NS5) contains enzymatic activities required for capping and replication of the viral RNA genome that occurs in the host cytoplasm. However, previous studies have shown that DENV-2 NS5 accumulates in the nucleus during infection. In this study, we examined the nuclear localization of NS5 for all four DENV serotypes. We demonstrate for the first time that there are serotypic differences in NS5 nuclear localization. Whereas the DENV-2 and -3 proteins accumulate in the nucleus, DENV-1 and -4 NS5 are predominantly if not exclusively localized to the cytoplasm. Comparative studies on the DENV-2 and -4 NS5 proteins revealed that the difference in DENV-4 NS5 nuclear localization was not due to rapid nuclear export but rather the lack of a functional nuclear localization sequence. Interaction studies using DENV-2 and -4 NS5 and human importin-α isoforms failed to identify an interaction that supported the differential nuclear localization of NS5. siRNA knockdown of the human importin-α isoform KPNA2, corresponding to the murine importin-α isoform previously shown to bind to DENV-2 NS5, did not substantially affect DENV-2 NS5 nuclear localization, whereas knockdown of importin-β did. The serotypic differences in NS5 nuclear localization did not correlate with differences in IL-8 gene expression. The results show that NS5 nuclear localization is not strictly required for virus replication but is more likely to have an auxiliary function in the life cycle of specific DENV serotypes.

  4. CRYSTAL STRUCTURE OF CLOSTRIDIUM BOTULINUM NEUROTOXIN SEROTYPE B

    SWAMINATHAN, S.; ESWARAMOORTHY, S.

    2001-01-01

    The toxigenic strains of Clostridium botulinum produce seven serologically distinct types of neurotoxins labeled A - G (EC 3.4.24.69), while Clostridium tetani produces tetanus neurotoxin (EC 3.4.24.68). Botulinum and tetanus neurotoxins (BoNTs and TeNT) are produced as single inactive chains of molecular mass of approximately 150 kDa. Most of these neurotoxins are released after being cleaved into two chains, a heavy chain (HI) of 100 kDa and a light chain (L) of 50 kDa held together by an interchain disulfide bond, by tissue proteinases. BoNT/E is released as a single chain but cleaved by host proteinases[1]. Clostvidium botulinum neurotoxins are extremely poisonous proteins with their LD(sub 50) for humans in the range of 0.1 - 1 ng kg(sup -1)[2]. Botulinum neurotoxins are responsible for neuroparalytic syndromes of botulism characterized by serious neurological disorders and flaccid paralysis. BoNTs block the release of acetylcholine at the neuromuscular junction causing flaccid paralysis while TeNT blocks the release of neurotransmitters like glycine and(gamma)-aminobutyric acid (GABA) in the inhibitory interneurons of the spinal cord resulting in spastic paralysis. In spite of different clinical symptoms, their aetiological agents intoxicate neuronal cells in the same way and these toxins have similar structural organization[3

  5. CRYSTAL STRUCTURE OF CLOSTRIDIUM BOTULINUM NEUROTOXIN SEROTYPE B.

    SWAMINATHAN,S.; ESWARAMOORTHY,S.

    2001-11-19

    The toxigenic strains of Clostridium botulinum produce seven serologically distinct types of neurotoxins labeled A - G (EC 3.4.24.69), while Clostridium tetani produces tetanus neurotoxin (EC 3.4.24.68). Botulinum and tetanus neurotoxins (BoNTs and TeNT) are produced as single inactive chains of molecular mass of approximately 150 kDa. Most of these neurotoxins are released after being cleaved into two chains, a heavy chain (HI) of 100 kDa and a light chain (L) of 50 kDa held together by an interchain disulfide bond, by tissue proteinases. BoNT/E is released as a single chain but cleaved by host proteinases [1]. Clostvidium botulinum neurotoxins are extremely poisonous proteins with their LD{sub 50} for humans in the range of 0.1 - 1 ng kg{sup -1} [2]. Botulinum neurotoxins are responsible for neuroparalytic syndromes of botulism characterized by serious neurological disorders and flaccid paralysis. BoNTs block the release of acetylcholine at the neuromuscular junction causing flaccid paralysis while TeNT blocks the release of neurotransmitters like glycine and {gamma}-aminobutyric acid (GABA) in the inhibitory interneurons of the spinal cord resulting in spastic paralysis. In spite of different clinical symptoms, their aetiological agents intoxicate neuronal cells in the same way and these toxins have similar structural organization [3].

  6. Antigenicity of envelop and non-structural proteins of dengue serotypes and their potentiality to elicit specifi antibody

    Ramesh Venkatachalam

    2015-06-01

    Full Text Available Objective: To find out the antigenic nature of envelop (E and non-structural (NS proteins and their ability to induce specific antibodies, and to investigate specific antibody produced by specific dengue virus (DENV serotypes. Methods: Amino acid sequences of E and NS proteins of dengue serotypes were analysed by using VaxiJen antigen predicition server. The transmembrane of topology analyses were conducted by using transmembrane prediction using hidden markov models. The Hex dock server was used for docking. Results: The antigenicity score and exomembrane potentiality of E and NS proteins were calculated. All those proteins were antigenic; these antigens were made to interact with antibodies such as immunoglobulin A, immunoglobulin G and immunoglobulin M. Higher energy values of immunoglobulin M were found in DENV-1 and DENV-2, and more energy values were found in immunoglobulin G of DENV-3, DENV-4, NS-1, NS-3 and NS-5. Conclusions: In the present study, DENV-1 and DENV-2 are positive to immunoglobulin M and involved in the primary infection. DENV 3, DENV 4 and all the NS proteins (NS-1, NS-3, NS-5 which elicit immunoglobulin G are involved in the secondary infection.

  7. Twinned crystals of adeno-associated virus serotype 3b prove suitable for structural studies

    Lerch, Thomas F.; Xie, Qing; Ongley, Heather M.; Hare, Joan; Chapman, Michael S.

    2009-01-01

    Crystals of adeno-associated virus serotype 3b, a human DNA virus with promise as a vector for gene therapy, have been grown, diffract X-rays to ∼2.6 Å resolution and are suitable for structure determination in spite of twinning. Adeno-associated viruses (AAVs) are leading candidate vectors for gene-therapy applications. The AAV-3b capsid is closely related to the well characterized AAV-2 capsid (87% identity), but sequence and presumably structural differences lead to distinct cell-entry and immune-recognition properties. In an effort to understand these differences and to perhaps harness them, diffraction-quality crystals of purified infectious AAV-3b particles have been grown and several partial diffraction data sets have been recorded. The crystals displayed varying levels of merohedral twinning that in earlier times would have rendered them unsuitable for structure determination, but here is shown to be a tractable complication

  8. A structural investigation of the capsular antigens of some Klebsiella and E. coli serotypes

    Parolis, L.A.S.

    1985-11-01

    The work described in this thesis forms part of a program concerned with the study of exocellular capsular polysaccharides of some Enterobacteriaceae. 1 H- and 13 C-n.m.r. spectroscopy have been used in this study. Klebsiella and Escherichia coli are of interest because they are often pathogenic to man; E. coli are commensal bacteria as well as opportunistic pathogens. The bacterial capsule is the first line of defence of the bacterial cell against attack by the host's immunological defences and administered antibiotics, and thus knowledge of its composition and characteristics is of importance in devising ways of combating infection by these organisms. The structure of the capsular polysaccharide has been investigated employing a combination of chemical and spectroscopic methods. Several oligo-saccharides were isolated and characterized by high resolution 1 H-n.m.r. spectroscopy and methylation analysis. The E. coli group of bacteria possesses seventy-four recognized polysaccharide capsules and the structures of approximately twenty percent of these have been reported. The emphasis of this research group is centered on the elucidation of the structures of E. coli capsules. The acidic capsular polysaccharide isolated from E. coli K9 has been investigated using the techniques of methylation analysis periodate oxidation, bacteriophage degradation and n.m.r. spectroscopy. This thesis however represents a transition period in the study of Enterobacteriaceae capsular polysaccharides and so includes the structure elucidation of two Klebsiella polysaccharides, that of the K14 and K68 serotypes, and one E. coli polysaccharide, that of the K9 serotype. Bacteriophage-borne enzyme degradations of Klebsiella K14 and E. coli K9 polysaccharides have been performed and are presented. The thesis also includes a comparative study of the 0-specific side-chains of the lipo-polysaccharides of E. coli 09 and 09a serogroups

  9. Adenovirus structural protein IIIa is involved in the serotype specificity of viral DNA packaging.

    Ma, Hsin-Chieh; Hearing, Patrick

    2011-08-01

    The packaging of the adenovirus (Ad) genome into a capsid displays serotype specificity. This specificity has been attributed to viral packaging proteins, the IVa2 protein and the L1-52/55K protein. We previously found that the Ad17 L1-52/55K protein was not able to complement the growth of an Ad5 L1-52/55K mutant virus, whereas two other Ad17 packaging proteins, IVa2 and L4-22K, could complement the growth of Ad5 viruses with mutations in the respective genes. In this report, we investigated why the Ad17 L1-52/55K protein was not able to complement the Ad5 L1-52/55K mutant virus. We demonstrate that the Ad17 L1-52/55K protein binds to the Ad5 IVa2 protein in vitro and the Ad5 packaging domain in vivo, activities previously associated with packaging function. The Ad17 L1-52/55K protein also associates with empty Ad5 capsids. Interestingly, we find that the Ad17 L1-52/55K protein is able to complement the growth of an Ad5 L1-52/55K mutant virus in conjunction with the Ad17 structural protein IIIa. The same result was found with the L1-52/55K and IIIa proteins of several other Ad serotypes, including Ad3 and Ad4. The Ad17 IIIa protein associates with empty Ad5 capsids. Consistent with the complementation results, we find that the IIIa protein interacts with the L1-52/55K protein in vitro and associates with the viral packaging domain in vivo. These results underscore the complex nature of virus assembly and genome encapsidation and provide a new model for how the viral genome may tether to the empty capsid during the encapsidation process.

  10. Crystallization and preliminary X-ray structural studies of adeno-associated virus serotype 6

    Xie, Qing; Ongley, Heather M.; Hare, Joan; Chapman, Michael S.

    2008-01-01

    Adeno-associated virus type 6, a human DNA virus that is being developed as a vector for gene therapy, has been crystallized in a form suitable for structure determination at about 3.2 Å resolution. Adeno-associated viruses are being developed as vectors for gene therapy and have been used in a number of clinical trials. Vectors to date have been based on the type species AAV-2, the structure of which was published in 2002. There is growing interest in modulating the cellular tropism and immune neutralization of AAV-2 with variants inspired by the properties of other serotypes. Towards the determination of a structure for AAV type 6, this paper reports the high-yield production, purification, crystallization and preliminary diffraction studies of infectious AAV-6 virions. The crystals diffracted to 3.2 Å resolution using synchrotron radiation. The most promising crystal form belonged to space group R3 and appeared to be suitable for initial structure determination

  11. Genetic characterization of the non-structural protein-3 gene of bluetongue virus serotype-2 isolate from India.

    Pudupakam, Raghavendra Sumanth; Raghunath, Shobana; Pudupakam, Meghanath; Daggupati, Sreenivasulu

    2017-03-01

    Sequence analysis and phylogenetic studies based on non-structural protein-3 (NS3) gene are important in understanding the evolution and epidemiology of bluetongue virus (BTV). This study was aimed at characterizing the NS3 gene sequence of Indian BTV serotype-2 (BTV2) to elucidate its genetic relationship to global BTV isolates. The NS3 gene of BTV2 was amplified from infected BHK-21 cell cultures, cloned and subjected to sequence analysis. The generated NS3 gene sequence was compared with the corresponding sequences of different BTV serotypes across the world, and a phylogenetic relationship was established. The NS3 gene of BTV2 showed moderate levels of variability in comparison to different BTV serotypes, with nucleotide sequence identities ranging from 81% to 98%. The region showed high sequence homology of 93-99% at amino acid level with various BTV serotypes. The PPXY/PTAP late domain motifs, glycosylation sites, hydrophobic domains, and the amino acid residues critical for virus-host interactions were conserved in NS3 protein. Phylogenetic analysis revealed that BTV isolates segregate into four topotypes and that the Indian BTV2 in subclade IA is closely related to Asian and Australian origin strains. Analysis of the NS3 gene indicated that Indian BTV2 isolate is closely related to strains from Asia and Australia, suggesting a common origin of infection. Although the pattern of evolution of BTV2 isolate is different from other global isolates, the deduced amino acid sequence of NS3 protein demonstrated high molecular stability.

  12. Structural prediction in aphasia

    Tessa Warren

    2015-05-01

    Full Text Available There is considerable evidence that young healthy comprehenders predict the structure of upcoming material, and that their processing is facilitated when they encounter material matching those predictions (e.g., Staub & Clifton, 2006; Yoshida, Dickey & Sturt, 2013. However, less is known about structural prediction in aphasia. There is evidence that lexical prediction may be spared in aphasia (Dickey et al., 2014; Love & Webb, 1977; cf. Mack et al, 2013. However, predictive mechanisms supporting facilitated lexical access may not necessarily support structural facilitation. Given that many people with aphasia (PWA exhibit syntactic deficits (e.g. Goodglass, 1993, PWA with such impairments may not engage in structural prediction. However, recent evidence suggests that some PWA may indeed predict upcoming structure (Hanne, Burchert, De Bleser, & Vashishth, 2015. Hanne et al. tracked the eyes of PWA (n=8 with sentence-comprehension deficits while they listened to reversible subject-verb-object (SVO and object-verb-subject (OVS sentences in German, in a sentence-picture matching task. Hanne et al. manipulated case and number marking to disambiguate the sentences’ structure. Gazes to an OVS or SVO picture during the unfolding of a sentence were assumed to indicate prediction of the structure congruent with that picture. According to this measure, the PWA’s structural prediction was impaired compared to controls, but they did successfully predict upcoming structure when morphosyntactic cues were strong and unambiguous. Hanne et al.’s visual-world evidence is suggestive, but their forced-choice sentence-picture matching task places tight constraints on possible structural predictions. Clearer evidence of structural prediction would come from paradigms where the content of upcoming material is not as constrained. The current study used self-paced reading study to examine structural prediction among PWA in less constrained contexts. PWA (n=17 who

  13. Some epitopes conservation in non structural 3 protein dengue virus serotype 4

    Tegar A. P. Siregar

    2016-03-01

    Full Text Available AbstrakLatar belakang: Protein Non Struktural 3 (NS3 virus dengue menginduksi respon antibodi netralisasidan respon sel T CD4+ dan CD8+, serta berperan dalam replikasi virus. Protein NS3 memiliki epitopepitopsel T dan B yang terdapat perbedaan kelestarian pada berbagai strain virus dengue serotipe 4(DENV-4. Penelitian ini bertujuan untuk mengetahui kelestarian epitop sel T dan B pada protein NS3DENV-4 strain-strain dunia dan keempat serotipe virus dengue strain Indonesia.Metode: Penelitian ini dilakukan di Departemen Mikrobiologi Fakultas Kedokteran UI sejak Juni 2013 - April2014. Sekuens asam amino NS3 DENV-4 strain 081 didapatkan setelah produk PCR gen NS3 DENV-4 081disekuensing. Epitop-epitop sel T dan sel B protein NS3 DENV-4 081 dianalisis dan dibandingkan dengansekuens asam amino protein NS3 dari 124 strain DENV-4 di dunia dan keempat serotipe DENV strain Indonesia.Strain-strain dunia merupakan strain yang ada di benua Amerika (Venezuela, Colombia, dll dan Asia (Cina,Singapura, dll. Referensi posisi epitop sel T dan B protein NS3 diperoleh dari laporan penelitian terdahulu.Hasil: Delapan epitop sel T dan 2 epitop sel B dari protein NS3 DENV-4 081 ternyata identik dan lestaripada protein NS3 dari 124 strain DENV-4 dunia. Epitop sel B di posisi asam amino 537-544 pada proteinNS3 DENV-4 081 ternyata identik dan lestari dengan epitop sel B protein NS3 dari keempat serotipeDENV strain Indonesia.Kesimpulan: Kelestarian yang luas dari epitop sel T dan B pada hampir seluruh strain DENV-4 dunia danserotipe-serotipe DENV strain Indonesia. (Health Science Journal of Indonesia 2015;6:126-31Kata kunci: virus dengue, protein NS3, epitop sel T, epitop sel B AbstractBackground: Non Structural 3 (NS3 protein of dengue virus (DENV is known to induce antibody, CD4+and CD8+ T cell responses, and playing role in viral replication. NS3 protein has T and B cell epitopes,which has conservation difference between DENV-4 strains. This study aimed to identify

  14. Linguistic Structure Prediction

    Smith, Noah A

    2011-01-01

    A major part of natural language processing now depends on the use of text data to build linguistic analyzers. We consider statistical, computational approaches to modeling linguistic structure. We seek to unify across many approaches and many kinds of linguistic structures. Assuming a basic understanding of natural language processing and/or machine learning, we seek to bridge the gap between the two fields. Approaches to decoding (i.e., carrying out linguistic structure prediction) and supervised and unsupervised learning of models that predict discrete structures as outputs are the focus. W

  15. Structural Studies of Adeno-Associated Virus Serotype 8 Capsid Transitions Associated with Endosomal Trafficking

    Nam, Hyun-Joo; Gurda, Brittney L.; McKenna, Robert; Potter, Mark; Byrne, Barry; Salganik, Maxim; Muzyczka, Nicholas; Agbandje-McKenna, Mavis (Florida)

    2012-09-17

    The single-stranded DNA (ssDNA) parvoviruses enter host cells through receptor-mediated endocytosis, and infection depends on processing in the early to late endosome as well as in the lysosome prior to nuclear entry for replication. However, the mechanisms of capsid endosomal processing, including the effects of low pH, are poorly understood. To gain insight into the structural transitions required for this essential step in infection, the crystal structures of empty and green fluorescent protein (GFP) gene-packaged adeno-associated virus serotype 8 (AAV8) have been determined at pH values of 6.0, 5.5, and 4.0 and then at pH 7.5 after incubation at pH 4.0, mimicking the conditions encountered during endocytic trafficking. While the capsid viral protein (VP) topologies of all the structures were similar, significant amino acid side chain conformational rearrangements were observed on (i) the interior surface of the capsid under the icosahedral 3-fold axis near ordered nucleic acid density that was lost concomitant with the conformational change as pH was reduced and (ii) the exterior capsid surface close to the icosahedral 2-fold depression. The 3-fold change is consistent with DNA release from an ordering interaction on the inside surface of the capsid at low pH values and suggests transitions that likely trigger the capsid for genome uncoating. The surface change results in disruption of VP-VP interface interactions and a decrease in buried surface area between VP monomers. This disruption points to capsid destabilization which may (i) release VP1 amino acids for its phospholipase A2 function for endosomal escape and nuclear localization signals for nuclear targeting and (ii) trigger genome uncoating.

  16. Optimal serotype compositions for Pneumococcal conjugate vaccination under serotype replacement.

    Nurhonen, Markku; Auranen, Kari

    2014-02-01

    Pneumococcal conjugate vaccination has proved highly effective in eliminating vaccine-type pneumococcal carriage and disease. However, the potential adverse effects of serotype replacement remain a major concern when implementing routine childhood pneumococcal conjugate vaccination programmes. Applying a concise predictive model, we present a ready-to-use quantitative tool to investigate the implications of serotype replacement on the net effectiveness of vaccination against invasive pneumococcal disease (IPD) and to guide in the selection of optimal vaccine serotype compositions. We utilise pre-vaccination data on pneumococcal carriage and IPD and assume partial or complete elimination of vaccine-type carriage, its replacement by non-vaccine-type carriage, and stable case-to-carrier ratios (probability of IPD per carriage episode). The model predicts that the post-vaccination IPD incidences in Finland for currently available vaccine serotype compositions can eventually decrease among the target age group of children replacement through herd effects, the decrease among the older population is predicted to be much less (20-40%). We introduce a sequential algorithm for the search of optimal serotype compositions and assess the robustness of inferences to uncertainties in data and assumptions about carriage and IPD. The optimal serotype composition depends on the age group of interest and some serotypes may be highly beneficial vaccine types in one age category (e.g. 6B in children), while being disadvantageous in another. The net effectiveness will be improved only if the added serotype has a higher case-to-carrier ratio than the average case-to-carrier ratio of the current non-vaccine types and the degree of improvement in effectiveness depends on the carriage incidence of the serotype. The serotype compositions of currently available pneumococcal vaccines are not optimal and the effectiveness of vaccination in the population at large could be improved by including

  17. Genetic characterization of the non-structural protein-3 gene of bluetongue virus serotype-2 isolate from India

    Raghavendra Sumanth Pudupakam

    2017-03-01

    Full Text Available Aim: Sequence analysis and phylogenetic studies based on non-structural protein-3 (NS3 gene are important in understanding the evolution and epidemiology of bluetongue virus (BTV. This study was aimed at characterizing the NS3 gene sequence of Indian BTV serotype-2 (BTV2 to elucidate its genetic relationship to global BTV isolates. Materials and Methods: The NS3 gene of BTV2 was amplified from infected BHK-21 cell cultures, cloned and subjected to sequence analysis. The generated NS3 gene sequence was compared with the corresponding sequences of different BTV serotypes across the world, and a phylogenetic relationship was established. Results: The NS3 gene of BTV2 showed moderate levels of variability in comparison to different BTV serotypes, with nucleotide sequence identities ranging from 81% to 98%. The region showed high sequence homology of 93-99% at amino acid level with various BTV serotypes. The PPXY/PTAP late domain motifs, glycosylation sites, hydrophobic domains, and the amino acid residues critical for virus-host interactions were conserved in NS3 protein. Phylogenetic analysis revealed that BTV isolates segregate into four topotypes and that the Indian BTV2 in subclade IA is closely related to Asian and Australian origin strains. Conclusion: Analysis of the NS3 gene indicated that Indian BTV2 isolate is closely related to strains from Asia and Australia, suggesting a common origin of infection. Although the pattern of evolution of BTV2 isolate is different from other global isolates, the deduced amino acid sequence of NS3 protein demonstrated high molecular stability.

  18. Prediction and characterization of novel epitopes of serotype A foot-and-mouth disease viruses circulating in East Africa using site-directed mutagenesis

    Bari, Fufa Dawo; Parida, Satya; Asfor, Amin S.; Haydon, Daniel T.; Reeve, Richard; Paton, David J.

    2015-01-01

    Epitopes on the surface of the foot-and-mouth disease virus (FMDV) capsid have been identified by monoclonal antibody (mAb) escape mutant studies leading to the designation of four antigenic sites in serotype A FMDV. Previous work focused on viruses isolated mainly from Asia, Europe and Latin America. In this study we report on the prediction of epitopes in African serotype A FMDVs and testing of selected epitopes using reverse genetics. Twenty-four capsid amino acid residues were predicted to be of antigenic significance by analysing the capsid sequences (n = 56) using in silico methods, and six residues by correlating capsid sequence with serum–virus neutralization data. The predicted residues were distributed on the surface-exposed capsid regions, VP1–VP3. The significance of residue changes at eight of the predicted epitopes was tested by site-directed mutagenesis using a cDNA clone resulting in the generation of 12 mutant viruses involving seven sites. The effect of the amino acid substitutions on the antigenic nature of the virus was assessed by virus neutralization (VN) test. Mutations at four different positions, namely VP1-43, VP1-45, VP2-191 and VP3-132, led to significant reduction in VN titre (P value = 0.05, 0.05, 0.001 and 0.05, respectively). This is the first time, to our knowledge, that the antigenic regions encompassing amino acids VP1-43 to -45 (equivalent to antigenic site 3 in serotype O), VP2-191 and VP3-132 have been predicted as epitopes and evaluated serologically for serotype A FMDVs. This identifies novel capsid epitopes of recently circulating serotype A FMDVs in East Africa. PMID:25614587

  19. Genomic Epidemiology of Salmonella enterica Serotype Enteritidis based on Population Structure of Prevalent Lineages

    Deng, Xiangyu; Desai, Prerak T.; den Bakker, Henk C.

    2014-01-01

    serotype Nitra strains. Single-nucleotide polymorphisms were filtered to identify 4,887 reliable loci that distinguished all isolates from each other. Our whole-genome single-nucleotide polymorphism typing approach was robust for S. enterica Enteritidis subtyping with combined data for different strains...

  20. Epidemiology and population structure of serotypes 1, 5 and 7f carried by children in Portugal from 1996-2010 before introduction of the 10-valent and 13-valent pneumococcal conjugate vaccines.

    Sónia T Almeida

    Full Text Available Among the over 90 serotypes of Streptococcus pneumoniae described, serotypes 1, 5, and 7F account for a significant proportion of invasive disease worldwide and are now covered by the most recent 10- and 13-valent pneumococcal conjugate vaccines (PCVs. The epidemiology of these serotypes in carriage remains poorly studied because they are rarely detected. We aimed to gain insights into the epidemiology and population structure of serotypes 1, 5 and 7F carried by children in Portugal before PCV10 and PCV13 became widely used. Isolates obtained in cross-sectional studies carried out over a 15-year period (1996-2010 were retrospectively pooled and characterized. Of 5,123 pneumococci obtained, 70 were associated with serotypes 1 (n = 21, 5 (n = 7, and 7F (n = 42. The highest prevalence detected was 3.3% for serotype 1 in 2006, 1% for serotype 5 in 2009, and 3.3% for serotype 7F in 2006; Serotype 1 was associated with PMEN international clones Sweden(1-28(ST306 and Sweden(1-40(ST304; serotype 5 was associated with Colombia(5-19(ST289; and serotype 7F was associated with Netherlands(7F-39(ST191. All these isolates were fully susceptible. Most carriers of serotypes 1 (86%, 5 (86%, and 7F (91% were older than two years but a significant association with older age was only observed for serotype 7F (p = 0.006. Evidence for cross-transmission was obtained. In conclusion, we were able to detect and characterize the rarely carried serotypes 1, 5, and 7F among healthy children in Portugal. These data will constitute an important baseline for upcoming surveillance studies aimed to establish the impact of novel PCVs targeting these serotypes in carriage.

  1. Evolutionary Analysis of Structural Protein Gene VP1 of Foot-and-Mouth Disease Virus Serotype Asia 1

    Zhang, Qingxun; Liu, Xinsheng; Fang, Yuzhen; Pan, Li; Lv, Jianliang; Zhang, Zhongwang; Zhou, Peng; Ding, Yaozhong; Chen, Haotai; Shao, Junjun; Zhao, Furong; Lin, Tong; Chang, Huiyun; Zhang, Jie; Wang, Yonglu; Zhang, Yongguang

    2015-01-01

    Foot-and-mouth disease virus (FMDV) serotype Asia 1 was mostly endemic in Asia and then was responsible for economically important viral disease of cloven-hoofed animals, but the study on its selection and evolutionary process is comparatively rare. In this study, we characterized 377 isolates from Asia collected up until 2012, including four vaccine strains. Maximum likelihood analysis suggested that the strains circulating in Asia were classified into 8 different groups (groups I–VIII) or were unclassified (viruses collected before 2000). On the basis of divergence time analyses, we infer that the TMRCA of Asia 1 virus existed approximately 86.29 years ago. The result suggested that the virus had a high mutation rate (5.745 × 10−3 substitutions/site/year) in comparison to the other serotypes of FMDV VP1 gene. Furthermore, the structural protein VP1 was under lower selection pressure and the positive selection occurred at many sites, and four codons (positions 141, 146, 151, and 169) were located in known critical antigenic residues. The remaining sites were not located in known functional regions and were moderately conserved, and the reason for supporting all sites under positive selection remains to be elucidated because the power of these analyses was largely unknown. PMID:25793223

  2. Polypeptide structure and encoding location of the adenovirus serotype 2 late, nonstructural 33K protein

    Oosterom-Dragon, E.A.; Anderson, C.W.

    1983-01-01

    Radiochemical microsequence analysis of selected tryptic peptides of the adenovirus type 2 33K nonstructural protein has revealed the precise region of the genomic nucleotide sequence that encodes this protein. The initiation codon for the 33K protein lies 606 nucleotides to the right of the EcoRI restriction site at 70.7 map units and 281 nucleotides to the left of the postulated carboxyterminal codon of the adenovirus 100K protein. The coding regions for these two proteins thus overlap; however, the 33K protein is derived from the +1 frame with respect to the postulated 100K reading frame. Our results contradict an earlier published report suggesting that these two proteins share extensive amino acid sequence homology. The published nucleotide sequence of the Ad2 EcoRI-F fragment (70.7 to 75.9 map units) cannot accomodate in a single reading frame the peptide sequences of the 33K protein that we have determined. Sequence analysis of DNA fragments derived from virus has confirmed the published nucleotide sequence in all critical regions with respect to the coding region for the 33K protein. Consequently, our data are only consistent with the existence of an mRNA splice within the coding for 33K. Consensus donor and acceptor splice sequences have been located that would predict the removal of 202 nucleotides from the transcripts for the 33K protein. Removal of these nucleotides would explain the structure of a peptide that cannot otherwise be directly encoded by the EcoRI-F fragment. Identification of the precise splice points by peptide sequencing has permitted a prediction of the complete amino acid sequence for the 33K protein

  3. Structural insight into exosite binding and discovery of novel exosite inhibitors of botulinum neurotoxin serotype A through in silico screening

    Hu, Xin; Legler, Patricia M.; Southall, Noel; Maloney, David J.; Simeonov, Anton; Jadhav, Ajit

    2014-07-01

    Botulinum neurotoxin serotype A (BoNT/A) is the most lethal toxin among the Tier 1 Select Agents. Development of potent and selective small molecule inhibitors against BoNT/A zinc metalloprotease remains a challenging problem due to its exceptionally large substrate binding surface and conformational plasticity. The exosites of the catalytic domain of BoNT/A are intriguing alternative sites for small molecule intervention, but their suitability for inhibitor design remains largely unexplored. In this study, we employed two recently identified exosite inhibitors, D-chicoric acid and lomofungin, to probe the structural features of the exosites and molecular mechanisms of synergistic inhibition. The results showed that D-chicoric acid favors binding at the α-exosite, whereas lomofungin preferentially binds at the β-exosite by mimicking the substrate β-sheet binding interaction. Molecular dynamics simulations and binding interaction analysis of the exosite inhibitors with BoNT/A revealed key elements and hotspots that likely contribute to the inhibitor binding and synergistic inhibition. Finally, we performed database virtual screening for novel inhibitors of BoNT/A targeting the exosites. Hits C1 and C2 showed non-competitive inhibition and likely target the α- and β-exosites, respectively. The identified exosite inhibitors may provide novel candidates for structure-based development of therapeutics against BoNT/A intoxication.

  4. Structural conservation, variability, and immunogenicity of the T6 backbone pilin of serotype M6 Streptococcus pyogenes.

    Young, Paul G; Moreland, Nicole J; Loh, Jacelyn M; Bell, Anita; Atatoa Carr, Polly; Proft, Thomas; Baker, Edward N

    2014-07-01

    Group A streptococcus (GAS; Streptococcus pyogenes) is a Gram-positive human pathogen that causes a broad range of diseases ranging from acute pharyngitis to the poststreptococcal sequelae of acute rheumatic fever. GAS pili are highly diverse, long protein polymers that extend from the cell surface. They have multiple roles in infection and are promising candidates for vaccine development. This study describes the structure of the T6 backbone pilin (BP; Lancefield T-antigen) from the important M6 serotype. The structure reveals a modular arrangement of three tandem immunoglobulin-like domains, two with internal isopeptide bonds. The T6 pilin lysine, essential for polymerization, is located in a novel VAKS motif that is structurally homologous to the canonical YPKN pilin lysine in other three- and four-domain Gram-positive pilins. The T6 structure also highlights a conserved pilin core whose surface is decorated with highly variable loops and extensions. Comparison to other Gram-positive BPs shows that many of the largest variable extensions are found in conserved locations. Studies with sera from patients diagnosed with GAS-associated acute rheumatic fever showed that each of the three T6 domains, and the largest of the variable extensions (V8), are targeted by IgG during infection in vivo. Although the GAS BP show large variations in size and sequence, the modular nature of the pilus proteins revealed by the T6 structure may aid the future design of a pilus-based vaccine. Copyright © 2014, American Society for Microbiology. All Rights Reserved.

  5. Prediction of molecular crystal structures

    Beyer, Theresa

    2001-01-01

    The ab initio prediction of molecular crystal structures is a scientific challenge. Reliability of first-principle prediction calculations would show a fundamental understanding of crystallisation. Crystal structure prediction is also of considerable practical importance as different crystalline arrangements of the same molecule in the solid state (polymorphs)are likely to have different physical properties. A method of crystal structure prediction based on lattice energy minimisation has been developed in this work. The choice of the intermolecular potential and of the molecular model is crucial for the results of such studies and both of these criteria have been investigated. An empirical atom-atom repulsion-dispersion potential for carboxylic acids has been derived and applied in a crystal structure prediction study of formic, benzoic and the polymorphic system of tetrolic acid. As many experimental crystal structure determinations at different temperatures are available for the polymorphic system of paracetamol (acetaminophen), the influence of the variations of the molecular model on the crystal structure lattice energy minima, has also been studied. The general problem of prediction methods based on the assumption that the experimental thermodynamically stable polymorph corresponds to the global lattice energy minimum, is that more hypothetical low lattice energy structures are found within a few kJ mol -1 of the global minimum than are likely to be experimentally observed polymorphs. This is illustrated by the results for molecule I, 3-oxabicyclo(3.2.0)hepta-1,4-diene, studied for the first international blindtest for small organic crystal structures organised by the Cambridge Crystallographic Data Centre (CCDC) in May 1999. To reduce the number of predicted polymorphs, additional factors to thermodynamic criteria have to be considered. Therefore the elastic constants and vapour growth morphologies have been calculated for the lowest lattice energy

  6. Prediction of molecular crystal structures

    Beyer, Theresa

    2001-07-01

    The ab initio prediction of molecular crystal structures is a scientific challenge. Reliability of first-principle prediction calculations would show a fundamental understanding of crystallisation. Crystal structure prediction is also of considerable practical importance as different crystalline arrangements of the same molecule in the solid state (polymorphs)are likely to have different physical properties. A method of crystal structure prediction based on lattice energy minimisation has been developed in this work. The choice of the intermolecular potential and of the molecular model is crucial for the results of such studies and both of these criteria have been investigated. An empirical atom-atom repulsion-dispersion potential for carboxylic acids has been derived and applied in a crystal structure prediction study of formic, benzoic and the polymorphic system of tetrolic acid. As many experimental crystal structure determinations at different temperatures are available for the polymorphic system of paracetamol (acetaminophen), the influence of the variations of the molecular model on the crystal structure lattice energy minima, has also been studied. The general problem of prediction methods based on the assumption that the experimental thermodynamically stable polymorph corresponds to the global lattice energy minimum, is that more hypothetical low lattice energy structures are found within a few kJ mol{sup -1} of the global minimum than are likely to be experimentally observed polymorphs. This is illustrated by the results for molecule I, 3-oxabicyclo(3.2.0)hepta-1,4-diene, studied for the first international blindtest for small organic crystal structures organised by the Cambridge Crystallographic Data Centre (CCDC) in May 1999. To reduce the number of predicted polymorphs, additional factors to thermodynamic criteria have to be considered. Therefore the elastic constants and vapour growth morphologies have been calculated for the lowest lattice energy

  7. Algorithms for Protein Structure Prediction

    Paluszewski, Martin

    -trace. Here we present three different approaches for reconstruction of C-traces from predictable measures. In our first approach [63, 62], the C-trace is positioned on a lattice and a tabu-search algorithm is applied to find minimum energy structures. The energy function is based on half-sphere-exposure (HSE......) is more robust than standard Monte Carlo search. In the second approach for reconstruction of C-traces, an exact branch and bound algorithm has been developed [67, 65]. The model is discrete and makes use of secondary structure predictions, HSE, CN and radius of gyration. We show how to compute good lower...... bounds for partial structures very fast. Using these lower bounds, we are able to find global minimum structures in a huge conformational space in reasonable time. We show that many of these global minimum structures are of good quality compared to the native structure. Our branch and bound algorithm...

  8. Structural studies of some bacterial capsular polysaccharides from the family enterobacteriaceae: Klebsiella serotypes K79 and K35 and Escherichia coli serotype K44

    Lim, A.V.S.

    1986-01-01

    The techniques of sugar analysis, methylation, chromic acid oxidation, deamination, base-catalyzed uronic acid degradation, Smith degradation and partial hydrolysis were used in the structural elucidation of bacterial capsular polysaccharides. Methods such as gas-liquid chromatography, gas chromatography-mass spectrometry, gel permeation, ion-exchange and paper-chromatography were used to isolate and characterize oligosaccharides obtained from degradative procedures. N.m.r. spectroscopy (/sup 1/H and /sup 13/C) was widely used in the characterization of the polysaccharides and of derived poly- and oligo-saccharides. In a few instances, n.m.r. spectroscopy and mass spectrometry were used to delineate the sequence of the sugars in the structure of the poly- and oligo-saccharides. The bacteriophage-induced depolymerizations of the capsular polysaccharides of Klebsiella K79 and E. coli K44 are also reported in this thesis. The sum of these experiments demonstrated that the endoglycanase associated with Klebsiella Phi 79 had ..beta..-galactosidase activity. The endoglycanase from E. coli Phi 44 exhibited ..beta..-N-acetyl-galactosaminidase activity which is novel, in that it is the first reported action of this nature in the bacteriophages isolated for the species E. coli.

  9. Neural Networks for protein Structure Prediction

    Bohr, Henrik

    1998-01-01

    This is a review about neural network applications in bioinformatics. Especially the applications to protein structure prediction, e.g. prediction of secondary structures, prediction of surface structure, fold class recognition and prediction of the 3-dimensional structure of protein backbones...

  10. Structural features of NS3 of Dengue virus serotypes 2 and 4 in solution and insight into RNA binding and the inhibitory role of quercetin.

    Pan, Ankita; Saw, Wuan Geok; Subramanian Manimekalai, Malathy Sony; Grüber, Ardina; Joon, Shin; Matsui, Tsutomu; Weiss, Thomas M; Grüber, Gerhard

    2017-05-01

    Dengue virus (DENV), which has four serotypes (DENV-1 to DENV-4), is the causative agent of the viral infection dengue. DENV nonstructural protein 3 (NS3) comprises a serine protease domain and an RNA helicase domain which has nucleotide triphosphatase activities that are essential for RNA replication and viral assembly. Here, solution X-ray scattering was used to provide insight into the overall structure and flexibility of the entire NS3 and its recombinant helicase and protease domains for Dengue virus serotypes 2 and 4 in solution. The DENV-2 and DENV-4 NS3 forms are elongated and flexible in solution. The importance of the linker residues in flexibility and domain-domain arrangement was shown by the compactness of the individual protease and helicase domains. Swapping of the 174 PPAVP 179 linker stretch of the related Hepatitis C virus (HCV) NS3 into DENV-2 NS3 did not alter the elongated shape of the engineered mutant. Conformational alterations owing to RNA binding are described in the protease domain, which undergoes substantial conformational alterations that are required for the optimal catalysis of bound RNA. Finally, the effects of ATPase inhibitors on the enzymatically active DENV-2 and DENV-4 NS3 and the individual helicases are presented, and insight into the allosteric effect of the inhibitor quercetin is provided.

  11. Small RNA sequence analysis of adenovirus VA RNA-derived miRNAs reveals an unexpected serotype-specific difference in structure and abundance.

    Wael Kamel

    Full Text Available Human adenoviruses (HAds encode for one or two highly abundant virus-associated RNAs, designated VA RNAI and VA RNAII, which fold into stable hairpin structures resembling miRNA precursors. Here we show that the terminal stem of the VA RNAs originating from Ad4, Ad5, Ad11 and Ad37, all undergo Dicer dependent processing into virus-specific miRNAs (so-called mivaRNAs. We further show that the mivaRNA duplex is subjected to a highly asymmetric RISC loading with the 3'-strand from all VA RNAs being the favored strand, except for the Ad37 VA RNAII, where the 5'-mivaRNAII strand was preferentially assembled into RISC. Although the mivaRNA seed sequences are not fully conserved between the HAds a bioinformatics prediction approach suggests that a large fraction of the VA RNAII-, but not the VA RNAI-derived mivaRNAs still are able to target the same cellular genes. Using small RNA deep sequencing we demonstrate that the Dicer processing event in the terminal stem of the VA RNAs is not unique and generates 3'-mivaRNAs with a slight variation of the position of the 5' terminal nucleotide in the RISC loaded guide strand. Also, we show that all analyzed VA RNAs, except Ad37 VA RNAI and Ad5 VA RNAII, utilize an alternative upstream A start site in addition to the classical +1 G start site. Further, the 5'-mivaRNAs with an A start appears to be preferentially incorporated into RISC. Although the majority of mivaRNA research has been done using Ad5 as the model system our analysis demonstrates that the mivaRNAs expressed in Ad11- and Ad37-infected cells are the most abundant mivaRNAs associated with Ago2-containing RISC. Collectively, our results show an unexpected variability in Dicer processing of the VA RNAs and a serotype-specific loading of mivaRNAs into Ago2-based RISC.

  12. Structural insight and flexible features of NS5 proteins from all four serotypes of Dengue virus in solution

    Saw, Wuan Geok; Tria, Giancarlo; Grüber, Ardina; Subramanian Manimekalai, Malathy Sony; Zhao, Yongqian; Chandramohan, Arun; Srinivasan Anand, Ganesh; Matsui, Tsutomu; Weiss, Thomas M.; Vasudevan, Subhash G.; Grüber, Gerhard

    2015-10-31

    Infection by the four serotypes ofDengue virus(DENV-1 to DENV-4) causes an important arthropod-borne viral disease in humans. The multifunctional DENV nonstructural protein 5 (NS5) is essential for capping and replication of the viral RNA and harbours a methyltransferase (MTase) domain and an RNA-dependent RNA polymerase (RdRp) domain. In this study, insights into the overall structure and flexibility of the entire NS5 of all fourDengue virusserotypes in solution are presented for the first time. The solution models derived revealed an arrangement of the full-length NS5 (NS5FL) proteins with the MTase domain positioned at the top of the RdRP domain. The DENV-1 to DENV-4 NS5 forms are elongated and flexible in solution, with DENV-4 NS5 being more compact relative to NS5 from DENV-1, DENV-2 and DENV-3. Solution studies of the individual MTase and RdRp domains show the compactness of the RdRp domain as well as the contribution of the MTase domain and the ten-residue linker region to the flexibility of the entire NS5. Swapping the ten-residue linker between DENV-4 NS5FL and DENV-3 NS5FL demonstrated its importance in MTase–RdRp communication and in concerted interaction with viral and host proteins, as probed by amide hydrogen/deuterium mass spectrometry. Conformational alterations owing to RNA binding are presented.

  13. Nucleic acid secondary structure prediction and display.

    Stüber, K

    1986-01-01

    A set of programs has been developed for the prediction and display of nucleic acid secondary structures. Information from experimental data can be used to restrict or enforce secondary structural elements. The predictions can be displayed either on normal line printers or on graphic devices like plotters or graphic terminals.

  14. Applications of contact predictions to structural biology

    Felix Simkovic

    2017-05-01

    Full Text Available Evolutionary pressure on residue interactions, intramolecular or intermolecular, that are important for protein structure or function can lead to covariance between the two positions. Recent methodological advances allow much more accurate contact predictions to be derived from this evolutionary covariance signal. The practical application of contact predictions has largely been confined to structural bioinformatics, yet, as this work seeks to demonstrate, the data can be of enormous value to the structural biologist working in X-ray crystallography, cryo-EM or NMR. Integrative structural bioinformatics packages such as Rosetta can already exploit contact predictions in a variety of ways. The contribution of contact predictions begins at construct design, where structural domains may need to be expressed separately and contact predictions can help to predict domain limits. Structure solution by molecular replacement (MR benefits from contact predictions in diverse ways: in difficult cases, more accurate search models can be constructed using ab initio modelling when predictions are available, while intermolecular contact predictions can allow the construction of larger, oligomeric search models. Furthermore, MR using supersecondary motifs or large-scale screens against the PDB can exploit information, such as the parallel or antiparallel nature of any β-strand pairing in the target, that can be inferred from contact predictions. Contact information will be particularly valuable in the determination of lower resolution structures by helping to assign sequence register. In large complexes, contact information may allow the identity of a protein responsible for a certain region of density to be determined and then assist in the orientation of an available model within that density. In NMR, predicted contacts can provide long-range information to extend the upper size limit of the technique in a manner analogous but complementary to experimental

  15. An atypical biotype I Actinobacillus pleuropneumoniae serotype 13 is present in North America

    Perry, Malcolm B.; Angen, Øystein; MacLean, Leann L.

    2012-01-01

    Atypical Actinobacillus pleuropneumoniae serotype 13 strains present in North America are described here for the first time. Different from serotype 13 strains described in Europe, North America strains are biotype I and antigenically related to both, serotypes 13 and 10. Chemical and structural...

  16. Fourth human parechovirus serotype

    Benschop, Kimberley S. M.; Schinkel, Janke; Luken, Manon E.; van den Broek, Peter J. M.; Beersma, Matthias F. C.; Menelik, Negassi; van Eijk, Hetty W. M.; Zaaijer, Hans L.; Vandenbroucke-Grauls, Christina M. J. E.; Beld, Marcel G. H. M.; Wolthers, Katja C.

    2006-01-01

    We identified a novel human parechovirus (HPeV) type (K251176-02) from a neonate with fever. Analysis of the complete genome showed K251176-02 to be a new HPeV genotype. Since K251176-02 could not be neutralized with antibodies against known HPeV serotypes 1-3, it should be classified as a fourth

  17. European bluetongue serotype 8

    Drolet, Barbara S.; Reister-Hendricks, Lindsey M.; Podell, Brendan K.; Breitenbach, Jonathan E.; Mcvey, D.S.; Rijn, van Piet A.; Bowen, Richard A.

    2016-01-01

    Bluetongue virus (BTV) is an orbivirus transmitted by biting midges (Culicoides spp.) that can result in moderate to high morbidity and mortality primarily in sheep and white-tailed deer. Although only 5 serotypes of BTV are considered endemic to the United States, as many as 11 incursive

  18. Evaluation and use of in-silico structure-based epitope prediction with foot-and-mouth disease virus.

    Daryl W Borley

    Full Text Available Understanding virus antigenicity is of fundamental importance for the development of better, more cross-reactive vaccines. However, as far as we are aware, no systematic work has yet been conducted using the 3D structure of a virus to identify novel epitopes. Therefore we have extended several existing structural prediction algorithms to build a method for identifying epitopes on the appropriate outer surface of intact virus capsids (which are structurally different from globular proteins in both shape and arrangement of multiple repeated elements and applied it here as a proof of principle concept to the capsid of foot-and-mouth disease virus (FMDV. We have analysed how reliably several freely available structure-based B cell epitope prediction programs can identify already known viral epitopes of FMDV in the context of the viral capsid. To do this we constructed a simple objective metric to measure the sensitivity and discrimination of such algorithms. After optimising the parameters for five methods using an independent training set we used this measure to evaluate the methods. Individually any one algorithm performed rather poorly (three performing better than the other two suggesting that there may be value in developing virus-specific software. Taking a very conservative approach requiring a consensus between all three top methods predicts a number of previously described antigenic residues as potential epitopes on more than one serotype of FMDV, consistent with experimental results. The consensus results identified novel residues as potential epitopes on more than one serotype. These include residues 190-192 of VP2 (not previously determined to be antigenic, residues 69-71 and 193-197 of VP3 spanning the pentamer-pentamer interface, and another region incorporating residues 83, 84 and 169-174 of VP1 (all only previously experimentally defined on serotype A. The computer programs needed to create a semi-automated procedure for carrying out

  19. Predicting RNA Structure Using Mutual Information

    Freyhult, E.; Moulton, V.; Gardner, P. P.

    2005-01-01

    , to display and predict conserved RNA secondary structure (including pseudoknots) from an alignment. Results: We show that MIfold can be used to predict simple pseudoknots, and that the performance can be adjusted to make it either more sensitive or more selective. We also demonstrate that the overall...... package. Conclusion: MIfold provides a useful supplementary tool to programs such as RNA Structure Logo, RNAalifold and COVE, and should be useful for automatically generating structural predictions for databases such as Rfam. Availability: MIfold is freely available from http......Background: With the ever-increasing number of sequenced RNAs and the establishment of new RNA databases, such as the Comparative RNA Web Site and Rfam, there is a growing need for accurately and automatically predicting RNA structures from multiple alignments. Since RNA secondary structure...

  20. Computational predictions of zinc oxide hollow structures

    Tuoc, Vu Ngoc; Huan, Tran Doan; Thao, Nguyen Thi

    2018-03-01

    Nanoporous materials are emerging as potential candidates for a wide range of technological applications in environment, electronic, and optoelectronics, to name just a few. Within this active research area, experimental works are predominant while theoretical/computational prediction and study of these materials face some intrinsic challenges, one of them is how to predict porous structures. We propose a computationally and technically feasible approach for predicting zinc oxide structures with hollows at the nano scale. The designed zinc oxide hollow structures are studied with computations using the density functional tight binding and conventional density functional theory methods, revealing a variety of promising mechanical and electronic properties, which can potentially find future realistic applications.

  1. Protein secondary structure: category assignment and predictability

    Andersen, Claus A.; Bohr, Henrik; Brunak, Søren

    2001-01-01

    In the last decade, the prediction of protein secondary structure has been optimized using essentially one and the same assignment scheme known as DSSP. We present here a different scheme, which is more predictable. This scheme predicts directly the hydrogen bonds, which stabilize the secondary......-forward neural network with one hidden layer on a data set identical to the one used in earlier work....

  2. Stochastic Extreme Load Predictions for Marine Structures

    Jensen, Jørgen Juncher

    1999-01-01

    Development of rational design criteria for marine structures requires reliable estimates for the maximum wave-induced loads the structure may encounter during its operational lifetime. The paper discusses various methods for extreme value predictions taking into account the non-linearity of the ......Development of rational design criteria for marine structures requires reliable estimates for the maximum wave-induced loads the structure may encounter during its operational lifetime. The paper discusses various methods for extreme value predictions taking into account the non......-linearity of the waves and the response. As example the wave-induced bending moment in the ship hull girder is considered....

  3. A Kernel for Protein Secondary Structure Prediction

    Guermeur , Yann; Lifchitz , Alain; Vert , Régis

    2004-01-01

    http://mitpress.mit.edu/catalog/item/default.asp?ttype=2&tid=10338&mode=toc; International audience; Multi-class support vector machines have already proved efficient in protein secondary structure prediction as ensemble methods, to combine the outputs of sets of classifiers based on different principles. In this chapter, their implementation as basic prediction methods, processing the primary structure or the profile of multiple alignments, is investigated. A kernel devoted to the task is in...

  4. Characteristics and Prediction of RNA Structure

    Hengwu Li

    2014-01-01

    Full Text Available RNA secondary structures with pseudoknots are often predicted by minimizing free energy, which is NP-hard. Most RNAs fold during transcription from DNA into RNA through a hierarchical pathway wherein secondary structures form prior to tertiary structures. Real RNA secondary structures often have local instead of global optimization because of kinetic reasons. The performance of RNA structure prediction may be improved by considering dynamic and hierarchical folding mechanisms. This study is a novel report on RNA folding that accords with the golden mean characteristic based on the statistical analysis of the real RNA secondary structures of all 480 sequences from RNA STRAND, which are validated by NMR or X-ray. The length ratios of domains in these sequences are approximately 0.382L, 0.5L, 0.618L, and L, where L is the sequence length. These points are just the important golden sections of sequence. With this characteristic, an algorithm is designed to predict RNA hierarchical structures and simulate RNA folding by dynamically folding RNA structures according to the above golden section points. The sensitivity and number of predicted pseudoknots of our algorithm are better than those of the Mfold, HotKnots, McQfold, ProbKnot, and Lhw-Zhu algorithms. Experimental results reflect the folding rules of RNA from a new angle that is close to natural folding.

  5. Facilitating RNA structure prediction with microarrays.

    Kierzek, Elzbieta; Kierzek, Ryszard; Turner, Douglas H; Catrina, Irina E

    2006-01-17

    Determining RNA secondary structure is important for understanding structure-function relationships and identifying potential drug targets. This paper reports the use of microarrays with heptamer 2'-O-methyl oligoribonucleotides to probe the secondary structure of an RNA and thereby improve the prediction of that secondary structure. When experimental constraints from hybridization results are added to a free-energy minimization algorithm, the prediction of the secondary structure of Escherichia coli 5S rRNA improves from 27 to 92% of the known canonical base pairs. Optimization of buffer conditions for hybridization and application of 2'-O-methyl-2-thiouridine to enhance binding and improve discrimination between AU and GU pairs are also described. The results suggest that probing RNA with oligonucleotide microarrays can facilitate determination of secondary structure.

  6. Spatial pattern of foot-and-mouth disease virus serotypes in North Central Nigeria

    Yiltawe Simwal Wungak

    2017-04-01

    Full Text Available Aim: This study aimed to determine the foot-and-mouth disease virus (FMDV serotypes circulating, the prevalence of FMDV serotypes, and the spatial distribution of FMDV among sedentary and pastoral cattle herds in the North-Central Nigeria. Materials and Methods: A cross-sectional study was undertaken, during which a total of 155 sera that tested positive for foot-and-mouth disease (FMD 3ABC non-structural protein antibodies were selected and screened for FMD structural protein serotypes, A, O, SAT 1, and SAT 2 using a solid-phase competitive enzyme-linked immunosorbent assay (ELISA. Epithelial tissue specimens were collected during outbreak investigations which were tested for FMD using an antigen capture ELISA for serotype A, O, SAT 1, and SAT 2. Results: An overall serotype-specific prevalence of 79.35 (95% confidence interval [CI]: 72.4-85.18 was recorded for serotype O, 65.2% (95% CI: 57.41-72.3 for serotype A, 52.9% (95% CI: 45.03-60.67 for SAT-2, and 33.55% (95% CI: 26.45-41.26 for SAT-1. Evidence of exposure to multiple FMDV serotypes showed that 12.26% of the sera samples had antibodies against four serotypes circulating, 30.97% had antibodies against three serotypes circulating, 22.58% had antibodies against two serotypes, and 17% showed exposure to only one serotype. Clinical specimens (epithelial tissue collected during outbreak investigations showed that serotype O has the highest proportion of 50% with serotype A - 25%; SAT 2 - 20.8%; and SAT 1 - 4.1%. Conclusion: The study detected diffuse and co-circulation of serotypes A, O, SAT1, and SAT2 within the study area, and hence the need for the appropriately matched multivalent vaccine is strongly advocated for FMD control in Nigeria.

  7. Development and characterization of serotype-specific monoclonal antibodies against the dengue virus-4 (DENV-4) non-structural protein (NS1).

    Gelanew, Tesfaye; Hunsperger, Elizabeth

    2018-02-06

    Dengue, caused by one of the four serologically distinct dengue viruses (DENV-1 to - 4), is a mosquito-borne disease of serious global health significance. Reliable and cost-effective diagnostic tests, along with effective vaccines and vector-control strategies, are highly required to reduce dengue morbidity and mortality. Evaluation studies revealed that many commercially available NS1 antigen (Ag) tests have limited sensitivity to DENV-4 serotype compared to the other three serotypes. These studies indicated the need for development of new NS1 Ag detection test with improved sensitivity to DENV-4. An NS1 capture enzyme linked immunoassay (ELISA) specific to DENV-4 may improve the detection of DENV-4 cases worldwide. In addition, a serotype-specific NS1 Ag test identifies both DENV and the infecting serotype. In this study, we used a small-ubiquitin-like modifier (SUMO*) cloning vector to express a SUMO*-DENV-4 rNS1 fusion protein to develop NS1 DENV-4 specific monoclonal antibodies (MAbs). These newly developed MAbs were then optimized for use in an anti-NS1 DENV-4 capture ELISA. The serotype specificity and sensitivity of this ELISA was evaluated using (i) supernatants from DENV (1-4)-infected Vero cell cultures, (ii) rNS1s from all the four DENV (1-4) and, (iii) rNS1s of related flaviviruses (yellow fever virus; YFV and West Nile virus; WNV). From the evaluation studies of the newly developed MAbs, we identified three DENV-4 specific anti-NS1 MAbs: 3H7A9, 8A6F2 and 6D4B10. Two of these MAbs were optimal for use in a DENV-4 serotype-specific NS1 capture ELISA: MAb 8A6F2 as the capture antibody and 6D4B10 as a detection antibody. This ELISA was sensitive and specific to DENV-4 with no cross-reactivity to other three DENV (1-3) serotypes and other heterologous flaviviruses. Taken together these data indicated that our MAbs are useful reagents for the development of DENV-4 immunodiagnostic tests.

  8. A matrix-focused structure-activity and binding site flexibility study of quinolinol inhibitors of botulinum neurotoxin serotype A.

    Harrell, William A; Vieira, Rebecca C; Ensel, Susan M; Montgomery, Vicki; Guernieri, Rebecca; Eccard, Vanessa S; Campbell, Yvette; Roxas-Duncan, Virginia; Cardellina, John H; Webb, Robert P; Smith, Leonard A

    2017-02-01

    Our initial discovery of 8-hydroxyquinoline inhibitors of BoNT/A and separation/testing of enantiomers of one of the more active leads indicated considerable flexibility in the binding site. We designed a limited study to investigate this flexibility and probe structure-activity relationships; utilizing the Betti reaction, a 36 compound matrix of quinolinol BoNT/A LC inhibitors was developed using three 8-hydroxyquinolines, three heteroaromatic amines, and four substituted benzaldehydes. This study has revealed some of the most effective quinolinol-based BoNT/A inhibitors to date, with 7 compounds displaying IC 50 values ⩽1μM and 11 effective at ⩽2μM in an ex vivo assay. Published by Elsevier Ltd.

  9. Protein Structure Prediction by Protein Threading

    Xu, Ying; Liu, Zhijie; Cai, Liming; Xu, Dong

    The seminal work of Bowie, Lüthy, and Eisenberg (Bowie et al., 1991) on "the inverse protein folding problem" laid the foundation of protein structure prediction by protein threading. By using simple measures for fitness of different amino acid types to local structural environments defined in terms of solvent accessibility and protein secondary structure, the authors derived a simple and yet profoundly novel approach to assessing if a protein sequence fits well with a given protein structural fold. Their follow-up work (Elofsson et al., 1996; Fischer and Eisenberg, 1996; Fischer et al., 1996a,b) and the work by Jones, Taylor, and Thornton (Jones et al., 1992) on protein fold recognition led to the development of a new brand of powerful tools for protein structure prediction, which we now term "protein threading." These computational tools have played a key role in extending the utility of all the experimentally solved structures by X-ray crystallography and nuclear magnetic resonance (NMR), providing structural models and functional predictions for many of the proteins encoded in the hundreds of genomes that have been sequenced up to now.

  10. RNA secondary structure prediction using soft computing.

    Ray, Shubhra Sankar; Pal, Sankar K

    2013-01-01

    Prediction of RNA structure is invaluable in creating new drugs and understanding genetic diseases. Several deterministic algorithms and soft computing-based techniques have been developed for more than a decade to determine the structure from a known RNA sequence. Soft computing gained importance with the need to get approximate solutions for RNA sequences by considering the issues related with kinetic effects, cotranscriptional folding, and estimation of certain energy parameters. A brief description of some of the soft computing-based techniques, developed for RNA secondary structure prediction, is presented along with their relevance. The basic concepts of RNA and its different structural elements like helix, bulge, hairpin loop, internal loop, and multiloop are described. These are followed by different methodologies, employing genetic algorithms, artificial neural networks, and fuzzy logic. The role of various metaheuristics, like simulated annealing, particle swarm optimization, ant colony optimization, and tabu search is also discussed. A relative comparison among different techniques, in predicting 12 known RNA secondary structures, is presented, as an example. Future challenging issues are then mentioned.

  11. Predicting Protein Secondary Structure with Markov Models

    Fischer, Paul; Larsen, Simon; Thomsen, Claus

    2004-01-01

    we are considering here, is to predict the secondary structure from the primary one. To this end we train a Markov model on training data and then use it to classify parts of unknown protein sequences as sheets, helices or coils. We show how to exploit the directional information contained...... in the Markov model for this task. Classifications that are purely based on statistical models might not always be biologically meaningful. We present combinatorial methods to incorporate biological background knowledge to enhance the prediction performance....

  12. Antibody structural modeling with prediction of immunoglobulin structure (PIGS)

    Marcatili, Paolo; Olimpieri, Pier Paolo; Chailyan, Anna

    2014-01-01

    Antibodies (or immunoglobulins) are crucial for defending organisms from pathogens, but they are also key players in many medical, diagnostic and biotechnological applications. The ability to predict their structure and the specific residues involved in antigen recognition has several useful...... applications in all of these areas. Over the years, we have developed or collaborated in developing a strategy that enables researchers to predict the 3D structure of antibodies with a very satisfactory accuracy. The strategy is completely automated and extremely fast, requiring only a few minutes (∼10 min...... on average) to build a structural model of an antibody. It is based on the concept of canonical structures of antibody loops and on our understanding of the way light and heavy chains pack together....

  13. Antibody structural modeling with prediction of immunoglobulin structure (PIGS)

    Marcatili, Paolo

    2014-11-06

    © 2014 Nature America, Inc. All rights reserved. Antibodies (or immunoglobulins) are crucial for defending organisms from pathogens, but they are also key players in many medical, diagnostic and biotechnological applications. The ability to predict their structure and the specific residues involved in antigen recognition has several useful applications in all of these areas. Over the years, we have developed or collaborated in developing a strategy that enables researchers to predict the 3D structure of antibodies with a very satisfactory accuracy. The strategy is completely automated and extremely fast, requiring only a few minutes (~10 min on average) to build a structural model of an antibody. It is based on the concept of canonical structures of antibody loops and on our understanding of the way light and heavy chains pack together.

  14. Structure prediction of AlnOm clusters

    Smok, P

    2011-01-01

    Genetic algorithm simulations, using Buckingham potential to represent the anion-anion and cation-anion short-range interactions, were performed in order to predict the equilibrium positions of the Al and O ions in Al n O m clusters. In order to find the equilibrium structures of compounds a self-organizing genetic algorithm were constructed. The calculation were carried out for several clusters Al n O m , with different numbers of aluminium and oxygen atoms.

  15. Evolutionary Structure Prediction of Stoichiometric Compounds

    Zhu, Qiang; Oganov, Artem

    2014-03-01

    In general, for a given ionic compound AmBn\\ at ambient pressure condition, its stoichiometry reflects the valence state ratio between per chemical specie (i.e., the charges for each anion and cation). However, compounds under high pressure exhibit significantly behavior, compared to those analogs at ambient condition. Here we developed a method to solve the crystal structure prediction problem based on the evolutionary algorithms, which can predict both the stable compounds and their crystal structures at arbitrary P,T-conditions, given just the set of chemical elements. By applying this method to a wide range of binary ionic systems (Na-Cl, Mg-O, Xe-O, Cs-F, etc), we discovered a lot of compounds with brand new stoichimetries which can become thermodynamically stable. Further electronic structure analysis on these novel compounds indicates that several factors can contribute to this extraordinary phenomenon: (1) polyatomic anions; (2) free electron localization; (3) emergence of new valence states; (4) metallization. In particular, part of the results have been confirmed by experiment, which warrants that this approach can play a crucial role in new materials design under extreme pressure conditions. This work is funded by DARPA (Grants No. W31P4Q1210008 and W31P4Q1310005), NSF (EAR-1114313 and DMR-1231586).

  16. Alpha complexes in protein structure prediction

    Winter, Pawel; Fonseca, Rasmus

    2015-01-01

    Reducing the computational effort and increasing the accuracy of potential energy functions is of utmost importance in modeling biological systems, for instance in protein structure prediction, docking or design. Evaluating interactions between nonbonded atoms is the bottleneck of such computations......-complexes from scratch for every configuration encountered during the search for the native structure would make this approach hopelessly slow. However, it is argued that kinetic a-complexes can be used to reduce the computational effort of determining the potential energy when "moving" from one configuration...... to a neighboring one. As a consequence, relatively expensive (initial) construction of an a-complex is expected to be compensated by subsequent fast kinetic updates during the search process. Computational results presented in this paper are limited. However, they suggest that the applicability of a...

  17. Protein structure based prediction of catalytic residues.

    Fajardo, J Eduardo; Fiser, Andras

    2013-02-22

    Worldwide structural genomics projects continue to release new protein structures at an unprecedented pace, so far nearly 6000, but only about 60% of these proteins have any sort of functional annotation. We explored a range of features that can be used for the prediction of functional residues given a known three-dimensional structure. These features include various centrality measures of nodes in graphs of interacting residues: closeness, betweenness and page-rank centrality. We also analyzed the distance of functional amino acids to the general center of mass (GCM) of the structure, relative solvent accessibility (RSA), and the use of relative entropy as a measure of sequence conservation. From the selected features, neural networks were trained to identify catalytic residues. We found that using distance to the GCM together with amino acid type provide a good discriminant function, when combined independently with sequence conservation. Using an independent test set of 29 annotated protein structures, the method returned 411 of the initial 9262 residues as the most likely to be involved in function. The output 411 residues contain 70 of the annotated 111 catalytic residues. This represents an approximately 14-fold enrichment of catalytic residues on the entire input set (corresponding to a sensitivity of 63% and a precision of 17%), a performance competitive with that of other state-of-the-art methods. We found that several of the graph based measures utilize the same underlying feature of protein structures, which can be simply and more effectively captured with the distance to GCM definition. This also has the added the advantage of simplicity and easy implementation. Meanwhile sequence conservation remains by far the most influential feature in identifying functional residues. We also found that due the rapid changes in size and composition of sequence databases, conservation calculations must be recalibrated for specific reference databases.

  18. Virulence of six capsular serotypes of Porphyromonas gingivalis in a mouse model

    Laine, ML; van Winkelhoff, AJ

    1998-01-01

    Capsular structures of Porphyromonas gingivalis have been correlated to the pathogenicity in animal models. Six polysaccharide capsular serotypes have recently been described in P. gingivalis. In the present study, virulence of the P. gingivalis strains of the six capsular serotypes was compared

  19. Biofilm formation capacity of Salmonella serotypes at different temperature conditions

    Karen A. Borges

    Full Text Available ABSTRACT: Salmonella spp. are one of the most important agents of foodborne disease in several countries, including Brazil. Poultry-derived products are the most common food products, including meat and eggs, involved in outbreaks of human salmonellosis. Salmonella has the capacity to form biofilms on both biotic and abiotic surfaces. The biofilm formation process depends on an interaction among bacterial cells, the attachment surface and environmental conditions. These structures favor bacterial survival in hostile environments, such as slaughterhouses and food processing plants. Biofilms are also a major problem for public health because breakage of these structures can cause the release of pathogenic microorganisms and, consequently, product contamination. The aim of this study was to determine the biofilm production capacity of Salmonella serotypes at four different temperatures of incubation. Salmonella strains belonging to 11 different serotypes, isolated from poultry or from food involved in salmonellosis outbreaks, were selected for this study. Biofilm formation was investigated under different temperature conditions (37°, 28°, 12° and 3°C using a microtiter plate assay. The tested temperatures are important for the Salmonella life cycle and to the poultry-products process. A total of 92.2% of the analyzed strains were able to produce biofilm on at least one of the tested temperatures. In the testing, 71.6% of the strains produced biofilm at 37°C, 63% at 28°C, 52.3% at 12°C and 39.5% at 3°C, regardless of the serotype. The results indicate that there is a strong influence of temperature on biofilm production, especially for some serotypes, such as S. Enteritidis, S. Hadar and S. Heidelberg. The production of these structures is partially associated with serotype. There were also significant differences within strains of the same serotype, indicating that biofilm production capacity may be strain-dependent.

  20. Structure of nonevaporating sprays - Measurements and predictions

    Solomon, A. S. P.; Shuen, J.-S.; Zhang, Q.-F.; Faeth, G. M.

    1984-01-01

    Structure measurements were completed within the dilute portion of axisymmetric nonevaporating sprays (SMD of 30 and 87 microns) injected into a still air environment, including: mean and fluctuating gas velocities and Reynolds stress using laser-Doppler anemometry; mean liquid fluxes using isokinetic sampling; drop sizes using slide impaction; and drop sizes and velocities using multiflash photography. The new measurements were used to evaluate three representative models of sprays: (1) a locally homogeneous flow (LHF) model, where slip between the phases was neglected; (2) a deterministic separated flow (DSF) model, where slip was considered but effects of drop interaction with turbulent fluctuations were ignored; and (3) a stochastic separated flow (SSF) model, where effects of both interphase slip and turbulent fluctuations were considered using random sampling for turbulence properties in conjunction with random-walk computations for drop motion. The LHF and DSF models were unsatisfactory for present test conditions-both underestimating flow widths and the rate of spread of drops. In contrast, the SSF model provided reasonably accurate predictions, including effects of enhanced spreading rates of sprays due to drop dispersion by turbulence, with all empirical parameters fixed from earlier work.

  1. Prediction of the Secondary Structure of HIV-1 gp120

    Hansen, Jan; Lund, Ole; Nielsen, Jens O.

    1996-01-01

    Fourier transform infrared spectroscopy. The predicted secondary structure of gp120 compared well with data from NMR analysis of synthetic peptides from the V3 loop and the C4 region. As a first step towards modeling the tertiary structure of gp120, the predicted secondary structure may guide the design......The secondary structure of HIV-1 gp120 was predicted using multiple alignment and a combination of two independent methods based on neural network and nearest-neighbor algorithms. The methods agreed on the secondary structure for 80% of the residues in BH10 gp120. Six helices were predicted in HIV...

  2. RNA-SSPT: RNA Secondary Structure Prediction Tools.

    Ahmad, Freed; Mahboob, Shahid; Gulzar, Tahsin; Din, Salah U; Hanif, Tanzeela; Ahmad, Hifza; Afzal, Muhammad

    2013-01-01

    The prediction of RNA structure is useful for understanding evolution for both in silico and in vitro studies. Physical methods like NMR studies to predict RNA secondary structure are expensive and difficult. Computational RNA secondary structure prediction is easier. Comparative sequence analysis provides the best solution. But secondary structure prediction of a single RNA sequence is challenging. RNA-SSPT is a tool that computationally predicts secondary structure of a single RNA sequence. Most of the RNA secondary structure prediction tools do not allow pseudoknots in the structure or are unable to locate them. Nussinov dynamic programming algorithm has been implemented in RNA-SSPT. The current studies shows only energetically most favorable secondary structure is required and the algorithm modification is also available that produces base pairs to lower the total free energy of the secondary structure. For visualization of RNA secondary structure, NAVIEW in C language is used and modified in C# for tool requirement. RNA-SSPT is built in C# using Dot Net 2.0 in Microsoft Visual Studio 2005 Professional edition. The accuracy of RNA-SSPT is tested in terms of Sensitivity and Positive Predicted Value. It is a tool which serves both secondary structure prediction and secondary structure visualization purposes.

  3. A novel plasmid-encoded serotype conversion mechanism through addition of phosphoethanolamine to the O-antigen of Shigella flexneri.

    Qiangzheng Sun

    Full Text Available Shigella flexneri is the major pathogen causing bacillary dysentery in developing countries. S. flexneri is divided into at least 16 serotypes based on the combination of antigenic determinants present in the O-antigen. All the serotypes (except for serotype 6 share a basic O-unit containing one N-acetyl-d-glucosamine and three l-rhamnose residues, whereas differences between the serotypes are conferred by phage-encoded glucosylation and/or O-acetylation. Serotype Xv is a newly emerged and the most prevalent serotype in China, which can agglutinate with both MASF IV-1 and 7,8 monoclonal antibodies. The factor responsible for the presence of MASF IV-1 (E1037 epitope has not yet been identified. In this study, we analyzed the LPS structure of serotype Xv strains and found that the MASF IV-1 positive phenotype depends on an O-antigen modification with a phosphoethanolamine (PEtN group attached at position 3 of one of the rhamnose residues. A plasmid carried gene, lpt-O (LPS phosphoethanolamine transferase for O-antigen, mediates the addition of PEtN for serotype Xv and other MASF IV-1 positive strains. These findings reveal a novel serotype conversion mechanism in S. flexneri and show the necessity of further extension of the serotype classification scheme recognizing the MASF IV-1 positive strains as distinctive subtypes.

  4. A comprehensive comparison of comparative RNA structure prediction approaches

    Gardner, P. P.; Giegerich, R.

    2004-01-01

    -finding and multiple-sequence-alignment algorithms. Results Here we evaluate a number of RNA folding algorithms using reliable RNA data-sets and compare their relative performance. Conclusions We conclude that comparative data can enhance structure prediction but structure-prediction-algorithms vary widely in terms......Background An increasing number of researchers have released novel RNA structure analysis and prediction algorithms for comparative approaches to structure prediction. Yet, independent benchmarking of these algorithms is rarely performed as is now common practice for protein-folding, gene...

  5. PSPP: a protein structure prediction pipeline for computing clusters.

    Michael S Lee

    2009-07-01

    Full Text Available Protein structures are critical for understanding the mechanisms of biological systems and, subsequently, for drug and vaccine design. Unfortunately, protein sequence data exceed structural data by a factor of more than 200 to 1. This gap can be partially filled by using computational protein structure prediction. While structure prediction Web servers are a notable option, they often restrict the number of sequence queries and/or provide a limited set of prediction methodologies. Therefore, we present a standalone protein structure prediction software package suitable for high-throughput structural genomic applications that performs all three classes of prediction methodologies: comparative modeling, fold recognition, and ab initio. This software can be deployed on a user's own high-performance computing cluster.The pipeline consists of a Perl core that integrates more than 20 individual software packages and databases, most of which are freely available from other research laboratories. The query protein sequences are first divided into domains either by domain boundary recognition or Bayesian statistics. The structures of the individual domains are then predicted using template-based modeling or ab initio modeling. The predicted models are scored with a statistical potential and an all-atom force field. The top-scoring ab initio models are annotated by structural comparison against the Structural Classification of Proteins (SCOP fold database. Furthermore, secondary structure, solvent accessibility, transmembrane helices, and structural disorder are predicted. The results are generated in text, tab-delimited, and hypertext markup language (HTML formats. So far, the pipeline has been used to study viral and bacterial proteomes.The standalone pipeline that we introduce here, unlike protein structure prediction Web servers, allows users to devote their own computing assets to process a potentially unlimited number of queries as well as perform

  6. QCD dipole prediction for dis and diffractive structure functions

    Royon, CH.

    1996-01-01

    The F 2 , F G , R = F L /F T proton structure functions are derived in the QCD dipole picture of BFKL dynamics. We get a three parameter fit describing the 1994 H1 proton structure function F 2 data in the low x, moderate Q 2 range. Without any additional parameter, the gluon density and the longitudinal structure functions are predicted. The diffractive dissociation processes are also discussed, and a new prediction for the proton diffractive structure function is obtained. (author)

  7. Critical Features of Fragment Libraries for Protein Structure Prediction.

    Trevizani, Raphael; Custódio, Fábio Lima; Dos Santos, Karina Baptista; Dardenne, Laurent Emmanuel

    2017-01-01

    The use of fragment libraries is a popular approach among protein structure prediction methods and has proven to substantially improve the quality of predicted structures. However, some vital aspects of a fragment library that influence the accuracy of modeling a native structure remain to be determined. This study investigates some of these features. Particularly, we analyze the effect of using secondary structure prediction guiding fragments selection, different fragments sizes and the effect of structural clustering of fragments within libraries. To have a clearer view of how these factors affect protein structure prediction, we isolated the process of model building by fragment assembly from some common limitations associated with prediction methods, e.g., imprecise energy functions and optimization algorithms, by employing an exact structure-based objective function under a greedy algorithm. Our results indicate that shorter fragments reproduce the native structure more accurately than the longer. Libraries composed of multiple fragment lengths generate even better structures, where longer fragments show to be more useful at the beginning of the simulations. The use of many different fragment sizes shows little improvement when compared to predictions carried out with libraries that comprise only three different fragment sizes. Models obtained from libraries built using only sequence similarity are, on average, better than those built with a secondary structure prediction bias. However, we found that the use of secondary structure prediction allows greater reduction of the search space, which is invaluable for prediction methods. The results of this study can be critical guidelines for the use of fragment libraries in protein structure prediction.

  8. A crystal structure prediction enigma solved

    Hoser, Anna Agnieszka; Sovago, Ioana; Lanzac, A.

    2017-01-01

    The seemingly unpredictable structure of gallic acid monohydrate form IV has been investigated using accurate X-ray diffraction measurements at temperatures of 10 and 123 K. The measurements demonstrate that the structure is commensurately modulated at 10 K and disordered at higher temperatures...

  9. Viral IRES prediction system - a web server for prediction of the IRES secondary structure in silico.

    Jun-Jie Hong

    Full Text Available The internal ribosomal entry site (IRES functions as cap-independent translation initiation sites in eukaryotic cells. IRES elements have been applied as useful tools for bi-cistronic expression vectors. Current RNA structure prediction programs are unable to predict precisely the potential IRES element. We have designed a viral IRES prediction system (VIPS to perform the IRES secondary structure prediction. In order to obtain better results for the IRES prediction, the VIPS can evaluate and predict for all four different groups of IRESs with a higher accuracy. RNA secondary structure prediction, comparison, and pseudoknot prediction programs were implemented to form the three-stage procedure for the VIPS. The backbone of VIPS includes: the RNAL fold program, aimed to predict local RNA secondary structures by minimum free energy method; the RNA Align program, intended to compare predicted structures; and pknotsRG program, used to calculate the pseudoknot structure. VIPS was evaluated by using UTR database, IRES database and Virus database, and the accuracy rate of VIPS was assessed as 98.53%, 90.80%, 82.36% and 80.41% for IRES groups 1, 2, 3, and 4, respectively. This advance useful search approach for IRES structures will facilitate IRES related studies. The VIPS on-line website service is available at http://140.135.61.250/vips/.

  10. Predicting Career Advancement with Structural Equation Modelling

    Heimler, Ronald; Rosenberg, Stuart; Morote, Elsa-Sofia

    2012-01-01

    Purpose: The purpose of this paper is to use the authors' prior findings concerning basic employability skills in order to determine which skills best predict career advancement potential. Design/methodology/approach: Utilizing survey responses of human resource managers, the employability skills showing the largest relationships to career…

  11. RNAstructure: software for RNA secondary structure prediction and analysis.

    Reuter, Jessica S; Mathews, David H

    2010-03-15

    To understand an RNA sequence's mechanism of action, the structure must be known. Furthermore, target RNA structure is an important consideration in the design of small interfering RNAs and antisense DNA oligonucleotides. RNA secondary structure prediction, using thermodynamics, can be used to develop hypotheses about the structure of an RNA sequence. RNAstructure is a software package for RNA secondary structure prediction and analysis. It uses thermodynamics and utilizes the most recent set of nearest neighbor parameters from the Turner group. It includes methods for secondary structure prediction (using several algorithms), prediction of base pair probabilities, bimolecular structure prediction, and prediction of a structure common to two sequences. This contribution describes new extensions to the package, including a library of C++ classes for incorporation into other programs, a user-friendly graphical user interface written in JAVA, and new Unix-style text interfaces. The original graphical user interface for Microsoft Windows is still maintained. The extensions to RNAstructure serve to make RNA secondary structure prediction user-friendly. The package is available for download from the Mathews lab homepage at http://rna.urmc.rochester.edu/RNAstructure.html.

  12. Prediction of concrete strength in massive structures

    Sakamoto, T.; Makino, H.; Nakane, S.; Kawaguchi, T.; Ohike, T.

    1989-01-01

    Reinforced concrete structures of a nuclear power plant are mostly of mass concrete with cross-sectional dimensions larger than 1.0 m. The temperature of concrete inside after placement rises due to heat of hydration of cement. It is well known that concrete strengths of mass concrete structure subjected to such temperature hysteresis are generally not equal to strengths of cylinders subjected to standard curing. In order to construct a mass concrete structure of high reliability in which the specified concrete strength is satisfied by the specified age, it is necessary to have a thorough understanding of the strength gain property of concrete in the structure and its relationships with the water-cement ratio of the mix, strength of standard-cured cylinders and the internal temperature hysteresis. This report describes the result of studies on methods of controlling concrete strength in actual construction projects

  13. Can Morphing Methods Predict Intermediate Structures?

    Weiss, Dahlia R.; Levitt, Michael

    2009-01-01

    Movement is crucial to the biological function of many proteins, yet crystallographic structures of proteins can give us only a static snapshot. The protein dynamics that are important to biological function often happen on a timescale that is unattainable through detailed simulation methods such as molecular dynamics as they often involve crossing high-energy barriers. To address this coarse-grained motion, several methods have been implemented as web servers in which a set of coordinates is usually linearly interpolated from an initial crystallographic structure to a final crystallographic structure. We present a new morphing method that does not extrapolate linearly and can therefore go around high-energy barriers and which can produce different trajectories between the same two starting points. In this work, we evaluate our method and other established coarse-grained methods according to an objective measure: how close a coarse-grained dynamics method comes to a crystallographically determined intermediate structure when calculating a trajectory between the initial and final crystal protein structure. We test this with a set of five proteins with at least three crystallographically determined on-pathway high-resolution intermediate structures from the Protein Data Bank. For simple hinging motions involving a small conformational change, segmentation of the protein into two rigid sections outperforms other more computationally involved methods. However, large-scale conformational change is best addressed using a nonlinear approach and we suggest that there is merit in further developing such methods. PMID:18996395

  14. Structural determination of Streptococcus pneumoniae repeat units in serotype 41A and 41F capsular polysaccharides to probe gene functions in the corresponding capsular biosynthetic loci

    Petersen, Bent O.; Skovsted, Ian C.; Paulsen, Berit Smestad

    2014-01-01

    (1) by anambitionto help close the remaining gaps in S. pneumoniaecapsular polysaccharide structures, and (2)by the attempt to derive functional annotationsof carbohydrate active enzymes in the biosynthesis of bacterial polysaccharides from the determined structures. Anactivitypresent in 41F...

  15. QCD dipole predictions for DIS and diffractive structure functions

    Royon, C.

    1997-01-01

    The proton structure function F 2 , the gluon density F G , and the longitudinal structure function F L are derived in the QCD dipole picture of BFKL dynamics. We use a three parameter fit to describe the 1994 H1 proton structure function F 2 data in the low x, moderate Q 2 range. Without any additional parameter, the gluon density and the longitudinal structure functions are predicted. The diffractive dissociation processes are also discussed within the same framework, and a new prediction for the proton diffractive structure function is obtained

  16. Structure Prediction and Analysis of Neuraminidase Sequence Variants

    Thayer, Kelly M.

    2016-01-01

    Analyzing protein structure has become an integral aspect of understanding systems of biochemical import. The laboratory experiment endeavors to introduce protein folding to ascertain structures of proteins for which the structure is unavailable, as well as to critically evaluate the quality of the prediction obtained. The model system used is the…

  17. QCD predictions for weak neutral current structure functions

    Wu Jimin

    1987-01-01

    Employing the analytic expression (to the next leading order) for non-singlet component of structure function which the author got from QCD theory and putting recent experiment result of neutral current structure function at Q 2 = 11 (GeV/C) 2 as input, the QCD prediction for neutral current structure function of their scaling violation behaviours was given

  18. Antibody structural modeling with prediction of immunoglobulin structure (PIGS)

    Marcatili, Paolo; Olimpieri, Pier Paolo; Chailyan, Anna; Tramontano, Anna

    2014-01-01

    of antibodies with a very satisfactory accuracy. The strategy is completely automated and extremely fast, requiring only a few minutes (~10 min on average) to build a structural model of an antibody. It is based on the concept of canonical structures of antibody

  19. Ensemble-based prediction of RNA secondary structures.

    Aghaeepour, Nima; Hoos, Holger H

    2013-04-24

    Accurate structure prediction methods play an important role for the understanding of RNA function. Energy-based, pseudoknot-free secondary structure prediction is one of the most widely used and versatile approaches, and improved methods for this task have received much attention over the past five years. Despite the impressive progress that as been achieved in this area, existing evaluations of the prediction accuracy achieved by various algorithms do not provide a comprehensive, statistically sound assessment. Furthermore, while there is increasing evidence that no prediction algorithm consistently outperforms all others, no work has been done to exploit the complementary strengths of multiple approaches. In this work, we present two contributions to the area of RNA secondary structure prediction. Firstly, we use state-of-the-art, resampling-based statistical methods together with a previously published and increasingly widely used dataset of high-quality RNA structures to conduct a comprehensive evaluation of existing RNA secondary structure prediction procedures. The results from this evaluation clarify the performance relationship between ten well-known existing energy-based pseudoknot-free RNA secondary structure prediction methods and clearly demonstrate the progress that has been achieved in recent years. Secondly, we introduce AveRNA, a generic and powerful method for combining a set of existing secondary structure prediction procedures into an ensemble-based method that achieves significantly higher prediction accuracies than obtained from any of its component procedures. Our new, ensemble-based method, AveRNA, improves the state of the art for energy-based, pseudoknot-free RNA secondary structure prediction by exploiting the complementary strengths of multiple existing prediction procedures, as demonstrated using a state-of-the-art statistical resampling approach. In addition, AveRNA allows an intuitive and effective control of the trade-off between

  20. Computational methods in sequence and structure prediction

    Lang, Caiyi

    This dissertation is organized into two parts. In the first part, we will discuss three computational methods for cis-regulatory element recognition in three different gene regulatory networks as the following: (a) Using a comprehensive "Phylogenetic Footprinting Comparison" method, we will investigate the promoter sequence structures of three enzymes (PAL, CHS and DFR) that catalyze sequential steps in the pathway from phenylalanine to anthocyanins in plants. Our result shows there exists a putative cis-regulatory element "AC(C/G)TAC(C)" in the upstream of these enzyme genes. We propose this cis-regulatory element to be responsible for the genetic regulation of these three enzymes and this element, might also be the binding site for MYB class transcription factor PAP1. (b) We will investigate the role of the Arabidopsis gene glutamate receptor 1.1 (AtGLR1.1) in C and N metabolism by utilizing the microarray data we obtained from AtGLR1.1 deficient lines (antiAtGLR1.1). We focus our investigation on the putatively co-regulated transcript profile of 876 genes we have collected in antiAtGLR1.1 lines. By (a) scanning the occurrence of several groups of known abscisic acid (ABA) related cisregulatory elements in the upstream regions of 876 Arabidopsis genes; and (b) exhaustive scanning of all possible 6-10 bps motif occurrence in the upstream regions of the same set of genes, we are able to make a quantative estimation on the enrichment level of each of the cis-regulatory element candidates. We finally conclude that one specific cis-regulatory element group, called "ABRE" elements, are statistically highly enriched within the 876-gene group as compared to their occurrence within the genome. (c) We will introduce a new general purpose algorithm, called "fuzzy REDUCE1", which we have developed recently for automated cis-regulatory element identification. In the second part, we will discuss our newly devised protein design framework. With this framework we have developed

  1. EVA: continuous automatic evaluation of protein structure prediction servers.

    Eyrich, V A; Martí-Renom, M A; Przybylski, D; Madhusudhan, M S; Fiser, A; Pazos, F; Valencia, A; Sali, A; Rost, B

    2001-12-01

    Evaluation of protein structure prediction methods is difficult and time-consuming. Here, we describe EVA, a web server for assessing protein structure prediction methods, in an automated, continuous and large-scale fashion. Currently, EVA evaluates the performance of a variety of prediction methods available through the internet. Every week, the sequences of the latest experimentally determined protein structures are sent to prediction servers, results are collected, performance is evaluated, and a summary is published on the web. EVA has so far collected data for more than 3000 protein chains. These results may provide valuable insight to both developers and users of prediction methods. http://cubic.bioc.columbia.edu/eva. eva@cubic.bioc.columbia.edu

  2. Serotyping of Actinobacillus pleuropneumoniae serotype 5 strains using a monoclonal-based polystyrene agglutination test

    Dubreuil, J.D.; Letellier, A.; Stenbæk, Eva

    1996-01-01

    A polystyrene agglutination test has been developed for serotyping Actinobacillus pleuropneumoniae serotype 5a and 5b strains. Protein A-coated polystyrene microparticles were sensitized with a murine monoclonal antibody recognizing an epitope on serotype 5 LPS-O chain as shown by SDS-PAGE and We......A polystyrene agglutination test has been developed for serotyping Actinobacillus pleuropneumoniae serotype 5a and 5b strains. Protein A-coated polystyrene microparticles were sensitized with a murine monoclonal antibody recognizing an epitope on serotype 5 LPS-O chain as shown by SDS...... suspension of bacterial cells grown for 18 h. All A, pleuropneumoniae strains had been previously serotyped using standard procedures, The polystyrene agglutination test was rapid (less than 3 min) and easy to perform. Overall a very good correlation (97.3%) with the standard techniques was found...

  3. PCR specific for Actinobacillus pleuropneumoniae serotype 3

    Zhou, L.; Jones, S.C.P.; Angen, Øystein

    2008-01-01

    , but the method has liminations, for example, cross-reactions between serotypes 3, 6, and 8. This study describes the development of a serotype 3-specific PCR, based on the capsule locus, which can be used in a multiplex format with the organism's specific gene apxIV. The PCR test was evaluated on 266 strains...

  4. Effects prediction guidelines for structures subjected to ground motion

    1975-07-01

    Part of the planning for an underground nuclear explosion (UNE) is determining the effects of expected ground motion on exposed structures. Because of the many types of structures and the wide variation in ground motion intensity typically encountered, no single prediction method is both adequate and feasible for a complete evaluation. Furthermore, the nature and variability of ground motion and structure damage prescribe effects predictions that are made probabilistically. Initially, prediction for a UNE involves a preliminary assessment of damage to establish overall project feasibility. Subsequent efforts require more detailed damage evaluations, based on structure inventories and analyses of specific structures, so that safety problems can be identified and safety and remedial measures can be recommended. To cover this broad range of effects prediction needs for a typical UNE project, three distinct but interrelated methods have been developed and are described. First, the fundamental practical and theoretical aspects of predicting the effects of dynamic ground motion on structures are summarized. Next, experimentally derived and theoretically determined observations of the behavior of typical structures subjected to ground motion are presented. Then, based on these fundamental considerations and on the observed behavior of structures, the formulation of the three effects prediction procedures is described, along with guidelines regarding their applicability. Example damage predictions for hypothetical UNEs demonstrate these procedures. To aid in identifying the vibration properties of complex structures, one chapter discusses alternatives in vibration testing, instrumentation, and data analysis. Finally, operational guidelines regarding data acquisition procedures, safety criteria, and remedial measures involved in conducting structure effects evaluations are discussed. (U.S.)

  5. Mesoscopic structure prediction of nanoparticle assembly and coassembly: Theoretical foundation

    Hur, Kahyun; Hennig, Richard G.; Escobedo, Fernando A.; Wiesner, Ulrich

    2010-01-01

    structures and interactions. We validate our approach by comparing its predictions with previous simulation results for model systems. We illustrate the flexibility of our approach by applying it to hybrid systems composed of block copolymers and ligand

  6. Predicting nucleic acid binding interfaces from structural models of proteins.

    Dror, Iris; Shazman, Shula; Mukherjee, Srayanta; Zhang, Yang; Glaser, Fabian; Mandel-Gutfreund, Yael

    2012-02-01

    The function of DNA- and RNA-binding proteins can be inferred from the characterization and accurate prediction of their binding interfaces. However, the main pitfall of various structure-based methods for predicting nucleic acid binding function is that they are all limited to a relatively small number of proteins for which high-resolution three-dimensional structures are available. In this study, we developed a pipeline for extracting functional electrostatic patches from surfaces of protein structural models, obtained using the I-TASSER protein structure predictor. The largest positive patches are extracted from the protein surface using the patchfinder algorithm. We show that functional electrostatic patches extracted from an ensemble of structural models highly overlap the patches extracted from high-resolution structures. Furthermore, by testing our pipeline on a set of 55 known nucleic acid binding proteins for which I-TASSER produces high-quality models, we show that the method accurately identifies the nucleic acids binding interface on structural models of proteins. Employing a combined patch approach we show that patches extracted from an ensemble of models better predicts the real nucleic acid binding interfaces compared with patches extracted from independent models. Overall, these results suggest that combining information from a collection of low-resolution structural models could be a valuable approach for functional annotation. We suggest that our method will be further applicable for predicting other functional surfaces of proteins with unknown structure. Copyright © 2011 Wiley Periodicals, Inc.

  7. Evolutionary rate variation and RNA secondary structure prediction

    Knudsen, B.; Andersen, E.S.; Damgaard, C.

    2004-01-01

    Predicting RNA secondary structure using evolutionary history can be carried out by using an alignment of related RNA sequences with conserved structure. Accurately determining evolutionary substitution rates for base pairs and single stranded nucleotides is a concern for methods based on this type...... by applying rates derived from tRNA and rRNA to the prediction of the much more rapidly evolving 5'-region of HIV-1. We find that the HIV-1 prediction is in agreement with experimental data, even though the relative evolutionary rate between A and G is significantly increased, both in stem and loop regions...

  8. Evaluation of cross-protection of bluetongue virus serotype 4 with other serotypes in sheep

    Gcwalisile B. Zulu

    2014-10-01

    Full Text Available Bluetongue (BT is a non-contagious disease of sheep and other domestic and wild ruminants caused by the bluetongue virus (BTV. Currently 26 serotypes of the virus have been identified. In South Africa, 22 serotypes have been identified and BT is controlled mainly by annual vaccinations using a freeze-dried live attenuated polyvalent BTV vaccine. The vaccine is constituted of 15 BTV serotypes divided into three separate bottles and the aim is to develop a vaccine using fewer serotypes without compromising the immunity against the disease. This study is based on previously reported cross-neutralisation of specific BTV serotypes in in vitro studies. Bluetongue virus serotype 4 was selected for this trial and was tested for cross-protection against serotype 4 (control, 1 (unrelated serotype, 9, 10 and 11 in sheep using the serum neutralisation test. The purpose of the study was to determine possible cross-protection of different serotypes in sheep. Of those vaccinated with BTV-4 and challenged with BTV-1, which is not directly related to BTV-4, 20% were completely protected and 80% showed clinical signs, but the reaction was not as severe as amongst the unvaccinated animals. In the group challenged with BTV-10, some showed good protection and some became very sick. Those challenged with BTV-9 and BTV-11 had good protection. The results showed that BTV-4 does not only elicit a specific immune response but can also protect against other serotypes.

  9. Prediction of Seismic Damage-Based Degradation in RC Structures

    Kirkegaard, Poul Henning; Gupta, Vinay K.; Nielsen, Søren R.K.

    Estimation of structural damage from known increase in the fundamental period of a structure after an earthquake or prediction of degradation of stiffness and strength for known damage requires reliable correlations between these response functionals. This study proposes a modified Clough-Johnsto...

  10. Correlating Structural Order with Structural Rearrangement in Dusty Plasma Liquids: Can Structural Rearrangement be Predicted by Static Structural Information?

    Su, Yen-Shuo; Liu, Yu-Hsuan; I, Lin

    2012-11-01

    Whether the static microstructural order information is strongly correlated with the subsequent structural rearrangement (SR) and their predicting power for SR are investigated experimentally in the quenched dusty plasma liquid with microheterogeneities. The poor local structural order is found to be a good alarm to identify the soft spot and predict the short term SR. For the site with good structural order, the persistent time for sustaining the structural memory until SR has a large mean value but a broad distribution. The deviation of the local structural order from that averaged over nearest neighbors serves as a good second alarm to further sort out the short time SR sites. It has the similar sorting power to that using the temporal fluctuation of the local structural order over a small time interval.

  11. Molecular serotyping, virulence gene profiling and pathogenicity of Streptococcus agalactiae isolated from tilapia farms in Thailand by multiplex PCR.

    Kannika, K; Pisuttharachai, D; Srisapoome, P; Wongtavatchai, J; Kondo, H; Hirono, I; Unajak, S; Areechon, N

    2017-06-01

    This study aimed to biotype Streptococcus agalactiae isolated from tilapia farms in Thailand based on molecular biotyping methods and to determine the correlation between the serotype and virulence of bacteria. In addition to a biotyping (serotyping) technique based on multiplex PCR of cps genes, in this study, we developed multiplex PCR typing of Group B streptococcus (GBS) virulence genes to examine three clusters of virulence genes and their correlation with the pathogenicity of S. agalactiae. The epidemiology of S. agalactiae in Thailand was analysed to provide bacterial genetic information towards a future rational vaccine strategy for tilapia culture systems. Streptococcus agalactiae were isolated from diseased tilapia from different areas of Thailand. A total of 124 S. agalactiae isolates were identified by phenotypic analysis and confirmed by 16S rRNA PCR. Bacterial genotyping was conducted based on (i) molecular serotyping of the capsular polysaccharide (cps) gene cluster and (ii) virulence gene profiling using multiplex PCR analysis of 14 virulence genes (lmb, scpB, pavA, cspA, spb1, cyl, bca, rib, fbsA, fbsB, cfb, hylB, bac and pbp1A/ponA). Only serotypes Ia and III were found in this study; serotype Ia lacks the lmb, scpB and spb1 genes, whereas serotype III lacks only the bac gene. Virulence tests in juvenile Nile tilapia demonstrated a correlation between the pathogenicity of the bacteria and their virulence gene profile, with serotype III showing higher virulence than serotype Ia. Epidemiological analysis showed an almost equal distribution in all regions of Thailand, except serotype III was found predominantly in the southern areas. Only two serotypes of S. agalactiae were isolated from diseased tilapia in Thailand. Serotype Ia showed fewer virulence genes and lower virulence than serotype III. Both serotypes showed a similar distribution throughout Thailand. We identified two major serotypes of S. agalactiae isolates associated with the outbreak in

  12. Protein structure prediction using bee colony optimization metaheuristic

    Fonseca, Rasmus; Paluszewski, Martin; Winter, Pawel

    2010-01-01

    of the proteins structure, an energy potential and some optimization algorithm that ¿nds the structure with minimal energy. Bee Colony Optimization (BCO) is a relatively new approach to solving opti- mization problems based on the foraging behaviour of bees. Several variants of BCO have been suggested......Predicting the native structure of proteins is one of the most challenging problems in molecular biology. The goal is to determine the three-dimensional struc- ture from the one-dimensional amino acid sequence. De novo prediction algorithms seek to do this by developing a representation...... our BCO method to generate good solutions to the protein structure prediction problem. The results show that BCO generally ¿nds better solutions than simulated annealing which so far has been the metaheuristic of choice for this problem....

  13. Blind Test of Physics-Based Prediction of Protein Structures

    Shell, M. Scott; Ozkan, S. Banu; Voelz, Vincent; Wu, Guohong Albert; Dill, Ken A.

    2009-01-01

    We report here a multiprotein blind test of a computer method to predict native protein structures based solely on an all-atom physics-based force field. We use the AMBER 96 potential function with an implicit (GB/SA) model of solvation, combined with replica-exchange molecular-dynamics simulations. Coarse conformational sampling is performed using the zipping and assembly method (ZAM), an approach that is designed to mimic the putative physical routes of protein folding. ZAM was applied to the folding of six proteins, from 76 to 112 monomers in length, in CASP7, a community-wide blind test of protein structure prediction. Because these predictions have about the same level of accuracy as typical bioinformatics methods, and do not utilize information from databases of known native structures, this work opens up the possibility of predicting the structures of membrane proteins, synthetic peptides, or other foldable polymers, for which there is little prior knowledge of native structures. This approach may also be useful for predicting physical protein folding routes, non-native conformations, and other physical properties from amino acid sequences. PMID:19186130

  14. Prediction of RNA secondary structure using generalized centroid estimators.

    Hamada, Michiaki; Kiryu, Hisanori; Sato, Kengo; Mituyama, Toutai; Asai, Kiyoshi

    2009-02-15

    Recent studies have shown that the methods for predicting secondary structures of RNAs on the basis of posterior decoding of the base-pairing probabilities has an advantage with respect to prediction accuracy over the conventionally utilized minimum free energy methods. However, there is room for improvement in the objective functions presented in previous studies, which are maximized in the posterior decoding with respect to the accuracy measures for secondary structures. We propose novel estimators which improve the accuracy of secondary structure prediction of RNAs. The proposed estimators maximize an objective function which is the weighted sum of the expected number of the true positives and that of the true negatives of the base pairs. The proposed estimators are also improved versions of the ones used in previous works, namely CONTRAfold for secondary structure prediction from a single RNA sequence and McCaskill-MEA for common secondary structure prediction from multiple alignments of RNA sequences. We clarify the relations between the proposed estimators and the estimators presented in previous works, and theoretically show that the previous estimators include additional unnecessary terms in the evaluation measures with respect to the accuracy. Furthermore, computational experiments confirm the theoretical analysis by indicating improvement in the empirical accuracy. The proposed estimators represent extensions of the centroid estimators proposed in Ding et al. and Carvalho and Lawrence, and are applicable to a wide variety of problems in bioinformatics. Supporting information and the CentroidFold software are available online at: http://www.ncrna.org/software/centroidfold/.

  15. Phylogenetic analyses of the polyprotein coding sequences of serotype O foot-and-mouth disease viruses in East Africa: evidence for interserotypic recombination

    Balinda, Sheila; Siegismund, Hans; Muwanika, Vincent

    2010-01-01

    from both serotypes A and O. Conclusions Sequences of the VP1 coding region from recent serotype O FMDVs from Kenya and Uganda are all representatives of a specific East African lineage (topotype EA-2), a probable indication that hardly any FMD introductions of this serotype have occurred from outside...... the region in the recent past. Furthermore, evidence for interserotypic recombination, within the non-structural protein coding regions, between FMDVs of serotypes A and O has been obtained. In addition to characterization using the VP1 coding region, analyses involving the non-structural protein coding...

  16. Pneumococcal Serotypes Colonise the Nasopharynx in Children at Different Densities.

    Fernanda Rodrigues

    Full Text Available Prevalence of pneumococcal serotypes in carriage and disease has been described but absolute serotype colonisation densities have not been reported. 515 paediatric nasal swab DNA extracts were subjected to lytA qPCR and molecular serotyping by microarray. Absolute serotype densities were derived from total pneumococcal density (qPCR cycle threshold and standard curve and relative abundance (microarray and varied widely. Compared to all serotype densities observed, the strongest evidence of differences was seen for serotypes 21 and 35B (higher and 3, 38 and non-typeables (lower (p<0.05 with a similar hierarchy when only a single serotype carriage was assessed. There was no evidence of any overall density differences between children with single or multiple serotypes detected but serotypes with mid-range densities were more prevalent. The hierarchy of distinct pneumococcal serotype carriage densities described here for the first time, may help explain the dynamics of transmission between children.

  17. RNA folding: structure prediction, folding kinetics and ion electrostatics.

    Tan, Zhijie; Zhang, Wenbing; Shi, Yazhou; Wang, Fenghua

    2015-01-01

    Beyond the "traditional" functions such as gene storage, transport and protein synthesis, recent discoveries reveal that RNAs have important "new" biological functions including the RNA silence and gene regulation of riboswitch. Such functions of noncoding RNAs are strongly coupled to the RNA structures and proper structure change, which naturally leads to the RNA folding problem including structure prediction and folding kinetics. Due to the polyanionic nature of RNAs, RNA folding structure, stability and kinetics are strongly coupled to the ion condition of solution. The main focus of this chapter is to review the recent progress in the three major aspects in RNA folding problem: structure prediction, folding kinetics and ion electrostatics. This chapter will introduce both the recent experimental and theoretical progress, while emphasize the theoretical modelling on the three aspects in RNA folding.

  18. Contingency Table Browser - prediction of early stage protein structure.

    Kalinowska, Barbara; Krzykalski, Artur; Roterman, Irena

    2015-01-01

    The Early Stage (ES) intermediate represents the starting structure in protein folding simulations based on the Fuzzy Oil Drop (FOD) model. The accuracy of FOD predictions is greatly dependent on the accuracy of the chosen intermediate. A suitable intermediate can be constructed using the sequence-structure relationship information contained in the so-called contingency table - this table expresses the likelihood of encountering various structural motifs for each tetrapeptide fragment in the amino acid sequence. The limited accuracy with which such structures could previously be predicted provided the motivation for a more indepth study of the contingency table itself. The Contingency Table Browser is a tool which can visualize, search and analyze the table. Our work presents possible applications of Contingency Table Browser, among them - analysis of specific protein sequences from the point of view of their structural ambiguity.

  19. Prediction of degradation and fracture of structural materials

    Tomkins, B.

    1992-01-01

    Prediction of materials performance in an engineering integrity context requires the underpinning of predictive modelling tuned by inputs from design, fabrication, operating experience, and laboratory testing. In this regard, in addition to fracture resistance four important areas of time dependent degradation are considered - mechanical, environmental, irradiation and thermal. The status of prediction of materials performance is discussed in relation to a number of important components such as LWR reactor pressure vessels and steam generators, and Fast Reactor high temperature structures. In each case the role of materials modelling is examined and the balance of factors which contribute to the overall prediction of component integrity/reliability noted. Structural integrity arguments must follow a clear strategy if the required level of confidence is to be established. Various strategies and their evolution are discussed. (author)

  20. Cascaded bidirectional recurrent neural networks for protein secondary structure prediction.

    Chen, Jinmiao; Chaudhari, Narendra

    2007-01-01

    Protein secondary structure (PSS) prediction is an important topic in bioinformatics. Our study on a large set of non-homologous proteins shows that long-range interactions commonly exist and negatively affect PSS prediction. Besides, we also reveal strong correlations between secondary structure (SS) elements. In order to take into account the long-range interactions and SS-SS correlations, we propose a novel prediction system based on cascaded bidirectional recurrent neural network (BRNN). We compare the cascaded BRNN against another two BRNN architectures, namely the original BRNN architecture used for speech recognition as well as Pollastri's BRNN that was proposed for PSS prediction. Our cascaded BRNN achieves an overall three state accuracy Q3 of 74.38\\%, and reaches a high Segment OVerlap (SOV) of 66.0455. It outperforms the original BRNN and Pollastri's BRNN in both Q3 and SOV. Specifically, it improves the SOV score by 4-6%.

  1. Improving the accuracy of protein secondary structure prediction using structural alignment

    Gallin Warren J

    2006-06-01

    Full Text Available Abstract Background The accuracy of protein secondary structure prediction has steadily improved over the past 30 years. Now many secondary structure prediction methods routinely achieve an accuracy (Q3 of about 75%. We believe this accuracy could be further improved by including structure (as opposed to sequence database comparisons as part of the prediction process. Indeed, given the large size of the Protein Data Bank (>35,000 sequences, the probability of a newly identified sequence having a structural homologue is actually quite high. Results We have developed a method that performs structure-based sequence alignments as part of the secondary structure prediction process. By mapping the structure of a known homologue (sequence ID >25% onto the query protein's sequence, it is possible to predict at least a portion of that query protein's secondary structure. By integrating this structural alignment approach with conventional (sequence-based secondary structure methods and then combining it with a "jury-of-experts" system to generate a consensus result, it is possible to attain very high prediction accuracy. Using a sequence-unique test set of 1644 proteins from EVA, this new method achieves an average Q3 score of 81.3%. Extensive testing indicates this is approximately 4–5% better than any other method currently available. Assessments using non sequence-unique test sets (typical of those used in proteome annotation or structural genomics indicate that this new method can achieve a Q3 score approaching 88%. Conclusion By using both sequence and structure databases and by exploiting the latest techniques in machine learning it is possible to routinely predict protein secondary structure with an accuracy well above 80%. A program and web server, called PROTEUS, that performs these secondary structure predictions is accessible at http://wishart.biology.ualberta.ca/proteus. For high throughput or batch sequence analyses, the PROTEUS programs

  2. New tips for structure prediction by comparative modeling

    Rayan, Anwar

    2009-01-01

    Comparative modelling is utilized to predict the 3-dimensional conformation of a given protein (target) based on its sequence alignment to experimentally determined protein structure (template). The use of such technique is already rewarding and increasingly widespread in biological research and drug development. The accuracy of the predictions as commonly accepted depends on the score of sequence identity of the target protein to the template. To assess the relationship between sequence iden...

  3. Combining neural networks for protein secondary structure prediction

    Riis, Søren Kamaric

    1995-01-01

    In this paper structured neural networks are applied to the problem of predicting the secondary structure of proteins. A hierarchical approach is used where specialized neural networks are designed for each structural class and then combined using another neural network. The submodels are designed...... by using a priori knowledge of the mapping between protein building blocks and the secondary structure and by using weight sharing. Since none of the individual networks have more than 600 adjustable weights over-fitting is avoided. When ensembles of specialized experts are combined the performance...

  4. Effect of pneumococcal conjugate vaccination on serotype-specific carriage and invasive disease in England: a cross-sectional study.

    Stefan Flasche

    2011-04-01

    Full Text Available BACKGROUND: We investigated the effect of the 7-valent pneumococcal conjugate vaccine (PCV7 programme in England on serotype-specific carriage and invasive disease to help understand its role in serotype replacement and predict the impact of higher valency vaccines. METHODS AND FINDINGS: Nasopharyngeal swabs were taken from children <5 y old and family members (n=400 2 y after introduction of PCV7 into routine immunization programs. Proportions carrying Streptococcus pneumoniae and serotype distribution among carried isolates were compared with a similar population prior to PCV7 introduction. Serotype-specific case carrier ratios (CCRs were estimated using national data on invasive disease. In vaccinated children and their contacts vaccine-type (VT carriage decreased, but was offset by an increase in non-VT carriage, with no significant overall change in carriage prevalence, odds ratio 1.06 (95% confidence interval 0.76-1.49. The lower CCRs of the replacing serotypes resulted in a net reduction in invasive disease in children. The additional serotypes covered by higher valency vaccines had low carriage but high disease prevalence. Serotype 11C emerged as predominant in carriage but caused no invasive disease whereas 8, 12F, and 22F emerged in disease but had very low carriage prevalence. CONCLUSION: Because the additional serotypes included in PCV10/13 have high CCRs but low carriage prevalence, vaccinating against them is likely to significantly reduce invasive disease with less risk of serotype replacement. However, a few serotypes with high CCRs could mitigate the benefits of higher valency vaccines. Assessment of the effect of PCV on carriage as well as invasive disease should be part of enhanced surveillance activities for PCVs. Please see later in the article for the Editors' Summary.

  5. escherichia coli serotypes confirmed in experimental mammary ...

    DJFLEX

    VARIATIONS IN VIRULENCE OF THREE (3) ESCHERICHIA COLI. SEROTYPES CONFIRMED IN ... ows are susceptible to E. coli infection because. E. coli exist in the .... Coli infections in mice: A laboratory animal model for research in.

  6. Distance matrix-based approach to protein structure prediction.

    Kloczkowski, Andrzej; Jernigan, Robert L; Wu, Zhijun; Song, Guang; Yang, Lei; Kolinski, Andrzej; Pokarowski, Piotr

    2009-03-01

    Much structural information is encoded in the internal distances; a distance matrix-based approach can be used to predict protein structure and dynamics, and for structural refinement. Our approach is based on the square distance matrix D = [r(ij)(2)] containing all square distances between residues in proteins. This distance matrix contains more information than the contact matrix C, that has elements of either 0 or 1 depending on whether the distance r (ij) is greater or less than a cutoff value r (cutoff). We have performed spectral decomposition of the distance matrices D = sigma lambda(k)V(k)V(kT), in terms of eigenvalues lambda kappa and the corresponding eigenvectors v kappa and found that it contains at most five nonzero terms. A dominant eigenvector is proportional to r (2)--the square distance of points from the center of mass, with the next three being the principal components of the system of points. By predicting r (2) from the sequence we can approximate a distance matrix of a protein with an expected RMSD value of about 7.3 A, and by combining it with the prediction of the first principal component we can improve this approximation to 4.0 A. We can also explain the role of hydrophobic interactions for the protein structure, because r is highly correlated with the hydrophobic profile of the sequence. Moreover, r is highly correlated with several sequence profiles which are useful in protein structure prediction, such as contact number, the residue-wise contact order (RWCO) or mean square fluctuations (i.e. crystallographic temperature factors). We have also shown that the next three components are related to spatial directionality of the secondary structure elements, and they may be also predicted from the sequence, improving overall structure prediction. We have also shown that the large number of available HIV-1 protease structures provides a remarkable sampling of conformations, which can be viewed as direct structural information about the

  7. (PS)2: protein structure prediction server version 3.0.

    Huang, Tsun-Tsao; Hwang, Jenn-Kang; Chen, Chu-Huang; Chu, Chih-Sheng; Lee, Chi-Wen; Chen, Chih-Chieh

    2015-07-01

    Protein complexes are involved in many biological processes. Examining coupling between subunits of a complex would be useful to understand the molecular basis of protein function. Here, our updated (PS)(2) web server predicts the three-dimensional structures of protein complexes based on comparative modeling; furthermore, this server examines the coupling between subunits of the predicted complex by combining structural and evolutionary considerations. The predicted complex structure could be indicated and visualized by Java-based 3D graphics viewers and the structural and evolutionary profiles are shown and compared chain-by-chain. For each subunit, considerations with or without the packing contribution of other subunits cause the differences in similarities between structural and evolutionary profiles, and these differences imply which form, complex or monomeric, is preferred in the biological condition for the subunit. We believe that the (PS)(2) server would be a useful tool for biologists who are interested not only in the structures of protein complexes but also in the coupling between subunits of the complexes. The (PS)(2) is freely available at http://ps2v3.life.nctu.edu.tw/. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

  8. Parallel protein secondary structure prediction based on neural networks.

    Zhong, Wei; Altun, Gulsah; Tian, Xinmin; Harrison, Robert; Tai, Phang C; Pan, Yi

    2004-01-01

    Protein secondary structure prediction has a fundamental influence on today's bioinformatics research. In this work, binary and tertiary classifiers of protein secondary structure prediction are implemented on Denoeux belief neural network (DBNN) architecture. Hydrophobicity matrix, orthogonal matrix, BLOSUM62 and PSSM (position specific scoring matrix) are experimented separately as the encoding schemes for DBNN. The experimental results contribute to the design of new encoding schemes. New binary classifier for Helix versus not Helix ( approximately H) for DBNN produces prediction accuracy of 87% when PSSM is used for the input profile. The performance of DBNN binary classifier is comparable to other best prediction methods. The good test results for binary classifiers open a new approach for protein structure prediction with neural networks. Due to the time consuming task of training the neural networks, Pthread and OpenMP are employed to parallelize DBNN in the hyperthreading enabled Intel architecture. Speedup for 16 Pthreads is 4.9 and speedup for 16 OpenMP threads is 4 in the 4 processors shared memory architecture. Both speedup performance of OpenMP and Pthread is superior to that of other research. With the new parallel training algorithm, thousands of amino acids can be processed in reasonable amount of time. Our research also shows that hyperthreading technology for Intel architecture is efficient for parallel biological algorithms.

  9. Cloud prediction of protein structure and function with PredictProtein for Debian.

    Kaján, László; Yachdav, Guy; Vicedo, Esmeralda; Steinegger, Martin; Mirdita, Milot; Angermüller, Christof; Böhm, Ariane; Domke, Simon; Ertl, Julia; Mertes, Christian; Reisinger, Eva; Staniewski, Cedric; Rost, Burkhard

    2013-01-01

    We report the release of PredictProtein for the Debian operating system and derivatives, such as Ubuntu, Bio-Linux, and Cloud BioLinux. The PredictProtein suite is available as a standard set of open source Debian packages. The release covers the most popular prediction methods from the Rost Lab, including methods for the prediction of secondary structure and solvent accessibility (profphd), nuclear localization signals (predictnls), and intrinsically disordered regions (norsnet). We also present two case studies that successfully utilize PredictProtein packages for high performance computing in the cloud: the first analyzes protein disorder for whole organisms, and the second analyzes the effect of all possible single sequence variants in protein coding regions of the human genome.

  10. Child Support Payment: A Structural Model of Predictive Variables.

    Wright, David W.; Price, Sharon J.

    A major area of concern in divorced families is compliance with child support payments. Aspects of the former spouse relationship that are predictive of compliance with court-ordered payment of child support were investigated in a sample of 58 divorced persons all of whom either paid or received child support. Structured interviews and…

  11. The prediction and discovery of Rayleigh line fine structure

    Fabelinskii, Immanuil L

    2000-01-01

    The history of the theoretical prediction and experimental discovery of the Rayleigh line fine structure (which belongs to one of the most important phenomena in optics and physics of condensed matter) is discussed along with the history of first publications concerning this topic. (from the history of physics)

  12. Towards a unified fatigue life prediction method for marine structures

    Cui, Weicheng; Wang, Fang

    2014-01-01

    In order to apply the damage tolerance design philosophy to design marine structures, accurate prediction of fatigue crack growth under service conditions is required. Now, more and more people have realized that only a fatigue life prediction method based on fatigue crack propagation (FCP) theory has the potential to explain various fatigue phenomena observed. In this book, the issues leading towards the development of a unified fatigue life prediction (UFLP) method based on FCP theory are addressed. Based on the philosophy of the UFLP method, the current inconsistency between fatigue design and inspection of marine structures could be resolved. This book presents the state-of-the-art and recent advances, including those by the authors, in fatigue studies. It is designed to lead the future directions and to provide a useful tool in many practical applications. It is intended to address to engineers, naval architects, research staff, professionals and graduates engaged in fatigue prevention design and survey ...

  13. Structure life prediction at high temperature: present and future capabilities

    Chaboche, J.L.

    1987-01-01

    The life prediction techniques for high temperature conditions include several aspects which are considered successively in this article. Crack initiation criteria themselves, defined for the isolated volume element (the tension-compression specimen for example), including parametric relationships and continuous damage approaches and calculation of local stress and strain fields in the structure and their evolution under cyclic plasticity, which poses several difficult problems to obtain stabilized cyclic solutions are examined. The use of crack initiation criteria or damage rules from the result of the cyclic inelastic analysis and the prediction of crack growth in the structure are considered. Different levels are considered for the predictive tools: the classical approach, future methods presently under development and intermediate rules, which are already in use. Several examples are given on materials and components used either in the nuclear industry or in gas turbine engines. (author)

  14. I-TASSER server for protein 3D structure prediction

    Zhang Yang

    2008-01-01

    Full Text Available Abstract Background Prediction of 3-dimensional protein structures from amino acid sequences represents one of the most important problems in computational structural biology. The community-wide Critical Assessment of Structure Prediction (CASP experiments have been designed to obtain an objective assessment of the state-of-the-art of the field, where I-TASSER was ranked as the best method in the server section of the recent 7th CASP experiment. Our laboratory has since then received numerous requests about the public availability of the I-TASSER algorithm and the usage of the I-TASSER predictions. Results An on-line version of I-TASSER is developed at the KU Center for Bioinformatics which has generated protein structure predictions for thousands of modeling requests from more than 35 countries. A scoring function (C-score based on the relative clustering structural density and the consensus significance score of multiple threading templates is introduced to estimate the accuracy of the I-TASSER predictions. A large-scale benchmark test demonstrates a strong correlation between the C-score and the TM-score (a structural similarity measurement with values in [0, 1] of the first models with a correlation coefficient of 0.91. Using a C-score cutoff > -1.5 for the models of correct topology, both false positive and false negative rates are below 0.1. Combining C-score and protein length, the accuracy of the I-TASSER models can be predicted with an average error of 0.08 for TM-score and 2 Å for RMSD. Conclusion The I-TASSER server has been developed to generate automated full-length 3D protein structural predictions where the benchmarked scoring system helps users to obtain quantitative assessments of the I-TASSER models. The output of the I-TASSER server for each query includes up to five full-length models, the confidence score, the estimated TM-score and RMSD, and the standard deviation of the estimations. The I-TASSER server is freely available

  15. Automatic prediction of facial trait judgments: appearance vs. structural models.

    Mario Rojas

    Full Text Available Evaluating other individuals with respect to personality characteristics plays a crucial role in human relations and it is the focus of attention for research in diverse fields such as psychology and interactive computer systems. In psychology, face perception has been recognized as a key component of this evaluation system. Multiple studies suggest that observers use face information to infer personality characteristics. Interactive computer systems are trying to take advantage of these findings and apply them to increase the natural aspect of interaction and to improve the performance of interactive computer systems. Here, we experimentally test whether the automatic prediction of facial trait judgments (e.g. dominance can be made by using the full appearance information of the face and whether a reduced representation of its structure is sufficient. We evaluate two separate approaches: a holistic representation model using the facial appearance information and a structural model constructed from the relations among facial salient points. State of the art machine learning methods are applied to a derive a facial trait judgment model from training data and b predict a facial trait value for any face. Furthermore, we address the issue of whether there are specific structural relations among facial points that predict perception of facial traits. Experimental results over a set of labeled data (9 different trait evaluations and classification rules (4 rules suggest that a prediction of perception of facial traits is learnable by both holistic and structural approaches; b the most reliable prediction of facial trait judgments is obtained by certain type of holistic descriptions of the face appearance; and c for some traits such as attractiveness and extroversion, there are relationships between specific structural features and social perceptions.

  16. Predictive modeling of neuroanatomic structures for brain atrophy detection

    Hu, Xintao; Guo, Lei; Nie, Jingxin; Li, Kaiming; Liu, Tianming

    2010-03-01

    In this paper, we present an approach of predictive modeling of neuroanatomic structures for the detection of brain atrophy based on cross-sectional MRI image. The underlying premise of applying predictive modeling for atrophy detection is that brain atrophy is defined as significant deviation of part of the anatomy from what the remaining normal anatomy predicts for that part. The steps of predictive modeling are as follows. The central cortical surface under consideration is reconstructed from brain tissue map and Regions of Interests (ROI) on it are predicted from other reliable anatomies. The vertex pair-wise distance between the predicted vertex and the true one within the abnormal region is expected to be larger than that of the vertex in normal brain region. Change of white matter/gray matter ratio within a spherical region is used to identify the direction of vertex displacement. In this way, the severity of brain atrophy can be defined quantitatively by the displacements of those vertices. The proposed predictive modeling method has been evaluated by using both simulated atrophies and MRI images of Alzheimer's disease.

  17. New tips for structure prediction by comparative modeling

    Rayan, Anwar

    2009-01-01

    Comparative modelling is utilized to predict the 3-dimensional conformation of a given protein (target) based on its sequence alignment to experimentally determined protein structure (template). The use of such technique is already rewarding and increasingly widespread in biological research and drug development. The accuracy of the predictions as commonly accepted depends on the score of sequence identity of the target protein to the template. To assess the relationship between sequence identity and model quality, we carried out an analysis of a set of 4753 sequence and structure alignments. Throughout this research, the model accuracy was measured by root mean square deviations of Cα atoms of the target-template structures. Surprisingly, the results show that sequence identity of the target protein to the template is not a good descriptor to predict the accuracy of the 3-D structure model. However, in a large number of cases, comparative modelling with lower sequence identity of target to template proteins led to more accurate 3-D structure model. As a consequence of this study, we suggest new tips for improving the quality of omparative models, particularly for models whose target-template sequence identity is below 50%. PMID:19255646

  18. Transplacental Transmission of Bluetongue Virus Serotype 1 and Serotype 8 in Sheep: Virological and Pathological Findings

    Sluijs, van der M.T.W.; Schroer-Joosten, D.P.H.; Fid-Fourkour, A.; Vrijenhoek, M.P.; Debyser, I.; Moulin, V.; Moormann, R.J.M.; Smit, de A.J.

    2013-01-01

    The Bluetongue virus serotype 8 (BTV-8) strain, which emerged in Europe in 2006, had an unusually high ability to cause foetal infection in pregnant ruminants. Other serotypes of BTV had already been present in Europe for more than a decade, but transplacental transmission of these strains had never

  19. Three-dimensional protein structure prediction: Methods and computational strategies.

    Dorn, Márcio; E Silva, Mariel Barbachan; Buriol, Luciana S; Lamb, Luis C

    2014-10-12

    A long standing problem in structural bioinformatics is to determine the three-dimensional (3-D) structure of a protein when only a sequence of amino acid residues is given. Many computational methodologies and algorithms have been proposed as a solution to the 3-D Protein Structure Prediction (3-D-PSP) problem. These methods can be divided in four main classes: (a) first principle methods without database information; (b) first principle methods with database information; (c) fold recognition and threading methods; and (d) comparative modeling methods and sequence alignment strategies. Deterministic computational techniques, optimization techniques, data mining and machine learning approaches are typically used in the construction of computational solutions for the PSP problem. Our main goal with this work is to review the methods and computational strategies that are currently used in 3-D protein prediction. Copyright © 2014 Elsevier Ltd. All rights reserved.

  20. Application of Functional Use Predictions to Aid in Structure ...

    Humans are potentially exposed to thousands of anthropogenic chemicals in commerce. Recent work has shown that the bulk of this exposure may occur in near-field indoor environments (e.g., home, school, work, etc.). Advances in suspect screening analyses (SSA) now allow an improved understanding of the chemicals present in these environments. However, due to the nature of suspect screening techniques, investigators are often left with chemical formula predictions, with the possibility of many chemical structures matching to each formula. Here, newly developed quantitative structure-use relationship (QSUR) models are used to identify potential exposure sources for candidate structures. Previously, a suspect screening workflow was introduced and applied to house dust samples collected from the U.S. Department of Housing and Urban Development’s American Healthy Homes Survey (AHHS) [Rager, et al., Env. Int. 88 (2016)]. This workflow utilized the US EPA’s Distributed Structure-Searchable Toxicity (DSSTox) Database to link identified molecular features to molecular formulas, and ultimately chemical structures. Multiple QSUR models were applied to support the evaluation of candidate structures. These QSURs predict the likelihood of a chemical having a functional use commonly associated with consumer products having near-field use. For 3,228 structures identified as possible chemicals in AHHS house dust samples, we were able to obtain the required descriptors to appl

  1. Genomic Characterization of Flavobacterium psychrophilum Serotypes and Development of a Multiplex PCR-Based Serotyping Scheme

    Tatiana Rochat

    2017-09-01

    Full Text Available Flavobacterium psychrophilum is a devastating bacterial pathogen of salmonids reared in freshwater worldwide. So far, serological diversity between isolates has been described but the underlying molecular factors remain unknown. By combining complete genome sequence analysis and the serotyping method proposed by Lorenzen and Olesen (1997 for a set of 34 strains, we identified key molecular determinants of the serotypes. This knowledge allowed us to develop a robust multiplex PCR-based serotyping scheme, which was applied to 244 bacterial isolates. The results revealed a striking association between PCR-serotype and fish host species and illustrate the use of this approach as a simple and cost-effective method for the determination of F. psychrophilum serogroups. PCR-based serotyping could be a useful tool in a range of applications such as disease surveillance, selection of salmonids for bacterial coldwater disease resistance and future vaccine formulation.

  2. Protein 8-class secondary structure prediction using conditional neural fields.

    Wang, Zhiyong; Zhao, Feng; Peng, Jian; Xu, Jinbo

    2011-10-01

    Compared with the protein 3-class secondary structure (SS) prediction, the 8-class prediction gains less attention and is also much more challenging, especially for proteins with few sequence homologs. This paper presents a new probabilistic method for 8-class SS prediction using conditional neural fields (CNFs), a recently invented probabilistic graphical model. This CNF method not only models the complex relationship between sequence features and SS, but also exploits the interdependency among SS types of adjacent residues. In addition to sequence profiles, our method also makes use of non-evolutionary information for SS prediction. Tested on the CB513 and RS126 data sets, our method achieves Q8 accuracy of 64.9 and 64.7%, respectively, which are much better than the SSpro8 web server (51.0 and 48.0%, respectively). Our method can also be used to predict other structure properties (e.g. solvent accessibility) of a protein or the SS of RNA. Copyright © 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  3. Seroepidemiological investigation of foot-and-mouth disease virus serotypes in cattle around Lake Mburo National Park in South-Western Uganda

    Mwiine, Frank Norbert; Ayebazibwe, Chrisostom; Alexandersen, Søren

    2010-01-01

    Foot-and-mouth disease (FMD) outbreaks in cattle occur annually in Uganda. In this study the authors investigated antibodies against FMD virus (FMDV) in cattle in surrounding areas of Lake Mburo National Park in South-western Uganda. Two hundred and eleven serum samples from 23 cattle herds were...... examined for the presence of antibodies against FMDV non-structural proteins and structural proteins using Ceditest® FMDV-NS and Ceditest® FMDV type O (Cedi Diagnostics BV, Lelystad, The Netherlands). Furthermore, serotype-specific antibodies against the seven serotypes of FMDV were determined using in......-house serotype-specific Solid Phase Blocking ELISAs (SPBE). Of the sera tested, 42.7% (90/211) were positive in the ELISA for antibodies against non-structural proteins, while 75.4% (159/211) had antibodies against the structural proteins of FMDV serotype O. Titres of ≥ 1:160 of serotype-specific antibodies...

  4. Predicting beta-turns and their types using predicted backbone dihedral angles and secondary structures.

    Kountouris, Petros; Hirst, Jonathan D

    2010-07-31

    Beta-turns are secondary structure elements usually classified as coil. Their prediction is important, because of their role in protein folding and their frequent occurrence in protein chains. We have developed a novel method that predicts beta-turns and their types using information from multiple sequence alignments, predicted secondary structures and, for the first time, predicted dihedral angles. Our method uses support vector machines, a supervised classification technique, and is trained and tested on three established datasets of 426, 547 and 823 protein chains. We achieve a Matthews correlation coefficient of up to 0.49, when predicting the location of beta-turns, the highest reported value to date. Moreover, the additional dihedral information improves the prediction of beta-turn types I, II, IV, VIII and "non-specific", achieving correlation coefficients up to 0.39, 0.33, 0.27, 0.14 and 0.38, respectively. Our results are more accurate than other methods. We have created an accurate predictor of beta-turns and their types. Our method, called DEBT, is available online at http://comp.chem.nottingham.ac.uk/debt/.

  5. A multicontroller structure for teaching and designing predictive control strategies

    Hodouin, D.; Desbiens, A.

    1999-01-01

    The paper deals with the unification of the existing linear control algorithms in order to facilitate their transfer to the engineering students and to industry's engineers. The resulting control algorithm is the Global Predictive Control (GlobPC), which is now taught at the graduate and continuing education levels. GlobPC is based on an internal model framework where three independent control criteria are minimized: one for tracking, one for regulation and one for feedforward. This structure allows to obtain desired tracking, regulation and feedforward behaviors in an optimal way while keeping them perfectly separated. It also cleanly separates the deterministic and stochastic predictions of the process model output. (author)

  6. Mesoscopic structure prediction of nanoparticle assembly and coassembly: Theoretical foundation

    Hur, Kahyun

    2010-01-01

    In this work, we present a theoretical framework that unifies polymer field theory and density functional theory in order to efficiently predict ordered nanostructure formation of systems having considerable complexity in terms of molecular structures and interactions. We validate our approach by comparing its predictions with previous simulation results for model systems. We illustrate the flexibility of our approach by applying it to hybrid systems composed of block copolymers and ligand coated nanoparticles. We expect that our approach will enable the treatment of multicomponent self-assembly with a level of molecular complexity that approaches experimental systems. © 2010 American Institute of Physics.

  7. Predicted crystal structures of molybdenum under high pressure

    Wang, Bing; Zhang, Guang Biao [Institute for Computational Materials Science, School of Physics and Electronics, Henan University, Kaifeng 475004 (China); Wang, Yuan Xu, E-mail: wangyx@henu.edu.cn [Institute for Computational Materials Science, School of Physics and Electronics, Henan University, Kaifeng 475004 (China); Guizhou Provincial Key Laboratory of Computational Nano-Material Science, Institute of Applied Physics, Guizhou Normal College, Guiyang 550018 (China)

    2013-04-15

    Highlights: ► A double-hexagonal close-packed (dhcp) structure of molybdenum is predicted. ► Calculated acoustic velocity confirms the bcc–dhcp phase transition at 660 GPa. ► The valence electrons of dhcp Mo are mostly localized in the interstitial sites. -- Abstract: The high-pressure structures of molybdenum (Mo) at zero temperature have been extensively explored through the newly developed particle swarm optimization (PSO) algorithm on crystal structural prediction. All the experimental and earlier theoretical structures were successfully reproduced in certain pressure ranges, validating our methodology in application to Mo. A double-hexagonal close-packed (dhcp) structure found by Mikhaylushkin et al. (2008) [12] is confirmed by the present PSO calculations. The lattice parameters and physical properties of the dhcp phase were investigated based on first principles calculations. The phase transition occurs only from bcc phase to dhcp phase at 660 GPa and at zero temperature. The calculated acoustic velocities also indicate a transition from the bcc to dhcp phases for Mo. More intriguingly, the calculated density of states (DOS) shows that the dhcp structure remains metallic. The calculated electron density difference (EDD) reveals that its valence electrons are localized in the interstitial regions.

  8. Predicting protein structures with a multiplayer online game.

    Cooper, Seth; Khatib, Firas; Treuille, Adrien; Barbero, Janos; Lee, Jeehyung; Beenen, Michael; Leaver-Fay, Andrew; Baker, David; Popović, Zoran; Players, Foldit

    2010-08-05

    People exert large amounts of problem-solving effort playing computer games. Simple image- and text-recognition tasks have been successfully 'crowd-sourced' through games, but it is not clear if more complex scientific problems can be solved with human-directed computing. Protein structure prediction is one such problem: locating the biologically relevant native conformation of a protein is a formidable computational challenge given the very large size of the search space. Here we describe Foldit, a multiplayer online game that engages non-scientists in solving hard prediction problems. Foldit players interact with protein structures using direct manipulation tools and user-friendly versions of algorithms from the Rosetta structure prediction methodology, while they compete and collaborate to optimize the computed energy. We show that top-ranked Foldit players excel at solving challenging structure refinement problems in which substantial backbone rearrangements are necessary to achieve the burial of hydrophobic residues. Players working collaboratively develop a rich assortment of new strategies and algorithms; unlike computational approaches, they explore not only the conformational space but also the space of possible search strategies. The integration of human visual problem-solving and strategy development capabilities with traditional computational algorithms through interactive multiplayer games is a powerful new approach to solving computationally-limited scientific problems.

  9. Protein Secondary Structure Prediction Using Deep Convolutional Neural Fields.

    Wang, Sheng; Peng, Jian; Ma, Jianzhu; Xu, Jinbo

    2016-01-11

    Protein secondary structure (SS) prediction is important for studying protein structure and function. When only the sequence (profile) information is used as input feature, currently the best predictors can obtain ~80% Q3 accuracy, which has not been improved in the past decade. Here we present DeepCNF (Deep Convolutional Neural Fields) for protein SS prediction. DeepCNF is a Deep Learning extension of Conditional Neural Fields (CNF), which is an integration of Conditional Random Fields (CRF) and shallow neural networks. DeepCNF can model not only complex sequence-structure relationship by a deep hierarchical architecture, but also interdependency between adjacent SS labels, so it is much more powerful than CNF. Experimental results show that DeepCNF can obtain ~84% Q3 accuracy, ~85% SOV score, and ~72% Q8 accuracy, respectively, on the CASP and CAMEO test proteins, greatly outperforming currently popular predictors. As a general framework, DeepCNF can be used to predict other protein structure properties such as contact number, disorder regions, and solvent accessibility.

  10. Predicting and validating protein interactions using network structure.

    Pao-Yang Chen

    2008-07-01

    Full Text Available Protein interactions play a vital part in the function of a cell. As experimental techniques for detection and validation of protein interactions are time consuming, there is a need for computational methods for this task. Protein interactions appear to form a network with a relatively high degree of local clustering. In this paper we exploit this clustering by suggesting a score based on triplets of observed protein interactions. The score utilises both protein characteristics and network properties. Our score based on triplets is shown to complement existing techniques for predicting protein interactions, outperforming them on data sets which display a high degree of clustering. The predicted interactions score highly against test measures for accuracy. Compared to a similar score derived from pairwise interactions only, the triplet score displays higher sensitivity and specificity. By looking at specific examples, we show how an experimental set of interactions can be enriched and validated. As part of this work we also examine the effect of different prior databases upon the accuracy of prediction and find that the interactions from the same kingdom give better results than from across kingdoms, suggesting that there may be fundamental differences between the networks. These results all emphasize that network structure is important and helps in the accurate prediction of protein interactions. The protein interaction data set and the program used in our analysis, and a list of predictions and validations, are available at http://www.stats.ox.ac.uk/bioinfo/resources/PredictingInteractions.

  11. Comparative genomic analysis of Vibrio parahaemolyticus: serotype conversion and virulence

    Gil Ana I

    2011-06-01

    Full Text Available Abstract Background Vibrio parahaemolyticus is a common cause of foodborne disease. Beginning in 1996, a more virulent strain having serotype O3:K6 caused major outbreaks in India and other parts of the world, resulting in the emergence of a pandemic. Other serovariants of this strain emerged during its dissemination and together with the original O3:K6 were termed strains of the pandemic clone. Two genomes, one of this virulent strain and one pre-pandemic strain have been sequenced. We sequenced four additional genomes of V. parahaemolyticus in this study that were isolated from different geographical regions and time points. Comparative genomic analyses of six strains of V. parahaemolyticus isolated from Asia and Peru were performed in order to advance knowledge concerning the evolution of V. parahaemolyticus; specifically, the genetic changes contributing to serotype conversion and virulence. Two pre-pandemic strains and three pandemic strains, isolated from different geographical regions, were serotype O3:K6 and either toxin profiles (tdh+, trh- or (tdh-, trh+. The sixth pandemic strain sequenced in this study was serotype O4:K68. Results Genomic analyses revealed that the trh+ and tdh+ strains had different types of pathogenicity islands and mobile elements as well as major structural differences between the tdh pathogenicity islands of the pre-pandemic and pandemic strains. In addition, the results of single nucleotide polymorphism (SNP analysis showed that 94% of the SNPs between O3:K6 and O4:K68 pandemic isolates were within a 141 kb region surrounding the O- and K-antigen-encoding gene clusters. The "core" genes of V. parahaemolyticus were also compared to those of V. cholerae and V. vulnificus, in order to delineate differences between these three pathogenic species. Approximately one-half (49-59% of each species' core genes were conserved in all three species, and 14-24% of the core genes were species-specific and in different

  12. Constraint Logic Programming approach to protein structure prediction

    Fogolari Federico

    2004-11-01

    Full Text Available Abstract Background The protein structure prediction problem is one of the most challenging problems in biological sciences. Many approaches have been proposed using database information and/or simplified protein models. The protein structure prediction problem can be cast in the form of an optimization problem. Notwithstanding its importance, the problem has very seldom been tackled by Constraint Logic Programming, a declarative programming paradigm suitable for solving combinatorial optimization problems. Results Constraint Logic Programming techniques have been applied to the protein structure prediction problem on the face-centered cube lattice model. Molecular dynamics techniques, endowed with the notion of constraint, have been also exploited. Even using a very simplified model, Constraint Logic Programming on the face-centered cube lattice model allowed us to obtain acceptable results for a few small proteins. As a test implementation their (known secondary structure and the presence of disulfide bridges are used as constraints. Simplified structures obtained in this way have been converted to all atom models with plausible structure. Results have been compared with a similar approach using a well-established technique as molecular dynamics. Conclusions The results obtained on small proteins show that Constraint Logic Programming techniques can be employed for studying protein simplified models, which can be converted into realistic all atom models. The advantage of Constraint Logic Programming over other, much more explored, methodologies, resides in the rapid software prototyping, in the easy way of encoding heuristics, and in exploiting all the advances made in this research area, e.g. in constraint propagation and its use for pruning the huge search space.

  13. Constraint Logic Programming approach to protein structure prediction.

    Dal Palù, Alessandro; Dovier, Agostino; Fogolari, Federico

    2004-11-30

    The protein structure prediction problem is one of the most challenging problems in biological sciences. Many approaches have been proposed using database information and/or simplified protein models. The protein structure prediction problem can be cast in the form of an optimization problem. Notwithstanding its importance, the problem has very seldom been tackled by Constraint Logic Programming, a declarative programming paradigm suitable for solving combinatorial optimization problems. Constraint Logic Programming techniques have been applied to the protein structure prediction problem on the face-centered cube lattice model. Molecular dynamics techniques, endowed with the notion of constraint, have been also exploited. Even using a very simplified model, Constraint Logic Programming on the face-centered cube lattice model allowed us to obtain acceptable results for a few small proteins. As a test implementation their (known) secondary structure and the presence of disulfide bridges are used as constraints. Simplified structures obtained in this way have been converted to all atom models with plausible structure. Results have been compared with a similar approach using a well-established technique as molecular dynamics. The results obtained on small proteins show that Constraint Logic Programming techniques can be employed for studying protein simplified models, which can be converted into realistic all atom models. The advantage of Constraint Logic Programming over other, much more explored, methodologies, resides in the rapid software prototyping, in the easy way of encoding heuristics, and in exploiting all the advances made in this research area, e.g. in constraint propagation and its use for pruning the huge search space.

  14. Identification of critical residues of the serotype modifying O-acetyltransferase of Shigella flexneri

    Thanweer Farzaana

    2012-07-01

    Full Text Available Abstract Background Thirteen serotypes of Shigella flexneri (S. flexneri have been recognised, all of which are capable of causing bacillary dysentery or shigellosis. With the emergence of the newer S. flexneri serotypes, the development of an effective vaccine has only become more challenging. One of the factors responsible for the generation of serotype diversity is an LPS O-antigen modifying, integral membrane protein known as O-acetyltransferase or Oac. Oac functions by adding an acetyl group to a specific O-antigen sugar, thus changing the antigenic signature of the parent S. flexneri strain. Oac is a membrane protein, consisting of hydrophobic and hydrophilic components. Oac bears homology to several known and predicted acetyltransferases with most homology existing in the N-terminal transmembrane (TM regions. Results In this study, the conserved motifs in the TM regions and in hydrophilic loops of S. flexneri Oac were targeted for mutagenesis with the aim of identifying the amino acid residues essential for the function of Oac. We previously identified three critical arginines–R73, R75 and R76 in the cytoplasmic loop 3 of Oac. Re-establishing that these arginines are critical, in this study we suggest a catalytic role for R73 and a structural role for R75 and R76 in O-acetylation. Serine-glycine motifs (SG 52–53, GS 138–139 and SYG 274–276, phenylalanine-proline motifs (FP 78–79 and FPV 282–84 and a tryptophan-threonine motif (WT141-142 found in TM segments and residues RK 110–111, GR 269–270 and D333 found in hydrophilic loops were also found to be critical to Oac function. Conclusions By studying the effect of the mutations on Oac’s function and assembly, an insight into the possible roles played by the chosen amino acids in Oac was gained. The transmembrane serine-glycine motifs and hydrophilic residues (RK 110–111, GR 269–270 and D333 were shown to have an affect on Oac assembly which suggests a structural role

  15. Virality Prediction and Community Structure in Social Networks

    Weng, Lilian; Menczer, Filippo; Ahn, Yong-Yeol

    2013-08-01

    How does network structure affect diffusion? Recent studies suggest that the answer depends on the type of contagion. Complex contagions, unlike infectious diseases (simple contagions), are affected by social reinforcement and homophily. Hence, the spread within highly clustered communities is enhanced, while diffusion across communities is hampered. A common hypothesis is that memes and behaviors are complex contagions. We show that, while most memes indeed spread like complex contagions, a few viral memes spread across many communities, like diseases. We demonstrate that the future popularity of a meme can be predicted by quantifying its early spreading pattern in terms of community concentration. The more communities a meme permeates, the more viral it is. We present a practical method to translate data about community structure into predictive knowledge about what information will spread widely. This connection contributes to our understanding in computational social science, social media analytics, and marketing applications.

  16. PCI-SS: MISO dynamic nonlinear protein secondary structure prediction

    Aboul-Magd Mohammed O

    2009-07-01

    Full Text Available Abstract Background Since the function of a protein is largely dictated by its three dimensional configuration, determining a protein's structure is of fundamental importance to biology. Here we report on a novel approach to determining the one dimensional secondary structure of proteins (distinguishing α-helices, β-strands, and non-regular structures from primary sequence data which makes use of Parallel Cascade Identification (PCI, a powerful technique from the field of nonlinear system identification. Results Using PSI-BLAST divergent evolutionary profiles as input data, dynamic nonlinear systems are built through a black-box approach to model the process of protein folding. Genetic algorithms (GAs are applied in order to optimize the architectural parameters of the PCI models. The three-state prediction problem is broken down into a combination of three binary sub-problems and protein structure classifiers are built using 2 layers of PCI classifiers. Careful construction of the optimization, training, and test datasets ensures that no homology exists between any training and testing data. A detailed comparison between PCI and 9 contemporary methods is provided over a set of 125 new protein chains guaranteed to be dissimilar to all training data. Unlike other secondary structure prediction methods, here a web service is developed to provide both human- and machine-readable interfaces to PCI-based protein secondary structure prediction. This server, called PCI-SS, is available at http://bioinf.sce.carleton.ca/PCISS. In addition to a dynamic PHP-generated web interface for humans, a Simple Object Access Protocol (SOAP interface is added to permit invocation of the PCI-SS service remotely. This machine-readable interface facilitates incorporation of PCI-SS into multi-faceted systems biology analysis pipelines requiring protein secondary structure information, and greatly simplifies high-throughput analyses. XML is used to represent the input

  17. Predicting protein structures with a multiplayer online game

    Cooper, Seth; Khatib, Firas; Treuille, Adrien; Barbero, Janos; Lee, Jeehyung; Beenen, Michael; Leaver-Fay, Andrew; Baker, David; Popović, Zoran

    2010-01-01

    People exert significant amounts of problem solving effort playing computer games. Simple image- and text-recognition tasks have been successfully crowd-sourced through gamesi, ii, iii, but it is not clear if more complex scientific problems can be similarly solved with human-directed computing. Protein structure prediction is one such problem: locating the biologically relevant native conformation of a protein is a formidable computational challenge given the very large size of the search sp...

  18. Improved hybrid optimization algorithm for 3D protein structure prediction.

    Zhou, Changjun; Hou, Caixia; Wei, Xiaopeng; Zhang, Qiang

    2014-07-01

    A new improved hybrid optimization algorithm - PGATS algorithm, which is based on toy off-lattice model, is presented for dealing with three-dimensional protein structure prediction problems. The algorithm combines the particle swarm optimization (PSO), genetic algorithm (GA), and tabu search (TS) algorithms. Otherwise, we also take some different improved strategies. The factor of stochastic disturbance is joined in the particle swarm optimization to improve the search ability; the operations of crossover and mutation that are in the genetic algorithm are changed to a kind of random liner method; at last tabu search algorithm is improved by appending a mutation operator. Through the combination of a variety of strategies and algorithms, the protein structure prediction (PSP) in a 3D off-lattice model is achieved. The PSP problem is an NP-hard problem, but the problem can be attributed to a global optimization problem of multi-extremum and multi-parameters. This is the theoretical principle of the hybrid optimization algorithm that is proposed in this paper. The algorithm combines local search and global search, which overcomes the shortcoming of a single algorithm, giving full play to the advantage of each algorithm. In the current universal standard sequences, Fibonacci sequences and real protein sequences are certified. Experiments show that the proposed new method outperforms single algorithms on the accuracy of calculating the protein sequence energy value, which is proved to be an effective way to predict the structure of proteins.

  19. Probabilistic approaches to life prediction of nuclear plant structural components

    Villain, B.; Pitner, P.; Procaccia, H.

    1996-01-01

    In the last decade there has been an increasing interest at EDF in developing and applying probabilistic methods for a variety of purposes. In the field of structural integrity and reliability they are used to evaluate the effect of deterioration due to aging mechanisms, mainly on major passive structural components such as steam generators, pressure vessels and piping in nuclear plants. Because there can be numerous uncertainties involved in a assessment of the performance of these structural components, probabilistic methods. The benefits of a probabilistic approach are the clear treatment of uncertainly and the possibility to perform sensitivity studies from which it is possible to identify and quantify the effect of key factors and mitigative actions. They thus provide information to support effective decisions to optimize In-Service Inspection planning and maintenance strategies and for realistic lifetime prediction or reassessment. The purpose of the paper is to discuss and illustrate the methods available at EDF for probabilistic component life prediction. This includes a presentation of software tools in classical, Bayesian and structural reliability, and an application on two case studies (steam generator tube bundle, reactor pressure vessel). (authors)

  20. Probabilistic approaches to life prediction of nuclear plant structural components

    Villain, B.; Pitner, P.; Procaccia, H.

    1996-01-01

    In the last decade there has been an increasing interest at EDF in developing and applying probabilistic methods for a variety of purposes. In the field of structural integrity and reliability they are used to evaluate the effect of deterioration due to aging mechanisms, mainly on major passive structural components such as steam generators, pressure vessels and piping in nuclear plants. Because there can be numerous uncertainties involved in an assessment of the performance of these structural components, probabilistic methods provide an attractive alternative or supplement to more conventional deterministic methods. The benefits of a probabilistic approach are the clear treatment of uncertainty and the possibility to perform sensitivity studies from which it is possible to identify and quantify the effect of key factors and mitigative actions. They thus provide information to support effective decisions to optimize In-Service Inspection planning and maintenance strategies and for realistic lifetime prediction or reassessment. The purpose of the paper is to discuss and illustrate the methods available at EDF for probabilistic component life prediction. This includes a presentation of software tools in classical, Bayesian and structural reliability, and an application on two case studies (steam generator tube bundle, reactor pressure vessel)

  1. Serotype distribution in non-bacteremic pneumococcal pneumonia

    Benfield, Thomas Lars Vibe; Skovgaard, Marlene; Schønheyder, Henrik Carl

    2013-01-01

    There is limited knowledge of serotypes that cause non-bacteremic pneumococcal pneumonia (NBP). Here we report serotypes, their associated disease potential and coverage of pneumococcal conjugate vaccines (PCV) in adults with NBP and compare these to bacteremic pneumonia (BP).......There is limited knowledge of serotypes that cause non-bacteremic pneumococcal pneumonia (NBP). Here we report serotypes, their associated disease potential and coverage of pneumococcal conjugate vaccines (PCV) in adults with NBP and compare these to bacteremic pneumonia (BP)....

  2. Predicting Reactive Transport Dynamics in Carbonates using Initial Pore Structure

    Menke, H. P.; Nunes, J. P. P.; Blunt, M. J.

    2017-12-01

    Understanding rock-fluid interaction at the pore-scale is imperative for accurate predictive modelling of carbon storage permanence. However, coupled reactive transport models are computationally expensive, requiring either a sacrifice of resolution or high performance computing to solve relatively simple geometries. Many recent studies indicate that initial pore structure many be the dominant mechanism in determining the dissolution regime. Here we investigate how well the initial pore structure is predictive of distribution and amount of dissolution during reactive flow using particle tracking on the initial image. Two samples of carbonate rock with varying initial pore space heterogeneity were reacted with reservoir condition CO2-saturated brine and scanned dynamically during reactive flow at a 4-μm resolution between 4 and 40 times using 4D X-ray micro-tomography over the course of 1.5 hours using μ-CT. Flow was modelled on the initial binarized image using a Navier-Stokes solver. Particle tracking was then run on the velocity fields, the streamlines were traced, and the streamline density was calculated both on a voxel-by-voxel and a channel-by-channel basis. The density of streamlines was then compared to the amount of dissolution in subsequent time steps during reaction. It was found that for the flow and transport regimes studied, the streamline density distribution in the initial image accurately predicted the dominant pathways of dissolution and gave good indicators of the type of dissolution regime that would later develop. This work suggests that the eventual reaction-induced changes in pore structure are deterministic rather than stochastic and can be predicted with high resolution imaging of unreacted rock.

  3. Prediction of Cracking Induced by Indirect Actions in RC Structures

    Anerdi, Costanza; Bertagnoli, Gabriele; Gino, Diego; Malavisi, Marzia; Mancini, Giuseppe

    2017-10-01

    Cracking of concrete plays a key role in reinforced concrete (RC) structures design, especially in serviceability conditions. A variety of reasons contribute to develop cracking and its presence in concrete structures is to be considered as almost unavoidable. Therefore, a good control of the phenomenon in order to provide durability is required. Cracking development is due to tensile stresses that arise in concrete structures as a result of the action of direct external loads or restrained endogenous deformations. This paper focuses on cracking induced by indirect actions. In fact, there is very limited literature regarding this particular phenomenon if compared to its high incidence in the construction practice. As a consequence, the correct prediction of the crack opening, width and position when structures are subjected to imposed deformations, such as massive castings or other highly restrained structures, becomes a compelling task, not so much for the structural capacity, as for their durability. However, this is only partially addressed by commonly used design methods, which are usually intended for direct actions. A set of non-linear analysis on simple tie models is performed using the Finite Element Method in order to study the cracking process under imposed deformations. Different concrete grades have been considered and analysed. The results of this study have been compared with the provisions of the most common codes.

  4. Bitter or not? BitterPredict, a tool for predicting taste from chemical structure.

    Dagan-Wiener, Ayana; Nissim, Ido; Ben Abu, Natalie; Borgonovo, Gigliola; Bassoli, Angela; Niv, Masha Y

    2017-09-21

    Bitter taste is an innately aversive taste modality that is considered to protect animals from consuming toxic compounds. Yet, bitterness is not always noxious and some bitter compounds have beneficial effects on health. Hundreds of bitter compounds were reported (and are accessible via the BitterDB http://bitterdb.agri.huji.ac.il/dbbitter.php ), but numerous additional bitter molecules are still unknown. The dramatic chemical diversity of bitterants makes bitterness prediction a difficult task. Here we present a machine learning classifier, BitterPredict, which predicts whether a compound is bitter or not, based on its chemical structure. BitterDB was used as the positive set, and non-bitter molecules were gathered from literature to create the negative set. Adaptive Boosting (AdaBoost), based on decision trees machine-learning algorithm was applied to molecules that were represented using physicochemical and ADME/Tox descriptors. BitterPredict correctly classifies over 80% of the compounds in the hold-out test set, and 70-90% of the compounds in three independent external sets and in sensory test validation, providing a quick and reliable tool for classifying large sets of compounds into bitter and non-bitter groups. BitterPredict suggests that about 40% of random molecules, and a large portion (66%) of clinical and experimental drugs, and of natural products (77%) are bitter.

  5. Structural syntactic prediction measured with ELAN: evidence from ERPs.

    Fonteneau, Elisabeth

    2013-02-08

    The current study used event-related potentials (ERPs) to investigate how and when argument structure information is used during the processing of sentences with a filler-gap dependency. We hypothesize that one specific property - animacy (living vs. non-living) - is used by the parser during the building of the syntactic structure. Participants heard sentences that were rated off-line as having an expected noun (Who did the Lion King chase the caravan with?) or an unexpected noun (Who did Lion King chase the animal with?). This prediction is based on the animacy properties relation between the wh-word and the noun in the object position. ERPs from the noun in the unexpected condition (animal) elicited a typical Early Left Anterior Negativity (ELAN)/P600 complex compared to the noun in the expected condition (caravan). Firstly, these results demonstrate that the ELAN reflects not only grammatical category violation but also animacy property expectations in filler-gap dependency. Secondly, our data suggests that the language comprehension system is able to make detailed predictions about aspects of the upcoming words to build up the syntactic structure. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

  6. Integrating chemical footprinting data into RNA secondary structure prediction.

    Kourosh Zarringhalam

    Full Text Available Chemical and enzymatic footprinting experiments, such as shape (selective 2'-hydroxyl acylation analyzed by primer extension, yield important information about RNA secondary structure. Indeed, since the [Formula: see text]-hydroxyl is reactive at flexible (loop regions, but unreactive at base-paired regions, shape yields quantitative data about which RNA nucleotides are base-paired. Recently, low error rates in secondary structure prediction have been reported for three RNAs of moderate size, by including base stacking pseudo-energy terms derived from shape data into the computation of minimum free energy secondary structure. Here, we describe a novel method, RNAsc (RNA soft constraints, which includes pseudo-energy terms for each nucleotide position, rather than only for base stacking positions. We prove that RNAsc is self-consistent, in the sense that the nucleotide-specific probabilities of being unpaired in the low energy Boltzmann ensemble always become more closely correlated with the input shape data after application of RNAsc. From this mathematical perspective, the secondary structure predicted by RNAsc should be 'correct', in as much as the shape data is 'correct'. We benchmark RNAsc against the previously mentioned method for eight RNAs, for which both shape data and native structures are known, to find the same accuracy in 7 out of 8 cases, and an improvement of 25% in one case. Furthermore, we present what appears to be the first direct comparison of shape data and in-line probing data, by comparing yeast asp-tRNA shape data from the literature with data from in-line probing experiments we have recently performed. With respect to several criteria, we find that shape data appear to be more robust than in-line probing data, at least in the case of asp-tRNA.

  7. The sequential structure of brain activation predicts skill.

    Anderson, John R; Bothell, Daniel; Fincham, Jon M; Moon, Jungaa

    2016-01-29

    In an fMRI study, participants were trained to play a complex video game. They were scanned early and then again after substantial practice. While better players showed greater activation in one region (right dorsal striatum) their relative skill was better diagnosed by considering the sequential structure of whole brain activation. Using a cognitive model that played this game, we extracted a characterization of the mental states that are involved in playing a game and the statistical structure of the transitions among these states. There was a strong correspondence between this measure of sequential structure and the skill of different players. Using multi-voxel pattern analysis, it was possible to recognize, with relatively high accuracy, the cognitive states participants were in during particular scans. We used the sequential structure of these activation-recognized states to predict the skill of individual players. These findings indicate that important features about information-processing strategies can be identified from a model-based analysis of the sequential structure of brain activation. Copyright © 2015 Elsevier Ltd. All rights reserved.

  8. Protein Loop Structure Prediction Using Conformational Space Annealing.

    Heo, Seungryong; Lee, Juyong; Joo, Keehyoung; Shin, Hang-Cheol; Lee, Jooyoung

    2017-05-22

    We have developed a protein loop structure prediction method by combining a new energy function, which we call E PLM (energy for protein loop modeling), with the conformational space annealing (CSA) global optimization algorithm. The energy function includes stereochemistry, dynamic fragment assembly, distance-scaled finite ideal gas reference (DFIRE), and generalized orientation- and distance-dependent terms. For the conformational search of loop structures, we used the CSA algorithm, which has been quite successful in dealing with various hard global optimization problems. We assessed the performance of E PLM with two widely used loop-decoy sets, Jacobson and RAPPER, and compared the results against the DFIRE potential. The accuracy of model selection from a pool of loop decoys as well as de novo loop modeling starting from randomly generated structures was examined separately. For the selection of a nativelike structure from a decoy set, E PLM was more accurate than DFIRE in the case of the Jacobson set and had similar accuracy in the case of the RAPPER set. In terms of sampling more nativelike loop structures, E PLM outperformed E DFIRE for both decoy sets. This new approach equipped with E PLM and CSA can serve as the state-of-the-art de novo loop modeling method.

  9. Offspring social network structure predicts fitness in families.

    Royle, Nick J; Pike, Thomas W; Heeb, Philipp; Richner, Heinz; Kölliker, Mathias

    2012-12-22

    Social structures such as families emerge as outcomes of behavioural interactions among individuals, and can evolve over time if families with particular types of social structures tend to leave more individuals in subsequent generations. The social behaviour of interacting individuals is typically analysed as a series of multiple dyadic (pair-wise) interactions, rather than a network of interactions among multiple individuals. However, in species where parents feed dependant young, interactions within families nearly always involve more than two individuals simultaneously. Such social networks of interactions at least partly reflect conflicts of interest over the provision of costly parental investment. Consequently, variation in family network structure reflects variation in how conflicts of interest are resolved among family members. Despite its importance in understanding the evolution of emergent properties of social organization such as family life and cooperation, nothing is currently known about how selection acts on the structure of social networks. Here, we show that the social network structure of broods of begging nestling great tits Parus major predicts fitness in families. Although selection at the level of the individual favours large nestlings, selection at the level of the kin-group primarily favours families that resolve conflicts most effectively.

  10. Optimal neural networks for protein-structure prediction

    Head-Gordon, T.; Stillinger, F.H.

    1993-01-01

    The successful application of neural-network algorithms for prediction of protein structure is stymied by three problem areas: the sparsity of the database of known protein structures, poorly devised network architectures which make the input-output mapping opaque, and a global optimization problem in the multiple-minima space of the network variables. We present a simplified polypeptide model residing in two dimensions with only two amino-acid types, A and B, which allows the determination of the global energy structure for all possible sequences of pentamer, hexamer, and heptamer lengths. This model simplicity allows us to compile a complete structural database and to devise neural networks that reproduce the tertiary structure of all sequences with absolute accuracy and with the smallest number of network variables. These optimal networks reveal that the three problem areas are convoluted, but that thoughtful network designs can actually deconvolute these detrimental traits to provide network algorithms that genuinely impact on the ability of the network to generalize or learn the desired mappings. Furthermore, the two-dimensional polypeptide model shows sufficient chemical complexity so that transfer of neural-network technology to more realistic three-dimensional proteins is evident

  11. Mapping monomeric threading to protein-protein structure prediction.

    Guerler, Aysam; Govindarajoo, Brandon; Zhang, Yang

    2013-03-25

    The key step of template-based protein-protein structure prediction is the recognition of complexes from experimental structure libraries that have similar quaternary fold. Maintaining two monomer and dimer structure libraries is however laborious, and inappropriate library construction can degrade template recognition coverage. We propose a novel strategy SPRING to identify complexes by mapping monomeric threading alignments to protein-protein interactions based on the original oligomer entries in the PDB, which does not rely on library construction and increases the efficiency and quality of complex template recognitions. SPRING is tested on 1838 nonhomologous protein complexes which can recognize correct quaternary template structures with a TM score >0.5 in 1115 cases after excluding homologous proteins. The average TM score of the first model is 60% and 17% higher than that by HHsearch and COTH, respectively, while the number of targets with an interface RMSD benchmark proteins. Although the relative performance of SPRING and ZDOCK depends on the level of homology filters, a combination of the two methods can result in a significantly higher model quality than ZDOCK at all homology thresholds. These data demonstrate a new efficient approach to quaternary structure recognition that is ready to use for genome-scale modeling of protein-protein interactions due to the high speed and accuracy.

  12. A probabilistic fragment-based protein structure prediction algorithm.

    David Simoncini

    Full Text Available Conformational sampling is one of the bottlenecks in fragment-based protein structure prediction approaches. They generally start with a coarse-grained optimization where mainchain atoms and centroids of side chains are considered, followed by a fine-grained optimization with an all-atom representation of proteins. It is during this coarse-grained phase that fragment-based methods sample intensely the conformational space. If the native-like region is sampled more, the accuracy of the final all-atom predictions may be improved accordingly. In this work we present EdaFold, a new method for fragment-based protein structure prediction based on an Estimation of Distribution Algorithm. Fragment-based approaches build protein models by assembling short fragments from known protein structures. Whereas the probability mass functions over the fragment libraries are uniform in the usual case, we propose an algorithm that learns from previously generated decoys and steers the search toward native-like regions. A comparison with Rosetta AbInitio protocol shows that EdaFold is able to generate models with lower energies and to enhance the percentage of near-native coarse-grained decoys on a benchmark of [Formula: see text] proteins. The best coarse-grained models produced by both methods were refined into all-atom models and used in molecular replacement. All atom decoys produced out of EdaFold's decoy set reach high enough accuracy to solve the crystallographic phase problem by molecular replacement for some test proteins. EdaFold showed a higher success rate in molecular replacement when compared to Rosetta. Our study suggests that improving low resolution coarse-grained decoys allows computational methods to avoid subsequent sampling issues during all-atom refinement and to produce better all-atom models. EdaFold can be downloaded from http://www.riken.jp/zhangiru/software.html [corrected].

  13. Effect of Serotype on Pneumococcal Competition in a Mouse Colonization Model.

    Trzciński, Krzysztof; Li, Yuan; Weinberger, Daniel M; Thompson, Claudette M; Cordy, Derrick; Bessolo, Andrew; Malley, Richard; Lipsitch, Marc

    2015-09-15

    Competitive interactions between Streptococcus pneumoniae strains during host colonization could influence the serotype distribution in nasopharyngeal carriage and pneumococcal disease. We evaluated the competitive fitness of strains of serotypes 6B, 14, 19A, 19F, 23F, and 35B in a mouse model of multiserotype carriage. Isogenic variants were constructed using clinical strains as the capsule gene donors. Animals were intranasally inoculated with a mixture of up to six pneumococcal strains of different serotypes, with separate experiments involving either clinical isolates or isogenic capsule-switch variants of clinical strain TIGR4. Upper-respiratory-tract samples were repeatedly collected from animals in order to monitor changes in the serotype ratios using quantitative PCR. A reproducible hierarchy of capsular types developed in the airways of mice inoculated with multiple strains. Serotype ranks in this hierarchy were similar among pneumococcal strains of different genetic backgrounds in different strains of mice and were not altered when tested under a range of host conditions. This rank correlated with the measure of the metabolic cost of capsule synthesis and in vitro measure of pneumococcal cell surface charge, both parameters considered to be predictors of serotype-specific fitness in carriage. This study demonstrates the presence of a robust competitive hierarchy of pneumococcal serotypes in vivo that is driven mainly, but not exclusively, by the capsule itself. Streptococcus pneumoniae (pneumococcus) is the leading cause of death due to respiratory bacterial infections but also a commensal frequently carried in upper airways. Available vaccines induce immune responses against polysaccharides coating pneumococcal cells, but with over 90 different capsular types (serotypes) identified, they can only target strains of the selected few serotypes most prevalent in disease. Vaccines not only protect vaccinated individuals against disease but also protect by

  14. Phenotypic and molecular characterization of Salmonella serotypes ...

    The presence of Salmonella and human pathogens in unpasteurized milk remains a public health hazard. The study reported the phenotypic and molecular characterization of Salmonella serotypes in cow raw milk, cheese and traditional yoghurt marketed for man's consumption in Nigeria. Isolation of Salmonella was done ...

  15. Virulence, serotype and phylogenetic groups of diarrhoeagenic ...

    Dr DADIE Thomas

    2014-02-17

    Feb 17, 2014 ... The virulence, serotype and phylogenetic traits of diarrhoeagenic Escherichia coli were detected in 502 strains isolated during digestive infections. Molecular detection of the target virulence genes, rfb gene of operon O and phylogenetic grouping genes Chua, yjaA and TSPE4.C2 was performed.

  16. Facial Structure Predicts Sexual Orientation in Both Men and Women.

    Skorska, Malvina N; Geniole, Shawn N; Vrysen, Brandon M; McCormick, Cheryl M; Bogaert, Anthony F

    2015-07-01

    Biological models have typically framed sexual orientation in terms of effects of variation in fetal androgen signaling on sexual differentiation, although other biological models exist. Despite marked sex differences in facial structure, the relationship between sexual orientation and facial structure is understudied. A total of 52 lesbian women, 134 heterosexual women, 77 gay men, and 127 heterosexual men were recruited at a Canadian campus and various Canadian Pride and sexuality events. We found that facial structure differed depending on sexual orientation; substantial variation in sexual orientation was predicted using facial metrics computed by a facial modelling program from photographs of White faces. At the univariate level, lesbian and heterosexual women differed in 17 facial features (out of 63) and four were unique multivariate predictors in logistic regression. Gay and heterosexual men differed in 11 facial features at the univariate level, of which three were unique multivariate predictors. Some, but not all, of the facial metrics differed between the sexes. Lesbian women had noses that were more turned up (also more turned up in heterosexual men), mouths that were more puckered, smaller foreheads, and marginally more masculine face shapes (also in heterosexual men) than heterosexual women. Gay men had more convex cheeks, shorter noses (also in heterosexual women), and foreheads that were more tilted back relative to heterosexual men. Principal components analysis and discriminant functions analysis generally corroborated these results. The mechanisms underlying variation in craniofacial structure--both related and unrelated to sexual differentiation--may thus be important in understanding the development of sexual orientation.

  17. Protein Function Prediction Based on Sequence and Structure Information

    Smaili, Fatima Z.

    2016-05-25

    The number of available protein sequences in public databases is increasing exponentially. However, a significant fraction of these sequences lack functional annotation which is essential to our understanding of how biological systems and processes operate. In this master thesis project, we worked on inferring protein functions based on the primary protein sequence. In the approach we follow, 3D models are first constructed using I-TASSER. Functions are then deduced by structurally matching these predicted models, using global and local similarities, through three independent enzyme commission (EC) and gene ontology (GO) function libraries. The method was tested on 250 “hard” proteins, which lack homologous templates in both structure and function libraries. The results show that this method outperforms the conventional prediction methods based on sequence similarity or threading. Additionally, our method could be improved even further by incorporating protein-protein interaction information. Overall, the method we use provides an efficient approach for automated functional annotation of non-homologous proteins, starting from their sequence.

  18. Prediction of Chloride Diffusion in Concrete Structure Using Meshless Methods

    Ling Yao

    2016-01-01

    Full Text Available Degradation of RC structures due to chloride penetration followed by reinforcement corrosion is a serious problem in civil engineering. The numerical simulation methods at present mainly involve finite element methods (FEM, which are based on mesh generation. In this study, element-free Galerkin (EFG and meshless weighted least squares (MWLS methods are used to solve the problem of simulation of chloride diffusion in concrete. The range of a scaling parameter is presented using numerical examples based on meshless methods. One- and two-dimensional numerical examples validated the effectiveness and accuracy of the two meshless methods by comparing results obtained by MWLS with results computed by EFG and FEM and results calculated by an analytical method. A good agreement is obtained among MWLS and EFG numerical simulations and the experimental data obtained from an existing marine concrete structure. These results indicate that MWLS and EFG are reliable meshless methods that can be used for the prediction of chloride ingress in concrete structures.

  19. The extended evolutionary synthesis: its structure, assumptions and predictions

    Laland, Kevin N.; Uller, Tobias; Feldman, Marcus W.; Sterelny, Kim; Müller, Gerd B.; Moczek, Armin; Jablonka, Eva; Odling-Smee, John

    2015-01-01

    Scientific activities take place within the structured sets of ideas and assumptions that define a field and its practices. The conceptual framework of evolutionary biology emerged with the Modern Synthesis in the early twentieth century and has since expanded into a highly successful research program to explore the processes of diversification and adaptation. Nonetheless, the ability of that framework satisfactorily to accommodate the rapid advances in developmental biology, genomics and ecology has been questioned. We review some of these arguments, focusing on literatures (evo-devo, developmental plasticity, inclusive inheritance and niche construction) whose implications for evolution can be interpreted in two ways—one that preserves the internal structure of contemporary evolutionary theory and one that points towards an alternative conceptual framework. The latter, which we label the ‘extended evolutionary synthesis' (EES), retains the fundaments of evolutionary theory, but differs in its emphasis on the role of constructive processes in development and evolution, and reciprocal portrayals of causation. In the EES, developmental processes, operating through developmental bias, inclusive inheritance and niche construction, share responsibility for the direction and rate of evolution, the origin of character variation and organism–environment complementarity. We spell out the structure, core assumptions and novel predictions of the EES, and show how it can be deployed to stimulate and advance research in those fields that study or use evolutionary biology. PMID:26246559

  20. The experimental search for new predicted binary-alloy structures

    Erb, K. C.; Richey, Lauren; Lang, Candace; Campbell, Branton; Hart, Gus

    2010-10-01

    Predicting new ordered phases in metallic alloys is a productive line of inquiry because configurational ordering in an alloy can dramatically alter their useful material properties. One is able to infer the existence of an ordered phase in an alloy using first-principles calculated formation enthalpies.ootnotetextG. L. W. Hart, ``Where are Nature's missing structures?,'' Nature Materials 6 941-945 2007 Using this approach, we have been able to identify stable (i.e. lowest energy) orderings in a variety of binary metallic alloys. Many of these phases have been observed experimentally in the past, though others have not. In pursuit of several of the missing structures, we have characterized potential orderings in PtCd, PtPd and PtMo alloys using synchrotron x-ray powder diffraction and symmetry-analysis tools.ootnotetextB. J. Campbell, H. T. Stokes, D. E. Tanner, and D. M. Hatch, ``ISODISPLACE: a web-based tool for exploring structural distortions,'' J. Appl. Cryst. 39, 607-614 (2006)

  1. Structural Acoustic Prediction and Interior Noise Control Technology

    Mathur, G. P.; Chin, C. L.; Simpson, M. A.; Lee, J. T.; Palumbo, Daniel L. (Technical Monitor)

    2001-01-01

    This report documents the results of Task 14, "Structural Acoustic Prediction and Interior Noise Control Technology". The task was to evaluate the performance of tuned foam elements (termed Smart Foam) both analytically and experimentally. Results taken from a three-dimensional finite element model of an active, tuned foam element are presented. Measurements of sound absorption and sound transmission loss were taken using the model. These results agree well with published data. Experimental performance data were taken in Boeing's Interior Noise Test Facility where 12 smart foam elements were applied to a 757 sidewall. Several configurations were tested. Noise reductions of 5-10 dB were achieved over the 200-800 Hz bandwidth of the controller. Accelerometers mounted on the panel provided a good reference for the controller. Configurations with far-field error microphones outperformed near-field cases.

  2. Simple neural substrate predicts complex rhythmic structure in duetting birds

    Amador, Ana; Trevisan, M. A.; Mindlin, G. B.

    2005-09-01

    Horneros (Furnarius Rufus) are South American birds well known for their oven-looking nests and their ability to sing in couples. Previous work has analyzed the rhythmic organization of the duets, unveiling a mathematical structure behind the songs. In this work we analyze in detail an extended database of duets. The rhythms of the songs are compatible with the dynamics presented by a wide class of dynamical systems: forced excitable systems. Compatible with this nonlinear rule, we build a biologically inspired model for how the neural and the anatomical elements may interact to produce the observed rhythmic patterns. This model allows us to synthesize songs presenting the acoustic and rhythmic features observed in real songs. We also make testable predictions in order to support our hypothesis.

  3. Predicting DNA-binding proteins and binding residues by complex structure prediction and application to human proteome.

    Huiying Zhao

    Full Text Available As more and more protein sequences are uncovered from increasingly inexpensive sequencing techniques, an urgent task is to find their functions. This work presents a highly reliable computational technique for predicting DNA-binding function at the level of protein-DNA complex structures, rather than low-resolution two-state prediction of DNA-binding as most existing techniques do. The method first predicts protein-DNA complex structure by utilizing the template-based structure prediction technique HHblits, followed by binding affinity prediction based on a knowledge-based energy function (Distance-scaled finite ideal-gas reference state for protein-DNA interactions. A leave-one-out cross validation of the method based on 179 DNA-binding and 3797 non-binding protein domains achieves a Matthews correlation coefficient (MCC of 0.77 with high precision (94% and high sensitivity (65%. We further found 51% sensitivity for 82 newly determined structures of DNA-binding proteins and 56% sensitivity for the human proteome. In addition, the method provides a reasonably accurate prediction of DNA-binding residues in proteins based on predicted DNA-binding complex structures. Its application to human proteome leads to more than 300 novel DNA-binding proteins; some of these predicted structures were validated by known structures of homologous proteins in APO forms. The method [SPOT-Seq (DNA] is available as an on-line server at http://sparks-lab.org.

  4. Assessment of CASP7 structure predictions for template free targets.

    Jauch, Ralf; Yeo, Hock Chuan; Kolatkar, Prasanna R; Clarke, Neil D

    2007-01-01

    In CASP7, protein structure prediction targets that lacked substantial similarity to a protein in the PDB at the time of assessment were considered to be free modeling targets (FM). We assessed predictions for 14 FM targets as well as four other targets that were deemed to be on the borderline between FM targets and template based modeling targets (TBM/FM). GDT_TS was used as one measure of model quality. Model quality was also assessed by visual inspection. Visual inspection was performed by three independent assessors who were blinded to GDT_TS scores and other quantitative measures of model quality. The best models by visual inspection tended to rank among the top few percent by GDT_TS, but were typically not the highest scoring models. Thus, visual inspection remains an essential component of assessment for FM targets. Overall, group TS020 (Baker) performed best, but success on individual targets was widely distributed among many groups. Among these other groups, TS024 and TS025 (Zhang and Zhang server) performed notably well without exceptionally large computing resources. This should be considered encouraging for future CASPs. There was a sense of progress in template FM relative to CASP6, but we were unable to demonstrate this progress objectively. (c) 2007 Wiley-Liss, Inc.

  5. Lifetime prediction of structures submitted to thermal fatigue loadings

    Amiable, S.

    2006-01-01

    The aim of this work is to predict the lifetime of structures submitted to thermal fatigue loadings. This work lies within the studies undertaken by the CEA on the thermal fatigue problems from the french reactor of Civaux. In particular we study the SPLASH test: a specimen is heated continuously and cyclically cooled down by a water spray. This loading generates important temperature gradients in space and time and leads to the initiation and the propagation of a crack network. We propose a new thermo-mechanical model to simulate the SPLASH experiment and we propose a new fatigue criterion to predict the lifetime of the SPLASH specimen. We propose and compare several numerical models with various complexity to estimate the mechanical response of the SPLASH specimen. The practical implications of this work are the reevaluation of the hypothesis used in the French code RCC, which are used to simulate thermal shock and to interpret the results in terms of fatigue. This work leads to new perspectives on the mechanical interpretation of the fatigue criterion. (author)

  6. Inhibition of Metalloprotease Botulinum Serotype A from a Pseudo-Peptide Binding Mode to a Small Molecule that is Active in Primary Neurons

    Burnett, James C; Ruthel, Gordon; Stegmann, Christian M; Panchal, Rekha G; Nguyen, Tam L; Hermone, Ann R; Stafford, Robert G; Lane, Douglas J; Kenny, Tara A; McGarth, Connor F

    2007-01-01

    An efficient research strategy integrating empirically-guided, structure-based modeling and chemoinformatics was used to discover potent small molecule inhibitors of the botulinum neurotoxin serotype A light chain...

  7. Occupational Structure in European Countries: What do Forecasts Predict?

    Nina Vishnevskaya

    2017-12-01

    Full Text Available This paper analyzes the future occupational structure of the labour force in European members of the Organisation for Co-operation and Development (OECD. Occupational structure forecasts allow researchers to evaluate the quality of job openings and, consequently, overall future labour market performance. Identification of demand for certain occupations in Europe can facilitate assessment of whether processes occurring in the Russian labour market are consistent with global trends. The paper discusses the methodology of labour force forecasting and basic research approaches to the prediction of occupational structure changes. It emphasizes the dynamics of demand for representatives of certain occupations in Europe by identifying the fastest growing and declining occupations and suggests possible reasons for changing demand. The paper demonstrates that the main occupational trend over the next decade will consist in the increasing importance of professionals, as well as technicians and associate professionals. The increase in demand for health professionals and representatives of occupations providing scientific and technological innovation will be most significant. At the same time, it is expected that demand for elementary occupations will also rise. This process will evolve simultaneously with the decrease in the total number of skilled and semi-skilled blue-collar occupations due to globalization and the reduction of industrial production in developed economies. The ongoing “mechanization” of many job functions will not eliminate the need for occupations such as cleaners, labourers, domestic servants or personal workers. The need for these jobs allow employees with low levels of education to enter the labour market rather than depending on the social benefit system. Another tendency for all countries with developed economies will be reduced demand for many whitecollar occupations as modern computer technologies and the automation of many

  8. Evolution of foot-and-mouth disease virus serotype A capsid coding (P1) region on a timescale of three decades in an endemic context.

    Das, Biswajit; Mohapatra, Jajati K; Pande, Veena; Subramaniam, Saravanan; Sanyal, Aniket

    2016-07-01

    Three decades-long (1977-2013) evolutionary trend of the capsid coding (P1) region of foot-and-mouth disease virus (FMDV) serotype A isolated in India was analysed. The exclusive presence of genotype 18 since 2001 and the dominance of the VP3(59)-deletion group of genotype 18 was evident in the recent years. Clade 18c was found to be currently the only active one among the three clades (18a, 18b and 18c) identified in the deletion group. The rate of evolution of the Indian isolates at the capsid region was found to be 4.96×10(-3)substitutions/site/year. The timescale analysis predicted the most recent common ancestor to have existed during 1962 for Indian FMDV serotype A and around 1998 for the deletion group. The evolutionary pattern of serotype A in India appears to be homogeneous as no spatial or temporal structure was observed. Bayesian skyline plots indicate a sharp decline in the effective number of infections after 2008, which might be a result of mass vaccination or inherent loss of virus fitness. Analyses of variability at 38 known antigenically critical positions in a countrywide longitudinal data set suggested that the substitutions neither followed any specific trend nor remained fixed for a long period since frequent reversions and convergence was noticed. A maximum of 6 different amino acid residues was seen in the gene pool at any antigenically critical site over the decades, suggesting a limited combination of residues being responsible for the observed antigenic variation. Evidence of positive selection at some of the antigenically critical residues and the structurally proximal positions suggest a possible role of pre-existing immunity in the host population in driving evolution. The VP1 C-terminus neither revealed variability nor positive selection, suggesting the possibility that this stretch does not contribute to the antigenic variation and adaptation under immune selection. Copyright © 2016 Elsevier B.V. All rights reserved.

  9. Estimation of sensitivity, specificity and predictive values of two serologic tests for the detection of antibodies against Actinobacillus pleuropneumoniae serotype 2 in the absence of a reference test (gold standard)

    Enøe, Claes; Andersen, Søren; Sørensen, Vibeke

    2001-01-01

    Latent-class models were used to determine the sensitivity, specificity and predictive values of a polyclonal blocking enzyme-linked immunosorbent assay (ELISA) and a modified complement-fixation test (CFT) when there was no reference test. The tests were used for detection of antibodies against ...

  10. Prediction of beta-turns at over 80% accuracy based on an ensemble of predicted secondary structures and multiple alignments.

    Zheng, Ce; Kurgan, Lukasz

    2008-10-10

    beta-turn is a secondary protein structure type that plays significant role in protein folding, stability, and molecular recognition. To date, several methods for prediction of beta-turns from protein sequences were developed, but they are characterized by relatively poor prediction quality. The novelty of the proposed sequence-based beta-turn predictor stems from the usage of a window based information extracted from four predicted three-state secondary structures, which together with a selected set of position specific scoring matrix (PSSM) values serve as an input to the support vector machine (SVM) predictor. We show that (1) all four predicted secondary structures are useful; (2) the most useful information extracted from the predicted secondary structure includes the structure of the predicted residue, secondary structure content in a window around the predicted residue, and features that indicate whether the predicted residue is inside a secondary structure segment; (3) the PSSM values of Asn, Asp, Gly, Ile, Leu, Met, Pro, and Val were among the top ranked features, which corroborates with recent studies. The Asn, Asp, Gly, and Pro indicate potential beta-turns, while the remaining four amino acids are useful to predict non-beta-turns. Empirical evaluation using three nonredundant datasets shows favorable Q total, Q predicted and MCC values when compared with over a dozen of modern competing methods. Our method is the first to break the 80% Q total barrier and achieves Q total = 80.9%, MCC = 0.47, and Q predicted higher by over 6% when compared with the second best method. We use feature selection to reduce the dimensionality of the feature vector used as the input for the proposed prediction method. The applied feature set is smaller by 86, 62 and 37% when compared with the second and two third-best (with respect to MCC) competing methods, respectively. Experiments show that the proposed method constitutes an improvement over the competing prediction

  11. The use of oligonucleotide probes for meningococcal serotype characterization

    SACCHI Claudio Tavares

    1998-01-01

    Full Text Available In the present study we examine the potential use of oligonucleotide probes to characterize Neisseria meningitidis serotypes without the use of monoclonal antibodies (MAbs. Antigenic diversity on PorB protein forms the bases of serotyping method. However, the current panel of MAbs underestimated, by at least 50% the PorB variability, presumably because reagents for several PorB variable regions (VRs are lacking, or because a number of VR variants are not recognized by serotype-defining MAbs12. We analyzed the use of oligonucleotide probes to characterize serotype 10 and serotype 19 of N. meningitidis. The porB gene sequence for the prototype strain of serotype 10 was determined, aligned with 7 other porB sequences from different serotypes, and analysis of individual VRs were performed. The results of DNA probes 21U (VR1-A and 615U (VR3-B used against 72 N. meningitidis strains confirm that VR1 type A and VR3 type B encode epitopes for serotype-defined MAbs 19 and 10, respectively. The use of probes for characterizing serotypes possible can type 100% of the PorB VR diversity. It is a simple and rapid method specially useful for analysis of large number of samples.

  12. Ab Initio Predictions of Structures and Densities of Energetic Solids

    Rice, Betsy M; Sorescu, Dan C

    2004-01-01

    We have applied a powerful simulation methodology known as ab initio crystal prediction to assess the ability of a generalized model of CHNO intermolecular interactions to predict accurately crystal...

  13. Predictive modeling of pedestal structure in KSTAR using EPED model

    Han, Hyunsun; Kim, J. Y. [National Fusion Research Institute, Daejeon 305-806 (Korea, Republic of); Kwon, Ohjin [Department of Physics, Daegu University, Gyeongbuk 712-714 (Korea, Republic of)

    2013-10-15

    A predictive calculation is given for the structure of edge pedestal in the H-mode plasma of the KSTAR (Korea Superconducting Tokamak Advanced Research) device using the EPED model. Particularly, the dependence of pedestal width and height on various plasma parameters is studied in detail. The two codes, ELITE and HELENA, are utilized for the stability analysis of the peeling-ballooning and kinetic ballooning modes, respectively. Summarizing the main results, the pedestal slope and height have a strong dependence on plasma current, rapidly increasing with it, while the pedestal width is almost independent of it. The plasma density or collisionality gives initially a mild stabilization, increasing the pedestal slope and height, but above some threshold value its effect turns to a destabilization, reducing the pedestal width and height. Among several plasma shape parameters, the triangularity gives the most dominant effect, rapidly increasing the pedestal width and height, while the effect of elongation and squareness appears to be relatively weak. Implication of these edge results, particularly in relation to the global plasma performance, is discussed.

  14. Identification of a serotype-independent linear epitope of foot-and-mouth disease virus.

    Yang, Baolin; Wang, Mingxia; Liu, Wenming; Xu, Zhiqiang; Wang, Haiwei; Yang, Decheng; Ma, Wenge; Zhou, Guohui; Yu, Li

    2017-12-01

    Foot-and-mouth disease (FMD), caused by foot-and-mouth disease virus (FMDV), is a highly contagious infectious disease that affects domestic and wild cloven-hoofed animals worldwide. VP2 is a structural protein of FMDV. In this study, an FMDV serotype-independent monoclonal antibody (MAb), 10B10, against the viral capsid protein VP2 was generated, and a series of GST fusion proteins expressing a truncated peptide of VP2 was subjected to Western blot analysis using MAb 10B10. Their results indicated that the peptide 8 TLLEDRILT 16 of VP2 is the minimal requirement of the epitope recognized by MAb 10B10. Importantly, this linear epitope was highly conserved among all seven serotypes of FMDV in a sequence alignment analysis. Subsequent alanine-scanning mutagenesis analysis revealed that the residues Thr 8 and Asp 12 of the epitope were crucial for MAb-10B10 binding. Furthermore, Western blot analysis also revealed that the MAb 10B10-directed epitope could be recognized by positive sera from FMDV-infected cattle. The discovery that MAb 10B10 recognizes a serotype-independent linear epitope of FMDV suggests potential applications for this MAb in the development of serotype-independent tests for FMDV.

  15. Co-circulation and co-infections of all dengue virus serotypes in Hyderabad, India 2014.

    Vaddadi, K; Gandikota, C; Jain, P K; Prasad, V S V; Venkataramana, M

    2017-09-01

    The burden of dengue virus infections increased globally during recent years. Though India is considered as dengue hyper-endemic country, limited data are available on disease epidemiology. The present study includes molecular characterization of dengue virus strains occurred in Hyderabad, India, during the year 2014. A total of 120 febrile cases were recruited for this study, which includes only children and 41 were serologically confirmed for dengue positive infections using non-structural (NS1) and/or IgG/IgM ELISA tests. RT-PCR, nucleotide sequencing and evolutionary analyses were carried out to identify the circulating serotypes/genotypes. The data indicated a high percent of severe dengue (63%) in primary infections. Simultaneous circulation of all four serotypes and co-infections were observed for the first time in Hyderabad, India. In total, 15 patients were co-infected with more than one dengue serotype and 12 (80%) of them had severe dengue. One of the striking findings of the present study is the identification of serotype Den-1 as the first report from this region and this strain showed close relatedness to the Thailand 1980 strains but not to any of the strains reported from India until now. Phylogenetically, all four strains of the present study showed close relatedness to the strains, which are reported to be high virulent.

  16. Genesis of a novel Shigella flexneri serotype by sequential infection of serotype-converting bacteriophages SfX and SfI

    Sun Qiangzheng

    2011-12-01

    Full Text Available Abstract Background Shigella flexneri is the major pathogen causing bacillary dysentery. Fifteen serotypes have been recognized up to now. The genesis of new S. flexneri serotypes is commonly mediated by serotype-converting bacteriophages. Untypeable or novel serotypes from natural infections had been reported worldwide but have not been generated in laboratory. Results A new S. flexneri serotype-serotype 1 d was generated when a S. flexneri serotype Y strain (native LPS was sequentially infected with 2 serotype-converting bacteriophages, SfX first and then SfI. The new serotype 1 d strain agglutinated with both serotype X-specific anti-7;8 grouping serum and serotype 1a-specific anti- I typing serum, and differed from subserotypes 1a, 1b and 1c. Twenty four S. flexneri clinical isolates of serotype X were all converted to serotype 1 d by infection with phage SfI. PCR and sequencing revealed that SfI and SfX were integrated in tandem into the proA-yaiC region of the host chromosome. Conclusions These findings suggest a new S. flexneri serotype could be created in nature. Such a conversion may be constrained by susceptibility of a strain to infection by a given serotype-converting bacteriophage. This finding has significant implications in the emergence of new S. flexneri serotypes in nature.

  17. RNA secondary structure prediction with pseudoknots: Contribution of algorithm versus energy model.

    Jabbari, Hosna; Wark, Ian; Montemagno, Carlo

    2018-01-01

    RNA is a biopolymer with various applications inside the cell and in biotechnology. Structure of an RNA molecule mainly determines its function and is essential to guide nanostructure design. Since experimental structure determination is time-consuming and expensive, accurate computational prediction of RNA structure is of great importance. Prediction of RNA secondary structure is relatively simpler than its tertiary structure and provides information about its tertiary structure, therefore, RNA secondary structure prediction has received attention in the past decades. Numerous methods with different folding approaches have been developed for RNA secondary structure prediction. While methods for prediction of RNA pseudoknot-free structure (structures with no crossing base pairs) have greatly improved in terms of their accuracy, methods for prediction of RNA pseudoknotted secondary structure (structures with crossing base pairs) still have room for improvement. A long-standing question for improving the prediction accuracy of RNA pseudoknotted secondary structure is whether to focus on the prediction algorithm or the underlying energy model, as there is a trade-off on computational cost of the prediction algorithm versus the generality of the method. The aim of this work is to argue when comparing different methods for RNA pseudoknotted structure prediction, the combination of algorithm and energy model should be considered and a method should not be considered superior or inferior to others if they do not use the same scoring model. We demonstrate that while the folding approach is important in structure prediction, it is not the only important factor in prediction accuracy of a given method as the underlying energy model is also as of great value. Therefore we encourage researchers to pay particular attention in comparing methods with different energy models.

  18. Prediction of beta-turns at over 80% accuracy based on an ensemble of predicted secondary structures and multiple alignments

    Kurgan Lukasz

    2008-10-01

    Full Text Available Abstract Background β-turn is a secondary protein structure type that plays significant role in protein folding, stability, and molecular recognition. To date, several methods for prediction of β-turns from protein sequences were developed, but they are characterized by relatively poor prediction quality. The novelty of the proposed sequence-based β-turn predictor stems from the usage of a window based information extracted from four predicted three-state secondary structures, which together with a selected set of position specific scoring matrix (PSSM values serve as an input to the support vector machine (SVM predictor. Results We show that (1 all four predicted secondary structures are useful; (2 the most useful information extracted from the predicted secondary structure includes the structure of the predicted residue, secondary structure content in a window around the predicted residue, and features that indicate whether the predicted residue is inside a secondary structure segment; (3 the PSSM values of Asn, Asp, Gly, Ile, Leu, Met, Pro, and Val were among the top ranked features, which corroborates with recent studies. The Asn, Asp, Gly, and Pro indicate potential β-turns, while the remaining four amino acids are useful to predict non-β-turns. Empirical evaluation using three nonredundant datasets shows favorable Qtotal, Qpredicted and MCC values when compared with over a dozen of modern competing methods. Our method is the first to break the 80% Qtotal barrier and achieves Qtotal = 80.9%, MCC = 0.47, and Qpredicted higher by over 6% when compared with the second best method. We use feature selection to reduce the dimensionality of the feature vector used as the input for the proposed prediction method. The applied feature set is smaller by 86, 62 and 37% when compared with the second and two third-best (with respect to MCC competing methods, respectively. Conclusion Experiments show that the proposed method constitutes an

  19. First Principles Prediction of Structure, Structure Selectivity, and Thermodynamic Stability under Realistic Conditions

    Ceder, Gerbrand [Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States). Dept. of Materials and Engineering

    2018-01-28

    Novel materials are often the enabler for new energy technologies. In ab-initio computational materials science, method are developed to predict the behavior of materials starting from the laws of physics, so that properties can be predicted before compounds have to be synthesized and tested. As such, a virtual materials laboratory can be constructed, saving time and money. The objectives of this program were to develop first-principles theory to predict the structure and thermodynamic stability of materials. Since its inception the program focused on the development of the cluster expansion to deal with the increased complexity of complex oxides. This research led to the incorporation of vibrational degrees of freedom in ab-initio thermodynamics, developed methods for multi-component cluster expansions, included the explicit configurational degrees of freedom of localized electrons, developed the formalism for stability in aqueous environments, and culminated in the first ever approach to produce exact ground state predictions of the cluster expansion. Many of these methods have been disseminated to the larger theory community through the Materials Project, pymatgen software, or individual codes. We summarize three of the main accomplishments.

  20. Lessons From Globally Coordinated Cessation of Serotype 2 Oral Poliovirus Vaccine for the Remaining Serotypes.

    Thompson, Kimberly M; Duintjer Tebbens, Radboud J

    2017-07-01

    Comparing model expectations with the experience of oral poliovirus vaccine (OPV) containing serotype 2 (OPV2) cessation can inform risk management for the expected cessation of OPV containing serotypes 1 and 3 (OPV13). We compare the expected post-OPV2-cessation OPV2-related viruses from models with the evidence available approximately 6 months after OPV2 cessation. We also model the trade-offs of use vs nonuse of monovalent OPV (mOPV) for outbreak response considering all 3 serotypes. Although too early to tell definitively, the observed die-out of OPV2-related viruses in populations that attained sufficiently intense trivalent OPV (tOPV) use prior to OPV2 cessation appears consistent with model expectations. As expected, populations that did not intensify tOPV use prior to OPV2 cessation show continued circulation of serotype 2 vaccine-derived polioviruses (VDPVs). Failure to aggressively use mOPV to respond to circulating VDPVs results in a high risk of uncontrolled outbreaks that would require restarting OPV. Ensuring a successful endgame requires more aggressive OPV cessation risk management than has occurred to date for OPV2 cessation. This includes maintaining high population immunity to transmission up until OPV13 cessation, meeting all prerequisites for OPV cessation, and ensuring sufficient vaccine supply to prevent and respond to outbreaks. © The Author 2017. Published by Oxford University Press for the Infectious Diseases Society of America.

  1. Genomic characterization of Flavobacterium psychrophilum serotypes and development of a multiplex PCR-based serotyping scheme

    Rochat, Tatiana; Fujiwara-Nagata, Erina; Calvez, Ségolène

    2017-01-01

    Flavobacterium psychrophilum is a devastating bacterial pathogen of salmonids reared in freshwater worldwide. So far, serological diversity between isolates has been described but the underlying molecular factors remain unknown. By combining complete genome sequence analysis and the serotyping me...... for bacterial coldwater disease resistance and future vaccine formulation....

  2. PREDICTING APHASIA TYPE FROM BRAIN DAMAGE MEASURED WITH STRUCTURAL MRI

    Yourganov, Grigori; Smith, Kimberly G.; Fridriksson, Julius; Rorden, Chris

    2015-01-01

    Chronic aphasia is a common consequence of a left-hemisphere stroke. Since the early insights by Broca and Wernicke, studying the relationship between the loci of cortical damage and patterns of language impairment has been one of the concerns of aphasiology. We utilized multivariate classification in a cross-validation framework to predict the type of chronic aphasia from the spatial pattern of brain damage. Our sample consisted of 98 patients with five types of aphasia (Broca’s, Wernicke’s, global, conduction, and anomic), classified based on scores on the Western Aphasia Battery. Binary lesion maps were obtained from structural MRI scans (obtained at least 6 months poststroke, and within 2 days of behavioural assessment); after spatial normalization, the lesions were parcellated into a disjoint set of brain areas. The proportion of damage to the brain areas was used to classify patients’ aphasia type. To create this parcellation, we relied on five brain atlases; our classifier (support vector machine) could differentiate between different kinds of aphasia using any of the five parcellations. In our sample, the best classification accuracy was obtained when using a novel parcellation that combined two previously published brain atlases, with the first atlas providing the segmentation of grey matter, and the second atlas used to segment the white matter. For each aphasia type, we computed the relative importance of different brain areas for distinguishing it from other aphasia types; our findings were consistent with previously published reports of lesion locations implicated in different types of aphasia. Overall, our results revealed that automated multivariate classification could distinguish between aphasia types based on damage to atlas-defined brain areas. PMID:26465238

  3. Predicting aphasia type from brain damage measured with structural MRI.

    Yourganov, Grigori; Smith, Kimberly G; Fridriksson, Julius; Rorden, Chris

    2015-12-01

    Chronic aphasia is a common consequence of a left-hemisphere stroke. Since the early insights by Broca and Wernicke, studying the relationship between the loci of cortical damage and patterns of language impairment has been one of the concerns of aphasiology. We utilized multivariate classification in a cross-validation framework to predict the type of chronic aphasia from the spatial pattern of brain damage. Our sample consisted of 98 patients with five types of aphasia (Broca's, Wernicke's, global, conduction, and anomic), classified based on scores on the Western Aphasia Battery (WAB). Binary lesion maps were obtained from structural MRI scans (obtained at least 6 months poststroke, and within 2 days of behavioural assessment); after spatial normalization, the lesions were parcellated into a disjoint set of brain areas. The proportion of damage to the brain areas was used to classify patients' aphasia type. To create this parcellation, we relied on five brain atlases; our classifier (support vector machine - SVM) could differentiate between different kinds of aphasia using any of the five parcellations. In our sample, the best classification accuracy was obtained when using a novel parcellation that combined two previously published brain atlases, with the first atlas providing the segmentation of grey matter, and the second atlas used to segment the white matter. For each aphasia type, we computed the relative importance of different brain areas for distinguishing it from other aphasia types; our findings were consistent with previously published reports of lesion locations implicated in different types of aphasia. Overall, our results revealed that automated multivariate classification could distinguish between aphasia types based on damage to atlas-defined brain areas. Copyright © 2015 Elsevier Ltd. All rights reserved.

  4. Update on protein structure prediction: results of the 1995 IRBM workshop

    Hubbard, Tim; Tramontano, Anna; Hansen, Jan

    1996-01-01

    Computational tools for protein structure prediction are of great interest to molecular, structural and theoretical biologists due to a rapidly increasing number of protein sequences with no known structure. In October 1995, a workshop was held at IRBM to predict as much as possible about a numbe...

  5. RNA Secondary Structure Prediction by Using Discrete Mathematics: An Interdisciplinary Research Experience for Undergraduate Students

    Ellington, Roni; Wachira, James; Nkwanta, Asamoah

    2010-01-01

    The focus of this Research Experience for Undergraduates (REU) project was on RNA secondary structure prediction by using a lattice walk approach. The lattice walk approach is a combinatorial and computational biology method used to enumerate possible secondary structures and predict RNA secondary structure from RNA sequences. The method uses…

  6. Update on protein structure prediction: results of the 1995 IRBM workshop

    Hubbard, Tim; Tramontano, Anna; Hansen, Jan

    1996-01-01

    Computational tools for protein structure prediction are of great interest to molecular, structural and theoretical biologists due to a rapidly increasing number of protein sequences with no known structure. In October 1995, a workshop was held at IRBM to predict as much as possible about a number...

  7. Evolving stochastic context-free grammars for RNA secondary structure prediction

    Anderson, James WJ; Tataru, Paula Cristina; Stains, Joe

    2012-01-01

    Background Stochastic Context-Free Grammars (SCFGs) were applied successfully to RNA secondary structure prediction in the early 90s, and used in combination with comparative methods in the late 90s. The set of SCFGs potentially useful for RNA secondary structure prediction is very large, but a few...... to structure prediction as has been previously suggested. Results These search techniques were applied to predict RNA secondary structure on a maximal data set and revealed new and interesting grammars, though none are dramatically better than classic grammars. In general, results showed that many grammars...... with quite different structure could have very similar predictive ability. Many ambiguous grammars were found which were at least as effective as the best current unambiguous grammars. Conclusions Overall the method of evolving SCFGs for RNA secondary structure prediction proved effective in finding many...

  8. Structural Dynamic Analyses And Test Predictions For Spacecraft Structures With Non-Linearities

    Vergniaud, Jean-Baptiste; Soula, Laurent; Newerla, Alfred

    2012-07-01

    The overall objective of the mechanical development and verification process is to ensure that the spacecraft structure is able to sustain the mechanical environments encountered during launch. In general the spacecraft structures are a-priori assumed to behave linear, i.e. the responses to a static load or dynamic excitation, respectively, will increase or decrease proportionally to the amplitude of the load or excitation induced. However, past experiences have shown that various non-linearities might exist in spacecraft structures and the consequences of their dynamic effects can significantly affect the development and verification process. Current processes are mainly adapted to linear spacecraft structure behaviour. No clear rules exist for dealing with major structure non-linearities. They are handled outside the process by individual analysis and margin policy, and analyses after tests to justify the CLA coverage. Non-linearities can primarily affect the current spacecraft development and verification process on two aspects. Prediction of flights loads by launcher/satellite coupled loads analyses (CLA): only linear satellite models are delivered for performing CLA and no well-established rules exist how to properly linearize a model when non- linearities are present. The potential impact of the linearization on the results of the CLA has not yet been properly analyzed. There are thus difficulties to assess that CLA results will cover actual flight levels. Management of satellite verification tests: the CLA results generated with a linear satellite FEM are assumed flight representative. If the internal non- linearities are present in the tested satellite then there might be difficulties to determine which input level must be passed to cover satellite internal loads. The non-linear behaviour can also disturb the shaker control, putting the satellite at risk by potentially imposing too high levels. This paper presents the results of a test campaign performed in

  9. Antibiotic Susceptibilities and Serotyping of Clinical Streptococcus Agalactiae Isolates

    Altay Atalay

    2011-11-01

    Full Text Available Objective: Streptococcus agalactiae (Group B streptococci, GBS are frequently responsible for sepsis and meningitis seen in the early weeks of life. GBS may cause perinatal infection and premature birth in pregnant women. The aim of this study was to serotype GBS strains isolated from clinical samples and evaluate their serotype distribution according to their susceptibilities to antibiotics and isolation sites. Material and Methods: One hundred thirty one S. agalactiae strains isolated from the clinical samples were included in the study. Of the strains, 99 were isolated from urine, 20 from soft tissue, 10 from blood and 2 from vaginal swab. Penicillin G and ceftriaxone susceptibilities of GBS were determined by the agar dilution method. Susceptibilities to erythromycin, clindamycin, vancomycin and tetracycline were determined by the Kirby-Bauer method according to CLSI criteria. Serotyping was performed using the latex aglutination method using specific antisera (Ia, Ib, II-VIII. Results: While in 131 GBS strains, serotypes VII and VIII were not detected, the most frequently isolated serotypes were types Ia (36%, III (30.5% and II (13% respectively. Serotype Ia was the most frequently seen serotype in all samples. All GBS isolates were susceptible to penicilin G, ceftriaxone and vancomycin. Among the strains, tetracycline, erythromycin and clindamycin resistance rates were determined as 90%, 14.5%, and 13% respectively. Conclusion: Penicillin is still the first choice of treatment for the infections with all serotypes of S. agalactiae in Turkey.

  10. Validation of Molecular Dynamics Simulations for Prediction of Three-Dimensional Structures of Small Proteins.

    Kato, Koichi; Nakayoshi, Tomoki; Fukuyoshi, Shuichi; Kurimoto, Eiji; Oda, Akifumi

    2017-10-12

    Although various higher-order protein structure prediction methods have been developed, almost all of them were developed based on the three-dimensional (3D) structure information of known proteins. Here we predicted the short protein structures by molecular dynamics (MD) simulations in which only Newton's equations of motion were used and 3D structural information of known proteins was not required. To evaluate the ability of MD simulationto predict protein structures, we calculated seven short test protein (10-46 residues) in the denatured state and compared their predicted and experimental structures. The predicted structure for Trp-cage (20 residues) was close to the experimental structure by 200-ns MD simulation. For proteins shorter or longer than Trp-cage, root-mean square deviation values were larger than those for Trp-cage. However, secondary structures could be reproduced by MD simulations for proteins with 10-34 residues. Simulations by replica exchange MD were performed, but the results were similar to those from normal MD simulations. These results suggest that normal MD simulations can roughly predict short protein structures and 200-ns simulations are frequently sufficient for estimating the secondary structures of protein (approximately 20 residues). Structural prediction method using only fundamental physical laws are useful for investigating non-natural proteins, such as primitive proteins and artificial proteins for peptide-based drug delivery systems.

  11. Assessment of tropism and effectiveness of new primate-derived hybrid recombinant AAV serotypes in the mouse and primate retina.

    Peter Charbel Issa

    Full Text Available Adeno-associated viral vectors (AAV have been shown to be safe in the treatment of retinal degenerations in clinical trials. Thus, improving the efficiency of viral gene delivery has become increasingly important to increase the success of clinical trials. In this study, structural domains of different rAAV serotypes isolated from primate brain were combined to create novel hybrid recombinant AAV serotypes, rAAV2/rec2 and rAAV2/rec3. The efficacy of these novel serotypes were assessed in wild type mice and in two models of retinal degeneration (the Abca4(-/- mouse which is a model for Stargardt disease and in the Pde6b(rd1/rd1 mouse in vivo, in primate tissue ex-vivo, and in the human-derived SH-SY5Y cell line, using an identical AAV2 expression cassette. We show that these novel hybrid serotypes can transduce retinal tissue in mice and primates efficiently, although no more than AAV2/2 and rAAV2/5 serotypes. Transduction efficiency appeared lower in the Abca4(-/- mouse compared to wild type with all vectors tested, suggesting an effect of specific retinal diseases on the efficiency of gene delivery. Shuffling of AAV capsid domains may have clinical applications for patients who develop T-cell immune responses following AAV gene therapy, as specific peptide antigen sequences could be substituted using this technique prior to vector re-treatments.

  12. Assessment of tropism and effectiveness of new primate-derived hybrid recombinant AAV serotypes in the mouse and primate retina.

    Charbel Issa, Peter; De Silva, Samantha R; Lipinski, Daniel M; Singh, Mandeep S; Mouravlev, Alexandre; You, Qisheng; Barnard, Alun R; Hankins, Mark W; During, Matthew J; Maclaren, Robert E

    2013-01-01

    Adeno-associated viral vectors (AAV) have been shown to be safe in the treatment of retinal degenerations in clinical trials. Thus, improving the efficiency of viral gene delivery has become increasingly important to increase the success of clinical trials. In this study, structural domains of different rAAV serotypes isolated from primate brain were combined to create novel hybrid recombinant AAV serotypes, rAAV2/rec2 and rAAV2/rec3. The efficacy of these novel serotypes were assessed in wild type mice and in two models of retinal degeneration (the Abca4(-/-) mouse which is a model for Stargardt disease and in the Pde6b(rd1/rd1) mouse) in vivo, in primate tissue ex-vivo, and in the human-derived SH-SY5Y cell line, using an identical AAV2 expression cassette. We show that these novel hybrid serotypes can transduce retinal tissue in mice and primates efficiently, although no more than AAV2/2 and rAAV2/5 serotypes. Transduction efficiency appeared lower in the Abca4(-/-) mouse compared to wild type with all vectors tested, suggesting an effect of specific retinal diseases on the efficiency of gene delivery. Shuffling of AAV capsid domains may have clinical applications for patients who develop T-cell immune responses following AAV gene therapy, as specific peptide antigen sequences could be substituted using this technique prior to vector re-treatments.

  13. Modelling microbial interactions and food structure in predictive microbiology

    Malakar, P.K.

    2002-01-01

    Keywords: modelling, dynamic models, microbial interactions, diffusion, microgradients, colony growth, predictive microbiology.

    Growth response of microorganisms in foods is a complex process. Innovations in food production and preservation techniques have resulted in adoption of

  14. Protein Domain Analysis of C. botulinum Type A Neurotoxin and Its Relationship with Other Botulinum Serotypes

    Sharma, Shashi K.; Basavanna, Uma; Shukla, Hem D.

    2009-01-01

    Botulinum neurotoxins (BoNTs) are highly potent poisons produced by seven serotypes of Clostridium botulinum. The mechanism of neurotoxin action is a multistep process which leads to the cleavage of one of three different SNARE proteins essential for synaptic vesicle fusion and transmission of the nerve signals to muscles: synaptobrevin, syntaxin, or SNAP-25. In order to understand the precise mechanism of neurotoxin in a host, the domain structure of the neurotoxin was analyzed among differe...

  15. [Establishment of chemiluminescent enzyme immunoassay for detecting antibodies against foot-and-mouth disease virus serotype O in swine].

    Cui, Chen; Huang, Ligang; Li, Jing; Zou, Xingqi; Zhu, Yuanyuan; Xie, Lei; Zhao, Qizu; Yang, Limin; Liu, Wenjun

    2016-11-25

    Recombinant structural protein VP1 of foot-and-mouth disease virus serotype O was expressed in Escherichia coli and then purified using Nickel affinity chromatography. A chemiluminescent enzyme immunoassay (CLEIA) method was established using the purified recombinant protein as coating antigen to detect antibody of foot-and-mouth disease virus serotype O in swine. The specificity of VP1-CLEIA method is 100%. The coefficients of variation in the plate and between plates are 1.10%-6.70% and 0.66%-4.80%, respectively. Comparing with the commercial indirect ELISA kit or liquid phase block ELISA kit, the calculated coincidence rate is 93.50% or 94.00%. The high specificity and stability suggested this detection method can be used to monitor the antibody level of foot-and-mouth disease virus serotype O in swine.

  16. Perspective: Role of structure prediction in materials discovery and design

    Richard J. Needs

    2016-05-01

    Full Text Available Materials informatics owes much to bioinformatics and the Materials Genome Initiative has been inspired by the Human Genome Project. But there is more to bioinformatics than genomes, and the same is true for materials informatics. Here we describe the rapidly expanding role of searching for structures of materials using first-principles electronic-structure methods. Structure searching has played an important part in unraveling structures of dense hydrogen and in identifying the record-high-temperature superconducting component in hydrogen sulfide at high pressures. We suggest that first-principles structure searching has already demonstrated its ability to determine structures of a wide range of materials and that it will play a central and increasing part in materials discovery and design.

  17. Effective Energy Methods for Global Optimization for Biopolymer Structure Prediction

    Shalloway, David

    1998-01-01

    .... Its main strength is that it uncovers and exploits the intrinsic "hidden structures" of biopolymer energy landscapes to efficiently perform global minimization using a hierarchical search procedure...

  18. Improved fuzzy PID controller design using predictive functional control structure.

    Wang, Yuzhong; Jin, Qibing; Zhang, Ridong

    2017-11-01

    In conventional PID scheme, the ensemble control performance may be unsatisfactory due to limited degrees of freedom under various kinds of uncertainty. To overcome this disadvantage, a novel PID control method that inherits the advantages of fuzzy PID control and the predictive functional control (PFC) is presented and further verified on the temperature model of a coke furnace. Based on the framework of PFC, the prediction of the future process behavior is first obtained using the current process input signal. Then, the fuzzy PID control based on the multi-step prediction is introduced to acquire the optimal control law. Finally, the case study on a temperature model of a coke furnace shows the effectiveness of the fuzzy PID control scheme when compared with conventional PID control and fuzzy self-adaptive PID control. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

  19. Structural health monitoring for fatigue life prediction of orthotropic brdige decks

    Pijpers, R.J.M.; Pahlavan, P.L.; Paulissen, J.H.; Hakkesteegt, H.C.; Jansen, T.H.

    2013-01-01

    Infrastructure asset owners are more and more confronted with structures reaching the end of their structural life. Structural Health Monitoring (SHM) systems should provide up-to-date information about the actual condition, as well predict the structural life and required maintenance of the assets

  20. Selective and genetic constraints on pneumococcal serotype switching.

    Nicholas J Croucher

    2015-03-01

    Full Text Available Streptococcus pneumoniae isolates typically express one of over 90 immunologically distinguishable polysaccharide capsules (serotypes, which can be classified into "serogroups" based on cross-reactivity with certain antibodies. Pneumococci can alter their serotype through recombinations affecting the capsule polysaccharide synthesis (cps locus. Twenty such "serotype switching" events were fully characterised using a collection of 616 whole genome sequences from systematic surveys of pneumococcal carriage. Eleven of these were within-serogroup switches, representing a highly significant (p < 0.0001 enrichment based on the observed serotype distribution. Whereas the recombinations resulting in between-serogroup switches all spanned the entire cps locus, some of those that caused within-serogroup switches did not. However, higher rates of within-serogroup switching could not be fully explained by either more frequent, shorter recombinations, nor by genetic linkage to genes involved in β-lactam resistance. This suggested the observed pattern was a consequence of selection for preserving serogroup. Phenotyping of strains constructed to express different serotypes in common genetic backgrounds was used to test whether genotypes were physiologically adapted to particular serogroups. These data were consistent with epistatic interactions between the cps locus and the rest of the genome that were specific to serotype, but not serogroup, meaning they were unlikely to account for the observed distribution of capsule types. Exclusion of these genetic and physiological hypotheses suggested future work should focus on alternative mechanisms, such as host immunity spanning multiple serotypes within the same serogroup, which might explain the observed pattern.

  1. Antibiofilm activity of Actinobacillus pleuropneumoniae serotype 5 capsular polysaccharide.

    Michael T Karwacki

    Full Text Available Cell-free extracts isolated from colony biofilms of Actinobacillus pleuropneumoniae serotype 5 were found to inhibit biofilm formation by Staphylococcus aureus, S. epidermidis and Aggregatibacter actinomycetemcomitans, but not by A. pleuropneumoniae serotype 5 itself, in a 96-well microtiter plate assay. Physical and chemical analyses indicated that the antibiofilm activity in the extract was due to high-molecular-weight polysaccharide. Extracts isolated from a mutant strain deficient in the production of serotype 5 capsular polysaccharide did not exhibit antibiofilm activity. A plasmid harboring the serotype 5 capsule genes restored the antibiofilm activity in the mutant extract. Purified serotype 5 capsular polysaccharide also exhibited antibiofilm activity against S. aureus. A. pleuropneumoniae wild-type extracts did not inhibit S. aureus growth, but did inhibit S. aureus intercellular adhesion and binding of S. aureus cells to stainless steel surfaces. Furthermore, polystyrene surfaces coated with A. pleuropneumoniae wild-type extracts, but not with capsule-mutant extracts, resisted S. aureus biofilm formation. Our findings suggest that the A. pleuropneumoniae serotype 5 capsule inhibits cell-to-cell and cell-to-surface interactions of other bacteria. A. pleuropneumoniae serotype 5 capsular polysaccharide is one of a growing number of bacterial polysaccharides that exhibit broad-spectrum, nonbiocidal antibiofilm activity. Future studies on these antibiofilm polysaccharides may uncover novel functions for bacterial polysaccharides in nature, and may lead to the development of new classes of antibiofilm agents for industrial and clinical applications.

  2. Vfold: a web server for RNA structure and folding thermodynamics prediction.

    Xu, Xiaojun; Zhao, Peinan; Chen, Shi-Jie

    2014-01-01

    The ever increasing discovery of non-coding RNAs leads to unprecedented demand for the accurate modeling of RNA folding, including the predictions of two-dimensional (base pair) and three-dimensional all-atom structures and folding stabilities. Accurate modeling of RNA structure and stability has far-reaching impact on our understanding of RNA functions in human health and our ability to design RNA-based therapeutic strategies. The Vfold server offers a web interface to predict (a) RNA two-dimensional structure from the nucleotide sequence, (b) three-dimensional structure from the two-dimensional structure and the sequence, and (c) folding thermodynamics (heat capacity melting curve) from the sequence. To predict the two-dimensional structure (base pairs), the server generates an ensemble of structures, including loop structures with the different intra-loop mismatches, and evaluates the free energies using the experimental parameters for the base stacks and the loop entropy parameters given by a coarse-grained RNA folding model (the Vfold model) for the loops. To predict the three-dimensional structure, the server assembles the motif scaffolds using structure templates extracted from the known PDB structures and refines the structure using all-atom energy minimization. The Vfold-based web server provides a user friendly tool for the prediction of RNA structure and stability. The web server and the source codes are freely accessible for public use at "http://rna.physics.missouri.edu".

  3. Improving 3D structure prediction from chemical shift data

    Schot, Gijs van der [Utrecht University, Computational Structural Biology, Bijvoet Center for Biomolecular Research, Faculty of Science-Chemistry (Netherlands); Zhang, Zaiyong [Technische Universitaet Muenchen, Biomolecular NMR and Munich Center for Integrated Protein Science, Department Chemie (Germany); Vernon, Robert [University of Washington, Department of Biochemistry (United States); Shen, Yang [National Institutes of Health, Laboratory of Chemical Physics, National Institute of Diabetes and Digestive and Kidney Diseases (United States); Vranken, Wim F. [VIB, Department of Structural Biology (Belgium); Baker, David [University of Washington, Department of Biochemistry (United States); Bonvin, Alexandre M. J. J., E-mail: a.m.j.j.bonvin@uu.nl [Utrecht University, Computational Structural Biology, Bijvoet Center for Biomolecular Research, Faculty of Science-Chemistry (Netherlands); Lange, Oliver F., E-mail: oliver.lange@tum.de [Technische Universitaet Muenchen, Biomolecular NMR and Munich Center for Integrated Protein Science, Department Chemie (Germany)

    2013-09-15

    We report advances in the calculation of protein structures from chemical shift nuclear magnetic resonance data alone. Our previously developed method, CS-Rosetta, assembles structures from a library of short protein fragments picked from a large library of protein structures using chemical shifts and sequence information. Here we demonstrate that combination of a new and improved fragment picker and the iterative sampling algorithm RASREC yield significant improvements in convergence and accuracy. Moreover, we introduce improved criteria for assessing the accuracy of the models produced by the method. The method was tested on 39 proteins in the 50-100 residue size range and yields reliable structures in 70 % of the cases. All structures that passed the reliability filter were accurate (<2 A RMSD from the reference)

  4. Leptospira interrogans serotype hardjo in dairy cows

    Vidić Branka M.

    2003-01-01

    Full Text Available Data on L. hardjo infection of dairy cows in the world pint out its important role in the occurrence of health and economic problem. L. interrogans serotype hardjo has been described as the cause of miscarriages, stillbirts, or the birhs of poorly vital calves, agalactia, mastitis, and low fertility in cows. Two L. hardjo genotypes have been identified in cows, namely, hardjopraitno and hardjobovis. Serological investigations have established a drastic increase in this leptospiral infection in cows. L. hardjo has become adapted to cattle as the primary host, so that an infection is maintained in herds and becomes deeply rooted because of the permanent presence of the source of infection. It was believed that sheep were accidental hosts, but the latest research suggest that they are yet another, transitory, host for maintining this leptospira serotype. L. hardjo is also important from the aspect of human health, especially of persons who are professionally exposed to this infection. L. hardjo infection is detected using serological tests and by proving the presence of leptospira. The medicine of choice in the therapy of leptospiral infections is streptomycin (DSM. Therapy using oxytetracyclines for clinical mastitis was also proven effective. Treatment is most successful in the early stage of the disease. A single dose of streptomycin administered in infected herds reduces the duration period of leptospira excretion through urine, thus preventing the spread of infection thorugh contaminated urine. The basic components of the plan to contain leptospira are the following: serological investigations, sanitary-higiene measures, the elimination of animals which excrete leptospira through urine, therapy, vaccination, quarantine.

  5. Protein structure predictions with Monte Carlo simulated annealing: Case for the β-sheet

    Okamoto, Y.; Fukugita, M.; Kawai, H.; Nakazawa, T.

    Work is continued for a prediction of three-dimensional structure of peptides and proteins with Monte Carlo simulated annealing using only a generic energy function and amino acid sequence as input. We report that β-sheet like structure is successfully predicted for a fragment of bovine pancreatic trypsin inhibitor which is known to have the β-sheet structure in nature. Together with the results for α-helix structure reported earlier, this means that a successful prediction can be made, at least at a qualitative level, for two dominant building blocks of proteins, α-helix and β-sheet, from the information of amino acid sequence alone.

  6. Development of laboratory acceleration test method for service life prediction of concrete structures

    Cho, M. S.; Song, Y. C.; Bang, K. S.; Lee, J. S.; Kim, D. K.

    1999-01-01

    Service life prediction of nuclear power plants depends on the application of history of structures, field inspection and test, the development of laboratory acceleration tests, their analysis method and predictive model. In this study, laboratory acceleration test method for service life prediction of concrete structures and application of experimental test results are introduced. This study is concerned with environmental condition of concrete structures and is to develop the acceleration test method for durability factors of concrete structures e.g. carbonation, sulfate attack, freeze-thaw cycles and shrinkage-expansion etc

  7. Lung abscess caused by Streptococcus pneumoniae serotype 6B.

    Ito, Yuhei; Toyoshima, Hirokazu; Suzuki, Takehiro; Iwamoto, Keisuke; Sasano, Hajime; Itani, Hidetoshi; Kondo, Shigeto; Tanigawa, Motoaki

    2018-01-01

    Lung abscess has been considered to be a rare complication of pneumococcal infection, and most cases are reported to be Streptococcus pneumoniae serotype 3. A 67-year-old man presented with fever and was diagnosed to have lung abscess caused by S. pneumoniae serotype 6B. The minimal inhibitory concentration (MIC) of penicillin for the isolate was 1 μg/mL. He was treated with high-dose intravenous sulbactam/ampicillin as definitive therapy based on susceptibility testing for S. pneumoniae and recovered successfully without surgical intervention. S. pneumoniae serotype 6B can cause lung abscess.

  8. Chromosome sizes and phylogenetic relationships between serotypes of Actinobacillus pleuropneumoniae

    Chevallier, Bruno; Dugourd, Dominique; Tarasiuk, Kazimirez; Harel, Josée; Gottschalk, Marcelo; Kobisch, Marylène; Frey, Joachim

    2017-01-01

    The genome size of Actinobacillus pleuropneumoniae was determined by pulsed field gel electrophoresis of AscI and ApaI digested chromosomal DNA. The genome size of the type strain 4074T (serotype 1) was determined to be 2404±40 kb. The chromosome sizes for the reference strains of the other serotypes range between 2.3 and 2.4 Mb. The restriction pattern profiles of AscI, ApaI and NheI digested chromosomes showed a high degree of polymorphism among the different serotype reference strains and ...

  9. Phylogenetic analyses of the polyprotein coding sequences of serotype O foot-and-mouth disease viruses in East Africa: evidence for interserotypic recombination

    Balinda Sheila N

    2010-08-01

    Full Text Available Abstract Background Foot-and-mouth disease (FMD is endemic in East Africa with the majority of the reported outbreaks attributed to serotype O virus. In this study, phylogenetic analyses of the polyprotein coding region of serotype O FMD viruses from Kenya and Uganda has been undertaken to infer evolutionary relationships and processes responsible for the generation and maintenance of diversity within this serotype. FMD virus RNA was obtained from six samples following virus isolation in cell culture and in one case by direct extraction from an oropharyngeal sample. Following RT-PCR, the single long open reading frame, encoding the polyprotein, was sequenced. Results Phylogenetic comparisons of the VP1 coding region showed that the recent East African viruses belong to one lineage within the EA-2 topotype while an older Kenyan strain, K/52/1992 is a representative of the topotype EA-1. Evolutionary relationships between the coding regions for the leader protease (L, the capsid region and almost the entire coding region are monophyletic except for the K/52/1992 which is distinct. Furthermore, phylogenetic relationships for the P2 and P3 regions suggest that the K/52/1992 is a probable recombinant between serotypes A and O. A bootscan analysis of K/52/1992 with East African FMD serotype A viruses (A21/KEN/1964 and A23/KEN/1965 and serotype O viral isolate (K/117/1999 revealed that the P2 region is probably derived from a serotype A strain while the P3 region appears to be a mosaic derived from both serotypes A and O. Conclusions Sequences of the VP1 coding region from recent serotype O FMDVs from Kenya and Uganda are all representatives of a specific East African lineage (topotype EA-2, a probable indication that hardly any FMD introductions of this serotype have occurred from outside the region in the recent past. Furthermore, evidence for interserotypic recombination, within the non-structural protein coding regions, between FMDVs of serotypes A

  10. Protein Function Prediction Based on Sequence and Structure Information

    Smaili, Fatima Z.

    2016-01-01

    operate. In this master thesis project, we worked on inferring protein functions based on the primary protein sequence. In the approach we follow, 3D models are first constructed using I-TASSER. Functions are then deduced by structurally matching

  11. Pathways to Structure-Property Relationships of Peptide-Materials Interfaces: Challenges in Predicting Molecular Structures.

    Walsh, Tiffany R

    2017-07-18

    challenges in their successful application to model the biotic-abiotic interface, related to several factors. For instance, simulations require a plausible description of the chemistry and the physics of the interface, which comprises two very different states of matter, in the presence of liquid water. Also, it is essential that the conformational ensemble be comprehensively characterized under these conditions; this is especially challenging because intrinsically disordered peptides do not typically admit one single structure or set of structures. Moreover, a plausible structural model of the substrate is required, which may require a high level of detail, even for single-element materials such as Au surfaces or graphene. Developing and applying strategies to make credible predictions of the conformational ensemble of adsorbed peptides and using these to construct structure-property relationships of these interfaces have been the goals of our efforts. We have made substantial progress in developing interatomic potentials for these interfaces and adapting advanced conformational sampling approaches for these purposes. This Account summarizes our progress in the development and deployment of interfacial force fields and molecular simulation techniques for the purpose of elucidating these insights at biomolecule-materials interfaces, using examples from our laboratories ranging from noble-metal interfaces to graphitic substrates (including carbon nanotubes and graphene) and oxide materials (such as titania). In addition to the well-established application areas of plasmonic materials, biosensing, and the production of medical implant materials, we outline new directions for this field that have the potential to bring new advances in areas such as energy materials and regenerative medicine.

  12. Harmonic Structure Predicts the Enjoyment of Uplifting Trance Music.

    Agres, Kat; Herremans, Dorien; Bigo, Louis; Conklin, Darrell

    2016-01-01

    An empirical investigation of how local harmonic structures (e.g., chord progressions) contribute to the experience and enjoyment of uplifting trance (UT) music is presented. The connection between rhythmic and percussive elements and resulting trance-like states has been highlighted by musicologists, but no research, to our knowledge, has explored whether repeated harmonic elements influence affective responses in listeners of trance music. Two alternative hypotheses are discussed, the first highlighting the direct relationship between repetition/complexity and enjoyment, and the second based on the theoretical inverted-U relationship described by the Wundt curve. We investigate the connection between harmonic structure and subjective enjoyment through interdisciplinary behavioral and computational methods: First we discuss an experiment in which listeners provided enjoyment ratings for computer-generated UT anthems with varying levels of harmonic repetition and complexity. The anthems were generated using a statistical model trained on a corpus of 100 uplifting trance anthems created for this purpose, and harmonic structure was constrained by imposing particular repetition structures (semiotic patterns defining the order of chords in the sequence) on a professional UT music production template. Second, the relationship between harmonic structure and enjoyment is further explored using two computational approaches, one based on average Information Content, and another that measures average tonal tension between chords. The results of the listening experiment indicate that harmonic repetition does in fact contribute to the enjoyment of uplifting trance music. More compelling evidence was found for the second hypothesis discussed above, however some maximally repetitive structures were also preferred. Both computational models provide evidence for a Wundt-type relationship between complexity and enjoyment. By systematically manipulating the structure of chord

  13. Building a Better Fragment Library for De Novo Protein Structure Prediction

    de Oliveira, Saulo H. P.; Shi, Jiye; Deane, Charlotte M.

    2015-01-01

    Fragment-based approaches are the current standard for de novo protein structure prediction. These approaches rely on accurate and reliable fragment libraries to generate good structural models. In this work, we describe a novel method for structure fragment library generation and its application in fragment-based de novo protein structure prediction. The importance of correct testing procedures in assessing the quality of fragment libraries is demonstrated. In particular, the exclusion of homologs to the target from the libraries to correctly simulate a de novo protein structure prediction scenario, something which surprisingly is not always done. We demonstrate that fragments presenting different predominant predicted secondary structures should be treated differently during the fragment library generation step and that exhaustive and random search strategies should both be used. This information was used to develop a novel method, Flib. On a validation set of 41 structurally diverse proteins, Flib libraries presents both a higher precision and coverage than two of the state-of-the-art methods, NNMake and HHFrag. Flib also achieves better precision and coverage on the set of 275 protein domains used in the two previous experiments of the the Critical Assessment of Structure Prediction (CASP9 and CASP10). We compared Flib libraries against NNMake libraries in a structure prediction context. Of the 13 cases in which a correct answer was generated, Flib models were more accurate than NNMake models for 10. “Flib is available for download at: http://www.stats.ox.ac.uk/research/proteins/resources”. PMID:25901595

  14. Building a better fragment library for de novo protein structure prediction.

    Saulo H P de Oliveira

    Full Text Available Fragment-based approaches are the current standard for de novo protein structure prediction. These approaches rely on accurate and reliable fragment libraries to generate good structural models. In this work, we describe a novel method for structure fragment library generation and its application in fragment-based de novo protein structure prediction. The importance of correct testing procedures in assessing the quality of fragment libraries is demonstrated. In particular, the exclusion of homologs to the target from the libraries to correctly simulate a de novo protein structure prediction scenario, something which surprisingly is not always done. We demonstrate that fragments presenting different predominant predicted secondary structures should be treated differently during the fragment library generation step and that exhaustive and random search strategies should both be used. This information was used to develop a novel method, Flib. On a validation set of 41 structurally diverse proteins, Flib libraries presents both a higher precision and coverage than two of the state-of-the-art methods, NNMake and HHFrag. Flib also achieves better precision and coverage on the set of 275 protein domains used in the two previous experiments of the the Critical Assessment of Structure Prediction (CASP9 and CASP10. We compared Flib libraries against NNMake libraries in a structure prediction context. Of the 13 cases in which a correct answer was generated, Flib models were more accurate than NNMake models for 10. "Flib is available for download at: http://www.stats.ox.ac.uk/research/proteins/resources".

  15. Structure prediction of boron-doped graphene by machine learning

    M. Dieb, Thaer; Hou, Zhufeng; Tsuda, Koji

    2018-06-01

    Heteroatom doping has endowed graphene with manifold aspects of material properties and boosted its applications. The atomic structure determination of doped graphene is vital to understand its material properties. Motivated by the recently synthesized boron-doped graphene with relatively high concentration, here we employ machine learning methods to search the most stable structures of doped boron atoms in graphene, in conjunction with the atomistic simulations. From the determined stable structures, we find that in the free-standing pristine graphene, the doped boron atoms energetically prefer to substitute for the carbon atoms at different sublattice sites and that the para configuration of boron-boron pair is dominant in the cases of high boron concentrations. The boron doping can increase the work function of graphene by 0.7 eV for a boron content higher than 3.1%.

  16. A semi-supervised learning approach for RNA secondary structure prediction.

    Yonemoto, Haruka; Asai, Kiyoshi; Hamada, Michiaki

    2015-08-01

    RNA secondary structure prediction is a key technology in RNA bioinformatics. Most algorithms for RNA secondary structure prediction use probabilistic models, in which the model parameters are trained with reliable RNA secondary structures. Because of the difficulty of determining RNA secondary structures by experimental procedures, such as NMR or X-ray crystal structural analyses, there are still many RNA sequences that could be useful for training whose secondary structures have not been experimentally determined. In this paper, we introduce a novel semi-supervised learning approach for training parameters in a probabilistic model of RNA secondary structures in which we employ not only RNA sequences with annotated secondary structures but also ones with unknown secondary structures. Our model is based on a hybrid of generative (stochastic context-free grammars) and discriminative models (conditional random fields) that has been successfully applied to natural language processing. Computational experiments indicate that the accuracy of secondary structure prediction is improved by incorporating RNA sequences with unknown secondary structures into training. To our knowledge, this is the first study of a semi-supervised learning approach for RNA secondary structure prediction. This technique will be useful when the number of reliable structures is limited. Copyright © 2015 Elsevier Ltd. All rights reserved.

  17. Prediction of Vibration Transmission within Periodic Bar Structures

    Domadiya, Parthkumar Gandalal; Andersen, Lars Vabbersgaard; Sorokin, Sergey

    2012-01-01

    The present analysis focuses on vibration transmission within semi-infinite bar structure. The bar is consisting of two different materials in a periodic manner. A periodic bar model is generated using two various methods: The Finite Element method (FEM) and a Floquet theory approach. A parameter...... study is carried out regarding the influence of the number of periods at various frequencies within a semi-infinite bar, stop bands are illustrated at certain periodic intervals within the structure. The computations are carried out in frequency domain in the range below 500 Hz. Results from both...

  18. Model Predictive Vibration Control Efficient Constrained MPC Vibration Control for Lightly Damped Mechanical Structures

    Takács, Gergely

    2012-01-01

    Real-time model predictive controller (MPC) implementation in active vibration control (AVC) is often rendered difficult by fast sampling speeds and extensive actuator-deformation asymmetry. If the control of lightly damped mechanical structures is assumed, the region of attraction containing the set of allowable initial conditions requires a large prediction horizon, making the already computationally demanding on-line process even more complex. Model Predictive Vibration Control provides insight into the predictive control of lightly damped vibrating structures by exploring computationally efficient algorithms which are capable of low frequency vibration control with guaranteed stability and constraint feasibility. In addition to a theoretical primer on active vibration damping and model predictive control, Model Predictive Vibration Control provides a guide through the necessary steps in understanding the founding ideas of predictive control applied in AVC such as: ·         the implementation of ...

  19. Prediction of backbone dihedral angles and protein secondary structure using support vector machines

    Hirst Jonathan D

    2009-12-01

    Full Text Available Abstract Background The prediction of the secondary structure of a protein is a critical step in the prediction of its tertiary structure and, potentially, its function. Moreover, the backbone dihedral angles, highly correlated with secondary structures, provide crucial information about the local three-dimensional structure. Results We predict independently both the secondary structure and the backbone dihedral angles and combine the results in a loop to enhance each prediction reciprocally. Support vector machines, a state-of-the-art supervised classification technique, achieve secondary structure predictive accuracy of 80% on a non-redundant set of 513 proteins, significantly higher than other methods on the same dataset. The dihedral angle space is divided into a number of regions using two unsupervised clustering techniques in order to predict the region in which a new residue belongs. The performance of our method is comparable to, and in some cases more accurate than, other multi-class dihedral prediction methods. Conclusions We have created an accurate predictor of backbone dihedral angles and secondary structure. Our method, called DISSPred, is available online at http://comp.chem.nottingham.ac.uk/disspred/.

  20. Lung abscess caused by Streptococcus pneumoniae serotype 6B

    Yuhei Ito

    Full Text Available Lung abscess has been considered to be a rare complication of pneumococcal infection, and most cases are reported to be Streptococcus pneumoniae serotype 3. A 67-year-old man presented with fever and was diagnosed to have lung abscess caused by S. pneumoniae serotype 6B. The minimal inhibitory concentration (MIC of penicillin for the isolate was 1 μg/mL. He was treated with high-dose intravenous sulbactam/ampicillin as definitive therapy based on susceptibility testing for S. pneumoniae and recovered successfully without surgical intervention. S. pneumoniae serotype 6B can cause lung abscess. Keywords: Streptococcus pneumoniae, Lung abscess, Serotype 6B, Penicillin-resistant Streptococcus pneumoniae

  1. Crystal structure prediction of flexible molecules using parallel genetic algorithms with a standard force field.

    Kim, Seonah; Orendt, Anita M; Ferraro, Marta B; Facelli, Julio C

    2009-10-01

    This article describes the application of our distributed computing framework for crystal structure prediction (CSP) the modified genetic algorithms for crystal and cluster prediction (MGAC), to predict the crystal structure of flexible molecules using the general Amber force field (GAFF) and the CHARMM program. The MGAC distributed computing framework includes a series of tightly integrated computer programs for generating the molecule's force field, sampling crystal structures using a distributed parallel genetic algorithm and local energy minimization of the structures followed by the classifying, sorting, and archiving of the most relevant structures. Our results indicate that the method can consistently find the experimentally known crystal structures of flexible molecules, but the number of missing structures and poor ranking observed in some crystals show the need for further improvement of the potential. Copyright 2009 Wiley Periodicals, Inc.

  2. Ab-initio theoretical predictions of structural properties of semiconductors

    Rodriguez, C.O.; Peltzer y Blanca, E.L.; Cappannini, O.M.

    1983-01-01

    Calculations of the total energies of Si, GaP and C together with related structural properties are presented. The results show good agreement with experimental values (differences of less than 6%). They also agree with other recent theoretical results. Calculations for Si and GaP have already been reported and are given here as a reference. (L.C.) [pt

  3. Water balance and topography predict fire and forest structure patterns

    Van R. Kane; James A. Lutz; C. Alina Cansler; Nicholas A. Povak; Derek J. Churchill; Douglas F. Smith; Jonathan T. Kane; Malcolm P. North

    2015-01-01

    Mountainous topography creates fine-scale environmental mosaics that vary in precipitation, temperature, insolation, and slope position. This mosaic in turn influences fuel accumulation and moisture and forest structure. We studied these the effects of varying environmental conditions across a 27,104 ha landscape within Yosemite National Park, California, USA, on the...

  4. Ab-initio theoretical predictions of structure properties of semiconductors

    Rodriguez, C.O.; Peltzer y Blanca, E.L.; Cappannini, O.M.

    1983-01-01

    In this paper, calculations of the total energies and related structural properties of Si, GaP and C are presented showing good agreement with experimental values. The total energy is calculated within the local-density functional formalism using first principles non-local pseudopotentials. (A.C.A.S.) [pt

  5. Structure Building Predicts Grades in College Psychology and Biology

    Arnold, Kathleen M.; Daniel, David B.; Jensen, Jamie L.; McDaniel, Mark A.; Marsh, Elizabeth J.

    2016-01-01

    Knowing what skills underlie college success can allow students, teachers, and universities to identify and to help at-risk students. One skill that may underlie success across a variety of subject areas is structure building, the ability to create mental representations of narratives (Gernsbacher, Varner, & Faust, 1990). We tested if…

  6. Predicting structural properties of fluids by thermodynamic extrapolation

    Mahynski, Nathan A.; Jiao, Sally; Hatch, Harold W.; Blanco, Marco A.; Shen, Vincent K.

    2018-05-01

    We describe a methodology for extrapolating the structural properties of multicomponent fluids from one thermodynamic state to another. These properties generally include features of a system that may be computed from an individual configuration such as radial distribution functions, cluster size distributions, or a polymer's radius of gyration. This approach is based on the principle of using fluctuations in a system's extensive thermodynamic variables, such as energy, to construct an appropriate Taylor series expansion for these structural properties in terms of intensive conjugate variables, such as temperature. Thus, one may extrapolate these properties from one state to another when the series is truncated to some finite order. We demonstrate this extrapolation for simple and coarse-grained fluids in both the canonical and grand canonical ensembles, in terms of both temperatures and the chemical potentials of different components. The results show that this method is able to reasonably approximate structural properties of such fluids over a broad range of conditions. Consequently, this methodology may be employed to increase the computational efficiency of molecular simulations used to measure the structural properties of certain fluid systems, especially those used in high-throughput or data-driven investigations.

  7. Whole-brain functional connectivity predicted by indirect structural connections

    Røge, Rasmus; Ambrosen, Karen Marie Sandø; Albers, Kristoffer Jon

    2017-01-01

    Modern functional and diffusion magnetic resonance imaging (fMRI and dMRI) provide data from which macro-scale networks of functional and structural whole brain connectivity can be estimated. Although networks derived from these two modalities describe different properties of the human brain, the...

  8. Social structure predicts genital morphology in African mole-rats.

    Marianne L Seney

    2009-10-01

    Full Text Available African mole-rats (Bathyergidae, Rodentia exhibit a wide range of social structures, from solitary to eusocial. We previously found a lack of sex differences in the external genitalia and morphology of the perineal muscles associated with the phallus in the eusocial naked mole-rat. This was quite surprising, as the external genitalia and perineal muscles are sexually dimorphic in all other mammals examined. We hypothesized that the lack of sex differences in naked mole-rats might be related to their unusual social structure.We compared the genitalia and perineal muscles in three African mole-rat species: the naked mole-rat, the solitary silvery mole-rat, and the Damaraland mole-rat, a species considered to be eusocial, but with less reproductive skew than naked mole-rats. Our findings support a relationship between social structure, mating system, and sexual differentiation. Naked mole-rats lack sex differences in genitalia and perineal morphology, silvery mole-rats exhibit sex differences, and Damaraland mole-rats are intermediate.The lack of sex differences in naked mole-rats is not an attribute of all African mole-rats, but appears to have evolved in relation to their unusual social structure and reproductive biology.

  9. Social structure predicts genital morphology in African mole-rats.

    Seney, Marianne L; Kelly, Diane A; Goldman, Bruce D; Sumbera, Radim; Forger, Nancy G

    2009-10-15

    African mole-rats (Bathyergidae, Rodentia) exhibit a wide range of social structures, from solitary to eusocial. We previously found a lack of sex differences in the external genitalia and morphology of the perineal muscles associated with the phallus in the eusocial naked mole-rat. This was quite surprising, as the external genitalia and perineal muscles are sexually dimorphic in all other mammals examined. We hypothesized that the lack of sex differences in naked mole-rats might be related to their unusual social structure. We compared the genitalia and perineal muscles in three African mole-rat species: the naked mole-rat, the solitary silvery mole-rat, and the Damaraland mole-rat, a species considered to be eusocial, but with less reproductive skew than naked mole-rats. Our findings support a relationship between social structure, mating system, and sexual differentiation. Naked mole-rats lack sex differences in genitalia and perineal morphology, silvery mole-rats exhibit sex differences, and Damaraland mole-rats are intermediate. The lack of sex differences in naked mole-rats is not an attribute of all African mole-rats, but appears to have evolved in relation to their unusual social structure and reproductive biology.

  10. The Prediction of Botulinum Toxin Structure Based on in Silico and in Vitro Analysis

    Suzuki, Tomonori; Miyazaki, Satoru

    2011-01-01

    Many of biological system mediated through protein-protein interactions. Knowledge of protein-protein complex structure is required for understanding the function. The determination of huge size and flexible protein-protein complex structure by experimental studies remains difficult, costly and five-consuming, therefore computational prediction of protein structures by homolog modeling and docking studies is valuable method. In addition, MD simulation is also one of the most powerful methods allowing to see the real dynamics of proteins. Here, we predict protein-protein complex structure of botulinum toxin to analyze its property. These bioinformatics methods are useful to report the relation between the flexibility of backbone structure and the activity.

  11. LocARNA-P: Accurate boundary prediction and improved detection of structural RNAs

    Will, Sebastian; Joshi, Tejal; Hofacker, Ivo L.

    2012-01-01

    Current genomic screens for noncoding RNAs (ncRNAs) predict a large number of genomic regions containing potential structural ncRNAs. The analysis of these data requires highly accurate prediction of ncRNA boundaries and discrimination of promising candidate ncRNAs from weak predictions. Existing...... methods struggle with these goals because they rely on sequence-based multiple sequence alignments, which regularly misalign RNA structure and therefore do not support identification of structural similarities. To overcome this limitation, we compute columnwise and global reliabilities of alignments based...... on sequence and structure similarity; we refer to these structure-based alignment reliabilities as STARs. The columnwise STARs of alignments, or STAR profiles, provide a versatile tool for the manual and automatic analysis of ncRNAs. In particular, we improve the boundary prediction of the widely used nc...

  12. Hand, Foot, and Mouth Disease in China: Modeling Epidemic Dynamics of Enterovirus Serotypes and Implications for Vaccination.

    Takahashi, Saki; Liao, Qiaohong; Van Boeckel, Thomas P; Xing, Weijia; Sun, Junling; Hsiao, Victor Y; Metcalf, C Jessica E; Chang, Zhaorui; Liu, Fengfeng; Zhang, Jing; Wu, Joseph T; Cowling, Benjamin J; Leung, Gabriel M; Farrar, Jeremy J; van Doorn, H Rogier; Grenfell, Bryan T; Yu, Hongjie

    2016-02-01

    Hand, foot, and mouth disease (HFMD) is a common childhood illness caused by serotypes of the Enterovirus A species in the genus Enterovirus of the Picornaviridae family. The disease has had a substantial burden throughout East and Southeast Asia over the past 15 y. China reported 9 million cases of HFMD between 2008 and 2013, with the two serotypes Enterovirus A71 (EV-A71) and Coxsackievirus A16 (CV-A16) being responsible for the majority of these cases. Three recent phase 3 clinical trials showed that inactivated monovalent EV-A71 vaccines manufactured in China were highly efficacious against HFMD associated with EV-A71, but offered no protection against HFMD caused by CV-A16. To better inform vaccination policy, we used mathematical models to evaluate the effect of prospective vaccination against EV-A71-associated HFMD and the potential risk of serotype replacement by CV-A16. We also extended the model to address the co-circulation, and implications for vaccination, of additional non-EV-A71, non-CV-A16 serotypes of enterovirus. Weekly reports of HFMD incidence from 31 provinces in Mainland China from 1 January 2009 to 31 December 2013 were used to fit multi-serotype time series susceptible-infected-recovered (TSIR) epidemic models. We obtained good model fit for the two-serotype TSIR with cross-protection, capturing the seasonality and geographic heterogeneity of province-level transmission, with strong correlation between the observed and simulated epidemic series. The national estimate of the basic reproduction number, R0, weighted by provincial population size, was 26.63 for EV-A71 (interquartile range [IQR]: 23.14, 30.40) and 27.13 for CV-A16 (IQR: 23.15, 31.34), with considerable variation between provinces (however, predictions about the overall impact of vaccination were robust to this variation). EV-A71 incidence was projected to decrease monotonically with higher coverage rates of EV-A71 vaccination. Across provinces, CV-A16 incidence in the post-EV-A71

  13. Hand, Foot, and Mouth Disease in China: Modeling Epidemic Dynamics of Enterovirus Serotypes and Implications for Vaccination.

    Saki Takahashi

    2016-02-01

    Full Text Available Hand, foot, and mouth disease (HFMD is a common childhood illness caused by serotypes of the Enterovirus A species in the genus Enterovirus of the Picornaviridae family. The disease has had a substantial burden throughout East and Southeast Asia over the past 15 y. China reported 9 million cases of HFMD between 2008 and 2013, with the two serotypes Enterovirus A71 (EV-A71 and Coxsackievirus A16 (CV-A16 being responsible for the majority of these cases. Three recent phase 3 clinical trials showed that inactivated monovalent EV-A71 vaccines manufactured in China were highly efficacious against HFMD associated with EV-A71, but offered no protection against HFMD caused by CV-A16. To better inform vaccination policy, we used mathematical models to evaluate the effect of prospective vaccination against EV-A71-associated HFMD and the potential risk of serotype replacement by CV-A16. We also extended the model to address the co-circulation, and implications for vaccination, of additional non-EV-A71, non-CV-A16 serotypes of enterovirus.Weekly reports of HFMD incidence from 31 provinces in Mainland China from 1 January 2009 to 31 December 2013 were used to fit multi-serotype time series susceptible-infected-recovered (TSIR epidemic models. We obtained good model fit for the two-serotype TSIR with cross-protection, capturing the seasonality and geographic heterogeneity of province-level transmission, with strong correlation between the observed and simulated epidemic series. The national estimate of the basic reproduction number, R0, weighted by provincial population size, was 26.63 for EV-A71 (interquartile range [IQR]: 23.14, 30.40 and 27.13 for CV-A16 (IQR: 23.15, 31.34, with considerable variation between provinces (however, predictions about the overall impact of vaccination were robust to this variation. EV-A71 incidence was projected to decrease monotonically with higher coverage rates of EV-A71 vaccination. Across provinces, CV-A16 incidence in the

  14. Draft Genome Sequences of Actinobacillus pleuropneumoniae Serotypes 2 and 6

    Zhan, Bujie; Angen, Øystein; Hedegaard, Jakob

    2010-01-01

    Actinobacillus pleuropneumoniae is a bacterial pathogen that causes highly contagious respiratory infection in pigs and has a serious impact on the production economy and animal welfare. As clear differences in virulence between serotypes have been observed, the genetic basis should be investigat...... at the genomic level. Here, we present the draft genome sequences of the A. pleuropneumoniae serotypes 2 (strain 4226) and 6 (strain Femo)....

  15. Serotype-specific mortality from invasive Streptococcus pneumoniae disease revisited

    Martens, Pernille; Worm, Signe Westring; Lundgren, Bettina

    2004-01-01

    Serotype-specific mortality from invasive Streptococcus pneumoniae disease revisited.Martens P, Worm SW, Lundgren B, Konradsen HB, Benfield T. Department of Infectious Diseases 144, Hvidovre University Hospital, DK-2650 Hvidovre, Denmark. pernillemartens@yahoo.com BACKGROUND: Invasive infection...... with Streptococcus pneumoniae (pneumococci) causes significant morbidity and mortality. Case series and experimental data have shown that the capsular serotype is involved in the pathogenesis and a determinant of disease outcome. METHODS: Retrospective review of 464 cases of invasive disease among adults diagnosed...

  16. Hill-Climbing search and diversification within an evolutionary approach to protein structure prediction.

    Chira, Camelia; Horvath, Dragos; Dumitrescu, D

    2011-07-30

    Proteins are complex structures made of amino acids having a fundamental role in the correct functioning of living cells. The structure of a protein is the result of the protein folding process. However, the general principles that govern the folding of natural proteins into a native structure are unknown. The problem of predicting a protein structure with minimum-energy starting from the unfolded amino acid sequence is a highly complex and important task in molecular and computational biology. Protein structure prediction has important applications in fields such as drug design and disease prediction. The protein structure prediction problem is NP-hard even in simplified lattice protein models. An evolutionary model based on hill-climbing genetic operators is proposed for protein structure prediction in the hydrophobic - polar (HP) model. Problem-specific search operators are implemented and applied using a steepest-ascent hill-climbing approach. Furthermore, the proposed model enforces an explicit diversification stage during the evolution in order to avoid local optimum. The main features of the resulting evolutionary algorithm - hill-climbing mechanism and diversification strategy - are evaluated in a set of numerical experiments for the protein structure prediction problem to assess their impact to the efficiency of the search process. Furthermore, the emerging consolidated model is compared to relevant algorithms from the literature for a set of difficult bidimensional instances from lattice protein models. The results obtained by the proposed algorithm are promising and competitive with those of related methods.

  17. Hill-Climbing search and diversification within an evolutionary approach to protein structure prediction

    Chira Camelia

    2011-07-01

    Full Text Available Abstract Proteins are complex structures made of amino acids having a fundamental role in the correct functioning of living cells. The structure of a protein is the result of the protein folding process. However, the general principles that govern the folding of natural proteins into a native structure are unknown. The problem of predicting a protein structure with minimum-energy starting from the unfolded amino acid sequence is a highly complex and important task in molecular and computational biology. Protein structure prediction has important applications in fields such as drug design and disease prediction. The protein structure prediction problem is NP-hard even in simplified lattice protein models. An evolutionary model based on hill-climbing genetic operators is proposed for protein structure prediction in the hydrophobic - polar (HP model. Problem-specific search operators are implemented and applied using a steepest-ascent hill-climbing approach. Furthermore, the proposed model enforces an explicit diversification stage during the evolution in order to avoid local optimum. The main features of the resulting evolutionary algorithm - hill-climbing mechanism and diversification strategy - are evaluated in a set of numerical experiments for the protein structure prediction problem to assess their impact to the efficiency of the search process. Furthermore, the emerging consolidated model is compared to relevant algorithms from the literature for a set of difficult bidimensional instances from lattice protein models. The results obtained by the proposed algorithm are promising and competitive with those of related methods.

  18. Solubility Temperature Dependence Predicted from 2D Structure

    Alex Avdeef

    2015-12-01

    Full Text Available The objective of the study was to find a computational procedure to normalize solubility data determined at various temperatures (e.g., 10 – 50 oC to values at a “reference” temperature (e.g., 25 °C. A simple procedure was devised to predict enthalpies of solution, ΔHsol, from which the temperature dependence of intrinsic (uncharged form solubility, log S0, could be calculated. As dependent variables, values of ΔHsol at 25 °C were subjected to multiple linear regression (MLR analysis, using melting points (mp and Abraham solvation descriptors. Also, the enthalpy data were subjected to random forest regression (RFR and recursive partition tree (RPT analyses. A total of 626 molecules were examined, drawing on 2040 published solubility values measured at various temperatures, along with 77 direct calori    metric measurements. The three different prediction methods (RFR, RPT, MLR all indicated that the estimated standard deviations in the enthalpy data are 11-15 kJ mol-1, which is concordant with the 10 kJ mol-1 propagation error estimated from solubility measurements (assuming 0.05 log S errors, and consistent with the 7 kJ mol-1 average reproducibility in enthalpy values from interlaboratory replicates. According to the MLR model, higher values of mp, H‑bond acidity, polarizability/dipolarity, and dispersion forces relate to more positive (endothermic enthalpy values. However, molecules that are large and have high H-bond basicity are likely to possess negative (exothermic enthalpies of solution. With log S0 values normalized to 25 oC, it was shown that the interlaboratory average standard deviations in solubility measurement are reduced to 0.06 ‑ 0.17 log unit, with higher errors for the least-soluble druglike molecules. Such improvements in data mining are expected to contribute to more reliable in silico prediction models of solubility for use in drug discovery.

  19. Exponential Repulsion Improves Structural Predictability of Molecular Docking

    Bazgier, Václav; Berka, K.; Otyepka, M.; Banáš, P.

    2016-01-01

    Roč. 37, č. 28 (2016), s. 2485-2494 ISSN 0192-8651 Institutional support: RVO:61389030 Keywords : cyclin-dependent kinases * structure-based design * scoring functions * cdk2 inhibitors * force-field * ligand interactions * drug discovery * purine * potent * protein-kinase-2 * molecular docking * dock 6.6 * drug design * cyclin-dependent kinase 2 * directory of decoys Subject RIV: CF - Physical ; Theoretical Chemistry Impact factor: 3.229, year: 2016

  20. Structural and Function Prediction of Musa acuminata subsp. Malaccensis Protein

    Anum Munir

    2016-03-01

    Full Text Available Hypothetical proteins (HPs are the proteins whose presence has been anticipated, yet in vivo function has not been built up. Illustrating the structural and functional privileged insights of these HPs might likewise prompt a superior comprehension of the protein-protein associations or networks in diverse types of life. Bananas (Musa acuminata spp., including sweet and cooking types, are giant perennial monocotyledonous herbs of the order Zingiberales, a sister grouped to the all-around considered Poales, which incorporate oats. Bananas are crucial for nourishment security in numerous tropical and subtropical nations and the most prominent organic product in industrialized nations. In the present study, the hypothetical protein of M. acuminata (Banana was chosen for analysis and modeling by distinctive bioinformatics apparatuses and databases. As indicated by primary and secondary structure analysis, XP_009393594.1 is a stable hydrophobic protein containing a noteworthy extent of α-helices; Homology modeling was done utilizing SWISS-MODEL server where the templates identity with XP_009393594.1 protein was less which demonstrated novelty of our protein. Ab initio strategy was conducted to produce its 3D structure. A few evaluations of quality assessment and validation parameters determined the generated protein model as stable with genuinely great quality. Functional analysis was completed by ProtFun 2.2, and KEGG (KAAS, recommended that the hypothetical protein is a transcription factor with cytoplasmic domain as zinc finger. The protein was observed to be vital for translation process, involved in metabolism, signaling and cellular processes, genetic information processing and Zinc ion binding. It is suggested that further test approval would help to anticipate the structures and functions of other uncharacterized proteins of different plants and living being.

  1. Quantum chemical prediction of antennae structures in lanthanide complexes

    Ottonelli, M.; Musso, G.F.; Rizzo, F.; Dellepiane, G.; Porzio, W.; Destri, S.

    2008-01-01

    In this paper the quantum chemical semiempirical procedure recently proposed by us to predict ground- and excited-state geometries of lanthanide complexes, the pseudo coordination centre method (PCC), is preliminarily compared with the semiempirical sparkle model for the calculation of lanthanide complexes (SMLC). Contrary to the SMLC method, where the rare-earth ion is replaced by a reparameterized sparkle atom, in our approach we replace it with a metal ion which is already present in the chosen semiempirical parameterization. This implies that in the optimization of the geometry of the complexes a different weight is implicitly given to the complex region including the rare-earth ion and its neighbour atoms with respect to the region of the ligands aggregate. As a consequence our approach is expected to reproduce better than the SMLC one the geometry of the ligands aggregate embedded in the complex, while the contrary happens for the coordination distances

  2. Serotype determination of Salmonella by xTAG assay.

    Zheng, Zhibei; Zheng, Wei; Wang, Haoqiu; Pan, Jincao; Pu, Xiaoying

    2017-10-01

    Currently, no protocols or commercial kits are available to determine the serotypes of Salmonella by using Luminex MAGPIX®. In this study, an xTAG assay for serotype determination of Salmonella suitable for Luminex MAGPIX® is described and 228 Salmonella isolates were serotype determined by this xTAG assay. The xTAG assay consists of two steps: 1) Multiplex PCR to amplify simultaneously O, H and Vi antigen genes of Salmonella, and 2) Magplex-TAG™ microsphere hybridization to identify accurately the specific PCR products of different antigens. Compared with the serotyping results of traditional serum agglutination test, the sensitivity and specificity of the xTAG assay were 95.1% and 100%, respectively. The agreement rate of these two assays was 95.2%. Compared with Luminex xMAP® Salmonella Serotyping Assay (SSA) kit, the advantages of this xTAG assay are: First, the magnetic beads make it applicable to both the Luminex®100/200™ and MAGPIX® systems. Second, only primers rather than both primers and probes are needed in the xTAG assay, and the process of coupling antigen-specific oligonucleotide probes to beads is circumvented, which make the xTAG assay convenient to be utilized by other laboratories. The xTAG assay may serve as a rapid alternative or complementary method for traditional Salmonella serotyping tests, especially for laboratories that utilize the MAGPIX® systems. Copyright © 2017 Elsevier B.V. All rights reserved.

  3. Serotype tracking of Salmonella through integrated broiler chicken operations.

    Bailey, J S; Cox, N A; Craven, S E; Cosby, D E

    2002-05-01

    The widespread presence of Salmonella in all phases of broiler chicken production and processing is well documented. However, little information is available to indicate the identity and movement of specific serotypes of Salmonella through the different phases of an integrated operation. In this study, samples were collected from the breeder farm, from the hatchery, from the previous grow-out flock, from the flock during grow-out, and from carcasses after processing. Salmonella were recovered from 6, 98, 24, 60, and 7% of the samples, respectively, in the first trial and from 7, 98, 26, 22, and 36% of the samples, respectively, in the second trial. Seven different serotypes were identified in the first trial, and 12 different serotypes were identified in the second trial. For both trials there was poor correlation between the serotypes found in the breeder farms and those found in the hatchery. This finding and the fact that similar serotypes were found in the hatchery in both trials suggests that there was an endemic population of Salmonella in the hatchery. An association between the serotypes found in the hatchery and those found on the final processed carcasses was observed in both trials. This study confirms that a successful intervention program for broiler production operations must be multifaceted, with one component being disinfection in the hatchery.

  4. Java project on periodontal diseases : serotype distribution of Aggregatibacter actinomycetemcomitans and serotype dynamics over an 8-year period

    van der Reijden, Wil A.; Bosch-Tijhof, Carolien J.; van der Velden, Ubele; van Winkelhoff, Arie Jan

    Objective: To investigate the serotype distribution and stability of Aggregatibacter actinomycetemcomitans over an 8-year period in untreated Indonesian subjects. Material and Methods: Clinical periodontal status and the presence of A. actinomycetemcomitans were established in 1994 and 2002 in 107

  5. Genetic analysis of the capsular biosynthetic locus from all 90 pneumococcal serotypes.

    Stephen D Bentley

    2006-03-01

    Full Text Available Several major invasive bacterial pathogens are encapsulated. Expression of a polysaccharide capsule is essential for survival in the blood, and thus for virulence, but also is a target for host antibodies and the basis for effective vaccines. Encapsulated species typically exhibit antigenic variation and express one of a number of immunochemically distinct capsular polysaccharides that define serotypes. We provide the sequences of the capsular biosynthetic genes of all 90 serotypes of Streptococcus pneumoniae and relate these to the known polysaccharide structures and patterns of immunological reactivity of typing sera, thereby providing the most complete understanding of the genetics and origins of bacterial polysaccharide diversity, laying the foundations for molecular serotyping. This is the first time, to our knowledge, that a complete repertoire of capsular biosynthetic genes has been available, enabling a holistic analysis of a bacterial polysaccharide biosynthesis system. Remarkably, the total size of alternative coding DNA at this one locus exceeds 1.8 Mbp, almost equivalent to the entire S. pneumoniae chromosomal complement.

  6. Antigenic heterogeneity of capsid protein VP1 in foot-and-mouth disease virus (FMDV serotype Asia1

    Alam SM

    2013-08-01

    Full Text Available SM Sabbir Alam,1 Ruhul Amin,1 Mohammed Ziaur Rahman,2 M Anwar Hossain,1 Munawar Sultana11Department of Microbiology, University of Dhaka, Dhaka, Bangladesh; 2International Centre for Diarrhoeal Disease Research, Dhaka, BangladeshAbstract: Foot and mouth disease virus (FMDV, with its seven serotypes, is a highly contagious virus infecting mainly cloven-hoofed animals. The serotype Asia1 occurs mainly in Asian regions. An in-silico approach was taken to reveal the antigenic heterogeneities within the capsid protein VP1 of Asia1. A total of 47 VP1 sequences of Asia1 isolates from different countries of South Asian regions were selected, retrieved from database, and were aligned. The structure of VP1 protein was modeled using a homology modeling approach. Several antigenic sites were identified and mapped onto the three-dimensional protein structure. Variations at these antigenic sites were analyzed by calculating the protein variability index and finding mutation combinations. The data suggested that vaccine escape mutants have derived from only few mutations at several antigenic sites. Five antigenic peptides have been identified as the least variable epitopes, with just fewer amino acid substitutions. Only a limited number of serotype Asia1 antigenic variants were found to be circulated within the South Asian region. This emphasizes a possibility of formulating synthetic vaccines for controlling foot-and-mouth disease by Asia1 serotypes.Keywords: protein modeling, antigenic sites, sequence variation

  7. Failure/leakage predictions of concrete structures containing cracks

    Pan, Y.C.; Marchertas, A.H.; Kennedy, J.M.

    1984-06-01

    An approach is presented for studying the cracking and radioactive release of a reactor containment during severe accidents and extreme environments. The cracking of concrete is modeled as the blunt crack. The initiation and propagation of a crack are determined by using the maximum strength and the J-integral criteria. Furthermore, the extent of cracking is related to the leakage calculation by using a model developed by Rizkalla, Lau and Simmonds. Numerical examples are given for a three-point bending problem and a hypothetical case of a concrete containment structure subjected to high internal pressure during an accident

  8. Reduced Fragment Diversity for Alpha and Alpha-Beta Protein Structure Prediction using Rosetta.

    Abbass, Jad; Nebel, Jean-Christophe

    2017-01-01

    Protein structure prediction is considered a main challenge in computational biology. The biannual international competition, Critical Assessment of protein Structure Prediction (CASP), has shown in its eleventh experiment that free modelling target predictions are still beyond reliable accuracy, therefore, much effort should be made to improve ab initio methods. Arguably, Rosetta is considered as the most competitive method when it comes to targets with no homologues. Relying on fragments of length 9 and 3 from known structures, Rosetta creates putative structures by assembling candidate fragments. Generally, the structure with the lowest energy score, also known as first model, is chosen to be the "predicted one". A thorough study has been conducted on the role and diversity of 3-mers involved in Rosetta's model "refinement" phase. Usage of the standard number of 3-mers - i.e. 200 - has been shown to degrade alpha and alpha-beta protein conformations initially achieved by assembling 9-mers. Therefore, a new prediction pipeline is proposed for Rosetta where the "refinement" phase is customised according to a target's structural class prediction. Over 8% improvement in terms of first model structure accuracy is reported for alpha and alpha-beta classes when decreasing the number of 3- mers. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

  9. Identification of Actinobacillus pleuropneumoniae serotypes 1, 7, and 12 by multiplex PCR based on genes involved in encapsulation

    Angen, Øystein; Jessing, Stine Graakjær; Ahrens, Peter

    2005-01-01

    . However, a number of isolates show cross-reaction between serotypes and this has urged the development of quick, serotype specific DNA-based methods necessary. Serotype specific tests have until now been described for the serotypes 2, 5, and 6 (Jessing et al, 2003) and serotypes 1, 2 and 8 (Schuchert et...

  10. TMDIM: an improved algorithm for the structure prediction of transmembrane domains of bitopic dimers

    Cao, Han; Ng, Marcus C. K.; Jusoh, Siti Azma; Tai, Hio Kuan; Siu, Shirley W. I.

    2017-09-01

    α-Helical transmembrane proteins are the most important drug targets in rational drug development. However, solving the experimental structures of these proteins remains difficult, therefore computational methods to accurately and efficiently predict the structures are in great demand. We present an improved structure prediction method TMDIM based on Park et al. (Proteins 57:577-585, 2004) for predicting bitopic transmembrane protein dimers. Three major algorithmic improvements are introduction of the packing type classification, the multiple-condition decoy filtering, and the cluster-based candidate selection. In a test of predicting nine known bitopic dimers, approximately 78% of our predictions achieved a successful fit (RMSD PHP, MySQL and Apache, with all major browsers supported.

  11. Predictive model for early math skills based on structural equations.

    Aragón, Estíbaliz; Navarro, José I; Aguilar, Manuel; Cerda, Gamal; García-Sedeño, Manuel

    2016-12-01

    Early math skills are determined by higher cognitive processes that are particularly important for acquiring and developing skills during a child's early education. Such processes could be a critical target for identifying students at risk for math learning difficulties. Few studies have considered the use of a structural equation method to rationalize these relations. Participating in this study were 207 preschool students ages 59 to 72 months, 108 boys and 99 girls. Performance with respect to early math skills, early literacy, general intelligence, working memory, and short-term memory was assessed. A structural equation model explaining 64.3% of the variance in early math skills was applied. Early literacy exhibited the highest statistical significance (β = 0.443, p < 0.05), followed by intelligence (β = 0.286, p < 0.05), working memory (β = 0.220, p < 0.05), and short-term memory (β = 0.213, p < 0.05). Correlations between the independent variables were also significant (p < 0.05). According to the results, cognitive variables should be included in remedial intervention programs. © 2016 Scandinavian Psychological Associations and John Wiley & Sons Ltd.

  12. Study on effect of mean stress on fatigue life prediction of thin film structure

    Shin, Myung Soo [Ahtti Co., Seongnam (Korea, Republic of); Park, Jun Hyu [Tongmyong University, Busan (Korea, Republic of); Kim, Jung Yup [Korea Institute of Machinery and Materials, Daejeon (Korea, Republic of)

    2016-04-15

    This paper describes the effect of mean stress on fatigue life prediction of structure made with thin film. It is well known that the mean stress influences fatigue life prediction of mechanical structure. We investigated a reasonable method for considering mean stress when fatigue strength assessment of micro structure of thin film should be performed. Fatigue tests of smooth specimen of beryllium-copper (BeCu) thin film were performed in ambient air at R = 0.1 with 5 Hz. A micro probe was designed and made with BeCu thin film by the precision press process. Fatigue tests of micro structure were performed with 5 Hz frequency, in ambient air to verify the fatigue life predicted by computer simulation through FE analysis. The fatigue life predicted by the Sa -N curve modified by Goodman method with principal stress through FE analysis shows a more reasonable result than other methods.

  13. Adaptive Neuro-Fuzzy Inference System Models for Force Prediction of a Mechatronic Flexible Structure

    Achiche, S.; Shlechtingen, M.; Raison, M.

    2016-01-01

    This paper presents the results obtained from a research work investigating the performance of different Adaptive Neuro-Fuzzy Inference System (ANFIS) models developed to predict excitation forces on a dynamically loaded flexible structure. For this purpose, a flexible structure is equipped...... obtained from applying a random excitation force on the flexible structure. The performance of the developed models is evaluated by analyzing the prediction capabilities based on a normalized prediction error. The frequency domain is considered to analyze the similarity of the frequencies in the predicted...... of the sampling frequency and sensor location on the model performance is investigated. The results obtained in this paper show that ANFIS models can be used to set up reliable force predictors for dynamical loaded flexible structures, when a certain degree of inaccuracy is accepted. Furthermore, the comparison...

  14. Study on effect of mean stress on fatigue life prediction of thin film structure

    Shin, Myung Soo; Park, Jun Hyu; Kim, Jung Yup

    2016-01-01

    This paper describes the effect of mean stress on fatigue life prediction of structure made with thin film. It is well known that the mean stress influences fatigue life prediction of mechanical structure. We investigated a reasonable method for considering mean stress when fatigue strength assessment of micro structure of thin film should be performed. Fatigue tests of smooth specimen of beryllium-copper (BeCu) thin film were performed in ambient air at R = 0.1 with 5 Hz. A micro probe was designed and made with BeCu thin film by the precision press process. Fatigue tests of micro structure were performed with 5 Hz frequency, in ambient air to verify the fatigue life predicted by computer simulation through FE analysis. The fatigue life predicted by the Sa -N curve modified by Goodman method with principal stress through FE analysis shows a more reasonable result than other methods

  15. CentroidFold: a web server for RNA secondary structure prediction

    Sato, Kengo; Hamada, Michiaki; Asai, Kiyoshi; Mituyama, Toutai

    2009-01-01

    The CentroidFold web server (http://www.ncrna.org/centroidfold/) is a web application for RNA secondary structure prediction powered by one of the most accurate prediction engine. The server accepts two kinds of sequence data: a single RNA sequence and a multiple alignment of RNA sequences. It responses with a prediction result shown as a popular base-pair notation and a graph representation. PDF version of the graph representation is also available. For a multiple alignment sequence, the ser...

  16. Ranking beta sheet topologies with applications to protein structure prediction

    Fonseca, Rasmus; Helles, Glennie; Winter, Pawel

    2011-01-01

    One reason why ab initio protein structure predictors do not perform very well is their inability to reliably identify long-range interactions between amino acids. To achieve reliable long-range interactions, all potential pairings of ß-strands (ß-topologies) of a given protein are enumerated......, including the native ß-topology. Two very different ß-topology scoring methods from the literature are then used to rank all potential ß-topologies. This has not previously been attempted for any scoring method. The main result of this paper is a justification that one of the scoring methods, in particular......, consistently top-ranks native ß-topologies. Since the number of potential ß-topologies grows exponentially with the number of ß-strands, it is unrealistic to expect that all potential ß-topologies can be enumerated for large proteins. The second result of this paper is an enumeration scheme of a subset of ß-topologies...

  17. Reliability prediction for structures under cyclic loads and recurring inspections

    Alberto W. S. Mello Jr

    2009-06-01

    Full Text Available This work presents a methodology for determining the reliability of fracture control plans for structures subjected to cyclic loads. It considers the variability of the parameters involved in the problem, such as initial flaw and crack growth curve. The probability of detection (POD curve of the field non-destructive inspection method and the condition/environment are used as important factors for structural confidence. According to classical damage tolerance analysis (DTA, inspection intervals are based on detectable crack size and crack growth rate. However, all variables have uncertainties, which makes the final result totally stochastic. The material properties, flight loads, engineering tools and even the reliability of inspection methods are subject to uncertainties which can affect significantly the final maintenance schedule. The present methodology incorporates all the uncertainties in a simulation process, such as Monte Carlo, and establishes a relationship between the reliability of the overall maintenance program and the proposed inspection interval, forming a “cascade” chart. Due to the scatter, it also defines the confidence level of the “acceptable” risk. As an example, the damage tolerance analysis (DTA results are presented for the upper cockpit longeron splice bolt of the BAF upgraded F-5EM. In this case, two possibilities of inspection intervals were found: one that can be characterized as remote risk, with a probability of failure (integrity nonsuccess of 1 in 10 million, per flight hour; and other as extremely improbable, with a probability of nonsuccess of 1 in 1 billion, per flight hour, according to aviation standards. These two results are compared with the classical military airplane damage tolerance requirements.

  18. Free energy minimization to predict RNA secondary structures and computational RNA design.

    Churkin, Alexander; Weinbrand, Lina; Barash, Danny

    2015-01-01

    Determining the RNA secondary structure from sequence data by computational predictions is a long-standing problem. Its solution has been approached in two distinctive ways. If a multiple sequence alignment of a collection of homologous sequences is available, the comparative method uses phylogeny to determine conserved base pairs that are more likely to form as a result of billions of years of evolution than by chance. In the case of single sequences, recursive algorithms that compute free energy structures by using empirically derived energy parameters have been developed. This latter approach of RNA folding prediction by energy minimization is widely used to predict RNA secondary structure from sequence. For a significant number of RNA molecules, the secondary structure of the RNA molecule is indicative of its function and its computational prediction by minimizing its free energy is important for its functional analysis. A general method for free energy minimization to predict RNA secondary structures is dynamic programming, although other optimization methods have been developed as well along with empirically derived energy parameters. In this chapter, we introduce and illustrate by examples the approach of free energy minimization to predict RNA secondary structures.

  19. In silico serotyping of E. coli from short read data identifies limited novel O-loci but extensive diversity of O:H serotype combinations within and between pathogenic lineages.

    Ingle, Danielle J; Valcanis, Mary; Kuzevski, Alex; Tauschek, Marija; Inouye, Michael; Stinear, Tim; Levine, Myron M; Robins-Browne, Roy M; Holt, Kathryn E

    2016-07-01

    The lipopolysaccharide (O) and flagellar (H) surface antigens of Escherichia coli are targets for serotyping that have traditionally been used to identify pathogenic lineages. These surface antigens are important for the survival of E. coli within mammalian hosts. However, traditional serotyping has several limitations, and public health reference laboratories are increasingly moving towards whole genome sequencing (WGS) to characterize bacterial isolates. Here we present a method to rapidly and accurately serotype E. coli isolates from raw, short read WGS data. Our approach bypasses the need for de novo genome assembly by directly screening WGS reads against a curated database of alleles linked to known and novel E. coli O-groups and H-types (the EcOH database) using the software package srst2. We validated the approach by comparing in silico results for 197 enteropathogenic E. coli isolates with those obtained by serological phenotyping in an independent laboratory. We then demonstrated the utility of our method to characterize isolates in public health and clinical settings, and to explore the genetic diversity of >1500 E. coli genomes from multiple sources. Importantly, we showed that transfer of O- and H-antigen loci between E. coli chromosomal backbones is common, with little evidence of constraints by host or pathotype, suggesting that E. coli ' strain space' may be virtually unlimited, even within specific pathotypes. Our findings show that serotyping is most useful when used in combination with strain genotyping to characterize microevolution events within an inferred population structure.

  20. MASTR: multiple alignment and structure prediction of non-coding RNAs using simulated annealing

    Lindgreen, Stinus; Gardner, Paul P; Krogh, Anders

    2007-01-01

    function that considers sequence conservation, covariation and basepairing probabilities. The results show that the method is very competitive to similar programs available today, both in terms of accuracy and computational efficiency. AVAILABILITY: Source code available from http://mastr.binf.ku.dk/......MOTIVATION: As more non-coding RNAs are discovered, the importance of methods for RNA analysis increases. Since the structure of ncRNA is intimately tied to the function of the molecule, programs for RNA structure prediction are necessary tools in this growing field of research. Furthermore......, it is known that RNA structure is often evolutionarily more conserved than sequence. However, few existing methods are capable of simultaneously considering multiple sequence alignment and structure prediction. RESULT: We present a novel solution to the problem of simultaneous structure prediction...

  1. Bridge Structure Deformation Prediction Based on GNSS Data Using Kalman-ARIMA-GARCH Model.

    Xin, Jingzhou; Zhou, Jianting; Yang, Simon X; Li, Xiaoqing; Wang, Yu

    2018-01-19

    Bridges are an essential part of the ground transportation system. Health monitoring is fundamentally important for the safety and service life of bridges. A large amount of structural information is obtained from various sensors using sensing technology, and the data processing has become a challenging issue. To improve the prediction accuracy of bridge structure deformation based on data mining and to accurately evaluate the time-varying characteristics of bridge structure performance evolution, this paper proposes a new method for bridge structure deformation prediction, which integrates the Kalman filter, autoregressive integrated moving average model (ARIMA), and generalized autoregressive conditional heteroskedasticity (GARCH). Firstly, the raw deformation data is directly pre-processed using the Kalman filter to reduce the noise. After that, the linear recursive ARIMA model is established to analyze and predict the structure deformation. Finally, the nonlinear recursive GARCH model is introduced to further improve the accuracy of the prediction. Simulation results based on measured sensor data from the Global Navigation Satellite System (GNSS) deformation monitoring system demonstrated that: (1) the Kalman filter is capable of denoising the bridge deformation monitoring data; (2) the prediction accuracy of the proposed Kalman-ARIMA-GARCH model is satisfactory, where the mean absolute error increases only from 3.402 mm to 5.847 mm with the increment of the prediction step; and (3) in comparision to the Kalman-ARIMA model, the Kalman-ARIMA-GARCH model results in superior prediction accuracy as it includes partial nonlinear characteristics (heteroscedasticity); the mean absolute error of five-step prediction using the proposed model is improved by 10.12%. This paper provides a new way for structural behavior prediction based on data processing, which can lay a foundation for the early warning of bridge health monitoring system based on sensor data using sensing

  2. Bridge Structure Deformation Prediction Based on GNSS Data Using Kalman-ARIMA-GARCH Model

    Jingzhou Xin

    2018-01-01

    Full Text Available Bridges are an essential part of the ground transportation system. Health monitoring is fundamentally important for the safety and service life of bridges. A large amount of structural information is obtained from various sensors using sensing technology, and the data processing has become a challenging issue. To improve the prediction accuracy of bridge structure deformation based on data mining and to accurately evaluate the time-varying characteristics of bridge structure performance evolution, this paper proposes a new method for bridge structure deformation prediction, which integrates the Kalman filter, autoregressive integrated moving average model (ARIMA, and generalized autoregressive conditional heteroskedasticity (GARCH. Firstly, the raw deformation data is directly pre-processed using the Kalman filter to reduce the noise. After that, the linear recursive ARIMA model is established to analyze and predict the structure deformation. Finally, the nonlinear recursive GARCH model is introduced to further improve the accuracy of the prediction. Simulation results based on measured sensor data from the Global Navigation Satellite System (GNSS deformation monitoring system demonstrated that: (1 the Kalman filter is capable of denoising the bridge deformation monitoring data; (2 the prediction accuracy of the proposed Kalman-ARIMA-GARCH model is satisfactory, where the mean absolute error increases only from 3.402 mm to 5.847 mm with the increment of the prediction step; and (3 in comparision to the Kalman-ARIMA model, the Kalman-ARIMA-GARCH model results in superior prediction accuracy as it includes partial nonlinear characteristics (heteroscedasticity; the mean absolute error of five-step prediction using the proposed model is improved by 10.12%. This paper provides a new way for structural behavior prediction based on data processing, which can lay a foundation for the early warning of bridge health monitoring system based on sensor data

  3. G-LoSA for Prediction of Protein-Ligand Binding Sites and Structures.

    Lee, Hui Sun; Im, Wonpil

    2017-01-01

    Recent advances in high-throughput structure determination and computational protein structure prediction have significantly enriched the universe of protein structure. However, there is still a large gap between the number of available protein structures and that of proteins with annotated function in high accuracy. Computational structure-based protein function prediction has emerged to reduce this knowledge gap. The identification of a ligand binding site and its structure is critical to the determination of a protein's molecular function. We present a computational methodology for predicting small molecule ligand binding site and ligand structure using G-LoSA, our protein local structure alignment and similarity measurement tool. All the computational procedures described here can be easily implemented using G-LoSA Toolkit, a package of standalone software programs and preprocessed PDB structure libraries. G-LoSA and G-LoSA Toolkit are freely available to academic users at http://compbio.lehigh.edu/GLoSA . We also illustrate a case study to show the potential of our template-based approach harnessing G-LoSA for protein function prediction.

  4. Aromatic claw: A new fold with high aromatic content that evades structural prediction: Aromatic Claw

    Sachleben, Joseph R. [Biomolecular NMR Core Facility, University of Chicago, Chicago Illinois; Adhikari, Aashish N. [Department of Chemistry, University of Chicago, Chicago Illinois; Gawlak, Grzegorz [Department of Biochemistry and Molecular Biology, University of Chicago, Chicago Illinois; Hoey, Robert J. [Department of Biochemistry and Molecular Biology, University of Chicago, Chicago Illinois; Liu, Gaohua [Northeast Structural Genomics Consortium (NESG), Department of Molecular Biology and Biochemistry, School of Arts and Sciences, and Department of Biochemistry and Molecular Biology, Robert Wood Johnson Medical School, and Center for Advanced Biotechnology and Medicine, Rutgers, The State University of New Jersey, Piscataway New Jersey; Joachimiak, Andrzej [Department of Biochemistry and Molecular Biology, University of Chicago, Chicago Illinois; Biological Sciences Division, Argonne National Laboratory, Argonne Illinois; Montelione, Gaetano T. [Northeast Structural Genomics Consortium (NESG), Department of Molecular Biology and Biochemistry, School of Arts and Sciences, and Department of Biochemistry and Molecular Biology, Robert Wood Johnson Medical School, and Center for Advanced Biotechnology and Medicine, Rutgers, The State University of New Jersey, Piscataway New Jersey; Sosnick, Tobin R. [Department of Biochemistry and Molecular Biology, University of Chicago, Chicago Illinois; Koide, Shohei [Department of Biochemistry and Molecular Biology, University of Chicago, Chicago Illinois; Department of Biochemistry and Molecular Pharmacology and the Perlmutter Cancer Center, New York University School of Medicine, New York New York

    2016-11-10

    We determined the NMR structure of a highly aromatic (13%) protein of unknown function, Aq1974 from Aquifex aeolicus (PDB ID: 5SYQ). The unusual sequence of this protein has a tryptophan content five times the normal (six tryptophan residues of 114 or 5.2% while the average tryptophan content is 1.0%) with the tryptophans occurring in a WXW motif. It has no detectable sequence homology with known protein structures. Although its NMR spectrum suggested that the protein was rich in β-sheet, upon resonance assignment and solution structure determination, the protein was found to be primarily α-helical with a small two-stranded β-sheet with a novel fold that we have termed an Aromatic Claw. As this fold was previously unknown and the sequence unique, we submitted the sequence to CASP10 as a target for blind structural prediction. At the end of the competition, the sequence was classified a hard template based model; the structural relationship between the template and the experimental structure was small and the predictions all failed to predict the structure. CSRosetta was found to predict the secondary structure and its packing; however, it was found that there was little correlation between CSRosetta score and the RMSD between the CSRosetta structure and the NMR determined one. This work demonstrates that even in relatively small proteins, we do not yet have the capacity to accurately predict the fold for all primary sequences. The experimental discovery of new folds helps guide the improvement of structural prediction methods.

  5. Protein Secondary Structure Prediction Using AutoEncoder Network and Bayes Classifier

    Wang, Leilei; Cheng, Jinyong

    2018-03-01

    Protein secondary structure prediction is belong to bioinformatics,and it's important in research area. In this paper, we propose a new prediction way of protein using bayes classifier and autoEncoder network. Our experiments show some algorithms including the construction of the model, the classification of parameters and so on. The data set is a typical CB513 data set for protein. In terms of accuracy, the method is the cross validation based on the 3-fold. Then we can get the Q3 accuracy. Paper results illustrate that the autoencoder network improved the prediction accuracy of protein secondary structure.

  6. Exploiting the Past and the Future in Protein Secondary Structure Prediction

    Baldi, Pierre; Brunak, Søren; Frasconi, P

    1999-01-01

    predictions based on variable ranges of dependencies. These architectures extend recurrent neural networks, introducing non-causal bidirectional dynamics to capture both upstream and downstream information. The prediction algorithm is completed by the use of mixtures of estimators that leverage evolutionary......Motivation: Predicting the secondary structure of a protein (alpha-helix, beta-sheet, coil) is an important step towards elucidating its three-dimensional structure, as well as its function. Presently, the best predictors are based on machine learning approaches, in particular neural network...

  7. Predicting adverse drug reaction profiles by integrating protein interaction networks with drug structures.

    Huang, Liang-Chin; Wu, Xiaogang; Chen, Jake Y

    2013-01-01

    The prediction of adverse drug reactions (ADRs) has become increasingly important, due to the rising concern on serious ADRs that can cause drugs to fail to reach or stay in the market. We proposed a framework for predicting ADR profiles by integrating protein-protein interaction (PPI) networks with drug structures. We compared ADR prediction performances over 18 ADR categories through four feature groups-only drug targets, drug targets with PPI networks, drug structures, and drug targets with PPI networks plus drug structures. The results showed that the integration of PPI networks and drug structures can significantly improve the ADR prediction performance. The median AUC values for the four groups were 0.59, 0.61, 0.65, and 0.70. We used the protein features in the best two models, "Cardiac disorders" (median-AUC: 0.82) and "Psychiatric disorders" (median-AUC: 0.76), to build ADR-specific PPI networks with literature supports. For validation, we examined 30 drugs withdrawn from the U.S. market to see if our approach can predict their ADR profiles and explain why they were withdrawn. Except for three drugs having ADRs in the categories we did not predict, 25 out of 27 withdrawn drugs (92.6%) having severe ADRs were successfully predicted by our approach. © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  8. Germline bias dictates cross-serotype reactivity in a common dengue-virus-specific CD8+ T cell response.

    Culshaw, Abigail; Ladell, Kristin; Gras, Stephanie; McLaren, James E; Miners, Kelly L; Farenc, Carine; van den Heuvel, Heleen; Gostick, Emma; Dejnirattisai, Wanwisa; Wangteeraprasert, Apirath; Duangchinda, Thaneeya; Chotiyarnwong, Pojchong; Limpitikul, Wannee; Vasanawathana, Sirijitt; Malasit, Prida; Dong, Tao; Rossjohn, Jamie; Mongkolsapaya, Juthathip; Price, David A; Screaton, Gavin R

    2017-11-01

    Adaptive immune responses protect against infection with dengue virus (DENV), yet cross-reactivity with distinct serotypes can precipitate life-threatening clinical disease. We found that clonotypes expressing the T cell antigen receptor (TCR) β-chain variable region 11 (TRBV11-2) were 'preferentially' activated and mobilized within immunodominant human-leukocyte-antigen-(HLA)-A*11:01-restricted CD8 + T cell populations specific for variants of the nonstructural protein epitope NS3 133 that characterize the serotypes DENV1, DENV3 and DENV4. In contrast, the NS3 133 -DENV2-specific repertoire was largely devoid of such TCRs. Structural analysis of a representative TRBV11-2 + TCR demonstrated that cross-serotype reactivity was governed by unique interplay between the variable antigenic determinant and germline-encoded residues in the second β-chain complementarity-determining region (CDR2β). Extensive mutagenesis studies of three distinct TRBV11-2 + TCRs further confirmed that antigen recognition was dependent on key contacts between the serotype-defined peptide and discrete residues in the CDR2β loop. Collectively, these data reveal an innate-like mode of epitope recognition with potential implications for the outcome of sequential exposure to heterologous DENVs.

  9. Prediction of protein-protein interaction sites in sequences and 3D structures by random forests.

    Mile Sikić

    2009-01-01

    Full Text Available Identifying interaction sites in proteins provides important clues to the function of a protein and is becoming increasingly relevant in topics such as systems biology and drug discovery. Although there are numerous papers on the prediction of interaction sites using information derived from structure, there are only a few case reports on the prediction of interaction residues based solely on protein sequence. Here, a sliding window approach is combined with the Random Forests method to predict protein interaction sites using (i a combination of sequence- and structure-derived parameters and (ii sequence information alone. For sequence-based prediction we achieved a precision of 84% with a 26% recall and an F-measure of 40%. When combined with structural information, the prediction performance increases to a precision of 76% and a recall of 38% with an F-measure of 51%. We also present an attempt to rationalize the sliding window size and demonstrate that a nine-residue window is the most suitable for predictor construction. Finally, we demonstrate the applicability of our prediction methods by modeling the Ras-Raf complex using predicted interaction sites as target binding interfaces. Our results suggest that it is possible to predict protein interaction sites with quite a high accuracy using only sequence information.

  10. Prediction of coronal structure of the solar eclipse of October 23, 1976

    Schatten, K.H.

    1976-01-01

    Earlier work on the prediction of solar eclipse coronal structures is briefly summarised. A computer drawn plot made on October 18 1976 showed the field time structure predicted for the time of the solar eclipse on October 23. A very dipolar coronal field was indicated, and a very large equatorial streamer was predicted for both the east and west limbs of the Sun, due to the lack of very strong active regions near either limb. Nested coronal arches were seen within this equatorial streamer, and many small arches were also seen on both limbs. The main feature, however, is the prediction of the two large bright streamers marking the solar equator, with polar plumes in a characteristic dipole fashion. At the time of the eclipse it is hoped that a high resolution photograph will allow much of the structure to be discovered. (U.K.)

  11. The equivalent thermal conductivity of lattice core sandwich structure: A predictive model

    Cheng, Xiangmeng; Wei, Kai; He, Rujie; Pei, Yongmao; Fang, Daining

    2016-01-01

    Highlights: • A predictive model of the equivalent thermal conductivity was established. • Both the heat conduction and radiation were considered. • The predictive results were in good agreement with experiment and FEM. • Some methods for improving the thermal protection performance were proposed. - Abstract: The equivalent thermal conductivity of lattice core sandwich structure was predicted using a novel model. The predictive results were in good agreement with experimental and Finite Element Method results. The thermal conductivity of the lattice core sandwich structure was attributed to both core conduction and radiation. The core conduction caused thermal conductivity only relied on the relative density of the structure. And the radiation caused thermal conductivity increased linearly with the thickness of the core. It was found that the equivalent thermal conductivity of the lattice core sandwich structure showed a highly dependent relationship on temperature. At low temperatures, the structure exhibited a nearly thermal insulated behavior. With the temperature increasing, the thermal conductivity of the structure increased owing to radiation. Therefore, some attempts, such as reducing the emissivity of the core or designing multilayered structure, are believe to be of benefit for improving the thermal protection performance of the structure at high temperatures.

  12. Structural properties of MHC class II ligands, implications for the prediction of MHC class II epitopes.

    Kasper Winther Jørgensen

    2010-12-01

    Full Text Available Major Histocompatibility class II (MHC-II molecules sample peptides from the extracellular space allowing the immune system to detect the presence of foreign microbes from this compartment. Prediction of MHC class II ligands is complicated by the open binding cleft of the MHC class II molecule, allowing binding of peptides extending out of the binding groove. Furthermore, only a few HLA-DR alleles have been characterized with a sufficient number of peptides (100-200 peptides per allele to derive accurate description of their binding motif. Little work has been performed characterizing structural properties of MHC class II ligands. Here, we perform one such large-scale analysis. A large set of SYFPEITHI MHC class II ligands covering more than 20 different HLA-DR molecules was analyzed in terms of their secondary structure and surface exposure characteristics in the context of the native structure of the corresponding source protein. We demonstrated that MHC class II ligands are significantly more exposed and have significantly more coil content than other peptides in the same protein with similar predicted binding affinity. We next exploited this observation to derive an improved prediction method for MHC class II ligands by integrating prediction of MHC- peptide binding with prediction of surface exposure and protein secondary structure. This combined prediction method was shown to significantly outperform the state-of-the-art MHC class II peptide binding prediction method when used to identify MHC class II ligands. We also tried to integrate N- and O-glycosylation in our prediction methods but this additional information was found not to improve prediction performance. In summary, these findings strongly suggest that local structural properties influence antigen processing and/or the accessibility of peptides to the MHC class II molecule.

  13. Structural features that predict real-value fluctuations of globular proteins.

    Jamroz, Michal; Kolinski, Andrzej; Kihara, Daisuke

    2012-05-01

    It is crucial to consider dynamics for understanding the biological function of proteins. We used a large number of molecular dynamics (MD) trajectories of nonhomologous proteins as references and examined static structural features of proteins that are most relevant to fluctuations. We examined correlation of individual structural features with fluctuations and further investigated effective combinations of features for predicting the real value of residue fluctuations using the support vector regression (SVR). It was found that some structural features have higher correlation than crystallographic B-factors with fluctuations observed in MD trajectories. Moreover, SVR that uses combinations of static structural features showed accurate prediction of fluctuations with an average Pearson's correlation coefficient of 0.669 and a root mean square error of 1.04 Å. This correlation coefficient is higher than the one observed in predictions by the Gaussian network model (GNM). An advantage of the developed method over the GNMs is that the former predicts the real value of fluctuation. The results help improve our understanding of relationships between protein structure and fluctuation. Furthermore, the developed method provides a convienient practial way to predict fluctuations of proteins using easily computed static structural features of proteins. Copyright © 2012 Wiley Periodicals, Inc.

  14. Improving protein fold recognition and structural class prediction accuracies using physicochemical properties of amino acids.

    Raicar, Gaurav; Saini, Harsh; Dehzangi, Abdollah; Lal, Sunil; Sharma, Alok

    2016-08-07

    Predicting the three-dimensional (3-D) structure of a protein is an important task in the field of bioinformatics and biological sciences. However, directly predicting the 3-D structure from the primary structure is hard to achieve. Therefore, predicting the fold or structural class of a protein sequence is generally used as an intermediate step in determining the protein's 3-D structure. For protein fold recognition (PFR) and structural class prediction (SCP), two steps are required - feature extraction step and classification step. Feature extraction techniques generally utilize syntactical-based information, evolutionary-based information and physicochemical-based information to extract features. In this study, we explore the importance of utilizing the physicochemical properties of amino acids for improving PFR and SCP accuracies. For this, we propose a Forward Consecutive Search (FCS) scheme which aims to strategically select physicochemical attributes that will supplement the existing feature extraction techniques for PFR and SCP. An exhaustive search is conducted on all the existing 544 physicochemical attributes using the proposed FCS scheme and a subset of physicochemical attributes is identified. Features extracted from these selected attributes are then combined with existing syntactical-based and evolutionary-based features, to show an improvement in the recognition and prediction performance on benchmark datasets. Copyright © 2016 Elsevier Ltd. All rights reserved.

  15. Serotype Specificity of Antibodies against Foot-and-Mouth Disease Virus in Cattle in Selected Districts in Uganda

    Mwiine, F.N.; Ayebazibwe, C.; Olaho-Mukani, W.

    2010-01-01

    Uganda had an unusually large number of foot-and-mouth disease (FMD) outbreaks in 2006, and all clinical reports were in cattle. A serological investigation was carried out to confirm circulating antibodies against foot-and-mouth disease virus (FMDV) by ELISA for antibodies against non-structural......Uganda had an unusually large number of foot-and-mouth disease (FMD) outbreaks in 2006, and all clinical reports were in cattle. A serological investigation was carried out to confirm circulating antibodies against foot-and-mouth disease virus (FMDV) by ELISA for antibodies against non......-structural proteins and structural proteins. Three hundred and forty-nine cattle sera were collected from seven districts in Uganda, and 65% of these were found positive for antibodies against the non-structural proteins of FMDV. A subset of these samples were analysed for serotype specificity of the identified...... antibodies. High prevalences of antibodies against non-structural proteins and structural proteins of FMDV serotype O were demonstrated in herds with typical visible clinical signs of FMD, while prevalences were low in herds without clinical signs of FMD. Antibody titres were higher against serotype O than...

  16. Listeria monocytogenes serotype prevalence and biodiversity in diverse food products.

    Hadjilouka, Agni; Andritsos, Nikolaos D; Paramithiotis, Spiros; Mataragas, Marios; Drosinos, Eleftherios H

    2014-12-01

    The aim of this study was to assess serotype prevalence and biodiversity of Listeria monocytogenes strains isolated from diverse food products, i.e., minced pork, fruits, and vegetables. Three hundred twenty-six samples previously purchased from supermarkets and street markets within the Athens area were studied for L. monocytogenes prevalence. A total of 121 strains were isolated from the 36 samples that were positive for L. monocytogenes. Serotyping was performed with multiplex PCR, and biodiversity was assessed with random amplified polymorphic DNA (RAPD) PCR analysis using M13, UBC155, and HLWL85 as primers and with repetitive element palindromic (rep) PCR analysis using (GTG)5 as the primer. The majority (17 of 22) of the contaminated minced pork samples contained strains identified as serotype 1/2a, either alone or in combination with strains belonging to serotypes 1/2b, 4a, 4c, or 4ab. However, all L. monocytogenes isolates from fruits and vegetables belonged to serotype 4b. Rep-PCR provided better differentiation of the isolates than did RAPD PCR and resulted in discrimination of the isolates into a larger number of unique profiles. Complete differentiation was achieved only with the combination of these subtyping techniques.

  17. Salmonella serotype distribution in the Dutch broiler supply chain.

    van Asselt, E D; Thissen, J T N M; van der Fels-Klerx, H J

    2009-12-01

    Salmonella serotype distribution can give insight in contamination routes and persistence along a production chain. Therefore, it is important to determine not only Salmonella prevalence but also to specify the serotypes involved at the different stages of the supply chain. For this purpose, data from a national monitoring program in the Netherlands were used to estimate the serotype distribution and to determine whether this distribution differs for the available sampling points in the broiler supply chain. Data covered the period from 2002 to 2005, all slaughterhouses (n = 22), and the following 6 sampling points: departure from hatchery, arrival at the farm, departure from the farm, arrival at the slaughterhouse, departure from the slaughterhouse, and end of processing. Furthermore, retail data for 2005 were used for comparison with slaughterhouse data. The following serotypes were followed throughout the chain: Salmonella Enteritidis, Salmonella Typhimurium, Salmonella Paratyphi B var. Java (Salmonella Java), Salmonella Infantis, Salmonella Virchow, and Salmonella Mbandaka. Results showed that serotype distribution varied significantly throughout the supply chain (P supply chain up to the retail phase.

  18. Salmonella serotypes in reptiles and humans, French Guiana.

    Gay, Noellie; Le Hello, Simon; Weill, François-Xavier; de Thoisy, Benoit; Berger, Franck

    2014-05-14

    In French Guiana, a French overseas territory located in the South American northern coast, nearly 50% of Salmonella serotypes isolated from human infections belong to serotypes rarely encountered in metropolitan France. A reptilian source of contamination has been investigated. Between April and June 2011, in the area around Cayenne, 151 reptiles were collected: 38 lizards, 37 snakes, 32 turtles, 23 green iguanas and 21 caimans. Cloacal swab samples were collected and cultured. Isolated Salmonella strains were identified biochemically and serotyped. The overall carriage frequency of carriage was 23.2% (95% confidence interval: 16.7-30.4) with 23 serotyped strains. The frequency of Salmonella carriage was significantly higher for wild reptiles. Near two-thirds of the Salmonella serotypes isolated from reptiles were also isolated from patients in French Guiana. Our results highlight the risk associated with the handling and consumption of reptiles and their role in the spread of Salmonella in the environment. Copyright © 2014 Elsevier B.V. All rights reserved.

  19. From structure prediction to genomic screens for novel non-coding RNAs

    Gorodkin, Jan; Hofacker, Ivo L.

    2011-01-01

    Abstract: Non-coding RNAs (ncRNAs) are receiving more and more attention not only as an abundant class of genes, but also as regulatory structural elements (some located in mRNAs). A key feature of RNA function is its structure. Computational methods were developed early for folding and prediction....... This and the increased amount of available genomes have made it possible to employ structure-based methods for genomic screens. The field has moved from folding prediction of single sequences to computational screens for ncRNAs in genomic sequence using the RNA structure as the main characteristic feature. Whereas early...... upon some of the concepts in current methods that have been applied in genomic screens for de novo RNA structures in searches for novel ncRNA genes and regulatory RNA structure on mRNAs. We discuss the strengths and weaknesses of the different strategies and how they can complement each other....

  20. Why Is There a Glass Ceiling for Threading Based Protein Structure Prediction Methods?

    Skolnick, Jeffrey; Zhou, Hongyi

    2017-04-20

    Despite their different implementations, comparison of the best threading approaches to the prediction of evolutionary distant protein structures reveals that they tend to succeed or fail on the same protein targets. This is true despite the fact that the structural template library has good templates for all cases. Thus, a key question is why are certain protein structures threadable while others are not. Comparison with threading results on a set of artificial sequences selected for stability further argues that the failure of threading is due to the nature of the protein structures themselves. Using a new contact map based alignment algorithm, we demonstrate that certain folds are highly degenerate in that they can have very similar coarse grained fractions of native contacts aligned and yet differ significantly from the native structure. For threadable proteins, this is not the case. Thus, contemporary threading approaches appear to have reached a plateau, and new approaches to structure prediction are required.

  1. Manual for the prediction of blast and fragment loadings on structures

    1980-11-01

    The purpose of this manual is to provide Architect-Engineer (AE) firms guidance for the prediction of air blast, ground shock and fragment loadings on structures as a result of accidental explosions in or near these structures. Information in this manual is the result of an extensive literature survey and data gathering effort, supplemented by some original analytical studies on various aspects of blast phenomena. Many prediction equations and graphs are presented, accompanied by numerous example problems illustrating their use. The manual is complementary to existing structural design manuals and is intended to reflect the current state-of-the-art in prediction of blast and fragment loads for accidental explosions of high explosives at the Pantex Plant. In some instances, particularly for explosions within blast-resistant structures of complex geometry, rational estimation of these loads is beyond the current state-of-the-art.

  2. Potent new small-molecule inhibitor of botulinum neurotoxin serotype A endopeptidase developed by synthesis-based computer-aided molecular design.

    Yuan-Ping Pang

    2009-11-01

    Full Text Available Botulinum neurotoxin serotype A (BoNTA causes a life-threatening neuroparalytic disease known as botulism. Current treatment for post exposure of BoNTA uses antibodies that are effective in neutralizing the extracellular toxin to prevent further intoxication but generally cannot rescue already intoxicated neurons. Effective small-molecule inhibitors of BoNTA endopeptidase (BoNTAe are desirable because such inhibitors potentially can neutralize the intracellular BoNTA and offer complementary treatment for botulism. Previously we reported a serotype-selective, small-molecule BoNTAe inhibitor with a K(i (app value of 3.8+/-0.8 microM. This inhibitor was developed by lead identification using virtual screening followed by computer-aided optimization of a lead with an IC(50 value of 100 microM. However, it was difficult to further improve the lead from micromolar to even high nanomolar potency due to the unusually large enzyme-substrate interface of BoNTAe. The enzyme-substrate interface area of 4,840 A(2 for BoNTAe is about four times larger than the typical protein-protein interface area of 750-1,500 A(2. Inhibitors must carry several functional groups to block the unusually large interface of BoNTAe, and syntheses of such inhibitors are therefore time-consuming and expensive. Herein we report the development of a serotype-selective, small-molecule, and competitive inhibitor of BoNTAe with a K(i value of 760+/-170 nM using synthesis-based computer-aided molecular design (SBCAMD. This new approach accounts the practicality and efficiency of inhibitor synthesis in addition to binding affinity and selectivity. We also report a three-dimensional model of BoNTAe in complex with the new inhibitor and the dynamics of the complex predicted by multiple molecular dynamics simulations, and discuss further structural optimization to achieve better in vivo efficacy in neutralizing BoNTA than those of our early micromolar leads. This work provides new insight

  3. Structural protein descriptors in 1-dimension and their sequence-based predictions.

    Kurgan, Lukasz; Disfani, Fatemeh Miri

    2011-09-01

    The last few decades observed an increasing interest in development and application of 1-dimensional (1D) descriptors of protein structure. These descriptors project 3D structural features onto 1D strings of residue-wise structural assignments. They cover a wide-range of structural aspects including conformation of the backbone, burying depth/solvent exposure and flexibility of residues, and inter-chain residue-residue contacts. We perform first-of-its-kind comprehensive comparative review of the existing 1D structural descriptors. We define, review and categorize ten structural descriptors and we also describe, summarize and contrast over eighty computational models that are used to predict these descriptors from the protein sequences. We show that the majority of the recent sequence-based predictors utilize machine learning models, with the most popular being neural networks, support vector machines, hidden Markov models, and support vector and linear regressions. These methods provide high-throughput predictions and most of them are accessible to a non-expert user via web servers and/or stand-alone software packages. We empirically evaluate several recent sequence-based predictors of secondary structure, disorder, and solvent accessibility descriptors using a benchmark set based on CASP8 targets. Our analysis shows that the secondary structure can be predicted with over 80% accuracy and segment overlap (SOV), disorder with over 0.9 AUC, 0.6 Matthews Correlation Coefficient (MCC), and 75% SOV, and relative solvent accessibility with PCC of 0.7 and MCC of 0.6 (0.86 when homology is used). We demonstrate that the secondary structure predicted from sequence without the use of homology modeling is as good as the structure extracted from the 3D folds predicted by top-performing template-based methods.

  4. Prediction and constancy of cognitive-motivational structures in mothers and their adolescents.

    Malerstein, A J; Ahern, M M; Pulos, S; Arasteh, J D

    1995-01-01

    Three clinically-derived, cognitive-motivational structures were predicted in 68 adolescents from their caregiving situations as revealed in their mothers' interviews, elicited six years earlier. Basic to each structure is a motivational concern and its related social cognitive style, a style which corresponds to a Piagetian cognitive stage: concrete operational, intuitive or symbolic. Because these structure types parse a non-clinical population, current views of health and accordingly goals of treatment may need modification.

  5. Hydrogen-bond coordination in organic crystal structures: statistics, predictions and applications.

    Galek, Peter T A; Chisholm, James A; Pidcock, Elna; Wood, Peter A

    2014-02-01

    Statistical models to predict the number of hydrogen bonds that might be formed by any donor or acceptor atom in a crystal structure have been derived using organic structures in the Cambridge Structural Database. This hydrogen-bond coordination behaviour has been uniquely defined for more than 70 unique atom types, and has led to the development of a methodology to construct hypothetical hydrogen-bond arrangements. Comparing the constructed hydrogen-bond arrangements with known crystal structures shows promise in the assessment of structural stability, and some initial examples of industrially relevant polymorphs, co-crystals and hydrates are described.

  6. What’s in a Name? Species-Wide Whole-Genome Sequencing Resolves Invasive and Noninvasive Lineages of Salmonella enterica Serotype Paratyphi B

    Thomas R. Connor

    2016-08-01

    Full Text Available For 100 years, it has been obvious that Salmonella enterica strains sharing the serotype with the formula 1,4,[5],12:b:1,2—now known as Paratyphi B—can cause diseases ranging from serious systemic infections to self-limiting gastroenteritis. Despite considerable predicted diversity between strains carrying the common Paratyphi B serotype, there remain few methods that subdivide the group into groups that are congruent with their disease phenotypes. Paratyphi B therefore represents one of the canonical examples in Salmonella where serotyping combined with classical microbiological tests fails to provide clinically informative information. Here, we use genomics to provide the first high-resolution view of this serotype, placing it into a wider genomic context of the Salmonella enterica species. These analyses reveal why it has been impossible to subdivide this serotype based upon phenotypic and limited molecular approaches. By examining the genomic data in detail, we are able to identify common features that correlate with strains of clinical importance. The results presented here provide new diagnostic targets, as well as posing important new questions about the basis for the invasive disease phenotype observed in a subset of strains.

  7. Emerging, Non-PCV13 Serotypes 11A and 35B of Streptococcus pneumoniae Show High Potential for Biofilm Formation In Vitro.

    Mirian Domenech

    Full Text Available Since the use of pneumococcal conjugate vaccines PCV7 and PCV13 in children became widespread, invasive pneumococcal disease (IPD has dramatically decreased. Nevertheless, there has been a rise in incidence of Streptococcus pneumoniae non-vaccine serotypes (NVT colonising the human nasopharynx. Nasopharyngeal colonisation, an essential step in the development of S. pneumoniae-induced IPD, is associated with biofilm formation. Although the capsule is the main pneumococcal virulence factor, the formation of pneumococcal biofilms might, in fact, be limited by the presence of capsular polysaccharide (CPS.We used clinical isolates of 16 emerging, non-PCV13 serotypes as well as isogenic transformants of the same serotypes. The biofilm formation capacity of isogenic transformants expressing CPSs from NVT was evaluated in vitro to ascertain whether this trait can be used to predict the emergence of NVT. Fourteen out of 16 NVT analysed were not good biofilm formers, presumably because of the presence of CPS. In contrast, serotypes 11A and 35B formed ≥45% of the biofilm produced by the non-encapsulated M11 strain.This study suggest that emerging, NVT serotypes 11A and 35B deserve a close surveillance.

  8. Exploring high-pressure FeB{sub 2}: Structural and electronic properties predictions

    Harran, Ismail [School of Physical Science and Technology, Key Laboratory of Advanced Technologies of Materials, Ministry of Education of China, Southwest Jiaotong University, Chengdu, 610031 (China); Al Fashir University (Sudan); Wang, Hongyan [School of Physical Science and Technology, Key Laboratory of Advanced Technologies of Materials, Ministry of Education of China, Southwest Jiaotong University, Chengdu, 610031 (China); Chen, Yuanzheng, E-mail: cyz@calypso.org.cn [School of Physical Science and Technology, Key Laboratory of Advanced Technologies of Materials, Ministry of Education of China, Southwest Jiaotong University, Chengdu, 610031 (China); Jia, Mingzhen [School of Physical Science and Technology, Key Laboratory of Advanced Technologies of Materials, Ministry of Education of China, Southwest Jiaotong University, Chengdu, 610031 (China); Wu, Nannan [School of Mathematics, Physics and Biological Engineering, Inner Mongolia University of Science & Technology, Baotou, 014010 (China)

    2016-09-05

    The high pressure (HP) structural phase of FeB{sub 2} compound is investigated by using first-principles crystal structure prediction based on the CALYPSO technique. A thermodynamically stable phase of FeB{sub 2} with space group Imma is predicted at pressure above 225 GPa, which is characterized by a layered orthorhombic structure containing puckered graphite-like boron layers. Its electronic and mechanical properties are identified and analyzed. The feature of band structures favors the occurrence of superconductivity, whereas, the calculated Pugh's ratio reveals that the HP Imma structure exhibits ductile mechanical property. - Highlights: • The high pressure structural phase of FeB{sub 2} compound is firstly investigated by the CALYPSO technique. • A thermodynamically stable Imma phase of FeB{sub 2} is predicted at pressure above 225 GPa. • The Imma structure is characterized by a 2D boron network containing puckered graphite-like boron layers. • The band feature of Imma structure favors the occurrence of superconductivity. • The calculated Pugh's ratio suggests that the Imma structure exhibits ductile mechanical property.

  9. Compound Structure-Independent Activity Prediction in High-Dimensional Target Space.

    Balfer, Jenny; Hu, Ye; Bajorath, Jürgen

    2014-08-01

    Profiling of compound libraries against arrays of targets has become an important approach in pharmaceutical research. The prediction of multi-target compound activities also represents an attractive task for machine learning with potential for drug discovery applications. Herein, we have explored activity prediction in high-dimensional target space. Different types of models were derived to predict multi-target activities. The models included naïve Bayesian (NB) and support vector machine (SVM) classifiers based upon compound structure information and NB models derived on the basis of activity profiles, without considering compound structure. Because the latter approach can be applied to incomplete training data and principally depends on the feature independence assumption, SVM modeling was not applicable in this case. Furthermore, iterative hybrid NB models making use of both activity profiles and compound structure information were built. In high-dimensional target space, NB models utilizing activity profile data were found to yield more accurate activity predictions than structure-based NB and SVM models or hybrid models. An in-depth analysis of activity profile-based models revealed the presence of correlation effects across different targets and rationalized prediction accuracy. Taken together, the results indicate that activity profile information can be effectively used to predict the activity of test compounds against novel targets. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  10. Ab-initio conformational epitope structure prediction using genetic algorithm and SVM for vaccine design.

    Moghram, Basem Ameen; Nabil, Emad; Badr, Amr

    2018-01-01

    T-cell epitope structure identification is a significant challenging immunoinformatic problem within epitope-based vaccine design. Epitopes or antigenic peptides are a set of amino acids that bind with the Major Histocompatibility Complex (MHC) molecules. The aim of this process is presented by Antigen Presenting Cells to be inspected by T-cells. MHC-molecule-binding epitopes are responsible for triggering the immune response to antigens. The epitope's three-dimensional (3D) molecular structure (i.e., tertiary structure) reflects its proper function. Therefore, the identification of MHC class-II epitopes structure is a significant step towards epitope-based vaccine design and understanding of the immune system. In this paper, we propose a new technique using a Genetic Algorithm for Predicting the Epitope Structure (GAPES), to predict the structure of MHC class-II epitopes based on their sequence. The proposed Elitist-based genetic algorithm for predicting the epitope's tertiary structure is based on Ab-Initio Empirical Conformational Energy Program for Peptides (ECEPP) Force Field Model. The developed secondary structure prediction technique relies on Ramachandran Plot. We used two alignment algorithms: the ROSS alignment and TM-Score alignment. We applied four different alignment approaches to calculate the similarity scores of the dataset under test. We utilized the support vector machine (SVM) classifier as an evaluation of the prediction performance. The prediction accuracy and the Area Under Receiver Operating Characteristic (ROC) Curve (AUC) were calculated as measures of performance. The calculations are performed on twelve similarity-reduced datasets of the Immune Epitope Data Base (IEDB) and a large dataset of peptide-binding affinities to HLA-DRB1*0101. The results showed that GAPES was reliable and very accurate. We achieved an average prediction accuracy of 93.50% and an average AUC of 0.974 in the IEDB dataset. Also, we achieved an accuracy of 95

  11. A fast and robust iterative algorithm for prediction of RNA pseudoknotted secondary structures

    2014-01-01

    Background Improving accuracy and efficiency of computational methods that predict pseudoknotted RNA secondary structures is an ongoing challenge. Existing methods based on free energy minimization tend to be very slow and are limited in the types of pseudoknots that they can predict. Incorporating known structural information can improve prediction accuracy; however, there are not many methods for prediction of pseudoknotted structures that can incorporate structural information as input. There is even less understanding of the relative robustness of these methods with respect to partial information. Results We present a new method, Iterative HFold, for pseudoknotted RNA secondary structure prediction. Iterative HFold takes as input a pseudoknot-free structure, and produces a possibly pseudoknotted structure whose energy is at least as low as that of any (density-2) pseudoknotted structure containing the input structure. Iterative HFold leverages strengths of earlier methods, namely the fast running time of HFold, a method that is based on the hierarchical folding hypothesis, and the energy parameters of HotKnots V2.0. Our experimental evaluation on a large data set shows that Iterative HFold is robust with respect to partial information, with average accuracy on pseudoknotted structures steadily increasing from roughly 54% to 79% as the user provides up to 40% of the input structure. Iterative HFold is much faster than HotKnots V2.0, while having comparable accuracy. Iterative HFold also has significantly better accuracy than IPknot on our HK-PK and IP-pk168 data sets. Conclusions Iterative HFold is a robust method for prediction of pseudoknotted RNA secondary structures, whose accuracy with more than 5% information about true pseudoknot-free structures is better than that of IPknot, and with about 35% information about true pseudoknot-free structures compares well with that of HotKnots V2.0 while being significantly faster. Iterative HFold and all data used in

  12. SGC method for predicting the standard enthalpy of formation of pure compounds from their molecular structures

    Albahri, Tareq A.; Aljasmi, Abdulla F.

    2013-01-01

    Highlights: • ΔH° f is predicted from the molecular structure of the compounds alone. • ANN-SGC model predicts ΔH° f with a correlation coefficient of 0.99. • ANN-MNLR model predicts ΔH° f with a correlation coefficient of 0.90. • Better definition of the atom-type molecular groups is presented. • The method is better than others in terms of combined simplicity, accuracy and generality. - Abstract: A theoretical method for predicting the standard enthalpy of formation of pure compounds from various chemical families is presented. Back propagation artificial neural networks were used to investigate several structural group contribution (SGC) methods available in literature. The networks were used to probe the structural groups that have significant contribution to the overall enthalpy of formation property of pure compounds and arrive at the set of groups that can best represent the enthalpy of formation for about 584 substances. The 51 atom-type structural groups listed provide better definitions of group contributions than others in the literature. The proposed method can predict the standard enthalpy of formation of pure compounds with an AAD of 11.38 kJ/mol and a correlation coefficient of 0.9934 from only their molecular structure. The results are further compared with those of the traditional SGC method based on MNLR as well as other methods in the literature

  13. CompaRNA: a server for continuous benchmarking of automated methods for RNA secondary structure prediction

    Puton, Tomasz; Kozlowski, Lukasz P.; Rother, Kristian M.; Bujnicki, Janusz M.

    2013-01-01

    We present a continuous benchmarking approach for the assessment of RNA secondary structure prediction methods implemented in the CompaRNA web server. As of 3 October 2012, the performance of 28 single-sequence and 13 comparative methods has been evaluated on RNA sequences/structures released weekly by the Protein Data Bank. We also provide a static benchmark generated on RNA 2D structures derived from the RNAstrand database. Benchmarks on both data sets offer insight into the relative performance of RNA secondary structure prediction methods on RNAs of different size and with respect to different types of structure. According to our tests, on the average, the most accurate predictions obtained by a comparative approach are generated by CentroidAlifold, MXScarna, RNAalifold and TurboFold. On the average, the most accurate predictions obtained by single-sequence analyses are generated by CentroidFold, ContextFold and IPknot. The best comparative methods typically outperform the best single-sequence methods if an alignment of homologous RNA sequences is available. This article presents the results of our benchmarks as of 3 October 2012, whereas the rankings presented online are continuously updated. We will gladly include new prediction methods and new measures of accuracy in the new editions of CompaRNA benchmarks. PMID:23435231

  14. CompaRNA: a server for continuous benchmarking of automated methods for RNA secondary structure prediction.

    Puton, Tomasz; Kozlowski, Lukasz P; Rother, Kristian M; Bujnicki, Janusz M

    2013-04-01

    We present a continuous benchmarking approach for the assessment of RNA secondary structure prediction methods implemented in the CompaRNA web server. As of 3 October 2012, the performance of 28 single-sequence and 13 comparative methods has been evaluated on RNA sequences/structures released weekly by the Protein Data Bank. We also provide a static benchmark generated on RNA 2D structures derived from the RNAstrand database. Benchmarks on both data sets offer insight into the relative performance of RNA secondary structure prediction methods on RNAs of different size and with respect to different types of structure. According to our tests, on the average, the most accurate predictions obtained by a comparative approach are generated by CentroidAlifold, MXScarna, RNAalifold and TurboFold. On the average, the most accurate predictions obtained by single-sequence analyses are generated by CentroidFold, ContextFold and IPknot. The best comparative methods typically outperform the best single-sequence methods if an alignment of homologous RNA sequences is available. This article presents the results of our benchmarks as of 3 October 2012, whereas the rankings presented online are continuously updated. We will gladly include new prediction methods and new measures of accuracy in the new editions of CompaRNA benchmarks.

  15. STRUCTURAL SCALE LIFE PREDICTION OF AERO STRUCTURES EXPERIENCING COMBINED EXTREME ENVIRONMENTS

    2017-07-01

    complex loading environments. Today’s state of the art methods cannot address structural reliability under combined environment conditions due to...probabilistically assess the structural life under complex loading environments. Today’s state of the art methods cannot address structural reliability...Institute of Aeronautics and Astronautics, San Diego, CA, January 4th‐8th, 2016. Clark, L. D., Bae, H., Gobal, K., and Penmetsa, R., “ Engineering

  16. Genomic Evolution Of The Mdr Serotype O12 Pseudomonas Aeruginosa Clone

    Thrane, Sandra Wingaard; Taylor, Véronique L.; Freschi, Luca

    2015-01-01

    that serotype switching in combination with an antibiotic resistance determinant contributed to the dissemination of the O12 serotype in the clinic. This selective advantage coincides with the introduction of fluoroquinolones in the clinic. With the PAst program isolates can be serotyped using WGS data......Introduction: Since the 1980’s the serotype O12 of Pseudomonas aeruginosa has emerged as the predominant serotype in clinical settings and in epidemic outbreaks. These serotype O12 isolates exhibit high levels of resistance to various classes of antibiotics.Methods: In this study, we explore how......).Results: While most serotypes were closely linked to the core genome phylogeny we observed horizontal exchange of LPS genes among distinct P. aeruginosa strains. Specifically, we identified a ‘serotype island’ containing the P. aeruginosa O12 LPS gene cluster and an antibiotic resistance determinant (gyrAC248T...

  17. Statistical properties of thermodynamically predicted RNA secondary structures in viral genomes

    Spanò, M.; Lillo, F.; Miccichè, S.; Mantegna, R. N.

    2008-10-01

    By performing a comprehensive study on 1832 segments of 1212 complete genomes of viruses, we show that in viral genomes the hairpin structures of thermodynamically predicted RNA secondary structures are more abundant than expected under a simple random null hypothesis. The detected hairpin structures of RNA secondary structures are present both in coding and in noncoding regions for the four groups of viruses categorized as dsDNA, dsRNA, ssDNA and ssRNA. For all groups, hairpin structures of RNA secondary structures are detected more frequently than expected for a random null hypothesis in noncoding rather than in coding regions. However, potential RNA secondary structures are also present in coding regions of dsDNA group. In fact, we detect evolutionary conserved RNA secondary structures in conserved coding and noncoding regions of a large set of complete genomes of dsDNA herpesviruses.

  18. Determination of Serotypes and Antibiotic Resistance in Streptococcus Pneumoniae

    Deniz Akgun Karapinar

    2016-01-01

    Full Text Available Aim: In this study, the distribution of serogroup/serotype and antibiotic susceptibility testing of Streptococcus pneumoniae strains, recovered from pediatric and adult patients were evaluated. Material and Method: A total of 80 clinical isolates recovered from 19 pediatric and 61 adult patients were performed by latex aglutination method and antibiotic susceptibility tests in Istanbul University, Istanbul Faculty of Medicine, Medical Microbiology Laboratories. Results: Sixty-two strains (76 %, were serogroup/serotyped and 18 (23 % strains couldn%u2019t serogroup/serotyped. The most frequent identified serogroups were 19, 14, 23, 6, 4 in pediatrics, and 3, 19, 23 and 9 in adults. In adults, serogroups 3, 9, 5, 8, 18, 1, 15 were determined, but these serogroups weren%u2019t found in pediatrics. Vaccine serotypes rates were found as 53 % in pediatric and as 85 % in adults. The serogroups 2, 7, 10, 11, 12, 17, 20, 22, 33 were not detected, which are available in vaccine serotypes. Only 1 (1 % strain was found to exhibit low level resistance to penicillin and high level resistance wasn%u2019t found in any strain. Resistant results for trimethoprim-sulfamethoxazole, erythromycin, chloramphenicol and ofloxacin were found as 45 (56 %, 22 (27.5 %, 7 (9 %, 2 (2.5 %, respectively. All strains were found susceptible to vancomycin, linezolid and levofloxacin. The most resistant serogroups were 19, 23, 9 and 14 in the tested antibiotics. Multidrug resistance was found in 9 (11 % strains and these strains were found as serogroups 19, 23, 9, 6 and 14. Discussion: The epidemiological studies are important that the distribution of serotype and antibiotic resistance vary depending on many factors like age, and geographic region.

  19. A mosaic adenovirus possessing serotype Ad5 and serotype Ad3 knobs exhibits expanded tropism

    Takayama, Koichi; Reynolds, Paul N.; Short, Joshua J.; Kawakami, Yosuke; Adachi, Yasuo; Glasgow, Joel N.; Rots, Marianne G.; Krasnykh, Victor; Douglas, Joanne T.; Curiel, David T.

    2003-01-01

    The efficiency of cancer gene therapy with recombinant adenoviruses based on serotype 5 (Ad5) has been limited partly because of variable, and often low, expression by human primary cancer cells of the primary cellular-receptor which recognizes the knob domain of the fiber protein, the coxsackie and adenovirus receptor (CAR). As a means of circumventing CAR deficiency, Ad vectors have been retargeted by utilizing chimeric fibers possessing knob domains of alternate Ad serotypes. We have reported that ovarian cancer cells possess a primary receptor for Ad3 to which the Ad3 knob binds independently of the CAR-Ad5 knob interaction. Furthermore, an Ad5-based chimeric vector, designated Ad5/3, containing a chimeric fiber proteins possessing the Ad3 knob, demonstrates CAR-independent tropism by virtue of targeting the Ad3 receptor. Based on these findings, we hypothesized that a mosaic virus possessing both the Ad5 knob and the Ad3 knob on the same virion could utilize either primary receptor, resulting in expanded tropism. In this study, we generated a dual-knob mosaic virus by coinfection of 293 cells with Ad5-based and Ad5/3-based vectors. Characterization of the resultant virions confirmed the incorporation of both Ad5 and Ad3 knobs in the same particle. Furthermore, this mosaic virus was able to utilize either receptor, CAR and the Ad3 receptor, for virus attachment to cells. Enhanced Ad infectivity with the mosaic virus was shown in a panel of cell lines, with receptor profiles ranging from CAR-dominant to Ad3 receptor-dominant. Thus, this mosaic virus strategy may offer the potential to improve Ad-based gene therapy approaches by infectivity enhancement and tropism expansion

  20. RNA 3D modules in genome-wide predictions of RNA 2D structure

    Theis, Corinna; Zirbel, Craig L; Zu Siederdissen, Christian Höner

    2015-01-01

    . These modules can, for example, occur inside structural elements which in RNA 2D predictions appear as internal loops. Hence one question is if the use of such RNA 3D information can improve the prediction accuracy of RNA secondary structure at a genome-wide level. Here, we use RNAz in combination with 3D......Recent experimental and computational progress has revealed a large potential for RNA structure in the genome. This has been driven by computational strategies that exploit multiple genomes of related organisms to identify common sequences and secondary structures. However, these computational...... approaches have two main challenges: they are computationally expensive and they have a relatively high false discovery rate (FDR). Simultaneously, RNA 3D structure analysis has revealed modules composed of non-canonical base pairs which occur in non-homologous positions, apparently by independent evolution...

  1. Theoretical prediction of low-density hexagonal ZnO hollow structures

    Tuoc, Vu Ngoc, E-mail: tuoc.vungoc@hust.edu.vn [Institute of Engineering Physics, Hanoi University of Science and Technology, 1 Dai Co Viet Road, Hanoi (Viet Nam); Huan, Tran Doan [Institute of Materials Science, University of Connecticut, Storrs, Connecticut 06269-3136 (United States); Thao, Nguyen Thi [Institute of Engineering Physics, Hanoi University of Science and Technology, 1 Dai Co Viet Road, Hanoi (Viet Nam); Hong Duc University, 307 Le Lai, Thanh Hoa City (Viet Nam); Tuan, Le Manh [Hong Duc University, 307 Le Lai, Thanh Hoa City (Viet Nam)

    2016-10-14

    Along with wurtzite and zinc blende, zinc oxide (ZnO) has been found in a large number of polymorphs with substantially different properties and, hence, applications. Therefore, predicting and synthesizing new classes of ZnO polymorphs are of great significance and have been gaining considerable interest. Herein, we perform a density functional theory based tight-binding study, predicting several new series of ZnO hollow structures using the bottom-up approach. The geometry of the building blocks allows for obtaining a variety of hexagonal, low-density nanoporous, and flexible ZnO hollow structures. Their stability is discussed by means of the free energy computed within the lattice-dynamics approach. Our calculations also indicate that all the reported hollow structures are wide band gap semiconductors in the same fashion with bulk ZnO. The electronic band structures of the ZnO hollow structures are finally examined in detail.

  2. Model structural uncertainty quantification and hydrologic parameter and prediction error analysis using airborne electromagnetic data

    Minsley, B. J.; Christensen, Nikolaj Kruse; Christensen, Steen

    Model structure, or the spatial arrangement of subsurface lithological units, is fundamental to the hydrological behavior of Earth systems. Knowledge of geological model structure is critically important in order to make informed hydrological predictions and management decisions. Model structure...... is never perfectly known, however, and incorrect assumptions can be a significant source of error when making model predictions. We describe a systematic approach for quantifying model structural uncertainty that is based on the integration of sparse borehole observations and large-scale airborne...... electromagnetic (AEM) data. Our estimates of model structural uncertainty follow a Bayesian framework that accounts for both the uncertainties in geophysical parameter estimates given AEM data, and the uncertainties in the relationship between lithology and geophysical parameters. Using geostatistical sequential...

  3. MCTBI: a web server for predicting metal ion effects in RNA structures.

    Sun, Li-Zhen; Zhang, Jing-Xiang; Chen, Shi-Jie

    2017-08-01

    Metal ions play critical roles in RNA structure and function. However, web servers and software packages for predicting ion effects in RNA structures are notably scarce. Furthermore, the existing web servers and software packages mainly neglect ion correlation and fluctuation effects, which are potentially important for RNAs. We here report a new web server, the MCTBI server (http://rna.physics.missouri.edu/MCTBI), for the prediction of ion effects for RNA structures. This server is based on the recently developed MCTBI, a model that can account for ion correlation and fluctuation effects for nucleic acid structures and can provide improved predictions for the effects of metal ions, especially for multivalent ions such as Mg 2+ effects, as shown by extensive theory-experiment test results. The MCTBI web server predicts metal ion binding fractions, the most probable bound ion distribution, the electrostatic free energy of the system, and the free energy components. The results provide mechanistic insights into the role of metal ions in RNA structure formation and folding stability, which is important for understanding RNA functions and the rational design of RNA structures. © 2017 Sun et al.; Published by Cold Spring Harbor Laboratory Press for the RNA Society.

  4. De novo protein structure prediction by dynamic fragment assembly and conformational space annealing.

    Lee, Juyong; Lee, Jinhyuk; Sasaki, Takeshi N; Sasai, Masaki; Seok, Chaok; Lee, Jooyoung

    2011-08-01

    Ab initio protein structure prediction is a challenging problem that requires both an accurate energetic representation of a protein structure and an efficient conformational sampling method for successful protein modeling. In this article, we present an ab initio structure prediction method which combines a recently suggested novel way of fragment assembly, dynamic fragment assembly (DFA) and conformational space annealing (CSA) algorithm. In DFA, model structures are scored by continuous functions constructed based on short- and long-range structural restraint information from a fragment library. Here, DFA is represented by the full-atom model by CHARMM with the addition of the empirical potential of DFIRE. The relative contributions between various energy terms are optimized using linear programming. The conformational sampling was carried out with CSA algorithm, which can find low energy conformations more efficiently than simulated annealing used in the existing DFA study. The newly introduced DFA energy function and CSA sampling algorithm are implemented into CHARMM. Test results on 30 small single-domain proteins and 13 template-free modeling targets of the 8th Critical Assessment of protein Structure Prediction show that the current method provides comparable and complementary prediction results to existing top methods. Copyright © 2011 Wiley-Liss, Inc.

  5. Knowledge base and neural network approach for protein secondary structure prediction.

    Patel, Maulika S; Mazumdar, Himanshu S

    2014-11-21

    Protein structure prediction is of great relevance given the abundant genomic and proteomic data generated by the genome sequencing projects. Protein secondary structure prediction is addressed as a sub task in determining the protein tertiary structure and function. In this paper, a novel algorithm, KB-PROSSP-NN, which is a combination of knowledge base and modeling of the exceptions in the knowledge base using neural networks for protein secondary structure prediction (PSSP), is proposed. The knowledge base is derived from a proteomic sequence-structure database and consists of the statistics of association between the 5-residue words and corresponding secondary structure. The predicted results obtained using knowledge base are refined with a Backpropogation neural network algorithm. Neural net models the exceptions of the knowledge base. The Q3 accuracy of 90% and 82% is achieved on the RS126 and CB396 test sets respectively which suggest improvement over existing state of art methods. Copyright © 2014 Elsevier Ltd. All rights reserved.

  6. Synthesis and Molecular Structure of the 5-Methoxycarbonylpentyl α-Glycoside of the Upstream, Terminal Moiety of the O-Specific Polysaccharide of Vibrio cholerae O1, Serotype Inaba

    Peng Xu

    2015-02-01

    Full Text Available The trimethylsilyl trifluoromethanesulfonate (TMSOTf-catalyzed reaction of methyl 6-hydroxyhexanoate with 3-O-benzyl-4-(2,4-di-O-acetyl-3-deoxy-L-glycero-tetronamido-4,6-dideoxy-2-O-levulinoyl-α-d-mannopyranosyl trichloroacetimidate followed by a two-step deprotection (hydrogenolysis over Pd/C catalyst and Zemplén deacylation, to simultaneously remove the acetyl and levulinoyl groups gave 5-(methoxycarbonylpentyl 4-(3-deoxy-L-glycero-tetronamido-4,6-dideoxy-α-D-mannopyranoside. The structure of the latter, for which crystals were obtained in the analytically pure state for the first time, followed from its NMR and high-resolution mass spectra and was confirmed by X-ray crystallography. The molecule has two approximately linear components; a line through the aglycon intersects a line through the mannosyl and tetronylamido groups at 120°. The crystal packing separates the aglycon groups from the tetronylamido and mannosyl groups, with only C-H…O hydrogen bonding among the aglycon groups and N-H…O, O-H…O and C-H…O links among the tetronylamido and mannosyl groups. A carbonyl oxygen atom accepts the strongest O-H…O hydrogen bond and two strong C-H…O hydrogen bonds. The geometric properties were compared with those of related molecules.

  7. Detection of Foot-and-mouth Disease Serotype O by ELISA Using a Monoclonal Antibody

    Chen, Hao-tai; Peng, Yun-hua; Zhang, Yong-guang; Liu, Xiang-tao

    2012-01-01

    An ELISA assay with monoclonal antibody (MELISA) was used to type serotype O of foot-and-mouth disease virus (FMDV). All FMDV serotype O reference strains were positive by MELISA, while other viruses such as FMDV serotypes Asia 1, C, and A and classical swine fever virus, swine vesicular disease virus, and porcine reproductive and respiratory syndrome virus remained negative. Furthermore, FMDV serotype O positive samples were able to be detected by MELISA. This assay may be particularly suita...

  8. Novel structures of oxygen adsorbed on a Zr(0001) surface predicted from first principles

    Gao, Bo [State Key Laboratory of Superhard Materials, Jilin University, Changchun, 130012 (China); Beijing computational science research center, Beijing,100084 (China); Wang, Jianyun [State Key Laboratory of Superhard Materials, Jilin University, Changchun, 130012 (China); Lv, Jian [State Key Laboratory of Superhard Materials, Jilin University, Changchun, 130012 (China); College of Materials Science and Engineering, Jilin University, Changchun, 130012 (China); Gao, Xingyu [Laboratory of Computational Physics, Institute of Applied Physics and Computational Mathematics, Beijing, 100088 (China); CAEP Software Center for High Performance Numerical Simulation, Beijing, 100088 (China); Zhao, Yafan [CAEP Software Center for High Performance Numerical Simulation, Beijing, 100088 (China); Wang, Yanchao, E-mail: wyc@calypso.cn [State Key Laboratory of Superhard Materials, Jilin University, Changchun, 130012 (China); Beijing computational science research center, Beijing,100084 (China); College of Materials Science and Engineering, Jilin University, Changchun, 130012 (China); Song, Haifeng, E-mail: song_haifeng@iapcm.ac.cn [Laboratory of Computational Physics, Institute of Applied Physics and Computational Mathematics, Beijing, 100088 (China); CAEP Software Center for High Performance Numerical Simulation, Beijing, 100088 (China); Ma, Yanming [State Key Laboratory of Superhard Materials, Jilin University, Changchun, 130012 (China); Beijing computational science research center, Beijing,100084 (China)

    2017-01-30

    Highlights: • Two stable structures of O adsorbed on a Zr(0001) surface are predicted with SLAM. • A stable structure of O adsorbed on a Zr(0001) surface is proposed with MLAM. • The calculated work function change is agreement with experimental value. - Abstract: The structures of O atoms adsorbed on a metal surface influence the metal properties significantly. Thus, studying O chemisorption on a Zr surface is of great interest. We investigated O adsorption on a Zr(0001) surface using our newly developed structure-searching method combined with first-principles calculations. A novel structural prototype with a unique combination of surface face-centered cubic (SFCC) and surface hexagonal close-packed (SHCP) O adsorption sites was predicted using a single-layer adsorption model (SLAM) for a 0.5 and 1.0 monolayer (ML) O coverage. First-principles calculations based on the SLAM revealed that the new predicted structures are energetically favorable compared with the well-known SFCC structures for a low O coverage (0.5 and 1.0 ML). Furthermore, on basis of our predicted SFCC + SHCP structures, a new structure within multi-layer adsorption model (MLAM) was proposed to be more stable at the O coverage of 1.0 ML, in which adsorbed O atoms occupy the SFCC + SHCP sites and the substitutional octahedral sites. The calculated work functions indicate that the SFCC + SHCP configuration has the lowest work function of all known structures at an O coverage of 0.5 ML within the SLAM, which agrees with the experimental trend of work function with variation in O coverage.

  9. Integration of QUARK and I-TASSER for Ab Initio Protein Structure Prediction in CASP11.

    Zhang, Wenxuan; Yang, Jianyi; He, Baoji; Walker, Sara Elizabeth; Zhang, Hongjiu; Govindarajoo, Brandon; Virtanen, Jouko; Xue, Zhidong; Shen, Hong-Bin; Zhang, Yang

    2016-09-01

    We tested two pipelines developed for template-free protein structure prediction in the CASP11 experiment. First, the QUARK pipeline constructs structure models by reassembling fragments of continuously distributed lengths excised from unrelated proteins. Five free-modeling (FM) targets have the model successfully constructed by QUARK with a TM-score above 0.4, including the first model of T0837-D1, which has a TM-score = 0.736 and RMSD = 2.9 Å to the native. Detailed analysis showed that the success is partly attributed to the high-resolution contact map prediction derived from fragment-based distance-profiles, which are mainly located between regular secondary structure elements and loops/turns and help guide the orientation of secondary structure assembly. In the Zhang-Server pipeline, weakly scoring threading templates are re-ordered by the structural similarity to the ab initio folding models, which are then reassembled by I-TASSER based structure assembly simulations; 60% more domains with length up to 204 residues, compared to the QUARK pipeline, were successfully modeled by the I-TASSER pipeline with a TM-score above 0.4. The robustness of the I-TASSER pipeline can stem from the composite fragment-assembly simulations that combine structures from both ab initio folding and threading template refinements. Despite the promising cases, challenges still exist in long-range beta-strand folding, domain parsing, and the uncertainty of secondary structure prediction; the latter of which was found to affect nearly all aspects of FM structure predictions, from fragment identification, target classification, structure assembly, to final model selection. Significant efforts are needed to solve these problems before real progress on FM could be made. Proteins 2016; 84(Suppl 1):76-86. © 2015 Wiley Periodicals, Inc. © 2015 Wiley Periodicals, Inc.

  10. RANDOM FUNCTIONS AND INTERVAL METHOD FOR PREDICTING THE RESIDUAL RESOURCE OF BUILDING STRUCTURES

    Shmelev Gennadiy Dmitrievich

    2017-11-01

    Full Text Available Subject: possibility of using random functions and interval prediction method for estimating the residual life of building structures in the currently used buildings. Research objectives: coordination of ranges of values to develop predictions and random functions that characterize the processes being predicted. Materials and methods: when performing this research, the method of random functions and the method of interval prediction were used. Results: in the course of this work, the basic properties of random functions, including the properties of families of random functions, are studied. The coordination of time-varying impacts and loads on building structures is considered from the viewpoint of their influence on structures and representation of the structures’ behavior in the form of random functions. Several models of random functions are proposed for predicting individual parameters of structures. For each of the proposed models, its scope of application is defined. The article notes that the considered approach of forecasting has been used many times at various sites. In addition, the available results allowed the authors to develop a methodology for assessing the technical condition and residual life of building structures for the currently used facilities. Conclusions: we studied the possibility of using random functions and processes for the purposes of forecasting the residual service lives of structures in buildings and engineering constructions. We considered the possibility of using an interval forecasting approach to estimate changes in defining parameters of building structures and their technical condition. A comprehensive technique for forecasting the residual life of building structures using the interval approach is proposed.

  11. Bi-objective integer programming for RNA secondary structure prediction with pseudoknots.

    Legendre, Audrey; Angel, Eric; Tahi, Fariza

    2018-01-15

    RNA structure prediction is an important field in bioinformatics, and numerous methods and tools have been proposed. Pseudoknots are specific motifs of RNA secondary structures that are difficult to predict. Almost all existing methods are based on a single model and return one solution, often missing the real structure. An alternative approach would be to combine different models and return a (small) set of solutions, maximizing its quality and diversity in order to increase the probability that it contains the real structure. We propose here an original method for predicting RNA secondary structures with pseudoknots, based on integer programming. We developed a generic bi-objective integer programming algorithm allowing to return optimal and sub-optimal solutions optimizing simultaneously two models. This algorithm was then applied to the combination of two known models of RNA secondary structure prediction, namely MEA and MFE. The resulting tool, called BiokoP, is compared with the other methods in the literature. The results show that the best solution (structure with the highest F 1 -score) is, in most cases, given by BiokoP. Moreover, the results of BiokoP are homogeneous, regardless of the pseudoknot type or the presence or not of pseudoknots. Indeed, the F 1 -scores are always higher than 70% for any number of solutions returned. The results obtained by BiokoP show that combining the MEA and the MFE models, as well as returning several optimal and several sub-optimal solutions, allow to improve the prediction of secondary structures. One perspective of our work is to combine better mono-criterion models, in particular to combine a model based on the comparative approach with the MEA and the MFE models. This leads to develop in the future a new multi-objective algorithm to combine more than two models. BiokoP is available on the EvryRNA platform: https://EvryRNA.ibisc.univ-evry.fr .

  12. Prediction of material damage in orthotropic metals for virtual structural testing

    Ravindran, S.

    2010-01-01

    Models based on the Continuum Damage Mechanics principle are increasingly used for predicting the initiation and growth of damage in materials. The growing reliance on 3-D finite element (FE) virtual structural testing demands implementation and validation of robust material models that can predict the material behaviour accurately. The use of these models within numerical analyses requires suitable material data. EU aerospace companies along with Cranfield University and other similar resear...

  13. Prediction of residues in discontinuous B-cell epitopes using protein 3D structures

    Andersen, P.H.; Nielsen, Morten; Lund, Ole

    2006-01-01

    . We show that the new structure-based method has a better performance for predicting residues of discontinuous epitopes than methods based solely on sequence information, and that it can successfully predict epitope residues that have been identified by different techniques. DiscoTope detects 15...... experimental epitope mapping in both rational vaccine design and development of diagnostic tools, and may lead to more efficient epitope identification....

  14. MLST and Whole-Genome-Based Population Analysis of Cryptococcus gattii VGIII Links Clinical, Veterinary and Environmental Strains, and Reveals Divergent Serotype Specific Sub-populations and Distant Ancestors

    Firacative, Carolina; Roe, Chandler C.; Malik, Richard; Ferreira-Paim, Kennio; Escandón, Patricia; Sykes, Jane E.; Castañón-Olivares, Laura Rocío; Contreras-Peres, Cudberto; Samayoa, Blanca; Sorrell, Tania C.; Castañeda, Elizabeth; Lockhart, Shawn R.; Engelthaler, David M.; Meyer, Wieland

    2016-01-01

    The emerging pathogen Cryptococcus gattii causes life-threatening disease in immunocompetent and immunocompromised hosts. Of the four major molecular types (VGI-VGIV), the molecular type VGIII has recently emerged as cause of disease in otherwise healthy individuals, prompting a need to investigate its population genetic structure to understand if there are potential genotype-dependent characteristics in its epidemiology, environmental niche(s), host range and clinical features of disease. Multilocus sequence typing (MLST) of 122 clinical, environmental and veterinary C. gattii VGIII isolates from Australia, Colombia, Guatemala, Mexico, New Zealand, Paraguay, USA and Venezuela, and whole genome sequencing (WGS) of 60 isolates representing all established MLST types identified four divergent sub-populations. The majority of the isolates belong to two main clades, corresponding either to serotype B or C, indicating an ongoing species evolution. Both major clades included clinical, environmental and veterinary isolates. The C. gattii VGIII population was genetically highly diverse, with minor differences between countries, isolation source, serotype and mating type. Little to no recombination was found between the two major groups, serotype B and C, at the whole and mitochondrial genome level. C. gattii VGIII is widespread in the Americas, with sporadic cases occurring elsewhere, WGS revealed Mexico and USA as a likely origin of the serotype B VGIII population and Colombia as a possible origin of the serotype C VGIII population. Serotype B isolates are more virulent than serotype C isolates in a murine model of infection, causing predominantly pulmonary cryptococcosis. No specific link between genotype and virulence was observed. Antifungal susceptibility testing against six antifungal drugs revealed that serotype B isolates are more susceptible to azoles than serotype C isolates, highlighting the importance of strain typing to guide effective treatment to improve the

  15. SVM-PB-Pred: SVM based protein block prediction method using sequence profiles and secondary structures.

    Suresh, V; Parthasarathy, S

    2014-01-01

    We developed a support vector machine based web server called SVM-PB-Pred, to predict the Protein Block for any given amino acid sequence. The input features of SVM-PB-Pred include i) sequence profiles (PSSM) and ii) actual secondary structures (SS) from DSSP method or predicted secondary structures from NPS@ and GOR4 methods. There were three combined input features PSSM+SS(DSSP), PSSM+SS(NPS@) and PSSM+SS(GOR4) used to test and train the SVM models. Similarly, four datasets RS90, DB433, LI1264 and SP1577 were used to develop the SVM models. These four SVM models developed were tested using three different benchmarking tests namely; (i) self consistency, (ii) seven fold cross validation test and (iii) independent case test. The maximum possible prediction accuracy of ~70% was observed in self consistency test for the SVM models of both LI1264 and SP1577 datasets, where PSSM+SS(DSSP) input features was used to test. The prediction accuracies were reduced to ~53% for PSSM+SS(NPS@) and ~43% for PSSM+SS(GOR4) in independent case test, for the SVM models of above two same datasets. Using our method, it is possible to predict the protein block letters for any query protein sequence with ~53% accuracy, when the SP1577 dataset and predicted secondary structure from NPS@ server were used. The SVM-PB-Pred server can be freely accessed through http://bioinfo.bdu.ac.in/~svmpbpred.

  16. ORION: a web server for protein fold recognition and structure prediction using evolutionary hybrid profiles.

    Ghouzam, Yassine; Postic, Guillaume; Guerin, Pierre-Edouard; de Brevern, Alexandre G; Gelly, Jean-Christophe

    2016-06-20

    Protein structure prediction based on comparative modeling is the most efficient way to produce structural models when it can be performed. ORION is a dedicated webserver based on a new strategy that performs this task. The identification by ORION of suitable templates is performed using an original profile-profile approach that combines sequence and structure evolution information. Structure evolution information is encoded into profiles using structural features, such as solvent accessibility and local conformation -with Protein Blocks-, which give an accurate description of the local protein structure. ORION has recently been improved, increasing by 5% the quality of its results. The ORION web server accepts a single protein sequence as input and searches homologous protein structures within minutes. Various databases such as PDB, SCOP and HOMSTRAD can be mined to find an appropriate structural template. For the modeling step, a protein 3D structure can be directly obtained from the selected template by MODELLER and displayed with global and local quality model estimation measures. The sequence and the predicted structure of 4 examples from the CAMEO server and a recent CASP11 target from the 'Hard' category (T0818-D1) are shown as pertinent examples. Our web server is accessible at http://www.dsimb.inserm.fr/ORION/.

  17. From structure prediction to genomic screens for novel non-coding RNAs.

    Gorodkin, Jan; Hofacker, Ivo L

    2011-08-01

    Non-coding RNAs (ncRNAs) are receiving more and more attention not only as an abundant class of genes, but also as regulatory structural elements (some located in mRNAs). A key feature of RNA function is its structure. Computational methods were developed early for folding and prediction of RNA structure with the aim of assisting in functional analysis. With the discovery of more and more ncRNAs, it has become clear that a large fraction of these are highly structured. Interestingly, a large part of the structure is comprised of regular Watson-Crick and GU wobble base pairs. This and the increased amount of available genomes have made it possible to employ structure-based methods for genomic screens. The field has moved from folding prediction of single sequences to computational screens for ncRNAs in genomic sequence using the RNA structure as the main characteristic feature. Whereas early methods focused on energy-directed folding of single sequences, comparative analysis based on structure preserving changes of base pairs has been efficient in improving accuracy, and today this constitutes a key component in genomic screens. Here, we cover the basic principles of RNA folding and touch upon some of the concepts in current methods that have been applied in genomic screens for de novo RNA structures in searches for novel ncRNA genes and regulatory RNA structure on mRNAs. We discuss the strengths and weaknesses of the different strategies and how they can complement each other.

  18. On the relevance of sophisticated structural annotations for disulfide connectivity pattern prediction.

    Julien Becker

    Full Text Available Disulfide bridges strongly constrain the native structure of many proteins and predicting their formation is therefore a key sub-problem of protein structure and function inference. Most recently proposed approaches for this prediction problem adopt the following pipeline: first they enrich the primary sequence with structural annotations, second they apply a binary classifier to each candidate pair of cysteines to predict disulfide bonding probabilities and finally, they use a maximum weight graph matching algorithm to derive the predicted disulfide connectivity pattern of a protein. In this paper, we adopt this three step pipeline and propose an extensive study of the relevance of various structural annotations and feature encodings. In particular, we consider five kinds of structural annotations, among which three are novel in the context of disulfide bridge prediction. So as to be usable by machine learning algorithms, these annotations must be encoded into features. For this purpose, we propose four different feature encodings based on local windows and on different kinds of histograms. The combination of structural annotations with these possible encodings leads to a large number of possible feature functions. In order to identify a minimal subset of relevant feature functions among those, we propose an efficient and interpretable feature function selection scheme, designed so as to avoid any form of overfitting. We apply this scheme on top of three supervised learning algorithms: k-nearest neighbors, support vector machines and extremely randomized trees. Our results indicate that the use of only the PSSM (position-specific scoring matrix together with the CSP (cysteine separation profile are sufficient to construct a high performance disulfide pattern predictor and that extremely randomized trees reach a disulfide pattern prediction accuracy of [Formula: see text] on the benchmark dataset SPX[Formula: see text], which corresponds to

  19. Protein secondary structure prediction for a single-sequence using hidden semi-Markov models

    Borodovsky Mark

    2006-03-01

    Full Text Available Abstract Background The accuracy of protein secondary structure prediction has been improving steadily towards the 88% estimated theoretical limit. There are two types of prediction algorithms: Single-sequence prediction algorithms imply that information about other (homologous proteins is not available, while algorithms of the second type imply that information about homologous proteins is available, and use it intensively. The single-sequence algorithms could make an important contribution to studies of proteins with no detected homologs, however the accuracy of protein secondary structure prediction from a single-sequence is not as high as when the additional evolutionary information is present. Results In this paper, we further refine and extend the hidden semi-Markov model (HSMM initially considered in the BSPSS algorithm. We introduce an improved residue dependency model by considering the patterns of statistically significant amino acid correlation at structural segment borders. We also derive models that specialize on different sections of the dependency structure and incorporate them into HSMM. In addition, we implement an iterative training method to refine estimates of HSMM parameters. The three-state-per-residue accuracy and other accuracy measures of the new method, IPSSP, are shown to be comparable or better than ones for BSPSS as well as for PSIPRED, tested under the single-sequence condition. Conclusions We have shown that new dependency models and training methods bring further improvements to single-sequence protein secondary structure prediction. The results are obtained under cross-validation conditions using a dataset with no pair of sequences having significant sequence similarity. As new sequences are added to the database it is possible to augment the dependency structure and obtain even higher accuracy. Current and future advances should contribute to the improvement of function prediction for orphan proteins inscrutable

  20. Phylogenetic variation of Aggregatibacter actinomycetemcomitans serotype e reveals an aberrant distinct evolutionary stable lineage

    van der Reijden, Wil A.; Brunner, Jorg; Bosch-Tijhof, Carolien J.; van Trappen, Stefanie; Rijnsburger, Martine C.; de Graaff, Marcel P. W.; van Winkelhoff, Arie J.; Cleenwerck, Ilse; de Vos, Paul

    2010-01-01

    The periodontal pathogen Aggregatibacter actinomycetemcomitans that comprises six serotypes (a-f), is often identified by PCR-based techniques targeting the 16S rRNA gene. In this study, 16S rRNA gene sequence analysis revealed an aberrant cluster of 19 strains within serotype e, denoted as serotype

  1. Comparison of Toxicological Properties of Botulinum Neurotoxin Serotypes A and B in Mice

    Botulinum neurotoxins (BoNTs) are among the most toxic biological toxins for humans. Of the seven known serotypes (A-G) of BoNT, serotypes A, B and E cause most of the human foodborne intoxications. In this study, we compared the toxicological properties of BoNT serotype A and B holotoxins and compl...

  2. Structure-aided prediction of mammalian transcription factor complexes in conserved non-coding elements

    Guturu, H.

    2013-11-11

    Mapping the DNA-binding preferences of transcription factor (TF) complexes is critical for deciphering the functions of cis-regulatory elements. Here, we developed a computational method that compares co-occurring motif spacings in conserved versus unconserved regions of the human genome to detect evolutionarily constrained binding sites of rigid TF complexes. Structural data were used to estimate TF complex physical plausibility, explore overlapping motif arrangements seldom tackled by non-structure-aware methods, and generate and analyse three-dimensional models of the predicted complexes bound to DNA. Using this approach, we predicted 422 physically realistic TF complex motifs at 18% false discovery rate, the majority of which (326, 77%) contain some sequence overlap between binding sites. The set of mostly novel complexes is enriched in known composite motifs, predictive of binding site configurations in TF-TF-DNA crystal structures, and supported by ChIP-seq datasets. Structural modelling revealed three cooperativity mechanisms: direct protein-protein interactions, potentially indirect interactions and \\'through-DNA\\' interactions. Indeed, 38% of the predicted complexes were found to contain four or more bases in which TF pairs appear to synergize through overlapping binding to the same DNA base pairs in opposite grooves or strands. Our TF complex and associated binding site predictions are available as a web resource at http://bejerano.stanford.edu/complex.

  3. MUFOLD-SS: New deep inception-inside-inception networks for protein secondary structure prediction.

    Fang, Chao; Shang, Yi; Xu, Dong

    2018-05-01

    Protein secondary structure prediction can provide important information for protein 3D structure prediction and protein functions. Deep learning offers a new opportunity to significantly improve prediction accuracy. In this article, a new deep neural network architecture, named the Deep inception-inside-inception (Deep3I) network, is proposed for protein secondary structure prediction and implemented as a software tool MUFOLD-SS. The input to MUFOLD-SS is a carefully designed feature matrix corresponding to the primary amino acid sequence of a protein, which consists of a rich set of information derived from individual amino acid, as well as the context of the protein sequence. Specifically, the feature matrix is a composition of physio-chemical properties of amino acids, PSI-BLAST profile, and HHBlits profile. MUFOLD-SS is composed of a sequence of nested inception modules and maps the input matrix to either eight states or three states of secondary structures. The architecture of MUFOLD-SS enables effective processing of local and global interactions between amino acids in making accurate prediction. In extensive experiments on multiple datasets, MUFOLD-SS outperformed the best existing methods and other deep neural networks significantly. MUFold-SS can be downloaded from http://dslsrv8.cs.missouri.edu/~cf797/MUFoldSS/download.html. © 2018 Wiley Periodicals, Inc.

  4. Structure-aided prediction of mammalian transcription factor complexes in conserved non-coding elements

    Guturu, H.; Doxey, A. C.; Wenger, A. M.; Bejerano, G.

    2013-01-01

    Mapping the DNA-binding preferences of transcription factor (TF) complexes is critical for deciphering the functions of cis-regulatory elements. Here, we developed a computational method that compares co-occurring motif spacings in conserved versus unconserved regions of the human genome to detect evolutionarily constrained binding sites of rigid TF complexes. Structural data were used to estimate TF complex physical plausibility, explore overlapping motif arrangements seldom tackled by non-structure-aware methods, and generate and analyse three-dimensional models of the predicted complexes bound to DNA. Using this approach, we predicted 422 physically realistic TF complex motifs at 18% false discovery rate, the majority of which (326, 77%) contain some sequence overlap between binding sites. The set of mostly novel complexes is enriched in known composite motifs, predictive of binding site configurations in TF-TF-DNA crystal structures, and supported by ChIP-seq datasets. Structural modelling revealed three cooperativity mechanisms: direct protein-protein interactions, potentially indirect interactions and 'through-DNA' interactions. Indeed, 38% of the predicted complexes were found to contain four or more bases in which TF pairs appear to synergize through overlapping binding to the same DNA base pairs in opposite grooves or strands. Our TF complex and associated binding site predictions are available as a web resource at http://bejerano.stanford.edu/complex.

  5. Rosetta Structure Prediction as a Tool for Solving Difficult Molecular Replacement Problems.

    DiMaio, Frank

    2017-01-01

    Molecular replacement (MR), a method for solving the crystallographic phase problem using phases derived from a model of the target structure, has proven extremely valuable, accounting for the vast majority of structures solved by X-ray crystallography. However, when the resolution of data is low, or the starting model is very dissimilar to the target protein, solving structures via molecular replacement may be very challenging. In recent years, protein structure prediction methodology has emerged as a powerful tool in model building and model refinement for difficult molecular replacement problems. This chapter describes some of the tools available in Rosetta for model building and model refinement specifically geared toward difficult molecular replacement cases.

  6. A Deep Learning Network Approach to ab initio Protein Secondary Structure Prediction.

    Spencer, Matt; Eickholt, Jesse; Jianlin Cheng

    2015-01-01

    Ab initio protein secondary structure (SS) predictions are utilized to generate tertiary structure predictions, which are increasingly demanded due to the rapid discovery of proteins. Although recent developments have slightly exceeded previous methods of SS prediction, accuracy has stagnated around 80 percent and many wonder if prediction cannot be advanced beyond this ceiling. Disciplines that have traditionally employed neural networks are experimenting with novel deep learning techniques in attempts to stimulate progress. Since neural networks have historically played an important role in SS prediction, we wanted to determine whether deep learning could contribute to the advancement of this field as well. We developed an SS predictor that makes use of the position-specific scoring matrix generated by PSI-BLAST and deep learning network architectures, which we call DNSS. Graphical processing units and CUDA software optimize the deep network architecture and efficiently train the deep networks. Optimal parameters for the training process were determined, and a workflow comprising three separately trained deep networks was constructed in order to make refined predictions. This deep learning network approach was used to predict SS for a fully independent test dataset of 198 proteins, achieving a Q3 accuracy of 80.7 percent and a Sov accuracy of 74.2 percent.

  7. Fast computational methods for predicting protein structure from primary amino acid sequence

    Agarwal, Pratul Kumar [Knoxville, TN

    2011-07-19

    The present invention provides a method utilizing primary amino acid sequence of a protein, energy minimization, molecular dynamics and protein vibrational modes to predict three-dimensional structure of a protein. The present invention also determines possible intermediates in the protein folding pathway. The present invention has important applications to the design of novel drugs as well as protein engineering. The present invention predicts the three-dimensional structure of a protein independent of size of the protein, overcoming a significant limitation in the prior art.

  8. Artificial Intelligence in Prediction of Secondary Protein Structure Using CB513 Database

    Avdagic, Zikrija; Purisevic, Elvir; Omanovic, Samir; Coralic, Zlatan

    2009-01-01

    In this paper we describe CB513 a non-redundant dataset, suitable for development of algorithms for prediction of secondary protein structure. A program was made in Borland Delphi for transforming data from our dataset to make it suitable for learning of neural network for prediction of secondary protein structure implemented in MATLAB Neural-Network Toolbox. Learning (training and testing) of neural network is researched with different sizes of windows, different number of neurons in the hidden layer and different number of training epochs, while using dataset CB513. PMID:21347158

  9. Virulence Studies of Different Sequence Types and Geographical Origins of Streptococcus suis Serotype 2 in a Mouse Model of Infection

    Jean-Philippe Auger

    2016-07-01

    Full Text Available Multilocus sequence typing previously identified three predominant sequence types (STs of Streptococcus suis serotype 2: ST1 strains predominate in Eurasia while North American (NA strains are generally ST25 and ST28. However, ST25/ST28 and ST1 strains have also been isolated in Asia and NA, respectively. Using a well-standardized mouse model of infection, the virulence of strains belonging to different STs and different geographical origins was evaluated. Results demonstrated that although a certain tendency may be observed, S. suis serotype 2 virulence is difficult to predict based on ST and geographical origin alone; strains belonging to the same ST presented important differences of virulence and did not always correlate with origin. The only exception appears to be NA ST28 strains, which were generally less virulent in both systemic and central nervous system (CNS infection models. Persistent and high levels of bacteremia accompanied by elevated CNS inflammation are required to cause meningitis. Although widely used, in vitro tests such as phagocytosis and killing assays require further standardization in order to be used as predictive tests for evaluating virulence of strains. The use of strains other than archetypal strains has increased our knowledge and understanding of the S. suis serotype 2 population dynamics.

  10. Topotactic decomposition and crystal structure of white molybdenum trioxide--monohydrate: prediction of structure by topotaxy

    Oswald, H.R.; Guenter, J.R.; Dubler, E.

    1975-01-01

    Single crystals of the white MoO 3 . H 2 O modification (''α-molybdic acid'') were transformed by heating to 160 0 C into perfect pseudomorphs built up from oriented MoO 3 crystallites of known structure. From the mutual orientation relationship of the unit cells of both phases involved in this topotactic reaction, as determined by X-ray photographs, a model for the so far unknown crystal structure of white MoO 3 . H 2 O could be deduced. Independently, this structure was determined by X-ray diffractometer data then: space group P anti 1, a = 7.388, b = 3.700, c = 6.673 A, α = 107.8, β = 113.6, γ = 91.2 0 , Z = 2. The structure was solved from the Patterson function and refined until R = 0.088. It is built up from isolated double chains of strongly distorted [MoO 5 (H 2 O)]-octahedra sharing two common edges with each other. This result agrees well with the model derived from topotaxy, and it becomes evident how the MoO 3 lattice is formed through corner linking of the isolated double chains after the water molecules are removed. The study of topotactic phenomena seems rather generally applicable to deduce the main features of structures involved and for better understanding of structural relationships. (U.S.)

  11. Predicting protein folding pathways at the mesoscopic level based on native interactions between secondary structure elements

    Sze Sing-Hoi

    2008-07-01

    Full Text Available Abstract Background Since experimental determination of protein folding pathways remains difficult, computational techniques are often used to simulate protein folding. Most current techniques to predict protein folding pathways are computationally intensive and are suitable only for small proteins. Results By assuming that the native structure of a protein is known and representing each intermediate conformation as a collection of fully folded structures in which each of them contains a set of interacting secondary structure elements, we show that it is possible to significantly reduce the conformation space while still being able to predict the most energetically favorable folding pathway of large proteins with hundreds of residues at the mesoscopic level, including the pig muscle phosphoglycerate kinase with 416 residues. The model is detailed enough to distinguish between different folding pathways of structurally very similar proteins, including the streptococcal protein G and the peptostreptococcal protein L. The model is also able to recognize the differences between the folding pathways of protein G and its two structurally similar variants NuG1 and NuG2, which are even harder to distinguish. We show that this strategy can produce accurate predictions on many other proteins with experimentally determined intermediate folding states. Conclusion Our technique is efficient enough to predict folding pathways for both large and small proteins at the mesoscopic level. Such a strategy is often the only feasible choice for large proteins. A software program implementing this strategy (SSFold is available at http://faculty.cs.tamu.edu/shsze/ssfold.

  12. LoopIng: a template-based tool for predicting the structure of protein loops.

    Messih, Mario Abdel

    2015-08-06

    Predicting the structure of protein loops is very challenging, mainly because they are not necessarily subject to strong evolutionary pressure. This implies that, unlike the rest of the protein, standard homology modeling techniques are not very effective in modeling their structure. However, loops are often involved in protein function, hence inferring their structure is important for predicting protein structure as well as function.We describe a method, LoopIng, based on the Random Forest automated learning technique, which, given a target loop, selects a structural template for it from a database of loop candidates. Compared to the most recently available methods, LoopIng is able to achieve similar accuracy for short loops (4-10 residues) and significant enhancements for long loops (11-20 residues). The quality of the predictions is robust to errors that unavoidably affect the stem regions when these are modeled. The method returns a confidence score for the predicted template loops and has the advantage of being very fast (on average: 1 min/loop).www.biocomputing.it/loopinganna.tramontano@uniroma1.itSupplementary data are available at Bioinformatics online.

  13. Molecular Phylogeny and Predicted 3D Structure of Plant beta-D-N-Acetylhexosaminidase

    Md. Anowar Hossain

    2014-01-01

    Full Text Available beta-D-N-Acetylhexosaminidase, a family 20 glycosyl hydrolase, catalyzes the removal of β-1,4-linked N-acetylhexosamine residues from oligosaccharides and their conjugates. We constructed phylogenetic tree of β-hexosaminidases to analyze the evolutionary history and predicted functions of plant hexosaminidases. Phylogenetic analysis reveals the complex history of evolution of plant β-hexosaminidase that can be described by gene duplication events. The 3D structure of tomato β-hexosaminidase (β-Hex-Sl was predicted by homology modeling using 1now as a template. Structural conformity studies of the best fit model showed that more than 98% of the residues lie inside the favoured and allowed regions where only 0.9% lie in the unfavourable region. Predicted 3D structure contains 531 amino acids residues with glycosyl hydrolase20b domain-I and glycosyl hydrolase20 superfamily domain-II including the (β/α8 barrel in the central part. The α and β contents of the modeled structure were found to be 33.3% and 12.2%, respectively. Eleven amino acids were found to be involved in ligand-binding site; Asp(330 and Glu(331 could play important roles in enzyme-catalyzed reactions. The predicted model provides a structural framework that can act as a guide to develop a hypothesis for β-Hex-Sl mutagenesis experiments for exploring the functions of this class of enzymes in plant kingdom.

  14. Molecular phylogeny and predicted 3D structure of plant beta-D-N-acetylhexosaminidase.

    Hossain, Md Anowar; Roslan, Hairul Azman

    2014-01-01

    beta-D-N-Acetylhexosaminidase, a family 20 glycosyl hydrolase, catalyzes the removal of β-1,4-linked N-acetylhexosamine residues from oligosaccharides and their conjugates. We constructed phylogenetic tree of β-hexosaminidases to analyze the evolutionary history and predicted functions of plant hexosaminidases. Phylogenetic analysis reveals the complex history of evolution of plant β-hexosaminidase that can be described by gene duplication events. The 3D structure of tomato β-hexosaminidase (β-Hex-Sl) was predicted by homology modeling using 1now as a template. Structural conformity studies of the best fit model showed that more than 98% of the residues lie inside the favoured and allowed regions where only 0.9% lie in the unfavourable region. Predicted 3D structure contains 531 amino acids residues with glycosyl hydrolase20b domain-I and glycosyl hydrolase20 superfamily domain-II including the (β/α)8 barrel in the central part. The α and β contents of the modeled structure were found to be 33.3% and 12.2%, respectively. Eleven amino acids were found to be involved in ligand-binding site; Asp(330) and Glu(331) could play important roles in enzyme-catalyzed reactions. The predicted model provides a structural framework that can act as a guide to develop a hypothesis for β-Hex-Sl mutagenesis experiments for exploring the functions of this class of enzymes in plant kingdom.

  15. Characterization of human monoclonal antibodies that neutralize multiple poliovirus serotypes.

    Puligedda, Rama Devudu; Kouiavskaia, Diana; Al-Saleem, Fetweh H; Kattala, Chandana Devi; Nabi, Usman; Yaqoob, Hamid; Bhagavathula, V Sandeep; Sharma, Rashmi; Chumakov, Konstantin; Dessain, Scott K

    2017-10-04

    Following the eradication of wild poliovirus (PV), achieving and maintaining a polio-free status will require eliminating potentially pathogenic PV strains derived from the oral attenuated vaccine. For this purpose, a combination of non-cross-resistant drugs, such as small molecules and neutralizing monoclonal antibodies (mAbs), may be ideal. We previously isolated chimpanzee and human mAbs capable of neutralizing multiple PV types (cross-neutralization). Here, we describe three additional human mAbs that neutralize types 1 and 2 PV and one mAb that neutralizes all three types. Most bind conformational epitopes and have unusually long heavy chain complementarity determining 3 domains (HC CDR3). We assessed the ability of the mAbs to neutralize A12 escape mutant PV strains, and found that the neutralizing activities of the mAbs were disrupted by different amino acid substitutions. Competitive binding studies further suggested that the specific mAb:PV interactions that enable cross-neutralization differ among mAbs and serotypes. All of the cloned mAbs bind PV in the vicinity of the "canyon", a circular depression around the 5-fold axis of symmetry through which PV recognizes its cellular receptor. We were unable to generate escape mutants to two of the mAbs, suggesting that their epitopes are important for the PV life cycle. These data indicate that PV cross-neutralization involves binding to highly conserved structures within the canyon that binds to the cellular receptor. These may be facilitated by the long HC CDR3 domains, which may adopt alternative binding configurations. We propose that the human and chimpanzee mAbs described here could have potential as anti-PV therapeutics. Copyright © 2017 Elsevier Ltd. All rights reserved.

  16. Methodology for predicting ultimate pressure capacity of the ACR-1000 containment structure

    Saudy, A.M.; Awad, A.; Elgohary, M.

    2006-01-01

    The Advanced CANDU Reactor or the ACR-1000 is developed by Atomic Energy of Canada Limited (AECL) to be the next step in the evolution of the CANDU product line. It is based on the proven CANDU technology and incorporates advanced design technologies. The ACR containment structure is an essential element of the overall defense in depth approach to reactor safety, and is a physical barrier against the release of radioactive material to the environment. Therefore, it is important to provide a robust design with an adequate margin of safety. One of the key design requirements of the ACR containment structure is to have an ultimate pressure capacity that is at least twice the design pressure Using standard design codes, the containment structure is expected to behave elastically at least up to 1.5 times the design pressure. Beyond this pressure level, the concrete containment structure with reinforcements and post-tension tendons behaves in a highly non-linear manner and exhibits a complex response when cracks initiate and propagate. To predict the structural non-linear responses, at least two critical features are involved. These are: the structural idealization by the geometry and material property models, and the adopted solution algorithm. Therefore, detailed idealization of the concrete structure is needed in order to accurately predict its ultimate pressure capacity. This paper summarizes the analysis methodology to be carried out to establish the ultimate pressure capacity of the ACR containment structure and to confirm that the structure meets the specified design requirements. (author)

  17. Structural prediction and analysis of VIH-related peptides from selected crustacean species.

    Nagaraju, Ganji Purna Chandra; Kumari, Nunna Siva; Prasad, Ganji Lakshmi Vara; Rajitha, Balney; Meenu, Madan; Rao, Manam Sreenivasa; Naik, Bannoth Reddya

    2009-08-17

    The tentative elucidation of the 3D-structure of vitellogenesis inhibiting hormone (VIH) peptides is conversely underprivileged by difficulties in gaining enough peptide or protein, diffracting crystals, and numerous extra technical aspects. As a result, no structural information is available for VIH peptide sequences registered in the Genbank. In this situation, it is not surprising that predictive methods have achieved great interest. Here, in this study the molt-inhibiting hormone (MIH) of the kuruma prawn (Marsupenaeus japonicus) is used, to predict the structure of four VIHrelated peptides in the crustacean species. The high similarity of the 3D-structures and the calculated physiochemical characteristics of these peptides suggest a common fold for the entire family.

  18. Antibody modeling using the prediction of immunoglobulin structure (PIGS) web server [corrected].

    Marcatili, Paolo; Olimpieri, Pier Paolo; Chailyan, Anna; Tramontano, Anna

    2014-12-01

    Antibodies (or immunoglobulins) are crucial for defending organisms from pathogens, but they are also key players in many medical, diagnostic and biotechnological applications. The ability to predict their structure and the specific residues involved in antigen recognition has several useful applications in all of these areas. Over the years, we have developed or collaborated in developing a strategy that enables researchers to predict the 3D structure of antibodies with a very satisfactory accuracy. The strategy is completely automated and extremely fast, requiring only a few minutes (∼10 min on average) to build a structural model of an antibody. It is based on the concept of canonical structures of antibody loops and on our understanding of the way light and heavy chains pack together.

  19. Critical assessment of methods of protein structure prediction (CASP)-round IX

    Moult, John; Fidelis, Krzysztof; Kryshtafovych, Andriy; Tramontano, Anna

    2011-01-01

    This article is an introduction to the special issue of the journal PROTEINS, dedicated to the ninth Critical Assessment of Structure Prediction (CASP) experiment to assess the state of the art in protein structure modeling. The article describes the conduct of the experiment, the categories of prediction included, and outlines the evaluation and assessment procedures. Methods for modeling protein structure continue to advance, although at a more modest pace than in the early CASP experiments. CASP developments of note are indications of improvement in model accuracy for some classes of target, an improved ability to choose the most accurate of a set of generated models, and evidence of improvement in accuracy for short "new fold" models. In addition, a new analysis of regions of models not derivable from the most obvious template structure has revealed better performance than expected.

  20. Sparse RNA folding revisited: space-efficient minimum free energy structure prediction.

    Will, Sebastian; Jabbari, Hosna

    2016-01-01

    RNA secondary structure prediction by energy minimization is the central computational tool for the analysis of structural non-coding RNAs and their interactions. Sparsification has been successfully applied to improve the time efficiency of various structure prediction algorithms while guaranteeing the same result; however, for many such folding problems, space efficiency is of even greater concern, particularly for long RNA sequences. So far, space-efficient sparsified RNA folding with fold reconstruction was solved only for simple base-pair-based pseudo-energy models. Here, we revisit the problem of space-efficient free energy minimization. Whereas the space-efficient minimization of the free energy has been sketched before, the reconstruction of the optimum structure has not even been discussed. We show that this reconstruction is not possible in trivial extension of the method for simple energy models. Then, we present the time- and space-efficient sparsified free energy minimization algorithm SparseMFEFold that guarantees MFE structure prediction. In particular, this novel algorithm provides efficient fold reconstruction based on dynamically garbage-collected trace arrows. The complexity of our algorithm depends on two parameters, the number of candidates Z and the number of trace arrows T; both are bounded by [Formula: see text], but are typically much smaller. The time complexity of RNA folding is reduced from [Formula: see text] to [Formula: see text]; the space complexity, from [Formula: see text] to [Formula: see text]. Our empirical results show more than 80 % space savings over RNAfold [Vienna RNA package] on the long RNAs from the RNA STRAND database (≥2500 bases). The presented technique is intentionally generalizable to complex prediction algorithms; due to their high space demands, algorithms like pseudoknot prediction and RNA-RNA-interaction prediction are expected to profit even stronger than "standard" MFE folding. SparseMFEFold is free

  1. Mathematical Model to Predict the Permeability of Water Transport in Concrete Structure

    Solomon Ndubuisi Eluozo

    2013-01-01

    Mathematical model to predict the permeability of water transport in concrete has been established, the model is to monitor the rate of water transport in concrete structure. The process of this water transport is based on the constituent in the mixture of concrete. Permeability established a relation on the influence of the micropores on the constituent that made of concrete, the method of concrete placement determine the rate of permeability deposition in concrete structure, permeability es...

  2. Inelastic spectra to predict period elongation of structures under earthquake loading

    Katsanos, Evangelos; Sextos, A.G.

    2015-01-01

    Period lengthening, exhibited by structures when subjected to strong ground motions, constitutes an implicit proxy of structural inelasticity and associated damage. However, the reliable prediction of the inelastic period is tedious and a multi-parametric task, which is related to both epistemic ...... for period lengthening as a function of Ry and Tel. These equations may be used in the framework of the earthquake record selection and scaling....

  3. The calcium binding properties and structure prediction of the Hax-1 protein.

    Balcerak, Anna; Rowinski, Sebastian; Szafron, Lukasz M; Grzybowska, Ewa A

    2017-01-01

    Hax-1 is a protein involved in regulation of different cellular processes, but its properties and exact mechanisms of action remain unknown. In this work, using purified, recombinant Hax-1 and by applying an in vitro autoradiography assay we have shown that this protein binds Ca 2+ . Additionally, we performed structure prediction analysis which shows that Hax-1 displays definitive structural features, such as two α-helices, short β-strands and four disordered segments.

  4. Predictive Methods for Dense Polymer Networks: Combating Bias with Bio-Based Structures

    2016-03-16

    Combating bias with bio - based structures 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) Andrew J. Guenthner...unlimited. PA Clearance 16152 Integrity  Service  Excellence Predictive methods for dense polymer networks: Combating bias with bio -based...Architectural Bias • Comparison of Petroleum-Based and Bio -Based Chemical Architectures • Continuing Research on Structure-Property Relationships using

  5. PCR deduction of invasive and colonizing pneumococcal serotypes from Venezuela: a critical appraisal.

    Bello Gonzalez, Teresita; Rivera-Olivero, Ismar Alejandra; Sisco, María Carolina; Spadola, Enza; Hermans, Peter W; de Waard, Jacobus H

    2014-04-15

    Serotype surveillance of Streptococcus pneumoniae is indispensable for evaluating the potential impact of pneumococcal conjugate vaccines. Serotyping by the standard Quellung reaction is technically demanding, time consuming, and expensive. A simple and economical strategy is multiplex PCR-based serotyping. We evaluated the cost effectiveness of a modified serial multiplex PCR (mPCR), resolving 24 serotypes in four PCR reactions and optimally targeting the most prevalent invasive and colonizing pneumococcal serotypes found in Venezuela. A total of 223 pneumococcal isolates, 140 invasive and 83 carriage isolates, previously serotyped by the Quellung reaction and representing the 18 most common serotypes/groups identified in Venezuela, were serotyped with the adapted mPCR. The mPCR serotyped 76% of all the strains in the first two PCR reactions and 91% after four reactions, correctly identifying 17 serotypes/groups. An isolate could be serotyped with mPCR in less than 2 minutes versus 15 minutes for the Quellung reaction, considerably lowering labor costs. A restrictive weakness of mPCR was found for the detection of 19F strains. Most Venezuelan 19F strains were not typeable using the mPCR, and two 19F cps serotype variants were identified. The mPCR assay is an accurate, rapid, and economical method for the identification of the vast majority of the serotypes from Venezuela and can be used in place of the standard Quellung reaction. An exception is the identification of serotype 19F. In this setting, most 19F strains were not detectable with mPCR, demonstrating a need of serology-based quality control for PCR-based serotyping.

  6. GalaxyHomomer: a web server for protein homo-oligomer structure prediction from a monomer sequence or structure.

    Baek, Minkyung; Park, Taeyong; Heo, Lim; Park, Chiwook; Seok, Chaok

    2017-07-03

    Homo-oligomerization of proteins is abundant in nature, and is often intimately related with the physiological functions of proteins, such as in metabolism, signal transduction or immunity. Information on the homo-oligomer structure is therefore important to obtain a molecular-level understanding of protein functions and their regulation. Currently available web servers predict protein homo-oligomer structures either by template-based modeling using homo-oligomer templates selected from the protein structure database or by ab initio docking of monomer structures resolved by experiment or predicted by computation. The GalaxyHomomer server, freely accessible at http://galaxy.seoklab.org/homomer, carries out template-based modeling, ab initio docking or both depending on the availability of proper oligomer templates. It also incorporates recently developed model refinement methods that can consistently improve model quality. Moreover, the server provides additional options that can be chosen by the user depending on the availability of information on the monomer structure, oligomeric state and locations of unreliable/flexible loops or termini. The performance of the server was better than or comparable to that of other available methods when tested on benchmark sets and in a recent CASP performed in a blind fashion. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

  7. Prediction of the Fundamental Period of Infilled RC Frame Structures Using Artificial Neural Networks

    Panagiotis G. Asteris

    2016-01-01

    Full Text Available The fundamental period is one of the most critical parameters for the seismic design of structures. There are several literature approaches for its estimation which often conflict with each other, making their use questionable. Furthermore, the majority of these approaches do not take into account the presence of infill walls into the structure despite the fact that infill walls increase the stiffness and mass of structure leading to significant changes in the fundamental period. In the present paper, artificial neural networks (ANNs are used to predict the fundamental period of infilled reinforced concrete (RC structures. For the training and the validation of the ANN, a large data set is used based on a detailed investigation of the parameters that affect the fundamental period of RC structures. The comparison of the predicted values with analytical ones indicates the potential of using ANNs for the prediction of the fundamental period of infilled RC frame structures taking into account the crucial parameters that influence its value.

  8. Critical assessment of methods of protein structure prediction (CASP) - round x

    Moult, John; Fidelis, Krzysztof; Kryshtafovych, Andriy; Schwede, Torsten; Tramontano, Anna

    2013-01-01

    This article is an introduction to the special issue of the journal PROTEINS, dedicated to the tenth Critical Assessment of Structure Prediction (CASP) experiment to assess the state of the art in protein structure modeling. The article describes the conduct of the experiment, the categories of prediction included, and outlines the evaluation and assessment procedures. The 10 CASP experiments span almost 20 years of progress in the field of protein structure modeling, and there have been enormous advances in methods and model accuracy in that period. Notable in this round is the first sustained improvement of models with refinement methods, using molecular dynamics. For the first time, we tested the ability of modeling methods to make use of sparse experimental three-dimensional contact information, such as may be obtained from new experimental techniques, with encouraging results. On the other hand, new contact prediction methods, though holding considerable promise, have yet to make an impact in CASP testing. The nature of CASP targets has been changing in recent CASPs, reflecting shifts in experimental structural biology, with more irregular structures, more multi-domain and multi-subunit structures, and less standard versions of known folds. When allowance is made for these factors, we continue to see steady progress in the overall accuracy of models, particularly resulting from improvement of non-template regions.

  9. Critical assessment of methods of protein structure prediction (CASP) - round x

    Moult, John

    2013-12-17

    This article is an introduction to the special issue of the journal PROTEINS, dedicated to the tenth Critical Assessment of Structure Prediction (CASP) experiment to assess the state of the art in protein structure modeling. The article describes the conduct of the experiment, the categories of prediction included, and outlines the evaluation and assessment procedures. The 10 CASP experiments span almost 20 years of progress in the field of protein structure modeling, and there have been enormous advances in methods and model accuracy in that period. Notable in this round is the first sustained improvement of models with refinement methods, using molecular dynamics. For the first time, we tested the ability of modeling methods to make use of sparse experimental three-dimensional contact information, such as may be obtained from new experimental techniques, with encouraging results. On the other hand, new contact prediction methods, though holding considerable promise, have yet to make an impact in CASP testing. The nature of CASP targets has been changing in recent CASPs, reflecting shifts in experimental structural biology, with more irregular structures, more multi-domain and multi-subunit structures, and less standard versions of known folds. When allowance is made for these factors, we continue to see steady progress in the overall accuracy of models, particularly resulting from improvement of non-template regions.

  10. Evaluation of immunity against poliovirus serotypes among children ...

    Eight (4%) of the children had no detectable antibody, 178 (89%), 180 (90%) and 181 (90.5%) were positive for antibodies to poliovirus types 1, 2 and 3, respectively. Overall, 162 (81%) of the children had antibodies to the three poliovirus serotypes at a titre of at least 1:8. The study shows the need for proper monitoring of ...

  11. Antibiogram of E. coli serotypes isolated from children aged under ...

    Background: Diarrheal disease and its complications remain a major cause of morbidity and mortality in children. The prevalence and antibiogram of E. coli as causative agents of diarrhea vary from region to region, and even within countries in the same geographical area. Objectives: To determine the serotype and ...

  12. Antibiogram of E. coli serotypes isolated from children aged under ...

    Antibiogram of E. coli serotypes isolated from children aged under five with acute diarrhea in Bahir Dar town. Ayrikim Adugna1, Mulugeta Kibret1, Bayeh Abera2, Endalkachew Nibret1, Melaku Adal1. 1. Department of Biology, Science College, Bahir Dar University. 2. Department of Microbiology, Parasitology and ...

  13. An Increase in Streptococcus pneumoniae Serotype 12F

    2018-02-08

    Dr. Cynthia Whitney, a CDC medical doctor and Epidemiologist, discusses serotype 12F pneumoniae.  Created: 2/8/2018 by National Center for Emerging and Zoonotic Infectious Diseases (NCEZID).   Date Released: 2/8/2018.

  14. Adhesion of Porphyromonas gingivalis serotypes to pocket epithelium

    Dierickx, K; Pauwels, M; Laine, ML; Van Eldere, J; Cassiman, JJ; van Winkelhoff, AJ; van Steenberghe, D; Quirynen, M

    Background: Porphyromonas gingivalis, a key pathogen in periodontitis, is able to adhere to and invade the pocket epithelium. Different capsular antigens of P gingivalis have been identified (K-serotyping). These P gingivalis capsular types show differences in adhesion capacity to human cell lines

  15. Two barley yellow dwarf luteovirus serotypes associated with ...

    Barley yellow dwarf luteovirus (BYDV) serotypes PAV and RPV were identified from irrigated wheat (Triticum aestivum L.) samples from three provinces of Zambia by double antibody sandwich enzyme-linked immunosorbent assay using polyclonal and monoclonal antisera. Nine wheat cultivars were surveyed in 11 wheat ...

  16. Streptococcus agalactiae Serotype IV in Humans and Cattle, Northern Europe

    Lyhs, Ulrike; Kulkas, Laura; Katholm, Jorgen

    2016-01-01

    not differentiate between populations isolated from different host species. Isolates from humans and cattle differed in lactose fermentation, which is encoded on the accessory genome and represents an adaptation to the bovine mammary gland. Serotype IV-ST196 isolates were obtained from multiple dairy herds in both...

  17. Modified Displacement Transfer Functions for Deformed Shape Predictions of Slender Curved Structures with Varying Curvatives

    Ko, William L.; Fleischer, Van Tran

    2014-01-01

    To eliminate the need to use finite-element modeling for structure shape predictions, a new method was invented. This method is to use the Displacement Transfer Functions to transform the measured surface strains into deflections for mapping out overall structural deformed shapes. The Displacement Transfer Functions are expressed in terms of rectilinearly distributed surface strains, and contain no material properties. This report is to apply the patented method to the shape predictions of non-symmetrically loaded slender curved structures with different curvatures up to a full circle. Because the measured surface strains are not available, finite-element analysis had to be used to analytically generate the surface strains. Previously formulated straight-beam Displacement Transfer Functions were modified by introducing the curvature-effect correction terms. Through single-point or dual-point collocations with finite-elementgenerated deflection curves, functional forms of the curvature-effect correction terms were empirically established. The resulting modified Displacement Transfer Functions can then provide quite accurate shape predictions. Also, the uniform straight-beam Displacement Transfer Function was applied to the shape predictions of a section-cut of a generic capsule (GC) outer curved sandwich wall. The resulting GC shape predictions are quite accurate in partial regions where the radius of curvature does not change sharply.

  18. Advancing viral RNA structure prediction: measuring the thermodynamics of pyrimidine-rich internal loops.

    Phan, Andy; Mailey, Katherine; Saeki, Jessica; Gu, Xiaobo; Schroeder, Susan J

    2017-05-01

    Accurate thermodynamic parameters improve RNA structure predictions and thus accelerate understanding of RNA function and the identification of RNA drug binding sites. Many viral RNA structures, such as internal ribosome entry sites, have internal loops and bulges that are potential drug target sites. Current models used to predict internal loops are biased toward small, symmetric purine loops, and thus poorly predict asymmetric, pyrimidine-rich loops with >6 nucleotides (nt) that occur frequently in viral RNA. This article presents new thermodynamic data for 40 pyrimidine loops, many of which can form UU or protonated CC base pairs. Uracil and protonated cytosine base pairs stabilize asymmetric internal loops. Accurate prediction rules are presented that account for all thermodynamic measurements of RNA asymmetric internal loops. New loop initiation terms for loops with >6 nt are presented that do not follow previous assumptions that increasing asymmetry destabilizes loops. Since the last 2004 update, 126 new loops with asymmetry or sizes greater than 2 × 2 have been measured. These new measurements significantly deepen and diversify the thermodynamic database for RNA. These results will help better predict internal loops that are larger, pyrimidine-rich, and occur within viral structures such as internal ribosome entry sites. © 2017 Phan et al.; Published by Cold Spring Harbor Laboratory Press for the RNA Society.

  19. External validation of structure-biodegradation relationship (SBR) models for predicting the biodegradability of xenobiotics.

    Devillers, J; Pandard, P; Richard, B

    2013-01-01

    Biodegradation is an important mechanism for eliminating xenobiotics by biotransforming them into simple organic and inorganic products. Faced with the ever growing number of chemicals available on the market, structure-biodegradation relationship (SBR) and quantitative structure-biodegradation relationship (QSBR) models are increasingly used as surrogates of the biodegradation tests. Such models have great potential for a quick and cheap estimation of the biodegradation potential of chemicals. The Estimation Programs Interface (EPI) Suite™ includes different models for predicting the potential aerobic biodegradability of organic substances. They are based on different endpoints, methodologies and/or statistical approaches. Among them, Biowin 5 and 6 appeared the most robust, being derived from the largest biodegradation database with results obtained only from the Ministry of International Trade and Industry (MITI) test. The aim of this study was to assess the predictive performances of these two models from a set of 356 chemicals extracted from notification dossiers including compatible biodegradation data. Another set of molecules with no more than four carbon atoms and substituted by various heteroatoms and/or functional groups was also embodied in the validation exercise. Comparisons were made with the predictions obtained with START (Structural Alerts for Reactivity in Toxtree). Biowin 5 and Biowin 6 gave satisfactorily prediction results except for the prediction of readily degradable chemicals. A consensus model built with Biowin 1 allowed the diminution of this tendency.

  20. Structure-based methods to predict mutational resistance to diarylpyrimidine non-nucleoside reverse transcriptase inhibitors.

    Azeem, Syeda Maryam; Muwonge, Alecia N; Thakkar, Nehaben; Lam, Kristina W; Frey, Kathleen M

    2018-01-01

    Resistance to non-nucleoside reverse transcriptase inhibitors (NNRTIs) is a leading cause of HIV treatment failure. Often included in antiviral therapy, NNRTIs are chemically diverse compounds that bind an allosteric pocket of enzyme target reverse transcriptase (RT). Several new NNRTIs incorporate flexibility in order to compensate for lost interactions with amino acid conferring mutations in RT. Unfortunately, even successful inhibitors such as diarylpyrimidine (DAPY) inhibitor rilpivirine are affected by mutations in RT that confer resistance. In order to aid drug design efforts, it would be efficient and cost effective to pre-evaluate NNRTI compounds in development using a structure-based computational approach. As proof of concept, we applied a residue scan and molecular dynamics strategy using RT crystal structures to predict mutations that confer resistance to DAPYs rilpivirine, etravirine, and investigational microbicide dapivirine. Our predictive values, changes in affinity and stability, are correlative with fold-resistance data for several RT mutants. Consistent with previous studies, mutation K101P is predicted to confer high-level resistance to DAPYs. These findings were further validated using structural analysis, molecular dynamics, and an enzymatic reverse transcription assay. Our results confirm that changes in affinity and stability for mutant complexes are predictive parameters of resistance as validated by experimental and clinical data. In future work, we believe that this computational approach may be useful to predict resistance mutations for inhibitors in development. Published by Elsevier Inc.

  1. Predicting community structure in snakes on Eastern Nearctic islands using ecological neutral theory and phylogenetic methods.

    Burbrink, Frank T; McKelvy, Alexander D; Pyron, R Alexander; Myers, Edward A

    2015-11-22

    Predicting species presence and richness on islands is important for understanding the origins of communities and how likely it is that species will disperse and resist extinction. The equilibrium theory of island biogeography (ETIB) and, as a simple model of sampling abundances, the unified neutral theory of biodiversity (UNTB), predict that in situations where mainland to island migration is high, species-abundance relationships explain the presence of taxa on islands. Thus, more abundant mainland species should have a higher probability of occurring on adjacent islands. In contrast to UNTB, if certain groups have traits that permit them to disperse to islands better than other taxa, then phylogeny may be more predictive of which taxa will occur on islands. Taking surveys of 54 island snake communities in the Eastern Nearctic along with mainland communities that have abundance data for each species, we use phylogenetic assembly methods and UNTB estimates to predict island communities. Species richness is predicted by island area, whereas turnover from the mainland to island communities is random with respect to phylogeny. Community structure appears to be ecologically neutral and abundance on the mainland is the best predictor of presence on islands. With regard to young and proximate islands, where allopatric or cladogenetic speciation is not a factor, we find that simple neutral models following UNTB and ETIB predict the structure of island communities. © 2015 The Author(s).

  2. MemBrain: An Easy-to-Use Online Webserver for Transmembrane Protein Structure Prediction

    Yin, Xi; Yang, Jing; Xiao, Feng; Yang, Yang; Shen, Hong-Bin

    2018-03-01

    Membrane proteins are an important kind of proteins embedded in the membranes of cells and play crucial roles in living organisms, such as ion channels, transporters, receptors. Because it is difficult to determinate the membrane protein's structure by wet-lab experiments, accurate and fast amino acid sequence-based computational methods are highly desired. In this paper, we report an online prediction tool called MemBrain, whose input is the amino acid sequence. MemBrain consists of specialized modules for predicting transmembrane helices, residue-residue contacts and relative accessible surface area of α-helical membrane proteins. MemBrain achieves a prediction accuracy of 97.9% of A TMH, 87.1% of A P, 3.2 ± 3.0 of N-score, 3.1 ± 2.8 of C-score. MemBrain-Contact obtains 62%/64.1% prediction accuracy on training and independent dataset on top L/5 contact prediction, respectively. And MemBrain-Rasa achieves Pearson correlation coefficient of 0.733 and its mean absolute error of 13.593. These prediction results provide valuable hints for revealing the structure and function of membrane proteins. MemBrain web server is free for academic use and available at www.csbio.sjtu.edu.cn/bioinf/MemBrain/. [Figure not available: see fulltext.

  3. Multidrug resistance among different serotypes of clinical Salmonella isolates in Taiwan

    Lauderdale, T. L.; Aarestrup, Frank Møller; Chen, P. C.

    2006-01-01

    (41%) and was highly prevalent in Salmonella enterica serotype Typhimurium (72.7%, 176/242) the most common serotype. Additional resistance to trimethoprim was present in 155 (19.4% overall) of the ACSSuT R-type isolates from several serotypes. Reduced susceptibility to fluoroquinolone (FQ...... multiresistant to other antimicrobials. Studies are needed to determine the sources of different multidrug-resistant serotypes. Continued national surveillance is underway to monitor changes in resistance trends and to detect further emergence of resistant Salmonella serotypes in Taiwan. (c) 2006 Elsevier Inc...

  4. Ground-State Gas-Phase Structures of Inorganic Molecules Predicted by Density Functional Theory Methods

    Minenkov, Yury; Cavallo, Luigi

    2017-01-01

    -GGA approximations with B3PW91, APF, TPSSh, mPW1PW91, PBE0, mPW1PBE, B972, and B98 functionals, resulting in lowest errors. We recommend using these methods to predict accurate three-dimensional structures of inorganic molecules when intramolecular dispersion

  5. LANDIS PRO: a landscape model that predicts forest composition and structure changes at regional scales

    Wen J. Wang; Hong S. He; Jacob S. Fraser; Frank R. Thompson; Stephen R. Shifley; Martin A. Spetich

    2014-01-01

    LANDIS PRO predicts forest composition and structure changes incorporating species-, stand-, and landscape-scales processes at regional scales. Species-scale processes include tree growth, establishment, and mortality. Stand-scale processes contain density- and size-related resource competition that regulates self-thinning and seedling establishment. Landscapescale...

  6. Bioinformatical approaches to RNA structure prediction & Sequencing of an ancient human genome

    Lindgreen, Stinus

    Stinus Lindgreen has been working in two different fields during his Ph.D. The first part has been focused on computational approaches to predict the structure of non-coding RNA molecules at the base pairing level. This has resulted in the analysis of various measures of the base pairing potentia...

  7. Prediction of protein structural features by use of artificial neural networks

    Petersen, Bent

    . There is a huge over-representation of DNA sequences when comparing the amount of experimentally verified proteins with the amount of DNA sequences. The academic and industrial research community therefore has to rely on structure predictions instead of waiting for the time consuming experimentally determined...

  8. Aircraft interior noise prediction using a structural-acoustic analogy in NASTRAN modal synthesis

    Grosveld, Ferdinand W.; Sullivan, Brenda M.; Marulo, Francesco

    1988-01-01

    The noise induced inside a cylindrical fuselage model by shaker excitation is investigated theoretically and experimentally. The NASTRAN modal-synthesis program is used in the theoretical analysis, and the predictions are compared with experimental measurements in extensive graphs. Good general agreement is obtained, but the need for further refinements to account for acoustic-cavity damping and structural-acoustic interaction is indicated.

  9. RaptorX-Property: a web server for protein structure property prediction.

    Wang, Sheng; Li, Wei; Liu, Shiwang; Xu, Jinbo

    2016-07-08

    RaptorX Property (http://raptorx2.uchicago.edu/StructurePropertyPred/predict/) is a web server predicting structure property of a protein sequence without using any templates. It outperforms other servers, especially for proteins without close homologs in PDB or with very sparse sequence profile (i.e. carries little evolutionary information). This server employs a powerful in-house deep learning model DeepCNF (Deep Convolutional Neural Fields) to predict secondary structure (SS), solvent accessibility (ACC) and disorder regions (DISO). DeepCNF not only models complex sequence-structure relationship by a deep hierarchical architecture, but also interdependency between adjacent property labels. Our experimental results show that, tested on CASP10, CASP11 and the other benchmarks, this server can obtain ∼84% Q3 accuracy for 3-state SS, ∼72% Q8 accuracy for 8-state SS, ∼66% Q3 accuracy for 3-state solvent accessibility, and ∼0.89 area under the ROC curve (AUC) for disorder prediction. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

  10. Comparison of moments from the valence structure function with QCD predictions

    Groot, J.G.H. de; Hansl, T.; Holder, M.; Knobloch, J.; May, J.; Paar, H.P.; Palazzi, P.; Para, A.; Ranjard, F.; Schlatter, D.; Steinberger, J.; Suter, H.; Rueden, W. von; Wahl, H.; Whitaker, S.; Williams, E.G.H.; Eisele, F.; Kleinknecht, K.; Lierl, H.; Spahn, G.; Willutzki, H.J.; Dorth, W.; Dydak, F.; Geweniger, C.; Hepp, V.; Tittel, K.; Wotschack, J.; Bloch, P.; Devaux, B.; Loucatos, S.; Maillard, J.; Merlo, J.P.; Peyaud, B.; Rander, J.; Savoy-Navarro, A.; Turlay, R.; Navarria, F.L.

    1979-01-01

    Moments (both ordinary and Nachtmann) of the nucleon valence structure function measured in high Q 2 γFe scattering are presented, supplemented by data from deep inelastic eD scattering. These data seem to agree with QCD predictions for vector gluons. The QCD parameter Λ is found to be of the order 0.5 GeV. (Auth.)

  11. Application of structural reliability and risk assessment to life prediction and life extension decision making

    Meyer, T.A.; Balkey, K.R.; Bishop, B.A.

    1987-01-01

    There can be numerous uncertainties involved in performing component life assessments. In addition, sufficient data may be unavailable to make a useful life prediction. Structural Reliability and Risk Assessment (SRRA) is primarily an analytical methodology or tool that quantifies the impact of uncertainties on the structural life of plant components and can address the lack of data in component life prediction. As a prelude to discussing the technical aspects of SRRA, a brief review of general component life prediction methods is first made so as to better develop an understanding of the role of SRRA in such evaluations. SRRA is then presented as it is applied in component life evaluations with example applications being discussed for both nuclear and non-nuclear components

  12. Protein Tertiary Structure Prediction Based on Main Chain Angle Using a Hybrid Bees Colony Optimization Algorithm

    Mahmood, Zakaria N.; Mahmuddin, Massudi; Mahmood, Mohammed Nooraldeen

    Encoding proteins of amino acid sequence to predict classified into their respective families and subfamilies is important research area. However for a given protein, knowing the exact action whether hormonal, enzymatic, transmembranal or nuclear receptors does not depend solely on amino acid sequence but on the way the amino acid thread folds as well. This study provides a prototype system that able to predict a protein tertiary structure. Several methods are used to develop and evaluate the system to produce better accuracy in protein 3D structure prediction. The Bees Optimization algorithm which inspired from the honey bees food foraging method, is used in the searching phase. In this study, the experiment is conducted on short sequence proteins that have been used by the previous researches using well-known tools. The proposed approach shows a promising result.

  13. Prediction of elastic-plastic response of structural elements subjected to cyclic loading

    El Haddad, M.H.; Samaan, S.

    1985-01-01

    A simplified elastic-plastic analysis is developed to predict stress strain and force deformation response of structural metallic elements subjected to irregular cyclic loadings. In this analysis a simple elastic-plastic method for predicting the skeleton force deformation curve is developed. In this method, elastic and fully plastic solutions are first obtained for unknown quantities, such as deflection or local strains. Elastic and fully plastic contributions are then combined to obtain an elastic-plastic solution. The skeleton curve is doubled to establish the shape of the hysteresis loop. The complete force deformation response can therefore be simulated through reversal by reversal in accordance with hysteresis looping and material memory. Several examples of structural elements with various cross sections made from various materials and subjected to irregular cyclic loadings, are analysed. A close agreement is obtained between experimental results found in the literature and present predictions. (orig.)

  14. Structural maturation and brain activity predict future working memory capacity during childhood development.

    Ullman, Henrik; Almeida, Rita; Klingberg, Torkel

    2014-01-29

    Human working memory capacity develops during childhood and is a strong predictor of future academic performance, in particular, achievements in mathematics and reading. Predicting working memory development is important for the early identification of children at risk for poor cognitive and academic development. Here we show that structural and functional magnetic resonance imaging data explain variance in children's working memory capacity 2 years later, which was unique variance in addition to that predicted using cognitive tests. While current working memory capacity correlated with frontoparietal cortical activity, the future capacity could be inferred from structure and activity in basal ganglia and thalamus. This gives a novel insight into the neural mechanisms of childhood development and supports the idea that neuroimaging can have a unique role in predicting children's cognitive development.

  15. Less-structured time in children's daily lives predicts self-directed executive functioning.

    Barker, Jane E; Semenov, Andrei D; Michaelson, Laura; Provan, Lindsay S; Snyder, Hannah R; Munakata, Yuko

    2014-01-01

    Executive functions (EFs) in childhood predict important life outcomes. Thus, there is great interest in attempts to improve EFs early in life. Many interventions are led by trained adults, including structured training activities in the lab, and less-structured activities implemented in schools. Such programs have yielded gains in children's externally-driven executive functioning, where they are instructed on what goal-directed actions to carry out and when. However, it is less clear how children's experiences relate to their development of self-directed executive functioning, where they must determine on their own what goal-directed actions to carry out and when. We hypothesized that time spent in less-structured activities would give children opportunities to practice self-directed executive functioning, and lead to benefits. To investigate this possibility, we collected information from parents about their 6-7 year-old children's daily, annual, and typical schedules. We categorized children's activities as "structured" or "less-structured" based on categorization schemes from prior studies on child leisure time use. We assessed children's self-directed executive functioning using a well-established verbal fluency task, in which children generate members of a category and can decide on their own when to switch from one subcategory to another. The more time that children spent in less-structured activities, the better their self-directed executive functioning. The opposite was true of structured activities, which predicted poorer self-directed executive functioning. These relationships were robust (holding across increasingly strict classifications of structured and less-structured time) and specific (time use did not predict externally-driven executive functioning). We discuss implications, caveats, and ways in which potential interpretations can be distinguished in future work, to advance an understanding of this fundamental aspect of growing up.

  16. CNNH_PSS: protein 8-class secondary structure prediction by convolutional neural network with highway.

    Zhou, Jiyun; Wang, Hongpeng; Zhao, Zhishan; Xu, Ruifeng; Lu, Qin

    2018-05-08

    Protein secondary structure is the three dimensional form of local segments of proteins and its prediction is an important problem in protein tertiary structure prediction. Developing computational approaches for protein secondary structure prediction is becoming increasingly urgent. We present a novel deep learning based model, referred to as CNNH_PSS, by using multi-scale CNN with highway. In CNNH_PSS, any two neighbor convolutional layers have a highway to deliver information from current layer to the output of the next one to keep local contexts. As lower layers extract local context while higher layers extract long-range interdependencies, the highways between neighbor layers allow CNNH_PSS to have ability to extract both local contexts and long-range interdependencies. We evaluate CNNH_PSS on two commonly used datasets: CB6133 and CB513. CNNH_PSS outperforms the multi-scale CNN without highway by at least 0.010 Q8 accuracy and also performs better than CNF, DeepCNF and SSpro8, which cannot extract long-range interdependencies, by at least 0.020 Q8 accuracy, demonstrating that both local contexts and long-range interdependencies are indeed useful for prediction. Furthermore, CNNH_PSS also performs better than GSM and DCRNN which need extra complex model to extract long-range interdependencies. It demonstrates that CNNH_PSS not only cost less computer resource, but also achieves better predicting performance. CNNH_PSS have ability to extracts both local contexts and long-range interdependencies by combing multi-scale CNN and highway network. The evaluations on common datasets and comparisons with state-of-the-art methods indicate that CNNH_PSS is an useful and efficient tool for protein secondary structure prediction.

  17. Group B streptococcus serotype prevalence in reproductive-age women at a tertiary care military medical center relative to global serotype distribution

    Williams Julie

    2010-11-01

    Full Text Available Abstract Background Group B Streptococcus (GBS serotype (Ia, Ib, II-IX correlates with pathogen virulence and clinical prognosis. Epidemiological studies of seroprevalence are an important metric for determining the proportion of serotypes in a given population. The purpose of this study was to evaluate the prevalence of individual GBS serotypes at Madigan Healthcare System (Madigan, the largest military tertiary healthcare facility in the Pacific Northwestern United States, and to compare seroprevalences with international locations. Methods To determine serotype distribution at Madigan, we obtained GBS isolates from standard-of-care anogenital swabs from 207 women of indeterminate gravidity between ages 18-40 during a five month interval. Serotype was determined using a recently described molecular method of polymerase chain reaction by capsular polysaccharide synthesis (cps genes associated with pathogen virulence. Results Serotypes Ia, III, and V were the most prevalent (28%, 27%, and 17%, respectively. A systematic review of global GBS seroprevalence, meta-analysis, and statistical comparison revealed strikingly similar serodistibution at Madigan relative to civilian-sector populations in Canada and the United States. Serotype Ia was the only serotype consistently higher in North American populations relative to other geographic regions (p Conclusion This study establishes PCR-based serotyping as a viable strategy for GBS epidemiological surveillance. Our results suggest that GBS seroprevalence remains stable in North America over the past two decades.

  18. Capsid coding region diversity of re-emerging lineage C foot-and-mouth disease virus serotype Asia1 from India.

    Subramaniam, Saravanan; Mohapatra, Jajati K; Das, Biswajit; Sharma, Gaurav K; Biswal, Jitendra K; Mahajan, Sonalika; Misri, Jyoti; Dash, Bana B; Pattnaik, Bramhadev

    2015-07-01

    Foot-and-mouth disease virus (FMDV) serotype Asia1 was first reported in India in 1951, where three major genetic lineages (B, C and D) of this serotype have been described until now. In this study, the capsid protein coding region of serotype Asia1 viruses (n = 99) from India were analyzed, giving importance to the viruses circulating since 2007. All of the isolates (n = 50) recovered during 2007-2013 were found to group within the re-emerging cluster of lineage C (designated as sublineage C(R)). The evolutionary rate of sublineage C(R) was estimated to be slightly higher than that of the serotype as a whole, and the time of the most recent common ancestor for this cluster was estimated to be approximately 2001. In comparison to the older isolates of lineage C (1993-2001), the re-emerging viruses showed variation at eight amino acid positions, including substitutions at the antigenically critical residues VP279 and VP2131. However, no direct correlation was found between sequence variations and antigenic relationships. The number of codons under positive selection and the nature of the selection pressure varied widely among the structural proteins, implying a heterogeneous pattern of evolution in serotype Asia1. While episodic diversifying selection appears to play a major role in shaping the evolution of VP1 and VP3, selection pressure acting on codons of VP2 is largely pervasive. Further, episodic positive selection appears to be responsible for the early diversification of lineage C. Recombination events identified in the structural protein coding region indicates its probable role in adaptive evolution of serotype Asia1 viruses.

  19. Evidence of Hippocampal Structural Alterations in Gulf War Veterans With Predicted Exposure to the Khamisiyah Plume.

    Chao, Linda L; Raymond, Morgan R; Leo, Cynthia K; Abadjian, Linda R

    2017-10-01

    To replicate and expand our previous findings of smaller hippocampal volumes in Gulf War (GW) veterans with predicted exposure to the Khamisiyah plume. Total hippocampal and hippocampal subfield volumes were quantified from 3 Tesla magnetic resonance images in 113 GW veterans, 62 of whom had predicted exposure as per the Department of Defense exposure models. Veterans with predicted exposure had smaller total hippocampal and CA3/dentate gyrus volumes compared with unexposed veterans, even after accounting for potentially confounding genetic and clinical variables. Among veterans with predicted exposure, memory performance was positively correlated with hippocampal volume and negatively correlated with estimated exposure levels and self-reported memory difficulties. These results replicate and extend our previous finding that low-level exposure to chemical nerve agents from the Khamisiyah pit demolition has detrimental, lasting effects on brain structure and function.

  20. Vertical structure of predictability and information transport over the Northern Hemisphere

    Feng Ai-Xia; Wang Qi-Gang; Gong Zhi-Qiang; Feng Guo-Lin

    2014-01-01

    Based on nonlinear prediction and information theory, vertical heterogeneity of predictability and information loss rate in geopotential height field are obtained over the Northern Hemisphere. On a seasonal-to-interannual time scale, the predictability is low in the lower troposphere and high in the mid-upper troposphere. However, within mid-upper troposphere over the subtropics ocean area, there is a relatively poor predictability. These conclusions also fit the seasonal time scale. Moving to the interannual time scale, the predictability becomes high in the lower troposphere and low in the mid-upper troposphere, contrary to the former case. On the whole the interannual trend is more predictable than the seasonal trend. The average information loss rate is low over the mid-east Pacific, west of North America, Atlantic and Eurasia, and the atmosphere over other places has a relatively high information loss rate on all-time scales. Two channels are found steadily over the Pacific Ocean and Atlantic Ocean in subtropics. There are also unstable channels. The four-season influence on predictability and information communication are studied. The predictability is low, no matter which season data are removed and each season plays an important role in the existence of the channels, except for the winter. The predictability and teleconnections are paramount issues in atmospheric science, and the teleconnections may be established by communication channels. So, this work is interesting since it reveals the vertical structure of predictability distribution, channel locations, and the contributions of different time scales to them and their variations under different seasons. (geophysics, astronomy, and astrophysics)

  1. Managing uncertainty in metabolic network structure and improving predictions using EnsembleFBA.

    Matthew B Biggs

    2017-03-01

    Full Text Available Genome-scale metabolic network reconstructions (GENREs are repositories of knowledge about the metabolic processes that occur in an organism. GENREs have been used to discover and interpret metabolic functions, and to engineer novel network structures. A major barrier preventing more widespread use of GENREs, particularly to study non-model organisms, is the extensive time required to produce a high-quality GENRE. Many automated approaches have been developed which reduce this time requirement, but automatically-reconstructed draft GENREs still require curation before useful predictions can be made. We present a novel approach to the analysis of GENREs which improves the predictive capabilities of draft GENREs by representing many alternative network structures, all equally consistent with available data, and generating predictions from this ensemble. This ensemble approach is compatible with many reconstruction methods. We refer to this new approach as Ensemble Flux Balance Analysis (EnsembleFBA. We validate EnsembleFBA by predicting growth and gene essentiality in the model organism Pseudomonas aeruginosa UCBPP-PA14. We demonstrate how EnsembleFBA can be included in a systems biology workflow by predicting essential genes in six Streptococcus species and mapping the essential genes to small molecule ligands from DrugBank. We found that some metabolic subsystems contributed disproportionately to the set of predicted essential reactions in a way that was unique to each Streptococcus species, leading to species-specific outcomes from small molecule interactions. Through our analyses of P. aeruginosa and six Streptococci, we show that ensembles increase the quality of predictions without drastically increasing reconstruction time, thus making GENRE approaches more practical for applications which require predictions for many non-model organisms. All of our functions and accompanying example code are available in an open online repository.

  2. RNA secondary structure prediction by using discrete mathematics: an interdisciplinary research experience for undergraduate students.

    Ellington, Roni; Wachira, James; Nkwanta, Asamoah

    2010-01-01

    The focus of this Research Experience for Undergraduates (REU) project was on RNA secondary structure prediction by using a lattice walk approach. The lattice walk approach is a combinatorial and computational biology method used to enumerate possible secondary structures and predict RNA secondary structure from RNA sequences. The method uses discrete mathematical techniques and identifies specified base pairs as parameters. The goal of the REU was to introduce upper-level undergraduate students to the principles and challenges of interdisciplinary research in molecular biology and discrete mathematics. At the beginning of the project, students from the biology and mathematics departments of a mid-sized university received instruction on the role of secondary structure in the function of eukaryotic RNAs and RNA viruses, RNA related to combinatorics, and the National Center for Biotechnology Information resources. The student research projects focused on RNA secondary structure prediction on a regulatory region of the yellow fever virus RNA genome and on an untranslated region of an mRNA of a gene associated with the neurological disorder epilepsy. At the end of the project, the REU students gave poster and oral presentations, and they submitted written final project reports to the program director. The outcome of the REU was that the students gained transferable knowledge and skills in bioinformatics and an awareness of the applications of discrete mathematics to biological research problems.

  3. RNA Secondary Structure Prediction by Using Discrete Mathematics: An Interdisciplinary Research Experience for Undergraduate Students

    Ellington, Roni; Wachira, James

    2010-01-01

    The focus of this Research Experience for Undergraduates (REU) project was on RNA secondary structure prediction by using a lattice walk approach. The lattice walk approach is a combinatorial and computational biology method used to enumerate possible secondary structures and predict RNA secondary structure from RNA sequences. The method uses discrete mathematical techniques and identifies specified base pairs as parameters. The goal of the REU was to introduce upper-level undergraduate students to the principles and challenges of interdisciplinary research in molecular biology and discrete mathematics. At the beginning of the project, students from the biology and mathematics departments of a mid-sized university received instruction on the role of secondary structure in the function of eukaryotic RNAs and RNA viruses, RNA related to combinatorics, and the National Center for Biotechnology Information resources. The student research projects focused on RNA secondary structure prediction on a regulatory region of the yellow fever virus RNA genome and on an untranslated region of an mRNA of a gene associated with the neurological disorder epilepsy. At the end of the project, the REU students gave poster and oral presentations, and they submitted written final project reports to the program director. The outcome of the REU was that the students gained transferable knowledge and skills in bioinformatics and an awareness of the applications of discrete mathematics to biological research problems. PMID:20810968

  4. Advances in Rosetta structure prediction for difficult molecular-replacement problems

    DiMaio, Frank

    2013-01-01

    Modeling advances using Rosetta structure prediction to aid in solving difficult molecular-replacement problems are discussed. Recent work has shown the effectiveness of structure-prediction methods in solving difficult molecular-replacement problems. The Rosetta protein structure modeling suite can aid in the solution of difficult molecular-replacement problems using templates from 15 to 25% sequence identity; Rosetta refinement guided by noisy density has consistently led to solved structures where other methods fail. In this paper, an overview of the use of Rosetta for these difficult molecular-replacement problems is provided and new modeling developments that further improve model quality are described. Several variations to the method are introduced that significantly reduce the time needed to generate a model and the sampling required to improve the starting template. The improvements are benchmarked on a set of nine difficult cases and it is shown that this improved method obtains consistently better models in less running time. Finally, strategies for best using Rosetta to solve difficult molecular-replacement problems are presented and future directions for the role of structure-prediction methods in crystallography are discussed

  5. Electronic structure prediction via data-mining the empirical pseudopotential method

    Zenasni, H; Aourag, H [LEPM, URMER, Departement of Physics, University Abou Bakr Belkaid, Tlemcen 13000 (Algeria); Broderick, S R; Rajan, K [Department of Materials Science and Engineering, Iowa State University, Ames, Iowa 50011-2230 (United States)

    2010-01-15

    We introduce a new approach for accelerating the calculation of the electronic structure of new materials by utilizing the empirical pseudopotential method combined with data mining tools. Combining data mining with the empirical pseudopotential method allows us to convert an empirical approach to a predictive approach. Here we consider tetrahedrally bounded III-V Bi semiconductors, and through the prediction of form factors based on basic elemental properties we can model the band structure and charge density for these semi-conductors, for which limited results exist. This work represents a unique approach to modeling the electronic structure of a material which may be used to identify new promising semi-conductors and is one of the few efforts utilizing data mining at an electronic level. (Abstract Copyright [2010], Wiley Periodicals, Inc.)

  6. Bayesian Inference using Neural Net Likelihood Models for Protein Secondary Structure Prediction

    Seong-Gon Kim

    2011-06-01

    Full Text Available Several techniques such as Neural Networks, Genetic Algorithms, Decision Trees and other statistical or heuristic methods have been used to approach the complex non-linear task of predicting Alpha-helicies, Beta-sheets and Turns of a proteins secondary structure in the past. This project introduces a new machine learning method by using an offline trained Multilayered Perceptrons (MLP as the likelihood models within a Bayesian Inference framework to predict secondary structures proteins. Varying window sizes are used to extract neighboring amino acid information and passed back and forth between the Neural Net models and the Bayesian Inference process until there is a convergence of the posterior secondary structure probability.

  7. Tchebichef image moment approach to the prediction of protein secondary structures based on circular dichroism.

    Li, Sha Sha; Li, Bao Qiong; Liu, Jin Jin; Lu, Shao Hua; Zhai, Hong Lin

    2018-04-20

    Circular dichroism (CD) spectroscopy is a widely used technique for the evaluation of protein secondary structures that has a significant impact for the understanding of molecular biology. However, the quantitative analysis of protein secondary structures based on CD spectra is still a hard work due to the serious overlap of the spectra corresponding to different structural motifs. Here, Tchebichef image moment (TM) approach is introduced for the first time, which can effectively extract the chemical features in CD spectra for the quantitative analysis of protein secondary structures. The proposed approach was applied to analyze reference set. and the obtained results were evaluated by the strict statistical parameters such as correlation coefficient, cross-validation correlation coefficient and root mean squared error. Compared with several specialized prediction methods, TM approach provided satisfactory results, especially for turns and unordered structures. Our study indicates that TM approach can be regarded as a feasible tool for the analysis of the secondary structures of proteins based on CD spectra. An available TMs package is provided and can be used directly for secondary structures prediction. This article is protected by copyright. All rights reserved. © 2018 Wiley Periodicals, Inc.

  8. De novo prediction of human chromosome structures: Epigenetic marking patterns encode genome architecture.

    Di Pierro, Michele; Cheng, Ryan R; Lieberman Aiden, Erez; Wolynes, Peter G; Onuchic, José N

    2017-11-14

    Inside the cell nucleus, genomes fold into organized structures that are characteristic of cell type. Here, we show that this chromatin architecture can be predicted de novo using epigenetic data derived from chromatin immunoprecipitation-sequencing (ChIP-Seq). We exploit the idea that chromosomes encode a 1D sequence of chromatin structural types. Interactions between these chromatin types determine the 3D structural ensemble of chromosomes through a process similar to phase separation. First, a neural network is used to infer the relation between the epigenetic marks present at a locus, as assayed by ChIP-Seq, and the genomic compartment in which those loci reside, as measured by DNA-DNA proximity ligation (Hi-C). Next, types inferred from this neural network are used as an input to an energy landscape model for chromatin organization [Minimal Chromatin Model (MiChroM)] to generate an ensemble of 3D chromosome conformations at a resolution of 50 kilobases (kb). After training the model, dubbed Maximum Entropy Genomic Annotation from Biomarkers Associated to Structural Ensembles (MEGABASE), on odd-numbered chromosomes, we predict the sequences of chromatin types and the subsequent 3D conformational ensembles for the even chromosomes. We validate these structural ensembles by using ChIP-Seq tracks alone to predict Hi-C maps, as well as distances measured using 3D fluorescence in situ hybridization (FISH) experiments. Both sets of experiments support the hypothesis of phase separation being the driving process behind compartmentalization. These findings strongly suggest that epigenetic marking patterns encode sufficient information to determine the global architecture of chromosomes and that de novo structure prediction for whole genomes may be increasingly possible. Copyright © 2017 the Author(s). Published by PNAS.

  9. Probabilistic methods for condition assessment and life prediction of concrete structures in nuclear power plants

    Ellingwood, B.R.; Mori, Yasuhiro

    1993-01-01

    A probability-based methodology is being developed in support of the NRC Structural Aging Program to assist in evaluating the reliability of existing concrete structures in nuclear power plants under potential future operating loads and extreme evironmental and accidental events. The methodology includes models to predict structural deterioration due to environmental stressors, a database to support the use of these models, and methods for analyzing time-dependent reliability of concrete structural components subjected to stochastic loads. The methodology can be used to support a plant license extension application by providing evidence that safety-related concrete structures in their current (service) condition are able to withstand future extreme events with a level of reliability sufficient for public health and safety. (orig.)

  10. A Comparative Taxonomy of Parallel Algorithms for RNA Secondary Structure Prediction

    Al-Khatib, Ra’ed M.; Abdullah, Rosni; Rashid, Nur’Aini Abdul

    2010-01-01

    RNA molecules have been discovered playing crucial roles in numerous biological and medical procedures and processes. RNA structures determination have become a major problem in the biology context. Recently, computer scientists have empowered the biologists with RNA secondary structures that ease an understanding of the RNA functions and roles. Detecting RNA secondary structure is an NP-hard problem, especially in pseudoknotted RNA structures. The detection process is also time-consuming; as a result, an alternative approach such as using parallel architectures is a desirable option. The main goal in this paper is to do an intensive investigation of parallel methods used in the literature to solve the demanding issues, related to the RNA secondary structure prediction methods. Then, we introduce a new taxonomy for the parallel RNA folding methods. Based on this proposed taxonomy, a systematic and scientific comparison is performed among these existing methods. PMID:20458364

  11. Evaluation of multiple protein docking structures using correctly predicted pairwise subunits

    Esquivel-Rodríguez Juan

    2012-03-01

    Full Text Available Abstract Background Many functionally important proteins in a cell form complexes with multiple chains. Therefore, computational prediction of multiple protein complexes is an important task in bioinformatics. In the development of multiple protein docking methods, it is important to establish a metric for evaluating prediction results in a reasonable and practical fashion. However, since there are only few works done in developing methods for multiple protein docking, there is no study that investigates how accurate structural models of multiple protein complexes should be to allow scientists to gain biological insights. Methods We generated a series of predicted models (decoys of various accuracies by our multiple protein docking pipeline, Multi-LZerD, for three multi-chain complexes with 3, 4, and 6 chains. We analyzed the decoys in terms of the number of correctly predicted pair conformations in the decoys. Results and conclusion We found that pairs of chains with the correct mutual orientation exist even in the decoys with a large overall root mean square deviation (RMSD to the native. Therefore, in addition to a global structure similarity measure, such as the global RMSD, the quality of models for multiple chain complexes can be better evaluated by using the local measurement, the number of chain pairs with correct mutual orientation. We termed the fraction of correctly predicted pairs (RMSD at the interface of less than 4.0Å as fpair and propose to use it for evaluation of the accuracy of multiple protein docking.

  12. Polymer physics predicts the effects of structural variants on chromatin architecture.

    Bianco, Simona; Lupiáñez, Darío G; Chiariello, Andrea M; Annunziatella, Carlo; Kraft, Katerina; Schöpflin, Robert; Wittler, Lars; Andrey, Guillaume; Vingron, Martin; Pombo, Ana; Mundlos, Stefan; Nicodemi, Mario

    2018-05-01

    Structural variants (SVs) can result in changes in gene expression due to abnormal chromatin folding and cause disease. However, the prediction of such effects remains a challenge. Here we present a polymer-physics-based approach (PRISMR) to model 3D chromatin folding and to predict enhancer-promoter contacts. PRISMR predicts higher-order chromatin structure from genome-wide chromosome conformation capture (Hi-C) data. Using the EPHA4 locus as a model, the effects of pathogenic SVs are predicted in silico and compared to Hi-C data generated from mouse limb buds and patient-derived fibroblasts. PRISMR deconvolves the folding complexity of the EPHA4 locus and identifies SV-induced ectopic contacts and alterations of 3D genome organization in homozygous or heterozygous states. We show that SVs can reconfigure topologically associating domains, thereby producing extensive rewiring of regulatory interactions and causing disease by gene misexpression. PRISMR can be used to predict interactions in silico, thereby providing a tool for analyzing the disease-causing potential of SVs.

  13. Chemical structure-based predictive model for methanogenic anaerobic biodegradation potential.

    Meylan, William; Boethling, Robert; Aronson, Dallas; Howard, Philip; Tunkel, Jay

    2007-09-01

    Many screening-level models exist for predicting aerobic biodegradation potential from chemical structure, but anaerobic biodegradation generally has been ignored by modelers. We used a fragment contribution approach to develop a model for predicting biodegradation potential under methanogenic anaerobic conditions. The new model has 37 fragments (substructures) and classifies a substance as either fast or slow, relative to the potential to be biodegraded in the "serum bottle" anaerobic biodegradation screening test (Organization for Economic Cooperation and Development Guideline 311). The model correctly classified 90, 77, and 91% of the chemicals in the training set (n = 169) and two independent validation sets (n = 35 and 23), respectively. Accuracy of predictions of fast and slow degradation was equal for training-set chemicals, but fast-degradation predictions were less accurate than slow-degradation predictions for the validation sets. Analysis of the signs of the fragment coefficients for this and the other (aerobic) Biowin models suggests that in the context of simple group contribution models, the majority of positive and negative structural influences on ultimate degradation are the same for aerobic and methanogenic anaerobic biodegradation.

  14. Using quantitative structure-activity relationships (QSAR) to predict toxic endpoints for polycyclic aromatic hydrocarbons (PAH).

    Bruce, Erica D; Autenrieth, Robin L; Burghardt, Robert C; Donnelly, K C; McDonald, Thomas J

    2008-01-01

    Quantitative structure-activity relationships (QSAR) offer a reliable, cost-effective alternative to the time, money, and animal lives necessary to determine chemical toxicity by traditional methods. Additionally, humans are exposed to tens of thousands of chemicals in their lifetimes, necessitating the determination of chemical toxicity and screening for those posing the greatest risk to human health. This study developed models to predict toxic endpoints for three bioassays specific to several stages of carcinogenesis. The ethoxyresorufin O-deethylase assay (EROD), the Salmonella/microsome assay, and a gap junction intercellular communication (GJIC) assay were chosen for their ability to measure toxic endpoints specific to activation-, induction-, and promotion-related effects of polycyclic aromatic hydrocarbons (PAH). Shape-electronic, spatial, information content, and topological descriptors proved to be important descriptors in predicting the toxicity of PAH in these bioassays. Bioassay-based toxic equivalency factors (TEF(B)) were developed for several PAH using the quantitative structure-toxicity relationships (QSTR) developed. Predicting toxicity for a specific PAH compound, such as a bioassay-based potential potency (PP(B)) or a TEF(B), is possible by combining the predicted behavior from the QSTR models. These toxicity estimates may then be incorporated into a risk assessment for compounds that lack toxicity data. Accurate toxicity predictions are made by examining each type of endpoint important to the process of carcinogenicity, and a clearer understanding between composition and toxicity can be obtained.

  15. Structural MRI-Based Predictions in Patients with Treatment-Refractory Depression (TRD.

    Blair A Johnston

    Full Text Available The application of machine learning techniques to psychiatric neuroimaging offers the possibility to identify robust, reliable and objective disease biomarkers both within and between contemporary syndromal diagnoses that could guide routine clinical practice. The use of quantitative methods to identify psychiatric biomarkers is consequently important, particularly with a view to making predictions relevant to individual patients, rather than at a group-level. Here, we describe predictions of treatment-refractory depression (TRD diagnosis using structural T1-weighted brain scans obtained from twenty adult participants with TRD and 21 never depressed controls. We report 85% accuracy of individual subject diagnostic prediction. Using an automated feature selection method, the major brain regions supporting this significant classification were in the caudate, insula, habenula and periventricular grey matter. It was not, however, possible to predict the degree of 'treatment resistance' in individual patients, at least as quantified by the Massachusetts General Hospital (MGH-S clinical staging method; but the insula was again identified as a region of interest. Structural brain imaging data alone can be used to predict diagnostic status, but not MGH-S staging, with a high degree of accuracy in patients with TRD.

  16. QuaBingo: A Prediction System for Protein Quaternary Structure Attributes Using Block Composition

    Chi-Hua Tung

    2016-01-01

    Full Text Available Background. Quaternary structures of proteins are closely relevant to gene regulation, signal transduction, and many other biological functions of proteins. In the current study, a new method based on protein-conserved motif composition in block format for feature extraction is proposed, which is termed block composition. Results. The protein quaternary assembly states prediction system which combines blocks with functional domain composition, called QuaBingo, is constructed by three layers of classifiers that can categorize quaternary structural attributes of monomer, homooligomer, and heterooligomer. The building of the first layer classifier uses support vector machines (SVM based on blocks and functional domains of proteins, and the second layer SVM was utilized to process the outputs of the first layer. Finally, the result is determined by the Random Forest of the third layer. We compared the effectiveness of the combination of block composition, functional domain composition, and pseudoamino acid composition of the model. In the 11 kinds of functional protein families, QuaBingo is 23% of Matthews Correlation Coefficient (MCC higher than the existing prediction system. The results also revealed the biological characterization of the top five block compositions. Conclusions. QuaBingo provides better predictive ability for predicting the quaternary structural attributes of proteins.

  17. Sequential search leads to faster, more efficient fragment-based de novo protein structure prediction.

    de Oliveira, Saulo H P; Law, Eleanor C; Shi, Jiye; Deane, Charlotte M

    2018-04-01

    Most current de novo structure prediction methods randomly sample protein conformations and thus require large amounts of computational resource. Here, we consider a sequential sampling strategy, building on ideas from recent experimental work which shows that many proteins fold cotranslationally. We have investigated whether a pseudo-greedy search approach, which begins sequentially from one of the termini, can improve the performance and accuracy of de novo protein structure prediction. We observed that our sequential approach converges when fewer than 20 000 decoys have been produced, fewer than commonly expected. Using our software, SAINT2, we also compared the run time and quality of models produced in a sequential fashion against a standard, non-sequential approach. Sequential prediction produces an individual decoy 1.5-2.5 times faster than non-sequential prediction. When considering the quality of the best model, sequential prediction led to a better model being produced for 31 out of 41 soluble protein validation cases and for 18 out of 24 transmembrane protein cases. Correct models (TM-Score > 0.5) were produced for 29 of these cases by the sequential mode and for only 22 by the non-sequential mode. Our comparison reveals that a sequential search strategy can be used to drastically reduce computational time of de novo protein structure prediction and improve accuracy. Data are available for download from: http://opig.stats.ox.ac.uk/resources. SAINT2 is available for download from: https://github.com/sauloho/SAINT2. saulo.deoliveira@dtc.ox.ac.uk. Supplementary data are available at Bioinformatics online.

  18. From structure prediction to genomic screens for novel non-coding RNAs.

    Jan Gorodkin

    2011-08-01

    Full Text Available Non-coding RNAs (ncRNAs are receiving more and more attention not only as an abundant class of genes, but also as regulatory structural elements (some located in mRNAs. A key feature of RNA function is its structure. Computational methods were developed early for folding and prediction of RNA structure with the aim of assisting in functional analysis. With the discovery of more and more ncRNAs, it has become clear that a large fraction of these are highly structured. Interestingly, a large part of the structure is comprised of regular Watson-Crick and GU wobble base pairs. This and the increased amount of available genomes have made it possible to employ structure-based methods for genomic screens. The field has moved from folding prediction of single sequences to computational screens for ncRNAs in genomic sequence using the RNA structure as the main characteristic feature. Whereas early methods focused on energy-directed folding of single sequences, comparative analysis based on structure preserving changes of base pairs has been efficient in improving accuracy, and today this constitutes a key component in genomic screens. Here, we cover the basic principles of RNA folding and touch upon some of the concepts in current methods that have been applied in genomic screens for de novo RNA structures in searches for novel ncRNA genes and regulatory RNA structure on mRNAs. We discuss the strengths and weaknesses of the different strategies and how they can complement each other.

  19. Predicting acute aquatic toxicity of structurally diverse chemicals in fish using artificial intelligence approaches.

    Singh, Kunwar P; Gupta, Shikha; Rai, Premanjali

    2013-09-01

    The research aims to develop global modeling tools capable of categorizing structurally diverse chemicals in various toxicity classes according to the EEC and European Community directives, and to predict their acute toxicity in fathead minnow using set of selected molecular descriptors. Accordingly, artificial intelligence approach based classification and regression models, such as probabilistic neural networks (PNN), generalized regression neural networks (GRNN), multilayer perceptron neural network (MLPN), radial basis function neural network (RBFN), support vector machines (SVM), gene expression programming (GEP), and decision tree (DT) were constructed using the experimental toxicity data. Diversity and non-linearity in the chemicals' data were tested using the Tanimoto similarity index and Brock-Dechert-Scheinkman statistics. Predictive and generalization abilities of various models constructed here were compared using several statistical parameters. PNN and GRNN models performed relatively better than MLPN, RBFN, SVM, GEP, and DT. Both in two and four category classifications, PNN yielded a considerably high accuracy of classification in training (95.85 percent and 90.07 percent) and validation data (91.30 percent and 86.96 percent), respectively. GRNN rendered a high correlation between the measured and model predicted -log LC50 values both for the training (0.929) and validation (0.910) data and low prediction errors (RMSE) of 0.52 and 0.49 for two sets. Efficiency of the selected PNN and GRNN models in predicting acute toxicity of new chemicals was adequately validated using external datasets of different fish species (fathead minnow, bluegill, trout, and guppy). The PNN and GRNN models showed good predictive and generalization abilities and can be used as tools for predicting toxicities of structurally diverse chemical compounds. Copyright © 2013 Elsevier Inc. All rights reserved.

  20. Predicting deleterious nsSNPs: an analysis of sequence and structural attributes

    Saqi Mansoor AS

    2006-04-01

    Full Text Available Abstract Background There has been an explosion in the number of single nucleotide polymorphisms (SNPs within public databases. In this study we focused on non-synonymous protein coding single nucleotide polymorphisms (nsSNPs, some associated with disease and others which are thought to be neutral. We describe the distribution of both types of nsSNPs using structural and sequence based features and assess the relative value of these attributes as predictors of function using machine learning methods. We also address the common problem of balance within machine learning methods and show the effect of imbalance on nsSNP function prediction. We show that nsSNP function prediction can be significantly improved by 100% undersampling of the majority class. The learnt rules were then applied to make predictions of function on all nsSNPs within Ensembl. Results The measure of prediction success is greatly affected by the level of imbalance in the training dataset. We found the balanced dataset that included all attributes produced the best prediction. The performance as measured by the Matthews correlation coefficient (MCC varied between 0.49 and 0.25 depending on the imbalance. As previously observed, the degree of sequence conservation at the nsSNP position is the single most useful attribute. In addition to conservation, structural predictions made using a balanced dataset can be of value. Conclusion The predictions for all nsSNPs within Ensembl, based on a balanced dataset using all attributes, are available as a DAS annotation. Instructions for adding the track to Ensembl are at http://www.brightstudy.ac.uk/das_help.html

  1. Using Data Mining Approaches for Force Prediction of a Dynamically Loaded Flexible Structure

    Schlechtingen, Meik; Achiche, Sofiane; Lourenco Costa, Tiago

    2014-01-01

    -deterministic excitation forces with different excitation frequencies and amplitudes. Additionally, the influence of the sampling frequency and sensor location on the model performance is investigated. The results obtained in this paper show that most data mining approaches can be used, when a certain degree of inaccuracy...... of freedom and a force transducer for validation and training. The models are trained using data obtained from applying a random excitation force on the flexible structure. The performance of the developed models is evaluated by analyzing the prediction capabilities based on a normalized prediction error...

  2. Prediction of Individual Response to Electroconvulsive Therapy via Machine Learning on Structural Magnetic Resonance Imaging Data.

    Redlich, Ronny; Opel, Nils; Grotegerd, Dominik; Dohm, Katharina; Zaremba, Dario; Bürger, Christian; Münker, Sandra; Mühlmann, Lisa; Wahl, Patricia; Heindel, Walter; Arolt, Volker; Alferink, Judith; Zwanzger, Peter; Zavorotnyy, Maxim; Kugel, Harald; Dannlowski, Udo

    2016-06-01

    Electroconvulsive therapy (ECT) is one of the most effective treatments for severe depression. However, biomarkers that accurately predict a response to ECT remain unidentified. To investigate whether certain factors identified by structural magnetic resonance imaging (MRI) techniques are able to predict ECT response. In this nonrandomized prospective study, gray matter structure was assessed twice at approximately 6 weeks apart using 3-T MRI and voxel-based morphometry. Patients were recruited through the inpatient service of the Department of Psychiatry, University of Muenster, from March 11, 2010, to March 27, 2015. Two patient groups with acute major depressive disorder were included. One group received an ECT series in addition to antidepressants (n = 24); a comparison sample was treated solely with antidepressants (n = 23). Both groups were compared with a sample of healthy control participants (n = 21). Binary pattern classification was used to predict ECT response by structural MRI that was performed before treatment. In addition, univariate analysis was conducted to predict reduction of the Hamilton Depression Rating Scale score by pretreatment gray matter volumes and to investigate ECT-related structural changes. One participant in the ECT sample was excluded from the analysis, leaving 67 participants (27 men and 40 women; mean [SD] age, 43.7 [10.6] years). The binary pattern classification yielded a successful prediction of ECT response, with accuracy rates of 78.3% (18 of 23 patients in the ECT sample) and sensitivity rates of 100% (13 of 13 who responded to ECT). Furthermore, a support vector regression yielded a significant prediction of relative reduction in the Hamilton Depression Rating Scale score. The principal findings of the univariate model indicated a positive association between pretreatment subgenual cingulate volume and individual ECT response (Montreal Neurological Institute [MNI] coordinates x = 8, y = 21, z = -18

  3. The dual role of fragments in fragment-assembly methods for de novo protein structure prediction

    Handl, Julia; Knowles, Joshua; Vernon, Robert; Baker, David; Lovell, Simon C.

    2013-01-01

    In fragment-assembly techniques for protein structure prediction, models of protein structure are assembled from fragments of known protein structures. This process is typically guided by a knowledge-based energy function and uses a heuristic optimization method. The fragments play two important roles in this process: they define the set of structural parameters available, and they also assume the role of the main variation operators that are used by the optimiser. Previous analysis has typically focused on the first of these roles. In particular, the relationship between local amino acid sequence and local protein structure has been studied by a range of authors. The correlation between the two has been shown to vary with the window length considered, and the results of these analyses have informed directly the choice of fragment length in state-of-the-art prediction techniques. Here, we focus on the second role of fragments and aim to determine the effect of fragment length from an optimization perspective. We use theoretical analyses to reveal how the size and structure of the search space changes as a function of insertion length. Furthermore, empirical analyses are used to explore additional ways in which the size of the fragment insertion influences the search both in a simulation model and for the fragment-assembly technique, Rosetta. PMID:22095594

  4. Prediction of Protein Structural Class Based on Gapped-Dipeptides and a Recursive Feature Selection Approach

    Taigang Liu

    2015-12-01

    Full Text Available The prior knowledge of protein structural class may offer useful clues on understanding its functionality as well as its tertiary structure. Though various significant efforts have been made to find a fast and effective computational approach to address this problem, it is still a challenging topic in the field of bioinformatics. The position-specific score matrix (PSSM profile has been shown to provide a useful source of information for improving the prediction performance of protein structural class. However, this information has not been adequately explored. To this end, in this study, we present a feature extraction technique which is based on gapped-dipeptides composition computed directly from PSSM. Then, a careful feature selection technique is performed based on support vector machine-recursive feature elimination (SVM-RFE. These optimal features are selected to construct a final predictor. The results of jackknife tests on four working datasets show that our method obtains satisfactory prediction accuracies by extracting features solely based on PSSM and could serve as a very promising tool to predict protein structural class.

  5. Predicting Consensus Structures for RNA Alignments Via Pseudo-Energy Minimization

    Junilda Spirollari

    2009-01-01

    Full Text Available Thermodynamic processes with free energy parameters are often used in algorithms that solve the free energy minimization problem to predict secondary structures of single RNA sequences. While results from these algorithms are promising, an observation is that single sequence-based methods have moderate accuracy and more information is needed to improve on RNA secondary structure prediction, such as covariance scores obtained from multiple sequence alignments. We present in this paper a new approach to predicting the consensus secondary structure of a set of aligned RNA sequences via pseudo-energy minimization. Our tool, called RSpredict, takes into account sequence covariation and employs effective heuristics for accuracy improvement. RSpredict accepts, as input data, a multiple sequence alignment in FASTA or ClustalW format and outputs the consensus secondary structure of the input sequences in both the Vienna style Dot Bracket format and the Connectivity Table format. Our method was compared with some widely used tools including KNetFold, Pfold and RNAalifold. A comprehensive test on different datasets including Rfam sequence alignments and a multiple sequence alignment obtained from our study on the Drosophila X chromosome reveals that RSpredict is competitive with the existing tools on the tested datasets. RSpredict is freely available online as a web server and also as a jar file for download at http:// datalab.njit.edu/biology/RSpredict.

  6. A systematic review on popularity, application and characteristics of protein secondary structure prediction tools.

    Kashani-Amin, Elaheh; Tabatabaei-Malazy, Ozra; Sakhteman, Amirhossein; Larijani, Bagher; Ebrahim-Habibi, Azadeh

    2018-02-27

    Prediction of proteins' secondary structure is one of the major steps in the generation of homology models. These models provide structural information which is used to design suitable ligands for potential medicinal targets. However, selecting a proper tool between multiple secondary structure prediction (SSP) options is challenging. The current study is an insight onto currently favored methods and tools, within various contexts. A systematic review was performed for a comprehensive access to recent (2013-2016) studies which used or recommended protein SSP tools. Three databases, Web of Science, PubMed and Scopus were systematically searched and 99 out of 209 studies were finally found eligible to extract data. Four categories of applications for 59 retrieved SSP tools were: (I) prediction of structural features of a given sequence, (II) evaluation of a method, (III) providing input for a new SSP method and (IV) integrating a SSP tool as a component for a program. PSIPRED was found to be the most popular tool in all four categories. JPred and tools utilizing PHD (Profile network from HeiDelberg) method occupied second and third places of popularity in categories I and II. JPred was only found in the two first categories, while PHD was present in three fields. This study provides a comprehensive insight about the recent usage of SSP tools which could be helpful for selecting a proper tool's choice. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

  7. Genetic variation in the 3’ untranslated region of dengue virus serotype 3 strains isolated from mosquitoes and humans in Brazil

    de Castro Márcia Gonçalves

    2013-01-01

    Full Text Available Summary Background Dengue, a mosquito-borne viral infection caused by one of the four dengue virus (DENV serotypes (DENV-1 to 4, replicate alternately on the mosquito vector and human host and are responsible for infections throughout tropical and subtropical regions of the world. In Brazil, the disease has become a major public health problem and the introduction of DENV-3 in 2000 in Rio de Janeiro (RJ was associated with severe dengue epidemics. The potential emergence of strains associated with severe disease highlights the need for the surveillance of DENV in human host and vectors. Methods Aiming to contribute for DENV phylogenetic and vector-virus-human host studies, we sequenced the entire genome of one DENV-3 isolated from naturally infected Aedes aegypti from RJ in 2001 and characterized the 3’ UTR from strains isolated from mosquitoes and humans. Mosquitoes were pooled and submitted to virus isolation in Ae. albopictus C6/36 cells and the infecting serotype was identified by immunofluorescence using type-specific monoclonal antibody. Sequence analysis was performed using BioEdit software, the multiple alignments were performed using CLUSTAL W and the phylogenetic analysis by MEGA 5, using the Neighbor-joining method. Secondary structure prediction was performed by using the MFOLD program. Results Exclusive substitutions and a substitution leading to a stop codon on the NS5 gene were observed in the DENV-3 isolated from a naturally infected Ae. aegypti and fully sequenced. As an 8- nucleotides deletion was observed within the 11- nucleotides (nts insertion on the variable region (VR from the 3′UTR in this isolate, we further sequenced other DENV-3 from both mosquitoes and humans. The majority of DENV-3 from RJ analyzed were characterized by the 11-nts insertion in the VR of the 3′UTR, despite the observation of strains carrying the 8-nts deletion. The latter presented similar secondary structures, however not all strains

  8. Stress Prediction for Distributed Structural Health Monitoring Using Existing Measurements and Pattern Recognition.

    Lu, Wei; Teng, Jun; Zhou, Qiushi; Peng, Qiexin

    2018-02-01

    The stress in structural steel members is the most useful and directly measurable physical quantity to evaluate the structural safety in structural health monitoring, which is also an important index to evaluate the stress distribution and force condition of structures during structural construction and service phases. Thus, it is common to set stress as a measure in steel structural monitoring. Considering the economy and the importance of the structural members, there are only a limited number of sensors that can be placed, which means that it is impossible to obtain the stresses of all members directly using sensors. This study aims to develop a stress response prediction method for locations where there are insufficent sensors, using measurements from a limited number of sensors and pattern recognition. The detailed improved aspects are: (1) a distributed computing process is proposed, where the same pattern is recognized by several subsets of measurements; and (2) the pattern recognition using the subset of measurements is carried out by considering the optimal number of sensors and number of fusion patterns. The validity and feasibility of the proposed method are verified using two examples: the finite-element simulation of a single-layer shell-like steel structure, and the structural health monitoring of the space steel roof of Shenzhen Bay Stadium; for the latter, the anti-noise performance of this method is verified by the stress measurements from a real-world project.

  9. Predictive modeling of multicellular structure formation by using Cellular Particle Dynamics simulations

    McCune, Matthew; Shafiee, Ashkan; Forgacs, Gabor; Kosztin, Ioan

    2014-03-01

    Cellular Particle Dynamics (CPD) is an effective computational method for describing and predicting the time evolution of biomechanical relaxation processes of multicellular systems. A typical example is the fusion of spheroidal bioink particles during post bioprinting structure formation. In CPD cells are modeled as an ensemble of cellular particles (CPs) that interact via short-range contact interactions, characterized by an attractive (adhesive interaction) and a repulsive (excluded volume interaction) component. The time evolution of the spatial conformation of the multicellular system is determined by following the trajectories of all CPs through integration of their equations of motion. CPD was successfully applied to describe and predict the fusion of 3D tissue construct involving identical spherical aggregates. Here, we demonstrate that CPD can also predict tissue formation involving uneven spherical aggregates whose volumes decrease during the fusion process. Work supported by NSF [PHY-0957914]. Computer time provided by the University of Missouri Bioinformatics Consortium.

  10. An economic prediction of the finer resolution level wavelet coefficients in electronic structure calculations.

    Nagy, Szilvia; Pipek, János

    2015-12-21

    In wavelet based electronic structure calculations, introducing a new, finer resolution level is usually an expensive task, this is why often a two-level approximation is used with very fine starting resolution level. This process results in large matrices to calculate with and a large number of coefficients to be stored. In our previous work we have developed an adaptively refined solution scheme that determines the indices, where the refined basis functions are to be included, and later a method for predicting the next, finer resolution coefficients in a very economic way. In the present contribution, we would like to determine whether the method can be applied for predicting not only the first, but also the other, higher resolution level coefficients. Also the energy expectation values of the predicted wave functions are studied, as well as the scaling behaviour of the coefficients in the fine resolution limit.

  11. Lifetime prediction of structures submitted to thermal fatigue loadings; Prediction de duree de vie de structures sous chargement de fatigue thermique

    Amiable, S

    2006-01-15

    The aim of this work is to predict the lifetime of structures submitted to thermal fatigue loadings. This work lies within the studies undertaken by the CEA on the thermal fatigue problems from the french reactor of Civaux. In particular we study the SPLASH test: a specimen is heated continuously and cyclically cooled down by a water spray. This loading generates important temperature gradients in space and time and leads to the initiation and the propagation of a crack network. We propose a new thermo-mechanical model to simulate the SPLASH experiment and we propose a new fatigue criterion to predict the lifetime of the SPLASH specimen. We propose and compare several numerical models with various complexity to estimate the mechanical response of the SPLASH specimen. The practical implications of this work are the reevaluation of the hypothesis used in the French code RCC, which are used to simulate thermal shock and to interpret the results in terms of fatigue. This work leads to new perspectives on the mechanical interpretation of the fatigue criterion. (author)

  12. LiveBench-1: continuous benchmarking of protein structure prediction servers.

    Bujnicki, J M; Elofsson, A; Fischer, D; Rychlewski, L

    2001-02-01

    We present a novel, continuous approach aimed at the large-scale assessment of the performance of available fold-recognition servers. Six popular servers were investigated: PDB-Blast, FFAS, T98-lib, GenTHREADER, 3D-PSSM, and INBGU. The assessment was conducted using as prediction targets a large number of selected protein structures released from October 1999 to April 2000. A target was selected if its sequence showed no significant similarity to any of the proteins previously available in the structural database. Overall, the servers were able to produce structurally similar models for one-half of the targets, but significantly accurate sequence-structure alignments were produced for only one-third of the targets. We further classified the targets into two sets: easy and hard. We found that all servers were able to find the correct answer for the vast majority of the easy targets if a structurally similar fold was present in the server's fold libraries. However, among the hard targets--where standard methods such as PSI-BLAST fail--the most sensitive fold-recognition servers were able to produce similar models for only 40% of the cases, half of which had a significantly accurate sequence-structure alignment. Among the hard targets, the presence of updated libraries appeared to be less critical for the ranking. An "ideally combined consensus" prediction, where the results of all servers are considered, would increase the percentage of correct assignments by 50%. Each server had a number of cases with a correct assignment, where the assignments of all the other servers were wrong. This emphasizes the benefits of considering more than one server in difficult prediction tasks. The LiveBench program (http://BioInfo.PL/LiveBench) is being continued, and all interested developers are cordially invited to join.

  13. Automated glycan assembly of a S. pneumoniae serotype 3 CPS antigen

    Markus W. Weishaupt

    2016-07-01

    Full Text Available Vaccines against S. pneumoniae, one of the most prevalent bacterial infections causing severe disease, rely on isolated capsular polysaccharide (CPS that are conjugated to proteins. Such isolates contain a heterogeneous oligosaccharide mixture of different chain lengths and frame shifts. Access to defined synthetic S. pneumoniae CPS structures is desirable. Known syntheses of S. pneumoniae serotype 3 CPS rely on a time-consuming and low-yielding late-stage oxidation step, or use disaccharide building blocks which limits variability. Herein, we report the first iterative automated glycan assembly (AGA of a conjugation-ready S. pneumoniae serotype 3 CPS trisaccharide. This oligosaccharide was assembled using a novel glucuronic acid building block to circumvent the need for a late-stage oxidation. The introduction of a washing step with the activator prior to each glycosylation cycle greatly increased the yields by neutralizing any residual base from deprotection steps in the synthetic cycle. This process improvement is applicable to AGA of many other oligosaccharides.

  14. Machine-learning scoring functions to improve structure-based binding affinity prediction and virtual screening.

    Ain, Qurrat Ul; Aleksandrova, Antoniya; Roessler, Florian D; Ballester, Pedro J

    2015-01-01

    Docking tools to predict whether and how a small molecule binds to a target can be applied if a structural model of such target is available. The reliability of docking depends, however, on the accuracy of the adopted scoring function (SF). Despite intense research over the years, improving the accuracy of SFs for structure-based binding affinity prediction or virtual screening has proven to be a challenging task for any class of method. New SFs based on modern machine-learning regression models, which do not impose a predetermined functional form and thus are able to exploit effectively much larger amounts of experimental data, have recently been introduced. These machine-learning SFs have been shown to outperform a wide range of classical SFs at both binding affinity prediction and virtual screening. The emerging picture from these studies is that the classical approach of using linear regression with a small number of expert-selected structural features can be strongly improved by a machine-learning approach based on nonlinear regression allied with comprehensive data-driven feature selection. Furthermore, the performance of classical SFs does not grow with larger training datasets and hence this performance gap is expected to widen as more training data becomes available in the future. Other topics covered in this review include predicting the reliability of a SF on a particular target class, generating synthetic data to improve predictive performance and modeling guidelines for SF development. WIREs Comput Mol Sci 2015, 5:405-424. doi: 10.1002/wcms.1225 For further resources related to this article, please visit the WIREs website.

  15. Framingham coronary heart disease risk score can be predicted from structural brain images in elderly subjects.

    Jane Maryam Rondina

    2014-12-01

    Full Text Available Recent literature has presented evidence that cardiovascular risk factors (CVRF play an important role on cognitive performance in elderly individuals, both those who are asymptomatic and those who suffer from symptoms of neurodegenerative disorders. Findings from studies applying neuroimaging methods have increasingly reinforced such notion. Studies addressing the impact of CVRF on brain anatomy changes have gained increasing importance, as recent papers have reported gray matter loss predominantly in regions traditionally affected in Alzheimer’s disease (AD and vascular dementia in the presence of a high degree of cardiovascular risk. In the present paper, we explore the association between CVRF and brain changes using pattern recognition techniques applied to structural MRI and the Framingham score (a composite measure of cardiovascular risk largely used in epidemiological studies in a sample of healthy elderly individuals. We aim to answer the following questions: Is it possible to decode (i.e., to learn information regarding cardiovascular risk from structural brain images enabling individual predictions? Among clinical measures comprising the Framingham score, are there particular risk factors that stand as more predictable from patterns of brain changes? Our main findings are threefold: i we verified that structural changes in spatially distributed patterns in the brain enable statistically significant prediction of Framingham scores. This result is still significant when controlling for the presence of the APOE 4 allele (an important genetic risk factor for both AD and cardiovascular disease. ii When considering each risk factor singly, we found different levels of correlation between real and predicted factors; however, single factors were not significantly predictable from brain images when considering APOE4 allele presence as covariate. iii We found important gender differences, and the possible causes of that finding are discussed.

  16. In Silico Prediction of Chemical Toxicity for Drug Design Using Machine Learning Methods and Structural Alerts

    Yang, Hongbin; Sun, Lixia; Li, Weihua; Liu, Guixia; Tang, Yun

    2018-02-01

    For a drug, safety is always the most important issue, including a variety of toxicities and adverse drug effects, which should be evaluated in preclinical and clinical trial phases. This review article at first simply introduced the computational methods used in prediction of chemical toxicity for drug design, including machine learning methods and structural alerts. Machine learning methods have been widely applied in qualitative classification and quantitative regression studies, while structural alerts can be regarded as a complementary tool for lead optimization. The emphasis of this article was put on the recent progress of predictive models built for various toxicities. Available databases and web servers were also provided. Though the methods and models are very helpful for drug design, there are still some challenges and limitations to be improved for drug safety assessment in the future.

  17. In Silico Prediction of Chemical Toxicity for Drug Design Using Machine Learning Methods and Structural Alerts

    Hongbin Yang

    2018-02-01

    Full Text Available During drug development, safety is always the most important issue, including a variety of toxicities and adverse drug effects, which should be evaluated in preclinical and clinical trial phases. This review article at first simply introduced the computational methods used in prediction of chemical toxicity for drug design, including machine learning methods and structural alerts. Machine learning methods have been widely applied in qualitative classification and quantitative regression studies, while structural alerts can be regarded as a complementary tool for lead optimization. The emphasis of this article was put on the recent progress of predictive models built for various toxicities. Available databases and web servers were also provided. Though the methods and models are very helpful for drug design, there are still some challenges and limitations to be improved for drug safety assessment in the future.

  18. In Silico Prediction of Chemical Toxicity for Drug Design Using Machine Learning Methods and Structural Alerts.

    Yang, Hongbin; Sun, Lixia; Li, Weihua; Liu, Guixia; Tang, Yun

    2018-01-01

    During drug development, safety is always the most important issue, including a variety of toxicities and adverse drug effects, which should be evaluated in preclinical and clinical trial phases. This review article at first simply introduced the computational methods used in prediction of chemical toxicity for drug design, including machine learning methods and structural alerts. Machine learning methods have been widely applied in qualitative classification and quantitative regression studies, while structural alerts can be regarded as a complementary tool for lead optimization. The emphasis of this article was put on the recent progress of predictive models built for various toxicities. Available databases and web servers were also provided. Though the methods and models are very helpful for drug design, there are still some challenges and limitations to be improved for drug safety assessment in the future.

  19. Anisotropic Elastoplastic Damage Mechanics Method to Predict Fatigue Life of the Structure

    Hualiang Wan

    2016-01-01

    Full Text Available New damage mechanics method is proposed to predict the low-cycle fatigue life of metallic structures under multiaxial loading. The microstructure mechanical model is proposed to simulate anisotropic elastoplastic damage evolution. As the micromodel depends on few material parameters, the present method is very concise and suitable for engineering application. The material parameters in damage evolution equation are determined by fatigue experimental data of standard specimens. By employing further development on the ANSYS platform, the anisotropic elastoplastic damage mechanics-finite element method is developed. The fatigue crack propagation life of satellite structure is predicted using the present method and the computational results comply with the experimental data very well.

  20. The ordered network structure and its prediction for the big floods of the Changjiang River Basins

    Men, Ke-Pei; Zhao, Kai; Zhu, Shu-Dan [Nanjing Univ. of Information Science and Technology, Nanjing (China). College of Mathematics and Statistics

    2013-12-15

    According to the latest statistical data of hydrology, a total of 21 floods took place over the Changjiang (Yangtze) River Basins from 1827 to 2012 and showed an obvious commensurable orderliness. In the guidance of the information forecasting theory of Wen-Bo Weng, based on previous research results, combining ordered analysis with complex network technology, we focus on the summary of the ordered network structure of the Changjiang floods, supplement new information, further optimize networks, construct the 2D- and 3D-ordered network structure and make prediction research. Predictions show that the future big deluges will probably occur over the Changjiang River Basin around 2013-2014, 2020-2021, 2030, 2036, 2051, and 2058. (orig.)

  1. RDNAnalyzer: A tool for DNA secondary structure prediction and sequence analysis.

    Afzal, Muhammad; Shahid, Ahmad Ali; Shehzadi, Abida; Nadeem, Shahid; Husnain, Tayyab

    2012-01-01

    RDNAnalyzer is an innovative computer based tool designed for DNA secondary structure prediction and sequence analysis. It can randomly generate the DNA sequence or user can upload the sequences of their own interest in RAW format. It uses and extends the Nussinov dynamic programming algorithm and has various application for the sequence analysis. It predicts the DNA secondary structure and base pairings. It also provides the tools for routinely performed sequence analysis by the biological scientists such as DNA replication, reverse compliment generation, transcription, translation, sequence specific information as total number of nucleotide bases, ATGC base contents along with their respective percentages and sequence cleaner. RDNAnalyzer is a unique tool developed in Microsoft Visual Studio 2008 using Microsoft Visual C# and Windows Presentation Foundation and provides user friendly environment for sequence analysis. It is freely available. http://www.cemb.edu.pk/sw.html RDNAnalyzer - Random DNA Analyser, GUI - Graphical user interface, XAML - Extensible Application Markup Language.

  2. Phase change predictions for liquid fuel in contact with steel structure using the heat conduction equation

    Brear, D.J. [Power Reactor and Nuclear Fuel Development Corp., Oarai, Ibaraki (Japan). Oarai Engineering Center

    1998-01-01

    When liquid fuel makes contact with steel structure the liquid can freeze as a crust and the structure can melt at the surface. The melting and freezing processes that occur can influence the mode of fuel freezing and hence fuel relocation. Furthermore the temperature gradients established in the fuel and steel phases determine the rate at which heat is transferred from fuel to steel. In this memo the 1-D transient heat conduction equations are applied to the case of initially liquid UO{sub 2} brought into contact with solid steel using up-to-date materials properties. The solutions predict criteria for fuel crust formation and steel melting and provide a simple algorithm to determine the interface temperature when one or both of the materials is undergoing phase change. The predicted steel melting criterion is compared with available experimental results. (author)

  3. The ordered network structure and its prediction for the big floods of the Changjiang River Basins

    Men, Ke-Pei; Zhao, Kai; Zhu, Shu-Dan

    2013-01-01

    According to the latest statistical data of hydrology, a total of 21 floods took place over the Changjiang (Yangtze) River Basins from 1827 to 2012 and showed an obvious commensurable orderliness. In the guidance of the information forecasting theory of Wen-Bo Weng, based on previous research results, combining ordered analysis with complex network technology, we focus on the summary of the ordered network structure of the Changjiang floods, supplement new information, further optimize networks, construct the 2D- and 3D-ordered network structure and make prediction research. Predictions show that the future big deluges will probably occur over the Changjiang River Basin around 2013-2014, 2020-2021, 2030, 2036, 2051, and 2058. (orig.)

  4. Phase change predictions for liquid fuel in contact with steel structure using the heat conduction equation

    Brear, D.J.

    1998-01-01

    When liquid fuel makes contact with steel structure the liquid can freeze as a crust and the structure can melt at the surface. The melting and freezing processes that occur can influence the mode of fuel freezing and hence fuel relocation. Furthermore the temperature gradients established in the fuel and steel phases determine the rate at which heat is transferred from fuel to steel. In this memo the 1-D transient heat conduction equations are applied to the case of initially liquid UO 2 brought into contact with solid steel using up-to-date materials properties. The solutions predict criteria for fuel crust formation and steel melting and provide a simple algorithm to determine the interface temperature when one or both of the materials is undergoing phase change. The predicted steel melting criterion is compared with available experimental results. (author)

  5. Association of meningococcal serotypes with the course of disease: serotypes 2a and 2b in the Netherlands, 1959-1981

    Spanjaard, L.; Bol, P.; de Marie, S.; Zanen, H. C.

    1987-01-01

    Case histories of 692 patients with meningococcal disease due to serogroup B, C, or W (W-135) were reviewed to study the association of the serotypes 2a and 2b with the course of disease. The case-fatality rate in group B disease was significantly associated with serotype 2b (B:2b) strains (P =

  6. Ramsdellite-structured LiTiO 2: A new phase predicted from ab initio calculations

    Koudriachova, M. V.

    2008-06-01

    A new phase of highly lithiated titania with potential application as an anode in Li-rechargeable batteries is predicted on the basis of ab initio calculations. This phase has a composition LiTiO2 and may be accessed through electrochemical lithiation of ramsdellite-structured TiO2 at the lowest potential reported for titanium dioxide based materials. The potential remains constant over a wide range of Li-concentrations. The new phase is metastable with respect to a tetragonally distorted rock salt structure, which hitherto has been the only known polymorph of LiTiO2.

  7. SAAS: Short Amino Acid Sequence - A Promising Protein Secondary Structure Prediction Method of Single Sequence

    Zhou Yuan Wu

    2013-07-01

    Full Text Available In statistical methods of predicting protein secondary structure, many researchers focus on single amino acid frequencies in α-helices, β-sheets, and so on, or the impact near amino acids on an amino acid forming a secondary structure. But the paper considers a short sequence of amino acids (3, 4, 5 or 6 amino acids as integer, and statistics short sequence's probability forming secondary structure. Also, many researchers select low homologous sequences as statistical database. But this paper select whole PDB database. In this paper we propose a strategy to predict protein secondary structure using simple statistical method. Numerical computation shows that, short amino acids sequence as integer to statistics, which can easy see trend of short sequence forming secondary structure, and it will work well to select large statistical database (whole PDB database without considering homologous, and Q3 accuracy is ca. 74% using this paper proposed simple statistical method, but accuracy of others statistical methods is less than 70%.

  8. Prediction of protein–protein interactions: unifying evolution and structure at protein interfaces

    Tuncbag, Nurcan; Gursoy, Attila; Keskin, Ozlem

    2011-01-01

    The vast majority of the chores in the living cell involve protein–protein interactions. Providing details of protein interactions at the residue level and incorporating them into protein interaction networks are crucial toward the elucidation of a dynamic picture of cells. Despite the rapid increase in the number of structurally known protein complexes, we are still far away from a complete network. Given experimental limitations, computational modeling of protein interactions is a prerequisite to proceed on the way to complete structural networks. In this work, we focus on the question 'how do proteins interact?' rather than 'which proteins interact?' and we review structure-based protein–protein interaction prediction approaches. As a sample approach for modeling protein interactions, PRISM is detailed which combines structural similarity and evolutionary conservation in protein interfaces to infer structures of complexes in the protein interaction network. This will ultimately help us to understand the role of protein interfaces in predicting bound conformations

  9. SucStruct: Prediction of succinylated lysine residues by using structural properties of amino acids.

    López, Yosvany; Dehzangi, Abdollah; Lal, Sunil Pranit; Taherzadeh, Ghazaleh; Michaelson, Jacob; Sattar, Abdul; Tsunoda, Tatsuhiko; Sharma, Alok

    2017-06-15

    Post-Translational Modification (PTM) is a biological reaction which contributes to diversify the proteome. Despite many modifications with important roles in cellular activity, lysine succinylation has recently emerged as an important PTM mark. It alters the chemical structure of lysines, leading to remarkable changes in the structure and function of proteins. In contrast to the huge amount of proteins being sequenced in the post-genome era, the experimental detection of succinylated residues remains expensive, inefficient and time-consuming. Therefore, the development of computational tools for accurately predicting succinylated lysines is an urgent necessity. To date, several approaches have been proposed but their sensitivity has been reportedly poor. In this paper, we propose an approach that utilizes structural features of amino acids to improve lysine succinylation prediction. Succinylated and non-succinylated lysines were first retrieved from 670 proteins and characteristics such as accessible surface area, backbone torsion angles and local structure conformations were incorporated. We used the k-nearest neighbors cleaning treatment for dealing with class imbalance and designed a pruned decision tree for classification. Our predictor, referred to as SucStruct (Succinylation using Structural features), proved to significantly improve performance when compared to previous predictors, with sensitivity, accuracy and Mathew's correlation coefficient equal to 0.7334-0.7946, 0.7444-0.7608 and 0.4884-0.5240, respectively. Copyright © 2017 Elsevier Inc. All rights reserved.

  10. Protein secondary structure prediction using modular reciprocal bidirectional recurrent neural networks.

    Babaei, Sepideh; Geranmayeh, Amir; Seyyedsalehi, Seyyed Ali

    2010-12-01

    The supervised learning of recurrent neural networks well-suited for prediction of protein secondary structures from the underlying amino acids sequence is studied. Modular reciprocal recurrent neural networks (MRR-NN) are proposed to model the strong correlations between adjacent secondary structure elements. Besides, a multilayer bidirectional recurrent neural network (MBR-NN) is introduced to capture the long-range intramolecular interactions between amino acids in formation of the secondary structure. The final modular prediction system is devised based on the interactive integration of the MRR-NN and the MBR-NN structures to arbitrarily engage the neighboring effects of the secondary structure types concurrent with memorizing the sequential dependencies of amino acids along the protein chain. The advanced combined network augments the percentage accuracy (Q₃) to 79.36% and boosts the segment overlap (SOV) up to 70.09% when tested on the PSIPRED dataset in three-fold cross-validation. Copyright © 2010 Elsevier Ireland Ltd. All rights reserved.

  11. Computational tools for experimental determination and theoretical prediction of protein structure

    O`Donoghue, S.; Rost, B.

    1995-12-31

    This tutorial was one of eight tutorials selected to be presented at the Third International Conference on Intelligent Systems for Molecular Biology which was held in the United Kingdom from July 16 to 19, 1995. The authors intend to review the state of the art in the experimental determination of protein 3D structure (focus on nuclear magnetic resonance), and in the theoretical prediction of protein function and of protein structure in 1D, 2D and 3D from sequence. All the atomic resolution structures determined so far have been derived from either X-ray crystallography (the majority so far) or Nuclear Magnetic Resonance (NMR) Spectroscopy (becoming increasingly more important). The authors briefly describe the physical methods behind both of these techniques; the major computational methods involved will be covered in some detail. They highlight parallels and differences between the methods, and also the current limitations. Special emphasis will be given to techniques which have application to ab initio structure prediction. Large scale sequencing techniques increase the gap between the number of known proteins sequences and that of known protein structures. They describe the scope and principles of methods that contribute successfully to closing that gap. Emphasis will be given on the specification of adequate testing procedures to validate such methods.

  12. Streptococcus pneumoniae serotype-2 childhood meningitis in Bangladesh: a newly recognized pneumococcal infection threat.

    Samir K Saha

    Full Text Available BACKGROUND: Streptococcus pneumoniae is a leading cause of meningitis in countries where pneumococcal conjugate vaccines (PCV targeting commonly occurring serotypes are not routinely used. However, effectiveness of PCV would be jeopardized by emergence of invasive pneumococcal diseases (IPD caused by serotypes which are not included in PCV. Systematic hospital based surveillance in Bangladesh was established and progressively improved to determine the pathogens causing childhood sepsis and meningitis. This also provided the foundation for determining the spectrum of serotypes causing IPD. This article reports an unprecedented upsurge of serotype 2, an uncommon pneumococcal serotype, without any known intervention. METHODS AND FINDINGS: Cases with suspected IPD had blood or cerebrospinal fluid (CSF collected from the beginning of 2001 till 2009. Pneumococcal serotypes were determined by capsular swelling of isolates or PCR of culture-negative CSF specimens. Multicenter national surveillance, expanded from 2004, identified 45,437 patients with suspected bacteremia who were blood cultured and 10,618 suspected meningitis cases who had a lumber puncture. Pneumococcus accounted for 230 culture positive cases of meningitis in children <5 years. Serotype-2 was the leading cause of pneumococcal meningitis, accounting for 20.4% (45/221; 95% CI 15%-26% of cases. Ninety eight percent (45/46 of these serotype-2 strains were isolated from meningitis cases, yielding the highest serotype-specific odds ratio for meningitis (29.6; 95% CI 3.4-256.3. The serotype-2 strains had three closely related pulsed field gel electrophoresis types. CONCLUSIONS: S. pneumoniae serotype-2 was found to possess an unusually high potential for causing meningitis and was the leading serotype-specific cause of childhood meningitis in Bangladesh over the past decade. Persisting disease occurrence or progressive spread would represent a major potential infection threat since serotype-2

  13. Utilizing knowledge base of amino acids structural neighborhoods to predict protein-protein interaction sites.

    Jelínek, Jan; Škoda, Petr; Hoksza, David

    2017-12-06

    Protein-protein interactions (PPI) play a key role in an investigation of various biochemical processes, and their identification is thus of great importance. Although computational prediction of which amino acids take part in a PPI has been an active field of research for some time, the quality of in-silico methods is still far from perfect. We have developed a novel prediction method called INSPiRE which benefits from a knowledge base built from data available in Protein Data Bank. All proteins involved in PPIs were converted into labeled graphs with nodes corresponding to amino acids and edges to pairs of neighboring amino acids. A structural neighborhood of each node was then encoded into a bit string and stored in the knowledge base. When predicting PPIs, INSPiRE labels amino acids of unknown proteins as interface or non-interface based on how often their structural neighborhood appears as interface or non-interface in the knowledge base. We evaluated INSPiRE's behavior with respect to different types and sizes of the structural neighborhood. Furthermore, we examined the suitability of several different features for labeling the nodes. Our evaluations showed that INSPiRE clearly outperforms existing methods with respect to Matthews correlation coefficient. In this paper we introduce a new knowledge-based method for identification of protein-protein interaction sites called INSPiRE. Its knowledge base utilizes structural patterns of known interaction sites in the Protein Data Bank which are then used for PPI prediction. Extensive experiments on several well-established datasets show that INSPiRE significantly surpasses existing PPI approaches.

  14. Prediction of municipal solid waste generation using artificial neural network approach enhanced by structural break analysis.

    Adamović, Vladimir M; Antanasijević, Davor Z; Ristić, Mirjana Đ; Perić-Grujić, Aleksandra A; Pocajt, Viktor V

    2017-01-01

    This paper presents the development of a general regression neural network (GRNN) model for the prediction of annual municipal solid waste (MSW) generation at the national level for 44 countries of different size, population and economic development level. Proper modelling of MSW generation is essential for the planning of MSW management system as well as for the simulation of various environmental impact scenarios. The main objective of this work was to examine the potential influence of economy crisis (global or local) on the forecast of MSW generation obtained by the GRNN model. The existence of the so-called structural breaks that occur because of the economic crisis in the studied period (2000-2012) for each country was determined and confirmed using the Chow test and Quandt-Andrews test. Two GRNN models, one which did not take into account the influence of the economic crisis (GRNN) and another one which did (SB-GRNN), were developed. The novelty of the applied method is that it uses broadly available social, economic and demographic indicators and indicators of sustainability, together with GRNN and structural break testing for the prediction of MSW generation at the national level. The obtained results demonstrate that the SB-GRNN model provide more accurate predictions than the model which neglected structural breaks, with a mean absolute percentage error (MAPE) of 4.0 % compared to 6.7 % generated by the GRNN model. The proposed model enhanced with structural breaks can be a viable alternative for a more accurate prediction of MSW generation at the national level, especially for developing countries for which a lack of MSW data is notable.

  15. Electrostatics, structure prediction, and the energy landscapes for protein folding and binding.

    Tsai, Min-Yeh; Zheng, Weihua; Balamurugan, D; Schafer, Nicholas P; Kim, Bobby L; Cheung, Margaret S; Wolynes, Peter G

    2016-01-01

    While being long in range and therefore weakly specific, electrostatic interactions are able to modulate the stability and folding landscapes of some proteins. The relevance of electrostatic forces for steering the docking of proteins to each other is widely acknowledged, however, the role of electrostatics in establishing specifically funneled landscapes and their relevance for protein structure prediction are still not clear. By introducing Debye-Hückel potentials that mimic long-range electrostatic forces into the Associative memory, Water mediated, Structure, and Energy Model (AWSEM), a transferable protein model capable of predicting tertiary structures, we assess the effects of electrostatics on the landscapes of thirteen monomeric proteins and four dimers. For the monomers, we find that adding electrostatic interactions does not improve structure prediction. Simulations of ribosomal protein S6 show, however, that folding stability depends monotonically on electrostatic strength. The trend in predicted melting temperatures of the S6 variants agrees with experimental observations. Electrostatic effects can play a range of roles in binding. The binding of the protein complex KIX-pKID is largely assisted by electrostatic interactions, which provide direct charge-charge stabilization of the native state and contribute to the funneling of the binding landscape. In contrast, for several other proteins, including the DNA-binding protein FIS, electrostatics causes frustration in the DNA-binding region, which favors its binding with DNA but not with its protein partner. This study highlights the importance of long-range electrostatics in functional responses to problems where proteins interact with their charged partners, such as DNA, RNA, as well as membranes. © 2015 The Protein Society.

  16. Inorganic Nitrogen Application Affects Both Taxonomical and Predicted Functional Structure of Wheat Rhizosphere Bacterial Communities

    Vanessa N. Kavamura

    2018-05-01

    Full Text Available The effects of fertilizer regime on bulk soil microbial communities have been well studied, but this is not the case for the rhizosphere microbiome. The aim of this work was to assess the impact of fertilization regime on wheat rhizosphere microbiome assembly and 16S rRNA gene-predicted functions with soil from the long term Broadbalk experiment at Rothamsted Research. Soil from four N fertilization regimes (organic N, zero N, medium inorganic N and high inorganic N was sown with seeds of Triticum aestivum cv. Cadenza. 16S rRNA gene amplicon sequencing was performed with the Illumina platform on bulk soil and rhizosphere samples of 4-week-old and flowering plants (10 weeks. Phylogenetic and 16S rRNA gene-predicted functional analyses were performed. Fertilization regime affected the structure and composition of wheat rhizosphere bacterial communities. Acidobacteria and Planctomycetes were significantly depleted in treatments receiving inorganic N, whereas the addition of high levels of inorganic N enriched members of the phylum Bacteroidetes, especially after 10 weeks. Bacterial richness and diversity decreased with inorganic nitrogen inputs and was highest after organic treatment (FYM. In general, high levels of inorganic nitrogen fertilizers negatively affect bacterial richness and diversity, leading to a less stable bacterial community structure over time, whereas, more stable bacterial communities are provided by organic amendments. 16S rRNA gene-predicted functional structure was more affected by growth stage than by fertilizer treatment, although, some functions related to energy metabolism and metabolism of terpenoids and polyketides were enriched in samples not receiving any inorganic N, whereas inorganic N addition enriched predicted functions related to metabolism of other amino acids and carbohydrates. Understanding the impact of different fertilizers on the structure and dynamics of the rhizosphere microbiome is an important step

  17. Comparing methodologies for structural identification and fatigue life prediction of a highway bridge

    Pai, Sai Ganesh Sarvotham; Nussbaumer, Alain; Smith, Ian F. C.

    2018-01-01

    Accurate measurement-data interpretation leads to increased understanding of structural behavior and enhanced asset-management decision making. In this paper, four data-interpretation methodologies, residual minimization, traditional Bayesian model updating, modified Bayesian model updating (with an L∞-norm-based Gaussian likelihood function), and error-domain model falsification (EDMF), a method that rejects models that have unlikely differences between predictions and measurements, are comp...

  18. Comparing Structural Identification Methodologies for Fatigue Life Prediction of a Highway Bridge

    Pai, Sai G.S.; Nussbaumer, Alain; Smith, Ian F.C.

    2018-01-01

    Accurate measurement-data interpretation leads to increased understanding of structural behavior and enhanced asset-management decision making. In this paper, four data-interpretation methodologies, residual minimization, traditional Bayesian model updating, modified Bayesian model updating (with an L∞-norm-based Gaussian likelihood function), and error-domain model falsification (EDMF), a method that rejects models that have unlikely differences between predictions and measurements, are comp...

  19. In silico prediction of monovalent and chimeric tetravalent vaccines for prevention and treatment of dengue fever.

    Vijayakumar, Subramaniyan; Ramesh, Venkatachalam; Prabhu, Srinivasan; Manogar, Palani

    2017-11-01

    Reverse vaccinology method was used to predict the monovalent peptide vaccine candidate to produce antibodies for therapeutic purpose and to predict tetravalent vaccine candidate to act as a common vaccine to cover all the fever dengue virus serotypes. Envelope (E)-proteins of DENV-1-4 serotypes were used for vaccine prediction using NCBI, Uniprot/Swissprot, Swiss-prot viewer, VaxiJen V2.0, TMHMM, BCPREDS, Propred-1, Propred and MHC Pred,. E-proteins of DENV-1-4 serotypes were identified as antigen from which T cell epitopes, through B cell epitopes, were predicted to act as peptide vaccine candidates. Each selected T cell epitope of E-protein was confirmed to act as vaccine and to induce complementary antibody against particular serotype of dengue virus. Chimeric tetravalent vaccine was formed by the conjugation of four vaccines, each from four dengue serotypes to act as a common vaccine candidate for all the four dengue serotypes. It can be justifiably concluded that the monovalent 9-mer T cell epitope for each DENV serotypes can be used to produce specific antibody agaomst dengue virus and a chimeric common tetravalent vaccine candidate to yield a comparative vaccine to cover any of the four dengue virus serotype. This vaccine is expected to act as highly immunogenic against preventing dengue fever.

  20. The prevalence of dengue virus serotypes in asymptomatic blood donors reveals the emergence of serotype 4 in Saudi Arabia.

    Ashshi, Ahmed Mohamed

    2017-06-09

    Transmission of dengue virus (DENV) through blood transfusion has been documented and hence screening for DENV during blood donation has been recently recommended by the American Association of Blood Banks and Centres of Disease Control and Prevention. DENV is endemic in the Western province of the Kingdom of Saudi Arabia (KSA) and serotypes 1, 2 and 3, but not 4, have been detected. However, little is known regarding the rates of DENV during blood donation in the kingdom. The aim of this study was therefore to measure the prevalence of dengue virus and its serotypes in eligible Saudi blood donors in the endemic Western region of KSA. This was a cross-sectional study and serum samples were collected from 910 eligible Saudi male blood donors. DENV IgM and IgG antibodies were measured serologically by ELISA while viral serotypes were detected by a single step IVD CE certified multiplex RT-PCR kit. The overall prevalence was 39 and 5.5% for IgG+ and IgM+, respectively. There were 12 (1.3%) with exclusively IgM+, 317 (34.8%) exclusively IgG+ and 38 (4.2%) with dual IgM+/IgG+ donors. The overall prevalence was 3.2% (n = 29) and 2.3% (n = 21) for primary and secondary infections. PCR was positive in 5.5% (n = 50) and, DENV-2 (n = 24; 48%) was the most frequent serotype and was significantly higher than DENV-1 (20%; P = 0.02) and DENV-3 (2%; P = 0.1 × 10 -5 ) but not DENV-4 (30%; P = 0.2). There was no significant difference between both DENV-4 and DENV-1 (P = 0.4). The combination of the PCR and serology findings showed that 22 (2.4%) and 28 (3.1%) donors had primary and secondary viremic infections, respectively. The detected rates of DENV by PCR suggest a potential high risk of viral transmission by blood transfusion. To the best of our knowledge, this study is the first to report the detection of DENV-4 serotype in Saudi Arabia. More studies are required to measure the precise prevalence of DENV serotypes and their potential

  1. Prediction of protein structural classes by recurrence quantification analysis based on chaos game representation.

    Yang, Jian-Yi; Peng, Zhen-Ling; Yu, Zu-Guo; Zhang, Rui-Jie; Anh, Vo; Wang, Desheng

    2009-04-21

    In this paper, we intend to predict protein structural classes (alpha, beta, alpha+beta, or alpha/beta) for low-homology data sets. Two data sets were used widely, 1189 (containing 1092 proteins) and 25PDB (containing 1673 proteins) with sequence homology being 40% and 25%, respectively. We propose to decompose the chaos game representation of proteins into two kinds of time series. Then, a novel and powerful nonlinear analysis technique, recurrence quantification analysis (RQA), is applied to analyze these time series. For a given protein sequence, a total of 16 characteristic parameters can be calculated with RQA, which are treated as feature representation of protein sequences. Based on such feature representation, the structural class for each protein is predicted with Fisher's linear discriminant algorithm. The jackknife test is used to test and compare our method with other existing methods. The overall accuracies with step-by-step procedure are 65.8% and 64.2% for 1189 and 25PDB data sets, respectively. With one-against-others procedure used widely, we compare our method with five other existing methods. Especially, the overall accuracies of our method are 6.3% and 4.1% higher for the two data sets, respectively. Furthermore, only 16 parameters are used in our method, which is less than that used by other methods. This suggests that the current method may play a complementary role to the existing methods and is promising to perform the prediction of protein structural classes.

  2. 3dRPC: a web server for 3D RNA-protein structure prediction.

    Huang, Yangyu; Li, Haotian; Xiao, Yi

    2018-04-01

    RNA-protein interactions occur in many biological processes. To understand the mechanism of these interactions one needs to know three-dimensional (3D) structures of RNA-protein complexes. 3dRPC is an algorithm for prediction of 3D RNA-protein complex structures and consists of a docking algorithm RPDOCK and a scoring function 3dRPC-Score. RPDOCK is used to sample possible complex conformations of an RNA and a protein by calculating the geometric and electrostatic complementarities and stacking interactions at the RNA-protein interface according to the features of atom packing of the interface. 3dRPC-Score is a knowledge-based potential that uses the conformations of nucleotide-amino-acid pairs as statistical variables and that is used to choose the near-native complex-conformations obtained from the docking method above. Recently, we built a web server for 3dRPC. The users can easily use 3dRPC without installing it locally. RNA and protein structures in PDB (Protein Data Bank) format are the only needed input files. It can also incorporate the information of interface residues or residue-pairs obtained from experiments or theoretical predictions to improve the prediction. The address of 3dRPC web server is http://biophy.hust.edu.cn/3dRPC. yxiao@hust.edu.cn.

  3. Comparison of Comet Enflow and VA One Acoustic-to-Structure Power Flow Predictions

    Grosveld, Ferdinand W.; Schiller, Noah H.; Cabell, Randolph H.

    2010-01-01

    Comet Enflow is a commercially available, high frequency vibroacoustic analysis software based on the Energy Finite Element Analysis (EFEA). In this method the same finite element mesh used for structural and acoustic analysis can be employed for the high frequency solutions. Comet Enflow is being validated for a floor-equipped composite cylinder by comparing the EFEA vibroacoustic response predictions with Statistical Energy Analysis (SEA) results from the commercial software program VA One from ESI Group. Early in this program a number of discrepancies became apparent in the Enflow predicted response for the power flow from an acoustic space to a structural subsystem. The power flow anomalies were studied for a simple cubic, a rectangular and a cylindrical structural model connected to an acoustic cavity. The current investigation focuses on three specific discrepancies between the Comet Enflow and the VA One predictions: the Enflow power transmission coefficient relative to the VA One coupling loss factor; the importance of the accuracy of the acoustic modal density formulation used within Enflow; and the recommended use of fast solvers in Comet Enflow. The frequency region of interest for this study covers the one-third octave bands with center frequencies from 16 Hz to 4000 Hz.

  4. Computational Prediction of Atomic Structures of Helical Membrane Proteins Aided by EM Maps

    Kovacs, Julio A.; Yeager, Mark; Abagyan, Ruben

    2007-01-01

    Integral membrane proteins pose a major challenge for protein-structure prediction because only ≈100 high-resolution structures are available currently, thereby impeding the development of rules or empirical potentials to predict the packing of transmembrane α-helices. However, when an intermediate-resolution electron microscopy (EM) map is available, it can be used to provide restraints which, in combination with a suitable computational protocol, make structure prediction feasible. In this work we present such a protocol, which proceeds in three stages: 1), generation of an ensemble of α-helices by flexible fitting into each of the density rods in the low-resolution EM map, spanning a range of rotational angles around the main helical axes and translational shifts along the density rods; 2), fast optimization of side chains and scoring of the resulting conformations; and 3), refinement of the lowest-scoring conformations with internal coordinate mechanics, by optimizing the van der Waals, electrostatics, hydrogen bonding, torsional, and solvation energy contributions. In addition, our method implements a penalty term through a so-called tethering map, derived from the EM map, which restrains the positions of the α-helices. The protocol was validated on three test cases: GpA, KcsA, and MscL. PMID:17496035

  5. The Widespread Multidrug-Resistant Serotype O12 Pseudomonas aeruginosa Clone Emerged through Concomitant Horizontal Transfer of Serotype Antigen and Antibiotic Resistance Gene Clusters

    Thrane, Sandra Wingaard; Taylor, Véronique L.; Freschi, Luca

    2015-01-01

    . aeruginosa O12 OSA gene cluster, an antibiotic resistance determinant (gyrAC248T), and other genes that have been transferred between P. aeruginosa strains with distinct core genome architectures. We showed that these genes were likely acquired from an O12 serotype strain that is closely related to P...... in clinical settings and outbreaks. These serotype O12 isolates exhibit high levels of resistance to various classes of antibiotics. Here, we explore how the P. aeruginosa OSA biosynthesis gene clusters evolve in the population by investigating the association between the phylogenetic relationships among 83 P....... aeruginosa strains and their serotypes. While most serotypes were closely linked to the core genome phylogeny, we observed horizontal exchange of OSA biosynthesis genes among phylogenetically distinct P. aeruginosa strains. Specifically, we identified a "serotype island" ranging from 62 kb to 185 kb containing the P...

  6. Increasing quinolone resistance in Salmonella enterica serotype enteritidis

    Mølbak, K.; Gerner-Smidt, P.; Wegener, Henrik Caspar

    2002-01-01

    Until recently, Salmonella enterica serotype Enteritidis has remained sensitive to most antibiotics. However, national surveillance data from Denmark show that quinolone resistance in S. Enteritidis has increased from 0.8% in 1995 to 8.5% in 2000. These data support concerns that the current use...... of quinolone in food animals leads to increasing resistance in S. Enteritidis and that action should be taken to limit such use....

  7. Bilateral breast abscesses due to Salmonella Enterica serotype typhi

    Gagandeep Singh

    2011-01-01

    Full Text Available Focal infection is an uncommon complication of Salmonella septicemia, particularly in immunocompetent patients. The localization of Salmonella infection to breast tissue is regarded as a rare event. We report a case of bilateral breast abscesses due to Salmonella enterica serotype Typhi in a nonlactating female and highlight the fact that Salmonella spp. should be included in differential diagnosis of abscesses in individuals coming from endemic areas with the history of recent typhoid fever and should be treated accordingly.

  8. Imported dengue virus serotype 1 from Madeira to Finland 2012.

    Huhtamo, E; Korhonen, Em; Vapalahti, O

    2013-02-21

    Imported dengue cases originating from the Madeiran outbreak are increasingly reported. In 2012 five Finnish travellers returning from Madeira were diagnosed with dengue fever. Viral sequence data was obtained from two patients. The partial C-preM sequences (399 and 396 bp respectively) were found similar to that of an autochthonous case from Madeira. The partial E-gene sequence (933 bp) which was identical among the two patients grouped phylogenetically with South American strains of dengue virus serotype 1.

  9. All Serotypes of Dengue Viruses Circulating in Kuala Lumpur, Malaysia

    M.H. Chew; M.M. Rahman; J. Jelip; M.R. Hassan; I. Isahak

    2012-01-01

    Dengue is a severe disease caused by dengue virus (DENV), transmitted to human being by infected Aedes mosquitoes. It is a major public health concern in Southeast Asia due to its fatality in the form of hemorrhagic fever (DHF) and dengue shock syndrome (DSS). The objective of the study was to isolate and identify dengue virus serotypes prevalent in endemic areas of Kuala Lumpur and Selangor in Malaysia by virus culture, indirect immunoflurecent assay and molecular techniques. A total number ...

  10. Structure Based Thermostability Prediction Models for Protein Single Point Mutations with Machine Learning Tools.

    Lei Jia

    Full Text Available Thermostability issue of protein point mutations is a common occurrence in protein engineering. An application which predicts the thermostability of mutants can be helpful for guiding decision making process in protein design via mutagenesis. An in silico point mutation scanning method is frequently used to find "hot spots" in proteins for focused mutagenesis. ProTherm (http://gibk26.bio.kyutech.ac.jp/jouhou/Protherm/protherm.html is a public database that consists of thousands of protein mutants' experimentally measured thermostability. Two data sets based on two differently measured thermostability properties of protein single point mutations, namely the unfolding free energy change (ddG and melting temperature change (dTm were obtained from this database. Folding free energy change calculation from Rosetta, structural information of the point mutations as well as amino acid physical properties were obtained for building thermostability prediction models with informatics modeling tools. Five supervised machine learning methods (support vector machine, random forests, artificial neural network, naïve Bayes classifier, K nearest neighbor and partial least squares regression are used for building the prediction models. Binary and ternary classifications as well as regression models were built and evaluated. Data set redundancy and balancing, the reverse mutations technique, feature selection, and comparison to other published methods were discussed. Rosetta calculated folding free energy change ranked as the most influential features in all prediction models. Other descriptors also made significant contributions to increasing the accuracy of the prediction models.

  11. Microbes as engines of ecosystem function: when does community structure enhance predictions of ecosystem processes?

    Emily B. Graham

    2016-02-01

    Full Text Available Microorganisms are vital in mediating the earth’s biogeochemical cycles; yet, despite our rapidly increasing ability to explore complex environmental microbial communities, the relationship between microbial community structure and ecosystem processes remains poorly understood. Here, we address a fundamental and unanswered question in microbial ecology: ‘When do we need to understand microbial community structure to accurately predict function?’ We present a statistical analysis investigating the value of environmental data and microbial community structure independently and in combination for explaining rates of carbon and nitrogen cycling processes within 82 global datasets. Environmental variables were the strongest predictors of process rates but left 44% of variation unexplained on average, suggesting the potential for microbial data to increase model accuracy. Although only 29% of our datasets were significantly improved by adding information on microbial community structure, we observed improvement in models of processes mediated by narrow phylogenetic guilds via functional gene data, and conversely, improvement in models of facultative microbial processes via community diversity metrics. Our results also suggest that microbial diversity can strengthen predictions of respiration rates beyond microbial biomass parameters, as 53% of models were improved by incorporating both sets of predictors compared to 35% by microbial biomass alone. Our analysis represents the first comprehensive analysis of research examining links between microbial community structure and ecosystem function. Taken together, our results indicate that a greater understanding of microbial communities informed by ecological principles may enhance our ability to predict ecosystem process rates relative to assessments based on environmental variables and microbial physiology.

  12. Structural habitat predicts functional dispersal habitat of a large carnivore: how leopards change spots.

    Fattebert, Julien; Robinson, Hugh S; Balme, Guy; Slotow, Rob; Hunter, Luke

    2015-10-01

    Natal dispersal promotes inter-population linkage, and is key to spatial distribution of populations. Degradation of suitable landscape structures beyond the specific threshold of an individual's ability to disperse can therefore lead to disruption of functional landscape connectivity and impact metapopulation function. Because it ignores behavioral responses of individuals, structural connectivity is easier to assess than functional connectivity and is often used as a surrogate for landscape connectivity modeling. However using structural resource selection models as surrogate for modeling functional connectivity through dispersal could be erroneous. We tested how well a second-order resource selection function (RSF) models (structural connectivity), based on GPS telemetry data from resident adult leopard (Panthera pardus L.), could predict subadult habitat use during dispersal (functional connectivity). We created eight non-exclusive subsets of the subadult data based on differing definitions of dispersal to assess the predictive ability of our adult-based RSF model extrapolated over a broader landscape. Dispersing leopards used habitats in accordance with adult selection patterns, regardless of the definition of dispersal considered. We demonstrate that, for a wide-ranging apex carnivore, functional connectivity through natal dispersal corresponds to structural connectivity as modeled by a second-order RSF. Mapping of the adult-based habitat classes provides direct visualization of the potential linkages between populations, without the need to model paths between a priori starting and destination points. The use of such landscape scale RSFs may provide insight into predicting suitable dispersal habitat peninsulas in human-dominated landscapes where mitigation of human-wildlife conflict should be focused. We recommend the use of second-order RSFs for landscape conservation planning and propose a similar approach to the conservation of other wide-ranging large

  13. Trypsin diminishes the rat potency of polio serotype 3.

    ten Have, R; Westdijk, J; Levels, L M A R; Koedam, P; de Haan, A; Hamzink, M R J; Metz, B; Kersten, G F A

    2015-11-01

    This study addresses observations made in view of testing in practice the guideline in the European Pharmacopoeia (EP) on omitting the rat potency test for release of polio containing vaccines. In general, use of the guideline is valid and the D-antigen ELISA can indeed be used as an in vitro alternative for the in vivo test. However, the set-up of the ELISA is crucial and should include detection of antigenic site 1 in polio serotype 3 as destruction of that site by trypsin results in a reduced rat potency. Antigenic site 1 in polio serotype 2 may also be modified by trypsin, but the cleavage of viral protein 1 (VP1) did not affect the rat potency. Therefore, any antigenic site, except site 1, can be used for detection of polio serotype 2. It is advised to include testing of the effect of trypsin treatment in the EP-guideline. This allows polio vaccine manufacturers to check whether their in-house ELISA needs improvement. Copyright © 2015 The International Alliance for Biological Standardization. Published by Elsevier Ltd. All rights reserved.

  14. Identification and differentiation of the twenty six bluetongue virus serotypes by RT-PCR amplification of the serotype-specific genome segment 2.

    Narender S Maan

    Full Text Available Bluetongue (BT is an arthropod-borne viral disease, which primarily affects ruminants in tropical and temperate regions of the world. Twenty six bluetongue virus (BTV serotypes have been recognised worldwide, including nine from Europe and fifteen in the United States. Identification of BTV serotype is important for vaccination programmes and for BTV epidemiology studies. Traditional typing methods (virus isolation and serum or virus neutralisation tests (SNT or VNT are slow (taking weeks, depend on availability of reference virus-strains or antisera and can be inconclusive. Nucleotide sequence analyses and phylogenetic comparisons of genome segment 2 (Seg-2 encoding BTV outer-capsid protein VP2 (the primary determinant of virus serotype were completed for reference strains of BTV-1 to 26, as well as multiple additional isolates from different geographic and temporal origins. The resulting Seg-2 database has been used to develop rapid (within 24 h and reliable RT-PCR-based typing assays for each BTV type. Multiple primer-pairs (at least three designed for each serotype were widely tested, providing an initial identification of serotype by amplification of a cDNA product of the expected size. Serotype was confirmed by sequencing of the cDNA amplicons and phylogenetic comparisons to previously characterised reference strains. The results from RT-PCR and sequencing were in perfect agreement with VNT for reference strains of all 26 BTV serotypes, as well as the field isolates tested. The serotype-specific primers showed no cross-amplification with reference strains of the remaining 25 serotypes, or multiple other isolates of the more closely related heterologous BTV types. The primers and RT-PCR assays developed in this study provide a rapid, sensitive and reliable method for the identification and differentiation of the twenty-six BTV serotypes, and will be updated periodically to maintain their relevance to current BTV distribution and

  15. Toward Structure Prediction for Short Peptides Using the Improved SAAP Force Field Parameters

    Kenichi Dedachi

    2013-01-01

    Full Text Available Based on the observation that Ramachandran-type potential energy surfaces of single amino acid units in water are in good agreement with statistical structures of the corresponding amino acid residues in proteins, we recently developed a new all-atom force field called SAAP, in which the total energy function for a polypeptide is expressed basically as a sum of single amino acid potentials and electrostatic and Lennard-Jones potentials between the amino acid units. In this study, the SAAP force field (SAAPFF parameters were improved, and classical canonical Monte Carlo (MC simulation was carried out for short peptide models, that is, Met-enkephalin and chignolin, at 300 K in an implicit water model. Diverse structures were reasonably obtained for Met-enkephalin, while three folded structures, one of which corresponds to a native-like structure with three native hydrogen bonds, were obtained for chignolin. The results suggested that the SAAP-MC method is useful for conformational sampling for the short peptides. A protocol of SAAP-MC simulation followed by structural clustering and examination of the obtained structures by ab initio calculation or simply by the number of the hydrogen bonds (or the hardness was demonstrated to be an effective strategy toward structure prediction for short peptide molecules.

  16. Clinical Survey of Dengue Virus Circulation in the Republic of Djibouti between 2011 and 2014 Identifies Serotype 3 Epidemic and Recommends Clinical Diagnosis Guidelines for Resource Limited Settings.

    Erwan Le Gonidec

    2016-06-01

    Full Text Available Dengue virus is endemic globally, throughout tropical and sub-tropical regions. While the number of epidemics due to the four DENV serotypes is pronounced in East Africa, the total number of cases reported in Africa (16 million infections remained at low levels compared to Asia (70 million infections. The French Armed forces Health Service provides epidemiological surveillance support in the Republic of Djibouti through the Bouffard Military hospital. Between 2011 and 2014, clinical and biological data of suspected dengue syndromes were collected at the Bouffard Military hospital and analyzed to improve Dengue clinical diagnosis and evaluate its circulation in East Africa. Examining samples from patients that presented one or more Dengue-like symptoms the study evidenced 128 Dengue cases among 354 suspected cases (36.2% of the non-malarial Dengue-like syndromes. It also demonstrated the circulation of serotypes 1 and 2 and reports the first epidemic of serotype 3 infections in Djibouti which was found in all of the hospitalized patients in this study. Based on these results we have determined that screening for Malaria and the presence of the arthralgia, gastro-intestinal symptoms and lymphopenia < 1,000cell/ mm3 allows for negative predictive value and specificity of diagnosis in isolated areas superior to 80% up to day 6. This study also provides evidence for an epidemic of Dengue virus serotype 3 previously not detected in Djibouti.

  17. Structural predictions for Correlated Electron Materials Using the Functional Dynamical Mean Field Theory Approach

    Haule, Kristjan

    2018-04-01

    The Dynamical Mean Field Theory (DMFT) in combination with the band structure methods has been able to address reach physics of correlated materials, such as the fluctuating local moments, spin and orbital fluctuations, atomic multiplet physics and band formation on equal footing. Recently it is getting increasingly recognized that more predictive ab-initio theory of correlated systems needs to also address the feedback effect of the correlated electronic structure on the ionic positions, as the metal-insulator transition is almost always accompanied with considerable structural distortions. We will review recently developed extension of merger between the Density Functional Theory (DFT) and DMFT method, dubbed DFT+ embedded DMFT (DFT+eDMFT), whichsuccessfully addresses this challenge. It is based on the stationary Luttinger-Ward functional to minimize the numerical error, it subtracts the exact double-counting of DFT and DMFT, and implements self-consistent forces on all atoms in the unit cell. In a few examples, we will also show how the method elucidated the important feedback effect of correlations on crystal structure in rare earth nickelates to explain the mechanism of the metal-insulator transition. The method showed that such feedback effect is also essential to understand the dynamic stability of the high-temperature body-centered cubic phase of elemental iron, and in particular it predicted strong enhancement of the electron-phonon coupling over DFT values in FeSe, which was very recently verified by pioneering time-domain experiment.

  18. Structure Prediction of Outer Membrane Protease Protein of Salmonella typhimurium Using Computational Techniques

    Rozina Tabassum

    2016-03-01

    Full Text Available Salmonella typhimurium, a facultative gram-negative intracellular pathogen belonging to family Enterobacteriaceae, is the most frequent cause of human gastroenteritis worldwide. PgtE gene product, outer membrane protease emerges important in the intracellular phases of salmonellosis. The pgtE gene product of S. typhimurium was predicted to be capable of proteolyzing T7 RNA polymerase and localize in the outer membrane of these gram negative bacteria. PgtE product of S. enterica and OmpT of E. coli, having high sequence similarity have been revealed to degrade macrophages, causing salmonellosis and other diseases. The three-dimensional structure of the protein was not available through Protein Data Bank (PDB creating lack of structural information about E protein. In our study, by performing Comparative model building, the three dimensional structure of outer membrane protease protein was generated using the backbone of the crystal structure of Pla of Yersinia pestis, retrieved from PDB, with MODELLER (9v8. Quality of the model was assessed by validation tool PROCHECK, web servers like ERRAT and ProSA are used to certify the reliability of the predicted model. This information might offer clues for better understanding of E protein and consequently for developmet of better therapeutic treatment against pathogenic role of this protein in salmonellosis and other diseases.

  19. Switch region for pathogenic structural change in conformational disease and its prediction.

    Xin Liu

    2010-01-01

    Full Text Available Many diseases are believed to be related to abnormal protein folding. In the first step of such pathogenic structural changes, misfolding occurs in regions important for the stability of the native structure. This destabilizes the normal protein conformation, while exposing the previously hidden aggregation-prone regions, leading to subsequent errors in the folding pathway. Sites involved in this first stage can be deemed switch regions of the protein, and can represent perfect binding targets for drugs to block the abnormal folding pathway and prevent pathogenic conformational changes. In this study, a prediction algorithm for the switch regions responsible for the start of pathogenic structural changes is introduced. With an accuracy of 94%, this algorithm can successfully find short segments covering sites significant in triggering conformational diseases (CDs and is the first that can predict switch regions for various CDs. To illustrate its effectiveness in dealing with urgent public health problems, the reason of the increased pathogenicity of H5N1 influenza virus is analyzed; the mechanisms of the pandemic swine-origin 2009 A(H1N1 influenza virus in overcoming species barriers and in infecting large number of potential patients are also suggested. It is shown that the algorithm is a potential tool useful in the study of the pathology of CDs because: (1 it can identify the origin of pathogenic structural conversion with high sensitivity and specificity, and (2 it provides an ideal target for clinical treatment.

  20. Prediction of RNA secondary structures: from theory to models and real molecules

    Schuster, Peter

    2006-01-01

    RNA secondary structures are derived from RNA sequences, which are strings built form the natural four letter nucleotide alphabet, {AUGC}. These coarse-grained structures, in turn, are tantamount to constrained strings over a three letter alphabet. Hence, the secondary structures are discrete objects and the number of sequences always exceeds the number of structures. The sequences built from two letter alphabets form perfect structures when the nucleotides can form a base pair, as is the case with {GC} or {AU}, but the relation between the sequences and structures differs strongly from the four letter alphabet. A comprehensive theory of RNA structure is presented, which is based on the concepts of sequence space and shape space, being a space of structures. It sets the stage for modelling processes in ensembles of RNA molecules like evolutionary optimization or kinetic folding as dynamical phenomena guided by mappings between the two spaces. The number of minimum free energy (mfe) structures is always smaller than the number of sequences, even for two letter alphabets. Folding of RNA molecules into mfe energy structures constitutes a non-invertible mapping from sequence space onto shape space. The preimage of a structure in sequence space is defined as its neutral network. Similarly the set of suboptimal structures is the preimage of a sequence in shape space. This set represents the conformation space of a given sequence. The evolutionary optimization of structures in populations is a process taking place in sequence space, whereas kinetic folding occurs in molecular ensembles that optimize free energy in conformation space. Efficient folding algorithms based on dynamic programming are available for the prediction of secondary structures for given sequences. The inverse problem, the computation of sequences for predefined structures, is an important tool for the design of RNA molecules with tailored properties. Simultaneous folding or cofolding of two or more RNA

  1. Validation of Quantitative Structure-Activity Relationship (QSAR Model for Photosensitizer Activity Prediction

    Sharifuddin M. Zain

    2011-11-01

    Full Text Available Photodynamic therapy is a relatively new treatment method for cancer which utilizes a combination of oxygen, a photosensitizer and light to generate reactive singlet oxygen that eradicates tumors via direct cell-killing, vasculature damage and engagement of the immune system. Most of photosensitizers that are in clinical and pre-clinical assessments, or those that are already approved for clinical use, are mainly based on cyclic tetrapyrroles. In an attempt to discover new effective photosensitizers, we report the use of the quantitative structure-activity relationship (QSAR method to develop a model that could correlate the structural features of cyclic tetrapyrrole-based compounds with their photodynamic therapy (PDT activity. In this study, a set of 36 porphyrin derivatives was used in the model development where 24 of these compounds were in the training set and the remaining 12 compounds were in the test set. The development of the QSAR model involved the use of the multiple linear regression analysis (MLRA method. Based on the method, r2 value, r2 (CV value and r2 prediction value of 0.87, 0.71 and 0.70 were obtained. The QSAR model was also employed to predict the experimental compounds in an external test set. This external test set comprises 20 porphyrin-based compounds with experimental IC50 values ranging from 0.39 µM to 7.04 µM. Thus the model showed good correlative and predictive ability, with a predictive correlation coefficient (r2 prediction for external test set of 0.52. The developed QSAR model was used to discover some compounds as new lead photosensitizers from this external test set.

  2. Predicting taxonomic and functional structure of microbial communities in acid mine drainage.

    Kuang, Jialiang; Huang, Linan; He, Zhili; Chen, Linxing; Hua, Zhengshuang; Jia, Pu; Li, Shengjin; Liu, Jun; Li, Jintian; Zhou, Jizhong; Shu, Wensheng

    2016-06-01

    Predicting the dynamics of community composition and functional attributes responding to environmental changes is an essential goal in community ecology but remains a major challenge, particularly in microbial ecology. Here, by targeting a model system with low species richness, we explore the spatial distribution of taxonomic and functional structure of 40 acid mine drainage (AMD) microbial communities across Southeast China profiled by 16S ribosomal RNA pyrosequencing and a comprehensive microarray (GeoChip). Similar environmentally dependent patterns of dominant microbial lineages and key functional genes were observed regardless of the large-scale geographical isolation. Functional and phylogenetic β-diversities were significantly correlated, whereas functional metabolic potentials were strongly influenced by environmental conditions and community taxonomic structure. Using advanced modeling approaches based on artificial neural networks, we successfully predicted the taxonomic and functional dynamics with significantly higher prediction accuracies of metabolic potentials (average Bray-Curtis similarity 87.8) as compared with relative microbial abundances (similarity 66.8), implying that natural AMD microbial assemblages may be better predicted at the functional genes level rather than at taxonomic level. Furthermore, relative metabolic potentials of genes involved in many key ecological functions (for example, nitrogen and phosphate utilization, metals resistance and stress response) were extrapolated to increase under more acidic and metal-rich conditions, indicating a critical strategy of stress adaptation in these extraordinary communities. Collectively, our findings indicate that natural selection rather than geographic distance has a more crucial role in shaping the taxonomic and functional patterns of AMD microbial community that readily predicted by modeling methods and suggest that the model-based approach is essential to better understand natural

  3. Genetic programming based quantitative structure-retention relationships for the prediction of Kovats retention indices.

    Goel, Purva; Bapat, Sanket; Vyas, Renu; Tambe, Amruta; Tambe, Sanjeev S

    2015-11-13

    The development of quantitative structure-retention relationships (QSRR) aims at constructing an appropriate linear/nonlinear model for the prediction of the retention behavior (such as Kovats retention index) of a solute on a chromatographic column. Commonly, multi-linear regression and artificial neural networks are used in the QSRR development in the gas chromatography (GC). In this study, an artificial intelligence based data-driven modeling formalism, namely genetic programming (GP), has been introduced for the development of quantitative structure based models predicting Kovats retention indices (KRI). The novelty of the GP formalism is that given an example dataset, it searches and optimizes both the form (structure) and the parameters of an appropriate linear/nonlinear data-fitting model. Thus, it is not necessary to pre-specify the form of the data-fitting model in the GP-based modeling. These models are also less complex, simple to understand, and easy to deploy. The effectiveness of GP in constructing QSRRs has been demonstrated by developing models predicting KRIs of light hydrocarbons (case study-I) and adamantane derivatives (case study-II). In each case study, two-, three- and four-descriptor models have been developed using the KRI data available in the literature. The results of these studies clearly indicate that the GP-based models possess an excellent KRI prediction accuracy and generalization capability. Specifically, the best performing four-descriptor models in both the case studies have yielded high (>0.9) values of the coefficient of determination (R(2)) and low values of root mean squared error (RMSE) and mean absolute percent error (MAPE) for training, test and validation set data. The characteristic feature of this study is that it introduces a practical and an effective GP-based method for developing QSRRs in gas chromatography that can be gainfully utilized for developing other types of data-driven models in chromatography science

  4. Predicting binding within disordered protein regions to structurally characterised peptide-binding domains.

    Waqasuddin Khan

    Full Text Available Disordered regions of proteins often bind to structured domains, mediating interactions within and between proteins. However, it is difficult to identify a priori the short disordered regions involved in binding. We set out to determine if docking such peptide regions to peptide binding domains would assist in these predictions.We assembled a redundancy reduced dataset of SLiM (Short Linear Motif containing proteins from the ELM database. We selected 84 sequences which had an associated PDB structures showing the SLiM bound to a protein receptor, where the SLiM was found within a 50 residue region of the protein sequence which was predicted to be disordered. First, we investigated the Vina docking scores of overlapping tripeptides from the 50 residue SLiM containing disordered regions of the protein sequence to the corresponding PDB domain. We found only weak discrimination of docking scores between peptides involved in binding and adjacent non-binding peptides in this context (AUC 0.58.Next, we trained a bidirectional recurrent neural network (BRNN using as input the protein sequence, predicted secondary structure, Vina docking score and predicted disorder score. The results were very promising (AUC 0.72 showing that multiple sources of information can be combined to produce results which are clearly superior to any single source.We conclude that the Vina docking score alone has only modest power to define the location of a peptide within a larger protein region known to contain it. However, combining this information with other knowledge (using machine learning methods clearly improves the identification of peptide binding regions within a protein sequence. This approach combining docking with machine learning is primarily a predictor of binding to peptide-binding sites, and is not intended as a predictor of specificity of binding to particular receptors.

  5. Ground-State Gas-Phase Structures of Inorganic Molecules Predicted by Density Functional Theory Methods

    Minenkov, Yury

    2017-11-29

    We tested a battery of density functional theory (DFT) methods ranging from generalized gradient approximation (GGA) via meta-GGA to hybrid meta-GGA schemes as well as Møller–Plesset perturbation theory of the second order and a single and double excitation coupled-cluster (CCSD) theory for their ability to reproduce accurate gas-phase structures of di- and triatomic molecules derived from microwave spectroscopy. We obtained the most accurate molecular structures using the hybrid and hybrid meta-GGA approximations with B3PW91, APF, TPSSh, mPW1PW91, PBE0, mPW1PBE, B972, and B98 functionals, resulting in lowest errors. We recommend using these methods to predict accurate three-dimensional structures of inorganic molecules when intramolecular dispersion interactions play an insignificant role. The structures that the CCSD method predicts are of similar quality although at considerably larger computational cost. The structures that GGA and meta-GGA schemes predict are less accurate with the largest absolute errors detected with BLYP and M11-L, suggesting that these methods should not be used if accurate three-dimensional molecular structures are required. Because of numerical problems related to the integration of the exchange–correlation part of the functional and large scattering of errors, most of the Minnesota models tested, particularly MN12-L, M11, M06-L, SOGGA11, and VSXC, are also not recommended for geometry optimization. When maintaining a low computational budget is essential, the nonseparable gradient functional N12 might work within an acceptable range of error. As expected, the DFT-D3 dispersion correction had a negligible effect on the internuclear distances when combined with the functionals tested on nonweakly bonded di- and triatomic inorganic molecules. By contrast, the dispersion correction for the APF-D functional has been found to shorten the bonds significantly, up to 0.064 Å (AgI), in Ag halides, BaO, BaS, BaF, BaCl, Cu halides, and Li and

  6. A DNA Microarray-Based Assay to Detect Dual Infection with Two Dengue Virus Serotypes

    Alvaro Díaz-Badillo

    2014-04-01

    Full Text Available Here; we have described and tested a microarray based-method for the screening of dengue virus (DENV serotypes. This DNA microarray assay is specific and sensitive and can detect dual infections with two dengue virus serotypes and single-serotype infections. Other methodologies may underestimate samples containing more than one serotype. This technology can be used to discriminate between the four DENV serotypes. Single-stranded DNA targets were covalently attached to glass slides and hybridised with specific labelled probes. DENV isolates and dengue samples were used to evaluate microarray performance. Our results demonstrate that the probes hybridized specifically to DENV serotypes; with no detection of unspecific signals. This finding provides evidence that specific probes can effectively identify single and double infections in DENV samples.

  7. Emergence of group B Streptococcus serotype IV in women of child-bearing age in Ireland.

    Kiely, R A

    2011-02-01

    This study determined the carriage rate and serotype distribution of group B Streptococcus (GBS) in women of child-bearing age in the southern region of Ireland. A total of 2000 vaginal swabs collected in two periods in 2004 and 2006 were examined and revealed a GBS carriage rate of 16·1%. Serotyping of isolates showed that serotypes Ia, II, III, IV, and V were the most prevalent. A high prevalence of serotype IV was found, increasing from 7·6% to 15·2% between 2004 and 2006. Random amplified polymorphic DNA analysis demonstrated considerable genetic heterogeneity in the serotype IV isolates. This serotype should be considered for inclusion in potential vaccines for use in Ireland.

  8. Molecular diagnosis of non-serotypeable Shigella spp.: problems and prospects.

    Muthuirulandi Sethuvel, Dhiviya Prabaa; Devanga Ragupathi, Naveen Kumar; Anandan, Shalini; Walia, Kamini; Veeraraghavan, Balaji

    2017-02-01

    It is not always possible to identify Shigella serogroups/serotypes by biochemical properties alone. Specific identification requires serotyping. Occasionally, isolates that resemble Shigella spp. biochemically, but are non-agglutinable with available antisera, have been observed. Several mechanisms have been reported to limit the efficiency of the serotyping assay. Serotype conversion is a major mechanism in Shigella spp. to escape protective host immune responses. This easy conversion through significant modification of the O-antigen backbone results in different serotypes, which makes laboratory identification difficult. Furthermore, members of the family Enterobacteriaceae are closely related and there is antigenic cross-over (intra- and inter-specific cross-reaction) which affects the agglutination reaction. The performance of the available methods for identification of non-serotypeable Shigella is discussed here, and reveals them to be non-reliable. This shows a need for an alternative method for identification and typing of Shigella spp.

  9. Prediction of pressure induced structural phase transitions and internal mode frequency changes in solid N2+

    Etters, R.D.; Kobashi, K.; Chandrasekharan, V.

    1983-01-01

    A rhombohedral distortion of the Pm3n structure is introduced which shows that a low temperature phase transition occurs from P4 2 /mnm into the R3c calcite structure at P approx. = 19.2 kbar with a volume change of 0.125 cm 3 /mole. This transition agrees with recent Raman scattering measurements. Another transition from R3c into R3m is predicted at P approx. = 67.5 kbar, with a volume change of 0.1 cm 3 /mole. The pressure dependence of the intramolecular mode frequencies for the R3c structure is in reasonably good agreement with the two main branches observed experimentally

  10. Model structures amplify uncertainty in predicted soil carbon responses to climate change.

    Shi, Zheng; Crowell, Sean; Luo, Yiqi; Moore, Berrien

    2018-06-04

    Large model uncertainty in projected future soil carbon (C) dynamics has been well documented. However, our understanding of the sources of this uncertainty is limited. Here we quantify the uncertainties arising from model parameters, structures and their interactions, and how those uncertainties propagate through different models to projections of future soil carbon stocks. Both the vertically resolved model and the microbial explicit model project much greater uncertainties to climate change than the conventional soil C model, with both positive and negative C-climate feedbacks, whereas the conventional model consistently predicts positive soil C-climate feedback. Our findings suggest that diverse model structures are necessary to increase confidence in soil C projection. However, the larger uncertainty in the complex models also suggests that we need to strike a balance between model complexity and the need to include diverse model structures in order to forecast soil C dynamics with high confidence and low uncertainty.

  11. Structural modeling of natural citrus products as potential cross ...

    There are four serotypes of Dengue virus and there are existing drugs used against specific serotype. There is no drug that is effective against all strains of this virus. In this research, bioinformatics tools were used to predict the affinity of natural ligands for the glycoprotein E of Dengue virus by considering the conserved ...

  12. Structured Semantic Knowledge Can Emerge Automatically from Predicting Word Sequences in Child-Directed Speech

    Philip A. Huebner

    2018-02-01

    Full Text Available Previous research has suggested that distributional learning mechanisms may contribute to the acquisition of semantic knowledge. However, distributional learning mechanisms, statistical learning, and contemporary “deep learning” approaches have been criticized for being incapable of learning the kind of abstract and structured knowledge that many think is required for acquisition of semantic knowledge. In this paper, we show that recurrent neural networks, trained on noisy naturalistic speech to children, do in fact learn what appears to be abstract and structured knowledge. We trained two types of recurrent neural networks (Simple Recurrent Network, and Long Short-Term Memory to predict word sequences in a 5-million-word corpus of speech directed to children ages 0–3 years old, and assessed what semantic knowledge they acquired. We found that learned internal representations are encoding various abstract grammatical and semantic features that are useful for predicting word sequences. Assessing the organization of semantic knowledge in terms of the similarity structure, we found evidence of emergent categorical and hierarchical structure in both models. We found that the Long Short-term Memory (LSTM and SRN are both learning very similar kinds of representations, but the LSTM achieved higher levels of performance on a quantitative evaluation. We also trained a non-recurrent neural network, Skip-gram, on the same input to compare our results to the state-of-the-art in machine learning. We found that Skip-gram achieves relatively similar performance to the LSTM, but is representing words more in terms of thematic compared to taxonomic relations, and we provide reasons why this might be the case. Our findings show that a learning system that derives abstract, distributed representations for the purpose of predicting sequential dependencies in naturalistic language may provide insight into emergence of many properties of the developing

  13. Structured Semantic Knowledge Can Emerge Automatically from Predicting Word Sequences in Child-Directed Speech

    Huebner, Philip A.; Willits, Jon A.

    2018-01-01

    Previous research has suggested that distributional learning mechanisms may contribute to the acquisition of semantic knowledge. However, distributional learning mechanisms, statistical learning, and contemporary “deep learning” approaches have been criticized for being incapable of learning the kind of abstract and structured knowledge that many think is required for acquisition of semantic knowledge. In this paper, we show that recurrent neural networks, trained on noisy naturalistic speech to children, do in fact learn what appears to be abstract and structured knowledge. We trained two types of recurrent neural networks (Simple Recurrent Network, and Long Short-Term Memory) to predict word sequences in a 5-million-word corpus of speech directed to children ages 0–3 years old, and assessed what semantic knowledge they acquired. We found that learned internal representations are encoding various abstract grammatical and semantic features that are useful for predicting word sequences. Assessing the organization of semantic knowledge in terms of the similarity structure, we found evidence of emergent categorical and hierarchical structure in both models. We found that the Long Short-term Memory (LSTM) and SRN are both learning very similar kinds of representations, but the LSTM achieved higher levels of performance on a quantitative evaluation. We also trained a non-recurrent neural network, Skip-gram, on the same input to compare our results to the state-of-the-art in machine learning. We found that Skip-gram achieves relatively similar performance to the LSTM, but is representing words more in terms of thematic compared to taxonomic relations, and we provide reasons why this might be the case. Our findings show that a learning system that derives abstract, distributed representations for the purpose of predicting sequential dependencies in naturalistic language may provide insight into emergence of many properties of the developing semantic system. PMID

  14. Large-scale prediction of drug–target interactions using protein sequences and drug topological structures

    Cao Dongsheng; Liu Shao; Xu Qingsong; Lu Hongmei; Huang Jianhua; Hu Qiannan; Liang Yizeng

    2012-01-01

    Highlights: ► Drug–target interactions are predicted using an extended SAR methodology. ► A drug–target interaction is regarded as an event triggered by many factors. ► Molecular fingerprint and CTD descriptors are used to represent drugs and proteins. ► Our approach shows compatibility between the new scheme and current SAR methodology. - Abstract: The identification of interactions between drugs and target proteins plays a key role in the process of genomic drug discovery. It is both consuming and costly to determine drug–target interactions by experiments alone. Therefore, there is an urgent need to develop new in silico prediction approaches capable of identifying these potential drug–target interactions in a timely manner. In this article, we aim at extending current structure–activity relationship (SAR) methodology to fulfill such requirements. In some sense, a drug–target interaction can be regarded as an event or property triggered by many influence factors from drugs and target proteins. Thus, each interaction pair can be represented theoretically by using these factors which are based on the structural and physicochemical properties simultaneously from drugs and proteins. To realize this, drug molecules are encoded with MACCS substructure fingerings representing existence of certain functional groups or fragments; and proteins are encoded with some biochemical and physicochemical properties. Four classes of drug–target interaction networks in humans involving enzymes, ion channels, G-protein-coupled receptors (GPCRs) and nuclear receptors, are independently used for establishing predictive models with support vector machines (SVMs). The SVM models gave prediction accuracy of 90.31%, 88.91%, 84.68% and 83.74% for four datasets, respectively. In conclusion, the results demonstrate the ability of our proposed method to predict the drug–target interactions, and show a general compatibility between the new scheme and current SAR

  15. Genetic Diversity of Clinical and Environmental Strains of Salmonella enterica Serotype Weltevreden Isolated in Malaysia

    Thong, K. L.; Goh, Y. L.; Radu, S.; Noorzaleha, S.; Yasin, R.; Koh, Y. T.; Lim, V. K. E.; Rusul, G.; Puthucheary, S. D.

    2002-01-01

    The incidence of food-borne salmonellosis due to Salmonella enterica serotype Weltevreden is reported to be on the increase in Malaysia. The pulsed-field gel electrophoresis (PFGE) subtyping method was used to assess the extent of genetic diversity and clonality of Salmonella serotype Weltevreden strains from humans and the environment. PFGE of XbaI-digested chromosomal DNA from 95 strains of Salmonella serotype Weltevreden gave 39 distinct profiles with a wide range of Dice coefficients (0.2...

  16. Serological characterization of Actinobacillus pleuropneumoniae biotype 1 strains antigenically related to both serotypes 2 and 7

    Nielsen, R.; Andresen, Lars Ole; Plambeck, Tamara

    1996-01-01

    Nine Danish Actinobacillus pleuropneumoniae biotype 1 isolates were shown by latex agglutination and indirect haemagglutination to possess capsular polysaccharide epitopes identical to those of serotype 2 strain 1536 (reference strain of serotype 2) and strain 4226 (Danish serotype 2 strain). Imm...... in the LPS of strains 1536 and 7317 were revealed. Since an antigenic determinant specific for the 9 isolates could not be demonstrated with the methods used, the strains are proposed to be designated K2:O7....

  17. Analysis of energy-based algorithms for RNA secondary structure prediction

    Hajiaghayi Monir

    2012-02-01

    Full Text Available Abstract Background RNA molecules play critical roles in the cells of organisms, including roles in gene regulation, catalysis, and synthesis of proteins. Since RNA function depends in large part on its folded structures, much effort has been invested in developing accurate methods for prediction of RNA secondary structure from the base sequence. Minimum free energy (MFE predictions are widely used, based on nearest neighbor thermodynamic parameters of Mathews, Turner et al. or those of Andronescu et al. Some recently proposed alternatives that leverage partition function calculations find the structure with maximum expected accuracy (MEA or pseudo-expected accuracy (pseudo-MEA methods. Advances in prediction methods are typically benchmarked using sensitivity, positive predictive value and their harmonic mean, namely F-measure, on datasets of known reference structures. Since such benchmarks document progress in improving accuracy of computational prediction methods, it is important to understand how measures of accuracy vary as a function of the reference datasets and whether advances in algorithms or thermodynamic parameters yield statistically significant improvements. Our work advances such understanding for the MFE and (pseudo-MEA-based methods, with respect to the latest datasets and energy parameters. Results We present three main findings. First, using the bootstrap percentile method, we show that the average F-measure accuracy of the MFE and (pseudo-MEA-based algorithms, as measured on our largest datasets with over 2000 RNAs from diverse families, is a reliable estimate (within a 2% range with high confidence of the accuracy of a population of RNA molecules represented by this set. However, average accuracy on smaller classes of RNAs such as a class of 89 Group I introns used previously in benchmarking algorithm accuracy is not reliable enough to draw meaningful conclusions about the relative merits of the MFE and MEA-based algorithms

  18. Coupling between cracking and permeability, a model for structure service life prediction

    Lasne, M.; Gerard, B.; Breysse, D.

    1993-01-01

    Many authors have chosen permeability coefficients (permeation, diffusion) as a reference for material durability and for structure service life prediction. When we look for designing engineered barriers for radioactive waste storage we find these macroscopic parameters very essential. In order to work with a predictive model of transfer properties evolution in a porous media (concrete, mortar, rock) we introduce a 'micro-macro' hierarchical model of permeability whose data are the total porosity and the pore size distribution. In spite of the simplicity of the model (very small CPU time consuming) comparative studies show predictive results for sound cement pastes, mortars and concretes. Associated to these works we apply a model of damage due to hydration processes at early ages to a container as a preliminary underproject for the definitive storage of Low Level radioactive Waste (LLW). Data are geometry, cement properties and damage measurement of concrete. This model takes into account the mechanical property of the concrete maturation (volumic variations during cement hydration can damage the structures). Some local microcracking can appear and affect the long term durability. Following these works we introduce our research program for the concrete cracking analysis. An experimental campaign is designed in order to determine damage-cracking-porosity-permeability coupling. (authors). 12 figs., 16 refs

  19. Using sequence-specific chemical and structural properties of DNA to predict transcription factor binding sites.

    Amy L Bauer

    2010-11-01

    Full Text Available An important step in understanding gene regulation is to identify the DNA binding sites recognized by each transcription factor (TF. Conventional approaches to prediction of TF binding sites involve the definition of consensus sequences or position-specific weight matrices and rely on statistical analysis of DNA sequences of known binding sites. Here, we present a method called SiteSleuth in which DNA structure prediction, computational chemistry, and machine learning are applied to develop models for TF binding sites. In this approach, binary classifiers are trained to discriminate between true and false binding sites based on the sequence-specific chemical and structural features of DNA. These features are determined via molecular dynamics calculations in which we consider each base in different local neighborhoods. For each of 54 TFs in Escherichia coli, for which at least five DNA binding sites are documented in RegulonDB, the TF binding sites and portions of the non-coding genome sequence are mapped to feature vectors and used in training. According to cross-validation analysis and a comparison of computational predictions against ChIP-chip data available for the TF Fis, SiteSleuth outperforms three conventional approaches: Match, MATRIX SEARCH, and the method of Berg and von Hippel. SiteSleuth also outperforms QPMEME, a method similar to SiteSleuth in that it involves a learning algorithm. The main advantage of SiteSleuth is a lower false positive rate.

  20. Predicting complex syntactic structure in real time: Processing of negative sentences in Russian.

    Kazanina, Nina

    2017-11-01

    In Russian negative sentences the verb's direct object may appear either in the accusative case, which is licensed by the verb (as is common cross-linguistically), or in the genitive case, which is licensed by the negation (Russian-specific "genitive-of-negation" phenomenon). Such sentences were used to investigate whether case marking is employed for anticipating syntactic structure, and whether lexical heads other than the verb can be predicted on the basis of a case-marked noun phrase. Experiment 1, a completion task, confirmed that genitive-of-negation is part of Russian speakers' active grammatical repertoire. In Experiments 2 and 3, the genitive/accusative case manipulation on the preverbal object led to shorter reading times at the negation and verb in the genitive versus accusative condition. Furthermore, Experiment 3 manipulated linear order of the direct object and the negated verb in order to distinguish whether the abovementioned facilitatory effect was predictive or integrative in nature, and concluded that the parser actively predicts a verb and (otherwise optional) negation on the basis of a preceding genitive-marked object. Similarly to a head-final language, case-marking information on preverbal noun phrases (NPs) is used by the parser to enable incremental structure building in a free-word-order language such as Russian.

  1. Quantitative structure-activity relationship (QSAR) for insecticides: development of predictive in vivo insecticide activity models.

    Naik, P K; Singh, T; Singh, H

    2009-07-01

    Quantitative structure-activity relationship (QSAR) analyses were performed independently on data sets belonging to two groups of insecticides, namely the organophosphates and carbamates. Several types of descriptors including topological, spatial, thermodynamic, information content, lead likeness and E-state indices were used to derive quantitative relationships between insecticide activities and structural properties of chemicals. A systematic search approach based on missing value, zero value, simple correlation and multi-collinearity tests as well as the use of a genetic algorithm allowed the optimal selection of the descriptors used to generate the models. The QSAR models developed for both organophosphate and carbamate groups revealed good predictability with r(2) values of 0.949 and 0.838 as well as [image omitted] values of 0.890 and 0.765, respectively. In addition, a linear correlation was observed between the predicted and experimental LD(50) values for the test set data with r(2) of 0.871 and 0.788 for both the organophosphate and carbamate groups, indicating that the prediction accuracy of the QSAR models was acceptable. The models were also tested successfully from external validation criteria. QSAR models developed in this study should help further design of novel potent insecticides.

  2. Prediction of post translational modifications in avicennia marina Cu-Zn superoxide dismutase: implication of glycation on the enzyme structure

    Jabeen, U.; Salim, A.; Abbasi, A.

    2012-01-01

    3D homology model of Cu-Zn superoxide dismutase (SOD) from Avicennia marina (AMSOD) was constructed using the structural coordinates of Spinach SOD (SSOD). Prediction of post translational modification was done by PROSITE. The predicted sites were examined in the 3D model. AMSOD model was glycated using modeling software and changes in the structure was analyzed after glycation. The analysis revealed some potential sites and structural changes after glycation. (author)

  3. Multi-Scale Modeling for Predicting the Stiffness and Strength of Hollow-Structured Metal Foams with Structural Hierarchy

    Yong Yi

    2018-03-01

    Full Text Available This work was inspired by previous experiments which managed to establish an optimal template-dealloying route to prepare ultralow density metal foams. In this study, we propose a new analytical–numerical model of hollow-structured metal foams with structural hierarchy to predict its stiffness and strength. The two-level model comprises a main backbone and a secondary nanoporous structure. The main backbone is composed of hollow sphere-packing architecture, while the secondary one is constructed of a bicontinuous nanoporous network proposed to describe the nanoscale interactions in the shell. Firstly, two nanoporous models with different geometries are generated by Voronoi tessellation, then the scaling laws of the mechanical properties are determined as a function of relative density by finite volume simulation. Furthermore, the scaling laws are applied to identify the uniaxial compression behavior of metal foams. It is shown that the thickness and relative density highly influence the Young’s modulus and yield strength, and vacancy defect determines the foams being self-supported. The present study provides not only new insights into the mechanical behaviors of both nanoporous metals and metal foams, but also a practical guide for their fabrication and application.

  4. Predicting cognitive function of the Malaysian elderly: a structural equation modelling approach.

    Foong, Hui Foh; Hamid, Tengku Aizan; Ibrahim, Rahimah; Haron, Sharifah Azizah; Shahar, Suzana

    2018-01-01

    The aim of this study was to identify the predictors of elderly's cognitive function based on biopsychosocial and cognitive reserve perspectives. The study included 2322 community-dwelling elderly in Malaysia, randomly selected through a multi-stage proportional cluster random sampling from Peninsular Malaysia. The elderly were surveyed on socio-demographic information, biomarkers, psychosocial status, disability, and cognitive function. A biopsychosocial model of cognitive function was developed to test variables' predictive power on cognitive function. Statistical analyses were performed using SPSS (version 15.0) in conjunction with Analysis of Moment Structures Graphics (AMOS 7.0). The estimated theoretical model fitted the data well. Psychosocial stress and metabolic syndrome (MetS) negatively predicted cognitive function and psychosocial stress appeared as a main predictor. Socio-demographic characteristics, except gender, also had significant effects on cognitive function. However, disability failed to predict cognitive function. Several factors together may predict cognitive function in the Malaysian elderly population, and the variance accounted for it is large enough to be considered substantial. Key factor associated with the elderly's cognitive function seems to be psychosocial well-being. Thus, psychosocial well-being should be included in the elderly assessment, apart from medical conditions, both in clinical and community setting.

  5. Low-Quality Structural and Interaction Data Improves Binding Affinity Prediction via Random Forest.

    Li, Hongjian; Leung, Kwong-Sak; Wong, Man-Hon; Ballester, Pedro J

    2015-06-12

    Docking scoring functions can be used to predict the strength of protein-ligand binding. It is widely believed that training a scoring function with low-quality data is detrimental for its predictive performance. Nevertheless, there is a surprising lack of systematic validation experiments in support of this hypothesis. In this study, we investigated to which extent training a scoring function with data containing low-quality structural and binding data is detrimental for predictive performance. We actually found that low-quality data is not only non-detrimental, but beneficial for the predictive performance of machine-learning scoring functions, though the improvement is less important than that coming from high-quality data. Furthermore, we observed that classical scoring functions are not able to effectively exploit data beyond an early threshold, regardless of its quality. This demonstrates that exploiting a larger data volume is more important for the performance of machine-learning scoring functions than restricting to a smaller set of higher data quality.

  6. Improved understanding of physics processes in pedestal structure, leading to improved predictive capability for ITER

    Groebner, R.J.; Snyder, P.B.; Leonard, A.W.; Chang, C.S.; Maingi, R.; Boyle, D.P.; Diallo, A.; Hughes, J.W.; Davis, E.M.; Ernst, D.R.; Landreman, M.; Xu, X.Q.; Boedo, J.A.; Cziegler, I.; Diamond, P.H.; Eldon, D.P.; Callen, J.D.; Canik, J.M.; Elder, J.D.; Fulton, D.P.

    2013-01-01

    Joint experiment/theory/modelling research has led to increased confidence in predictions of the pedestal height in ITER. This work was performed as part of a US Department of Energy Joint Research Target in FY11 to identify physics processes that control the H-mode pedestal structure. The study included experiments on C-Mod, DIII-D and NSTX as well as interpretation of experimental data with theory-based modelling codes. This work provides increased confidence in the ability of models for peeling–ballooning stability, bootstrap current, pedestal width and pedestal height scaling to make correct predictions, with some areas needing further work also being identified. A model for pedestal pressure height has made good predictions in existing machines for a range in pressure of a factor of 20. This provides a solid basis for predicting the maximum pedestal pressure height in ITER, which is found to be an extrapolation of a factor of 3 beyond the existing data set. Models were studied for a number of processes that are proposed to play a role in the pedestal n e and T e profiles. These processes include neoclassical transport, paleoclassical transport, electron temperature gradient turbulence and neutral fuelling. All of these processes may be important, with the importance being dependent on the plasma regime. Studies with several electromagnetic gyrokinetic codes show that the gradients in and on top of the pedestal can drive a number of instabilities. (paper)

  7. Carriage and serotype distribution of Streptococcus agalactiae in third trimester pregnancy in southern Ghana

    Slotved, Hans-Christian; Dayie, Nicholas T K D; Banini, Josephine A N

    2017-01-01

    was performed on a single subcultured colony. Gram staining was performed, and isolates were evaluated for beta-haemolytic reactions. Furthermore, the isolates were serotyped using the GBS latex serotyping kit. RESULTS: The carriage rates were found to be 25.5% (95% CI: 19.6-32.1) to 28.0% (95% CI: 21...... of this study revealed that prevalence of GBS colonization in pregnant women in Greater Accra region is high and comparable to rates observed in South Africa and Western countries. The most prevalent serotypes were serotypes VII and IX, which have not been observed before in West Africa....

  8. Antimicrobial susceptibility and serotypes of Actinobacillus (Haemophilus) pleuropneumoniae recovered from Missouri swine.

    Fales, W H; Morehouse, L G; Mittal, K R; Bean-Knudsen, C; Nelson, S L; Kintner, L D; Turk, J R; Turk, M A; Brown, T P; Shaw, D P

    1989-01-01

    The antimicrobial susceptibility of 73 Actinobacillus (Haemophilus) pleuropneumoniae isolates from swine in Missouri was determined with a microdilution minimal inhibitory concentration test system. Serotyping was accomplished by means of co-agglutination. Serotype 1 (39/73) and serotype 5 (30/73) were commonly found, whereas serotype 7 (4/73) was infrequently encountered. Most isolates (MIC90) were found susceptible to ampicillin (amoxicillin), cephalothin, penicillin, erythromycin, gentamicin, and kanamycin. Marked resistance was found with oxytetracycline, tylosin, and sulfadimethoxine. The data indicate that use of ampicillin (amoxicillin) or penicillin may correlate well with the favorable outcome of treatment.

  9. [Isolation of Haemophilus influenzae serotypes from deep sites in sick children].

    Gatti, B M; Ramirez Gronda, G A; Etchevarría, M; Vescina, C M; Varea, A M; González Ayala, S E

    2004-01-01

    Haemophilus influenzae (Hi) is the causative agent of several human diseases such as sepsis, meningitis, celulitis, and osteoarthritis. We investigated the isolation of Hi serotypes from sterile sites in sick children. One hundred and seventy nine strains from 146 patients were studied, period 1996-2002, at the Microbiology Laboratory, Hospital de Niños Superiora Sor María Ludovica, Argentina. The serotype distribution was:1 a, 112 b,1 c,1 d, 4 e, 3 f y 24 no typable. Since the beginning of universal Hi b vaccination in 1998, we have observed the fast decrease of serotype b and a relative increase of other serotypes.

  10. Serotype diversity of astroviruses in Rawalpindi, Pakistan during 2009-2010.

    Muhammad Masroor Alam

    Full Text Available Astroviruses are globally known enteropathogens causing gastroenteritis and diarrhea, with eight well defined serotypes. Epidemiological studies have recognized serotype-1 as the most common subtype but no such data is available in Pakistan. During 2009-2010, we found astroviruses in 41 out of 535 (7% samples collected from hospitalized children. Thirty one strains belonged to serotype-1 and clustered into two distinct lineages. Serotype-3, -4 and -6 were detected with 97-98% genetic homology to Indian and Chinese strains.

  11. Can high-energy proton events in solar wind be predicted via classification of precursory structures?

    Hallerberg, Sarah [Chemnitz University of Technology (Germany); Ruzmaikin, Alexander; Feynman, Joan [Jet Propulsion Laboratory, California Institute of Technology (United States)

    2011-07-01

    Shock waves in the solar wind associated with solar coronal mass ejections produce fluxes of high-energy protons and ions with energies larger than 10 MeV. These fluxes present a danger to humans and electronic equipment in space, and also endanger passengers of over-pole air flights. The approaches that have been exploited for the prediction of high-energy particle events so far consist in training artificial neural networks on catalogues of events. Our approach towards this task is based on the identification of precursory structures in the fluxes of particles. In contrast to artificial neural networks that function as a ''black box'' transforming data into predictions, this classification approach can additionally provide information on relevant precursory events and thus might help to improve the understanding of underlying mechanisms of particle acceleration.

  12. Predicting effects of structural stress in a genome-reduced model bacterial metabolism

    Güell, Oriol; Sagués, Francesc; Serrano, M. Ángeles

    2012-08-01

    Mycoplasma pneumoniae is a human pathogen recently proposed as a genome-reduced model for bacterial systems biology. Here, we study the response of its metabolic network to different forms of structural stress, including removal of individual and pairs of reactions and knockout of genes and clusters of co-expressed genes. Our results reveal a network architecture as robust as that of other model bacteria regarding multiple failures, although less robust against individual reaction inactivation. Interestingly, metabolite motifs associated to reactions can predict the propagation of inactivation cascades and damage amplification effects arising in double knockouts. We also detect a significant correlation between gene essentiality and damages produced by single gene knockouts, and find that genes controlling high-damage reactions tend to be expressed independently of each other, a functional switch mechanism that, simultaneously, acts as a genetic firewall to protect metabolism. Prediction of failure propagation is crucial for metabolic engineering or disease treatment.

  13. Computational prediction of muon stopping sites using ab initio random structure searching (AIRSS)

    Liborio, Leandro; Sturniolo, Simone; Jochym, Dominik

    2018-04-01

    The stopping site of the muon in a muon-spin relaxation experiment is in general unknown. There are some techniques that can be used to guess the muon stopping site, but they often rely on approximations and are not generally applicable to all cases. In this work, we propose a purely theoretical method to predict muon stopping sites in crystalline materials from first principles. The method is based on a combination of ab initio calculations, random structure searching, and machine learning, and it has successfully predicted the MuT and MuBC stopping sites of muonium in Si, diamond, and Ge, as well as the muonium stopping site in LiF, without any recourse to experimental results. The method makes use of Soprano, a Python library developed to aid ab initio computational crystallography, that was publicly released and contains all the software tools necessary to reproduce our analysis.

  14. Predicting the structural development in Danish livestock and how it affects control strategies against FMD

    Christiansen, Lasse Engbo; Hisham Beshara Halasa, Tariq; Boklund, Anette

    2012-01-01

    farms were classified by production type and size each year. A total of 88 classes were used. For each species group (cattle, swine, and sheep and goat) a transition probability matrix (TPM) was estimated based on the ten year to year transitions. It was hypothesized that there might be regional......The purpose of this study was to assess if the optimal control strategy against foot-and-mouth disease (FMD) spread is invariant to structural development in Danish livestock until 2030. The DTU-DADS model as presented by Halasa et al. uses demographic information of all farms including...... significantly different TPMs. These TPMs were used in a Markov chain to predict the distribution of farms in year 2030. However, the predictions were unrealistic as far too many farms opened – since all closed farms were allowed to reopen. It was decided to make the closed state a terminal state and make...

  15. Predicting Dynamic Response of Structures under Earthquake Loads Using Logical Analysis of Data

    Ayman Abd-Elhamed

    2018-04-01

    Full Text Available In this paper, logical analysis of data (LAD is used to predict the seismic response of building structures employing the captured dynamic responses. In order to prepare the data, computational simulations using a single degree of freedom (SDOF building model under different ground motion records are carried out. The selected excitation records are real and of different peak ground accelerations (PGA. The sensitivity of the seismic response in terms of displacements of floors to the variation in earthquake characteristics, such as soil class, characteristic period, and time step of records, peak ground displacement, and peak ground velocity, have also been considered. The dynamic equation of motion describing the building model and the applied earthquake load are presented and solved incrementally using the Runge-Kutta method. LAD then finds the characteristic patterns which lead to forecast the seismic response of building structures. The accuracy of LAD is compared to that of an artificial neural network (ANN, since the latter is the most known machine learning technique. Based on the conducted study, the proposed LAD model has been proven to be an efficient technique to learn, simulate, and blindly predict the dynamic response behaviour of building structures subjected to earthquake loads.

  16. A Method to Predict the Structure and Stability of RNA/RNA Complexes.

    Xu, Xiaojun; Chen, Shi-Jie

    2016-01-01

    RNA/RNA interactions are essential for genomic RNA dimerization and regulation of gene expression. Intermolecular loop-loop base pairing is a widespread and functionally important tertiary structure motif in RNA machinery. However, computational prediction of intermolecular loop-loop base pairing is challenged by the entropy and free energy calculation due to the conformational constraint and the intermolecular interactions. In this chapter, we describe a recently developed statistical mechanics-based method for the prediction of RNA/RNA complex structures and stabilities. The method is based on the virtual bond RNA folding model (Vfold). The main emphasis in the method is placed on the evaluation of the entropy and free energy for the loops, especially tertiary kissing loops. The method also uses recursive partition function calculations and two-step screening algorithm for large, complicated structures of RNA/RNA complexes. As case studies, we use the HIV-1 Mal dimer and the siRNA/HIV-1 mutant (T4) to illustrate the method.

  17. Further Development of Ko Displacement Theory for Deformed Shape Predictions of Nonuniform Aerospace Structures

    Ko, William L.; Fleischer, Van Tran

    2009-01-01

    The Ko displacement theory previously formulated for deformed shape predictions of nonuniform beam structures is further developed mathematically. The further-developed displacement equations are expressed explicitly in terms of geometrical parameters of the beam and bending strains at equally spaced strain-sensing stations along the multiplexed fiber-optic sensor line installed on the bottom surface of the beam. The bending strain data can then be input into the displacement equations for calculations of local slopes, deflections, and cross-sectional twist angles for generating the overall deformed shapes of the nonuniform beam. The further-developed displacement theory can also be applied to the deformed shape predictions of nonuniform two-point supported beams, nonuniform panels, nonuniform aircraft wings and fuselages, and so forth. The high degree of accuracy of the further-developed displacement theory for nonuniform beams is validated by finite-element analysis of various nonuniform beam structures. Such structures include tapered tubular beams, depth-tapered unswept and swept wing boxes, width-tapered wing boxes, and double-tapered wing boxes, all under combined bending and torsional loads. The Ko displacement theory, combined with the fiber-optic strain-sensing system, provide a powerful tool for in-flight deformed shape monitoring of unmanned aerospace vehicles by ground-based pilots to maintain safe flights.

  18. Predicting algal growth inhibition toxicity: three-step strategy using structural and physicochemical properties.

    Furuhama, A; Hasunuma, K; Hayashi, T I; Tatarazako, N

    2016-05-01

    We propose a three-step strategy that uses structural and physicochemical properties of chemicals to predict their 72 h algal growth inhibition toxicities against Pseudokirchneriella subcapitata. In Step 1, using a log D-based criterion and structural alerts, we produced an interspecies QSAR between algal and acute daphnid toxicities for initial screening of chemicals. In Step 2, we categorized chemicals according to the Verhaar scheme for aquatic toxicity, and we developed QSARs for toxicities of Class 1 (non-polar narcotic) and Class 2 (polar narcotic) chemicals by means of simple regression with a hydrophobicity descriptor and multiple regression with a hydrophobicity descriptor and a quantum chemical descriptor. Using the algal toxicities of the Class 1 chemicals, we proposed a baseline QSAR for calculating their excess toxicities. In Step 3, we used structural profiles to predict toxicity either quantitatively or qualitatively and to assign chemicals to the following categories: Pesticide, Reactive, Toxic, Toxic low and Uncategorized. Although this three-step strategy cannot be used to estimate the algal toxicities of all chemicals, it is useful for chemicals within its domain. The strategy is also applicable as a component of Integrated Approaches to Testing and Assessment.

  19. A nucleobase-centered coarse-grained representation for structure prediction of RNA motifs.

    Poblete, Simón; Bottaro, Sandro; Bussi, Giovanni

    2018-02-28

    We introduce the SPlit-and-conQueR (SPQR) model, a coarse-grained (CG) representation of RNA designed for structure prediction and refinement. In our approach, the representation of a nucleotide consists of a point particle for the phosphate group and an anisotropic particle for the nucleoside. The interactions are, in principle, knowledge-based potentials inspired by the $\\mathcal {E}$SCORE function, a base-centered scoring function. However, a special treatment is given to base-pairing interactions and certain geometrical conformations which are lost in a raw knowledge-based model. This results in a representation able to describe planar canonical and non-canonical base pairs and base-phosphate interactions and to distinguish sugar puckers and glycosidic torsion conformations. The model is applied to the folding of several structures, including duplexes with internal loops of non-canonical base pairs, tetraloops, junctions and a pseudoknot. For the majority of these systems, experimental structures are correctly predicted at the level of individual contacts. We also propose a method for efficiently reintroducing atomistic detail from the CG representation.

  20. Structure-based function prediction of the expanding mollusk tyrosinase family

    Huang, Ronglian; Li, Li; Zhang, Guofan

    2017-11-01

    Tyrosinase (Ty) is a common enzyme found in many different animal groups. In our previous study, genome sequencing revealed that the Ty family is expanded in the Pacific oyster ( Crassostrea gigas). Here, we examine the larger number of Ty family members in the Pacific oyster by high-level structure prediction to obtain more information about their function and evolution, especially the unknown role in biomineralization. We verified 12 Ty gene sequences from Crassostrea gigas genome and Pinctada fucata martensii transcriptome. By using phylogenetic analysis of these Tys with functionally known Tys from other molluscan species, eight subgroups were identified (CgTy_s1, CgTy_s2, MolTy_s1, MolTy-s2, MolTy-s3, PinTy-s1, PinTy-s2 and PviTy). Structural data and surface pockets of the dinuclear copper center in the eight subgroups of molluscan Ty were obtained using the latest versions of prediction online servers. Structural comparison with other Ty proteins from the protein databank revealed functionally important residues (HA1, HA2, HA3, HB1, HB2, HB3, Z1-Z9) and their location within these protein structures. The structural and chemical features of these pockets which may related to the substrate binding showed considerable variability among mollusks, which undoubtedly defines Ty substrate binding. Finally, we discuss the potential driving forces of Ty family evolution in mollusks. Based on these observations, we conclude that the Ty family has rapidly evolved as a consequence of substrate adaptation in mollusks.