WorldWideScience

Sample records for tertiary structure prediction

  1. Protein Tertiary Structure Prediction Based on Main Chain Angle Using a Hybrid Bees Colony Optimization Algorithm

    Science.gov (United States)

    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.

  2. Molecular modelling of the Norrie disease protein predicts a cystine knot growth factor tertiary structure.

    Science.gov (United States)

    Meitinger, T; Meindl, A; Bork, P; Rost, B; Sander, C; Haasemann, M; Murken, J

    1993-12-01

    The X-lined gene for Norrie disease, which is characterized by blindness, deafness and mental retardation has been cloned recently. This gene has been thought to code for a putative extracellular factor; its predicted amino acid sequence is homologous to the C-terminal domain of diverse extracellular proteins. Sequence pattern searches and three-dimensional modelling now suggest that the Norrie disease protein (NDP) has a tertiary structure similar to that of transforming growth factor beta (TGF beta). Our model identifies NDP as a member of an emerging family of growth factors containing a cystine knot motif, with direct implications for the physiological role of NDP. The model also sheds light on sequence related domains such as the C-terminal domain of mucins and of von Willebrand factor.

  3. Genome-scale characterization of RNA tertiary structures and their functional impact by RNA solvent accessibility prediction.

    Science.gov (United States)

    Yang, Yuedong; Li, Xiaomei; Zhao, Huiying; Zhan, Jian; Wang, Jihua; Zhou, Yaoqi

    2017-01-01

    As most RNA structures are elusive to structure determination, obtaining solvent accessible surface areas (ASAs) of nucleotides in an RNA structure is an important first step to characterize potential functional sites and core structural regions. Here, we developed RNAsnap, the first machine-learning method trained on protein-bound RNA structures for solvent accessibility prediction. Built on sequence profiles from multiple sequence alignment (RNAsnap-prof), the method provided robust prediction in fivefold cross-validation and an independent test (Pearson correlation coefficients, r, between predicted and actual ASA values are 0.66 and 0.63, respectively). Application of the method to 6178 mRNAs revealed its positive correlation to mRNA accessibility by dimethyl sulphate (DMS) experimentally measured in vivo (r = 0.37) but not in vitro (r = 0.07), despite the lack of training on mRNAs and the fact that DMS accessibility is only an approximation to solvent accessibility. We further found strong association across coding and noncoding regions between predicted solvent accessibility of the mutation site of a single nucleotide variant (SNV) and the frequency of that variant in the population for 2.2 million SNVs obtained in the 1000 Genomes Project. Moreover, mapping solvent accessibility of RNAs to the human genome indicated that introns, 5' cap of 5' and 3' cap of 3' untranslated regions, are more solvent accessible, consistent with their respective functional roles. These results support conformational selections as the mechanism for the formation of RNA-protein complexes and highlight the utility of genome-scale characterization of RNA tertiary structures by RNAsnap. The server and its stand-alone downloadable version are available at http://sparks-lab.org. © 2016 Yang et al.; Published by Cold Spring Harbor Laboratory Press for the RNA Society.

  4. In silico modeling techniques for predicting the tertiary structure of human H4 receptor.

    Science.gov (United States)

    Zaid, Hilal; Raiyn, Jamal; Osman, Midhat; Falah, Mizied; Srouji, Samer; Rayan, Anwar

    2016-01-01

    First cloned in 2000, the human Histamine H4 Receptor (hH4R) is the last member of the histamine receptors family discovered so far, it belongs to the GPCR super-family and is involved in a wide variety of immunological and inflammatory responses. Potential hH4R antagonists are proposed to have therapeutic potential for the treatment of allergies, inflammation, asthma and colitis. So far, no hH4R ligands have been successfully introduced to the pharmaceutical market, which creates a strong demand for new selective ligands to be developed. in silico techniques and structural based modeling are likely to facilitate the achievement of this goal. In this review paper we attempt to cover the fundamental concepts of hH4R structure modeling and its implementations in drug discovery and development, especially those that have been experimentally tested and to highlight some ideas that are currently being discussed on the dynamic nature of hH4R and GPCRs, in regards to computerized techniques for 3-D structure modeling.

  5. Modelling the harmonized tertiary Institutions Salary Structure ...

    African Journals Online (AJOL)

    This paper analyses the Harmonized Tertiary Institution Salary Structure (HATISS IV) used in Nigeria. The irregularities in the structure are highlighted. A model that assumes a polynomial trend for the zero step salary, and exponential trend for the incremental rates, is suggested for the regularization of the structure.

  6. Tertiary lymphoid structures in cancer and beyond.

    Science.gov (United States)

    Dieu-Nosjean, Marie-Caroline; Goc, Jérémy; Giraldo, Nicolas A; Sautès-Fridman, Catherine; Fridman, Wolf Herman

    2014-11-01

    Tertiary lymphoid structures (TLS) are ectopic lymphoid formations found in inflamed, infected, or tumoral tissues. They exhibit all the characteristics of structures in the lymph nodes (LN) associated with the generation of an adaptive immune response, including a T cell zone with mature dendritic cells (DC), a germinal center with follicular dendritic cells (FDC) and proliferating B cells, and high endothelial venules (HEV). In this review, we discuss evidence for the roles of TLS in chronic infection, autoimmunity, and cancer, and address the question of whether TLS present beneficial or deleterious effects in these contexts. We examine the relationship between TLS in tumors and patient prognosis, and discuss the potential role of TLS in building and/or maintaining local immune responses and how this understanding may guide therapeutic interventions. Copyright © 2014 Elsevier Ltd. All rights reserved.

  7. Tertiary alphabet for the observable protein structural universe.

    Science.gov (United States)

    Mackenzie, Craig O; Zhou, Jianfu; Grigoryan, Gevorg

    2016-11-22

    Here, we systematically decompose the known protein structural universe into its basic elements, which we dub tertiary structural motifs (TERMs). A TERM is a compact backbone fragment that captures the secondary, tertiary, and quaternary environments around a given residue, comprising one or more disjoint segments (three on average). We seek the set of universal TERMs that capture all structure in the Protein Data Bank (PDB), finding remarkable degeneracy. Only ∼600 TERMs are sufficient to describe 50% of the PDB at sub-Angstrom resolution. However, more rare geometries also exist, and the overall structural coverage grows logarithmically with the number of TERMs. We go on to show that universal TERMs provide an effective mapping between sequence and structure. We demonstrate that TERM-based statistics alone are sufficient to recapitulate close-to-native sequences given either NMR or X-ray backbones. Furthermore, sequence variability predicted from TERM data agrees closely with evolutionary variation. Finally, locations of TERMs in protein chains can be predicted from sequence alone based on sequence signatures emergent from TERM instances in the PDB. For multisegment motifs, this method identifies spatially adjacent fragments that are not contiguous in sequence-a major bottleneck in structure prediction. Although all TERMs recur in diverse proteins, some appear specialized for certain functions, such as interface formation, metal coordination, or even water binding. Structural biology has benefited greatly from previously observed degeneracies in structure. The decomposition of the known structural universe into a finite set of compact TERMs offers exciting opportunities toward better understanding, design, and prediction of protein structure.

  8. Pushing the frontiers of atomic models for protein tertiary structure ...

    Indian Academy of Sciences (India)

    as an NP complete or NP hard problem.4,5 This notwith- standing, the dire need for tertiary structures of proteins in drug discovery and other areas6–8 has propelled the development of a multitude of computational recipes. In this article, we focus on ab initio/de novo strategies,. Bhageerath in particular, for protein tertiary ...

  9. Induction of secondary and tertiary lymphoid structures in the skin.

    NARCIS (Netherlands)

    Cupedo, T.; Jansen, W.; Kraal, G.; Mebius, R.E.

    2004-01-01

    During embryogenesis a developmental program leading to the formation of lymph nodes and Peyer's patches is initiated. We now show that lymph node-like structures as well as tertiary lymphoid structures can ectopically be induced by intradermal injection of newborn lymph node-derived cells.

  10. Study of tertiary creep instability in several elevated-temperature structural materials

    International Nuclear Information System (INIS)

    Booker, M.K.; Sikka, V.K.

    1978-01-01

    Data for a number of common elevated temperature structural materials have been analyzed to yield mathematical predictions for the time and strain to tertiary creep at various rupture lives and temperatures. Materials examined include types 304 and 316 stainless steel, 2 1/4 Cr-1 Mo steel, alloy 800H, alloy 718, Hastelloy alloy X, and ERNiCr--3 weld metal. Data were typically examined over a range of creep temperatures for rupture lives ranging from less than 100 to greater than 10,000 hours. Within a given material, trends in these quantities can be consistently described, but it is difficult to directly relate the onset of tertiary creep to failure-inducing instabilities. A series of discontinued tests for alloy 718 at 649 and 620 0 C showed that the material fails by intergranular cracking but that no significant intergranular cracking occurs until well after the onset of tertiary creep

  11. Tertiary structure in N-linked oligosaccharides.

    Science.gov (United States)

    Homans, S W; Dwek, R A; Rademacher, T W

    1987-10-06

    Distance constraints derived from two-dimensional nuclear Overhauser effect measurements have been used to define the orientation of the Man alpha 1-3Man beta linkage in seven different N-linked oligosaccharides, all containing the common pentasaccharide core Man alpha 1-6(Man alpha 1-3)Man beta 1-4GlcNAc beta 1-4GlcNAc. Conformational invariance of the Man alpha 1-3Man beta linkage was found for those structures bearing substitutions on the Man alpha 1-3Man beta antenna. However, the presence of either a GlcNAc residue in the beta 1-4 linkage to Man beta ("bisecting GlcNAc") or a xylose residue in the beta 1-2 linkage to Man beta of the trimannosyl core was found to generate conformational transitions that were similar. These transitions were accompanied by characteristic chemical shift perturbations of proton resonances in the vicinity of the Man alpha 1-3Man beta linkage. Molecular orbital energy calculations suggest that the conformational transition between the unsubstituted and substituted cores arises from energetic constraints in the vicinity of the Man alpha 1-3Man beta linkage, rather than specific long-range interactions. These data taken together with our previous results on the Man alpha 1-6Man beta linkage [Homans, S. W., Dwek R. A., Boyd, J., Mahmoudian, M., Richards, W. G., & Rademacher, T. W. (1986) Biochemistry 25, 6342] allow us to discuss the consequences of the modulation of oligosaccharide solution conformations.

  12. Analysis of Management Practices in Lagos State Tertiary Institutions through Total Quality Management Structural Framework

    Science.gov (United States)

    AbdulAzeez, Abbas Tunde

    2016-01-01

    This research investigated total quality management practices and quality teacher education in public tertiary institutions in Lagos State. The study was therefore designed to analyse management practices in Lagos state tertiary institutions through total quality management structural framework. The selected public tertiary institutions in Lagos…

  13. Beyond BLASTing: Tertiary and Quaternary Structure Analysis Helps Identify Major Vault Proteins

    Science.gov (United States)

    Daly, Toni K.; Sutherland-Smith, Andrew J.; Penny, David

    2013-01-01

    We examine the advantages of going beyond sequence similarity and use both protein three-dimensional (3D) structure prediction and then quaternary structure (docking) of inferred 3D structures to help evaluate whether comparable sequences can fold into homologous structures with sufficient lateral associations for quaternary structure formation. Our test case is the major vault protein (MVP) that oligomerizes in multiple copies to form barrel-like vault particles and is relatively widespread among eukaryotes. We used the iterative threading assembly refinement server (I-TASSER) to predict whether putative MVP sequences identified by BLASTp and PSI Basic Local Alignment Search Tool are structurally similar to the experimentally determined rodent MVP tertiary structures. Then two identical predicted quaternary structures from I-TASSER are analyzed by RosettaDock to test whether a pair-wise association occurs, and hence whether the oligomeric vault complex is likely to form for a given MVP sequence. Positive controls for the method are the experimentally determined rat (Rattus norvegicus) vault X-ray crystal structure and the purple sea urchin (Strongylocentrotus purpuratus) MVP sequence that forms experimentally observed vaults. These and two kinetoplast MVP structural homologs were predicted with high confidence value, and RosettaDock predicted that these MVP sequences would dock laterally and therefore could form oligomeric vaults. As the negative control, I-TASSER did not predict an MVP-like structure from a randomized rat MVP sequence, even when constrained to the rat MVP crystal structure (PDB:2ZUO), thus further validating the method. The protocol identified six putative homologous MVP sequences in the heterobolosean Naegleria gruberi within the excavate kingdom. Two of these sequences are predicted to be structurally similar to rat MVP, despite being in excess of 300 residues shorter. The method can be used generally to help test predictions of homology via

  14. Structural prediction in aphasia

    Directory of Open Access Journals (Sweden)

    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

  15. Linguistic Structure Prediction

    CERN Document Server

    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

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

    DEFF Research Database (Denmark)

    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...

  17. Characteristics and Prediction of RNA Structure

    Directory of Open Access Journals (Sweden)

    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.

  18. Examination of factors predicting secondary students' interest in tertiary STEM education

    Science.gov (United States)

    Chachashvili-Bolotin, Svetlana; Milner-Bolotin, Marina; Lissitsa, Sabina

    2016-02-01

    Based on the Social Cognitive Career Theory (SCCT), the study aims to investigate factors that predict students' interest in pursuing science, technology, engineering, and mathematics (STEM) fields in tertiary education both in general and in relation to their gender and socio-economic background. The results of the analysis of survey responses of 2458 secondary public school students in the fifth-largest Israeli city indicate that STEM learning experience positively associates with students' interest in pursuing STEM fields in tertiary education as opposed to non-STEM fields. Moreover, studying advanced science courses at the secondary school level decreases (but does not eliminate) the gender gap and eliminates the effect of family background on students' interest in pursuing STEM fields in the future. Findings regarding outcome expectations and self-efficacy beliefs only partially support the SCCT model. Outcome expectations and self-efficacy beliefs positively correlate with students' entering tertiary education but did not differentiate between their interests in the fields of study.

  19. [Changes in the secondary and tertiary structure of serum albumin in interactions with ligands of various structures].

    Science.gov (United States)

    Trinus, F P; Braver-Chernobul'skaia, B S; Luĭk, A I; Boldeskul, A E; Velichko, A N

    1984-01-01

    High affinity interactions between blood serum albumin and five substances of various chemical structure, exhibiting distinct physiological activity, were accompanied by alterations in the protein tertiary structure, while the albumin secondary structure was involved in conformational transformation after less effective affinity binding.

  20. Do health complaints in adolescence negatively predict the chance of entering tertiary education in young adulthood?

    Science.gov (United States)

    Låftman, Sara B; Magnusson, Charlotta

    2017-12-01

    Self-reported psychological and psychosomatic health complaints, such as nervousness, sadness, headache and stomach-ache, are common among adolescents, particularly among girls, and studies suggest that the prevalence has risen among adolescent girls during the last few decades. However, only a limited number of studies have investigated the potential long-term consequences of such health complaints. The aim of the current study was to assess whether psychological and psychosomatic health complaints in adolescence predict the chance of entering tertiary education in young adulthood among women and men. The data used are from the Swedish Young-LNU, which is based on a nationally representative sample with self-reported survey information from adolescents aged 10-18 years in 2000 and from the same individuals at ages 20-28 in 2010 ( n=783). Information was also collected from parents and from official registers. Linear probability models showed that self-reported psychological complaints in adolescence were associated with a lower chance of having entered tertiary education 10 years later. This association was accounted for by differences in grade point average (GPA), suggesting that GPA may mediate the association between psychological complaints and later education. The pattern was similar for both genders. Furthermore, among men, psychosomatic complaints in adolescence were significantly associated with a lower likelihood of having entered tertiary education 10 years later when adjusting for GPA and social class in adolescence. A similar but non-significant tendency was found among women. The findings suggest that health complaints in adolescence may have long-term consequences in terms of lower educational attainment.

  1. The roles of tertiary amine structure, background organic matter and chloramine species on NDMA formation.

    Science.gov (United States)

    Selbes, Meric; Kim, Daekyun; Ates, Nuray; Karanfil, Tanju

    2013-02-01

    N-nitrosodimethylamine (NDMA), a probable human carcinogen, is a disinfection by-product that has been detected in chloraminated and chlorinated drinking waters and wastewaters. Formation mechanisms and precursors of NDMA are still not well understood. The main objectives of this study were to systematically investigate (i) the effect of tertiary amine structure, (ii) the effect of background natural organic matter (NOM), and (iii) the roles of mono vs. dichloramine species on the NDMA formation. Dimethylamine (DMA) and 20 different tertiary aliphatic and aromatic amines were carefully examined based on their functional groups attached to the basic DMA structure. The wide range (0.02-83.9%) of observed NDMA yields indicated the importance of the structure of tertiary amines, and both stability and electron distribution of the leaving group of tertiary amines on NDMA formation. DMA associated with branched alkyl groups or benzyl like structures having only one carbon between the ring and DMA structure consistently gave higher NDMA yields. Compounds with electron withdrawing groups (EWG) reacted preferentially with monochloramine, whereas compounds with electron donating group (EDG) showed tendency to react with dichloramine to form NDMA. When the selected amines were present in NOM solutions, NDMA formation increased for compounds with EWG while decreased for compounds with EDG. This impact was attributed to the competitions between NOM and amines for chloramine species. The results provided additional information to the commonly accepted mechanism for NDMA formation including chloramine species reacting with tertiary amines and the role of the leaving group on overall NDMA conversion. Copyright © 2012 Elsevier Ltd. All rights reserved.

  2. PROGRAM SYSTEM AND INFORMATION METADATA BANK OF TERTIARY PROTEIN STRUCTURES

    Directory of Open Access Journals (Sweden)

    T. A. Nikitin

    2013-01-01

    Full Text Available The article deals with the architecture of metadata storage model for check results of three-dimensional protein structures. Concept database model was built. The service and procedure of database update as well as data transformation algorithms for protein structures and their quality were presented. Most important information about entries and their submission forms to store, access, and delivery to users were highlighted. Software suite was developed for the implementation of functional tasks using Java programming language in the NetBeans v.7.0 environment and JQL to query and interact with the database JavaDB. The service was tested and results have shown system effectiveness while protein structures filtration.

  3. Evolution of Tertiary Structure of Viral RNA Dependent Polymerases

    Czech Academy of Sciences Publication Activity Database

    Černý, Jiří; Černá, B.; Valdés, James J.; Grubhoffer, Libor; Růžek, Daniel

    2014-01-01

    Roč. 9, č. 5 (2014), e96070 E-ISSN 1932-6203 R&D Projects: GA ČR GAP502/11/2116; GA ČR GAP302/12/2490; GA MŠk(CZ) EE2.3.30.0032 Institutional support: RVO:60077344 Keywords : Q-BETA replicase * C virus RNA * crystal structure Subject RIV: EB - Genetics ; Molecular Biology Impact factor: 3.234, year: 2014

  4. Prediction of molecular crystal structures

    International Nuclear Information System (INIS)

    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

  5. Prediction of molecular crystal structures

    Energy Technology Data Exchange (ETDEWEB)

    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

  6. Thermodynamics and kinetics of RNA tertiary structure formation in the junctionless hairpin ribozyme.

    Science.gov (United States)

    White, Neil A; Hoogstraten, Charles G

    2017-09-01

    The hairpin ribozyme consists of two RNA internal loops that interact to form the catalytically active structure. This docking transition is a rare example of intermolecular formation of RNA tertiary structure without coupling to helix annealing. We have used temperature-dependent surface plasmon resonance (SPR) to characterize the thermodynamics and kinetics of RNA tertiary structure formation for the junctionless form of the ribozyme, in which loops A and B reside on separate molecules. We find docking to be strongly enthalpy-driven and to be accompanied by substantial activation barriers for association and dissociation, consistent with the structural reorganization of both internal loops upon complex formation. Comparisons with the parallel analysis of a ribozyme variant carrying a 2'-O-methyl modification at the self-cleavage site and with published data in other systems reveal a surprising diversity of thermodynamic signatures, emphasizing the delicate balance of contributions to the free energy of formation of RNA tertiary structure. Copyright © 2017 Elsevier B.V. All rights reserved.

  7. Factors predicting quality of work life among nurses in tertiary-level hospitals, Bangladesh.

    Science.gov (United States)

    Akter, N; Akkadechanunt, T; Chontawan, R; Klunklin, A

    2018-06-01

    This study examined the level of quality of work life and predictability of years of education, monthly income, years of experience, job stress, organizational commitment and work environment on quality of work life among nurses in tertiary-level hospitals in the People's Republic of Bangladesh. There is an acute shortage of nurses worldwide including Bangladesh. Quality of work life is important for quality of patient care and nurse retention. Nurses in Bangladesh are fighting to provide quality care for emerging health problems for the achievement of sustainable development goals. We collected data from 288 randomly selected registered nurses, from six tertiary-level hospitals. All nurses were requested to fill questionnaire consisted of Demographic Data Sheet, Quality of Nursing Work Life Survey, Expanded Nursing Stress Scale, Questionnaire of Organizational Commitment and Practice Environment Scale of the Nursing Work Index. Data were analysed by descriptive statistics and multiple regression. The quality of work life as perceived by nurses in Bangladesh was at moderate level. Monthly income was found as the best predictor followed by work environment, organizational commitment and job stress. A higher monthly income helps nurses to fulfil their personal needs; positive work environment helps to provide quality care to the patients. Quality of work life and predictors measured by self-report only may not reflect the original picture of the quality of work life among nurses. Findings provide information for nursing and health policymakers to develop policies to improve quality of work life among nurses that can contribute to quality of nursing care. This includes the working environment, commitment to the organization and measures to reduce job stress. © 2017 International Council of Nurses.

  8. Algorithms for Protein Structure Prediction

    DEFF Research Database (Denmark)

    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...

  9. Dynamic response of tertiary systems in structures subjected to base excitation

    International Nuclear Information System (INIS)

    Hernried, A.G.; Kai-sing Lau

    1988-01-01

    The dynamic response of very lightweight equipment (tertiary subsystem) attached to light equipment (secondary subsystem) which in turn is attached to a heavier structure (primary subsystem) that is subjected to ground shock or earthquake excitation is investigated. Both the single-degree-of-freedom and multi-degree-of-freedom subsystem models are considered. The systems are damped as well as undamped, completely detuned (all natural frequencies of the subsystems well spaced), singly tuned (one natural frequency of each subsystem equal or close to one another), or multiply tuned (more than one natural frequency of the subsystems close to each other). Efficient techniques for the determination of the tertiary subsystem response that avoid a computationally intensive numerical integration of the combined system equations are presented. (author)

  10. Computational prediction of vaccine potential epitopes and 3-dimensional structure of XAGE-1b for non-small cell lung cancer immunotherapy

    Directory of Open Access Journals (Sweden)

    Mohammad M. Tarek

    2018-04-01

    Conclusions: This study predicted a model of XAGE-1b tertiary structure which could explain its antigenic function and facilitate usage of predicted peptides for experimental validation towards designing immunotherapies against NSCLC.

  11. Neural Networks for protein Structure Prediction

    DEFF Research Database (Denmark)

    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...

  12. Fast protein tertiary structure retrieval based on global surface shape similarity.

    Science.gov (United States)

    Sael, Lee; Li, Bin; La, David; Fang, Yi; Ramani, Karthik; Rustamov, Raif; Kihara, Daisuke

    2008-09-01

    Characterization and identification of similar tertiary structure of proteins provides rich information for investigating function and evolution. The importance of structure similarity searches is increasing as structure databases continue to expand, partly due to the structural genomics projects. A crucial drawback of conventional protein structure comparison methods, which compare structures by their main-chain orientation or the spatial arrangement of secondary structure, is that a database search is too slow to be done in real-time. Here we introduce a global surface shape representation by three-dimensional (3D) Zernike descriptors, which represent a protein structure compactly as a series expansion of 3D functions. With this simplified representation, the search speed against a few thousand structures takes less than a minute. To investigate the agreement between surface representation defined by 3D Zernike descriptor and conventional main-chain based representation, a benchmark was performed against a protein classification generated by the combinatorial extension algorithm. Despite the different representation, 3D Zernike descriptor retrieved proteins of the same conformation defined by combinatorial extension in 89.6% of the cases within the top five closest structures. The real-time protein structure search by 3D Zernike descriptor will open up new possibility of large-scale global and local protein surface shape comparison. 2008 Wiley-Liss, Inc.

  13. Depression and catastrophizing predict suicidal ideation in tertiary care patients with interstitial cystitis/bladder pain syndrome

    DEFF Research Database (Denmark)

    Tripp, Dean A; Nickel, J Curtis; Krsmanovic, Adrijana

    2016-01-01

    is the first to implicate multiple psychosocial risk factors over and above IC/BPS-specific symptoms and patient pain experience in SI in women with IC/BPS. Depression in particular is uniquely important in predicting suicidality. These results support a multidisciplinary, proactive approach to IC......Introduction: We sought to evaluate psychosocial factors as predictors of suicidal ideation (SI) in a tertiary care outpatient sample of women suffering from interstitial cystitis/bladder pain syndrome (IC/BPS). Methods: The patients are women managed at tertiary care centres (n=190). Controls were...... of psychosocial variables on patient SI. Results: Compared to 6% in healthy controls, 23% of patients endorsed SI in the past two weeks. Correlations between SI, depression, and catastrophizing across controls and cases show that for controls, SI is associated with greater pain (0.31; pdepression only...

  14. Hydrogen bond indices and tertiary structure of yeast tRNA sup(Phe)

    International Nuclear Information System (INIS)

    Giambiagi, M.S. de; Giambiagi, M.; Esquivel, D.M.S.

    1982-01-01

    The rigidity and stability of the tertiary structure of yeast tRNA sup(Phe) is related to a bond index employed in an IEHT calculation. The index permits a quantitative estimate of the electronic cloud along the hydrogen bond, having thus an appealing physical meaning. The results indicate that Hoogsteen-type bonds have, as expected, greater electronic population than Watson-Crick type ones. Other non-Watson-Crick pairings, the wobble pair and G 15 -C 48 , exhibit high values of the index for the NH...O bond. In the triples, the electronic density of the hydrogen bridges does not weaken, comparing it with the one of the pairs involved. Contour density maps are shown and dipolar moments of pairs and triples are qualitatively discussed. (Author) [pt

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

    Science.gov (United States)

    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.

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

    Science.gov (United States)

    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.

  17. Tertiary Lymphoid Structures in Cancer: Drivers of Antitumor Immunity, Immunosuppression, or Bystander Sentinels in Disease?

    Science.gov (United States)

    Colbeck, Emily Jayne; Ager, Ann; Gallimore, Awen; Jones, Gareth Wyn

    2017-01-01

    Secondary lymphoid organs are integral to initiation and execution of adaptive immune responses. These organs provide a setting for interactions between antigen-specific lymphocytes and antigen-presenting cells recruited from local infected or inflamed tissues. Secondary lymphoid organs develop as a part of a genetically preprogrammed process during embryogenesis. However, organogenesis of secondary lymphoid tissues can also be recapitulated in adulthood during de novo lymphoid neogenesis of tertiary lymphoid structures (TLSs). These ectopic lymphoid-like structures form in the inflamed tissues afflicted by various pathological conditions, including cancer, autoimmunity, infection, or allograft rejection. Studies are beginning to shed light on the function of such structures in different disease settings, raising important questions regarding their contribution to progression or resolution of disease. Data show an association between the tumor-associated TLSs and a favorable prognosis in various types of human cancer, attracting the speculation that TLSs support effective local antitumor immune responses. However, definitive evidence for the role for TLSs in fostering immune responses in vivo are lacking, with current data remaining largely correlative by nature. In fact, some more recent studies have even demonstrated an immunosuppressive, tumor-promoting role for cancer-associated TLSs. In this review, we will discuss what is known about the development of cancer-associated TLSs and the current understanding of their potential role in the antitumor immune response. PMID:29312327

  18. Interaction Of Calcium Phosphate Nanoparticles With Human Chorionic Gonadotropin Modifies Secondary And Tertiary Protein Structure

    Directory of Open Access Journals (Sweden)

    Al-Hakeim Hussein K

    2015-12-01

    Full Text Available Calcium phosphate nanoparticles (CaPNP have good biocompatibility and bioactivity inside human body. In this study, the interaction between CaPNP and human chorionic gonadotropin (hCG was analyzed to determine the changes in the protein structure in the presence of CaPNP and the quantity of protein adsorbed on the CaPNP surface. The results showed a significant adsorption of hCG on the CaPNP nanoparticle surface. The optimal fit was achieved using the Sips isotherm equation with a maximum adsorption capacity of 68.23 µg/mg. The thermodynamic parameters, including ∆H° and ∆G°, of the adsorption process are positive, whereas ∆S° is negative. The circular dichroism results of the adsorption of hCG on CaPNP showed the changes in its secondary structure; such changes include the decomposition of α-helix strand and the increase in β-pleated sheet and random coil percentages. Fluorescence study indicated minimal changes in the tertiary structure near the microenvironment of the aromatic amino acids such as tyrosine and phenyl alanine caused by the interaction forces between the CaPNP and hCG protein. The desorption process showed that the quantity of the hCG desorbed significantly increases as temperature increases, which indicates the weak forces between hCG and the surface.

  19. Stratigraphic structure of the B1 Tertiary tectonostratigraphic unit in eastern Slovenia

    Directory of Open Access Journals (Sweden)

    Bogomir Jelen

    2002-06-01

    Full Text Available High inconsistency and incoherence in the stratigraphy of the Slovenian upper Paleogene and lower Miocene have remained unsolved in the past 150 years. To solve the problem, we tried to rigorously conduct the authentic Galilei’s scientific method. Steps of logical and empirical verification confirmed the existence of the posited B1 Tertiary tectonostratigraphic unit, and a general chronostratigraphic model of new positional relationships of lithologic units resulted from rather good biochronostratigraphic resolution achieved by nannoplankton and planktonic foraminifera biostratigraphy. The application of principles of newly developed fields in science helped us to avoid errors in transmission of messages (to reduce noise from the source (rock to the concept formation,which had been done previously. This in turn has strongly reduced inconsistency andincoherence (high information entropy = uncertainty. The released amount of information enabled us to answer also questions that reached beyond the original difficulty, e.g.: is the tectonostratigraphic structure of eastern Slovenia a manifestation of plate tectonics processes, and of which ones, are theories of continental escape in the Alps and associated dissection and offset of the formerly uniform Slovenian-Hungarian Paleogene basin tenableor not, are then there in the B1 stratigraphic equivalents of the Hungarian Paleogene basin formations, where are the important Eocene / Oligocene, Paleogene / Neogene, Rupelian / Chattian and Kiscellian / Egerian boundaries in Slovenia, and is there acontinuation of the B1 in Croatia and in the Mid-Hungarian tectonic zone?

  20. Building the Nonuniversity, Tertiary Care Center Hepatobiliary and Pancreatic Surgery Practice: Structural and Financial Considerations.

    Science.gov (United States)

    Baker, Erin H; Siddiqui, Imran; Vrochides, Dionisios; Iannitti, David A; Martinie, John B; Rorabaugh, Lauren; Jeyarajah, D Rohan; Swan, Ryan Z

    2016-12-01

    Early in their careers, many new surgeons lack the background and experience to understand essential components needed to build a surgical practice. Surgical resident education is often devoid of specific instruction on the business of medicine and practice management. In particular, hepatobiliary and pancreatic (HPB) surgeons require many key components to build a successful practice secondary to significant interdisciplinary coordination and a scope of complex surgery, which spans challenging benign and malignant disease processes. In the following, we describe the required clinical and financial components for developing a successful HPB surgery practice in the nonuniversity tertiary care center. We discuss significant financial considerations for understanding community need and hospital investment, contract establishment, billing, and coding. We summarize the structural elements and key personnel necessary for establishing an effectual HPB surgical team. This article provides useful, essential information for a new HPB surgeon looking to establish a surgical practice. It also provides insight for health-care administrators as to the value an HPB surgeon can bring to a hospital or health-care system.

  1. Are clinical features able to predict Helicobacter pylori gastritis patterns? Evidence from tertiary centers.

    Science.gov (United States)

    Carabotti, Marilia; Lahner, Edith; Porowska, Barbara; Colacci, Enzo; Trentino, Paolo; Annibale, Bruno; Severi, Carola

    2014-12-01

    Outcome of Helicobacter pylori infection is different according to gastritis extension (i.e. antrum-restricted gastritis or pangastritis). The aim of this study is to evaluate whether different gastritis patterns are associated with specific gastrointestinal symptoms or clinical signs that could be suggestive of the topography of gastritis. 236 consecutive symptomatic outpatients were recruited in two tertiary centers. They filled in a validated and self-administered Rome III modular symptomatic questionnaire, and underwent gastroscopy with histological sampling. 154 patients with Helicobacter pylori infection were included. Clinical presentation did not differ between antrum-restricted gastritis and pangastritis, gastro-esophageal reflux disease being present in 48.2 and 54.1 % of patients and dyspepsia in 51.8 and 45.9 %, respectively. However, pangastritis statistically differed from antrum-restricted gastritis in that the presence of clinical signs (p gastritis pattern whereas their association with signs, accurately detected, is indicative for the presence of pangastritis.

  2. FACTORS PREDICTING MORBIDITY IN PATIENTS WITH DENGUE FEVER IN A TERTIARY CARE HOSPITAL

    Directory of Open Access Journals (Sweden)

    Muhammad Imran Hasan Khan

    2013-02-01

    Full Text Available Introduction: Dengue virus (DENV affects over half the world’s population in 112 countries, and dengue fever (DF is the second largest arthropod borne infectious global hazard after malaria with complications like Dengue Hemorrhagic Fever (DHF and Dengue Shock Syndrome (DSS accounting for significant morbidity and mortality world over. Pakistan is significantly affected with DENV infection and to-date no study identifying risk factors associated with complications of DF has been done. Methods: 997 confirmed cases of DF were collected in a tertiary care hospital in Lahore, Pakistan and their clinical and biochemical data were collected. Univariate, multivariate and logistics regression analysis was performed to identify risk factors associated with development of DHF and DSS. Results: Bleeding OR 70.7 (CI 38.4-129.9, deranged liver function test OR 1.9 (CI 0.97-0.99, platelet count on admission less than 50,000 x109/L OR 0.16 (CI 0.13-0.19, presence of urinary red blood cells OR 1.4 (CI 0.179-0.900 and presence of urinary protein OR 1.1 (CI 0.191-0.974 were related to development of DHF and DSS.

  3. Phylogeography and genetic structure of a Tertiary relict tree species, Tapiscia sinensis (Tapisciaceae): implications for conservation.

    Science.gov (United States)

    Zhang, Jinju; Li, Zuozhou; Fritsch, Peter W; Tian, Hua; Yang, Aihong; Yao, Xiaohong

    2015-10-01

    The phylogeography of plant species in sub-tropical China remains largely unclear. This study used Tapiscia sinensis, an endemic and endangered tree species widely but disjunctly distributed in sub-tropical China, as a model to reveal the patterns of genetic diversity and phylogeographical history of Tertiary relict plant species in this region. The implications of the results are discussed in relation to its conservation management. Samples were taken from 24 populations covering the natural geographical distribution of T. sinensis. Genetic structure was investigated by analysis of molecular variance (AMOVA) and spatial analysis of molecular variance (SAMOVA). Phylogenetic relationships among haplotypes were constructed with maximum parsimony and haplotype network methods. Historical population expansion events were tested with pairwise mismatch distribution analysis and neutrality tests. Species potential range was deduced by ecological niche modelling (ENM). A low level of genetic diversity was detected at the population level. A high level of genetic differentiation and a significant phylogeographical structure were revealed. The mean divergence time of the haplotypes was approx. 1·33 million years ago. Recent range expansion in this species is suggested by a star-like haplotype network and by the results from the mismatch distribution analysis and neutrality tests. Climatic oscillations during the Pleistocene have had pronounced effects on the extant distribution of Tapiscia relative to the Last Glacial Maximum (LGM). Spatial patterns of molecular variation and ENM suggest that T. sinensis may have retreated in south-western and central China and colonized eastern China prior to the LGM. Multiple montane refugia for T. sinense existing during the LGM are inferred in central and western China. The populations adjacent to or within these refugia of T. sinense should be given high priority in the development of conservation policies and management strategies for

  4. The role of big five factors on predicting job crafting propensities amongst administrative employees in a South African tertiary institution

    Directory of Open Access Journals (Sweden)

    Clement Bell

    2016-04-01

    Full Text Available Orientation: Personality provides a foundation for understanding employee job behaviours. It determines and reflects how they respond to their work situations. There is a shortage of previous researches that have specifically dealt with the predictive role of personality on job crafting. Job crafting is also a significantly new concept in the South African work context. It has both positive and negative consequences on employee job behaviours. Research purpose: The present study investigated the role of big five factors on predicting job crafting propensities amongst administrative employees in Alice, South Africa. Motivation for the study: The present study aimed to determine the role of big five factors on predicting job crafting propensities amongst administrative employees. It was premised on previous research that the big five factors are associated with many employee job behaviours. Research approach, design and method: The present study employed a quantitative, crosssectional research design with a sample of 246 administrative employees in Alice, South Africa. A biographical questionnaire, a Big Five Inventory, and a job crafting questionnaire were used to collect data. Main findings: The findings showed that big five factors of Conscientiousness, Extraversion, Agreeableness, Openness to experience and Neuroticism play a significant role in predicting job crafting propensities. Practical implications: The present study suggests that big five factors of Conscientiousness, Extraversion, Agreeableness, Openness to experience and Neuroticism have a predictive role on job crafting behaviours. Managers of tertiary institutions can therefore consider these big five personalities to understand and predict the impacts of their job design strategies on administrative employees’ behaviours. Contribution: The contribution of the study was significant in that it contributed to research literature representing the influence of the big five factors in

  5. Facilities management: Structuring a body of knowledge for continuing and tertiary education in South Africa

    Directory of Open Access Journals (Sweden)

    A. C. Hauptfleisch

    2010-01-01

    is exacerbated by the fact that very little is being done at the level of continuing education and formal tertiary education in South Africa. The limited attempts in this regard are furthermore taking place in isolation, with practically no dialogue between the limited facilities management industry and the providers of the limited education that is available.Although the study that was undertaken did not have the objective to create comprehensive interaction between the existing role-players, the stated problems were addressed by involving the industry, participants in continuing education programmes and other role-players in the condensed research problem as stated below. The problem at hand is firstly to extract, from the present practice of facilities management, a knowledge framework and secondly to formulate the results in terms of suitable continuing and tertiary education programmes to address the shortcomings in South Africa. Research was therefore undertaken to address the structuring of educational programmes. The methodology applied comprised a comprehensive literature survey and three quantified and qualified data surveys, the latter conducted amongst facilities management practitioners, primarily the perceived beneficiaries of such programmes. These surveys provided information that led to the resultant educational activities stated below. The most important aspects that were determined were the following: It was possible to create and test amongst facilities management practitioners a diagrammatic presentation that contextualised facilities management. The literature survey, including international and South African sources, contributed towards compiling a primary knowledge framework for facilities management. The perceived value of the contents of a continuing education programme that had been attended by numerous groups of delegates over a number of years was established. A proposed tertiary education programme was presented to practitioners

  6. Nucleic acid secondary structure prediction and display.

    OpenAIRE

    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.

  7. Applications of contact predictions to structural biology

    Directory of Open Access Journals (Sweden)

    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

  8. Risk factors predicting mortality in patients with lung abscess in a public tertiary care center in Karachi, Pakistan.

    Science.gov (United States)

    Ghazal, Shaista; Kumar, Ashok; Shrestha, Binav; Sajid, Sana; Malik, Maria; Rizvi, Nadeen

    2013-01-01

    Lung abscess is a commonly encountered entity in South-East Asia but not much data regarding its outcome is available. The objective of this study was to identify the factors associated with increased mortality in patients diagnosed with lung abscess in a tertiary care center of Karachi, Pakistan. A retrospective case analysis was performed via hospital records, on patients admitted with lung abscess between January 2009 and January 2011 at the largest state-owned tertiary care centre in Karachi, Pakistan. Out of the 41 patients hospitalized, 17 could not survive and were evaluated for clinical, radiological and microbiological factors to determine association with heightened mortality. Mortality due to lung abscess stood at 41.4% (17 of 41 cases). Adult male patients were found to have higher mortality with 13 out of 17 (43%) dead patients being male. A majority (21/41, 51.2%) of the cases belonged to the 41-60 year old age group. Highest mortality was seen in patients200 mg/dL (56%) succumb to disease. Patients with a positive history of smoking, diabetes mellitus, and alcohol intake expressed mortality rates of 44%, 56%, and 50% respectively; while 29.4% of the mortalities were positive for Pseudomonas aeruginosa on sputum culture. A significant association was found with elevated mortality and low haemoglobin levels at time of admission; mortality was 58% (p=0.005) in patients with Hb less than or equal to 10 mg/dL. The risk factors involved with heightened mortality included male gender and history of smoking, diabetes and alcohol intake. High blood sugar levels and detection of Pseudomonas aeruginosa on sputum cultures were also implicated. Anemia (Hb level less than or equal to 10 mg/dl) was statistically significant predictive factor for increased mortality.

  9. Predicting RNA Structure Using Mutual Information

    DEFF Research Database (Denmark)

    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...

  10. Computational predictions of zinc oxide hollow structures

    Science.gov (United States)

    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.

  11. Optimal neural networks for protein-structure prediction

    International Nuclear Information System (INIS)

    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

  12. Predictive value of Borrelia burgdorferi IgG antibody levels in patients referred to a tertiary Lyme centre.

    Science.gov (United States)

    Zwerink, M; Zomer, T P; van Kooten, B; Blaauw, G; van Bemmel, T; van Hees, B C; Vermeeren, Y M; Landman, G W

    2018-03-01

    A two-step testing strategy is recommended in serological testing for Lyme borreliosis; positive and indeterminate enzyme-linked immunosorbent assay (ELISA) results are confirmed with immunoblots. Several ELISAs quantify the concentration of antibodies tested, however, no recommendation exists for an upper cut-off value at which an IgG ELISA is sufficient and the immunoblot can be omitted. The study objective was to determine at which IgG antibody level an immunoblot does not have any additional predictive value compared to ELISA results. Data of adult patients who visited a tertiary Lyme centre between 2008 and 2014 were analysed. Both an ELISA (Enzygnost Lyme link VlsE IgG) and immunoblot (recomLine blot Borrelia) were performed. Clinical data were extracted from the patient's digital medical record. Positive predictive values (PPVs) for either previous or active infection with Borrelia burgdorferi s.l. were calculated for different cut-off ELISA IgG antibody levels where the immunoblot was regarded as reference test. In total, 1454 patients were included. According to the two-step test strategy, 486 (33%), 69 (5%) and 899 (62%) patients had positive, indeterminate and negative Borrelia IgG serology, respectively. At IgG levels of 500 IU/ml and higher, all immunoblots were positive, resulting in a 100% PPV (95% CI: 97.0-100). At IgG levels of 200 IU/ml and higher, the PPV was 99.3% (95% CI: 97.4-99.8). In conclusion, at IgG levels of 200 IU/ml and higher, an ELISA was sufficient to detect antibodies to Borrelia burgdorferi s.l. At those IgG levels, a confirmatory immunoblot may be omitted in patients referred to a tertiary Lyme centre. Before these results can be implemented in routine diagnosis of Lyme borreliosis, confirmation of the results is necessary in other patient populations and using other quantitative ELISAs and immunoblots. Copyright © 2017 Elsevier GmbH. All rights reserved.

  13. Protein secondary structure: category assignment and predictability

    DEFF Research Database (Denmark)

    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....

  14. Stochastic Extreme Load Predictions for Marine Structures

    DEFF Research Database (Denmark)

    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....

  15. A Kernel for Protein Secondary Structure Prediction

    OpenAIRE

    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...

  16. Phocid seal leptin: tertiary structure and hydrophobic receptor binding site preservation during distinct leptin gene evolution.

    Directory of Open Access Journals (Sweden)

    John A Hammond

    Full Text Available The cytokine hormone leptin is a key signalling molecule in many pathways that control physiological functions. Although leptin demonstrates structural conservation in mammals, there is evidence of positive selection in primates, lagomorphs and chiropterans. We previously reported that the leptin genes of the grey and harbour seals (phocids have significantly diverged from other mammals. Therefore we further investigated the diversification of leptin in phocids, other marine mammals and terrestrial taxa by sequencing the leptin genes of representative species. Phylogenetic reconstruction revealed that leptin diversification was pronounced within the phocid seals with a high dN/dS ratio of 2.8, indicating positive selection. We found significant evidence of positive selection along the branch leading to the phocids, within the phocid clade, but not over the dataset as a whole. Structural predictions indicate that the individual residues under selection are away from the leptin receptor (LEPR binding site. Predictions of the surface electrostatic potential indicate that phocid seal leptin is notably different to other mammalian leptins, including the otariids. Cloning the grey seal leptin binding domain of LEPR confirmed that this was structurally conserved. These data, viewed in toto, support a hypothesis that phocid leptin divergence is unlikely to have arisen by random mutation. Based upon these phylogenetic and structural assessments, and considering the comparative physiology and varying life histories among species, we postulate that the unique phocid diving behaviour has produced this selection pressure. The Phocidae includes some of the deepest diving species, yet have the least modified lung structure to cope with pressure and volume changes experienced at depth. Therefore, greater surfactant production is required to facilitate rapid lung re-inflation upon surfacing, while maintaining patent airways. We suggest that this additional

  17. Tertiary Students ’ Entrepreneurship Learning Socialization : Factor Analysis and Structural Equation Modeling

    Directory of Open Access Journals (Sweden)

    Chun- Mei Chou

    2015-09-01

    Full Text Available This study examines 728 tertiary students’ entrepreneurship learning socialization and its influencing factors to serve as a school reference for the development of internship and entrepreneurship education. The results show that students’ internship experience has a significant direct effect on entrepreneurship learning socialization, and entrepreneurship intention has a significant effect on entrepreneurship learning socialization through internship experience. The influence pattern and empirical data of entrepreneurship intention and internship experience on entrepreneurship learning socialization has a good fit. This paper gives an insight from Taiwan tertiary institutions about entrepreneurial learning socialization of students and contributions to them. We describe the development of the influencing factors, discuss its implications for entrepreneurship and internship education, and finally offer suggestions for further entrepreneurship education development.

  18. Prediction of Wound Healing in Diabetic Foot Ulcers: an Observational Study in Tertiary Hospital in Indonesia

    Directory of Open Access Journals (Sweden)

    Pradana Soewondo

    2017-04-01

    Full Text Available Aim: to evaluate the role of clinical characteristics, functional markers of vasodilation, inflammatory response, and atherosclerosis in predicting wound healing in diabetic foot ulcer. Methods: a cohort study (February – October 2010 was conducted from 40 subjects with acute diabetic foot ulcer at clinical ward of Dr. Cipto Mangunkusumo National Central General Hospital, Jakarta, Indonesia. Each subject underwent at least two variable measurements, i.e. during inflammatory phase and proliferation phase. The studied variables were clinical characteristics, complete peripheral blood count (CBC and differential count, levels of HbA1c, ureum, creatinine, lipid profile, fasting blood glucose (FBG, marker of endothelial dysfunction (asymmetric dimethylarginine/ADMA, endothelin-1/ET-1, and flow-mediated dilation/FMD of brachial artery, and marker of vascular calcification (osteoprotegerin/OPG. Results: median of time achieving 50% granulation tissue in our study was 21 days. There were nine factors that contribute in the development of 50% granulation tissue, i.e. family history of diabetes mellitus (DM, previous history of wound, wound area, duration of existing wound, captopril and simvastatin medications, levels of ADMA, ET-1, and OPG. There were three out of the nine factors that significantly correlated with wound healing, i.e. wound area, OPG levels, and simvastatin medications. Conclusion: in acute diabetic foot ulcers, wound area and OPG levels had positive correlation with wound healing, whereas simvastatin medications had negative correlation with wound healing.

  19. Prediction of Wound Healing in Diabetic Foot Ulcers: an Observational Study in Tertiary Hospital in Indonesia.

    Science.gov (United States)

    Soewondo, Pradana; Suyono, Slamet; Sastrosuwignyo, Mpu Kanoko; Harahap, Alida R; Sutrisna, Bambang; Makmun, Lukman H

    2017-01-01

    to evaluate the role of clinical characteristics, functional markers of vasodilation, inflammatory response, and atherosclerosis in predicting wound healing in diabetic foot ulcer. a cohort study (February - October 2010) was conducted from 40 subjects with acute diabetic foot ulcer at clinical ward of Dr. Cipto Mangunkusumo National Central General Hospital, Jakarta, Indonesia. Each subject underwent at least two variable measurements, i.e. during inflammatory phase and proliferation phase. The studied variables were clinical characteristics, complete peripheral blood count (CBC) and differential count, levels of HbA1c, ureum, creatinine, lipid profile, fasting blood glucose (FBG), marker of endothelial dysfunction (asymmetric dimethylarginine/ADMA, endothelin-1/ET-1, and flow-mediated dilation/FMD of brachial artery), and marker of vascular calcification (osteoprotegerin/OPG). median of time achieving 50% granulation tissue in our study was 21 days. There were nine factors that contribute in the development of 50% granulation tissue, i.e. family history of diabetes mellitus (DM), previous history of wound, wound area, duration of existing wound, captopril and simvastatin medications, levels of ADMA, ET-1, and OPG. There were three out of the nine factors that significantly correlated with wound healing, i.e. wound area, OPG levels, and simvastatin medications. in acute diabetic foot ulcers, wound area and OPG levels had positive correlation with wound healing, whereas simvastatin medications had negative correlation with wound healing.

  20. Assessment of performance and utility of mortality prediction models in a single Indian mixed tertiary intensive care unit.

    Science.gov (United States)

    Sathe, Prachee M; Bapat, Sharda N

    2014-01-01

    To assess the performance and utility of two mortality prediction models viz. Acute Physiology and Chronic Health Evaluation II (APACHE II) and Simplified Acute Physiology Score II (SAPS II) in a single Indian mixed tertiary intensive care unit (ICU). Secondary objectives were bench-marking and setting a base line for research. In this observational cohort, data needed for calculation of both scores were prospectively collected for all consecutive admissions to 28-bedded ICU in the year 2011. After excluding readmissions, discharges within 24 h and age <18 years, the records of 1543 patients were analyzed using appropriate statistical methods. Both models overpredicted mortality in this cohort [standardized mortality ratio (SMR) 0.88 ± 0.05 and 0.95 ± 0.06 using APACHE II and SAPS II respectively]. Patterns of predicted mortality had strong association with true mortality (R (2) = 0.98 for APACHE II and R (2) = 0.99 for SAPS II). Both models performed poorly in formal Hosmer-Lemeshow goodness-of-fit testing (Chi-square = 12.8 (P = 0.03) for APACHE II, Chi-square = 26.6 (P = 0.001) for SAPS II) but showed good discrimination (area under receiver operating characteristic curve 0.86 ± 0.013 SE (P < 0.001) and 0.83 ± 0.013 SE (P < 0.001) for APACHE II and SAPS II, respectively). There were wide variations in SMRs calculated for subgroups based on International Classification of Disease, 10(th) edition (standard deviation ± 0.27 for APACHE II and 0.30 for SAPS II). Lack of fit of data to the models and wide variation in SMRs in subgroups put a limitation on utility of these models as tools for assessing quality of care and comparing performances of different units without customization. Considering comparable performance and simplicity of use, efforts should be made to adapt SAPS II.

  1. Facilitating RNA structure prediction with microarrays.

    Science.gov (United States)

    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.

  2. Hyperbilirubinaemia a predictive factor for complicated acute appendicitis: a study in a tertiary care hospital

    International Nuclear Information System (INIS)

    Jamaluddin, M.; Hussain, S.M.A.; Ahmad, H.

    2013-01-01

    Objective: To study the role of hyperbilirubinaemia as a predictive factor for appendiceal perforation in acute appendicitis. Methods: The prospective, descriptive study was conducted at the Abbasi Shaheed Hospital and the Karachi Medical and Dental College, Karachi, from January 2010 to June 2012. It comprised all patients coming to the surgical outpatient department and emergency department with pain in the right iliac fossa with duration less than seven days. They were clinically assessed for signs and symptoms of acute appendicitis and relevant tests were conducted. Patients were diagnosed as a case of acute appendicitis on the basis of clinical and ultrasound findings, and were prepared for appendicectomy. Per-operative findings were recorded and specimens were sent for histopathology to confirm the diagnosis. SPSS version 10 was used to analyse the data. Results: Of the 71 patients, 37 (52.10%) were male and 34 (47.90%) were female. The age range was 3-57 years, and most of the patients (n=33; 46.5%) were between 11 and 20 years. Besides, 63 (89%) patients had pain in the right iliac fossa of less than four-days duration, while 8 (11%) had pain of longer duration. Total leukocyte count was found to be elevated in 33 (46.5%) patients, while total serum bilirubin was elevated in 41 (57.70%). Ultrasound of abdomen showed 9 (12.70%) patients having normal appearance of appendix and 59 (83.30%) had inflamed appendix. Four (5.60%) patients had no signs of inflammation on naked eye appearance per operatively. Histopathology of appendix showed 10 (14.10%) patients had non-inflammatory appendix. Conclusion: Patients with signs and symptoms of acute appendicitis and a raised total serum bilirubin level indicated a complication of acute appendicitis requiring an early intervention to prevent peritonitis and septicaemia. A raised serum bilirubin level is a good indicator of complicated acute appendicitis, and should be included in the assessment of patients with

  3. Three-residue turns in alpha/beta-peptides and their application in the design of tertiary structures.

    Science.gov (United States)

    Sharma, Gangavaram V M; Nagendar, Pendem; Ramakrishna, Kallaganti V S; Chandramouli, Nagula; Choudhary, Madavi; Kunwar, Ajit C

    2008-06-02

    A new three-residue turn was serendipitously discovered in alpha/beta hybrid peptides derived from alternating C-linked carbo-beta-amino acids (beta-Caa) and L-Ala residues. The three-residue beta-alpha-beta turn at the C termini, nucleated by a helix at the N termini, resulted in helix-turn (HT) supersecondary structures in these peptides. The turn in the HT motif is stabilized by two H bonds-CO(i-2)-NH(i), with a seven-membered pseudoring (gamma turn) in the backward direction, and NH(i-2)-CO(i), with a 13-membered pseudoring in the forward direction (i being the last residue)--at the C termini. The study was extended to generalize the new three-residue turn (beta-alpha-beta) by using different alpha- and beta-amino acids. Furthermore, the HT motifs were efficiently converted, by an extension with helical oligomers at the C termini, into peptides with novel helix-turn-helix (HTH) tertiary structures. However, this resulted in the destabilization of the beta-alpha-beta turn with the concomitant nucleation of another three-residue turn, alpha-beta-beta, which is stabilized by 11- and 15-membered bifurcated H bonds. Extensive NMR spectroscopic studies were carried out to delineate the secondary and tertiary structures in these peptides, which are further supported by molecular dynamics (MD) investigations.

  4. Protein Structure Prediction by Protein Threading

    Science.gov (United States)

    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.

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

    Directory of Open Access Journals (Sweden)

    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/.

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

    Directory of Open Access Journals (Sweden)

    Aboul-Magd Mohammed O

    2009-07-01

    protein sequence data and also to encode the resulting structure prediction in a machine-readable format. To our knowledge, this represents the only publicly available SOAP-interface for a protein secondary structure prediction service with published WSDL interface definition. Conclusion Relative to the 9 contemporary methods included in the comparison cascaded PCI classifiers perform well, however PCI finds greatest application as a consensus classifier. When PCI is used to combine a sequence-to-structure PCI-based classifier with the current leading ANN-based method, PSIPRED, the overall error rate (Q3 is maintained while the rate of occurrence of a particularly detrimental error is reduced by up to 25%. This improvement in BAD score, combined with the machine-readable SOAP web service interface makes PCI-SS particularly useful for inclusion in a tertiary structure prediction pipeline.

  7. RNA secondary structure prediction using soft computing.

    Science.gov (United States)

    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.

  8. Calcium-induced tertiary structure modifications of endo-B-1,3-glucanase form Pyrococcus furiosus in 7.9 M guanidinium chloride

    NARCIS (Netherlands)

    Chiaraluce, R.; Gianese, G.; Angelaccio, S.; Florio, R.; Lieshout, van J.F.T.; Oost, van der J.; Consalvi, V.

    2005-01-01

    The family 16 endo-b-1,3 glucanase from the extremophilic archaeon Pyrococcus furiosus is a laminarinase, which in 7.9 M GdmCl (guanidinium chloride) maintains a significant amount of tertiary structure without any change of secondary structure. The addition of calcium to the enzyme in 7.9 M GdmCl

  9. Predicting Protein Secondary Structure with Markov Models

    DEFF Research Database (Denmark)

    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....

  10. Defining proximity relationships in the tertiary structure of the dopamine transporter. Identification of a conserved glutamic acid as a third coordinate in the endogenous Zn(2+)-binding site

    DEFF Research Database (Denmark)

    Løland, Claus Juul; Norregaard, L; Gether, U

    1999-01-01

    , high affinity Zn(2+)-binding site. To achieve further insight into the tertiary organization of hDAT, we set out to identify additional residues involved in Zn(2+) binding and subsequently to engineer artificial Zn(2+)-binding sites. Ten aspartic acids and glutamic acids, predicted...

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

    DEFF Research Database (Denmark)

    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....

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

    KAUST Repository

    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.

  13. Structure prediction of AlnOm clusters

    International Nuclear Information System (INIS)

    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.

  14. Upgrading and downgrading of prostate cancer from biopsy to radical prostatectomy: incidence and predictive factors using the modified Gleason grading system and factoring in tertiary grades.

    Science.gov (United States)

    Epstein, Jonathan I; Feng, Zhaoyong; Trock, Bruce J; Pierorazio, Phillip M

    2012-05-01

    Prior studies assessing the correlation of Gleason score (GS) at needle biopsy and corresponding radical prostatectomy (RP) predated the use of the modified Gleason scoring system and did not factor in tertiary grade patterns. To assess the relation of biopsy and RP grade in the largest study to date. A total of 7643 totally embedded RP and corresponding needle biopsies (2004-2010) were analyzed according to the updated Gleason system. All patients underwent prostate biopsy prior to RP. The relation of upgrading or downgrading to patient and cancer characteristics was compared using the chi-square test, Student t test, and multivariable logistic regression. A total of 36.3% of cases were upgraded from a needle biopsy GS 5-6 to a higher grade at RP (11.2% with GS 6 plus tertiary). Half of the cases had matching GS 3+4=7 at biopsy and RP with an approximately equal number of cases downgraded and upgraded at RP. With biopsy GS 4+3=7, RP GS was almost equally 3+4=7 and 4+3=7. Biopsy GS 8 led to an almost equal distribution between RP GS 4+3=7, 8, and 9-10. A total of 58% of the cases had matching GS 9-10 at biopsy and RP. In multivariable analysis, increasing age (pfactoring in multiple variables including the number of positive cores and the maximum percentage of cancer per core, the concordance indexes were not sufficiently high to justify the use of nomograms for predicting upgrading and downgrading for the individual patient. Almost 20% of RP cases have tertiary patterns. A needle biopsy can sample a tertiary higher Gleason pattern in the RP, which is then not recorded in the standard GS reporting, resulting in an apparent overgrading on the needle biopsy. Copyright © 2012 European Association of Urology. Published by Elsevier B.V. All rights reserved.

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

    Science.gov (United States)

    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

  16. Risk Factors Predicting Mortality in Patients with Lung Abscess in a Public Tertiary Care Center in Karachi, Pakistan

    OpenAIRE

    Shaista Ghazal; Ashok Kumar; Binav Shrestha; Sana Sajid; Maria Malik; Nadeen Rizvi

    2013-01-01

    Introduction: Lung abscess is a commonly encountered entity in South-East Asia but not much data regarding its outcome is available. The objective of this study was to identify the factors associated with increased mortality in patients diagnosed with lung abscess in a tertiary care center of Karachi, Pakistan. Methods: A retrospective case analysis was performed via hospital records, on patients admitted with lung abscess between January 2009 and January 2011 at the largest state...

  17. Causes and Predictive Factors Associated with "Diagnosis Changed" Outcomes in Patients Notified as Tuberculosis Cases in a Private Tertiary Hospital

    OpenAIRE

    Kang, Byung Ju; Jo, Kyung-Wook; Park, Tai Sun; Yoo, Jung-Wan; Lee, Sei Won; Choi, Chang-Min; Oh, Yeon-Mok; Lee, Sang-Do; Kim, Woo Sung; Kim, Dong Soon; Shim, Tae Sun

    2013-01-01

    Background The aim of our study was to evaluate the "diagnosis changed" rate in patients notified as tuberculosis (TB) on the Korean TB surveillance system (KTBS). Methods A total of 1,273 patients notified as TB cases on the KTBS in one private tertiary hospital in 2011 were enrolled in the present study. Patients were classified into three groups: "diagnosis maintained", "diagnosis changed" (initially notified as TB, but ultimately diagnosed as non-TB), and "administrative error" (notified ...

  18. Evolutionary Structure Prediction of Stoichiometric Compounds

    Science.gov (United States)

    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).

  19. Alpha complexes in protein structure prediction

    DEFF Research Database (Denmark)

    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...

  20. A Method to Predict the Structure and Stability of RNA/RNA Complexes.

    Science.gov (United States)

    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.

  1. Protein structure based prediction of catalytic residues.

    Science.gov (United States)

    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.

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

    Science.gov (United States)

    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.

  3. Tertiary Structures of the Escherichia coli and Human Chromosome 21 Molecules of DNA

    Czech Academy of Sciences Publication Activity Database

    Hanzálek, Petr; Kypr, Jaroslav

    2001-01-01

    Roč. 283, č. 1 (2001), s. 219-223 ISSN 0006-291X R&D Projects: GA AV ČR IAA5004802 Institutional research plan: CEZ:AV0Z5004920 Keywords : DNA crystal structures * phosphorus atom representation * genomic DNA molecules Subject RIV: BO - Biophysics Impact factor: 2.946, year: 2001

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

    Science.gov (United States)

    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.

  5. Regulation of Neurexin 1[beta] Tertiary Structure and Ligand Binding through Alternative Splicing

    Energy Technology Data Exchange (ETDEWEB)

    Shen, Kaiser C.; Kuczynska, Dorota A.; Wu, Irene J.; Murray, Beverly H.; Sheckler, Lauren R.; Rudenko, Gabby (Michigan)

    2008-08-04

    Neurexins and neuroligins play an essential role in synapse function, and their alterations are linked to autistic spectrum disorder. Interactions between neurexins and neuroligins regulate inhibitory and excitatory synaptogenesis in vitro through a splice-insert signaling code. In particular, neurexin 1{beta} carrying an alternative splice insert at site SS{number_sign}4 interacts with neuroligin 2 (found predominantly at inhibitory synapses) but much less so with other neuroligins (those carrying an insert at site B and prevalent at excitatory synapses). The structure of neurexin 1{beta}+SS{number_sign}4 reveals dramatic rearrangements to the 'hypervariable surface', the binding site for neuroligins. The splice insert protrudes as a long helix into space, triggers conversion of loop {beta}10-{beta}11 into a helix rearranging the binding site for neuroligins, and rearranges the Ca{sup 2+}-binding site required for ligand binding, increasing its affinity. Our structures reveal the mechanism by which neurexin 1{beta} isoforms acquire neuroligin splice isoform selectivity.

  6. Structure of nonevaporating sprays - Measurements and predictions

    Science.gov (United States)

    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.

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

    Science.gov (United States)

    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.

  8. Prediction of Protein Structural Class Based on Gapped-Dipeptides and a Recursive Feature Selection Approach

    Directory of Open Access Journals (Sweden)

    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.

  9. Automated and fast building of three-dimensional RNA structures.

    Science.gov (United States)

    Zhao, Yunjie; Huang, Yangyu; Gong, Zhou; Wang, Yanjie; Man, Jianfen; Xiao, Yi

    2012-01-01

    Building tertiary structures of non-coding RNA is required to understand their functions and design new molecules. Current algorithms of RNA tertiary structure prediction give satisfactory accuracy only for small size and simple topology and many of them need manual manipulation. Here, we present an automated and fast program, 3dRNA, for RNA tertiary structure prediction with reasonable accuracy for RNAs of larger size and complex topology.

  10. A comprehensive comparison of comparative RNA structure prediction approaches

    DEFF Research Database (Denmark)

    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...

  11. Epitope-Targeting of Tertiary Protein Structure Enables Target-Guided Synthesis of a Potent in Cell Inhibitor of Botulinum Neurotoxin**

    OpenAIRE

    Farrow, Blake; Wong, Michelle; Malette, Jacquie; Lai, Bert; Deyle, Kaycie M.; Das, Samir; Nag, Arundhati; Agnew, Heather D.; Heath, James R.

    2015-01-01

    Botulinum neurotoxin (BoNT) serotype A is the most lethal known toxin and has an occluded structure, which prevents direct inhibition of its active site before it enters the cytosol. Target-guided synthesis by in situ click chemistry is combined with synthetic epitope targeting to exploit the tertiary structure of the BoNT protein as a landscape for assembling a competitive inhibitor. A substrate-mimicking peptide macrocycle is used as a direct inhibitor of BoNT. An epitope-targeting in situ ...

  12. CNNH_PSS: protein 8-class secondary structure prediction by convolutional neural network with highway.

    Science.gov (United States)

    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.

  13. Prolonged Duration of Surgery Predicts Postoperative Hypoparathyroidism among Patients Undergoing Total Thyroidectomy in a Tertiary Referral Centre

    DEFF Research Database (Denmark)

    Sonne-Holm, Emilie; Holst Hahn, Christoffer

    2017-01-01

    , to identify early predictive risk factors. METHODS: Based on a single-institution retrospective review, we identified 582 patients who underwent total thyroidectomy between January 2010 and March 2015. Information on age, gender, pathological diagnosis, duration of surgery, autotransplantation of parathyroid...

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

    Directory of Open Access Journals (Sweden)

    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

  15. QCD dipole prediction for dis and diffractive structure functions

    International Nuclear Information System (INIS)

    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)

  16. Immune Checkpoint Molecules on Tumor-Infiltrating Lymphocytes and Their Association with Tertiary Lymphoid Structures in Human Breast Cancer

    Directory of Open Access Journals (Sweden)

    Cinzia Solinas

    2017-10-01

    Full Text Available There is an exponentially growing interest in targeting immune checkpoint molecules in breast cancer (BC, particularly in the triple-negative subtype where unmet treatment needs remain. This study was designed to analyze the expression, localization, and prognostic role of PD-1, PD-L1, PD-L2, CTLA-4, LAG3, and TIM3 in primary BC. Gene expression analysis using the METABRIC microarray dataset found that all six immune checkpoint molecules are highly expressed in basal-like and HER2-enriched compared to the other BC molecular subtypes. Flow cytometric analysis of fresh tissue homogenates from untreated primary tumors show that PD-1 is principally expressed on CD4+ or CD8+ T cells and CTLA-4 is expressed on CD4+ T cells. The global proportion of PD-L1+, PD-L2+, LAG3+, and TIM3+ tumor-infiltrating lymphocytes (TIL was low and detectable in only a small number of tumors. Immunohistochemically staining fixed tissues from the same tumors was employed to score TIL and tertiary lymphoid structures (TLS. PD-L1+, PD-L2+, LAG3+, and TIM3+ cells were detected in some TLS in a pattern that resembles secondary lymphoid organs. This observation suggests that TLS are important sites of immune activation and regulation, particularly in tumors with extensive baseline immune infiltration. Significantly improved overall survival was correlated with PD-1 expression in the HER2-enriched and PD-L1 or CTLA-4 expression in basal-like BC. PD-1 and CTLA-4 proteins were most frequently detected on TIL, which supports the correlations observed between their gene expression and improved long-term outcome in basal-like and HER2-enriched BC. PD-L1 expression by tumor or immune cells is uncommon in BC. Overall, the data presented here distinguish PD-1 as a marker of T cell activity in both the T and B cell areas of BC associated TLS. We found that immune checkpoint molecule expression parallels the extent of TIL and TLS, although there is a noteworthy amount of heterogeneity

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

    Science.gov (United States)

    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.

  18. A crystal structure prediction enigma solved

    DEFF Research Database (Denmark)

    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...

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

    Directory of Open Access Journals (Sweden)

    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/.

  20. Predicting Career Advancement with Structural Equation Modelling

    Science.gov (United States)

    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…

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

    Science.gov (United States)

    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.

  2. Factors predictive of abnormal semen parameters in male partners of couples attending the infertility clinic of a tertiary hospital in south-western Nigeria

    Directory of Open Access Journals (Sweden)

    Peter Olusola Aduloju

    2016-11-01

    Full Text Available Background: Infertility is a common gynaecological problem and male factor contributes significantly in the aetiology of infertility. Semen analysis has remained a useful investigation in the search for male factor infertility. Aim: This study assessed the pattern of semen parameters and predictive factors associated with abnormal parameters in male partners of infertile couples attending a Nigerian tertiary hospital. Methods: A descriptive study of infertile couples presenting at the clinic between January 2012and December 2015 was done at Ekiti State University Teaching Hospital, Ado-Ekiti. Seminal fluid from the male partners were analysed in the laboratory using the WHO 2010 criteria for human semen characteristics. Data was analysed using SPSS 17 and logistic regression analysis was used to determine the predictive factors associated with abnormal semen parameters. Results: A total of 443 men participated in the study and 38.2% had abnormal sperm parameters. Oligozoospermia (34.8% and asthenozoospermia (26.9% are leading single factor abnormality found, astheno-oligozoospermia occurred in 14.2% and oligo-astheno-teratozoospermia in 3.6% of cases. The prevalence of azoospermia was 3.4%. Smoking habit, past infection with mumps and previous groin surgery significantly predicted abnormal semen parameters with p values of 0.025, 0.040 and 0.017 respectively. Positive cultures were recorded in 36.2% of cases and staph aureus was the commonest organism. Conclusion: Male factor abnormalities remain significant contributors to infertility and men should be encouraged through advocacy to participate in investigation of infertility to reduce the level of stigmatization and ostracizing of women with infertility especially in sub-Saharan Africa.

  3. Factors predictive of abnormal semen parameters in male partners of couples attending the infertility clinic of a tertiary hospital in southwestern Nigeria

    Directory of Open Access Journals (Sweden)

    Peter Aduloju

    2016-12-01

    Full Text Available Background: Infertility is a common gynaecological problem and male factor contributes significantly in the aetiology of infertility. Semen analysis has remained a useful investigation in the search for male factor infertility.Aim: This study assessed the pattern of semen parameters and predictive factors associated with abnormal parameters in male partners of infertile couples attending a Nigerian tertiary hospital.Methods: A descriptive study of infertile couples presenting at the clinic between January 2012and December 2015 was done at Ekiti State University Teaching Hospital, Ado-Ekiti.  Seminal fluid from the male partners were analysed in the laboratory using the WHO 2010 criteria for human semen characteristics. Data was analysed using SPSS 17 and logistic regression analysis was used to determine the predictive factors associated with abnormal semen parameters.Results: A total of 443 men participated in the study and 38.2% had abnormal sperm parameters. Oligozoospermia (34.8% and asthenozoospermia (26.9% are leading single factor abnormality found, astheno-oligozoospermia occurred in 14.2% and oligo-astheno-teratozoospermia in 3.6% of cases. The prevalence of azoospermia was 3.4%. Smoking habit, past infection with mumps and previous groin surgery significantly predicted abnormal semen parameters with p values of 0.025, 0.040 and 0.017 respectively. Positive cultures were recorded in 36.2% of cases and staph aureus was the commonest organism.Conclusion: Male factor abnormalities remain significant contributors to infertility and men should be encouraged through advocacy to participate in investigation of infertility to reduce the level of stigmatization and ostracizing of women with infertility especially in sub-Saharan Africa.

  4. Prediction of concrete strength in massive structures

    International Nuclear Information System (INIS)

    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

  5. Can Morphing Methods Predict Intermediate Structures?

    Science.gov (United States)

    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

  6. PREDICTING PROGNOSTIC VALUE OF OCULAR TRAUMA SCORE (OTS IN AN OPEN GLOBE INJURY IN TERTIARY EYE CARE HOSPITAL

    Directory of Open Access Journals (Sweden)

    Rahul

    2015-10-01

    Full Text Available AIM : To evaluate the prognostic value of OTS in open globe injuries. MATERIAL METHOD : Retrospective analysis of 77 eyes with open globe injuries was done from 01/07/2013 to 31/12/2014. Patients were assigned raw score sum based on initial V/A, and ocular findings then classified into 5 categories for predicting final visual outcome based on ocular Trauma score (OTS. RESULT : We estimated final V/A in 77 cases of open globe injuries (64.93% had raw sc ore between 65.91 (category 3, 4 Six months after the injury, 42.85% patients of categories 1 (raw score 0 - 44 achieved V/A of PL/HM as compared to 17% in OTS study. 16 patients with raw compared to OTS study. We reported comparable visual outcome with OT S study except in category 1 & 2. CONCLUSION: OTS score is valuable in triage, patient counseling and decision making for the management of ocular trauma. We recommend that OTS should be used routinely for open globe injuries as it is a simple guide

  7. QCD dipole predictions for DIS and diffractive structure functions

    International Nuclear Information System (INIS)

    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

  8. Electrostatics, structure prediction, and the energy landscapes for protein folding and binding.

    Science.gov (United States)

    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.

  9. Structure Prediction and Analysis of Neuraminidase Sequence Variants

    Science.gov (United States)

    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…

  10. QCD predictions for weak neutral current structure functions

    International Nuclear Information System (INIS)

    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

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

    KAUST Repository

    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

  12. Ensemble-based prediction of RNA secondary structures.

    Science.gov (United States)

    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

  13. Computational methods in sequence and structure prediction

    Science.gov (United States)

    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

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

    Science.gov (United States)

    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

  15. Effects prediction guidelines for structures subjected to ground motion

    International Nuclear Information System (INIS)

    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.)

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

    KAUST Repository

    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

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

    Science.gov (United States)

    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.

  18. Evolutionary rate variation and RNA secondary structure prediction

    DEFF Research Database (Denmark)

    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...

  19. Molecular identification of aiiA homologous gene from endophytic Enterobacter species and in silico analysis of putative tertiary structure of AHL-lactonase.

    Science.gov (United States)

    Rajesh, P S; Rai, V Ravishankar

    2014-01-03

    The aiiA homologous gene known to encode AHL- lactonase enzyme which hydrolyze the N-acylhomoserine lactone (AHL) quorum sensing signaling molecules produced by Gram negative bacteria. In this study, the degradation of AHL molecules was determined by cell-free lysate of endophytic Enterobacter species. The percentage of quorum quenching was confirmed and quantified by HPLC method (pEnterobacter asburiae VT65, Enterobacter aerogenes VT66 and Enterobacter ludwigii VT70 strains. Sequence alignment analysis revealed the presence of two zinc binding sites, "HXHXDH" motif as well as tyrosine residue at the position 194. Based on known template available at Swiss-Model, putative tertiary structure of AHL-lactonase was constructed. The result showed that novel endophytic strains of Enterobacter genera encode the novel aiiA homologous gene and its structural importance for future study. Copyright © 2013 Elsevier Inc. All rights reserved.

  20. Model for an RNA tertiary interaction from the structure of an intermolecular complex between a GAAA tetraloop and an RNA helix.

    Science.gov (United States)

    Pley, H W; Flaherty, K M; McKay, D B

    1994-11-03

    In large structured RNAs, RNA hairpins in which the strands of the duplex stem are connected by a tetraloop of the consensus sequence 5'-GNRA (where N is any nucleotide, and R is either G or A) are unusually frequent. In group I introns there is a covariation in sequence between nucleotides in the third and fourth positions of the loop with specific distant base pairs in putative RNA duplex stems: GNAA loops correlate with successive 5'-C-C.G-C base pairs in stems, whereas GNGA loops correlate with 5'-C-U.G-A. This has led to the suggestion that GNRA tetraloops may be involved in specific long-range tertiary interactions, with each A in position 3 or 4 of the loop interacting with a C-G base pair in the duplex, and G in position 3 interacting with a U-A base pair. This idea is supported experimentally for the GAAA loop of the P5b extension of the group I intron of Tetrahymena thermophila and the L9 GUGA terminal loop of the td intron of bacteriophage T4 (ref. 4). NMR has revealed the overall structure of the tetraloop for 12-nucleotide hairpins with GCAA and GAAA loops and models have been proposed for the interaction of GNRA tetraloops with base pairs in the minor groove of A-form RNA. Here we describe the crystal structure of an intermolecular complex between a GAAA tetraloop and an RNA helix. The interactions we observe correlate with the specificity of GNRA tetraloops inferred from phylogenetic studies, suggesting that this complex is a legitimate model for intramolecular tertiary interactions mediated by GNRA tetraloops in large structured RNAs.

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

    DEFF Research Database (Denmark)

    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...

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

    Science.gov (United States)

    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.

  3. Can pre-operative axial CT imaging predict syndesmosis instability in patients sustaining ankle fractures? Seven years' experience in a tertiary trauma center

    Energy Technology Data Exchange (ETDEWEB)

    Yeung, Tsz Wai; Chan, Chung Yan Grace; Chan, Wun Cheung Samuel; Yuen, Ming Keung [Tuen Mun Hospital, Department of Radiology, Tuen Mun (China); Yeung, Yuk Nam [Tune Mun Hospital, Department of Orthopaedics and Traumatology, Tuen Mun (China)

    2015-06-01

    The purpose of this study is to explore the diagnostic accuracy of CT measurements in predicting syndesmosis instability of injured ankle, with correlation to operative findings. From July 2006 to June 2013, 123 patients presented to a single tertiary hospital who received pre-operative CT for ankle fractures were retrospectively reviewed. All patients underwent open reduction and internal fixation for fractures and intra-operative syndesmosis integrity tests. The morphology of incisura fibularis was categorized as deep or shallow. The tibiofibular distance (TFD) between the medial border of the fibula and the nearest point of the lateral border of tibia were measured at anterior (aTFD), middle (mTFD), posterior (pTFD), and maximal (maxTFD) portions across the syndesmosis on axial CT images at 10 mm proximal to the tibial plafond. Statistical analysis was performed with independent samples t test and ROC curve analysis. Intraobserver reproducibility and inter-observers agreement were also evaluated. Of the 123 patients, 39 (31.7 %) were operatively diagnosed with syndesmosis instability. No significant difference of incisura fibularis morphology (deep or shallow) and TFDs was demonstrated respective to genders. The axial CT measurements were significantly higher in ankles diagnosed with syndesmosis instability than the group without (maxTFD means 7.2 ± 2.96 mm vs. 4.6 ± 1.4 mm, aTFD mean 4.9 ± 3.7 mm vs. 1.8 ± 1.4 mm, mTFD mean 5.3 ± 2.4 mm vs. 3.2 ± 1.6 mm, pTFD mean 5.3 ± 1.8 mm vs. 4.1 ± 1.3 mm, p < 0.05). Their respective cutoff values with best sensitivity and specificity were calculated; the aTFD (AUC 0.798) and maxTFD (AUC 0.794) achieved the highest diagnostic accuracy. The optimal cutoff levels were aTFD = 4 mm (sensitivity, 56.4 %; specificity, 91.7 %) and maxTFD = 5.65 mm (sensitivity, 74.4 %; specificity, 79.8 %). The inter-observer agreement was good for all aTFD, mTFD, pTFD, and maxTFD measurements (ICC 0.959, 0.799, 0.783, and 0.865). The ICC

  4. Can pre-operative axial CT imaging predict syndesmosis instability in patients sustaining ankle fractures? Seven years' experience in a tertiary trauma center

    International Nuclear Information System (INIS)

    Yeung, Tsz Wai; Chan, Chung Yan Grace; Chan, Wun Cheung Samuel; Yuen, Ming Keung; Yeung, Yuk Nam

    2015-01-01

    The purpose of this study is to explore the diagnostic accuracy of CT measurements in predicting syndesmosis instability of injured ankle, with correlation to operative findings. From July 2006 to June 2013, 123 patients presented to a single tertiary hospital who received pre-operative CT for ankle fractures were retrospectively reviewed. All patients underwent open reduction and internal fixation for fractures and intra-operative syndesmosis integrity tests. The morphology of incisura fibularis was categorized as deep or shallow. The tibiofibular distance (TFD) between the medial border of the fibula and the nearest point of the lateral border of tibia were measured at anterior (aTFD), middle (mTFD), posterior (pTFD), and maximal (maxTFD) portions across the syndesmosis on axial CT images at 10 mm proximal to the tibial plafond. Statistical analysis was performed with independent samples t test and ROC curve analysis. Intraobserver reproducibility and inter-observers agreement were also evaluated. Of the 123 patients, 39 (31.7 %) were operatively diagnosed with syndesmosis instability. No significant difference of incisura fibularis morphology (deep or shallow) and TFDs was demonstrated respective to genders. The axial CT measurements were significantly higher in ankles diagnosed with syndesmosis instability than the group without (maxTFD means 7.2 ± 2.96 mm vs. 4.6 ± 1.4 mm, aTFD mean 4.9 ± 3.7 mm vs. 1.8 ± 1.4 mm, mTFD mean 5.3 ± 2.4 mm vs. 3.2 ± 1.6 mm, pTFD mean 5.3 ± 1.8 mm vs. 4.1 ± 1.3 mm, p < 0.05). Their respective cutoff values with best sensitivity and specificity were calculated; the aTFD (AUC 0.798) and maxTFD (AUC 0.794) achieved the highest diagnostic accuracy. The optimal cutoff levels were aTFD = 4 mm (sensitivity, 56.4 %; specificity, 91.7 %) and maxTFD = 5.65 mm (sensitivity, 74.4 %; specificity, 79.8 %). The inter-observer agreement was good for all aTFD, mTFD, pTFD, and maxTFD measurements (ICC 0.959, 0.799, 0.783, and 0.865). The ICC

  5. Protein structure prediction using bee colony optimization metaheuristic

    DEFF Research Database (Denmark)

    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....

  6. Formation mechanism of NDMA from ranitidine, trimethylamine, and other tertiary amines during chloramination: a computational study.

    Science.gov (United States)

    Liu, Yong Dong; Selbes, Meric; Zeng, Chengchu; Zhong, Rugang; Karanfil, Tanju

    2014-01-01

    Chloramination of drinking waters has been associated with N-nitrosodimethylamine (NDMA) formation as a disinfection byproduct. NDMA is classified as a probable carcinogen and thus its formation during chloramination has recently become the focus of considerable research interest. In this study, the formation mechanisms of NDMA from ranitidine and trimethylamine (TMA), as models of tertiary amines, during chloramination were investigated by using density functional theory (DFT). A new four-step formation pathway of NDMA was proposed involving nucleophilic substitution by chloramine, oxidation, and dehydration followed by nitrosation. The results suggested that nitrosation reaction is the rate-limiting step and determines the NDMA yield for tertiary amines. When 45 other tertiary amines were examined, the proposed mechanism was found to be more applicable to aromatic tertiary amines, and there may be still some additional factors or pathways that need to be considered for aliphatic tertiary amines. The heterolytic ONN(Me)2-R(+) bond dissociation energy to release NDMA and carbocation R(+) was found to be a criterion for evaluating the reactivity of aromatic tertiary amines. A structure-activity study indicates that tertiary amines with benzyl, aromatic heterocyclic ring, and diene-substituted methenyl adjacent to the DMA moiety are potentially significant NDMA precursors. The findings of this study are helpful for understanding NDMA formation mechanism and predicting NDMA yield of a precursor.

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

    Science.gov (United States)

    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

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

    Science.gov (United States)

    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/.

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

    Science.gov (United States)

    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.

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

    Science.gov (United States)

    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.

  11. Prediction of degradation and fracture of structural materials

    International Nuclear Information System (INIS)

    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)

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

    Science.gov (United States)

    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%.

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

    Directory of Open Access Journals (Sweden)

    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

  14. New tips for structure prediction by comparative modeling

    OpenAIRE

    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...

  15. Quantitative structure-activity relationships for predicting potential ecological hazard of organic chemicals for use in regulatory risk assessments.

    Science.gov (United States)

    Comber, Mike H I; Walker, John D; Watts, Chris; Hermens, Joop

    2003-08-01

    The use of quantitative structure-activity relationships (QSARs) for deriving the predicted no-effect concentration of discrete organic chemicals for the purposes of conducting a regulatory risk assessment in Europe and the United States is described. In the United States, under the Toxic Substances Control Act (TSCA), the TSCA Interagency Testing Committee and the U.S. Environmental Protection Agency (U.S. EPA) use SARs to estimate the hazards of existing and new chemicals. Within the Existing Substances Regulation in Europe, QSARs may be used for data evaluation, test strategy indications, and the identification and filling of data gaps. To illustrate where and when QSARs may be useful and when their use is more problematic, an example, methyl tertiary-butyl ether (MTBE), is given and the predicted and experimental data are compared. Improvements needed for new QSARs and tools for developing and using QSARs are discussed.

  16. Social media use profile, social skills, and nurse-patient interaction among Registered Nurses in tertiary hospitals: A structural equation model analysis.

    Science.gov (United States)

    Mariano, Micah Celine O; Maniego, John Christian M; Manila, Hariette Lou Marie D; Mapanoo, Ram Cedrick C; Maquiran, Kerwin Miguel A; Macindo, John Rey B; Tejero, Lourdes Marie S; Torres, Gian Carlo S

    2018-04-01

    Social media has become increasingly important over the past decades and has been integrated in various environments, including the healthcare setting. Yet, the influence of social media use on the social skills and nurse-patient interaction of nurses is an area in nursing that requires further studies. This study determined the interrelationships among social media use profile, social skills, and nurse-patient interaction of Registered Nurses in tertiary hospitals. Employing structural equation modeling, a descriptive-correlational study was conducted among 212 consecutively-selected nurses from two tertiary hospitals. Consenting respondents completed a two-part survey composed of the respondent profile sheet and the Social Skills Inventory. The respondent profile sheet assessed demographic profile and social media use profile in terms of the mode, frequency, and duration of utilization. Three trained team members observed each nurse-patient dyad and completed the Nurse-Patient Bonding Instrument. A good fit model illustrated the negative effects of frequent social media use to patient openness (β = -0.18, p social media on a daily basis, however, positively affected both dimensions of social skills. Accessing social media platforms using non-handheld devices showed the most influential positive effects to social skills and nurse-patient interaction. Additionally, although verbal social skills positively affected most dimensions of nurse-patient interaction, non-verbal social skills negatively influenced patient engagement (β = -0.19, p = 0.019) and nurse openness (β = -0.38, p ≤ 0.05). The structural model illustrates the effects of using social media on the social skills and nurse-patient interaction of nurses and emphasizes the need for implementing institutional policies on the judicious use and application of social media in the workplace. Further, social skills development programs geared toward having a balanced social skill must

  17. UTILITY OF THE DECAF SCORE IN PREDICTING IN HOSPITAL OUTCOME IN PATIENTS WITH ACUTE EXACERBATION OF CHRONIC OBSTRUCTIVE PULMONARY DISEASE IN A TERTIARY CARE HOSPITAL OF SOUTHERN INDIA

    Directory of Open Access Journals (Sweden)

    Ravi Chethan Kumar A. N

    2017-09-01

    Full Text Available BACKGROUND Acute exacerbation of chronic obstructive pulmonary disease being an all too common cause for hospital admissions Worldwide poses a logistical stress for the treating physicians and hospital administration with regards to morbidity and mortality rates. Identifying upon admission those at higher risk of dying in-hospital could be useful for triaging patients to the appropriate level of care, determining the aggressiveness of therapies and timing safe discharges. The aim of this study was to evaluate the utilisation of the DECAF score in predicting in hospital outcome in patients with acute exacerbation of chronic obstructive pulmonary disease (AECOPD in a Tertiary Care Hospital of Southern India. MATERIALS AND METHODS Patients admitted with COPD exacerbations in K.R. Hospital, Mysore Medical College And Research Institute, Mysuru in between the May 2017 and July 2017 were taken has study subjects. A total of 80 patients were taken into the study. The duration of hospital stay, ICU admission and deaths were noted. DECAF score is applied to all study subjects and the severity of AECOPD is graded at the time of admission. The data collected and complied were then analysed for the correlation between score and subsequent management and overall outcome. RESULTS Total of 80 patients were recruited in the study. Mean age for male was 66.47, female was 70.86. Length of hospital stay was more in patients with decaf score more than 3 (average hospital stay 10 days. Patients with DECAF score of 2, 70.4% required inhalations oxygen, remaining 29.6% were managed with only bronchodilators whereas patients with DECAF score of 5 (max score in our study group there was a 100% initiation of assisted ventilation 33.3% received NIV ventilation while 66.6% required endotracheal intubation with ventilator support. In present study, 85 percent patients were survived. Total 6 patients (7.5% had died, belonging to high risk DECAF group (score 3 to 6

  18. Combining neural networks for protein secondary structure prediction

    DEFF Research Database (Denmark)

    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...

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

    Science.gov (United States)

    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

  20. Cross-Disciplinary Perceptions of Structured Interprofessional Rounds in Promoting Teamwork Within an Academic Tertiary Care Obstetric Unit.

    Science.gov (United States)

    Chau, Anthony; Vijjeswarapu, Mary A; Hickey, Margaret; Acker, David; Huang, Chuan-Chin; Tsen, Lawrence C

    2017-06-01

    In 2005, physician and nursing leaders at Brigham and Women's Hospital initiated structured interprofessional rounds (SIPRs) on the labor and delivery (L&D) suite to improve team communication. We performed a cross-sectional analysis of providers' perceptions of SIPRs and their effectiveness in improving teamwork. We hypothesized that on average, providers would perceive SIPRs as being effective in promoting teamwork, but ratings would differ among professional groups. After a factor analysis and internal consistency assessment, a 19-item paper-based questionnaire was used to evaluate providers' perceptions using a 5-point Likert scale. Respondents included L&D nurses, midwives, obstetricians, and anesthesiologists who participate in SIPRs. The primary aim was to evaluate the providers' perceptions of SIPRs and their association with professional roles. The outcome was total response score for each provider, ranging from 19 to 95; perception of SIPRs as being effective in promoting teamwork was defined as having a total response score of >66.5 (mean score, >3.5 per question). A univariable linear regression model was performed, followed by a multivariable analysis adjusting for predictors that modified the outcome; predictors included years of professional practice, years of experience on the L&D suite, number of clinical work hours worked weekly, and principal shift assignment among nurses. The associations between these predictors and providers' perceptions were assessed as a secondary aim. A total of 234 practitioners responded (100% response rate). The mean total response score (SD) for all providers was 73.3 (9.5). After multivariable adjustment, the mean total response scores were significantly higher for obstetric providers than for anesthesia (Δ mean, 6.5, 95% CI, 0.3, 12.7 P = .036) and midwifery (Δ mean, 12.5, 95% CI, 2.0, 23.0, P = .009) providers. Providers scored significantly lower if they worked >60 clinical hours per week compared with ≤20 (

  1. Prediction of RNA secondary structures: from theory to models and real molecules

    International Nuclear Information System (INIS)

    Schuster, Peter

    2006-01-01

    empirical parameters can be determined and by principal deficiencies, for example by the lack of energy contributions resulting from tertiary interactions. In addition, native structures may be determined by folding kinetics rather than by thermodynamics. We address the first problem by considering base pair probabilities or base pairing entropies, which are derived from the partition function of conformations. A high base pair probability corresponding to a low pairing entropy is taken as an indicator of a high reliability of prediction. Pseudoknots are discussed as an example of a tertiary interaction that is highly important for RNA function. Moreover, pseudoknot formation is readily incorporated into structure prediction algorithms. Some examples of experimental data on RNA secondary structures that are readily explained using the landscape concept are presented. They deal with (i) properties of RNA molecules with random sequences, (ii) RNA molecules from restricted alphabets, (iii) existence of neutral networks, (iv) shape space covering, (v) riboswitches and (vi) evolution of non-coding RNAs as an example of evolution restricted to neutral networks

  2. Amphibian and reptile road-kills on tertiary roads in relation to landscape structure: using a citizen science approach with open-access land cover data.

    Science.gov (United States)

    Heigl, Florian; Horvath, Kathrin; Laaha, Gregor; Zaller, Johann G

    2017-06-26

    Amphibians and reptiles are among the most endangered vertebrate species worldwide. However, little is known how they are affected by road-kills on tertiary roads and whether the surrounding landscape structure can explain road-kill patterns. The aim of our study was to examine the applicability of open-access remote sensing data for a large-scale citizen science approach to describe spatial patterns of road-killed amphibians and reptiles on tertiary roads. Using a citizen science app we monitored road-kills of amphibians and reptiles along 97.5 km of tertiary roads covering agricultural, municipal and interurban roads as well as cycling paths in eastern Austria over two seasons. Surrounding landscape was assessed using open access land cover classes for the region (Coordination of Information on the Environment, CORINE). Hotspot analysis was performed using kernel density estimation (KDE+). Relations between land cover classes and amphibian and reptile road-kills were analysed with conditional probabilities and general linear models (GLM). We also estimated the potential cost-efficiency of a large scale citizen science monitoring project. We recorded 180 amphibian and 72 reptile road-kills comprising eight species mainly occurring on agricultural roads. KDE+ analyses revealed a significant clustering of road-killed amphibians and reptiles, which is an important information for authorities aiming to mitigate road-kills. Overall, hotspots of amphibian and reptile road-kills were next to the land cover classes arable land, suburban areas and vineyards. Conditional probabilities and GLMs identified road-kills especially next to preferred habitats of green toad, common toad and grass snake, the most often found road-killed species. A citizen science approach appeared to be more cost-efficient than monitoring by professional researchers only when more than 400 km of road are monitored. Our findings showed that freely available remote sensing data in combination with a

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

    Science.gov (United States)

    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.

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

    Science.gov (United States)

    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.

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

    Science.gov (United States)

    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…

  6. The prediction and discovery of Rayleigh line fine structure

    International Nuclear Information System (INIS)

    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)

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

    CERN Document Server

    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 ...

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

    International Nuclear Information System (INIS)

    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)

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

    Directory of Open Access Journals (Sweden)

    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

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

    Directory of Open Access Journals (Sweden)

    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.

  11. Predictive modeling of neuroanatomic structures for brain atrophy detection

    Science.gov (United States)

    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.

  12. Epitope targeting of tertiary protein structure enables target-guided synthesis of a potent in-cell inhibitor of botulinum neurotoxin.

    Science.gov (United States)

    Farrow, Blake; Wong, Michelle; Malette, Jacquie; Lai, Bert; Deyle, Kaycie M; Das, Samir; Nag, Arundhati; Agnew, Heather D; Heath, James R

    2015-06-08

    Botulinum neurotoxin (BoNT) serotype A is the most lethal known toxin and has an occluded structure, which prevents direct inhibition of its active site before it enters the cytosol. Target-guided synthesis by in situ click chemistry is combined with synthetic epitope targeting to exploit the tertiary structure of the BoNT protein as a landscape for assembling a competitive inhibitor. A substrate-mimicking peptide macrocycle is used as a direct inhibitor of BoNT. An epitope-targeting in situ click screen is utilized to identify a second peptide macrocycle ligand that binds to an epitope that, in the folded BoNT structure, is active-site-adjacent. A second in situ click screen identifies a molecular bridge between the two macrocycles. The resulting divalent inhibitor exhibits an in vitro inhibition constant of 165 pM against the BoNT/A catalytic chain. The inhibitor is carried into cells by the intact holotoxin, and demonstrates protection and rescue of BoNT intoxication in a human neuron model. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  13. New tips for structure prediction by comparative modeling

    Science.gov (United States)

    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

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

    Science.gov (United States)

    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.

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

    Science.gov (United States)

    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

  16. Location and extent of Tertiary structures in Cook Inlet Basin, Alaska, and mantle dynamics that focus deformation and subsidence

    Science.gov (United States)

    Haeussler, Peter J.; Saltus, Richard W.

    2011-01-01

    This report is a new compilation of the location and extent of folds and faults in Cook Inlet Basin, Alaska. Data sources are previously published maps, well locations, and seismic-reflection data. We also utilize interpretation of new aeromagnetic data and some proprietary seismic-reflection data. Some structures are remarkably well displayed on frequency-filtered aeromagnetic maps, which are a useful tool for constraining the length of some structures. Most anticlines in and around the basin have at least shows of oil or gas, and some structures are considered to be seismically active. The new map better displays the pattern of faulting and folding. Deformation is greatest in upper Cook Inlet, where structures are oriented slightly counterclockwise of the basin bounding faults. The north ends of these structures bend to the northeast, which gives a pattern consistent with right-transpressional deformation.

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

    Science.gov (United States)

    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.

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

    Science.gov (United States)

    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/.

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

    International Nuclear Information System (INIS)

    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)

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

    KAUST Repository

    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.

  1. Predicted crystal structures of molybdenum under high pressure

    Energy Technology Data Exchange (ETDEWEB)

    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.

  2. Predicting protein structures with a multiplayer online game.

    Science.gov (United States)

    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.

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

    Science.gov (United States)

    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.

  4. Predicting and validating protein interactions using network structure.

    Directory of Open Access Journals (Sweden)

    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.

  5. Using molecular principal axes for structural comparison: determining the tertiary changes of a FAB antibody domain induced by antigenic binding

    Directory of Open Access Journals (Sweden)

    Silverman B David

    2007-11-01

    Full Text Available Abstract Background Comparison of different protein x-ray structures has previously been made in a number of different ways; for example, by visual examination, by differences in the locations of secondary structures, by explicit superposition of structural elements, e.g. α-carbon atom locations, or by procedures that utilize a common symmetry element or geometrical feature of the structures to be compared. Results A new approach is applied to determine the structural changes that an antibody protein domain experiences upon its interaction with an antigenic target. These changes are determined with the use of two different, however comparable, sets of principal axes that are obtained by diagonalizing the second-order tensors that yield the moments-of-geometry as well as an ellipsoidal characterization of domain shape, prior to and after interaction. Determination of these sets of axes for structural comparison requires no internal symmetry features of the domains, depending solely upon their representation in three-dimensional space. This representation may involve atomic, Cα, or residue centroid coordinates. The present analysis utilizes residue centroids. When the structural changes are minimal, the principal axes of the domains, prior to and after interaction, are essentially comparable and consequently may be used for structural comparison. When the differences of the axes cannot be neglected, but are nevertheless slight, a smaller relatively invariant substructure of the domains may be utilized for comparison. The procedure yields two distance metrics for structural comparison. First, the displacements of the residue centroids due to antigenic binding, referenced to the ellipsoidal principal axes, are noted. Second, changes in the ellipsoidal distances with respect to the non-interacting structure provide a direct measure of the spatial displacements of the residue centroids, towards either the interior or exterior of the domain

  6. Evaluation of the suitability of free-energy minimization using nearest-neighbor energy parameters for RNA secondary structure prediction

    Directory of Open Access Journals (Sweden)

    Cobaugh Christian W

    2004-08-01

    Full Text Available Abstract Background A detailed understanding of an RNA's correct secondary and tertiary structure is crucial to understanding its function and mechanism in the cell. Free energy minimization with energy parameters based on the nearest-neighbor model and comparative analysis are the primary methods for predicting an RNA's secondary structure from its sequence. Version 3.1 of Mfold has been available since 1999. This version contains an expanded sequence dependence of energy parameters and the ability to incorporate coaxial stacking into free energy calculations. We test Mfold 3.1 by performing the largest and most phylogenetically diverse comparison of rRNA and tRNA structures predicted by comparative analysis and Mfold, and we use the results of our tests on 16S and 23S rRNA sequences to assess the improvement between Mfold 2.3 and Mfold 3.1. Results The average prediction accuracy for a 16S or 23S rRNA sequence with Mfold 3.1 is 41%, while the prediction accuracies for the majority of 16S and 23S rRNA structures tested are between 20% and 60%, with some having less than 20% prediction accuracy. The average prediction accuracy was 71% for 5S rRNA and 69% for tRNA. The majority of the 5S rRNA and tRNA sequences have prediction accuracies greater than 60%. The prediction accuracy of 16S rRNA base-pairs decreases exponentially as the number of nucleotides intervening between the 5' and 3' halves of the base-pair increases. Conclusion Our analysis indicates that the current set of nearest-neighbor energy parameters in conjunction with the Mfold folding algorithm are unable to consistently and reliably predict an RNA's correct secondary structure. For 16S or 23S rRNA structure prediction, Mfold 3.1 offers little improvement over Mfold 2.3. However, the nearest-neighbor energy parameters do work well for shorter RNA sequences such as tRNA or 5S rRNA, or for larger rRNAs when the contact distance between the base-pairs is less than 100 nucleotides.

  7. Constraint Logic Programming approach to protein structure prediction

    Directory of Open Access Journals (Sweden)

    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.

  8. Constraint Logic Programming approach to protein structure prediction.

    Science.gov (United States)

    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.

  9. Virality Prediction and Community Structure in Social Networks

    Science.gov (United States)

    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.

  10. Predicting protein structures with a multiplayer online game

    OpenAIRE

    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...

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

    Science.gov (United States)

    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.

  12. Methyltransferase That Modifies Guanine 966 of the 16 S rRNA: FUNCTIONAL IDENTIFICATION AND TERTIARY STRUCTURE*

    Science.gov (United States)

    Lesnyak, Dmitry V.; Osipiuk, Jerzy; Skarina, Tatiana; Sergiev, Petr V.; Bogdanov, Alexey A.; Edwards, Aled; Savchenko, Alexei; Joachimiak, Andrzej; Dontsova, Olga A.

    2010-01-01

    N2-Methylguanine 966 is located in the loop of Escherichia coli 16 S rRNA helix 31, forming a part of the P-site tRNA-binding pocket. We found yhhF to be a gene encoding for m2G966 specific 16 S rRNA methyltransferase. Disruption of the yhhF gene by kanamycin resistance marker leads to a loss of modification at G966. The modification could be rescued by expression of recombinant protein from the plasmid carrying the yhhF gene. Moreover, purified m2G966 methyltransferase, in the presence of S-adenosylomethionine (AdoMet), is able to methylate 30 S ribosomal subunits that were purified from yhhF knock-out strain in vitro. The methylation is specific for G966 base of the 16 S rRNA. The m2G966 methyltransferase was crystallized, and its structure has been determined and refined to 2.05 Å. The structure closely resembles RsmC rRNA methyltransferase, specific for m2G1207 of the 16 S rRNA. Structural comparisons and analysis of the enzyme active site suggest modes for binding AdoMet and rRNA to m2G966 methyltransferase. Based on the experimental data and current nomenclature the protein expressed from the yhhF gene was renamed to RsmD. A model for interaction of RsmD with ribosome has been proposed. PMID:17189261

  13. Methyltransferase that modifies guanine 966 of the 16 S rRNA: functional identification and tertiary structure.

    Science.gov (United States)

    Lesnyak, Dmitry V; Osipiuk, Jerzy; Skarina, Tatiana; Sergiev, Petr V; Bogdanov, Alexey A; Edwards, Aled; Savchenko, Alexei; Joachimiak, Andrzej; Dontsova, Olga A

    2007-02-23

    N(2)-Methylguanine 966 is located in the loop of Escherichia coli 16 S rRNA helix 31, forming a part of the P-site tRNA-binding pocket. We found yhhF to be a gene encoding for m(2)G966 specific 16 S rRNA methyltransferase. Disruption of the yhhF gene by kanamycin resistance marker leads to a loss of modification at G966. The modification could be rescued by expression of recombinant protein from the plasmid carrying the yhhF gene. Moreover, purified m(2)G966 methyltransferase, in the presence of S-adenosylomethionine (AdoMet), is able to methylate 30 S ribosomal subunits that were purified from yhhF knock-out strain in vitro. The methylation is specific for G966 base of the 16 S rRNA. The m(2)G966 methyltransferase was crystallized, and its structure has been determined and refined to 2.05A(.) The structure closely resembles RsmC rRNA methyltransferase, specific for m(2)G1207 of the 16 S rRNA. Structural comparisons and analysis of the enzyme active site suggest modes for binding AdoMet and rRNA to m(2)G966 methyltransferase. Based on the experimental data and current nomenclature the protein expressed from the yhhF gene was renamed to RsmD. A model for interaction of RsmD with ribosome has been proposed.

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

    International Nuclear Information System (INIS)

    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)

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

    International Nuclear Information System (INIS)

    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)

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

    Science.gov (United States)

    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.

  17. Ultraviolet light-induced crosslinking reveals a unique region of local tertiary structure in potato spindle tuber viroid and HeLa 5S RNA

    International Nuclear Information System (INIS)

    Branch, A.D.; Benenfeld, B.J.; Robertson, H.D.

    1985-01-01

    The positions of intramolecular crosslinks induced by irradiation with ultraviolet light were mapped into potato spindle tuber viroid RNA and HeLa 5S rRNA. Crosslinking in each of these molecules occurred at a single major site, which was located by RNA fingerprinting and secondary analysis. Various lines of evidence suggest that these crosslinks identify a previously undescribed element of local tertiary structure common to these two widely divergent RNA molecules: (i) both crosslinks occur in an identical eight-base context, with the sequence 5 GGGAA 3 on one side and the sequence 5 UAC 3 on the other; (ii) both crosslinks connect bases that are not thought to be involved in conventional hydrogen bonding, within regions usually depicted as single-stranded loops flanked by short helical segments; and (iii) both crosslinks connect a purine and a pyrimidine residue, and both may generate the same G-U dimer. Furthermore, it is likely that the crosslinking site is of functional significance because it is located within the most highly conserved region of the viroid sequence and involves bases that are essentially invariant among eukaryotic 5S rRNA molecules

  18. Role of Tertiary Lymphoid Structures (TLS) in Anti-Tumor Immunity: Potential Tumor-Induced Cytokines/Chemokines that Regulate TLS Formation in Epithelial-Derived Cancers

    Energy Technology Data Exchange (ETDEWEB)

    Pimenta, Erica M. [Rutgers Biomedical and Health Sciences, New Jersey Medical School-Cancer Center, Newark, NJ 07103 (United States); Barnes, Betsy J., E-mail: barnesbe@njms.rutgers.edu [Department of Biochemistry and Molecular Biology, Rutgers Biomedical and Health Sciences, New Jersey Medical School-Cancer Center, Newark, NJ 07103 (United States)

    2014-04-23

    Following the successes of monoclonal antibody immunotherapies (trastuzumab (Herceptin{sup ®}) and rituximab (Rituxan{sup ®})) and the first approved cancer vaccine, Provenge{sup ®} (sipuleucel-T), investigations into the immune system and how it can be modified by a tumor has become an exciting and promising new field of cancer research. Dozens of clinical trials for new antibodies, cancer and adjuvant vaccines, and autologous T and dendritic cell transfers are ongoing in hopes of identifying ways to re-awaken the immune system and force an anti-tumor response. To date, however, few consistent, reproducible, or clinically-relevant effects have been shown using vaccine or autologous cell transfers due in part to the fact that the immunosuppressive mechanisms of the tumor have not been overcome. Much of the research focus has been on re-activating or priming cytotoxic T cells to recognize tumor, in some cases completely disregarding the potential roles that B cells play in immune surveillance or how a solid tumor should be treated to maximize immunogenicity. Here, we will summarize what is currently known about the induction or evasion of humoral immunity via tumor-induced cytokine/chemokine expression and how formation of tertiary lymphoid structures (TLS) within the tumor microenvironment may be used to enhance immunotherapy response.

  19. Role of Tertiary Lymphoid Structures (TLS in Anti-Tumor Immunity: Potential Tumor-Induced Cytokines/Chemokines that Regulate TLS Formation in Epithelial-Derived Cancers

    Directory of Open Access Journals (Sweden)

    Erica M. Pimenta

    2014-04-01

    Full Text Available Following the successes of monoclonal antibody immunotherapies (trastuzumab (Herceptin® and rituximab (Rituxan® and the first approved cancer vaccine, Provenge® (sipuleucel-T, investigations into the immune system and how it can be modified by a tumor has become an exciting and promising new field of cancer research. Dozens of clinical trials for new antibodies, cancer and adjuvant vaccines, and autologous T and dendritic cell transfers are ongoing in hopes of identifying ways to re-awaken the immune system and force an anti-tumor response. To date, however, few consistent, reproducible, or clinically-relevant effects have been shown using vaccine or autologous cell transfers due in part to the fact that the immunosuppressive mechanisms of the tumor have not been overcome. Much of the research focus has been on re-activating or priming cytotoxic T cells to recognize tumor, in some cases completely disregarding the potential roles that B cells play in immune surveillance or how a solid tumor should be treated to maximize immunogenicity. Here, we will summarize what is currently known about the induction or evasion of humoral immunity via tumor-induced cytokine/chemokine expression and how formation of tertiary lymphoid structures (TLS within the tumor microenvironment may be used to enhance immunotherapy response.

  20. Isotopic and anatomical evidence of an herbivorous diet in the Early Tertiary giant bird Gastornis. Implications for the structure of Paleocene terrestrial ecosystems

    Science.gov (United States)

    Angst, D.; Lécuyer, C.; Amiot, R.; Buffetaut, E.; Fourel, F.; Martineau, F.; Legendre, S.; Abourachid, A.; Herrel, A.

    2014-04-01

    The mode of life of the early Tertiary giant bird Gastornis has long been a matter of controversy. Although it has often been reconstructed as an apex predator feeding on small mammals, according to other interpretations, it was in fact a large herbivore. To determine the diet of this bird, we analyze here the carbon isotope composition of the bone apatite from Gastornis and contemporaneous herbivorous mammals. Based on 13C-enrichment measured between carbonate and diet of carnivorous and herbivorous modern birds, the carbonate δ13C values of Gastornis bone remains, recovered from four Paleocene and Eocene French localities, indicate that this bird fed on plants. This is confirmed by a morphofunctional study showing that the reconstructed jaw musculature of Gastornis was similar to that of living herbivorous birds and unlike that of carnivorous forms. The herbivorous Gastornis was the largest terrestrial tetrapod in the Paleocene biota of Europe, unlike the situation in North America and Asia, where Gastornis is first recorded in the early Eocene, and the largest Paleocene animals were herbivorous mammals. The structure of the Paleocene terrestrial ecosystems of Europe may have been similar to that of some large islands, notably Madagascar, prior to the arrival of humans.

  1. Volumetric contributions of loop regions of G-quadruplex DNA to the formation of the tertiary structure.

    Science.gov (United States)

    Takahashi, Shuntaro; Sugimoto, Naoki

    2017-12-01

    DNA guanine-quadruplexes (G-quadruplexes) are unique DNA structures formed by guanine-rich sequences. The loop regions of G-quadruplexes play key roles in stability and topology of G-quadruplexes. Here, we investigated volumetric changes induced by pressure in the folding of the G-quadruplex formed by the thrombin binding aptamer (TBA) with mutations within the loop regions. The change of partial molar volume in the transition from coil to G-quadruplex, ∆V tr , of TBA with a mutation from T to A in the 5' most loop (TBA T3A) was 75.5cm 3 mol -1 , which was larger than that of TBA (54.6cm 3 mol -1 ). TBA with a G to T mutation in the central loop (TBA G8T) had thermal stability similar to TBA T3A but a smaller ∆V tr of 41.1cm 3 mol -1 . In the presence of poly(ethylene)glycol 200 (PEG200), ∆V tr values were 14.7cm 3 mol -1 for TBA T3A and 13.2cm 3 mol -1 for TBA G8T. These results suggest that the two mutations destabilize the G-quadruplex structure differently. Thus, volumetric data obtained using pressure-based thermodynamic analyses provides information about the dynamics of the loop regions and the roles of loops in the stabilities and folding of G-quadruplex structures. Copyright © 2017 Elsevier B.V. All rights reserved.

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

    Science.gov (United States)

    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.

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

    Science.gov (United States)

    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.

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

    Science.gov (United States)

    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.

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

    Directory of Open Access Journals (Sweden)

    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.

  6. The sequential structure of brain activation predicts skill.

    Science.gov (United States)

    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.

  7. Protein Loop Structure Prediction Using Conformational Space Annealing.

    Science.gov (United States)

    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.

  8. Offspring social network structure predicts fitness in families.

    Science.gov (United States)

    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.

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

    Science.gov (United States)

    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.

  10. Using autoregressive integrated moving average (ARIMA) models to predict and monitor the number of beds occupied during a SARS outbreak in a tertiary hospital in Singapore.

    Science.gov (United States)

    Earnest, Arul; Chen, Mark I; Ng, Donald; Sin, Leo Yee

    2005-05-11

    The main objective of this study is to apply autoregressive integrated moving average (ARIMA) models to make real-time predictions on the number of beds occupied in Tan Tock Seng Hospital, during the recent SARS outbreak. This is a retrospective study design. Hospital admission and occupancy data for isolation beds was collected from Tan Tock Seng hospital for the period 14th March 2003 to 31st May 2003. The main outcome measure was daily number of isolation beds occupied by SARS patients. Among the covariates considered were daily number of people screened, daily number of people admitted (including observation, suspect and probable cases) and days from the most recent significant event discovery. We utilized the following strategy for the analysis. Firstly, we split the outbreak data into two. Data from 14th March to 21st April 2003 was used for model development. We used structural ARIMA models in an attempt to model the number of beds occupied. Estimation is via the maximum likelihood method using the Kalman filter. For the ARIMA model parameters, we considered the simplest parsimonious lowest order model. We found that the ARIMA (1,0,3) model was able to describe and predict the number of beds occupied during the SARS outbreak well. The mean absolute percentage error (MAPE) for the training set and validation set were 5.7% and 8.6% respectively, which we found was reasonable for use in the hospital setting. Furthermore, the model also provided three-day forecasts of the number of beds required. Total number of admissions and probable cases admitted on the previous day were also found to be independent prognostic factors of bed occupancy. ARIMA models provide useful tools for administrators and clinicians in planning for real-time bed capacity during an outbreak of an infectious disease such as SARS. The model could well be used in planning for bed-capacity during outbreaks of other infectious diseases as well.

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

    Directory of Open Access Journals (Sweden)

    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].

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

    Science.gov (United States)

    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.

  13. Protein Function Prediction Based on Sequence and Structure Information

    KAUST Repository

    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.

  14. A high density of tertiary lymphoid structure B cells in lung tumors is associated with increased CD4+ T cell receptor repertoire clonality.

    Science.gov (United States)

    Zhu, Wei; Germain, Claire; Liu, Zheng; Sebastian, Yinong; Devi, Priyanka; Knockaert, Samantha; Brohawn, Philip; Lehmann, Kim; Damotte, Diane; Validire, Pierre; Yao, Yihong; Valge-Archer, Viia; Hammond, Scott A; Dieu-Nosjean, Marie-Caroline; Higgs, Brandon W

    2015-12-01

    T and B cell receptor (TCR and BCR, respectively) Vβ or immunoglobulin heavy chain complementarity-determining region 3 sequencing allows monitoring of repertoire changes through recognition, clonal expansion, affinity maturation, and T or B cell activation in response to antigen. TCR and BCR repertoire analysis can advance understanding of antitumor immune responses in the tumor microenvironment. TCR and BCR repertoires of sorted CD4 + , CD8 + or CD19 + cells in tumor, non-tumoral distant tissue (NT), and peripheral compartments (blood/draining lymph node [P]) from 47 non-small cell lung cancer (NSCLC) patients (age median = 68 y) were sequenced. The clonotype spectra were assessed among different tissues and correlated with clinical and immunological parameters. In all tissues, CD4 + and CD8 + TCR repertoires had greater clonality relative to CD19 + BCR. CD4 + T cells exhibited greater clonality in NT compared to tumor ( p = 0.002) and P ( p 68). Younger patients exhibited greater CD4 + T cell diversity in P compared to older patients ( p = 0.05), and greater CD4 + T cell clonality in tumor relative to P ( p cell clonality in tumor and P, respectively (both p = 0.05), correlated with high density of tumor-associated tertiary lymphoid structure (TLS) B cells, a biomarker of higher overall survival in NSCLC. Results indicate distinct adaptive immune responses in NSCLC, where peripheral T cell diversity is modulated by age, and tumor T cell clonal expansion is favored by the presence of TLSs in the tumor microenvironment.

  15. Using autoregressive integrated moving average (ARIMA models to predict and monitor the number of beds occupied during a SARS outbreak in a tertiary hospital in Singapore

    Directory of Open Access Journals (Sweden)

    Earnest Arul

    2005-05-01

    Full Text Available Abstract Background The main objective of this study is to apply autoregressive integrated moving average (ARIMA models to make real-time predictions on the number of beds occupied in Tan Tock Seng Hospital, during the recent SARS outbreak. Methods This is a retrospective study design. Hospital admission and occupancy data for isolation beds was collected from Tan Tock Seng hospital for the period 14th March 2003 to 31st May 2003. The main outcome measure was daily number of isolation beds occupied by SARS patients. Among the covariates considered were daily number of people screened, daily number of people admitted (including observation, suspect and probable cases and days from the most recent significant event discovery. We utilized the following strategy for the analysis. Firstly, we split the outbreak data into two. Data from 14th March to 21st April 2003 was used for model development. We used structural ARIMA models in an attempt to model the number of beds occupied. Estimation is via the maximum likelihood method using the Kalman filter. For the ARIMA model parameters, we considered the simplest parsimonious lowest order model. Results We found that the ARIMA (1,0,3 model was able to describe and predict the number of beds occupied during the SARS outbreak well. The mean absolute percentage error (MAPE for the training set and validation set were 5.7% and 8.6% respectively, which we found was reasonable for use in the hospital setting. Furthermore, the model also provided three-day forecasts of the number of beds required. Total number of admissions and probable cases admitted on the previous day were also found to be independent prognostic factors of bed occupancy. Conclusion ARIMA models provide useful tools for administrators and clinicians in planning for real-time bed capacity during an outbreak of an infectious disease such as SARS. The model could well be used in planning for bed-capacity during outbreaks of other infectious

  16. Secondary and tertiary hyperparathyroidism.

    Science.gov (United States)

    Jamal, Sophie A; Miller, Paul D

    2013-01-01

    We reviewed the etiology and management of secondary and tertiary hyperparathyroidism. Secondary hyperparathyroidism is characterized by an increase in parathyroid hormone (PTH) that is appropriate and in response to a stimulus, most commonly low serum calcium. In secondary hyperparathyroidism, the serum calcium is normal and the PTH level is elevated. Tertiary hyperparathyroidism is characterized by excessive secretion of PTH after longstanding secondary hyperparathyroidism, in which hypercalcemia has ensued. Tertiary hyperparathyroidism typically occurs in men and women with chronic kidney disease usually after kidney transplant. The etiology and treatment of secondary hyperparathyroidism is relatively straightforward whereas data on the management of tertiary hyperparathyroidism is limited to a few small trials with short follow-up. Copyright © 2013 The International Society for Clinical Densitometry. Published by Elsevier Inc. All rights reserved.

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

    Directory of Open Access Journals (Sweden)

    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.

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

    Science.gov (United States)

    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

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

    Science.gov (United States)

    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)

  20. Structural Acoustic Prediction and Interior Noise Control Technology

    Science.gov (United States)

    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.

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

    Science.gov (United States)

    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.

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

    Directory of Open Access Journals (Sweden)

    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.

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

    Science.gov (United States)

    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.

  4. Lifetime prediction of structures submitted to thermal fatigue loadings

    International Nuclear Information System (INIS)

    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)

  5. Predicting long-term renal damage in children with vesicoureteral reflux under conservative initial management: 205 cases in a tertiary referral center.

    Science.gov (United States)

    Alvarez, Natalia; Alvira, Reyes Delgado; Ruiz, Yurema Gonzalez; Atuan, Rafael Fernandez; Hinojosa, Alexander Siles; Heras, Miguel Angel Rihuete; Roldan, Marisa Justa; Romero, Jesus Gracia

    2018-01-01

    Vesicoureteral reflux (VUR) is one of the most common ailments in children. Evidence-based guidelines recommend conservative treatment in children with VUR, followed by endoscopic surgery in those with breakthrough febrile urinary tract infections (UTIs). Despite this fact, the management of VUR is still controversial. Our objective is to evaluate the conservative strategy in children with primary VUR in terms of renal function and scarring, and identify factors associated with poor prognosis in those children. A retrospective study was carried out in a tertiary center in children with primary VUR under conservative strategy treatment from 1989 to 2015. Data extracted included age of presentation, family and prenatal backgrounds, radiographic evaluation including ultrasound (US), dimercaptosuccinic acid (DMSA) scans and voiding cystourethrogram (VCUG). The SPSS program was used for statistical analysis. Two-hundred and five patients were diagnosed and followed a conservative therapy scheme (49.8% males, 50.2% females) after febrile UTI (73.17%) or prenatal diagnosis (26.83%). VCUG showed 53.20% of low-moderate VUR grade, 46.80% high VUR grade. Renal damage was present at diagnosis in 40.89%. Mean follow-up reakthrough recurrent febrile UTIs and underwent surgery. Conservative therapy was followed in 189 patients. Renal scarring or decreased kidney function were shown in 15.12% respectively. Renal damage was identified as a risk factor for poor prognosis (p-value Conservative strategy is a feasible treatment for primary VUR in children. The majority of cases could be managed conservatively with good outcomes after long-term follow-up. Decreased renal function is more frequent in patients with high-grade VUR. Renal damage at diagnosis increases the risk for surgical treatment.

  6. Clinical predictive value of the ABCD2 score for early risk of stroke in patients who have had transient ischaemic attack and who present to an Australian tertiary hospital.

    Science.gov (United States)

    Sanders, Lauren M; Srikanth, Velandai K; Psihogios, Helen; Wong, Kitty K; Ramsay, David; Phan, Thanh G

    2011-02-07

    To determine the predictive value of the ABCD(2) score for early risk of stroke in Australian patients who have had transient ischaemic attack (TIA). Cohort study of 512 consecutive patients with suspected TIA referred by the emergency department to the acute stroke unit (in accordance with the TIA pathway) of an urban tertiary hospital in Melbourne, Victoria, between 1 June 2004 and 30 November 2007. Overall accuracy, estimated by the area under the curve (AUC) of receiver operating characteristic plots (of true positive rate v false positive rate), and sensitivity, specificity, predictive values and likelihood ratios at prespecified cut-off ABCD(2) scores for stroke within 2, 7 and 90 days. 24 patients were excluded because their symptoms lasted more than 24 hours. All included patients were reviewed by a stroke physician; TIA was confirmed in 301/488 (61.7%). Most (289/301; 96.0%) had complete follow-up. Stroke occurred in 4/292 patients (1.37%; 95% CI, 0.37%-3.47%) within 2 days and 7/289 (2.42%; 95% CI, 0.98%-4.93%) within 90 days; no patient had a stroke between 2 and 7 days. The AUCs for stroke in patients with confirmed TIA were 0.80 (95% CI, 0.68-0.91) and 0.62 (95% CI, 0.40-0.83) for stroke within 2 days and 90 days, respectively. At a cut-off of ≥ 5, the ABCD(2) score had modest specificity for stroke within 2 days (0.58) and 90 days (0.58), but positive predictive values (2 days, 0.03; 90 days, 0.04) and positive likelihood ratios (2 days, 2.40; 90 days, 1.71) were both poor. The score performed similarly poorly at other prespecified cut-off scores. Given its poor predictive value, the use of the ABCD(2) score alone may not be dependable for guiding clinical treatment decisions or service organisation in an Australian tertiary setting. Validation in other Australian settings is recommended before it can be applied with confidence.

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

    Directory of Open Access Journals (Sweden)

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

    Science.gov (United States)

    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

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

    National Research Council Canada - National Science Library

    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...

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

    Energy Technology Data Exchange (ETDEWEB)

    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.

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

    Directory of Open Access Journals (Sweden)

    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

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

    Energy Technology Data Exchange (ETDEWEB)

    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.

  13. 2. Tertiary Foraminifera

    NARCIS (Netherlands)

    Umbgrove, J.H.F.

    1931-01-01

    In his review of the palaeozoology of Java, K. Martin could in 1919, record 49 foraminifera from tertiary strata of Java, on the strength of a critical study of the existant literature, and especially on the strength of his own studies and knowledge of the above mentioned fossils (Bibl. 49). In

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

    Science.gov (United States)

    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

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

    Science.gov (United States)

    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.

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

    DEFF Research Database (Denmark)

    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...

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

    Science.gov (United States)

    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…

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

    DEFF Research Database (Denmark)

    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...

  19. The Ability of the Acute Physiology and Chronic Health Evaluation (APACHE IV Score to Predict Mortality in a Single Tertiary Hospital

    Directory of Open Access Journals (Sweden)

    Jae Woo Choi

    2017-08-01

    Full Text Available Background The Acute Physiology and Chronic Health Evaluation (APACHE II model has been widely used in Korea. However, there have been few studies on the APACHE IV model in Korean intensive care units (ICUs. The aim of this study was to compare the ability of APACHE IV and APACHE II in predicting hospital mortality, and to investigate the ability of APACHE IV as a critical care triage criterion. Methods The study was designed as a prospective cohort study. Measurements of discrimination and calibration were performed using the area under the receiver operating characteristic curve (AUROC and the Hosmer-Lemeshow goodness-of-fit test respectively. We also calculated the standardized mortality ratio (SMR. Results The APACHE IV score, the Charlson Comorbidity index (CCI score, acute respiratory distress syndrome, and unplanned ICU admissions were independently associated with hospital mortality. The calibration, discrimination, and SMR of APACHE IV were good (H = 7.67, P = 0.465; C = 3.42, P = 0.905; AUROC = 0.759; SMR = 1.00. However, the explanatory power of an APACHE IV score >93 alone on hospital mortality was low at 44.1%. The explanatory power was increased to 53.8% when the hospital mortality was predicted using a model that considers APACHE IV >93 scores, medical admission, and risk factors for CCI >3 coincidentally. However, the discriminative ability of the prediction model was unsatisfactory (C index <0.70. Conclusions The APACHE IV presented good discrimination, calibration, and SMR for hospital mortality.

  20. Pushing the size limit of de novo structure ensemble prediction guided by sparse SDSL-EPR restraints to 200 residues: The monomeric and homodimeric forms of BAX

    Science.gov (United States)

    Fischer, Axel W.; Bordignon, Enrica; Bleicken, Stephanie; García-Sáez, Ana J.; Jeschke, Gunnar; Meiler, Jens

    2016-01-01

    Structure determination remains a challenge for many biologically important proteins. In particular, proteins that adopt multiple conformations often evade crystallization in all biologically relevant states. Although computational de novo protein folding approaches often sample biologically relevant conformations, the selection of the most accurate model for different functional states remains a formidable challenge, in particular, for proteins with more than about 150 residues. Electron paramagnetic resonance (EPR) spectroscopy can obtain limited structural information for proteins in well-defined biological states and thereby assist in selecting biologically relevant conformations. The present study demonstrates that de novo folding methods are able to accurately sample the folds of 192-residue long soluble monomeric Bcl-2-associated X protein (BAX). The tertiary structures of the monomeric and homodimeric forms of BAX were predicted using the primary structure as well as 25 and 11 EPR distance restraints, respectively. The predicted models were subsequently compared to respective NMR/X-ray structures of BAX. EPR restraints improve the protein-size normalized root-mean-square-deviation (RMSD100) of the most accurate models with respect to the NMR/crystal structure from 5.9 Å to 3.9 Å and from 5.7 Å to 3.3 Å, respectively. Additionally, the model discrimination is improved, which is demonstrated by an improvement of the enrichment from 5% to 15% and from 13% to 21%, respectively. PMID:27129417

  1. Development of Tertiary Basins of SE Asia from the South China Sea to the Andaman Sea region ; a comparative view on structure and timing

    Science.gov (United States)

    Pubellier, Manuel; Sautter, Benjamin

    2016-04-01

    Basins of SE Asia have developed since the end of Cretaceous times to the detriment of a Mesozoic andean arc which surrounded Sundaland. The arc was broader in the Eastern part along the Pacific Subduction Zone including theSouth China Sea (SCS), than in the Western part along the Sumatra Subduction Zone (Myanmar, Andaman Sea (AS), Malay Peninsula). By the end of the Upper Cretaceous, this arc died out and a widespread rifting with astonishing resemblances started in the whole Sundaland. We compare and discuss the basins similarities and differences in structure and timing between the two sides. A relaxation stage is evidenced in Western Sunda, represented by poorly exposed Late Cretaceous red beds filling the pre-existing morphostructures without clear fault-controlled basins. These deposits are also observed on seismic data offshore in the Gulf of Thailand and AS). On the opposite side along the Chinese margin, thick molasse-type deposits of Late Cretaceous age are on the contrary well expressed offshore and restricted to narrow valleys, indicating that stretching had already begun. There, the Paleogene is marked by strong extension with large crustal blocks rotated by often counter-regional normal faults creating half grabens. Crust was extended and extremely thinned particularly around the SCS. Basins reached the spreading stage in the Celebes Sea, the North Makassar basin and the SCS. On the western side, this period corresponds to narrow deep grabens (e.g. Mergui basins and part of western Malacca) with continental deposits, meaning that the stretching was localized. There, thinning of the crust took place during the Oligocene up to the Middle Miocene where large basins develop mostly to the outer edges of the Yenshanian Arc. Extension resumed in the Pliocene with the opening of the Andaman basin in an even more external position. To the eastern side the uppermost Miocene and the Pliocene were marked mostly by a deepening of the margins and the SCS ocean

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

    DEFF Research Database (Denmark)

    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...

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

    Science.gov (United States)

    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

  4. Improving search for low energy protein structures with an iterative niche genetic algorithm

    DEFF Research Database (Denmark)

    Helles, Glennie

    2010-01-01

    In attempts to predict the tertiary structure of proteins we use almost exclusively metaheuristics. However, despite known differences in performance of metaheuristics for different problems, the effect of the choice of metaheuristic has received precious little attention in this field. Particula......In attempts to predict the tertiary structure of proteins we use almost exclusively metaheuristics. However, despite known differences in performance of metaheuristics for different problems, the effect of the choice of metaheuristic has received precious little attention in this field...

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

    Science.gov (United States)

    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.

  6. Measuring case-mix complexity of tertiary care hospitals using DRGs.

    Science.gov (United States)

    Park, Hayoung; Shin, Youngsoo

    2004-02-01

    The objectives of the study were to develop a model that measures and evaluates case-mix complexity of tertiary care hospitals, and to examine the characteristics of such a model. Physician panels defined three classes of case complexity and assigned disease categories represented by Adjacent Diagnosis Related Groups (ADRGs) to one of three case complexity classes. Three types of scores, indicating proportions of inpatients in each case complexity class standardized by the proportions at the national level, were defined to measure the case-mix complexity of a hospital. Discharge information for about 10% of inpatient episodes at 85 hospitals with bed size larger than 400 and their input structure and research and education activity were used to evaluate the case-mix complexity model. Results show its power to predict hospitals with the expected functions of tertiary care hospitals, i.e. resource intensive care, expensive input structure, and high levels of research and education activities.

  7. Modelling microbial interactions and food structure in predictive microbiology

    NARCIS (Netherlands)

    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

  8. [Mortality in patients with potentially severe trauma in a tertiary care hospital emergency department and evaluation of risk prediction with the GAP prognostic scale].

    Science.gov (United States)

    Martín Quirós, Alejandro; Borobia Pérez, Alberto; Pertejo Fernández, Ana; Pérez Perilla, Patricia; Rivera Núñez, Angélica; Martínez Virto, Ana María; Quintana Díaz, Manuel

    2015-01-01

    To assess mortality in patients with potentially severe injuries and explore the correlation between mortality and the score on the GAP scale (Glasgow Coma Scale, age, and systolic blood pressure). Retrospective observational study of all patients with potentially severe injuries treated in an emergency department (ED) over a period of 15 months. We recorded epidemiologic variables, cause of injury, type of transport, need for prehospital orotracheal intubation, substance abuse, Charlson Comorbidity Index (CCI), variables for the GAP prognostic score, destination on discharge from the ED and at the end of the episode, and mortality. Data for 864 patients entered the final analysis. Mortality was higher in older patients (mean [SD] age, 57.9 [26.6] vs 41.1 [17.4], P<.05) and those with a higher mean CCI (3.3 [2.9] vs 0.9 [1.7]). Accident type was a precipitating factor associated with mortality (P<.001), but substance abuse was unrelated. Patients who died had lower mean Glasgow scores (9.1 [5.3] vs 14.8 [1.2], P<.001) and lower mean systolic and diastolic pressures (respectively, 113.8 [19.8] vs 131.3 [20.7] mm Hg, P=.012, and 60.1 [16.8] vs 77.7 [11.7] mm Hg, P=.002). Patients who died also had lower mean GAP scores than survivors (15.1 [4.8] vs 22.6 [1.7], P<.001). Risk factors that remained significant in the multivariate analysis were CCI (odds ratio [OR], 0.704; 95% CI, 0.52-0.96) and GAP score (OR, 1.8; 95% CI, 1.45-2.20). Mortality in our patient series was lower than rates in previously published reports. The GAP score was a useful tool for predicting mortality in the series we studied.

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

    Directory of Open Access Journals (Sweden)

    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.

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

    National Research Council Canada - National Science Library

    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...

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

    Science.gov (United States)

    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.

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

    NARCIS (Netherlands)

    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

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

    Science.gov (United States)

    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".

  14. Improving 3D structure prediction from chemical shift data

    Energy Technology Data Exchange (ETDEWEB)

    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)

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

    Science.gov (United States)

    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.

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

    International Nuclear Information System (INIS)

    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

  17. Protein Function Prediction Based on Sequence and Structure Information

    KAUST Repository

    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

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

    Science.gov (United States)

    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.

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

    Science.gov (United States)

    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

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

    Science.gov (United States)

    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

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

    Directory of Open Access Journals (Sweden)

    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".

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

    Science.gov (United States)

    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%.

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

    Science.gov (United States)

    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.

  4. SECTORAL ANALYSIS: GROWTH ACCOUNTING OF TERTIARY INDUSTRIES

    Directory of Open Access Journals (Sweden)

    Yahya Z. ALSHEHHI

    2017-08-01

    Full Text Available The tertiary sector is one of the modern styles of economic systems in view of the share it occupies in the field of production as well as employment occupied share. Hence, just like other lands, the UAE, witnessed an economic structural change similar to developed and developing nations, where the tertiary industries contributed 55.4% in 2015 to total country’s income. The empirical study aimed to analyze the contribution portion of growth in the tertiary industries through using the growth accounting framework in time-series from 1990 to 2015. The empirical study found that most of the industries contributed significantly to the growth of the tertiary sector. The contribution shares of growth due to labor and capital varied among industries. The main observed results show that there was a vice versa relationship between TFP performance and the size of labor, where the TFP positively corresponded with the decline in the size of labor specifically from 2010-2015.

  5. Prediction of Vibration Transmission within Periodic Bar Structures

    DEFF Research Database (Denmark)

    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...

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

    CERN Document Server

    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 ...

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

    Science.gov (United States)

    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.

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

    International Nuclear Information System (INIS)

    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

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

    Science.gov (United States)

    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...

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

    International Nuclear Information System (INIS)

    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

  11. Structure Building Predicts Grades in College Psychology and Biology

    Science.gov (United States)

    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…

  12. Predicting structural properties of fluids by thermodynamic extrapolation

    Science.gov (United States)

    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.

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

    DEFF Research Database (Denmark)

    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...

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

    Directory of Open Access Journals (Sweden)

    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.

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

    Science.gov (United States)

    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.

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

    Science.gov (United States)

    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.

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

    DEFF Research Database (Denmark)

    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...

  18. Water-stable helical structure of tertiary amides of bicyclic β-amino acid bearing 7-azabicyclo[2.2.1]heptane. Full control of amide cis-trans equilibrium by bridgehead substitution.

    Science.gov (United States)

    Hosoya, Masahiro; Otani, Yuko; Kawahata, Masatoshi; Yamaguchi, Kentaro; Ohwada, Tomohiko

    2010-10-27

    Helical structures of oligomers of non-natural β-amino acids are significantly stabilized by intramolecular hydrogen bonding between main-chain amide moieties in many cases, but the structures are generally susceptible to the environment; that is, helices may unfold in protic solvents such as water. For the generation of non-hydrogen-bonded ordered structures of amides (tertiary amides in most cases), control of cis-trans isomerization is crucial, even though there is only a small sterical difference with respect to cis and trans orientations. We have established methods for synthesis of conformationally constrained β-proline mimics, that is, bridgehead-substituted 7-azabicyclo[2.2.1]heptane-2-endo-carboxylic acids. Our crystallographic, 1D- and 2D-NMR, and CD spectroscopic studies in solution revealed that a bridgehead methoxymethyl substituent completely biased the cis-trans equilibrium to the cis-amide structure along the main chain, and helical structures based on the cis-amide linkage were generated independently of the number of residues, from the minimalist dimer through the tetramer, hexamer, and up to the octamer, and irrespective of the solvent (e.g., water, alcohol, halogenated solvents, and cyclohexane). Generality of the control of the amide equilibrium by bridgehead substitution was also examined.

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

    Science.gov (United States)

    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.

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

    Directory of Open Access Journals (Sweden)

    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.

  1. Hydrogeology, groundwater levels, and generalized potentiometric-surface map of the Green River Basin lower Tertiary aquifer system, 2010–14, in the northern Green River structural basin

    Science.gov (United States)

    Bartos, Timothy T.; Hallberg, Laura L.; Eddy-Miller, Cheryl

    2015-07-14

    In cooperation with the Bureau of Land Management, groundwater levels in wells located in the northern Green River Basin in Wyoming, an area of ongoing energy development, were measured by the U.S. Geological Survey from 2010 to 2014. The wells were completed in the uppermost aquifers of the Green River Basin lower Tertiary aquifer system, which is a complex regional aquifer system that provides water to most wells in the area. Except for near perennial streams, groundwater-level altitudes in most aquifers generally decreased with increasing depth, indicating a general downward potential for groundwater movement in the study area. Drilled depth of the wells was observed as a useful indicator of depth to groundwater such that deeper wells typically had a greater depth to groundwater. Comparison of a subset of wells included in this study that had historical groundwater levels that were measured during the 1960s and 1970s and again between 2012 and 2014 indicated that, overall, most of the wells showed a net decline in groundwater levels.

  2. Ar-40 to Ar-39 ages of the large impact structures Kara and Manicouagan and their relevance to the Cretaceous-Tertiary and the Triassic-Jurassic boundary

    Science.gov (United States)

    Trieloff, M.; Jessberger, E. K.

    Since the discovery of the Ir enrichment in Cretaceous-Tertiary boundary clays in 1980, the effects of a 10-km asteroid impacting on the Earth 65 Ma ago have been discussed as the possible reason for the mass extinction--including the extinction of the dinosaurs--at the end of the Cretaceous. But up to now no crater of this age that is large enough (ca. 200 km in diameter) has been found. One candidate is the Kara Crater in northern Siberia. Kolesnikov et al. determined a K-Ar isochron of 65.6 +/- 0.5 Ma, indistinguishable from the age of the K-T boundary and interpreted this as confirmation of earlier proposals that the Kara bolide would have been at least one of the K-T impactors. Koeberl et al. determined Ar-40 to Ar-39 ages ranging from 70 to 82 Ma and suggested an association to the Campanian-Maastrichtian boundary, another important extinction horizon 73 Ma ago. We dated four impact melts, KA2-306, KA2-305, SA1-302, and AN9-182. Results from the investigation are discussed.

  3. Solubility Temperature Dependence Predicted from 2D Structure

    Directory of Open Access Journals (Sweden)

    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.

  4. Exponential Repulsion Improves Structural Predictability of Molecular Docking

    Czech Academy of Sciences Publication Activity Database

    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

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

    Directory of Open Access Journals (Sweden)

    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.

  6. Quantum chemical prediction of antennae structures in lanthanide complexes

    International Nuclear Information System (INIS)

    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

  7. Failure/leakage predictions of concrete structures containing cracks

    International Nuclear Information System (INIS)

    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.

    Science.gov (United States)

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

    Science.gov (United States)

    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.

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

    Science.gov (United States)

    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.

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

    Energy Technology Data Exchange (ETDEWEB)

    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.

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

    DEFF Research Database (Denmark)

    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...

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

    International Nuclear Information System (INIS)

    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

  14. Stratigraphy, structure, and some petrographic features of Tertiary volcanic rocks at the USW G-2 drill hole, Yucca Mountain, Nye County, Nevada

    International Nuclear Information System (INIS)

    Maldonado, F.; Koether, S.L.

    1983-01-01

    A continuously cored drill hole penetrated 1830.6 m of Tertiary volcanic strata comprised of the following in descending order: Paintbrush Tuff, tuffaceous beds of Calico Hills, Crater Flat Tuff, lava and flow breccia (rhyodacitic), tuff of Lithic Ridge, bedded and ash-flow tuff, lava and flow breccia bedded tuff, conglomerate and ash-flow tuff, and older tuffs of USW G-2. Comparison of unit thicknesses at USW G-2 to unit thicknesses at previously drilled holes at Yucca Mountain indicate: (1) thickening of the Paintbrush Tuff members and tuffaceous beds of Calico Hills toward the northern part of Yucca Mountain; (2) thickening of the Prow Pass Member but thinning of the Bullfrog Member and Tram unit; (3) thinning of the tuff of Lithic Ridge; (4) presence of about 280 m of lava and flow breccia not previously penetrated by any drill hole; and (5) presence of an ash-flow tuff unit at the bottom of the drill hole not previously intersected, apparently the oldest unit penetrated at Yucca Mountain to date. Petrographic features of some of the units include: (1) decrease in quartz and K-feldspar and increases in biotite and plagioclase with depth in the tuffaceous beds of Calico Hills; (2) an increase in quartz phenocrysts from the top to the bottom members of the Crater Flat Tuff; (3) a low quartz content in the tuff of Lithic Ridge, suggesting tapping of the magma chamber at quartz-poor levels; (4) a change in zeolitic alteration from heulandite to clinoptilolite to mordenite with increasing depth; (5) lavas characterized by a rhyolitic top and dacitic base, suggesting reverse compositional zoning; and (6) presence of hydrothermal mineralization in the lavas that could be related to an itrusive under Yucca Mountain or to volcanism associated with the Timber Mountain-Claim Canyon caldera complex. A fracture analysis of the core resulted in tabulation of 7848 fractures, predominately open and high angle

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

    OpenAIRE

    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

    DEFF Research Database (Denmark)

    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

    Directory of Open Access Journals (Sweden)

    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.

    Science.gov (United States)

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

    DEFF Research Database (Denmark)

    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...

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

    Science.gov (United States)

    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

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

    Directory of Open Access Journals (Sweden)

    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

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

    Science.gov (United States)

    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.

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

    Energy Technology Data Exchange (ETDEWEB)

    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.

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

    Science.gov (United States)

    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.

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

    DEFF Research Database (Denmark)

    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...

  6. Stereoinversion of tertiary alcohols to tertiary-alkyl isonitriles and amines.

    Science.gov (United States)

    Pronin, Sergey V; Reiher, Christopher A; Shenvi, Ryan A

    2013-09-12

    The SN2 reaction (bimolecular nucleophilic substitution) is a well-known chemical transformation that can be used to join two smaller molecules together into a larger molecule or to exchange one functional group for another. The SN2 reaction proceeds in a very predictable manner: substitution occurs with inversion of stereochemistry, resulting from the 'backside attack' of the electrophilic carbon by the nucleophile. A significant limitation of the SN2 reaction is its intolerance for tertiary carbon atoms: whereas primary and secondary alcohols are viable precursor substrates, tertiary alcohols and their derivatives usually either fail to react or produce stereochemical mixtures of products. Here we report the stereochemical inversion of chiral tertiary alcohols with a nitrogenous nucleophile facilitated by a Lewis-acid-catalysed solvolysis. The method is chemoselective against secondary and primary alcohols, thereby complementing the selectivity of the SN2 reaction. Furthermore, this method for carbon-nitrogen bond formation mimics a putative biosynthetic step in the synthesis of marine terpenoids and enables their preparation from the corresponding terrestrial terpenes. We expect that the general attributes of the methodology will allow chiral tertiary alcohols to be considered viable substrates for stereoinversion reactions.

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

    Science.gov (United States)

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

    Directory of Open Access Journals (Sweden)

    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.

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

    International Nuclear Information System (INIS)

    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.)

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

    International Nuclear Information System (INIS)

    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.

  11. Functional region prediction with a set of appropriate homologous sequences-an index for sequence selection by integrating structure and sequence information with spatial statistics

    Science.gov (United States)

    2012-01-01

    Background The detection of conserved residue clusters on a protein structure is one of the effective strategies for the prediction of functional protein regions. Various methods, such as Evolutionary Trace, have been developed based on this strategy. In such approaches, the conserved residues are identified through comparisons of homologous amino acid sequences. Therefore, the selection of homologous sequences is a critical step. It is empirically known that a certain degree of sequence divergence in the set of homologous sequences is required for the identification of conserved residues. However, the development of a method to select homologous sequences appropriate for the identification of conserved residues has not been sufficiently addressed. An objective and general method to select appropriate homologous sequences is desired for the efficient prediction of functional regions. Results We have developed a novel index to select the sequences appropriate for the identification of conserved residues, and implemented the index within our method to predict the functional regions of a protein. The implementation of the index improved the performance of the functional region prediction. The index represents the degree of conserved residue clustering on the tertiary structure of the protein. For this purpose, the structure and sequence information were integrated within the index by the application of spatial statistics. Spatial statistics is a field of statistics in which not only the attributes but also the geometrical coordinates of the data are considered simultaneously. Higher degrees of clustering generate larger index scores. We adopted the set of homologous sequences with the highest index score, under the assumption that the best prediction accuracy is obtained when the degree of clustering is the maximum. The set of sequences selected by the index led to higher functional region prediction performance than the sets of sequences selected by other sequence

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

    Directory of Open Access Journals (Sweden)

    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. Tertiary structure in 7.9 M guanidinium chloride: the role of Glu-53 and Asp-287 in Pyrococcus furiosus endo-beta-1,3-glucanase

    NARCIS (Netherlands)

    Chiaraluce, R.; Florio, R.; Angelaccio, S.; Gianese, G.; Lieshout, van J.F.T.; Oost, van der J.; Consalvi, V.

    2007-01-01

    The thermodynamic stability of family 16 endo-ß-1,3-glucanase (EC 3.2.1.39) from the hyperthermophilic archaeon Pyrococcus furiosus is decreased upon single (D287A, E53A) and double (E53A/D287A) mutation of Asp287 and Glu53. In accordance with the homology model prediction, both carboxylic acids are

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

    Science.gov (United States)

    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.

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

    Science.gov (United States)

    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.

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

    DEFF Research Database (Denmark)

    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....

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

    Science.gov (United States)

    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.

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

    Energy Technology Data Exchange (ETDEWEB)

    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.

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

    Science.gov (United States)

    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.

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

    Science.gov (United States)

    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.

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

    Science.gov (United States)

    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.

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

    Energy Technology Data Exchange (ETDEWEB)

    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.

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

    Science.gov (United States)

    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.

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

    Science.gov (United States)

    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

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

    International Nuclear Information System (INIS)

    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

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

    Science.gov (United States)

    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

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

    Science.gov (United States)

    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.

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

    Science.gov (United States)

    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

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

    Science.gov (United States)

    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.

  10. Base pair probability estimates improve the prediction accuracy of RNA non-canonical base pairs.

    Directory of Open Access Journals (Sweden)

    Michael F Sloma

    2017-11-01

    Full Text Available Prediction of RNA tertiary structure from sequence is an important problem, but generating accurate structure models for even short sequences remains difficult. Predictions of RNA tertiary structure tend to be least accurate in loop regions, where non-canonical pairs are important for determining the details of structure. Non-canonical pairs can be predicted using a knowledge-based model of structure that scores nucleotide cyclic motifs, or NCMs. In this work, a partition function algorithm is introduced that allows the estimation of base pairing probabilities for both canonical and non-canonical interactions. Pairs that are predicted to be probable are more likely to be found in the true structure than pairs of lower probability. Pair probability estimates can be further improved by predicting the structure conserved across multiple homologous sequences using the TurboFold algorithm. These pairing probabilities, used in concert with prior knowledge of the canonical secondary structure, allow accurate inference of non-canonical pairs, an important step towards accurate prediction of the full tertiary structure. Software to predict non-canonical base pairs and pairing probabilities is now provided as part of the RNAstructure software package.

  11. Base pair probability estimates improve the prediction accuracy of RNA non-canonical base pairs.

    Science.gov (United States)

    Sloma, Michael F; Mathews, David H

    2017-11-01

    Prediction of RNA tertiary structure from sequence is an important problem, but generating accurate structure models for even short sequences remains difficult. Predictions of RNA tertiary structure tend to be least accurate in loop regions, where non-canonical pairs are important for determining the details of structure. Non-canonical pairs can be predicted using a knowledge-based model of structure that scores nucleotide cyclic motifs, or NCMs. In this work, a partition function algorithm is introduced that allows the estimation of base pairing probabilities for both canonical and non-canonical interactions. Pairs that are predicted to be probable are more likely to be found in the true structure than pairs of lower probability. Pair probability estimates can be further improved by predicting the structure conserved across multiple homologous sequences using the TurboFold algorithm. These pairing probabilities, used in concert with prior knowledge of the canonical secondary structure, allow accurate inference of non-canonical pairs, an important step towards accurate prediction of the full tertiary structure. Software to predict non-canonical base pairs and pairing probabilities is now provided as part of the RNAstructure software package.

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

    DEFF Research Database (Denmark)

    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...

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

    Energy Technology Data Exchange (ETDEWEB)

    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.

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

    DEFF Research Database (Denmark)

    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...

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

    Science.gov (United States)

    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.

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

    Science.gov (United States)

    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.

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

    Energy Technology Data Exchange (ETDEWEB)

    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.

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

    Science.gov (United States)

    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.

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

    Directory of Open Access Journals (Sweden)

    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.

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

    Science.gov (United States)

    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 .

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

    OpenAIRE

    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...

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

    DEFF Research Database (Denmark)

    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....

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

    Science.gov (United States)

    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.

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

    Science.gov (United States)

    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/.

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

    Science.gov (United States)

    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.

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

    Directory of Open Access Journals (Sweden)

    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

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

    Directory of Open Access Journals (Sweden)

    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

  8. Tertiary classes–after Chern-Simons theory

    Indian Academy of Sciences (India)

    J.N. Iyer Institute of Mathematical Sciences Chennai, India

    2013-11-08

    Nov 8, 2013 ... Euler characteristic class. In early twentieth century, the notion of local product structure, i.e. fiber spaces and their generalizations appeared, in the study of topological spaces (with additional structures). J.N. Iyer. IMSc, Chennai. Tertiary classes–after Chern-Simons theory ...

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

    KAUST Repository

    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.

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

    Science.gov (United States)

    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.

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

    KAUST Repository

    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.

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

    Science.gov (United States)

    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.

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

    Science.gov (United States)

    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.

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

    Science.gov (United States)

    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

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

    International Nuclear Information System (INIS)

    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.)

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

    Directory of Open Access Journals (Sweden)

    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.

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

    KAUST Repository

    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.

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

    Directory of Open Access Journals (Sweden)

    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.

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

    Science.gov (United States)

    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.

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

    International Nuclear Information System (INIS)

    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)

  1. Differential effects of CSF-1R D802V and KIT D816V homologous mutations on receptor tertiary structure and allosteric communication.

    Directory of Open Access Journals (Sweden)

    Priscila Da Silva Figueiredo Celestino Gomes

    Full Text Available The colony stimulating factor-1 receptor (CSF-1R and the stem cell factor receptor KIT, type III receptor tyrosine kinases (RTKs, are important mediators of signal transduction. The normal functions of these receptors can be compromised by gain-of-function mutations associated with different physiopatological impacts. Whereas KIT D816V/H mutation is a well-characterized oncogenic event and principal cause of systemic mastocytosis, the homologous CSF-1R D802V has not been identified in human cancers. The KIT D816V oncogenic mutation triggers resistance to the RTK inhibitor Imatinib used as first line treatment against chronic myeloid leukemia and gastrointestinal tumors. CSF-1R is also sensitive to Imatinib and this sensitivity is altered by mutation D802V. Previous in silico characterization of the D816V mutation in KIT evidenced that the mutation caused a structure reorganization of the juxtamembrane region (JMR and facilitated its departure from the kinase domain (KD. In this study, we showed that the equivalent CSF-1R D802V mutation does not promote such structural effects on the JMR despite of a reduction on some key H-bonds interactions controlling the JMR binding to the KD. In addition, this mutation disrupts the allosteric communication between two essential regulatory fragments of the receptors, the JMR and the A-loop. Nevertheless, the mutation-induced shift towards an active conformation observed in KIT D816V is not observed in CSF-1R D802V. The distinct impact of equivalent mutation in two homologous RTKs could be associated with the sequence difference between both receptors in the native form, particularly in the JMR region. A local mutation-induced perturbation on the A-loop structure observed in both receptors indicates the stabilization of an inactive non-inhibited form, which Imatinib cannot bind.

  2. Differential Effects of CSF-1R D802V and KIT D816V Homologous Mutations on Receptor Tertiary Structure and Allosteric Communication

    Science.gov (United States)

    Da Silva Figueiredo Celestino Gomes, Priscila; Panel, Nicolas; Laine, Elodie; Pascutti, Pedro Geraldo; Solary, Eric; Tchertanov, Luba

    2014-01-01

    The colony stimulating factor-1 receptor (CSF-1R) and the stem cell factor receptor KIT, type III receptor tyrosine kinases (RTKs), are important mediators of signal transduction. The normal functions of these receptors can be compromised by gain-of-function mutations associated with different physiopatological impacts. Whereas KIT D816V/H mutation is a well-characterized oncogenic event and principal cause of systemic mastocytosis, the homologous CSF-1R D802V has not been identified in human cancers. The KIT D816V oncogenic mutation triggers resistance to the RTK inhibitor Imatinib used as first line treatment against chronic myeloid leukemia and gastrointestinal tumors. CSF-1R is also sensitive to Imatinib and this sensitivity is altered by mutation D802V. Previous in silico characterization of the D816V mutation in KIT evidenced that the mutation caused a structure reorganization of the juxtamembrane region (JMR) and facilitated its departure from the kinase domain (KD). In this study, we showed that the equivalent CSF-1R D802V mutation does not promote such structural effects on the JMR despite of a reduction on some key H-bonds interactions controlling the JMR binding to the KD. In addition, this mutation disrupts the allosteric communication between two essential regulatory fragments of the receptors, the JMR and the A-loop. Nevertheless, the mutation-induced shift towards an active conformation observed in KIT D816V is not observed in CSF-1R D802V. The distinct impact of equivalent mutation in two homologous RTKs could be associated with the sequence difference between both receptors in the native form, particularly in the JMR region. A local mutation-induced perturbation on the A-loop structure observed in both receptors indicates the stabilization of an inactive non-inhibited form, which Imatinib cannot bind. PMID:24828813

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

    Science.gov (United States)

    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.

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

    Science.gov (United States)

    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.

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

    KAUST Repository

    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.

  6. A folding algorithm for extended RNA secondary structures.

    Science.gov (United States)

    Höner zu Siederdissen, Christian; Bernhart, Stephan H; Stadler, Peter F; Hofacker, Ivo L

    2011-07-01

    RNA secondary structure contains many non-canonical base pairs of different pair families. Successful prediction of these structural features leads to improved secondary structures with applications in tertiary structure prediction and simultaneous folding and alignment. We present a theoretical model capturing both RNA pair families and extended secondary structure motifs with shared nucleotides using 2-diagrams. We accompany this model with a number of programs for parameter optimization and structure prediction. All sources (optimization routines, RNA folding, RNA evaluation, extended secondary structure visualization) are published under the GPLv3 and available at www.tbi.univie.ac.at/software/rnawolf/.

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

    Science.gov (United States)

    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

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

    OpenAIRE

    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...

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

    DEFF Research Database (Denmark)

    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....

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

    Science.gov (United States)

    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.

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

    Science.gov (United States)

    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

  12. Tertiary climatic fluctuations and methods of analysis of tertiary floras

    Science.gov (United States)

    Wolfe, J.A.

    1971-01-01

    On theoretical grounds, an analysis of the physiognomy of a Tertiary leaf assemblage is more direct and reliable than a circuitous floristic analysis in assigning thermal regimes to fossil assemblages. Using primarily foliar physiognomy and secondarily floristic composition, it can be shown that: (1) some middle latitude Tertiary assemblages probably lived under meteoroligically tropical climates; (2) a major and rapid climatic deterioration occurred in the Oligocene; and (3) a major climatic fluctuation probably occurred in the Late Eocene. These analyses thus substantiate the conclusions of several other paleobotanists regarding climatic fluctuations. Recent criticisms of these analyses are shown to be invalid and to be based largely on misinterpretations. ?? 1971.

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

    Science.gov (United States)

    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.

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

    Directory of Open Access Journals (Sweden)

    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.

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

    KAUST Repository

    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.

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

    KAUST Repository

    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.

  17. Modified Displacement Transfer Functions for Deformed Shape Predictions of Slender Curved Structures with Varying Curvatives

    Science.gov (United States)

    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.

    Science.gov (United States)

    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.

    Science.gov (United States)

    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.

    Science.gov (United States)

    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. Binding Affects the Tertiary and Quaternary Structures of the Shigella Translocator Protein IpaB and its Chaperone IpgC†

    Science.gov (United States)

    Adam, Philip R.; Patil, Mrinalini K.; Dickenson, Nicholas E.; Choudhari, Shyamal; Barta, Michael; Geisbrecht, Brian V.; Picking, Wendy L.; Picking, William D.

    2012-01-01

    Shigella flexneri uses its type III secretion system (T3SS) to promote invasion of human intestinal epithelial cells as the first step in causing shigellosis, a life threatening form of dysentery. The Shigella type III secretion apparatus (T3SA) consists of a basal body that spans the bacterial envelope and an exposed needle that injects effector proteins into target cells. The nascent Shigella T3SA needle is topped with a pentamer of the needle tip protein invasion plasmid antigen D (IpaD). Bile salts trigger recruitment of the first hydrophobic translocator protein, IpaB, to the tip complex where it senses contact with a host membrane. In the bacterial cytoplasm, IpaB exists in a complex with its chaperone IpgC. Several structures of IpgC have been solved and we recently reported the 2.1-Å crystal structure of the N-terminal domain (IpaB74.224) of IpaB. Like IpgC, the IpaB N-terminal domain exists as a homodimer in solution. We now report that when the two are mixed, these homodimers dissociate and form heterodimers having a nanomolar dissociation constant. This is consistent with the equivalent complexes co-purified after being co-expressed in E. coli. Fluorescence data presented here also indicate that the N-terminal domain of IpaB possesses two regions that appear to contribute additively to chaperone binding. It is also likely that the IpaB N terminus adopts an alternative conformation as a result of chaperone binding. The importance of these findings within the functional context of these proteins is discussed. PMID:22497344

  2. Predicting community structure in snakes on Eastern Nearctic islands using ecological neutral theory and phylogenetic methods.

    Science.gov (United States)

    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).

  3. MemBrain: An Easy-to-Use Online Webserver for Transmembrane Protein Structure Prediction

    Science.gov (United States)

    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.

  4. Ground-State Gas-Phase Structures of Inorganic Molecules Predicted by Density Functional Theory Methods

    KAUST Repository

    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

    Science.gov (United States)

    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

    DEFF Research Database (Denmark)

    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

    DEFF Research Database (Denmark)

    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

    Science.gov (United States)

    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.

    Science.gov (United States)

    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

    International Nuclear Information System (INIS)

    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

    International Nuclear Information System (INIS)

    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. Prediction of elastic-plastic response of structural elements subjected to cyclic loading

    International Nuclear Information System (INIS)

    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.)

  13. Structural maturation and brain activity predict future working memory capacity during childhood development.

    Science.gov (United States)

    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.

  14. Less-structured time in children's daily lives predicts self-directed executive functioning.

    Science.gov (United States)

    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.

  15. Evidence of Hippocampal Structural Alterations in Gulf War Veterans With Predicted Exposure to the Khamisiyah Plume.

    Science.gov (United States)

    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.

  16. Vertical structure of predictability and information transport over the Northern Hemisphere

    International Nuclear Information System (INIS)

    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)

  17. Inducible tertiary lymphoid structures, autoimmunity and exocrine dysfunction in a novel model of salivary gland inflammation in C57BL/6 mice§

    Science.gov (United States)

    Bombardieri, Michele; Barone, Francesca; Lucchesi, Davide; Nayar, Saba; van den Berg, Wim B; Proctor, Gordon; Buckley, Christopher D; Pitzalis, Costantino

    2012-01-01

    Salivary glands in patients with Sjögren’s syndrome (SS) develop ectopic lymphoid structures (ELS) characterized by B/T cell compartmentalization, the formation of high endothelial venules (HEV), follicular dendritic cell networks (FDCs), functional B cell activation with expression of activation-induced cytidine deaminase (AID) as well as local differentiation of autoreactive plasma cells. The mechanisms triggering ELS formation, autoimmunity and exocrine dysfunction in SS are largely unknown. Here we present a novel model of inducible ectopic lymphoid tissue formation, breach of humoral self-tolerance and salivary hypofunction following delivery of a replication-deficient adenovirus-5 (AdV5) in submandibular glands of C57BL/6 mice through retrograde excretory duct cannulation. In this model, inflammation rapidly and consistently evolves from diffuse infiltration towards the development of SS-like periductal lymphoid aggregates within 2 weeks from AdV delivery. These infiltrates progressively acquire ELS features and support functional GL7+/AID+ germinal centers. Formation of ELS is preceded by ectopic expression of lymphoid chemokines CXCL13, CCL19 and lymphotoxin-β and is associated with development of anti-nuclear antibodies in up to 75% of mice. Finally, reduction in salivary flow was observed over 3 weeks post AdV infection consistent with exocrine gland dysfunction as a consequence of the inflammatory response. This novel model has the potential to unravel the cellular and molecular mechanisms regulating ELS formation and their role in exocrine dysfunction and autoimmunity in SS. PMID:22942425

  18. Inducible tertiary lymphoid structures, autoimmunity, and exocrine dysfunction in a novel model of salivary gland inflammation in C57BL/6 mice.

    Science.gov (United States)

    Bombardieri, Michele; Barone, Francesca; Lucchesi, Davide; Nayar, Saba; van den Berg, Wim B; Proctor, Gordon; Buckley, Christopher D; Pitzalis, Costantino

    2012-10-01

    Salivary glands in patients with Sjögren's syndrome (SS) develop ectopic lymphoid structures (ELS) characterized by B/T cell compartmentalization, the formation of high endothelial venules, follicular dendritic cell networks, functional B cell activation with expression of activation-induced cytidine deaminase, as well as local differentiation of autoreactive plasma cells. The mechanisms that trigger ELS formation, autoimmunity, and exocrine dysfunction in SS are largely unknown. In this article, we present a novel model of inducible ectopic lymphoid tissue formation, breach of humoral self-tolerance, and salivary hypofunction after delivery of a replication-deficient adenovirus-5 in submandibular glands of C57BL/6 mice through retrograde excretory duct cannulation. In this model, inflammation rapidly and consistently evolves from diffuse infiltration toward the development of SS-like periductal lymphoid aggregates within 2 wk from AdV delivery. These infiltrates progressively acquire ELS features and support functional GL7(+)/activation-induced cytidine deaminase(+) germinal centers. Formation of ELS is preceded by ectopic expression of lymphoid chemokines CXCL13, CCL19, and lymphotoxin-β, and is associated with development of anti-nuclear Abs in up to 75% of mice. Finally, reduction in salivary flow was observed over 3 wk post-AdV infection, consistent with exocrine gland dysfunction as a consequence of the inflammatory response. This novel model has the potential to unravel the cellular and molecular mechanisms that regulate ELS formation and their role in exocrine dysfunction and autoimmunity in SS.

  19. Managing uncertainty in metabolic network structure and improving predictions using EnsembleFBA.

    Directory of Open Access Journals (Sweden)

    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.

  20. Social and structural vulnerability as a barrier in HIV and/or AIDS communication campaigns: Perceptions of undergraduate students at a South African tertiary institution

    Directory of Open Access Journals (Sweden)

    Olivia Kunguma

    2018-03-01

    Full Text Available The multicultural nature of a higher academic institution comprising students from different backgrounds can either negatively or positively influence student behaviour. Students might engage in high-risk practices, which in turn can make them vulnerable to HIV infection. Higher academic institutions are then tasked with finding strategies that can help to reduce this risk and vulnerability to HIV and/or AIDS. However, there are many issues and barriers, both from the institution and students, which can impede the success of any communication strategy. The University of the Free State’s main campus was selected for this study. A sample of 402 students from a total of 17 591 undergraduate students participated in the study. A structured questionnaire was randomly distributed to the undergraduate students. The sample was compiled across all faculties, as well as on campus and off campus. A transact walk on campus with an observation checklist was also used for triangulation purposes. The observation checklist helped to collect data on the visibility of male and female condoms in toilet facilities, and HIV and/or AIDS information on noticeboards, bins, stationery, billboards, etc. The main finding indicated that students were not knowledgeable about HIV and/or AIDS campaigns rolled out on campus. To support this, the observational transact walk results indicated that there were no visible campaigns on campus. Also, problems with existing communication and organisational barriers were found not only with the students but also with the implementation office. This study recommends that the university needs to engage with the students by identifying the root cause of their vulnerability. The university should explore and make use of all the available resources for a successful intervention, thereby building students’ resilience in preventing HIV infection.

  1. RNA secondary structure prediction by using discrete mathematics: an interdisciplinary research experience for undergraduate students.

    Science.gov (United States)

    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.

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

    Science.gov (United States)

    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

  3. Advances in Rosetta structure prediction for difficult molecular-replacement problems

    International Nuclear Information System (INIS)

    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

  4. Electronic structure prediction via data-mining the empirical pseudopotential method

    Energy Technology Data Exchange (ETDEWEB)

    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.)

  5. Bayesian Inference using Neural Net Likelihood Models for Protein Secondary Structure Prediction

    Directory of Open Access Journals (Sweden)

    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.

  6. Tchebichef image moment approach to the prediction of protein secondary structures based on circular dichroism.

    Science.gov (United States)

    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.

  7. De novo prediction of human chromosome structures: Epigenetic marking patterns encode genome architecture.

    Science.gov (United States)

    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.

  8. Why Do Tertiary Education Graduates Regret Their Study Program? A Comparison between Spain and the Netherlands

    Science.gov (United States)

    Kucel, Aleksander; Vilalta-Bufi, Montserrat

    2013-01-01

    In this paper we investigate the determinants of regret of study program for tertiary education graduates in Spain and the Netherlands. These two countries differ in their educational system in terms of the tracking structure in their secondary education and the strength of their education-labor market linkages in tertiary education. Therefore, by…

  9. RAISING ESP STUDENTS’ AWARENESS OF THE GENERIC STRUCTURES AND LINGUISTIC FEATURES OF JOB APPLICATION LETTERS THROUGH THE APPLICATION OF GENRE-BASED INSTRUCTION AT THE TERTIARY LEVEL: THE CASE OF 3 rd YEAR MANAGEMENT STUDENTS DJILLALI LIABES UNIVERSITY, SIDI BEL ABBES

    OpenAIRE

    SEKKAL, Faiza

    2012-01-01

    The current study is an attempt to investigate the genre of job application letter using genre-based analysis as a powerful educational tool, in order to improve the teaching of business writing at the tertiary level with reference to third- year management students by raising their awareness of the generic structures and linguistic features of the target genre. In this regard, this research work is based on the theory of genre analysis in ESP; it aims to help future graduates ...

  10. Probabilistic methods for condition assessment and life prediction of concrete structures in nuclear power plants

    International Nuclear Information System (INIS)

    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.)

  11. A Comparative Taxonomy of Parallel Algorithms for RNA Secondary Structure Prediction

    Science.gov (United States)

    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

  12. Evaluation of multiple protein docking structures using correctly predicted pairwise subunits

    Directory of Open Access Journals (Sweden)

    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.

  13. Polymer physics predicts the effects of structural variants on chromatin architecture.

    Science.gov (United States)

    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.

  14. Chemical structure-based predictive model for methanogenic anaerobic biodegradation potential.

    Science.gov (United States)

    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.

  15. Using quantitative structure-activity relationships (QSAR) to predict toxic endpoints for polycyclic aromatic hydrocarbons (PAH).

    Science.gov (United States)

    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.

  16. Structural MRI-Based Predictions in Patients with Treatment-Refractory Depression (TRD.

    Directory of Open Access Journals (Sweden)

    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.

  17. QuaBingo: A Prediction System for Protein Quaternary Structure Attributes Using Block Composition

    Directory of Open Access Journals (Sweden)

    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.

  18. Sequential search leads to faster, more efficient fragment-based de novo protein structure prediction.

    Science.gov (United States)

    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.

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

    Directory of Open Access Journals (Sweden)

    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.

  20. Predicting acute aquatic toxicity of structurally diverse chemicals in fish using artificial intelligence approaches.

    Science.gov (United States)

    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.

  1. Predicting deleterious nsSNPs: an analysis of sequence and structural attributes

    Directory of Open Access Journals (Sweden)

    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

  2. Using Data Mining Approaches for Force Prediction of a Dynamically Loaded Flexible Structure

    DEFF Research Database (Denmark)

    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...

  3. Rethinking the Tertiary Mathematics Curriculum

    Science.gov (United States)

    Petocz, Peter; Reid, Anna

    2005-01-01

    Mathematics curriculum at the tertiary level is located within a range of social and cultural theories, and is often constructed by academics seeking to promulgate a particular view of mathematics. We argue that such a curriculum should incorporate a real acknowledgement of the different ways in which students understand the nature of mathematics…

  4. Prediction of Individual Response to Electroconvulsive Therapy via Machine Learning on Structural Magnetic Resonance Imaging Data.

    Science.gov (United States)

    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

  5. The dual role of fragments in fragment-assembly methods for de novo protein structure prediction

    Science.gov (United States)

    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

  6. Predicting Consensus Structures for RNA Alignments Via Pseudo-Energy Minimization

    Directory of Open Access Journals (Sweden)

    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.

  7. A systematic review on popularity, application and characteristics of protein secondary structure prediction tools.

    Science.gov (United States)

    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.

  8. Stress Prediction for Distributed Structural Health Monitoring Using Existing Measurements and Pattern Recognition.

    Science.gov (United States)

    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. An Examination of the Local Cellular Immune Response to Examples of Both Ductal Carcinoma In Situ (DCIS) of the Breast and DCIS With Microinvasion, With Emphasis on Tertiary Lymphoid Structures and Tumor Infiltrating Lymphoctytes.

    Science.gov (United States)

    Kim, Ahrong; Heo, Sun-Hee; Kim, Young-Ae; Gong, Gyungyub; Jin Lee, Hee

    2016-07-01

    We tried to describe cellular immune response (tertiary lymphoid structures (TLSs), lymphoid aggregates, tumor infiltrating lymphocytes (TILs)) in neoplastic microenvironment of ductal carcinoma in situ (DCIS) with or without associated microinvasion. The histopathologic parameters of 177 DCIS and 27 DCIS with microinvasion were evaluated. We determined number of ducts involved by DCIS, and calculated percentage of these ducts surrounded by TLSs. TILs were quantitated in 27 microinvasive cases. Tumors having higher percentage of DCIS ducts associated with TLSs had higher incidence of microinvasion (P < .001). Percentage of DCIS ducts involved by TLSs was also higher in hormone receptor (HR)-/human epidermal growth factor receptor 2 (HER2)+ and TNBC subtypes of DCIS than in HR+/HER2- and HR+/HER2+ subtypes (38.04 ± 25.8%, 32.6 ± 32.4%, 2.5 ± 7.3% and 17.4 ± 23.3%, respectively, P < .001). In DCIS without microinvasion, HR+/HER2- subtype predominated (P < .001). In microinvasive cases, HR-/HER2+ subtype was most common. TNBC was more common in microinvasive carcinoma than DCIS (P < .001). Among 27 microinvasive ductal carcinomas, increased TLS amount was associated with increased TILs (P = .013). TLS abundance around DCIS was associated with HER2+ and TNBC subtypes and microinvasion. Pathologists should be aware of microinvasion when diagnosing DCIS lesions with abundant TLSs. © American Society for Clinical Pathology, 2016. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

  10. Relevance of Donation to Special Federal Tertiary Institution ...

    African Journals Online (AJOL)

    While structured and open-ended interview and relevant documents, including gift and donation files, acquisition records and library accession registers were used to collect data for the study. The population of the study consisted of the Special Federal Tertiary Institution Libraries under study while the subjects of the study ...

  11. PASS Student Leader and Mentor Roles: A Tertiary Leadership Pathway

    Science.gov (United States)

    Skalicky, Jane; Caney, Annaliese

    2010-01-01

    In relation to developing leadership skills during tertiary studies, this paper considers the leadership pathway afforded by a Peer Assisted Study Sessions (PASS) program which includes the traditional PASS Leader role and a more senior PASS Mentor role. Data was collected using a structured survey with open-ended questions designed to capture the…

  12. Discovery of tertiary sulfonamides as potent liver X receptor antagonists.

    Science.gov (United States)

    Zuercher, William J; Buckholz, Richard G; Campobasso, Nino; Collins, Jon L; Galardi, Cristin M; Gampe, Robert T; Hyatt, Stephen M; Merrihew, Susan L; Moore, John T; Oplinger, Jeffrey A; Reid, Paul R; Spearing, Paul K; Stanley, Thomas B; Stewart, Eugene L; Willson, Timothy M

    2010-04-22

    Tertiary sulfonamides were identified in a HTS as dual liver X receptor (LXR, NR1H2, and NR1H3) ligands, and the binding affinity of the series was increased through iterative analogue synthesis. A ligand-bound cocrystal structure was determined which elucidated key interactions for high binding affinity. Further characterization of the tertiary sulfonamide series led to the identification of high affinity LXR antagonists. GSK2033 (17) is the first potent cell-active LXR antagonist described to date. 17 may be a useful chemical probe to explore the cell biology of this orphan nuclear receptor.

  13. Predictive modeling of multicellular structure formation by using Cellular Particle Dynamics simulations

    Science.gov (United States)

    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.

  14. An economic prediction of the finer resolution level wavelet coefficients in electronic structure calculations.

    Science.gov (United States)

    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.

  15. Lifetime prediction of structures submitted to thermal fatigue loadings; Prediction de duree de vie de structures sous chargement de fatigue thermique

    Energy Technology Data Exchange (ETDEWEB)

    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)

  16. LiveBench-1: continuous benchmarking of protein structure prediction servers.

    Science.gov (United States)

    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.

  17. Machine-learning scoring functions to improve structure-based binding affinity prediction and virtual screening.

    Science.gov (United States)

    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.

  18. Framingham coronary heart disease risk score can be predicted from structural brain images in elderly subjects.

    Directory of Open Access Journals (Sweden)

    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.

  19. In Silico Prediction of Chemical Toxicity for Drug Design Using Machine Learning Methods and Structural Alerts

    Science.gov (United States)

    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.

  20. In Silico Prediction of Chemical Toxicity for Drug Design Using Machine Learning Methods and Structural Alerts

    Directory of Open Access Journals (Sweden)

    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.

  1. In Silico Prediction of Chemical Toxicity for Drug Design Using Machine Learning Methods and Structural Alerts.

    Science.gov (United States)

    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.

  2. Anisotropic Elastoplastic Damage Mechanics Method to Predict Fatigue Life of the Structure

    Directory of Open Access Journals (Sweden)

    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.

  3. The ordered network structure and its prediction for the big floods of the Changjiang River Basins

    Energy Technology Data Exchange (ETDEWEB)

    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.)

  4. RDNAnalyzer: A tool for DNA secondary structure prediction and sequence analysis.

    Science.gov (United States)

    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.

  5. Phase change predictions for liquid fuel in contact with steel structure using the heat conduction equation

    Energy Technology Data Exchange (ETDEWEB)

    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)

  6. The ordered network structure and its prediction for the big floods of the Changjiang River Basins

    International Nuclear Information System (INIS)

    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.)

  7. Phase change predictions for liquid fuel in contact with steel structure using the heat conduction equation

    International Nuclear Information System (INIS)

    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)

  8. Ramsdellite-structured LiTiO 2: A new phase predicted from ab initio calculations

    Science.gov (United States)

    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.

  9. SAAS: Short Amino Acid Sequence - A Promising Protein Secondary Structure Prediction Method of Single Sequence

    Directory of Open Access Journals (Sweden)

    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%.

  10. Prediction of protein–protein interactions: unifying evolution and structure at protein interfaces

    International Nuclear Information System (INIS)

    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

  11. SucStruct: Prediction of succinylated lysine residues by using structural properties of amino acids.

    Science.gov (United States)

    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.

  12. Protein secondary structure prediction using modular reciprocal bidirectional recurrent neural networks.

    Science.gov (United States)

    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.

  13. Computational tools for experimental determination and theoretical prediction of protein structure

    Energy Technology Data Exchange (ETDEWEB)

    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.

  14. Utilizing knowledge base of amino acids structural neighborhoods to predict protein-protein interaction sites.

    Science.gov (United States)

    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.

  15. Prediction of municipal solid waste generation using artificial neural network approach enhanced by structural break analysis.

    Science.gov (United States)

    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.

  16. Inorganic Nitrogen Application Affects Both Taxonomical and Predicted Functional Structure of Wheat Rhizosphere Bacterial Communities

    Directory of Open Access Journals (Sweden)

    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

    OpenAIRE

    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

    OpenAIRE

    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. Prediction of protein structural classes by recurrence quantification analysis based on chaos game representation.

    Science.gov (United States)

    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.

  20. 3dRPC: a web server for 3D RNA-protein structure prediction.

    Science.gov (United States)

    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.

  1. Comparison of Comet Enflow and VA One Acoustic-to-Structure Power Flow Predictions

    Science.gov (United States)

    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.

  2. Computational Prediction of Atomic Structures of Helical Membrane Proteins Aided by EM Maps

    Science.gov (United States)

    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

  3. Structure Based Thermostability Prediction Models for Protein Single Point Mutations with Machine Learning Tools.

    Directory of Open Access Journals (Sweden)

    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.

  4. Spatio-temporal observations of the tertiary ozone maximum

    Directory of Open Access Journals (Sweden)

    V. F. Sofieva

    2009-07-01

    Full Text Available We present spatio-temporal distributions of the tertiary ozone maximum (TOM, based on GOMOS (Global Ozone Monitoring by Occultation of Stars ozone measurements in 2002–2006. The tertiary ozone maximum is typically observed in the high-latitude winter mesosphere at an altitude of ~72 km. Although the explanation for this phenomenon has been found recently – low concentrations of odd-hydrogen cause the subsequent decrease in odd-oxygen losses – models have had significant deviations from existing observations until recently. Good coverage of polar night regions by GOMOS data has allowed for the first time to obtain spatial and temporal observational distributions of night-time ozone mixing ratio in the mesosphere.

    The distributions obtained from GOMOS data have specific features, which are variable from year to year. In particular, due to a long lifetime of ozone in polar night conditions, the downward transport of polar air by the meridional circulation is clearly observed in the tertiary ozone maximum time series. Although the maximum tertiary ozone mixing ratio is achieved close to the polar night terminator (as predicted by the theory, TOM can be observed also at very high latitudes, not only in the beginning and at the end, but also in the middle of winter. We have compared the observational spatio-temporal distributions of the tertiary ozone maximum with that obtained using WACCM (Whole Atmosphere Community Climate Model and found that the specific features are reproduced satisfactorily by the model.

    Since ozone in the mesosphere is very sensitive to HOx concentrations, energetic particle precipitation can significantly modify the shape of the ozone profiles. In particular, GOMOS observations have shown that the tertiary ozone maximum was temporarily destroyed during the January 2005 and December 2006 solar proton events as a result of the HOx enhancement from the increased ionization.

  5. Microbes as engines of ecosystem function: when does community structure enhance predictions of ecosystem processes?

    Directory of Open Access Journals (Sweden)

    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.

  6. Structural habitat predicts functional dispersal habitat of a large carnivore: how leopards change spots.

    Science.gov (United States)

    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

  7. Toward Structure Prediction for Short Peptides Using the Improved SAAP Force Field Parameters

    Directory of Open Access Journals (Sweden)

    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.

  8. Structural predictions for Correlated Electron Materials Using the Functional Dynamical Mean Field Theory Approach

    Science.gov (United States)

    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.

  9. Structure Prediction of Outer Membrane Protease Protein of Salmonella typhimurium Using Computational Techniques

    Directory of Open Access Journals (Sweden)

    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.

  10. Switch region for pathogenic structural change in conformational disease and its prediction.

    Directory of Open Access Journals (Sweden)

    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.

  11. Parathyroid carcinoma in tertiary hyperparathyroidism.

    Science.gov (United States)

    Kim, Byung Seup; Ryu, Han Suk; Kang, Kyung Ho; Park, Sung Jun

    2016-10-01

    Parathyroid carcinoma is a rare disease of unknown etiology. This study presents a case of parathyroid carcinoma in a patient with tertiary hyperparathyroidism. Despite a successful kidney transplantation, the intact parathyroid hormone (iPTH) level of the patient was elevated consistently and could not be controlled by medical therapy. Due to the development of tertiary hyperparathyroidism with bone pain and osteoporosis, subtotal parathyroidectomy was performed 4 months after the kidney transplantation. Histological evaluation revealed that one of four parathyroid lesions was a parathyroid carcinoma, while the others were diffuse hyperplasia. Postoperative laboratory studies indicated a decreased level of iPTH. A positron emission tomography-computed tomography performed 6 months after the operation revealed no evidence of local recurrence or distant metastasis. Copyright © 2013. Published by Elsevier Taiwan.

  12. Validation of Quantitative Structure-Activity Relationship (QSAR Model for Photosensitizer Activity Prediction

    Directory of Open Access Journals (Sweden)

    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.

  13. Predicting taxonomic and functional structure of microbial communities in acid mine drainage.

    Science.gov (United States)

    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

  14. Genetic programming based quantitative structure-retention relationships for the prediction of Kovats retention indices.

    Science.gov (United States)

    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

  15. Predicting binding within disordered protein regions to structurally characterised peptide-binding domains.

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    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.

  16. Ground-State Gas-Phase Structures of Inorganic Molecules Predicted by Density Functional Theory Methods

    KAUST Repository

    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

  17. Prediction of pressure induced structural phase transitions and internal mode frequency changes in solid N2+

    International Nuclear Information System (INIS)

    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

  18. Model structures amplify uncertainty in predicted soil carbon responses to climate change.

    Science.gov (United States)

    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.

  19. Comparative analysis of QSAR models for predicting pK(a) of organic oxygen acids and nitrogen bases from molecular structure.

    Science.gov (United States)

    Yu, Haiying; Kühne, Ralph; Ebert, Ralf-Uwe; Schüürmann, Gerrit

    2010-11-22

    For 1143 organic compounds comprising 580 oxygen acids and 563 nitrogen bases that cover more than 17 orders of experimental pK(a) (from -5.00 to 12.23), the pK(a) prediction performances of ACD, SPARC, and two calibrations of a semiempirical quantum chemical (QC) AM1 approach have been analyzed. The overall root-mean-square errors (rms) for the acids are 0.41, 0.58 (0.42 without ortho-substituted phenols with intramolecular H-bonding), and 0.55 and for the bases are 0.65, 0.70, 1.17, and 1.27 for ACD, SPARC, and both QC methods, respectively. Method-specific performances are discussed in detail for six acid subsets (phenols and aromatic and aliphatic carboxylic acids with different substitution patterns) and nine base subsets (anilines, primary, secondary and tertiary amines, meta/para-substituted and ortho-substituted pyridines, pyrimidines, imidazoles, and quinolines). The results demonstrate an overall better performance for acids than for bases but also a substantial variation across subsets. For the overall best-performing ACD, rms ranges from 0.12 to 1.11 and 0.40 to 1.21 pK(a) units for the acid and base subsets, respectively. With regard to the squared correlation coefficient r², the results are 0.86 to 0.96 (acids) and 0.79 to 0.95 (bases) for ACD, 0.77 to 0.95 (acids) and 0.85 to 0.97 (bases) for SPARC, and 0.64 to 0.87 (acids) and 0.43 to 0.83 (bases) for the QC methods, respectively. Attention is paid to structural and method-specific causes for observed pitfalls. The significant subset dependence of the prediction performances suggests a consensus modeling approach.

  20. Structured Semantic Knowledge Can Emerge Automatically from Predicting Word Sequences in Child-Directed Speech

    Directory of Open Access Journals (Sweden)

    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

  1. Structured Semantic Knowledge Can Emerge Automatically from Predicting Word Sequences in Child-Directed Speech

    Science.gov (United States)

    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

  2. Large-scale prediction of drug–target interactions using protein sequences and drug topological structures

    International Nuclear Information System (INIS)

    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

  3. Analysis of energy-based algorithms for RNA secondary structure prediction

    Directory of Open Access Journals (Sweden)

    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

  4. Coupling between cracking and permeability, a model for structure service life prediction

    International Nuclear Information System (INIS)

    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

  5. Using sequence-specific chemical and structural properties of DNA to predict transcription factor binding sites.

    Directory of Open Access Journals (Sweden)

    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.

  6. Predicting complex syntactic structure in real time: Processing of negative sentences in Russian.

    Science.gov (United States)

    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.

  7. Quantitative structure-activity relationship (QSAR) for insecticides: development of predictive in vivo insecticide activity models.

    Science.gov (United States)

    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.

  8. Prediction of post translational modifications in avicennia marina Cu-Zn superoxide dismutase: implication of glycation on the enzyme structure

    International Nuclear Information System (INIS)

    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)

  9. Multi-Scale Modeling for Predicting the Stiffness and Strength of Hollow-Structured Metal Foams with Structural Hierarchy

    Directory of Open Access Journals (Sweden)

    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.

  10. Predicting cognitive function of the Malaysian elderly: a structural equation modelling approach.

    Science.gov (United States)

    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.

  11. Low-Quality Structural and Interaction Data Improves Binding Affinity Prediction via Random Forest.

    Science.gov (United States)

    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.

  12. Improved understanding of physics processes in pedestal structure, leading to improved predictive capability for ITER

    International Nuclear Information System (INIS)

    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)

  13. Can high-energy proton events in solar wind be predicted via classification of precursory structures?

    Energy Technology Data Exchange (ETDEWEB)

    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.

  14. Predicting effects of structural stress in a genome-reduced model bacterial metabolism

    Science.gov (United States)

    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.

  15. Computational prediction of muon stopping sites using ab initio random structure searching (AIRSS)

    Science.gov (United States)

    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.

  16. Predicting the structural development in Danish livestock and how it affects control strategies against FMD

    DEFF Research Database (Denmark)

    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...

  17. Predicting Dynamic Response of Structures under Earthquake Loads Using Logical Analysis of Data

    Directory of Open Access Journals (Sweden)

    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.

  18. Further Development of Ko Displacement Theory for Deformed Shape Predictions of Nonuniform Aerospace Structures

    Science.gov (United States)

    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.

  19. Predicting algal growth inhibition toxicity: three-step strategy using structural and physicochemical properties.

    Science.gov (United States)

    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.

  20. A nucleobase-centered coarse-grained representation for structure prediction of RNA motifs.

    Science.gov (United States)

    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.