Sample records for predicting b-dna structure

  1. Structural correlations and melting of B-DNA fibers

    SciTech Connect

    Wildes, Andrew [Institut Laue Langevin, B.P. 156, 6 rue Jules Horowitz, F-38042 Grenoble Cedex 9 (France); Theodorakopoulos, Nikos [Theoretical and Physical Chemistry Institute, National Hellenic Research Foundation, Vasileos Constantinou 48, G-116 35 Athens (Greece); Fachbereich Physik, Universitaet Konstanz, D-78457 Konstanz (Germany); Valle-Orero, Jessica [Institut Laue Langevin, B.P. 156, 6 rue Jules Horowitz, F-38042 Grenoble Cedex 9 (France); Universite de Lyon, Ecole Normale Superieure de Lyon, Laboratoire de Physique CNRS UMR 5672, 46 allee d'Italie, F-69364 Lyon Cedex 7 (France); Cuesta-Lopez, Santiago; Peyrard, Michel [Universite de Lyon, Ecole Normale Superieure de Lyon, Laboratoire de Physique CNRS UMR 5672, 46 allee d'Italie, F-69364 Lyon Cedex 7 (France); Garden, Jean-Luc [Institut Neel, CNRS, Universite Joseph Fourier, 25 rue des Martyrs, B.P. 166, F-38042 Grenoble Cedex 9 (France)

    2011-06-15

    Despite numerous attempts, understanding the thermal denaturation of DNA is still a challenge due to the lack of structural data on the transition since standard experimental approaches to DNA melting are made in solution and do not provide spatial information. We report a measurement using neutron scattering from oriented DNA fibers to determine the size of the regions that stay in the double-helix conformation as the melting temperature is approached from below. A Bragg peak from the B form of DNA is observed as a function of temperature and its width and integrated intensity are measured. These results, complemented by a differential calorimetry study of the melting of B-DNA fibers as well as electrophoresis and optical observation data, are analyzed in terms of a one-dimensional mesoscopic model of DNA.

  2. NMR proton chemical shift prediction of C·C mismatches in B-DNA

    NASA Astrophysics Data System (ADS)

    Ng, Kui Sang; Lam, Sik Lok

    2015-03-01

    Accurate prediction of DNA chemical shifts facilitates resonance assignment and allows recognition of different conformational features. Based on the nearest neighbor model and base pair replacement approach, we have determined a set of triplet chemical shift values and correction factors for predicting the proton chemical shifts of B-DNA containing an internal C·C mismatch. Our results provide a reliable chemical shift prediction with an accuracy of 0.07 ppm for non-labile protons and 0.09 ppm for labile protons. In addition, we have also shown that the correction factors for C·C mismatches can be used interchangeably with those for T·T mismatches. As a result, we have generalized a set of correction factors for predicting the flanking residue chemical shifts of pyrimidine·pyrimidine mismatches.

  3. Structural change in a B-DNA helix with hydrostatic pressure

    PubMed Central

    Wilton, David J.; Ghosh, Mahua; Chary, K. V. A.; Akasaka, Kazuyuki; Williamson, Mike P.

    2008-01-01

    Study of the effects of pressure on macromolecular structure improves our understanding of the forces governing structure, provides details on the relevance of cavities and packing in structure, increases our understanding of hydration and provides a basis to understand the biology of high-pressure organisms. A study of DNA, in particular, helps us to understand how pressure can affect gene activity. Here we present the first high-resolution experimental study of B-DNA structure at high pressure, using NMR data acquired at pressures up to 200 MPa (2 kbar). The structure of DNA compresses very little, but is distorted so as to widen the minor groove, and to compress hydrogen bonds, with AT pairs compressing more than GC pairs. The minor groove changes are suggested to lead to a compression of the hydration water in the minor groove. PMID:18515837

  4. A 5-nanosecond molecular dynamics trajectory for B-DNA: analysis of structure, motions, and solvation.

    PubMed Central

    Young, M A; Ravishanker, G; Beveridge, D L

    1997-01-01

    We report the results of four new molecular dynamics (MD) simulations on the DNA duplex of sequence d(CGCGAATTCGCG)2, including explicit consideration of solvent water, and a sufficient number of Na+ counterions to provide electroneutrality to the system. Our simulations are configured particularly to characterize the latest MD models of DNA, and to provide a basis for examining the sensitivity of MD results to the treatment of boundary conditions, electrostatics, initial placement of solvent, and run lengths. The trajectories employ the AMBER 4.1 force field. The simulations use particle mesh Ewald summation for boundary conditions, and range in length from 500 ps to 5.0 ns. Analysis of the results is carried out by means of time series for conformationalm, helicoidal parameters, newly developed indices of DNA axis bending, and groove widths. The results support a dynamically stable model of B-DNA for d(CGCGAATTCGCG)2 over the entire length of the trajectory. The MD results are compared with corresponding crystallographic and NMR studies on the d(CGCGAATTCGCG)2 duplex, and placed in the context of observed behavior of B-DNA by comparisons with the complete crystallographic data base of B-form structures. The calculated distributions of mobile solvent molecules, both water and counterions, are displayed. The calculated solvent structure of the primary solvation shell is compared with the location of ordered solvent positions in the corresponding crystal structure. The results indicate that ordered solvent positions in crystals are roughly twice as structured as bulk water. Detailed analysis of the solvent dynamics reveals evidence of the incorporation of ions in the primary solvation of the minor groove B-form DNA. The idea of localized complexation of otherwise mobile counterions in electronegative pockets in the grooves of DNA helices introduces an additional source of sequence-dependent effects on local conformational, helicoidal, and morphological structure, and may have important implications for understanding the functional energetics and specificity of the interactions of DNA and RNA with regulatory proteins, pharmaceutical agents, and other ligands. Images FIGURE 2 FIGURE 3 FIGURE 4 FIGURE 10 FIGURE 11 FIGURE 13 FIGURE 14 FIGURE 15 PMID:9370428

  5. B-DNA structure is intrinsically polymorphic: even at the level of base pair positions

    SciTech Connect

    Maehigashi, Tatsuya; Hsiao, Chiaolong; Woods, Kristen Kruger; Moulaei, Tinoush; Hud, Nicholas V.; Williams, Loren Dean (GIT)

    2012-10-23

    Increasingly exact measurement of single crystal X-ray diffraction data offers detailed characterization of DNA conformation, hydration and electrostatics. However, instead of providing a more clear and unambiguous image of DNA, highly accurate diffraction data reveal polymorphism of the DNA atomic positions and conformation and hydration. Here we describe an accurate X-ray structure of B-DNA, painstakingly fit to a multistate model that contains multiple competing positions of most of the backbone and of entire base pairs. Two of ten base-pairs of CCAGGCCTGG are in multiple states distinguished primarily by differences in slide. Similarly, all the surrounding ions are seen to fractionally occupy discrete competing and overlapping sites. And finally, the vast majority of water molecules show strong evidence of multiple competing sites. Conventional resolution appears to give a false sense of homogeneity in conformation and interactions of DNA. In addition, conventional resolution yields an average structure that is not accurate, in that it is different from any of the multiple discrete structures observed at high resolution. Because base pair positional heterogeneity has not always been incorporated into model-building, even some high and ultrahigh-resolution structures of DNA do not indicate the full extent of conformational polymorphism.

  6. Single-Nucleotide Mutation Matrix: A New Model for Predicting the NF-?B DNA Binding Sites

    PubMed Central

    Wang, Tingting; Wang, Jinke

    2014-01-01

    In this study, we established a single nucleotide mutation matrix (SNMM) model based on the relative binding affinities of NF-?B p50 homodimer to a wild-type binding site (GGGACTTTCC) and its all single-nucleotide mutants detected with the double-stranded DNA microarray. We evaluated this model by scoring different groups of 10-bp DNA sequences with this model and analyzing the correlations between the scores and the relative binding affinities detected with three wet experiments, including the electrophoresis mobility shift assay (EMSA), the protein-binding microarray (PBM) and the systematic evolution of ligands by exponential enrichment-sequencing (SELEX-Seq). The results revealed that the SNMM scores were strongly correlated with the detected binding affinities. We also scored the DNA sequences with other three models, including the principal coordinate (PC) model, the position weight matrix scoring algorithm (PWMSA) model and the Match model, and analyzed the correlations between the scores and the detected binding affinities. In comparison with these models, the SNMM model achieved reliable results. We finally determined 0.747 as the optimal threshold for predicting the NF-?B DNA-binding sites with the SNMM model. The SNMM model thus provides a new alternative model for scoring the relative binding affinities of NF-?B to the 10-bp DNA sequences and predicting the NF-?B DNA-binding sites. PMID:24992458

  7. Non-B DB v2.0: a database of predicted non-B DNA-forming motifs and its associated tools

    PubMed Central

    Cer, Regina Z.; Donohue, Duncan E.; Mudunuri, Uma S.; Temiz, Nuri A.; Loss, Michael A.; Starner, Nathan J.; Halusa, Goran N.; Volfovsky, Natalia; Yi, Ming; Luke, Brian T.; Bacolla, Albino; Collins, Jack R.; Stephens, Robert M.

    2013-01-01

    The non-B DB, available at http://nonb.abcc.ncifcrf.gov, catalogs predicted non-B DNA-forming sequence motifs, including Z-DNA, G-quadruplex, A-phased repeats, inverted repeats, mirror repeats, direct repeats and their corresponding subsets: cruciforms, triplexes and slipped structures, in several genomes. Version 2.0 of the database revises and re-implements the motif discovery algorithms to better align with accepted definitions and thresholds for motifs, expands the non-B DNA-forming motifs coverage by including short tandem repeats and adds key visualization tools to compare motif locations relative to other genomic annotations. Non-B DB v2.0 extends the ability for comparative genomics by including re-annotation of the five organisms reported in non-B DB v1.0, human, chimpanzee, dog, macaque and mouse, and adds seven additional organisms: orangutan, rat, cow, pig, horse, platypus and Arabidopsis thaliana. Additionally, the non-B DB v2.0 provides an overall improved graphical user interface and faster query performance. PMID:23125372

  8. Crystal structures of B-DNA dodecamer containing the epigenetic modifications 5-hydroxymethylcytosine or 5-methylcytosine

    PubMed Central

    Renciuk, Daniel; Blacque, Olivier; Vorlickova, Michaela; Spingler, Bernhard

    2013-01-01

    5-Hydroxymethylcytosine (5-hmC) was recently identified as a relatively frequent base in eukaryotic genomes. Its physiological function is still unclear, but it is supposed to serve as an intermediate in DNA de novo demethylation. Using X-ray diffraction, we solved five structures of four variants of the d(CGCGAATTCGCG) dodecamer, containing either 5-hmC or 5-methylcytosine (5-mC) at position 3 or at position 9. The observed resolutions were between 1.42 and 1.99 Å. Cytosine modification in all cases influences neither the whole B-DNA double helix structure nor the modified base pair geometry. The additional hydroxyl group of 5-hmC with rotational freedom along the C5-C5A bond is preferentially oriented in the 3? direction. A comparison of thermodynamic properties of the dodecamers shows no effect of 5-mC modification and a sequence-dependent only slight destabilizing effect of 5-hmC modification. Also taking into account the results of a previous functional study [Münzel et al. (2011) (Improved synthesis and mutagenicity of oligonucleotides containing 5-hydroxymethylcytosine, 5-formylcytosine and 5-carboxylcytosine. Chem. Eur. J., 17, 13782?13788)], we conclude that the 5 position of cytosine is an ideal place to encode epigenetic information. Like this, neither the helical structure nor the thermodynamics are changed, and polymerases cannot distinguish 5-hmC and 5-mC from unmodified cytosine, all these effects are making the former ones non-mutagenic. PMID:23963698

  9. Structure and mechanism of the UvrA?UvrB DNA damage sensor

    SciTech Connect

    Pakotiprapha, Danaya; Samuels, Martin; Shen, Koning; Hu, Johnny H.; Jeruzalmi, David (Harvard)

    2012-04-17

    Nucleotide excision repair (NER) is used by all organisms to eliminate DNA lesions. We determined the structure of the Geobacillus stearothermophilus UvrA-UvrB complex, the damage-sensor in bacterial NER and a new structure of UvrA. We observe that the DNA binding surface of UvrA, previously found in an open shape that binds damaged DNA, also exists in a closed groove shape compatible with native DNA only. The sensor contains two UvrB molecules that flank the UvrA dimer along the predicted path for DNA, {approx}80 {angstrom} from the lesion. We show that the conserved signature domain II of UvrA mediates a nexus of contacts among UvrA, UvrB and DNA. Further, in our new structure of UvrA, this domain adopts an altered conformation while an adjacent nucleotide binding site is vacant. Our findings raise unanticipated questions about NER and also suggest a revised picture of its early stages.

  10. DNA polymerases ? and Rev1 mediate error-prone bypass of non-B DNA structures

    PubMed Central

    Northam, Matthew R.; Moore, Elizabeth A.; Mertz, Tony M.; Binz, Sara K.; Stith, Carrie M.; Stepchenkova, Elena I.; Wendt, Kathern L.; Burgers, Peter M. J.; Shcherbakova, Polina V.

    2014-01-01

    DNA polymerase ? (Pol ?) and Rev1 are key players in translesion DNA synthesis. The error-prone Pol ? can also participate in replication of undamaged DNA when the normal replisome is impaired. Here we define the nature of the replication disturbances that trigger the recruitment of error-prone polymerases in the absence of DNA damage and describe the specific roles of Rev1 and Pol ? in handling these disturbances. We show that Pol ?/Rev1-dependent mutations occur at sites of replication stalling at short repeated sequences capable of forming hairpin structures. The Rev1 deoxycytidyl transferase can take over the stalled replicative polymerase and incorporate an additional ‘C’ at the hairpin base. Full hairpin bypass often involves template-switching DNA synthesis, subsequent realignment generating multiply mismatched primer termini and extension of these termini by Pol ?. The postreplicative pathway dependent on polyubiquitylation of proliferating cell nuclear antigen provides a backup mechanism for accurate bypass of these sequences that is primarily used when the Pol ?/Rev1-dependent pathway is inactive. The results emphasize the pivotal role of noncanonical DNA structures in mutagenesis and reveal the long-sought-after mechanism of complex mutations that represent a unique signature of Pol ?. PMID:24049079

  11. Breakpoints of gross deletions coincide with non-B DNA conformations

    PubMed Central

    Bacolla, Albino; Jaworski, Adam; Larson, Jacquelynn E.; Jakupciak, John P.; Chuzhanova, Nadia; Abeysinghe, Shaun S.; O'Connell, Catherine D.; Cooper, David N.; Wells, Robert D.

    2004-01-01

    Genomic rearrangements are a frequent source of instability, but the mechanisms involved are poorly understood. A 2.5-kbp poly(purine·pyrimidine) sequence from the human PKD1 gene, known to form non-B DNA structures, induced long deletions and other instabilities in plasmids that were mediated by mismatch repair and, in some cases, transcription. The breakpoints occurred at predicted non-B DNA structures. Distance measurements also indicated a significant proximity of alternating purine-pyrimidine and oligo(purine·pyrimidine) tracts to breakpoint junctions in 222 gross deletions and translocations, respectively, involved in human diseases. In 11 deletions analyzed, breakpoints were explicable by non-B DNA structure formation. We conclude that alternative DNA conformations trigger genomic rearrangements through recombination-repair activities. PMID:15377784

  12. Breakpoints of gross deletions coincide with non-B DNA conformations.

    PubMed

    Bacolla, Albino; Jaworski, Adam; Larson, Jacquelynn E; Jakupciak, John P; Chuzhanova, Nadia; Abeysinghe, Shaun S; O'Connell, Catherine D; Cooper, David N; Wells, Robert D

    2004-09-28

    Genomic rearrangements are a frequent source of instability, but the mechanisms involved are poorly understood. A 2.5-kbp poly(purine.pyrimidine) sequence from the human PKD1 gene, known to form non-B DNA structures, induced long deletions and other instabilities in plasmids that were mediated by mismatch repair and, in some cases, transcription. The breakpoints occurred at predicted non-B DNA structures. Distance measurements also indicated a significant proximity of alternating purine-pyrimidine and oligo(purine.pyrimidine) tracts to breakpoint junctions in 222 gross deletions and translocations, respectively, involved in human diseases. In 11 deletions analyzed, breakpoints were explicable by non-B DNA structure formation. We conclude that alternative DNA conformations trigger genomic rearrangements through recombination-repair activities. PMID:15377784

  13. Structural insights into the effect of isonucleosides on B-DNA duplexes using molecular-dynamics simulations

    Microsoft Academic Search

    Hongwei Jin; Suxin Zheng; Zhanli Wang; Cheng Luo; Jianhua Shen; Hualiang Jiang; Liangren Zhang; Lihe Zhang

    2006-01-01

    Some structural insights into the conformations of the isonucleosides containing duplexes have been provided. Unrestrained molecular-dynamics simulations on 18-mer duplexes with isonucleosides incorporated at the 3'-end or in the center of one strand have been carried out with explicit solvent under periodic boundary conditions using the AMBER force field and the particle mesh Ewald method. The Watson–Crick hydrogen-bonding patterns of

  14. The DnaB·DnaC complex: a structure based on dimers assembled around an occluded channel

    PubMed Central

    Bárcena, Montserrat; Ruiz, Teresa; Donate, Luis Enrique; Brown, Susan E.; Dixon, Nicholas E.; Radermacher, Michael; Carazo, José María

    2001-01-01

    Replicative helicases are motor proteins that unwind DNA at replication forks. Escherichia coli DnaB is the best characterized member of this family of enzymes. We present the 26 ? resolution three-dimensional structure of the DnaB hexamer in complex with its loading partner, DnaC, obtained from cryo-electron microscopy. Analysis of the volume brings insight into the elaborate way the two proteins interact, and provides a structural basis for control of the symmetry state and inactivation of the helicase by DnaC. The complex is arranged on the basis of interactions among DnaC and DnaB dimers. DnaC monomers are observed for the first time to arrange as three dumb-bell-shaped dimers that interlock into one of the faces of the helicase. This could be responsible for the freezing of DnaB in a C3 architecture by its loading partner. The central channel of the helicase is almost occluded near the end opposite to DnaC, such that even single-stranded DNA could not pass through. We propose that the DnaB N-terminal domain is located at this face. PMID:11250911

  15. Crystal Structure and Prediction

    NASA Astrophysics Data System (ADS)

    Thakur, Tejender S.; Dubey, Ritesh; Desiraju, Gautam R.

    2015-04-01

    The notion of structure is central to the subject of chemistry. This review traces the development of the idea of crystal structure since the time when a crystal structure could be determined from a three-dimensional diffraction pattern and assesses the feasibility of computationally predicting an unknown crystal structure of a given molecule. Crystal structure prediction is of considerable fundamental and applied importance, and its successful execution is by no means a solved problem. The ease of crystal structure determination today has resulted in the availability of large numbers of crystal structures of higher-energy polymorphs and pseudopolymorphs. These structural libraries lead to the concept of a crystal structure landscape. A crystal structure of a compound may accordingly be taken as a data point in such a landscape.

  16. The guanine-quadruplex structure in the human c-myc gene's promoter is converted into B-DNA form by the human poly(ADP-ribose)polymerase-1.

    PubMed

    Fekete, Anna; Kenesi, Erzsebet; Hunyadi-Gulyas, Eva; Durgo, Hajnalka; Berko, Barbara; Dunai, Zsuzsanna A; Bauer, Pal I

    2012-01-01

    The important regulatory role of the guanine-quadruplex (GQ) structure, present in the nuclease hypersensitive element (NHE) III(1) region of the human c-myc (h c-myc) gene's promoter, in the regulation of the transcription of that gene has been documented. Here we present evidences, that the human nuclear poly(ADP-ribose)polymerase-1 (h PARP-1) protein participates in the regulation of the h c-myc gene expression through its interaction with this GQ structure, characterized by binding assays, fluorescence energy transfer (FRET) experiments and by affinity pull-down experiments in vitro, and by chromatin immunoprecipitation (ChIP)-qPCR analysis and h c-myc-promoter-luciferase reporter determinations in vivo. We surmise that h PARP-1 binds to the GQ structure and participates in the conversion of that structure into the transcriptionally more active B-DNA form. The first Zn-finger structure present in h PARP-1 participates in this interaction. PARP-1 might be a new member of the group of proteins participating in the regulation of transcription through their interactions with GQ structures present in the promoters of different genes. PMID:22880082

  17. The Guanine-Quadruplex Structure in the Human c-myc Gene's Promoter Is Converted into B-DNA Form by the Human Poly(ADP-Ribose)Polymerase-1

    PubMed Central

    Fekete, Anna; Kenesi, Erzsebet; Hunyadi-Gulyas, Eva; Durgo, Hajnalka; Berko, Barbara; Dunai, Zsuzsanna A.; Bauer, Pal I.

    2012-01-01

    The important regulatory role of the guanine-quadruplex (GQ) structure, present in the nuclease hypersensitive element (NHE) III1 region of the human c-myc (h c-myc) gene's promoter, in the regulation of the transcription of that gene has been documented. Here we present evidences, that the human nuclear poly(ADP-ribose)polymerase-1 (h PARP-1) protein participates in the regulation of the h c-myc gene expression through its interaction with this GQ structure, characterized by binding assays, fluorescence energy transfer (FRET) experiments and by affinity pull-down experiments in vitro, and by chromatin immunoprecipitation (ChIP)-qPCR analysis and h c-myc-promoter-luciferase reporter determinations in vivo. We surmise that h PARP-1 binds to the GQ structure and participates in the conversion of that structure into the transcriptionally more active B-DNA form. The first Zn-finger structure present in h PARP-1 participates in this interaction. PARP-1 might be a new member of the group of proteins participating in the regulation of transcription through their interactions with GQ structures present in the promoters of different genes. PMID:22880082

  18. Protein Structure Prediction Jayanthi Sourirajan

    E-print Network

    Protein Structure Prediction Jayanthi Sourirajan Final Project Computational Molecular Biology BIOC218 June 4, 2004 #12;Protein Structure Prediction Proteins are building blocks of life. Proteins exhibit more sequence and chemical complexity than DNA or RNA. A protein sequence is a linear hetero

  19. Protein Structure Prediction and Structural Genomics

    NSDL National Science Digital Library

    David Baker (University of Washington; Howard Hughes Medical Institute)

    2001-10-05

    Genome sequencing projects are producing linear amino acid sequences, but full understanding of the biological role of these proteins will require knowledge of their structure and function. Although experimental structure determination methods are providing high-resolution structure information about a subset of the proteins, computational structure prediction methods will provide valuable information for the large fraction of sequences whose structures will not be determined experimentally. In this Viewpoint, we begin by describing the essential features of the methods, the accuracy of the models, and their application to the prediction and understanding of protein function, both for single proteins and on the scale of whole genomes. We then discuss the important role that protein structure prediction methods play in the growing worldwide effort in structural genomics.

  20. Protein structure Predictive methods

    E-print Network

    Sjölander, Kimmen

    ;3 Principles of Protein Structure GFCHIKAYTRLIMVG... Anabaena7120 Anacystisnidulans Condruscrispus MODELING 0 (100)2 (50) 1 (80) Ca RMSD Å (% EQV) % SEQUENCE IDENTITY 20 50 100 Anabaena 7120 Anacystis

  1. Local protein structure prediction using discriminative models

    Microsoft Academic Search

    Oliver Sander; Ingolf Sommer; Thomas Lengauer

    2006-01-01

    Background: In recent years protein structure prediction methods using local structure information have shown promising improvements. The quality of new fold predictions has risen significantly and in fold recognition incorporation of local structure predictions led to improvements in the accuracy of results. We developed a local structure prediction method to be integrated into either fold recognition or new fold prediction

  2. Ab Initio Protein Structure Prediction

    Microsoft Academic Search

    Jooyoung Lee; Sitao Wu; Yang Zhang

    Predicting protein 3D structures from the amino acid sequence still remains as an unsolved problem after five decades of efforts.\\u000a If the target protein has a homologue already solved, the task is relatively easy and high-resolution models can be built\\u000a by copying the framework of the solved structure. However, such a modelling procedure does not help answer the question of

  3. Water in protein structure prediction

    PubMed Central

    Papoian, Garegin A.; Ulander, Johan; Eastwood, Michael P.; Luthey-Schulten, Zaida; Wolynes, Peter G.

    2004-01-01

    Proteins have evolved to use water to help guide folding. A physically motivated, nonpairwise-additive model of water-mediated interactions added to a protein structure prediction Hamiltonian yields marked improvement in the quality of structure prediction for larger proteins. Free energy profile analysis suggests that long-range water-mediated potentials guide folding and smooth the underlying folding funnel. Analyzing simulation trajectories gives direct evidence that water-mediated interactions facilitate native-like packing of supersecondary structural elements. Long-range pairing of hydrophilic groups is an integral part of protein architecture. Specific water-mediated interactions are a universal feature of biomolecular recognition landscapes in both folding and binding. PMID:14988499

  4. Bayesian Nonparametric Methods for Protein Structure Prediction 

    E-print Network

    Lennox, Kristin Patricia

    2011-10-21

    The protein structure prediction problem consists of determining a protein’s three-dimensional structure from the underlying sequence of amino acids. A standard approach for predicting such structures is to conduct a ...

  5. TRITIUM RESERVOIR STRUCTURAL PERFORMANCE PREDICTION

    SciTech Connect

    Lam, P.S.; Morgan, M.J

    2005-11-10

    The burst test is used to assess the material performance of tritium reservoirs in the surveillance program in which reservoirs have been in service for extended periods of time. A materials system model and finite element procedure were developed under a Savannah River Site Plant-Directed Research and Development (PDRD) program to predict the structural response under a full range of loading and aged material conditions of the reservoir. The results show that the predicted burst pressure and volume ductility are in good agreement with the actual burst test results for the unexposed units. The material tensile properties used in the calculations were obtained from a curved tensile specimen harvested from a companion reservoir by Electric Discharge Machining (EDM). In the absence of exposed and aged material tensile data, literature data were used for demonstrating the methodology in terms of the helium-3 concentration in the metal and the depth of penetration in the reservoir sidewall. It can be shown that the volume ductility decreases significantly with the presence of tritium and its decay product, helium-3, in the metal, as was observed in the laboratory-controlled burst tests. The model and analytical procedure provides a predictive tool for reservoir structural integrity under aging conditions. It is recommended that benchmark tests and analysis for aged materials be performed. The methodology can be augmented to predict performance for reservoir with flaws.

  6. Non-B DNA-forming Sequences and WRN Deficiency Independently Increase the Frequency of Base Substitution in Human Cells*

    PubMed Central

    Bacolla, Albino; Wang, Guliang; Jain, Aklank; Chuzhanova, Nadia A.; Cer, Regina Z.; Collins, Jack R.; Cooper, David N.; Bohr, Vilhelm A.; Vasquez, Karen M.

    2011-01-01

    Although alternative DNA secondary structures (non-B DNA) can induce genomic rearrangements, their associated mutational spectra remain largely unknown. The helicase activity of WRN, which is absent in the human progeroid Werner syndrome, is thought to counteract this genomic instability. We determined non-B DNA-induced mutation frequencies and spectra in human U2OS osteosarcoma cells and assessed the role of WRN in isogenic knockdown (WRN-KD) cells using a supF gene mutation reporter system flanked by triplex- or Z-DNA-forming sequences. Although both non-B DNA and WRN-KD served to increase the mutation frequency, the increase afforded by WRN-KD was independent of DNA structure despite the fact that purified WRN helicase was found to resolve these structures in vitro. In U2OS cells, ?70% of mutations comprised single-base substitutions, mostly at G·C base-pairs, with the remaining ?30% being microdeletions. The number of mutations at G·C base-pairs in the context of NGNN/NNCN sequences correlated well with predicted free energies of base stacking and ionization potentials, suggesting a possible origin via oxidation reactions involving electron loss and subsequent electron transfer (hole migration) between neighboring bases. A set of ?40,000 somatic mutations at G·C base pairs identified in a lung cancer genome exhibited similar correlations, implying that hole migration may also be involved. We conclude that alternative DNA conformations, WRN deficiency and lung tumorigenesis may all serve to increase the mutation rate by promoting, through diverse pathways, oxidation reactions that perturb the electron orbitals of neighboring bases. It follows that such “hole migration” is likely to play a much more widespread role in mutagenesis than previously anticipated. PMID:21285356

  7. NF-?B DNA binding activity of sesquiterpene lactones from Anthemis arvensis and Anthemis cotula

    Microsoft Academic Search

    Ivan Vu?kovi?; Ljubodrag Vujisi?; Christoph A. Klaas; Irmgard Merfort; Slobodan Milosavljevi?

    2011-01-01

    Previous investigations of Anthemis arvensis L. and Anthemis cotula L., species growing wild in Serbia, revealed linear sesquiterpene lactones (SLs) with unusual types of skeleton, named antheindurolides and anthecotuloides. The NF-?B DNA binding activity of four of those SLs as well as from compounds representing structural parts of the SLs is reported in this article. The relationship between their structure

  8. Practical lessons from protein structure prediction

    PubMed Central

    Ginalski, Krzysztof; Grishin, Nick V.; Godzik, Adam; Rychlewski, Leszek

    2005-01-01

    Despite recent efforts to develop automated protein structure determination protocols, structural genomics projects are slow in generating fold assignments for complete proteomes, and spatial structures remain unknown for many protein families. Alternative cheap and fast methods to assign folds using prediction algorithms continue to provide valuable structural information for many proteins. The development of high-quality prediction methods has been boosted in the last years by objective community-wide assessment experiments. This paper gives an overview of the currently available practical approaches to protein structure prediction capable of generating accurate fold assignment. Recent advances in assessment of the prediction quality are also discussed. PMID:15805122

  9. NF-?B DNA binding activity of sesquiterpene lactones from Anthemis arvensis and Anthemis cotula.

    PubMed

    Vuckovi?, Ivan; Vujisi?, Ljubodrag; Klaas, Christoph A; Merfort, Irmgard; Milosavljevi?, Slobodan

    2011-04-01

    Previous investigations of Anthemis arvensis L. and Anthemis cotula L., species growing wild in Serbia, revealed linear sesquiterpene lactones (SLs) with unusual types of skeleton, named antheindurolides and anthecotuloides. The NF-?B DNA binding activity of four of those SLs as well as from compounds representing structural parts of the SLs is reported in this article. The relationship between their structure and NF-?B inhibition potency is briefly discussed. PMID:20603773

  10. Computational Approach for Protein Structure Prediction

    PubMed Central

    Gopal, Jeyakodi; Candavelou, Manimozhi; Gollapalli, Sowjanya; Karthikeyan, Kayathri

    2013-01-01

    Objectives To predict the structure of protein, which dictates the function it performs, a newly designed algorithm is developed which blends the concept of self-organization and the genetic algorithm. Methods Among many other approaches, genetic algorithm is found to be a promising cooperative computational method to solve protein structure prediction in a reasonable time. To automate the right choice of parameter values the influence of self-organization is adopted to design a new genetic operator to optimize the process of prediction. Torsion angles, the local structural parameters which define the backbone of protein are considered to encode the chromosome that enhances the quality of the confirmation. Newly designed self-configured genetic operators are used to develop self-organizing genetic algorithm to facilitate the accurate structure prediction. Results Peptides are used to gauge the validity of the proposed algorithm. As a result, the structure predicted shows clear improvements in the root mean square deviation on overlapping the native indicates the overall performance of the algorithm. In addition, the Ramachandran plot results implies that the conformations of phi-psi angles in the predicted structure are better as compared to native and also free from steric hindrances. Conclusions The proposed algorithm is promising which contributes to the prediction of a native-like structure by eliminating the time constraint and effort demand. In addition, the energy of the predicted structure is minimized to a greater extent, which proves the stability of protein. PMID:23882419

  11. Protein structure Predictive methods and

    E-print Network

    Sjölander, Kimmen

    ." Baxevanis & Ouellette (Ch. 9, p.224, Wishart) Principles of Protein Structure GFCHIKAYTRLIMVG... Anabaena (% EQV) % SEQUENCE IDENTITY 20 50 100 Anabaena 7120 Anacystis nidulans Condrus crispus Desulfovibrio

  12. Transmembrane beta-barrel protein structure prediction

    NASA Astrophysics Data System (ADS)

    Randall, Arlo; Baldi, Pierre

    Transmembrane ?-barrel (TMB) proteins are embedded in the outer membranes of mitochondria, Gram-negative bacteria, and chloroplasts. These proteins perform critical functions, including active ion-transport and passive nutrient intake. Therefore, there is a need for accurate prediction of secondary and tertiary structures of TMB proteins. A variety of methods have been developed for predicting the secondary structure and these predictions are very useful for constructing a coarse topology of TMB structure; however, they do not provide enough information to construct a low-resolution tertiary structure for a TMB protein. In addition, while the overall structural architecture is well conserved among TMB proteins, the amino acid sequences are highly divergent. Thus, traditional homology modeling methods cannot be applied to many putative TMB proteins. Here, we describe the TMBpro: a pipeline of methods for predicting TMB secondary structure, ?-residue contacts, and finally tertiary structure. The tertiary prediction method relies on the specific construction rules that TMB proteins adhere to and on the predicted ?-residue contacts to dramatically reduce the search space for the model building procedure.

  13. SCRATCH: a protein structure and structural feature prediction server

    PubMed Central

    Cheng, J.; Randall, A. Z.; Sweredoski, M. J.; Baldi, P.

    2005-01-01

    SCRATCH is a server for predicting protein tertiary structure and structural features. The SCRATCH software suite includes predictors for secondary structure, relative solvent accessibility, disordered regions, domains, disulfide bridges, single mutation stability, residue contacts versus average, individual residue contacts and tertiary structure. The user simply provides an amino acid sequence and selects the desired predictions, then submits to the server. Results are emailed to the user. The server is available at . PMID:15980571

  14. Gene structure prediction in syntenic DNA segments

    Microsoft Academic Search

    Jonathan E. Moore; James A. Lake

    2003-01-01

    The accurate prediction of higher eukaryotic gene structures and regulatory elements directly from genomic sequences is an important early step in the understanding of newly assembled contigs and finished genomes. As more new genomes are sequenced, comparative approaches are becoming increasingly practical and valuable for predicting genes and regulatory elements. We demonstrate the effectiveness of a comparative method called pattern

  15. Short Specialist Review Gene structure prediction

    E-print Network

    Brendel, Volker

    as well as genome-survey sequencing is in progress for dozens of plant species, all of which appearShort Specialist Review Gene structure prediction in plant genomes Volker Brendel Iowa State prediction in vertebrates. The second reason is pragmatic. Expressed Sequence Tag (EST) sequencing and whole-genome

  16. Data Mining for Protein Secondary Structure Prediction

    Microsoft Academic Search

    Haitao Cheng; Taner Z. Sen; Robert L. Jernigan; Andrzej Kloczkowski

    \\u000a Accurate protein secondary structure prediction from the amino acid sequence is essential for almost all theoretical and experimental\\u000a studies on protein structure and function. After a brief discussion of application of data mining for optimization of crystallization\\u000a conditions for target proteins we show that data mining of structural fragments of proteins from known structures in the protein\\u000a data bank (PDB)

  17. Interface Structure Prediction from First-Principles

    SciTech Connect

    Zhao, Xin; Shu, Qiang; Nguyen, Manh Cuong; Wang, Yangang; Ji, Min; Xiang, Hongjun; Ho, Kai-Ming; Gong, Xingao; Wang, Cai-Zhuang

    2014-05-08

    Information about the atomic structures at solid–solid interfaces is crucial for understanding and predicting the performance of materials. Due to the complexity of the interfaces, it is very challenging to resolve their atomic structures using either experimental techniques or computer simulations. In this paper, we present an efficient first-principles computational method for interface structure prediction based on an adaptive genetic algorithm. This approach significantly reduces the computational cost, while retaining the accuracy of first-principles prediction. The method is applied to the investigation of both stoichiometric and nonstoichiometric SrTiO3 ?3(112)[1?10] grain boundaries with unit cell containing up to 200 atoms. Several novel low-energy structures are discovered, which provide fresh insights into the structure and stability of the grain boundaries.

  18. Structure prediction methods (2D and 3D)

    E-print Network

    Sjölander, Kimmen

    · Overview of protein structure: primary, secondary, tertiary, and quaternary · Overview of protein folding · Secondary structure prediction methods · Solvent accessibility prediction · 3D fold prediction ­ Ab initio ­ Critical Assessment of protein Fold Prediction (CASP) http://predictioncenter.org/ ­ EVA (real

  19. Characteristics and Prediction of RNA Structure

    PubMed Central

    Zhu, Daming; Zhang, Caiming; Han, Huijian; Crandall, Keith A.

    2014-01-01

    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. PMID:25110687

  20. Predicting the Fatigue life of Structures

    NASA Technical Reports Server (NTRS)

    Besuner, P. M.; Harris, D. O.; Thomas, J. M.; Allison, D. E.; Bannantine, J. M.; Brown, S. B.; Davis, C. S.; Derbalian, G. A.; Eischen, J. W.; Fowler, G. F.; Osteraas, J. D.; Robinson, J. N.; Sire, R. A.; Vroman, G. A.

    1985-01-01

    Report reviews fracture-mechanics technology for predicting life expectancy of structural components subjected to cyclic loads. Report covers analytical tools for modeling and forecasting subcritical fatigue-crack growth in structures. It emphasizes use of tools in practical, day-to-day problems of engineering design, development, and decisionmaking.

  1. Exploiting Protein Structures to Predict Protein Functions

    Microsoft Academic Search

    Alison Cuff; Oliver Redfern; Benoit Dessailly; Christine Orengo

    \\u000a The exponential growth of experimentally determined protein structures in the Protein Data Bank (PDB) has provided structural\\u000a data for an ever increasing proportion of genomic sequences. In combination with enhanced functional annotation from sequence,\\u000a it has become possible to predict protein function from structure. In this chapter we discuss a range of methods which aim\\u000a to recognise enzyme active sites

  2. A dynamic programming algorithm for RNA structure prediction including pseudoknots

    E-print Network

    Eddy, Sean

    A dynamic programming algorithm for RNA structure prediction including pseudoknots Elena Rivas describe a dynamic programming algorithm for predicting opti­ mal RNA secondary structure, including structure prediction, pseudoknots, dynamic pro­ gramming, thermodynamic stability. 1 To whom correspondence

  3. Improving RNA secondary structure prediction with structure mapping data.

    PubMed

    Sloma, Michael F; Mathews, David H

    2015-01-01

    Methods to probe RNA secondary structure, such as small molecule modifying agents, secondary structure-specific nucleases, inline probing, and SHAPE chemistry, are widely used to study the structure of functional RNA. Computational secondary structure prediction programs can incorporate probing data to predict structure with high accuracy. In this chapter, an overview of current methods for probing RNA secondary structure is provided, including modern high-throughput methods. Methods for guiding secondary structure prediction algorithms using these data are explained, and best practices for using these data are provided. This chapter concludes by listing a number of open questions about how to best use probing data, and what these data can provide. PMID:25726462

  4. Site-directed chemical modification of archaeal Thermococcus litoralis Sh1B DNA polymerase: Acquired ability to read through template-strand uracils

    Microsoft Academic Search

    Edita Gaidamaviciute; Daiva Tauraite; Julius Gagilas; Arunas Lagunavicius

    2010-01-01

    We present site-directed chemical modification (SDCM), a tool for engineering U-resistant archaeal DNA polymerases of family B. The Thermococcus litoralis Sh1B DNA polymerase (GenBank: GQ891548) was chosen as the object of the study. Similar to D.Tok, Kod1, Pfu, Tgo and other archaeal members of this family, the T. litoralis Sh1B DNA polymerase is a domain structured, proofreading-proficient enzyme that has

  5. Large Margin Methods for Structured Output Prediction

    Microsoft Academic Search

    Elisa Ricci; Renzo Perfetti

    2008-01-01

    Many real-life data problems require effective classification algorithms able to model structural dependencies between multiple\\u000a labels and to perform classification in a multivariate setting, i.e. such that complex, non-scalar predictions must be produced\\u000a in correspondence to input vectors. Examples of these tasks range from natural language parsing to speech recognition, machine\\u000a translation, image segmentation, handwritten character recognition or gene prediction.

  6. Protein Structure Prediction with Evolutionary Algorithms

    SciTech Connect

    Hart, W.E.; Krasnogor, N.; Pelta, D.A.; Smith, J.

    1999-02-08

    Evolutionary algorithms have been successfully applied to a variety of molecular structure prediction problems. In this paper we reconsider the design of genetic algorithms that have been applied to a simple protein structure prediction problem. Our analysis considers the impact of several algorithmic factors for this problem: the confirmational representation, the energy formulation and the way in which infeasible conformations are penalized, Further we empirically evaluated the impact of these factors on a small set of polymer sequences. Our analysis leads to specific recommendations for both GAs as well as other heuristic methods for solving PSP on the HP model.

  7. Method for predicting RNA secondary structure.

    PubMed Central

    Pipas, J M; McMahon, J E

    1975-01-01

    We report a method for predicting the most stable secondary structure of RNA from its primary sequence of nucleotides. The technique consists of a series of three computer programs interfaced to take the nucleotide sequence of any RNA and (a) list all possible helical regions, using modified Watson-Crick base-pairing rules; (b) create all possible secondary structures by forming permutations of compatible helical regions; and (c)evaluate each structure for total free energy of formation from a completely extended chain. A free energy distribution and the base-by-base bonding interactions of each possible structure are catalogued by the system and are readily available for examination. The method has been applied to 62 tRNA sequences. The total free-energy of the predicted most stable structures ranged from -19 to -41 kcal/mole (-22 to -49 kJ/mole). The number of structures created was also highly sequence-dependent and ranged from 200 to 13,000. In nearly all cases the cloverleaf is predicted to be the structure with the lowest free energy of formation. PMID:1056009

  8. Cascaded multiple classifiers for secondary structure prediction.

    PubMed Central

    Ouali, M.; King, R. D.

    2000-01-01

    We describe a new classifier for protein secondary structure prediction that is formed by cascading together different types of classifiers using neural networks and linear discrimination. The new classifier achieves an accuracy of 76.7% (assessed by a rigorous full Jack-knife procedure) on a new nonredundant dataset of 496 nonhomologous sequences (obtained from G.J. Barton and J.A. Cuff). This database was especially designed to train and test protein secondary structure prediction methods, and it uses a more stringent definition of homologous sequence than in previous studies. We show that it is possible to design classifiers that can highly discriminate the three classes (H, E, C) with an accuracy of up to 78% for beta-strands, using only a local window and resampling techniques. This indicates that the importance of long-range interactions for the prediction of beta-strands has been probably previously overestimated. PMID:10892809

  9. Structure-based predictions of activity cliffs.

    PubMed

    Husby, Jarmila; Bottegoni, Giovanni; Kufareva, Irina; Abagyan, Ruben; Cavalli, Andrea

    2015-05-26

    In drug discovery, it is generally accepted that neighboring molecules in a given descriptor's space display similar activities. However, even in regions that provide strong predictability, structurally similar molecules can occasionally display large differences in potency. In QSAR jargon, these discontinuities in the activity landscape are known as "activity cliffs". In this study, we assessed the reliability of ligand docking and virtual ligand screening schemes in predicting activity cliffs. We performed our calculations on a diverse, independently collected database of cliff-forming cocrystals. Starting from ideal situations, which allowed us to establish our baseline, we progressively moved toward simulating more realistic scenarios. Ensemble- and template-docking achieved a significant level of accuracy, suggesting that, despite the well-known limitations of empirical scoring schemes, activity cliffs can be accurately predicted by advanced structure-based methods. PMID:25918827

  10. Twin Gaussian Processes for Structured Prediction

    Microsoft Academic Search

    Liefeng Bo; Cristian Sminchisescu

    2010-01-01

    We describe twin Gaussian processes (TGP), a generic structured prediction method that uses Gaussian process (GP) priors on both covariates and responses, both multivariate, and estimates outputs by minimizing the Kullback-Leibler divergence between two GP modeled as normal distributions over finite index sets of training and testing examples, emphasizing the goal that similar inputs should produce similar percepts and this

  11. TRITIUM RESERVOIR STRUCTURAL PERFORMANCE PREDICTION (U)

    Microsoft Academic Search

    P. S. Lam; M. J. Morgan

    2005-01-01

    The burst test is used to assess the material performance of tritium reservoirs in the surveillance program in which reservoirs have been in service for extended periods of time. A materials system model and finite element procedure were developed under a Savannah River Site Plant-Directed Research and Development (PDRD) program to predict the structural response under a full range of

  12. Predicting protein structure using hidden Markov models

    E-print Network

    Karplus, Kevin

    Predicting protein structure using hidden Markov models Kevin Karplusy Kimmen Sjolanderz Christian Santa Cruz, CA 95064 USA abstract We discuss how methods based on hidden Markov models performed in the fold recogni- tion section of the CASP2 experiment. Hidden Markov models were built for a set of about

  13. Protein complex compositions predicted by structural similarity

    E-print Network

    Eddy, Sean

    Protein complex compositions predicted by structural similarity Fred P. Davis1,2 , Hannes Braberg1 of Biopharmaceutical Sciences and 2 Department of Pharmaceutical Chemistry, California Institute for Quantitative Proteins function through interactions with other molecules. Thus, the network of physical interac- tions

  14. Evaluation of gene structure prediction programs

    SciTech Connect

    Burset, M.; Guigo, R. [Institut Municipal d`Investigacio Medica (IMIM), Barcelona (Spain)] [Institut Municipal d`Investigacio Medica (IMIM), Barcelona (Spain)

    1996-06-15

    We evaluate a number of computer programs designed to predict the structure of protein coding genes in genomic DNA sequences. Computational gene identification is set to play an increasingly important role in the development of the genome projects, as emphasis turns from mapping to large-scale sequencing. The evaluation presented here serves both to assess the current status of the problem and to identify the most promising approaches to ensure further progress. The programs analyzed were uniformly tested on a large set of vertebrate sequences with simple gene structure, and several measures of predictive accuracy were computed at the nucleotide, exon, and protein product levels. The results indicated that the predictive accuracy of the programs analyzed was lower than originally found. The accuracy was even lower when considering only those sequences that had recently been entered and that did not show any similarity to previously entered sequences. This indicates that the programs are overly dependent on the particularities of the examples they learn from. For most of the programs, accuracy in this test set ranged from 0.60 to 0.70 as measured by the Correlation Coefficient (where 1.0 corresponds to a perfect prediction and 0.0 is the value expected for a random prediction), and the average percentage of exons exactly identified was less than 50%. Only those programs including protein sequence database searches showed substantially greater accuracy. 47 refs., 6 tabs.

  15. A Structured Approach to Sediment Transport Prediction

    NASA Astrophysics Data System (ADS)

    Wilcock, Peter

    2013-04-01

    There are two types of sediment transport problem. One, flow competence, concerns the conditions that initiate motion of grains on the bed surface. The other, transport capacity, concerns the rate at which sediment is transported and involves sediment found locally on the bed as well as sediment delivered from upstream. The two problems can be linked by the critical stress for incipient motion. A model for critical stress is used directly to predict flow competence. The Ashida/Parker similarity hypothesis provides a useful approximation of transport rates and incorporates local sediment effects entirely via the reference stress, a surrogate for critical stress. Although critical stress is key to both predictions, its application is quite different. The difficult problem of wash load - sizes found in transport in quantities much larger than would be predicted by their presence in the bed - makes the distinction clear and challenges any attempt to predict transport rate from a competence-like approach based on hydraulics and bed material alone. The Shields Diagram and a hiding function provide models for critical stress for uni-size and mixed-size sediment. In addition to grain size - both absolute and relative - other factors alter the critical stress of bed material. These include the proportion of fine-grained material, the aging or freshening of bed material via biologically mediated processes, and the development of bed structure at flows close to the critical stress. Although these factors directly influence the prediction of competent flows, their effect on transport rate is less clear. As flow increases, to what extent does bed strengthening through structuring and other mechanisms persist in dampening transport rate? The answer involves the condition of partial transport in which some grains in a size fraction are active and others remain inactive. Tracing of grains in the flume and field provide guidance on the domain of partial transport and thus on the influence on transport rates of bed strengthening. A surface-based transport model can be used with a bed-sorting algorithm to predict the evolution of the bed surface under active transport. The same transport model can be used in inverse form to predict the combination of flow, transport, and bed surface grain size under steady-state conditions. These formulas provide a useful starting point for documenting the effect of bed structuring on sediment transport rate. Careful (although not complex) consideration of the type of transport problem - competence or capacity - and the nature of the time-varying boundary conditions are needed to make accurate predictions of sediment transport.

  16. The solution structure of a B-DNA undecamer comprising a portion of the specific target site for the cAMP receptor protein in the gal operon. Refinement on the basis of interproton distance data.

    PubMed Central

    Clore, G M; Gronenborn, A M

    1985-01-01

    A restrained least squares refinement of the solution structure of the double-stranded DNA undecamer 5'd(AAGTGT-GACAT).5'd(ATGTCACACTT) comprising a portion of the specific target site of the cAMP receptor protein in the gal operon is presented. The structure is refined on the basis of both distance and planarity restraints, 2331 in all. The distance restraints comprise 150 interproton distances determined from pre-steady state nuclear Overhauser enhancement measurements and 2159 other interatomic distances derived from idealized geometry (i.e., distances between covalently bonded atoms, between atoms defining fixed bond angles, and between atoms defining hydrogen bonding in AT and GC base pairs). Two refinements were carried out and in both cases the final RMS difference between the experimental and calculated interproton distances was 0.2 A. The difference between the two refined structures is small (overall RMS difference of 0.23 A) and represents the error in the refined coordinates. Although the refined structures have an overall B-type conformation there are large variations in many of the local conformational parameters including backbone and glycosidic bond torsion angles, helical twist and propellor twist, base roll and base tilt angles. Images Fig. 2. PMID:3891324

  17. Sequence comparison and protein structure prediction.

    PubMed

    Dunbrack, Roland L

    2006-06-01

    Sequence comparison is a major step in the prediction of protein structure from existing templates in the Protein Data Bank. The identification of potentially remote homologues to be used as templates for modeling target sequences of unknown structure and their accurate alignment remain challenges, despite many years of study. The most recent advances have been in combining as many sources of information as possible--including amino acid variation in the form of profiles or hidden Markov models for both the target and template families, known and predicted secondary structures of the template and target, respectively, the combination of structure alignment for distant homologues and sequence alignment for close homologues to build better profiles, and the anchoring of certain regions of the alignment based on existing biological data. Newer technologies have been applied to the problem, including the use of support vector machines to tackle the fold classification problem for a target sequence and the alignment of hidden Markov models. Finally, using the consensus of many fold recognition methods, whether based on profile-profile alignments, threading or other approaches, continues to be one of the most successful strategies for both recognition and alignment of remote homologues. Although there is still room for improvement in identification and alignment methods, additional progress may come from model building and refinement methods that can compensate for large structural changes between remotely related targets and templates, as well as for regions of misalignment. PMID:16713709

  18. Excluded volume and ion-ion correlation effects on the ionic atmosphere around B-DNA: Theory, simulations, and experiments

    NASA Astrophysics Data System (ADS)

    Ovanesyan, Zaven; Medasani, Bharat; Fenley, Marcia O.; Guerrero-García, Guillermo Iván; Olvera de la Cruz, Mónica; Marucho, Marcelo

    2014-12-01

    The ionic atmosphere around a nucleic acid regulates its stability in aqueous salt solutions. One major source of complexity in biological activities involving nucleic acids arises from the strong influence of the surrounding ions and water molecules on their structural and thermodynamic properties. Here, we implement a classical density functional theory for cylindrical polyelectrolytes embedded in aqueous electrolytes containing explicit (neutral hard sphere) water molecules at experimental solvent concentrations. Our approach allows us to include ion correlations as well as solvent and ion excluded volume effects for studying the structural and thermodynamic properties of highly charged cylindrical polyelectrolytes. Several models of size and charge asymmetric mixtures of aqueous electrolytes at physiological concentrations are studied. Our results are in good agreement with Monte Carlo simulations. Our numerical calculations display significant differences in the ion density profiles for the different aqueous electrolyte models studied. However, similar results regarding the excess number of ions adsorbed to the B-DNA molecule are predicted by our theoretical approach for different aqueous electrolyte models. These findings suggest that ion counting experimental data should not be used alone to validate the performance of aqueous DNA-electrolyte models.

  19. Toward structure prediction of cyclic peptides.

    PubMed

    Yu, Hongtao; Lin, Yu-Shan

    2015-02-14

    Cyclic peptides are a promising class of molecules that can be used to target specific protein-protein interactions. A computational method to accurately predict their structures would substantially advance the development of cyclic peptides as modulators of protein-protein interactions. Here, we develop a computational method that integrates bias-exchange metadynamics simulations, a Boltzmann reweighting scheme, dihedral principal component analysis and a modified density peak-based cluster analysis to provide a converged structural description for cyclic peptides. Using this method, we evaluate the performance of a number of popular protein force fields on a model cyclic peptide. All the tested force fields seem to over-stabilize the ?-helix and PPII/? regions in the Ramachandran plot, commonly populated by linear peptides and proteins. Our findings suggest that re-parameterization of a force field that well describes the full Ramachandran plot is necessary to accurately model cyclic peptides. PMID:25566700

  20. Service life prediction of reinforced concrete structures

    SciTech Connect

    Liang, M.T.; Wang, K.L.; Liang, C.H.

    1999-09-01

    This paper is focused on the estimation of durability and service life of reinforced concrete structures. Assuming that the chloride ion in concrete can be absorbed on tricalcium aluminate, calcium silicate hydrate, and by other constituents of hardened cement paste, hydrated or not, the exact analytical solution of the governing partial differential equation together with its boundary and initial conditions can be obtained through nondimensional parameters and Laplace's transform. When the results of an exact analytical solution using suitable parameters were compared with the results of previous experimental work, the differences were found to be very small. This suggests that the absorption model is of considerable value. The exact analytical solution with the saturation parameter and time and diffusion coefficients under different effective electrical potential could be used to predict both the experimental results and the service life of reinforced concrete structures.

  1. A SOFTWARE PIPELINE FOR PROTEIN STRUCTURE PREDICTION Michael S. Lee

    E-print Network

    A SOFTWARE PIPELINE FOR PROTEIN STRUCTURE PREDICTION Michael S. Lee Computational Sciences a software suite to predict protein structures from sequence through the integration of multiple non structure prediction is an integral tool for the current proteomic and systems biology efforts taking place

  2. Protein Secondary Structure Prediction Method Based on Neural Networks

    Microsoft Academic Search

    Vasilka Dzikovska; M. Oreskovic; S. Kalajdziski; K. Trivodaliev; D. Davcev

    2008-01-01

    Protein secondary structure prediction remains an open and important problem in life sciences as a first step towards the crucial tertiary structure prediction. In [3], a protein secondary structure prediction algorithm called PSIPRED presents an innovative approach - feeding the neural network (NN) with a position specific scoring matrix as input data. Starting from this idea, in this paper we

  3. Structure prediction for multicomponent materials using biminima.

    PubMed

    Schebarchov, D; Wales, D J

    2014-10-10

    The potential energy surface of a heteroparticle system will contain points that are local minima in both coordinate space and permutation space for the different species. We introduce the term biminima to describe these special points, and we formulate a deterministic scheme for finding them. Our search algorithm generates a converging sequence of particle-identity swaps, each accompanied by a number of local geometry relaxations. For selected binary atomic clusters of size N = N(A) + N(B) ? 98, convergence to a biminimum on average takes 3 N(A)N(B) relaxations, and the number of biminima grows with the preference for mixing. The new framework unifies continuous and combinatorial optimization, providing a powerful tool for structure prediction and rational design of multicomponent materials. PMID:25375724

  4. Pretty Good Guessing: Protein Structure Prediction at CASP5

    Microsoft Academic Search

    Rosemarie Swanson; Jerry Tsai

    2003-01-01

    In this special issue of the Journal of Bacteriology, bacteriol- ogists look into the smallest organisms even deeper than be- fore, down to the molecular level. The focus is on experimen- tally determined molecular structures. However, structure prediction from amino acid sequence data is becoming a usable source of protein structure information as well. Interest in protein structure prediction is

  5. Predicting missing links via structural similarity

    NASA Astrophysics Data System (ADS)

    Lyu, Guo-Dong; Fan, Chang-Jun; Yu, Lian-Fei; Xiu, Bao-Xin; Zhang, Wei-Ming

    2015-04-01

    Predicting missing links in networks plays a significant role in modern science. On the basis of structural similarity, our paper proposes a new node-similarity-based measure called biased resource allocation (BRA), which is motivated by the resource allocation (RA) measure. Comparisons between BRA and nine well-known node-similarity-based measures on five real networks indicate that BRA performs no worse than RA, which was the best node-similarity-based index in previous researches. Afterwards, based on localPath (LP) and Katz measure, we propose another two improved measures, named Im-LocalPath and Im-Katz respectively. Numerical results show that the prediction accuracy of both Im-LP and Im-Katz measure improve compared with the original LP and Katz measure. Finally, a new path-similarity-based measure and its improved measure, called LYU and Im-LYU measure, are proposed and especially, Im-LYU measure is shown to perform more remarkably than other mentioned measures.

  6. Protein Structure Prediction by Comparative Modeling: An Analysis of Methodology

    E-print Network

    Protein Structure Prediction by Comparative Modeling: An Analysis of Methodology Jennifer Wang, Biochemistry 218 Submitted December 11, 2009 1. Introduction Protein structure determination has become an important area of research in molecular biology and structural genomics. Understanding the tertiary

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

    PubMed

    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. PMID:24250115

  8. Current approaches to predicting molecular organic crystal structures

    Microsoft Academic Search

    Graeme M. Day

    2011-01-01

    Considerable effort has been invested in developing methods for predicting the crystalline structure(s) of a given compound, ideally starting from no more than a structural formula of the molecule. Reliable computational predictions would be of great value in many areas of materials chemistry, from the design of materials with novel properties to the avoidance of an undesirable change of form

  9. RNA-SSPT: RNA Secondary Structure Prediction Tools

    PubMed Central

    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. PMID:24250115

  10. Statistical energy analysis response prediction methods for structural systems

    NASA Technical Reports Server (NTRS)

    Davis, R. F.

    1979-01-01

    The results of an effort to document methods for accomplishing response predictions for commonly encountered aerospace structural configurations is presented. Application of these methods to specified aerospace structure to provide sample analyses is included. An applications manual, with the structural analyses appended as example problems is given. Comparisons of the response predictions with measured data are provided for three of the example problems.

  11. Evolutionary Crystal Structure Prediction and Novel High-Pressure Phases

    Microsoft Academic Search

    Artem R. Oganov; Yanming Ma; Andriy O. Lyakhov; Mario Valle; Carlo Gatti

    2010-01-01

    \\u000a Prediction of stable crystal structures at given pressure-temperature conditions, based only on the knowledge of the chemical\\u000a composition, is a central problem of condensed matter physics. This extremely challenging problem is often termed “crystal\\u000a structure prediction problem”, and recently developed evolutionary algorithm USPEX (Universal Structure Predictor: Evolutionary\\u000a Xtallography) made an important progress in solving it, enabling efficient and reliable prediction

  12. Molecular dynamics of a ?B DNA element: base flipping via cross-strand intercalative stacking in a microsecond-scale simulation

    PubMed Central

    Mura, Cameron; McCammon, J. Andrew

    2008-01-01

    The sequence-dependent structural variability and conformational dynamics of DNA play pivotal roles in many biological milieus, such as in the site-specific binding of transcription factors to target regulatory elements. To better understand DNA structure, function, and dynamics in general, and protein···DNA recognition in the ‘?B’ family of genetic regulatory elements in particular, we performed molecular dynamics simulations of a 20-bp DNA encompassing a cognate ?B site recognized by the proto-oncogenic ‘c-Rel’ subfamily of NF-?B transcription factors. Simulations of the ?B DNA in explicit water were extended to microsecond duration, providing a broad, atomically detailed glimpse into the structural and dynamical behavior of double helical DNA over many timescales. Of particular note, novel (and structurally plausible) conformations of DNA developed only at the long times sampled in this simulation—including a peculiar state arising at ?0.7 ?s and characterized by cross-strand intercalative stacking of nucleotides within a longitudinally sheared base pair, followed (at ?1 ?s) by spontaneous base flipping of a neighboring thymine within the A-rich duplex. Results and predictions from the microsecond-scale simulation include implications for a dynamical NF-?B recognition motif, and are amenable to testing and further exploration via specific experimental approaches that are suggested herein. PMID:18653524

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

    PubMed

    Hong, Jun-Jie; Wu, Tzong-Yuan; Chang, Tsair-Yuan; Chen, Chung-Yung

    2013-01-01

    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/. PMID:24223923

  14. Changes to the Natural Killer Cell Repertoire after Therapeutic Hepatitis B DNA Vaccination

    E-print Network

    Paris-Sud XI, Université de

    Changes to the Natural Killer Cell Repertoire after Therapeutic Hepatitis B DNA Vaccination Daniel responses could be achieved by inducing specific natural killer (NK) cell subsets which can cooperate, Michel M-L (2010) Changes to the Natural Killer Cell Repertoire after Therapeutic Hepatitis B DNA

  15. Atomic Structure Prediction with Large-Scale High Performance Computing

    NASA Astrophysics Data System (ADS)

    Wang, Cai-Zhuang; Harmon, Bruce; Nguyen, Manh Cuong; Zhao, Xin; Ho, Kai-Ming; Ames Lab, US DOE Team

    2014-03-01

    Many unknown binary or ternary materials for energy applications have very complex crystal structures, containing large number of atoms in their unit cells and possible uncertainty in composition. Computational prediction for atomic structures of such complex materials is a highly demanding work. Advances in modern large-scale high performance computational resources and computational algorithms now make it feasible to perform an efficient crystal structure prediction. We developed an adaptive genetic algorithm to perform large-scale structure search on high performance supercomputer. Examples of successful structure prediction/solving of complex materials will be presented. Further applications of the adaptive genetic algorithm to aid material discoveries will be discussed.

  16. Protein structure prediction: challenging targets for CASP10

    Microsoft Academic Search

    Ashish Runthala

    2012-01-01

    Functional characterization of proteins being one of the major issues in molecular biology is still unsolved due to several resource and technical limitations of experimental structure determination methods. A suitable methodology for accurate prediction of protein confirmations simply from sequence is therefore emerging as the primary modeling goal of researchers today. Global blind protein structure prediction summit, entitled Critical Assessment

  17. Predicting crystal structure by merging data mining with quantum mechanics

    Microsoft Academic Search

    Christopher C. Fischer; Kevin J. Tibbetts; Dane Morgan; Gerbrand Ceder

    2006-01-01

    Modern methods of quantum mechanics have proved to be effective tools to understand and even predict materials properties. An essential element of the materials design process, relevant to both new materials and the optimization of existing ones, is knowing which crystal structures will form in an alloy system. Crystal structure can only be predicted effectively with quantum mechanics if an

  18. Structure-based function prediction: approaches and applications

    Microsoft Academic Search

    Pier Federico Gherardini; Manuela Helmer-Citterich

    2008-01-01

    The ever increasing number of protein structures determined by structural genomic projects has spurred much interest in the development of methods for structure-based function prediction. Existing methods can be roughly classified in two groups: some use a comparative approach looking for the presence of structural motifs possibly associated with a known biochemical function. Other methods try to identify functional patches

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

    PubMed Central

    2010-01-01

    Background 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. Results 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. Conclusion 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. PMID:20230624

  20. RBO Aleph: leveraging novel information sources for protein structure prediction

    PubMed Central

    Mabrouk, Mahmoud; Putz, Ines; Werner, Tim; Schneider, Michael; Neeb, Moritz; Bartels, Philipp; Brock, Oliver

    2015-01-01

    RBO Aleph is a novel protein structure prediction web server for template-based modeling, protein contact prediction and ab initio structure prediction. The server has a strong emphasis on modeling difficult protein targets for which templates cannot be detected. RBO Aleph's unique features are (i) the use of combined evolutionary and physicochemical information to perform residue–residue contact prediction and (ii) leveraging this contact information effectively in conformational space search. RBO Aleph emerged as one of the leading approaches to ab initio protein structure prediction and contact prediction during the most recent Critical Assessment of Protein Structure Prediction experiment (CASP11, 2014). In addition to RBO Aleph's main focus on ab initio modeling, the server also provides state-of-the-art template-based modeling services. Based on template availability, RBO Aleph switches automatically between template-based modeling and ab initio prediction based on the target protein sequence, facilitating use especially for non-expert users. The RBO Aleph web server offers a range of tools for visualization and data analysis, such as the visualization of predicted models, predicted contacts and the estimated prediction error along the model's backbone. The server is accessible at http://compbio.robotics.tu-berlin.de/rbo_aleph/. PMID:25897112

  1. RBO Aleph: leveraging novel information sources for protein structure prediction.

    PubMed

    Mabrouk, Mahmoud; Putz, Ines; Werner, Tim; Schneider, Michael; Neeb, Moritz; Bartels, Philipp; Brock, Oliver

    2015-07-01

    RBO Aleph is a novel protein structure prediction web server for template-based modeling, protein contact prediction and ab initio structure prediction. The server has a strong emphasis on modeling difficult protein targets for which templates cannot be detected. RBO Aleph's unique features are (i) the use of combined evolutionary and physicochemical information to perform residue-residue contact prediction and (ii) leveraging this contact information effectively in conformational space search. RBO Aleph emerged as one of the leading approaches to ab initio protein structure prediction and contact prediction during the most recent Critical Assessment of Protein Structure Prediction experiment (CASP11, 2014). In addition to RBO Aleph's main focus on ab initio modeling, the server also provides state-of-the-art template-based modeling services. Based on template availability, RBO Aleph switches automatically between template-based modeling and ab initio prediction based on the target protein sequence, facilitating use especially for non-expert users. The RBO Aleph web server offers a range of tools for visualization and data analysis, such as the visualization of predicted models, predicted contacts and the estimated prediction error along the model's backbone. The server is accessible at http://compbio.robotics.tu-berlin.de/rbo_aleph/. PMID:25897112

  2. Designing and benchmarking the MULTICOM protein structure prediction system

    PubMed Central

    2013-01-01

    Background Predicting protein structure from sequence is one of the most significant and challenging problems in bioinformatics. Numerous bioinformatics techniques and tools have been developed to tackle almost every aspect of protein structure prediction ranging from structural feature prediction, template identification and query-template alignment to structure sampling, model quality assessment, and model refinement. How to synergistically select, integrate and improve the strengths of the complementary techniques at each prediction stage and build a high-performance system is becoming a critical issue for constructing a successful, competitive protein structure predictor. Results Over the past several years, we have constructed a standalone protein structure prediction system MULTICOM that combines multiple sources of information and complementary methods at all five stages of the protein structure prediction process including template identification, template combination, model generation, model assessment, and model refinement. The system was blindly tested during the ninth Critical Assessment of Techniques for Protein Structure Prediction (CASP9) in 2010 and yielded very good performance. In addition to studying the overall performance on the CASP9 benchmark, we thoroughly investigated the performance and contributions of each component at each stage of prediction. Conclusions Our comprehensive and comparative study not only provides useful and practical insights about how to select, improve, and integrate complementary methods to build a cutting-edge protein structure prediction system but also identifies a few new sources of information that may help improve the design of a protein structure prediction system. Several components used in the MULTICOM system are available at: http://sysbio.rnet.missouri.edu/multicom_toolbox/. PMID:23442819

  3. Neural network definitions of highly predictable protein secondary structure classes

    SciTech Connect

    Lapedes, A. [Los Alamos National Lab., NM (United States)]|[Santa Fe Inst., NM (United States); Steeg, E. [Toronto Univ., ON (Canada). Dept. of Computer Science; Farber, R. [Los Alamos National Lab., NM (United States)

    1994-02-01

    We use two co-evolving neural networks to determine new classes of protein secondary structure which are significantly more predictable from local amino sequence than the conventional secondary structure classification. Accurate prediction of the conventional secondary structure classes: alpha helix, beta strand, and coil, from primary sequence has long been an important problem in computational molecular biology. Neural networks have been a popular method to attempt to predict these conventional secondary structure classes. Accuracy has been disappointingly low. The algorithm presented here uses neural networks to similtaneously examine both sequence and structure data, and to evolve new classes of secondary structure that can be predicted from sequence with significantly higher accuracy than the conventional classes. These new classes have both similarities to, and differences with the conventional alpha helix, beta strand and coil.

  4. A physical approach to protein structure prediction: CASP4 results

    SciTech Connect

    Crivelli, Silvia; Eskow, Elizabeth; Bader, Brett; Lamberti, Vincent; Byrd, Richard; Schnabel, Robert; Head-Gordon, Teresa

    2001-02-27

    We describe our global optimization method called Stochastic Perturbation with Soft Constraints (SPSC), which uses information from known proteins to predict secondary structure, but not in the tertiary structure predictions or in generating the terms of the physics-based energy function. Our approach is also characterized by the use of an all atom energy function that includes a novel hydrophobic solvation function derived from experiments that shows promising ability for energy discrimination against misfolded structures. We present the results obtained using our SPSC method and energy function for blind prediction in the 4th Critical Assessment of Techniques for Protein Structure Prediction (CASP4) competition, and show that our approach is more effective on targets for which less information from known proteins is available. In fact our SPSC method produced the best prediction for one of the most difficult targets of the competition, a new fold protein of 240 amino acids.

  5. Quantifying variances in comparative RNA secondary structure prediction

    PubMed Central

    2013-01-01

    Background With the advancement of next-generation sequencing and transcriptomics technologies, regulatory effects involving RNA, in particular RNA structural changes are being detected. These results often rely on RNA secondary structure predictions. However, current approaches to RNA secondary structure modelling produce predictions with a high variance in predictive accuracy, and we have little quantifiable knowledge about the reasons for these variances. Results In this paper we explore a number of factors which can contribute to poor RNA secondary structure prediction quality. We establish a quantified relationship between alignment quality and loss of accuracy. Furthermore, we define two new measures to quantify uncertainty in alignment-based structure predictions. One of the measures improves on the “reliability score” reported by PPfold, and considers alignment uncertainty as well as base-pair probabilities. The other measure considers the information entropy for SCFGs over a space of input alignments. Conclusions Our predictive accuracy improves on the PPfold reliability score. We can successfully characterize many of the underlying reasons for and variances in poor prediction. However, there is still variability unaccounted for, which we therefore suggest comes from the RNA secondary structure predictive model itself. PMID:23634662

  6. Site-directed chemical modification of archaeal Thermococcus litoralis Sh1B DNA polymerase: Acquired ability to read through template-strand uracils.

    PubMed

    Gaidamaviciute, Edita; Tauraite, Daiva; Gagilas, Julius; Lagunavicius, Arunas

    2010-06-01

    We present site-directed chemical modification (SDCM), a tool for engineering U-resistant archaeal DNA polymerases of family B. The Thermococcus litoralis Sh1B DNA polymerase (GenBank: GQ891548) was chosen as the object of the study. Similar to D.Tok, Kod1, Pfu, Tgo and other archaeal members of this family, the T. litoralis Sh1B DNA polymerase is a domain structured, proofreading-proficient enzyme that has the polymerization and 3'-->5' DNA exonucleolytic activities and contains N-terminally located highly conserved template-strand U-binding pocket. The tight binding of template uracil in the enzyme pocket during polymerization blocks the replication of DNA containing uracils. This effect can be alleviated by mutations in key amino acids of the U-binding pocket. We altered T. litoralis Sh1B DNA polymerase's ability to read through the template-strand uracils by applying SDCM. Specific modification of individual cysteine residues in U-binding pocket - targets introduced into certain positions by site-directed mutagenesis - enables the enzyme to effectively replicate DNA containing uracils. We demonstrate that the acquired resistance of chemically modified T. litoralis Sh1B DNA polymerase to DNA uracil correlates with its decreased affinity for template-strand uracil. PMID:20152943

  7. Secondary Structure Prediction of Proposed RNAi

    E-print Network

    supported by additional information from RNase sensitivity and nucleotide chemical base reactivity assays of multiple sequence alignments. The aim of this exploration is to examine the effectiveness of two web;3 published structures based on multiple methods of structure determination are available. BACKGROUND

  8. Prediction of structure and phase transformations

    E-print Network

    Widom, Michael

    diagrams and crystal structures of most binary intermetallics, many ternary phase diagrams have not been-Nb-Ta-W, and their binary and ternary subsystems. 1 Introduction High entropy alloys form when multiple chemical species diagrams. While the stable crystal structures of almost all pure elements are known, as are the phase

  9. PRISM: Protein-protein Interaction Prediction by Structural Matching

    PubMed Central

    Keskin, Ozlem; Nussinov, Ruth; Gursoy, Attila

    2009-01-01

    Prism (Protein Interactions by Structural Matching) is a system which employs a novel prediction algorithm for protein-protein interactions. It adopts a bottom-up approach that combines structure and sequence conservation in protein interfaces. The algorithm seeks possible binary interactions between proteins through the structure similarity and evolutionary conservation of known interfaces. It is composed of a database holding protein interface structures derived from the Protein Data Bank (PDB) and predicted protein-protein interactions. It also provides related information about the proteins, and an interactive protein interface viewer. In the current version, 3799 structurally non-redundant interfaces are used to predict the interactions among 6170 proteins. A substantial number of interactions are verified in two publicly available interaction databases (DIP and BIND). As the verified interactions demonstrate the suitability of our approach, unverified ones may point to undiscovered interactions. Prism can be accessed through a user friendly web site (http://prism.ccbb.ku.edu.tr) and it is planned to be updated regularly as new protein structures become available in the PDB. Users may browse through the non-redundant dataset of representative interfaces which the prediction algorithm depends on, retrieve the list of similar structures to these interfaces, or see the results of interaction predictions for a particular protein. Another service provided is the interactive prediction. This is done by running the algorithm for the user input structures. PMID:18592198

  10. Dynamic network structure identification with prediction error methods -basic

    E-print Network

    Van den Hof, Paul

    Dynamic network structure identification with prediction error methods - basic examples Arne G important in different fields of science. When identifying the structure and dynamics of a network dynamics) in a sys- tem/measurement/excitation structure that is clearly well defined a priori. One knows

  11. Dynamic network structure identification with prediction error methods -basic

    E-print Network

    Van den Hof, Paul

    Dynamic network structure identification with prediction error methods - basic examples Arne G important in different fields of science. When identifying the structure and dynamics of a network (and possibly noise dynamics) in a sys- tem/measurement/excitation structure that is clearly well

  12. Online Structured Prediction via Coactive Learning

    E-print Network

    Shivaswamy, Pannaga

    2012-01-01

    We propose Coactive Learning as a model of interaction between a learning system and a human user, where both have the common goal of providing results of maximum utility to the user. At each step, the system (e.g. search engine) receives a context (e.g. query) and predicts an object (e.g. ranking). The user responds by correcting the system if necessary, providing a slightly improved -- but not necessarily optimal -- object as feedback. We argue that such feedback can often be inferred from observable user behavior, for example, from clicks in web-search. Evaluating predictions by their cardinal utility to the user, we propose efficient learning algorithms that have ${\\cal O}(\\frac{1}{\\sqrt{T}})$ average regret, even though the learning algorithm never observes cardinal utility values as in conventional online learning. We demonstrate the applicability of our model and learning algorithms on a movie recommendation task, as well as ranking for web-search.

  13. Data mining for structure type prediction

    E-print Network

    Tibbetts, Kevin (Kevin Joseph)

    2004-01-01

    Determining the stable structure types of an alloy is critical to determining many properties of that material. This can be done through experiment or computation. Both methods can be expensive and time consuming. Computational ...

  14. Towards de novo RNA 3D structure prediction

    E-print Network

    Bottaro, Sandro; Bussi, Giovanni

    2015-01-01

    RNA is a fundamental class of biomolecules that mediate a large variety of molecular processes within the cell. Computational algorithms can be of great help in the understanding of RNA structure-function relationship. One of the main challenges in this field is the development of structure-prediction algorithms, which aim at the prediction of the three-dimensional (3D) native fold from the sole knowledge of the sequence. In a recent paper, we have introduced a scoring function for RNA structure prediction. Here, we analyze in detail the performance of the method, we underline strengths and shortcomings, and we discuss the results with respect to state-of-the-art techniques. These observations provide a starting point for improving current methodologies, thus paving the way to the advances of more accurate approaches for RNA 3D structure prediction.

  15. Advanced Structured Prediction Sebastian Nowozin Sebastian.Nowozin@microsoft.com

    E-print Network

    Heermann, Dieter W.

    Advanced Structured Prediction Editors: Sebastian Nowozin Sebastian.Nowozin@microsoft.com Microsoft for Intelligent Systems 72076 T¨ubingen, Germany Jeremy Jancsary jermyj@microsoft.com Microsoft Research Cambridge

  16. WeFold: a coopetition for protein structure prediction.

    PubMed

    Khoury, George A; Liwo, Adam; Khatib, Firas; Zhou, Hongyi; Chopra, Gaurav; Bacardit, Jaume; Bortot, Leandro O; Faccioli, Rodrigo A; Deng, Xin; He, Yi; Krupa, Pawel; Li, Jilong; Mozolewska, Magdalena A; Sieradzan, Adam K; Smadbeck, James; Wirecki, Tomasz; Cooper, Seth; Flatten, Jeff; Xu, Kefan; Baker, David; Cheng, Jianlin; Delbem, Alexandre C B; Floudas, Christodoulos A; Keasar, Chen; Levitt, Michael; Popovi?, Zoran; Scheraga, Harold A; Skolnick, Jeffrey; Crivelli, Silvia N

    2014-09-01

    The protein structure prediction problem continues to elude scientists. Despite the introduction of many methods, only modest gains were made over the last decade for certain classes of prediction targets. To address this challenge, a social-media based worldwide collaborative effort, named WeFold, was undertaken by 13 labs. During the collaboration, the laboratories were simultaneously competing with each other. Here, we present the first attempt at "coopetition" in scientific research applied to the protein structure prediction and refinement problems. The coopetition was possible by allowing the participating labs to contribute different components of their protein structure prediction pipelines and create new hybrid pipelines that they tested during CASP10. This manuscript describes both successes and areas needing improvement as identified throughout the first WeFold experiment and discusses the efforts that are underway to advance this initiative. A footprint of all contributions and structures are publicly accessible at http://www.wefold.org. PMID:24677212

  17. Structural imaging biomarkers of Alzheimer's disease: predicting disease progression

    E-print Network

    Paris-Sud XI, Université de

    1 Structural imaging biomarkers of Alzheimer's disease: predicting disease the Alzheimer's Disease Neuroimaging Initiative (ADNI) database (www of Alzheimer's disease (AD) may allow earlier detection and refined pre- diction

  18. Structure Prediction in Temporal Networks using Frequent Subgraphs

    E-print Network

    Lahiri, Mayank

    extensively are the evolution of bibliographic databases [1]­[5], the web and other information networks [6 structure prediction in tem- poral networks. Our algorithm does not rely on any domain- specific features

  19. JPred4: a protein secondary structure prediction server.

    PubMed

    Drozdetskiy, Alexey; Cole, Christian; Procter, James; Barton, Geoffrey J

    2015-07-01

    JPred4 (http://www.compbio.dundee.ac.uk/jpred4) is the latest version of the popular JPred protein secondary structure prediction server which provides predictions by the JNet algorithm, one of the most accurate methods for secondary structure prediction. In addition to protein secondary structure, JPred also makes predictions of solvent accessibility and coiled-coil regions. The JPred service runs up to 94 000 jobs per month and has carried out over 1.5 million predictions in total for users in 179 countries. The JPred4 web server has been re-implemented in the Bootstrap framework and JavaScript to improve its design, usability and accessibility from mobile devices. JPred4 features higher accuracy, with a blind three-state (?-helix, ?-strand and coil) secondary structure prediction accuracy of 82.0% while solvent accessibility prediction accuracy has been raised to 90% for residues <5% accessible. Reporting of results is enhanced both on the website and through the optional email summaries and batch submission results. Predictions are now presented in SVG format with options to view full multiple sequence alignments with and without gaps and insertions. Finally, the help-pages have been updated and tool-tips added as well as step-by-step tutorials. PMID:25883141

  20. Learning message-passing inference machines for structured prediction

    Microsoft Academic Search

    Stephane Ross; Daniel Munoz; Martial Hebert; J. Andrew Bagnell

    2011-01-01

    Nearly every structured prediction problem in computer vision requires approximate inference due to large and complex dependencies among output labels. While graphical models provide a clean separation between modeling and inference, learning these models with approximate inference is not well understood. Furthermore, even if a good model is learned, predictions are often inaccurate due to approximations. In this work, instead

  1. JPred4: a protein secondary structure prediction server

    PubMed Central

    Drozdetskiy, Alexey; Cole, Christian; Procter, James; Barton, Geoffrey J.

    2015-01-01

    JPred4 (http://www.compbio.dundee.ac.uk/jpred4) is the latest version of the popular JPred protein secondary structure prediction server which provides predictions by the JNet algorithm, one of the most accurate methods for secondary structure prediction. In addition to protein secondary structure, JPred also makes predictions of solvent accessibility and coiled-coil regions. The JPred service runs up to 94 000 jobs per month and has carried out over 1.5 million predictions in total for users in 179 countries. The JPred4 web server has been re-implemented in the Bootstrap framework and JavaScript to improve its design, usability and accessibility from mobile devices. JPred4 features higher accuracy, with a blind three-state (?-helix, ?-strand and coil) secondary structure prediction accuracy of 82.0% while solvent accessibility prediction accuracy has been raised to 90% for residues <5% accessible. Reporting of results is enhanced both on the website and through the optional email summaries and batch submission results. Predictions are now presented in SVG format with options to view full multiple sequence alignments with and without gaps and insertions. Finally, the help-pages have been updated and tool-tips added as well as step-by-step tutorials. PMID:25883141

  2. SVM learning of IP address structure for latency prediction

    Microsoft Academic Search

    Robert Beverly; Karen R. Sollins; Arthur Berger

    2006-01-01

    We examine the ability to exploit the hierarchical struc- ture of Internet addresses in order to endow network agents with predictive capabilities. Specifically, we consider Sup- port Vector Machines (SVMs) for prediction of round-trip latency to random network destinations the agent has not previously interacted with. We use kernel functions to trans- form the structured, yet fragmented and discontinuous, IP

  3. OPTIMIZATION BIAS IN ENERGY-BASED STRUCTURE PREDICTION

    PubMed Central

    Petrella, Robert J.

    2014-01-01

    Physics-based computational approaches to predicting the structure of macromolecules such as proteins are gaining increased use, but there are remaining challenges. In the current work, it is demonstrated that in energy-based prediction methods, the degree of optimization of the sampled structures can influence the prediction results. In particular, discrepancies in the degree of local sampling can bias the predictions in favor of the oversampled structures by shifting the local probability distributions of the minimum sampled energies. In simple systems, it is shown that the magnitude of the errors can be calculated from the energy surface, and for certain model systems, derived analytically. Further, it is shown that for energy wells whose forms differ only by a randomly assigned energy shift, the optimal accuracy of prediction is achieved when the sampling around each structure is equal. Energy correction terms can be used in cases of unequal sampling to reproduce the total probabilities that would occur under equal sampling, but optimal corrections only partially restore the prediction accuracy lost to unequal sampling. For multiwell systems, the determination of the correction terms is a multibody problem; it is shown that the involved cross-correlation multiple integrals can be reduced to simpler integrals. The possible implications of the current analysis for macromolecular structure prediction are discussed. PMID:25552783

  4. Hurricane Damage Prediction Model for Residential Structures

    Microsoft Academic Search

    Jean-Paul Pinelli; Emil Simiu; Kurt Gurley; Chelakara Subramanian; Liang Zhang; Anne Cope; James J. Filliben; Shahid Hamid

    2004-01-01

    The paper reports progress in the development of a practical probabilistic model for the estimation of expected annual damage induced by hurricane winds in residential structures. The estimation of the damage is accomplished in several steps. First, basic damage modes for components of specific building types are defined. Second, the damage modes are combined in possible damage states, whose probabilities

  5. Improving structure-based function prediction using molecular dynamics

    PubMed Central

    Glazer, Dariya S.; Radmer, Randall J.; Altman, Russ B.

    2009-01-01

    Summary The number of molecules with solved three-dimensional structure but unknown function is increasing rapidly. Particularly problematic are novel folds with little detectable similarity to molecules of known function. Experimental assays can determine the functions of such molecules, but are time-consuming and expensive. Computational approaches can identify potential functional sites; however, these approaches generally rely on single static structures and do not use information about dynamics. In fact, structural dynamics can enhance function prediction: we coupled molecular dynamics simulations with structure-based function prediction algorithms that identify Ca2+ binding sites. When applied to 11 challenging proteins, both methods showed substantial improvement in performance, revealing 22 more sites in one case and 12 more in the other, with a modest increase in apparent false positives. Thus, we show that treating molecules as dynamic entities improves the performance of structure-based function prediction methods. PMID:19604472

  6. Computational methods in sequence and structure prediction

    NASA Astrophysics Data System (ADS)

    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 a software package which is capable of designing novel protein structures at the atomic resolution. This software package allows us to perform protein structure design with a flexible backbone. The backbone flexibility includes loop region relaxation as well as a secondary structure collective mode relaxation scheme. (Abstract shortened by UMI.)

  7. Predicting crystal structure by merging data mining with quantum mechanics

    E-print Network

    Ceder, Gerbrand

    ARTICLES Predicting crystal structure by merging data mining with quantum mechanics CHRISTOPHER C@mit.edu Published online: 9 July 2006; doi:10.1038/nmat1691 Modern methods of quantum mechanics have proved with quantum mechanics if an algorithm to direct the search through the large space of possible structures

  8. Predicted Solution Structure of Zymogen Human Coagulation FVII

    E-print Network

    Perera, Lalith

    Predicted Solution Structure of Zymogen Human Coagulation FVII LALITH PERERA,1 THOMAS A. DARDEN,2-ray crystallographic structure of human coagulation FVIIa/TF complex bound with calcium ions (Banner et al., Nature dynamics simulations; FVII; tissue factor; EGF-like domains; serine protease Introduction Blood coagulation

  9. Smoking Outcome Expectancies: Factor Structure, Predictive Validity, and Discriminant Validity

    Microsoft Academic Search

    David W. Wetter; Stevens S. Smith; Susan L. Kenford; Douglas E. Jorenby; Michael C. Fiore; Richard D. Hurt; Kenneth P. Offord; Timothy B. Baker

    1994-01-01

    Recent models of addiction posit that drug outcome expectancies are influential determinants of drug use. The current research examines the dimensional structure, predictive validity, and discriminant validity of expectancies for cigarette smoking in a prospective study. There was a good fit between the factor structure of the Smoking Consequences Questionnaire and the observed data. In addition, the internal consistency of

  10. Analytical Predictions of the Air Gap Response of Floating Structures

    Microsoft Academic Search

    Lance Manuel; Bert Sweetman; Steven R. Winterstein

    2001-01-01

    Two separate studies are presented here that deal with analytical predictions of the air gap for floating structures. (1) To obtain an understanding of the importance of first- and second-order incident and diffracted wave effects as well as to determine the influence of the structure's motions on the instantaneous air gap, statistics of the air gap response are studied under

  11. Ceramide structure predicts tumor ganglioside immunosuppressive activity.

    PubMed

    Ladisch, S; Li, R; Olson, E

    1994-03-01

    Molecular determinants of biological activity of gangliosides are generally believed to be carbohydrate in nature. However, our studies of immunomodulation by highly purified naturally occurring tumor gangliosides provide another perspective: while the immunosuppressive activity of gangliosides requires the intact molecule (both carbohydrate and ceramide moieties), ceramide structure strikingly influences ganglioside immunosuppressive activity. Molecular species of human neuroblastoma GD2 ganglioside in which the ceramide contains a shorter fatty acyl chain (C16:0, C18:0) were 6- to 10-fold more active than those with a longer fatty acyl chain (C22:0/C24:1, C24:0). These findings were confirmed in studies of ceramide species of human leukemia sialosylparagloboside and murine lymphoma GalNAcGM1b. Gangliosides that contain shorter-chain fatty acids (and are most immunosuppressive) are known to be preferentially shed by tumor cells. Therefore, the results suggest that the tumor cell is optimized to protect itself from host immune destruction by selective shedding of highly active ceramide species of gangliosides. PMID:8127917

  12. Protein structure prediction enhanced with evolutionary diversity : SPEED.

    SciTech Connect

    DeBartolo, J.; Hocky, G.; Wilde, M.; Xu, J.; Freed, K. F.; Sosnick, T. R.; Univ. of Chicago; Toyota Technological Inst. at Chicago

    2010-03-01

    For naturally occurring proteins, similar sequence implies similar structure. Consequently, multiple sequence alignments (MSAs) often are used in template-based modeling of protein structure and have been incorporated into fragment-based assembly methods. Our previous homology-free structure prediction study introduced an algorithm that mimics the folding pathway by coupling the formation of secondary and tertiary structure. Moves in the Monte Carlo procedure involve only a change in a single pair of {phi},{psi} backbone dihedral angles that are obtained from a Protein Data Bank-based distribution appropriate for each amino acid, conditional on the type and conformation of the flanking residues. We improve this method by using MSAs to enrich the sampling distribution, but in a manner that does not require structural knowledge of any protein sequence (i.e., not homologous fragment insertion). In combination with other tools, including clustering and refinement, the accuracies of the predicted secondary and tertiary structures are substantially improved and a global and position-resolved measure of confidence is introduced for the accuracy of the predictions. Performance of the method in the Critical Assessment of Structure Prediction (CASP8) is discussed.

  13. Structure prediction of polyglutamine disease proteins: comparison of methods

    PubMed Central

    2014-01-01

    Background The expansion of polyglutamine (poly-Q) repeats in several unrelated proteins is associated with at least ten neurodegenerative diseases. The length of the poly-Q regions plays an important role in the progression of the diseases. The number of glutamines (Q) is inversely related to the onset age of these polyglutamine diseases, and the expansion of poly-Q repeats has been associated with protein misfolding. However, very little is known about the structural changes induced by the expansion of the repeats. Computational methods can provide an alternative to determine the structure of these poly-Q proteins, but it is important to evaluate their performance before large scale prediction work is done. Results In this paper, two popular protein structure prediction programs, I-TASSER and Rosetta, have been used to predict the structure of the N-terminal fragment of a protein associated with Huntington's disease with 17 glutamines. Results show that both programs have the ability to find the native structures, but I-TASSER performs better for the overall task. Conclusions Both I-TASSER and Rosetta can be used for structure prediction of proteins with poly-Q repeats. Knowledge of poly-Q structure may significantly contribute to development of therapeutic strategies for poly-Q diseases. PMID:25080018

  14. The post-SCF quantum chemistry characteristics of the energetic heterogeneity of stacked guanine–guanine pairs found in B-DNA and A-DNA crystals

    Microsoft Academic Search

    Piotr Cysewski

    2008-01-01

    The energies of homo-guanine pairs (5?G\\/G-3?) in stacked conformations found in crystallographic B-DNA and A-DNA were estimated by means of DF-MP2\\/aDZ method with inclusion of the correction for basis superposition error. The significant heterogeneity was noticed related to the structural properties, the intermolecular interaction energies, the values of the ionization potential and the localization of the HOMO densities. The direct

  15. Protein structure prediction and structure-based protein function annotation

    E-print Network

    Roy, Ambrish

    2011-12-31

    .............................................................................................................2 1.1.2. Protein databases ..............................................................................................................................4 1.1.3. Analyzing similarity between two proteins... .............................................................................51.1.3.1. Sequence based alignment............................................................................................................... 51.1.3.2. Structure based alignment...

  16. A lattice model for protein structure prediction at low resolution.

    PubMed Central

    Hinds, D A; Levitt, M

    1992-01-01

    The prediction of the folded structure of a protein from its sequence has proven to be a very difficult computational problem. We have developed an exceptionally simple representation of a polypeptide chain, with which we can enumerate all possible backbone conformations of small proteins. A protein is represented by a self-avoiding path of connected vertices on a tetrahedral lattice, with several amino acid residues assigned to each lattice vertex. For five small structurally dissimilar proteins, we find that we can separate native-like structures from the vast majority of non-native folds by using only simple structural and energetic criteria. This method demonstrates significant generality and predictive power without requiring foreknowledge of any native structural details. Images PMID:1557356

  17. A life prediction model for laminated composite structural components

    NASA Technical Reports Server (NTRS)

    Allen, David H.

    1990-01-01

    A life prediction methodology for laminated continuous fiber composites subjected to fatigue loading conditions was developed. A summary is presented of research completed. A phenomenological damage evolution law was formulated for matrix cracking which is independent of stacking sequence. Mechanistic and physical support was developed for the phenomenological evolution law proposed above. The damage evolution law proposed above was implemented to a finite element computer program. And preliminary predictions were obtained for a structural component undergoing fatigue loading induced damage.

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

    PubMed

    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. PMID:23971032

  19. Structure-based drug metabolism predictions for drug design.

    PubMed

    Sun, Hao; Scott, Dennis O

    2010-01-01

    Significant progress has been made in structure-based drug design by pharmaceutical companies at different stages of drug discovery such as identifying new hits, enhancing molecule binding affinity in hit-to-lead, and reducing toxicities in lead optimization. Drug metabolism is a major consideration for modifying drug clearance and also a primary source for drug metabolite-induced toxicity. With major cytochrome P450 structures identified and characterized recently, structure-based drug metabolism prediction becomes increasingly attractive. In silico methods based on molecular and quantum mechanics such as docking, molecular dynamics and ab initio chemical reactivity calculations bring us closer to understand drug metabolism and predict drug-drug interactions. In this study, we review important progress in drug metabolism and common in silico techniques adopted to predict drug regioselectivity, stereoselectivity, reactive metabolites, induction, inhibition and mechanism-based inactivation, as well as their implementation in hit-to-lead drug discovery. PMID:19878193

  20. HLA-peptide binding prediction using structural and modeling principles.

    PubMed

    Kangueane, Pandjassarame; Sakharkar, Meena Kishore

    2007-01-01

    Short peptides binding to specific human leukocyte antigen (HLA) alleles elicit immune response. These candidate peptides have potential utility in peptide vaccine design and development. The binding of peptides to allele-specific HLA molecule is estimated using competitive binding assay and biochemical binding constants. Application of this method for proteome-wide screening in parasites, viruses, and virulent bacterial strains is laborious and expensive. However, short listing of candidate peptides using prediction approaches have been realized lately. Prediction of peptide binding to HLA alleles using structural and modeling principles has gained momentum in recent years. Here, we discuss the current status of such prediction. PMID:18450009

  1. Combining local-structure, fold-recognition, and new-fold methods for protein structure prediction

    E-print Network

    Karplus, Kevin

    Combining local-structure, fold-recognition, and new-fold methods for protein structure prediction an overview of the SAM-T02 method for protein fold recognition and the undertaker pro- gram for ab initio predictions. The SAM-T02 server is an automatic method that uses two-track hidden Markov models (hmms) to find

  2. Sizing Structures and Predicting Weight of a Spacecraft

    NASA Technical Reports Server (NTRS)

    Cerro, Jeffrey; Shore, C. P.

    2006-01-01

    EZDESIT is a computer program for choosing the sizes of structural components and predicting the weight of a spacecraft, aircraft, or other vehicle. In designing a vehicle, EZDESIT is used in conjunction with a finite-element structural- analysis program: Each structural component is sized within EZDESIT to withstand the loads expected to be encountered during operation, then the weights of all the structural finite elements are added to obtain the structural weight of the vehicle. The sizing of the structural components elements also alters the stiffness properties of the finiteelement model. The finite-element analysis and structural component sizing are iterated until the weight of the vehicle converges to a prescribed iterative difference.

  3. Workshop—Predicting the Structure of Biological Molecules

    PubMed Central

    2004-01-01

    This April, in Cambridge (UK), principal investigators from the Mathematical Biology Group of the Medical Research Council's National Institute of Medical Research organized a workshop in structural bioinformatics at the Centre for Mathematical Sciences. Bioinformatics researchers of several nationalities from labs around the country presented and discussed their computational work in biomolecular structure prediction and analysis, and in protein evolution. The meeting was intensive and lively and gave attendees an overview of the healthy state of protein bioinformatics in the UK. PMID:18629142

  4. Computational Methods for Protein Structure Prediction and Fold Recognition

    Microsoft Academic Search

    Iwona Cymerman; Marcin Feder; Marcin Paw?owski; Michal Kurowski; Janusz Bujnicki

    Amino acid sequence analysis provides important insight into the structure of proteins,which in turn greatly facilitates the\\u000a understanding of its biochemical and cellular function. Efforts to use computational methods in predicting protein structure\\u000a based only on sequence information started 30 years ago (Nagano 1973; Chou and Fasman 1974).However, only during the last\\u000a decade, has the introduction of new computational techniques

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

    PubMed Central

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

    2015-01-01

    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/. PMID:25943546

  6. Protein structure prediction: assembly of secondary structure elements by basin-hopping.

    PubMed

    Hoffmann, Falk; Vancea, Ioan; Kamat, Sanjay G; Strodel, Birgit

    2014-10-20

    The prediction of protein tertiary structure from primary structure remains a challenging task. One possible approach to this problem is the application of basin-hopping global optimization combined with an all-atom force field. In this work, the efficiency of basin-hopping is improved by introducing an approach that derives tertiary structures from the secondary structure assignments of individual residues. This approach is termed secondary-to-tertiary basin-hopping and benchmarked for three miniproteins: trpzip, trp-cage and ER-10. For each of the three miniproteins, the secondary-to-tertiary basin-hopping approach successfully and reliably predicts their three-dimensional structure. When it is applied to larger proteins, correctly folded structures are obtained. It can be concluded that the assembly of secondary structure elements using basin-hopping is a promising tool for de novo protein structure prediction. PMID:25056272

  7. Predictions of microwavePredictions of microwave breakdown in rf structuresbreakdown in rf structures

    E-print Network

    Yu, Ming

    of the breakdown phenomenon). 2. Electron interaction with microwave field (rough estimates of parameters which effect) electron impact ionization of neutral gas molecules #12;4 Predictions of microwave breakdown breakdown in rf structures Electron acceleration in microwaveElectron acceleration in microwave field under

  8. PSPP: A Protein Structure Prediction Pipeline for Computing Clusters

    E-print Network

    PSPP: A Protein Structure Prediction Pipeline for Computing Clusters Michael S. Lee1,2,3 , Rajkumar. Methodology/Principal Findings: The pipeline consists of a Perl core that integrates more than 20 individual-delimited, and hypertext markup language (HTML) formats. So far, the pipeline has been used to study viral and bacterial

  9. Structural Damage Prediction and Analysis for Hypervelocity Impact: Consulting

    NASA Technical Reports Server (NTRS)

    1995-01-01

    A portion of the contract NAS8-38856, 'Structural Damage Prediction and Analysis for Hypervelocity Impacts,' from NASA Marshall Space Flight Center (MSFC), included consulting which was to be documented in the final report. This attachment to the final report contains memos produced as part of that consulting.

  10. Predicting Modes of Toxic Action from Chemical Structure: An Overview

    Microsoft Academic Search

    S. P. Bradbury

    1994-01-01

    In the field of environmental toxicology, and especially aquatic toxicology, quantitative structure activity relationships (QSARs) have developed as scientifically-credible tools for predicting the toxicity of chemicals when little or no empirical data are available. A basic and fundamental understanding of toxicological principles has been considered crucial to the continued acceptance and application of these techniques as biologically relevant. As a

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

    E-print Network

    Zhang, Yang

    2008-01-23

    -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...

  12. proteinsSTRUCTURE O FUNCTION O BIOINFORMATICS PREDICTION REPORT

    E-print Network

    Zhang, Yang

    structure prediction in CASP8 Yang Zhang* Center for Bioinformatics, University of Kansas, Lawrence, Kansas 66047 Department of Molecular Bioscience, University of Kansas, Lawrence, Kansas 66047 INTRODUCTION When for Bioinformatics and Department of Molecular Bioscience, University of Kansas, 2030 Becker Dr, Lawrence, KS 66047

  13. Prediction of RNA secondary structure including kissing hairpin motifs

    E-print Network

    Moeller, Ralf

    Prediction of RNA secondary structure including kissing hairpin motifs Corinna Theis, Stefan including kissing hairpin motifs. The new idea is to construct a kissing hairpin motif from an overlay's Principle of Optimality, and the kissing hairpin cannot simply be built from optimal pseudoknots. Our

  14. Predicting protein structures with a multiplayer online game

    E-print Network

    Baker, David

    LETTERS Predicting protein structures with a multiplayer online game Seth Cooper1 , Firas Khatib2 com- puter games. Simple image- and text-recognition tasks have been successfully `crowd-sourced' through games1­3 , but it is not clear if more complex scientific problems can be solved with human

  15. Predicting protein structure using hidden Markov models Kevin Karplusy

    E-print Network

    Karplus, Kevin

    Predicting protein structure using hidden Markov models Kevin Karplusy Computer Engineering U, Supplement 1, 1997 Abstract We discuss how methods based on hidden Markov models performed in the fold-recognition section of the CASP2 experiment. Hidden Markov models were built for a representative set of just over one

  16. Computational Predictions of Structures of Multichromosomes of Budding Yeast

    E-print Network

    Liang, Jie

    Computational Predictions of Structures of Multichromosomes of Budding Yeast (Accepted, Conf Proc of budding yeast nucleus. We successfully generated a large number of model genomes of yeast with appropriate yeast genome realistically. The model developed here provides a general computational framework

  17. Molecular dynamics in the endgame of protein structure prediction1

    Microsoft Academic Search

    Matthew R. Lee; Jerry Tsai; David Baker; Peter A. Kollman

    2001-01-01

    In order adequately to sample conformational space, methods for protein structure prediction make necessary simplifications that also prevent them from being as accurate as desired. Thus, the idea of feeding them, hierarchically, into a more accurate method that samples less effectively was introduced a decade ago but has not met with more than limited success in a few isolated instances.

  18. Process for predicting structural performance of mechanical systems

    DOEpatents

    Gardner, D.R.; Hendrickson, B.A.; Plimpton, S.J.; Attaway, S.W.; Heinstein, M.W.; Vaughan, C.T.

    1998-05-19

    A process for predicting the structural performance of a mechanical system represents the mechanical system by a plurality of surface elements. The surface elements are grouped according to their location in the volume occupied by the mechanical system so that contacts between surface elements can be efficiently located. The process is well suited for efficient practice on multiprocessor computers. 12 figs.

  19. Virality Prediction and Community Structure in Social Networks

    E-print Network

    Ahn, Yong-Yeol

    Virality Prediction and Community Structure in Social Networks Lilian Weng, Filippo Menczer & Yong, and marketing applications. D iseases, ideas, innovations, and behaviors spread through social networks1 marketing6,19 , network science20,21 , commun- ication22 , and social media analytics23­25 . Network

  20. Automatic Prediction of Facial Trait Judgments: Appearance vs. Structural Models

    PubMed Central

    Rojas Q., Mario; Masip, David; Todorov, Alexander; Vitria, Jordi

    2011-01-01

    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. PMID:21858069

  1. RNA Secondary Structure Prediction Using High-throughput SHAPE

    PubMed Central

    Purzycka, Katarzyna J.; Rausch, Jason W.; Le Grice, Stuart F.J.

    2013-01-01

    Understanding the function of RNA involved in biological processes requires a thorough knowledge of RNA structure. Toward this end, the methodology dubbed "high-throughput selective 2' hydroxyl acylation analyzed by primer extension", or SHAPE, allows prediction of RNA secondary structure with single nucleotide resolution. This approach utilizes chemical probing agents that preferentially acylate single stranded or flexible regions of RNA in aqueous solution. Sites of chemical modification are detected by reverse transcription of the modified RNA, and the products of this reaction are fractionated by automated capillary electrophoresis (CE). Since reverse transcriptase pauses at those RNA nucleotides modified by the SHAPE reagents, the resulting cDNA library indirectly maps those ribonucleotides that are single stranded in the context of the folded RNA. Using ShapeFinder software, the electropherograms produced by automated CE are processed and converted into nucleotide reactivity tables that are themselves converted into pseudo-energy constraints used in the RNAStructure (v5.3) prediction algorithm. The two-dimensional RNA structures obtained by combining SHAPE probing with in silico RNA secondary structure prediction have been found to be far more accurate than structures obtained using either method alone. PMID:23748604

  2. A dynamic programming algorithm for RNA structure prediction including pseudoknots.

    PubMed

    Rivas, E; Eddy, S R

    1999-02-01

    We describe a dynamic programming algorithm for predicting optimal RNA secondary structure, including pseudoknots. The algorithm has a worst case complexity of O(N6) in time and O(N4) in storage. The description of the algorithm is complex, which led us to adopt a useful graphical representation (Feynman diagrams) borrowed from quantum field theory. We present an implementation of the algorithm that generates the optimal minimum energy structure for a single RNA sequence, using standard RNA folding thermodynamic parameters augmented by a few parameters describing the thermodynamic stability of pseudoknots. We demonstrate the properties of the algorithm by using it to predict structures for several small pseudoknotted and non-pseudoknotted RNAs. Although the time and memory demands of the algorithm are steep, we believe this is the first algorithm to be able to fold optimal (minimum energy) pseudoknotted RNAs with the accepted RNA thermodynamic model. PMID:9925784

  3. Protein structure prediction using sparse dipolar coupling data

    PubMed Central

    Qu, Youxing; Guo, Jun-tao; Olman, Victor; Xu, Ying

    2004-01-01

    Residual dipolar coupling (RDC) represents one of the most exciting emerging NMR techniques for protein structure studies. However, solving a protein structure using RDC data alone is still a highly challenging problem. We report here a computer program, RDC-PROSPECT, for protein structure prediction based on a structural homolog or analog of the target protein in the Protein Data Bank (PDB), which best aligns with the 15N–1H RDC data of the protein recorded in a single ordering medium. Since RDC-PROSPECT uses only RDC data and predicted secondary structure information, its performance is virtually independent of sequence similarity between a target protein and its structural homolog/analog, making it applicable to protein targets beyond the scope of current protein threading techniques. We have tested RDC-PROSPECT on all 15N–1H RDC data (representing 43 proteins) deposited in the BioMagResBank (BMRB) database. The program correctly identified structural folds for 83.7% of the target proteins, and achieved an average alignment accuracy of 98.1% residues within a four-residue shift. PMID:14744980

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

    PubMed

    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. PMID:21805636

  5. Structure-Based Predictive Models for Allosteric Hot Spots

    PubMed Central

    Demerdash, Omar N. A.; Daily, Michael D.; Mitchell, Julie C.

    2009-01-01

    In allostery, a binding event at one site in a protein modulates the behavior of a distant site. Identifying residues that relay the signal between sites remains a challenge. We have developed predictive models using support-vector machines, a widely used machine-learning method. The training data set consisted of residues classified as either hotspots or non-hotspots based on experimental characterization of point mutations from a diverse set of allosteric proteins. Each residue had an associated set of calculated features. Two sets of features were used, one consisting of dynamical, structural, network, and informatic measures, and another of structural measures defined by Daily and Gray [1]. The resulting models performed well on an independent data set consisting of hotspots and non-hotspots from five allosteric proteins. For the independent data set, our top 10 models using Feature Set 1 recalled 68–81% of known hotspots, and among total hotspot predictions, 58–67% were actual hotspots. Hence, these models have precision P?=?58–67% and recall R?=?68–81%. The corresponding models for Feature Set 2 had P?=?55–59% and R?=?81–92%. We combined the features from each set that produced models with optimal predictive performance. The top 10 models using this hybrid feature set had R?=?73–81% and P?=?64–71%, the best overall performance of any of the sets of models. Our methods identified hotspots in structural regions of known allosteric significance. Moreover, our predicted hotspots form a network of contiguous residues in the interior of the structures, in agreement with previous work. In conclusion, we have developed models that discriminate between known allosteric hotspots and non-hotspots with high accuracy and sensitivity. Moreover, the pattern of predicted hotspots corresponds to known functional motifs implicated in allostery, and is consistent with previous work describing sparse networks of allosterically important residues. PMID:19816556

  6. Local Dielectric Environment of B-DNA in Solution: Results from a 14 ns Molecular Dynamics Trajectory

    E-print Network

    Jayaram, Bhyravabotla

    trajectory of B-DNA developed in a medium of explicit waters and sodium counterions with particle mesh Ewald Trajectory M. A. Young, B. Jayaram, and D. L. Beveridge* Department of Chemistry and Program in Molecular molecular dynamics trajectory of B-DNA developed in a medium of explicit TIP3P waters and Na+ counterions

  7. Constraint Logic Programming approach to protein structure prediction

    PubMed Central

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

    2004-01-01

    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. PMID:15571634

  8. Structural imaging biomarkers of Alzheimer's disease: predicting disease progression.

    PubMed

    Eskildsen, Simon F; Coupé, Pierrick; Fonov, Vladimir S; Pruessner, Jens C; Collins, D Louis

    2015-01-01

    Optimized magnetic resonance imaging (MRI)-based biomarkers of Alzheimer's disease (AD) may allow earlier detection and refined prediction of the disease. In addition, they could serve as valuable tools when designing therapeutic studies of individuals at risk of AD. In this study, we combine (1) a novel method for grading medial temporal lobe structures with (2) robust cortical thickness measurements to predict AD among subjects with mild cognitive impairment (MCI) from a single T1-weighted MRI scan. Using AD and cognitively normal individuals, we generate a set of features potentially discriminating between MCI subjects who convert to AD and those who remain stable over a period of 3 years. Using mutual information-based feature selection, we identify 5 key features optimizing the classification of MCI converters. These features are the left and right hippocampi gradings and cortical thicknesses of the left precuneus, left superior temporal sulcus, and right anterior part of the parahippocampal gyrus. We show that these features are highly stable in cross-validation and enable a prediction accuracy of 72% using a simple linear discriminant classifier, the highest prediction accuracy obtained on the baseline Alzheimer's Disease Neuroimaging Initiative first phase cohort to date. The proposed structural features are consistent with Braak stages and previously reported atrophic patterns in AD and are easy to transfer to new cohorts and to clinical practice. PMID:25260851

  9. 3D Protein structure prediction with genetic tabu search algorithm

    PubMed Central

    2010-01-01

    Background Protein structure prediction (PSP) has important applications in different fields, such as drug design, disease prediction, and so on. In protein structure prediction, there are two important issues. The first one is the design of the structure model and the second one is the design of the optimization technology. Because of the complexity of the realistic protein structure, the structure model adopted in this paper is a simplified model, which is called off-lattice AB model. After the structure model is assumed, optimization technology is needed for searching the best conformation of a protein sequence based on the assumed structure model. However, PSP is an NP-hard problem even if the simplest model is assumed. Thus, many algorithms have been developed to solve the global optimization problem. In this paper, a hybrid algorithm, which combines genetic algorithm (GA) and tabu search (TS) algorithm, is developed to complete this task. Results In order to develop an efficient optimization algorithm, several improved strategies are developed for the proposed genetic tabu search algorithm. The combined use of these strategies can improve the efficiency of the algorithm. In these strategies, tabu search introduced into the crossover and mutation operators can improve the local search capability, the adoption of variable population size strategy can maintain the diversity of the population, and the ranking selection strategy can improve the possibility of an individual with low energy value entering into next generation. Experiments are performed with Fibonacci sequences and real protein sequences. Experimental results show that the lowest energy obtained by the proposed GATS algorithm is lower than that obtained by previous methods. Conclusions The hybrid algorithm has the advantages from both genetic algorithm and tabu search algorithm. It makes use of the advantage of multiple search points in genetic algorithm, and can overcome poor hill-climbing capability in the conventional genetic algorithm by using the flexible memory functions of TS. Compared with some previous algorithms, GATS algorithm has better performance in global optimization and can predict 3D protein structure more effectively. PMID:20522256

  10. Structural transitions in hypersphere fluids: predictions of Kirkwood's approximation

    E-print Network

    Jaroslaw Piasecki; Piotr Szymczak; John J. Kozak

    2011-08-15

    We use an analytic criterion for vanishing of exponential damping of correlations developed previously (Piasecki et al, J. Chem. Phys., 133, 164507, 2010) to determine the threshold volume fractions for structural transitions in hard sphere systems in dimensions D=3,4,5 and 6, proceeding from the YBG hierarchy and using the Kirkwood superposition approximation. We conclude that the theory does predict phase transitions in qualitative agreement with numerical studies. We also derive, within the superposition approximation, the asymptotic form of the analytic condition for occurence of a structural transition in the D->Infinity limit .

  11. Improving protein structure prediction with model-based search

    Microsoft Academic Search

    T. J. Brunette; Oliver Brock

    2005-01-01

    Motivation: De novo protein structure prediction can be formu- lated as search in a high-dimensional space. One of the most frequently used computational tools to solve such search pro- blems is the Monte Carlo method. We present a novel search technique, called model-based search. This method samples the high-dimensional search space to build an approximate model of the underlying function.

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

    PubMed

    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. PMID:25069136

  13. How Good Are Simplified Models for Protein Structure Prediction?

    PubMed Central

    Newton, M. A. Hakim; Rashid, Mahmood A.; Pham, Duc Nghia; Sattar, Abdul

    2014-01-01

    Protein structure prediction (PSP) has been one of the most challenging problems in computational biology for several decades. The challenge is largely due to the complexity of the all-atomic details and the unknown nature of the energy function. Researchers have therefore used simplified energy models that consider interaction potentials only between the amino acid monomers in contact on discrete lattices. The restricted nature of the lattices and the energy models poses a twofold concern regarding the assessment of the models. Can a native or a very close structure be obtained when structures are mapped to lattices? Can the contact based energy models on discrete lattices guide the search towards the native structures? In this paper, we use the protein chain lattice fitting (PCLF) problem to address the first concern; we developed a constraint-based local search algorithm for the PCLF problem for cubic and face-centered cubic lattices and found very close lattice fits for the native structures. For the second concern, we use a number of techniques to sample the conformation space and find correlations between energy functions and root mean square deviation (RMSD) distance of the lattice-based structures with the native structures. Our analysis reveals weakness of several contact based energy models used that are popular in PSP. PMID:24876837

  14. Functional Structure of Biological Communities Predicts Ecosystem Multifunctionality

    PubMed Central

    Mouillot, David; Villéger, Sébastien; Scherer-Lorenzen, Michael; Mason, Norman W. H.

    2011-01-01

    The accelerating rate of change in biodiversity patterns, mediated by ever increasing human pressures and global warming, demands a better understanding of the relationship between the structure of biological communities and ecosystem functioning (BEF). Recent investigations suggest that the functional structure of communities, i.e. the composition and diversity of functional traits, is the main driver of ecological processes. However, the predictive power of BEF research is still low, the integration of all components of functional community structure as predictors is still lacking, and the multifunctionality of ecosystems (i.e. rates of multiple processes) must be considered. Here, using a multiple-processes framework from grassland biodiversity experiments, we show that functional identity of species and functional divergence among species, rather than species diversity per se, together promote the level of ecosystem multifunctionality with a predictive power of 80%. Our results suggest that primary productivity and decomposition rates, two key ecosystem processes upon which the global carbon cycle depends, are primarily sustained by specialist species, i.e. those that hold specialized combinations of traits and perform particular functions. Contrary to studies focusing on single ecosystem functions and considering species richness as the sole measure of biodiversity, we found a linear and non-saturating effect of the functional structure of communities on ecosystem multifunctionality. Thus, sustaining multiple ecological processes would require focusing on trait dominance and on the degree of community specialization, even in species-rich assemblages. PMID:21423747

  15. EVO—Evolutionary algorithm for crystal structure prediction

    NASA Astrophysics Data System (ADS)

    Bahmann, Silvia; Kortus, Jens

    2013-06-01

    We present EVO—an evolution strategy designed for crystal structure search and prediction. The concept and main features of biological evolution such as creation of diversity and survival of the fittest have been transferred to crystal structure prediction. EVO successfully demonstrates its applicability to find crystal structures of the elements of the 3rd main group with their different spacegroups. For this we used the number of atoms in the conventional cell and multiples of it. Running EVO with different numbers of carbon atoms per unit cell yields graphite as the lowest energy structure as well as a diamond-like structure, both in one run. Our implementation also supports the search for 2D structures and was able to find a boron sheet with structural features so far not considered in literature. Program summaryProgram title: EVO Catalogue identifier: AEOZ_v1_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEOZ_v1_0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: GNU General Public License version 3 No. of lines in distributed program, including test data, etc.: 23488 No. of bytes in distributed program, including test data, etc.: 1830122 Distribution format: tar.gz Programming language: Python. Computer: No limitations known. Operating system: Linux. RAM: Negligible compared to the requirements of the electronic structure programs used Classification: 7.8. External routines: Quantum ESPRESSO (http://www.quantum-espresso.org/), GULP (https://projects.ivec.org/gulp/) Nature of problem: Crystal structure search is a global optimisation problem in 3N+3 dimensions where N is the number of atoms in the unit cell. The high dimensional search space is accompanied by an unknown energy landscape. Solution method: Evolutionary algorithms transfer the main features of biological evolution to use them in global searches. The combination of the "survival of the fittest" (deterministic) and the randomised choice of the parents and normally distributed mutation steps (non-deterministic) provides a thorough search. Restrictions: The algorithm is in principle only restricted by a huge search space and simultaneously increasing calculation time (memory, etc.), which is not a problem for our piece of code but for the used electronic structure programs. Running time: The simplest provided case runs serially and takes 30 minutes to one hour. All other calculations run for significantly longer time depending on the parameters like the number and sort of atoms and the electronic structure program in use as well as the level of parallelism included.

  16. Combining Local-Structure, Fold-Recognition, and New Fold Methods for Protein Structure Prediction

    E-print Network

    Mandel-Gutfreund, Yael

    Combining Local-Structure, Fold-Recognition, and New Fold Methods for Protein Structure Prediction This article presents an overview of the SAM-T02 method for protein fold recognition and the UNDERTAKER program for ab initio predic- tions. The SAM-T02 server is an automatic method that uses two-track hidden Markov

  17. Protein Structure Prediction: From Recognition of Matches with Known Structures to Recombination of Fragments

    Microsoft Academic Search

    Michal J. Gajda; Marcin Pawlowski; Janusz M. Bujnicki

    \\u000a The field of protein structure prediction has been revolutionized by the application of “mix-and-match” methods both in template-based\\u000a homology modeling, as well as in template-free, “de novo” folding. Automated generation of models that are closer to the native\\u000a structure of the target protein than the structure of its closest homolog is currently possible by recombination of fragments\\u000a copied from known

  18. Development of advanced structural analysis methodologies for predicting widespread fatigue damage in aircraft structures

    Microsoft Academic Search

    Charles E. Harris; James H. Starnes Jr.; James C. Newman Jr.

    1995-01-01

    NASA is developing a 'tool box' that includes a number of advanced structural analysis computer codes which, taken together, represent the comprehensive fracture mechanics capability required to predict the onset of widespread fatigue damage. These structural analysis tools have complementary and specialized capabilities ranging from a finite-element-based stress-analysis code for two- and three-dimensional built-up structures with cracks to a fatigue

  19. FOURIER ANALYSIS OF EXTENDED FINE STRUCTURE WITH AUTOREGRESSIVE PREDICTION

    SciTech Connect

    Barton, J.; Shirley, D.A.

    1985-01-01

    Autoregressive prediction is adapted to double the resolution of Angle-Resolved Photoemission Extended Fine Structure (ARPEFS) Fourier transforms. Even with the optimal taper (weighting function), the commonly used taper-and-transform Fourier method has limited resolution: it assumes the signal is zero beyond the limits of the measurement. By seeking the Fourier spectrum of an infinite extent oscillation consistent with the measurements but otherwise having maximum entropy, the errors caused by finite data range can be reduced. Our procedure developed to implement this concept applies autoregressive prediction to extrapolate the signal to an extent controlled by a taper width. Difficulties encountered when processing actual ARPEFS data are discussed. A key feature of this approach is the ability to convert improved measurements (signal-to-noise or point density) into improved Fourier resolution.

  20. Gene Function Prediction Based on the Gene Ontology Hierarchical Structure

    PubMed Central

    Cheng, Liangxi; Lin, Hongfei; Hu, Yuncui; Wang, Jian; Yang, Zhihao

    2014-01-01

    The information of the Gene Ontology annotation is helpful in the explanation of life science phenomena, and can provide great support for the research of the biomedical field. The use of the Gene Ontology is gradually affecting the way people store and understand bioinformatic data. To facilitate the prediction of gene functions with the aid of text mining methods and existing resources, we transform it into a multi-label top-down classification problem and develop a method that uses the hierarchical relationships in the Gene Ontology structure to relieve the quantitative imbalance of positive and negative training samples. Meanwhile the method enhances the discriminating ability of classifiers by retaining and highlighting the key training samples. Additionally, the top-down classifier based on a tree structure takes the relationship of target classes into consideration and thus solves the incompatibility between the classification results and the Gene Ontology structure. Our experiment on the Gene Ontology annotation corpus achieves an F-value performance of 50.7% (precision: 52.7% recall: 48.9%). The experimental results demonstrate that when the size of training set is small, it can be expanded via topological propagation of associated documents between the parent and child nodes in the tree structure. The top-down classification model applies to the set of texts in an ontology structure or with a hierarchical relationship. PMID:25192339

  1. Gene function prediction based on the Gene Ontology hierarchical structure.

    PubMed

    Cheng, Liangxi; Lin, Hongfei; Hu, Yuncui; Wang, Jian; Yang, Zhihao

    2014-01-01

    The information of the Gene Ontology annotation is helpful in the explanation of life science phenomena, and can provide great support for the research of the biomedical field. The use of the Gene Ontology is gradually affecting the way people store and understand bioinformatic data. To facilitate the prediction of gene functions with the aid of text mining methods and existing resources, we transform it into a multi-label top-down classification problem and develop a method that uses the hierarchical relationships in the Gene Ontology structure to relieve the quantitative imbalance of positive and negative training samples. Meanwhile the method enhances the discriminating ability of classifiers by retaining and highlighting the key training samples. Additionally, the top-down classifier based on a tree structure takes the relationship of target classes into consideration and thus solves the incompatibility between the classification results and the Gene Ontology structure. Our experiment on the Gene Ontology annotation corpus achieves an F-value performance of 50.7% (precision: 52.7% recall: 48.9%). The experimental results demonstrate that when the size of training set is small, it can be expanded via topological propagation of associated documents between the parent and child nodes in the tree structure. The top-down classification model applies to the set of texts in an ontology structure or with a hierarchical relationship. PMID:25192339

  2. Failure prediction of thin beryllium sheets used in spacecraft structures

    NASA Technical Reports Server (NTRS)

    Roschke, Paul N.; Papados, Photios; Mascorro, Edward

    1991-01-01

    In an attempt to predict failure for cross-rolled beryllium sheet structures, high order macroscopic failure criteria are used. These require the knowledge of in-plane uniaxial and shear strengths. Test results are included for in-plane biaxial tension, uniaxial compression for two different material orientations, and shear. All beryllium specimens have the same chemical composition. In addition, all experimental work was performed in a controlled laboratory environment. Numerical simulation complements these tests. A brief bibliography supplements references listed in a previous report.

  3. RNA-Puzzles: A CASP-like evaluation of RNA three-dimensional structure prediction

    PubMed Central

    Cruz, José Almeida; Blanchet, Marc-Frédérick; Boniecki, Michal; Bujnicki, Janusz M.; Chen, Shi-Jie; Cao, Song; Das, Rhiju; Ding, Feng; Dokholyan, Nikolay V.; Flores, Samuel Coulbourn; Huang, Lili; Lavender, Christopher A.; Lisi, Véronique; Major, François; Mikolajczak, Katarzyna; Patel, Dinshaw J.; Philips, Anna; Puton, Tomasz; Santalucia, John; Sijenyi, Fredrick; Hermann, Thomas; Rother, Kristian; Rother, Magdalena; Serganov, Alexander; Skorupski, Marcin; Soltysinski, Tomasz; Sripakdeevong, Parin; Tuszynska, Irina; Weeks, Kevin M.; Waldsich, Christina; Wildauer, Michael; Leontis, Neocles B.; Westhof, Eric

    2012-01-01

    We report the results of a first, collective, blind experiment in RNA three-dimensional (3D) structure prediction, encompassing three prediction puzzles. The goals are to assess the leading edge of RNA structure prediction techniques; compare existing methods and tools; and evaluate their relative strengths, weaknesses, and limitations in terms of sequence length and structural complexity. The results should give potential users insight into the suitability of available methods for different applications and facilitate efforts in the RNA structure prediction community in ongoing efforts to improve prediction tools. We also report the creation of an automated evaluation pipeline to facilitate the analysis of future RNA structure prediction exercises. PMID:22361291

  4. ?ABC: a systematic microsecond molecular dynamics study of tetranucleotide sequence effects in B-DNA.

    PubMed

    Pasi, Marco; Maddocks, John H; Beveridge, David; Bishop, Thomas C; Case, David A; Cheatham, Thomas; Dans, Pablo D; Jayaram, B; Lankas, Filip; Laughton, Charles; Mitchell, Jonathan; Osman, Roman; Orozco, Modesto; Pérez, Alberto; Petkevi?i?t?, Daiva; Spackova, Nada; Sponer, Jiri; Zakrzewska, Krystyna; Lavery, Richard

    2014-10-29

    We present the results of microsecond molecular dynamics simulations carried out by the ABC group of laboratories on a set of B-DNA oligomers containing the 136 distinct tetranucleotide base sequences. We demonstrate that the resulting trajectories have extensively sampled the conformational space accessible to B-DNA at room temperature. We confirm that base sequence effects depend strongly not only on the specific base pair step, but also on the specific base pairs that flank each step. Beyond sequence effects on average helical parameters and conformational fluctuations, we also identify tetranucleotide sequences that oscillate between several distinct conformational substates. By analyzing the conformation of the phosphodiester backbones, it is possible to understand for which sequences these substates will arise, and what impact they will have on specific helical parameters. PMID:25260586

  5. ?ABC: a systematic microsecond molecular dynamics study of tetranucleotide sequence effects in B-DNA

    PubMed Central

    Pasi, Marco; Maddocks, John H.; Beveridge, David; Bishop, Thomas C.; Case, David A.; Cheatham, Thomas; Dans, Pablo D.; Jayaram, B.; Lankas, Filip; Laughton, Charles; Mitchell, Jonathan; Osman, Roman; Orozco, Modesto; Pérez, Alberto; Petkevi?i?t?, Daiva; Spackova, Nada; Sponer, Jiri; Zakrzewska, Krystyna; Lavery, Richard

    2014-01-01

    We present the results of microsecond molecular dynamics simulations carried out by the ABC group of laboratories on a set of B-DNA oligomers containing the 136 distinct tetranucleotide base sequences. We demonstrate that the resulting trajectories have extensively sampled the conformational space accessible to B-DNA at room temperature. We confirm that base sequence effects depend strongly not only on the specific base pair step, but also on the specific base pairs that flank each step. Beyond sequence effects on average helical parameters and conformational fluctuations, we also identify tetranucleotide sequences that oscillate between several distinct conformational substates. By analyzing the conformation of the phosphodiester backbones, it is possible to understand for which sequences these substates will arise, and what impact they will have on specific helical parameters. PMID:25260586

  6. Characterization of Protein Structure and Function at Genome Scale Using a Computational Prediction Pipeline

    E-print Network

    Prediction Pipeline Dong Xu1 *, Dongsup Kim1 , Phuongan Dam1 , Manesh Shah1 , Edward C. Uberbacher1 Pipeline. Keywords: protein structure prediction; fold recognition; threading; genome annotation; structure and integrates systematic data generation, computational data interpretation, and experimental validation

  7. Electronic Structure Methods for Predicting the Properties Materials: Grids in Space

    E-print Network

    Stathopoulos, Andreas

    Electronic Structure Methods for Predicting the Properties Materials: Grids in Space James phys­ chemical properties can accurately determined without resorting experiment. However, determining condensed matter physics the prediction the electronic structure complex systems such amorphous solids

  8. The MP2 quantum chemistry study on the local minima of guanine stacked with all four nucleic acid bases in conformations corresponding to mean B-DNA

    Microsoft Academic Search

    Piotr Cysewski; ?aneta Czy?nikowska-Balcerak

    2005-01-01

    Ab initio calculations at the MP2\\/6-31G*(d=0.25) level were used to perform an energy scan of guanine stacked with all four canonical DNA bases. The structures that were studied correspond to potential energy surface points B-DNA. Seven stacking complexes were analyzed in details: 5?-G\\/G-3?, 5?-G\\/A-3?, 5?-A\\/G-3?, 5?-G\\/T-3?, 5?-T\\/G-3?, 5?-G\\/C-3? and 5?-C\\/G-3?. In all cases, local minima on potential energy surface were

  9. Predicting fracture in micron-scale polycrystalline silicon MEMS structures.

    SciTech Connect

    Hazra, Siddharth S. (Carnegie Mellon University, Pittsburgh, PA); de Boer, Maarten Pieter (Carnegie Mellon University, Pittsburgh, PA); Boyce, Brad Lee; Ohlhausen, James Anthony; Foulk, James W., III; Reedy, Earl David, Jr.

    2010-09-01

    Designing reliable MEMS structures presents numerous challenges. Polycrystalline silicon fractures in a brittle manner with considerable variability in measured strength. Furthermore, it is not clear how to use a measured tensile strength distribution to predict the strength of a complex MEMS structure. To address such issues, two recently developed high throughput MEMS tensile test techniques have been used to measure strength distribution tails. The measured tensile strength distributions enable the definition of a threshold strength as well as an inferred maximum flaw size. The nature of strength-controlling flaws has been identified and sources of the observed variation in strength investigated. A double edge-notched specimen geometry was also tested to study the effect of a severe, micron-scale stress concentration on the measured strength distribution. Strength-based, Weibull-based, and fracture mechanics-based failure analyses were performed and compared with the experimental results.

  10. Factors Influencing Progressive Failure Analysis Predictions for Laminated Composite Structure

    NASA Technical Reports Server (NTRS)

    Knight, Norman F., Jr.

    2008-01-01

    Progressive failure material modeling methods used for structural analysis including failure initiation and material degradation are presented. Different failure initiation criteria and material degradation models are described that define progressive failure formulations. These progressive failure formulations are implemented in a user-defined material model for use with a nonlinear finite element analysis tool. The failure initiation criteria include the maximum stress criteria, maximum strain criteria, the Tsai-Wu failure polynomial, and the Hashin criteria. The material degradation model is based on the ply-discounting approach where the local material constitutive coefficients are degraded. Applications and extensions of the progressive failure analysis material model address two-dimensional plate and shell finite elements and three-dimensional solid finite elements. Implementation details are described in the present paper. Parametric studies for laminated composite structures are discussed to illustrate the features of the progressive failure modeling methods that have been implemented and to demonstrate their influence on progressive failure analysis predictions.

  11. Simulating regime structures in weather and climate prediction models

    NASA Astrophysics Data System (ADS)

    Dawson, A.; Palmer, T. N.; Corti, S.

    2012-11-01

    It is shown that a global atmospheric model with horizontal resolution typical of that used in operational numerical weather prediction is able to simulate non-gaussian probability distributions associated with the climatology of quasi-persistent Euro-Atlantic weather regimes. The spatial patterns of these simulated regimes are remarkably accurate. By contrast, the same model, integrated at a resolution more typical of current climate models, shows no statistically significant evidence of such non-gaussian regime structures, and the spatial structure of the corresponding clusters are not accurate. Hence, whilst studies typically show incremental improvements in first and second moments of climatological distributions of the large-scale flow with increasing model resolution, here a real step change in the higher-order moments is found. It is argued that these results have profound implications for the ability of high resolution limited-area models, forced by low resolution global models, to simulate reliably, regional climate change signals.

  12. Effects of magnesium salt concentrations on B-DNA overstretching transition

    Microsoft Academic Search

    H. Fu; H. Chen; C. G. Koh; C. T. Lim

    2009-01-01

    In this study, we use optical tweezers to investigate the ionic effects of magnesium salt solutions on the overstretching\\u000a transition of single B-DNA molecules. The experimental data are compared with those in sodium salt solutions. The overstretching\\u000a transition force increases when the NaCl or MgCl2 salt concentration increases. Magnesium cations have much stronger effects on the overstretching transition force than

  13. Effects of magnesium salt concentrations on B-DNA overstretching transition

    Microsoft Academic Search

    H. Fu; H. Chen; C. G. Koh; C. T. Lim

    2009-01-01

    In this study, we use optical tweezers to investigate the ionic effects of magnesium salt solutions on the overstretching transition of single B-DNA molecules. The experimental data are compared with those in sodium salt solutions. The overstretching transition force increases when the NaCl or MgCl2 salt concentration increases. Magnesium cations have much stronger effects on the overstretching transition force than

  14. Structure-Based Predictive model for Coal Char Combustion.

    SciTech Connect

    Hurt, R.; Colo, J [Brown Univ., Providence, RI (United States). Div. of Engineering; Essenhigh, R.; Hadad, C [Ohio State Univ., Columbus, OH (United States). Dept. of Chemistry; Stanley, E. [Boston Univ., MA (United States). Dept. of Physics

    1997-09-24

    During the third quarter of this project, progress was made on both major technical tasks. Progress was made in the chemistry department at OSU on the calculation of thermodynamic properties for a number of model organic compounds. Modelling work was carried out at Brown to adapt a thermodynamic model of carbonaceous mesophase formation, originally applied to pitch carbonization, to the prediction of coke texture in coal combustion. This latter work makes use of the FG-DVC model of coal pyrolysis developed by Advanced Fuel Research to specify the pool of aromatic clusters that participate in the order/disorder transition. This modelling approach shows promise for the mechanistic prediction of the rank dependence of char structure and will therefore be pursued further. Crystalline ordering phenomena were also observed in a model char prepared from phenol-formaldehyde carbonized at 900{degrees}C and 1300{degrees}C using high-resolution TEM fringe imaging. Dramatic changes occur in the structure between 900 and 1300{degrees}C, making this char a suitable candidate for upcoming in situ work on the hot stage TEM. Work also proceeded on molecular dynamics simulations at Boston University and on equipment modification and testing for the combustion experiments with widely varying flame types at Ohio State.

  15. DM-pred Method: A New Method to Predict Secondary Structures Based on Data Mining Techniques

    Microsoft Academic Search

    Sondes Fayech; Nadia Essoussi; Mohamed Limam

    2011-01-01

    Protein secondary structure prediction is a key step in prediction of protein tertiary structure. There have emerged many methods based on machine learning techniques, such as neural networks (NN) and support vector machines (SVM), to focus on the prediction of the secondary structures. In this paper a new method, DM-pred, was proposed based on a protein clustering method to detect

  16. Development of advanced structural analysis methodologies for predicting widespread fatigue damage in aircraft structures

    NASA Technical Reports Server (NTRS)

    Harris, Charles E.; Starnes, James H., Jr.; Newman, James C., Jr.

    1995-01-01

    NASA is developing a 'tool box' that includes a number of advanced structural analysis computer codes which, taken together, represent the comprehensive fracture mechanics capability required to predict the onset of widespread fatigue damage. These structural analysis tools have complementary and specialized capabilities ranging from a finite-element-based stress-analysis code for two- and three-dimensional built-up structures with cracks to a fatigue and fracture analysis code that uses stress-intensity factors and material-property data found in 'look-up' tables or from equations. NASA is conducting critical experiments necessary to verify the predictive capabilities of the codes, and these tests represent a first step in the technology-validation and industry-acceptance processes. NASA has established cooperative programs with aircraft manufacturers to facilitate the comprehensive transfer of this technology by making these advanced structural analysis codes available to industry.

  17. High-accuracy prediction of protein structural classes using PseAA structural properties and secondary structural patterns.

    PubMed

    Wang, Junru; Li, Yan; Liu, Xiaoqing; Dai, Qi; Yao, Yuhua; He, Pingan

    2014-06-01

    Since introduction of PseAAs and functional domains, promising results have been achieved in protein structural class predication, but some challenges still exist in the representation of the PseAA structural correlation and structural domains. This paper proposed a high-accuracy prediction method using novel PseAA structural properties and secondary structural patterns, reflecting the long-range and local structural properties of the PseAAs and certain compact structural domains. The proposed prediction method was tested against the competing prediction methods with four experiments. The experiment results indicate that the proposed method achieved the best performance. Its overall accuracies for datasets 25 PDB, D640, FC699 and 1189 are 88.8%, 90.9%, 96.4% and 87.4%, which are 4.5%, 7.6%, 2% and 3.9% higher than the existing best-performing method. This understanding can be used to guide development of more powerful methods for protein structural class prediction. The software and supplement material are freely available at http://bioinfo.zstu.edu.cn/PseAA-SSP. PMID:24412731

  18. Artificial Neural Networks and Hidden Markov Models for Predicting the Protein Structures: The Secondary Structure

    E-print Network

    1 Artificial Neural Networks and Hidden Markov Models for Predicting the Protein Structures advice on the development of this project #12;2 Artificial Neural Networks and Hidden Markov Models learning methods: artificial neural networks (ANN) and hidden Markov models (HMM) (Rost 2002; Karplus et al

  19. Lifetime Reliability Prediction of Ceramic Structures Under Transient Thermomechanical Loads

    NASA Technical Reports Server (NTRS)

    Nemeth, Noel N.; Jadaan, Osama J.; Gyekenyesi, John P.

    2005-01-01

    An analytical methodology is developed to predict the probability of survival (reliability) of ceramic components subjected to harsh thermomechanical loads that can vary with time (transient reliability analysis). This capability enables more accurate prediction of ceramic component integrity against fracture in situations such as turbine startup and shutdown, operational vibrations, atmospheric reentry, or other rapid heating or cooling situations (thermal shock). The transient reliability analysis methodology developed herein incorporates the following features: fast-fracture transient analysis (reliability analysis without slow crack growth, SCG); transient analysis with SCG (reliability analysis with time-dependent damage due to SCG); a computationally efficient algorithm to compute the reliability for components subjected to repeated transient loading (block loading); cyclic fatigue modeling using a combined SCG and Walker fatigue law; proof testing for transient loads; and Weibull and fatigue parameters that are allowed to vary with temperature or time. Component-to-component variation in strength (stochastic strength response) is accounted for with the Weibull distribution, and either the principle of independent action or the Batdorf theory is used to predict the effect of multiaxial stresses on reliability. The reliability analysis can be performed either as a function of the component surface (for surface-distributed flaws) or component volume (for volume-distributed flaws). The transient reliability analysis capability has been added to the NASA CARES/ Life (Ceramic Analysis and Reliability Evaluation of Structures/Life) code. CARES/Life was also updated to interface with commercially available finite element analysis software, such as ANSYS, when used to model the effects of transient load histories. Examples are provided to demonstrate the features of the methodology as implemented in the CARES/Life program.

  20. Interactions of the Escherichia coli DnaB–DnaC Protein Complex with Nucleotide Cofactors. 1. Allosteric Conformational Transitions of the Complex†

    PubMed Central

    Roychowdhury, Anasuya; Szymanski, Michal R.; Jezewska, Maria J.; Bujalowski, Wlodzimierz

    2011-01-01

    Interactions of nucleotide cofactors with both protein components of the Escherichia coli DnaB helicase complex with the replication factor, the DnaC protein, have been examined using MANT-nucleotide analogues. At saturation, in all examined stationary complexes, including the binary, DnaB–DnaC, and tertiary, DnaB–DnaC–ssDNA, complexes, the helicase binds six cofactor molecules. Thus, protein–protein and protein–DNA interactions do not affect the maximum stoichiometry of the helicase–nucleotide interactions. The single-stranded DNA dramatically increases the ATP analogue affinity, while it has little effect on the affinity of the NDP analogues, indicating that stationary complexes reflect allosteric interactions between the DNA- and NTP-binding site prior to the cofactor hydrolysis step and subsequent to product release. In the binary complex, the DnaC protein diminishes the intrinsic affinity and increases the negative cooperativity in the cofactor binding to the helicase; an opposite effect of the protein on the cofactor–helicase interactions occurs in the tertiary complex. The DnaC protein retains its nucleotide binding capability in the binary and tertiary complexes with the helicase. Surprisingly, the DnaC protein–nucleotide interactions, in the binary and tertiary complexes, are characterized by positive cooperativity. The DnaC assembles on the helicase as a hexamer, which exists in two conformational states and undergoes an allosteric transition, induced by the cofactor. Cooperativity of the allosteric transition depends on the structure of the phosphate group of the nucleotide. The significance of the results for the DnaB–DnaC complex activities is discussed. PMID:19569622

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

    PubMed

    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. PMID:25550146

  2. Predicting the structure of protein complexes: a step in the right direction

    Microsoft Academic Search

    Brian K. Shoichet; Irwin D. Kuntz

    1996-01-01

    In a blind test of protein-docking algorithms, six groups used different methods to predict the structure of a protein complex. All six predicted structures were close enough to the experimental complex to be useful; nevertheless, several important details of the experimental complex were missed or only partially predicted.

  3. Structural Time Series Model for El Niño Prediction

    NASA Astrophysics Data System (ADS)

    Petrova, Desislava; Koopman, Siem Jan; Ballester, Joan; Rodo, Xavier

    2015-04-01

    ENSO is a dominant feature of climate variability on inter-annual time scales destabilizing weather patterns throughout the globe, and having far-reaching socio-economic consequences. It does not only lead to extensive rainfall and flooding in some regions of the world, and anomalous droughts in others, thus ruining local agriculture, but also substantially affects the marine ecosystems and the sustained exploitation of marine resources in particular coastal zones, especially the Pacific South American coast. As a result, forecasting of ENSO and especially of the warm phase of the oscillation (El Niño/EN) has long been a subject of intense research and improvement. Thus, the present study explores a novel method for the prediction of the Niño 3.4 index. In the state-of-the-art the advantageous statistical modeling approach of Structural Time Series Analysis has not been applied. Therefore, we have developed such a model using a State Space approach for the unobserved components of the time series. Its distinguishing feature is that observations consist of various components - level, seasonality, cycle, disturbance, and regression variables incorporated as explanatory covariates. These components are aimed at capturing the various modes of variability of the N3.4 time series. They are modeled separately, then combined in a single model for analysis and forecasting. Customary statistical ENSO prediction models essentially use SST, SLP and wind stress in the equatorial Pacific. We introduce new regression variables - subsurface ocean temperature in the western equatorial Pacific, motivated by recent (Ramesh and Murtugudde, 2012) and classical research (Jin, 1997), (Wyrtki, 1985), showing that subsurface processes and heat accumulation there are fundamental for initiation of an El Niño event; and a southern Pacific temperature-difference tracer, the Rossbell dipole, leading EN by about nine months (Ballester, 2011).

  4. Prediction and classification of ncRNAs using structural information

    PubMed Central

    2014-01-01

    Background Evidence is accumulating that non-coding transcripts, previously thought to be functionally inert, play important roles in various cellular activities. High throughput techniques like next generation sequencing have resulted in the generation of vast amounts of sequence data. It is therefore desirable, not only to discriminate coding and non-coding transcripts, but also to assign the noncoding RNA (ncRNA) transcripts into respective classes (families). Although there are several algorithms available for this task, their classification performance remains a major concern. Acknowledging the crucial role that non-coding transcripts play in cellular processes, it is required to develop algorithms that are able to precisely classify ncRNA transcripts. Results In this study, we initially develop prediction tools to discriminate coding or non-coding transcripts and thereafter classify ncRNAs into respective classes. In comparison to the existing methods that employed multiple features, our SVM-based method by using a single feature (tri-nucleotide composition), achieved MCC of 0.98. Knowing that the structure of a ncRNA transcript could provide insights into its biological function, we use graph properties of predicted ncRNA structures to classify the transcripts into 18 different non-coding RNA classes. We developed classification models using a variety of algorithms (BayeNet, NaiveBayes, MultilayerPerceptron, IBk, libSVM, SMO and RandomForest) and observed that model based on RandomForest performed better than other models. As compared to the GraPPLE study, the sensitivity (of 13 classes) and specificity (of 14 classes) was higher. Moreover, the overall sensitivity of 0.43 outperforms the sensitivity of GraPPLE (0.33) whereas the overall MCC measure of 0.40 (in contrast to MCC of 0.29 of GraPPLE) was significantly higher for our method. This clearly demonstrates that our models are more accurate than existing models. Conclusions This work conclusively demonstrates that a simple feature, tri-nucleotide composition, is sufficient to discriminate between coding and non-coding RNA sequences. Similarly, graph properties based feature set along with RandomForest algorithm are most suitable to classify different ncRNA classes. We have also developed an online and standalone tool-- RNAcon ( http://crdd.osdd.net/raghava/rnacon). PMID:24521294

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

    E-print Network

    Joshi, T.

    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 ...

  6. proteinsSTRUCTURE O FUNCTION O BIOINFORMATICS PFP: Automated prediction of gene ontology

    E-print Network

    Kihara, Daisuke

    proteinsSTRUCTURE O FUNCTION O BIOINFORMATICS PFP: Automated prediction of gene ontology functional introduced PFP (Protein Function Prediction) as our sequence-based predictor of Gene Ontology (GO) functional

  7. A Computational Pipeline for Protein Structure Prediction and Analysis at Genome Scale

    E-print Network

    1 A Computational Pipeline for Protein Structure Prediction and Analysis at Genome Scale Manesh that they can complement the existing experimental techniques. In this paper, we present an automated pipeline for protein structure prediction. The centerpiece of the pipeline is a threading-based protein structure

  8. Saini, H. et al. Protein Structural Class Prediction via k-Separated Bigrams

    E-print Network

    the structure of a protein plays a very im- portant role in fields like molecular biology, cell biol- ogySaini, H. et al. Paper: Protein Structural Class Prediction via k-Separated Bigrams Using Position February 15, 2014] Protein structural class prediction (SCP) is as impor- tant task in identifying protein

  9. Automated de novo prediction of native-like RNA tertiary structures

    E-print Network

    Baker, David

    of biomolecules (1). The latter tasks typically require the attainment of complex, three-dimensional structures, and it has long been noted that the problem of predicting the folds of stable, structured RNA moleculesAutomated de novo prediction of native-like RNA tertiary structures Rhiju Das and David Baker

  10. Predicting the secondary structure of globular proteins using neural network models

    Microsoft Academic Search

    Ning Qian; Terrence J. Sejnowski

    1988-01-01

    We present a new method for predicting the secondary structure of globular proteins based on non-linear neural network models. Network models learn from existing protein structures how to predict the secondary structure of local sequences of amino acids. The average success rate of our method on a testing set of proteins non-homologous with the corresponding training set was 643% on

  11. Efficient pairwise RNA structure prediction using probabilistic alignment constraints in Dynalign

    Microsoft Academic Search

    Arif Ozgun Harmanci; Gaurav Sharma; David H. Mathews

    2007-01-01

    Background: Joint alignment and secondary structure prediction of two RNA sequences can significantly improve the accuracy of the structural predictions. Methods addressing this problem, however, are forced to employ constraints that reduce computation by restricting the alignments and\\/or structures (i.e. folds) that are permissible. In this paper, a new methodology is presented for the purpose of establishing alignment constraints based

  12. Prediction of Silicon-Based Layered Structures for Optoelectronic Applications

    NASA Astrophysics Data System (ADS)

    Luo, Wei; Ma, Yanming; Gong, Xingao; Xiang, Hongjun; CCMG Team

    2015-03-01

    A method based on the particle swarm optimization (PSO) algorithm is presented to design quasi-two-dimensional (Q2D) materials. With this development, various single-layer and bi-layer materials in C, Si, Ge, Sn, and Pb were predicted. A new Si bi-layer structure is found to have a much-favored energy than the previously widely accepted configuration. Both single-layer and bi-layer Si materials have small band gaps, limiting their usages in optoelectronic applications. Hydrogenation has therefore been used to tune the electronic and optical properties of Si layers. We discover two hydrogenated materials of layered Si8H2andSi6H2 possessing quasi-direct band gaps of 0.75 eV and 1.59 eV, respectively. Their potential applications for light emitting diode and photovoltaics are proposed and discussed. Our study opened up the possibility of hydrogenated Si layered materials as next-generation optoelectronic devices.

  13. Prediction of a Structural Transition in the Hard Disk Fluid

    E-print Network

    Jaroslaw Piasecki; Piotr Szymczak; John J. Kozak

    2010-09-16

    Starting from the second equilibrium equation in the BBGKY hierarchy under the Kirkwood superposition closure, we implement a new method for studying the asymptotic decay of correlations in the hard disk fluid in the high density regime. From our analysis and complementary numerical studies, we find that exponentially damped oscillations can occur only up to a packing fraction {\\eta}*~0.718, a value which is in substantial agreement with the packing fraction, {\\eta}~0.723, believed to characterize the transition from the ordered solid phase to a dense fluid phase, as inferred from Mak's Monte Carlo simulations [Phys. Rev. E 73, 065104 (2006)]. We next show that the same method of analysis predicts that exponential damping of oscillations in the hard sphere fluid becomes impossible when \\lambda = 4n\\pi {\\sigma}^3 [1 + H(1)]>/- 34.81, where H(1) is the contact value of the correlation function, n is the number density and {\\sigma} is the sphere diameter, in exact agreement with the condition, \\lambda >/- 34.8, first reported in a numerical study of the Kirkwood equation by Kirkwood et al. [J. Chem. Phys. 18, 1040 (1950)]. Finally, we show that our method confirms the absence of any structural transition in hard rods for the entire range of densities below close packing.

  14. B-DNA characteristics are preserved in double stranded d(A)3·d(T)3 and d(G)3·d(C)3 mini-helixes: conclusions from DFT/M06-2X study.

    PubMed

    Zubatiuk, Tetiana A; Shishkin, Oleg V; Gorb, Leonid; Hovorun, Dmytro M; Leszczynski, Jerzy

    2013-11-01

    We report the results of the first comprehensive DFT study on the d(A)3·d(T)3 and d(G)3·d(C)3 nucleic acid duplexes. The ability of mini-helixes to preserve the conformation of B-DNA in the gas phase and under the influence of such factors as: solvent, uncompensated charge, and counter-ions was evaluated using M06-2X functional with 6-31G(d,p) basis set. The accuracy of the models was ascertained based on their ability to reproduce key structural features of natural B-DNA. Analysis of the helicity suggests that the helical conformations adopt geometrical parameters which are close to those of the B-DNA form. The torsion angles fall somewhere between the values observed for BI/BII conformational classes. The comparative analysis of parameters of isolated Watson-Crick base pairs versus B-DNA-like conformations indicates the same tendency of base-pair polarization and hydration. Specifically, effects of polarization of nucleobases in continuum type dielectric medium mimicking water are stronger than those caused by the presence of backbone. Polar environment as well as the presence of counterions stabilizes duplexes, facilitating helix formation. Substantial conformational changes of nucleotides upon duplex formation decrease the binding energy. In spite of structural and energetic changes, the placement of a mini-helix into the gas phase does not lead to significant disruption of the structure. On the contrary, the duplex preserves its helicity and the strands remain bound. PMID:24065071

  15. Ab-initio prediction and reliability of protein structural genomics by PROPAINOR algorithm.

    PubMed

    Joshi, Rajani R; Jyothi, S

    2003-07-01

    We have formulated the ab-initio prediction of the 3D-structure of proteins as a probabilistic programming problem where the inter-residue 3D-distances are treated as random variables. Lower and upper bounds for these random variables and the corresponding probabilities are estimated by nonparametric statistical methods and knowledge-based heuristics. In this paper we focus on the probabilistic computation of the 3D-structure using these distance interval estimates. Validation of the predicted structures shows our method to be more accurate than other computational methods reported so far. Our method is also found to be computationally more efficient than other existing ab-initio structure prediction methods. Moreover, we provide a reliability index for the predicted structures too. Because of its computational simplicity and its applicability to any random sequence, our algorithm called PROPAINOR (PROtein structure Prediction by AI an Nonparametric Regression) has significant scope in computational protein structural genomics. PMID:12927100

  16. Towards Fully Automated Structure-Based Function Prediction In Structural Genomics: A Case Study

    PubMed Central

    Watson, James D.; Sanderson, Steve; Ezersky, Alexandra; Savchenko, Alexei; Edwards, Aled; Orengo, Christine; Joachimiak, Andrzej; Laskowski, Roman A.; Thornton, Janet M.

    2007-01-01

    Summary As the global Structural Genomics projects have picked up pace the number of structures annotated in the Protein Data Bank as “hypothetical protein” or “unknown function” has grown significantly. A major challenge now involves the development of computational methods to accurately and automatically assign functions to these proteins. As part of the Midwest Center for Structural Genomics (MCSG) we have developed a fully automated functional analysis server, ProFunc, which performs a battery of analyses on a submitted structure. The analyses combine a number of sequence-based and structure-based methods to identify functional clues. After the first stage of the Protein Structure Initiative (PSI) we review the success of the pipeline and the importance of structure-based function prediction. As a dataset we have chosen all structures solved by the MCSG during the 5 years of the first PSI. Our analysis suggests that two of the structure-based methods are particularly successful and provide examples of local similarity difficult to identify using current sequence methods. No one method is successful in all cases so through the use of a number of complementary sequence and structural approaches, the ProFunc server increases the chance that at least one method will find a significant hit that can help elucidate function. Manual assessment of the results is a time-consuming process and subject to individual interpretation and human error. We present a method based on the Gene Ontology schema using GO-slims that can allow the automated assessment of hits with a success rate approaching that of expert manual assessment. PMID:17316683

  17. Prediction of three social cognitive-motivational structure types.

    PubMed

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

    2001-10-01

    Previously, using interviews from Baumrind's longitudinal study, three cognitive-motivational structures (CMSs) were predicted in 68 adolescents from caregiving settings and from the CMS types of their mothers, based on the mothers' interviews elicited six years earlier. CMS theory proposes that during Piaget's Concrete Operational Period care-receiving influences the child's adoption of a social cognitive style, which corresponds to one of Piaget's stages of cognitive development. One who is classified as an Operational experiences the caregiving setting as tuned to the child's long-term interests, becomes focused on function and control of function and grasps the distinctions between and gradations of social attributes. One classified as future Intuitive experiences the caregiving as insufficient or unreliable and becomes focused on getting and having, and assesses social situations based on current striking dimensions. A person classified as being future Symbolic experiences the caregiving as out of tune with the self or the world, becomes focused on identity and emotional closeness, and may define self or object by a single attribute. This previous study did not distinguish between the influence of caregiving (including mothers' CMS) on the formation of adolescent CMS type and the possible constancy of CMS type from ages 9 to 15 years. The current study was designed to distinguish between these two possibilities, using data from 67 of the same mothers. Mothers' interviews were purged of descriptions of her child's behavior. Another interview was composed of the purged descriptions of child behavior. This was also done for interviews held when the child was 4 and 15 as well as at 9. From interviews with descriptions of child behavior purged, mother's CMS type at the child's age of 4 and 9 yr. agreed with her adolescent's previously assigned CMS type (p<.05), and caregiving setting at 9 years predicted the adolescent's CMS type (p<.05). From interviews composed of descriptions of only the child's behavior, adolescent CMS type agreed with previously assigned adolescent CMS type (p<.01). Findings were consonant with the idea that CMS type formation is influenced at about Age 9 and sufficiently established to be recognized at Age 15. PMID:11783566

  18. Prediction of Antisense Oligonucleotide Efficacy Using Local and Global Structure Information With Support Vector Machines

    E-print Network

    Liao, Li

    1 Prediction of Antisense Oligonucleotide Efficacy Using Local and Global Structure Information Newark, Delaware 19716, USA Email: lliao@cis.udel.edu Absract Designing antisense oligonucleotides on various sequential and structural features to differentiate the high efficacy antisense oligonucleotides

  19. PREDICTING TOXICOLOGICAL ENDPOINTS OF CHEMICALS USING QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIPS (QSARS)

    EPA Science Inventory

    Quantitative structure-activity relationships (QSARs) are being developed to predict the toxicological endpoints for untested chemicals similar in structure to chemicals that have known experimental toxicological data. Based on a very large number of predetermined descriptors, a...

  20. CONSIDERATION OF REACTION INTERMEDIATES IN STRUCTURE-ACTIVITY RELATIONSHIPS: A KEY TO UNDERSTANDING AND PREDICTION

    EPA Science Inventory

    Consideration of Reaction Intermediates in Structure- Activity Relationships: A Key to Understanding and Prediction A structure-activity relationship (SAR) represents an empirical means for generalizing chemical information relative to biological activity, and is frequent...

  1. Optimal Mutation Sites for PRE Data Collection and Membrane Protein Structure Prediction

    PubMed Central

    Chen, Huiling; Ji, Fei; Olman, Victor; Mobley, Charles K.; Liu, Yizhou; Zhou, Yunpeng; Bushweller, John H.; Prestegard, James H.; Xu, Ying

    2011-01-01

    Summary NMR paramagnetic relaxation enhancement (PRE) measures long-range distances to isotopically labeled residues, providing useful constraints for protein structure prediction. The method usually requires labor-intensive conjugation of nitroxide labels to multiple locations on the protein, one at a time. Here a computational procedure, based on protein sequence and simple secondary structure models, is presented to facilitate optimal placement of a minimum number of labels needed to determine the correct topology of a helical transmembrane protein. Test on DsbB (4 helices) using just one label leads to correct topology prediction in four of five cases, with the predicted structures <6Å to the native structure. Benchmark results using simulated PRE data show we can generally predict correct topology for five and six-to-seven helices using two and three labels, respectively, with an average success rate of 76% and structures of similar precision, showing promises in facilitating experimentally constrained structure prediction of membrane proteins. PMID:21481772

  2. Knowledge-based prediction of protein structures and the design of novel molecules

    NASA Astrophysics Data System (ADS)

    Blundell, T. L.; Sibanda, B. L.; Sternberg, M. J. E.; Thornton, J. M.

    1987-03-01

    Prediction of the tertiary structures of proteins may be carried out using a knowledge-based approach. This depends on identification of analogies in secondary structures, motifs, domains or ligand interactions between a protein to be modelled and those of known three-dimensional structures. Such techniques are of value in prediction of receptor structures to aid the design of drugs, herbicides or pesticides, antigens in vaccine design, and novel molecules in protein engineering.

  3. Predicting the structural evolution of networks by applying multivariate time series

    NASA Astrophysics Data System (ADS)

    Huang, Qiangjuan; Zhao, Chengli; Wang, Xiaojie; Zhang, Xue; Yi, Dongyun

    2015-06-01

    In practice, complex systems often change over time, and the temporal characteristics of a complex network make their behavior difficult to predict. Traditional link prediction methods based on structural similarity are good for mining underlying information from static networks, but do not always capture the temporal relevance of dynamic networks. However, time series analysis is an effective tool for examining dynamic evolution. In this paper, we combine link prediction with multivariate time series analysis to describe the structural evolution of dynamic networks using both temporal information and structure information. An empirical analysis demonstrates the effectiveness of our method in predicting undiscovered linkages in two classic networks.

  4. GeneSeqer@PlantGDB: gene structure prediction in plant genomes

    E-print Network

    Brendel, Volker

    GeneSeqer@PlantGDB: gene structure prediction in plant genomes Shannon D. Schlueter1 , Qunfeng Dong, 2003 ABSTRACT The GeneSeqer@PlantGDB Web server (http:// www.plantgdb.org/cgi-bin/GeneSeqer.cgi) provides a gene structure prediction tool tailored for applica- tions to plant genomic sequences

  5. Automatic protein structure prediction system enabling rapid and accurate model building for enzyme screening

    Microsoft Academic Search

    Joo-Hyun Seo; Gang-Seong Lee; Juhan Kim; Byung-Kwan Cho; Keehyoung Joo; Jooyoung Lee; Byung-Gee Kim

    2009-01-01

    Protein structure prediction has great potential of understanding the function of proteins at the molecular level and designing novel protein functions. Here, we report rapid and accurate structure prediction system running in an automated manner. Since fold recognition of the target protein to be modeled is the starting point of the template-guided model building process, various approaches – such as

  6. Video Article A Protocol for Computer-Based Protein Structure and Function Prediction

    E-print Network

    Zhang, Yang

    Video Article A Protocol for Computer-Based Protein Structure and Function Prediction Ambrish Roy1,2, Dong Xu1, Jonathan Poisson1, Yang Zhang1,2 Correspondence to: Yang Zhang at zhng@umich.edu URL: http://www.jove.com/details.php., Zhang, Y. A Protocol for Computer-Based Protein Structure and Function Prediction. J. Vis. Exp. (57), e

  7. Aircraft Structural Mass Property Prediction Using Conceptual-Level Structural Analysis

    NASA Technical Reports Server (NTRS)

    Sexstone, Matthew G.

    1998-01-01

    This paper describes a methodology that extends the use of the Equivalent LAminated Plate Solution (ELAPS) structural analysis code from conceptual-level aircraft structural analysis to conceptual-level aircraft mass property analysis. Mass property analysis in aircraft structures has historically depended upon parametric weight equations at the conceptual design level and Finite Element Analysis (FEA) at the detailed design level ELAPS allows for the modeling of detailed geometry, metallic and composite materials, and non-structural mass coupled with analytical structural sizing to produce high-fidelity mass property analyses representing fully configured vehicles early in the design process. This capability is especially valuable for unusual configuration and advanced concept development where existing parametric weight equations are inapplicable and FEA is too time consuming for conceptual design. This paper contrasts the use of ELAPS relative to empirical weight equations and FEA. ELAPS modeling techniques are described and the ELAPS-based mass property analysis process is detailed Examples of mass property stochastic calculations produced during a recent systems study are provided This study involved the analysis of three remotely piloted aircraft required to carry scientific payloads to very high altitudes at subsonic speeds. Due to the extreme nature of this high-altitude flight regime,few existing vehicle designs are available for use in performance and weight prediction. ELAPS was employed within a concurrent engineering analysis process that simultaneously produces aerodynamic, structural, and static aeroelastic results for input to aircraft performance analyses. The ELAPS models produced for each concept were also used to provide stochastic analyses of wing structural mass properties. The results of this effort indicate that ELAPS is an efficient means to conduct multidisciplinary trade studies at the conceptual design level.

  8. Aircraft Structural Mass Property Prediction Using Conceptual-Level Structural Analysis

    NASA Technical Reports Server (NTRS)

    Sexstone, Matthew G.

    1998-01-01

    This paper describes a methodology that extends the use of the Equivalent LAminated Plate Solution (ELAPS) structural analysis code from conceptual-level aircraft structural analysis to conceptual-level aircraft mass property analysis. Mass property analysis in aircraft structures has historically depended upon parametric weight equations at the conceptual design level and Finite Element Analysis (FEA) at the detailed design level. ELAPS allows for the modeling of detailed geometry, metallic and composite materials, and non-structural mass coupled with analytical structural sizing to produce high-fidelity mass property analyses representing fully configured vehicles early in the design process. This capability is especially valuable for unusual configuration and advanced concept development where existing parametric weight equations are inapplicable and FEA is too time consuming for conceptual design. This paper contrasts the use of ELAPS relative to empirical weight equations and FEA. ELAPS modeling techniques are described and the ELAPS-based mass property analysis process is detailed. Examples of mass property stochastic calculations produced during a recent systems study are provided. This study involved the analysis of three remotely piloted aircraft required to carry scientific payloads to very high altitudes at subsonic speeds. Due to the extreme nature of this high-altitude flight regime, few existing vehicle designs are available for use in performance and weight prediction. ELAPS was employed within a concurrent engineering analysis process that simultaneously produces aerodynamic, structural, and static aeroelastic results for input to aircraft performance analyses. The ELAPS models produced for each concept were also used to provide stochastic analyses of wing structural mass properties. The results of this effort indicate that ELAPS is an efficient means to conduct multidisciplinary trade studies at the conceptual design level.

  9. Anatomy of the herpes simplex virus 1 strain F glycoprotein B gene: primary sequence and predicted protein structure of the wild type and of monoclonal antibody-resistant mutants.

    PubMed Central

    Pellett, P E; Kousoulas, K G; Pereira, L; Roizman, B

    1985-01-01

    In this paper we report the nucleotide sequence and predicted amino acid sequence of glycoprotein B of herpes simplex virus 1 strain F and the amino acid substitutions in the domains of the glycoprotein B gene of three mutants selected for resistance to monoclonal antibody H126-5 or H233 but not to both. Analyses of the amino acid sequence with respect to hydropathicity and secondary structure yielded a two-dimensional model of the protein. The model predicts an N-terminal, 29-amino-acid cleavable signal sequence, a 696-amino-acid hydrophilic surface domain containing six potential sites for N-linked glycosylation, a 69-amino-acid hydrophobic domain containing three segments traversing the membrane, and a charged 109-amino-acid domain projecting into the cytoplasm and previously shown to marker rescue glycoprotein B syn mutations. The nucleotide sequence of the mutant glycoprotein B DNA fragments previously shown to marker transfer or rescue the mutations revealed that the amino acid substitutions cluster in the hydrophilic surface domain between amino acids 273 and 305. Analyses of the secondary structure of these regions, coupled with the experimentally derived observation that the H126-5- and H233-antibody cognitive sites do not overlap, indicate the approximate locations of the epitopes of these neutralizing, surface-reacting, and immune-precipitating monoclonal antibodies. The predicted perturbations in the secondary structure introduced by the amino acid substitutions correlate with the extent of loss of reactivity with monoclonal antibodies in various immunoassays. Images PMID:2981343

  10. Surrogate measures to optimize structures for robust and predictable progressive failure

    Microsoft Academic Search

    Kun Marhadi; Satchi Venkataraman

    2009-01-01

    Optimizing complex structures for robust and predictable progressive failure using probabilistic approaches is computationally\\u000a expensive. In this paper we investigate the progressive failure characteristics of structures subjected to random variability\\u000a and deduce patterns to identify surrogate measures that correlate with robustness and predictability of the design’s progressive\\u000a failure. The procedure is demonstrated for the optimization of robustness and predictability in

  11. Virtual screening against p50 NF-kappaB transcription factor for the identification of inhibitors of the NF-kappaB-DNA interaction and expression of NF-kappaB upregulated genes.

    PubMed

    Piccagli, Laura; Fabbri, Enrica; Borgatti, Monica; Bianchi, Nicoletta; Bezzerri, Valentino; Mancini, Irene; Nicolis, Elena; Dechecchi, Cristina M; Lampronti, Ilaria; Cabrini, Giulio; Gambari, Roberto

    2009-12-01

    Virtual screening against NF-kappaB p50 using docking simulations was applied by starting from a three-dimensional (3D) database containing more than 4.6 million commercially available structures. This database was filtered by specifying a subset of commercially available compounds sharing a (2E,Z)-3-(2-hydroxyphenyl)-2-propenoate substructure and relevant druglike properties. Docking to p50 NF-kappaB was performed with a test set of six known inhibitors of NF-kappaB-DNA interactions. In agreement with docking results, the highest-scored compound displayed a high level of inhibitory activity in electrophoretic mobility shift assay (EMSA) experiments (inhibition of NF-kappaB-DNA interactions) and on biological functions dependent on NF-kappaB activity (inhibition of IL-8 gene expression in cystic fibrosis IB3-1 cells). We found that this in silico screening approach is suitable for the identification of low-molecular-weight compounds that inhibit NF-kappaB-DNA interactions and NF-kappaB-dependent functions. Information deduced from the discovery of the new lead compound and its binding mode could result in further lead optimization resulting in more potent NF-kappaB inhibitors. PMID:19806632

  12. Bayesian Model of Protein Primary Sequence for Secondary Structure Prediction

    E-print Network

    Vannucci, Marina

    improving secondary structure reduction given the primary structure, we propose a Bayesian model based made obtaining protein sequences relatively cheap, accurate and fast, in comparison to the costly design of protein structure [4] and enzymatic function [5] as well as in drug development [6]. Depending

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

    Microsoft Academic Search

    James R. Green; Michael J. Korenberg; Mohammed O. Aboul-magd

    2009-01-01

    BACKGROUND: Since the function of a protein is largely dictated by its three dimensional configuration, determining a protein's structure is of fundamental importance to biology. Here we report on a novel approach to determining the one dimensional secondary structure of proteins (distinguishing ?-helices, ?-strands, and non-regular structures) from primary sequence data which makes use of Parallel Cascade Identification (PCI), a

  14. Climate and Species Richness Predict the Phylogenetic Structure of African Mammal Communities

    PubMed Central

    Kamilar, Jason M.; Beaudrot, Lydia; Reed, Kaye E.

    2015-01-01

    We have little knowledge of how climatic variation (and by proxy, habitat variation) influences the phylogenetic structure of tropical communities. Here, we quantified the phylogenetic structure of mammal communities in Africa to investigate how community structure varies with respect to climate and species richness variation across the continent. In addition, we investigated how phylogenetic patterns vary across carnivores, primates, and ungulates. We predicted that climate would differentially affect the structure of communities from different clades due to between-clade biological variation. We examined 203 communities using two metrics, the net relatedness (NRI) and nearest taxon (NTI) indices. We used simultaneous autoregressive models to predict community phylogenetic structure from climate variables and species richness. We found that most individual communities exhibited a phylogenetic structure consistent with a null model, but both climate and species richness significantly predicted variation in community phylogenetic metrics. Using NTI, species rich communities were composed of more distantly related taxa for all mammal communities, as well as for communities of carnivorans or ungulates. Temperature seasonality predicted the phylogenetic structure of mammal, carnivoran, and ungulate communities, and annual rainfall predicted primate community structure. Additional climate variables related to temperature and rainfall also predicted the phylogenetic structure of ungulate communities. We suggest that both past interspecific competition and habitat filtering have shaped variation in tropical mammal communities. The significant effect of climatic factors on community structure has important implications for the diversity of mammal communities given current models of future climate change. PMID:25875361

  15. Metabolism site prediction based on xenobiotic structural formulas and PASS prediction algorithm.

    PubMed

    Rudik, Anastasia V; Dmitriev, Alexander V; Lagunin, Alexey A; Filimonov, Dmitry A; Poroikov, Vladimir V

    2014-02-24

    A new ligand-based method for the prediction of sites of metabolism (SOMs) for xenobiotics has been developed on the basis of the LMNA (labeled multilevel neighborhoods of atom) descriptors and the PASS (prediction of activity spectra for substances) algorithm and applied to predict the SOMs of the 1A2, 2C9, 2C19, 2D6, and 3A4 isoforms of cytochrome P450. An average IAP (invariant accuracy of prediction) of SOMs calculated by the leave-one-out cross-validation procedure was 0.89 for the developed method. The external validation was made with evaluation sets containing data on biotransformations for 57 cardiovascular drugs. An average IAP of regioselectivity for evaluation sets was 0.83. It was shown that the proposed method exceeds accuracy of SOM prediction by RS-Predictor for CYP 1A2, 2D6, 2C9, 2C19, and 3A4 and is comparable to or better than SMARTCyp for CYP 2C9 and 2D6. PMID:24417355

  16. Automated Detection of Eruptive Structures for Solar Eruption Prediction

    NASA Astrophysics Data System (ADS)

    Georgoulis, Manolis K.

    2012-07-01

    The problem of data processing and assimilation for solar eruption prediction is, for contemporary solar physics, more pressing than the problem of data acquisition. Although critical solar data, such as the coronal magnetic field, are still not routinely available, space-based observatories deliver diverse, high-quality information at such a high rate that a manual or semi-manual processing becomes meaningless. We discuss automated data analysis methods and explain, using basic physics, why some of them are unlikely to advance eruption prediction. From this finding we also understand why solar eruption prediction is likely to remain inherently probabilistic. We discuss some promising eruption prediction measures and report on efforts to adapt them for use with high-resolution, high-cadence photospheric and coronal data delivered by the Solar Dynamics Observatory. Concluding, we touch on the problem of physical understanding and synthesis of different results: combining different measures inferred by different data sets is a yet-to-be-done exercise that, however, presents our best opportunity of realizing benefits in solar eruption prediction via a meaningful, targeted assimilation of solar data.

  17. Prediction of structural features and application to outer membrane protein identification

    PubMed Central

    Yan, Renxiang; Wang, Xiaofeng; Huang, Lanqing; Yan, Feidi; Xue, Xiaoyu; Cai, Weiwen

    2015-01-01

    Protein three-dimensional (3D) structures provide insightful information in many fields of biology. One-dimensional properties derived from 3D structures such as secondary structure, residue solvent accessibility, residue depth and backbone torsion angles are helpful to protein function prediction, fold recognition and ab initio folding. Here, we predict various structural features with the assistance of neural network learning. Based on an independent test dataset, protein secondary structure prediction generates an overall Q3 accuracy of ~80%. Meanwhile, the prediction of relative solvent accessibility obtains the highest mean absolute error of 0.164, and prediction of residue depth achieves the lowest mean absolute error of 0.062. We further improve the outer membrane protein identification by including the predicted structural features in a scoring function using a simple profile-to-profile alignment. The results demonstrate that the accuracy of outer membrane protein identification can be improved by ~3% at a 1% false positive level when structural features are incorporated. Finally, our methods are available as two convenient and easy-to-use programs. One is PSSM-2-Features for predicting secondary structure, relative solvent accessibility, residue depth and backbone torsion angles, the other is PPA-OMP for identifying outer membrane proteins from proteomes. PMID:26104144

  18. Prediction of structural features and application to outer membrane protein identification.

    PubMed

    Yan, Renxiang; Wang, Xiaofeng; Huang, Lanqing; Yan, Feidi; Xue, Xiaoyu; Cai, Weiwen

    2015-01-01

    Protein three-dimensional (3D) structures provide insightful information in many fields of biology. One-dimensional properties derived from 3D structures such as secondary structure, residue solvent accessibility, residue depth and backbone torsion angles are helpful to protein function prediction, fold recognition and ab initio folding. Here, we predict various structural features with the assistance of neural network learning. Based on an independent test dataset, protein secondary structure prediction generates an overall Q3 accuracy of ~80%. Meanwhile, the prediction of relative solvent accessibility obtains the highest mean absolute error of 0.164, and prediction of residue depth achieves the lowest mean absolute error of 0.062. We further improve the outer membrane protein identification by including the predicted structural features in a scoring function using a simple profile-to-profile alignment. The results demonstrate that the accuracy of outer membrane protein identification can be improved by ~3% at a 1% false positive level when structural features are incorporated. Finally, our methods are available as two convenient and easy-to-use programs. One is PSSM-2-Features for predicting secondary structure, relative solvent accessibility, residue depth and backbone torsion angles, the other is PPA-OMP for identifying outer membrane proteins from proteomes. PMID:26104144

  19. Identification of a New Motif in Family B DNA Polymerases by Mutational Analyses of the Bacteriophage T4 DNA Polymerase

    PubMed Central

    Li, Vincent; Hogg, Matthew; Reha-Krantz, Linda J.

    2011-01-01

    Structure-based protein sequence alignments of family B DNA polymerases revealed a conserved motif that is formed from interacting residues between loops from the N-terminal and palm domains and between the N-terminal loop and a conserved proline residue. The importance of the motif for function of the bacteriophage T4 DNA polymerase was revealed by suppressor analysis. T4 DNA polymerases that form weak replicating complexes cannot replicate DNA when the dGTP pool is reduced. The conditional lethality provides the means to identify amino acid substitutions that restore replication activity under low dGTP conditions by either correcting the defect produced by the first amino acid substitution or by generally increasing the stability of polymerase complexes; the second type are global suppressors that can effectively counter the reduced stability caused by a variety of amino acid substitutions. Some amino acid substitutions that increase the stability of polymerase complexes produce a new phenotype - sensitivity to the antiviral drug phosphonoacetic acid. Amino acid substitutions that confer decreased ability to replicate DNA under low dGTP conditions or drug sensitivity were identified in the new motif, which suggests that the motif functions in regulating the stability of polymerase complexes. Additional suppressor analyses revealed an apparent network of interactions that link the new motif to the fingers domain and to two patches of conserved residues that bind DNA. The collection of mutant T4 DNA polymerases provides a foundation for future biochemical studies to determine how DNA polymerases remain stably associated with DNA while waiting for the next available dNTP, how DNA polymerases translocate, and the biochemical basis for sensitivity to antiviral drugs. PMID:20493878

  20. Predicting Gene Structures from Multiple RT-PCR Tests

    NASA Astrophysics Data System (ADS)

    Ková?, Jakub; Vina?, Tomáš; Brejová, Bro?a

    It has been demonstrated that the use of additional information such as ESTs and protein homology can significantly improve accuracy of gene prediction. However, many sources of external information are still being omitted from consideration. Here, we investigate the use of product lengths from RT-PCR experiments in gene finding. We present hardness results and practical algorithms for several variants of the problem and apply our methods to a real RT-PCR data set in the Drosophila genome. We conclude that the use of RT-PCR data can improve the sensitivity of gene prediction and locate novel splicing variants.

  1. Electronic polarization stabilizes tertiary structure prediction of HP-36.

    PubMed

    Duan, Li L; Zhu, Tong; Zhang, Qing G; Tang, Bo; Zhang, John Z H

    2014-04-01

    Molecular dynamic (MD) simulations with both implicit and explicit solvent models have been carried out to study the folding dynamics of HP-36 protein. Starting from the extended conformation, the secondary structure of all three helices in HP-36 was formed in about 50 ns and remained stable in the remaining simulation. However, the formation of the tertiary structure was difficult. Although some intermediates were close to the native structure, the overall conformation was not stable. Further analysis revealed that the large structure fluctuation of loop and hydrophobic core regions was devoted mostly to the instability of the structure during MD simulation. The backbone root-mean-square deviation (RMSD) of the loop and hydrophobic core regions showed strong correlation with the backbone RMSD of the whole protein. The free energy landscape indicated that the distribution of main chain torsions in loop and turn regions was far away from the native state. Starting from an intermediate structure extracted from the initial AMBER simulation, HP-36 was found to generally fold to the native state under the dynamically adjusted polarized protein-specific charge (DPPC) simulation, while the peptide did not fold into the native structure when AMBER force filed was used. The two best folded structures were extracted and taken into further simulations in water employing AMBER03 charge and DPPC for 25 ns. Result showed that introducing polarization effect into interacting potential could stabilize the near-native protein structure. PMID:24715046

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

    PubMed

    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. PMID:25748534

  3. RNA-Puzzles Round II: assessment of RNA structure prediction programs applied to three large RNA structures.

    PubMed

    Miao, Zhichao; Adamiak, Ryszard W; Blanchet, Marc-Frédérick; Boniecki, Michal; Bujnicki, Janusz M; Chen, Shi-Jie; Cheng, Clarence; Chojnowski, Grzegorz; Chou, Fang-Chieh; Cordero, Pablo; Cruz, José Almeida; Ferré-D'Amaré, Adrian R; Das, Rhiju; Ding, Feng; Dokholyan, Nikolay V; Dunin-Horkawicz, Stanislaw; Kladwang, Wipapat; Krokhotin, Andrey; Lach, Grzegorz; Magnus, Marcin; Major, François; Mann, Thomas H; Masquida, Benoît; Matelska, Dorota; Meyer, Mélanie; Peselis, Alla; Popenda, Mariusz; Purzycka, Katarzyna J; Serganov, Alexander; Stasiewicz, Juliusz; Szachniuk, Marta; Tandon, Arpit; Tian, Siqi; Wang, Jian; Xiao, Yi; Xu, Xiaojun; Zhang, Jinwei; Zhao, Peinan; Zok, Tomasz; Westhof, Eric

    2015-06-01

    This paper is a report of a second round of RNA-Puzzles, a collective and blind experiment in three-dimensional (3D) RNA structure prediction. Three puzzles, Puzzles 5, 6, and 10, represented sequences of three large RNA structures with limited or no homology with previously solved RNA molecules. A lariat-capping ribozyme, as well as riboswitches complexed to adenosylcobalamin and tRNA, were predicted by seven groups using RNAComposer, ModeRNA/SimRNA, Vfold, Rosetta, DMD, MC-Fold, 3dRNA, and AMBER refinement. Some groups derived models using data from state-of-the-art chemical-mapping methods (SHAPE, DMS, CMCT, and mutate-and-map). The comparisons between the predictions and the three subsequently released crystallographic structures, solved at diffraction resolutions of 2.5-3.2 Å, were carried out automatically using various sets of quality indicators. The comparisons clearly demonstrate the state of present-day de novo prediction abilities as well as the limitations of these state-of-the-art methods. All of the best prediction models have similar topologies to the native structures, which suggests that computational methods for RNA structure prediction can already provide useful structural information for biological problems. However, the prediction accuracy for non-Watson-Crick interactions, key to proper folding of RNAs, is low and some predicted models had high Clash Scores. These two difficulties point to some of the continuing bottlenecks in RNA structure prediction. All submitted models are available for download at http://ahsoka.u-strasbg.fr/rnapuzzles/. PMID:25883046

  4. RNA-Puzzles Round II: assessment of RNA structure prediction programs applied to three large RNA structures

    PubMed Central

    Miao, Zhichao; Adamiak, Ryszard W.; Blanchet, Marc-Frédérick; Boniecki, Michal; Bujnicki, Janusz M.; Chen, Shi-Jie; Cheng, Clarence; Chojnowski, Grzegorz; Chou, Fang-Chieh; Cordero, Pablo; Cruz, José Almeida; Ferré-D'Amaré, Adrian R.; Das, Rhiju; Ding, Feng; Dokholyan, Nikolay V.; Dunin-Horkawicz, Stanislaw; Kladwang, Wipapat; Krokhotin, Andrey; Lach, Grzegorz; Magnus, Marcin; Major, François; Mann, Thomas H.; Masquida, Benoît; Matelska, Dorota; Meyer, Mélanie; Peselis, Alla; Popenda, Mariusz; Purzycka, Katarzyna J.; Serganov, Alexander; Stasiewicz, Juliusz; Szachniuk, Marta; Tandon, Arpit; Tian, Siqi; Wang, Jian; Xiao, Yi; Xu, Xiaojun; Zhang, Jinwei; Zhao, Peinan; Zok, Tomasz; Westhof, Eric

    2015-01-01

    This paper is a report of a second round of RNA-Puzzles, a collective and blind experiment in three-dimensional (3D) RNA structure prediction. Three puzzles, Puzzles 5, 6, and 10, represented sequences of three large RNA structures with limited or no homology with previously solved RNA molecules. A lariat-capping ribozyme, as well as riboswitches complexed to adenosylcobalamin and tRNA, were predicted by seven groups using RNAComposer, ModeRNA/SimRNA, Vfold, Rosetta, DMD, MC-Fold, 3dRNA, and AMBER refinement. Some groups derived models using data from state-of-the-art chemical-mapping methods (SHAPE, DMS, CMCT, and mutate-and-map). The comparisons between the predictions and the three subsequently released crystallographic structures, solved at diffraction resolutions of 2.5–3.2 Å, were carried out automatically using various sets of quality indicators. The comparisons clearly demonstrate the state of present-day de novo prediction abilities as well as the limitations of these state-of-the-art methods. All of the best prediction models have similar topologies to the native structures, which suggests that computational methods for RNA structure prediction can already provide useful structural information for biological problems. However, the prediction accuracy for non-Watson–Crick interactions, key to proper folding of RNAs, is low and some predicted models had high Clash Scores. These two difficulties point to some of the continuing bottlenecks in RNA structure prediction. All submitted models are available for download at http://ahsoka.u-strasbg.fr/rnapuzzles/. PMID:25883046

  5. OPTIMAL CHARACTERIZATION OF STRUCTURE FOR PREDICTION OF PROPERTIES

    EPA Science Inventory

    Different topological and physicochemical parameters have been used to predict hydrophobicity (log P, octanol-water) of chemicals. We calculated a hydrogen bonding parameter (HB1) and a large number of molecular connectivity and complexity indices for adverse set of 382 molecules...

  6. Prediction of Harmful Human Health Effects of Chemicals from Structure

    Microsoft Academic Search

    Mark T. D. Cronin

    2010-01-01

    There is a great need to assess the harmful effects of chemicals to which man is exposed. Various in silico techniques including chemical grouping and category formation, as well as the use of (Q)SARs can be applied to predict the toxicity of chemicals for a number of toxicological effects. This chapter provides an overview of the state of the art

  7. Multivariable predictive control for vibrating structures: An application

    Microsoft Academic Search

    L. Bossi; C. Rottenbacher; G. Mimmi; L. Magni

    2011-01-01

    This paper proposes the use of Model Predictive Control (MPC) to control a fast mechanical system. In particular an MPC strategy is applied to a laboratory flexible arm to perform a fast positioning of the end-effector with limited oscillations during the maneuver. The on-line implementation of a fast MPC is obtained with an ad hoc platform based on C++ and

  8. Incomplete gene structure prediction with almost 100% specificity 

    E-print Network

    Chin, See Loong

    2004-09-30

    . . . . . . . . . . . . . . . . . . . 23 IV SUMMARY AND CONCLUSION : : : : : : : : : : : : : : : : : 28 V FUTURE WORK : : : : : : : : : : : : : : : : : : : : : : : : : : 30 REFERENCES : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 31 VITA... organism. A model trained with statistical attributes from one organism can be used to predict the coding regions of homologous species [2], [3], [4] and [5]. 1. Statistical Approach Burge and Karlin [2] proposed a general probabilistic model based...

  9. Structure Prediction in Temporal Networks using Frequent Subgraphs

    Microsoft Academic Search

    Mayank Lahiri; Tanya Y. Berger-wolf

    2007-01-01

    There are several types of processes which can be modeled explicitly by recording the interactions between a set of actors over time. In such applications, a common objective is, given a series of observations, to predict exactly when certain interactions will occur in the future. We propose a representation for this type of temporal data and a generic, streaming, adaptive

  10. Predicting AE Attenuation in Structures by Geometric Analysis

    Microsoft Academic Search

    Theodore Lim; P. Nivesrangsan; Jonathan R. Corney; J. A. Steel; R. L. Reuben

    2005-01-01

    This paper investigates the feasibility of predicting the attenuation of AE signals travelling within a complex solid body. Such AE can occur due to external stimulation (e.g. impact) or internal events (i.e. crack propagation). The attenuation of these signals is affected not only by material properties but also by the geometry of the object. For example, wave propagation on a

  11. Prediction of optimal inspection time for structural fatigue life

    Microsoft Academic Search

    Guangwei Meng; Feng Li; Lirong Sha; Zhenping Zhou

    2007-01-01

    An optimal inspection time model for structural fatigue life based on stochastic finite element method (SFEM) and first order reliability method (FORM) was presented. The uncertainties such as material parameters and loads which affect the fatigue life of the structure were regarded as random variables. Taylor expansion stochastic finite element method (TSFEM) was introduced to simulate the material behavior of

  12. Finite Element Based HWB Centerbody Structural Optimization and Weight Prediction

    NASA Technical Reports Server (NTRS)

    Gern, Frank H.

    2012-01-01

    This paper describes a scalable structural model suitable for Hybrid Wing Body (HWB) centerbody analysis and optimization. The geometry of the centerbody and primary wing structure is based on a Vehicle Sketch Pad (VSP) surface model of the aircraft and a FLOPS compatible parameterization of the centerbody. Structural analysis, optimization, and weight calculation are based on a Nastran finite element model of the primary HWB structural components, featuring centerbody, mid section, and outboard wing. Different centerbody designs like single bay or multi-bay options are analyzed and weight calculations are compared to current FLOPS results. For proper structural sizing and weight estimation, internal pressure and maneuver flight loads are applied. Results are presented for aerodynamic loads, deformations, and centerbody weight.

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

    NASA Astrophysics Data System (ADS)

    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.

  14. Large-scale prediction of protein structure and function from sequence.

    PubMed

    Tosatto, S C E; Toppo, S

    2006-01-01

    The identification of novel drug targets from genomic data involves the large-scale analysis of many protein sequences. Methods for automated structure and function prediction are an essential tool for this purpose. In this review we concentrate on the recent developments in the field of protein structure prediction and how these can be used to gain hints about the function of proteins. The current state-of-the-art is highlighted through recent community-wide experiments aimed at comparing different approaches. For structure prediction this allows the identification of key improvements to increase the crucial sequence to structure alignment needed for accurate models. Function prediction is a rapidly maturing field that is still being benchmarked. Definitions for protein function are presented and available methods, mostly concentrating on functional site descriptors and structural motifs, presented. PMID:16796556

  15. Rational methodology for the prediction of structural response due to collisions of ships

    SciTech Connect

    Chang, P.Y.; Seibold, F.; Thasanatorn, C.

    1980-01-01

    The potentially serious hazards involved in LNG tanker collisions demand the development of safety regulations and structural design requirements; however, because the collision response is highly transient and nonlinear, it has defied analytical solution. This period methodology for predicting the structural responses of two colliding ships is a synthesis of the modern finite-element techniques, the classic collapse theorems, and experimental data from collision-model and other structural tests. Its basic principle is to use the most reliable method for each particular step of the prediction procedure. Comparisons of the analytical prediction with the collision model-test results indicate a good correlation.

  16. PREDICTION OF DELAM INATION IN WIND TURBINE BLADE STRUCTURAL DETAILS John F. Mandell, Douglas S. Cairns

    E-print Network

    1 PREDICTION OF DELAM INATION IN WIND TURBINE BLADE STRUCTURAL DETAILS John F. Mandell, Douglas S materials structures such as wind turbine blades. Design methodologies to prevent such failures have static and fatigue loading. INTRODUCTION Composite material structures such as wind turbine blades

  17. Conformational Transitions upon Ligand Binding: Holo-Structure Prediction from Apo Conformations

    E-print Network

    de Groot, Bert

    Conformational Transitions upon Ligand Binding: Holo- Structure Prediction from Apo Conformations design. Hence, if only an unbound (apo) structure is available distinct from the ligand of protein/ligand complexes based solely on the apo structure, the ligand and the radius of gyration

  18. Automatic Construction of 3D Structural Motifs for Protein Function Prediction

    Microsoft Academic Search

    Mike P. Liang; Douglas L. Brutlag; Russ B. Altman

    2003-01-01

    Structural genomics initiatives are on the verge of generating a vast number of protein structures. The biological roles for many of these proteins are still unknown, and high-throughput methods for determining their function are necessary. Understanding the function of these proteins will have profound impact in drug development and protein engineering. Current methods for protein function prediction on structures require

  19. Damage predictions of aluminum thin-walled structures subjected to explosive loads

    Microsoft Academic Search

    W. Venner Saul; Phillip L. Reu; Jeffrey Donald Gruda; Kimberly K. Haulenbeek; Marvin Elwood Larsen; James M. Phelan; Jerome H. Stofleth; Edmundo Corona; Kenneth West Gwinn

    2010-01-01

    Predicting failure of thin-walled structures from explosive loading is a very complex task. The problem can be divided into two parts; the detonation of the explosive to produce the loading on the structure, and secondly the structural response. First, the factors that affect the explosive loading include: size, shape, stand-off, confinement, and chemistry of the explosive. The goal of the

  20. Hidden Markov Models That Use Predicted Local Structure for Fold Recognition: Alphabets of Backbone Geometry

    E-print Network

    Karplus, Kevin

    Hidden Markov Models That Use Predicted Local Structure for Fold Recognition: Alphabets of Backbone-recognition methods.1­5 In this article, we evaluate the results of enriching hidden Markov models (HMMs), built using- dicted local structure, a generalization of secondary structure, into two-track profile hidden Markov mod

  1. Structure and dynamics of glass formers: Predictability at large length scales Ludovic Berthier*

    E-print Network

    Berthier, Ludovic

    Structure and dynamics of glass formers: Predictability at large length scales Ludovic Berthier formers has been related to their static structure using the concept of dynamic propensity. We reexamine dynamical relaxation 2­11 , but their structure, as measured by two-point correlation functions, appears

  2. Pair stochastic tree adjoining grammars for aligning and predicting pseudoknot RNA structures

    Microsoft Academic Search

    Hiroshi Matsui; Kengo Sato; Yasubumi Sakakibara

    2005-01-01

    Motivation: Since the whole genome sequences for many species are currently available, computational predictions of RNA secondary structures and computational identifi- cations of those non-coding RNA regions by comparative genomics become important, and require more advanced alignment methods. Recently, an approach of structural alignments for RNA sequences has been introduced to solve these problems. By structural alignments, we mean a

  3. Pair stochastic tree adjoining grammars for aligning and predicting pseudoknot RNA structures

    Microsoft Academic Search

    Hiroshi Matsui; Kengo Sato; Yasubumi Sakakibara

    2004-01-01

    Since the whole genome sequences for many species are currently available, computational predictions of RNA secondary structures and computational identifications of those non-coding RNA regions by comparative genomics have become important, and require more advanced alignment methods. Recently, an approach of structural alignments for RNA sequences has been introduced to solve these problems. By structural alignments, we mean a pair-wise

  4. Practical theories for service life prediction of critical aerospace structural components

    NASA Technical Reports Server (NTRS)

    Ko, William L.; Monaghan, Richard C.; Jackson, Raymond H.

    1992-01-01

    A new second-order theory was developed for predicting the service lives of aerospace structural components. The predictions based on this new theory were compared with those based on the Ko first-order theory and the classical theory of service life predictions. The new theory gives very accurate service life predictions. An equivalent constant-amplitude stress cycle method was proposed for representing the random load spectrum for crack growth calculations. This method predicts the most conservative service life. The proposed use of minimum detectable crack size, instead of proof load established crack size as an initial crack size for crack growth calculations, could give a more realistic service life.

  5. Prediction of Harmful Human Health Effects of Chemicals from Structure

    Microsoft Academic Search

    Mark T. D. Cronin

    \\u000a There is a great need to assess the harmful effects of chemicals to which man is exposed. Various in silico techniques including\\u000a chemical grouping and category formation, as well as the use of (Q)SARs can be applied to predict the toxicity of chemicals\\u000a for a number of toxicological effects. This chapter provides an overview of the state of the art

  6. Computational prediction of coiled-coil interaction structure specificity

    E-print Network

    Gutwin, Karl N. (Karl Nickolai)

    2009-01-01

    The alpha-helical coiled coil is a protein sequence and structural motif that consists of two or more helices in a parallel or antiparallel orientation supercoiling around a central axis. Coiled coils have been observed ...

  7. Efficient interfacing of #uid and structure for aeroelastic instability predictions

    Microsoft Academic Search

    Nesrin Sarigul-Klijn

    SUMMARY An e$cient procedure for multidisciplinary computation of #uid and structure interaction problems of aerospace vehicles is presented. It features the use of Meshless Methods, Finite Elements, Rayleigh}Ritz, and Kernel Functions enabling the aeroelastic requirements to be included in design without demanding prohibitive computational e!orts. Improvements made are in terms of choosing meshless #uid}structure interface for displacement and aerodynamic load

  8. Thermodynamic and phylogenetic prediction of RNA secondary structures in the coding region of hepatitis C virus.

    PubMed Central

    Tuplin, Andrew; Wood, Jonny; Evans, David J; Patel, Arvind H; Simmonds, Peter

    2002-01-01

    The existence and functional importance of RNA secondary structure in the replication of positive-stranded RNA viruses is increasingly recognized. We applied several computational methods to detect RNA secondary structure in the coding region of hepatitis C virus (HCV), including thermodynamic prediction, calculation of free energy on folding, and a newly developed method to scan sequences for covariant sites and associated secondary structures using a parsimony-based algorithm. Each of the prediction methods provided evidence for complex RNA folding in the core- and NS5B-encoding regions of the genome. The positioning of covariant sites and associated predicted stem-loop structures coincided with thermodynamic predictions of RNA base pairing, and localized precisely in parts of the genome with marked suppression of variability at synonymous sites. Combined, there was evidence for a total of six evolutionarily conserved stem-loop structures in the NS5B-encoding region and two in the core gene. The virus most closely related to HCV, GB virus-B (GBV-B) also showed evidence for similar internal base pairing in its coding region, although predictions of secondary structures were limited by the absence of comparative sequence data for this virus. While the role(s) of stem-loops in the coding region of HCV and GBV-B are currently unknown, the structure predictions in this study could provide the starting point for functional investigations using recently developed self-replicating clones of HCV. PMID:12088154

  9. Multiple classifier integration for the prediction of protein structural classes.

    PubMed

    Chen, Lei; Lu, Lin; Feng, Kairui; Li, Wenjin; Song, Jie; Zheng, Lulu; Yuan, Youlang; Zeng, Zhenbin; Feng, Kaiyan; Lu, Wencong; Cai, Yudong

    2009-11-15

    Supervised classifiers, such as artificial neural network, partition trees, and support vector machines, are often used for the prediction and analysis of biological data. However, choosing an appropriate classifier is not straightforward because each classifier has its own strengths and weaknesses, and each biological dataset has its own characteristics. By integrating many classifiers together, people can avoid the dilemma of choosing an individual classifier out of many to achieve an optimized classification results (Rahman et al., Multiple Classifier Combination for Character Recognition: Revisiting the Majority Voting System and Its Variation, Springer, Berlin, 2002, 167-178). The classification algorithms come from Weka (Witten and Frank, Data Mining: Practical Machine Learning Tools and Techniques, Morgan Kaufmann, San Francisco, 2005) (a collection of software tools for machine learning algorithms). By integrating many predictors (classifiers) together through simple voting, the correct prediction (classification) rates are 65.21% and 65.63% for a basic training dataset and an independent test set, respectively. These results are better than any single machine learning algorithm collected in Weka when exactly the same data are used. Furthermore, we introduce an integration strategy which takes care of both classifier weightings and classifier redundancy. A feature selection strategy, called minimum redundancy maximum relevance (mRMR), is transferred into algorithm selection to deal with classifier redundancy in this research, and the weightings are based on the performance of each classifier. The best classification results are obtained when 11 algorithms are selected by mRMR method, and integrated together through majority votes with weightings. As a result, the prediction correct rates are 68.56% and 69.29% for the basic training dataset and the independent test dataset, respectively. The web-server is available at http://chemdata.shu.edu.cn/protein_st/. PMID:19274708

  10. An adaptive genetic algorithm for crystal structure prediction

    SciTech Connect

    Wu, Shunqing [Ames Laboratory; Ji, Min [Ames Laboratory; Wang, Cai-Zhuang [Ames Laboratory; Nguyen, Manh Cuong [Ames Laboratory; Zhao, Xin [Ames Laboratory; Umemoto, K. [Ames Laboratory; Wentzcovitch, R. M. [University of Minnesota; Ho, Kai-Ming [Ames Laboratory

    2013-10-31

    We present a genetic algorithm (GA) for structural search that combines the speed of structure exploration by classical potentials with the accuracy of density functional theory (DFT) calculations in an adaptive and iterative way. This strategy increases the efficiency of the DFT-based GA by several orders of magnitude. This gain allows a considerable increase in the size and complexity of systems that can be studied by first principles. The performance of the method is illustrated by successful structure identifications of complex binary and ternary intermetallic compounds with 36 and 54 atoms per cell, respectively. The discovery of a multi-TPa Mg-silicate phase with unit cell containing up to 56 atoms is also reported. Such a phase is likely to be an essential component of terrestrial exoplanetary mantles.

  11. DSSTOX WEBSITE LAUNCH: IMPROVING PUBLIC ACCESS TO DATABASES FOR BUILDING STRUCTURE-TOXICITY PREDICTION MODELS

    EPA Science Inventory

    DSSTox Website Launch: Improving Public Access to Databases for Building Structure-Toxicity Prediction Models Ann M. Richard US Environmental Protection Agency, Research Triangle Park, NC, USA Distributed: Decentralized set of standardized, field-delimited databases,...

  12. APS/123-QED Decagonal quasicrystal with novel composition and structure predicted from rst

    E-print Network

    Widom, Michael

    APS/123-QED Decagonal quasicrystal with novel composition and structure predicted from #12;rst. Hexagon (H) and boat (B) tiles [5] are inscribed on these #12;gures to show how the atoms decorate a set

  13. Direct comparison of Neural Network, Fuzzy Logic and Model Prediction Variable Structure vortex flow controllers 

    E-print Network

    Joshi, Praveen Sudhakar

    1999-01-01

    Predictive Variable Structure and Fuzzy Logic based controllers for the same benchmark problem. Evaluation criteria consist of closed-loop system performance, activity level of the VFC nozzles, ease of controller synthesis, time required to synthesize...

  14. A Molecular Mechanics Knowledge Base Applied to Template Based Structure Prediction 

    E-print Network

    Qu, Xiaotao

    2011-02-22

    Predicting protein structure using its primary sequence has always been a challenging topic in biochemistry. Although it seems as simple as finding the minimal energy conformation, it has been quite difficult to provide an accurate yet reliable...

  15. A Quantitative Structure-Property Relationship for Predicting Drug Solubility in PEG 400\\/Water Cosolvent Systems

    Microsoft Academic Search

    Erik Rytting; Kimberley A. Lentz; Xue-Qing Chen; Feng Qian; Srini Venkatesh

    2004-01-01

    Purpose. A quantitative structure-property relationship (QSPR) was developed to predict drug solubility in binary mixtures of polyethylene glycol (PEG) 400 and water. The ability of the QSPR model to predict solubility was assessed and compared to the classic log-linear cosolvency model.

  16. Sequence-Based Prediction of Protein Folding Rates Using Contacts, Secondary Structures and Support Vector Machines

    E-print Network

    Cheng, Jianlin Jack

    Sequence-Based Prediction of Protein Folding Rates Using Contacts, Secondary Structures and Support, Columbia, Missouri * Corresponding author: chengji@missouri.edu Abstract Predicting protein folding rate is useful for understanding protein folding process and guiding protein design. Here we developed a method

  17. Structure-based activity prediction for an enzyme of unknown function

    Microsoft Academic Search

    Johannes C. Hermann; Ricardo Marti-Arbona; Alexander A. Fedorov; Elena Fedorov; Steven C. Almo; Brian K. Shoichet; Frank M. Raushel

    2007-01-01

    With many genomes sequenced, a pressing challenge in biology is predicting the function of the proteins that the genes encode. When proteins are unrelated to others of known activity, bioinformatics inference for function becomes problematic. It would thus be useful to interrogate protein structures for function directly. Here, we predict the function of an enzyme of unknown activity, Tm0936 from

  18. Family Structure versus Family Relationships for Predicting to Substance Use/Abuse and Illegal Behavior.

    ERIC Educational Resources Information Center

    Friedman, Alfred S.; Terras, Arlene; Glassman, Kimberly

    2000-01-01

    Study looked at sample of African-American adolescent males to determine the degree to which family structure (e.g., single parent vs. two-parent families) vs. the nature of the family relationships predict sons' involvement in substance use/abuse and illegal behavior. Of 33 relationships measures analyzed, 3 predicted the degree of recent…

  19. GeneSeqer add PlantGDB: gene structure prediction in plant genomes

    Microsoft Academic Search

    Shannon D. Schlueter; Qunfeng Dong; Volker Brendel

    2003-01-01

    The GeneSeqer@PlantGDB Web server (http:\\/\\/ www.plantgdb.org\\/cgi-bin\\/GeneSeqer.cgi) provides a gene structure prediction tool tailored for applica- tions to plant genomic sequences. Predictions are based on spliced alignment with source-native ESTs and full-length cDNAs or non-native probes derived from putative homologous genes. The tool is illustrated with applications to refinement of current gene structure annotation and de novo annotation of draft genomic

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

    Microsoft Academic Search

    Julio A. Kovacs; Mark Yeager; Ruben Abagyan

    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 ?-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

  1. Making Sense of Olfaction through Predictions of the 3-D Structure and Function of Olfactory Receptors

    Microsoft Academic Search

    Wely B. Floriano; Nagarajan Vaidehi; William A. Goddard

    2004-01-01

    We used the MembStruk first principles computational technique to predict the three-dimensional (3-D) structure of six mouse olfactory receptors (S6, S18, S19, S25, S46 and S50) for which experimental odorant recognition profiles are available for a set of 24 odorants (4-9 carbons aliphatic alcohols, acids, bromo-acids and diacids). We used the HierDock method to scan each predicted OR structure for

  2. A Structural Equation Model for Predicting Business Student Performance

    ERIC Educational Resources Information Center

    Pomykalski, James J.; Dion, Paul; Brock, James L.

    2008-01-01

    In this study, the authors developed a structural equation model that accounted for 79% of the variability of a student's final grade point average by using a sample size of 147 students. The model is based on student grades in 4 foundational business courses: introduction to business, macroeconomics, statistics, and using databases. Educators and…

  3. The prediction and measurement of sound radiated by structures

    NASA Technical Reports Server (NTRS)

    Lyon, R. H.; Brito, J. D.

    1976-01-01

    Theories regarding the radiation of sound are reviewed and the implementation in strategies for explaining or measuring the sound produced by practical strucutres are discussed. Particular attention is given to those aspects that relate to the determination of the relative amounts of sound generated by various parts of a machine or structure, which can be very useful information for noise reduction efforts.

  4. A Model to Predict Hurricanes Induced Losses for Residential Structures

    Microsoft Academic Search

    Jean-Paul Pinelli; Chelakara. Subramanian; Liang Zhang; Kurtis Gurley; Anne Cope; Emil Simiu; Sofia Diniz

    This paper presents a practical probabilistic model for the projection of annualized damage costs to residential structures due to hurricanes. The estimation of the damage is accomplished by first defining the basic damage modes for components of specific building types and their probabilities of occurrence as functions of estimated wind speeds. The damage modes are then combined in possible damage

  5. Evolutionary design of the energy function for protein structure prediction

    E-print Network

    Krasnogor, Natalio

    . Garibaldi Natalio Krasnogor Abstract-- Automatic protein structure predictors use the notion of energy folding was formulated by Christian Anfinsen. In a Nobel prize winning experiment he found that a refolded to fold a protein has to be contained in its sequence and nature is applying a "folding algorithm

  6. Prediction of Complete Gene Structures in Human Genomic DNA

    Microsoft Academic Search

    Christopher B. Burge; Samuel Karlin

    1997-01-01

    transcriptional, translational and splicing signals, as well as length distri- ,butions and compositional features of exons, introns and intergenic regions. Distinct sets of model parameters are derived to account for the many substantial differences in gene density and structure observed in distinct C + G compositional regions of the human genome. In addition, new models of the donor and acceptor

  7. Autonomous impact damage detection and isolation prediction for aerospace structures

    Microsoft Academic Search

    Michael J. Roemer; Jianhua Ge; Alex Liberson; G. P. Tandon; R. Y. Kim

    2005-01-01

    This paper presents a practical yet innovative impact damage identification and prognosis approach for aerospace structures that uses an optimized suite of reliable COTS sensors coupled with advanced damage detection and modeling algorithms. The presented methodology utilizes a monitoring approach based on acceleration measurements that are analyzed using advanced signal processing and dispersive wave theory models that capture frequency and

  8. Controlling failure using structural fuses for predictable progressive failure of composite laminates

    Microsoft Academic Search

    Pablo Salas; Satchi Venkataraman

    2007-01-01

    Structural fuses have been used to bias and control failures in structural applications where predictability of the progressive\\u000a failure or collapse response is important. Tailoring structural fuses by trial and error in large structures that have numerous\\u000a possible load and failure paths is not possible because the optimum failure sequence is not known a priori. Using nondeterministic\\u000a methods to tailor

  9. The predicted secondary structures of class I fructose-bisphosphate aldolases.

    PubMed Central

    Sawyer, L; Fothergill-Gilmore, L A; Freemont, P S

    1988-01-01

    The results of several secondary-structure prediction programs were combined to produce an estimate of the regions of alpha-helix, beta-sheet and reverse turns for fructose-bisphosphate aldolases from human and rat muscle and liver, from Trypanosoma brucei and from Drosophila melanogaster. All the aldolase sequences gave essentially the same pattern of secondary-structure predictions despite having sequences up to 50% different. One exception to this pattern was an additional strongly predicted helix in the rat liver and Drosophila enzymes. Regions of relatively high sequence variation generally were predicted as reverse turns, and probably occur as surface loops. Most of the positions corresponding to exon boundaries are located between regions predicted to have secondary-structural elements consistent with a compact structure. The predominantly alternating alpha/beta structure predicted is consistent with the alpha/beta-barrel structure indicated by preliminary high-resolution X-ray diffraction studies on rabbit muscle aldolase [Sygusch, Beaudry & Allaire (1986) Biophys. J. 49, 287a]. Images Fig. 1. (cont.) Fig. 1. PMID:3128269

  10. Inhibition of NF ?B–DNA binding by mercuric ion: utility of the non-thiol reductant, tris(2-carboxyethyl)phosphine hydrochloride (TCEP), on detection of impaired NF ?B–DNA binding by thiol-directed agents

    Microsoft Academic Search

    F. J Dieguez-Acuña; J. S Woods

    2000-01-01

    Mercuric ion (Hg2+), a potent thiol inhibitor, prevents expression of nuclear factor ?B (NF-?B) by mercaptide bond formation with a critical cysteine moiety (cys62) on the p50 subunit required for DNA binding. NF-?B–DNA binding is typically measured in reaction mixtures in which dithiothreitol (DTT) or other thiol reductants are used to maintain cys62 in the reduced state. However, the presence

  11. Predicting Homogeneous Pilus Structure from Monomeric Data and Sparse Constraints

    PubMed Central

    Xiao, Ke; Shu, Chuanjun; Yan, Qin; Sun, Xiao

    2015-01-01

    Type IV pili (T4P) and T2SS (Type II Secretion System) pseudopili are filaments extending beyond microbial surfaces, comprising homologous subunits called “pilins.” In this paper, we presented a new approach to predict pseudo atomic models of pili combining ambiguous symmetric constraints with sparse distance information obtained from experiments and based neither on electronic microscope (EM) maps nor on accurate a priori symmetric details. The approach was validated by the reconstruction of the gonococcal (GC) pilus from Neisseria gonorrhoeae, the type IVb toxin-coregulated pilus (TCP) from Vibrio cholerae, and pseudopilus of the pullulanase T2SS (the PulG pilus) from Klebsiella oxytoca. In addition, analyses of computational errors showed that subunits should be treated cautiously, as they are slightly flexible and not strictly rigid bodies. A global sampling in a wider range was also implemented and implied that a pilus might have more than one but fewer than many possible intact conformations.

  12. The structure of evaporating and combusting sprays: Measurements and predictions

    NASA Technical Reports Server (NTRS)

    Shuen, J. S.; Solomon, A. S. P.; Faeth, G. M.

    1984-01-01

    An apparatus developed, to allow observations of monodisperse sprays, consists of a methane-fueled turbulent jet diffusion flame with monodisperse methanol drops injected at the burner exit. Mean and fluctuating-phase velocities, drop sizes, drop-mass fluxes and mean-gas temperatures were measured. Initial drop diameters of 100 and 180 microns are being considered in order to vary drop penetration in the flow and effects of turbulent dispersion. Baseline tests of the burner flame with no drops present were also conducted. Calibration tests, needed to establish methods for predicting drop transport, involve drops supported in the post-flame region of a flat-flame burner operated at various mixture ratios. Spray models which are being evaluated include: (1) locally homogeneous flow (LFH) analysis, (2) deterministic separated flow (DSF) analysis and (3) stochastic separated flow (SSF) analysis.

  13. Prediction of rodent carcinogenicity bioassays from molecular structure using inductive logic programming.

    PubMed Central

    King, R D; Srinivasan, A

    1996-01-01

    The machine learning program Progol was applied to the problem of forming the structure-activity relationship (SAR) for a set of compounds tested for carcinogenicity in rodent bioassays by the U.S. National Toxicology Program (NTP). Progol is the first inductive logic programming (ILP) algorithm to use a fully relational method for describing chemical structure in SARs, based on using atoms and their bond connectivities. Progol is well suited to forming SARs for carcinogenicity as it is designed to produce easily understandable rules (structural alerts) for sets of noncongeneric compounds. The Progol SAR method was tested by prediction of a set of compounds that have been widely predicted by other SAR methods (the compounds used in the NTP's first round of carcinogenesis predictions). For these compounds no method (human or machine) was significantly more accurate than Progol. Progol was the most accurate method that did not use data from biological tests on rodents (however, the difference in accuracy is not significant). The Progol predictions were based solely on chemical structure and the results of tests for Salmonella mutagenicity. Using the full NTP database, the prediction accuracy of Progol was estimated to be 63% (+/- 3%) using 5-fold cross validation. A set of structural alerts for carcinogenesis was automatically generated and the chemical rationale for them investigated- these structural alerts are statistically independent of the Salmonella mutagenicity. Carcinogenicity is predicted for the compounds used in the NTP's second round of carcinogenesis predictions. The results for prediction of carcinogenesis, taken together with the previous successful applications of predicting mutagenicity in nitroaromatic compounds, and inhibition of angiogenesis by suramin analogues, show that Progol has a role to play in understanding the SARs of cancer-related compounds. PMID:8933051

  14. Lattice-free prediction of three-dimensional structure of programmed DNA assemblies

    PubMed Central

    Pan, Keyao; Kim, Do-Nyun; Zhang, Fei; Adendorff, Matthew R.; Yan, Hao; Bathe, Mark

    2014-01-01

    DNA can be programmed to self-assemble into high molecular weight 3D assemblies with precise nanometer-scale structural features. Although numerous sequence design strategies exist to realize these assemblies in solution, there is currently no computational framework to predict their 3D structures on the basis of programmed underlying multi-way junction topologies constrained by DNA duplexes. Here, we introduce such an approach and apply it to assemblies designed using the canonical immobile four-way junction. The procedure is used to predict the 3D structure of high molecular weight planar and spherical ring-like origami objects, a tile-based sheet-like ribbon, and a 3D crystalline tensegrity motif, in quantitative agreement with experiments. Our framework provides a new approach to predict programmed nucleic acid 3D structure on the basis of prescribed secondary structure motifs, with possible application to the design of such assemblies for use in biomolecular and materials science. PMID:25470497

  15. Lattice-free prediction of three-dimensional structure of programmed DNA assemblies

    NASA Astrophysics Data System (ADS)

    Pan, Keyao; Kim, Do-Nyun; Zhang, Fei; Adendorff, Matthew R.; Yan, Hao; Bathe, Mark

    2014-12-01

    DNA can be programmed to self-assemble into high molecular weight 3D assemblies with precise nanometer-scale structural features. Although numerous sequence design strategies exist to realize these assemblies in solution, there is currently no computational framework to predict their 3D structures on the basis of programmed underlying multi-way junction topologies constrained by DNA duplexes. Here, we introduce such an approach and apply it to assemblies designed using the canonical immobile four-way junction. The procedure is used to predict the 3D structure of high molecular weight planar and spherical ring-like origami objects, a tile-based sheet-like ribbon, and a 3D crystalline tensegrity motif, in quantitative agreement with experiments. Our framework provides a new approach to predict programmed nucleic acid 3D structure on the basis of prescribed secondary structure motifs, with possible application to the design of such assemblies for use in biomolecular and materials science.

  16. Predictive modelling of structure evolution in sandbox experiments

    NASA Astrophysics Data System (ADS)

    Crook, A. J. L.; Willson, S. M.; Yu, J. G.; Owen, D. R. J.

    2006-05-01

    In this paper a computational approach is presented that is able to forward model complex structural evolution with multiple intersecting faults that exhibit large relative movement. The approach adopts the Lagrangian method, complemented by robust and efficient automated adaptive meshing techniques, a constitutive model based on critical state concepts and global energy dissipation regularized by inclusion of fracture energy in the equations governing state variable evolution. The efficacy of the approach is benchmarked by forward simulation of two extensional analogue experiments that exhibit the development of a roll-over anticline with a series of superimposed crestal collapse graben systems. These sandbox experiments are excellent benchmarks for computational models, as both intersecting localisations with different rates of slip and large relative movement on localisations must be represented, while other complex phenomena associated with structural evolution over geological timeframes are not present.

  17. Interarm vortices predicted by laboratory simulation of spiral structure

    NASA Astrophysics Data System (ADS)

    Nezlin, M. V.; Polyachenko, V. L.; Snezhkin, E. N.; Trubnikov, A. S.; Fridman, A. M.

    1986-08-01

    A rotating, two-zone, shallow-water laboratory simulation, designed to illustrate how spiral structure might be generated in galaxies whose rotation curve shows a jump in velocity, discloses anticyclonic, banana-shaped vortices at points of minimum surface density between successive spiral arms. Fluid particles trapped in these 'whorls' will enter and flow along the arms, achieving substantial radial velocities near the corotation circle (the rotation-curve discontinuity). This behavior, interpreted theoretically, provides new insight into the relative motion of the spiral arms and the gas in a galaxy disk. A self-consistent spiral/whorl structure emerges even if the peripheral zone rotates so fast that the Rossby-Obukhov radius is an order of magntitude shorter than the arms. The model results are compared with observations of NGC 1566. In some SB galaxies the bar phenomenon may be an artifact of spiral/whorl structure in the gaseous disk. If a rotating spiral galaxy interacts with a companion orbiting in the opposite direction, spiral arms can be produced which intrinsically lead: the pattern would rotate, tips forward, opposite to the rotation in the galaxy's interior.

  18. In silico predicted structural and functional robustness of piscine steroidogenesis.

    PubMed

    Hala, D; Huggett, D B

    2014-03-21

    Assessments of metabolic robustness or susceptibility are inherently dependent on quantitative descriptions of network structure and associated function. In this paper a stoichiometric model of piscine steroidogenesis was constructed and constrained with productions of selected steroid hormones. Structural and flux metrics of this in silico model were quantified by calculating extreme pathways and optimal flux distributions (using linear programming). Extreme pathway analysis showed progestin and corticosteroid synthesis reactions to be highly participant in extreme pathways. Furthermore, reaction participation in extreme pathways also fitted a power law distribution (degree exponent ?=2.3), which suggested that progestin and corticosteroid reactions act as 'hubs' capable of generating other functionally relevant pathways required to maintain steady-state functionality of the network. Analysis of cofactor usage (O2 and NADPH) showed progestin synthesis reactions to exhibit high robustness, whereas estrogen productions showed highest energetic demands with low associated robustness to maintain such demands. Linear programming calculated optimal flux distributions showed high heterogeneity of flux values with a near-random power law distribution (degree exponent ??2.7). Subsequently, network robustness was tested by assessing maintenance of metabolite flux-sum subject to targeted deletions of rank-ordered (low to high metric) extreme pathway participant and optimal flux reactions. Network robustness was susceptible to deletions of extreme pathway participant reactions, whereas minimal impact of high flux reaction deletion was observed. This analysis shows that the steroid network is susceptible to perturbation of structurally relevant (extreme pathway) reactions rather than those carrying high flux. PMID:24333207

  19. Multithreaded comparative RNA secondary structure prediction using stochastic context-free grammars

    PubMed Central

    2011-01-01

    Background The prediction of the structure of large RNAs remains a particular challenge in bioinformatics, due to the computational complexity and low levels of accuracy of state-of-the-art algorithms. The pfold model couples a stochastic context-free grammar to phylogenetic analysis for a high accuracy in predictions, but the time complexity of the algorithm and underflow errors have prevented its use for long alignments. Here we present PPfold, a multithreaded version of pfold, which is capable of predicting the structure of large RNA alignments accurately on practical timescales. Results We have distributed both the phylogenetic calculations and the inside-outside algorithm in PPfold, resulting in a significant reduction of runtime on multicore machines. We have addressed the floating-point underflow problems of pfold by implementing an extended-exponent datatype, enabling PPfold to be used for large-scale RNA structure predictions. We have also improved the user interface and portability: alongside standalone executable and Java source code of the program, PPfold is also available as a free plugin to the CLC Workbenches. We have evaluated the accuracy of PPfold using BRaliBase I tests, and demonstrated its practical use by predicting the secondary structure of an alignment of 24 complete HIV-1 genomes in 65 minutes on an 8-core machine and identifying several known structural elements in the prediction. Conclusions PPfold is the first parallelized comparative RNA structure prediction algorithm to date. Based on the pfold model, PPfold is capable of fast, high-quality predictions of large RNA secondary structures, such as the genomes of RNA viruses or long genomic transcripts. The techniques used in the parallelization of this algorithm may be of general applicability to other bioinformatics algorithms. PMID:21501497

  20. All-atom 3D structure prediction of transmembrane ?-barrel proteins from sequences

    PubMed Central

    Hayat, Sikander; Sander, Chris; Marks, Debora S.

    2015-01-01

    Transmembrane ?-barrels (TMBs) carry out major functions in substrate transport and protein biogenesis but experimental determination of their 3D structure is challenging. Encouraged by successful de novo 3D structure prediction of globular and ?-helical membrane proteins from sequence alignments alone, we developed an approach to predict the 3D structure of TMBs. The approach combines the maximum-entropy evolutionary coupling method for predicting residue contacts (EVfold) with a machine-learning approach (boctopus2) for predicting ?-strands in the barrel. In a blinded test for 19 TMB proteins of known structure that have a sufficient number of diverse homologous sequences available, this combined method (EVfold_bb) predicts hydrogen-bonded residue pairs between adjacent ?-strands at an accuracy of ?70%. This accuracy is sufficient for the generation of all-atom 3D models. In the transmembrane barrel region, the average 3D structure accuracy [template-modeling (TM) score] of top-ranked models is 0.54 (ranging from 0.36 to 0.85), with a higher (44%) number of residue pairs in correct strand–strand registration than in earlier methods (18%). Although the nonbarrel regions are predicted less accurately overall, the evolutionary couplings identify some highly constrained loop residues and, for FecA protein, the barrel including the structure of a plug domain can be accurately modeled (TM score = 0.68). Lower prediction accuracy tends to be associated with insufficient sequence information and we therefore expect increasing numbers of ?-barrel families to become accessible to accurate 3D structure prediction as the number of available sequences increases. PMID:25858953

  1. An Integrated Theory for Predicting the Hydrothermomechanical Response of Advanced Composite Structural Components

    NASA Technical Reports Server (NTRS)

    Chamis, C. C.; Lark, R. F.; Sinclair, J. H.

    1977-01-01

    An integrated theory is developed for predicting the hydrothermomechanical (HDTM) response of fiber composite components. The integrated theory is based on a combined theoretical and experimental investigation. In addition to predicting the HDTM response of components, the theory is structured to assess the combined hydrothermal effects on the mechanical properties of unidirectional composites loaded along the material axis and off-axis, and those of angleplied laminates. The theory developed predicts values which are in good agreement with measured data at the micromechanics, macromechanics, laminate analysis and structural analysis levels.

  2. Structure Based Predictive Model for Coal Char Combustion

    SciTech Connect

    Robert Hurt; Joseph Calo; Robert Essenhigh; Christopher Hadad

    2000-12-30

    This unique collaborative project has taken a very fundamental look at the origin of structure, and combustion reactivity of coal chars. It was a combined experimental and theoretical effort involving three universities and collaborators from universities outside the U.S. and from U.S. National Laboratories and contract research companies. The project goal was to improve our understanding of char structure and behavior by examining the fundamental chemistry of its polyaromatic building blocks. The project team investigated the elementary oxidative attack on polyaromatic systems, and coupled with a study of the assembly processes that convert these polyaromatic clusters to mature carbon materials (or chars). We believe that the work done in this project has defined a powerful new science-based approach to the understanding of char behavior. The work on aromatic oxidation pathways made extensive use of computational chemistry, and was led by Professor Christopher Hadad in the Department of Chemistry at Ohio State University. Laboratory experiments on char structure, properties, and combustion reactivity were carried out at both OSU and Brown, led by Principle Investigators Joseph Calo, Robert Essenhigh, and Robert Hurt. Modeling activities were divided into two parts: first unique models of crystal structure development were formulated by the team at Brown (PI'S Hurt and Calo) with input from Boston University and significant collaboration with Dr. Alan Kerstein at Sandia and with Dr. Zhong-Ying chen at SAIC. Secondly, new combustion models were developed and tested, led by Professor Essenhigh at OSU, Dieter Foertsch (a collaborator at the University of Stuttgart), and Professor Hurt at Brown. One product of this work is the CBK8 model of carbon burnout, which has already found practical use in CFD codes and in other numerical models of pulverized fuel combustion processes, such as EPRI's NOxLOI Predictor. The remainder of the report consists of detailed technical discussion organized into chapters whose organization is dictated by the nature of the research performed. Chapter 2 is entitled 'Experimental Work on Char Structure, Properties, and Reactivity', and focuses on fundamental structural studies at Brown using both phenollformaldehyde resin chars as model carbons and real coal chars. This work includes the first known in site high resolution TEM studies of carbonization processes, and some intriguing work on 'memory loss', a form of interaction between annealing and oxidation phenomena in chars. Chapter 3 entitled 'Computational Chemistry of Aromatic Oxidation Pathways' presents in detail the OSU work targeted at understanding the elementary molecular pathways of aromatic oxidation. Chapter 4 describes the 'Mesoscale Structural Models', using a combination of thermodynamic (equilibrium) approaches based on liquid crystal theory and kinetic simulations accounting for the effects of limited layer mobility in many fossil fuel derived carbons containing cross-linking agents. Chapter 5 entitled 'Combustion Modeling' presents work on extinction in the late stages of combustion and the development and features of the CBK8 model.

  3. Lessons from application of the UNRES force field to predictions of structures of CASP10 targets

    PubMed Central

    He, Yi; Mozolewska, Magdalena A.; Krupa, Pawe?; Sieradzan, Adam K.; Wirecki, Tomasz K.; Liwo, Adam; Kachlishvili, Khatuna; Rackovsky, Shalom; Jagie?a, Dawid; ?lusarz, Rafa?; Czaplewski, Cezary R.; O?dziej, Stanis?aw; Scheraga, Harold A.

    2013-01-01

    The performance of the physics-based protocol, whose main component is the United Residue (UNRES) physics-based coarse-grained force field, developed in our laboratory for the prediction of protein structure from amino acid sequence, is illustrated. Candidate models are selected, based on probabilities of the conformational families determined by multiplexed replica-exchange simulations, from the 10th Community Wide Experiment on the Critical Assessment of Techniques for Protein Structure Prediction (CASP10). For target T0663, classified as a new fold, which consists of two domains homologous to those of known proteins, UNRES predicted the correct symmetry of packing, in which the domains are rotated with respect to each other by 180° in the experimental structure. By contrast, models obtained by knowledge-based methods, in which each domain is modeled very accurately but not rotated, resulted in incorrect packing. Two UNRES models of this target were featured by the assessors. Correct domain packing was also predicted by UNRES for the homologous target T0644, which has a similar structure to that of T0663, except that the two domains are not rotated. Predictions for two other targets, T0668 and T0684_D2, are among the best ones by global distance test score. These results suggest that our physics-based method has substantial predictive power. In particular, it has the ability to predict domain–domain orientations, which is a significant advance in the state of the art. PMID:23980156

  4. Challenging the state-of-the-art in protein structure prediction: Highlights of experimental target structures for the 10th Critical Assessment of Techniques for Protein Structure Prediction Experiment CASP10

    PubMed Central

    Kryshtafovych, Andriy; Moult, John; Bales, Patrick; Bazan, J. Fernando; Biasini, Marco; Burgin, Alex; Chen, Chen; Cochran, Frank V.; Craig, Timothy K.; Das, Rhiju; Fass, Deborah; Garcia-Doval, Carmela; Herzberg, Osnat; Lorimer, Donald; Luecke, Hartmut; Ma, Xiaolei; Nelson, Daniel C.; van Raaij, Mark J.; Rohwer, Forest; Segall, Anca; Seguritan, Victor; Zeth, Kornelius; Schwede, Torsten

    2014-01-01

    For the last two decades, CASP has assessed the state of the art in techniques for protein structure prediction and identified areas which required further development. CASP would not have been possible without the prediction targets provided by the experimental structural biology community. In the latest experiment, CASP10, over 100 structures were suggested as prediction targets, some of which appeared to be extraordinarily difficult for modeling. In this paper, authors of some of the most challenging targets discuss which specific scientific question motivated the experimental structure determination of the target protein, which structural features were especially interesting from a structural or functional perspective, and to what extent these features were correctly reproduced in the predictions submitted to CASP10. Specifically, the following targets will be presented: the acid-gated urea channel, a difficult to predict trans-membrane protein from the important human pathogen Helicobacter pylori; the structure of human interleukin IL-34, a recently discovered helical cytokine; the structure of a functionally uncharacterized enzyme OrfY from Thermoproteus tenax formed by a gene duplication and a novel fold; an ORFan domain of mimivirus sulfhydryl oxidase R596; the fibre protein gp17 from bacteriophage T7; the Bacteriophage CBA-120 tailspike protein; a virus coat protein from metagenomic samples of the marine environment; and finally an unprecedented class of structure prediction targets based on engineered disulfide-rich small proteins. PMID:24318984

  5. Bad to the bone: facial structure predicts unethical behaviour

    PubMed Central

    Haselhuhn, Michael P.; Wong, Elaine M.

    2012-01-01

    Researchers spanning many scientific domains, including primatology, evolutionary biology and psychology, have sought to establish an evolutionary basis for morality. While researchers have identified social and cognitive adaptations that support ethical behaviour, a consensus has emerged that genetically determined physical traits are not reliable signals of unethical intentions or actions. Challenging this view, we show that genetically determined physical traits can serve as reliable predictors of unethical behaviour if they are also associated with positive signals in intersex and intrasex selection. Specifically, we identify a key physical attribute, the facial width-to-height ratio, which predicts unethical behaviour in men. Across two studies, we demonstrate that men with wider faces (relative to facial height) are more likely to explicitly deceive their counterparts in a negotiation, and are more willing to cheat in order to increase their financial gain. Importantly, we provide evidence that the link between facial metrics and unethical behaviour is mediated by a psychological sense of power. Our results demonstrate that static physical attributes can indeed serve as reliable cues of immoral action, and provide additional support for the view that evolutionary forces shape ethical judgement and behaviour. PMID:21733897

  6. Prediction of Protein Structure by Template-Based Modeling Combined with the UNRES Force Field.

    PubMed

    Krupa, Pawe?; Mozolewska, Magdalena A; Joo, Keehyoung; Lee, Jooyoung; Czaplewski, Cezary; Liwo, Adam

    2015-06-22

    A new approach to the prediction of protein structures that uses distance and backbone virtual-bond dihedral angle restraints derived from template-based models and simulations with the united residue (UNRES) force field is proposed. The approach combines the accuracy and reliability of template-based methods for the segments of the target sequence with high similarity to those having known structures with the ability of UNRES to pack the domains correctly. Multiplexed replica-exchange molecular dynamics with restraints derived from template-based models of a given target, in which each restraint is weighted according to the accuracy of the prediction of the corresponding section of the molecule, is used to search the conformational space, and the weighted histogram analysis method and cluster analysis are applied to determine the families of the most probable conformations, from which candidate predictions are selected. To test the capability of the method to recover template-based models from restraints, five single-domain proteins with structures that have been well-predicted by template-based methods were used; it was found that the resulting structures were of the same quality as the best of the original models. To assess whether the new approach can improve template-based predictions with incorrectly predicted domain packing, four such targets were selected from the CASP10 targets; for three of them the new approach resulted in significantly better predictions compared with the original template-based models. The new approach can be used to predict the structures of proteins for which good templates can be found for sections of the sequence or an overall good template can be found for the entire sequence but the prediction quality is remarkably weaker in putative domain-linker regions. PMID:25965196

  7. Structure Prediction of Domain Insertion Proteins from Structures of Individual Domains

    E-print Network

    Gray, Jeffrey J.

    structure of domain insertion proteins from structures of the individual domains. The word ``domain'' has- cation in structural biology defines a domain as a compact, inde- pendently folding unit with structural a structural definition, including the manually curated Structural Classification of Proteins (SCOP; Barton

  8. Hand use predicts the structure of representations in sensorimotor cortex.

    PubMed

    Ejaz, Naveed; Hamada, Masashi; Diedrichsen, Jörn

    2015-07-01

    Fine finger movements are controlled by the population activity of neurons in the hand area of primary motor cortex. Experiments using microstimulation and single-neuron electrophysiology suggest that this area represents coordinated multi-joint, rather than single-finger movements. However, the principle by which these representations are organized remains unclear. We analyzed activity patterns during individuated finger movements using functional magnetic resonance imaging (fMRI). Although the spatial layout of finger-specific activity patterns was variable across participants, the relative similarity between any pair of activity patterns was well preserved. This invariant organization was better explained by the correlation structure of everyday hand movements than by correlated muscle activity. This also generalized to an experiment using complex multi-finger movements. Finally, the organizational structure correlated with patterns of involuntary co-contracted finger movements for high-force presses. Together, our results suggest that hand use shapes the relative arrangement of finger-specific activity patterns in sensory-motor cortex. PMID:26030847

  9. Analysis of an optimal hidden Markov model for secondary structure prediction

    PubMed Central

    Martin, Juliette; Gibrat, Jean-François; Rodolphe, François

    2006-01-01

    Background Secondary structure prediction is a useful first step toward 3D structure prediction. A number of successful secondary structure prediction methods use neural networks, but unfortunately, neural networks are not intuitively interpretable. On the contrary, hidden Markov models are graphical interpretable models. Moreover, they have been successfully used in many bioinformatic applications. Because they offer a strong statistical background and allow model interpretation, we propose a method based on hidden Markov models. Results Our HMM is designed without prior knowledge. It is chosen within a collection of models of increasing size, using statistical and accuracy criteria. The resulting model has 36 hidden states: 15 that model ?-helices, 12 that model coil and 9 that model ?-strands. Connections between hidden states and state emission probabilities reflect the organization of protein structures into secondary structure segments. We start by analyzing the model features and see how it offers a new vision of local structures. We then use it for secondary structure prediction. Our model appears to be very efficient on single sequences, with a Q3 score of 68.8%, more than one point above PSIPRED prediction on single sequences. A straightforward extension of the method allows the use of multiple sequence alignments, rising the Q3 score to 75.5%. Conclusion The hidden Markov model presented here achieves valuable prediction results using only a limited number of parameters. It provides an interpretable framework for protein secondary structure architecture. Furthermore, it can be used as a tool for generating protein sequences with a given secondary structure content. PMID:17166267

  10. Crystal structure prediction could have helped the experimentalists with polymorphism in benzamide

    Microsoft Academic Search

    Juergen Thun; Markus Schoeffel; Josef Breu

    2008-01-01

    Benzamide was the first molecular material for which polymorphism was reported, as long as 176 years ago. Unfortunately, due to very similar cell metrics leading to massive peak overlap, the metastable form reported by Liebig escaped structural characterisation by XRD until recently. With the help of crystal structure prediction this old riddle of ‘Liebig's’ polymorph of benzamide could have been

  11. Predictive mapping of forest composition and structure with direct gradient analysis and nearest-

    E-print Network

    Predictive mapping of forest composition and structure with direct gradient analysis and nearest explicit information on the species composition and structure of forest vegetation is needed at broad spatial scales for natural resource policy analysis and ecological research. We present a method

  12. I-TASSER server: new development for protein structure and function predictions

    PubMed Central

    Yang, Jianyi; Zhang, Yang

    2015-01-01

    The I-TASSER server (http://zhanglab.ccmb.med.umich.edu/I-TASSER) is an online resource for automated protein structure prediction and structure-based function annotation. In I-TASSER, structural templates are first recognized from the PDB using multiple threading alignment approaches. Full-length structure models are then constructed by iterative fragment assembly simulations. The functional insights are finally derived by matching the predicted structure models with known proteins in the function databases. Although the server has been widely used for various biological and biomedical investigations, numerous comments and suggestions have been reported from the user community. In this article, we summarize recent developments on the I-TASSER server, which were designed to address the requirements from the user community and to increase the accuracy of modeling predictions. Focuses have been made on the introduction of new methods for atomic-level structure refinement, local structure quality estimation and biological function annotations. We expect that these new developments will improve the quality of the I-TASSER server and further facilitate its use by the community for high-resolution structure and function prediction. PMID:25883148

  13. Distill, Distill_human Distill: protein structure prediction by Machine Learning

    E-print Network

    Pollastri, Gianluca

    Distill, Distill_human Distill: protein structure prediction by Machine Learning C. Mirabello1, G.pollastri@ucd.ie Distill has two main components: a set of predictors of protein features based on machine learning" of PDB structures suggested by our fold recognition algorithm. The only difference between Distill

  14. SEGMENTATION OF RODENT BRAINS FROM MRI BASED ON A NOVEL STATISTICAL STRUCTURE PREDICTION METHOD

    E-print Network

    SEGMENTATION OF RODENT BRAINS FROM MRI BASED ON A NOVEL STATISTICAL STRUCTURE PREDICTION METHOD in under- stating the relationships between anatomy and mental diseases in brains. Volumetric analysis the relationships between anatomy and mental diseases in brains [1]. Volumetric analysis of rodent brain structures

  15. PROTEIN SECONDARY STRUCTURE PREDICTION BASED ON THE AMINO ACIDS CONFORMATIONAL CLASSIFICATION AND NEURAL NETWORK TECHNIQUE

    E-print Network

    Hefei Institute of Intelligent Machines

    PROTEIN SECONDARY STRUCTURE PREDICTION BASED ON THE AMINO ACIDS CONFORMATIONAL CLASSIFICATION@iim.ac.cn ABSTRACT In this paper, based on the 340 protein sequences and their corresponding secondary structures got from the Protein Data Bank (PDB), we group the 20 different amino acids into f (Former), b (Breaker

  16. Electronic Structure Methods for Predicting the Properties of Materials: Grids in Space

    E-print Network

    Stathopoulos, Andreas

    Electronic Structure Methods for Predicting the Properties of Materials: Grids in Space James R material is known, then many phys- ical and chemical properties can be accurately determined without. INTRODUCTION: THE ELECTRONIC STRUCTURE PROBLEM. A fundamental problem in condensed matter physics

  17. Prediction Intervals for NAR Model Structures Using a Bootstrap De Brabanter J.,

    E-print Network

    Prediction Intervals for NAR Model Structures Using a Bootstrap Method De Brabanter J structure. Our approach relies on the external bootstrap procedure [1]. This method is contrasted with a more traditional approach relying on the Gaus- sian strategy, showing improved results. 1. Introduction

  18. Predicting total organic halide formation from drinking water chlorination using quantitative structure–property relationships

    Microsoft Academic Search

    G. B. Luilo; S. E. Cabaniss

    2011-01-01

    Chlorinating water which contains dissolved organic matter (DOM) produces disinfection byproducts, the majority of unknown structure. Hence, the total organic halide (TOX) measurement is used as a surrogate for toxic disinfection byproducts. This work derives a robust quantitative structure–property relationship (QSPR) for predicting the TOX formation potential of model compounds. Literature data for 49 compounds were used to train the

  19. Predicting Three-Dimensional Structures of Transmembrane Domains of -Barrel Membrane Proteins

    E-print Network

    Dai, Yang

    Predicting Three-Dimensional Structures of Transmembrane Domains of -Barrel Membrane Proteins, mitochondria, and chloroplasts. They are important for pore formation, membrane anchoring, and enzyme activity. These proteins are also often responsible for bacterial virulence. Due to difficulties in experimental structure

  20. Structural features based genome-wide characterization and prediction of nucleosome organization

    PubMed Central

    2012-01-01

    Background Nucleosome distribution along chromatin dictates genomic DNA accessibility and thus profoundly influences gene expression. However, the underlying mechanism of nucleosome formation remains elusive. Here, taking a structural perspective, we systematically explored nucleosome formation potential of genomic sequences and the effect on chromatin organization and gene expression in S. cerevisiae. Results We analyzed twelve structural features related to flexibility, curvature and energy of DNA sequences. The results showed that some structural features such as DNA denaturation, DNA-bending stiffness, Stacking energy, Z-DNA, Propeller twist and free energy, were highly correlated with in vitro and in vivo nucleosome occupancy. Specifically, they can be classified into two classes, one positively and the other negatively correlated with nucleosome occupancy. These two kinds of structural features facilitated nucleosome binding in centromere regions and repressed nucleosome formation in the promoter regions of protein-coding genes to mediate transcriptional regulation. Based on these analyses, we integrated all twelve structural features in a model to predict more accurately nucleosome occupancy in vivo than the existing methods that mainly depend on sequence compositional features. Furthermore, we developed a novel approach, named DLaNe, that located nucleosomes by detecting peaks of structural profiles, and built a meta predictor to integrate information from different structural features. As a comparison, we also constructed a hidden Markov model (HMM) to locate nucleosomes based on the profiles of these structural features. The result showed that the meta DLaNe and HMM-based method performed better than the existing methods, demonstrating the power of these structural features in predicting nucleosome positions. Conclusions Our analysis revealed that DNA structures significantly contribute to nucleosome organization and influence chromatin structure and gene expression regulation. The results indicated that our proposed methods are effective in predicting nucleosome occupancy and positions and that these structural features are highly predictive of nucleosome organization. The implementation of our DLaNe method based on structural features is available online. PMID:22449207

  1. Structure Prediction of the Second Extracellular Loop in G-Protein-Coupled Receptors

    PubMed Central

    Kmiecik, Sebastian; Jamroz, Michal; Kolinski, Michal

    2014-01-01

    G-protein-coupled receptors (GPCRs) play key roles in living organisms. Therefore, it is important to determine their functional structures. The second extracellular loop (ECL2) is a functionally important region of GPCRs, which poses significant challenge for computational structure prediction methods. In this work, we evaluated CABS, a well-established protein modeling tool for predicting ECL2 structure in 13 GPCRs. The ECL2s (with between 13 and 34 residues) are predicted in an environment of other extracellular loops being fully flexible and the transmembrane domain fixed in its x-ray conformation. The modeling procedure used theoretical predictions of ECL2 secondary structure and experimental constraints on disulfide bridges. Our approach yielded ensembles of low-energy conformers and the most populated conformers that contained models close to the available x-ray structures. The level of similarity between the predicted models and x-ray structures is comparable to that of other state-of-the-art computational methods. Our results extend other studies by including newly crystallized GPCRs. PMID:24896119

  2. Biochemical functional predictions for protein structures of unknown or uncertain function

    PubMed Central

    Mills, Caitlyn L.; Beuning, Penny J.; Ondrechen, Mary Jo

    2015-01-01

    With the exponential growth in the determination of protein sequences and structures via genome sequencing and structural genomics efforts, there is a growing need for reliable computational methods to determine the biochemical function of these proteins. This paper reviews the efforts to address the challenge of annotating the function at the molecular level of uncharacterized proteins. While sequence- and three-dimensional-structure-based methods for protein function prediction have been reviewed previously, the recent trends in local structure-based methods have received less attention. These local structure-based methods are the primary focus of this review. Computational methods have been developed to predict the residues important for catalysis and the local spatial arrangements of these residues can be used to identify protein function. In addition, the combination of different types of methods can help obtain more information and better predictions of function for proteins of unknown function. Global initiatives, including the Enzyme Function Initiative (EFI), COMputational BRidges to EXperiments (COMBREX), and the Critical Assessment of Function Annotation (CAFA), are evaluating and testing the different approaches to predicting the function of proteins of unknown function. These initiatives and global collaborations will increase the capability and reliability of methods to predict biochemical function computationally and will add substantial value to the current volume of structural genomics data by reducing the number of absent or inaccurate functional annotations. PMID:25848497

  3. Assessment and refinement of eukaryotic gene structure prediction with gene-structure-aware multiple protein sequence alignment

    PubMed Central

    2014-01-01

    Background Accurate computational identification of eukaryotic gene organization is a long-standing problem. Despite the fundamental importance of precise annotation of genes encoded in newly sequenced genomes, the accuracy of predicted gene structures has not been critically evaluated, mostly due to the scarcity of proper assessment methods. Results We present a gene-structure-aware multiple sequence alignment method for gene prediction using amino acid sequences translated from homologous genes from many genomes. The approach provides rich information concerning the reliability of each predicted gene structure. We have also devised an iterative method that attempts to improve the structures of suspiciously predicted genes based on a spliced alignment algorithm using consensus sequences or reliable homologs as templates. Application of our methods to cytochrome P450 and ribosomal proteins from 47 plant genomes indicated that 50?~?60 % of the annotated gene structures are likely to contain some defects. Whereas more than half of the defect-containing genes may be intrinsically broken, i.e. they are pseudogenes or gene fragments, located in unfinished sequencing areas, or corresponding to non-productive isoforms, the defects found in a majority of the remaining gene candidates can be remedied by our iterative refinement method. Conclusions Refinement of eukaryotic gene structures mediated by gene-structure-aware multiple protein sequence alignment is a useful strategy to dramatically improve the overall prediction quality of a set of homologous genes. Our method will be applicable to various families of protein-coding genes if their domain structures are evolutionarily stable. It is also feasible to apply our method to gene families from all kingdoms of life, not just plants. PMID:24927652

  4. A predictive model for secondary RNA structure using graph theory and a neural network

    PubMed Central

    2010-01-01

    Background Determining the secondary structure of RNA from the primary structure is a challenging computational problem. A number of algorithms have been developed to predict the secondary structure from the primary structure. It is agreed that there is still room for improvement in each of these approaches. In this work we build a predictive model for secondary RNA structure using a graph-theoretic tree representation of secondary RNA structure. We model the bonding of two RNA secondary structures to form a larger secondary structure with a graph operation we call merge. We consider all combinatorial possibilities using all possible tree inputs, both those that are RNA-like in structure and those that are not. The resulting data from each tree merge operation is represented by a vector. We use these vectors as input values for a neural network and train the network to recognize a tree as RNA-like or not, based on the merge data vector. The network estimates the probability of a tree being RNA-like. Results The network correctly assigned a high probability of RNA-likeness to trees previously identified as RNA-like and a low probability of RNA-likeness to those classified as not RNA-like. We then used the neural network to predict the RNA-likeness of the unclassified trees. Conclusions There are a number of secondary RNA structure prediction algorithms available online. These programs are based on finding the secondary structure with the lowest total free energy. In this work, we create a predictive tool for secondary RNA structures using graph-theoretic values as input for a neural network. The use of a graph operation to theoretically describe the bonding of secondary RNA is novel and is an entirely different approach to the prediction of secondary RNA structures. Our method correctly predicted trees to be RNA-like or not RNA-like for all known cases. In addition, our results convey a measure of likelihood that a tree is RNA-like or not RNA-like. Given that the majority of secondary RNA folding algorithms return more than one possible outcome, our method provides a means of determining the best or most likely structures among all of the possible outcomes. PMID:20946605

  5. Geometric programming prediction of design trends for OMV protective structures

    NASA Technical Reports Server (NTRS)

    Mog, R. A.; Horn, J. R.

    1990-01-01

    The global optimization trends of protective honeycomb structural designs for spacecraft subject to hypervelocity meteroid and space debris are presented. This nonlinear problem is first formulated for weight minimization of the orbital maneuvering vehicle (OMV) using a generic monomial predictor. Five problem formulations are considered, each dependent on the selection of independent design variables. Each case is optimized by considering the dual geometric programming problem. The dual variables are solved for in terms of the generic estimated exponents of the monomial predictor. The primal variables are then solved for by conversion. Finally, parametric design trends are developed for ranges of the estimated regression parameters. Results specify nonmonotonic relationships for the optimal first and second sheet mass per unit areas in terms of the estimated exponents.

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

    SciTech Connect

    Not Available

    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.

  7. TASSER_WT: A Protein Structure Prediction Algorithm with Accurate Predicted Contact Restraints for Difficult Protein Targets

    PubMed Central

    Lee, Seung Yup; Skolnick, Jeffrey

    2010-01-01

    To improve the prediction accuracy in the regime where template alignment quality is poor, an updated version of TASSER_2.0, namely TASSER_WT, was developed. TASSER_WT incorporates more accurate contact restraints from a new method, COMBCON. COMBCON uses confidence-weighted contacts from PROSPECTOR_3.5, the latest version, PROSPECTOR_4, and a new local structural fragment-based threading algorithm, STITCH, implemented in two variants depending on expected fragment prediction accuracy. TASSER_WT is tested on 622 Hard proteins, the most difficult targets (incorrect alignments and/or templates and incorrect side-chain contact restraints) in a comprehensive benchmark of 2591 nonhomologous, single domain proteins ?200 residues that cover the PDB at 35% pairwise sequence identity. For 454 of 622 Hard targets, COMBCON provides contact restraints with higher accuracy and number of contacts per residue. As contact coverage with confidence weight ?3 (Fwt?3cov) increases, the more improved are TASSER_WT models. When Fwt?3cov > 1.0 and > 0.4, the average root mean-square deviation of TASSER_WT (TASSER_2.0) models is 4.11 Å (6.72 Å) and 5.03 Å (6.40 Å), respectively. Regarding a structure prediction as successful when a model has a TM-score to the native structure ?0.4, when Fwt?3cov > 1.0 and > 0.4, the success rate of TASSER_WT (TASSER_2.0) is 98.8% (76.2%) and 93.7% (81.1%), respectively. PMID:21044605

  8. Prediction of protein secondary structure content for the twilight zone sequences.

    PubMed

    Homaeian, Leila; Kurgan, Lukasz A; Ruan, Jishou; Cios, Krzysztof J; Chen, Ke

    2007-11-15

    Secondary protein structure carries information about local structural arrangements, which include three major conformations: alpha-helices, beta-strands, and coils. Significant majority of successful methods for prediction of the secondary structure is based on multiple sequence alignment. However, multiple alignment fails to provide accurate results when a sequence comes from the twilight zone, that is, it is characterized by low (<30%) homology. To this end, we propose a novel method for prediction of secondary structure content through comprehensive sequence representation, called PSSC-core. The method uses a multiple linear regression model and introduces a comprehensive feature-based sequence representation to predict amount of helices and strands for sequences from the twilight zone. The PSSC-core method was tested and compared with two other state-of-the-art prediction methods on a set of 2187 twilight zone sequences. The results indicate that our method provides better predictions for both helix and strand content. The PSSC-core is shown to provide statistically significantly better results when compared with the competing methods, reducing the prediction error by 5-7% for helix and 7-9% for strand content predictions. The proposed feature-based sequence representation uses a comprehensive set of physicochemical properties that are custom-designed for each of the helix and strand content predictions. It includes composition and composition moment vectors, frequency of tetra-peptides associated with helical and strand conformations, various property-based groups like exchange groups, chemical groups of the side chains and hydrophobic group, auto-correlations based on hydrophobicity, side-chain masses, hydropathy, and conformational patterns for beta-sheets. The PSSC-core method provides an alternative for predicting the secondary structure content that can be used to validate and constrain results of other structure prediction methods. At the same time, it also provides useful insight into design of successful protein sequence representations that can be used in developing new methods related to prediction of different aspects of the secondary protein structure. PMID:17623861

  9. Computer Prediction of Possible Toxic Action from Chemical Structure; The DEREK System

    Microsoft Academic Search

    D. M. Sanderson; C. G. Earnshaw

    1991-01-01

    1 The development of DEREK, a computer-based expert system (derived from the LHASA chemical synthesis design program) for the qualitative prediction of possible toxic action of compounds on the basis of their chemical structure is described.2 The system is able to perceive chemical sub-structures within molecules and relate these to a rulebase linking the sub-structures with likely types of toxicity.3

  10. From Structure Prediction to Genomic Screens for Novel Non-Coding RNAs

    Microsoft Academic Search

    Jan Gorodkin; Ivo L. Hofacker

    2011-01-01

    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

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

    PubMed

    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. PMID:7736804

  12. Computational prediction of riboswitch tertiary structures including pseudoknots by RAGTOP: a hierarchical graph sampling approach.

    PubMed

    Kim, Namhee; Zahran, Mai; Schlick, Tamar

    2015-01-01

    The modular organization of RNA structure has been exploited in various computational and theoretical approaches to identify RNA tertiary (3D) motifs and assemble RNA structures. Riboswitches exemplify this modularity in terms of both structural and functional adaptability of RNA components. Here, we extend our computational approach based on tree graph sampling to the prediction of riboswitch topologies by defining additional edges to mimick pseudoknots. Starting from a secondary (2D) structure, we construct an initial graph deduced from predicted junction topologies by our data-mining algorithm RNAJAG trained on known RNAs; we sample these graphs in 3D space guided by knowledge-based statistical potentials derived from bending and torsion measures of internal loops as well as radii of gyration for known RNAs. We present graph sampling results for 10 representative riboswitches, 6 of them with pseudoknots, and compare our predictions to solved structures based on global and local RMSD measures. Our results indicate that the helical arrangements in riboswitches can be approximated using our combination of modified 3D tree graph representations for pseudoknots, junction prediction, graph moves, and scoring functions. Future challenges in the field of riboswitch prediction and design are also discussed. PMID:25726463

  13. The Ordered Network Structure and Prediction Summary for M?7 Earthquakes in Xinjiang Region of China

    NASA Astrophysics Data System (ADS)

    Men, Ke-Pei; Zhao, Kai

    2014-12-01

    M ?7 earthquakes have showed an obvious commensurability and orderliness in Xinjiang of China and its adjacent region since 1800. The main orderly values are 30 a × k (k = 1,2,3), 11 ~ 12 a, 41 ~ 43 a, 18 ~ 19 a, and 5 ~ 6 a. In the guidance of the information forecasting theory of Wen-Bo Weng, based on previous research results, combining ordered network structure analysis with complex network technology, we focus on the prediction summary of M ? 7 earthquakes by using the ordered network structure, and add new information to further optimize network, hence construct the 2D- and 3D-ordered network structure of M ? 7 earthquakes. In this paper, the network structure revealed fully the regularity of seismic activity of M ? 7 earthquakes in the study region during the past 210 years. Based on this, the Karakorum M7.1 earthquake in 1996, the M7.9 earthquake on the frontier of Russia, Mongol, and China in 2003, and two Yutian M7.3 earthquakes in 2008 and 2014 were predicted successfully. At the same time, a new prediction opinion is presented that the future two M ? 7 earthquakes will probably occur around 2019 - 2020 and 2025 - 2026 in this region. The results show that large earthquake occurred in defined region can be predicted. The method of ordered network structure analysis produces satisfactory results for the mid-and-long term prediction of M ? 7 earthquakes.

  14. Structural link prediction based on ant colony approach in social networks

    NASA Astrophysics Data System (ADS)

    Sherkat, Ehsan; Rahgozar, Maseud; Asadpour, Masoud

    2015-02-01

    As the size and number of online social networks are increasing day by day, social network analysis has become a popular issue in many branches of science. The link prediction is one of the key rolling issues in the analysis of social network's evolution. As the size of social networks is increasing, the necessity for scalable link prediction algorithms is being felt more. The aim of this paper is to introduce a new unsupervised structural link prediction algorithm based on the ant colony approach. Recently, ant colony approach has been used for solving some graph problems. Different kinds of networks are used for testing the proposed approach. In some networks, the proposed scalable algorithm has the best result in comparison to other structural unsupervised link prediction algorithms. In order to evaluate the algorithm results, methods like the top- n precision, area under the Receiver Operating Characteristic (ROC) and Precision-Recall curves are carried out on real-world networks.

  15. PSP_MCSVM: brainstorming consensus prediction of protein secondary structures using two-stage multiclass support vector machines

    Microsoft Academic Search

    Piyali Chatterjee; Subhadip Basu; Mahantapas Kundu; Mita Nasipuri; Dariusz Plewczynski

    Secondary structure prediction is a crucial task for understanding the variety of protein structures and performed biological\\u000a functions. Prediction of secondary structures for new proteins using their amino acid sequences is of fundamental importance\\u000a in bioinformatics. We propose a novel technique to predict protein secondary structures based on position-specific scoring\\u000a matrices (PSSMs) and physico-chemical properties of amino acids. It is

  16. Predicting RNA 3D structure using a coarse-grain helix-centered model.

    PubMed

    Kerpedjiev, Peter; Höner Zu Siederdissen, Christian; Hofacker, Ivo L

    2015-06-01

    A 3D model of RNA structure can provide information about its function and regulation that is not possible with just the sequence or secondary structure. Current models suffer from low accuracy and long running times and either neglect or presume knowledge of the long-range interactions which stabilize the tertiary structure. Our coarse-grained, helix-based, tertiary structure model operates with only a few degrees of freedom compared with all-atom models while preserving the ability to sample tertiary structures given a secondary structure. It strikes a balance between the precision of an all-atom tertiary structure model and the simplicity and effectiveness of a secondary structure representation. It provides a simplified tool for exploring global arrangements of helices and loops within RNA structures. We provide an example of a novel energy function relying only on the positions of stems and loops. We show that coupling our model to this energy function produces predictions as good as or better than the current state of the art tools. We propose that given the wide range of conformational space that needs to be explored, a coarse-grain approach can explore more conformations in less iterations than an all-atom model coupled to a fine-grain energy function. Finally, we emphasize the overarching theme of providing an ensemble of predicted structures, something which our tool excels at, rather than providing a handful of the lowest energy structures. PMID:25904133

  17. Predicting RNA 3D structure using a coarse-grain helix-centered model

    PubMed Central

    Kerpedjiev, Peter; Höner zu Siederdissen, Christian; Hofacker, Ivo L.

    2015-01-01

    A 3D model of RNA structure can provide information about its function and regulation that is not possible with just the sequence or secondary structure. Current models suffer from low accuracy and long running times and either neglect or presume knowledge of the long-range interactions which stabilize the tertiary structure. Our coarse-grained, helix-based, tertiary structure model operates with only a few degrees of freedom compared with all-atom models while preserving the ability to sample tertiary structures given a secondary structure. It strikes a balance between the precision of an all-atom tertiary structure model and the simplicity and effectiveness of a secondary structure representation. It provides a simplified tool for exploring global arrangements of helices and loops within RNA structures. We provide an example of a novel energy function relying only on the positions of stems and loops. We show that coupling our model to this energy function produces predictions as good as or better than the current state of the art tools. We propose that given the wide range of conformational space that needs to be explored, a coarse-grain approach can explore more conformations in less iterations than an all-atom model coupled to a fine-grain energy function. Finally, we emphasize the overarching theme of providing an ensemble of predicted structures, something which our tool excels at, rather than providing a handful of the lowest energy structures. PMID:25904133

  18. Analytical Methodology for Predicting the Onset of Widespread Fatigue Damage in Fuselage Structure

    NASA Technical Reports Server (NTRS)

    Harris, Charles E.; Newman, James C., Jr.; Piascik, Robert S.; Starnes, James H., Jr.

    1996-01-01

    NASA has developed a comprehensive analytical methodology for predicting the onset of widespread fatigue damage in fuselage structure. The determination of the number of flights and operational hours of aircraft service life that are related to the onset of widespread fatigue damage includes analyses for crack initiation, fatigue crack growth, and residual strength. Therefore, the computational capability required to predict analytically the onset of widespread fatigue damage must be able to represent a wide range of crack sizes from the material (microscale) level to the global structural-scale level. NASA studies indicate that the fatigue crack behavior in aircraft structure can be represented conveniently by the following three analysis scales: small three-dimensional cracks at the microscale level, through-the-thickness two-dimensional cracks at the local structural level, and long cracks at the global structural level. The computational requirements for each of these three analysis scales are described in this paper.

  19. Finite element models to predict the structural response of 120-mm sabot\\/rods during launch

    Microsoft Academic Search

    D. A. Rabern; K. A. Bannister

    1990-01-01

    Numerical modeling techniques in two- and three-dimensions were used to predict the structural and mechanical behavior of sabot\\/rod systems while inbore and just after muzzle exit. Three-dimensional transient numerical simulations were used to predict the rod deformations and states of stress and strain caused by axial and lateral accelerations during launch. The numerical models include the launch tube, recoil motion,

  20. An Energy Based Fatigue Life Prediction Framework for In-Service Structural Components

    Microsoft Academic Search

    H. Ozaltun; M.-H. H. Shen; T. George; C. Cross

    2011-01-01

    An energy based fatigue life prediction framework has been developed for calculation of remaining fatigue life of in service\\u000a gas turbine materials. The purpose of the life prediction framework is to account aging effect caused by cyclic loadings on\\u000a fatigue strength of gas turbine engines structural components which are usually designed for very long life. Previous studies\\u000a indicate the total

  1. Multithreaded comparative RNA secondary structure prediction using stochastic context-free grammars

    Microsoft Academic Search

    Zsuzsanna Sukosd; Bjarne Knudsen; Morten Vaerum; Jorgen Kjems; Ebbe Sloth Andersen

    2011-01-01

    Background  The prediction of the structure of large RNAs remains a particular challenge in bioinformatics, due to the computational complexity\\u000a and low levels of accuracy of state-of-the-art algorithms. The pfold model couples a stochastic context-free grammar to phylogenetic analysis for a high accuracy in predictions, but the time\\u000a complexity of the algorithm and underflow errors have prevented its use for long

  2. Protein Function Prediction from Structure in Structural Genomics and its Contribution to the Study of Health and Disease

    NASA Astrophysics Data System (ADS)

    Watson, James D.; Thornton, Janet M.

    The various structural genomics projects throughout the globe have developed high-throughput protein structure determination pipelines which have been responsible for the deposition of a vast number of protein structures. As a consequence of the need for rapid data release and their target selection strategy, these projects have deposited a large number of proteins with little or no functional information. As the experimental characterization of protein function is expensive and time consuming, the bio-informatics community was prompted to address the problem of protein function prediction from sequence and structure. Over the years many methods have been developed and show varying degrees of success. Here we will discuss the main types of approach, the problems faced and, with examples from the Midwest Center for Structural Genomics (MCSG), illustrate how these structures and the techniques developed can have a significant impact on the study of health and disease.

  3. ASTRO-FOLD 2.0: an Enhanced Framework for Protein Structure Prediction

    PubMed Central

    Subramani, A.; Wei, Y.; Floudas, C. A.

    2011-01-01

    The three-dimensional (3-D) structure prediction of proteins, given their amino acid sequence, is addressed using the first principles–based approach ASTRO-FOLD 2.0. The key features presented are: (1) Secondary structure prediction using a novel optimization-based consensus approach, (2) ?-sheet topology prediction using mixed-integer linear optimization (MILP), (3) Residue-to-residue contact prediction using a high-resolution distance-dependent force field and MILP formulation, (4) Tight dihedral angle and distance bound generation for loop residues using dihedral angle clustering and non-linear optimization (NLP), (5) 3-D structure prediction using deterministic global optimization, stochastic conformational space annealing, and the full-atomistic ECEPP/3 potential, (6) Near-native structure selection using a traveling salesman problem-based clustering approach, ICON, and (7) Improved bound generation using chemical shifts of subsets of heavy atoms, generated by SPARTA and CS23D. Computational results of ASTRO-FOLD 2.0 on 47 blind targets of the recently concluded CASP9 experiment are presented. PMID:23049093

  4. Ab-initio crystal structure prediction. A case study: NaBH{sub 4}

    SciTech Connect

    Caputo, Riccarda, E-mail: riccarda.caputo@inorg.chem.ethz.ch [ETH Zuerich, Department of Chemistry and Applied Biosciences, Laboratory of Inorganic Chemistry, Wolfgang-Pauli Str 10, CH-8093 Zuerich (Switzerland); Tekin, Adem, E-mail: adem.tekin@be.itu.edu.tr [Informatics Institute, Istanbul Technical University, 34469 Maslak, Istanbul (Turkey)

    2011-07-15

    Crystal structure prediction from first principles is still one of the most challenging and interesting issue in condensed matter science. we explored the potential energy surface of NaBH{sub 4} by a combined ab-initio approach, based on global structure optimizations and quantum chemistry. In particular, we used simulated annealing (SA) and density functional theory (DFT) calculations. The methodology enabled the identification of several local minima, of which the global minimum corresponded to the tetragonal ground-state structure (P4{sub 2}/nmc), and the prediction of higher energy stable structures, among them a monoclinic (Pm) one was identified to be 22.75 kJ/mol above the ground-state at T=298 K. In between, orthorhombic and cubic structures were recovered, in particular those with Pnma and F4-bar 3m symmetries. - Graphical abstract: The total electron energy difference of the calculated stable structures. Here, the tetragonal (IT 137) and the monoclinic (IT 6) symmetry groups corresponded to the lowest and the highest energy structures, respectively. Highlights: > Potential energy surface of NaBH{sub 4} is investigated. > This is done a combination of global structure optimizations based on simulated annealing and density functional calculations. > We successfully reproduced experimentally found tetragonal and orthorhombic structures of NaBH{sub 4}. > Furthermore, we found a new stable high energy structure.

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

    DOEpatents

    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.

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

    PubMed

    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

  7. Analysis and Design of Fuselage Structures Including Residual Strength Prediction Methodology

    NASA Technical Reports Server (NTRS)

    Knight, Norman F.

    1998-01-01

    The goal of this research project is to develop and assess methodologies for the design and analysis of fuselage structures accounting for residual strength. Two primary objectives are included in this research activity: development of structural analysis methodology for predicting residual strength of fuselage shell-type structures; and the development of accurate, efficient analysis, design and optimization tool for fuselage shell structures. Assessment of these tools for robustness, efficient, and usage in a fuselage shell design environment will be integrated with these two primary research objectives.

  8. Mediating role of multivalent cations in DNA electrostatics: an epsilon-modified Poisson-Boltzmann study of B-DNA-B-DNA interactions in mixture of NaCl and MgCl2 solutions.

    PubMed

    Gavryushov, Sergei

    2009-02-19

    Potentials of mean force acting between two ions in SPC/E water have been determined via molecular dynamics simulations using the spherical cavity approach ( J. Phys. Chem. B 2006 , 110 , 10878 ). The potentials were obtained for Me(2+)-Me(+) pairs, where Me(2+) means cations Mg(2+) and Ca(2+) and Me(+) denotes monovalent ions Li(+), Na(+), and K(+). The hard-core interaction distance for effective Me(2+)-Me(+) potentials appears to be of about 5 A that looks like a sum of the effective radii of a Me(2+) ion (3 A) and of an alkali metal ion Me(+) (about 2 A). These ion-ion interaction parameters were used in the epsilon-Modified Poisson-Boltzmann (epsilon-MPB) calculations ( J. Phys. Chem. B 2007 , 111 , 5264 ) of ionic distributions around DNA generalized for the arbitrary mixture of different ion species. Ionic distributions around an all-atom geometry model of B-DNA in solution of a mixture of NaCl and MgCl(2) were obtained. It was found that even a small fraction of ions Mg(2+) led to sharp condensation of Mg(2+) near the phosphate groups of DNA due to polarization deficiency of cluster [Mg(H(2)O)(6)](2+) in an external field. The epsilon-MPB calculations of the B-DNA-B-DNA interaction energies suggest that adding 1 mM of Mg(2+) to 50 mM solution of NaCl notably affects the force acting between the two macromolecules. Being compared to Poisson-Boltzmann results and to MPB calculations for the primitive model of ions, the epsilon-MPB results also indicate an important contribution of dielectric saturation effects to the mediating role of divalent cations in the DNA-DNA interaction energies. PMID:19199702

  9. Prediction of re-entrant regions and other structural features beyond traditional topology models

    NASA Astrophysics Data System (ADS)

    Granseth, Erik

    A topology model of a membrane protein is a two-dimensional representation of the three-dimensional structure. Most often, it is the only structural information available and it can either come from computer predictions, experiments or a combination of both. However, it has lately become clear that some membrane protein structures contain features that cannot be described by a traditional topology model. They might contain kinks in their transmembrane helices, have interface helices that lie parallel to the membrane surface or contain re-entrant regions that only partially enter the membrane. Since these structural features are almost always functionally important and there are more and more structures available each year, there has been an increasing effort in predicting them. This chapter describes transmembrane helix kinks, interface helices, amphipathic membrane anchors, and re-entrant regions in detail, both from a biological perspective and from the methods that try to predict them. Additionally, prediction of free energy of membrane insertion and Z-coordinates is also covered.

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

    PubMed Central

    Hossain, Md. Anowar

    2014-01-01

    beta-D-N-Acetylhexosaminidase, a family 20 glycosyl hydrolase, catalyzes the removal of ?-1,4-linked N-acetylhexosamine residues from oligosaccharides and their conjugates. We constructed phylogenetic tree of ?-hexosaminidases to analyze the evolutionary history and predicted functions of plant hexosaminidases. Phylogenetic analysis reveals the complex history of evolution of plant ?-hexosaminidase that can be described by gene duplication events. The 3D structure of tomato ?-hexosaminidase (?-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 (?/?)8 barrel in the central part. The ? and ? 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 ?-Hex-Sl mutagenesis experiments for exploring the functions of this class of enzymes in plant kingdom. PMID:25165734

  11. Structural predictions based on the compositions of cathodic materials by first-principles calculations

    NASA Astrophysics Data System (ADS)

    Li, Yang; Lian, Fang; Chen, Ning; Hao, Zhen-jia; Chou, Kuo-chih

    2015-05-01

    A first-principles method is applied to comparatively study the stability of lithium metal oxides with layered or spinel structures to predict the most energetically favorable structure for different compositions. The binding and reaction energies of the real or virtual layered LiMO2 and spinel LiM2O4 (M = Sc-Cu, Y-Ag, Mg-Sr, and Al-In) are calculated. The effect of element M on the structural stability, especially in the case of multiple-cation compounds, is discussed herein. The calculation results indicate that the phase stability depends on both the binding and reaction energies. The oxidation state of element M also plays a role in determining the dominant structure, i.e., layered or spinel phase. Moreover, calculation-based theoretical predictions of the phase stability of the doped materials agree with the previously reported experimental data.

  12. Aircraft Structural Mass Property Prediction Using Conceptual-Level Structural Analysis

    Microsoft Academic Search

    Matthew G. Sexstone

    1998-01-01

    This paper describes a methodology that extends the use of the Equivalent LAminated Plate Solution (ELAPS) structural analysis code from conceptual-level aircraft structural analysis to conceptual-level aircraft mass property analysis. Mass property analysis in aircraft structures has historically depended upon parametric we ight equations at the conceptual design level and Finite Element Analysis (FEA) at the detailed design level. ELAPS

  13. Protein structure prediction by all-atom free-energy refinement

    Microsoft Academic Search

    Abhinav Verma; Wolfgang Wenzel

    2007-01-01

    BACKGROUND: The reliable prediction of protein tertiary structure from the amino acid sequence remains challenging even for small proteins. We have developed an all-atom free-energy protein forcefield (PFF01) that we could use to fold several small proteins from completely extended conformations. Because the computational cost of de-novo folding studies rises steeply with system size, this approach is unsuitable for structure

  14. A dual-scale approach toward structure prediction of retinal proteins

    Microsoft Academic Search

    C.-C. Chen; C.-M. Chen

    2009-01-01

    We propose a dual-scale approach to predict the native structures of retinal proteins (RPs) by combining coarse-grained (CG) Monte-Carlo simulations and all-atom (AA) molecular dynamics simulations to pack their transmembrane helices correctly. This approach has been applied to obtain the structures of five RPs, including bacteriorhodopsin (BR), halorhodopsin (HR), sensory rhodopsin I (SRI), sensory rhodopsin II (SRII), and (bovine) rhodopsin.

  15. Application of multiple sequence alignment profiles to improve protein secondary structure prediction

    Microsoft Academic Search

    James A. Cuff; Geoffrey J. Barton

    2000-01-01

    ABSTRACT The effect of training a neural net- work secondary structure prediction algorithm with different types of multiple sequence alignment pro- files derived from the same sequences, is shown to provide a range of accuracy from 70.5% to 76.4%. The best accuracy of 76.4% (standard deviation 8.4%), is 3.1% (2000 Wiley-Liss, Inc. Key words: protein; secondary structure predic-

  16. Predicting Protein Ligand Binding Sites by Combining Evolutionary Sequence Conservation and 3D Structure

    Microsoft Academic Search

    John A. Capra; Roman A. Laskowski; Janet M. Thornton; Mona Singh; Thomas A. Funkhouser

    2009-01-01

    Identifying a protein's functional sites is an important step towards characterizing its molecular function. Numerous structure- and sequence-based methods have been developed for this problem. Here we introduce ConCavity, a small molecule binding site prediction algorithm that integrates evolutionary sequence conservation estimates with structure-based methods for identifying protein surface cavities. In large-scale testing on a diverse set of single- and

  17. De novo structure prediction and experimental characterization of folded peptoid oligomers

    PubMed Central

    Butterfoss, Glenn L.; Yoo, Barney; Jaworski, Jonathan N.; Chorny, Ilya; Dill, Ken A.; Zuckermann, Ronald N.; Bonneau, Richard; Kirshenbaum, Kent; Voelz, Vincent A.

    2012-01-01

    Peptoid molecules are biomimetic oligomers that can fold into unique three-dimensional structures. As part of an effort to advance computational design of folded oligomers, we present blind-structure predictions for three peptoid sequences using a combination of Replica Exchange Molecular Dynamics (REMD) simulation and Quantum Mechanical refinement. We correctly predicted the structure of a N-aryl peptoid trimer to within 0.2 ? rmsd-backbone and a cyclic peptoid nonamer to an accuracy of 1.0 ? rmsd-backbone. X-ray crystallographic structures are presented for a linear N-alkyl peptoid trimer and for the cyclic peptoid nonamer. The peptoid macrocycle structure features a combination of cis and trans backbone amides, significant nonplanarity of the amide bonds, and a unique “basket” arrangement of (S)-N(1-phenylethyl) side chains encompassing a bound ethanol molecule. REMD simulations of the peptoid trimers reveal that well folded peptoids can exhibit funnel-like conformational free energy landscapes similar to those for ordered polypeptides. These results indicate that physical modeling can successfully perform de novo structure prediction for small peptoid molecules. PMID:22908242

  18. Thermodynamic ground state of MgB{sub 6} predicted from first principles structure search methods

    SciTech Connect

    Wang, Hui [State Key Lab of Superhard Materials, Jilin University, Changchun 130012 (China) [State Key Lab of Superhard Materials, Jilin University, Changchun 130012 (China); Department of Physics and Engineering Physics, University of Saskatchewan, Saskatoon, Saskatchewan S7N 5E2 (Canada); LeBlanc, K. A. [Department of Physics and Engineering Physics, University of Saskatchewan, Saskatoon, Saskatchewan S7N 5E2 (Canada)] [Department of Physics and Engineering Physics, University of Saskatchewan, Saskatoon, Saskatchewan S7N 5E2 (Canada); Gao, Bo [State Key Lab of Superhard Materials, Jilin University, Changchun 130012 (China)] [State Key Lab of Superhard Materials, Jilin University, Changchun 130012 (China); Yao, Yansun, E-mail: yansun.yao@usask.ca [Department of Physics and Engineering Physics, University of Saskatchewan, Saskatoon, Saskatchewan S7N 5E2 (Canada) [Department of Physics and Engineering Physics, University of Saskatchewan, Saskatoon, Saskatchewan S7N 5E2 (Canada); Canadian Light Source, Saskatoon, Saskatchewan S7N 0X4 (Canada)

    2014-01-28

    Crystalline structures of magnesium hexaboride, MgB{sub 6}, were investigated using unbiased structure searching methods combined with first principles density functional calculations. An orthorhombic Cmcm structure was predicted as the thermodynamic ground state of MgB{sub 6}. The energy of the Cmcm structure is significantly lower than the theoretical MgB{sub 6} models previously considered based on a primitive cubic arrangement of boron octahedra. The Cmcm structure is stable against the decomposition to elemental magnesium and boron solids at atmospheric pressure and high pressures up to 18.3 GPa. A unique feature of the predicted Cmcm structure is that the boron atoms are clustered into two forms: localized B{sub 6} octahedra and extended B{sub ?} ribbons. Within the boron ribbons, the electrons are delocalized and this leads to a metallic ground state with vanished electric dipoles. The present prediction is in contrast to the previous proposal that the crystalline MgB{sub 6} maintains a semiconducting state with permanent dipole moments. MgB{sub 6} is estimated to have much weaker electron-phonon coupling compared with that of MgB{sub 2}, and therefore it is not expected to be able to sustain superconductivity at high temperatures.

  19. Rational Design of Temperature-Sensitive Alleles Using Computational Structure Prediction

    PubMed Central

    Poultney, Christopher S.; Butterfoss, Glenn L.; Gutwein, Michelle R.; Drew, Kevin; Gresham, David; Gunsalus, Kristin C.; Shasha, Dennis E.; Bonneau, Richard

    2011-01-01

    Temperature-sensitive (ts) mutations are mutations that exhibit a mutant phenotype at high or low temperatures and a wild-type phenotype at normal temperature. Temperature-sensitive mutants are valuable tools for geneticists, particularly in the study of essential genes. However, finding ts mutations typically relies on generating and screening many thousands of mutations, which is an expensive and labor-intensive process. Here we describe an in silico method that uses Rosetta and machine learning techniques to predict a highly accurate “top 5” list of ts mutations given the structure of a protein of interest. Rosetta is a protein structure prediction and design code, used here to model and score how proteins accommodate point mutations with side-chain and backbone movements. We show that integrating Rosetta relax-derived features with sequence-based features results in accurate temperature-sensitive mutation predictions. PMID:21912654

  20. Synthesis of a specified, silica molecular sieve by using computationally predicted organic structure-directing agents.

    PubMed

    Schmidt, Joel E; Deem, Michael W; Davis, Mark E

    2014-08-01

    Crystalline molecular sieves are used in numerous applications, where the properties exploited for each technology are the direct consequence of structural features. New materials are typically discovered by trial and error, and in many cases, organic structure-directing agents (OSDAs) are used to direct their formation. Here, we report the first successful synthesis of a specified molecular sieve through the use of an OSDA that was predicted from a recently developed computational method that constructs chemically synthesizable OSDAs. Pentamethylimidazolium is computationally predicted to have the largest stabilization energy in the STW framework, and is experimentally shown to strongly direct the synthesis of pure-silica STW. Other OSDAs with lower stabilization energies did not form STW. The general method demonstrated here to create STW may lead to new, simpler OSDAs for existing frameworks and provide a way to predict OSDAs for desired, theoretical frameworks. PMID:24961789

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

    NASA Astrophysics Data System (ADS)

    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. Combining Structure and Sequence Information Allows Automated Prediction of Substrate Specificities within Enzyme Families

    PubMed Central

    Röttig, Marc; Rausch, Christian; Kohlbacher, Oliver

    2010-01-01

    An important aspect of the functional annotation of enzymes is not only the type of reaction catalysed by an enzyme, but also the substrate specificity, which can vary widely within the same family. In many cases, prediction of family membership and even substrate specificity is possible from enzyme sequence alone, using a nearest neighbour classification rule. However, the combination of structural information and sequence information can improve the interpretability and accuracy of predictive models. The method presented here, Active Site Classification (ASC), automatically extracts the residues lining the active site from one representative three-dimensional structure and the corresponding residues from sequences of other members of the family. From a set of representatives with known substrate specificity, a Support Vector Machine (SVM) can then learn a model of substrate specificity. Applied to a sequence of unknown specificity, the SVM can then predict the most likely substrate. The models can also be analysed to reveal the underlying structural reasons determining substrate specificities and thus yield valuable insights into mechanisms of enzyme specificity. We illustrate the high prediction accuracy achieved on two benchmark data sets and the structural insights gained from ASC by a detailed analysis of the family of decarboxylating dehydrogenases. The ASC web service is available at http://asc.informatik.uni-tuebingen.de/. PMID:20072606

  3. Biomedical event extraction from abstracts and full papers using search-based structured prediction

    PubMed Central

    2012-01-01

    Background Biomedical event extraction has attracted substantial attention as it can assist researchers in understanding the plethora of interactions among genes that are described in publications in molecular biology. While most recent work has focused on abstracts, the BioNLP 2011 shared task evaluated the submitted systems on both abstracts and full papers. In this article, we describe our submission to the shared task which decomposes event extraction into a set of classification tasks that can be learned either independently or jointly using the search-based structured prediction framework. Our intention is to explore how these two learning paradigms compare in the context of the shared task. Results We report that models learned using search-based structured prediction exceed the accuracy of independently learned classifiers by 8.3 points in F-score, with the gains being more pronounced on the more complex Regulation events (13.23 points). Furthermore, we show how the trade-off between recall and precision can be adjusted in both learning paradigms and that search-based structured prediction achieves better recall at all precision points. Finally, we report on experiments with a simple domain-adaptation method, resulting in the second-best performance achieved by a single system. Conclusions We demonstrate that joint inference using the search-based structured prediction framework can achieve better performance than independently learned classifiers, thus demonstrating the potential of this learning paradigm for event extraction and other similarly complex information-extraction tasks. PMID:22759459

  4. DOI: 10.1002/cmdc.201000175 Prediction of the Three-Dimensional Structure for the Rat

    E-print Network

    Goddard III, William A.

    DOI: 10.1002/cmdc.201000175 Prediction of the Three-Dimensional Structure for the Rat Urotensin II Receptor, and Comparison of the Antagonist Binding Sites and Binding Selectivity between Human and Rat (Kd =~4 nm from a molecular binding assay). However, it binds ~400-times more weakly to rat UT2R (rUT2

  5. Factor Structure, Stability, and Predictive Validity of College Students' Relationship Self-Efficacy Beliefs

    ERIC Educational Resources Information Center

    Lopez, Frederick G.; Morua, Wendy; Rice, Kenneth G.

    2007-01-01

    This study explored the underlying structure, stability, and predictive validity of college students' scores on a measure of relationship maintenance self-efficacy beliefs. Three identified efficacy-related factors were found to be stable; related in expected directions with gender, commitment status, and adult attachment orientations; and…

  6. Protein Secondary Structure Prediction Based on Position-specific Scoring Matrices

    Microsoft Academic Search

    David T. Jones

    1999-01-01

    A two-stage neural network has been used to predict protein secondary structure based on the position specific scoring matrices generated by PSI-BLAST. Despite the simplicity and convenience of the approach used, the results are found to be superior to those produced by other methods, including the popular PHD method according to our own benchmarking results and the results from the

  7. Predicting hepatotoxicity using ToxCast in vitro bioactivity and chemical structure

    EPA Science Inventory

    Background: The U.S. EPA ToxCastTM program is screening thousands of environmental chemicals for bioactivity using hundreds of high-throughput in vitro assays to build predictive models of toxicity. We represented chemicals based on bioactivity and chemical structure descriptors ...

  8. Properties of the BFKL equation and structure function predictions for HERA

    E-print Network

    A. J. Askew; J. Kwiecinski; A. D. Martin; P. J. Sutton

    1993-10-11

    The general properties of the Lipatov or BFKL equation are reviewed. Modifications to the infrared region are proposed. Numerical predictions for the deep-inelastic electron-proton structure functions at small $x$ are presented and confronted with recent HERA measurements.

  9. Predicted 3D structure for the human 2 adrenergic receptor and its binding site for agonists

    E-print Network

    Goddard III, William A.

    , Rene J. Trabanino, Victor Wai Tak Kam, and William A. Goddard III* Materials and Process Simulation, January 5, 2004 We report the 3D structure of human 2 adrenergic receptor (AR) predicted by using the Memb on ligand-binding sites and mutational analysis with which to compare our results (2, 3). We use the Memb

  10. Dynalign II: common secondary structure prediction for RNA homologs with domain insertions

    PubMed Central

    Fu, Yinghan; Sharma, Gaurav; Mathews, David H.

    2014-01-01

    Homologous non-coding RNAs frequently exhibit domain insertions, where a branch of secondary structure is inserted in a sequence with respect to its homologs. Dynamic programming algorithms for common secondary structure prediction of multiple RNA homologs, however, do not account for these domain insertions. This paper introduces a novel dynamic programming algorithm methodology that explicitly accounts for the possibility of inserted domains when predicting common RNA secondary structures. The algorithm is implemented as Dynalign II, an update to the Dynalign software package for predicting the common secondary structure of two RNA homologs. This update is accomplished with negligible increase in computational cost. Benchmarks on ncRNA families with domain insertions validate the method. Over base pairs occurring in inserted domains, Dynalign II improves accuracy over Dynalign, attaining 80.8% sensitivity (compared with 14.4% for Dynalign) and 91.4% positive predictive value (PPV) for tRNA; 66.5% sensitivity (compared with 38.9% for Dynalign) and 57.0% PPV for RNase P RNA; and 50.1% sensitivity (compared with 24.3% for Dynalign) and 58.5% PPV for SRP RNA. Compared with Dynalign, Dynalign II also exhibits statistically significant improvements in overall sensitivity and PPV. Dynalign II is available as a component of RNAstructure, which can be downloaded from http://rna.urmc.rochester.edu/RNAstructure.html. PMID:25416799

  11. Novel Use of a Genetic Algorithm for Protein Structure Prediction: Searching Template and Sequence

    E-print Network

    Moreira, Bruno Contreras

    Novel Use of a Genetic Algorithm for Protein Structure Prediction: Searching Template and Sequence Laboratories, London, United Kingdom ABSTRACT A novel genetic algorithm was ap- plied to all CASP5 targets recognition; comparative model- ing; genetic algorithms; template selec- tion; alignment errors INTRODUCTION

  12. Pruned tree-structured vector quantization of medical images with segmentation and improved prediction

    Microsoft Academic Search

    Giovanni Poggi; Richard A. Olshen

    1995-01-01

    The authors use predictive pruned tree-structured vector quantization for the compression of medical images. Their goal is to obtain a high compression ratio without impairing the image quality, at least so far as diagnostic purposes are concerned. The authors use a priori knowledge of the class of images to be encoded to help them segment the images and thereby to

  13. Predicted structure of agonist-bound glucagon-like peptide 1 receptor, a class

    E-print Network

    Goddard III, William A.

    Predicted structure of agonist-bound glucagon-like peptide 1 receptor, a class B G protein and Process Simulation Center (139-74), California Institute of Technology, Pasadena, CA 91125 Contributed by William A. Goddard III, October 18, 2012 (sent for review July 9, 2012) The glucagon-like peptide 1

  14. Computational prediction of the lifetime of self-healing CMC structures M. Geneta,1,

    E-print Network

    Boyer, Edmond

    chemins, 33187 Le Haillan Cedex, France Abstract Self-healing Ceramic Matrix Composites (CMCs) are good Matrix Composites (CMCs) have very good ther- momechanical properties and, thanks to self-healingComputational prediction of the lifetime of self-healing CMC structures M. Geneta,1, , L. Marcina,2

  15. Human microRNA prediction through a probabilistic co-learning model of sequence and structure

    E-print Network

    Human microRNA prediction through a probabilistic co-learning model of sequence and structure Jin been identified through experimental complementary DNA cloning methods and computational efforts-validation with 136 referenced human data- sets, the efficiency of the classification shows 73% sensitivity and 96

  16. Prediction of trabecular bone principal structural orientation using quantitative ultrasound scanning.

    PubMed

    Lin, Liangjun; Cheng, Jiqi; Lin, Wei; Qin, Yi-Xian

    2012-06-26

    Bone has the ability to adapt its structure in response to the mechanical environment as defined as Wolff's Law. The alignment of trabecular structure is intended to adapt to the particular mechanical milieu applied to it. Due to the absence of normal mechanical loading, it will be extremely important to assess the anisotropic deterioration of bone during the extreme conditions, i.e., long term space mission and disease orientated disuse, to predict risk of fractures. The propagation of ultrasound wave in trabecular bone is substantially influenced by the anisotropy of the trabecular structure. Previous studies have shown that both ultrasound velocity and amplitude is dependent on the incident angle of the ultrasound signal into the bone sample. In this work, seven bovine trabecular bone balls were used for rotational ultrasound measurement around three anatomical axes to elucidate the ability of ultrasound to identify trabecular orientation. Both ultrasound attenuation (ATT) and fast wave velocity (UV) were used to calculate the principal orientation of the trabecular bone. By comparing to the mean intercept length (MIL) tensor obtained from ?CT, the angle difference of the prediction by UV was 4.45°, while it resulted in 11.67° angle difference between direction predicted by ?CT and the prediction by ATT. This result demonstrates the ability of ultrasound as a non-invasive measurement tool for the principal structural orientation of the trabecular bone. PMID:22560370

  17. The performance of minima hopping and evolutionary algorithms for cluster structure prediction

    E-print Network

    Oganov, Artem R.

    The performance of minima hopping and evolutionary algorithms for cluster structure prediction compare evolutionary algorithms with minima hopping for global optimization in the field of cluster it with previously used operators. Minima hopping is improved with a softening method and a stronger feedback

  18. SAM-T04: what's new in protein-structure prediction for CASP6 Kevin Karplus

    E-print Network

    Karplus, Kevin

    . Copyright 2005. Abstract 1 Introduction In previous casp experiments, our team has concentrated on fold [5]. In 2000, we started incorporating secondary structure prediction in our fold-recognition method for casp4 [3]. We entered two automatic servers in casp6, both of which are somewhat old: the SAM-T99

  19. A TOOL FOR PREDICTING VIBRATION AND STRUCTURE-BORNE NOISE IMMISSIONS CAUSED BY RAILWAYS

    Microsoft Academic Search

    H. Kuppelwieser; A. Ziegler

    1996-01-01

    Due to Swiss environment legislation, the Swiss Federal Railways (SBB) has to minimize the negative influences of vibration in buildings near railways. For every newly constructed or extended railway track, costly measurements and calculations have to be undertaken to assess future immissions of vibration and structure-borne noise. To predict such immissions and determine corresponding measures in a less expensive and

  20. PROSPECT-PSPP: an automatic computational pipeline for protein structure prediction

    E-print Network

    PROSPECT-PSPP: an automatic computational pipeline for protein structure prediction Jun-tao Guo1 generation. The centerpiece of the pipe- line is our threading-based program PROSPECT. The pipeline with the production rate of protein sequences due to the limitations of the current technology. Computational

  1. An Improved Parallel Simulated Annealing Algorithm Used for Protein Structure Prediction

    Microsoft Academic Search

    Yun-Ling Liu; Lan Tao

    2006-01-01

    This paper introduces an improved simulated annealing - parallel simulated annealing with genetic crossover: PSAGC, and uses this method in energy minimization problem of protein structure prediction. Through experiments on three real proteins, PSAGC is proved more effective than SA and PSA, it can achieve conformations which have lower energy values. Then the paper investigates crossover interval and crossover method

  2. Secondary structure of NADPH: protochlorophyllide oxidoreductase examined by circular dichroism and prediction methods.

    PubMed Central

    Birve, S J; Selstam, E; Johansson, L B

    1996-01-01

    To study the secondary structure of the enzyme NADPH: protochlorophyllide oxidoreductase (PCOR), a novel method of enzyme isolation was developed. The detergent isotridecyl poly-(ethylene glycol) ether (Genapol X-080) selectively solubilizes the enzyme from a prolamellar-body fraction isolated from wheat (Triticum aestivum L.). The solubilized fraction was further purified by ion-exchange chromatography. The isolated enzyme was studied by fluorescence spectroscopy at 77 K, and by CD spectroscopy. The fluorescence-emission spectra revealed that the binding properties of the substrate and co-substrate were preserved and that photo-reduction occurred. The CD spectra of PCOR were analysed for the relative amounts of the secondary structures, alpha-helix, beta-sheet, turn and random coil. The secondary structure composition was estimated to be 33% alpha-helix, 19% beta-sheet, 20% turn and 28% random coil. These values are in agreement with those predicted by the Predict Heidelberg Deutschland and self-optimized prediction method from alignments methods. The enzyme has some amino acid identity with other NADPH-binding enzymes containing the Rossmann fold. The Rossmann-fold fingerprint motif is localized in the N-terminal region and at the expected positions in the predicted secondary structure. It is suggested that PCOR is anchored to the interfacial region of the membrane by either a beta-sheet or an alpha-helical region containing tryptophan residues. A hydrophobic loop-region could also be involved in membrane anchoring. PMID:8713084

  3. DICOVERY OF "BIOMARKERS" FOR ALZHEIMER'S DISEASE PREDICTION FROM STRUCTURAL MR IMAGES

    E-print Network

    DICOVERY OF "BIOMARKERS" FOR ALZHEIMER'S DISEASE PREDICTION FROM STRUCTURAL MR IMAGES Y. Liu1 a computational framework for learning pre- dictive image features as "biomarkers" for Alzheimer's Dis- ease "biomarkers" and the spatial distributions of 20 Mild cognitive impairment (MCI) subjects in the discrim

  4. Improved Displacement Transfer Functions for Structure Deformed Shape Predictions Using Discretely Distributed Surface Strains

    NASA Technical Reports Server (NTRS)

    Ko, William L.; Fleischer, Van Tran

    2012-01-01

    In the formulations of earlier Displacement Transfer Functions for structure shape predictions, the surface strain distributions, along a strain-sensing line, were represented with piecewise linear functions. To improve the shape-prediction accuracies, Improved Displacement Transfer Functions were formulated using piecewise nonlinear strain representations. Through discretization of an embedded beam (depth-wise cross section of a structure along a strain-sensing line) into multiple small domains, piecewise nonlinear functions were used to describe the surface strain distributions along the discretized embedded beam. Such piecewise approach enabled the piecewise integrations of the embedded beam curvature equations to yield slope and deflection equations in recursive forms. The resulting Improved Displacement Transfer Functions, written in summation forms, were expressed in terms of beam geometrical parameters and surface strains along the strain-sensing line. By feeding the surface strains into the Improved Displacement Transfer Functions, structural deflections could be calculated at multiple points for mapping out the overall structural deformed shapes for visual display. The shape-prediction accuracies of the Improved Displacement Transfer Functions were then examined in view of finite-element-calculated deflections using different tapered cantilever tubular beams. It was found that by using the piecewise nonlinear strain representations, the shape-prediction accuracies could be greatly improved, especially for highly-tapered cantilever tubular beams.

  5. Available online at www.sciencedirect.com Progress and challenges in protein structure prediction

    E-print Network

    Zhang, Yang

    have been witnessed in folding small proteins to atomic resolutions. However, predicting structures combination of the PSI-BLAST search and the comparative modeling technique [4 ]. Development of more-chain atoms. The first two steps are actually done in a single procedure called threading (or fold recognition

  6. proteinsSTRUCTURE O FUNCTION O BIOINFORMATICS Predicting drug resistance of the HIV-1

    E-print Network

    Wang, Wei

    Genotypic and phenotypic re- sistance testing has become an important step in drug develop- ment- pared with amprenavir, our analysis suggested that darunavir might be more potent to combat drug resistproteinsSTRUCTURE O FUNCTION O BIOINFORMATICS Predicting drug resistance of the HIV-1 protease

  7. 1 INTRODUCTION Fluid flow predictions for ships and marine structures are often significantly more complex

    E-print Network

    Andrzejak, Artur

    aspects of ship hydrodynamic behaviour. Therefore, in order to maintain its competitive edge, the European1 INTRODUCTION Fluid flow predictions for ships and marine structures are often significantly more problem when targeting an in- tegrated approach to ship simulation and optimisation during design

  8. A model recognition approach to the prediction of all-helical membrane protein structure and topology.

    PubMed

    Jones, D T; Taylor, W R; Thornton, J M

    1994-03-15

    This paper describes a new method for the prediction of the secondary structure and topology of integral membrane proteins based on the recognition of topological models. The method employs a set of statistical tables (log likelihoods) complied from well-characterized membrane protein data, and a novel dynamic programming algorithm to recognize membrane topology models by expectation maximization. The statistical tables show definite biases toward certain amino acid species on the inside, middle, and outside of a cellular membrane. Using a set of 83 integral membrane protein sequences taken from a variety of bacterial, plant, and animal species, and a strict jackknifing procedure, where each protein (along with any detectable homologues) is removed from the training set used to calculate the tables before prediction, the method successfully predicted 64 of the 83 topologies, and of the 37 complex multispanning topologies 34 were predicted correctly. PMID:8130217

  9. Predicting binding affinity of CSAR ligands using both structure-based and ligand-based approaches

    PubMed Central

    Fourches, Denis; Muratov, Eugene; Ding, Feng; Dokholyan, Nikolay V.; Tropsha, Alexander

    2013-01-01

    We report on the prediction accuracy of ligand-based (2D QSAR) and structure-based (MedusaDock) methods used both independently and in consensus for ranking the congeneric series of ligands binding to three protein targets (UK, ERK2, and CHK1) from the CSAR 2011 benchmark exercise. An ensemble of predictive QSAR models was developed using known binders of these three targets extracted from the publicly-available ChEMBL database. Selected models were used to predict the binding affinity of CSAR compounds towards the corresponding targets and rank them accordingly; the overall ranking accuracy evaluated by Spearman correlation was as high as 0.78 for UK, 0.60 for ERK2, and 0.56 for CHK1, placing our predictions in top-10% among all the participants. In parallel, MedusaDock designed to predict reliable docking poses was also used for ranking the CSAR ligands according to their docking scores; the resulting accuracy (Spearman correlation) for UK, ERK2, and CHK1 were 0.76, 0.31, and 0.26, respectively. In addition, performance of several consensus approaches combining MedusaDock and QSAR predicted ranks altogether has been explored; the best approach yielded Spearman correlation coefficients for UK, ERK2, and CHK1 of 0.82, 0.50, and 0.45, respectively. This study shows that (i) externally validated 2D QSAR models were capable of ranking CSAR ligands at least as accurately as more computationally intensive structure-based approaches used both by us and by other groups and (ii) ligand-based QSAR models can complement structure-based approaches by boosting the prediction performances when used in consensus. PMID:23809015

  10. I-TASSER: fully automated protein structure prediction in CASP8.

    PubMed

    Zhang, Yang

    2009-01-01

    The I-TASSER algorithm for 3D protein structure prediction was tested in CASP8, with the procedure fully automated in both the Server and Human sections. The quality of the server models is close to that of human ones but the human predictions incorporate more diverse templates from other servers which improve the human predictions in some of the distant homology targets. For the first time, the sequence-based contact predictions from machine learning techniques are found helpful for both template-based modeling (TBM) and template-free modeling (FM). In TBM, although the accuracy of the sequence based contact predictions is on average lower than that from template-based ones, the novel contacts in the sequence-based predictions, which are complementary to the threading templates in the weakly or unaligned regions, are important to improve the global and local packing in these regions. Moreover, the newly developed atomic structural refinement algorithm was tested in CASP8 and found to improve the hydrogen-bonding networks and the overall TM-score, which is mainly due to its ability of removing steric clashes so that the models can be generated from cluster centroids. Nevertheless, one of the major issues of the I-TASSER pipeline is the model selection where the best models could not be appropriately recognized when the correct templates are detected only by the minority of the threading algorithms. There are also problems related with domain-splitting and mirror image recognition which mainly influences the performance of I-TASSER modeling in the FM-based structure predictions. PMID:19768687

  11. Functional insights from structural predictions: analysis of the Escherichia coli genome.

    PubMed Central

    Rychlewski, L.; Zhang, B.; Godzik, A.

    1999-01-01

    Fold assignments for proteins from the Escherichia coli genome are carried out using BASIC, a profile-profile alignment algorithm, recently tested on fold recognition benchmarks and on the Mycoplasma genitalium genome and PSI BLAST, the newest generation of the de facto standard in homology search algorithms. The fold assignments are followed by automated modeling and the resulting three-dimensional models are analyzed for possible function prediction. Close to 30% of the proteins encoded in the E. coli genome can be recognized as homologous to a protein family with known structure. Most of these homologies (23% of the entire genome) can be recognized both by PSI BLAST and BASIC algorithms, but the latter recognizes an additional 260 homologies. Previous estimates suggested that only 10-15% of E. coli proteins can be characterized this way. This dramatic increase in the number of recognized homologies between E. coli proteins and structurally characterized protein families is partly due to the rapid increase of the database of known protein structures, but mostly it is due to the significant improvement in prediction algorithms. Knowing protein structure adds a new dimension to our understanding of its function and the predictions presented here can be used to predict function for uncharacterized proteins. Several examples, analyzed in more detail in this paper, include the DPS protein protecting DNA from oxidative damage (predicted to be homologous to ferritin with iron ion acting as a reducing agent) and the ahpC/tsa family of proteins, which provides resistance to various oxidating agents (predicted to be homologous to glutathione peroxidase). PMID:10091664

  12. Contact Prediction for Beta and Alpha-Beta Proteins Using Integer Linear Optimization and its Impact on the First Principles 3D Structure Prediction Method ASTRO-FOLD

    PubMed Central

    Rajgaria, R.; Wei, Y.; Floudas, C. A.

    2010-01-01

    An integer linear optimization model is presented to predict residue contacts in ?, ? + ?, and ?/? proteins. The total energy of a protein is expressed as sum of a C? – C? distance dependent contact energy contribution and a hydrophobic contribution. The model selects contacts that assign lowest energy to the protein structure while satisfying a set of constraints that are included to enforce certain physically observed topological information. A new method based on hydrophobicity is proposed to find the ?-sheet alignments. These ?-sheet alignments are used as constraints for contacts between residues of ?-sheets. This model was tested on three independent protein test sets and CASP8 test proteins consisting of ?, ? + ?, ?/? proteins and was found to perform very well. The average accuracy of the predictions (separated by at least six residues) was approximately 61%. The average true positive and false positive distances were also calculated for each of the test sets and they are 7.58 Å and 15.88 Å, respectively. Residue contact prediction can be directly used to facilitate the protein tertiary structure prediction. This proposed residue contact prediction model is incorporated into the first principles protein tertiary structure prediction approach, ASTRO-FOLD. The effectiveness of the contact prediction model was further demonstrated by the improvement in the quality of the protein structure ensemble generated using the predicted residue contacts for a test set of 10 proteins. PMID:20225257

  13. Accurate secondary structure prediction and fold recognition for circular dichroism spectroscopy

    PubMed Central

    Micsonai, András; Wien, Frank; Kernya, Linda; Lee, Young-Ho; Goto, Yuji; Réfrégiers, Matthieu; Kardos, József

    2015-01-01

    Circular dichroism (CD) spectroscopy is a widely used technique for the study of protein structure. Numerous algorithms have been developed for the estimation of the secondary structure composition from the CD spectra. These methods often fail to provide acceptable results on ?/?-mixed or ?-structure–rich proteins. The problem arises from the spectral diversity of ?-structures, which has hitherto been considered as an intrinsic limitation of the technique. The predictions are less reliable for proteins of unusual ?-structures such as membrane proteins, protein aggregates, and amyloid fibrils. Here, we show that the parallel/antiparallel orientation and the twisting of the ?-sheets account for the observed spectral diversity. We have developed a method called ?-structure selection (BeStSel) for the secondary structure estimation that takes into account the twist of ?-structures. This method can reliably distinguish parallel and antiparallel ?-sheets and accurately estimates the secondary structure for a broad range of proteins. Moreover, the secondary structure components applied by the method are characteristic to the protein fold, and thus the fold can be predicted to the level of topology in the CATH classification from a single CD spectrum. By constructing a web server, we offer a general tool for a quick and reliable structure analysis using conventional CD or synchrotron radiation CD (SRCD) spectroscopy for the protein science research community. The method is especially useful when X-ray or NMR techniques fail. Using BeStSel on data collected by SRCD spectroscopy, we investigated the structure of amyloid fibrils of various disease-related proteins and peptides. PMID:26038575

  14. Accurate secondary structure prediction and fold recognition for circular dichroism spectroscopy.

    PubMed

    Micsonai, András; Wien, Frank; Kernya, Linda; Lee, Young-Ho; Goto, Yuji; Réfrégiers, Matthieu; Kardos, József

    2015-06-16

    Circular dichroism (CD) spectroscopy is a widely used technique for the study of protein structure. Numerous algorithms have been developed for the estimation of the secondary structure composition from the CD spectra. These methods often fail to provide acceptable results on ?/?-mixed or ?-structure-rich proteins. The problem arises from the spectral diversity of ?-structures, which has hitherto been considered as an intrinsic limitation of the technique. The predictions are less reliable for proteins of unusual ?-structures such as membrane proteins, protein aggregates, and amyloid fibrils. Here, we show that the parallel/antiparallel orientation and the twisting of the ?-sheets account for the observed spectral diversity. We have developed a method called ?-structure selection (BeStSel) for the secondary structure estimation that takes into account the twist of ?-structures. This method can reliably distinguish parallel and antiparallel ?-sheets and accurately estimates the secondary structure for a broad range of proteins. Moreover, the secondary structure components applied by the method are characteristic to the protein fold, and thus the fold can be predicted to the level of topology in the CATH classification from a single CD spectrum. By constructing a web server, we offer a general tool for a quick and reliable structure analysis using conventional CD or synchrotron radiation CD (SRCD) spectroscopy for the protein science research community. The method is especially useful when X-ray or NMR techniques fail. Using BeStSel on data collected by SRCD spectroscopy, we investigated the structure of amyloid fibrils of various disease-related proteins and peptides. PMID:26038575

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

    PubMed Central

    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. PMID:25071617

  16. STITCHER: Dynamic assembly of likely amyloid and prion beta-structures from secondary structure predictions

    E-print Network

    Bryan, Allen W.

    The supersecondary structure of amyloids and prions, proteins of intense clinical and biological interest, are difficult to determine by standard experimental or computational means. In addition, significant conformational ...

  17. Structural response measurements and predictions for the SANDIA 34-meter test bed

    NASA Astrophysics Data System (ADS)

    Ashwill, Thomas D.; Veers, Paul S.

    Measurements of structural response during operation of the 34-Meter Test Bed vertical axis wind turbine are compared with analytical predictions. Measured structural data include stationary and rotating modal frequencies, cable natural frequencies, and operating stresses. These data are compared to analytical results obtained with the use of NASTRAN-based structural codes. In the case of operating stresses, analytical results with and without turbulence are compared to measured stresses. Data taken during two significant events, a high wind over-speed condition with an emergency stop and a cable resonance that couples with a tower natural frequency, are shown.

  18. Staple Fitness: A Concept to Understand and Predict the Structures of Thiolated Gold Nanoclusters

    SciTech Connect

    Jiang, Deen [ORNL

    2011-01-01

    A profound connection has been found between the structures of thiolated gold clusters and the combinatorial problem of pairing up dots on a surface. The bridge is the concept of staple fitness: the fittest combination corresponds to the experimental structure. This connection has been demonstrated for both Au{sub 25}(SR){sub 18} and Au{sub 38}(SR){sub 24} (-SR being a thiolate group) and applied to predict a promising structure for the recently synthesized Au{sub 19}(SR){sub 13}.

  19. Prediction of the rodent carcinogenicity of organic compounds from their chemical structures using the FALS method.

    PubMed Central

    Moriguchi, I; Hirano, H; Hirono, S

    1996-01-01

    Fuzzy adaptive least-squares (FALS), a pattern recognition method recently developed in our laboratory for correlating structure with activity rating, was used to generate quantitative structure-activity relationship (QSAR) models on the carcinogenicity of organic compounds of several chemical classes. Using the predictive models obtained from the chemical class-based FALS QSAR approach, the rodent carcinogenicity or noncarcinogenicity of a group of organic chemicals currently being tested by the U.S. National Toxicology Program was estimated from their chemical structures. PMID:8933054

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

    SciTech Connect

    DiMaio, Frank, E-mail: dimaio@u.washington.edu [University of Washington, UW Box 357350, Seattle, WA 98195 (United States)

    2013-11-01

    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.

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

    PubMed Central

    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

  2. Gene conversion causing human inherited disease: evidence for involvement of non-B-DNA-forming sequences and recombination-promoting motifs in DNA breakage and repair

    PubMed Central

    Chuzhanova, Nadia; Chen, Jian-Min; Bacolla, Albino; Patrinos, George P.; Férec, Claude; Wells, Robert D.; Cooper, David N.

    2009-01-01

    A variety of DNA sequence motifs including inverted repeats, minisatellites, and the ? recombination hotspot, have been reported in association with gene conversion in human genes causing inherited disease. However, no methodical statistically-based analysis has been performed to formalize these observations. We have performed an in silico analysis of the DNA sequence tracts involved in 27 non-overlapping gene conversion events in 19 different genes reported in the context of inherited disease. We found that gene conversion events tend to occur within (C+G)- and CpG-rich regions and that sequences with the potential to form non-B-DNA structures, and which may be involved in the generation of double-strand breaks that could in turn serve to promote gene conversion, occur disproportionately within maximal converted tracts and/or short flanking regions. Maximal converted tracts were also found to be enriched (p<0.01) in a truncated version of the ?-element (a TGGTGG motif), immunoglobulin heavy chain class switch repeats, translin target sites and several novel motifs including (or overlapping) the classical meiotic recombination hotspot, CCTCCCCT. Finally, gene conversions tend to occur in genomic regions that have the potential to fold into stable hairpin conformations. These findings support the concept that recombination-inducing motifs, in association with alternative DNA conformations, can promote recombination in the human genome. PMID:19431182

  3. Effective 3D protein structure prediction with local adjustment genetic-annealing algorithm.

    PubMed

    Zhang, Xiao-Long; Lin, Xiao-Li

    2010-09-01

    The protein folding problem consists of predicting protein tertiary structure from a given amino acid sequence by minimizing the energy function. The protein folding structure prediction is computationally challenging and has been shown to be NP-hard problem when the 3D off-lattice AB model is employed. In this paper, the local adjustment genetic-annealing (LAGA) algorithm was used to search the ground state of 3D offlattice AB model for protein folding structure. The algorithm included an improved crossover strategy and an improved mutation strategy, where a local adjustment strategy was also used to enhance the searching ability. The experiments were carried out with the Fibonacci sequences. The experimental results demonstrate that the LAGA algorithm appears to have better performance and accuracy compared to the previous methods. PMID:20658338

  4. Prediction and confirmation of new MB4 crystal structures (M=Cr,Fe,Mn)

    NASA Astrophysics Data System (ADS)

    van der Geest, Abram; Kolmogorov, Aleksey; Kolmogorov Group Team

    2014-03-01

    The family of 3 d transition metals tetraborides has recently attracted a lot of interest due to the materials' unusual structural, mechanical, and superconducting properties. We overview our computational work on the determination of their ground state structures and show that all the predictions have been confirmed by experimental groups. Namely, the true ground state of the known CrB4 and MnB4 compounds have been determined to be new orthorhombic and monoclinic structures, as predicted by a combination of high-throughput and evolutionary searches. The proposed brand-new FeB4 superconducting compound has been synthesized by our colleagues and shown to be a superhard superconductor. We discuss the possibility of raising the material's superconducting critical temperature by doping.

  5. Sequence-structure relationships in DNA oligomers: a computational approach.

    PubMed

    Packer, M J; Hunter, C A

    2001-08-01

    A collective-variable model for DNA structure is used to predict the conformation of a set of 30 octamer, decamer, and dodecamer oligomers for which high-resolution crystal structures are available. The model combines an all-atom base pair representation with an empirical backbone, emphasizing the role of base stacking in fixing sequence-dependent structure. We are able to reproduce trends in roll and twist to within 5 degrees across a large database of both A- and B-DNA oligomers. A genetic algorithm approach is used to search for global minimum structures and this is augmented by a grid search to identify local minimums. We find that the number of local minimums is highly sequence dependent, with certain sequences having a set of minimums that span the entire range between canonical A- and B-DNA conformations. Although the global minimum does not always agree with the crystal structure, for 24 of the 30 oligomers, we find low-energy local minimums that match the experimental step parameters. Discrepancies throw some light on the role of crystal packing in determining the solid-state conformation of double-helical DNA. PMID:11472171

  6. Displacement Theories for In-Flight Deformed Shape Predictions of Aerospace Structures

    NASA Technical Reports Server (NTRS)

    Ko, William L.; Richards, W. L.; Tran, Van t.

    2007-01-01

    Displacement theories are developed for a variety of structures with the goal of providing real-time shape predictions for aerospace vehicles during flight. These theories are initially developed for a cantilever beam to predict the deformed shapes of the Helios flying wing. The main structural configuration of the Helios wing is a cantilever wing tubular spar subjected to bending, torsion, and combined bending and torsion loading. The displacement equations that are formulated are expressed in terms of strains measured at multiple sensing stations equally spaced on the surface of the wing spar. Displacement theories for other structures, such as tapered cantilever beams, two-point supported beams, wing boxes, and plates also are developed. The accuracy of the displacement theories is successfully validated by finite-element analysis and classical beam theory using input-strains generated by finite-element analysis. The displacement equations and associated strain-sensing system (such as fiber optic sensors) create a powerful means for in-flight deformation monitoring of aerospace structures. This method serves multiple purposes for structural shape sensing, loads monitoring, and structural health monitoring. Ultimately, the calculated displacement data can be visually displayed to the ground-based pilot or used as input to the control system to actively control the shape of structures during flight.

  7. Structure prediction for molecular crystals using evolutionary algorithms: methodology and applications

    NASA Astrophysics Data System (ADS)

    Zhu, Qiang

    2011-03-01

    Evolutionary crystal structure prediction proved to be a powerful approach in determining the atomic crystal structure of materials. Here, we present a specifically designed algorithm for the prediction of the structure of molecular crystals. The main feature of this new approach is that each molecule is treated as a whole body, which drastically reduces the search space and improves the efficiency, but necessitates the introduction of new variation operators described here. We illustrate the efficiency of this approach by a search for ice (H2O) structures at zero pressure and temperature, which easily finds the structures of ice Ih and Ic, as well as the thermodynamically stable at these conditions ice XI. We successfully apply this method to finding the hitherto unknown structures of plastic phases of methane at high pressure. These structures are distinguished by an icosahedral packing of the molecules, and are likely candidate solutions for methane A and B. The author thanks Intel Corporation, Research Foundation of Stony Brook University, Rosnauka (Russia,contract 02.740.11.5102), and DARPA (grant 54751) for funding.

  8. Computational neural networks in chemistry: Model free mapping devices for predicting chemical reactivity from molecular structure

    SciTech Connect

    Elrod, D.W.

    1992-01-01

    Computational neural networks (CNNs) are a computational paradigm inspired by the brain's massively parallel network of highly interconnected neurons. The power of computational neural networks derives not so much from their ability to model the brain as from their ability to learn by example and to map highly complex, nonlinear functions, without the need to explicitly specify the functional relationship. Two central questions about CNNs were investigated in the context of predicting chemical reactions: (1) the mapping properties of neural networks and (2) the representation of chemical information for use in CNNs. Chemical reactivity is here considered an example of a complex, nonlinear function of molecular structure. CNN's were trained using modifications of the back propagation learning rule to map a three dimensional response surface similar to those typically observed in quantitative structure-activity and structure-property relationships. The computational neural network's mapping of the response surface was found to be robust to the effects of training sample size, noisy data and intercorrelated input variables. The investigation of chemical structure representation led to the development of a molecular structure-based connection-table representation suitable for neural network training. An extension of this work led to a BE-matrix structure representation that was found to be general for several classes of reactions. The CNN prediction of chemical reactivity and regiochemistry was investigated for electrophilic aromatic substitution reactions, Markovnikov addition to alkenes, Saytzeff elimination from haloalkanes, Diels-Alder cycloaddition, and retro Diels-Alder ring opening reactions using these connectivity-matrix derived representations. The reaction predictions made by the CNNs were more accurate than those of an expert system and were comparable to predictions made by chemists.

  9. Prediction Accuracy of a Novel Dynamic Structure–Function Model for Glaucoma Progression

    PubMed Central

    Hu, Rongrong; Marín-Franch, Iván; Racette, Lyne

    2014-01-01

    Purpose. To assess the prediction accuracy of a novel dynamic structure–function (DSF) model to monitor glaucoma progression. Methods. Longitudinal data of paired rim area (RA) and mean sensitivity (MS) from 220 eyes with ocular hypertension or primary open-angle glaucoma enrolled in the Diagnostic Innovations in Glaucoma Study or the African Descent and Glaucoma Evaluation Study were included. Rim area and MS were expressed as percent of mean normal based on an independent dataset of 91 healthy eyes. The DSF model uses centroids as estimates of the current state of the disease and velocity vectors as estimates of direction and rate of change over time. The first three visits were used to predict the fourth visit; the first four visits were used to predict the fifth visit, and so on up to the 11th visit. The prediction error (PE) was compared to that of ordinary least squares linear regression (OLSLR) using Wilcoxon signed-rank test. Results. For predictions at visit 4 to visit 7, the average PE for the DSF model was significantly lower than OLSLR by 1.19% to 3.42% of mean normal. No significant difference was observed for the predictions at visit 8 to visit 11. The DSF model had lower PE than OLSLR for 70% of eyes in predicting visit 4 and approximately 60% in predicting visits 5, 6, and 7. Conclusions. The two models had similar prediction capabilities, and the DSF model performed better in shorter time series. The DSF model could be clinically useful when only limited follow-ups are available. (ClinicalTrials.gov numbers, NCT00221923, NCT00221897.) PMID:25358735

  10. Band-structure predictions for A2BX4 discovery compounds

    NASA Astrophysics Data System (ADS)

    Lany, Stephan; Stevanovic, V.; Zunger, A.

    2012-02-01

    The inverse design of materials requires to predict the existence and the properties of previously unknown materials. We have performed a computational search for thermodynamically stable materials within the family of A2BX4 compounds (A, B = main group and 3d cations; X = O, S, Se, Te) resulting in the theoretical discovery of about 100 previously unreported compounds. The challenge for the prediction of band-structures and optical spectra is to obtain accurate results for a wide range of materials within a single computational scheme, so that unknown materials can be predicted with confidence. Whereas the main group chalcogenides are rather accurately predicted by many-body GW calculations, large deviations from experiment are observed for many 3d oxides. In particular, we find that the 3d orbitals consistently occur at too high energies, independent on whether they are occupied (e.g., Cu2O) or unoccupied (e.g., TiO2). While the exact nature of these issues are under investigation, we pursue here a pragmatic approach, using attractive on-site potentials with a single parameter for each 3d element, which leads to good agreement with experiment for binary and ternary 3d oxides. We use this approach to predict the band-structures of the discovery compounds.

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

    PubMed Central

    Johnston, Blair A.; Steele, J. Douglas; Tolomeo, Serenella; Christmas, David; Matthews, Keith

    2015-01-01

    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. PMID:26186455

  12. Enhanced hybrid search algorithm for protein structure prediction using the 3D-HP lattice model.

    PubMed

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

    2013-09-01

    The problem of protein structure prediction in the hydrophobic-polar (HP) lattice model is the prediction of protein tertiary structure. This problem is usually referred to as the protein folding problem. This paper presents a method for the application of an enhanced hybrid search algorithm to the problem of protein folding prediction, using the three dimensional (3D) HP lattice model. The enhanced hybrid search algorithm is a combination of the particle swarm optimizer (PSO) and tabu search (TS) algorithms. Since the PSO algorithm entraps local minimum in later evolution extremely easily, we combined PSO with the TS algorithm, which has properties of global optimization. Since the technologies of crossover and mutation are applied many times to PSO and TS algorithms, so enhanced hybrid search algorithm is called the MCMPSO-TS (multiple crossover and mutation PSO-TS) algorithm. Experimental results show that the MCMPSO-TS algorithm can find the best solutions so far for the listed benchmarks, which will help comparison with any future paper approach. Moreover, real protein sequences and Fibonacci sequences are verified in the 3D HP lattice model for the first time. Compared with the previous evolutionary algorithms, the new hybrid search algorithm is novel, and can be used effectively to predict 3D protein folding structure. With continuous development and changes in amino acids sequences, the new algorithm will also make a contribution to the study of new protein sequences. PMID:23824509

  13. Time-varying prediction filter for structural noise reduction in ultrasonic NDE.

    PubMed

    Izquierdo, M A G; Hernández, M G; Anaya, J J

    2006-12-22

    Predominant physical phenomenon in highly scattering materials is the attenuation due to dispersion. Therefore, received echo has high frequencies more severely attenuated than low frequencies and the structural noise can be modeled as a non-stationary random process. Most of the proposed techniques for enhancing the flaw visibility do not exploit the frequency dependency of the incoming flaw signal, assuming homogeneous behaviour of the insonified material. In this work, a new technique based on exploiting the non-stationary nature of the incoming UT signal is presented. Proposed technique is based on the prediction error obtained with a linear and time-varying parametric model of the noise. By this method, when the analyzed UT echo has only structural noise, the prediction error is low, however, if it contains a flaw, high prediction error occurs because a flaw is a non-predictable alteration of the material structure. Experiments with stainless steel show that this method has an excellent performance on SNR enhancement. PMID:16797660

  14. RosettaHoles: Rapid assessment of protein core packing for structure prediction, refinement, design, and validation

    PubMed Central

    Sheffler, Will; Baker, David

    2009-01-01

    We present a novel method called RosettaHoles for visual and quantitative assessment of underpacking in the protein core. RosettaHoles generates a set of spherical cavity balls that fill the empty volume between atoms in the protein interior. For visualization, the cavity balls are aggregated into contiguous overlapping clusters and small cavities are discarded, leaving an uncluttered representation of the unfilled regions of space in a structure. For quantitative analysis, the cavity ball data are used to estimate the probability of observing a given cavity in a high-resolution crystal structure. RosettaHoles provides excellent discrimination between real and computationally generated structures, is predictive of incorrect regions in models, identifies problematic structures in the Protein Data Bank, and promises to be a useful validation tool for newly solved experimental structures. PMID:19177366

  15. NMR chemical shift prediction of glycans: application of the computer program CASPER in structural analysis.

    PubMed

    Lundborg, Magnus; Widmalm, Göran

    2015-01-01

    Carbohydrate molecules have highly complex structures and the constituent monosaccharides and substituents are linked to each other in a large number of ways. NMR spectroscopy can be used to unravel these structures, but the process may be tedious and time-consuming. The computerized approach based on the CASPER program can facilitate rapid structural determination of glycans with little user intervention, which results in the most probable primary structure of the investigated carbohydrate material. Additionally, (1)H and (13)C NMR chemical shifts of a user-defined structure can be predicted, and this tool may thus be employed in many aspects where NMR spectroscopy plays an important part of a study. PMID:25753701

  16. Structure-Based Prediction of G-Protein-Coupled Receptor Ligand Function: A ?-Adrenoceptor Case Study.

    PubMed

    Kooistra, Albert J; Leurs, Rob; de Esch, Iwan J P; de Graaf, Chris

    2015-05-26

    The spectacular advances in G-protein-coupled receptor (GPCR) structure determination have opened up new possibilities for structure-based GPCR ligand discovery. The structure-based prediction of whether a ligand stimulates (full/partial agonist), blocks (antagonist), or reduces (inverse agonist) GPCR signaling activity is, however, still challenging. A total of 31 ?1 (?1R) and ?2 (?2R) adrenoceptor crystal structures, including antagonist, inverse agonist, and partial/full agonist-bound structures, allowed us to explore the possibilities and limitations of structure-based prediction of GPCR ligand function. We used all unique protein-ligand interaction fingerprints (IFPs) derived from all ligand-bound ?-adrenergic crystal structure monomers to post-process the docking poses of known ?1R/?2R partial/full agonists, antagonists/inverse agonists, and physicochemically similar decoys in each of the ?1R/?2R structures. The systematic analysis of these 1920 unique IFP-structure combinations offered new insights into the relative impact of protein conformation and IFP scoring on selective virtual screening (VS) for ligands with a specific functional effect. Our studies show that ligands with the same function can be efficiently classified on the basis of their protein-ligand interaction profile. Small differences between the receptor conformation (used for docking) and reference IFP (used for scoring of the docking poses) determine, however, the enrichment of specific ligand types in VS hit lists. Interestingly, the selective enrichment of partial/full agonists can be achieved by using agonist IFPs to post-process docking poses in agonist-bound as well as antagonist-bound structures. We have identified optimal structure-IFP combinations for the identification and discrimination of antagonists/inverse agonist and partial/full agonists, and defined a predicted IFP for the small full agonist norepinephrine that gave the highest retrieval rate of agonists over antagonists for all structures (with an enrichment factor of 46 for agonists and 8 for antagonists on average at a 1% false-positive rate). This ?-adrenoceptor case study provides new insights into the opportunities for selective structure-based discovery of GPCR ligands with a desired function and emphasizes the importance of IFPs in scoring docking poses. PMID:25848966

  17. Flow structure generated by perpendicular blade vortex interaction and implications for helicopter noise predictions

    NASA Technical Reports Server (NTRS)

    Devenport, William J.; Glegg, Stewart A. L.

    1994-01-01

    Activities carried out in support of research on flow structure generated by perpendicular blade vortex interaction and implications for helicopter noise prediction are summarized. Progress in the following areas is described: (1) construction of 8 inch-chord NACA 0012 full-span blade; (2) Acquisition of two full-span blades; (3) preparation for hot wire measurements; (4) related work on a modified Betz's theory; and (5) work related to helicopter noise prediction. In addition, a list of publications based on the results of prior experimentation is presented.

  18. Evaluation and improvement of multiple sequence methods for protein secondary structure prediction

    Microsoft Academic Search

    James A. Cuff; Geoffrey J. Barton

    1999-01-01

    ABSTRACT A new,dataset,of 396 protein,do- mains,is developed,and,used to evaluate,the perfor- mance,of the,protein,secondary,structure,predic- tion algorithms DSC, PHD, NNSSP, and PREDATOR. The maximum,theoretical Q3 accuracy,for combina- tion of these methods,is shown,to be 78%. A simple consensus prediction on the 396 domains, with auto- matically,generated,multiple,sequence,alignments gives an,average,Q3 prediction,accuracy,of 72.9%. This is a 1% improvement over PHD, which was the best single method,evaluated. Segment,Overlap

  19. From Structure Prediction to Genomic Screens for Novel Non-Coding RNAs

    PubMed Central

    Gorodkin, Jan; Hofacker, Ivo L.

    2011-01-01

    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. PMID:21829340

  20. Structure classification and melting temperature prediction in octet AB solids via machine learning

    NASA Astrophysics Data System (ADS)

    Pilania, G.; Gubernatis, J. E.; Lookman, T.

    2015-06-01

    Machine learning methods are being increasingly used in condensed matter physics and materials science to classify crystals structures and predict material properties. However, the reliability of these methods for a given problem, especially when large data sets are unavailable, has not been well studied. By addressing the tasks of classifying crystal structure and predicting melting temperatures of the octet subset of AB solids, we performed such a study and found potential problems with using machine learning methods on relatively small data sets. At the same time, however, we can reaffirm the potential power of such methods for these tasks. In particular, we uncovered an important new material feature, the excess Born effective charge, that significantly increased the accuracy of the predictions for the classification problem we defined. This discovery leads us to propose a new scale for the degree of ionicity and covalency in these solids. More specifically, we partitioned the crystal structures of a set of 75 octet solids into those that are ionic and covalent bonded and thus performed a binary classification task. We found that using the standard indices (r?,r?) , suggested by St. John and Bloch several decades ago, enabled an average success in classification of 92 % . Using just r? and the excess Born effective charge ? ZA of the A atom enabled an average success of 97 % , but we also found relatively large variations about these averages that were dependent on how certain machine learning methods were used and for which a standard deviation was not a proper measure of the degree of confidence we can place in either average. Instead, we calculated and report with 95 % confidence that the traditional classification pair predicts an accuracy in the interval [89 %,95 %] and the accuracy of the new pair lies in the interval [96 %,99 %] . For melting temperature predictions, the size of our data set was 46. We estimate the root-mean-squared error of our resulting model to be 11 % of the mean melting temperature of the data, but we note that if the accuracy of this predicted error is itself measured, our estimated fitting error itself has a root-mean-square error of 50 % . In short, what we illustrate is that classification and regression predictions can vary significantly, depending on the details of how machine learning methods are applied to small data sets. This variation makes it important, if not essential, to average the predictions and compute confidence intervals about these averages to report results meaningfully. However, when properly used, these statistical methods can advance our understanding and improve predictions of material properties even for small data sets.

  1. A comparative assessment and analysis of 20 representative sequence alignment methods for protein structure prediction

    PubMed Central

    Yan, Renxiang; Xu, Dong; Yang, Jianyi; Walker, Sara; Zhang, Yang

    2013-01-01

    Protein sequence alignment is essential for template-based protein structure prediction and function annotation. We collect 20 sequence alignment algorithms, 10 published and 10 newly developed, which cover all representative sequence- and profile-based alignment approaches. These algorithms are benchmarked on 538 non-redundant proteins for protein fold-recognition on a uniform template library. Results demonstrate dominant advantage of profile-profile based methods, which generate models with average TM-score 26.5% higher than sequence-profile methods and 49.8% higher than sequence-sequence alignment methods. There is no obvious difference in results between methods with profiles generated from PSI-BLAST PSSM matrix and hidden Markov models. Accuracy of profile-profile alignments can be further improved by 9.6% or 21.4% when predicted or native structure features are incorporated. Nevertheless, TM-scores from profile-profile methods including experimental structural features are still 37.1% lower than that from TM-align, demonstrating that the fold-recognition problem cannot be solved solely by improving accuracy of structure feature predictions. PMID:24018415

  2. Structure band-gap correlations in semiconductors: Implications for computational band gap prediction

    NASA Astrophysics Data System (ADS)

    Schneider, Guenter; Foster, David H.

    2014-03-01

    Large scale structure prediction for novel materials requires computationally inexpensive lattice relaxation methods, which are typically based on density functional theory (DFT) using a semi-local approximation for the exchange-correlation functional. These methods provide structural parameters accurate to within a few percent, but cannot predict band-gaps. Band-gap calculations, require much more computationally expensive methods such as hybrid functionals or the GW approximation. Such an accuracy-tiered method fails dramatically for Cu3PSe4. When the generalized gradient approximation (GGA) is used to relax the lattice and ions, band-gaps calculated using both the single shot GGA+GW method and the Heyd-Scuseria-Ernzerhof (HSE) hybrid functional method are a full 0.5 eV lower than the band gaps calculated for the unrelaxed, experimental structure. The GW and HSE methods predict accurate band gaps only when used with the correct experimental structure. We show that in Cu3PSe4, the calculated band-gap depends strongly on the P-Se bondlength, which can be explained by the P-Se* anti-bonding character of the lowest conduction band state. We show this effect for different lattice relaxation methods including recently developed meta-GGAs.

  3. A framework for inter-subject prediction of functional connectivity from structural networks.

    PubMed

    Deligianni, Fani; Varoquaux, Gael; Thirion, Bertrand; Sharp, David; Ledig, Christian; Leech, Robert; Rueckert, Daniel

    2013-08-01

    Functional connections between brain regions are supported by structural connectivity. Both functional and structural connectivity are estimated from in-vivo MRI and offer complementary information on brain organisation and function. However, imaging only provides noisy measures, and we lack a good neuroscientific understanding of the links between structure and function. Therefore, inter-subject joint modeling of structural and functional connectivity, the key to multimodal biomarkers, is an open challenge. We present a probabilistic framework to learn across subjects a mapping from structural to functional brain connectivity. Expanding on our previous work [1], our approach is based on a predictive framework with multiple sparse linear regression. We rely on the randomized LASSO to identify relevant anatomo-functional links with some confidence interval. In addition, we describe resting-state (rs)-fMRI in the setting of Gaussian graphical models, on the one hand imposing conditional independences from structural connectivity and on the other hand parameterizing the problem in terms of multivariate autoregressive models. We introduce an intrinsic measure of prediction error for functional connectivity that is independent of the parameterization chosen and provides the means for robust model selection. We demonstrate our methodology with regions within the default mode and the salience network as well as, atlas-based cortical parcellation. PMID:23934663

  4. Predicting Successful Memes using Network and Community Structure Lilian Weng and Filippo Menczer and Yong-Yeol Ahn

    E-print Network

    Ahn, Yong-Yeol

    Predicting Successful Memes using Network and Community Structure Lilian Weng and Filippo Menczer Indiana University, Bloomington, USA Abstract We investigate the predictability of successful memes using of fea- tures and develop an accurate model to predict future popu- larity of a meme given its early

  5. An open source multistep model to predict mutagenicity from statistical analysis and relevant structural alerts

    PubMed Central

    2010-01-01

    Background Mutagenicity is the capability of a substance to cause genetic mutations. This property is of high public concern because it has a close relationship with carcinogenicity and potentially with reproductive toxicity. Experimentally, mutagenicity can be assessed by the Ames test on Salmonella with an estimated experimental reproducibility of 85%; this intrinsic limitation of the in vitro test, along with the need for faster and cheaper alternatives, opens the road to other types of assessment methods, such as in silico structure-activity prediction models. A widely used method checks for the presence of known structural alerts for mutagenicity. However the presence of such alerts alone is not a definitive method to prove the mutagenicity of a compound towards Salmonella, since other parts of the molecule can influence and potentially change the classification. Hence statistically based methods will be proposed, with the final objective to obtain a cascade of modeling steps with custom-made properties, such as the reduction of false negatives. Results A cascade model has been developed and validated on a large public set of molecular structures and their associated Salmonella mutagenicity outcome. The first step consists in the derivation of a statistical model and mutagenicity prediction, followed by further checks for specific structural alerts in the "safe" subset of the prediction outcome space. In terms of accuracy (i.e., overall correct predictions of both negative and positives), the obtained model approached the 85% reproducibility of the experimental mutagenicity Ames test. Conclusions The model and the documentation for regulatory purposes are freely available on the CAESAR website. The input is simply a file of molecular structures and the output is the classification result. PMID:20678181

  6. Folding Molecular Dynamics Simulations Accurately Predict the Effect of Mutations on the Stability and Structure of a Vammin-

    E-print Network

    Glykos, Nikolaos

    repeatedly been shown to be able to accurately predict the structure and dynamics of peptides ranging from the effects of mutations on peptide structure and dynamics. The system we selected to study is based on twoFolding Molecular Dynamics Simulations Accurately Predict the Effect of Mutations on the Stability

  7. Predicting toxicity of benzene derivatives by molecular hologram derived quantitative structure-activity relationships (QSARS).

    PubMed

    Cui, S; Wang, X; Liu, S; Wang, L

    2003-06-01

    Holographic quantitative structure-activity relationship (HQSAR) is an emerging QSAR technique with the combined application of molecular hologram, which encodes the frequency of occurrence of various molecular fragment types, and the subsequent partial least squares (PLS) regression analysis. Based on molecular hologram, alignment-free QSAR models could be rapidly and easily developed with highly statistical significance and predictive ability. In this paper, the toxicity data for a series of 83 benzene derivatives to the autotrophic Chlorella vulgaris (IGC50, negative logarithmic form of 6-h 50% population growth inhibition concentration in mmol/l) were subjected to HQSAR analysis and this resulted in a model with a high predictive ability. The robustness and predictive ability of the model were validated by "leave-one-out" (LOO) cross-validation procedure and an external testing set. The influence of fragment distinction parameters and fragment size on the quality of the HQSAR model have been also discussed. PMID:12854655

  8. On an economic prediction of the finer resolution level wavelet coefficients in electron structure calculations

    E-print Network

    Szilvia Nagy; János Pipek

    2015-02-28

    In wavelet based electron 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 refining solution scheme that determines the indices, where 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.

  9. On an economic prediction of the finer resolution level wavelet coefficients in electron structure calculations

    E-print Network

    Nagy, Szilvia

    2015-01-01

    In wavelet based electron 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 refining solution scheme that determines the indices, where 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.

  10. Prediction of children's reading skills using behavioral, functional, and structural neuroimaging measures.

    PubMed

    Hoeft, Fumiko; Ueno, Takefumi; Reiss, Allan L; Meyler, Ann; Whitfield-Gabrieli, Susan; Glover, Gary H; Keller, Timothy A; Kobayashi, Nobuhisa; Mazaika, Paul; Jo, Booil; Just, Marcel Adam; Gabrieli, John D E

    2007-06-01

    The ability to decode letters into language sounds is essential for reading success, and accurate identification of children at high risk for decoding impairment is critical for reducing the frequency and severity of reading impairment. We examined the utility of behavioral (standardized tests), and functional and structural neuroimaging measures taken with children at the beginning of a school year for predicting their decoding ability at the end of that school year. Specific patterns of brain activation during phonological processing and morphology, as revealed by voxel-based morphometry (VBM) of gray and white matter densities, predicted later decoding ability. Further, a model combining behavioral and neuroimaging measures predicted decoding outcome significantly better than either behavioral or neuroimaging models alone. Results were validated using cross-validation methods. These findings suggest that neuroimaging methods may be useful in enhancing the early identification of children at risk for poor decoding and reading skills. PMID:17592952

  11. Blade-Vortex Interaction (BVI) Noise and Airload Prediction Using Loose Aerodynamic/Structural Coupling

    NASA Technical Reports Server (NTRS)

    Sim, B. W.; Lim, J. W.

    2007-01-01

    Predictions of blade-vortex interaction (BVI) noise, using blade airloads obtained from a coupled aerodynamic and structural methodology, are presented. This methodology uses an iterative, loosely-coupled trim strategy to cycle information between the OVERFLOW-2 (CFD) and CAMRAD-II (CSD) codes. Results are compared to the HART-II baseline, minimum noise and minimum vibration conditions. It is shown that this CFD/CSD state-of-the-art approach is able to capture blade airload and noise radiation characteristics associated with BVI. With the exception of the HART-II minimum noise condition, predicted advancing and retreating side BVI for the baseline and minimum vibration conditions agrees favorably with measured data. Although the BVI airloads and noise amplitudes are generally under-predicted, this CFD/CSD methodology provides an overall noteworthy improvement over the lifting line aerodynamics and free-wake models typically used in CSD comprehensive analysis codes.

  12. Structural and Predictive Equivalency of the Wisconsin Smoking Withdrawal Scale across Three Racial/Ethnic Groups

    PubMed Central

    Kendzor, Darla E.; Businelle, Michael S.; Mazas, Carlos A.; Cofta-Woerpel, Ludmila; Cinciripini, Paul M.; Wetter, David W.

    2011-01-01

    Introduction: The Wisconsin Smoking Withdrawal Scale (WSWS) is a valid and reliable scale among non-Latino Whites but has not been validated for use among other racial/ethnic groups despite increasing use with these populations. The current study examined the structural invariance and predictive equivalency of the WSWS across three racial/ethnic groups. Methods: The WSWS scores of 424 African American, Latino, and White smokers receiving smoking cessation treatment were analyzed in a series of factor analyses and multiple-group analyses. Additionally, hierarchical logistic regression analyses were conducted to determine whether WSWS scores differentially predicted smoking relapse across racial/ethnic groups. These analyses were consistent with a step-down hierarchical regression procedure for examination of test bias. Results: The 7-factor structure of the WSWS was largely confirmed in the current study, with the exception of the removal of two offending items. Evidence of full invariance across race/ethnicity was found in multiple-group analyses. The WSWS total score and subscales measuring anger, anxiety, concentration, and sadness predicted relapse, whereas the hunger, craving, and sleep subscales did not. None of these scales displayed differential predictive ability across race/ethnicity. The WSWS sleep subscale showed a significant interaction with race/ethnicity such that it was a significant predictor of relapse among Whites but not African Americans or Latinos. Conclusions: Overall, the WSWS is similar in structure and predictive of relapse across racial/ethnic groups. Caution should be exercised when using the WSWS sleep subscale with African Americans and Latinos. PMID:21454912

  13. Framingham Coronary Heart Disease Risk Score Can be Predicted from Structural Brain Images in Elderly Subjects

    PubMed Central

    Rondina, Jane Maryam; Squarzoni, Paula; Souza-Duran, Fabio Luis; Tamashiro-Duran, Jaqueline Hatsuko; Scazufca, Marcia; Menezes, Paulo Rossi; Vallada, Homero; Lotufo, Paulo A.; de Toledo Ferraz Alves, Tania Correa; Busatto Filho, Geraldo

    2014-01-01

    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. PMID:25520654

  14. Geological and geomorphological study of the Amudariya syneclise (middle Asia) for petroleum-bearing structure prediction

    SciTech Connect

    Smirnova, I. [Institute of Remote Sensing Methods for Geology, St. Petersburg (Russian Federation)

    1995-08-01

    The integrated analysis of geophysical, geological, geochemical, geomorphological and remotely sensed data was carried out using the computerized technology at two test sites of Amudaria syneclise: eastern part of the Charjou step and the area of gas field Gasly (Bukharskaya step). At the Charjou step the geological modelling of petroleum-bearing structures (anticlines, reefs) as well as different horizons of sedimentary cover was conducted. In the models we used the morphological parameters of structures from the depth of petroliferous units up to the surface by drilling and seismic data, gravity and magnetic data, geochemical characteristics of soils, characteristics of relief, landscape elements distribution, spectral characteristics extracted from remotely sensed data and others. The using in the models of the landscape data is based on the theory that landscape components, their distribution and changes are connected with deep geological structures due to neotectonic movements, alterations of the rocks covering petroleum pools and seeping fluids. The modelling of known structures allows to reveal the types of their expression in relief and to predict the location of these structures and their parameters (amplitude, size) on week investigated areas. The results of structural horizons modelling allow to compose the schemes of tectonic and petroleum zonation and to predict the spreading of rest formation on Charjou step. The results of retrospective multispectral satellite and field data processing have permitted to reveal the secondary gas pool formed at the depth near 200 meters after failure on exploration well. At the test site Gasly geomorphological investigations using retrospective aerial and satellite images were carried out for the study of geological consequences of large gas field exploitation in connection with two destructive earthquakes. We obtain the data connected with recent tectonic movement which may be used for prediction of the earthquakes.

  15. Investigation and prediction of the severity of p53 mutants using parameters from structural calculations

    PubMed Central

    Carlsson, Jonas; Soussi, Thierry; Persson, Bengt

    2009-01-01

    A method has been developed to predict the effects of mutations in the p53 cancer suppressor gene. The new method uses novel parameters combined with previously established parameters. The most important parameter is the stability measure of the mutated structure calculated using molecular modelling. For each mutant, a severity score is reported, which can be used for classification into deleterious and nondeleterious. Both structural features and sequence properties are taken into account. The method has a prediction accuracy of 77% on all mutants and 88% on breast cancer mutations affecting WAF1 promoter binding. When compared with earlier methods, using the same dataset, our method clearly performs better. As a result of the severity score calculated for every mutant, valuable knowledge can be gained regarding p53, a protein that is believed to be involved in over 50% of all human cancers. PMID:19558493

  16. A new method for failure prediction of SR-200 beryllium sheet structures

    NASA Technical Reports Server (NTRS)

    Papados, P. P.; Roschke, P. N.

    1994-01-01

    Contemporary applications of failure criteria frequently incorporate two-dimensional or simplified three-dimensional methodologies for prediction of stresses. Motivation behind the development of a new multi-dimensional failure criterion is due mainly to the lack of a sufficiently accurate mathematical tool that accounts for the behavior of brittle material with anisotropic properties. Such a criterion should be able to provide a reliable maximum load estimate so that design of the structure is not penalized in terms of excessive weight requirements. The failure criterion developed is represented by a fracture surface in a six-dimensional stress space. The criterion is applied for failure prediction of SR-200 beryllium sheet structures, a non-homogeneous orthotropic material used widely in space applications. Two experiments are used to verify the criterion.

  17. Reported and predicted structures of Ba(Co,Nb)(1-?)O? hexagonal perovskite phases.

    PubMed

    Bradley, Kathryn A; Collins, Christopher; Dyer, Matthew S; Claridge, John B; Darling, George R; Rosseinsky, Matthew J

    2014-10-21

    The Extended Module Materials Assembly computational method for structure solution and prediction has been implemented for close-packed lattices. Exploring the family of B-site deficient materials in hexagonal perovskite barium cobalt niobates, it is found that the EMMA procedure returns the experimental structures as the most stable for the known compositions of Ba3CoNb2O9, Ba5Nb4O15 and Ba8CoNb6O24. The unknown compositions Ba11Co2Nb8O33 and Ba13CoNb10O39, having longer stacking sequences, are predicted to form as intergrowths of Ba3CoNb2O9 and Ba5Nb4O15, and are found to have similar stability to pure Ba3CoNb2O9 and Ba5Nb4O15, indicating that it is likely they can be synthesised. PMID:24871400

  18. Data quality in predictive toxicology: identification of chemical structures and calculation of chemical properties.

    PubMed Central

    Helma, C; Kramer, S; Pfahringer, B; Gottmann, E

    2000-01-01

    Every technique for toxicity prediction and for the detection of structure-activity relationships relies on the accurate estimation and representation of chemical and toxicologic properties. In this paper we discuss the potential sources of errors associated with the identification of compounds, the representation of their structures, and the calculation of chemical descriptors. It is based on a case study where machine learning techniques were applied to data from noncongeneric compounds and a complex toxicologic end point (carcinogenicity). We propose methods applicable to the routine quality control of large chemical datasets, but our main intention is to raise awareness about this topic and to open a discussion about quality assurance in predictive toxicology. The accuracy and reproducibility of toxicity data will be reported in another paper. PMID:11102292

  19. Computational Analysis of structure-based interactions and ligand properties can predict efflux effects on antibiotics

    PubMed Central

    Sarkar, Aurijit; Anderson, Kelcey C.; Kellogg, Glen E.

    2012-01-01

    AcrA-AcrB-TolC efflux pumps extrude drugs of multiple classes from bacterial cells and are a leading cause for antimicrobial resistance. Thus, they are of paramount interest to those engaged in antibiotic discovery. Accurate prediction of antibiotic efflux has been elusive, despite several studies aimed at this purpose. Minimum inhibitory concentration (MIC) ratios of 32 ?-lactam antibiotics were collected from literature. 3-Dimensional Quantitative Structure Activity Relationship on the ?-lactam antibiotic structures revealed seemingly predictive models (q2 = 0.53), but the lack of a general superposition rule does not allow its use on antibiotics that lack the ?-lactam moiety. Since MIC ratios must depend on interactions of antibiotics with lipid membranes and transport proteins during influx, capture and extrusion of antibiotics from the bacterial cell, descriptors representing these factors were calculated and used in building mathematical models that quantitatively classify antibiotics as having high/low efflux (>93% accuracy). Our models provide preliminary evidence that it is possible to predict the effects of antibiotic efflux if the passage of antibiotics into, and out of, bacterial cells is taken into account – something descriptor and field-based QSAR models cannot do. While the paucity of data in the public domain remains the limiting factor in such studies, these models show significant improvements in predictions over simple LogP-based regression models and should pave the path towards further work in this field. This method should also be extensible to other pharmacologically and biologically relevant transport proteins. PMID:22483632

  20. Using the RosettaSurface Algorithm to Predict Protein Structure at Mineral Surfaces

    PubMed Central

    Pacella, Michael S.; Koo, Da Chen Emily; Thottungal, Robin A.; Gray, Jeffrey J.

    2014-01-01

    Determination of protein structure on mineral surfaces is necessary to understand biomineralization processes toward better treatment of biomineralization diseases and design of novel protein-synthesized materials. To date, limited atomic-resolution data have hindered experimental structure determination for proteins on mineral surfaces. Molecular simulation represents a complementary approach. In this chapter, we review RosettaSurface, a computational structure prediction-based algorithm designed to broadly sample conformational space to identify low-energy structures. We summarize the computational approaches, the published applications, and the new releases of the code in the Rosetta 3 framework. In addition, we provide a protocol capture to demonstrate the practical steps to employ RosettaSurface. As an example, we provide input files and output data analysis for a previously unstudied mineralization protein, osteocalcin. Finally, we summarize ongoing challenges in energy function optimization and conformational searching and suggest that the fusion between experiment and calculation is the best route forward. PMID:24188775

  1. Theoretical prediction of stable tin oxides: stoichiometry, electronic structure and possible applications

    NASA Astrophysics Data System (ADS)

    Wang, Junjie; Umezawa, Naoto; Theoretical design of environmental remediation materials Team

    2015-03-01

    We have carried out a computational materials search for stable crystal phases of tin oxides in different composition ratios under ambient pressure condition. By employing density-functional theory calculations combined with evolutionary algorithm, we have identified several thermodynamically stable phases of tin oxides and investigated their dynamical stabilities by computing phonon vibration frequencies. We revealed the mechanism of determining the electronic structures of tin oxide crystals/van der Waals heterostructures through a systematic computational study of chemical bonding, band structure and Bader charges. Based on our theoretical analysis, we demonstrated that the predicted structures can lead to a desirable band structure for photocatalytic hydrogen evolution from water solution. Therefore, the tin oxides proposed in the present work have great potential as an abundant, cheap and environmentally-benign solar-energy conversion catalyst.

  2. iFC²: an integrated web-server for improved prediction of protein structural class, fold type, and secondary structure content.

    PubMed

    Chen, Ke; Stach, Wojciech; Homaeian, Leila; Kurgan, Lukasz

    2011-03-01

    Several descriptors of protein structure at the sequence and residue levels have been recently proposed. They are widely adopted in the analysis and prediction of structural and functional characteristics of proteins. Numerous in silico methods have been developed for sequence-based prediction of these descriptors. However, many of them do not have a public web-server and only a few integrate multiple descriptors to improve the predictions. We introduce iFC² (integrated prediction of fold, class, and content) server that is the first to integrate three modern predictors of sequence-level descriptors. They concern fold type (PFRES), structural class (SCEC), and secondary structure content (PSSC-core). The server exploits relations between the three descriptors to implement a cross-evaluation procedure that improves over the predictions of the individual methods. The iFC² annotates fold and class predictions as potentially correct/incorrect. When tested on datasets with low-similarity chains, for the fold prediction iFC² labels 82% of the PFRES predictions as correct and the accuracy of these predictions equals 72%. The accuracy of the remaining 28% of the PFRES predictions equals 38%. Similarly, our server assigns correct labels for over 79% of SCEC predictions, which are shown to be 98% accurate, while the remaining SCEC predictions are only 15% accurate. These results are shown to be competitive when contrasted against recent relevant web-servers. Predictions on CASP8 targets show that the content predicted by iFC² is competitive when compared with the content computed from the tertiary structures predicted by three best-performing methods in CASP8. The iFC² server is available at http://biomine.ece.ualberta.ca/1D/1D.html . PMID:20730460

  3. A two-layer structure prediction framework for microscopy cell detection.

    PubMed

    Xu, Yan; Wu, Weiying; Chang, Eric I-Chao; Chen, Danny; Mu, Jian; Lee, Peter P; Blenman, Kim R M; Tu, Zhuowen

    2015-04-01

    The task of microscopy cell detection is of great biological and clinical importance. However, existing algorithms for microscopy cell detection usually ignore the large variations of cells and only focus on the shape feature/descriptor design. Here we propose a new two-layer model for cell centre detection by a two-layer structure prediction framework, which is respectively built on classification for the cell centres implicitly using rich appearances and contextual information and explicit structural information for the cells. Experimental results demonstrate the efficiency and effectiveness of the proposed method over competing state-of-the-art methods, providing a viable alternative for microscopy cell detection. PMID:25082065

  4. Large-Deformation Displacement Transfer Functions for Shape Predictions of Highly Flexible Slender Aerospace Structures

    NASA Technical Reports Server (NTRS)

    Ko, William L.; Fleischer, Van Tran

    2013-01-01

    Large deformation displacement transfer functions were formulated for deformed shape predictions of highly flexible slender structures like aircraft wings. In the formulation, the embedded beam (depth wise cross section of structure along the surface strain sensing line) was first evenly discretized into multiple small domains, with surface strain sensing stations located at the domain junctures. Thus, the surface strain (bending strains) variation within each domain could be expressed with linear of nonlinear function. Such piecewise approach enabled piecewise integrations of the embedded beam curvature equations [classical (Eulerian), physical (Lagrangian), and shifted curvature equations] to yield closed form slope and deflection equations in recursive forms.

  5. Neural network based prediction of protein structure and Function: Comparison with other machine learning methods

    Microsoft Academic Search

    M. Michael Gromiha; Shandar Ahmad; Makiko Suwa

    2008-01-01

    We have utilized neural networks in different applications of bioinformatics such as discrimination of beta-barrel membrane proteins, mesophilic and thermophilic proteins, different folding types of globular proteins, different classes of transporter proteins and predicting the secondary structures of beta-barrel membrane proteins. In these methods, we have used the information about amino acid composition, neighboring residue information, inter-residue contacts and amino

  6. Optimizing inter-view prediction structures for multi-view video coding using simulated annealing

    Microsoft Academic Search

    Zheng Zhu; Dong-xiao Li; Ming Zhang

    2011-01-01

    New video applications, such as 3D video and free viewpoint video, require efficient compression of multi-view video. In addition\\u000a to temporal redundancy, exploiting the inter-view redundancy is crucial to improve the performance of multi-view video coding.\\u000a In this paper, we present a novel method to construct the optimal inter-view prediction structure for multi-view video coding\\u000a using simulated annealing. In the

  7. Use of beta-strand Interaction Pseudo-Potentials in Protein Structure Prediction and Modeling

    Microsoft Academic Search

    Tim J. P. Hubbard

    1994-01-01

    A residue-residue interaction pseudo-potential has been developed specific for protein \\/spl beta\\/-sheets. The potential is derived by scoring the occurrence of all i-(j-2...J+2) residue-residue pairs between any two interacting \\/spl beta\\/-strands, subdividing according to 4 classes of hydrogen bond pattern. The potential can be useful in distinguishing between correct and incorrect alignments between \\/spl beta\\/-strands in a predicted protein structure.

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

    SciTech Connect

    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.

  9. Predicting band structure of 3D mechanical metamaterials with complex geometry via XFEM

    NASA Astrophysics Data System (ADS)

    Zhao, Jifeng; Li, Ying; Liu, Wing Kam

    2015-04-01

    Band structure characterizes the most important property of mechanical metamaterials. However, predicting the band structure of 3D metamaterials with complex microstructures through direct numerical simulation (DNS) is computationally inefficient due to the complexity of meshing. To overcome this issue, an extended finite element method (XFEM)-based method is developed to predict 3D metamaterial band structures. Since the microstructure and material interface are implicitly resolved by the level-set function embedded in the XFEM formulation, a non-conforming (such as uniform) mesh is used in the proposed method to avoid the difficulties in meshing complex geometries. The accuracy and mesh convergence of the proposed method have been validated and verified by studying the band structure of a spherical particle embedded in a cube and comparing the results with DNS. The band structures of 3D metamaterials with different microstructures have been studied using the proposed method with the same finite element mesh, indicating the flexibility of this method. This XFEM-based method opens new opportunities in design and optimization of mechanical metamaterials with target functions, e.g. location and width of the band gap, by eliminating the iterative procedure of re-building and re-meshing microstructures that is required by classical DNS type of methods.

  10. Structure- and Sequence-Based Function Prediction for Non-Homologous Proteins

    PubMed Central

    Sael, Lee; Chitale, Meghana; Kihara, Daisuke

    2012-01-01

    The structural genomics projects have been accumulating an increasing number of protein structures, many of which remain functionally unknown. In parallel effort to experimental methods, computational methods are expected to make a significant contribution for functional elucidation of such proteins. However, conventional computational methods that transfer functions from homologous proteins do not help much for these uncharacterized protein structures because they do not have apparent structural or sequence similarity with the known proteins. Here, we briefly review two avenues of computational function prediction methods, i.e. structure-based methods and sequence-based methods. The focus is on our recently developments of local structure-based methods and sequence-based methods, which can effectively extract function information from distantly related proteins. Two structure-based methods, Pocket-Surfer and Patch-Surfer, identify similar known ligand binding sites for pocket regions in a query protein without using global protein fold similarity information. Two sequence-based methods, PFP and ESG, make use of weakly similar sequences that are conventionally discarded in homology based function annotation. Combined together with experimental methods we hope that computational methods will make leading contribution in functional elucidation of the protein structures. PMID:22270458

  11. Protein-protein complex structure predictions by multimeric threading and template recombination

    PubMed Central

    Mukherjee, Srayanta; Zhang, Yang

    2011-01-01

    Summary The number of protein-protein complex structures is nearly 6-times smaller than that of tertiary structures in PDB which limits the power of homology-based approaches to complex structure modeling. We present a new threading-recombination approach, COTH, to boost the protein complex structure library by combining tertiary structure templates with complex alignments. The query sequences are first aligned to complex templates using a modified dynamic programming algorithm, guided by ab initio binding-site predictions. The monomer alignments are then shifted to the multimeric template framework by structural alignments. COTH was tested on 500 non-homologous dimeric proteins, which can successfully detect correct templates for half of the cases after homologous templates are excluded, which significantly outperforms conventional homology modeling algorithms. It also shows a higher accuracy in interface modeling than rigid-body docking of unbound structures from ZDOCK although with lower coverage. These data demonstrate new avenues to model complex structures from non-homologous templates. PMID:21742262

  12. Methods to determine DNA structural alterations and genetic instability

    PubMed Central

    Wang, Guliang; Zhao, Junhua; Vasquez, Karen M.

    2009-01-01

    Chromosomal DNA is a dynamic structure that can adopt a variety of non-canonical (i.e. non-B) conformations. In this regard, at least ten different forms of non-B DNA conformations have been identified, and many of them have been found to be mutagenic, and associated with human disease development. Despite the importance of non-B DNA structures in genetic instability and DNA metabolic processes, mechanisms remain largely undefined. The purpose of this review is to summarize current methodologies that are used to address questions in the field of non-B DNA structure-induced genetic instability. Advantages and disadvantages of each method will be discussed. A focused effort to further elucidate the mechanisms of non-B DNA-induced genetic instability will lead to a better understanding of how these structure-forming sequences contribute to the development of human disease. PMID:19245837

  13. Prediction of the As-Cast Structure of Al-4.0 Wt Pct Cu Ingots

    NASA Astrophysics Data System (ADS)

    Ahmadein, Mahmoud; Wu, M.; Li, J. H.; Schumacher, P.; Ludwig, A.

    2013-06-01

    A two-stage simulation strategy is proposed to predict the as-cast structure. During the first stage, a 3-phase model is used to simulate the mold-filling process by considering the nucleation, the initial growth of globular equiaxed crystals and the transport of the crystals. The three considered phases are the melt, air and globular equiaxed crystals. In the second stage, a 5-phase mixed columnar-equiaxed solidification model is used to simulate the formation of the as-cast structure including the distinct columnar and equiaxed zones, columnar-to-equiaxed transition, grain size distribution, macrosegregation, etc. The five considered phases are the extradendritic melt, the solid dendrite, the interdendritic melt inside the equiaxed grains, the solid dendrite, and the interdendritic melt inside the columnar grains. The extra- and interdendritic melts are treated as separate phases. In order to validate the above strategy, laboratory ingots (Al-4.0 wt pct Cu) are poured and analyzed, and a good agreement with the numerical predictions is achieved. The origin of the equiaxed crystals by the "big-bang" theory is verified to play a key role in the formation of the as-cast structure, especially for the castings poured at a low pouring temperature. A single-stage approach that only uses the 5-phase mixed columnar-equiaxed solidification model and ignores the mold filling can predict satisfactory results for a casting poured at high temperature, but it delivers false results for the casting poured at low temperature.

  14. Evolutionary conservation and predicted structure of the Drosophila extra sex combs repressor protein.

    PubMed

    Ng, J; Li, R; Morgan, K; Simon, J

    1997-11-01

    The Drosophila extra sex combs (esc) protein, a member of the Polycomb group (PcG), is a transcriptional repressor of homeotic genes. Genetic studies have shown that esc protein is required in early embryos at about the time that other PcG proteins become engaged in homeotic gene repression. The esc protein consists primarily of multiple copies of the WD repeat, a motif that has been implicated in protein-protein interaction. To further investigate the domain organization of esc protein, we have isolated and characterized esc homologs from divergent insect species. We report that esc protein is highly conserved in housefly (72% identical to Drosophila esc), butterfly (55% identical), and grasshopper (56% identical). We show that the butterfly homolog provides esc function in Drosophila, indicating that the sequence similarities reflect functional conservation. Homology modeling using the crystal structure of another WD repeat protein, the G-protein beta-subunit, predicts that esc protein adopts a beta-propeller structure. The sequence comparisons and modeling suggest that there are seven WD repeats in esc protein which together form a seven-bladed beta-propeller. We locate the conserved regions in esc protein with respect to this predicted structure. Site-directed mutagenesis of specific loops, predicted to extend from the propeller surface, identifies conserved parts of esc protein required for function in vivo. We suggest that these regions might mediate physical interaction with esc partner proteins. PMID:9343430

  15. An Energy Based Fatigue Life Prediction Framework for In-Service Structural Components

    SciTech Connect

    H. Ozaltun; M. H.H. Shen; T. George; C. Cross

    2011-06-01

    An energy based fatigue life prediction framework has been developed for calculation of remaining fatigue life of in service gas turbine materials. The purpose of the life prediction framework is to account aging effect caused by cyclic loadings on fatigue strength of gas turbine engines structural components which are usually designed for very long life. Previous studies indicate the total strain energy dissipated during a monotonic fracture process and a cyclic process is a material property that can be determined by measuring the area underneath the monotonic true stress-strain curve and the sum of the area within each hysteresis loop in the cyclic process, respectively. The energy-based fatigue life prediction framework consists of the following entities: (1) development of a testing procedure to achieve plastic energy dissipation per life cycle and (2) incorporation of an energy-based fatigue life calculation scheme to determine the remaining fatigue life of in-service gas turbine materials. The accuracy of the remaining fatigue life prediction method was verified by comparison between model approximation and experimental results of Aluminum 6061-T6. The comparison shows promising agreement, thus validating the capability of the framework to produce accurate fatigue life prediction.

  16. mRNA secondary structure optimization using a correlated stem–loop prediction

    PubMed Central

    Gaspar, Paulo; Moura, Gabriela; Santos, Manuel A. S.; Oliveira, José Luís

    2013-01-01

    Secondary structure of messenger RNA plays an important role in the bio-synthesis of proteins. Its negative impact on translation can reduce the yield of protein by slowing or blocking the initiation and movement of ribosomes along the mRNA, becoming a major factor in the regulation of gene expression. Several algorithms can predict the formation of secondary structures by calculating the minimum free energy of RNA sequences, or perform the inverse process of obtaining an RNA sequence for a given structure. However, there is still no approach to redesign an mRNA to achieve minimal secondary structure without affecting the amino acid sequence. Here we present the first strategy to optimize mRNA secondary structures, to increase (or decrease) the minimum free energy of a nucleotide sequence, without changing its resulting polypeptide, in a time-efficient manner, through a simplistic approximation to hairpin formation. Our data show that this approach can efficiently increase the minimum free energy by >40%, strongly reducing the strength of secondary structures. Applications of this technique range from multi-objective optimization of genes by controlling minimum free energy together with CAI and other gene expression variables, to optimization of secondary structures at the genomic level. PMID:23325845

  17. Methods to determine DNA structural alterations and genetic instability

    Microsoft Academic Search

    Guliang Wang; Junhua Zhao; Karen M. Vasquez

    2009-01-01

    Chromosomal DNA is a dynamic structure that can adopt a variety of non-canonical (i.e., non-B) conformations. In this regard, at least 10 different forms of non-B DNA conformations have been identified; many of them have been found to be mutagenic, and associated with human disease development. Despite the importance of non-B DNA structures in genetic instability and DNA metabolic processes,

  18. Ecotoxicity prediction by adaptive fuzzy partitioning: comparing descriptors computed on 2D and 3D structures.

    PubMed

    Piclin, N; Pintore, M; Wechman, C; Roncaglioni, A; Benfenati, E; Chretien, J R

    2006-04-01

    Classification models were established on four endpoints, i.e. trout, daphnia, quail and bee, including from 100 to 300 pesticides subdivided into 3 toxicity classes. For each species, five separate sets of molecular descriptors, computed by several software, were compared, including parameters related to 2D or 3D structures. The most relevant descriptors were selected with help of a procedure based on genetic algorithms. Then, structure-activity relationships were built by Adaptive Fuzzy Partition (AFP), a recursive partitioning method derived from Fuzzy Logic concepts.Globally, satisfactory results were obtained for each animal species. The best cross-validation and test set scores reached values of about 70-75%. More important, the relationships derived from the descriptors calculated from 2D structures were superior or similar to those computed from 3D structures. These results underline that the long computational time employed to compute 3D descriptors is often useless to improve the prediction ability of the ecotoxicity models. Finally, the differences in the prediction ability between the different software used were quite reduced and show the possibility to use different descriptor packages for obtaining similar satisfactory models. PMID:16644559

  19. A Parallel Framework for Multipoint Spiral Search in ab Initio Protein Structure Prediction

    PubMed Central

    Rashid, Mahmood A.; Newton, M. A. Hakim; Hoque, Md Tamjidul; Sattar, Abdul

    2014-01-01

    Protein structure prediction is computationally a very challenging problem. A large number of existing search algorithms attempt to solve the problem by exploring possible structures and finding the one with the minimum free energy. However, these algorithms perform poorly on large sized proteins due to an astronomically wide search space. In this paper, we present a multipoint spiral search framework that uses parallel processing techniques to expedite exploration by starting from different points. In our approach, a set of random initial solutions are generated and distributed to different threads. We allow each thread to run for a predefined period of time. The improved solutions are stored threadwise. When the threads finish, the solutions are merged together and the duplicates are removed. A selected distinct set of solutions are then split to different threads again. In our ab initio protein structure prediction method, we use the three-dimensional face-centred-cubic lattice for structure-backbone mapping. We use both the low resolution hydrophobic-polar energy model and the high-resolution 20 × 20 energy model for search guiding. The experimental results show that our new parallel framework significantly improves the results obtained by the state-of-the-art single-point search approaches for both energy models on three-dimensional face-centred-cubic lattice. We also experimentally show the effectiveness of mixing energy models within parallel threads. PMID:24744779

  20. Male heterozygosity predicts territory size, song structure and reproductive success in a cooperatively breeding bird.

    PubMed Central

    Seddon, Nathalie; Amos, William; Mulder, Raoul A.; Tobias, Joseph A.

    2004-01-01

    Recent studies of non-social animals have shown that sexually selected traits signal at least one measure of genetic quality: heterozygosity. To determine whether similar cues reveal group quality in more complex social systems, we examined the relationship between territory size, song structure and heterozygosity in the subdesert mesite (Monias benschi), a group-living bird endemic to Madagascar. Using nine polymorphic microsatellite loci, we found that heterozygosity predicted both the size of territories and the structure of songs used to defend them: more heterozygous groups had larger territories, and more heterozygous males used longer, lower-pitched trills in their songs. Heterozygosity was linked to territory size and song structure in males, but not in females, implying that these traits are sexually selected by female choice and/or male-male competition. To our knowledge, this study provides the first direct evidence in any animal that territory size is related to genetic diversity. We also found a positive association between seasonal reproductive success and heterozygosity, suggesting that this heritable characteristic is a reliable indicator of group quality and fitness. Given that heterozygosity predicts song structure in males, and can therefore be determined by listening to acoustic cues, we identify a mechanism by which social animals may assess rival groups, prospective partners and group mates, information of potential importance in guiding decisions related to conflict, breeding and dispersal. PMID:15315898

  1. Different approaches to quantitative structure-retention relationships in the prediction of oligonucleotide retention.

    PubMed

    Studzi?ska, Sylwia; Buszewski, Bogus?aw

    2015-06-01

    Quantitative structure-retention relationships studies were performed for cholesterol and alkylamide stationary phases, which were previously applied in the analysis of nucleotides and oligonucleotides. An octadecyl column was also tested. Twenty-four oligonucleotides of various sequences and length were chosen; next, their structural descriptors were determined with the use of quantum-mechanics method. The sequence features were related mainly to their surface area, hydrophobicity, and the nature of nucleobases. Moreover, for the first time models employing experimentally derived descriptors (the sum of retention factor for individual nucleotides) were developed in the quantitative structure-retention relationship studies of these compounds. The retention of oligonucleotides for alkylamide and cholesterol stationary phases may be effectively predicted with the use of quantitative structure-retention relationship models based only on molecularly modeled descriptors, as well as with models employing experimentally derived descriptors. Therefore, we recommend the first approach, since descriptors may be easily and quickly calculated. However, oligonucleotide retention prediction for octadecyl phases gives better results, when individual nucleotide retention factors are known and utilized for the creation of a mathematical model. PMID:25866200

  2. Combining secondary-structure and protein solvent-accessibility predictions in methionine substitution for anomalous dispersion.

    PubMed

    Wu, Hsin-Yi; Cheng, Yi-Sheng

    2014-03-01

    In X-ray crystallographic analysis, the single-wavelength and multi-wavelength anomalous diffraction (SAD and MAD) methods have been widely used in order to solve the phase problem. Selenium-labelled methionine has been shown to be very effective for anomalous dispersion phasing, and at least one selenomethionine is required for every 100 amino acids. Some proteins, such as the Arabidopsis thaliana thylakoid lumen protein AtTLP18.3, can be overexpressed in an Escherichia coli system and high-quality protein crystals can be obtained. However, AtTLP18.3 contains no methionine residues, and site-directed mutagenesis was required in order to introduce methionine residues into the protein. A criterion for the mutated residues is that they should avoid affecting the structure and function. In this study, several leucine and isoleucine residues were selected for methionine substitution by combining secondary-structure and solvent-accessibility predictions. From the secondary-structure prediction, mutated residues were first determined in the coil or loop regions at the junction of two secondary structures. Since leucine and isoleucine residues are hydrophobic and are normally buried within the protein core, these residues should have a higher solvent-accessibility prediction so that they would be partially buried or exposed in the protein. In addition, five residues (Leu107, Leu202, Ile133, Leu128 and Ile159) of AtTLP18.3 were mutated to methionine residues. After overexpression and purification, only two single-mutant lines, L128M and I159M, could be crystallized. Finally, a double-mutation line of truncated AtTLP18.3 with L128M and I159M mutations was constructed. The structure of the double mutant AtTLP18.3 protein was resolved using the single-wavelength anomalous diffraction method at 2.6?Å resolution. The results indicated that a combination of secondary-structure and solvent-accessibility prediction for methionine substitution is a useful method in SAD and MAD phasing. PMID:24598932

  3. a Method to Predict Acoustic Radiation from AN Enclosed Multicavity Structure

    NASA Astrophysics Data System (ADS)

    WU, J. H.; CHEN, H. L.

    2002-01-01

    The acoustical reciprocity theorem can be used to solve the problem of vibroacoustic coupling. However, the theorem can be used only on the presupposition that the scattered sound field of the elastic surface concerned is known. This is the key point and the most difficult point for many complicated surfaces, such as a multicavity structure. A new method, covering-domain method, which transforms the calculation of scattered sound field of an arbitrary-shaped closed shell into that of a series of simply closed spherical shells, is applied in this paper to calculate the scattered sound field of a multicavity structure with elastic surfaces. So the radiated sound pressure of an elastic multicavity structure excited by an external force can be predicted by using the acoustical reciprocity theorem. It is verified to be correct by a corresponding test in this paper.

  4. Computational Prediction of acyl-coA Binding Proteins Structure in Brassica napus

    PubMed Central

    Raboanatahiry, Nadia Haingotiana; Lu, Guangyuan; Li, Maoteng

    2015-01-01

    Acyl-coA binding proteins could transport acyl-coA esters from plastid to endoplasmic reticulum, prior to fatty acid biosynthesis, leading to the formation of triacylglycerol. The structure and the subcellular localization of acyl-coA binding proteins (ACBP) in Brassica napus were computationally predicted in this study. Earlier, the structure analysis of ACBPs was limited to the small ACBPs, the current study focused on all four classes of ACBPs. Physicochemical parameters including the size and the length, the intron-exon structure, the isoelectric point, the hydrophobicity, and the amino acid composition were studied. Furthermore, identification of conserved residues and conserved domains were carried out. Secondary structure and tertiary structure of ACBPs were also studied. Finally, subcellular localization of ACBPs was predicted. The findings indicated that the physicochemical parameters and subcellular localizations of ACBPs in Brassica napus were identical to Arabidopsis thaliana. Conserved domain analysis indicated that ACBPs contain two or three kelch domains that belong to different families. Identical residues in acyl-coA binding domains corresponded to eight amino acid residues in all ACBPs of B. napus. However, conserved residues of common ACBPs in all species of animal, plant, bacteria and fungi were only inclusive in small ACBPs. Alpha-helixes were displayed and conserved in all the acyl-coA binding domains, representing almost the half of the protein structure. The findings confirm high similarities in ACBPs between A. thaliana and B. napus, they might share the same functions but loss or gain might be possible. PMID:26065422

  5. AB Initio Protein Tertiary Structure Prediction: Comparative-Genetic Algorithm with Graph Theoretical Methods

    SciTech Connect

    Gregurick, S. K.

    2001-04-20

    During the period from September 1, 1998 until September 1, 2000 I was awarded a Sloan/DOE postdoctoral fellowship to work in collaboration with Professor John Moult at the Center for Advanced Research in Biotechnology (CARB). Our research project, ''Ab Initio Protein Tertiary Structure Prediction and a Comparative Genetic algorithm'', yielded promising initial results. In short, the project is designed to predict the native fold, or native tertiary structure, of a given protein by inputting only the primary sequence of the protein (one or three letter code). The algorithm is based on a general learning, or evolutionary algorithm and is called Genetic Algorithm (GAS). In our particular application of GAS, we search for native folds, or lowest energy structures, using two different descriptions for the interactions of the atoms and residues in a given protein sequence. One potential energy function is based on a free energy description, while the other function is a threading potential derived by Moult and Samudrala. This modified genetic algorithm was loosely termed a Comparative Genetic Algorithm and was designed to search for native folded structures on both potential energy surfaces, simultaneously. We tested the algorithm on a series of peptides ranging from 11 to 15 residues in length, which are thought to be independent folding units and thereby will fold to native structures independent of the larger protein environment. Our initial results indicated a modest increase in accuracy, as compared to a standard Genetic Algorithm. We are now in the process of improving the algorithm to increase the sensitivity to other inputs, such as secondary structure requirements. The project did not involve additional students and as of yet, the work has not been published.

  6. Finite element models to predict the structural response of 120-mm sabot/rods during launch

    SciTech Connect

    Rabern, D.A. (Los Alamos National Lab., NM (USA)); Bannister, K.A. (Army Armament Research and Development Command, Aberdeen Proving Ground, MD (USA). Ballistics Research Lab.)

    1990-01-01

    Numerical modeling techniques in two- and three-dimensions were used to predict the structural and mechanical behavior of sabot/rod systems while inbore and just after muzzle exit. Three-dimensional transient numerical simulations were used to predict the rod deformations and states of stress and strain caused by axial and lateral accelerations during launch. The numerical models include the launch tube, recoil motion, and sabot/rod system modeled as it transits the launch tube and exits. The simulated rod leaves the muzzle of the gun, and exit parameters, including transverse displacement, transverse velocity, pitch, and pitch rate are extracted from the analysis results. Results from the inbore numerical simulations were compared with previous full-scale experiments. The results of the comparisons indicated a predictive capability to model inbore three-dimensional phenomena. Two-dimensional analyses were used to model details of the structural behavior caused by the axial load environment. Methodology and results are presented for several launch environments. 7 refs., 16 figs., 5 tabs.

  7. Prediction of compounds in different local structure-activity relationship environments using emerging chemical patterns.

    PubMed

    Namasivayam, Vigneshwaran; Gupta-Ostermann, Disha; Balfer, Jenny; Heikamp, Kathrin; Bajorath, Jürgen

    2014-05-27

    Active compounds can participate in different local structure-activity relationship (SAR) environments and introduce different degrees of local SAR discontinuity, depending on their structural and potency relationships in data sets. Such SAR features have thus far mostly been analyzed using descriptive approaches, in particular, on the basis of activity landscape modeling. However, compounds in different local SAR environments have not yet been predicted. Herein, we adapt the emerging chemical patterns (ECP) method, a machine learning approach for compound classification, to systematically predict compounds with different local SAR characteristics. ECP analysis is shown to accurately assign many compounds to different local SAR environments across a variety of activity classes covering the entire range of observed local SARs. Control calculations using random forests and multiclass support vector machines were carried out and a variety of statistical performance measures were applied. In all instances, ECP calculations yielded comparable or better performance than controls. The approach presented herein can be applied to predict compounds that complement local SARs or prioritize compounds with different SAR characteristics. PMID:24803014

  8. Structure Based Prediction of Binding Residues on DNA-binding Proteins.

    PubMed

    Bhardwaj, Nitin; Langlois, Robert; Zhao, Guijun; Lu, Hui

    2005-01-01

    Annotation of the functional sites on the surface of a protein has been the subject of many studies. In this regard, the search for attributes and features characterizing these sites is of prime consequence. Here, we present an implementation of a kernel-based machine learning protocol for identifying residues on a DNA-binding protein form the interface with the DNA. Sequence and structural features including solvent accessibility, local composition, net charge and electrostatic potentials are examined. These features are then fed into Support Vector Machines (SVM) to predict the DNA-binding residues on the surface of the protein. In order to compare with published work, we predict binding residues by training on other binding and non-binding residues in the same protein for which we achieved an accuracy of 79%. The sensitivity and specificity are 59% and 89%. We also consider a more realistic approach, predicting the binding residues of proteins entirely withheld from the training set achieving values of 66%, 43% and 81%, respectively. Performances reported here are better than other published results. Moreover, since our protocol does not lean on sequence or structural homology, it can be used to annotate unclassified proteins and more generally to identify novel binding sites with no similarity to the known cases. PMID:17282773

  9. Using Sequence-Specific Chemical and Structural Properties of DNA to Predict Transcription Factor Binding Sites

    PubMed Central

    Bauer, Amy L.; Hlavacek, William S.; Unkefer, Pat J.; Mu, Fangping

    2010-01-01

    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. PMID:21124945

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

    PubMed

    Li, Hongjian; Leung, Kwong-Sak; Wong, Man-Hon; Ballester, Pedro J

    2015-01-01

    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. PMID:26076113

  11. Electronic structure Fermi liquid theory of high Tc superconductors: Comparison of predictions with experiments

    NASA Technical Reports Server (NTRS)

    Yu, Jaejun; Freeman, A. J.

    1991-01-01

    Predictions of local density functional (LDF) calculations of the electronic structure and transport properties of high T(sub c) superconductors are presented. As evidenced by the excellent agreement with both photoemission and positron annihilation experiments, a Fermi liquid nature of the 'normal' state of the high T(sub c) superconductors become clear for the metallic phase of these oxides. In addition, LDF predictions on the normal state transport properties are qualitatively in agreement with experiments on single crystals. It is emphasized that the signs of the Hall coefficients for the high T(sub c) superconductors are not consistent with the types of dopants (e.g., electron-doped or hole-doped) but are determined by the topology of the Fermi surfaces obtained from the LDF calculations.

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

    PubMed Central

    Güell, Oriol; Sagués, Francesc; Serrano, M. Ángeles

    2012-01-01

    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. PMID:22934134

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

    NASA Astrophysics Data System (ADS)

    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.

  14. Structural Dynamics Modeling of HIRENASD in Support of the Aeroelastic Prediction Workshop

    NASA Technical Reports Server (NTRS)

    Wieseman, Carol; Chwalowski, Pawel; Heeg, Jennifer; Boucke, Alexander; Castro, Jack

    2013-01-01

    An Aeroelastic Prediction Workshop (AePW) was held in April 2012 using three aeroelasticity case study wind tunnel tests for assessing the capabilities of various codes in making aeroelasticity predictions. One of these case studies was known as the HIRENASD model that was tested in the European Transonic Wind Tunnel (ETW). This paper summarizes the development of a standardized enhanced analytical HIRENASD structural model for use in the AePW effort. The modifications to the HIRENASD finite element model were validated by comparing modal frequencies, evaluating modal assurance criteria, comparing leading edge, trailing edge and twist of the wing with experiment and by performing steady and unsteady CFD analyses for one of the test conditions on the same grid, and identical processing of results.

  15. Structured Set Intra Prediction With Discriminative Learning in a Max-Margin Markov Network for High Efficiency Video Coding

    PubMed Central

    Dai, Wenrui; Xiong, Hongkai; Jiang, Xiaoqian; Chen, Chang Wen

    2014-01-01

    This paper proposes a novel model on intra coding for High Efficiency Video Coding (HEVC), which simultaneously predicts blocks of pixels with optimal rate distortion. It utilizes the spatial statistical correlation for the optimal prediction based on 2-D contexts, in addition to formulating the data-driven structural interdependences to make the prediction error coherent with the probability distribution, which is desirable for successful transform and coding. The structured set prediction model incorporates a max-margin Markov network (M3N) to regulate and optimize multiple block predictions. The model parameters are learned by discriminating the actual pixel value from other possible estimates to maximize the margin (i.e., decision boundary bandwidth). Compared to existing methods that focus on minimizing prediction error, the M3N-based model adaptively maintains the coherence for a set of predictions. Specifically, the proposed model concurrently optimizes a set of predictions by associating the loss for individual blocks to the joint distribution of succeeding discrete cosine transform coefficients. When the sample size grows, the prediction error is asymptotically upper bounded by the training error under the decomposable loss function. As an internal step, we optimize the underlying Markov network structure to find states that achieve the maximal energy using expectation propagation. For validation, we integrate the proposed model into HEVC for optimal mode selection on rate-distortion optimization. The proposed prediction model obtains up to 2.85% bit rate reduction and achieves better visual quality in comparison to the HEVC intra coding. PMID:25505829

  16. Protein secondary structure prediction using different encoding schemes and neural network architectures

    NASA Astrophysics Data System (ADS)

    Zhong, Wei; Pan, Yi; Harrison, Robert; Tai, Phang C.

    2004-04-01

    Protein secondary structure prediction is very important for drug design, protein engineering and immunological studies. This research uses fully connected multilayer perceptron (MLP) neural network with one, two and three hidden layers to predict protein secondary structure. Orthogonal matrix, BLOSUM62 matrix and hydrophobicity matrix are used for input profiles. To increase the input information for neural networks, the combined matrix from BLOSUM62 and orthogonal matrix and the combined matrix from BLOSUM62 and hydrophobicity matrix are also experimented. Binary classifiers indicate test accuracy of one hidden layer is better than that of two and three hidden layers. This may indicate that increasing complexity of architecture may not help neural network to recognize structural pattern of protein sequence more accurately. The results also show that the combined input profile of BLOSUM62 matrix and orthogonal matrix is the best one among five encoding schemes. While accuracy of the tertiary classifier reaches 63.20%, binary classifier for H/~H is 78.70%, which is comparable to other researchers" results.

  17. AGGRESCAN3D (A3D): server for prediction of aggregation properties of protein structures

    PubMed Central

    Zambrano, Rafael; Jamroz, Michal; Szczasiuk, Agata; Pujols, Jordi; Kmiecik, Sebastian; Ventura, Salvador

    2015-01-01

    Protein aggregation underlies an increasing number of disorders and constitutes a major bottleneck in the development of therapeutic proteins. Our present understanding on the molecular determinants of protein aggregation has crystalized in a series of predictive algorithms to identify aggregation-prone sites. A majority of these methods rely only on sequence. Therefore, they find difficulties to predict the aggregation properties of folded globular proteins, where aggregation-prone sites are often not contiguous in sequence or buried inside the native structure. The AGGRESCAN3D (A3D) server overcomes these limitations by taking into account the protein structure and the experimental aggregation propensity scale from the well-established AGGRESCAN method. Using the A3D server, the identified aggregation-prone residues can be virtually mutated to design variants with increased solubility, or to test the impact of pathogenic mutations. Additionally, A3D server enables to take into account the dynamic fluctuations of protein structure in solution, which may influence aggregation propensity. This is possible in A3D Dynamic Mode that exploits the CABS-flex approach for the fast simulations of flexibility of globular proteins. The A3D server can be accessed at http://biocomp.chem.uw.edu.pl/A3D/. PMID:25883144

  18. Rich stoichiometries of stable Ca-Bi system: Structure prediction and superconductivity

    NASA Astrophysics Data System (ADS)

    Dong, Xu; Fan, Changzeng

    2015-03-01

    Using a variable-composition ab initio evolutionary algorithm implemented in the USPEX code, we have performed a systematic search for stable compounds in the Ca-Bi system at different pressures. In addition to the well-known tI12-Ca2Bi and oS12-CaBi2, a few more structures were found by our calculations, among which phase transitions were also predicted in Ca2Bi (tI12 --> oI12 --> hP6), Ca3Bi2 (hP5 --> mC20 --> aP5) and CaBi (tI2 --> tI8), as well as a new phase (Ca3Bi) with a cF4 structure. All the newly predicted structures can be both dynamically and thermodynamically stable with increasing pressure. The superconductive properties of cF4-CaBi3, tI2-CaBi and cF4-Ca3Bi were studied and the superconducting critical temperature Tc can be as high as 5.16, 2.27 and 5.25 K, respectively. Different superconductivity behaviors with pressure increasing have been observed by further investigations.

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

    NASA Technical Reports Server (NTRS)

    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.

  20. The road not taken: retreat and diverge in local search for simplified protein structure prediction

    PubMed Central

    2013-01-01

    Background Given a protein's amino acid sequence, the protein structure prediction problem is to find a three dimensional structure that has the native energy level. For many decades, it has been one of the most challenging problems in computational biology. A simplified version of the problem is to find an on-lattice self-avoiding walk that minimizes the interaction energy among the amino acids. Local search methods have been preferably used in solving the protein structure prediction problem for their efficiency in finding very good solutions quickly. However, they suffer mainly from two problems: re-visitation and stagnancy. Results In this paper, we present an efficient local search algorithm that deals with these two problems. During search, we select the best candidate at each iteration, but store the unexplored second best candidates in a set of elite conformations, and explore them whenever the search faces stagnation. Moreover, we propose a new non-isomorphic encoding for the protein conformations to store the conformations and to check similarity when applied with a memory based search. This new encoding helps eliminate conformations that are equivalent under rotation and translation, and thus results in better prevention of re-visitation. Conclusion On standard benchmark proteins, our algorithm significantly outperforms the state-of-the art approaches for Hydrophobic-Polar energy models and Face Centered Cubic Lattice. PMID:23368768

  1. Immediate use of prosody and context in predicting a syntactic structure.

    PubMed

    Nakamura, Chie; Arai, Manabu; Mazuka, Reiko

    2012-11-01

    Numerous studies have reported an effect of prosodic information on parsing but whether prosody can impact even the initial parsing decision is still not evident. In a visual world eye-tracking experiment, we investigated the influence of contrastive intonation and visual context on processing temporarily ambiguous relative clause sentences in Japanese. Our results showed that listeners used the prosodic cue to make a structural prediction before hearing disambiguating information. Importantly, the effect was limited to cases where the visual scene provided an appropriate context for the prosodic cue, thus eliminating the explanation that listeners have simply associated marked prosodic information with a less frequent structure. Furthermore, the influence of the prosodic information was also evident following disambiguating information, in a way that reflected the initial analysis. The current study demonstrates that prosody, when provided with an appropriate context, influences the initial syntactic analysis and also the subsequent cost at disambiguating information. The results also provide first evidence for pre-head structural prediction driven by prosodic and contextual information with a head-final construction. PMID:22901508

  2. Improving prediction of secondary structure, local backbone angles, and solvent accessible surface area of proteins by iterative deep learning.

    PubMed

    Heffernan, Rhys; Paliwal, Kuldip; Lyons, James; Dehzangi, Abdollah; Sharma, Alok; Wang, Jihua; Sattar, Abdul; Yang, Yuedong; Zhou, Yaoqi

    2015-01-01

    Direct prediction of protein structure from sequence is a challenging problem. An effective approach is to break it up into independent sub-problems. These sub-problems such as prediction of protein secondary structure can then be solved independently. In a previous study, we found that an iterative use of predicted secondary structure and backbone torsion angles can further improve secondary structure and torsion angle prediction. In this study, we expand the iterative features to include solvent accessible surface area and backbone angles and dihedrals based on C? atoms. By using a deep learning neural network in three iterations, we achieved 82% accuracy for secondary structure prediction, 0.76 for the correlation coefficient between predicted and actual solvent accessible surface area, 19° and 30° for mean absolute errors of backbone ? and ? angles, respectively, and 8° and 32° for mean absolute errors of C?-based ? and ? angles, respectively, for an independent test dataset of 1199 proteins. The accuracy of the method is slightly lower for 72 CASP 11 targets but much higher than those of model structures from current state-of-the-art techniques. This suggests the potentially beneficial use of these predicted properties for model assessment and ranking. PMID:26098304

  3. Improving prediction of secondary structure, local backbone angles, and solvent accessible surface area of proteins by iterative deep learning

    PubMed Central

    Heffernan, Rhys; Paliwal, Kuldip; Lyons, James; Dehzangi, Abdollah; Sharma, Alok; Wang, Jihua; Sattar, Abdul; Yang, Yuedong; Zhou, Yaoqi

    2015-01-01

    Direct prediction of protein structure from sequence is a challenging problem. An effective approach is to break it up into independent sub-problems. These sub-problems such as prediction of protein secondary structure can then be solved independently. In a previous study, we found that an iterative use of predicted secondary structure and backbone torsion angles can further improve secondary structure and torsion angle prediction. In this study, we expand the iterative features to include solvent accessible surface area and backbone angles and dihedrals based on C? atoms. By using a deep learning neural network in three iterations, we achieved 82% accuracy for secondary structure prediction, 0.76 for the correlation coefficient between predicted and actual solvent accessible surface area, 19° and 30° for mean absolute errors of backbone ? and ? angles, respectively, and 8° and 32° for mean absolute errors of C?-based ? and ? angles, respectively, for an independent test dataset of 1199 proteins. The accuracy of the method is slightly lower for 72 CASP 11 targets but much higher than those of model structures from current state-of-the-art techniques. This suggests the potentially beneficial use of these predicted properties for model assessment and ranking. PMID:26098304

  4. Predicting performance and plasticity in the development of respiratory structures and metabolic systems.

    PubMed

    Greenlee, Kendra J; Montooth, Kristi L; Helm, Bryan R

    2014-07-01

    The scaling laws governing metabolism suggest that we can predict metabolic rates across taxonomic scales that span large differences in mass. Yet, scaling relationships can vary with development, body region, and environment. Within species, there is variation in metabolic rate that is independent of mass and which may be explained by genetic variation, the environment or their interaction (i.e., metabolic plasticity). Additionally, some structures, such as the insect tracheal respiratory system, change throughout development and in response to the environment to match the changing functional requirements of the organism. We discuss how study of the development of respiratory function meets multiple challenges set forth by the NSF Grand Challenges Workshop. Development of the structure and function of respiratory and metabolic systems (1) is inherently stable and yet can respond dynamically to change, (2) is plastic and exhibits sensitivity to environments, and (3) can be examined across multiple scales in time and space. Predicting respiratory performance and plasticity requires quantitative models that integrate information across scales of function from the expression of metabolic genes and mitochondrial biogenesis to the building of respiratory structures. We present insect models where data are available on the development of the tracheal respiratory system and of metabolic physiology and suggest what is needed to develop predictive models. Incorporating quantitative genetic data will enable mapping of genetic and genetic-by-environment variation onto phenotypes, which is necessary to understand the evolution of respiratory and metabolic systems and their ability to enable respiratory homeostasis as organisms walk the tightrope between stability and change. PMID:24812329

  5. Predicting Performance and Plasticity in the Development of Respiratory Structures and Metabolic Systems

    PubMed Central

    Montooth, Kristi L.; Helm, Bryan R.

    2014-01-01

    The scaling laws governing metabolism suggest that we can predict metabolic rates across taxonomic scales that span large differences in mass. Yet, scaling relationships can vary with development, body region, and environment. Within species, there is variation in metabolic rate that is independent of mass and which may be explained by genetic variation, the environment or their interaction (i.e., metabolic plasticity). Additionally, some structures, such as the insect tracheal respiratory system, change throughout development and in response to the environment to match the changing functional requirements of the organism. We discuss how study of the development of respiratory function meets multiple challenges set forth by the NSF Grand Challenges Workshop. Development of the structure and function of respiratory and metabolic systems (1) is inherently stable and yet can respond dynamically to change, (2) is plastic and exhibits sensitivity to environments, and (3) can be examined across multiple scales in time and space. Predicting respiratory performance and plasticity requires quantitative models that integrate information across scales of function from the expression of metabolic genes and mitochondrial biogenesis to the building of respiratory structures. We present insect models where data are available on the development of the tracheal respiratory system and of metabolic physiology and suggest what is needed to develop predictive models. Incorporating quantitative genetic data will enable mapping of genetic and genetic-by-environment variation onto phenotypes, which is necessary to understand the evolution of respiratory and metabolic systems and their ability to enable respiratory homeostasis as organisms walk the tightrope between stability and change. PMID:24812329

  6. Towards high performance computing for molecular structure prediction using IBM Cell Broadband Engine - an implementation perspective

    PubMed Central

    2010-01-01

    Background RNA structure prediction problem is a computationally complex task, especially with pseudo-knots. The problem is well-studied in existing literature and predominantly uses highly coupled Dynamic Programming (DP) solutions. The problem scale and complexity become embarrassingly humungous to handle as sequence size increases. This makes the case for parallelization. Parallelization can be achieved by way of networked platforms (clusters, grids, etc) as well as using modern day multi-core chips. Methods In this paper, we exploit the parallelism capabilities of the IBM Cell Broadband Engine to parallelize an existing Dynamic Programming (DP) algorithm for RNA secondary structure prediction. We design three different implementation strategies that exploit the inherent data, code and/or hybrid parallelism, referred to as C-Par, D-Par and H-Par, and analyze their performances. Our approach attempts to introduce parallelism in critical sections of the algorithm. We ran our experiments on SONY Play Station 3 (PS3), which is based on the IBM Cell chip. Results Our results suggest that introducing parallelism in DP algorithm allows it to easily handle longer sequences which otherwise would consume a large amount of time in single core computers. The results further demonstrate the speed-up gain achieved in exploiting the inherent parallelism in the problem and also elicits the advantages of using multi-core platforms towards designing more sophisticated methodologies for handling a fairly long sequence of RNA. Conclusion The speed-up performance reported here is promising, especially when sequence length is long. To the best of our literature survey, the work reported in this paper is probably the first-of-its-kind to utilize the IBM Cell Broadband Engine (a heterogeneous multi-core chip) to implement a DP. The results also encourage using multi-core platforms towards designing more sophisticated methodologies for handling a fairly long sequence of RNA to predict its secondary structure. PMID:20122209

  7. Seismic Structure of Inner Core Boundary Region Correlated with Predicted Outer Core Flow

    NASA Astrophysics Data System (ADS)

    Cormier, Vernon

    2015-04-01

    Seismic observations of the inner core reveal a pattern of lateral variation in elastic velocities, attenuation, scattering, and anisotropy having a strong but incompletely understood correlation with outer core flow predicted from dynamo simulations controlled by heat flow across the core-mantle boundary. Currently resolvable large spatial scales exhibit at least a tripartite latitudinal rather than hemispherical pattern in attenuation, elastic velocities, and anisotropy1. These large and smaller scale lateral variations are most densely sampled by seismic body waves traversing the equatorial region of the inner core, where cyclonic cylinders of outer core convection are tangent to its boundary. Correlations of inner core structure with predicted outer core flow include (a) a thin (10-40 km thick) zone of low P velocity and possibly near zero S velocity beneath the equatorial eastern Indian Ocean2, which is coincident with a predicted region of strong down-welling flow and inner core growth3; (b) a broad region beneath the central and eastern equatorial Pacific that more strongly attenuates PKIKP1, containing small-scale (1-10km) volumetric heterogeneities inferred from the coda of reflected PKiKP waves4, which is coincident with a predicted region of inner core melting5; and (c) a narrow region of fast P velocity 150 km beneath the eastern equatorial Atlantic6, which is coincident with a predicted secondary, weaker, longitudinal zone of down-welling5. To explain observed decadal time variations on the order of 0.1's of sec for the travel times of PKIKP, the mantle control suggested by these correlations requires either an small upper bound (

  8. Sex differences in structural brain asymmetry predict overt aggression in early adolescents.

    PubMed

    Visser, Troy A W; Ohan, Jeneva L; Whittle, Sarah; Yücel, Murat; Simmons, Julian G; Allen, Nicholas B

    2014-04-01

    The devastating social, emotional and economic consequences of human aggression are laid bare nightly on newscasts around the world. Aggression is principally mediated by neural circuitry comprising multiple areas of the prefrontal cortex and limbic system, including the orbitofrontal cortex (OFC), anterior cingulate cortex (ACC), amygdala and hippocampus. A striking characteristic of these regions is their structural asymmetry about the midline (i.e. left vs right hemisphere). Variations in these asymmetries have been linked to clinical disorders characterized by aggression and the rate of aggressive behavior in psychiatric patients. Here, we show for the first time that structural asymmetries in prefrontal cortical areas are also linked to aggression in a normal population of early adolescents. Our findings indicate a relationship between parent reports of aggressive behavior in adolescents and structural asymmetries in the limbic and paralimbic ACC and OFC, and moreover, that this relationship varies by sex. Furthermore, while there was no relationship between aggression and structural asymmetries in the amygdala or hippocampus, hippocampal volumes did predict aggression in females. Taken together, the results suggest that structural asymmetries in the prefrontal cortex may influence human aggression, and that the anatomical basis of aggression varies substantially by sex. PMID:23446839

  9. Performance assessment of different constraining potentials in computational structure prediction for disulfide-bridged proteins.

    PubMed

    Kondov, Ivan; Verma, Abhinav; Wenzel, Wolfgang

    2011-08-10

    The presence of disulfide bonds in proteins has very important implications on the three-dimensional structure and folding of proteins. An adequate treatment of disulfide bonds in de-novo protein simulations is therefore very important. Here we present a computational study of a set of small disulfide-bridged proteins using an all-atom stochastic search approach and including various constraining potentials to describe the disulfide bonds. The proposed potentials can easily be implemented in any code based on all-atom force fields and employed in simulations to achieve an improved prediction of protein structure. Exploring different potential parameters and comparing the structures to those from unconstrained simulations and to experimental structures by means of a scoring function we demonstrate that the inclusion of constraining potentials improves the quality of final structures significantly. For some proteins (1KVG and 1PG1) the native conformation is visited only in simulations in presence of constraints. Overall, we found that the Morse potential has optimal performance, in particular for the ?-sheet proteins. PMID:21864792

  10. XTALOPT version r7: An open-source evolutionary algorithm for crystal structure prediction

    NASA Astrophysics Data System (ADS)

    Lonie, David C.; Zurek, Eva

    2011-10-01

    A new version of XTALOPT, a user-friendly GPL-licensed evolutionary algorithm for crystal structure prediction, is available for download from the CPC library or the XTALOPT website, http://xtalopt.openmolecules.net. The new version now supports four external geometry optimization codes (VASP, GULP, PWSCF, and CASTEP), as well as three queuing systems: PBS, SGE, SLURM, and “Local”. The local queuing system allows the geometry optimizations to be performed on the user's workstation if an external computational cluster is unavailable. Support for the Windows operating system has been added, and a Windows installer is provided. Numerous bugfixes and feature enhancements have been made in the new release as well.

  11. Coupling continuous damage and debris fragmentation for energy absorption prediction by cfrp structures during crushing

    NASA Astrophysics Data System (ADS)

    Espinosa, Christine; Lachaud, Frédéric; Limido, Jérome; Lacome, Jean-Luc; Bisson, Antoine; Charlotte, Miguel

    2015-05-01

    Energy absorption during crushing is evaluated using a thermodynamic based continuum damage model inspired from the Matzenmiller-Lubliner-Taylors model. It was found that for crash-worthiness applications, it is necessary to couple the progressive ruin of the material to a representation of the matter openings and debris generation. Element kill technique (erosion) and/or cohesive elements are efficient but not predictive. A technique switching finite elements into discrete particles at rupture is used to create debris and accumulated mater during the crushing of the structure. Switching criteria are evaluated using the contribution of the different ruin modes in the damage evolution, energy absorption, and reaction force generation.

  12. The prediction on in-line vortex-induced vibration of slender marine structures

    NASA Astrophysics Data System (ADS)

    Xu, Wan-Hai; Gao, Xi-Feng; Du, Jie

    2012-10-01

    The in-line (IL) vortex-induced vibration (VIV) that occurs frequently in ocean engineering may cause severe fatigue damage in slender marine structures. To the best knowledge of the authors, in existing literatures, there is no efficient analytical model for predicting pure IL VIV. In this paper, a wake oscillator model capable of analyzing the IL VIV of slender marine structures has been developed. Two different kinds of van der Pol equations are used to describe the near wake dynamics related to the fluctuating nature of symmetric vortex shedding in the first excitation region and alternate vortex shedding in the second one. Some comparisons are carried out between the present model results and experimental data. It is found that many phenomena observed in experiments could be reproduced by the present wake oscillator model.

  13. Prediction of Shock Wave Structure in Weakly Ionized Gas Flow by Solving MGD Equation

    NASA Technical Reports Server (NTRS)

    Deng, Z. T.; Oviedo-Rojas, Ruben; Chow, Alan; Litchford, Ron J.; Cook, Stephen (Technical Monitor)

    2002-01-01

    This paper reports the recent research results of shockwave structure predictions using a new developed code. The modified Rankine-Hugoniot relations across a standing normal shock wave are discussed and adopted to obtain jump conditions. Coupling a electrostatic body force to the Burnett equations, the weakly ionized flow field across the shock wave was solved. Results indicated that the Modified Rankine-Hugoniot equations for shock wave are valid for a wide range of ionization fraction. However, this model breaks down with small free stream Mach number and with large ionization fraction. The jump conditions also depend on the value of free stream pressure, temperature and density. The computed shock wave structure with ionization provides results, which indicated that shock wave strength may be reduced by existence of weakly ionized gas.

  14. Does Adolescent Family Structure Predict Military Enlistment? A Comparison of Post-High School Activities

    PubMed Central

    Spence, Naomi J.; Henderson, Kathryn A.; Elder, Glen H.

    2013-01-01

    This paper investigates the link between adolescent family structure and the likelihood of military enlistment in young adulthood, as compared to alternative post-high school activities. We use data from the National Longitudinal Study of Adolescent Health and multinomial logistic regression analyses to compare the odds of military enlistment with college attendance or labor force involvement. We find that alternative family structures predict enlistment relative to college attendance. Living in a single-parent household during adolescence increased odds of military enlistment, but the effect is accounted for by socioeconomic status and early feelings of social isolation. Living with a stepparent or with neither biological parent more than doubles the odds of enlistment, independent of socioeconomic status, characteristics of parent-child relationships, or feelings of social isolation. Although college attendance is widely promoted as a valued post-high school activity, military service may offer a route to independence and a greater sense of belonging. PMID:24000268

  15. Correlation of predicted and measured thermal stresses on an advanced aircraft structure with similar materials

    NASA Technical Reports Server (NTRS)

    Jenkins, J. M.

    1979-01-01

    A laboratory heating test simulating hypersonic heating was conducted on a heat-sink type structure to provide basic thermal stress measurements. Six NASTRAN models utilizing various combinations of bar, shear panel, membrane, and plate elements were used to develop calculated thermal stresses. Thermal stresses were also calculated using a beam model. For a given temperature distribution there was very little variation in NASTRAN calculated thermal stresses when element types were interchanged for a given grid system. Thermal stresses calculated for the beam model compared similarly to the values obtained for the NASTRAN models. Calculated thermal stresses compared generally well to laboratory measured thermal stresses. A discrepancy of signifiance occurred between the measured and predicted thermal stresses in the skin areas. A minor anomaly in the laboratory skin heating uniformity resulted in inadequate temperature input data for the structural models.

  16. Does Adolescent Family Structure Predict Military Enlistment? A Comparison of Post-High School Activities.

    PubMed

    Spence, Naomi J; Henderson, Kathryn A; Elder, Glen H

    2013-09-01

    This paper investigates the link between adolescent family structure and the likelihood of military enlistment in young adulthood, as compared to alternative post-high school activities. We use data from the National Longitudinal Study of Adolescent Health and multinomial logistic regression analyses to compare the odds of military enlistment with college attendance or labor force involvement. We find that alternative family structures predict enlistment relative to college attendance. Living in a single-parent household during adolescence increased odds of military enlistment, but the effect is accounted for by socioeconomic status and early feelings of social isolation. Living with a stepparent or with neither biological parent more than doubles the odds of enlistment, independent of socioeconomic status, characteristics of parent-child relationships, or feelings of social isolation. Although college attendance is widely promoted as a valued post-high school activity, military service may offer a route to independence and a greater sense of belonging. PMID:24000268

  17. Structural relaxation in glassy polymers predicted by soft modes: a quantitative analysis.

    PubMed

    Smessaert, Anton; Rottler, Jörg

    2014-11-14

    We present a quantitative analysis of the correlation between quasi-localized, low energy vibrational modes and structural relaxation events in computer simulations of a quiescent, thermal polymer glass. Our results extend previous studies on glass forming binary mixtures in 2D, and show that the soft modes identify regions that undergo irreversible rearrangements with up to 7 times the average probability. We study systems in the supercooled- and aging-regimes and discuss temperature- as well as age-dependence of the correlation. In addition to the location of rearrangements, we find that soft modes also predict their direction on the molecular level. The soft regions are long lived structural features, and the observed correlations vanish only after >50% of the system has undergone rearrangements. PMID:25241966

  18. DEVELOPMENT OF QUANTITATIVE STRUCTURE ACTIVITY RELATIONSHIPS (QSARS) TO PREDICT TOXICITY FOR A VARIETY OF HUMAN AND ECOLOGICAL ENDPOINTS

    EPA Science Inventory

    A web accessible software tool is being developed to predict the toxicity of unknown chemicals for a wide variety of endpoints. The tool will enable a user to easily predict the toxicity of a query compound by simply entering its structure in a 2-dimensional (2-D) chemical sketc...

  19. How does the quality of phospholipidosis data influence the predictivity of structural alerts?

    PubMed

    Przybylak, Katarzyna R; Alzahrani, Abdullah Rzgallah; Cronin, Mark T D

    2014-08-25

    The ability of drugs to induce phospholipidosis (PLD) is linked directly to their molecular substructures: hydrophobic, cyclic moieties with hydrophilic, peripheral amine groups. These structural properties can be captured and coded into SMILES arbitrary target specification (SMARTS) patterns. Such structural alerts, which are capable of identifying potential PLD inducers, should ideally be developed on a relatively large but reliable data set. We had previously developed a model based on SMARTS patterns consisting of 32 structural fragments using information from 450 chemicals. In the present study, additional PLD structural alerts have been developed based on a newer and larger data set combining two data sets published recently by the United States Food and Drug Administration (US FDA). To assess the predictive performance of the updated SMARTS model, two publicly available data sets were considered. These data sets were constructed using different criteria and hence represent different standards for overall quality. In the first data set high quality was assured as all negative chemicals were confirmed by the gold standard method for the detection of PLD-transmission electron microscopy (EM). The second data set was constructed from seven previously published data sets and then curated by removing compounds where conflicting results were found for PLD activity. Evaluation of the updated SMARTS model showed a strong, positive correlation between predictive performance of the alerts and the quality of the data set used for the assessment. The results of this study confirm the importance of using high quality data for modeling and evaluation, especially in the case of PLD, where species, tissue, and dose dependence of results are additional confounding factors. PMID:25062434

  20. Structure based model for the prediction of phospholipidosis induction potential of small molecules.

    PubMed

    Sun, Hongmao; Shahane, Sampada; Xia, Menghang; Austin, Christopher P; Huang, Ruili

    2012-07-23

    Drug-induced phospholipidosis (PLD), characterized by an intracellular accumulation of phospholipids and formation of concentric lamellar bodies, has raised concerns in the drug discovery community, due to its potential adverse effects. To evaluate the PLD induction potential, 4,161 nonredundant drug-like molecules from the National Institutes of Health Chemical Genomics Center (NCGC) Pharmaceutical Collection (NPC), the Library of Pharmacologically Active Compounds (LOPAC), and the Tocris Biosciences collection were screened in a quantitative high-throughput screening (qHTS) format. The potential of drug-lipid complex formation can be linked directly to the structures of drug molecules, and many PLD inducing drugs were found to share common structural features. Support vector machine (SVM) models were constructed by using customized atom types or Molecular Operating Environment (MOE) 2D descriptors as structural descriptors. Either the compounds from LOPAC or randomly selected from the entire data set were used as the training set. The impact of training data with biased structural features and the impact of molecule descriptors emphasizing whole-molecule properties or detailed functional groups at the atom level on model performance were analyzed and discussed. Rebalancing strategies were applied to improve the predictive power of the SVM models. Using the undersampling method, the consensus model using one-third of the compounds randomly selected from the data set as the training set achieved high accuracy of 0.90 in predicting the remaining two-thirds of the compounds constituting the test set, as measured by the area under the receiver operator characteristic curve (AUC-ROC). PMID:22725677

  1. A novel quantitative structure–activity relationship model for prediction of biomagnification factor of some organochlorine pollutants

    Microsoft Academic Search

    Mohammad Hossein Fatemi; Elham Baher

    2009-01-01

    The biomagnification factor (BMF) is an important property for toxicology and environmental chemistry. In this work, quantitative\\u000a structure–activity relationship (QSAR) models were used for the prediction of BMF for a data set including 30 polychlorinated\\u000a biphenyls and 12 organochlorine pollutants. This set was divided into training and prediction sets. The result of diversity\\u000a test reveals that the structure of the

  2. Engineering Property Prediction Tools for Tailored Polymer Composite Structures (FY06 Annual Report)

    SciTech Connect

    Nguyen, Ba Nghiep; Holbery, Jim; Kunc, Vlastimil

    2006-12-31

    Recently, long-fiber injection molded thermoplastics (LFTs) have generated great interest within the automotive industry as these materials can be used for structural applications in order to reduce vehicle weight. However, injection-molding of these materials poses a great challenge because of two main reasons: (i) no process models for LFTs have been developed that can be used to predict the processing of an LFT part, and (ii) no experimental characterization methods exist to fully characterize the as-formed LFT microstructure to determine the fiber orientation and length distributions and fiber dispersion that are critical for any process model development. This report summarizes the work conducted during the fiscal year 2006 (FY06) that includes (i) the assessment of current process modeling approaches, (ii) experimental evaluation of LFT microstructure and mechanical properties, and (iii) the computation of thermoelastic properties using the measured and predicted orientation distributions as well as the measured fiber length distribution. Our objective is two-fold. First, it is necessary to assess current process models and characterization techniques in order to determine their capabilities and limitations, and the necessary developments for LFTs. Second, before modeling the nonlinear behaviors of LFTs, it is essential to develop computation tools for predicting the elastic and thermoelastic properties of these materials.

  3. Prediction of Structures and Atomization Energies of Small Silver Clusters, (Ag)n, n < 100

    SciTech Connect

    Chen, Mingyang; Dyer, Jason E.; Li, Keijing; Dixon, David A.

    2013-07-24

    Neutral silver clusters, Agn, were studied using density functional theory (DFT) followed by high level coupled cluster CCSD(T) calculations to determine the low energy isomers for each cluster size for small clusters. The normalized atomization energy, heats of formation, and average bond lengths were calculated for each of the different isomeric forms of the silver clusters. For n = 2?6, the preferred geometry is planar, and the larger n = 7?8 clusters prefer higher symmetry, three-dimensional geometries. The low spin state is predicted to be the ground state for every cluster size. A number of new low energy isomers for the heptamer and octamer were found. Additional larger Agn structures, n < 100, were initially optimized using a tree growth-hybrid genetic algorithm with an embedded atom method (EAM) potential. For n ? 20, DFT was used to optimize the geometries. DFT with benchmarked functionals were used to predict that the normalized atomization energies (?AE?s) for Agn start to converge slowly to the bulk at n = 55. The ?AE? for Ag99 is predicted to be ?50 kcal/mol.

  4. Population structure in the native range predicts the spread of introduced marine species.

    PubMed

    Gaither, Michelle R; Bowen, Brian W; Toonen, Robert J

    2013-06-01

    Forecasting invasion success remains a fundamental challenge in invasion biology. The effort to identify universal characteristics that predict which species become invasive has faltered in part because of the diversity of taxa and systems considered. Here, we use an alternative approach focused on the spread stage of invasions. FST, a measure of alternative fixation of alleles, is a common proxy for realized dispersal among natural populations, summarizing the combined influences of life history, behaviour, habitat requirements, population size, history and ecology. We test the hypothesis that population structure in the native range (FST) is negatively correlated with the geographical extent of spread of marine species in an introduced range. An analysis of the available data (29 species, nine phyla) revealed a significant negative correlation (R(2) = 0.245-0.464) between FST and the extent of spread of non-native species. Mode FST among pairwise comparisons between populations in the native range demonstrated the highest predictive power (R(2) = 0.464, p < 0.001). There was significant improvement when marker type was considered, with mtDNA datasets providing the strongest relationship (n = 21, R(2) = 0.333-0.516). This study shows that FST can be used to make qualitative predictions concerning the geographical extent to which a non-native marine species will spread once established in a new area. PMID:23595272

  5. A simple structure-based model for the prediction of HIV-1 co-receptor tropism

    PubMed Central

    2014-01-01

    Background Human Immunodeficiency Virus 1 enters host cells through interaction of its V3 loop (which is part of the gp120 protein) with the host cell receptor CD4 and one of two co-receptors, namely CCR5 or CXCR4. Entry inhibitors binding the CCR5 co-receptor can prevent viral entry. As these drugs are only available for CCR5-using viruses, accurate prediction of this so-called co-receptor tropism is important in order to ensure an effective personalized therapy. With the development of next-generation sequencing technologies, it is now possible to sequence representative subpopulations of the viral quasispecies. Results Here we present T-CUP 2.0, a model for predicting co-receptor tropism. Based on our recently published T-CUP model, we developed a more accurate and even faster solution. Similarly to its predecessor, T-CUP 2.0 models co-receptor tropism using information of the electrostatic potential and hydrophobicity of V3-loops. However, extracting this information from a simplified structural vacuum-model leads to more accurate and faster predictions. The area-under-the-ROC-curve (AUC) achieved with T-CUP 2.0 on the training set is 0.968±0.005 in a leave-one-patient-out cross-validation. When applied to an independent dataset, T-CUP 2.0 has an improved prediction accuracy of around 3% when compared to the original T-CUP. Conclusions We found that it is possible to model co-receptor tropism in HIV-1 based on a simplified structure-based model of the V3 loop. In this way, genotypic prediction of co-receptor tropism is very accurate, fast and can be applied to large datasets derived from next-generation sequencing technologies. The reduced complexity of the electrostatic modeling makes T-CUP 2.0 independent from third-party software, making it easy to install and use. PMID:25120583

  6. Evaluating factors that predict the structure of a commensalistic epiphyte–phorophyte network

    PubMed Central

    Sáyago, Roberto; Lopezaraiza-Mikel, Martha; Quesada, Mauricio; Álvarez-Añorve, Mariana Yolotl; Cascante-Marín, Alfredo; Bastida, Jesus Ma.

    2013-01-01

    A central issue in ecology is the understanding of the establishment of biotic interactions. We studied the factors that affect the assembly of the commensalistic interactions between vascular epiphytes and their host plants. We used an analytical approach that considers all individuals and species of epiphytic bromeliads and woody hosts and non-hosts at study plots. We built models of interaction probabilities among species to assess if host traits and abundance and spatial overlap of species predict the quantitative epiphyte–host network. Species abundance, species spatial overlap and host size largely predicted pairwise interactions and several network metrics. Wood density and bark texture of hosts also contributed to explain network structure. Epiphytes were more common on large hosts, on abundant woody species, with denser wood and/or rougher bark. The network had a low level of specialization, although several interactions were more frequent than expected by the models. We did not detect a phylogenetic signal on the network structure. The effect of host size on the establishment of epiphytes indicates that mature forests are necessary to preserve diverse bromeliad communities. PMID:23407832

  7. Evaluating factors that predict the structure of a commensalistic epiphyte-phorophyte network.

    PubMed

    Sáyago, Roberto; Lopezaraiza-Mikel, Martha; Quesada, Mauricio; Álvarez-Añorve, Mariana Yolotl; Cascante-Marín, Alfredo; Bastida, Jesus Ma

    2013-04-01

    A central issue in ecology is the understanding of the establishment of biotic interactions. We studied the factors that affect the assembly of the commensalistic interactions between vascular epiphytes and their host plants. We used an analytical approach that considers all individuals and species of epiphytic bromeliads and woody hosts and non-hosts at study plots. We built models of interaction probabilities among species to assess if host traits and abundance and spatial overlap of species predict the quantitative epiphyte-host network. Species abundance, species spatial overlap and host size largely predicted pairwise interactions and several network metrics. Wood density and bark texture of hosts also contributed to explain network structure. Epiphytes were more common on large hosts, on abundant woody species, with denser wood and/or rougher bark. The network had a low level of specialization, although several interactions were more frequent than expected by the models. We did not detect a phylogenetic signal on the network structure. The effect of host size on the establishment of epiphytes indicates that mature forests are necessary to preserve diverse bromeliad communities. PMID:23407832

  8. Measured and predicted structural behavior of the HiMAT tailored composite wing

    NASA Technical Reports Server (NTRS)

    Nelson, Lawrence H.

    1987-01-01

    A series of load tests was conducted on the HiMAT tailored composite wing. Coupon tests were also run on a series of unbalanced laminates, including the ply configuration of the wing, the purpose of which was to compare the measured and predicted behavior of unbalanced laminates, including - in the case of the wing - a comparison between the behavior of the full scale structure and coupon tests. Both linear and nonlinear finite element (NASTRAN) analyses were carried out on the wing. Both linear and nonlinear point-stress analyses were performed on the coupons. All test articles were instrumented with strain gages, and wing deflections measured. The leading and trailing edges were found to have no effect on the response of the wing to applied loads. A decrease in the stiffness of the wing box was evident over the 27-test program. The measured load-strain behavior of the wing was found to be linear, in contrast to coupon tests of the same laminate, which were nonlinear. A linear NASTRAN analysis of the wing generally correlated more favorably with measurements than did a nonlinear analysis. An examination of the predicted deflections in the wing root region revealed an anomalous behavior of the structural model that cannot be explained. Both hysteresis and creep appear to be less significant in the wing tests than in the corresponding laminate coupon tests.

  9. Montane refugia predict population genetic structure in the Large-blotched Ensatina salamander.

    PubMed

    Devitt, Thomas J; Devitt, Susan E Cameron; Hollingsworth, Bradford D; McGuire, Jimmy A; Moritz, Craig

    2013-03-01

    Understanding the biotic consequences of Pleistocene range shifts and fragmentation remains a fundamental goal in historical biogeography and evolutionary biology. Here, we combine species distribution models (SDM) from the present and two late Quaternary time periods with multilocus genetic data (mitochondrial DNA and microsatellites) to evaluate the effect of climate-induced habitat shifts on population genetic structure in the Large-blotched Ensatina (Ensatina eschscholtzii klauberi), a plethodontid salamander endemic to middle and high-elevation conifer forest in the Transverse and Peninsular Ranges of southern California and northern Baja California. A composite SDM representing the range through time predicts two disjunct refugia, one in southern California encompassing the core of the species range and the other in the Sierra San Pedro Mártir of northern Baja California at the southern limit of the species range. Based on our spatial model, we would expect a pattern of high connectivity among populations within the northern refugium and, conversely, a pattern of isolation due to long-term persistence of the Sierra San Pedro Mártir population. Our genetic results are consistent with these predictions based on the hypothetical refugia in that (i) historical measures of population connectivity among stable areas are correlated with gene flow estimates; and (ii) there is strong geographical structure between separate refugia. These results provide evidence for the role of recent climatic change in shaping patterns of population persistence and connectivity within the Transverse and Peninsular Ranges, an evolutionary hotspot. PMID:23379992

  10. Analytic Prediction of Baryonic Effects from the EFT of Large Scale Structures

    E-print Network

    Lewandowski, Matthew; Senatore, Leonardo

    2014-01-01

    The large scale structures of the universe will likely be the next leading source of cosmological information. It is therefore crucial to understand their behavior. The Effective Field Theory of Large Scale Structures provides a consistent way to perturbatively predict the clustering of dark matter at large distances. The fact that baryons move distances comparable to dark matter allows us to infer that baryons at large distances can be described in a similar formalism: the backreaction of short-distance non-linearities and of star-formation physics at long distances can be encapsulated in an effective stress tensor, characterized by a few parameters. The functional form of baryonic effects can therefore be predicted. In the power spectrum the leading contribution goes as $\\propto k^2 P(k)$, with $P(k)$ being the linear power spectrum and with the numerical prefactor depending on the details of the star-formation physics. We also perform the resummation of the contribution of the long-wavelength displacements...

  11. Fabrication of 3D nanostructures by multidirectional UV lithography and predictive structural modeling

    NASA Astrophysics Data System (ADS)

    Kim, Jungkwun; Kim, Cheolbok; Allen, Mark G.; ‘YK’ Yoon, Yong-Kyu

    2015-02-01

    This paper presents the fabrication and modeling of three-dimensional (3D) nanostructures by automated multidirectional ultraviolet (UV) lithography, which is a fast, cost-effective, manufacturable fabrication method. Multidirectional UV exposure is performed using a static UV light source equipped with a tilt-rotational substrate holder. A glass substrate with a nanopatterned chrome layer is utilized as both a photomask and a substrate, for which a backside UV exposure scheme is used. For the analytical modeling of the shape of fabricated nanostructures, UV exposure dosage, diffraction and refraction effects, and absorption rate are taken into account. For more accurate process predictive models, a commercially available multiphysics simulation tool is used. The structural shapes predicted from analytical calculation and simulation are compared with the fabricated ones for which various 3D nanoscale test structures are fabricated such as an inclined nanopillar array and a vertical triangular slab. Also, nanostructures with multiple heights are successfully implemented from single layer photoresist by controlling the UV exposure dosage and tilt angles. A tripod embedded horn and a triangular-slab embedded horn are demonstrated.

  12. The analysis of seismic data structure and oil and gas prediction

    NASA Astrophysics Data System (ADS)

    Wang, Shangxu; Lin, Changrong

    2004-10-01

    In this paper, a new concept called numerical structure of seismic data is introduced and the difference between numerical structure and numerical value of seismic data is explained. Our study shows that the numerical seismic structure is closely related to oil and gas-bearing reservoir, so it is very useful for a geologist or a geophysicist to precisely interpret the oil-bearing layers from the seismic data. This technology can be applied to any exploration or production stage. The new method has been tested on a series of exploratory or development wells and proved to be reliable in China. Hydrocarbon-detection with this new method for 39 exploration wells on 25 structures indicates a success ratio of over 80 percent. The new method of hydrocarbon prediction can be applied for: (1) depositional environment of reservoirs with marine fades, delta, or non-marine facies (including fluvial facies, lacustrine facies); (2) sedimentary rocks of reservoirs that are non-marine clastic rocks and carbonate rock; and (3) burial depths range from 300 m to 7000 m, and the minimum thickness of these reservoirs is over 8 m (main frequency is about 50 Hz).

  13. Critical analysis of the accuracy of models predicting or extracting liquid structure information.

    PubMed

    Van Houteghem, Marc; Ghysels, An; Verstraelen, Toon; Poelmans, Ward; Waroquier, Michel; Van Speybroeck, Veronique

    2014-03-01

    This work aims at a critical assessment of properties predicting or extracting information on the density and structure of liquids. State-of-the-art NVT and NpT molecular dynamics (MD) simulations have been performed on five liquids: methanol, chloroform, acetonitrile, tetrahydrofuran, and ethanol. These simulations allow the computation of properties based on first principles, including the equilibrium density and radial distribution functions (RDFs), characterizing the liquid structure. Refinements have been incorporated in the MD simulations by taking into account basis set superposition errors (BSSE). An extended BSSE model for an instantaneous evaluation of the BSSE corrections has been proposed, and their impact on the liquid properties has been assessed. If available, the theoretical RDFs have been compared with the experimentally derived RDFs. For some liquids, significant discrepancies have been observed, and a profound but critical investigation is presented to unravel the origin of these deficiencies. This discussion is focused on tetrahydrofuran where the experiment reveals some prominent peaks completely missing in any MD simulation. Experiments providing information on liquid structure consist mainly of neutron diffraction measurements offering total structure factors as the primary observables. The splitting of these factors in reciprocal space into intra- and intermolecular contributions is extensively discussed, together with their sensitivity in reproducing correct RDFs in coordinate space. PMID:24512612

  14. A supervised approach to predict the hierarchical structure of conversation threads for comments.

    PubMed

    Balali, A; Faili, H; Asadpour, M

    2014-01-01

    User-generated texts such as comments in social media are rich sources of information. In general, the reply structure of comments is not publicly accessible on the web. Websites present comments as a list in chronological order. This way, some information is lost. A solution for this problem is to reconstruct the thread structure (RTS) automatically. RTS predicts a semantic tree for the reply structure, useful for understanding users' behaviours and facilitating follow of the actual conversation streams. This paper works on RTS task in blogs, online news agencies, and news websites. These types of websites cover various types of articles reflecting the real-world events. People with different views participate in arguments by writing comments. Comments express opinions, sentiments, or ideas about articles. The reply structure of threads in these types of websites is basically different from threads in the forums, chats, and emails. To perform RTS, we define a set of textual and nontextual features. Then, we use supervised learning to combine these features. The proposed method is evaluated on five different datasets. The accuracy of the proposed method is compared with baselines. The results reveal higher accuracy for our method in comparison with baselines in all datasets. PMID:24672323

  15. Prediction of the 3D structure of rat MrgA G protein-coupled receptor and identification of its binding site

    E-print Network

    Goddard III, William A.

    Prediction of the 3D structure of rat MrgA G protein-coupled receptor and identification of its structure of rat MrgA (rMrgA) receptor [obtained from homology modeling to the recently validated predicted bonds to these two Asn residues. These results validate the predicted structure for rat MrgA and suggest

  16. Spliced mRNA Encoding the Murine Cytomegalovirus Chemokine Homolog Predicts a ? Chemokine of Novel Structure

    PubMed Central

    MacDonald, Margaret R.; Burney, Mary W.; Resnick, Stuart B.; Virgin, Herbert W.

    1999-01-01

    A viral mRNA of the late kinetic class expressed by murine cytomegalovirus (MCMV) contains an open reading frame (ORF) whose predicted protein, designated MCK-1, has homology to ? chemokines (M. R. MacDonald, X.-Y. Li, and H. W. Virgin IV, J. Virol. 71:1671–1678, 1997). The present study analyzed further the structure of the transcript in infected fibroblast cells. A splicing event removed the MCK-1 stop codon, bringing a downstream ORF into frame with the chemokine homolog and demonstrating that the MCK-1 ORF was an exon of a larger gene. The predicted 31.4-kDa protein, designated MCK-2, contains a putative amino-terminal signal sequence and a ? chemokine domain, followed by a carboxyl-terminal domain without significant homology to known proteins. Quantitative analysis of mRNA forms in MCMV-infected fibroblast cells at late times after infection indicated that the viral chemokine RNA was predominantly spliced. There was no evidence for expression of RNA encoding either MCK-1 or MCK-2 at immediate early or early times after infection with MCMV. Monoclonal antibodies generated against bacterially expressed MCK-2 recognized multiple proteins in the range of ?30 to ?45 kDa in Western blot analysis of MCK-2 expressed in transfected COS cells. The monoclonal antibodies immunoprecipitated a similar group of proteins in transfected COS cells metabolically labeled with radioactive cysteine. Radiolabelled protein of apparent higher molecular mass was immunoprecipitated from culture medium overlying the transfected cells, suggesting that posttranslationally modified MCK-2 can be secreted. Two proteins with apparent molecular mass suggestive of posttranslational modification were detected by Western blot analysis of cells harvested at late times after infection with MCMV. These studies show that MCMV encodes and expresses a ? chemokine homolog with a novel predicted structure. PMID:10196260

  17. B-Pred, a structure based B-cell epitopes prediction server

    PubMed Central

    Giacò, Luciano; Amicosante, Massimo; Fraziano, Maurizio; Gherardini, Pier Federico; Ausiello, Gabriele; Helmer-Citterich, Manuela; Colizzi, Vittorio; Cabibbo, Andrea

    2012-01-01

    The ability to predict immunogenic regions in selected proteins by in-silico methods has broad implications, such as allowing a quick selection of potential reagents to be used as diagnostics, vaccines, immunotherapeutics, or research tools in several branches of biological and biotechnological research. However, the prediction of antibody target sites in proteins using computational methodologies has proven to be a highly challenging task, which is likely due to the somewhat elusive nature of B-cell epitopes. This paper proposes a web-based platform for scoring potential immunological reagents based on the structures or 3D models of the proteins of interest. The method scores a protein’s peptides set, which is derived from a sliding window, based on the average solvent exposure, with a filter on the average local model quality for each peptide. The platform was validated on a custom-assembled database of 1336 experimentally determined epitopes from 106 proteins for which a reliable 3D model could be obtained through standard modeling techniques. Despite showing poor sensitivity, this method can achieve a specificity of 0.70 and a positive predictive value of 0.29 by combining these two simple parameters. These values are slightly higher than those obtained with other established sequence-based or structure-based methods that have been evaluated using the same epitopes dataset. This method is implemented in a web server called B-Pred, which is accessible at http://immuno.bio.uniroma2.it/bpred. The server contains a number of original features that allow users to perform personalized reagent searches by manipulating the sliding window’s width and sliding step, changing the exposure and model quality thresholds, and running sequential queries with different parameters. The B-Pred server should assist experimentalists in the rational selection of epitope antigens for a wide range of applications. PMID:22888263

  18. B-Pred, a structure based B-cell epitopes prediction server.

    PubMed

    Giacò, Luciano; Amicosante, Massimo; Fraziano, Maurizio; Gherardini, Pier Federico; Ausiello, Gabriele; Helmer-Citterich, Manuela; Colizzi, Vittorio; Cabibbo, Andrea

    2012-01-01

    The ability to predict immunogenic regions in selected proteins by in-silico methods has broad implications, such as allowing a quick selection of potential reagents to be used as diagnostics, vaccines, immunotherapeutics, or research tools in several branches of biological and biotechnological research. However, the prediction of antibody target sites in proteins using computational methodologies has proven to be a highly challenging task, which is likely due to the somewhat elusive nature of B-cell epitopes. This paper proposes a web-based platform for scoring potential immunological reagents based on the structures or 3D models of the proteins of interest. The method scores a protein's peptides set, which is derived from a sliding window, based on the average solvent exposure, with a filter on the average local model quality for each peptide. The platform was validated on a custom-assembled database of 1336 experimentally determined epitopes from 106 proteins for which a reliable 3D model could be obtained through standard modeling techniques. Despite showing poor sensitivity, this method can achieve a specificity of 0.70 and a positive predictive value of 0.29 by combining these two simple parameters. These values are slightly higher than those obtained with other established sequence-based or structure-based methods that have been evaluated using the same epitopes dataset. This method is implemented in a web server called B-Pred, which is accessible at http://immuno.bio.uniroma2.it/bpred. The server contains a number of original features that allow users to perform personalized reagent searches by manipulating the sliding window's width and sliding step, changing the exposure and model quality thresholds, and running sequential queries with different parameters. The B-Pred server should assist experimentalists in the rational selection of epitope antigens for a wide range of applications. PMID:22888263

  19. Influence of Finite Element Size in Residual Strength Prediction of Composite Structures

    NASA Technical Reports Server (NTRS)

    Satyanarayana, Arunkumar; Bogert, Philip B.; Karayev, Kazbek Z.; Nordman, Paul S.; Razi, Hamid

    2012-01-01

    The sensitivity of failure load to the element size used in a progressive failure analysis (PFA) of carbon composite center notched laminates is evaluated. The sensitivity study employs a PFA methodology previously developed by the authors consisting of Hashin-Rotem intra-laminar fiber and matrix failure criteria and a complete stress degradation scheme for damage simulation. The approach is implemented with a user defined subroutine in the ABAQUS/Explicit finite element package. The effect of element size near the notch tips on residual strength predictions was assessed for a brittle failure mode with a parametric study that included three laminates of varying material system, thickness and stacking sequence. The study resulted in the selection of an element size of 0.09 in. X 0.09 in., which was later used for predicting crack paths and failure loads in sandwich panels and monolithic laminated panels. Comparison of predicted crack paths and failure loads for these panels agreed well with experimental observations. Additionally, the element size vs. normalized failure load relationship, determined in the parametric study, was used to evaluate strength-scaling factors for three different element sizes. The failure loads predicted with all three element sizes provided converged failure loads with respect to that corresponding with the 0.09 in. X 0.09 in. element size. Though preliminary in nature, the strength-scaling concept has the potential to greatly reduce the computational time required for PFA and can enable the analysis of large scale structural components where failure is dominated by fiber failure in tension.

  20. The ABCs of molecular dynamics simulations on B-DNA, circa 2012

    PubMed Central

    Beveridge, David L; Cheatham, Thomas E; Mezei, Mihaly

    2013-01-01

    This article provides a retrospective on the ABC initiative in the area of all-atom molecular dynamics (MD) simulations including explicit solvent on all tetranucleotide steps of duplex B-form DNA duplex, ca. 2012. The ABC consortium has completed two phases of simulations, the most current being a set of 50–100 trajectories based on the AMBER ff99 force field together with the parmbsc0 modification. Some general perspectives on the field of MD on DNA and sequence effects on DNA structure are provided, followed by an overview our MD results, including a detailed comparison of the ff99/parmbsc0 results with crystal and NMR structures available for d(CGCGAATTCGCG). Some projects inspired by or related to the ABC initiative and database are also reviewed, including methods for the trajectory analyses, informatics of dealing with the large database of results, compressions of trajectories for efficacy of distribution, DNA solvation by water and ions, parameterization of coarse-grained models with applications and gene finding and genome annotation PMID:22750978