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Sample records for predicting b-dna structure

  1. A DFT study of 2-aminopurine-containing dinucleotides: prediction of stacked conformations with B-DNA structure.

    PubMed

    Smith, Darren A; Holroyd, Leo F; van Mourik, Tanja; Jones, Anita C

    2016-05-25

    The fluorescence properties of dinucleotides incorporating 2-aminopurine (2AP) suggest that the simplest oligonucleotides adopt conformations similar to those found in duplex DNA. However, there is a lack of structural data for these systems. We report a density functional theory (DFT) study of the structures of 2AP-containing dinucleotides (deoxydinucleoside monophosphates), including full geometry optimisation of the sugar-phosphate backbone. Our DFT calculations employ the M06-2X functional for reliable treatment of dispersion interactions and include implicit aqueous solvation. Dinucleotides with 2AP in the 5'-position and each of the natural bases in the 3'-position are examined, together with the analogous 5'-adenine-containing systems. Computed structures are compared in detail with typical B-DNA base-step parameters, backbone torsional angles and sugar pucker, derived from crystallographic data. We find that 2AP-containing dinucleotides adopt structures that closely conform to B-DNA in all characteristic parameters. The structures of 2AP-containing dinucleotides closely resemble those of their adenine-containing counterparts, demonstrating the fidelity of 2AP as a mimic of the natural base. As a first step towards exploring the conformational heterogeneity of dinucleotides, we also characterise an imperfectly stacked conformation and one in which the bases are completely unstacked. PMID:27186599

  2. Associations between intronic non-B DNA structures and exon skipping

    PubMed Central

    Tsai, Zing Tsung-Yeh; Chu, Wen-Yi; Cheng, Jen-Hao; Tsai, Huai-Kuang

    2014-01-01

    Non-B DNA structures are abundant in the genome and are often associated with critical biological processes, including gene regulation, chromosome rearrangement and genome stabilization. In particular, G-quadruplex (G4) may affect alternative splicing based on its ability to impede the activity of RNA polymerase II. However, the specific role of non-B DNA structures in splicing regulation still awaits investigation. Here, we provide a genome-wide and cross-species investigation of the associations between five non-B DNA structures and exon skipping. Our results indicate a statistically significant correlation of each examined non-B DNA structures with exon skipping in both human and mouse. We further show that the contributions of non-B DNA structures to exon skipping are influenced by the occurring region. These correlations and contributions are also significantly different in human and mouse. Finally, we detailed the effects of G4 by showing that occurring on the template strand and the length of G-run, which is highly related to the stability of a G4 structure, are significantly correlated with exon skipping activity. We thus show that, in addition to the well-known effects of RNA and protein structure, the relative positional arrangement of intronic non-B DNA structures may also impact exon skipping. PMID:24153112

  3. Non-B DNA Secondary Structures and Their Resolution by RecQ Helicases

    PubMed Central

    Sharma, Sudha

    2011-01-01

    In addition to the canonical B-form structure first described by Watson and Crick, DNA can adopt a number of alternative structures. These non-B-form DNA secondary structures form spontaneously on tracts of repeat sequences that are abundant in genomes. In addition, structured forms of DNA with intrastrand pairing may arise on single-stranded DNA produced transiently during various cellular processes. Such secondary structures have a range of biological functions but also induce genetic instability. Increasing evidence suggests that genomic instabilities induced by non-B DNA secondary structures result in predisposition to diseases. Secondary DNA structures also represent a new class of molecular targets for DNA-interactive compounds that might be useful for targeting telomeres and transcriptional control. The equilibrium between the duplex DNA and formation of multistranded non-B-form structures is partly dependent upon the helicases that unwind (resolve) these alternate DNA structures. With special focus on tetraplex, triplex, and cruciform, this paper summarizes the incidence of non-B DNA structures and their association with genomic instability and emphasizes the roles of RecQ-like DNA helicases in genome maintenance by resolution of DNA secondary structures. In future, RecQ helicases are anticipated to be additional molecular targets for cancer chemotherapeutics. PMID:21977309

  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

  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

    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. Electronic structure, stacking energy, partial charge, and hydrogen bonding in four periodic B-DNA models

    NASA Astrophysics Data System (ADS)

    Poudel, Lokendra; Rulis, Paul; Liang, Lei; Ching, W. Y.

    2014-08-01

    We present a theoretical study of the electronic structure of four periodic B-DNA models labeled (AT)10,(GC)10, (AT)5(GC)5, and (AT-GC)5 where A denotes adenine, T denotes thymine, G denotes guanine, and C denotes cytosine. Each model has ten base pairs with Na counterions to neutralize the negative phosphate group in the backbone. The (AT)5(GC)5 and (AT-GC)5 models contain two and five AT-GC bilayers, respectively. When compared against the average of the two pure models, we estimate the AT-GC bilayer interaction energy to be 19.015 Kcal/mol, which is comparable to the hydrogen bonding energy between base pairs obtained from the literature. Our investigation shows that the stacking of base pairs plays a vital role in the electronic structure, relative stability, bonding, and distribution of partial charges in the DNA models. All four models show a highest occupied molecular orbital (HOMO) to lowest unoccupied molecular orbital (LUMO) gap ranging from 2.14 to 3.12 eV with HOMO states residing on the PO4 + Na functional group and LUMO states originating from the bases. Our calculation implies that the electrical conductance of a DNA molecule should increase with increased base-pair mixing. Interatomic bonding effects in these models are investigated in detail by analyzing the distributions of the calculated bond order values for every pair of atoms in the four models including hydrogen bonding. The counterions significantly affect the gap width, the conductivity, and the distribution of partial charge on the DNA backbone. We also evaluate quantitatively the surface partial charge density on each functional group of the DNA models.

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

    PubMed

    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. Singlet Oxygen Attack on Guanine: Reactivity and Structural Signature within the B-DNA Helix.

    PubMed

    Dumont, Elise; Grüber, Raymond; Bignon, Emmanuelle; Morell, Christophe; Aranda, Juan; Ravanat, Jean-Luc; Tuñón, Iñaki

    2016-08-22

    Oxidatively generated DNA lesions are numerous and versatile, and have been the subject of intensive research since the discovery of 8-oxoguanine in 1984. Even for this prototypical lesion, the precise mechanism of formation remains elusive due to the inherent difficulties in characterizing high-energy intermediates. We have probed the stability of the guanine endoperoxide in B-DNA as a key intermediate and determined a unique activation free energy of around 6 kcal mol(-1) for the formation of the first C-O covalent bond upon the attack of singlet molecular oxygen ((1) O2 ) on the central guanine of a solvated 13 base-pair poly(dG-dC), described by means of quantum mechanics/molecular mechanics (QM/MM) simulations. The B-helix remains stable upon oxidation in spite of the bulky character of the guanine endoperoxide. Our modeling study has revealed the nature of the versatile (1) O2 attack in terms of free energy and shows a sensitivity to electrostatics and solvation as it involves a charge-separated intermediate. PMID:27440482

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

    PubMed

    Pakotiprapha, Danaya; Samuels, Martin; Shen, Koning; Hu, Johnny H; Jeruzalmi, David

    2012-03-01

    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, ~80 Å 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. PMID:22307053

  10. Structure and mechanism of the UvrA-UvrB DNA damage sensor

    SciTech Connect

    Pakotiprapha, Danaya; Samuels, Martin; Shen, Koning; Hu, Johnny H; Jeruzalmi, David

    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, ~80 Å 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.

  11. Double-strand break formation by the RAG complex at the bcl-2 major breakpoint region and at other non-B DNA structures in vitro.

    PubMed

    Raghavan, Sathees C; Swanson, Patrick C; Ma, Yunmei; Lieber, Michael R

    2005-07-01

    The most common chromosomal translocation in cancer, t(14;18) at the 150-bp bcl-2 major breakpoint region (Mbr), occurs in follicular lymphomas. The bcl-2 Mbr assumes a non-B DNA conformation, thus explaining its distinctive fragility. This non-B DNA structure is a target of the RAG complex in vivo, but not because of its primary sequence. Here we report that the RAG complex generates at least two independent nicks that lead to double-strand breaks in vitro, and this requires the non-B DNA structure at the bcl-2 Mbr. A 3-bp mutation is capable of abolishing the non-B structure formation and the double-strand breaks. The observations on the bcl-2 Mbr reflect more general properties of the RAG complex, which can bind and nick at duplex-single-strand transitions of other non-B DNA structures, resulting in double-strand breaks in vitro. Hence, the present study reveals novel insight into a third mechanism of action of RAGs on DNA, besides the standard heptamer/nonamer-mediated cleavage in V(D)J recombination and the in vitro transposase activity. PMID:15988007

  12. A non-B-DNA structure at the Bcl-2 major breakpoint region is cleaved by the RAG complex.

    PubMed

    Raghavan, Sathees C; Swanson, Patrick C; Wu, Xiantuo; Hsieh, Chih-Lin; Lieber, Michael R

    2004-03-01

    The causes of spontaneous chromosomal translocations in somatic cells of biological organisms are largely unknown, although double-strand DNA breaks are required in all proposed mechanisms. The most common chromosomal abnormality in human cancer is the reciprocal translocation between chromosomes 14 and 18 (t(14;18)), which occurs in follicular lymphomas. The break at the immunoglobulin heavy-chain locus on chromosome 14 is an interruption of the normal V(D)J recombination process. But the breakage on chromosome 18, at the Bcl-2 gene, occurs within a confined 150-base-pair region (the major breakpoint region or Mbr) for reasons that have remained enigmatic. We have reproduced key features of the translocation process on an episome that propagates in human cells. The RAG complex--which is the normal enzyme for DNA cleavage at V, D or J segments--nicks the Bcl-2 Mbr in vitro and in vivo in a manner that reflects the pattern of the chromosomal translocations; however, the Mbr is not a V(D)J recombination signal. Rather the Bcl-2 Mbr assumes a non-B-form DNA structure within the chromosomes of human cells at 20-30% of alleles. Purified DNA assuming this structure contains stable regions of single-strandedness, which correspond well to the translocation regions in patients. Hence, a stable non-B-DNA structure in the human genome appears to be the basis for the fragility of the Bcl-2 Mbr, and the RAG complex is able to cleave this structure. PMID:14999286

  13. B-DNA to Z-DNA structural transitions in the SV40 enhancer: stabilization of Z-DNA in negatively supercoiled DNA minicircles

    NASA Technical Reports Server (NTRS)

    Gruskin, E. A.; Rich, A.

    1993-01-01

    During replication and transcription, the SV40 control region is subjected to significant levels of DNA unwinding. There are three, alternating purine-pyrimidine tracts within this region that can adopt the Z-DNA conformation in response to negative superhelix density: a single copy of ACACACAT and two copies of ATGCATGC. Since the control region is essential for both efficient transcription and replication, B-DNA to Z-DNA transitions in these vital sequence tracts may have significant biological consequences. We have synthesized DNA minicircles to detect B-DNA to Z-DNA transitions in the SV40 enhancer, and to determine the negative superhelix density required to stabilize the Z-DNA. A variety of DNA sequences, including the entire SV40 enhancer and the two segments of the enhancer with alternating purine-pyrimidine tracts, were incorporated into topologically relaxed minicircles. Negative supercoils were generated, and the resulting topoisomers were resolved by electrophoresis. Using an anti-Z-DNA Fab and an electrophoretic mobility shift assay, Z-DNA was detected in the enhancer-containing minicircles at a superhelix density of -0.05. Fab saturation binding experiments demonstrated that three, independent Z-DNA tracts were stabilized in the supercoiled minicircles. Two other minicircles, each with one of the two alternating purine-pyrimidine tracts, also contained single Z-DNA sites. These results confirm the identities of the Z-DNA-forming sequences within the control region. Moreover, the B-DNA to Z-DNA transitions were detected at superhelix densities observed during normal replication and transcription processes in the SV40 life cycle.

  14. Molecular stripping in the NF-κB/IκB/DNA genetic regulatory network

    PubMed Central

    Potoyan, Davit A.; Zheng, Weihua; Komives, Elizabeth A.; Wolynes, Peter G.

    2016-01-01

    Genetic switches based on the NF-κB/IκB/DNA system are master regulators of an array of cellular responses. Recent kinetic experiments have shown that IκB can actively remove NF-κB bound to its genetic sites via a process called “molecular stripping.” This allows the NF-κB/IκB/DNA switch to function under kinetic control rather than the thermodynamic control contemplated in the traditional models of gene switches. Using molecular dynamics simulations of coarse-grained predictive energy landscape models for the constituent proteins by themselves and interacting with the DNA we explore the functional motions of the transcription factor NF-κB and its various binary and ternary complexes with DNA and the inhibitor IκB. These studies show that the function of the NF-κB/IκB/DNA genetic switch is realized via an allosteric mechanism. Molecular stripping occurs through the activation of a domain twist mode by the binding of IκB that occurs through conformational selection. Free energy calculations for DNA binding show that the binding of IκB not only results in a significant decrease of the affinity of the transcription factor for the DNA but also kinetically speeds DNA release. Projections of the free energy onto various reaction coordinates reveal the structural details of the stripping pathways. PMID:26699500

  15. 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

  16. Toolbox for Protein Structure Prediction.

    PubMed

    Roche, Daniel Barry; McGuffin, Liam James

    2016-01-01

    Protein tertiary structure prediction algorithms aim to predict, from amino acid sequence, the tertiary structure of a protein. In silico protein structure prediction methods have become extremely important, as in vitro-based structural elucidation is unable to keep pace with the current growth of sequence databases due to high-throughput next-generation sequencing, which has exacerbated the gaps in our knowledge between sequences and structures.Here we briefly discuss protein tertiary structure prediction, the biennial competition for the Critical Assessment of Techniques for Protein Structure Prediction (CASP) and its role in shaping the field. We also discuss, in detail, our cutting-edge web-server method IntFOLD2-TS for tertiary structure prediction. Furthermore, we provide a step-by-step guide on using the IntFOLD2-TS web server, along with some real world examples, where the IntFOLD server can and has been used to improve protein tertiary structure prediction and aid in functional elucidation. PMID:26519323

  17. De Novo Protein Structure Prediction

    NASA Astrophysics Data System (ADS)

    Hung, Ling-Hong; Ngan, Shing-Chung; Samudrala, Ram

    An unparalleled amount of sequence data is being made available from large-scale genome sequencing efforts. The data provide a shortcut to the determination of the function of a gene of interest, as long as there is an existing sequenced gene with similar sequence and of known function. This has spurred structural genomic initiatives with the goal of determining as many protein folds as possible (Brenner and Levitt, 2000; Burley, 2000; Brenner, 2001; Heinemann et al., 2001). The purpose of this is twofold: First, the structure of a gene product can often lead to direct inference of its function. Second, since the function of a protein is dependent on its structure, direct comparison of the structures of gene products can be more sensitive than the comparison of sequences of genes for detecting homology. Presently, structural determination by crystallography and NMR techniques is still slow and expensive in terms of manpower and resources, despite attempts to automate the processes. Computer structure prediction algorithms, while not providing the accuracy of the traditional techniques, are extremely quick and inexpensive and can provide useful low-resolution data for structure comparisons (Bonneau and Baker, 2001). Given the immense number of structures which the structural genomic projects are attempting to solve, there would be a considerable gain even if the computer structure prediction approach were applicable to a subset of proteins.

  18. Chemical synthesis and characterization of branched oligodeoxyribonucleotides (bDNA) for use as signal amplifiers in nucleic acid quantification assays.

    PubMed Central

    Horn, T; Chang, C A; Urdea, M S

    1997-01-01

    The divergent synthesis of bDNA structures is described. This new type of branched DNA contains one unique oligonucleotide, the primary sequence, covalently attached through a comb-like branching network to many identical copies of a different oligonucleotide, the secondary sequence. The bDNA comb molecules were assembled on a solid support using parameters optimized for bDNA synthesis. The chemistry was used to synthesize bDNA comb molecules containing 15 secondary sequences. The bDNA comb molecules were elaborated by enzymatic ligation into branched amplification multimers, large bDNA molecules (a total of 1068 nt) containing an average of 36 repeated DNA oligomer sequences, each capable of hybridizing specifically to an alkaline phosphatase-labeled oligonucleotide. The bDNA comb molecules were characterized by electrophoretic methods and by controlled cleavage at periodate-cleavable moieties incorporated during synthesis. The branched amplification multimers have been used as signal amplifiers in nucleic acid quantification assays for detection of viral infection. It is possible to detect as few as 50 molecules with bDNA technology. PMID:9365266

  19. 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.

  20. Protein structural domains: definition and prediction.

    PubMed

    Ezkurdia, Iakes; Tress, Michael L

    2011-11-01

    Recognition and prediction of structural domains in proteins is an important part of structure and function prediction. This unit lists the range of tools available for domain prediction, and describes sequence and structural analysis tools that complement domain prediction methods. Also detailed are the basic domain prediction steps, along with suggested strategies for different protein sequences and potential pitfalls in domain boundary prediction. The difficult problem of domain orientation prediction is also discussed. All the resources necessary for domain boundary prediction are accessible via publicly available Web servers and databases and do not require computational expertise. PMID:22045561

  1. De novo design of protein mimics of B-DNA.

    PubMed

    Yüksel, Deniz; Bianco, Piero R; Kumar, Krishna

    2016-01-01

    Structural mimicry of DNA is utilized in nature as a strategy to evade molecular defences mounted by host organisms. One such example is the protein Ocr - the first translation product to be expressed as the bacteriophage T7 infects E. coli. The structure of Ocr reveals an intricate and deliberate arrangement of negative charges that endows it with the ability to mimic ∼24 base pair stretches of B-DNA. This uncanny resemblance to DNA enables Ocr to compete in binding the type I restriction modification (R/M) system, and neutralizes the threat of hydrolytic cleavage of viral genomic material. Here, we report the de novo design and biophysical characterization of DNA mimicking peptides, and describe the inhibitory action of the designed helical bundles on a type I R/M enzyme, EcoR124I. This work validates the use of charge patterning as a design principle for creation of protein mimics of DNA, and serves as a starting point for development of therapeutic peptide inhibitors against human pathogens that employ molecular camouflage as part of their invasion stratagem. PMID:26568416

  2. 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

  3. Local backbone structure prediction of proteins.

    PubMed

    de Brevern, Alexandre G; Benros, Cristina; Gautier, Romain; Valadié, Héléne; Hazout, Serge; Etchebest, Catherine

    2004-01-01

    A statistical analysis of the PDB structures has led us to define a new set of small 3D structural prototypes called Protein Blocks (PBs). This structural alphabet includes 16 PBs, each one is defined by the (phi, psi) dihedral angles of 5 consecutive residues. The amino acid distributions observed in sequence windows encompassing these PBs are used to predict by a Bayesian approach the local 3D structure of proteins from the sole knowledge of their sequences. LocPred is a software which allows the users to submit a protein sequence and performs a prediction in terms of PBs. The prediction results are given both textually and graphically. PMID:15724288

  4. Molecular stripping in the NFκB / IκB / DNA genetic regulatory network

    NASA Astrophysics Data System (ADS)

    Potoyan, Davit; Wolynes, Peter

    Genetic switches based on the NFκB / IκB / DNA system are master regulators of an array of cellular responses. Recent kinetic experiments have shown that IκB can actively remove NF κB bound to its genetic sites via a process called ''molecular stripping''. This allows the NFκB / IκB / DNA switch to function under kinetic control rather than the thermodynamic control contemplated in the traditional models of gene switches. Using molecular dynamics simulations of coarse grained predictive energy landscape models for the constituent proteins by themselves and interacting with the DNA we explore the functional motions of the transcription factor NFκB and its various binary and ternary complexes with DNA and the inhibitor I κB. These studies show that the function of the NFκB / IκB / DNA genetic switch is realized via an allosteric mechanism. Molecular stripping occurs through the activation of a domain twist mode by the binding of IκB which occurs through conformational selection. Free energy calculations for DNA binding show that the binding of IκB not only results in a significant decrease of the affinity of the transcription factor for the DNA but also kinetically speeds DNA release. Projections of the

  5. Structure Prediction of Membrane Proteins

    NASA Astrophysics Data System (ADS)

    Hu, Xiche

    Membrane proteins play a central role in many cellular and physiological processes. It is estimated that integral membrane proteins make up about 20-30% of the proteome (Krogh et al., 2001b; Stevens and Arkin, 2000; von Heijne, 1999). They are essential mediators of material and information transfer across cell membranes. Their functions include active and passive transport of molecules into and out of cells and organelles; transduction of energy among various forms (light, electrical, and chemical energy); as well as reception and transduction of chemical and electrical signals across membranes (Avdonin, 2005; Bockaert et al., 2002; Pahl, 1999; Rehling et al., 2004; Stack et al., 1995). Identifying these transmembrane (TM) proteins and deciphering their molecular mechanisms, then, is of great importance, particularly as applied to biomedicine. Membrane proteins are the targets of a large number of pharmacologically and toxicologically active substances, and are directly involved in their uptake, metabolism, and clearance (Bettler et al., 1998; Cohen, 2002; Heusser and Jardieu, 1997; Tibes et al., 2005; Xu et al., 2005). Despite the importance of membrane proteins, the knowledge of their high-resolution structures and mechanisms of action has lagged far behind in comparison to that of water-soluble proteins: less than 1% of all three-dimensional structures deposited in the Protein Data Bank are of membrane proteins. This unfortunate disparity stems from difficulties in overexpression and the crystallization of membrane proteins (Grisshammer and Tate, 1995; Michel, 1991).

  6. Geometric prediction structure for multiview video coding

    NASA Astrophysics Data System (ADS)

    Lee, Seok; Wey, Ho-Cheon; Park, Du-Sik

    2010-02-01

    One of the critical issues to successful service of 3D video is how to compress huge amount of multi-view video data efficiently. In this paper, we described about geometric prediction structure for multi-view video coding. By exploiting the geometric relations between each camera pose, we can make prediction pair which maximizes the spatial correlation of each view. To analyze the relationship of each camera pose, we defined the mathematical view center and view distance in 3D space. We calculated virtual center pose by getting mean rotation matrix and mean translation vector. We proposed an algorithm for establishing the geometric prediction structure based on view center and view distance. Using this prediction structure, inter-view prediction is performed to camera pair of maximum spatial correlation. In our prediction structure, we also considered the scalability in coding and transmitting the multi-view videos. Experiments are done using JMVC (Joint Multiview Video Coding) software on MPEG-FTV test sequences. Overall performance of proposed prediction structure is measured in the PSNR and subjective image quality measure such as PSPNR.

  7. 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 Σ3(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.

  8. A structural alphabet for local protein structures: improved prediction methods.

    PubMed

    Etchebest, Catherine; Benros, Cristina; Hazout, Serge; de Brevern, Alexandre G

    2005-06-01

    Three-dimensional protein structures can be described with a library of 3D fragments that define a structural alphabet. We have previously proposed such an alphabet, composed of 16 patterns of five consecutive amino acids, called Protein Blocks (PBs). These PBs have been used to describe protein backbones and to predict local structures from protein sequences. The Q16 prediction rate reaches 40.7% with an optimization procedure. This article examines two aspects of PBs. First, we determine the effect of the enlargement of databanks on their definition. The results show that the geometrical features of the different PBs are preserved (local RMSD value equal to 0.41 A on average) and sequence-structure specificities reinforced when databanks are enlarged. Second, we improve the methods for optimizing PB predictions from sequences, revisiting the optimization procedure and exploring different local prediction strategies. Use of a statistical optimization procedure for the sequence-local structure relation improves prediction accuracy by 8% (Q16 = 48.7%). Better recognition of repetitive structures occurs without losing the prediction efficiency of the other local folds. Adding secondary structure prediction improved the accuracy of Q16 by only 1%. An entropy index (Neq), strongly related to the RMSD value of the difference between predicted PBs and true local structures, is proposed to estimate prediction quality. The Neq is linearly correlated with the Q16 prediction rate distributions, computed for a large set of proteins. An "expected" prediction rate QE16 is deduced with a mean error of 5%. PMID:15822101

  9. 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

  10. Predicting complex mineral structures using genetic algorithms.

    PubMed

    Mohn, Chris E; Kob, Walter

    2015-10-28

    We show that symmetry-adapted genetic algorithms are capable of finding the ground state of a range of complex crystalline phases including layered- and incommensurate super-structures. This opens the way for the atomistic prediction of complex crystal structures of functional materials and mineral phases. PMID:26441052

  11. Predicting protein dynamics from structural ensembles

    NASA Astrophysics Data System (ADS)

    Copperman, J.; Guenza, M. G.

    2015-12-01

    The biological properties of proteins are uniquely determined by their structure and dynamics. A protein in solution populates a structural ensemble of metastable configurations around the global fold. From overall rotation to local fluctuations, the dynamics of proteins can cover several orders of magnitude in time scales. We propose a simulation-free coarse-grained approach which utilizes knowledge of the important metastable folded states of the protein to predict the protein dynamics. This approach is based upon the Langevin Equation for Protein Dynamics (LE4PD), a Langevin formalism in the coordinates of the protein backbone. The linear modes of this Langevin formalism organize the fluctuations of the protein, so that more extended dynamical cooperativity relates to increasing energy barriers to mode diffusion. The accuracy of the LE4PD is verified by analyzing the predicted dynamics across a set of seven different proteins for which both relaxation data and NMR solution structures are available. Using experimental NMR conformers as the input structural ensembles, LE4PD predicts quantitatively accurate results, with correlation coefficient ρ = 0.93 to NMR backbone relaxation measurements for the seven proteins. The NMR solution structure derived ensemble and predicted dynamical relaxation is compared with molecular dynamics simulation-derived structural ensembles and LE4PD predictions and is consistent in the time scale of the simulations. The use of the experimental NMR conformers frees the approach from computationally demanding simulations.

  12. Predicting structure in nonsymmetric sparse matrix factorizations

    SciTech Connect

    Gilbert, J.R.; Ng, E.

    1991-12-31

    Many computations on sparse matrices have a phase that predicts the nonzero structure of the output, followed by a phase that actually performs the numerical computation. We study structure prediction for computations that involve nonsymmetric row and column permutations and nonsymmetric or non-square matrices. Our tools are bipartite graphs, matchings, and alternating paths. Our main new result concerns LU factorization with partial pivoting. We show that if a square matrix A has the strong Hall property (i.e., is fully indecomposable) then an upper bound due to George and Ng on the nonzero structure of L + U is as tight as possible. To show this, we prove a crucial result about alternating paths in strong Hall graphs. The alternating-paths theorem seems to be of independent interest: it can also be used to prove related results about structure prediction for QR factorization that are due to Coleman, Edenbrandt, Gilbert, Hare, Johnson, Olesky, Pothen, and van den Driessche.

  13. Predicting structure in nonsymmetric sparse matrix factorizations

    SciTech Connect

    Gilbert, J.R. ); Ng, E. )

    1991-01-01

    Many computations on sparse matrices have a phase that predicts the nonzero structure of the output, followed by a phase that actually performs the numerical computation. We study structure prediction for computations that involve nonsymmetric row and column permutations and nonsymmetric or non-square matrices. Our tools are bipartite graphs, matchings, and alternating paths. Our main new result concerns LU factorization with partial pivoting. We show that if a square matrix A has the strong Hall property (i.e., is fully indecomposable) then an upper bound due to George and Ng on the nonzero structure of L + U is as tight as possible. To show this, we prove a crucial result about alternating paths in strong Hall graphs. The alternating-paths theorem seems to be of independent interest: it can also be used to prove related results about structure prediction for QR factorization that are due to Coleman, Edenbrandt, Gilbert, Hare, Johnson, Olesky, Pothen, and van den Driessche.

  14. Predicting structure in nonsymmetric sparse matrix factorizations

    SciTech Connect

    Gilbert, J.R. ); Ng, E.G. )

    1992-10-01

    Many computations on sparse matrices have a phase that predicts the nonzero structure of the output, followed by a phase that actually performs the numerical computation. We study structure prediction for computations that involve nonsymmetric row and column permutations and nonsymmetric or non-square matrices. Our tools are bipartite graphs, matchings, and alternating paths. Our main new result concerns LU factorization with partial pivoting. We show that if a square matrix A has the strong Hall property (i.e., is fully indecomposable) then an upper bound due to George and Ng on the nonzero structure of L + U is as tight as possible. To show this, we prove a crucial result about alternating paths in strong Hall graphs. The alternating-paths theorem seems to be of independent interest: it can also be used to prove related results about structure prediction for QR factorization that are due to Coleman, Edenbrandt, Gilbert, Hare, Johnson, Olesky, Pothen, and van den Driessche.

  15. Predicting Odor Perceptual Similarity from Odor Structure

    PubMed Central

    Weiss, Tali; Frumin, Idan; Khan, Rehan M.; Sobel, Noam

    2013-01-01

    To understand the brain mechanisms of olfaction we must understand the rules that govern the link between odorant structure and odorant perception. Natural odors are in fact mixtures made of many molecules, and there is currently no method to look at the molecular structure of such odorant-mixtures and predict their smell. In three separate experiments, we asked 139 subjects to rate the pairwise perceptual similarity of 64 odorant-mixtures ranging in size from 4 to 43 mono-molecular components. We then tested alternative models to link odorant-mixture structure to odorant-mixture perceptual similarity. Whereas a model that considered each mono-molecular component of a mixture separately provided a poor prediction of mixture similarity, a model that represented the mixture as a single structural vector provided consistent correlations between predicted and actual perceptual similarity (r≥0.49, p<0.001). An optimized version of this model yielded a correlation of r = 0.85 (p<0.001) between predicted and actual mixture similarity. In other words, we developed an algorithm that can look at the molecular structure of two novel odorant-mixtures, and predict their ensuing perceptual similarity. That this goal was attained using a model that considers the mixtures as a single vector is consistent with a synthetic rather than analytical brain processing mechanism in olfaction. PMID:24068899

  16. Predicting polymeric crystal structures by evolutionary algorithms

    NASA Astrophysics Data System (ADS)

    Zhu, Qiang; Sharma, Vinit; Oganov, Artem R.; Ramprasad, Ramamurthy

    2014-10-01

    The recently developed evolutionary algorithm USPEX proved to be a tool that enables accurate and reliable prediction of structures. Here we extend this method to predict the crystal structure of polymers by constrained evolutionary search, where each monomeric unit is treated as a building block with fixed connectivity. This greatly reduces the search space and allows the initial structure generation with different sequences and packings of these blocks. The new constrained evolutionary algorithm is successfully tested and validated on a diverse range of experimentally known polymers, namely, polyethylene, polyacetylene, poly(glycolic acid), poly(vinyl chloride), poly(oxymethylene), poly(phenylene oxide), and poly (p-phenylene sulfide). By fixing the orientation of polymeric chains, this method can be further extended to predict the structures of complex linear polymers, such as all polymorphs of poly(vinylidene fluoride), nylon-6 and cellulose. The excellent agreement between predicted crystal structures and experimentally known structures assures a major role of this approach in the efficient design of the future polymeric materials.

  17. 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.

  18. Multipass Membrane Protein Structure Prediction Using Rosetta

    PubMed Central

    Yarov-Yarovoy, Vladimir; Schonbrun, Jack; Baker, David

    2006-01-01

    We describe the adaptation of the Rosetta de novo structure prediction method for prediction of helical transmembrane protein structures. The membrane environment is modeled by embedding the protein chain into a model membrane represented by parallel planes defining hydrophobic, interface, and polar membrane layers for each energy evaluation. The optimal embedding is determined by maximizing the exposure of surface hydrophobic residues within the membrane and minimizing hydrophobic exposure outside of the membrane. Protein conformations are built up using the Rosetta fragment assembly method and evaluated using a new membrane-specific version of the Rosetta low-resolution energy function in which residue–residue and residue–environment interactions are functions of the membrane layer in addition to amino acid identity, distance, and density. We find that lower energy and more native-like structures are achieved by sequential addition of helices to a growing chain, which may mimic some aspects of helical protein biogenesis after translocation, rather than folding the whole chain simultaneously as in the Rosetta soluble protein prediction method. In tests on 12 membrane proteins for which the structure is known, between 51 and 145 residues were predicted with root-mean-square deviation <4Å from the native structure. PMID:16372357

  19. Potential non-B DNA regions in the human genome are associated with higher rates of nucleotide mutation and expression variation

    PubMed Central

    Du, Xiangjun; Gertz, E. Michael; Wojtowicz, Damian; Zhabinskaya, Dina; Levens, David; Benham, Craig J.; Schäffer, Alejandro A.; Przytycka, Teresa M.

    2014-01-01

    While individual non-B DNA structures have been shown to impact gene expression, their broad regulatory role remains elusive. We utilized genomic variants and expression quantitative trait loci (eQTL) data to analyze genome-wide variation propensities of potential non-B DNA regions and their relation to gene expression. Independent of genomic location, these regions were enriched in nucleotide variants. Our results are consistent with previously observed mutagenic properties of these regions and counter a previous study concluding that G-quadruplex regions have a reduced frequency of variants. While such mutagenicity might undermine functionality of these elements, we identified in potential non-B DNA regions a signature of negative selection. Yet, we found a depletion of eQTL-associated variants in potential non-B DNA regions, opposite to what might be expected from their proposed regulatory role. However, we also observed that genes downstream of potential non-B DNA regions showed higher expression variation between individuals. This coupling between mutagenicity and tolerance for expression variability of downstream genes may be a result of evolutionary adaptation, which allows reconciling mutagenicity of non-B DNA structures with their location in functionally important regions and their potential regulatory role. PMID:25336616

  20. Ko Displacement Theory for Structural Shape Predictions

    NASA Technical Reports Server (NTRS)

    Ko, William L.

    2010-01-01

    The development of the Ko displacement theory for predictions of structure deformed shapes was motivated in 2003 by the Helios flying wing, which had a 247-ft (75-m) wing span with wingtip deflections reaching 40 ft (12 m). The Helios flying wing failed in midair in June 2003, creating the need to develop new technology to predict in-flight deformed shapes of unmanned aircraft wings for visual display before the ground-based pilots. Any types of strain sensors installed on a structure can only sense the surface strains, but are incapable to sense the overall deformed shapes of structures. After the invention of the Ko displacement theory, predictions of structure deformed shapes could be achieved by feeding the measured surface strains into the Ko displacement transfer functions for the calculations of out-of-plane deflections and cross sectional rotations at multiple locations for mapping out overall deformed shapes of the structures. The new Ko displacement theory combined with a strain-sensing system thus created a revolutionary new structure- shape-sensing technology.

  1. Structure based prediction of protein folding intermediates.

    PubMed

    Xie, D; Freire, E

    1994-09-01

    The complete unfolding of a protein involves the disruption of non-covalent intramolecular interactions within the protein and the subsequent hydration of the backbone and amino acid side-chains. The magnitude of the thermodynamic parameters associated with this process is known accurately for a growing number of globular proteins for which high-resolution structures are also available. The existence of this database of structural and thermodynamic information has facilitated the development of statistical procedures aimed at quantifying the relationships existing between protein structure and the thermodynamic parameters of folding/unfolding. Under some conditions proteins do not unfold completely, giving rise to states (commonly known as molten globules) in which the molecule retains some secondary structure and remains in a compact configuration after denaturation. This phenomenon is reflected in the thermodynamics of the process. Depending on the nature of the residual structure that exists after denaturation, the observed enthalpy, entropy and heat capacity changes will deviate in a particular and predictable way from the values expected for complete unfolding. For several proteins, these deviations have been shown to exhibit similar characteristics, suggesting that their equilibrium folding intermediates exhibit some common structural features. Employing empirically derived structure-energetic relationships, it is possible to identify in the native structure of the protein those regions with the higher probability of being structured in equilibrium partly folded states. In this work, a thermodynamic search algorithm aimed at identifying the structural determinants of the molten globule state has been applied to six globular proteins; alpha-lactalbumin, barnase, IIIGlc, interleukin-1 beta, phage T4 lysozyme and phage 434 repressor. Remarkably, the structural features of the predicted equilibrium intermediates coincide to a large extent with the known

  2. RNA secondary structure prediction using soft computing.

    PubMed

    Ray, Shubhra Sankar; Pal, Sankar K

    2013-01-01

    Prediction of RNA structure is invaluable in creating new drugs and understanding genetic diseases. Several deterministic algorithms and soft computing-based techniques have been developed for more than a decade to determine the structure from a known RNA sequence. Soft computing gained importance with the need to get approximate solutions for RNA sequences by considering the issues related with kinetic effects, cotranscriptional folding, and estimation of certain energy parameters. A brief description of some of the soft computing-based techniques, developed for RNA secondary structure prediction, is presented along with their relevance. The basic concepts of RNA and its different structural elements like helix, bulge, hairpin loop, internal loop, and multiloop are described. These are followed by different methodologies, employing genetic algorithms, artificial neural networks, and fuzzy logic. The role of various metaheuristics, like simulated annealing, particle swarm optimization, ant colony optimization, and tabu search is also discussed. A relative comparison among different techniques, in predicting 12 known RNA secondary structures, is presented, as an example. Future challenging issues are then mentioned. PMID:23702539

  3. Fractal structure enables temporal prediction in music.

    PubMed

    Rankin, Summer K; Fink, Philip W; Large, Edward W

    2014-10-01

    1/f serial correlations and statistical self-similarity (fractal structure) have been measured in various dimensions of musical compositions. Musical performances also display 1/f properties in expressive tempo fluctuations, and listeners predict tempo changes when synchronizing. Here the authors show that the 1/f structure is sufficient for listeners to predict the onset times of upcoming musical events. These results reveal what information listeners use to anticipate events in complex, non-isochronous acoustic rhythms, and this will entail innovative models of temporal synchronization. This finding could improve therapies for Parkinson's and related disorders and inform deeper understanding of how endogenous neural rhythms anticipate events in complex, temporally structured communication signals. PMID:25324107

  4. Predicting structured metadata from unstructured metadata

    PubMed Central

    Posch, Lisa; Panahiazar, Maryam; Dumontier, Michel; Gevaert, Olivier

    2016-01-01

    Enormous amounts of biomedical data have been and are being produced by investigators all over the world. However, one crucial and limiting factor in data reuse is accurate, structured and complete description of the data or data about the data—defined as metadata. We propose a framework to predict structured metadata terms from unstructured metadata for improving quality and quantity of metadata, using the Gene Expression Omnibus (GEO) microarray database. Our framework consists of classifiers trained using term frequency-inverse document frequency (TF-IDF) features and a second approach based on topics modeled using a Latent Dirichlet Allocation model (LDA) to reduce the dimensionality of the unstructured data. Our results on the GEO database show that structured metadata terms can be the most accurately predicted using the TF-IDF approach followed by LDA both outperforming the majority vote baseline. While some accuracy is lost by the dimensionality reduction of LDA, the difference is small for elements with few possible values, and there is a large improvement over the majority classifier baseline. Overall this is a promising approach for metadata prediction that is likely to be applicable to other datasets and has implications for researchers interested in biomedical metadata curation and metadata prediction. Database URL: http://www.yeastgenome.org/

  5. A Role for Non-B DNA Forming Sequences in Mediating Microlesions Causing Human Inherited Disease.

    PubMed

    Kamat, Mihir Anant; Bacolla, Albino; Cooper, David N; Chuzhanova, Nadia

    2016-01-01

    Missense/nonsense mutations and microdeletions/microinsertions (<21 bp) represent ∼ 76% of all mutations causing human inherited disease, and their occurrence has been associated with sequence motifs (direct, inverted, and mirror repeats; G-quartets) capable of adopting non-B DNA structures. We found that a significant proportion (∼ 21%) of both microdeletions and microinsertions occur within direct repeats, and are explicable by slipped misalignment. A novel mutational mechanism, DNA triplex formation followed by DNA repair, may explain ∼ 5% of microdeletions and microinsertions at mirror repeats. Further, G-quartets, direct, and inverted repeats also appear to play a prominent role in mediating missense mutations, whereas only direct and inverted repeats mediate nonsense mutations. We suggest a mutational mechanism involving slipped strand mispairing, slipped structure formation, and DNA repair, to explain ∼ 15% of missense and ∼ 12% of nonsense mutations yielding perfect direct repeats from imperfect repeats, or the extension of existing direct repeats. Similar proportions of missense and nonsense mutations were explicable by hairpin/loop formation and DNA repair, yielding perfect inverted repeats from imperfect repeats. We also propose a model for single base-pair substitution based on one-electron oxidation reactions at G-quadruplex DNA. Overall, the proposed mechanisms provide support for a role for non-B DNA structures in human gene mutagenesis. PMID:26466920

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

    SciTech Connect

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

    2014-12-14

    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.

  7. 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.

  8. Structural load prediction methods for space payloads

    NASA Technical Reports Server (NTRS)

    Wada, B. K.

    1982-01-01

    The state of the art in structural loads prediction procedures for spacecraft is summarized. Three categories of prediction techniques delineated by cost, complexity, comprehensiveness, accuracy, and applications are outlined. The lowest cost method has been used for earth resources, communications, and weather satellites, the medium cost method for sun-synchronous orbits and the large space telescope, and the most expensive for planetary missions, comet rendezvous, and out-of-ecliptic orbits, all assuming Shuttle launch. The lowest cost method involves a mass-acceleration curve. A shock spectra technique predicts a least upper bound for loads. A recovered transient method analyzes the interface acceleration of two connected launch vehicles. The most accurate method devised thus far is a transient analysis of the total launch vehicle/payload dynamic system.

  9. 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

  10. The intrinsic mechanics of B-DNA in solution characterized by NMR.

    PubMed

    Imeddourene, Akli Ben; Xu, Xiaoqian; Zargarian, Loussiné; Oguey, Christophe; Foloppe, Nicolas; Mauffret, Olivier; Hartmann, Brigitte

    2016-04-20

    Experimental characterization of the structural couplings in free B-DNA in solution has been elusive, because of subtle effects that are challenging to tackle. Here, the exploitation of the NMR measurements collected on four dodecamers containing a substantial set of dinucleotide sequences provides new, consistent correlations revealing the DNA intrinsic mechanics. The difference between two successive residual dipolar couplings (ΔRDCs) involving C6/8-H6/8, C3'-H3' and C4'-H4' vectors are correlated to the(31)P chemical shifts (δP), which reflect the populations of the BI and BII backbone states. The δPs are also correlated to the internucleotide distances (Dinter) involving H6/8, H2' and H2″ protons. Calculations of NMR quantities on high resolution X-ray structures and controlled models of DNA enable to interpret these couplings: the studied ΔRDCs depend mostly on roll, while Dinterare mainly sensitive to twist or slide. Overall, these relations demonstrate how δP measurements inform on key inter base parameters, in addition to probe the BI↔BII backbone equilibrium, and shed new light into coordinated motions of phosphate groups and bases in free B-DNA in solution. Inspection of the 5' and 3' ends of the dodecamers also supplies new information on the fraying events, otherwise neglected. PMID:26883628

  11. The intrinsic mechanics of B-DNA in solution characterized by NMR

    PubMed Central

    Imeddourene, Akli Ben; Xu, Xiaoqian; Zargarian, Loussiné; Oguey, Christophe; Foloppe, Nicolas; Mauffret, Olivier; Hartmann, Brigitte

    2016-01-01

    Experimental characterization of the structural couplings in free B-DNA in solution has been elusive, because of subtle effects that are challenging to tackle. Here, the exploitation of the NMR measurements collected on four dodecamers containing a substantial set of dinucleotide sequences provides new, consistent correlations revealing the DNA intrinsic mechanics. The difference between two successive residual dipolar couplings (ΔRDCs) involving C6/8-H6/8, C3′-H3′ and C4′-H4′ vectors are correlated to the 31P chemical shifts (δP), which reflect the populations of the BI and BII backbone states. The δPs are also correlated to the internucleotide distances (Dinter) involving H6/8, H2′ and H2″ protons. Calculations of NMR quantities on high resolution X-ray structures and controlled models of DNA enable to interpret these couplings: the studied ΔRDCs depend mostly on roll, while Dinter are mainly sensitive to twist or slide. Overall, these relations demonstrate how δP measurements inform on key inter base parameters, in addition to probe the BI↔BII backbone equilibrium, and shed new light into coordinated motions of phosphate groups and bases in free B-DNA in solution. Inspection of the 5′ and 3′ ends of the dodecamers also supplies new information on the fraying events, otherwise neglected. PMID:26883628

  12. Evolutionary Structure Prediction of Stoichiometric Compounds

    NASA Astrophysics Data System (ADS)

    Zhu, Qiang; Oganov, Artem

    2014-03-01

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

  13. Local sequential minimization of double stranded B-DNA using Monte Carlo annealing.

    PubMed

    Sfyrakis, Konstantinos; Provata, Astero; Povey, David C; Howlin, Brendan J

    2004-06-01

    A software algorithm has been developed to investigate the folding process in B-DNA structures in vacuum under a simple and accurate force field. This algorithm models linear double stranded B-DNA sequences based on a local, sequential minimization procedure. The original B-DNA structures were modeled using initial nucleotide structures taken from the Brookhaven database. The models contain information at the atomic level allowing one to investigate as accurately as possible the structure and characteristics of the resulting DNA structures. A variety of DNA sequences and sizes were investigated containing coding and non-coding, random and real, homogeneous or heterogeneous sequences in the range of 2 to 40 base pairs. The force field contains terms such as angle bend, Lennard-Jones, electrostatic interactions and hydrogen bonding which are set up using the Dreiding II force field and defined to account for the helical parameters such as twist, tilt and rise. A close comparison was made between this local minimization algorithm and a global one (previously published) in order to find out advantages and disadvantages of the different methods. From the comparison, this algorithm gives better and faster results than the previous method, allowing one to minimize larger DNA segments. DNA segments with a length of 40 bases need approximately 4 h, while 2.5 weeks are needed with the previous method. After each minimization the angles between phosphate-oxygen-carbon A1, the oxygen-phosphate-oxygen A2 and the average helical twists were calculated. From the generated fragments it was found that the bond angles are A1=150 degrees +/-2 degrees and A2=130 degrees +/-10 degrees, while the helical twist is 36.6 degrees +/-2 degrees in the A strand and A1=150 degrees +/-6 degrees and A2=130+/-6 degrees with helical twist 39.6 degrees +/-2 degrees in the B strand for the DNA segment with the same sequence as the Dickerson dodecamer. PMID:15042433

  14. A protein structural classes prediction method based on predicted secondary structure and PSI-BLAST profile.

    PubMed

    Ding, Shuyan; Li, Yan; Shi, Zhuoxing; Yan, Shoujiang

    2014-02-01

    Knowledge of protein secondary structural classes plays an important role in understanding protein folding patterns. In this paper, 25 features based on position-specific scoring matrices are selected to reflect evolutionary information. In combination with other 11 rational features based on predicted protein secondary structure sequences proposed by the previous researchers, a 36-dimensional representation feature vector is presented to predict protein secondary structural classes for low-similarity sequences. ASTRALtraining dataset is used to train and design our method, other three low-similarity datasets ASTRALtest, 25PDB and 1189 are used to test the proposed method. Comparisons with other methods show that our method is effective to predict protein secondary structural classes. Stand alone version of the proposed method (PSSS-PSSM) is written in MATLAB language and it can be downloaded from http://letsgob.com/bioinfo_PSSS_PSSM/. PMID:24067326

  15. Predicting road accidents: Structural time series approach

    NASA Astrophysics Data System (ADS)

    Junus, Noor Wahida Md; Ismail, Mohd Tahir

    2014-07-01

    In this paper, the model for occurrence of road accidents in Malaysia between the years of 1970 to 2010 was developed and throughout this model the number of road accidents have been predicted by using the structural time series approach. The models are developed by using stepwise method and the residual of each step has been analyzed. The accuracy of the model is analyzed by using the mean absolute percentage error (MAPE) and the best model is chosen based on the smallest Akaike information criterion (AIC) value. A structural time series approach found that local linear trend model is the best model to represent the road accidents. This model allows level and slope component to be varied over time. In addition, this approach also provides useful information on improving the conventional time series method.

  16. On lattice protein structure prediction revisited.

    PubMed

    Dotu, Ivan; Cebrián, Manuel; Van Hentenryck, Pascal; Clote, Peter

    2011-01-01

    Protein structure prediction is regarded as a highly challenging problem both for the biology and for the computational communities. In recent years, many approaches have been developed, moving to increasingly complex lattice models and off-lattice models. This paper presents a Large Neighborhood Search (LNS) to find the native state for the Hydrophobic-Polar (HP) model on the Face-Centered Cubic (FCC) lattice or, in other words, a self-avoiding walk on the FCC lattice having a maximum number of H-H contacts. The algorithm starts with a tabu-search algorithm, whose solution is then improved by a combination of constraint programming and LNS. The flexible framework of this hybrid algorithm allows an adaptation to the Miyazawa-Jernigan contact potential, in place of the HP model, thus suggesting its potential for tertiary structure prediction. Benchmarking statistics are given for our method against the hydrophobic core threading program HPstruct, an exact method which can be viewed as complementary to our method. PMID:21358007

  17. Phylogenetic Approaches to Natural Product Structure Prediction

    PubMed Central

    Ziemert, Nadine; Jensen, Paul R.

    2015-01-01

    Phylogenetics is the study of the evolutionary relatedness among groups of organisms. Molecular phylogenetics uses sequence data to infer these relationships for both organisms and the genes they maintain. With the large amount of publicly available sequence data, phylogenetic inference has become increasingly important in all fields of biology. In the case of natural product research, phylogenetic relationships are proving to be highly informative in terms of delineating the architecture and function of the genes involved in secondary metabolite biosynthesis. Polyketide synthases and nonribosomal peptide synthetases provide model examples in which individual domain phylogenies display different predictive capacities, resolving features ranging from substrate specificity to structural motifs associated with the final metabolic product. This chapter provides examples in which phylogeny has proven effective in terms of predicting functional or structural aspects of secondary metabolism. The basics of how to build a reliable phylogenetic tree are explained along with information about programs and tools that can be used for this purpose. Furthermore, it introduces the Natural Product Domain Seeker, a recently developed Web tool that employs phylogenetic logic to classify ketosynthase and condensation domains based on established enzyme architecture and biochemical function. PMID:23084938

  18. Accurate Prediction of Docked Protein Structure Similarity.

    PubMed

    Akbal-Delibas, Bahar; Pomplun, Marc; Haspel, Nurit

    2015-09-01

    One of the major challenges for protein-protein docking methods is to accurately discriminate nativelike structures. The protein docking community agrees on the existence of a relationship between various favorable intermolecular interactions (e.g. Van der Waals, electrostatic, desolvation forces, etc.) and the similarity of a conformation to its native structure. Different docking algorithms often formulate this relationship as a weighted sum of selected terms and calibrate their weights against specific training data to evaluate and rank candidate structures. However, the exact form of this relationship is unknown and the accuracy of such methods is impaired by the pervasiveness of false positives. Unlike the conventional scoring functions, we propose a novel machine learning approach that not only ranks the candidate structures relative to each other but also indicates how similar each candidate is to the native conformation. We trained the AccuRMSD neural network with an extensive dataset using the back-propagation learning algorithm. Our method achieved predicting RMSDs of unbound docked complexes with 0.4Å error margin. PMID:26335807

  19. Modeling intercalated PAH metabolites: Explanation for the stereochemical and shape selectivity of B-DNA for bay-region carcinogens

    SciTech Connect

    Szentpaly, L.V.; Shamovsky, I.L.

    1996-12-31

    The equilibrium structures of 22 intercalation complexes of different metabolites of polycyclic aromatic hydrocarbons (PAH) with the dG{sub 2}{lg_bullet}dC{sub 2} dinucleotide are obtained by AMBER and FLEX molecular modeling. The triol carbocations of highly potent carcinogens are stereochemically compatible with the dinucleotide and B-DNA. Their intercalation complexes are found (1) to be stabilized by two hydrogen bonds between DH groups of the triol cation and the N(3) atoms of the adjacent guanine residues, (2) to be {open_quotes}preorganized{close_quotes} for covalent bonding to the N(2) amino group of quanine, (3) to display only minor conformational changes with respect to the uncomplexed dinucleotide in B-DNA. A new explanation for the stereochemical and shape selectivity in the initiation of cancer by PAHa is presented. The molecular mechanics study is sugmented by HF/6-31G{sup I} calculations on the conformations of phenanthrene triol carbocation.

  20. Structure prediction of magnetosome-associated proteins.

    PubMed

    Nudelman, Hila; Zarivach, Raz

    2014-01-01

    Magnetotactic bacteria (MTB) are Gram-negative bacteria that can navigate along geomagnetic fields. This ability is a result of a unique intracellular organelle, the magnetosome. These organelles are composed of membrane-enclosed magnetite (Fe3O4) or greigite (Fe3S4) crystals ordered into chains along the cell. Magnetosome formation, assembly, and magnetic nano-crystal biomineralization are controlled by magnetosome-associated proteins (MAPs). Most MAP-encoding genes are located in a conserved genomic region - the magnetosome island (MAI). The MAI appears to be conserved in all MTB that were analyzed so far, although the MAI size and organization differs between species. It was shown that MAI deletion leads to a non-magnetic phenotype, further highlighting its important role in magnetosome formation. Today, about 28 proteins are known to be involved in magnetosome formation, but the structures and functions of most MAPs are unknown. To reveal the structure-function relationship of MAPs we used bioinformatics tools in order to build homology models as a way to understand their possible role in magnetosome formation. Here we present a predicted 3D structural models' overview for all known Magnetospirillum gryphiswaldense strain MSR-1 MAPs. PMID:24523717

  1. Evaluation, analysis and prediction of geologic structures

    NASA Astrophysics Data System (ADS)

    Woodward, Nicholas B.

    2012-08-01

    Balanced cross-sections claim to be better because they apply a rigorous set of rules to develop the conceptual model of the structures present in an area. Balanced cross-sections can be further improved and become more useful to understanding real physical problems by collection of additional data such as seismic reflection surveys, collection of additional stratigraphic data, or collection of rock fabric information. The additional information validates the initial model and provides details on deformation conditions and on local rock responses to the deformation. Although individual cross-sections are two dimensional, the objective of evaluation and analysis of deformed regions should be three dimensional whenever possible to recognize the challenges of the real world. Subsurface system analysis derived from the hydrologic community emphasizes conceptual model development through model verification, validation, uncertainty quantification, benchmarking and meta-analysis. Their approach includes many steps informally used by the structural geology community but in a much more explicit way. Newer geological applications of structural geology would benefit from this more rigorous approach for designing and doing performance predictions as technological needs become more socially sensitive such as for carbon storage sites, new areas of energy exploration in higher population density areas, or for nuclear waste storage facilities.

  2. Optimizing nondecomposable loss functions in structured prediction.

    PubMed

    Ranjbar, Mani; Lan, Tian; Wang, Yang; Robinovitch, Steven N; Li, Ze-Nian; Mori, Greg

    2013-04-01

    We develop an algorithm for structured prediction with nondecomposable performance measures. The algorithm learns parameters of Markov Random Fields (MRFs) and can be applied to multivariate performance measures. Examples include performance measures such as Fβ score (natural language processing), intersection over union (object category segmentation), Precision/Recall at k (search engines), and ROC area (binary classifiers). We attack this optimization problem by approximating the loss function with a piecewise linear function. The loss augmented inference forms a Quadratic Program (QP), which we solve using LP relaxation. We apply this approach to two tasks: object class-specific segmentation and human action retrieval from videos. We show significant improvement over baseline approaches that either use simple loss functions or simple scoring functions on the PASCAL VOC and H3D Segmentation datasets, and a nursing home action recognition dataset. PMID:22868650

  3. 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.

  4. Structure prediction of magnetosome-associated proteins

    PubMed Central

    Nudelman, Hila; Zarivach, Raz

    2014-01-01

    Magnetotactic bacteria (MTB) are Gram-negative bacteria that can navigate along geomagnetic fields. This ability is a result of a unique intracellular organelle, the magnetosome. These organelles are composed of membrane-enclosed magnetite (Fe3O4) or greigite (Fe3S4) crystals ordered into chains along the cell. Magnetosome formation, assembly, and magnetic nano-crystal biomineralization are controlled by magnetosome-associated proteins (MAPs). Most MAP-encoding genes are located in a conserved genomic region – the magnetosome island (MAI). The MAI appears to be conserved in all MTB that were analyzed so far, although the MAI size and organization differs between species. It was shown that MAI deletion leads to a non-magnetic phenotype, further highlighting its important role in magnetosome formation. Today, about 28 proteins are known to be involved in magnetosome formation, but the structures and functions of most MAPs are unknown. To reveal the structure–function relationship of MAPs we used bioinformatics tools in order to build homology models as a way to understand their possible role in magnetosome formation. Here we present a predicted 3D structural models’ overview for all known Magnetospirillum gryphiswaldense strain MSR-1 MAPs. PMID:24523717

  5. Crystal structure prediction of rigid molecules.

    PubMed

    Elking, Dennis M; Fusti-Molnar, Laszlo; Nichols, Anthony

    2016-08-01

    A non-polarizable force field based on atomic multipoles fit to reproduce experimental crystal properties and ab initio gas-phase dimers is described. The Ewald method is used to calculate both long-range electrostatic and 1/r(6) dispersion energies of crystals. The dispersion energy of a crystal calculated by a cutoff method is shown to converge slowly to the exact Ewald result. A method for constraining space-group symmetry during unit-cell optimization is derived. Results for locally optimizing 4427 unit cells including volume, cell parameters, unit-cell r.m.s.d. and CPU timings are given for both flexible and rigid molecule optimization. An algorithm for randomly generating rigid molecule crystals is described. Using the correct experimentally determined space group, the average and maximum number of random crystals needed to find the correct experimental structure is given for 2440 rigid single component crystals. The force field energy rank of the correct experimental structure is presented for the same set of 2440 rigid single component crystals assuming the correct space group. A complete crystal prediction is performed for two rigid molecules by searching over the 32 most probable space groups. PMID:27484371

  6. Studies of the B-Z transition of DNA: The temperature dependence of the free-energy difference, the composition of the counterion sheath in mixed salt, and the preparation of a sample of the 5'-d[T-(m(5) C-G)12 -T] duplex in pure B-DNA or Z-DNA form.

    PubMed

    Guéron, Maurice; Plateau, Pierre; Filoche, Marcel

    2016-07-01

    It is often envisioned that cations might coordinate at specific sites of nucleic acids and play an important structural role, for instance in the transition between B-DNA and Z-DNA. However, nucleic acid models explicitly devoid of specific sites may also exhibit features previously considered as evidence for specific binding. Such is the case of the "composite cylinder" (or CC) model which spreads out localized features of DNA structure and charge by cylindrical averaging, while sustaining the main difference between the B and Z structures, namely the better immersion of the B-DNA phosphodiester charges in the solution. Here, we analyze the non-electrostatic component of the free-energy difference between B-DNA and Z-DNA. We also compute the composition of the counterion sheath in a wide range of mixed-salt solutions and of temperatures: in contrast with the large difference of composition between the B-DNA and Z-DNA forms, the temperature dependence of sheath composition, previously unknown, is very weak. In order to validate the model, the mixed-salt predictions should be compared to experiment. We design a procedure for future measurements of the sheath composition based on Anomalous Small-Angle X-ray Scattering and complemented by (31) P NMR. With due consideration for the kinetics of the B-Z transition and for the capacity of generating at will the B or Z form in a single sample, the 5'-d[T-(m(5) C-G)12 -T] 26-mer emerges as a most suitable oligonucleotide for this study. Finally, the application of the finite element method to the resolution of the Poisson-Boltzmann equation is described in detail. © 2016 Wiley Periodicals, Inc. Biopolymers 105: 369-384, 2016. PMID:26900058

  7. 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.

  8. 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

  9. RNAComposer and RNA 3D structure prediction for nanotechnology.

    PubMed

    Biesiada, Marcin; Pachulska-Wieczorek, Katarzyna; Adamiak, Ryszard W; Purzycka, Katarzyna J

    2016-07-01

    RNAs adopt specific, stable tertiary architectures to perform their activities. Knowledge of RNA tertiary structure is fundamental to understand RNA functions beginning with transcription and ending with turnover. Contrary to advanced RNA secondary structure prediction algorithms, which allow good accuracy when experimental data are integrated into the prediction, tertiary structure prediction of large RNAs still remains a significant challenge. However, the field of RNA tertiary structure prediction is rapidly developing and new computational methods based on different strategies are emerging. RNAComposer is a user-friendly and freely available server for 3D structure prediction of RNA up to 500 nucleotide residues. RNAComposer employs fully automated fragment assembly based on RNA secondary structure specified by the user. Importantly, this method allows incorporation of distance restraints derived from the experimental data to strengthen the 3D predictions. The potential and limitations of RNAComposer are discussed and an application to RNA design for nanotechnology is presented. PMID:27016145

  10. Protein structure prediction and analysis using the Robetta server

    PubMed Central

    Kim, David E.; Chivian, Dylan; Baker, David

    2004-01-01

    The Robetta server (http://robetta.bakerlab.org) provides automated tools for protein structure prediction and analysis. For structure prediction, sequences submitted to the server are parsed into putative domains and structural models are generated using either comparative modeling or de novo structure prediction methods. If a confident match to a protein of known structure is found using BLAST, PSI-BLAST, FFAS03 or 3D-Jury, it is used as a template for comparative modeling. If no match is found, structure predictions are made using the de novo Rosetta fragment insertion method. Experimental nuclear magnetic resonance (NMR) constraints data can also be submitted with a query sequence for RosettaNMR de novo structure determination. Other current capabilities include the prediction of the effects of mutations on protein–protein interactions using computational interface alanine scanning. The Rosetta protein design and protein–protein docking methodologies will soon be available through the server as well. PMID:15215442

  11. Protein short loop prediction in terms of a structural alphabet.

    PubMed

    Tyagi, Manoj; Bornot, Aurélie; Offmann, Bernard; de Brevern, Alexandre G

    2009-08-01

    Loops connect regular secondary structures. In many instances, they are known to play crucial biological roles. To bypass the limitation of secondary structure description, we previously defined a structural alphabet composed of 16 structural prototypes, called Protein Blocks (PBs). It leads to an accurate description of every region of 3D protein backbones and has been used in local structure prediction. In the present study, we used our structural alphabet to predict the loops connecting two repetitive structures. Thus, we showed interest to take into account the flanking regions, leading to prediction rate improvement up to 19.8%, but we also underline the sensitivity of such an approach. This research can be used to propose different structures for the loops and to probe and sample their flexibility. It is a useful tool for ab initio loop prediction and leads to insights into flexible docking approach. PMID:19625218

  12. SAM-T08, HMM-based protein structure prediction

    PubMed Central

    Karplus, Kevin

    2009-01-01

    The SAM-T08 web server is a protein structure prediction server that provides several useful intermediate results in addition to the final predicted 3D structure: three multiple sequence alignments of putative homologs using different iterated search procedures, prediction of local structure features including various backbone and burial properties, calibrated E-values for the significance of template searches of PDB and residue–residue contact predictions. The server has been validated as part of the CASP8 assessment of structure prediction as having good performance across all classes of predictions. The SAM-T08 server is available at http://compbio.soe.ucsc.edu/SAM_T08/T08-query.html PMID:19483096

  13. Structural coding versus free-energy predictive coding.

    PubMed

    van der Helm, Peter A

    2016-06-01

    Focusing on visual perceptual organization, this article contrasts the free-energy (FE) version of predictive coding (a recent Bayesian approach) to structural coding (a long-standing representational approach). Both use free-energy minimization as metaphor for processing in the brain, but their formal elaborations of this metaphor are fundamentally different. FE predictive coding formalizes it by minimization of prediction errors, whereas structural coding formalizes it by minimization of the descriptive complexity of predictions. Here, both sides are evaluated. A conclusion regarding competence is that FE predictive coding uses a powerful modeling technique, but that structural coding has more explanatory power. A conclusion regarding performance is that FE predictive coding-though more detailed in its account of neurophysiological data-provides a less compelling cognitive architecture than that of structural coding, which, for instance, supplies formal support for the computationally powerful role it attributes to neuronal synchronization. PMID:26407895

  14. Prediction of binary hard-sphere crystal structures.

    PubMed

    Filion, Laura; Dijkstra, Marjolein

    2009-04-01

    We present a method based on a combination of a genetic algorithm and Monte Carlo simulations to predict close-packed crystal structures in hard-core systems. We employ this method to predict the binary crystal structures in a mixture of large and small hard spheres with various stoichiometries and diameter ratios between 0.4 and 0.84. In addition to known binary hard-sphere crystal structures similar to NaCl and AlB2, we predict additional crystal structures with the symmetry of CrB, gammaCuTi, alphaIrV, HgBr2, AuTe2, Ag2Se, and various structures for which an atomic analog was not found. In order to determine the crystal structures at infinite pressures, we calculate the maximum packing density as a function of size ratio for the crystal structures predicted by our GA using a simulated annealing approach. PMID:19518387

  15. Predicting Career Advancement with Structural Equation Modelling

    ERIC Educational Resources Information Center

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

    2012-01-01

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

  16. Effects of Complementary DNA and Salt on the Thermoresponsiveness of Poly(N-isopropylacrylamide)-b-DNA.

    PubMed

    Fujita, Masahiro; Hiramine, Hayato; Pan, Pengju; Hikima, Takaaki; Maeda, Mizuo

    2016-02-01

    The thermoresponsive structural transition of poly(N-isopropylacrylamide) (PNIPAAm)-b-DNA copolymers was explored. Molecular assembly of the block copolymers was facilitated by adding salt, and this assembly was not nucleated by the association between DNA strands but by the coil-globule transition of PNIPAAm blocks. Below the lower critical solution temperature (LCST) of PNIPAAm, the copolymer solution remained transparent even at high salt concentrations, regardless of whether DNA was hybridized with its complementary partner to form a double-strand (or single-strand) structure. At the LCST, the hybridized copolymer assembled in spherical nanoparticles, surrounded by double-stranded DNA; subsequently, the non-cross-linking aggregation occurred, while the nanoparticles were dispersed if the salt concentration was low or DNA blocks were unhybridized. When the DNA duplex was denatured to a single-stranded state by heating, the aggregated nanoparticles redispersed owing to the recovery of the steric repulsion of the DNA strands. The changes in the steric and electrostatic effects by hybridization and the addition of salt did not result in any specific attraction between DNA strands but merely decreased the repulsive interactions. The van der Waals attraction between the nanoparticles overcame such repulsive interactions so that the non-cross-linking aggregation of the micellar particles was mediated. PMID:26750407

  17. Defining and predicting structurally conserved regions in protein superfamilies

    PubMed Central

    Huang, Ivan K.; Grishin, Nick V.

    2013-01-01

    Motivation: The structures of homologous proteins are generally better conserved than their sequences. This phenomenon is demonstrated by the prevalence of structurally conserved regions (SCRs) even in highly divergent protein families. Defining SCRs requires the comparison of two or more homologous structures and is affected by their availability and divergence, and our ability to deduce structurally equivalent positions among them. In the absence of multiple homologous structures, it is necessary to predict SCRs of a protein using information from only a set of homologous sequences and (if available) a single structure. Accurate SCR predictions can benefit homology modelling and sequence alignment. Results: Using pairwise DaliLite alignments among a set of homologous structures, we devised a simple measure of structural conservation, termed structural conservation index (SCI). SCI was used to distinguish SCRs from non-SCRs. A database of SCRs was compiled from 386 SCOP superfamilies containing 6489 protein domains. Artificial neural networks were then trained to predict SCRs with various features deduced from a single structure and homologous sequences. Assessment of the predictions via a 5-fold cross-validation method revealed that predictions based on features derived from a single structure perform similarly to ones based on homologous sequences, while combining sequence and structural features was optimal in terms of accuracy (0.755) and Matthews correlation coefficient (0.476). These results suggest that even without information from multiple structures, it is still possible to effectively predict SCRs for a protein. Finally, inspection of the structures with the worst predictions pinpoints difficulties in SCR definitions. Availability: The SCR database and the prediction server can be found at http://prodata.swmed.edu/SCR. Contact: 91huangi@gmail.com or grishin@chop.swmed.edu Supplementary information: Supplementary data are available at Bioinformatics

  18. 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

  19. 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

  20. Protein structure prediction from sequence variation

    PubMed Central

    Marks, Debora S; Hopf, Thomas A; Sander, Chris

    2015-01-01

    Genomic sequences contain rich evolutionary information about functional constraints on macromolecules such as proteins. This information can be efficiently mined to detect evolutionary couplings between residues in proteins and address the long-standing challenge to compute protein three-dimensional structures from amino acid sequences. Substantial progress has recently been made on this problem owing to the explosive growth in available sequences and the application of global statistical methods. In addition to three-dimensional structure, the improved understanding of covariation may help identify functional residues involved in ligand binding, protein-complex formation and conformational changes. We expect computation of covariation patterns to complement experimental structural biology in elucidating the full spectrum of protein structures, their functional interactions and evolutionary dynamics. PMID:23138306

  1. Genome-wide Membrane Protein Structure Prediction

    PubMed Central

    Piccoli, Stefano; Suku, Eda; Garonzi, Marianna; Giorgetti, Alejandro

    2013-01-01

    Transmembrane proteins allow cells to extensively communicate with the external world in a very accurate and specific way. They form principal nodes in several signaling pathways and attract large interest in therapeutic intervention, as the majority pharmaceutical compounds target membrane proteins. Thus, according to the current genome annotation methods, a detailed structural/functional characterization at the protein level of each of the elements codified in the genome is also required. The extreme difficulty in obtaining high-resolution three-dimensional structures, calls for computational approaches. Here we review to which extent the efforts made in the last few years, combining the structural characterization of membrane proteins with protein bioinformatics techniques, could help describing membrane proteins at a genome-wide scale. In particular we analyze the use of comparative modeling techniques as a way of overcoming the lack of high-resolution three-dimensional structures in the human membrane proteome. PMID:24403851

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

    SciTech Connect

    Lapedes, A. |; Steeg, E.; Farber, R.

    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.

  3. 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.

  4. 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

  5. Comparative melting and healing of B-DNA and Z-DNA by an infrared laser pulse

    NASA Astrophysics Data System (ADS)

    Man, Viet Hoang; Pan, Feng; Sagui, Celeste; Roland, Christopher

    2016-04-01

    We explore the use of a fast laser melting simulation approach combined with atomistic molecular dynamics simulations in order to determine the melting and healing responses of B-DNA and Z-DNA dodecamers with the same d(5'-CGCGCGCGCGCG-3')2 sequence. The frequency of the laser pulse is specifically tuned to disrupt Watson-Crick hydrogen bonds, thus inducing melting of the DNA duplexes. Subsequently, the structures relax and partially refold, depending on the field strength. In addition to the inherent interest of the nonequilibrium melting process, we propose that fast melting by an infrared laser pulse could be used as a technique for a fast comparison of relative stabilities of same-sequence oligonucleotides with different secondary structures with full atomistic detail of the structures and solvent. This could be particularly useful for nonstandard secondary structures involving non-canonical base pairs, mismatches, etc.

  6. Protein Structure and Function Prediction Using I-TASSER

    PubMed Central

    Yang, Jianyi; Zhang, Yang

    2016-01-01

    I-TASSER is a hierarchical protocol for automated protein structure prediction and structure-based function annotation. Starting from the amino acid sequence of target proteins, I-TASSER first generates full-length atomic structural models from multiple threading alignments and iterative structural assembly simulations followed by atomic-level structure refinement. The biological functions of the protein, including ligand-binding sites, enzyme commission number, and gene ontology terms, are then inferred from known protein function databases based on sequence and structure profile comparisons. I-TASSER is freely available as both an on-line server and a stand-alone package. This unit describes how to use the I-TASSER protocol to generate structure and function prediction and how to interpret the prediction results, as well as alternative approaches for further improving the I-TASSER modeling quality for distant-homologous and multi-domain protein targets. PMID:26678386

  7. PredyFlexy: flexibility and local structure prediction from sequence

    PubMed Central

    de Brevern, Alexandre G.; Bornot, Aurélie; Craveur, Pierrick; Etchebest, Catherine; Gelly, Jean-Christophe

    2012-01-01

    Protein structures are necessary for understanding protein function at a molecular level. Dynamics and flexibility of protein structures are also key elements of protein function. So, we have proposed to look at protein flexibility using novel methods: (i) using a structural alphabet and (ii) combining classical X-ray B-factor data and molecular dynamics simulations. First, we established a library composed of structural prototypes (LSPs) to describe protein structure by a limited set of recurring local structures. We developed a prediction method that proposes structural candidates in terms of LSPs and predict protein flexibility along a given sequence. Second, we examine flexibility according to two different descriptors: X-ray B-factors considered as good indicators of flexibility and the root mean square fluctuations, based on molecular dynamics simulations. We then define three flexibility classes and propose a method based on the LSP prediction method for predicting flexibility along the sequence. This method does not resort to sophisticate learning of flexibility but predicts flexibility from average flexibility of predicted local structures. The method is implemented in PredyFlexy web server. Results are similar to those obtained with the most recent, cutting-edge methods based on direct learning of flexibility data conducted with sophisticated algorithms. PredyFlexy can be accessed at http://www.dsimb.inserm.fr/dsimb_tools/predyflexy/. PMID:22689641

  8. Structure Prediction and Analysis of Neuraminidase Sequence Variants

    ERIC Educational Resources Information Center

    Thayer, Kelly M.

    2016-01-01

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

  9. Structural class prediction of protein using novel feature extraction method from chaos game representation of predicted secondary structure.

    PubMed

    Zhang, Lichao; Kong, Liang; Han, Xiaodong; Lv, Jinfeng

    2016-07-01

    Protein structural class prediction plays an important role in protein structure and function analysis, drug design and many other biological applications. Extracting good representation from protein sequence is fundamental for this prediction task. In recent years, although several secondary structure based feature extraction strategies have been specially proposed for low-similarity protein sequences, the prediction accuracy still remains limited. To explore the potential of secondary structure information, this study proposed a novel feature extraction method from the chaos game representation of predicted secondary structure to mainly capture sequence order information and secondary structure segments distribution information in a given protein sequence. Several kinds of prediction accuracies obtained by the jackknife test are reported on three widely used low-similarity benchmark datasets (25PDB, 1189 and 640). Compared with the state-of-the-art prediction methods, the proposed method achieves the highest overall accuracies on all the three datasets. The experimental results confirm that the proposed feature extraction method is effective for accurate prediction of protein structural class. Moreover, it is anticipated that the proposed method could be extended to other graphical representations of protein sequence and be helpful in future research. PMID:27084358

  10. 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

  11. Status of research aimed at predicting structural integrity

    SciTech Connect

    Reuter, W.G.

    1997-12-31

    Considerable research has been performed throughout the world on measuring the fracture toughness of metals. The existing capability fills the need encountered when selecting materials, thermal-mechanical treatments, welding procedures, etc., but cannot predict the fracture process of structural components containing cracks. The Idaho National Engineering and Environmental Laboratory and the Massachusetts Institute of Technology have been collaborating for a number of years on developing capabilities for using fracture toughness results to predict structural integrity. Because of the high cost of fabricating and testing structural components, these studies have been limited to predicting the fracture process in specimens containing surface cracks. This paper summarizes the present status of the experimental studies of using fracture toughness data to predict crack growth initiation in specimens (structural components) containing surface cracks. These results are limited to homogeneous base materials.

  12. 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; Popović, 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

  13. WeFold: A Coopetition for Protein Structure Prediction

    PubMed Central

    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; Popović, Zoran; Scheraga, Harold A.; Skolnick, Jeffrey; Crivelli, Silvia N.; Players, Foldit

    2014-01-01

    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 thirteen labs. During the collaboration, the labs 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

  14. PREDICTING MODES OF TOXIC ACTION FROM CHEMICAL STRUCTURE: AN OVERVIEW

    EPA Science Inventory

    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. asic and fun...

  15. 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 (α-helix, β-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

  16. 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 (α-helix, β-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

  17. Nucleosome structure incorporated histone acetylation site prediction in arabidopsis thaliana

    PubMed Central

    2010-01-01

    Abstract Background Acetylation is a crucial post-translational modification for histones, and plays a key role in gene expression regulation. Due to limited data and lack of a clear acetylation consensus sequence, a few researches have focused on prediction of lysine acetylation sites. Several systematic prediction studies have been conducted for human and yeast, but less for Arabidopsis thaliana. Results Concerning the insufficient observation on acetylation site, we analyzed contributions of the peptide-alignment-based distance definition and 3D structure factors in acetylation prediction. We found that traditional structure contributes little to acetylation site prediction. Identified acetylation sites of histones in Arabidopsis thaliana are conserved and cross predictable with that of human by peptide based methods. However, the predicted specificity is overestimated, because of the existence of non-observed acetylable site. Here, by performing a complete exploration on the factors that affect the acetylability of lysines in histones, we focused on the relative position of lysine at nucleosome level, and defined a new structure feature to promote the performance in predicting the acetylability of all the histone lysines in A. thaliana. Conclusion We found a new spacial correlated acetylation factor, and defined a ε-N spacial location based feature, which contains five core spacial ellipsoid wired areas. By incorporating the new feature, the performance of predicting the acetylability of all the histone lysines in A. Thaliana was promoted, in which the previous mispredicted acetylable lysines were corrected by comparing to the peptide-based prediction. PMID:21047388

  18. A predictive structural model for bulk metallic glasses.

    PubMed

    Laws, K J; Miracle, D B; Ferry, M

    2015-01-01

    Great progress has been made in understanding the atomic structure of metallic glasses, but there is still no clear connection between atomic structure and glass-forming ability. Here we give new insights into perhaps the most important question in the field of amorphous metals: how can glass-forming ability be predicted from atomic structure? We give a new approach to modelling metallic glass atomic structures by solving three long-standing problems: we discover a new family of structural defects that discourage glass formation; we impose efficient local packing around all atoms simultaneously; and we enforce structural self-consistency. Fewer than a dozen binary structures satisfy these constraints, but extra degrees of freedom in structures with three or more different atom sizes significantly expand the number of relatively stable, 'bulk' metallic glasses. The present work gives a new approach towards achieving the long-sought goal of a predictive capability for bulk metallic glasses. PMID:26370667

  19. A predictive structural model for bulk metallic glasses

    PubMed Central

    Laws, K. J.; Miracle, D. B.; Ferry, M.

    2015-01-01

    Great progress has been made in understanding the atomic structure of metallic glasses, but there is still no clear connection between atomic structure and glass-forming ability. Here we give new insights into perhaps the most important question in the field of amorphous metals: how can glass-forming ability be predicted from atomic structure? We give a new approach to modelling metallic glass atomic structures by solving three long-standing problems: we discover a new family of structural defects that discourage glass formation; we impose efficient local packing around all atoms simultaneously; and we enforce structural self-consistency. Fewer than a dozen binary structures satisfy these constraints, but extra degrees of freedom in structures with three or more different atom sizes significantly expand the number of relatively stable, ‘bulk' metallic glasses. The present work gives a new approach towards achieving the long-sought goal of a predictive capability for bulk metallic glasses. PMID:26370667

  20. 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

  1. Gogny HFB prediction of nuclear structure properties

    SciTech Connect

    Goriely, S.; Hilaire, S.; Girod, M.

    2011-10-28

    Large scale mean field calculations from proton to neutron drip lines have been performed using the Hartree-Fock-Bogoliubov method based on the Gogny nucleon-nucleon effective interaction. This extensive study has shown the ability of the method to reproduce bulk nuclear structure data available experimentally. This includes nuclear masses, radii, matter densities, deformations, moment of inertia as well as collective mode (low energy and giant resonances). In particular, the first mass table based on a Gogny-Hartree-Fock-Bogolyubov calculation including an explicit and coherent account of all the quadrupole correlation energies is presented. The rms deviation with respect to essentially all the available mass data is 798 keV. Nearly 8000 nuclei have been studied under the axial symmetry hypothesis and going beyond the mean-field approach.

  2. Predicting RNA secondary structures from sequence and probing data.

    PubMed

    Lorenz, Ronny; Wolfinger, Michael T; Tanzer, Andrea; Hofacker, Ivo L

    2016-07-01

    RNA secondary structures have proven essential for understanding the regulatory functions performed by RNA such as microRNAs, bacterial small RNAs, or riboswitches. This success is in part due to the availability of efficient computational methods for predicting RNA secondary structures. Recent advances focus on dealing with the inherent uncertainty of prediction by considering the ensemble of possible structures rather than the single most stable one. Moreover, the advent of high-throughput structural probing has spurred the development of computational methods that incorporate such experimental data as auxiliary information. PMID:27064083

  3. Methods for evaluating the predictive accuracy of structural dynamic models

    NASA Technical Reports Server (NTRS)

    Hasselman, Timothy K.; Chrostowski, Jon D.

    1991-01-01

    Modeling uncertainty is defined in terms of the difference between predicted and measured eigenvalues and eigenvectors. Data compiled from 22 sets of analysis/test results was used to create statistical databases for large truss-type space structures and both pretest and posttest models of conventional satellite-type space structures. Modeling uncertainty is propagated through the model to produce intervals of uncertainty on frequency response functions, both amplitude and phase. This methodology was used successfully to evaluate the predictive accuracy of several structures, including the NASA CSI Evolutionary Structure tested at Langley Research Center. Test measurements for this structure were within + one-sigma intervals of predicted accuracy for the most part, demonstrating the validity of the methodology and computer code.

  4. SAbPred: a structure-based antibody prediction server.

    PubMed

    Dunbar, James; Krawczyk, Konrad; Leem, Jinwoo; Marks, Claire; Nowak, Jaroslaw; Regep, Cristian; Georges, Guy; Kelm, Sebastian; Popovic, Bojana; Deane, Charlotte M

    2016-07-01

    SAbPred is a server that makes predictions of the properties of antibodies focusing on their structures. Antibody informatics tools can help improve our understanding of immune responses to disease and aid in the design and engineering of therapeutic molecules. SAbPred is a single platform containing multiple applications which can: number and align sequences; automatically generate antibody variable fragment homology models; annotate such models with estimated accuracy alongside sequence and structural properties including potential developability issues; predict paratope residues; and predict epitope patches on protein antigens. The server is available at http://opig.stats.ox.ac.uk/webapps/sabpred. PMID:27131379

  5. Evolving networks-Using past structure to predict the future

    NASA Astrophysics Data System (ADS)

    Shang, Ke-ke; Yan, Wei-sheng; Small, Michael

    2016-08-01

    Many previous studies on link prediction have focused on using common neighbors to predict the existence of links between pairs of nodes. More broadly, research into the structural properties of evolving temporal networks and temporal link prediction methods have recently attracted increasing attention. In this study, for the first time, we examine the use of links between a pair of nodes to predict their common neighbors and analyze the relationship between the weight and the structure in static networks, evolving networks, and in the corresponding randomized networks. We propose both new unweighted and weighted prediction methods and use six kinds of real networks to test our algorithms. In unweighted networks, we find that if a pair of nodes connect to each other in the current network, they will have a higher probability to connect common nodes both in the current and the future networks-and the probability will decrease with the increase of the number of neighbors. Furthermore, we find that the original networks have their particular structure and statistical characteristics which benefit link prediction. In weighted networks, the prediction algorithm performance of networks which are dominated by human factors decrease with the decrease of weight and are in general better in static networks. Furthermore, we find that geographical position and link weight both have significant influence on the transport network. Moreover, the evolving financial network has the lowest predictability. In addition, we find that the structure of non-social networks has more robustness than social networks. The structure of engineering networks has both best predictability and also robustness.

  6. Structure Prediction of RNA Loops with a Probabilistic Approach

    PubMed Central

    Li, Jun; Zhang, Jian; Wang, Jun; Li, Wenfei; Wang, Wei

    2016-01-01

    The knowledge of the tertiary structure of RNA loops is important for understanding their functions. In this work we develop an efficient approach named RNApps, specifically designed for predicting the tertiary structure of RNA loops, including hairpin loops, internal loops, and multi-way junction loops. It includes a probabilistic coarse-grained RNA model, an all-atom statistical energy function, a sequential Monte Carlo growth algorithm, and a simulated annealing procedure. The approach is tested with a dataset including nine RNA loops, a 23S ribosomal RNA, and a large dataset containing 876 RNAs. The performance is evaluated and compared with a homology modeling based predictor and an ab initio predictor. It is found that RNApps has comparable performance with the former one and outdoes the latter in terms of structure predictions. The approach holds great promise for accurate and efficient RNA tertiary structure prediction. PMID:27494763

  7. Structure Prediction of RNA Loops with a Probabilistic Approach.

    PubMed

    Li, Jun; Zhang, Jian; Wang, Jun; Li, Wenfei; Wang, Wei

    2016-08-01

    The knowledge of the tertiary structure of RNA loops is important for understanding their functions. In this work we develop an efficient approach named RNApps, specifically designed for predicting the tertiary structure of RNA loops, including hairpin loops, internal loops, and multi-way junction loops. It includes a probabilistic coarse-grained RNA model, an all-atom statistical energy function, a sequential Monte Carlo growth algorithm, and a simulated annealing procedure. The approach is tested with a dataset including nine RNA loops, a 23S ribosomal RNA, and a large dataset containing 876 RNAs. The performance is evaluated and compared with a homology modeling based predictor and an ab initio predictor. It is found that RNApps has comparable performance with the former one and outdoes the latter in terms of structure predictions. The approach holds great promise for accurate and efficient RNA tertiary structure prediction. PMID:27494763

  8. Prediction of residual strength of impact damaged aerospace composite structures

    SciTech Connect

    Garg, A.C.

    1993-12-31

    The importance of composites for aerospace structures is well known and therefore its increased use is being made for such structural components. However, these structures may be damaged as a result of various causes. One of the important causes is the impact damage either during manufacture or service. The amount of damage by impact created in the structure depends on several parameters such as impactor mass and velocity (impact energy), the structure material and support conditions. Since the magnitude of damage depends on impact energy, the residual strength may be expressed as a function of impact energy. Using a three parametric approach, a model is proposed to predict the residual strength behavior of impact damaged structure. The predicted behavior is shown to compare favorably with the available test data.

  9. Text Prediction on Structured Data Entry in Healthcare

    PubMed Central

    Hua, L.; Wang, S.; Gong, Y.

    2014-01-01

    Summary Background Structured data entry pervades computerized patient safety event reporting systems and serves as a key component in collecting patient-related information in electronic health records. Clinicians would spend more time being with patients and arrive at a high probability of proper diagnosis and treatment, if data entry can be completed efficiently and effectively. Historically it has been proven text prediction holds potential for human performance regarding data entry in a variety of research areas. Objective This study aimed at examining a function of text prediction proposed for increasing efficiency and data quality in structured data entry. Methods We employed a two-group randomized design with fifty-two nurses in this usability study. Each participant was assigned the task of reporting patient falls by answering multiple choice questions either with or without the text prediction function. t-test statistics and linear regression model were applied to analyzing the results of the two groups. Results While both groups of participants exhibited a good capacity of accomplishing the assigned task, the results were an overall 13.0% time reduction and 3.9% increase of response accuracy for the group utilizing the prediction function. Conclusion As a primary attempt investigating the effectiveness of text prediction in healthcare, study findings validated the necessity of text prediction to structured date entry, and laid the ground for further research improving the effectiveness of text prediction in clinical settings. PMID:24734137

  10. 3D protein structure prediction using Imperialist Competitive algorithm and half sphere exposure prediction.

    PubMed

    Khaji, Erfan; Karami, Masoumeh; Garkani-Nejad, Zahra

    2016-02-21

    Predicting the native structure of proteins based on half-sphere exposure and contact numbers has been studied deeply within recent years. Online predictors of these vectors and secondary structures of amino acids sequences have made it possible to design a function for the folding process. By choosing variant structures and directs for each secondary structure, a random conformation can be generated, and a potential function can then be assigned. Minimizing the potential function utilizing meta-heuristic algorithms is the final step of finding the native structure of a given amino acid sequence. In this work, Imperialist Competitive algorithm was used in order to accelerate the process of minimization. Moreover, we applied an adaptive procedure to apply revolutionary changes. Finally, we considered a more accurate tool for prediction of secondary structure. The results of the computational experiments on standard benchmark show the superiority of the new algorithm over the previous methods with similar potential function. PMID:26718864

  11. 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.

  12. A-DNA and B-DNA: Comparing Their Historical X-Ray Fiber Diffraction Images

    ERIC Educational Resources Information Center

    Lucas, Amand A.

    2008-01-01

    A-DNA and B-DNA are two secondary molecular conformations (among other allomorphs) that double-stranded DNA drawn into a fiber can assume, depending on the relative water content and other chemical parameters of the fiber. They were the first two forms to be observed by X-ray fiber diffraction in the early 1950s, respectively by Wilkins and…

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

    PubMed

    Kalinowska, Barbara; Krzykalski, Artur; Roterman, Irena

    2015-01-01

    The Early Stage (ES) intermediate represents the starting structure in protein folding simulations based on the Fuzzy Oil Drop (FOD) model. The accuracy of FOD predictions is greatly dependent on the accuracy of the chosen intermediate. A suitable intermediate can be constructed using the sequence-structure relationship information contained in the so-called contingency table - this table expresses the likelihood of encountering various structural motifs for each tetrapeptide fragment in the amino acid sequence. The limited accuracy with which such structures could previously be predicted provided the motivation for a more indepth study of the contingency table itself. The Contingency Table Browser is a tool which can visualize, search and analyze the table. Our work presents possible applications of Contingency Table Browser, among them - analysis of specific protein sequences from the point of view of their structural ambiguity. PMID:26664034

  14. 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.

  15. Predicting crystal structures ab initio: group 14 nitrides and phosphides.

    PubMed

    Hart, Judy N; Allan, Neil L; Claeyssens, Frederik

    2010-08-14

    Crystal structures are predicted for a range of group 14 nitrides and phosphides with 1 : 1 stoichiometry, following our method of starting from the known structures for a range of binary compounds and looking for trends in the preferred local bonding environments in the optimised structures. We have previously applied this method to predict the structures of carbon nitride and phosphorus carbide. Here, we use a similar approach to predict the structures of silicon and germanium nitrides and phosphides with 1 : 1 stoichiometry. We find that the local bonding environments in the preferred structures for the nitrides are the same as those for the 3 : 4 stoichiometry. For the phosphides, we have found several possible structures with similar energies. Structures containing hypervalent phosphorus must be considered as these are often low in energy, particularly for GeP; these have not been included in previous work. The greater tendency to form hypervalent phosphorus in GeP than SiP can be rationalised by considering the bond enthalpies for the two compositions. PMID:20603659

  16. An atomistic geometrical model of the B-DNA configuration for DNA-radiation interaction simulations

    NASA Astrophysics Data System (ADS)

    Bernal, M. A.; Sikansi, D.; Cavalcante, F.; Incerti, S.; Champion, C.; Ivanchenko, V.; Francis, Z.

    2013-12-01

    In this paper, an atomistic geometrical model for the B-DNA configuration is explained. This model accounts for five organization levels of the DNA, up to the 30 nm chromatin fiber. However, fragments of this fiber can be used to construct the whole genome. The algorithm developed in this work is capable to determine which is the closest atom with respect to an arbitrary point in space. It can be used in any application in which a DNA geometrical model is needed, for instance, in investigations related to the effects of ionizing radiations on the human genetic material. Successful consistency checks were carried out to test the proposed model. Catalogue identifier: AEPZ_v1_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEPZ_v1_0.html Program obtainable from: CPC Program Library, Queen’s University, Belfast, N. Ireland Licensing provisions: Standard CPC licence, http://cpc.cs.qub.ac.uk/licence/licence.html No. of lines in distributed program, including test data, etc.: 1245 No. of bytes in distributed program, including test data, etc.: 6574 Distribution format: tar.gz Programming language: FORTRAN. Computer: Any. Operating system: Multi-platform. RAM: 2 Gb Classification: 3. Nature of problem: The Monte Carlo method is used to simulate the interaction of ionizing radiation with the human genetic material in order to determine DNA damage yields per unit absorbed dose. To accomplish this task, an algorithm to determine if a given energy deposition lies within a given target is needed. This target can be an atom or any other structure of the genetic material. Solution method: This is a stand-alone subroutine describing an atomic-resolution geometrical model of the B-DNA configuration. It is able to determine the closest atom to an arbitrary point in space. This model accounts for five organization levels of the human genetic material, from the nucleotide pair up to the 30 nm chromatin fiber. This subroutine carries out a series of coordinate transformations

  17. Confidence-Guided Local Structure Prediction with HHfrag

    PubMed Central

    Kalev, Ivan; Habeck, Michael

    2013-01-01

    We present a method to assess the reliability of local structure prediction from sequence. We introduce a greedy algorithm for filtering and enrichment of dynamic fragment libraries, compiled with remote-homology detection methods such as HHfrag. After filtering false hits at each target position, we reduce the fragment library to a minimal set of representative fragments, which are guaranteed to have correct local structure in regions of detectable conservation. We demonstrate that the location of conserved motifs in a protein sequence can be predicted by examining the recurrence and structural homogeneity of detected fragments. The resulting confidence score correlates with the local RMSD of the representative fragments and allows us to predict torsion angles from sequence with better accuracy compared to existing machine learning methods. PMID:24146881

  18. A new protein structure representation for efficient protein function prediction.

    PubMed

    Maghawry, Huda A; Mostafa, Mostafa G M; Gharib, Tarek F

    2014-12-01

    One of the challenging problems in bioinformatics is the prediction of protein function. Protein function is the main key that can be used to classify different proteins. Protein function can be inferred experimentally with very small throughput or computationally with very high throughput. Computational methods are sequence based or structure based. Structure-based methods produce more accurate protein function prediction. In this article, we propose a new protein structure representation for efficient protein function prediction. The representation is based on three-dimensional patterns of protein residues. In the analysis, we used protein function based on enzyme activity through six mechanistically diverse enzyme superfamilies: amidohydrolase, crotonase, haloacid dehalogenase, isoprenoid synthase type I, and vicinal oxygen chelate. We applied three different classification methods, naïve Bayes, k-nearest neighbors, and random forest, to predict the enzyme superfamily of a given protein. The prediction accuracy using the proposed representation outperforms a recently introduced representation method that is based only on the distance patterns. The results show that the proposed representation achieved prediction accuracy up to 98%, with improvement of about 10% on average. PMID:25343279

  19. Cloud Prediction of Protein Structure and Function with PredictProtein for Debian

    PubMed Central

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

  20. 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

  1. PredictProtein—an open resource for online prediction of protein structural and functional features

    PubMed Central

    Yachdav, Guy; Kloppmann, Edda; Kajan, Laszlo; Hecht, Maximilian; Goldberg, Tatyana; Hamp, Tobias; Hönigschmid, Peter; Schafferhans, Andrea; Roos, Manfred; Bernhofer, Michael; Richter, Lothar; Ashkenazy, Haim; Punta, Marco; Schlessinger, Avner; Bromberg, Yana; Schneider, Reinhard; Vriend, Gerrit; Sander, Chris; Ben-Tal, Nir; Rost, Burkhard

    2014-01-01

    PredictProtein is a meta-service for sequence analysis that has been predicting structural and functional features of proteins since 1992. Queried with a protein sequence it returns: multiple sequence alignments, predicted aspects of structure (secondary structure, solvent accessibility, transmembrane helices (TMSEG) and strands, coiled-coil regions, disulfide bonds and disordered regions) and function. The service incorporates analysis methods for the identification of functional regions (ConSurf), homology-based inference of Gene Ontology terms (metastudent), comprehensive subcellular localization prediction (LocTree3), protein–protein binding sites (ISIS2), protein–polynucleotide binding sites (SomeNA) and predictions of the effect of point mutations (non-synonymous SNPs) on protein function (SNAP2). Our goal has always been to develop a system optimized to meet the demands of experimentalists not highly experienced in bioinformatics. To this end, the PredictProtein results are presented as both text and a series of intuitive, interactive and visually appealing figures. The web server and sources are available at http://ppopen.rostlab.org. PMID:24799431

  2. Servers for sequence–structure relationship analysis and prediction

    PubMed Central

    Dosztányi, Zsuzsanna; Magyar, Csaba; Tusnády, Gábor E.; Cserző, Miklós; Fiser, András; Simon, István

    2003-01-01

    We describe several algorithms and public servers that were developed to analyze and predict various features of protein structures. These servers provide information about the covalent state of cysteine (CYSREDOX), as well as about residues involved in non-covalent cross links that play an important role in the structural stability of proteins (SCIDE and SCPRED). We also discuss methods and servers developed to identify helical transmembrane proteins from large databases and rough genomic data, including two of the most popular transmembrane prediction methods, DAS and HMMTOP. Several biologically interesting applications of these servers are also presented. The servers are available through http://www.enzim.hu/servers.html. PMID:12824327

  3. Servers for sequence-structure relationship analysis and prediction.

    PubMed

    Dosztányi, Zsuzsanna; Magyar, Csaba; Tusnády, Gábor E; Cserzo, Miklós; Fiser, András; Simon, István

    2003-07-01

    We describe several algorithms and public servers that were developed to analyze and predict various features of protein structures. These servers provide information about the covalent state of cysteine (CYSREDOX), as well as about residues involved in non-covalent cross links that play an important role in the structural stability of proteins (SCIDE and SCPRED). We also discuss methods and servers developed to identify helical transmembrane proteins from large databases and rough genomic data, including two of the most popular transmembrane prediction methods, DAS and HMMTOP. Several biologically interesting applications of these servers are also presented. The servers are available through http://www.enzim.hu/servers.html. PMID:12824327

  4. Adaptive modelling of structured molecular representations for toxicity prediction

    NASA Astrophysics Data System (ADS)

    Bertinetto, Carlo; Duce, Celia; Micheli, Alessio; Solaro, Roberto; Tiné, Maria Rosaria

    2012-12-01

    We investigated the possibility of modelling structure-toxicity relationships by direct treatment of the molecular structure (without using descriptors) through an adaptive model able to retain the appropriate structural information. With respect to traditional descriptor-based approaches, this provides a more general and flexible way to tackle prediction problems that is particularly suitable when little or no background knowledge is available. Our method employs a tree-structured molecular representation, which is processed by a recursive neural network (RNN). To explore the realization of RNN modelling in toxicological problems, we employed a data set containing growth impairment concentrations (IGC50) for Tetrahymena pyriformis.

  5. A novel fold recognition method using composite predicted secondary structures.

    PubMed

    An, Yuling; Friesner, Richard A

    2002-08-01

    In this work, we introduce a new method for fold recognition using composite secondary structures assembled from different secondary structure prediction servers for a given target sequence. An automatic, complete, and robust way of finding all possible combinations of predicted secondary structure segments (SSS) for the target sequence and clustering them into a few flexible clusters, each containing patterns with the same number of SSS, is developed. This program then takes two steps in choosing plausible homologues: (i) a SSS-based alignment excludes impossible templates whose SSS patterns are very different from any of those of the target; (ii) a residue-based alignment selects good structural templates based on sequence similarity and secondary structure similarity between the target and only those templates left in the first stage. The secondary structure of each residue in the target is selected from one of the predictions to find the best match with the template. Truncation is applied to a target where different predictions vary. In most cases, a target is also divided into N-terminal and C-terminal fragments, each of which is used as a separate subsequence. Our program was tested on the fold recognition targets from CASP3 with known PDB codes and some available targets from CASP4. The results are compared with a structural homologue list for each target produced by the CE program (Shindyalov and Bourne, Protein Eng 1998;11:739-747). The program successfully locates homologues with high Z-score and low root-mean-score deviation within the top 30-50 predictions in the overwhelming majority of cases. PMID:12112702

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

    PubMed Central

    Green, James R; Korenberg, Michael J; Aboul-Magd, Mohammed O

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

  7. Prediction of protein folding rates from simplified secondary structure alphabet.

    PubMed

    Huang, Jitao T; Wang, Titi; Huang, Shanran R; Li, Xin

    2015-10-21

    Protein folding is a very complicated and highly cooperative dynamic process. However, the folding kinetics is likely to depend more on a few key structural features. Here we find that secondary structures can determine folding rates of only large, multi-state folding proteins and fails to predict those for small, two-state proteins. The importance of secondary structures for protein folding is ordered as: extended β strand > α helix > bend > turn > undefined secondary structure>310 helix > isolated β strand > π helix. Only the first three secondary structures, extended β strand, α helix and bend, can achieve a good correlation with folding rates. This suggests that the rate-limiting step of protein folding would depend upon the formation of regular secondary structures and the buckling of chain. The reduced secondary structure alphabet provides a simplified description for the machine learning applications in protein design. PMID:26247139

  8. 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.

  9. Data-directed RNA secondary structure prediction using probabilistic modeling.

    PubMed

    Deng, Fei; Ledda, Mirko; Vaziri, Sana; Aviran, Sharon

    2016-08-01

    Structure dictates the function of many RNAs, but secondary RNA structure analysis is either labor intensive and costly or relies on computational predictions that are often inaccurate. These limitations are alleviated by integration of structure probing data into prediction algorithms. However, existing algorithms are optimized for a specific type of probing data. Recently, new chemistries combined with advances in sequencing have facilitated structure probing at unprecedented scale and sensitivity. These novel technologies and anticipated wealth of data highlight a need for algorithms that readily accommodate more complex and diverse input sources. We implemented and investigated a recently outlined probabilistic framework for RNA secondary structure prediction and extended it to accommodate further refinement of structural information. This framework utilizes direct likelihood-based calculations of pseudo-energy terms per considered structural context and can readily accommodate diverse data types and complex data dependencies. We use real data in conjunction with simulations to evaluate performances of several implementations and to show that proper integration of structural contexts can lead to improvements. Our tests also reveal discrepancies between real data and simulations, which we show can be alleviated by refined modeling. We then propose statistical preprocessing approaches to standardize data interpretation and integration into such a generic framework. We further systematically quantify the information content of data subsets, demonstrating that high reactivities are major drivers of SHAPE-directed predictions and that better understanding of less informative reactivities is key to further improvements. Finally, we provide evidence for the adaptive capability of our framework using mock probe simulations. PMID:27251549

  10. Predicting electrical measurements by applying scatterometry to complex spacer structures

    NASA Astrophysics Data System (ADS)

    Sendelbach, Matthew; Ayala, Javier; Herrera, Pedro

    2007-03-01

    The comparison of scatterometry measurements of complex spacer structures to electrical test measurements is discussed. Details of the NFET and PFET structures are presented, along with a summary of the scatterometry models used to represent the structures. Before comparison data are shown, a methodology and set of metrics are presented that assist in the analysis and interpretation of comparison data. The methodology, called Prediction Analysis, has its roots in TMU analysis, where both measurements are subject to error. But in Prediction Analysis, an "apples-to-apples" comparison of the measurements is not the goal, and the measurements may be reported in different units. The goal of Prediction Analysis is to analyze the components of error in a correlation and use this analysis to predict a measurement based on the knowledge of another measurement, such that the predicted measurement is bounded. This method is used in this work to determine how well scatterometry measurements of certain parameters correlate to electrical measurements of gate resistance, gate Lpoly, and transistor current Ion. Clear correlations are demonstrated, and physical explanations that explain these correlations are presented. Due to the correlations, the scatterometry measurements can be used as a predictor of electrical performance significantly before the electrical test occurs. Because of this, scatterometry can be a reliable measurement technique for improving spacer controls and reducing the mean time to detect (MTTD) some profile abnormalities.

  11. Prediction of reactive hazards based on molecular structure.

    PubMed

    Saraf, S R; Rogers, W J; Mannan, M S

    2003-03-17

    There is considerable interest in prediction of reactive hazards based on chemical structure. Calorimetric measurements to determine reactivity can be resource consuming, so computational methods to predict reactivity hazards present an attractive option. This paper reviews some of the commonly employed theoretical hazard evaluation techniques, including the oxygen-balance method, ASTM CHETAH, and calculated adiabatic reaction temperature (CART). It also discusses the development of a study table to correlate and predict calorimetric properties of pure compounds. Quantitative structure-property relationships (QSPR) based on quantum mechanical calculations can be employed to correlate calorimetrically measured onset temperatures, T(o), and energies of reaction, -deltaH, with molecular properties. To test the feasibility of this approach, the QSPR technique is used to correlate differential scanning calorimeter (DSC) data, T(o) and -deltaH, with molecular properties for 19 nitro compounds. PMID:12628775

  12. 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.

  13. 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.

  14. Process for predicting structural performance of mechanical systems

    DOEpatents

    Gardner, David R.; Hendrickson, Bruce A.; Plimpton, Steven J.; Attaway, Stephen W.; Heinstein, Martin W.; Vaughan, Courtenay T.

    1998-01-01

    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.

  15. Predictive modeling of neuroanatomic structures for brain atrophy detection

    NASA Astrophysics Data System (ADS)

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

    2010-03-01

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

  16. 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

  17. Predicting PDZ domain mediated protein interactions from structure

    PubMed Central

    2013-01-01

    Background PDZ domains are structural protein domains that recognize simple linear amino acid motifs, often at protein C-termini, and mediate protein-protein interactions (PPIs) in important biological processes, such as ion channel regulation, cell polarity and neural development. PDZ domain-peptide interaction predictors have been developed based on domain and peptide sequence information. Since domain structure is known to influence binding specificity, we hypothesized that structural information could be used to predict new interactions compared to sequence-based predictors. Results We developed a novel computational predictor of PDZ domain and C-terminal peptide interactions using a support vector machine trained with PDZ domain structure and peptide sequence information. Performance was estimated using extensive cross validation testing. We used the structure-based predictor to scan the human proteome for ligands of 218 PDZ domains and show that the predictions correspond to known PDZ domain-peptide interactions and PPIs in curated databases. The structure-based predictor is complementary to the sequence-based predictor, finding unique known and novel PPIs, and is less dependent on training–testing domain sequence similarity. We used a functional enrichment analysis of our hits to create a predicted map of PDZ domain biology. This map highlights PDZ domain involvement in diverse biological processes, some only found by the structure-based predictor. Based on this analysis, we predict novel PDZ domain involvement in xenobiotic metabolism and suggest new interactions for other processes including wound healing and Wnt signalling. Conclusions We built a structure-based predictor of PDZ domain-peptide interactions, which can be used to scan C-terminal proteomes for PDZ interactions. We also show that the structure-based predictor finds many known PDZ mediated PPIs in human that were not found by our previous sequence-based predictor and is less dependent on

  18. 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

  19. Ab Initio Prediction of Transcription Factor Targets Using Structural Knowledge

    PubMed Central

    Kaplan, Tommy; Friedman, Nir; Margalit, Hanah

    2005-01-01

    Current approaches for identification and detection of transcription factor binding sites rely on an extensive set of known target genes. Here we describe a novel structure-based approach applicable to transcription factors with no prior binding data. Our approach combines sequence data and structural information to infer context-specific amino acid–nucleotide recognition preferences. These are used to predict binding sites for novel transcription factors from the same structural family. We demonstrate our approach on the Cys2His2 Zinc Finger protein family, and show that the learned DNA-recognition preferences are compatible with experimental results. We use these preferences to perform a genome-wide scan for direct targets of Drosophila melanogaster Cys2His2 transcription factors. By analyzing the predicted targets along with gene annotation and expression data we infer the function and activity of these proteins. PMID:16103898

  20. 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

  1. Predicting inclusion behaviour and framework structures in organic crystals.

    PubMed

    Cruz-Cabeza, Aurora J; Day, Graeme M; Jones, William

    2009-12-01

    We have used well-established computational methods to generate and explore the crystal structure landscapes of four organic molecules of well-known inclusion behaviour. Using these methods, we are able to generate both close-packed crystal structures and high-energy open frameworks containing voids of molecular dimensions. Some of these high-energy open frameworks correspond to real structures observed experimentally when the appropriate guest molecules are present during crystallisation. We propose a combination of crystal structure prediction methodologies with structure rankings based on relative lattice energy and solvent-accessible volume as a way of selecting likely inclusion frameworks completely ab initio. This methodology can be used as part of a rational strategy in the design of inclusion compounds, and also for the anticipation of inclusion behaviour in organic molecules. PMID:19876969

  2. Protein Secondary Structure Prediction Using Deep Convolutional Neural Fields

    NASA Astrophysics Data System (ADS)

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

    2016-01-01

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

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

    PubMed

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

    2016-01-01

    Protein secondary structure (SS) prediction is important for studying protein structure and function. When only the sequence (profile) information is used as input feature, currently the best predictors can obtain ~80% Q3 accuracy, which has not been improved in the past decade. Here we present DeepCNF (Deep Convolutional Neural Fields) for protein SS prediction. DeepCNF is a Deep Learning extension of Conditional Neural Fields (CNF), which is an integration of Conditional Random Fields (CRF) and shallow neural networks. DeepCNF can model not only complex sequence-structure relationship by a deep hierarchical architecture, but also interdependency between adjacent SS labels, so it is much more powerful than CNF. Experimental results show that DeepCNF can obtain ~84% Q3 accuracy, ~85% SOV score, and ~72% Q8 accuracy, respectively, on the CASP and CAMEO test proteins, greatly outperforming currently popular predictors. As a general framework, DeepCNF can be used to predict other protein structure properties such as contact number, disorder regions, and solvent accessibility. PMID:26752681

  4. Protein Secondary Structure Prediction Using Deep Convolutional Neural Fields

    PubMed Central

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

    2016-01-01

    Protein secondary structure (SS) prediction is important for studying protein structure and function. When only the sequence (profile) information is used as input feature, currently the best predictors can obtain ~80% Q3 accuracy, which has not been improved in the past decade. Here we present DeepCNF (Deep Convolutional Neural Fields) for protein SS prediction. DeepCNF is a Deep Learning extension of Conditional Neural Fields (CNF), which is an integration of Conditional Random Fields (CRF) and shallow neural networks. DeepCNF can model not only complex sequence-structure relationship by a deep hierarchical architecture, but also interdependency between adjacent SS labels, so it is much more powerful than CNF. Experimental results show that DeepCNF can obtain ~84% Q3 accuracy, ~85% SOV score, and ~72% Q8 accuracy, respectively, on the CASP and CAMEO test proteins, greatly outperforming currently popular predictors. As a general framework, DeepCNF can be used to predict other protein structure properties such as contact number, disorder regions, and solvent accessibility. PMID:26752681

  5. Human papillomavirus type 6b DNA required for initiation but not maintenance of transformation of C127 mouse cells.

    PubMed Central

    Morgan, D; Pecoraro, G; Rosenberg, I; Defendi, V

    1990-01-01

    We describe the transformation of C127 mouse fibroblasts with human papillomavirus type 6b (HPV-6b) DNA, which is associated primarily with benign tumors of the human genital tract. The major transformed phenotype of the HPV-6b-transfected cells lines, which had been G418 selected, pooled, and maintained without subsequent selection, was tumorigenicity in nude mice. We found that, unlike that reported for other HPVs or papovaviruses, the transformed phenotype was expressed after a delay, in which the cells had undergone extensive culture passages (about 20 passages or 100 generations). Interestingly, the HPV-6b DNA had become reduced or nondetectable in copy number in the cells by the time the transformed phenotype was expressed and in most of the tumors induced by the cells in nude mice, indicating that high levels of HPV-6b DNA were not required for maintenance of the transformed phenotype. Clonal cell lines gave similar results. When continued G418 selection was used to maintain high-copy-number HPV-6b DNA, the cells were tumorigenic, indicating that high levels of HPV-6b DNA did not suppress tumorigenesis. These studies suggest that HPV-6b DNA initiates transformation of C127 cells but is dispensable for expression or maintenance of the transformed phenotype. Transformation by HPV-6b DNA in vitro may provide insights into the HPV type-specific association with benign versus malignant lesions in vivo and may elucidate some of the oncogenic processes involved in tumor progression. Images PMID:2154622

  6. Prediction of the structure of symmetrical protein assemblies

    PubMed Central

    André, Ingemar; Bradley, Philip; Wang, Chu; Baker, David

    2007-01-01

    Biological supramolecular systems are commonly built up by the self-assembly of identical protein subunits to produce symmetrical oligomers with cyclical, icosahedral, or helical symmetry that play roles in processes ranging from allosteric control and molecular transport to motor action. The large size of these systems often makes them difficult to structurally characterize using experimental techniques. We have developed a computational protocol to predict the structure of symmetrical protein assemblies based on the structure of a single subunit. The method carries out simultaneous optimization of backbone, side chain, and rigid-body degrees of freedom, while restricting the search space to symmetrical conformations. Using this protocol, we can reconstruct, starting from the structure of a single subunit, the structure of cyclic oligomers and the icosahedral virus capsid of satellite panicum virus using a rigid backbone approximation. We predict the oligomeric state of EscJ from the type III secretion system both in its proposed cyclical and crystallized helical form. Finally, we show that the method can recapitulate the structure of an amyloid-like fibril formed by the peptide NNQQNY from the yeast prion protein Sup35 starting from the amino acid sequence alone and searching the complete space of backbone, side chain, and rigid-body degrees of freedom. PMID:17978193

  7. Generalized Pattern Search Algorithm for Peptide Structure Prediction

    PubMed Central

    Nicosia, Giuseppe; Stracquadanio, Giovanni

    2008-01-01

    Finding the near-native structure of a protein is one of the most important open problems in structural biology and biological physics. The problem becomes dramatically more difficult when a given protein has no regular secondary structure or it does not show a fold similar to structures already known. This situation occurs frequently when we need to predict the tertiary structure of small molecules, called peptides. In this research work, we propose a new ab initio algorithm, the generalized pattern search algorithm, based on the well-known class of Search-and-Poll algorithms. We performed an extensive set of simulations over a well-known set of 44 peptides to investigate the robustness and reliability of the proposed algorithm, and we compared the peptide conformation with a state-of-the-art algorithm for peptide structure prediction known as PEPstr. In particular, we tested the algorithm on the instances proposed by the originators of PEPstr, to validate the proposed algorithm; the experimental results confirm that the generalized pattern search algorithm outperforms PEPstr by 21.17% in terms of average root mean-square deviation, RMSD Cα. PMID:18487293

  8. Structure based activity prediction of HIV-1 reverse transcriptase inhibitors.

    PubMed

    de Jonge, Marc R; Koymans, Lucien M H; Vinkers, H Maarten; Daeyaert, Frits F D; Heeres, Jan; Lewi, Paul J; Janssen, Paul A J

    2005-03-24

    We have developed a fast and robust computational method for prediction of antiviral activity in automated de novo design of HIV-1 reverse transcriptase inhibitors. This is a structure-based approach that uses a linear relation between activity and interaction energy with discrete orientation sampling and with localized interaction energy terms. The localization allows for the analysis of mutations of the protein target and for the separation of inhibition and a specific binding to the enzyme. We apply the method to the prediction of pIC(50) of HIV-1 reverse transcriptase inhibitors. The model predicts the activity of an arbitrary compound with a q(2) of 0.681 and an average absolute error of 0.66 log value, and it is fast enough to be used in high-throughput computational applications. PMID:15771460

  9. Predicting loop–helix tertiary structural contacts in RNA pseudoknots

    PubMed Central

    Cao, Song; Giedroc, David P.; Chen, Shi-Jie

    2010-01-01

    Tertiary interactions between loops and helical stems play critical roles in the biological function of many RNA pseudoknots. However, quantitative predictions for RNA tertiary interactions remain elusive. Here we report a statistical mechanical model for the prediction of noncanonical loop–stem base-pairing interactions in RNA pseudoknots. Central to the model is the evaluation of the conformational entropy for the pseudoknotted folds with defined loop–stem tertiary structural contacts. We develop an RNA virtual bond-based conformational model (Vfold model), which permits a rigorous computation of the conformational entropy for a given fold that contains loop–stem tertiary contacts. With the entropy parameters predicted from the Vfold model and the energy parameters for the tertiary contacts as inserted parameters, we can then predict the RNA folding thermodynamics, from which we can extract the tertiary contact thermodynamic parameters from theory–experimental comparisons. These comparisons reveal a contact enthalpy (ΔH) of −14 kcal/mol and a contact entropy (ΔS) of −38 cal/mol/K for a protonated C+•(G–C) base triple at pH 7.0, and (ΔH = −7 kcal/mol, ΔS = −19 cal/mol/K) for an unprotonated base triple. Tests of the model for a series of pseudoknots show good theory–experiment agreement. Based on the extracted energy parameters for the tertiary structural contacts, the model enables predictions for the structure, stability, and folding pathways for RNA pseudoknots with known or postulated loop–stem tertiary contacts from the nucleotide sequence alone. PMID:20100813

  10. A Hybrid Loss for Multiclass and Structured Prediction.

    PubMed

    Shi, Qinfeng; Reid, Mark; Caetano, Tiberio; van den Hengel, Anton; Wang, Zhenhua

    2015-01-01

    We propose a novel hybrid loss for multiclass and structured prediction problems that is a convex combination of a log loss for Conditional Random Fields (CRFs) and a multiclass hinge loss for Support Vector Machines (SVMs). We provide a sufficient condition for when the hybrid loss is Fisher consistent for classification. This condition depends on a measure of dominance between labels-specifically, the gap between the probabilities of the best label and the second best label. We also prove Fisher consistency is necessary for parametric consistency when learning models such as CRFs. We demonstrate empirically that the hybrid loss typically performs least as well as-and often better than-both of its constituent losses on a variety of tasks, such as human action recognition. In doing so we also provide an empirical comparison of the efficacy of probabilistic and margin based approaches to multiclass and structured prediction. PMID:26353204

  11. Structure-Based Prediction of Protein-Folding Transition Paths.

    PubMed

    Jacobs, William M; Shakhnovich, Eugene I

    2016-09-01

    We propose a general theory to describe the distribution of protein-folding transition paths. We show that transition paths follow a predictable sequence of high-free-energy transient states that are separated by free-energy barriers. Each transient state corresponds to the assembly of one or more discrete, cooperative units, which are determined directly from the native structure. We show that the transition state on a folding pathway is reached when a small number of critical contacts are formed between a specific set of substructures, after which folding proceeds downhill in free energy. This approach suggests a natural resolution for distinguishing parallel folding pathways and provides a simple means to predict the rate-limiting step in a folding reaction. Our theory identifies a common folding mechanism for proteins with diverse native structures and establishes general principles for the self-assembly of polymers with specific interactions. PMID:27602721

  12. One Single Static Measurement Predicts Wave Localization in Complex Structures

    NASA Astrophysics Data System (ADS)

    Lefebvre, Gautier; Gondel, Alexane; Dubois, Marc; Atlan, Michael; Feppon, Florian; Labbé, Aimé; Gillot, Camille; Garelli, Alix; Ernoult, Maxence; Mayboroda, Svitlana; Filoche, Marcel; Sebbah, Patrick

    2016-08-01

    A recent theoretical breakthrough has brought a new tool, called the localization landscape, for predicting the localization regions of vibration modes in complex or disordered systems. Here, we report on the first experiment which measures the localization landscape and demonstrates its predictive power. Holographic measurement of the static deformation under uniform load of a thin plate with complex geometry provides direct access to the landscape function. When put in vibration, this system shows modes precisely confined within the subregions delineated by the landscape function. Also the maxima of this function match the measured eigenfrequencies, while the minima of the valley network gives the frequencies at which modes become extended. This approach fully characterizes the low frequency spectrum of a complex structure from a single static measurement. It paves the way for controlling and engineering eigenmodes in any vibratory system, especially where a structural or microscopic description is not accessible.

  13. Virality Prediction and Community Structure in Social Networks

    NASA Astrophysics Data System (ADS)

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

    2013-08-01

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

  14. One Single Static Measurement Predicts Wave Localization in Complex Structures.

    PubMed

    Lefebvre, Gautier; Gondel, Alexane; Dubois, Marc; Atlan, Michael; Feppon, Florian; Labbé, Aimé; Gillot, Camille; Garelli, Alix; Ernoult, Maxence; Mayboroda, Svitlana; Filoche, Marcel; Sebbah, Patrick

    2016-08-12

    A recent theoretical breakthrough has brought a new tool, called the localization landscape, for predicting the localization regions of vibration modes in complex or disordered systems. Here, we report on the first experiment which measures the localization landscape and demonstrates its predictive power. Holographic measurement of the static deformation under uniform load of a thin plate with complex geometry provides direct access to the landscape function. When put in vibration, this system shows modes precisely confined within the subregions delineated by the landscape function. Also the maxima of this function match the measured eigenfrequencies, while the minima of the valley network gives the frequencies at which modes become extended. This approach fully characterizes the low frequency spectrum of a complex structure from a single static measurement. It paves the way for controlling and engineering eigenmodes in any vibratory system, especially where a structural or microscopic description is not accessible. PMID:27563967

  15. Virality prediction and community structure in social networks.

    PubMed

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

    2013-01-01

    How does network structure affect diffusion? Recent studies suggest that the answer depends on the type of contagion. Complex contagions, unlike infectious diseases (simple contagions), are affected by social reinforcement and homophily. Hence, the spread within highly clustered communities is enhanced, while diffusion across communities is hampered. A common hypothesis is that memes and behaviors are complex contagions. We show that, while most memes indeed spread like complex contagions, a few viral memes spread across many communities, like diseases. We demonstrate that the future popularity of a meme can be predicted by quantifying its early spreading pattern in terms of community concentration. The more communities a meme permeates, the more viral it is. We present a practical method to translate data about community structure into predictive knowledge about what information will spread widely. This connection contributes to our understanding in computational social science, social media analytics, and marketing applications. PMID:23982106

  16. Predicting olfactory receptor neuron responses from odorant structure

    PubMed Central

    Schmuker, Michael; de Bruyne, Marien; Hähnel, Melanie; Schneider, Gisbert

    2007-01-01

    Background Olfactory receptors work at the interface between the chemical world of volatile molecules and the perception of scent in the brain. Their main purpose is to translate chemical space into information that can be processed by neural circuits. Assuming that these receptors have evolved to cope with this task, the analysis of their coding strategy promises to yield valuable insight in how to encode chemical information in an efficient way. Results We mimicked olfactory coding by modeling responses of primary olfactory neurons to small molecules using a large set of physicochemical molecular descriptors and artificial neural networks. We then tested these models by recording in vivo receptor neuron responses to a new set of odorants and successfully predicted the responses of five out of seven receptor neurons. Correlation coefficients ranged from 0.66 to 0.85, demonstrating the applicability of our approach for the analysis of olfactory receptor activation data. The molecular descriptors that are best-suited for response prediction vary for different receptor neurons, implying that each receptor neuron detects a different aspect of chemical space. Finally, we demonstrate that receptor responses themselves can be used as descriptors in a predictive model of neuron activation. Conclusion The chemical meaning of molecular descriptors helps understand structure-response relationships for olfactory receptors and their "receptive fields". Moreover, it is possible to predict receptor neuron activation from chemical structure using machine-learning techniques, although this is still complicated by a lack of training data. PMID:17880742

  17. 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

  18. Prediction of protein structural classes and subcellular locations.

    PubMed

    Chou, K C

    2000-09-01

    The structural class and subcellular location are the two important features of proteins that are closely related to their biological functions. With the rapid increase in new protein sequences entering into data banks, it is highly desirable to develop a fast and accurate method for predicting the attributes of these features for them. This can expedite the functionality determination of new proteins and the process of prioritizing genes and proteins identified by genomics efforts as potential molecular targets for drug design. Various prediction methods have been developed during the last two decades. This review is devoted to presenting a systematic introduction and comparison of the existing methods in respect to the prediction algorithm and classification scheme. The attention is focused on the state-of-the-art, which is featured by the covarient-discriminant algorithm developed very recently, as well as some new classification schemes for protein structural classes and subcellular locations. Particularly, addressed are also the physical chemistry foundation of the existing prediction methods, and the essence why the covariant-discriminant algorithm is so powerful. PMID:12369916

  19. Structure-based mutant stability predictions on proteins of unknown structure.

    PubMed

    Gonnelli, Giulia; Rooman, Marianne; Dehouck, Yves

    2012-10-31

    The ability to rapidly and accurately predict the effects of mutations on the physicochemical properties of proteins holds tremendous importance in the rational design of modified proteins for various types of industrial, environmental or pharmaceutical applications, as well as in elucidating the genetic background of complex diseases. In many cases, the absence of an experimentally resolved structure represents a major obstacle, since most currently available predictive software crucially depend on it. We investigate here the relevance of combining coarse-grained structure-based stability predictions with a simple comparative modeling procedure. Strikingly, our results show that the use of average to high quality structural models leads to virtually no loss in predictive power compared to the use of experimental structures. Even in the case of low quality models, the decrease in performance is quite limited and this combined approach remains markedly superior to other methods based exclusively on the analysis of sequence features. PMID:22782143

  20. A tool for the prediction of structures of complex sugars.

    PubMed

    Xia, Junchao; Margulis, Claudio

    2008-12-01

    In two recent back to back articles(Xia et al., J Chem Theory Comput 3:1620-1628 and 1629-1643, 2007a, b) we have started to address the problem of complex oligosaccharide conformation and folding. The scheme previously presented was based on exhaustive searches in configuration space in conjunction with Nuclear Overhauser Effect (NOE) calculations and the use of a complex rotameric library that takes branching into account. NOEs are extremely useful for structural determination but only provide information about short range interactions and ordering. Instead, the measurement of residual dipolar couplings (RDC), yields information about molecular ordering or folding that is long range in nature. In this article we show the results obtained by incorporation RDC calculations into our prediction scheme. Using this new approach we are able to accurately predict the structure of six human milk sugars: LNF-1, LND-1, LNF-2, LNF-3, LNnT and LNT. Our exhaustive search in dihedral configuration space combined with RDC and NOE calculations allows for highly accurate structural predictions that, because of the non-ergodic nature of these molecules on a time scale compatible with molecular dynamics simulations, are extremely hard to obtain otherwise (Almond et al., Biochemistry 43:5853-5863, 2004). Molecular dynamics simulations in explicit solvent using as initial configurations the structures predicted by our algorithm show that the histo-blood group epitopes in these sugars are relatively rigid and that the whole family of oligosaccharides derives its conformational variability almost exclusively from their common linkage (beta-D: -GlcNAc-(1-->3)-beta-D: -Gal) which can exist in two distinct conformational states. A population analysis based on the conformational variability of this flexible glycosidic link indicates that the relative population of the two distinct states varies for different human milk oligosaccharides. PMID:18953494

  1. Improving protein structure prediction using multiple sequence-based contact predictions

    PubMed Central

    Wu, Sitao; Szilagyi, Andras; Zhang, Yang

    2011-01-01

    Summary Although residue-residue contact maps dictate the topology of proteins, sequence-based ab initio contact predictions have been found little use in actual structure prediction due to the low accuracy. We developed a composite set of nine SVM-based contact predictors which are used in I-TASSER simulation in combination with sparse template contact restraints. When testing the strategy on 273 non-homologous targets, remarkable improvements of I-TASSER models were observed for both easy and hard targets, with P-value by student s t-test below 0.00001 and 0.001, respectively. In several cases, TM-score increases by >30%, which essentially converts “non-foldable” targets into “foldable” ones. In CASP9, I-TASSER employed ab initio contact predictions, and generated models for 26 FM targets with a GDT-score 16% and 44% higher than the second and third best servers from other groups, respectively. These findings demonstrate a new avenue to improve the accuracy of protein structure prediction especially for free-modeling targets. PMID:21827953

  2. Residual Strength Prediction of Fuselage Structures with Multiple Site Damage

    NASA Technical Reports Server (NTRS)

    Chen, Chuin-Shan; Wawrzynek, Paul A.; Ingraffea, Anthony R.

    1999-01-01

    This paper summarizes recent results on simulating full-scale pressure tests of wide body, lap-jointed fuselage panels with multiple site damage (MSD). The crack tip opening angle (CTOA) fracture criterion and the FRANC3D/STAGS software program were used to analyze stable crack growth under conditions of general yielding. The link-up of multiple cracks and residual strength of damaged structures were predicted. Elastic-plastic finite element analysis based on the von Mises yield criterion and incremental flow theory with small strain assumption was used. A global-local modeling procedure was employed in the numerical analyses. Stress distributions from the numerical simulations are compared with strain gage measurements. Analysis results show that accurate representation of the load transfer through the rivets is crucial for the model to predict the stress distribution accurately. Predicted crack growth and residual strength are compared with test data. Observed and predicted results both indicate that the occurrence of small MSD cracks substantially reduces the residual strength. Modeling fatigue closure is essential to capture the fracture behavior during the early stable crack growth. Breakage of a tear strap can have a major influence on residual strength prediction.

  3. 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

  4. Protein secondary structure prediction using logic-based machine learning.

    PubMed

    Muggleton, S; King, R D; Sternberg, M J

    1992-10-01

    Many attempts have been made to solve the problem of predicting protein secondary structure from the primary sequence but the best performance results are still disappointing. In this paper, the use of a machine learning algorithm which allows relational descriptions is shown to lead to improved performance. The Inductive Logic Programming computer program, Golem, was applied to learning secondary structure prediction rules for alpha/alpha domain type proteins. The input to the program consisted of 12 non-homologous proteins (1612 residues) of known structure, together with a background knowledge describing the chemical and physical properties of the residues. Golem learned a small set of rules that predict which residues are part of the alpha-helices--based on their positional relationships and chemical and physical properties. The rules were tested on four independent non-homologous proteins (416 residues) giving an accuracy of 81% (+/- 2%). This is an improvement, on identical data, over the previously reported result of 73% by King and Sternberg (1990, J. Mol. Biol., 216, 441-457) using the machine learning program PROMIS, and of 72% using the standard Garnier-Osguthorpe-Robson method. The best previously reported result in the literature for the alpha/alpha domain type is 76%, achieved using a neural net approach. Machine learning also has the advantage over neural network and statistical methods in producing more understandable results. PMID:1480619

  5. Structure Prediction: New Insights into Decrypting Long Noncoding RNAs

    PubMed Central

    Yan, Kun; Arfat, Yasir; Li, Dijie; Zhao, Fan; Chen, Zhihao; Yin, Chong; Sun, Yulong; Hu, Lifang; Yang, Tuanmin; Qian, Airong

    2016-01-01

    Long noncoding RNAs (lncRNAs), which form a diverse class of RNAs, remain the least understood type of noncoding RNAs in terms of their nature and identification. Emerging evidence has revealed that a small number of newly discovered lncRNAs perform important and complex biological functions such as dosage compensation, chromatin regulation, genomic imprinting, and nuclear organization. However, understanding the wide range of functions of lncRNAs related to various processes of cellular networks remains a great experimental challenge. Structural versatility is critical for RNAs to perform various functions and provides new insights into probing the functions of lncRNAs. In recent years, the computational method of RNA structure prediction has been developed to analyze the structure of lncRNAs. This novel methodology has provided basic but indispensable information for the rapid, large-scale and in-depth research of lncRNAs. This review focuses on mainstream RNA structure prediction methods at the secondary and tertiary levels to offer an additional approach to investigating the functions of lncRNAs. PMID:26805815

  6. 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

  7. Translocation and deletion breakpoints in cancer genomes are associated with potential non-B DNA-forming sequences.

    PubMed

    Bacolla, Albino; Tainer, John A; Vasquez, Karen M; Cooper, David N

    2016-07-01

    Gross chromosomal rearrangements (including translocations, deletions, insertions and duplications) are a hallmark of cancer genomes and often create oncogenic fusion genes. An obligate step in the generation of such gross rearrangements is the formation of DNA double-strand breaks (DSBs). Since the genomic distribution of rearrangement breakpoints is non-random, intrinsic cellular factors may predispose certain genomic regions to breakage. Notably, certain DNA sequences with the potential to fold into secondary structures [potential non-B DNA structures (PONDS); e.g. triplexes, quadruplexes, hairpin/cruciforms, Z-DNA and single-stranded looped-out structures with implications in DNA replication and transcription] can stimulate the formation of DNA DSBs. Here, we tested the postulate that these DNA sequences might be found at, or in close proximity to, rearrangement breakpoints. By analyzing the distribution of PONDS-forming sequences within ±500 bases of 19 947 translocation and 46 365 sequence-characterized deletion breakpoints in cancer genomes, we find significant association between PONDS-forming repeats and cancer breakpoints. Specifically, (AT)n, (GAA)n and (GAAA)n constitute the most frequent repeats at translocation breakpoints, whereas A-tracts occur preferentially at deletion breakpoints. Translocation breakpoints near PONDS-forming repeats also recur in different individuals and patient tumor samples. Hence, PONDS-forming sequences represent an intrinsic risk factor for genomic rearrangements in cancer genomes. PMID:27084947

  8. Translocation and deletion breakpoints in cancer genomes are associated with potential non-B DNA-forming sequences

    PubMed Central

    Bacolla, Albino; Tainer, John A.; Vasquez, Karen M.; Cooper, David N.

    2016-01-01

    Gross chromosomal rearrangements (including translocations, deletions, insertions and duplications) are a hallmark of cancer genomes and often create oncogenic fusion genes. An obligate step in the generation of such gross rearrangements is the formation of DNA double-strand breaks (DSBs). Since the genomic distribution of rearrangement breakpoints is non-random, intrinsic cellular factors may predispose certain genomic regions to breakage. Notably, certain DNA sequences with the potential to fold into secondary structures [potential non-B DNA structures (PONDS); e.g. triplexes, quadruplexes, hairpin/cruciforms, Z-DNA and single-stranded looped-out structures with implications in DNA replication and transcription] can stimulate the formation of DNA DSBs. Here, we tested the postulate that these DNA sequences might be found at, or in close proximity to, rearrangement breakpoints. By analyzing the distribution of PONDS-forming sequences within ±500 bases of 19 947 translocation and 46 365 sequence-characterized deletion breakpoints in cancer genomes, we find significant association between PONDS-forming repeats and cancer breakpoints. Specifically, (AT)n, (GAA)n and (GAAA)n constitute the most frequent repeats at translocation breakpoints, whereas A-tracts occur preferentially at deletion breakpoints. Translocation breakpoints near PONDS-forming repeats also recur in different individuals and patient tumor samples. Hence, PONDS-forming sequences represent an intrinsic risk factor for genomic rearrangements in cancer genomes. PMID:27084947

  9. Predicting the stability of large structured food webs.

    PubMed

    Allesina, Stefano; Grilli, Jacopo; Barabás, György; Tang, Si; Aljadeff, Johnatan; Maritan, Amos

    2015-01-01

    The stability of ecological systems has been a long-standing focus of ecology. Recently, tools from random matrix theory have identified the main drivers of stability in ecological communities whose network structure is random. However, empirical food webs differ greatly from random graphs. For example, their degree distribution is broader, they contain few trophic cycles, and they are almost interval. Here we derive an approximation for the stability of food webs whose structure is generated by the cascade model, in which 'larger' species consume 'smaller' ones. We predict the stability of these food webs with great accuracy, and our approximation also works well for food webs whose structure is determined empirically or by the niche model. We find that intervality and broad degree distributions tend to stabilize food webs, and that average interaction strength has little influence on stability, compared with the effect of variance and correlation. PMID:26198207

  10. Predicting the stability of large structured food webs

    PubMed Central

    Allesina, Stefano; Grilli, Jacopo; Barabás, György; Tang, Si; Aljadeff, Johnatan; Maritan, Amos

    2015-01-01

    The stability of ecological systems has been a long-standing focus of ecology. Recently, tools from random matrix theory have identified the main drivers of stability in ecological communities whose network structure is random. However, empirical food webs differ greatly from random graphs. For example, their degree distribution is broader, they contain few trophic cycles, and they are almost interval. Here we derive an approximation for the stability of food webs whose structure is generated by the cascade model, in which ‘larger' species consume ‘smaller' ones. We predict the stability of these food webs with great accuracy, and our approximation also works well for food webs whose structure is determined empirically or by the niche model. We find that intervality and broad degree distributions tend to stabilize food webs, and that average interaction strength has little influence on stability, compared with the effect of variance and correlation. PMID:26198207

  11. RNAex: an RNA secondary structure prediction server enhanced by high-throughput structure-probing data.

    PubMed

    Wu, Yang; Qu, Rihao; Huang, Yiming; Shi, Binbin; Liu, Mengrong; Li, Yang; Lu, Zhi John

    2016-07-01

    Several high-throughput technologies have been developed to probe RNA base pairs and loops at the transcriptome level in multiple species. However, to obtain the final RNA secondary structure, extensive effort and considerable expertise is required to statistically process the probing data and combine them with free energy models. Therefore, we developed an RNA secondary structure prediction server that is enhanced by experimental data (RNAex). RNAex is a web interface that enables non-specialists to easily access cutting-edge structure-probing data and predict RNA secondary structures enhanced by in vivo and in vitro data. RNAex annotates the RNA editing, RNA modification and SNP sites on the predicted structures. It provides four structure-folding methods, restrained MaxExpect, SeqFold, RNAstructure (Fold) and RNAfold that can be selected by the user. The performance of these four folding methods has been verified by previous publications on known structures. We re-mapped the raw sequencing data of the probing experiments to the whole genome for each species. RNAex thus enables users to predict secondary structures for both known and novel RNA transcripts in human, mouse, yeast and Arabidopsis The RNAex web server is available at http://RNAex.ncrnalab.org/. PMID:27137891

  12. RNAex: an RNA secondary structure prediction server enhanced by high-throughput structure-probing data

    PubMed Central

    Wu, Yang; Qu, Rihao; Huang, Yiming; Shi, Binbin; Liu, Mengrong; Li, Yang; Lu, Zhi John

    2016-01-01

    Several high-throughput technologies have been developed to probe RNA base pairs and loops at the transcriptome level in multiple species. However, to obtain the final RNA secondary structure, extensive effort and considerable expertise is required to statistically process the probing data and combine them with free energy models. Therefore, we developed an RNA secondary structure prediction server that is enhanced by experimental data (RNAex). RNAex is a web interface that enables non-specialists to easily access cutting-edge structure-probing data and predict RNA secondary structures enhanced by in vivo and in vitro data. RNAex annotates the RNA editing, RNA modification and SNP sites on the predicted structures. It provides four structure-folding methods, restrained MaxExpect, SeqFold, RNAstructure (Fold) and RNAfold that can be selected by the user. The performance of these four folding methods has been verified by previous publications on known structures. We re-mapped the raw sequencing data of the probing experiments to the whole genome for each species. RNAex thus enables users to predict secondary structures for both known and novel RNA transcripts in human, mouse, yeast and Arabidopsis. The RNAex web server is available at http://RNAex.ncrnalab.org/. PMID:27137891

  13. Addressing the Role of Conformational Diversity in Protein Structure Prediction

    PubMed Central

    Parisi, Gustavo; Fornasari, Maria Silvina

    2016-01-01

    Computational modeling of tertiary structures has become of standard use to study proteins that lack experimental characterization. Unfortunately, 3D structure prediction methods and model quality assessment programs often overlook that an ensemble of conformers in equilibrium populates the native state of proteins. In this work we collected sets of publicly available protein models and the corresponding target structures experimentally solved and studied how they describe the conformational diversity of the protein. For each protein, we assessed the quality of the models against known conformers by several standard measures and identified those models ranked best. We found that model rankings are defined by both the selected target conformer and the similarity measure used. 70% of the proteins in our datasets show that different models are structurally closest to different conformers of the same protein target. We observed that model building protocols such as template-based or ab initio approaches describe in similar ways the conformational diversity of the protein, although for template-based methods this description may depend on the sequence similarity between target and template sequences. Taken together, our results support the idea that protein structure modeling could help to identify members of the native ensemble, highlight the importance of considering conformational diversity in protein 3D quality evaluations and endorse the study of the variability of the native structure for a meaningful biological analysis. PMID:27159429

  14. Addressing the Role of Conformational Diversity in Protein Structure Prediction.

    PubMed

    Palopoli, Nicolas; Monzon, Alexander Miguel; Parisi, Gustavo; Fornasari, Maria Silvina

    2016-01-01

    Computational modeling of tertiary structures has become of standard use to study proteins that lack experimental characterization. Unfortunately, 3D structure prediction methods and model quality assessment programs often overlook that an ensemble of conformers in equilibrium populates the native state of proteins. In this work we collected sets of publicly available protein models and the corresponding target structures experimentally solved and studied how they describe the conformational diversity of the protein. For each protein, we assessed the quality of the models against known conformers by several standard measures and identified those models ranked best. We found that model rankings are defined by both the selected target conformer and the similarity measure used. 70% of the proteins in our datasets show that different models are structurally closest to different conformers of the same protein target. We observed that model building protocols such as template-based or ab initio approaches describe in similar ways the conformational diversity of the protein, although for template-based methods this description may depend on the sequence similarity between target and template sequences. Taken together, our results support the idea that protein structure modeling could help to identify members of the native ensemble, highlight the importance of considering conformational diversity in protein 3D quality evaluations and endorse the study of the variability of the native structure for a meaningful biological analysis. PMID:27159429

  15. PREDICTING RNA STRUCTURE BY MULTIPLE TEMPLATE HOMOLOGY MODELING

    PubMed Central

    FLORES, SAMUEL C.; WAN, YAQI; RUSSELL, RICK; ALTMAN, RUSS B.

    2010-01-01

    Despite the importance of 3D structure to understand the myriad functions of RNAs in cells, most RNA molecules remain out of reach of crystallographic and NMR methods. However, certain structural information such as base pairing and some tertiary contacts can be determined readily for many RNAs by bioinformatics or relatively low cost experiments. Further, because RNA structure is highly modular, it is possible to deduce local 3D structure from the solved structures of evolutionarily related RNAs or even unrelated RNAs that share the same module. RNABuilder is a software package that generates model RNA structures by treating the kinematics and forces at separate, multiple levels of resolution. Kinematically, bonds in bases, certain stretches of residues, and some entire molecules are rigid while other bonds remain flexible. Forces act on the rigid bases and selected individual atoms. Here we use RNABuilder to predict the structure of the 200-nucleotide Azoarcus group I intron by homology modeling against fragments of the distantly-related Twort and Tetrahymena group I introns and by incorporating base pairing forces where necessary. In the absence of any information from the solved Azoarcus intron crystal structure, the model accurately depicts the global topology, secondary and tertiary connections, and gives an overall RMSD value of 4.6 Å relative to the crystal structure. The accuracy of the model is even higher in the intron core (RMSD = 3.5 Å), whereas deviations are modestly larger for peripheral regions that differ more substantially between the different introns. These results lay the groundwork for using this approach for larger and more diverse group I introns, as well for still larger RNAs and RNA-protein complexes such as group II introns and the ribosomal subunits. PMID:19908374

  16. 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.

  17. A protein structural class prediction method based on novel features.

    PubMed

    Zhang, Lichao; Zhao, Xiqiang; Kong, Liang

    2013-09-01

    In this study, a 12-dimensional feature vector is constructed to reflect the general contents and spatial arrangements of the secondary structural elements of a given protein sequence. Among the 12 features, 6 novel features are specially designed to improve the prediction accuracies for α/β and α + β classes based on the distributions of α-helices and β-strands and the characteristics of parallel β-sheets and anti-parallel β-sheets. To evaluate our method, the jackknife cross-validating test is employed on two widely-used datasets, 25PDB and 1189 datasets with sequence similarity lower than 40% and 25%, respectively. The performance of our method outperforms the recently reported methods in most cases, and the 6 newly-designed features have significant positive effect to the prediction accuracies, especially for α/β and α + β classes. PMID:23770446

  18. Protein-protein interface prediction based on hexagon structure similarity.

    PubMed

    Guo, Fei; Ding, Yijie; Li, Shuai Cheng; Shen, Chao; Wang, Lusheng

    2016-08-01

    Studies on protein-protein interaction are important in proteome research. How to build more effective models based on sequence information, structure information and physicochemical characteristics, is the key technology in protein-protein interface prediction. In this paper, we study the protein-protein interface prediction problem. We propose a novel method for identifying residues on interfaces from an input protein with both sequence and 3D structure information, based on hexagon structure similarity. Experiments show that our method achieves better results than some state-of-the-art methods for identifying protein-protein interface. Comparing to existing methods, our approach improves F-measure value by at least 0.03. On a common dataset consisting of 41 complexes, our method has overall precision and recall values of 63% and 57%. On Benchmark v4.0, our method has overall precision and recall values of 55% and 56%. On CAPRI targets, our method has overall precision and recall values of 52% and 55%. PMID:26936323

  19. Cortical structure predicts success in performing musical transformation judgments.

    PubMed

    Foster, Nicholas E V; Zatorre, Robert J

    2010-10-15

    Recognizing melodies by their interval structure, or "relative pitch," is a fundamental aspect of musical perception. By using relative pitch, we are able to recognize tunes regardless of the key in which they are played. We sought to determine the cortical areas important for relative pitch processing using two morphometric techniques. Cortical differences have been reported in musicians within right auditory cortex (AC), a region considered important for pitch-based processing, and we have previously reported a functional correlation between relative pitch processing in the anterior intraparietal sulcus (IPS). We addressed the hypothesis that regional variation of cortical structure within AC and IPS is related to relative pitch ability using two anatomical techniques, cortical thickness (CT) analysis and voxel-based morphometry (VBM) of magnetic resonance imaging data. Persons with variable amounts of formal musical training were tested on a melody transposition task, as well as two musical control tasks and a speech control task. We found that gray matter concentration and cortical thickness in right Heschl's sulcus and bilateral IPS both predicted relative pitch task performance and correlated to a lesser extent with performance on the two musical control tasks. After factoring out variance explained by musical training, only relative pitch performance was predicted by cortical structure in these regions. These results directly demonstrate the functional relevance of previously reported anatomical differences in the auditory cortex of musicians. The findings in the IPS provide further support for the existence of a multimodal network for systematic transformation of stimulus information in this region. PMID:20600982

  20. Symmetry-adapted digital modeling II. The double-helix B-DNA.

    PubMed

    Janner, A

    2016-05-01

    The positions of phosphorus in B-DNA have the remarkable property of occurring (in axial projection) at well defined points in the three-dimensional space of a projected five-dimensional decagonal lattice, subdividing according to the golden mean ratio τ:1:τ [with τ = (1+\\sqrt {5})/2] the edges of an enclosing decagon. The corresponding planar integral indices n1, n2, n3, n4 (which are lattice point coordinates) are extended to include the axial index n5 as well, defined for each P position of the double helix with respect to the single decagonal lattice ΛP(aP, cP) with aP = 2.222 Å and cP = 0.676 Å. A finer decagonal lattice Λ(a, c), with a = aP/6 and c = cP, together with a selection of lattice points for each nucleotide with a given indexed P position (so as to define a discrete set in three dimensions) permits the indexing of the atomic positions of the B-DNA d(AGTCAGTCAG) derived by M. J. P. van Dongen. This is done for both DNA strands and the single lattice Λ. Considered first is the sugar-phosphate subsystem, and then each nucleobase guanine, adenine, cytosine and thymine. One gets in this way a digital modeling of d(AGTCAGTCAG) in a one-to-one correspondence between atomic and indexed positions and a maximal deviation of about 0.6 Å (for the value of the lattice parameters given above). It is shown how to get a digital modeling of the B-DNA double helix for any given code. Finally, a short discussion indicates how this procedure can be extended to derive coarse-grained B-DNA models. An example is given with a reduction factor of about 2 in the number of atomic positions. A few remarks about the wider interest of this investigation and possible future developments conclude the paper. PMID:27126108

  1. Tailor-made force fields for crystal-structure prediction.

    PubMed

    Neumann, Marcus A

    2008-08-14

    A general procedure is presented to derive a complete set of force-field parameters for flexible molecules in the crystalline state on a case-by-case basis. The force-field parameters are fitted to the electrostatic potential as well as to accurate energies and forces generated by means of a hybrid method that combines solid-state density functional theory (DFT) calculations with an empirical van der Waals correction. All DFT calculations are carried out with the VASP program. The mathematical structure of the force field, the generation of reference data, the choice of the figure of merit, the optimization algorithm, and the parameter-refinement strategy are discussed in detail. The approach is applied to cyclohexane-1,4-dione, a small flexible ring. The tailor-made force field obtained for cyclohexane-1,4-dione is used to search for low-energy crystal packings in all 230 space groups with one molecule per asymmetric unit, and the most stable crystal structures are reoptimized in a second step with the hybrid method. The experimental crystal structure is found as the most stable predicted crystal structure both with the tailor-made force field and the hybrid method. The same methodology has also been applied successfully to the four compounds of the fourth CCDC blind test on crystal-structure prediction. For the five aforementioned compounds, the root-mean-square deviations between lattice energies calculated with the tailor-made force fields and the hybrid method range from 0.024 to 0.053 kcal/mol per atom around an average value of 0.034 kcal/mol per atom. PMID:18642947

  2. Structure prediction and analysis of neuraminidase sequence variants.

    PubMed

    Thayer, Kelly M

    2016-07-01

    Analyzing protein structure has become an integral aspect of understanding systems of biochemical import. The laboratory experiment endeavors to introduce protein folding to ascertain structures of proteins for which the structure is unavailable, as well as to critically evaluate the quality of the prediction obtained. The model system used is the highly mutable influenza virus protein neuraminidase, which is the key target in the development of therapeutics. In light of recent pandemics, understanding how mutations confer drug resistance, which translates at the molecular level to understanding how different sequence variants differ, constitutes an area of great interest because of the ramifications in public health. This lab targets upper level undergraduate biochemistry students, and aims to introduce tools to be used to explore protein folding and protein visualization in the context of the neuraminidase case study. Students proceed to critically evaluate the folded models by comparison with crystallographic structures. When validity is established, they fold a neuraminidase sequence for which a structure is not available. Through structural alignment and visual inspection of the 150 loop, students gain molecular insight into two possible conformations of the protein, which are actively being studied. Folding the third chosen sequence mimics a true research environment in allowing students to generate a structure from a sequence for which a structure was not previously available, and to assess whether their particular variant has an open or closed loop. From this vantage, they are then challenged to speculate about the connection between loop conformation and drug susceptibility. © 2016 by The International Union of Biochemistry and Molecular Biology, 44(4):361-376, 2016. PMID:26900942

  3. 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.

  4. The sequential structure of brain activation predicts skill.

    PubMed

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

    2016-01-29

    In an fMRI study, participants were trained to play a complex video game. They were scanned early and then again after substantial practice. While better players showed greater activation in one region (right dorsal striatum) their relative skill was better diagnosed by considering the sequential structure of whole brain activation. Using a cognitive model that played this game, we extracted a characterization of the mental states that are involved in playing a game and the statistical structure of the transitions among these states. There was a strong correspondence between this measure of sequential structure and the skill of different players. Using multi-voxel pattern analysis, it was possible to recognize, with relatively high accuracy, the cognitive states participants were in during particular scans. We used the sequential structure of these activation-recognized states to predict the skill of individual players. These findings indicate that important features about information-processing strategies can be identified from a model-based analysis of the sequential structure of brain activation. PMID:26707716

  5. 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.

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

    SciTech Connect

    Hazra, Siddharth S.; de Boer, Maarten Pieter; 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.

  7. Methods for evaluating the predictive accuracy of structural dynamic models

    NASA Technical Reports Server (NTRS)

    Hasselman, T. K.; Chrostowski, Jon D.

    1990-01-01

    Uncertainty of frequency response using the fuzzy set method and on-orbit response prediction using laboratory test data to refine an analytical model are emphasized with respect to large space structures. Two aspects of the fuzzy set approach were investigated relative to its application to large structural dynamics problems: (1) minimizing the number of parameters involved in computing possible intervals; and (2) the treatment of extrema which may occur in the parameter space enclosed by all possible combinations of the important parameters of the model. Extensive printer graphics were added to the SSID code to help facilitate model verification, and an application of this code to the LaRC Ten Bay Truss is included in the appendix to illustrate this graphics capability.

  8. Prediction of Halocarbon Toxicity from Structure: A Hierarchical QSAR Approach

    SciTech Connect

    Gute, B D; Balasubramanian, K; Geiss, K; Basak, S C

    2003-04-11

    Mathematical structural invariants and quantum theoretical descriptors have been used extensively in quantitative structure-activity relationships (QSARs) for the estimation of pharmaceutical activities, biological properties, physicochemical properties, and the toxicities of chemicals. Recently our research team has explored the relative importance of various levels of chemodescriptors, i.e., topostructural, topochemical, geometrical, and quantum theoretical descriptors, in property estimation. This study examines the contribution of chemodescriptors ranging from topostructural to quantum theoretic calculations up to the Gaussian STO-3G level in the prediction of the toxicity of a set of twenty halocarbons. We also report the results of experimental cell-level toxicity studies on these twenty halocarbons to validate our models.

  9. Predicted novel hydrogen hydrate structures under pressure from first principles

    NASA Astrophysics Data System (ADS)

    Qian, Guangrui; Lyakhov, Andriy; Zhu, Qiang; Oganov, Artem; Dong, Xiao

    2014-03-01

    Gas hydrates are systems of prime importance. In particular, hydrogen hydrates are potential materials of icy satellites and comets, and may be used for hydrogen storage. We explore the H2O-H2 system at pressures in the range 0 ~ 100 GPa with ab initio variable-composition evolutionary simulations. According to our calculation and previous experiments, the H2O-H2 system undergoes a series of transformations with pressure, and adopts the known open-network clathrate structures (sII, C0), dense ``filled ice'' structures (C1, C2) and two novel hydrogen hydrate phases. One of these structures is based on the hexagonal ice framework and has the same H2O:H2 ratio (2:1) as the C0 phase at low pressures and similar enthalpy (we name this phase Ih-C0). The other newly predicted hydrate phase has a 1:2 H2O:H2 ratio and structure based on cubic ice. This phase (which we name C3) is predicted to be thermodynamically stable above 38 GPa when including van der Waals interactions and zero-point vibrational energy. This is the hydrogen-richest hydrate and this phase has the highest gravimetric densities (18 wt.%) of extractable hydrogen among all known materials. We thank the DARPA (Grants No. W31P4Q1310005 and No. W31P4Q1210008), National Science Founda- tion (EAR-1114313, DMR-1231586), AFOSR (FA9550- 13-C-0037), DOE (DE-AC02-98CH10886), CRDF Global (UKE2-7034-KV-11) for financial support. We thank Purdue University Teragrid for providing computational resources and technical support for this work (Charge No.: TG-DMR110058).

  10. Strain Concentration at Structural Discontinuities and Its Prediction Based on Characteristics of Compliance Change in Structures

    NASA Astrophysics Data System (ADS)

    Kasahara, Naoto

    Elevated temperature structural design codes pay attention to strain concentration at structural discontinuities due to creep and plasticity, since it causes an increase in creep-fatigue damage of materials. One of the difficulties in predicting strain concentration is its dependence on the magnitude of loading, the constitutive equations, and the duration of loading. In this study, the author investigated the fundamental mechanism of strain concentration and its main factors. The results revealed that strain concentration is caused by strain redistribution between elastic and inelastic regions, which can be quantified by the characteristics of structural compliance. The characteristics of structural compliance are controlled by elastic region in structures and are insensitive to constitutive equations. It means that inelastic analysis can be easily applied to obtain compliance characteristics. By utilizing this fact, a simplified inelastic analysis method was proposed based on the characteristics of compliance change for the prediction of strain concentration.

  11. Unbiased charge oscillations in B-DNA: monomer polymers and dimer polymers.

    PubMed

    Lambropoulos, K; Chatzieleftheriou, M; Morphis, A; Kaklamanis, K; Theodorakou, M; Simserides, C

    2015-09-01

    We call monomer a B-DNA base pair and examine, analytically and numerically, electron or hole oscillations in monomer and dimer polymers, i.e., periodic sequences with repetition unit made of one or two monomers. We employ a tight-binding (TB) approach at the base-pair level to readily determine the spatiotemporal evolution of a single extra carrier along a N base-pair B-DNA segment. We study highest occupied molecular orbital and lowest unoccupied molecular orbital eigenspectra as well as the mean over time probabilities to find the carrier at a particular monomer. We use the pure mean transfer rate k to evaluate the easiness of charge transfer. The inverse decay length β for exponential fits k(d), where d is the charge transfer distance, and the exponent η for power-law fits k(N) are computed; generally power-law fits are better. We illustrate that increasing the number of different parameters involved in the TB description, the fall of k(d) or k(N) becomes steeper and show the range covered by β and η. Finally, for both the time-independent and the time-dependent problems, we analyze the palindromicity and the degree of eigenspectrum dependence of the probabilities to find the carrier at a particular monomer. PMID:26465516

  12. Unbiased charge oscillations in B-DNA: Monomer polymers and dimer polymers

    NASA Astrophysics Data System (ADS)

    Lambropoulos, K.; Chatzieleftheriou, M.; Morphis, A.; Kaklamanis, K.; Theodorakou, M.; Simserides, C.

    2015-09-01

    We call monomer a B-DNA base pair and examine, analytically and numerically, electron or hole oscillations in monomer and dimer polymers, i.e., periodic sequences with repetition unit made of one or two monomers. We employ a tight-binding (TB) approach at the base-pair level to readily determine the spatiotemporal evolution of a single extra carrier along a N base-pair B-DNA segment. We study highest occupied molecular orbital and lowest unoccupied molecular orbital eigenspectra as well as the mean over time probabilities to find the carrier at a particular monomer. We use the pure mean transfer rate k to evaluate the easiness of charge transfer. The inverse decay length β for exponential fits k (d ) , where d is the charge transfer distance, and the exponent η for power-law fits k (N ) are computed; generally power-law fits are better. We illustrate that increasing the number of different parameters involved in the TB description, the fall of k (d ) or k (N ) becomes steeper and show the range covered by β and η . Finally, for both the time-independent and the time-dependent problems, we analyze the palindromicity and the degree of eigenspectrum dependence of the probabilities to find the carrier at a particular monomer.

  13. Predicting the bifurcation structure of localized snaking patterns

    NASA Astrophysics Data System (ADS)

    Makrides, Elizabeth; Sandstede, Björn

    2014-02-01

    We expand upon a general framework for studying the bifurcation diagrams of localized spatially oscillatory structures. Building on work by Beck et al., the present work provides rigorous analytical results on the effects of perturbations to systems exhibiting snaking behavior. Starting with a reversible variational system possessing an additional Z2 symmetry, we elucidate the distinct effects of breaking symmetry and breaking variational structure, and characterize the resulting changes in both the bifurcation diagram and the solutions themselves. We show how to predict the branch reorganization and drift speeds induced by any particular given perturbative term, and illustrate our results via numerical continuation. We further demonstrate the utility of our methods in understanding the effects of particular perturbations breaking reversibility. Our approach yields an analytical explanation for previous numerical results on the effects of perturbations in the one-dimensional cubic-quintic Swift-Hohenberg model and allows us to make predictions on the effects of perturbations in more general settings, including planar systems. While our numerical results involve the Swift-Hohenberg model system, we emphasize the general applicability of the analytical results.

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

    SciTech Connect

    Hurt, R.; Colo, J; Essenhigh, R.; Hadad, C; Stanley, E.

    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. Predicting protein structural class with AdaBoost Learner.

    PubMed

    Niu, Bing; Cai, Yu-Dong; Lu, Wen-Cong; Li, Guo-Zheng; Chou, Kuo-Chen

    2006-01-01

    The structural class is an important feature in characterizing the overall topological folding type of a protein or the domains therein. Prediction of protein structural classification has attracted the attention and efforts from many investigators. In this paper a novel predictor, the AdaBoost Learner, was introduced to deal with this problem. The essence of the AdaBoost Learner is that a combination of many 'weak' learning algorithms, each performing just slightly better than a random guessing algorithm, will generate a 'strong' learning algorithm. Demonstration thru jackknife cross-validation on two working datasets constructed by previous investigators indicated that AdaBoost outperformed other predictors such as SVM (support vector machine), a powerful algorithm widely used in biological literatures. It has not escaped our notice that AdaBoost may hold a high potential for improving the quality in predicting the other protein features as well, such as subcellular location and receptor type, among many others. Or at the very least, it will play a complementary role to many of the existing algorithms in this regard. PMID:16800803

  16. Evaluation of the information content of RNA structure mapping data for secondary structure prediction.

    PubMed

    Quarrier, Scott; Martin, Joshua S; Davis-Neulander, Lauren; Beauregard, Arthur; Laederach, Alain

    2010-06-01

    Structure mapping experiments (using probes such as dimethyl sulfate [DMS], kethoxal, and T1 and V1 RNases) are used to determine the secondary structures of RNA molecules. The process is iterative, combining the results of several probes with constrained minimum free-energy calculations to produce a model of the structure. We aim to evaluate whether particular probes provide more structural information, and specifically, how noise in the data affects the predictions. Our approach involves generating "decoy" RNA structures (using the sFold Boltzmann sampling procedure) and evaluating whether we are able to identify the correct structure from this ensemble of structures. We show that with perfect information, we are always able to identify the optimal structure for five RNAs of known structure. We then collected orthogonal structure mapping data (DMS and RNase T1 digest) under several solution conditions using our high-throughput capillary automated footprinting analysis (CAFA) technique on two group I introns of known structure. Analysis of these data reveals the error rates in the data under optimal (low salt) and suboptimal solution conditions (high MgCl(2)). We show that despite these errors, our computational approach is less sensitive to experimental noise than traditional constraint-based structure prediction algorithms. Finally, we propose a novel approach for visualizing the interaction of chemical and enzymatic mapping data with RNA structure. We project the data onto the first two dimensions of a multidimensional scaling of the sFold-generated decoy structures. We are able to directly visualize the structural information content of structure mapping data and reconcile multiple data sets. PMID:20413617

  17. Protein structure prediction with local adjust tabu search algorithm

    PubMed Central

    2014-01-01

    Background Protein folding structure prediction is one of the most challenging problems in the bioinformatics domain. Because of the complexity of the realistic protein structure, the simplified structure model and the computational method should be adopted in the research. The AB off-lattice model is one of the simplification models, which only considers two classes of amino acids, hydrophobic (A) residues and hydrophilic (B) residues. Results The main work of this paper is to discuss how to optimize the lowest energy configurations in 2D off-lattice model and 3D off-lattice model by using Fibonacci sequences and real protein sequences. In order to avoid falling into local minimum and faster convergence to the global minimum, we introduce a novel method (SATS) to the protein structure problem, which combines simulated annealing algorithm and tabu search algorithm. Various strategies, such as the new encoding strategy, the adaptive neighborhood generation strategy and the local adjustment strategy, are adopted successfully for high-speed searching the optimal conformation corresponds to the lowest energy of the protein sequences. Experimental results show that some of the results obtained by the improved SATS are better than those reported in previous literatures, and we can sure that the lowest energy folding state for short Fibonacci sequences have been found. Conclusions Although the off-lattice models is not very realistic, they can reflect some important characteristics of the realistic protein. It can be found that 3D off-lattice model is more like native folding structure of the realistic protein than 2D off-lattice model. In addition, compared with some previous researches, the proposed hybrid algorithm can more effectively and more quickly search the spatial folding structure of a protein chain. PMID:25474708

  18. Crystal Structure Prediction from First Principles: The Crystal Structures of Glycine

    PubMed Central

    Lund, Albert M.; Pagola, Gabriel I.; Orendt, Anita M.; Ferraro, Marta B.; Facelli, Julio C.

    2015-01-01

    Here we present the results of our unbiased searches of glycine polymorphs obtained using the Genetic Algorithms search implemented in Modified Genetic Algorithm for Crystals coupled with the local optimization and energy evaluation provided by Quantum Espresso. We demonstrate that it is possible to predict the crystal structures of a biomedical molecule using solely first principles calculations. We were able to find all the ambient pressure stable glycine polymorphs, which are found in the same energetic ordering as observed experimentally and the agreement between the experimental and predicted structures is of such accuracy that the two are visually almost indistinguishable. PMID:25843964

  19. Crystal structure prediction from first principles: The crystal structures of glycine

    NASA Astrophysics Data System (ADS)

    Lund, Albert M.; Pagola, Gabriel I.; Orendt, Anita M.; Ferraro, Marta B.; Facelli, Julio C.

    2015-04-01

    Here we present the results of our unbiased searches of glycine polymorphs obtained using the genetic algorithms search implemented in MGAC, modified genetic algorithm for crystals, coupled with the local optimization and energy evaluation provided by Quantum Espresso. We demonstrate that it is possible to predict the crystal structures of a biomedical molecule using solely first principles calculations. We were able to find all the ambient pressure stable glycine polymorphs, which are found in the same energetic ordering as observed experimentally and the agreement between the experimental and predicted structures is of such accuracy that the two are visually almost indistinguishable.

  20. Interaction of Iron II Complexes with B-DNA. Insights from Molecular Modeling, Spectroscopy, and Cellular Biology.

    PubMed

    Gattuso, Hugo; Duchanois, Thibaut; Besancenot, Vanessa; Barbieux, Claire; Assfeld, Xavier; Becuwe, Philippe; Gros, Philippe C; Grandemange, Stephanie; Monari, Antonio

    2015-01-01

    We report the characterization of the interaction between B-DNA and three terpyridin iron II complexes. Relatively long time-scale molecular dynamics (MD) is used in order to characterize the stable interaction modes. By means of molecular modeling and UV-vis spectroscopy, we prove that they may lead to stable interactions with the DNA duplex. Furthermore, the presence of larger π-conjugated moieties also leads to the appearance of intercalation binding mode. Non-covalent stabilizing interactions between the iron complexes and the DNA are also characterized and evidenced by the analysis of the gradient of the electronic density. Finally, the structural deformations induced on the DNA in the different binding modes are also evidenced. The synthesis and chemical characterization of the three complexes is reported, as well as their absorption spectra in presence of DNA duplexes to prove the interaction with DNA. Finally, their effects on human cell cultures have also been evidenced to further enlighten their biological effects. PMID:26734600

  1. Interaction of Iron II Complexes with B-DNA. Insights from Molecular Modeling, Spectroscopy, and Cellular Biology

    PubMed Central

    Gattuso, Hugo; Duchanois, Thibaut; Besancenot, Vanessa; Barbieux, Claire; Assfeld, Xavier; Becuwe, Philippe; Gros, Philippe C.; Grandemange, Stephanie; Monari, Antonio

    2015-01-01

    We report the characterization of the interaction between B-DNA and three terpyridin iron II complexes. Relatively long time-scale molecular dynamics (MD) is used in order to characterize the stable interaction modes. By means of molecular modeling and UV-vis spectroscopy, we prove that they may lead to stable interactions with the DNA duplex. Furthermore, the presence of larger π-conjugated moieties also leads to the appearance of intercalation binding mode. Non-covalent stabilizing interactions between the iron complexes and the DNA are also characterized and evidenced by the analysis of the gradient of the electronic density. Finally, the structural deformations induced on the DNA in the different binding modes are also evidenced. The synthesis and chemical characterization of the three complexes is reported, as well as their absorption spectra in presence of DNA duplexes to prove the interaction with DNA. Finally, their effects on human cell cultures have also been evidenced to further enlighten their biological effects. PMID:26734600

  2. 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.

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

    PubMed Central

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

    2015-01-01

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

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

    PubMed

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

    2015-08-22

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

  5. 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.

  6. Optimizing Non-Decomposable Loss Functions in Structured Prediction

    PubMed Central

    Ranjbar, Mani; Lan, Tian; Wang, Yang; Robinovitch, Steven N.; Li, Ze-Nian; Mori, Greg

    2012-01-01

    We develop an algorithm for structured prediction with non-decomposable performance measures. The algorithm learns parameters of Markov random fields and can be applied to multivariate performance measures. Examples include performance measures such as Fβ score (natural language processing), intersection over union (object category segmentation), Precision/Recall at k (search engines) and ROC area (binary classifiers). We attack this optimization problem by approximating the loss function with a piecewise linear function. The loss augmented inference forms a quadratic program (QP), which we solve using LP relaxation. We apply this approach to two tasks: object class-specific segmentation and human action retrieval from videos. We show significant improvement over baseline approaches that either use simple loss functions or simple scoring functions on the PASCAL VOC and H3D Segmentation datasets, and a nursing home action recognition dataset. PMID:22868650

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

    NASA Astrophysics Data System (ADS)

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

    2005-09-01

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

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

    PubMed

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

    2005-09-01

    Horneros (Furnarius Rufus) are South American birds well known for their oven-looking nests and their ability to sing in couples. Previous work has analyzed the rhythmic organization of the duets, unveiling a mathematical structure behind the songs. In this work we analyze in detail an extended database of duets. The rhythms of the songs are compatible with the dynamics presented by a wide class of dynamical systems: forced excitable systems. Compatible with this nonlinear rule, we build a biologically inspired model for how the neural and the anatomical elements may interact to produce the observed rhythmic patterns. This model allows us to synthesize songs presenting the acoustic and rhythmic features observed in real songs. We also make testable predictions in order to support our hypothesis. PMID:16241480

  9. Structural Acoustic Prediction and Interior Noise Control Technology

    NASA Technical Reports Server (NTRS)

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

    2001-01-01

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

  10. Applications of tree-structured regression for regional precipitation prediction

    NASA Astrophysics Data System (ADS)

    Li, Xiangshang

    2000-11-01

    This thesis presents a Tree-Structured Regression (TSR) method to relate daily precipitation with a variety of free atmosphere variables. Historical data were used to identify distinct weather patterns associated with differing types of precipitation events. Models were developed using 67% of the data for training and the remaining data for model validation. Seasonal models were built for each of four U.S. sites; New Orleans Louisiana, San Antonio and Amarillo of Texas as well as San Francisco California. The average correlation by site between observed and simulated daily precipitation data series range from 0.69 to 0.79 for the training set, and 0.64 to 0.79 for the validation set. Relative humidity related variables were found to be the dominant variables in these TSR models. Output from an NCAR Climate System Model (CSM) transient simulation of climate change were then used to drive the TSR models for predicting precipitation characteristics under climate change. A preliminary screening of the GCM output variables for current climate, however, revealed significant problems for the New Orleans, San Antonio and Amarillo sites. Specifically, the CSM missed the annual trends in humidity for the grid cells containing these sites. CSM output for the San Francisco site was found to be much more reliable. Therefore, we present future precipitation estimates only for the San Francisco site. While both GCM and TSR predict very small change in overall annual precipitation, they differ significantly from season to season.

  11. Automatic measurement of voice onset time using discriminative structured prediction.

    PubMed

    Sonderegger, Morgan; Keshet, Joseph

    2012-12-01

    A discriminative large-margin algorithm for automatic measurement of voice onset time (VOT) is described, considered as a case of predicting structured output from speech. Manually labeled data are used to train a function that takes as input a speech segment of an arbitrary length containing a voiceless stop, and outputs its VOT. The function is explicitly trained to minimize the difference between predicted and manually measured VOT; it operates on a set of acoustic feature functions designed based on spectral and temporal cues used by human VOT annotators. The algorithm is applied to initial voiceless stops from four corpora, representing different types of speech. Using several evaluation methods, the algorithm's performance is near human intertranscriber reliability, and compares favorably with previous work. Furthermore, the algorithm's performance is minimally affected by training and testing on different corpora, and remains essentially constant as the amount of training data is reduced to 50-250 manually labeled examples, demonstrating the method's practical applicability to new datasets. PMID:23231126

  12. High Precision Prediction of Functional Sites in Protein Structures

    PubMed Central

    Buturovic, Ljubomir; Wong, Mike; Tang, Grace W.; Altman, Russ B.; Petkovic, Dragutin

    2014-01-01

    We address the problem of assigning biological function to solved protein structures. Computational tools play a critical role in identifying potential active sites and informing screening decisions for further lab analysis. A critical parameter in the practical application of computational methods is the precision, or positive predictive value. Precision measures the level of confidence the user should have in a particular computed functional assignment. Low precision annotations lead to futile laboratory investigations and waste scarce research resources. In this paper we describe an advanced version of the protein function annotation system FEATURE, which achieved 99% precision and average recall of 95% across 20 representative functional sites. The system uses a Support Vector Machine classifier operating on the microenvironment of physicochemical features around an amino acid. We also compared performance of our method with state-of-the-art sequence-level annotator Pfam in terms of precision, recall and localization. To our knowledge, no other functional site annotator has been rigorously evaluated against these key criteria. The software and predictive models are incorporated into the WebFEATURE service at http://feature.stanford.edu/wf4.0-beta. PMID:24632601

  13. Engineering Property Prediction Tools for Tailored Polymer Composite Structures

    SciTech Connect

    Nguyen, Ba Nghiep; Foss, Peter; Wyzgoski, Michael; Trantina, Gerry; Kunc, Vlastimil; Schutte, Carol; Smith, Mark T.

    2009-12-23

    This report summarizes our FY 2009 research activities for the project titled:"Engineering Property Prediction Tools for Tailored Polymer Composite Structures." These activities include (i) the completion of the development of a fiber length attrition model for injection-molded long-fiber thermoplastics (LFTs), (ii) development of the a fatigue damage model for LFTs and its implementation in ABAQUS, (iii) development of an impact damage model for LFTs and its implementation in ABAQUS, (iv) development of characterization methods for fatigue testing, (v) characterization of creep and fatigue responses of glass-fiber/polyamide (PA6,6) and glass-fiber/polypropylene (PP), (vi) characterization of fiber length distribution along the flow length of glass/PA6,6 and glass-fiber/PP, and (vii) characterization of impact responses of glass-fiber/PA6,6. The fiber length attrition model accurately captures the fiber length distribution along the flow length of the studied glass-fiber/PP material. The fatigue damage model is able to predict the S-N and stiffness reduction data which are valuable to the fatigue design of LFTs. The impact damage model correctly captures damage accumulation observed in experiments of glass-fiber/PA6,6 plaques.Further work includes validations of these models for representative LFT materials and a complex LFT part.

  14. An RNA secondary structure prediction method based on minimum and suboptimal free energy structures.

    PubMed

    Fu, Haoyue; Yang, Lianping; Zhang, Xiangde

    2015-09-01

    The function of an RNA-molecule is mainly determined by its tertiary structures. And its secondary structure is an important determinant of its tertiary structure. The comparative methods usually give better results than the single-sequence methods. Based on minimum and suboptimal free energy structures, the paper presents a novel method for predicting conserved secondary structure of a group of related RNAs. In the method, the information from the known RNA structures is used as training data in a SVM (Support Vector Machine) classifier. Our method has been tested on the benchmark dataset given by Puton et al. The results show that the average sensitivity of our method is higher than that of other comparative methods such as CentroidAlifold, MXScrana, RNAalifold, and TurboFold. PMID:26100179

  15. Molecular Dynamics of 8-oxoguanine Lesioned B-DNA Molecule — Structure and Energy Analysis

    NASA Astrophysics Data System (ADS)

    Pinak, M.; O'Neill, P.; Fujimoto, H.; Nemoto, T.

    2004-04-01

    The molecular dynamics (MD) simulation of DNA mutagenic oxidative lesion — 7,8-dihydro-8-oxoguanine (8-oxoG), complexed with the repair enzyme — human oxoguanine glycosylase 1 (hOGG1) was performed for 1 nanosecond (ns) in order to describe the dynamical process of DNA-enzyme complex formation. After 900 picoseconds of MD the lesioned DNA and enzyme formed a complex that lasted until the end of the simulation at 1 ns. The amino group of arginine 324 was located close to the phosphodiester bond of nucleotide with 8-oxoG enabling chemical reactions between amino acid and lesion. Phosphodiester bond at C5' of 8-oxoG was displaced to the position close to the amino group of arginine 324. In the background simulation of the identical molecular system with the native DNA, neither the complex nor the water mediated hydrogen bond network were observed. The electrostatic energy is supposed to be significant factor causing the disruption of DNA base stacking in DNA duplex and may also to serve as a signal toward the repair enzyme informing on the presence of the lesion.

  16. Application of cytochrome b DNA sequences for the authentication of endangered snake species.

    PubMed

    Wong, Ka-Lok; Wang, Jun; But, Paul Pui-Hay; Shaw, Pang-Chui

    2004-01-01

    In order to enforce the conservation program and curbing the illegal trading and consumption of endangered snake species, the value of cytochrome b sequence in the authentication of snake species was evaluated. As an illustration, DNA was extracted, selected cytochrome b DNA sequences amplified and sequenced from six snakes commonly consumed in Hong Kong. Cataloging with sequences available in public, a cytochrome b database containing 90 species of snakes was constructed. In this database, sequence homology between snakes ranged from 70.68 to 95.11%. On the other hand, intraspecific variation of three tested snakes was 0-0.98%. Using the database, we were able to determine the identity of six meat samples confiscated by the Agriculture, Fisheries and Conservation Department, HKSAR. PMID:14687773

  17. Shape and secondary structure prediction for ncRNAs including pseudoknots based on linear SVM

    PubMed Central

    2013-01-01

    Background Accurate secondary structure prediction provides important information to undefirstafinding the tertiary structures and thus the functions of ncRNAs. However, the accuracy of the native structure derivation of ncRNAs is still not satisfactory, especially on sequences containing pseudoknots. It is recently shown that using the abstract shapes, which retain adjacency and nesting of structural features but disregard the length details of helix and loop regions, can improve the performance of structure prediction. In this work, we use SVM-based feature selection to derive the consensus abstract shape of homologous ncRNAs and apply the predicted shape to structure prediction including pseudoknots. Results Our approach was applied to predict shapes and secondary structures on hundreds of ncRNA data sets with and without psuedoknots. The experimental results show that we can achieve 18% higher accuracy in shape prediction than the state-of-the-art consensus shape prediction tools. Using predicted shapes in structure prediction allows us to achieve approximate 29% higher sensitivity and 10% higher positive predictive value than other pseudoknot prediction tools. Conclusions Extensive analysis of RNA properties based on SVM allows us to identify important properties of sequences and structures related to their shapes. The combination of mass data analysis and SVM-based feature selection makes our approach a promising method for shape and structure prediction. The implemented tools, Knot Shape and Knot Structure are open source software and can be downloaded at: http://www.cse.msu.edu/~achawana/KnotShape. PMID:23369147

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

    NASA Technical Reports Server (NTRS)

    Roschke, Paul N.; Mascorro, Edward; Papados, Photios; Serna, Oscar R.

    1991-01-01

    The primary objective of this study is to develop a method for prediction of failure of thin beryllium sheets that undergo complex states of stress. Major components of the research include experimental evaluation of strength parameters for cross-rolled beryllium sheet, application of the Tsai-Wu failure criterion to plate bending problems, development of a high order failure criterion, application of the new criterion to a variety of structures, and incorporation of both failure criteria into a finite element code. A Tsai-Wu failure model for SR-200 sheet material is developed from available tensile data, experiments carried out by NASA on two circular plates, and compression and off-axis experiments performed in this study. The failure surface obtained from the resulting criterion forms an ellipsoid. By supplementing experimental data used in the the two-dimensional criterion and modifying previously suggested failure criteria, a multi-dimensional failure surface is proposed for thin beryllium structures. The new criterion for orthotropic material is represented by a failure surface in six-dimensional stress space. In order to determine coefficients of the governing equation, a number of uniaxial, biaxial, and triaxial experiments are required. Details of these experiments and a complementary ultrasonic investigation are described in detail. Finally, validity of the criterion and newly determined mechanical properties is established through experiments on structures composed of SR200 sheet material. These experiments include a plate-plug arrangement under a complex state of stress and a series of plates with an out-of-plane central point load. Both criteria have been incorporated into a general purpose finite element analysis code. Numerical simulation incrementally applied loads to a structural component that is being designed and checks each nodal point in the model for exceedance of a failure criterion. If stresses at all locations do not exceed the failure

  19. Accurate prediction of protein secondary structure and solvent accessibility by consensus combiners of sequence and structure information

    PubMed Central

    Pollastri, Gianluca; Martin, Alberto JM; Mooney, Catherine; Vullo, Alessandro

    2007-01-01

    Background Structural properties of proteins such as secondary structure and solvent accessibility contribute to three-dimensional structure prediction, not only in the ab initio case but also when homology information to known structures is available. Structural properties are also routinely used in protein analysis even when homology is available, largely because homology modelling is lower throughput than, say, secondary structure prediction. Nonetheless, predictors of secondary structure and solvent accessibility are virtually always ab initio. Results Here we develop high-throughput machine learning systems for the prediction of protein secondary structure and solvent accessibility that exploit homology to proteins of known structure, where available, in the form of simple structural frequency profiles extracted from sets of PDB templates. We compare these systems to their state-of-the-art ab initio counterparts, and with a number of baselines in which secondary structures and solvent accessibilities are extracted directly from the templates. We show that structural information from templates greatly improves secondary structure and solvent accessibility prediction quality, and that, on average, the systems significantly enrich the information contained in the templates. For sequence similarity exceeding 30%, secondary structure prediction quality is approximately 90%, close to its theoretical maximum, and 2-class solvent accessibility roughly 85%. Gains are robust with respect to template selection noise, and significant for marginal sequence similarity and for short alignments, supporting the claim that these improved predictions may prove beneficial beyond the case in which clear homology is available. Conclusion The predictive system are publicly available at the address . PMID:17570843

  20. Structural kinematics based damage zone prediction in gradient structures using vibration database

    NASA Astrophysics Data System (ADS)

    Talha, Mohammad; Ashokkumar, Chimpalthradi R.

    2014-05-01

    To explore the applications of functionally graded materials (FGMs) in dynamic structures, structural kinematics based health monitoring technique becomes an important problem. Depending upon the displacements in three dimensions, the health of the material to withstand dynamic loads is inferred in this paper, which is based on the net compressive and tensile displacements that each structural degree of freedom takes. These net displacements at each finite element node predicts damage zones of the FGM where the material is likely to fail due to a vibration response which is categorized according to loading condition. The damage zone prediction of a dynamically active FGMs plate have been accomplished using Reddy's higher-order theory. The constituent material properties are assumed to vary in the thickness direction according to the power-law behavior. The proposed C0 finite element model (FEM) is applied to get net tensile and compressive displacement distributions across the structures. A plate made of Aluminum/Ziconia is considered to illustrate the concept of structural kinematics-based health monitoring aspects of FGMs.

  1. Composite failure prediction of π-joint structures under bending

    NASA Astrophysics Data System (ADS)

    Huang, Hong-mei; Yuan, Shen-fang

    2012-03-01

    In this article, the composite -joint is investigated under bending loads. The "L" preform is the critical component regarding composite -joint failure. The study is presented in the failure detection of a carbon fiber composite -joint structure under bending loads using fiber Bragg grating (FBG) sensor. Firstly, based on the general finite element method (FEM) software, the 3-D finite element (FE) model of composite -joint is established, and the failure process and every lamina failure load of composite -joint are investigated by maximum stress criteria. Then, strain distributions along the length of FBG are extracted, and the reflection spectra of FBG are calculated according to the strain distribution. Finally, to verify the numerical results, a test scheme is performed and the experimental spectra of FBG are recorded. The experimental results indicate that the failure sequence and the corresponding critical loads of failure are consistent with the numerical predictions, and the computational error of failure load is less than 6.4%. Furthermore, it also verifies the feasibility of the damage detection system.

  2. Detecting and representing predictable structure during auditory scene analysis.

    PubMed

    Sohoglu, Ediz; Chait, Maria

    2016-01-01

    We use psychophysics and MEG to test how sensitivity to input statistics facilitates auditory-scene-analysis (ASA). Human subjects listened to 'scenes' comprised of concurrent tone-pip streams (sources). On occasional trials a new source appeared partway. Listeners were more accurate and quicker to detect source appearance in scenes comprised of temporally-regular (REG), rather than random (RAND), sources. MEG in passive listeners and those actively detecting appearance events revealed increased sustained activity in auditory and parietal cortex in REG relative to RAND scenes, emerging ~400 ms of scene-onset. Over and above this, appearance in REG scenes was associated with increased responses relative to RAND scenes. The effect of temporal structure on appearance-evoked responses was delayed when listeners were focused on the scenes relative to when listening passively, consistent with the notion that attention reduces 'surprise'. Overall, the results implicate a mechanism that tracks predictability of multiple concurrent sources to facilitate active and passive ASA. PMID:27602577

  3. Detecting and representing predictable structure during auditory scene analysis

    PubMed Central

    Sohoglu, Ediz; Chait, Maria

    2016-01-01

    We use psychophysics and MEG to test how sensitivity to input statistics facilitates auditory-scene-analysis (ASA). Human subjects listened to ‘scenes’ comprised of concurrent tone-pip streams (sources). On occasional trials a new source appeared partway. Listeners were more accurate and quicker to detect source appearance in scenes comprised of temporally-regular (REG), rather than random (RAND), sources. MEG in passive listeners and those actively detecting appearance events revealed increased sustained activity in auditory and parietal cortex in REG relative to RAND scenes, emerging ~400 ms of scene-onset. Over and above this, appearance in REG scenes was associated with increased responses relative to RAND scenes. The effect of temporal structure on appearance-evoked responses was delayed when listeners were focused on the scenes relative to when listening passively, consistent with the notion that attention reduces ‘surprise’. Overall, the results implicate a mechanism that tracks predictability of multiple concurrent sources to facilitate active and passive ASA. DOI: http://dx.doi.org/10.7554/eLife.19113.001 PMID:27602577

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

    SciTech Connect

    Han, Hyunsun; Kim, J. Y.; Kwon, Ohjin

    2013-10-15

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

  5. STRUCTURE-BASED PREDICTIVE MODEL FOR COAL CHAR COMBUSTION

    SciTech Connect

    CHRISTOPHER M. HADAD; JOSEPH M. CALO; ROBERT H. ESSENHIGH; ROBERT H. HURT

    1998-06-04

    During the past quarter of this project, significant progress continued was made on both major technical tasks. Progress was made at OSU on advancing the application of computational chemistry to oxidative attack on model polyaromatic hydrocarbons (PAHs) and graphitic structures. This work is directed at the application of quantitative ab initio molecular orbital theory to address the decomposition products and mechanisms of coal char reactivity. Previously, it was shown that the �hybrid� B3LYP method can be used to provide quantitative information concerning the stability of the corresponding radicals that arise by hydrogen atom abstraction from monocyclic aromatic rings. In the most recent quarter, these approaches have been extended to larger carbocyclic ring systems, such as coronene, in order to compare the properties of a large carbonaceous PAH to that of the smaller, monocyclic aromatic systems. It was concluded that, at least for bond dissociation energy considerations, the properties of the large PAHs can be modeled reasonably well by smaller systems. In addition to the preceding work, investigations were initiated on the interaction of selected radicals in the �radical pool� with the different types of aromatic structures. In particular, the different pathways for addition vs. abstraction to benzene and furan by H and OH radicals were examined. Thus far, the addition channel appears to be significantly favored over abstraction on both kinetic and thermochemical grounds. Experimental work at Brown University in support of the development of predictive structural models of coal char combustion was focused on elucidating the role of coal mineral matter impurities on reactivity. An �inverse� approach was used where a carbon material was doped with coal mineral matter. The carbon material was derived from a high carbon content fly ash (Fly Ash 23 from the Salem Basin Power Plant. The ash was obtained from Pittsburgh #8 coal (PSOC 1451). Doped

  6. The Proteome Folding Project: Proteome-scale prediction of structure and function

    PubMed Central

    Drew, Kevin; Winters, Patrick; Butterfoss, Glenn L.; Berstis, Viktors; Uplinger, Keith; Armstrong, Jonathan; Riffle, Michael; Schweighofer, Erik; Bovermann, Bill; Goodlett, David R.; Davis, Trisha N.; Shasha, Dennis; Malmström, Lars; Bonneau, Richard

    2011-01-01

    The incompleteness of proteome structure and function annotation is a critical problem for biologists and, in particular, severely limits interpretation of high-throughput and next-generation experiments. We have developed a proteome annotation pipeline based on structure prediction, where function and structure annotations are generated using an integration of sequence comparison, fold recognition, and grid-computing-enabled de novo structure prediction. We predict protein domain boundaries and three-dimensional (3D) structures for protein domains from 94 genomes (including human, Arabidopsis, rice, mouse, fly, yeast, Escherichia coli, and worm). De novo structure predictions were distributed on a grid of more than 1.5 million CPUs worldwide (World Community Grid). We generated significant numbers of new confident fold annotations (9% of domains that are otherwise unannotated in these genomes). We demonstrate that predicted structures can be combined with annotations from the Gene Ontology database to predict new and more specific molecular functions. PMID:21824995

  7. STRUCTURE-BASED PREDICTIVE MODEL FOR COAL CHAR COMBUSTION

    SciTech Connect

    CHRISTOPHER M. HADAD; JOSEPH M. CALO; ROBERT H. ESSENHIGH; ROBERT H. HURT

    1998-09-11

    Progress was made this period on a number of tasks. A significant advance was made in the incorporation of macrostructural ideas into high temperature combustion models. Work at OSU by R. Essenhigh in collaboration with the University of Stuttgart has led to a theory that the zone I / II transition in char combustion lies within the range of conditions of interest for pulverized char combustion. The group has presented evidence that some combustion data, previously interpreted with zone II models, in fact takes place in the transition from zone II to zone 1. This idea was used at Brown to make modifications to the CBK model (a char kinetics package specially designed for carbon burnout prediction, currently used by a number of research and furnace modeling groups in academia and industry). The resulting new model version, CBK8, shows improved ability to predict extinction behavior in the late stages of combustion, especially for particles with low ash content. The full development and release of CBK8, along with detailed descriptions of the role of the zone 1/2 transition will be reported on in subsequent reports. ABB-CE is currently implementing CBK7 into a special version of the CFD code Fluent for use in the modeling and design of their boilers. They have been appraised of the development, and have expressed interest in incorporating the new feature, realizing full CBK8 capabilities into their combustion codes. The computational chemistry task at OSU continued to study oxidative pathways for PAH, with emphasis this period on heteroatom containing ring compounds. Preliminary XPS studies were also carried out. Combustion experiments were also carried out at OSU this period, leading to the acquisition of samples at various residence times and the measurement of their oxidation reactivity by nonisothermal TGA techniques. Several members of the project team attended the Carbon Conference this period and made contacts with representatives from the new FETC Consortium

  8. 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

  9. Accurate prediction of protein structural classes by incorporating predicted secondary structure information into the general form of Chou's pseudo amino acid composition.

    PubMed

    Kong, Liang; Zhang, Lichao; Lv, Jinfeng

    2014-03-01

    Extracting good representation from protein sequence is fundamental for protein structural classes prediction tasks. In this paper, we propose a novel and powerful method to predict protein structural classes based on the predicted secondary structure information. At the feature extraction stage, a 13-dimensional feature vector is extracted to characterize general contents and spatial arrangements of the secondary structural elements of a given protein sequence. Specially, four segment-level features are designed to elevate discriminative ability for proteins from the α/β and α+β classes. After the features are extracted, a multi-class non-linear support vector machine classifier is used to implement protein structural classes prediction. We report extensive experiments comparing the proposed method to the state-of-the-art in protein structural classes prediction on three widely used low-similarity benchmark datasets: FC699, 1189 and 640. Our method achieves competitive performance on prediction accuracies, especially for the overall prediction accuracies which have exceeded the best reported results on all of the three datasets. PMID:24316044

  10. AWSEM-MD: Protein Structure Prediction Using Coarse-grained Physical Potentials and Bioinformatically Based Local Structure Biasing

    PubMed Central

    Davtyan, Aram; Schafer, Nicholas P.; Zheng, Weihua; Clementi, Cecilia; Wolynes, Peter G.; Papoian, Garegin A.

    2012-01-01

    The Associative memory, Water mediated, Structure and Energy Model (AWSEM) is a coarse-grained protein force field. AWSEM contains physically motivated terms, such as hydrogen bonding, as well as a bioinformatically based local structure biasing term, which efficiently takes into account many-body effects that are modulated by the local sequence. When combined with appropriate local or global alignments to choose memories, AWSEM can be used to perform de novo protein structure prediction. Herein we present structure prediction results for a particular choice of local sequence alignment method based on short residue sequences called fragments. We demonstrate the model’s structure prediction capabilities for three levels of global homology between the target sequence and those proteins used for local structure biasing, all of which assume that the structure of the target sequence is not known. When there are no homologs in the database of structures used for local structure biasing, AWSEM calculations produce structural predictions that are somewhat improved compared with prior works using related approaches. The inclusion of a small number of structures from homologous sequences improves structure prediction only marginally but when the fragment search is restricted to only homologous sequences, AWSEM can perform high resolution structure prediction and can be used for kinetics and dynamics studies. PMID:22545654

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

    PubMed

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

    2015-12-01

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

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

    ERIC Educational Resources Information Center

    Ellington, Roni; Wachira, James; Nkwanta, Asamoah

    2010-01-01

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

  13. Theoretical prediction of electronic structures of fully π-conjugated zinc oligoporphyrins with curved surface structures

    NASA Astrophysics Data System (ADS)

    Yamaguchi, Yoichi

    2004-05-01

    A theoretical prediction of the electronic structures of fully π-conjugated zinc oligoporphyrins with curved surface, ring, tube, and ball-shaped structures was conducted as the objective for the future development of triply meso-meso-, β-β-, and β-β-linked planar zinc oligoporphyrins. The excitation energies and oscillator strengths for the optimal ring and ball structures were calculated using the time-dependent density functional theory (DFT). Although there is an extremely small energy difference of <0.1 eV between the highest occupied molecular orbital (HOMO) and the lowest unoccupied molecular orbital (LUMO) of the ring structure relative to the same-sized triply linked planar one, the Q and B bands of the former are smaller redshifted excitation energies and intensified oscillator strengths than those of the latter due to the structurally shortened effective π-conjugated lengths for the electron transition. It is expected that the ball structure becomes an excellent electron acceptor and shows the highly redshifted Q' band in the near-IR region relative to the monomer. The minimum value of the HOMO-LUMO energy gaps of the infinite-length ring structures was estimated using periodic boundary conditions within the DFT, resulting in the metallic characters of both the tube structures with and without the spiral triply linked porphyrin array. The relation between the diameters and strain energies of the tube and ball structures was also examined. The present fused zinc porphyrins may become more colorful materials with new optelectronic properties including artificial photosynthesis than the carbon nanotubes and fullerenes when the axial coordinations of the central metal of porphyrins are functionally used.

  14. Energy-based RNA consensus secondary structure prediction in multiple sequence alignments.

    PubMed

    Washietl, Stefan; Bernhart, Stephan H; Kellis, Manolis

    2014-01-01

    Many biologically important RNA structures are conserved in evolution leading to characteristic mutational patterns. RNAalifold is a widely used program to predict consensus secondary structures in multiple alignments by combining evolutionary information with traditional energy-based RNA folding algorithms. Here we describe the theory and applications of the RNAalifold algorithm. Consensus secondary structure prediction not only leads to significantly more accurate structure models, but it also allows to study structural conservation of functional RNAs. PMID:24639158

  15. A permutation based simulated annealing algorithm to predict pseudoknotted RNA secondary structures.

    PubMed

    Tsang, Herbert H; Wiese, Kay C

    2015-01-01

    Pseudoknots are RNA tertiary structures which perform essential biological functions. This paper discusses SARNA-Predict-pk, a RNA pseudoknotted secondary structure prediction algorithm based on Simulated Annealing (SA). The research presented here extends previous work of SARNA-Predict and further examines the effect of the new algorithm to include prediction of RNA secondary structure with pseudoknots. An evaluation of the performance of SARNA-Predict-pk in terms of prediction accuracy is made via comparison with several state-of-the-art prediction algorithms using 20 individual known structures from seven RNA classes. We measured the sensitivity and specificity of nine prediction algorithms. Three of these are dynamic programming algorithms: Pseudoknot (pknotsRE), NUPACK, and pknotsRG-mfe. One is using the statistical clustering approach: Sfold and the other five are heuristic algorithms: SARNA-Predict-pk, ILM, STAR, IPknot and HotKnots algorithms. The results presented in this paper demonstrate that SARNA-Predict-pk can out-perform other state-of-the-art algorithms in terms of prediction accuracy. This supports the use of the proposed method on pseudoknotted RNA secondary structure prediction of other known structures. PMID:26558299

  16. Rapid prediction of structural responses of double-bottom structures in shoal grounding scenario

    NASA Astrophysics Data System (ADS)

    Hu, Zhiqiang; Wang, Ge; Yao, Qi; Yu, Zhaolong

    2016-03-01

    This study presents a simplified analytical model for predicting the structural responses of double-bottom ships in a shoal grounding scenario. This solution is based on a series of analytical models developed from elastic-plastic mechanism theories for different structural components, including bottom girders, floors, bottom plating, and attached stiffeners. We verify this simplified analytical model by numerical simulation, and establish finite element models for a typical tanker hold and a rigid indenter representing seabed obstacles. Employing the LS-DYNA finite element solver, we conduct numerical simulations for shoal-grounding cases with a wide range of slope angles and indentation depths. In comparison with numerical simulations, we verify the proposed simplified analytical model with respect to the total energy dissipation and the horizontal grounding resistance. We also investigate the interaction effect of deformation patterns between bottom structure components. Our results show that the total energy dissipation and resistances predicted by the analytical model agree well with those from numerical simulations.

  17. A survey of machine learning methods for secondary and supersecondary protein structure prediction.

    PubMed

    Ho, Hui Kian; Zhang, Lei; Ramamohanarao, Kotagiri; Martin, Shawn

    2013-01-01

    In this chapter we provide a survey of protein secondary and supersecondary structure prediction using methods from machine learning. Our focus is on machine learning methods applicable to β-hairpin and β-sheet prediction, but we also discuss methods for more general supersecondary structure prediction. We provide background on the secondary and supersecondary structures that we discuss, the features used to describe them, and the basic theory behind the machine learning methods used. We survey the machine learning methods available for secondary and supersecondary structure prediction and compare them where possible. PMID:22987348

  18. Self-entanglement of long linear DNA vectors using transient non-B-DNA attachment points: a new concept for improvement of non-viral therapeutic gene delivery.

    PubMed

    Tolmachov, Oleg E

    2012-05-01

    The cell-specific and long-term expression of therapeutic transgenes often requires a full array of native gene control elements including distal enhancers, regulatory introns and chromatin organisation sequences. The delivery of such extended gene expression modules to human cells can be accomplished with non-viral high-molecular-weight DNA vectors, in particular with several classes of linear DNA vectors. All high-molecular-weight DNA vectors are susceptible to damage by shear stress, and while for some of the vectors the harmful impact of shear stress can be minimised through the transformation of the vectors to compact topological configurations by supercoiling and/or knotting, linear DNA vectors with terminal loops or covalently attached terminal proteins cannot be self-compacted in this way. In this case, the only available self-compacting option is self-entangling, which can be defined as the folding of single DNA molecules into a configuration with mutual restriction of molecular motion by the individual segments of bent DNA. A negatively charged phosphate backbone makes DNA self-repulsive, so it is reasonable to assume that a certain number of 'sticky points' dispersed within DNA could facilitate the entangling by bringing DNA segments into proximity and by interfering with the DNA slipping away from the entanglement. I propose that the spontaneous entanglement of vector DNA can be enhanced by the interlacing of the DNA with sites capable of mutual transient attachment through the formation of non-B-DNA forms, such as interacting cruciform structures, inter-segment triplexes, slipped-strand DNA, left-handed duplexes (Z-forms) or G-quadruplexes. It is expected that the non-B-DNA based entanglement of the linear DNA vectors would consist of the initial transient and co-operative non-B-DNA mediated binding events followed by tight self-ensnarement of the vector DNA. Once in the nucleoplasm of the target human cells, the DNA can be disentangled by type II

  19. Surface pressure profiles, vortex structure and initialization for hurricane prediction. Part II: numerical simulations of track, structure and intensity

    NASA Astrophysics Data System (ADS)

    Davidson, Noel E.; Ma, Yimin

    2012-07-01

    In part 1 of this study, an assessment of commonly used surface pressure profiles to represent TC structures was made. Using the Australian tropical cyclone model, the profiles are tested in case studies of high-resolution prediction of track, structure and intensity. We demonstrate that: (1) track forecasts are mostly insensitive to the imposed structure; (2) in some cases [here Katrina (2005)], specification of vortex structure can have a large impact on prediction of structure and intensity; (3) the forecast model mostly preserves the characteristics of the initial structure and so correct structure at t = 0 is a requirement for improved structure forecasting; and (4) skilful prediction of intensity does not guarantee skilful prediction of structure. It is shown that for Ivan (2004) the initial structure from each profile is preserved during the simulations, and that markedly different structures can have similar intensities. Evidence presented suggests that different initial profiles can sometimes change the timing of intensification. Thus, correct initial vortex structure is an essential ingredient for more accurate intensity and structure prediction.

  20. 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.

  1. 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.

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

    NASA Astrophysics Data System (ADS)

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

    2012-07-01

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

  3. CASP11 - An Evaluation of a Modular BCL::Fold-Based Protein Structure Prediction Pipeline.

    PubMed

    Fischer, Axel W; Heinze, Sten; Putnam, Daniel K; Li, Bian; Pino, James C; Xia, Yan; Lopez, Carlos F; Meiler, Jens

    2016-01-01

    In silico prediction of a protein's tertiary structure remains an unsolved problem. The community-wide Critical Assessment of Protein Structure Prediction (CASP) experiment provides a double-blind study to evaluate improvements in protein structure prediction algorithms. We developed a protein structure prediction pipeline employing a three-stage approach, consisting of low-resolution topology search, high-resolution refinement, and molecular dynamics simulation to predict the tertiary structure of proteins from the primary structure alone or including distance restraints either from predicted residue-residue contacts, nuclear magnetic resonance (NMR) nuclear overhauser effect (NOE) experiments, or mass spectroscopy (MS) cross-linking (XL) data. The protein structure prediction pipeline was evaluated in the CASP11 experiment on twenty regular protein targets as well as thirty-three 'assisted' protein targets, which also had distance restraints available. Although the low-resolution topology search module was able to sample models with a global distance test total score (GDT_TS) value greater than 30% for twelve out of twenty proteins, frequently it was not possible to select the most accurate models for refinement, resulting in a general decay of model quality over the course of the prediction pipeline. In this study, we provide a detailed overall analysis, study one target protein in more detail as it travels through the protein structure prediction pipeline, and evaluate the impact of limited experimental data. PMID:27046050

  4. CASP11 – An Evaluation of a Modular BCL::Fold-Based Protein Structure Prediction Pipeline

    PubMed Central

    Fischer, Axel W.; Heinze, Sten; Putnam, Daniel K.; Li, Bian; Pino, James C.; Xia, Yan; Lopez, Carlos F.; Meiler, Jens

    2016-01-01

    In silico prediction of a protein’s tertiary structure remains an unsolved problem. The community-wide Critical Assessment of Protein Structure Prediction (CASP) experiment provides a double-blind study to evaluate improvements in protein structure prediction algorithms. We developed a protein structure prediction pipeline employing a three-stage approach, consisting of low-resolution topology search, high-resolution refinement, and molecular dynamics simulation to predict the tertiary structure of proteins from the primary structure alone or including distance restraints either from predicted residue-residue contacts, nuclear magnetic resonance (NMR) nuclear overhauser effect (NOE) experiments, or mass spectroscopy (MS) cross-linking (XL) data. The protein structure prediction pipeline was evaluated in the CASP11 experiment on twenty regular protein targets as well as thirty-three ‘assisted’ protein targets, which also had distance restraints available. Although the low-resolution topology search module was able to sample models with a global distance test total score (GDT_TS) value greater than 30% for twelve out of twenty proteins, frequently it was not possible to select the most accurate models for refinement, resulting in a general decay of model quality over the course of the prediction pipeline. In this study, we provide a detailed overall analysis, study one target protein in more detail as it travels through the protein structure prediction pipeline, and evaluate the impact of limited experimental data. PMID:27046050

  5. 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...

  6. STRUCTURE-ACTIVITY RELATIONSHIP STUIDES AND THEIR ROLE IN PREDICTING AND INVESTIGATING CHEMICAL TOXICITY

    EPA Science Inventory

    Structure-Activity Relationship Studies and their Role in Predicting and Investigating Chemical Toxicity

    Structure-activity relationships (SAR) represent attempts to generalize chemical information relative to biological activity for the twin purposes of generating insigh...

  7. 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...

  8. 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.

  9. Predicting Gene Structures from Multiple RT-PCR Tests

    NASA Astrophysics Data System (ADS)

    Kováč, Jakub; Vinař, Tomáš; Brejová, Broňa

    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.

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

    NASA Astrophysics Data System (ADS)

    Needs, Richard J.; Pickard, Chris J.

    2016-05-01

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