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Sample records for predicting b-dna structure

  1. NMR proton chemical shift prediction of T·T mismatches in B-DNA duplexes

    NASA Astrophysics Data System (ADS)

    Kwok, Chun Kit; Lam, Sik Lok

    2013-09-01

    A proton chemical shift prediction scheme for B-DNA duplexes containing a T·T mismatch has been established. The scheme employs a set of T·T mismatch triplet chemical shift values, 5?- and 3?-correction factors extracted from reference sequences, and also the B-DNA chemical shift values predicted by Altona et al. The prediction scheme was tested by eight B-DNA duplexes containing T·T mismatches. Based on 560 sets of predicted and experimental proton chemical shift values, the overall prediction accuracy for non-labile protons was determined to be 0.07 ppm with an excellent correlation coefficient of 0.9996. In addition, the prediction accuracy for 96 sets of labile protons was found to be 0.22 ppm with a correlation coefficient of 0.9961. The prediction scheme developed herein can facilitate resonance assignments of B-DNA duplexes containing T·T mismatches and be generalized for the chemical shift prediction of other DNA mismatches. Our chemical shift data will also be useful for establishing structure-chemical shift information in B-DNA containing mismatches.

  2. Structural correlations and melting of B-DNA fibers

    SciTech Connect

    Wildes, Andrew; Theodorakopoulos, Nikos; Valle-Orero, Jessica; Cuesta-Lopez, Santiago; Peyrard, Michel; Garden, Jean-Luc

    2011-06-15

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

  3. Structural correlations and melting of B-DNA fibers.

    PubMed

    Wildes, Andrew; Theodorakopoulos, Nikos; Valle-Orero, Jessica; Cuesta-López, Santiago; Garden, Jean-Luc; Peyrard, Michel

    2011-06-01

    Despite numerous attempts, understanding the thermal denaturation of DNA is still a challenge due to the lack of structural data on the transition since standard experimental approaches to DNA melting are made in solution and do not provide spatial information. We report a measurement using neutron scattering from oriented DNA fibers to determine the size of the regions that stay in the double-helix conformation as the melting temperature is approached from below. A Bragg peak from the B form of DNA is observed as a function of temperature and its width and integrated intensity are measured. These results, complemented by a differential calorimetry study of the melting of B-DNA fibers as well as electrophoresis and optical observation data, are analyzed in terms of a one-dimensional mesoscopic model of DNA. PMID:21797419

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

    NASA Astrophysics Data System (ADS)

    Ng, Kui Sang; Lam, Sik Lok

    2015-03-01

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

  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. Multistep modeling (MSM) of biomolecular structure application to the A-G mispair in the B-DNA environment

    NASA Technical Reports Server (NTRS)

    Srinivasan, S.; Raghunathan, G.; Shibata, M.; Rein, R.

    1986-01-01

    A multistep modeling procedure has been evolved to study the structural changes introduced by lesions in DNA. We report here the change in the structure of regular B-DNA geometry due to the incorporation of Ganti-Aanti mispair in place of a regular G-C pair, preserving the helix continuity. The energetics of the structure so obtained is compared with the Ganti-Asyn configuration under similar constrained conditions. We present the methodology adopted and discuss the results.

  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. 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.

  9. B-DNA structure and stability: the role of hydrogen bonding, ?-? stacking interactions, twist-angle, and solvation.

    PubMed

    Poater, Jordi; Swart, Marcel; Bickelhaupt, F Matthias; Fonseca Guerra, Célia

    2014-07-14

    We have computationally investigated the structure and stability of B-DNA. To this end, we have analyzed the bonding in a series of 47 stacks consisting of two base pairs, in which the base pairs cover the full range of natural Watson-Crick pairs, mismatched pairs, and artificial DNA base pairs. Our analyses provide detailed insight into the role and relative importance of the various types of interactions, such as, hydrogen bonding, ?-? stacking interactions, and solvation/desolvation. Furthermore, we have analyzed the functionality of the twist-angle on the stability of the structure. Interestingly, we can show that all stacked base pairs benefit from a stabilization by 6 to 12 kcal mol(-1) if stacked base pairs are twisted from 0° to 36°, that is, if they are mutually rotated from a congruent superposition to the mutually twisted stacking configuration that occurs in B-DNA. This holds especially for stacked AT pairs but also for other stacked base pairs, including GC. The electronic mechanism behind this preference for a twisted arrangement depends on the base pairs involved. We also show that so-called "diagonal interactions" (or cross terms) in the stacked base pairs are crucial for understanding the stability of B-DNA, in particular, in GC-rich sequences. PMID:24871817

  10. DNA polymerase 3'?5' exonuclease activity: Different roles of the beta hairpin structure in family-B DNA polymerases.

    PubMed

    Darmawan, Hariyanto; Harrison, Melissa; Reha-Krantz, Linda J

    2015-05-01

    Proofreading by the bacteriophage T4 and RB69 DNA polymerases requires a ? hairpin structure that resides in the exonuclease domain. Genetic, biochemical and structural studies demonstrate that the phage ? hairpin acts as a wedge to separate the primer-end from the template strand in exonuclease complexes. Single amino acid substitutions in the tip of the hairpin or deletion of the hairpin prevent proofreading and create "mutator" DNA polymerases. There is little known, however, about the function of similar hairpin structures in other family B DNA polymerases. We present mutational analysis of the yeast (Saccharomyces cerevisiae) DNA polymerase ? hairpin. Deletion of the DNA polymerase ? hairpin (hp?) did not significantly reduce DNA replication fidelity; thus, the ? hairpin structure in yeast DNA polymerase ? is not essential for proofreading. However, replication efficiency was reduced as indicated by a slow growth phenotype. In contrast, the G447D amino acid substitution in the tip of the hairpin increased frameshift mutations and sensitivity to hydroxyurea (HU). A chimeric yeast DNA polymerase ? was constructed in which the T4 DNA polymerase hairpin (T4hp) replaced the yeast DNA polymerase ? hairpin; a strong increase in frameshift mutations was observed and the mutant strain was sensitive to HU and to the pyrophosphate analog, phosphonoacetic acid (PAA). But all phenotypes - slow growth, HU-sensitivity, PAA-sensitivity, and reduced fidelity, were observed only in the absence of mismatch repair (MMR), which implicates a role for MMR in mediating DNA polymerase ? replication problems. In comparison, another family B DNA polymerase, DNA polymerase ?, has only an atrophied hairpin with no apparent function. Thus, while family B DNA polymerases share conserved motifs and general structural features, the ? hairpin has evolved to meet specific needs. PMID:25753811

  11. 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, {approx}80 {angstrom} from the lesion. We show that the conserved signature domain II of UvrA mediates a nexus of contacts among UvrA, UvrB and DNA. Further, in our new structure of UvrA, this domain adopts an altered conformation while an adjacent nucleotide binding site is vacant. Our findings raise unanticipated questions about NER and also suggest a revised picture of its early stages.

  12. Base-pair opening and spermine binding--B-DNA features displayed in the crystal structure of a gal operon fragment: implications for protein-DNA recognition.

    PubMed Central

    Tari, L W; Secco, A S

    1995-01-01

    A sequence that is represented frequently in functionally important sites involving protein-DNA interactions is GTG/CAC, suggesting that the trimer may play a role in regulatory processes. The 2.5 A resolution structure of d(CGGTGG)/d(CCACCG), a part of the interior operator (OI, nucleotides +44 to +49) of the gal operon, co-crystallized with spermine, is described herein. The crystal packing arrangement in this structure is unprecedented in a crystal of B-DNA, revealing a close packing of columns of stacked DNA resembling a 5-stranded twisted wire cable. The final structure contains one hexamer duplex, 17 water molecules and 1.5 spermine molecules per crystallographic asymmetric unit. The hexamer exhibits base-pair opening and shearing at T.A resulting in a novel non-Watson-Crick hydrogen-bonding scheme between adenine and thymine in the GTG region. The ability of this sequence to adopt unusual conformations in its GTG region may be a critical factor conferring sequence selectivity on the binding of Gal repressor. In addition, this is the first conclusive example of a crystal structure of spermine with native B-DNA, providing insight into the mechanics of polyamine-DNA binding, as well as possible explanations for the biological action of spermine. Images PMID:7596838

  13. Searching for non-B DNA-forming motifs using nBMST (non-B DNA Motif Search Tool)

    PubMed Central

    Cer, RZ; Bruce, KH; Donohue, DE; Temiz, NA; Mudunuri, US; Yi, M; Volfovsky, N; Bacolla, A; Luke, BT; Collins; Stephens, RM

    2012-01-01

    This unit describes basic protocols on using the non-B DNA Motif Search Tool (nBMST) to search for sequence motifs predicted to form alternative DNA conformations that differ from the canonical right-handed Watson-Crick double-helix, collectively known as non-B DNA and on using the associated PolyBrowse, a GBrowse (Stein et al., 2002) based genomic browser. The nBMST is a web-based resource that allows users to submit one or more DNA sequences to search for inverted repeats (cruciform DNA), mirror repeats (triplex DNA), direct/tandem repeats (slipped/hairpin structures), G4 motifs (tetraplex, G-quadruplex DNA), alternating purine-pyrimidine tracts (left-handed Z-DNA), and Aphased repeats (static bending). Basic protocol 1 illustrates different ways of submitting sequences, the required file input format, results comprising downloadable Generic Feature Format (GFF) files, static Portable Network Graphics (PNG) images, dynamic PolyBrowse link, and accessing documentation through the Help and Frequently Asked Questions (FAQs) pages. Basic Protocol 2 illustrates a brief overview of some of the PolyBrowse functionalities, particularly with reference to possible associations between predicted non-B DNA forming motifs and disease causing effects. The nBMST is versatile, simple to use, does not require bioinformatics skills, and can be applied to any type of DNA sequences, including viral and bacterial genomes, up to 20 megabytes (MB). PMID:22470144

  14. Crystal structure and prediction.

    PubMed

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

    2015-04-01

    The notion of structure is central to the subject of chemistry. This review traces the development of the idea of crystal structure since the time when a crystal structure could be determined from a three-dimensional diffraction pattern and assesses the feasibility of computationally predicting an unknown crystal structure of a given molecule. Crystal structure prediction is of considerable fundamental and applied importance, and its successful execution is by no means a solved problem. The ease of crystal structure determination today has resulted in the availability of large numbers of crystal structures of higher-energy polymorphs and pseudopolymorphs. These structural libraries lead to the concept of a crystal structure landscape. A crystal structure of a compound may accordingly be taken as a data point in such a landscape. PMID:25422850

  15. 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.

  16. Protein structure prediction.

    PubMed

    Al-Lazikani, B; Jung, J; Xiang, Z; Honig, B

    2001-02-01

    The prediction of protein structure, based primarily on sequence and structure homology, has become an increasingly important activity. Homology models have become more accurate and their range of applicability has increased. Progress has come, in part, from the flood of sequence and structure information that has appeared over the past few years, and also from improvements in analysis tools. These include profile methods for sequence searches, the use of three-dimensional structure information in sequence alignment and new homology modeling tools, specifically in the prediction of loop and side-chain conformations. There have also been important advances in understanding the physical chemical basis of protein stability and the corresponding use of physical chemical potential functions to identify correctly folded from incorrectly folded protein conformations. PMID:11166648

  17. On predicting secondary structure transition.

    PubMed

    Loganantharaj, Raja; Philip, Vivek

    2007-01-01

    A function of a protein is dependent on its structure; therefore, predicting a protein structure from an amino acid sequence is an active area of research. To improve the accuracy of validation of structures, we are studying the predictability of secondary structure transitions using the following machine learning algorithms: naive Bayes, C4.5 decision tree, and random forest. The annotated data sets from PDB that have agreement with DSSP and STRIDE are used for training and testing. We have demonstrated that predicting structure transition with high degree of certainty is possible and we were able to get as high as 97.5% of prediction accuracy. PMID:18048311

  18. An improved divergent synthesis of comb-type branched oligodeoxyribonucleotides (bDNA) containing multiple secondary sequences.

    PubMed Central

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

    1997-01-01

    The divergent synthesis of branched DNA (bDNA) comb structures is described. This new type of bDNA contains one unique oligonucleotide, the primary sequence, covalently attached through a comb-like branch network to many identical copies of a different oligonucleotide, the secondary sequence. The bDNA comb structures were assembled on a solid support and several synthesis parameters were investigated and optimized. The bDNA comb molecules were characterized by polyacrylamide gel electrophoretic methods and by controlled cleavage at periodate-cleavable moieties incorporated during synthesis. The developed chemistry allows synthesis of bDNA comb molecules containing multiple secondary sequences. In the accompanying article we describe the synthesis and characterization of large bDNA combs containing all four deoxynucleotides for use as signal amplifiers in nucleic acid quantification assays. PMID:9365265

  19. Protein Structure Prediction Jayanthi Sourirajan

    E-print Network

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

  20. 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

  1. Structure prediction of membrane proteins.

    PubMed

    Zhou, Chunlong; Zheng, Yao; Zhou, Yan

    2004-02-01

    There is a large gap between the number of membrane protein (MP) sequences and that of their decoded 3D structures, especially high-resolution structures, due to difficulties in crystal preparation of MPs. However, detailed knowledge of the 3D structure is required for the fundamental understanding of the function of an MP and the interactions between the protein and its inhibitors or activators. In this paper, some computational approaches that have been used to predict MP structures are discussed and compared. PMID:15629037

  2. 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.

  3. Local protein structure prediction using discriminative models

    PubMed Central

    Sander, Oliver; Sommer, Ingolf; Lengauer, Thomas

    2006-01-01

    Background In recent years protein structure prediction methods using local structure information have shown promising improvements. The quality of new fold predictions has risen significantly and in fold recognition incorporation of local structure predictions led to improvements in the accuracy of results. We developed a local structure prediction method to be integrated into either fold recognition or new fold prediction methods. For each local sequence window of a protein sequence the method predicts probability estimates for the sequence to attain particular local structures from a set of predefined local structure candidates. The first step is to define a set of local structure representatives based on clustering recurrent local structures. In the second step a discriminative model is trained to predict the local structure representative given local sequence information. Results The step of clustering local structures yields an average RMSD quantization error of 1.19 Ĺ for 27 structural representatives (for a fragment length of 7 residues). In the prediction step the area under the ROC curve for detection of the 27 classes ranges from 0.68 to 0.88. Conclusion The described method yields probability estimates for local protein structure candidates, giving signals for all kinds of local structure. These local structure predictions can be incorporated either into fold recognition algorithms to improve alignment quality and the overall prediction accuracy or into new fold prediction methods. PMID:16405736

  4. Theoretical prediction of crystal structures of rubrene

    NASA Astrophysics Data System (ADS)

    Obata, Shigeaki; Miura, Toshiaki; Shimoi, Yukihiro

    2014-01-01

    We theoretically predict crystal structures and molecular arrangements for rubrene molecule using CONFLEX program and compare them with the experimental ones. The most, second-most, and fourth-most stable predicted crystal structures show good agreement with the triclinic, orthorhombic, and monoclinic polymorphs of rubrene, respectively. The change in molecular conformation is also predicted between crystalline and gas phases: the tetracene backbone takes flat conformation in crystalline phase as in the observed structure. Meanwhile, it is twisted in gas phase. The theoretical prediction method used in this work provides the successful results on the determination of the three kinds of crystal structures and molecular arrangements for rubrene molecule.

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

    PubMed

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

    2015-12-15

    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

  6. Protein structure prediction using hybrid AI methods

    SciTech Connect

    Guan, X.; Mural, R.J.; Uberbacher, E.C.

    1993-11-01

    This paper describes a new approach for predicting protein structures based on Artificial Intelligence methods and genetic algorithms. We combine nearest neighbor searching algorithms, neural networks, heuristic rules and genetic algorithms to form an integrated system to predict protein structures from their primary amino acid sequences. First we describe our methods and how they are integrated, and then apply our methods to several protein sequences. The results are very close to the real structures obtained by crystallography. Parallel genetic algorithms are also implemented.

  7. Protein Structure Prediction by Tempering Spatial Constraints

    PubMed Central

    2006-01-01

    Summary The probability to predict correctly a protein structure can be enhanced through introduction of spatial constraints - either from NMR experiments or from homologous structures. However, the additional constraints lead often to new local energy minima and worse sampling efficiency in simulations. In this work we present a new parallel tempering variant that alleviates the energy barriers resulting from spatial constraints and therefore yields to an enhanced sampling in structure prediction simulations. PMID:16267688

  8. Secondary structural predictions for the clostridial neurotoxins.

    PubMed

    Lebeda, F J; Olson, M A

    1994-12-01

    The primary structures of a family of ten clostridial neurotoxins have recently been deduced yet little information is presently available concerning their secondary or tertiary structures. Because the overall similarity percentage of multiply aligned sequences is high, the secondary structures of these metalloendopeptidases are also expected to be conserved. The neural net program, PHD (Rost and Sander, Proc. Natl. Acad. Sci. USA 90:7558-7562, 1993), predicted that the secondary structures of the neurotoxins were indeed conserved in both single and multiple sequence modes of analysis. Predictions for the amounts of helical, extended, and loop states from the single sequence analyses were consistent with previously published data from circular dichroism studies on some of these neurotoxins. In the single analysis mode, only the aligned regions were predicted to show conservation of the three-state structure. In contrast, the multiple sequence analysis predicted that a conserved state (variable loops) also exists in non-aligned regions. Alignments with the primary structure of the prototypic metalloendopeptidase thermolysin showed that about 25% of the residues within this enzyme are similar to those in the neurotoxins. A comparison of thermolysin's known secondary structure with the predictions from this study showed that about 80% of thermolysin's residues could be structurally aligned with those in the neurotoxins. These predictions provide the necessary framework to build a homologous low-resolution tertiary structure of the neurotoxin active site that will be essential in the development of synthetic inhibitors. PMID:7731948

  9. A physical approach to protein structure prediction.

    PubMed

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

    2002-01-01

    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 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. PMID:11751294

  10. Protein secondary structure prediction with SPARROW.

    PubMed

    Bettella, Francesco; Rasinski, Dawid; Knapp, Ernst Walter

    2012-02-27

    A first step toward predicting the structure of a protein is to determine its secondary structure. The secondary structure information is generally used as starting point to solve protein crystal structures. In the present study, a machine learning approach based on a complete set of two-class scoring functions was used. Such functions discriminate between two specific structural classes or between a single specific class and the rest. The approach uses a hierarchical scheme of scoring functions and a neural network. The parameters are determined by optimizing the recall of learning data. Quality control is performed by predicting separate independent test data. A first set of scoring functions is trained to correlate the secondary structures of residues with profiles of sequence windows of width 15, centered at these residues. The sequence profiles are obtained by multiple sequence alignment with PSI-BLAST. A second set of scoring functions is trained to correlate the secondary structures of the center residues with the secondary structures of all other residues in the sequence windows used in the first step. Finally, a neural network is trained using the results from the second set of scoring functions as input to make a decision on the secondary structure class of the residue in the center of the sequence window. Here, we consider the three-class problem of helix, strand, and other secondary structures. The corresponding prediction scheme "SPARROW" was trained with the ASTRAL40 database, which contains protein domain structures with less than 40% sequence identity. The secondary structures were determined with DSSP. In a loose assignment, the helix class contains all DSSP helix types (?, 3-10, ?), the strand class contains ?-strand and ?-bridge, and the third class contains the other structures. In a tight assignment, the helix and strand classes contain only ?-helix and ?-strand classes, respectively. A 10-fold cross validation showed less than 0.8% deviation in the fraction of correct structure assignments between true prediction and recall of data used for training. Using sequences of 140,000 residues as a test data set, 80.46% ± 0.35% of secondary structures are predicted correctly in the loose assignment, a prediction performance, which is very close to the best results in the field. Most applications are done with the loose assignment. However, the tight assignment yields 2.25% better prediction performance. With each individual prediction, we also provide a confidence measure providing the probability that the prediction is correct. The SPARROW software can be used and downloaded on the Web page http://agknapp.chemie.fu-berlin.de/sparrow/ . PMID:22224407

  11. Sparsification of RNA structure prediction including pseudoknots

    E-print Network

    Mohl, Mathias

    Abstract Background Although many RNA molecules contain pseudoknots, computational prediction of pseudoknotted RNA structure is still in its infancy due to high running time and space consumption implied by the dynamic ...

  12. Interface Structure Prediction from First-Principles

    SciTech Connect

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

    2014-05-08

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

  13. Protein secondary structure: category assignment and predictability.

    PubMed

    Andersen, C A; Bohr, H; Brunak, S

    2001-10-19

    In the last decade, the prediction of protein secondary structure has been optimized using essentially one and the same assignment scheme known as DSSP. We present here a different scheme, which is more predictable. This scheme predicts directly the hydrogen bonds, which stabilize the secondary structures. Single sequence prediction of the new three category assignment gives an overall prediction improvement of 3.1% and 5.1% compared to the DSSP assignment and schemes where the helix category consists of alpha-helix and 3(10)-helix, respectively. These results were achieved using a standard feed-forward neural network with one hidden layer on a data set identical to the one used in earlier work. PMID:11682049

  14. Predicting complex mineral structures using genetic algorithms

    NASA Astrophysics Data System (ADS)

    Mohn, Chris E.; Kob, Walter

    2015-10-01

    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.

  15. Characteristics and prediction of RNA structure.

    PubMed

    Li, Hengwu; 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

  16. 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

  17. 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 ? = 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.

  18. Predicting protein dynamics from structural ensembles.

    PubMed

    Copperman, J; Guenza, M G

    2015-12-28

    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 ? = 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. PMID:26723616

  19. 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.

  20. Significant role of the DNA backbone in mediating the transition origin of electronic excitations of B-DNA--implication from long range corrected TDDFT and quantified NTO analysis.

    PubMed

    Li, Jian-Hao; Chai, Jeng-Da; Guo, Guang-Yu; Hayashi, Michitoshi

    2012-07-01

    We systematically investigate the possible complex transition origin of electronic excitations of giant molecular systems by using the recently proposed QNTO analysis [J.-H. Li, J.-D. Chai, G. Y. Guo and M. Hayashi, Chem. Phys. Lett., 2011, 514, 362.] combined with long-range corrected TDDFT calculations. Thymine (Thy) related excitations of a B-DNA biomolecule are then studied as examples, where the model systems have been constructed by extracting from the perfect or an X-ray crystal (PDB code 3BSE) B-DNA structure with at least one Thy included. In the first part, we consider the systems composed of a core molecular segment (e.g. Thy, or di-Thy) and a surrounding physical/chemical environment of interest (e.g. backbone, adjacent stacking nucleobases) in gas phase and examine how the excitation properties of the core vary in response to the environment. We find that the orbitals contributed by the DNA backbone and surrounding nucleobases often participate in a transition of Thy-related excitations affecting their composition, absorption energy, and oscillator strength. A vast number of strongly backbone-orbital involved excitations are also found at an absorption wavelength below ?180 nm predicted by TD-?B97X. In the second part, we take into account geometrically induced variation of the excitation properties of various B-DNA segments, e.g. di-Thy, dTpdT etc., obtained from different sources (ideal and 3BSE). It is found that the transition origin of several Thy-related excitations of these segments is sensitive to slight conformational variations, suggesting that DNA with thermal motions may from time to time exhibit very different photo-induced physical and/or chemical processes. PMID:22641198

  1. 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.

  2. Dynamic matching algorithm for viral structure prediction.

    PubMed

    Li, Hengwu; Zhu, Daming; Zhang, Caiming; Liu, Zhengdong; Han, Huijian; Xu, Zhenzhong

    2014-07-01

    Most viruses have RNA genomes, their biological functions are expressed more by folded architecture than by sequence. Among the various RNA structures, pseudoknots are the most typical. In general, RNA secondary structures prediction doesn't contain pseudoknots because of its difficulty in modeling. Here we present an algorithm of dynamic matching to predict RNA secondary structures with pseudoknots by combining the merits of comparative and thermodynamic approaches. We have tested and verified our algorithm on some viral RNA. Comparisons show that our algorithm and loop matching method has similar accuracy and time complexity, and are more sensitive than the maximum weighted matching method and Rivas algorithm. Among the four methods, our algorithm has the best prediction specificity. The results show that our algorithm is more reliable and efficient than the other methods. PMID:25016258

  3. 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.

  4. Bayesian Nonparametric Methods for Protein Structure Prediction 

    E-print Network

    Lennox, Kristin Patricia

    2011-10-21

    distributions, they are considered to be clustered together. Hence, we are discussing a kind of mixture model with a draw from a Dirichlet process serving as a prior on mixture components. Note that some authors follow the convention of Antoniak in referring... of Protein Structure . . . . . . . . . 22 2.6 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 III A DIRICHLET PROCESS MIXTURE OF HIDDEN MARKOV MODELS FOR PROTEIN STRUCTURE PREDICTION : : : : : : 34 3.1 Introduction...

  5. 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.

  6. Structure-Based Predictions of Activity Cliffs

    PubMed Central

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

    2015-01-01

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

  7. Protein Structure Prediction with Lattice Models

    E-print Network

    Newman, Alantha

    that catalyze most cellular biochemical reactions. Amino acids are joined end-to-end during protein synthesis1 Protein Structure Prediction with Lattice Models William E. Hart Sandia National Laboratories ............................................ 1-21 1.1 Introduction A protein is a complex biological macromolecule composed of a sequence

  8. Gene structure prediction by linguistic methods

    SciTech Connect

    Dong, S.; Searls, D.B.

    1994-10-01

    The higher-order structure of genes and other features of biological sequences can be described by means of formal grammars. These grammars can then be used by general-purpose parsers to detect and to assemble such structures by means of syntactic pattern recognition. We describe a grammar and parser for eukaryotic protein-encoding genes, which by some measures is as effective as current connectionist and combinatorial algorithms in predicting gene structures for sequence database entries. Parameters of the grammar rules are optimized for several different species, and mixing experiments are performed to determine the degree of species specificity and the relative importance of compositional, signal-based, and syntactic components in gene prediction. 24 refs., 5 figs., 3 tabs.

  9. Predicting protein dynamics from structural ensembles

    E-print Network

    Copperman, J

    2015-01-01

    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 e...

  10. 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

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

    PubMed Central

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

    2014-01-01

    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. PMID:25494770

  12. 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.

  13. 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.

  14. RNAstructure: Web servers for RNA secondary structure prediction and analysis.

    PubMed

    Bellaousov, Stanislav; Reuter, Jessica S; Seetin, Matthew G; Mathews, David H

    2013-07-01

    RNAstructure is a software package for RNA secondary structure prediction and analysis. This contribution describes a new set of web servers to provide its functionality. The web server offers RNA secondary structure prediction, including free energy minimization, maximum expected accuracy structure prediction and pseudoknot prediction. Bimolecular secondary structure prediction is also provided. Additionally, the server can predict secondary structures conserved in either two homologs or more than two homologs. Folding free energy changes can be predicted for a given RNA structure using nearest neighbor rules. Secondary structures can be compared using circular plots or the scoring methods, sensitivity and positive predictive value. Additionally, structure drawings can be rendered as SVG, postscript, jpeg or pdf. The web server is freely available for public use at: http://rna.urmc.rochester.edu/RNAstructureWeb. PMID:23620284

  15. 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 0.4Ĺ error margin. PMID:26335807

  16. A scoring framework for predicting protein structures

    NASA Astrophysics Data System (ADS)

    Zou, Xiaoqin

    2013-03-01

    We have developed a statistical mechanics-based iterative method to extract statistical atomic interaction potentials from known, non-redundant protein structures. Our method circumvents the long-standing reference state problem in deriving traditional knowledge-based scoring functions, by using rapid iterations through a physical, global convergence function. The rapid convergence of this physics-based method, unlike other parameter optimization methods, warrants the feasibility of deriving distance-dependent, all-atom statistical potentials to keep the scoring accuracy. The derived potentials, referred to as ITScore/Pro, have been validated using three diverse benchmarks: the high-resolution decoy set, the AMBER benchmark decoy set, and the CASP8 decoy set. Significant improvement in performance has been achieved. Finally, comparisons between the potentials of our model and potentials of a knowledge-based scoring function with a randomized reference state have revealed the reason for the better performance of our scoring function, which could provide useful insight into the development of other physical scoring functions. The potentials developed in the present study are generally applicable for structural selection in protein structure prediction.

  17. 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

  18. SCFGs in RNA secondary structure prediction RNA secondary structure prediction: a hands-on approach.

    PubMed

    Sükösd, Zsuzsanna; Andersen, Ebbe S; Lyngsř, Rune

    2014-01-01

    Stochastic context-free grammars (SCFGs) were first established in the context of natural language modelling, and only later found their applications in RNA secondary structure prediction. In this chapter, we discuss the basic SCFG algorithms (CYK and inside-outside algorithms) in an application-centered manner and use the pfold grammar as a case study to show how the algorithms can be adapted to a grammar in a nonstandard form. We extend our discussion to the use of grammars with additional information (such as evolutionary information) to improve the quality of predictions. Finally, we provide a brief survey of programs that use stochastic context-free grammars for RNA secondary structure prediction and modelling. PMID:24639159

  19. 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.

  20. Displacements of backbone vibrational modes of A-DNA and B-DNA.

    PubMed Central

    Lu, K C; Van Zandt, L L; Prohofsky, E W

    1979-01-01

    We display the displacement vectors or eigenvectors of calculations of the A- and B-DNA backbones. These calculations are based on a refinement scheme that simultaneously fit several backbone modes of A-DNA, B-DNA, and A-RNA. We discuss the role of symmetry operations in mode calculations and the relevance of these displacement vectors to the interpretation of linear dichroism measurements performed on the A- and B-DNA helix. PMID:262445

  1. Both intra- and interstrand charge-transfer excited states in aqueous B-DNA are present at energies comparable to, or just above, the (1)pipi* excitonic bright states.

    PubMed

    Lange, Adrian W; Herbert, John M

    2009-03-25

    Vertical electronic excitations in model systems representing single- and double-stranded B-DNA are characterized using electronic structure theory, including both time-dependent density functional theory (TD-DFT) and correlated wave function techniques. Previous TD-DFT predictions of charge-transfer (CT) states well below the optically bright (1)pipi* states are shown to be artifacts of the improper long-range behavior of standard density-functional exchange approximations, which we rectify here using a long-range correction (LRC) procedure. For nucleobase dimers (hydrogen-bonded or pi-stacked), TD-LRC-DFT affords vertical excitation energies in reasonable agreement with the wave function methods, not only for the (1)npi* and (1)pipi* states but also for the CT states, and qualitatively reproduces well-known base-stacking effects on the absorption spectrum of DNA. The emergence of (1)pipi* Frenkel exciton states, localized on a single strand, is clearly evident, and these states (rather than low-energy CT states) are primarily responsible for the fact that DNA's absorption spectrum exhibits a red tail that is absent in monomer absorption spectra. For B-DNA in aqueous solution, the low-energy tail of the CT band (representing both intra- and interstrand CT states) appears at energies comparable to those of the optically bright (1)pipi* exciton states. In systems with more than one base pair, we also observe the emergence of delocalized, interstrand CT excitations, whose excitation energies may be significantly lower than the lowest CT excitation in a single base pair. Together, these observations suggest that a single Watson-Crick base pair is an inadequate model of the photophysics of B-DNA. PMID:19292489

  2. Functional Inferences from Blind ab Initio Protein Structure Predictions

    E-print Network

    Baker, David

    Functional Inferences from Blind ab Initio Protein Structure Predictions Richard Bonneau, Jerry-resolution predicted structures using examples from blind ab initio structure pre- dictions from the third and fourth (Sippl et al., 1999; Venclovas et al., 1999). The results from these first two blind struc- ture

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

    E-print Network

    , subsequently, for drug and vaccine design. Unfortunately, protein sequence data exceed structural data statistics. The structures of the individual domains are then predicted using template-based modeling or abPSPP: A Protein Structure Prediction Pipeline for Computing Clusters Michael S. Lee1,2,3 , Rajkumar

  4. RNA-SSPT: RNA Secondary Structure Prediction Tools

    PubMed Central

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

    2013-01-01

    The prediction of RNA structure is useful for understanding evolution for both in silico and in vitro studies. Physical methods like NMR studies to predict RNA secondary structure are expensive and difficult. Computational RNA secondary structure prediction is easier. Comparative sequence analysis provides the best solution. But secondary structure prediction of a single RNA sequence is challenging. RNA-SSPT is a tool that computationally predicts secondary structure of a single RNA sequence. Most of the RNA secondary structure prediction tools do not allow pseudoknots in the structure or are unable to locate them. Nussinov dynamic programming algorithm has been implemented in RNA-SSPT. The current studies shows only energetically most favorable secondary structure is required and the algorithm modification is also available that produces base pairs to lower the total free energy of the secondary structure. For visualization of RNA secondary structure, NAVIEW in C language is used and modified in C# for tool requirement. RNA-SSPT is built in C# using Dot Net 2.0 in Microsoft Visual Studio 2005 Professional edition. The accuracy of RNA-SSPT is tested in terms of Sensitivity and Positive Predicted Value. It is a tool which serves both secondary structure prediction and secondary structure visualization purposes. PMID:24250115

  5. SOPMA: significant improvements in protein secondary structure prediction by consensus prediction from multiple alignments.

    PubMed

    Geourjon, C; Deléage, G

    1995-12-01

    Recently a new method called the self-optimized prediction method (SOPM) has been described to improve the success rate in the prediction of the secondary structure of proteins. In this paper we report improvements brought about by predicting all the sequences of a set of aligned proteins belonging to the same family. This improved SOPM method (SOPMA) correctly predicts 69.5% of amino acids for a three-state description of the secondary structure (alpha-helix, beta-sheet and coil) in a whole database containing 126 chains of non-homologous (less than 25% identity) proteins. Joint prediction with SOPMA and a neural networks method (PHD) correctly predicts 82.2% of residues for 74% of co-predicted amino acids. Predictions are available by Email to deleage@ibcp.fr or on a Web page (http:@www.ibcp.fr/predict.html). PMID:8808585

  6. General Approach in Protein Structural Prediction Flowchart Robert Russell 1999

    E-print Network

    Brutlag, Doug

    #12;#12;General Approach in Protein Structural Prediction Flowchart ©Robert Russell 1999 #12 adgkvsqnypivqnlqgqmvhqaisprtlnawvkvieekafspevipm #12;General Approach in Protein Structural Prediction Flowchart ©Robert Russell 1999 #12 or methionine, followed by any amino acid (x), followed by glycine,... [etc.]" ( Robert Russell http

  7. Training Structured Prediction Models with Extrinsic Loss Functions

    E-print Network

    Cortes, Corinna

    Training Structured Prediction Models with Extrinsic Loss Functions Keith Hall Ryan McDonald Slav for training structured prediction mod- els with extrinsic loss functions. This allows us to extend a standard with a distinct dataset. Our augmented-loss training framework (Section 2) simply iterates over the various loss-functions

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

    E-print Network

    Todorov, Alex

    Automatic Prediction of Facial Trait Judgments: Appearance vs. Structural Models Mario Rojas Q.1 characteristics plays a crucial role in human relations and it is the focus of attention for research in diverse Prediction of Facial Trait Judgments: Appearance vs. Structural Models. PLoS ONE 6(8): e23323. doi:10

  9. A Coprocessor Architecture for Fast Protein Structure Prediction

    E-print Network

    Kambhampati, Subbarao

    to develop faster and simpler techniques for protein structure prediction. There are numerous methods [2 template matching and use various fold recognition techniques to predict the tertiary structural model accurate PSIPRED algorithm [6] which is based on PSI-BLAST (Position Specific Iterated BLAST) [7]. It uses

  10. 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

  11. 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.

  12. Bayesian Structured Prediction using Gaussian Processes

    E-print Network

    Bratičres, Sébastien; Quadrianto, Novi; Ghahramani, Zoubin

    2014-10-31

    , and Michael I. Jordan. Loopy belief propagation for approximate inference: An empirical study. In UAI, 1999. [17] Hannes Nickisch. Approximations for Binary Gaussian Pro- cess Classification. JMLR, 2008. [18] Iain Murray, Ryan P. Adams, and David J. C. Mac... very limited impact on the error rate, cf. figure 3 (middle). Similarly, how many samples f?|f do we need? Do we need any at all, or could we use only the mean of the predictive posterior? This would save computing the predictive variance, which...

  13. 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…

  14. 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.

  15. Protein Structure and Function Prediction Using I-TASSER.

    PubMed

    Yang, Jianyi; Zhang, Yang

    2015-01-01

    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. © 2015 by John Wiley & Sons, Inc. PMID:26678386

  16. 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

  17. Can Morphing Methods Predict Intermediate Structures?

    PubMed Central

    Weiss, Dahlia R.; Levitt, Michael

    2009-01-01

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

  18. Graphlet Kernels for Prediction of Functional Residues in Protein Structures

    E-print Network

    Lonardi, Stefano

    Graphlet Kernels for Prediction of Functional Residues in Protein Structures VLADIMIR VACIC,1 LILIA-based kernel method for annotating functional residues in protein structures. A structure is first modeled as a protein contact graph, where nodes correspond to residues and edges connect spatially neighboring residues

  19. High-resolution structure prediction and the crystallographic phase problem

    E-print Network

    Das, Rhiju

    The energy-based refinement of low-resolution protein structure models to atomic-level accuracy is a major structure to relax in a physically realistic all-atom force field. In applications to models produced usingARTICLES High-resolution structure prediction and the crystallographic phase problem Bin Qian1

  20. Structure of Jupiter's upper atmosphere: Predictions for Galileo

    E-print Network

    Young, Leslie A.

    1 1 Structure of Jupiter's upper atmosphere: Predictions for Galileo Roger V. Yelle1,3, Leslie A information on the thermal structure of Jupiter's upper atmosphere that bear on this outstanding problem. We present an analysis of Jupiter's thermal structure using constraints from H3 + emissions, Voyager UVS

  1. An Atomic Environment Potential for use in Protein Structure Prediction

    E-print Network

    Summa, Christopher M.

    An Atomic Environment Potential for use in Protein Structure Prediction Christopher M. Summa1 of a knowledge-based atomic environment potential for the modeling of protein structural energetics. An analysis of the probabilities of atomic interactions in a dataset of high- resolution protein structures shows

  2. Prediction of Local Structure in Proteins Using a Library of Sequence-Structure Motifs

    E-print Network

    Bystroff, Chris

    Prediction of Local Structure in Proteins Using a Library of Sequence-Structure Motifs Christopher We describe a new method for local protein structure prediction based on a library of short sequence pattern that correlate strongly with protein three-dimensional structural elements. The library

  3. General overview on structure prediction of twilight-zone proteins.

    PubMed

    Khor, Bee Yin; Tye, Gee Jun; Lim, Theam Soon; Choong, Yee Siew

    2015-01-01

    Protein structure prediction from amino acid sequence has been one of the most challenging aspects in computational structural biology despite significant progress in recent years showed by critical assessment of protein structure prediction (CASP) experiments. When experimentally determined structures are unavailable, the predictive structures may serve as starting points to study a protein. If the target protein consists of homologous region, high-resolution (typically <1.5 Ĺ) model can be built via comparative modelling. However, when confronted with low sequence similarity of the target protein (also known as twilight-zone protein, sequence identity with available templates is less than 30%), the protein structure prediction has to be initiated from scratch. Traditionally, twilight-zone proteins can be predicted via threading or ab initio method. Based on the current trend, combination of different methods brings an improved success in the prediction of twilight-zone proteins. In this mini review, the methods, progresses and challenges for the prediction of twilight-zone proteins were discussed. PMID:26338054

  4. WeFold: a coopetition for protein structure prediction.

    PubMed

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

    2014-09-01

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

  5. 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...

  6. A machine learning approach to crystal structure prediction

    E-print Network

    Fischer, Christopher Carl

    2007-01-01

    This thesis develops a machine learning framework for predicting crystal structure and applies it to binary metallic alloys. As computational materials science turns a promising eye towards design, routine encounters with ...

  7. 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

  8. Evaluation of predictive accuracy in structural dynamic models

    NASA Technical Reports Server (NTRS)

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

    1989-01-01

    The evaluation of the predictive accuracy of dynamic models for future large space structures is addressed. Mass and stiffness uncertainties derived from a comparison of analytical and experimental modes are used to evaluate the uncertainty of response predictions based on the analytical model.

  9. JPred4: a protein secondary structure prediction server

    PubMed Central

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

    2015-01-01

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

  10. JPred4: a protein secondary structure prediction server.

    PubMed

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

    2015-07-01

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

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

    E-print Network

    Roy, Ambrish

    2011-12-31

    relatives from sequence alone becomes difficult. Thankfully, even after many changes at the sequence level, the protein three-dimensional structures are often conserved and hence protein structural similarity usually provide more clues on evolution...

  12. 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.

  13. GSAFold: a new application of GSA to protein structure prediction.

    PubMed

    Melo, Marcelo C R; Bernardi, Rafael C; Fernandes, Tácio V A; Pascutti, Pedro G

    2012-08-01

    The folding process defines three-dimensional protein structures from their amino acid chains. A protein's structure determines its activity and properties; thus knowing such conformation on an atomic level is essential for both basic and applied studies of protein function and dynamics. However, the acquisition of such structures by experimental methods is slow and expensive, and current computational methods mostly depend on previously known structures to determine new ones. Here we present a new software called GSAFold that applies the generalized simulated annealing (GSA) algorithm on ab initio protein structure prediction. The GSA is a stochastic search algorithm employed in energy minimization and used in global optimization problems, especially those that depend on long-range interactions, such as gravity models and conformation optimization of small molecules. This new implementation applies, for the first time in ab initio protein structure prediction, an analytical inverse for the Visitation function of GSA. It also employs the broadly used NAMD Molecular Dynamics package to carry out energy calculations, allowing the user to select different force fields and parameterizations. Moreover, the software also allows the execution of several simulations simultaneously. Applications that depend on protein structures include rational drug design and structure-based protein function prediction. Applying GSAFold in a test peptide, it was possible to predict the structure of mastoparan-X to a root mean square deviation of 3.00 Ĺ. PMID:22622959

  14. Prediction of Conserved and Consensus RNA Structures

    E-print Network

    Wien, Universität

    ;Conserved Structures in Picornaviruses 2B 2C 3A 3B 3C 3D 1A 2A 2B 2C 3D3A3B 3C AAA HEPATOVIRUS 1B 1D1C 2B2A1D1C1B1A 3D3C3A3B2C AAA ENTEROVIRUS AAA 2B 3B 3C 3D2C2A 3A1B 1C 1D1A RHINOVIRUS 1C 1D AAA 2A 2B 2C 3A

  15. Computational methods in sequence and structure prediction

    NASA Astrophysics Data System (ADS)

    Lang, Caiyi

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

  16. proteinsSTRUCTURE O FUNCTION O BIOINFORMATICS De novo protein structure prediction by

    E-print Network

    Lee, Jooyoung

    . In this article, we present an ab initio structure prediction method which combines a recently suggested novel wayproteinsSTRUCTURE O FUNCTION O BIOINFORMATICS De novo protein structure prediction by dynamic Masaki Sasai,2,4 Chaok Seok,1,2 * and Jooyoung Lee2 * 1 Department of Chemistry, Seoul National

  17. A comprehensive analysis of 40 blind protein structure predictions

    PubMed Central

    Samudrala, Ram; Levitt, Michael

    2002-01-01

    Background We thoroughly analyse the results of 40 blind predictions for which an experimental answer was made available at the fourth meeting on the critical assessment of protein structure methods (CASP4). Using our comparative modelling and fold recognition methodologies, we made 29 predictions for targets that had sequence identities ranging from 50% to 10% to the nearest related protein with known structure. Using our ab initio methodologies, we made eleven predictions for targets that had no detectable sequence relationships. Results For 23 of these proteins, we produced models ranging from 1.0 to 6.0 Ĺ root mean square deviation (RMSD) for the C? atoms between the model and the corresponding experimental structure for all or large parts of the protein, with model accuracies scaling fairly linearly with respect to sequence identity (i.e., the higher the sequence identity, the better the prediction). We produced nine models with accuracies ranging from 4.0 to 6.0 Ĺ C? RMSD for 60–100 residue proteins (or large fragments of a protein), with a prediction accuracy of 4.0 Ĺ C? RMSD for residues 1–80 for T110/rbfa. Conclusions The areas of protein structure prediction that work well, and areas that need improvement, are discernable by examining how our methods have performed over the past four CASP experiments. These results have implications for modelling the structure of all tractable proteins encoded by the genome of an organism. PMID:12150712

  18. Prediction of Complete Gene Structures in Human Genomic DNA

    E-print Network

    Shatkay, Hagit

    and RNA splicing. On the other hand, with the recent shift in the emphasis of the Human Genome ProjectPrediction of Complete Gene Structures in Human Genomic DNA Chris Burge* and Samuel Karlin model of the gene structure of human genomic sequences which incorporates descriptions of the basic

  19. Predicting equilibrium structures in freezing processes Dieter Gottwald

    E-print Network

    Likos, Christos N.

    Predicting equilibrium structures in freezing processes Dieter Gottwald Center for Computational candidate structures into which a simple fluid can freeze. In contrast to the conventional approach where American Institute of Physics. DOI: 10.1063/1.1901585 I. INTRODUCTION The freezing behavior of simple

  20. Representations of Structural Motifs for Protein Function Prediction

    E-print Network

    Moll, Mark

    Representations of Structural Motifs for Protein Function Prediction Brian Y. Chen1, Drew H. Bryant, Marek Kimmel4, Olivier Lichtarge4,5, Lydia E. Kavraki1,2,5 1Department of Computer Science, Rice University, 2Department of Bioengineering, Rice University, 3Program in Structural and Computational Biology

  1. Bayesian Network Multi-classifiers for Protein Secondary Structure Prediction

    E-print Network

    Grosu, Radu

    and evolutionary in- formation from the divergence of proteins in the same structural family. At the alignmentBayesian Network Multi-classifiers for Protein Secondary Structure Prediction V´ictor Robles a , Pedro Larra~naga b , Jos´e M. Pe~na a , Ernestina Menasalvas a , Mar´ia S. P´erez a , Vanessa Herves

  2. The prediction of protein structural class using averaged chemical shifts.

    PubMed

    Lin, Hao; Ding, Chen; Song, Qiang; Yang, Ping; Ding, Hui; Deng, Ke-Jun; Chen, Wei

    2012-01-01

    Knowledge of protein structural class can provide important information about its folding patterns. Many approaches have been developed for the prediction of protein structural classes. However, the information used by these approaches is primarily based on amino acid sequences. In this study, a novel method is presented to predict protein structural classes by use of chemical shift (CS) information derived from nuclear magnetic resonance spectra. Firstly, 399 non-homologue (about 15% identity) proteins were constructed to investigate the distribution of averaged CS values of six nuclei ((13)CO, (13)C?, (13)C?, (1)HN, (1)H? and (15)N) in three protein structural classes. Subsequently, support vector machine was proposed to predict three protein structural classes by using averaged CS information of six nuclei. Overall accuracy of jackknife cross-validation achieves 87.0%. Finally, the feature selection technique is applied to exclude redundant information and find out an optimized feature set. Results show that the overall accuracy increased to 88.0% by using the averaged CSs of (13)CO, (1)H? and (15)N. The proposed approach outperformed other state-of-the-art methods in terms of predictive accuracy in particular for low-similarity protein data. We expect that our proposed approach will be an excellent alternative to traditional methods for protein structural class prediction. PMID:22545995

  3. Protein structure prediction enhanced with evolutionary diversity: SPEED

    PubMed Central

    DeBartolo, Joe; Hocky, Glen; Wilde, Michael; Xu, Jinbo; Freed, Karl F; Sosnick, Tobin R

    2010-01-01

    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 ?,? 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. PMID:20066664

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

    PubMed

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

    2015-01-01

    Beyond the "traditional" functions such as gene storage, transport and protein synthesis, recent discoveries reveal that RNAs have important "new" biological functions including the RNA silence and gene regulation of riboswitch. Such functions of noncoding RNAs are strongly coupled to the RNA structures and proper structure change, which naturally leads to the RNA folding problem including structure prediction and folding kinetics. Due to the polyanionic nature of RNAs, RNA folding structure, stability and kinetics are strongly coupled to the ion condition of solution. The main focus of this chapter is to review the recent progress in the three major aspects in RNA folding problem: structure prediction, folding kinetics and ion electrostatics. This chapter will introduce both the recent experimental and theoretical progress, while emphasize the theoretical modelling on the three aspects in RNA folding. PMID:25387965

  5. Contingency Table Browser ? prediction of early stage protein structure

    PubMed Central

    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.

  6. 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.

  7. 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

  8. 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

  9. 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.

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

    PubMed Central

    Montgomerie, Scott; Sundararaj, Shan; Gallin, Warren J; Wishart, David S

    2006-01-01

    Background The accuracy of protein secondary structure prediction has steadily improved over the past 30 years. Now many secondary structure prediction methods routinely achieve an accuracy (Q3) of about 75%. We believe this accuracy could be further improved by including structure (as opposed to sequence) database comparisons as part of the prediction process. Indeed, given the large size of the Protein Data Bank (>35,000 sequences), the probability of a newly identified sequence having a structural homologue is actually quite high. Results We have developed a method that performs structure-based sequence alignments as part of the secondary structure prediction process. By mapping the structure of a known homologue (sequence ID >25%) onto the query protein's sequence, it is possible to predict at least a portion of that query protein's secondary structure. By integrating this structural alignment approach with conventional (sequence-based) secondary structure methods and then combining it with a "jury-of-experts" system to generate a consensus result, it is possible to attain very high prediction accuracy. Using a sequence-unique test set of 1644 proteins from EVA, this new method achieves an average Q3 score of 81.3%. Extensive testing indicates this is approximately 4–5% better than any other method currently available. Assessments using non sequence-unique test sets (typical of those used in proteome annotation or structural genomics) indicate that this new method can achieve a Q3 score approaching 88%. Conclusion By using both sequence and structure databases and by exploiting the latest techniques in machine learning it is possible to routinely predict protein secondary structure with an accuracy well above 80%. A program and web server, called PROTEUS, that performs these secondary structure predictions is accessible at . For high throughput or batch sequence analyses, the PROTEUS programs, databases (and server) can be downloaded and run locally. PMID:16774686

  11. Workshop—Predicting the Structure of Biological Molecules

    PubMed Central

    2004-01-01

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

  12. Improved Chou-Fasman method for protein secondary structure prediction

    PubMed Central

    Chen, Hang; Gu, Fei; Huang, Zhengge

    2006-01-01

    Background Protein secondary structure prediction is a fundamental and important component in the analytical study of protein structure and functions. The prediction technique has been developed for several decades. The Chou-Fasman algorithm, one of the earliest methods, has been successfully applied to the prediction. However, this method has its limitations due to low accuracy, unreliable parameters, and over prediction. Thanks to the recent development in protein folding type-specific structure propensities and wavelet transformation, the shortcomings in Chou-Fasman method are able to be overcome. Results We improved Chou-Fasman method in three aspects. (a) Replace the nucleation regions with extreme values of coefficients calculated by the continuous wavelet transform. (b) Substitute the original secondary structure conformational parameters with folding type-specific secondary structure propensities. (c) Modify Chou-Fasman rules. The CB396 data set was tested by using improved Chou-Fasman method and three indices: Q3, Qpre, SOV were used to measure this method. We compared the indices with those obtained from the original Chou-Fasman method and other four popular methods. The results showed that our improved Chou-Fasman method performs better than the original one in all indices, about 10–18% improvement. It is also comparable to other currently popular methods considering all the indices. Conclusion Our method has greatly improved Chou-Fasman method. It is able to predict protein secondary structure as good as current popular methods. By locating nucleation regions with refined wavelet transform technology and by calculating propensity factors with larger size data set, it is likely to get a better result. PMID:17217506

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

    PubMed

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

    2015-07-01

    Protein complexes are involved in many biological processes. Examining coupling between subunits of a complex would be useful to understand the molecular basis of protein function. Here, our updated (PS)(2) web server predicts the three-dimensional structures of protein complexes based on comparative modeling; furthermore, this server examines the coupling between subunits of the predicted complex by combining structural and evolutionary considerations. The predicted complex structure could be indicated and visualized by Java-based 3D graphics viewers and the structural and evolutionary profiles are shown and compared chain-by-chain. For each subunit, considerations with or without the packing contribution of other subunits cause the differences in similarities between structural and evolutionary profiles, and these differences imply which form, complex or monomeric, is preferred in the biological condition for the subunit. We believe that the (PS)(2) server would be a useful tool for biologists who are interested not only in the structures of protein complexes but also in the coupling between subunits of the complexes. The (PS)(2) is freely available at http://ps2v3.life.nctu.edu.tw/. PMID:25943546

  14. Extracting physicochemical features to predict protein secondary structure.

    PubMed

    Huang, Yin-Fu; Chen, Shu-Ying

    2013-01-01

    We propose a protein secondary structure prediction method based on position-specific scoring matrix (PSSM) profiles and four physicochemical features including conformation parameters, net charges, hydrophobic, and side chain mass. First, the SVM with the optimal window size and the optimal parameters of the kernel function is found. Then, we train the SVM using the PSSM profiles generated from PSI-BLAST and the physicochemical features extracted from the CB513 data set. Finally, we use the filter to refine the predicted results from the trained SVM. For all the performance measures of our method, Q 3 reaches 79.52, SOV94 reaches 86.10, and SOV99 reaches 74.60; all the measures are higher than those of the SVMpsi method and the SVMfreq method. This validates that considering these physicochemical features in predicting protein secondary structure would exhibit better performances. PMID:23766688

  15. Constraint Techniques for Solving the Protein Structure Prediction Problem

    E-print Network

    Will, Sebastian

    are interesting if one wants to design artificial proteins (for drug design). For the time being, one problem. Examples of how lat­ tice proteins can be used for predicting the native structure or for investigating letter alphabet, namely H and P. H represents hydrophobic amino acids, whereas P represent polar

  16. 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.

  17. Conformational sampling in protein structure prediction Sabareesh Subramaniam

    E-print Network

    Senes, Alessandro

    Conformational sampling in protein structure prediction by Sabareesh Subramaniam A dissertation dreams. #12;iii contents Contents iii List of Tables v List of Figures vi Abstract xii 1 Conformational applications . . . . . . . . . 10 1.4 Conformational sampling in protein modeling . . . . . . 15 1.5 Overview

  18. Process for predicting structural performance of mechanical systems

    DOEpatents

    Gardner, David R. (Albuquerque, NM); Hendrickson, Bruce A. (Albuquerque, NM); Plimpton, Steven J. (Albuquerque, NM); Attaway, Stephen W. (Cedar Crest, NM); Heinstein, Martin W. (Albuquerque, NM); Vaughan, Courtenay T. (Albuquerque, NM)

    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.

  19. Structured Prediction for Object Detection in Deep Neural Networks

    E-print Network

    Behnke, Sven

    ,behnke}@ais.uni-bonn.de Abstract. Deep convolutional neural networks are currently applied to computer vision tasks, especially 1 Introduction After great success in image classification, neural network research has recentlyStructured Prediction for Object Detection in Deep Neural Networks Hannes Schulz and Sven Behnke

  20. Problems on RNA Secondary Structure Prediction and Design

    E-print Network

    Condon, Anne

    Problems on RNA Secondary Structure Prediction and Design Anne Condon The Department of Computer world - the Chemistry Department. This short walk was the start of a rewarding ongoing journey. Along their home in the heads of us theoreticians, there to remain indefinitely. In this article, I will describe

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

    E-print Network

    Vannucci, Marina

    but distant in sequence. By performing an assessment of our method on 2 test sets we show how incorporation of the predictions. Software implementing the methods is provided as a web application and a stand. All structural files are available from the Protein Data Bank at http://www.rcsb.org. Funding

  2. proteinsSTRUCTURE O FUNCTION O BIOINFORMATICS PREDICTION REPORT

    E-print Network

    Das, Rhiju

    75390, Texas INTRODUCTION The CASP8 experiment provided an invaluable opportunity to stress-test our new object oriented Rosetta software suite and inspired new ideas for de novo structure prediction model was better than the best alignment to the best template in the Protein Data Bank for 24 cases

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

    E-print Network

    Gong, Xingao

    lower power consumption than classical transistors. Recently, few-layer black phosphorus crystals is presented to design quasi-two-dimensional materials. With this development, various single-layer and bilayer materials of C, Si, Ge, Sn, and Pb were predicted. A new Si bilayer structure is found to have a more

  4. CRISPR revisited: structure prediction of CRISPR repeats Sita Lange1

    E-print Network

    Will, Sebastian

    CRISPR revisited: structure prediction of CRISPR repeats Sita Lange1 , Omer S. Alkhnbashi 1 Regularly Interspaced Short Palindromic Repeats (CRISPRs), illustrated to the right. The CRISPR transcripts sequences have been found to match foreign virus or plasmid DNA. A set of CRISPR-associated (Cas) proteins

  5. 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.

  6. Automatic prediction of catalytic residues by modeling residue structural neighborhood

    PubMed Central

    2010-01-01

    Background Prediction of catalytic residues is a major step in characterizing the function of enzymes. In its simpler formulation, the problem can be cast into a binary classification task at the residue level, by predicting whether the residue is directly involved in the catalytic process. The task is quite hard also when structural information is available, due to the rather wide range of roles a functional residue can play and to the large imbalance between the number of catalytic and non-catalytic residues. Results We developed an effective representation of structural information by modeling spherical regions around candidate residues, and extracting statistics on the properties of their content such as physico-chemical properties, atomic density, flexibility, presence of water molecules. We trained an SVM classifier combining our features with sequence-based information and previously developed 3D features, and compared its performance with the most recent state-of-the-art approaches on different benchmark datasets. We further analyzed the discriminant power of the information provided by the presence of heterogens in the residue neighborhood. Conclusions Our structure-based method achieves consistent improvements on all tested datasets over both sequence-based and structure-based state-of-the-art approaches. Structural neighborhood information is shown to be responsible for such results, and predicting the presence of nearby heterogens seems to be a promising direction for further improvements. PMID:20199672

  7. 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

  8. Unexpected BII conformer substate population in unoriented hydrated films of the d(CGCGAATTCGCG)2 dodecamer and of native B-DNA from salmon testes.

    PubMed Central

    Pichler, A; Rüdisser, S; Mitterböck, M; Huber, C G; Winger, R H; Liedl, K R; Hallbrucker, A; Mayer, E

    1999-01-01

    Conformational substates of B-DNA had been observed so far in synthetic oligonucleotides but not in naturally occurring highly polymeric B-DNA. Our low-temperature experiments show that native B-DNA from salmon testes and the d(CGCGAATTCGCG)2 dodecamer have the same BI and BII substates. Nonequilibrium distribution of conformer population was generated by quenching hydrated unoriented films to 200 K, and isothermal structural relaxation toward equilibrium by interconversion of substates was followed by Fourier transform infrared spectroscopy. BI interconverts into BII on isothermal relaxation at 200 K, whereas on slow cooling from ambient temperature, BII interconverts into BI. Our estimation of the dodecamer's BI-to-BII conformer substate population by curve resolution of the symmetrical stretching vibration of the ionic phosphate is 2.4 +/- 0.5 to 1 at 200 K, and it is 1.3 +/- 0.5 to 1 between 270 and 290 K. Pronounced spectral changes upon BI-to-BII interconversion are consistent with base destacking coupled with migration of water from ionic phosphate toward the phosphodiester and sugar moieties. Nonspecific interaction of proteins with the DNA backbone could become specific by induced-fit-type interactions with either BI or BII backbone conformations. This suggests that the BI-to-BII substate interconversion could be a major contributor to the protein recognition process. PMID:10388766

  9. A graphic approach to evaluate algorithms of secondary structure prediction.

    PubMed

    Zhang, C T; Zhang, R

    2000-04-01

    Algorithms of secondary structure prediction have undergone the developments of nearly 30 years. However, the problem of how to appropriately evaluate and compare algorithms has not yet completely solved. A graphic method to evaluate algorithms of secondary structure prediction has been proposed here. Traditionally, the performance of an algorithm is evaluated by a number, i.e., accuracy of various definitions. Instead of a number, we use a graph to completely evaluate an algorithm, in which the mapping points are distributed in a three-dimensional space. Each point represents the predictive result of the secondary structure of a protein. Because the distribution of mapping points in the 3D space generally contains more information than a number or a set of numbers, it is expected that algorithms may be evaluated and compared by the proposed graphic method more objectively. Based on the point distribution, six evaluation parameters are proposed, which describe the overall performance of the algorithm evaluated. Furthermore, the graphic method is simple and intuitive. As an example of application, two advanced algorithms, i.e., the PHD and NNpredict methods, are evaluated and compared. It is shown that there is still much room for further improvement for both algorithms. It is pointed out that the accuracy for predicting either the alpha-helix or beta-strand in proteins with higher alpha-helix or beta-strand content, respectively, should be greatly improved for both algorithms. PMID:10798528

  10. 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 ?-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.

  11. 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…

  12. 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 to find which is the closest atom containing an arbitrary point in space. Atom sizes are according to the corresponding van der Waals radii. Restrictions: The geometrical model presented here does not include the chromosome organization level but it could be easily build up by using fragments of the 30 nm chromatin fiber. Unusual features: To our knowledge, this is the first open source atomic-resolution DNA geometrical model developed for DNA-radiation interaction Monte Carlo simulations. In our tests, the current model took into account the explicit position of about 56×106 atoms, although the user may enhance this amount according to the necessities. Running time: This subroutine can process about 2 million points within a few minutes in a typical current computer.

  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. 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

  15. Fast and accurate automatic structure prediction with HHpred.

    PubMed

    Hildebrand, Andrea; Remmert, Michael; Biegert, Andreas; Söding, Johannes

    2009-01-01

    Automated protein structure prediction is becoming a mainstream tool for biological research. This has been fueled by steady improvements of publicly available automated servers over the last decade, in particular their ability to build good homology models for an increasing number of targets by reliably detecting and aligning more and more remotely homologous templates. Here, we describe the three fully automated versions of the HHpred server that participated in the community-wide blind protein structure prediction competition CASP8. What makes HHpred unique is the combination of usability, short response times (typically under 15 min) and a model accuracy that is competitive with those of the best servers in CASP8. PMID:19626712

  16. Approximation algorithms for predicting RNA secondary structures with arbitrary pseudoknots.

    PubMed

    Jiang, Minghui

    2010-01-01

    We study three closely related problems motivated by the prediction of RNA secondary structures with arbitrary pseudoknots: the problem 2-Interval Pattern proposed by Vialette, the problem Maximum Base Pair Stackings proposed by Leong et al., and the problem Maximum Stacking Base Pairs proposed by Lyngsř. For the 2-Interval Pattern, we present polynomialtime approximation algorithms for the problem over the preceding-and-crossing model and on input with the unitary restriction. For Maximum Base Pair Stackings and Maximum Stacking Base Pairs, we present polynomial-time approximation algorithms for the two problems on explicit input of candidate base pairs. We also propose a new problem called Length-Weighted Balanced 2-Interval Pattern, which is natural in the context of RNA secondary structure prediction. PMID:20431151

  17. Predicting Earthquake Response of Civil Structures from Ambient Noise

    NASA Astrophysics Data System (ADS)

    Prieto, G.; Lawrence, J. F.; Chung, A. I.; Kohler, M. D.

    2009-12-01

    Increased monitoring of civil structures for response to earthquake motions is fundamental for reducing seismic hazard. Seismic monitoring is difficult because typically only a few useful, intermediate to large earthquakes occur per decade near instrumented structures. Here we demonstrate that the impulse response function (IRF) of a multi-story building can be generated from ambient noise. Estimated shear-wave velocity, attenuation values, and resonance frequencies from the IRFs agree with previous estimates for the instrumented UCLA Factor building. The accuracy of the approach is demonstrated by predicting the Factor building’s response to an M4.2 earthquake. The methodology described here allows for rapid non-invasive determination of structural parameters from the IRFs within days and could be used as a new tool for stateof- health monitoring of civil structures (buildings, bridges, etc.) before and/or after major earthquakes.

  18. The Computational Complexity of Protein Structure Prediction in

    E-print Network

    Istrail, Sorin

    in molecular biology. 0-8493-8597-0/01/$0.00+$1.50 c 2001 by CRC Press, LLC 1-1 #12;1-2 3 C H C O R1 N C H C O [10], protein structure prediction in general has proven to be quite difficult. The central dogma ............................................ 1-21 1.1 Introduction A protein is a complex biological macromolecule composed of a sequence

  19. Fiber composite structural durability and damage tolerance: Simplified predictive methods

    NASA Technical Reports Server (NTRS)

    Chamis, Christos C.; Ginty, Carol A.

    1987-01-01

    Simplified predictive methods and models (theory) to evaluate fiber/polymer-matrix composite material for determining structural durability and damage tolerance are presented and described. This theory includes equations for (1) fatigue and fracture of composites without and with defects, (2) impact resistance and residual strength after impact, (3) thermal fatigue, and (4) combined stress fatigue. Several examples are included to illustrate applications of the theory and to identify significant parameters and sensitivities. Comparisons with limited experimental data are made.

  20. 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

  1. Evaluating predictive performance of network biomarkers with network structures.

    PubMed

    Gao, Shang; Karakira, Ibrahim; Afra, Salim; Naji, Ghada; Alhajj, Reda; Zeng, Jia; Demetrick, Douglas

    2014-10-01

    Network is a powerful structure which reveals valuable characteristics of the underlying data. However, previous work on evaluating the predictive performance of network-based biomarkers does not take nodal connectedness into account. We argue that it is necessary to maximize the benefit from the network structure by employing appropriate techniques. To address this, we aim to learn a weight coefficient for each node in the network from the quantitative measure such as gene expression data. The weight coefficients are computed from an optimization problem which minimizes the total weighted difference between nodes in a network structure; this can be expressed in terms of graph Laplacian. After obtaining the coefficient vector for the network markers, we can then compute the corresponding network predictor. We demonstrate the effectiveness of the proposed method by conducting experiments using published breast cancer biomarkers with three patient cohorts. Network markers are first grouped based on GO terms related to cancer hallmarks. We compare the predictive performance of each network marker group across gene expression datasets. We also evaluate the network predictor against the average method for feature aggregation. The reported results show that the predictive performance of network markers is generally not consistent across patient cohorts. PMID:25219385

  2. 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

  3. 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.

  4. Predicting RNA secondary structures with pseudoknots by MCMC sampling.

    PubMed

    Metzler, Dirk; Nebel, Markus E

    2008-01-01

    The most probable secondary structure of an RNA molecule, given the nucleotide sequence, can be computed efficiently if a stochastic context-free grammar (SCFG) is used as the prior distribution of the secondary structure. The structures of some RNA molecules contain so-called pseudoknots. Allowing all possible configurations of pseudoknots is not compatible with context-free grammar models and makes the search for an optimal secondary structure NP-complete. We suggest a probabilistic model for RNA secondary structures with pseudoknots and present a Markov-chain Monte-Carlo Method for sampling RNA structures according to their posterior distribution for a given sequence. We favor Bayesian sampling over optimization methods in this context, because it makes the uncertainty of RNA structure predictions assessable. We demonstrate the benefit of our method in examples with tmRNA and also with simulated data. McQFold, an implementation of our method, is freely available from http://www.cs.uni-frankfurt.de/~metzler/McQFold. PMID:17589847

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

    PubMed Central

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

    2014-01-01

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

  6. Machine learning approach for the prediction of protein secondary structure.

    PubMed

    King, R D; Sternberg, M J

    1990-11-20

    PROMIS (protein machine induction system), a program for machine learning, was used to generalize rules that characterize the relationship between primary and secondary structure in globular proteins. These rules can be used to predict an unknown secondary structure from a known primary structure. The symbolic induction method used by PROMIS was specifically designed to produce rules that are meaningful in terms of chemical properties of the residues. The rules found were compared with existing knowledge of protein structure: some features of the rules were already recognized (e.g. amphipathic nature of alpha-helices). Other features are not understood, and are under investigation. The rules produced a prediction accuracy for three states (alpha-helix, beta-strand and coil) of 60% for all proteins, 73% for proteins of known alpha domain type, 62% for proteins of known beta domain type and 59% for proteins of known alpha/beta domain type. We conclude that machine learning is a useful tool in the examination of the large databases generated in molecular biology. PMID:2254939

  7. Exploring polymorphisms in B-DNA helical conformations

    PubMed Central

    Dans, Pablo D.; Pérez, Alberto; Faustino, Ignacio; Lavery, Richard; Orozco, Modesto

    2012-01-01

    The traditional mesoscopic paradigm represents DNA as a series of base-pair steps whose energy response to equilibrium perturbations is elastic, with harmonic oscillations (defining local stiffness) around a single equilibrium conformation. In addition, base sequence effects are often analysed as a succession of independent XpY base-pair steps (i.e. a nearest-neighbour (NN) model with only 10 unique cases). Unfortunately, recent massive simulations carried out by the ABC consortium suggest that the real picture of DNA flexibility may be much more complex. The paradigm of DNA flexibility therefore needs to be revisited. In this article, we explore in detail one of the most obvious violations of the elastic NN model of flexibility: the bimodal distributions of some helical parameters. We perform here an in-depth statistical analysis of a very large set of MD trajectories and also of experimental structures, which lead to very solid evidence of bimodality. We then suggest ways to improve mesoscopic models to account for this deviation from the elastic regime. PMID:23012264

  8. EVO—Evolutionary algorithm for crystal structure prediction

    NASA Astrophysics Data System (ADS)

    Bahmann, Silvia; Kortus, Jens

    2013-06-01

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

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

    PubMed Central

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

    2014-01-01

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

  12. 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.

  13. MUFOLD-DB: a processed protein structure database for protein structure prediction and analysis

    PubMed Central

    2014-01-01

    Background Protein structure data in Protein Data Bank (PDB) are widely used in studies of protein function and evolution and in protein structure prediction. However, there are two main barriers in large-scale usage of PDB data: 1) PDB data are highly redundant in terms of sequence and structure similarity; and 2) many PDB files have issues due to inconsistency of data and standards as well as missing residues, so that automated retrieval and analysis are often difficult. Description To address these issues, we have created MUFOLD-DB http://mufold.org/mufolddb.php, a web-based database, to collect and process the weekly PDB files thereby providing users with non-redundant, cleaned and partially-predicted structure data. For each of the non-redundant sequences, we annotate the SCOP domain classification and predict structures of missing regions by loop modelling. In addition, evolutional information, secondary structure, disorder region, and processed three-dimensional structure are computed and visualized to help users better understand the protein. Conclusions MUFOLD-DB integrates processed PDB sequence and structure data and multiple computational results, provides a friendly interface for users to retrieve, browse and download these data, and offers several useful functionalities to facilitate users' data operation. PMID:25559128

  14. The systematic structure and predictability of urban business diversity

    E-print Network

    Youn, Hyejin; Lobo, José; Strumsky, Deborah; Samaniego, Horacio; West, Geoffrey B

    2014-01-01

    Understanding cities is central to addressing major global challenges from climate and health to economic resilience. Although increasingly perceived as fundamental socio-economic units, the detailed fabric of urban economic activities is only now accessible to comprehensive analyses with the availability of large datasets. Here, we study abundances of business categories across U.S. metropolitan statistical areas to investigate how diversity of economic activities depends on city size. A universal structure common to all cities is revealed, manifesting self-similarity in internal economic structure as well as aggregated metrics (GDP, patents, crime). A derivation is presented that explains universality and the observed empirical distribution. The model incorporates a generalized preferential attachment process with ceaseless introduction of new business types. Combined with scaling analyses for individual categories, the theory quantitatively predicts how individual business types systematically change rank ...

  15. 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.

  16. 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.

  17. 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).

  18. 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.

  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. 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

  1. Predicting Physical-Chemical Properties of Compounds from Molecular Structures by Recursive Neural Networks

    E-print Network

    Sperduti, Alessandro

    Predicting Physical-Chemical Properties of Compounds from Molecular Structures by Recursive Neural of a recently developed neural network for structures applied to the prediction of physical chemical properties. INTRODUCTION To predict the physical-chemical properties of com- pounds, starting from the molecular structure

  2. Atomic-Accuracy Prediction of Protein Loop Structures through an RNA-Inspired Ansatz

    E-print Network

    Das, Rhiju

    Atomic-Accuracy Prediction of Protein Loop Structures through an RNA-Inspired Ansatz Rhiju Das Abstract Consistently predicting biopolymer structure at atomic resolution from sequence alone remains: Das R (2013) Atomic-Accuracy Prediction of Protein Loop Structures through an RNA-Inspired Ansatz. PLo

  3. 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.

  4. 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

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

    NASA Astrophysics Data System (ADS)

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

    2010-10-01

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

  6. 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

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

    E-print Network

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

  8. 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.

  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. Can computationally designed protein sequences improve secondary structure prediction?

    PubMed

    Bondugula, Rajkumar; Wallqvist, Anders; Lee, Michael S

    2011-05-01

    Computational sequence design methods are used to engineer proteins with desired properties such as increased thermal stability and novel function. In addition, these algorithms can be used to identify an envelope of sequences that may be compatible with a particular protein fold topology. In this regard, we hypothesized that sequence-property prediction, specifically secondary structure, could be significantly enhanced by using a large database of computationally designed sequences. We performed a large-scale test of this hypothesis with 6511 diverse protein domains and 50 designed sequences per domain. After analysis of the inherent accuracy of the designed sequences database, we realized that it was necessary to put constraints on what fraction of the native sequence should be allowed to change. With mutational constraints, accuracy was improved vs. no constraints, but the diversity of designed sequences, and hence effective size of the database, was moderately reduced. Overall, the best three-state prediction accuracy (Q(3)) that we achieved was nearly a percentage point improved over using a natural sequence database alone, well below the theoretical possibility for improvement of 8-10 percentage points. Furthermore, our nascent method was used to augment the state-of-the-art PSIPRED program by a percentage point. PMID:21282334

  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. 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.

  13. Improving protein secondary structure prediction based on short subsequences with local structure similarity

    PubMed Central

    2010-01-01

    Background When characterizing the structural topology of proteins, protein secondary structure (PSS) plays an important role in analyzing and modeling protein structures because it represents the local conformation of amino acids into regular structures. Although PSS prediction has been studied for decades, the prediction accuracy reaches a bottleneck at around 80%, and further improvement is very difficult. Results In this paper, we present an improved dictionary-based PSS prediction method called SymPred, and a meta-predictor called SymPsiPred. We adopt the concept behind natural language processing techniques and propose synonymous words to capture local sequence similarities in a group of similar proteins. A synonymous word is an n-gram pattern of amino acids that reflects the sequence variation in a protein’s evolution. We generate a protein-dependent synonymous dictionary from a set of protein sequences for PSS prediction. On a large non-redundant dataset of 8,297 protein chains (DsspNr-25), the average Q3 of SymPred and SymPsiPred are 81.0% and 83.9% respectively. On the two latest independent test sets (EVA Set_1 and EVA_Set2), the average Q3 of SymPred is 78.8% and 79.2% respectively. SymPred outperforms other existing methods by 1.4% to 5.4%. We study two factors that may affect the performance of SymPred and find that it is very sensitive to the number of proteins of both known and unknown structures. This finding implies that SymPred and SymPsiPred have the potential to achieve higher accuracy as the number of protein sequences in the NCBInr and PDB databases increases. Conclusions Our experiment results show that local similarities in protein sequences typically exhibit conserved structures, which can be used to improve the accuracy of secondary structure prediction. For the application of synonymous words, we demonstrate an example of a sequence alignment which is generated by the distribution of shared synonymous words of a pair of protein sequences. We can align the two sequences nearly perfectly which are very dissimilar at the sequence level but very similar at the structural level. The SymPred and SymPsiPred prediction servers are available at http://bio-cluster.iis.sinica.edu.tw/SymPred/. PMID:21143813

  14. Structure activity relationships: their function in biological prediction

    SciTech Connect

    Schultz, T.W.

    1982-01-01

    Quantitative structure activity relationships provide a means of ranking or predicting biological effects based on chemical structure. For each compound used to formulate a structure activity model two kinds of quantitative information are required: (1) biological activity and (2) molecular properties. Molecular properties are of three types: (1) molecular shape, (2) physiochemical parameters, and (3) abstract quantitations of molecular structure. Currently the two best descriptors are the hydrophobic parameter, log 1-octanol/water partition coefficient (log P), and the /sup 1/X/sup v/(one-chi-v) molecular connectivity index. Biological responses can be divided into three main categories: (1) non-specific effects due to membrane perturbation, (2) non-specific effects due to interaction with functional groups of proteins, and (3) specific effects due to interaction with receptors. Twenty-six synthetic fossil fuel-related nitrogen-containing aromatic compounds were examined to determine the quantitative correlation between log P and /sup 1/X/sup v/ and population growth impairment of Tetrahymena pyriformis. Nitro-containing compounds are the most active, followed by amino-containing compounds and azaarenes. Within each analog series activity increases with alkyl substitution and ring addition. The planar model log BR = 0.5564 log P + 0.3000 /sup 1/X/sup v/ -2.0138 was determined using mono-nitrogen substituted compounds. Attempts to extrapolate this model to dinitrogen-containing molecules were, for the most part, unsuccessful because of a change in mode of action from membrane perturbation to uncoupling of oxidative phosphoralation.

  15. Using Markov Chains for Structural Link Prediction in Adaptive Web Sites

    E-print Network

    Zhu, Jianhan

    Using Markov Chains for Structural Link Prediction in Adaptive Web Sites Jianhan Zhu1 School from Markov chains to acquire structural knowledge about Web sites. The structural knowledge clusters. The predicted Web pages and acquired Web structures are further integrated to assist Web users

  16. A composite score for predicting errors in protein structure models.

    PubMed

    Eramian, David; Shen, Min-yi; Devos, Damien; Melo, Francisco; Sali, Andrej; Marti-Renom, Marc A

    2006-07-01

    Reliable prediction of model accuracy is an important unsolved problem in protein structure modeling. To address this problem, we studied 24 individual assessment scores, including physics-based energy functions, statistical potentials, and machine learning-based scoring functions. Individual scores were also used to construct approximately 85,000 composite scoring functions using support vector machine (SVM) regression. The scores were tested for their abilities to identify the most native-like models from a set of 6000 comparative models of 20 representative protein structures. Each of the 20 targets was modeled using a template of <30% sequence identity, corresponding to challenging comparative modeling cases. The best SVM score outperformed all individual scores by decreasing the average RMSD difference between the model identified as the best of the set and the model with the lowest RMSD (DeltaRMSD) from 0.63 A to 0.45 A, while having a higher Pearson correlation coefficient to RMSD (r=0.87) than any other tested score. The most accurate score is based on a combination of the DOPE non-hydrogen atom statistical potential; surface, contact, and combined statistical potentials from MODPIPE; and two PSIPRED/DSSP scores. It was implemented in the SVMod program, which can now be applied to select the final model in various modeling problems, including fold assignment, target-template alignment, and loop modeling. PMID:16751606

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

    E-print Network

    Skolnick, Jeff

    TASSER_WT: A Protein Structure Prediction Algorithm with Accurate Predicted Contact Restraints_WT, was developed. TASSER_WT incorporates more accurate contact restraints from a new method, COMBCON. COMBCON uses (incorrect alignments and/or templates and incorrect side-chain contact restraints) in a comprehensive

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

    E-print Network

    Das, Rhiju

    RNA-Puzzles Round II: assessment of RNA structure prediction programs applied to three large RNA of RNA-Puzzles, a collective and blind experiment in three-dimensional (3D) RNA structure prediction. Three puzzles, Puzzles 5, 6, and 10, represented sequences of three large RNA structures with limited

  19. Algorithms for predicting the secondary structure of pairs and combinatorial sets of nucleic acid strands

    E-print Network

    Hutter, Frank

    Algorithms for predicting the secondary structure of pairs and combinatorial sets of nucleic acid;Abstract Secondary structure prediction of nucleic acid molecules is a very important prob- lem prediction of pairs of nucleic acid molecules (PairFold), and (2) finding which sequences, formed from

  20. Blind Predictions of Local Protein Structure in CASP2 Targets Using the I-Sites Library

    E-print Network

    Bystroff, Chris

    Blind Predictions of Local Protein Structure in CASP2 Targets Using the I-Sites Library Christopher ABSTRACT Blind predictions of the local structure of nine CASP2 targets were made using the I-sites library dataset to optimize the method may intro- duce a bias in favor of that set. Blind predictions of data

  1. Structural Time Series Model for El Nińo Prediction

    NASA Astrophysics Data System (ADS)

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

    2015-04-01

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

  2. 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

  3. 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 criterion, the load is increased and the process is repeated. Failure results for the plate-plug and clamped plate tests are accurate to within 2 percent.

  4. Unwinding of primer-templates by archaeal family-B DNA polymerases in response to template-strand uracil

    PubMed Central

    Richardson, Tomas T.; Wu, Xiaohua; Keith, Brian J.; Heslop, Pauline; Jones, Anita C.; Connolly, Bernard A.

    2013-01-01

    Archaeal family-B DNA polymerases bind tightly to deaminated bases and stall replication on encountering uracil in template strands, four bases ahead of the primer-template junction. Should the polymerase progress further towards the uracil, for example, to position uracil only two bases in front of the junction, 3?–5? proof-reading exonuclease activity becomes stimulated, trimming the primer and re-setting uracil to the +4 position. Uracil sensing prevents copying of the deaminated base and permanent mutation in 50% of the progeny. This publication uses both steady-state and time-resolved 2-aminopurine fluorescence to show pronounced unwinding of primer-templates with Pyrococcus furiosus (Pfu) polymerase–DNA complexes containing uracil at +2; much less strand separation is seen with uracil at +4. DNA unwinding has long been recognized as necessary for proof-reading exonuclease activity. The roles of M247 and Y261, amino acids suggested by structural studies to play a role in primer-template unwinding, have been probed. M247 appears to be unimportant, but 2-aminopurine fluorescence measurements show that Y261 plays a role in primer-template strand separation. Y261 is also required for full exonuclease activity and contributes to the fidelity of the polymerase. PMID:23303790

  5. Unwinding of primer-templates by archaeal family-B DNA polymerases in response to template-strand uracil.

    PubMed

    Richardson, Tomas T; Wu, Xiaohua; Keith, Brian J; Heslop, Pauline; Jones, Anita C; Connolly, Bernard A

    2013-02-01

    Archaeal family-B DNA polymerases bind tightly to deaminated bases and stall replication on encountering uracil in template strands, four bases ahead of the primer-template junction. Should the polymerase progress further towards the uracil, for example, to position uracil only two bases in front of the junction, 3'-5' proof-reading exonuclease activity becomes stimulated, trimming the primer and re-setting uracil to the +4 position. Uracil sensing prevents copying of the deaminated base and permanent mutation in 50% of the progeny. This publication uses both steady-state and time-resolved 2-aminopurine fluorescence to show pronounced unwinding of primer-templates with Pyrococcus furiosus (Pfu) polymerase-DNA complexes containing uracil at +2; much less strand separation is seen with uracil at +4. DNA unwinding has long been recognized as necessary for proof-reading exonuclease activity. The roles of M247 and Y261, amino acids suggested by structural studies to play a role in primer-template unwinding, have been probed. M247 appears to be unimportant, but 2-aminopurine fluorescence measurements show that Y261 plays a role in primer-template strand separation. Y261 is also required for full exonuclease activity and contributes to the fidelity of the polymerase. PMID:23303790

  6. 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.

  7. 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 ? for exponential fits k(d), where d is the charge transfer distance, and the exponent ? 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 ? and ?. 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

  8. 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 ? for exponential fits k (d ) , where d is the charge transfer distance, and the exponent ? 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 ? and ? . 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.

  9. 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…

  10. Web Page Segmentation with Structured Prediction and its Application in Web Page Classification

    E-print Network

    Murphy, Robert F.

    Web Page Segmentation with Structured Prediction and its Application in Web Page Classification perform Web page seg- mentation with a structured prediction approach. It formu- lates the segmentation task as a structured labeling prob- lem on a transformed Web page segmentation graph (WPS- graph). WPS

  11. HAAD: A Quick Algorithm for Accurate Prediction of Hydrogen Atoms in Protein Structures

    E-print Network

    Zhang, Yang

    HAAD: A Quick Algorithm for Accurate Prediction of Hydrogen Atoms in Protein Structures Yunqi Li diffraction structures decreased by 26% and 11%, respectively. Furthermore, hydrogen atoms placed by HAAD have Algorithm for Accurate Prediction of Hydrogen Atoms in Protein Structures. PLoS ONE 4(8): e6701. doi:10

  12. 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

  13. Spatial structure and potential predictability of summer precipitation in Ethiopia

    NASA Astrophysics Data System (ADS)

    Wild, S.; Eden, J. M.; Widmann, M.; Leckebusch, G. C.

    2012-04-01

    Variations in sea surface temperature (SST) and atmospheric circulation on both regional and global scales substantially influence interannual variability of precipitation in Ethiopia and the surrounding countries. Previous studies have revealed links between ENSO and summer rainfall in East Africa. As this region has been frequently affected by severe droughts during the last few decades, most recently in 2011, improving understanding of these influences is crucial for developing prediction methods for seasonal precipitation variability. More than half of the Ethiopian precipitation occurs during the Kiremt season (JJAS), which is therefore closely related to drought events. In the northwestern part the Kiremt rains are most prominent whereas the Belg precipitation (FMAM) is important for the southeastern part. We here objectively define homogenous rainfall regions in East Africa and analyse links between the rainfall in these regions with global SST. PCA of the gridded GPCP dataset (1979-2010), which includes station records and satellite data, reveals a dipole structure with two precipitation regimes divided geographically by the Ethiopian Rift Valley. We will show the response of precipitation in these regions to changes in Pacific SST, using the HadSST2 dataset. First results of concurrent relationships between Ethiopian precipitation (for the total over the whole country and for the northwestern part) and SST are consistent with an ENSO signal with positive correlation in the north- and southwestern Pacific, as well as negative correlation in the central eastern Pacific. Further investigations will also include lagged correlations. These findings corroborate the results of previous studies but extend them by using cross-validated principal component multiple linear regression (PC-MLR) models to estimate NW-, SE- and total Ethiopian rainfall from Pacific SST. It has already been shown by Eden et al. (see Poster in Session CL3.3/NP5.4, EGU2012-10302) that spring variability of an individual precipitation record from Addis Ababa can be partly estimated from Pacific SST. Considering our findings in seasonal prediction models may improve drought forecasting across East Africa.

  14. 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 for Premium Carbon Products from Coal. Possibilities for interactions with this new center will be explored. Also this period, an invited review paper was prepared for the 27th International Symposium on Combustion, to be held in Boulder, Colorado in August. The paper is entitled; "Structure, Properties, and Reactivity of Solid Fuels," and reports on a number of advances made in this collaborative project.

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

    E-print Network

    de Groot, Bert

    Conformational Transitions upon Ligand Binding: Holo- Structure Prediction from Apo Conformations in the ligand- bound state (holo structures), however, are a prerequisite for successful structure-based drug of the holo structure. The method is applied to ten cases in which proteins undergo structural rearrangements

  16. A Bayesian Model for Protein Secondary Structure Prediction

    E-print Network

    Dahl, David B.

    Structure (DSSP) for protein secondary structure with single letter codes. We consider the following 4 block types (in italics) from the original 8 structures defined in DSSP (in parentheses): 1. Helix "H": 310

  17. 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.

  18. 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...

  19. 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...

  20. RNA Folding with Soft Constraints: Reconciliation of Probing Data and Thermodynamic Secondary Structure Prediction

    E-print Network

    Mag Washietl, Stefan

    Thermodynamic folding algorithms and structure probing experiments are commonly used to determine the secondary structure of RNAs. Here we propose a formal framework to reconcile information from both prediction algorithms ...

  1. 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...

  2. Information theory provides a comprehensive framework for the evaluation of protein structure predictions

    PubMed Central

    Swanson, Rosemarie; Vannucci, Marina; Tsai, Jerry W.

    2008-01-01

    Protein structure prediction has a number of important ad hoc similarity measures for evaluating predictions, but would benefit from a measure that is able to provide a common framework for a broad range of comparisons. Here we show that a mutual information-like measure can provide a comprehensive framework for evaluating protein structure prediction of all types. We discuss the concept of information, its application to secondary structure, and the obstacle to applying it to 3D structure. Based on insights from the secondary structure case, we present an approach to work around the 3D difficulties, and develop a method to measure the mutual information provided by a 3D structure prediction. We integrate the evaluation of all types of protein structure prediction into a single frame work, and compare the amount of information provided by various prediction methods, including secondary structure prediction. Within this broadened framework, the idea that structure is better preserved than sequence during evolution is evaluated quantitatively for the globin family. A nearly perfect sequence match in the globin family corresponds to about 300 bits of information, whereas a nearly perfect structural match for the same two proteins corresponds to about 2500 bits of information, where bits of information describes the probability of obtaining a match of similar closeness by chance. Mutual information provides both a theoretical basis for evaluating structure similarity and an explanatory surround for existing similarity measures. PMID:18704942

  3. Measures for the assessment of fuzzy predictions of protein secondary structure.

    PubMed

    Lee, Julian

    2006-11-01

    Many of the recent secondary structure prediction methods incorporate the idea of fuzzy set theory, where instead of assigning a definite secondary structure to a query residue, probability for the residue being in each of the conformational states is estimated. Moreover, continuous assignment of conformational states to the experimentally observed protein structures can be performed in order to reflect inherent flexibility. Although various measures have been developed for evaluating performances of secondary structure prediction methods, they depend only on the most probable secondary structures. They do not assess the accuracy of the probabilities produced by fuzzy prediction methods, and they cannot incorporate information contained in continuous assignments of conformational states to observed structures. Three important measures for evaluating performance of a secondary structure prediction algorithm, Q score, Segment OVerlap (SOV) measure, and the k-state correlation coefficient (Corr), are deformed into fuzzy measures F score, Fuzzy OVerlap (FOV) measure, and the fuzzy correlation coefficient (Forr), so that the new measures not only assess probabilistic outputs of fuzzy prediction methods, but also incorporate information from continuous assignments of secondary structure. As an example of application, prediction results of four fuzzy secondary structure prediction methods, PSIPRED, PROFking, SABLE, and PREDICT, are assessed using the new fuzzy measures. PMID:16948155

  4. 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.

  5. 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.

  6. 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 the frame of an ESA TRP study [1]. A bread-board including typical non-linearities has been designed, manufactured and tested through a typical spacecraft dynamic test campaign. The study has demonstrate the capabilities to perform non-linear dynamic test predictions on a flight representative spacecraft, the good correlation of test results with respect to Finite Elements Model (FEM) prediction and the possibility to identify modal behaviour and to characterize non-linearities characteristics from test results. As a synthesis for this study, overall guidelines have been derived on the mechanical verification process to improve level of expertise on tests involving spacecraft including non-linearity.

  7. Incomplete gene structure prediction with almost 100% specificity 

    E-print Network

    Chin, See Loong

    2004-09-30

    -intron boundaries of a gene. Gene prediction follows two general approaches: statistical patterns identification and sequence similarity comparison. Similarity based approaches have gained increasing popularity with the recent vast increase in genomic data in Gen...

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

    NASA Astrophysics Data System (ADS)

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

    2015-06-01

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

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

    E-print Network

    prediction to generate atomic structure models, (6) quality assessment of predicted structure, and (7 functions of these genes at a comparable pace. BLAST/PSI-BLAST (Altschul et al., 1997) have been one assignments at the molecular or cellular level, using sequence- based approaches like BLAST (Altschul et al

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

    E-print Network

    Wang, Wei

    proteinsSTRUCTURE O FUNCTION O BIOINFORMATICS Predicting drug resistance of the HIV-1 protease regimen. In this study, we applied a structure-based computational approach to predict mutants of the HIV Inhibitors of human immunodeficiency virus type 1 (HIV-1) protease are widely used in the clinical treatment

  11. Application of Sparse NMR Restraints to Large-Scale Protein Structure Prediction

    E-print Network

    Zhang, Yang

    Application of Sparse NMR Restraints to Large-Scale Protein Structure Prediction Wei Li, Yang Zhang ABSTRACT The protein structure prediction algorithm TOUCHSTONEX that uses sparse distance restraints consisting of 1365 proteins up to 200 residues was employed. Using N/8 simulated long-range restraints, where

  12. Loading and Response of Offshore Wind Turbine Support Structures: Prediction with Comparison to Measured Data

    E-print Network

    Sweetman, Bert

    Loading and Response of Offshore Wind Turbine Support Structures: Prediction with Comparison turbines, but without the turbine itself. This effectiveness is assessed by comparison of predicted of the response process. 1. INTRODUCTION Historically, design of offshore turbine structures has evolved from

  13. Atomic structure of the (310) twin in niobium: Experimental determination and comparison with theoretical predictions

    NASA Astrophysics Data System (ADS)

    Campbell, Geoffrey H.; Foiles, Stephen M.; Gumbsch, Peter; Rühle, Manfred; King, Wayne E.

    1993-01-01

    The atomic structure of the (310) twin in Nb was predicted using interatomic potentials derived from the embedded atom method (EAM), Finnis-Sinclair theory (FS), and the model generalized pseudopotential theory (MGPT). The EAM and FS predicted structures with crystal translations which break mirror symmetry. The MGPT predicted one stable structure which possessed mirror symmetry. This defect was experimentally determined to have mirror symmetry. These findings emphasize that the angular dependent interactions modeled by the MGPT are important for determining defect structures in bcc transition metals.

  14. Climate and species richness predict the phylogenetic structure of African mammal communities.

    PubMed

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

    2015-01-01

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

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

    PubMed Central

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

    2015-01-01

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

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

    PubMed

    Patel, Maulika S; Mazumdar, Himanshu S

    2014-11-21

    Protein structure prediction is of great relevance given the abundant genomic and proteomic data generated by the genome sequencing projects. Protein secondary structure prediction is addressed as a sub task in determining the protein tertiary structure and function. In this paper, a novel algorithm, KB-PROSSP-NN, which is a combination of knowledge base and modeling of the exceptions in the knowledge base using neural networks for protein secondary structure prediction (PSSP), is proposed. The knowledge base is derived from a proteomic sequence-structure database and consists of the statistics of association between the 5-residue words and corresponding secondary structure. The predicted results obtained using knowledge base are refined with a Backpropogation neural network algorithm. Neural net models the exceptions of the knowledge base. The Q3 accuracy of 90% and 82% is achieved on the RS126 and CB396 test sets respectively which suggest improvement over existing state of art methods. PMID:25128736

  17. Predicting Gene Structures from Multiple RT-PCR Tests

    NASA Astrophysics Data System (ADS)

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

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

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

    PubMed

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

    2015-01-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2015-06-01

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

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

    PubMed Central

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

    2015-01-01

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

  1. proteinsSTRUCTURE O FUNCTION O BIOINFORMATICS PREDICTION REPORT

    E-print Network

    Zhang, Yang

    -based contact predictions from machine learning techniques are found helpful for both template- based modeling, with the procedure fully auto- mated in both the Server and Human sections. The quality of the server models is close-splitting and mirror image recognition which mainly influences the performance of I-TASSER modeling in the FM

  2. Predicting Gene Structures from Multiple RT-PCR Tests

    E-print Network

    Vinar, Tomas

    splicing variants. Keywords: gene finding, RT-PCR, NP-completeness, dynamic program- ming, splicing graph 1 in the presence of ubiquitous alternative splicing (Guigo et al., 2006). Nonetheless, prediction accuracy of gene. As an input to our algorithm, we use a set of potential transcripts of one gene represented as a splicing

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

    PubMed Central

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

    2015-01-01

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

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

    NASA Technical Reports Server (NTRS)

    Gern, Frank H.

    2012-01-01

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

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

    PubMed Central

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

    2015-01-01

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

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

    PubMed

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

    2015-06-01

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

  7. On the crystallographic accuracy of structure prediction by implicit water models: Tests for cyclic peptides

    NASA Astrophysics Data System (ADS)

    Goldtzvik, Yonathan; Goldstein, Moshe; Benny Gerber, R.

    2013-03-01

    Five small cyclic peptides and four implicit water models, were selected for this study. DEEPSAM, a structure prediction algorithm built upon TINKER, was used. Structures predicted using implicit water models were compared with experimental data, and with predictions calculated in the gas phase. The existence of very accurate X-ray crystallographic data allowed firm and conclusive comparisons between predictions and experiment. The introduction of implicit water models into the calculations improved the RMSD from experiment by about 13% compared with computations neglecting the presence of water. GBSA is shown to be consistently the best implicit water model.

  8. Crystal Structure Prediction (CSP) of Flexible Molecules using Parallel Genetic Algorithms with a Standard Force Field

    PubMed Central

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

    2009-01-01

    This paper describes the application of our distributed computing framework for crystal structure prediction (CSP), Modified Genetic Algorithms for Crystal and Cluster Prediction (MGAC) to predict the crystal structure of flexible molecules using the General Amber Force Field (GAFF) and the CHARMM program. The MGAC distributed computing framework which includes a series of tightly integrated computer programs for generating the molecule’s force field, sampling crystal structures using a distributed parallel genetic algorithm, local energy minimization of the structures followed by the classifying, sorting and archiving of the most relevant structures. Our results indicate that the method can consistently find the experimentally known crystal structures of flexible molecules, but the number of missing structures and poor ranking observed in some crystals show the need for further improvement of the potential. PMID:19130496

  9. Validation of finite element and boundary element methods for predicting structural vibration and radiated noise

    NASA Technical Reports Server (NTRS)

    Seybert, A. F.; Wu, X. F.; Oswald, Fred B.

    1992-01-01

    Analytical and experimental validation of methods to predict structural vibration and radiated noise are presented. A rectangular box excited by a mechanical shaker was used as a vibrating structure. Combined finite element method (FEM) and boundary element method (BEM) models of the apparatus were used to predict the noise radiated from the box. The FEM was used to predict the vibration, and the surface vibration was used as input to the BEM to predict the sound intensity and sound power. Vibration predicted by the FEM model was validated by experimental modal analysis. Noise predicted by the BEM was validated by sound intensity measurements. Three types of results are presented for the total radiated sound power: (1) sound power predicted by the BEM modeling using vibration data measured on the surface of the box; (2) sound power predicted by the FEM/BEM model; and (3) sound power measured by a sound intensity scan. The sound power predicted from the BEM model using measured vibration data yields an excellent prediction of radiated noise. The sound power predicted by the combined FEM/BEM model also gives a good prediction of radiated noise except for a shift of the natural frequencies that are due to limitations in the FEM model.

  10. Ab initio NMR Confirmed Evolutionary Structure Prediction for Organic Molecular Crystals

    NASA Astrophysics Data System (ADS)

    Pham, Cong-Huy; Kucukbenli, Emine; de Gironcoli, Stefano

    2015-03-01

    Ab initio crystal structure prediction of even small organic compounds is extremely challenging due to polymorphism, molecular flexibility and difficulties in addressing the dispersion interaction from first principles. We recently implemented vdW-aware density functionals and demonstrated their success in energy ordering of aminoacid crystals. In this work we combine this development with the evolutionary structure prediction method to study cholesterol polymorphs. Cholesterol crystals have paramount importance in various diseases, from cancer to atherosclerosis. The structure of some polymorphs (e.g. ChM, ChAl, ChAh) have already been resolved while some others, which display distinct NMR spectra and are involved in disease formation, are yet to be determined. Here we thoroughly assess the applicability of evolutionary structure prediction to address such real world problems. We validate the newly predicted structures with ab initio NMR chemical shift data using secondary referencing for an improved comparison with experiments.

  11. Computational prediction of coiled-coil interaction structure specificity

    E-print Network

    Gutwin, Karl N. (Karl Nickolai)

    2009-01-01

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

  12. An Overview of Protein Structure Prediction: From Homology to Ab Initio

    E-print Network

    , comparative modeling, threading or fold recognition, and Ab Initio prediction, is described. The accuracy structure. Thus there is enormous benefit in knowing the three dimensional structures of all the proteins structure database. In the following, I will briefly discuss each kind of methods and their accuracy

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

    E-print Network

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

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

    NASA Technical Reports Server (NTRS)

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

    1992-01-01

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

  15. Monitoring and restabilizing structures under external excitations through detection and prediction of changes in structural properties

    NASA Astrophysics Data System (ADS)

    Sebastijanovic, Nebojsa

    The primary goal of this dissertation is the development of methods for prediction and detection of damage in structures under external excitations through the use of sensors and actuators. The first example involves developing an active flutter suppression algorithm for a flat panel in flight and space vehicles using embedded piezoceramic actuators. A basic eigenvector orientation approach is used to evaluate the possibility of controlling the onset of panel flutter. Eigenvectors for two consecutive modes are usually orthogonal and the onset of flutter condition can be observed earlier as they start to lose their orthogonality. Piezoelectric layers are assumed to be bonded to the top and bottom surfaces of the panel in order to provide counter-bending moments at joints between elements. The controllers are designed to modify the stiffness of the structure and re-stabilize the system; as a result, flutter occurrence can be offset to a higher flutter speed. To illustrate the applicability and effectiveness of the developed method, several simple wide beam examples using piezoelectric layers as actuators are studied and presented. Controllers based on different control objectives are considered and the effects of control moment locations are studied. Potential applications of this basic method may be straightforwardly applied to plate and shell structures of laminated composites. The second example includes developing a method for detecting, locating, and quantifying structural damage using acceleration measurements as feedback. This method directly uses time domain structural vibration measurements and the effects of different damages are decoupled in the controller design. The effectiveness of the proposed method is evaluated with illustrative examples of a three and an eight-story model as well as a single story steel frame model with changes in joint flexibility. Finally, the progress on developing a hybrid structural health monitoring system is presented through the results on the combined use of the proposed global damage detection algorithm and local infrared imaging for the single bay three-story steel frame with local defect in a beam with channel cross section.

  16. Improved prediction of protein secondary structure by use of sequence profiles and neural networks.

    PubMed Central

    Rost, B; Sander, C

    1993-01-01

    The explosive accumulation of protein sequences in the wake of large-scale sequencing projects is in stark contrast to the much slower experimental determination of protein structures. Improved methods of structure prediction from the gene sequence alone are therefore needed. Here, we report a substantial increase in both the accuracy and quality of secondary-structure predictions, using a neural-network algorithm. The main improvements come from the use of multiple sequence alignments (better overall accuracy), from "balanced training" (better prediction of beta-strands), and from "structure context training" (better prediction of helix and strand lengths). This method, cross-validated on seven different test sets purged of sequence similarity to learning sets, achieves a three-state prediction accuracy of 69.7%, significantly better than previous methods. In addition, the predicted structures have a more realistic distribution of helix and strand segments. The predictions may be suitable for use in practice as a first estimate of the structural type of newly sequenced proteins. PMID:8356056

  17. A method of precise mRNA/DNA homology-based gene structure prediction

    PubMed Central

    Churbanov, Alexander; Pauley, Mark; Quest, Daniel; Ali, Hesham

    2005-01-01

    Background Accurate and automatic gene finding and structural prediction is a common problem in bioinformatics, and applications need to be capable of handling non-canonical splice sites, micro-exons and partial gene structure predictions that span across several genomic clones. Results We present a mRNA/DNA homology based gene structure prediction tool, GIGOgene. We use a new affine gap penalty splice-enhanced global alignment algorithm running in linear memory for a high quality annotation of splice sites. Our tool includes a novel algorithm to assemble partial gene structure predictions using interval graphs. GIGOgene exhibited a sensitivity of 99.08% and a specificity of 99.98% on the Genie learning set, and demonstrated a higher quality of gene structural prediction when compared to Sim4, est2genome, Spidey, Galahad and BLAT, including when genes contained micro-exons and non-canonical splice sites. GIGOgene showed an acceptable loss of prediction quality when confronted with a noisy Genie learning set simulating ESTs. Conclusion GIGOgene shows a higher quality of gene structure prediction for mRNA/DNA spliced alignment when compared to other available tools. PMID:16242044

  18. An adaptive genetic algorithm for crystal structure prediction.

    PubMed

    Wu, S Q; Ji, M; Wang, C Z; Nguyen, M C; Zhao, X; Umemoto, K; Wentzcovitch, R M; Ho, K M

    2014-01-22

    We present a genetic algorithm (GA) for structural search that combines the speed of structure exploration by classical potentials with the accuracy of density functional theory (DFT) calculations in an adaptive and iterative way. This strategy increases the efficiency of the DFT-based GA by several orders of magnitude. This gain allows a considerable increase in the size and complexity of systems that can be studied by first principles. The performance of the method is illustrated by successful structure identifications of complex binary and ternary intermetallic compounds with 36 and 54 atoms per cell, respectively. The discovery of a multi-TPa Mg-silicate phase with unit cell containing up to 56 atoms is also reported. Such a phase is likely to be an essential component of terrestrial exoplanetary mantles. PMID:24351274

  19. An adaptive genetic algorithm for crystal structure prediction

    SciTech Connect

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

    2013-10-31

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

  20. PreSSAPro: a software for the prediction of secondary structure by amino acid properties.

    PubMed

    Costantini, Susan; Colonna, Giovanni; Facchiano, Angelo M

    2007-10-01

    PreSSAPro is a software, available to the scientific community as a free web service designed to provide predictions of secondary structures starting from the amino acid sequence of a given protein. Predictions are based on our recently published work on the amino acid propensities for secondary structures in either large but not homogeneous protein data sets, as well as in smaller but homogeneous data sets corresponding to protein structural classes, i.e. all-alpha, all-beta, or alpha-beta proteins. Predictions result improved by the use of propensities evaluated for the right protein class. PreSSAPro predicts the secondary structure according to the right protein class, if known, or gives a multiple prediction with reference to the different structural classes. The comparison of these predictions represents a novel tool to evaluate what sequence regions can assume different secondary structures depending on the structural class assignment, in the perspective of identifying proteins able to fold in different conformations. The service is available at the URL http://bioinformatica.isa.cnr.it/PRESSAPRO/. PMID:17888742

  1. A Structural Equation Model for Predicting Business Student Performance

    ERIC Educational Resources Information Center

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

    2008-01-01

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

  2. Protein Secondary Structure Prediction Using Sigmoid Belief Networks to Parameterize

    E-print Network

    Edinburgh, University of

    dependency in residue sequences. The results of numerical experiments indicate the usefulness of a protein from its amino acid sequence. Beginning with the seminal work of Qian and Sejnowski [4], many-side publi., ISBN 2-930307-04-8, pp. 81-86 #12;sequence-structure probability distribution based

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

    E-print Network

    and Engineering Branch U. S. Army Research Laboratory Department of Cell Biology and Biochemistry U. S. Army and vaccine design as well as many areas of basic biological research. In this work, initial assessments translated proteins. Accurate structural models of proteins can be used in computational drug and vaccine

  4. A Historical Perspective and Overview of Protein Structure Prediction

    NASA Astrophysics Data System (ADS)

    Wooley, John C.; Ye, Yuzhen

    Carrying on many different biological functions, proteins are all composed of one or more polypeptide chains, each containing from several to hundreds or even thousands of the 20 amino acids. During the 1950s at the dawn of modern biochemistry, an essential question for biochemists was to understand the structure and function of these polypeptide chains. The sequences of protein, also referred to as their primary structures, determine the different chemical properties for different proteins, and thus continue to captivate much of the attention of biochemists. As an early step in characterizing protein chemistry, British biochemist Frederick Sanger designed an experimental method to identify the sequence of insulin (Sanger et al., 1955). He became the first person to obtain the primary structure of a protein and in 1958 won his first Nobel Price in Chemistry. This important progress in sequencing did not answer the question of whether a single (individual) protein has a distinctive shape in three dimensions (3D), and if so, what factors determine its 3D architecture. However, during the period when Sanger was studying the primary structure of proteins, American biochemist Christian Anfinsen observed that the active polypeptide chain of a model protein, bovine pancreatic ribonuclease (RNase), could fold spontaneously into a unique 3D structure, which was later called native conformation of the protein (Anfinsen et al., 1954). Anfinsen also studied the refolding of RNase enzyme and observed that an enzyme unfolded under extreme chemical environment could refold spontaneously back into its native conformation upon changing the environment back to natural conditions (Anfinsen et al., 1961). By 1962, Anfinsen had developed his theory of protein folding (which was summarized in his 1972 Nobel acceptance speech): "The native conformation is determined by the totality of interatomic interactions and hence, by the amino acid sequence, in a given environment."

  5. Three-dimensional structure of a halotolerant algal carbonic anhydrase predicts halotolerance

    E-print Network

    Sussman, Joel L.

    Three-dimensional structure of a halotolerant algal carbonic anhydrase predicts halotolerance electrostatic potential primarily due to the high abundance of surface acidic residues (1­6). Halo- philic

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

    E-print Network

    Zhang, Yang

    Video Article A Protocol for Computer-Based Protein Structure and Function Prediction Ambrish Roy1 who are using the on-line I-TASSER server. Video Link The video component of this article can be found

  7. An On/Off Lattice Approach to Protein Structure Prediction from Contact Maps

    E-print Network

    Passerini, Andrea

    acid sequence. The PSP problem is of enormous interest, because the function of proteins is a direct of predicted decoys for analysis, or for selecting the best candi- date structures through reranking techniques

  8. Heuristics, Optimizations, and Parallelism for Protein Structure Prediction in CLP(FD)

    E-print Network

    Dal Palů, Alessandro

    Heuristics, Optimizations, and Parallelism for Protein Structure Prediction in CLP(FD) Alessandro and to plug-and-play different search strategies and heuristics. A preliminary approach to the use of CLP

  9. Extended Aging Theories for Predictions of Safe Operational Life of Critical Airborne Structural Components

    NASA Technical Reports Server (NTRS)

    Ko, William L.; Chen, Tony

    2006-01-01

    The previously developed Ko closed-form aging theory has been reformulated into a more compact mathematical form for easier application. A new equivalent loading theory and empirical loading theories have also been developed and incorporated into the revised Ko aging theory for the prediction of a safe operational life of airborne failure-critical structural components. The new set of aging and loading theories were applied to predict the safe number of flights for the B-52B aircraft to carry a launch vehicle, the structural life of critical components consumed by load excursion to proof load value, and the ground-sitting life of B-52B pylon failure-critical structural components. A special life prediction method was developed for the preflight predictions of operational life of failure-critical structural components of the B-52H pylon system, for which no flight data are available.

  10. LOMETS: A local meta-threading-server for protein structure prediction

    E-print Network

    Wu, Sitao; Zhang, Yang

    2007-05-03

    We developed LOMETS, a local threading meta-server, for quick and automated predictions of protein tertiary structures and spatial constraints. Nine state-of-the-art threading programs are installed and run in a local computer cluster, which ensure...

  11. Integrins and other cell surface receptors have been fertile grounds for structure prediction experiments. Recently

    E-print Network

    Springer, Timothy A.

    802 Integrins and other cell surface receptors have been fertile grounds for structure prediction predictions, and also reveal how ligand binding by integrins is conformationally regulated. Addresses Center-EGF integrin EGF LDLR low-density lipoprotein receptor LGA local global alignment MIDAS metal ion dependent

  12. Protein Structure Prediction using String Kernels Huzefa Rangwala, Kevin DeRonne, George Karypis

    E-print Network

    Minnesota, University of

    Protein Structure Prediction using String Kernels Huzefa Rangwala, Kevin DeRonne, George Karypis in large scale sequencing technologies, we have seen an exponential growth in protein sequence information and evolutionary nature of proteins. Specifically, we focus on three common prediction problems, secondary

  13. Hydraulic trade-offs and space filling enable better predictions of vascular structure and function

    E-print Network

    Thomas, David D.

    Hydraulic trade-offs and space filling enable better predictions of vascular structure and function-offs between hydraulic safety and efficiency. Including these evolutionary drivers leads to predictions for sap how hydraulic safety and efficiency may have shaped the evolution of vascular networks (2, 17), (ii

  14. Incremental Bayesian Networks for Structure Prediction Ivan Titov ivan.titov@cui.unige.ch

    E-print Network

    Titov, Ivan

    of the output structure. Incremental Sigmoid Belief Networks (ISBNs) avoid the need to sum over the possible.g. prediction of phrase struc- ture trees for sentences), biology (e.g. protein struc- ture prediction mod- els which we call Incremental Sigmoid Belief Networks (ISBNs), which are closely related

  15. A Global Optimization Strategy for Predicting Protein Tertiary Structure: aaHelical Proteins

    E-print Network

    Schnabel, Bobby

    A Global Optimization Strategy for Predicting Protein Tertiary Structure: aa­Helical Proteins­Gordon thg@water.lbl.gov 510­486­7365 (phone) 510­486­6488 (Fax) Short title: Global Optimization Strategy, hydrophobic effect #12; Abstract We present a global optimization strategy that incorporates predicted soft

  16. Semiempirical Predictions of Chemical Degradation Reaction Mechanisms of CL-20 as Related to Molecular Structure

    SciTech Connect

    Qasim, Mohammad M.; Furey, John; Fredrickson, Herbert L.; Szecsody, Jim E.; Mcgrath, Chris J.; Bajpai, Rakesh

    2004-10-01

    Quantum mechanical methods and force field molecular mechanics were used to characterize cage cyclic nitramines and to predict environmental degradation mechanisms. Due to structural similarities it is predicted that, under homologous circumstances, the major environmental RDX degradation pathways should also be effective for CL-20 and similar cyclic nitramines.

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

    ERIC Educational Resources Information Center

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

    2000-01-01

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

  18. Reduction of model structure bias in the prediction of critical source areas

    NASA Astrophysics Data System (ADS)

    Frey, M.; Stamm, C.; Schneider, M. K.; Reichert, P.

    2009-04-01

    Effective mitigation strategies to reduce the contamination of surface waters by agrochemicals rely on an accurate identification of critical source areas (CSA). We used a spatially distributed hydrological model to identify CSA in a small agricultural catchment in Switzerland. Since the knowledge about model parameters is coarse, prior predictions of CSA involve large uncertainties. We investigated to which degree river discharge data can constrain parameter values and improve the prediction. Thereby, we combined the prior knowledge used for the prior prediction with additional river discharge data within a Bayesian inference approach. In order to consider the effect of uncertainty in input data and in the model structure we formulated the likelihood function with an autoregressive error model additive to the river discharge calculated by the deterministic hydrological model. The additional information gained from river discharge data slightly reduced the width of some of the marginal parameter distributions and the prediction uncertainty for high or low-risk areas. However, the analysis of the statistical assumptions of the inference process revealed deficits in the model structure. Thus the base flow during dry periods tended to be overestimated. By making the percolation process water table dependent the base flow prediction could be improved. These improvements in model structure significantly reduced the model structure bias and thus improved the statistical basis of the probabilistic CSA prediction. Furthermore, the improved model structure led to a large constraint of the CSA prediction uncertainty.

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

    NASA Astrophysics Data System (ADS)

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

    1984-07-01

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

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

    NASA Technical Reports Server (NTRS)

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

    1984-01-01

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

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

    NASA Technical Reports Server (NTRS)

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

    1982-01-01

    An apparatus was constructed to provide measurements in open sprays with no zones of recirculation, in order to provide well-defined conditions for use in evaluating spray models. Measurements were completed in a gas jet, in order to test experimental methods, and are currently in progress for nonevaporating sprays. A locally homogeneous flow (LHF) model where interphase transport rates are assumed to be infinitely fast; a separated flow (SF) model which allows for finite interphase transport rates but neglects effects of turbulent fluctuations on drop motion; and a stochastic SF model which considers effects of turbulent fluctuations on drop motion were evaluated using existing data on particle-laden jets. The LHF model generally overestimates rates of particle dispersion while the SF model underestimates dispersion rates. The stochastic SF flow yield satisfactory predictions except at high particle mass loadings where effects of turbulence modulation may have caused the model to overestimate turbulence levels.

  2. Correlation of predicted and measured thermal stresses on a truss-type aircraft structure

    NASA Technical Reports Server (NTRS)

    Jenkins, J. M.; Schuster, L. S.; Carter, A. L.

    1978-01-01

    A test structure representing a portion of a hypersonic vehicle was instrumented with strain gages and thermocouples. This test structure was then subjected to laboratory heating representative of supersonic and hypersonic flight conditions. A finite element computer model of this structure was developed using several types of elements with the NASA structural analysis (NASTRAN) computer program. Temperature inputs from the test were used to generate predicted model thermal stresses and these were correlated with the test measurements.

  3. Structural Damage Prediction and Analysis for Hypervelocity Impacts: Handbook

    NASA Technical Reports Server (NTRS)

    Elfer, N. C.

    1996-01-01

    This handbook reviews the analysis of structural damage on spacecraft due to hypervelocity impacts by meteoroid and space debris. These impacts can potentially cause structural damage to a Space Station module wall. This damage ranges from craters, bulges, minor penetrations, and spall to critical damage associated with a large hole, or even rupture. The analysis of damage depends on a variety of assumptions and the area of most concern is at a velocity beyond well controlled laboratory capability. In the analysis of critical damage, one of the key questions is how much momentum can actually be transfered to the pressure vessel wall. When penetration occurs without maximum bulging at high velocity and obliquities (if less momentum is deposited in the rear wall), then large tears and rupture may be avoided. In analysis of rupture effects of cylindrical geometry, biaxial loading, bending of the crack, a central hole strain rate and R-curve effects are discussed.

  4. RNA 3D Modules in Genome-Wide Predictions of RNA 2D Structure

    PubMed Central

    Theis, Corinna; Zirbel, Craig L.; zu Siederdissen, Christian Höner; Anthon, Christian; Hofacker, Ivo L.; Nielsen, Henrik; Gorodkin, Jan

    2015-01-01

    Recent experimental and computational progress has revealed a large potential for RNA structure in the genome. This has been driven by computational strategies that exploit multiple genomes of related organisms to identify common sequences and secondary structures. However, these computational approaches have two main challenges: they are computationally expensive and they have a relatively high false discovery rate (FDR). Simultaneously, RNA 3D structure analysis has revealed modules composed of non-canonical base pairs which occur in non-homologous positions, apparently by independent evolution. These modules can, for example, occur inside structural elements which in RNA 2D predictions appear as internal loops. Hence one question is if the use of such RNA 3D information can improve the prediction accuracy of RNA secondary structure at a genome-wide level. Here, we use RNAz in combination with 3D module prediction tools and apply them on a 13-way vertebrate sequence-based alignment. We find that RNA 3D modules predicted by metaRNAmodules and JAR3D are significantly enriched in the screened windows compared to their shuffled counterparts. The initially estimated FDR of 47.0% is lowered to below 25% when certain 3D module predictions are present in the window of the 2D prediction. We discuss the implications and prospects for further development of computational strategies for detection of RNA 2D structure in genomic sequence. PMID:26509713

  5. Full-length RNA structure prediction of the HIV-1 genome reveals a conserved core domain.

    PubMed

    Sükösd, Zsuzsanna; Andersen, Ebbe S; Seemann, Stefan E; Jensen, Mads Krogh; Hansen, Mathias; Gorodkin, Jan; Kjems, Jřrgen

    2015-12-01

    A distance constrained secondary structural model of the ?10 kb RNA genome of the HIV-1 has been predicted but higher-order structures, involving long distance interactions, are currently unknown. We present the first global RNA secondary structure model for the HIV-1 genome, which integrates both comparative structure analysis and information from experimental data in a full-length prediction without distance constraints. Besides recovering known structural elements, we predict several novel structural elements that are conserved in HIV-1 evolution. Our results also indicate that the structure of the HIV-1 genome is highly variable in most regions, with a limited number of stable and conserved RNA secondary structures. Most interesting, a set of long distance interactions form a core organizing structure (COS) that organize the genome into three major structural domains. Despite overlapping protein-coding regions the COS is supported by a particular high frequency of compensatory base changes, suggesting functional importance for this element. This new structural element potentially organizes the whole genome into three major domains protruding from a conserved core structure with potential roles in replication and evolution for the virus. PMID:26476446

  6. Full-length RNA structure prediction of the HIV-1 genome reveals a conserved core domain

    PubMed Central

    Sükösd, Zsuzsanna; Andersen, Ebbe S.; Seemann, Stefan E.; Jensen, Mads Krogh; Hansen, Mathias; Gorodkin, Jan; Kjems, Jřrgen

    2015-01-01

    A distance constrained secondary structural model of the ?10 kb RNA genome of the HIV-1 has been predicted but higher-order structures, involving long distance interactions, are currently unknown. We present the first global RNA secondary structure model for the HIV-1 genome, which integrates both comparative structure analysis and information from experimental data in a full-length prediction without distance constraints. Besides recovering known structural elements, we predict several novel structural elements that are conserved in HIV-1 evolution. Our results also indicate that the structure of the HIV-1 genome is highly variable in most regions, with a limited number of stable and conserved RNA secondary structures. Most interesting, a set of long distance interactions form a core organizing structure (COS) that organize the genome into three major structural domains. Despite overlapping protein-coding regions the COS is supported by a particular high frequency of compensatory base changes, suggesting functional importance for this element. This new structural element potentially organizes the whole genome into three major domains protruding from a conserved core structure with potential roles in replication and evolution for the virus. PMID:26476446

  7. Structure Based Predictive Model for Coal Char Combustion

    SciTech Connect

    Robert Hurt; Joseph Calo; Robert Essenhigh; Christopher Hadad

    2000-12-30

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

  8. Experimental validation of finite element and boundary element methods for predicting structural vibration and radiated noise

    NASA Technical Reports Server (NTRS)

    Seybert, A. F.; Wu, T. W.; Wu, X. F.

    1994-01-01

    This research report is presented in three parts. In the first part, acoustical analyses were performed on modes of vibration of the housing of a transmission of a gear test rig developed by NASA. The modes of vibration of the transmission housing were measured using experimental modal analysis. The boundary element method (BEM) was used to calculate the sound pressure and sound intensity on the surface of the housing and the radiation efficiency of each mode. The radiation efficiency of each of the transmission housing modes was then compared to theoretical results for a finite baffled plate. In the second part, analytical and experimental validation of methods to predict structural vibration and radiated noise are presented. A rectangular box excited by a mechanical shaker was used as a vibrating structure. Combined finite element method (FEM) and boundary element method (BEM) models of the apparatus were used to predict the noise level radiated from the box. The FEM was used to predict the vibration, while the BEM was used to predict the sound intensity and total radiated sound power using surface vibration as the input data. Vibration predicted by the FEM model was validated by experimental modal analysis; noise predicted by the BEM was validated by measurements of sound intensity. Three types of results are presented for the total radiated sound power: sound power predicted by the BEM model using vibration data measured on the surface of the box; sound power predicted by the FEM/BEM model; and sound power measured by an acoustic intensity scan. In the third part, the structure used in part two was modified. A rib was attached to the top plate of the structure. The FEM and BEM were then used to predict structural vibration and radiated noise respectively. The predicted vibration and radiated noise were then validated through experimentation.

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

    PubMed Central

    King, R D; Srinivasan, A

    1996-01-01

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

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

    PubMed Central

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

    2014-01-01

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

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

    PubMed

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

    2014-01-01

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

  12. Direct-Coupling Analysis of nucleotide coevolution facilitates RNA secondary and tertiary structure prediction

    PubMed Central

    De Leonardis, Eleonora; Lutz, Benjamin; Ratz, Sebastian; Cocco, Simona; Monasson, Rémi; Schug, Alexander; Weigt, Martin

    2015-01-01

    Despite the biological importance of non-coding RNA, their structural characterization remains challenging. Making use of the rapidly growing sequence databases, we analyze nucleotide coevolution across homologous sequences via Direct-Coupling Analysis to detect nucleotide-nucleotide contacts. For a representative set of riboswitches, we show that the results of Direct-Coupling Analysis in combination with a generalized Nussinov algorithm systematically improve the results of RNA secondary structure prediction beyond traditional covariance approaches based on mutual information. Even more importantly, we show that the results of Direct-Coupling Analysis are enriched in tertiary structure contacts. By integrating these predictions into molecular modeling tools, systematically improved tertiary structure predictions can be obtained, as compared to using secondary structure information alone. PMID:26420827

  13. Direct-Coupling Analysis of nucleotide coevolution facilitates RNA secondary and tertiary structure prediction.

    PubMed

    De Leonardis, Eleonora; Lutz, Benjamin; Ratz, Sebastian; Cocco, Simona; Monasson, Rémi; Schug, Alexander; Weigt, Martin

    2015-12-01

    Despite the biological importance of non-coding RNA, their structural characterization remains challenging. Making use of the rapidly growing sequence databases, we analyze nucleotide coevolution across homologous sequences via Direct-Coupling Analysis to detect nucleotide-nucleotide contacts. For a representative set of riboswitches, we show that the results of Direct-Coupling Analysis in combination with a generalized Nussinov algorithm systematically improve the results of RNA secondary structure prediction beyond traditional covariance approaches based on mutual information. Even more importantly, we show that the results of Direct-Coupling Analysis are enriched in tertiary structure contacts. By integrating these predictions into molecular modeling tools, systematically improved tertiary structure predictions can be obtained, as compared to using secondary structure information alone. PMID:26420827

  14. Direct-Coupling Analysis of nucleotide coevolution facilitates RNA secondary and tertiary structure prediction

    E-print Network

    De Leonardis, Eleonora; Ratz, Sebastian; Cocco, Simona; Monasson, Remi; Schug, Alexander; Weigt, Martin

    2015-01-01

    Despite the biological importance of non-coding RNA, their structural characterization remains challenging. Making use of the rapidly growing sequence databases, we analyze nucleotide coevolution across homologous sequences via Direct-Coupling Analysis to detect nucleotide-nucleotide contacts. For a representative set of riboswitches, we show that the results of Direct-Coupling Analysis in combination with a generalized Nussinov algorithm systematically improve the results of RNA secondary structure prediction beyond traditional covariance approaches based on mutual information. Even more importantly, we show that the results of Direct-Coupling Analysis are enriched in tertiary structure contacts. By integrating these predictions into molecular modeling tools, systematically improved tertiary structure predictions can be obtained, as compared to using secondary structure information alone.

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

    PubMed

    Hayat, Sikander; Sander, Chris; Marks, Debora S; Elofsson, Arne

    2015-04-28

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

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

    PubMed Central

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

    2015-01-01

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

  17. Hybrid experimental/analytical models of structural dynamics - Creation and use for predictions

    NASA Technical Reports Server (NTRS)

    Balmes, Etienne

    1993-01-01

    An original complete methodology for the construction of predictive models of damped structural vibrations is introduced. A consistent definition of normal and complex modes is given which leads to an original method to accurately identify non-proportionally damped normal mode models. A new method to create predictive hybrid experimental/analytical models of damped structures is introduced, and the ability of hybrid models to predict the response to system configuration changes is discussed. Finally a critical review of the overall methodology is made by application to the case of the MIT/SERC interferometer testbed.

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

    PubMed Central

    Haselhuhn, Michael P.; Wong, Elaine M.

    2012-01-01

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

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

    PubMed

    Ejaz, Naveed; Hamada, Masashi; Diedrichsen, Jörn

    2015-07-01

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

  20. Evaluation and improvement of multiple sequence methods for protein secondary structure prediction.

    PubMed

    Cuff, J A; Barton, G J

    1999-03-01

    A new dataset of 396 protein domains is developed and used to evaluate the performance of the protein secondary structure prediction algorithms DSC, PHD, NNSSP, and PREDATOR. The maximum theoretical Q3 accuracy for combination of these methods is shown to be 78%. A simple consensus prediction on the 396 domains, with automatically generated multiple sequence alignments gives an average Q3 prediction accuracy of 72.9%. This is a 1% improvement over PHD, which was the best single method evaluated. Segment Overlap Accuracy (SOV) is 75.4% for the consensus method on the 396-protein set. The secondary structure definition method DSSP defines 8 states, but these are reduced by most authors to 3 for prediction. Application of the different published 8- to 3-state reduction methods shows variation of over 3% on apparent prediction accuracy. This suggests that care should be taken to compare methods by the same reduction method. Two new sequence datasets (CB513 and CB251) are derived which are suitable for cross-validation of secondary structure prediction methods without artifacts due to internal homology. A fully automatic World Wide Web service that predicts protein secondary structure by a combination of methods is available via http://barton.ebi.ac.uk/. PMID:10081963

  1. Improving protein secondary structure prediction using a multi-modal BP method.

    PubMed

    Qu, Wu; Sui, Haifeng; Yang, Bingru; Qian, Wenbin

    2011-10-01

    Methods for predicting protein secondary structures provide information that is useful both in ab initio structure prediction and as additional restraints for fold recognition algorithms. Secondary structure predictions may also be used to guide the design of site directed mutagenesis studies, and to locate potential functionally important residues. In this article, we propose a multi-modal back propagation neural network (MMBP) method for predicting protein secondary structures. Using a Knowledge Discovery Theory based on Inner Cognitive Mechanism (KDTICM) method, we have constructed a compound pyramid model (CPM), which is composed of three layers of intelligent interface that integrate multi-modal back propagation neural network (MMBP), mixed-modal SVM (MMS), modified Knowledge Discovery in Databases (KDD(?)) process and so on. The CPM method is both an integrated web server and a standalone application that exploits recent advancements in knowledge discovery and machine learning to perform very accurate protein secondary structure predictions. Using a non-redundant test dataset of 256 proteins from RCASP256, the CPM method achieves an average Q(3) score of 86.13% (SOV99=84.66%). Extensive testing indicates that this is significantly better than any other method currently available. Assessments using RS126 and CB513 datasets indicate that the CPM method can achieve average Q(3) score approaching 83.99% (SOV99=80.25%) and 85.58% (SOV99=81.15%). By using both sequence and structure databases and by exploiting the latest techniques in machine learning it is possible to routinely predict protein secondary structure with an accuracy well above 80%. A program and web server, called CPM, which performs these secondary structure predictions, is accessible at http://kdd.ustb.edu.cn/protein_Web/. PMID:21880310

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

    PubMed Central

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

    2014-01-01

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

  3. HYPROSP: a hybrid protein secondary structure prediction algorithm--a knowledge-based approach.

    PubMed

    Wu, Kuen-Pin; Lin, Hsin-Nan; Chang, Jia-Ming; Sung, Ting-Yi; Hsu, Wen-Lian

    2004-01-01

    We develop a knowledge-based approach (called PROSP) for protein secondary structure prediction. The knowledge base contains small peptide fragments together with their secondary structural information. A quantitative measure M, called match rate, is defined to measure the amount of structural information that a target protein can extract from the knowledge base. Our experimental results show that proteins with a higher match rate will likely be predicted more accurately based on PROSP. That is, there is roughly a monotone correlation between the prediction accuracy and the amount of structure matching with the knowledge base. To fully utilize the strength of our knowledge base, a hybrid prediction method is proposed as follows: if the match rate of a target protein is at least 80%, we use the extracted information to make the prediction; otherwise, we adopt a popular machine-learning approach. This comprises our hybrid protein structure prediction (HYPROSP) approach. We use the DSSP and EVA data as our datasets and PSIPRED as our underlying machine-learning algorithm. For target proteins with match rate at least 80%, the average Q3 of PROSP is 3.96 and 7.2 better than that of PSIPRED on DSSP and EVA data, respectively. PMID:15448186

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

    NASA Technical Reports Server (NTRS)

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

    1983-01-01

    The structure of particle-laden jets and nonevaporating and evaporating sprays was measured in order to evaluate models of these processes. Three models are being evaluated: (1) a locally homogeneous flow model, where slip between the phases is neglected and the flow is assumed to be in local thermodynamic equilibrium; (2) a deterministic separated flow model, where slip and finite interphase transport rates are considered but effects of particle/drop dispersion by turbulence and effects of turbulence on interphase transport rates are ignored; and (3) a stochastic separated flow model, where effects of interphase slip, turbulent dispersion and turbulent fluctuations are considered using random sampling for turbulence properties in conjunction with random-walk computations for particle motion. All three models use a k-e-g turbulence model. All testing and data reduction are completed for the particle laden jets. Mean and fluctuating velocities of the continuous phase and mean mixture fraction were measured in the evaporating sprays.

  5. STRUCTURE-BASED PREDICTIVE MODEL FOR COAL CHAR COMBUSTION

    SciTech Connect

    Robert H. Hurt; Eric M. Suuberg

    2000-05-03

    This report is part on the ongoing effort at Brown University and Ohio State University to develop structure based models of coal combustion. A very fundamental approach is taken to the description of coal chars and their reaction processes, and the results are therefore expected to have broad applicability to the spectrum of carbon materials of interest in energy technologies. This quarter, our work on structure development in carbons continued. A combination of hot stage in situ and ex situ polarized light microscopy was used to identify the preferred orientational of graphene layers at gas interfaces in pitches used as carbon material precursors. The experiments show that edge-on orientation is the equilibrium state of the gas/pitch interface, implying that basal-rich surfaces have higher free energies than edge-rich surfaces in pitch. This result is in agreement with previous molecular modeling studies and TEM observations in the early stages of carbonization. The results may have important implications for the design of tailored carbons with edge-rich or basal-rich surfaces. In the computational chemistry task, we have continued our investigations into the reactivity of large aromatic rings. The role of H-atom abstraction as well as radical addition to monocyclic aromatic rings has been examined, and a manuscript is currently being revised after peer review. We have also shown that OH radical is more effective than H atom in the radical addition process with monocyclic rings. We have extended this analysis to H-atom and OH-radical addition to phenanthrene. Work on combustion kinetics focused on the theoretical analysis of the data previously gathered using thermogravametric analysis.

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

    PubMed Central

    Roy, Ambrish; Xu, Dong; Poisson, Jonathan; Zhang, Yang

    2011-01-01

    Genome sequencing projects have ciphered millions of protein sequence, which require knowledge of their structure and function to improve the understanding of their biological role. Although experimental methods can provide detailed information for a small fraction of these proteins, computational modeling is needed for the majority of protein molecules which are experimentally uncharacterized. The I-TASSER server is an on-line workbench for high-resolution modeling of protein structure and function. Given a protein sequence, a typical output from the I-TASSER server includes secondary structure prediction, predicted solvent accessibility of each residue, homologous template proteins detected by threading and structure alignments, up to five full-length tertiary structural models, and structure-based functional annotations for enzyme classification, Gene Ontology terms and protein-ligand binding sites. All the predictions are tagged with a confidence score which tells how accurate the predictions are without knowing the experimental data. To facilitate the special requests of end users, the server provides channels to accept user-specified inter-residue distance and contact maps to interactively change the I-TASSER modeling; it also allows users to specify any proteins as template, or to exclude any template proteins during the structure assembly simulations. The structural information could be collected by the users based on experimental evidences or biological insights with the purpose of improving the quality of I-TASSER predictions. The server was evaluated as the best programs for protein structure and function predictions in the recent community-wide CASP experiments. There are currently >20,000 registered scientists from over 100 countries who are using the on-line I-TASSER server. PMID:22082966

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

    SciTech Connect

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

    2010-11-01

    Predicting failure of thin-walled structures from explosive loading is a very complex task. The problem can be divided into two parts; the detonation of the explosive to produce the loading on the structure, and secondly the structural response. First, the factors that affect the explosive loading include: size, shape, stand-off, confinement, and chemistry of the explosive. The goal of the first part of the analysis is predicting the pressure on the structure based on these factors. The hydrodynamic code CTH is used to conduct these calculations. Secondly, the response of a structure from the explosive loading is predicted using a detailed finite element model within the explicit analysis code Presto. Material response, to failure, must be established in the analysis to model the failure of this class of structures; validation of this behavior is also required to allow these analyses to be predictive for their intended use. The presentation will detail the validation tests used to support this program. Validation tests using explosively loaded aluminum thin flat plates were used to study all the aspects mentioned above. Experimental measurements of the pressures generated by the explosive and the resulting plate deformations provided data for comparison against analytical predictions. These included pressure-time histories and digital image correlation of the full field plate deflections. The issues studied in the structural analysis were mesh sensitivity, strain based failure metrics, and the coupling methodologies between the blast and structural models. These models have been successfully validated using these tests, thereby increasing confidence of the results obtained in the prediction of failure thresholds of complex structures, including aircraft.

  8. New Secondary Structure Prediction software package using automatically trained Bayesian Networks

    E-print Network

    to protein structure A protein is a string of amino acids (or residues). The catalytic, binding this hierarchy should be. #12;One commonly accepted hierarchy is: · primary structure: The linear amino acid). Predicting the three-dimensional shape of proteins from their amino acid sequence is widely believed

  9. A METHOD FOR IDENTIFYING REPETITION STRUCTURE IN MUSICAL AUDIO BASED ON TIME SERIES PREDICTION

    E-print Network

    Dixon, Simon

    A METHOD FOR IDENTIFYING REPETITION STRUCTURE IN MUSICAL AUDIO BASED ON TIME SERIES PREDICTION annotation as a source of information. In particular, the task of music structure analysis has re- cently a frequently occurring audio segment [4]. In this work, we propose a novel approach for detecting repetition

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

    E-print Network

    Mandel-Gutfreund, Yael

    Hidden Markov Models That Use Predicted Local Structure for Fold Recognition: Alphabets of Backbone, California 2 Affymetrix, Inc., Emeryville, California 3 Department of Chemistry and Biochemistry, University the structure of the large number of putative proteins discovered by genome sequencing projects. Fold

  11. to confirm our structure's predicted negative refraction, using the interfaces of the

    E-print Network

    Jones, James Holland

    to confirm our structure's predicted negative refraction, using the interfaces of the photonic of the ­M interface. Our structure exhibits the maximum angular range of negative refraction at an operating corresponds to negative refraction7 . The negative index of refraction was determined to be 1.94, which

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

    E-print Network

    Pollastri, Gianluca

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

  13. Automated structure prediction of weakly homologous proteins on a genomic scale

    E-print Network

    Zhang, Yang

    Automated structure prediction of weakly homologous proteins on a genomic scale Yang Zhang for their success is the completeness of the library of solved structures in the Protein Data Bank (PDB) (10). Recently, it was demonstrated that the PDB library is most likely complete for single domain protein

  14. Protein structure prediction, fold recognition, homology modeling and design rely mainly on statistical effective energy

    E-print Network

    Lazaridis, Themis

    139 Protein structure prediction, fold recognition, homology modeling and design rely mainly on statistical effective energy functions. Although the theoretical foundation of such functions is not clear@tammy.harvard.edu Current Opinion in Structural Biology 2000, 10:139­145 0959-440X/00/$ -- see front matter © 2000 Elsevier

  15. Data Mining Approach to Ab-Initio Prediction of Crystal Structure Dane Morgan, Gerbrand Ceder

    E-print Network

    Curtarolo, Stefano

    Data Mining Approach to Ab-Initio Prediction of Crystal Structure Dane Morgan, Gerbrand Ceder of possible structures for new alloys. Here we describe ongoing work on a novel method (Data Mining of Quantum Calculations, or DMQC) that applies data mining techniques to existing ab initio data in order to increase

  16. Pfold: RNA secondary structure prediction using stochastic context-free grammars

    E-print Network

    Hein, Jotun

    Pfold: RNA secondary structure prediction using stochastic context-free grammars Bjarne Knudsen Building for Pathogen Research, South Parks Road, Oxford OX1 3SY, UK Received February 15, 2003; Revised and Accepted April 5, 2003 ABSTRACT RNA secondary structures are important in many biological processes

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

    PubMed Central

    Kmiecik, Sebastian; Jamroz, Michal; Kolinski, Michal

    2014-01-01

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

  18. Predicting three-dimensional structures of transmembrane domains of ?-barrel membrane proteins.

    PubMed

    Naveed, Hammad; Xu, Yun; Jackups, Ronald; Liang, Jie

    2012-01-25

    ?-Barrel membrane proteins are found in the outer membrane of gram-negative bacteria, mitochondria, and chloroplasts. They are important for pore formation, membrane anchoring, and enzyme activity. These proteins are also often responsible for bacterial virulence. Due to difficulties in experimental structure determination, they are sparsely represented in the protein structure databank. We have developed a computational method for predicting structures of the transmembrane (TM) domains of ?-barrel membrane proteins. Based on physical principles, our method can predict structures of the TM domain of ?-barrel membrane proteins of novel topology, including those from eukaryotic mitochondria. Our method is based on a model of physical interactions, a discrete conformational state space, an empirical potential function, as well as a model to account for interstrand loop entropy. We are able to construct three-dimensional atomic structure of the TM domains from sequences for a set of 23 nonhomologous proteins (resolution 1.8-3.0 Ĺ). The median rmsd of TM domains containing 75-222 residues between predicted and measured structures is 3.9 Ĺ for main chain atoms. In addition, stability determinants and protein-protein interaction sites can be predicted. Such predictions on eukaryotic mitochondria outer membrane protein Tom40 and VDAC are confirmed by independent mutagenesis and chemical cross-linking studies. These results suggest that our model captures key components of the organization principles of ?-barrel membrane protein assembly. PMID:22148174

  19. Rotor Airloads Prediction Using Loose Aerodynamic Structural Coupling

    NASA Technical Reports Server (NTRS)

    Potsdam, Mark; Yeo, Hyeonsoo; Johnson, Wayne

    2004-01-01

    This work couples a computational fluid dynamics (CFD) code and rotorcraft computational structural dynamics (CSD) code to calculate helicopter rotor airloads across a range of flight conditions. An iterative loose (weak) coupling methodology is used to couple the CFD and CSD codes on a per revolution, periodic basis. The CFD uses a high fidelity, Navier-Stokes, overset grid methodology with first principles-based wake capturing. Modifications are made to the CFD code for aeroelastic analysis. For a UH-60A Blackhawk helicopter, four challenging level flight conditions are computed: 1) low speed (u = 0.15) with blade-vortex interaction, 2) high speed (u = 0.37) with advancing blade negative lift, 3) high thrust with dynamic stall (u = 0.24), and 4) hover. Results are compared with UH-60A Airloads Program fight test data. Most importantly, for all cases the loose coupling methodology is shown to be stable, convergent, and robust with full coupling of normal force, pitching moment, and chord force. In comparison with flight test data, normal force and pitching moment magnitudes are in good agreement. For the high speed and dynamic stall cases a phase lag in comparison with the data is seen, nonetheless, the shapes of the curves are very good. Overall, the results are noteworthy improvement over lifting line aerodynamics used in rotorcraft comprehensive codes.

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

    SciTech Connect

    Not Available

    1980-11-01

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

  1. SPINE X: improving protein secondary structure prediction by multistep learning coupled with prediction of solvent accessible surface area and backbone torsion angles.

    PubMed

    Faraggi, Eshel; Zhang, Tuo; Yang, Yuedong; Kurgan, Lukasz; Zhou, Yaoqi

    2012-01-30

    Accurate prediction of protein secondary structure is essential for accurate sequence alignment, three-dimensional structure modeling, and function prediction. The accuracy of ab initio secondary structure prediction from sequence, however, has only increased from around 77 to 80% over the past decade. Here, we developed a multistep neural-network algorithm by coupling secondary structure prediction with prediction of solvent accessibility and backbone torsion angles in an iterative manner. Our method called SPINE X was applied to a dataset of 2640 proteins (25% sequence identity cutoff) previously built for the first version of SPINE and achieved a 82.0% accuracy based on 10-fold cross validation (Q(3)). Surpassing 81% accuracy by SPINE X is further confirmed by employing an independently built test dataset of 1833 protein chains, a recently built dataset of 1975 proteins and 117 CASP 9 targets (critical assessment of structure prediction techniques) with an accuracy of 81.3%, 82.3% and 81.8%, respectively. The prediction accuracy is further improved to 83.8% for the dataset of 2640 proteins if the DSSP assignment used above is replaced by a more consistent consensus secondary structure assignment method. Comparison to the popular PSIPRED and CASP-winning structure-prediction techniques is made. SPINE X predicts number of helices and sheets correctly for 21.0% of 1833 proteins, compared to 17.6% by PSIPRED. It further shows that SPINE X consistently makes more accurate prediction in helical residues (6%) without over prediction while PSIPRED makes more accurate prediction in coil residues (3-5%) and over predicts them by 7%. SPINE X Server and its training/test datasets are available at http://sparks.informatics.iupui.edu/ PMID:22045506

  2. Combined multiple sequence reduced protein model approach to predict the tertiary structure of small proteins.

    PubMed

    Ortiz, A R; Kolinski, A; Skolnick, J

    1998-01-01

    By incorporating predicted secondary and tertiary restraints into ab initio folding simulations, low resolution tertiary structures of a test set of 20 nonhomologous proteins have been predicted. These proteins, which represent all secondary structural classes, contain from 37 to 100 residues. Secondary structural restraints are provided by the PHD secondary structure prediction algorithm that incorporates multiple sequence information. Predicted tertiary restraints are obtained from multiple sequence alignments via a two-step process: First, "seed" side chain contacts are identified from a correlated mutation analysis, and then, the seed contacts are "expanded" by an inverse folding algorithm. These predicted restraints are then incorporated into a lattice based, reduced protein model. Depending upon fold complexity, the resulting nativelike topologies exhibit a coordinate root-mean-square deviation, cRMSD, from native between 3.1 and 6.7 A. Overall, this study suggests that the use of restraints derived from multiple sequence alignments combined with a fold assembly algorithm is a promising approach to the prediction of the global topology of small proteins. PMID:9697197

  3. Predicting 3D Structure, Flexibility, and Stability of RNA Hairpins in Monovalent and Divalent Ion Solutions.

    PubMed

    Shi, Ya-Zhou; Jin, Lei; Wang, Feng-Hua; Zhu, Xiao-Long; Tan, Zhi-Jie

    2015-12-15

    A full understanding of RNA-mediated biology would require the knowledge of three-dimensional (3D) structures, structural flexibility, and stability of RNAs. To predict RNA 3D structures and stability, we have previously proposed a three-bead coarse-grained predictive model with implicit salt/solvent potentials. In this study, we further develop the model by improving the implicit-salt electrostatic potential and including a sequence-dependent coaxial stacking potential to enable the model to simulate RNA 3D structure folding in divalent/monovalent ion solutions. The model presented here can predict 3D structures of RNA hairpins with bulges/internal loops (<77 nucleotides) from their sequences at the corresponding experimental ion conditions with an overall improved accuracy compared to the experimental data; the model also makes reliable predictions for the flexibility of RNA hairpins with bulge loops of different lengths at several divalent/monovalent ion conditions. In addition, the model successfully predicts the stability of RNA hairpins with various loops/stems in divalent/monovalent ion solutions. PMID:26682822

  4. Distributions in Protein Conformation Space: Implications for Structure Prediction and Entropy

    PubMed Central

    Sullivan, David C.; Kuntz, Irwin D.

    2004-01-01

    By considering how polymer structures are distributed in conformation space, we show that it is possible to quantify the difficulty of structural prediction and to provide a measure of progress for prediction calculations. The critical issue is the probability that a conformation is found within a specified distance of another conformer. We address this question by constructing a cumulative distribution function (CDF) for the average probability from observations about its limiting behavior at small displacements and numerical simulations of polyalanine chains. We can use the CDF to estimate the likelihood that a structure prediction is better than random chance. For example, the chance of randomly predicting the native backbone structure of a 150-amino-acid protein to low resolution, say within 6 Ĺ, is 10?14. A high-resolution structural prediction, say to 2 Ĺ, is immensely more difficult (10?57). With additional assumptions, the CDF yields the conformational entropy of protein folding from native-state coordinate variance. Or, using values of the conformational entropy change on folding, we can estimate the native state's conformational span. For example, for a 150-mer protein, equilibrium ?-carbon displacements in the native ensemble would be 0.3–0.5 Ĺ based on T?S of 1.42 kcal/(mol residue). PMID:15240450

  5. DOX: A new computational protocol for accurate prediction of the protein-ligand binding structures.

    PubMed

    Rao, Li; Chi, Bo; Ren, Yanliang; Li, Yongjian; Xu, Xin; Wan, Jian

    2016-01-30

    Molecular docking techniques have now been widely used to predict the protein-ligand binding modes, especially when the structures of crystal complexes are not available. Most docking algorithms are able to effectively generate and rank a large number of probable binding poses. However, it is hard for them to accurately evaluate these poses and identify the most accurate binding structure. In this study, we first examined the performance of some docking programs, based on a testing set made of 15 crystal complexes with drug statins for the human 3-hydroxy-3-methylglutaryl coenzyme A reductase (HMGR). We found that most of the top ranking HMGR-statin binding poses, predicted by the docking programs, were energetically unstable as revealed by the high theoretical-level calculations, which were usually accompanied by the large deviations from the geometric parameters of the corresponding crystal binding structures. Subsequently, we proposed a new computational protocol, DOX, based on the joint use of molecular Docking, ONIOM, and eXtended ONIOM (XO) methods to predict the accurate binding structures for the protein-ligand complexes of interest. Our testing results demonstrate that the DOX protocol can efficiently predict accurate geometries for all 15 HMGR-statin crystal complexes without exception. This study suggests a promising computational route, as an effective alternative to the experimental one, toward predicting the accurate binding structures, which is the prerequisite for all the deep understandings of the properties, functions, and mechanisms of the protein-ligand complexes. © 2015 Wiley Periodicals, Inc. PMID:26459237

  6. Predicting protein secondary structure and solvent accessibility with an improved multiple linear regression method.

    PubMed

    Qin, Sanbo; He, Yun; Pan, Xian-Ming

    2005-11-15

    We have improved the multiple linear regression (MLR) algorithm for protein secondary structure prediction by combining it with the evolutionary information provided by multiple sequence alignment of PSI-BLAST. On the CB513 dataset, the three states average overall per-residue accuracy, Q(3), reached 76.4%, while segment overlap accuracy, SOV99, reached 73.2%, using a rigorous jackknife procedure and the strictest reduction of eight states DSSP definition to three states. This represents an improvement of approximately 5% on overall per-residue accuracy compared with previous work. The relative solvent accessibility prediction also benefited from this combination of methods. The system achieved 77.7% average jackknifed accuracy for two states prediction based on a 25% relative solvent accessibility mode, with a Mathews' correlation coefficient of 0.548. The improved MLR secondary structure and relative solvent accessibility prediction server is available at http://spg.biosci.tsinghua.edu.cn/. PMID:16152601

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

    NASA Astrophysics Data System (ADS)

    Sherkat, Ehsan; Rahgozar, Maseud; Asadpour, Masoud

    2015-02-01

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

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

    PubMed Central

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

    2015-01-01

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

  9. Selective refinement and selection of near-native models in protein structure prediction

    PubMed Central

    Zhang, Jiong; Barz, Bagdan; Zhang, Jingfen; Xu, Dong; Kosztin, Ioan

    2015-01-01

    In recent years in silico protein structure prediction reached a level where fully automated servers can generate large pools of near-native structures. However, the identification and further refinement of the best structures from the pool of models remain problematic. To address these issues, we have developed (i) a target-specific selective refinement (SR) protocol; and (ii) molecular dynamics (MD) simulation based ranking (SMDR) method. In SR the all-atom refinement of structures is accomplished via the Rosetta Relax protocol, subject to specific constraints determined by the size and complexity of the target. The best-refined models are selected with SMDR by testing their relative stability against gradual heating through all-atom MD simulations. Through extensive testing we have found that Mufold-MD, our fully automated protein structure prediction server updated with the SR and SMDR modules consistently outperformed its previous versions. PMID:26214389

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

    NASA Technical Reports Server (NTRS)

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

    1996-01-01

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

  11. Atomic structure of the 310 twin in niobium: Theoretical predictions and comparison with experimental observation

    NASA Astrophysics Data System (ADS)

    Campbell, G. H.; King, W. E.; Foiles, S. M.; Ruehle, M.

    1992-11-01

    High-resolution transmission electron microscopy (HREM) has been used to characterize the atomic structure of the symmetric 36.9(degrees) tilt grain boundary with zone (001) tilt axes forming a twin about (310) in Nb. The projected structure was imaged along two different directions in the plane of the boundary and was compared to model structures through high-resolution image simulation. The atomic structure of this (Sigma)-5 boundary was predicted with atomistic simulations using interatomic potentials derived from the Embedded Atom Method (EAM), Finnis-Sinclair (FS), and the Model Generalized Pseudopotential Theory (MGPT). The EAM and FS predicted structures with translations of the adjacent crystals which break mirror symmetry. The MGPT predicted one stable structure with mirror symmetry. The atomic structure of the (310) twin in Nb was found by HREM to be mirror symmetric. These findings indicate that the angular dependent interactions modeled in the MGPT are important for determining the grain boundary structures of bcc transition metals.

  12. Prediction of vibration characteristics in beam structure using sub-scale modeling with experimental validation

    NASA Astrophysics Data System (ADS)

    Zai, Behzad Ahmed; Sami, Saad; Khan, M. Amir; Ahmad, Furqan; Park, Myung Kyun

    2015-09-01

    Geometric or sub-scale modeling techniques are used for the evaluation of large and complex dynamic structures to ensure accurate reproduction of load path and thus leading to true dynamic characteristics of such structures. The sub-scale modeling technique is very effective in the prediction of vibration characteristics of original large structure when the experimental testing is not feasible due to the absence of a large testing facility. Previous researches were more focused on free and harmonic vibration case with little or no consideration for readily encountered random vibration. A sub-scale modeling technique is proposed for estimating the vibration characteristics of any large scale structure such as Launch vehicles, Mega structures, etc., under various vibration load cases by utilizing precise scaled-down model of that dynamic structure. In order to establish an analytical correlation between the original structure and its scaled models, different scale models of isotropic cantilever beam are selected and analyzed under various vibration conditions( i.e. free, harmonic and random) using finite element package ANSYS. The developed correlations are also validated through experimental testing. The prediction made from the vibratory response of the scaled-down beam through the established sets of correlation are found similar to the response measured from the testing of original beam structure. The established correlations are equally applicable in the prediction of dynamic characteristics of any complex structure through its scaled-down models. This paper presents modified sub-scale modeling technique that enables accurate prediction of vibration characteristics of large and complex structure under not only sinusoidal but also for random vibrations.

  13. Molecular stripping in the NF-?B/I?B/DNA genetic regulatory network.

    PubMed

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

    2016-01-01

    Genetic switches based on the [Formula: see text] system are master regulators of an array of cellular responses. Recent kinetic experiments have shown that [Formula: see text] can actively remove NF-?B bound to its genetic sites via a process called "molecular stripping." This allows the [Formula: see text] 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 [Formula: see text] and its various binary and ternary complexes with DNA and the inhibitor I?B. These studies show that the function of the [Formula: see text] genetic switch is realized via an allosteric mechanism. Molecular stripping occurs through the activation of a domain twist mode by the binding of [Formula: see text] that occurs through conformational selection. Free energy calculations for DNA binding show that the binding of [Formula: see text] 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

  14. Molecular docking studies of phytochemicals from Phyllanthus niruri against Hepatitis B DNA Polymerase

    PubMed Central

    Mohan, Mekha; James, Priyanka; Valsalan, Ravisankar; Nazeem, Puthiyaveetil Abdulla

    2015-01-01

    Hepatitis B virus (HBV) infection is the leading cause for liver disorders and can lead to hepatocellular carcinoma, cirrhosis and liver damage which in turn can cause death of patients. HBV DNA Polymerase is essential for HBV replication in the host and hence is used as one of the most potent pharmacological target for the inhibition of HBV. Chronic hepatitis B is currently treated with nucleotide analogues that suppress viral reverse transcriptase activity and most of them are reported to have viral resistance. Therefore, it is of interest to model HBV DNA polymerase to dock known phytochemicals. The present study focuses on homology modeling and molecular docking analysis of phytocompounds from the traditional antidote Phyllanthus niruri and other nucleoside analogues against HBV DNA Polymerase using the software Discovery studio 4.0. 3D structure of HBV DNA Polymerase was predicted based on previously reported alignment. Docking studies revealed that a few phytochemicals from Phyllanthus niruri had good interactions with HBV DNA Polymerase. These compounds had acceptable binding properties for further in vitro validation. Thus the study puts forth experimental validation for traditional antidote and these phytocompounds could be further promoted as potential lead molecule. PMID:26527851

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

    PubMed

    Suresh, V; Parthasarathy, S

    2014-01-01

    We developed a support vector machine based web server called SVM-PB-Pred, to predict the Protein Block for any given amino acid sequence. The input features of SVM-PB-Pred include i) sequence profiles (PSSM) and ii) actual secondary structures (SS) from DSSP method or predicted secondary structures from NPS@ and GOR4 methods. There were three combined input features PSSM+SS(DSSP), PSSM+SS(NPS@) and PSSM+SS(GOR4) used to test and train the SVM models. Similarly, four datasets RS90, DB433, LI1264 and SP1577 were used to develop the SVM models. These four SVM models developed were tested using three different benchmarking tests namely; (i) self consistency, (ii) seven fold cross validation test and (iii) independent case test. The maximum possible prediction accuracy of ~70% was observed in self consistency test for the SVM models of both LI1264 and SP1577 datasets, where PSSM+SS(DSSP) input features was used to test. The prediction accuracies were reduced to ~53% for PSSM+SS(NPS@) and ~43% for PSSM+SS(GOR4) in independent case test, for the SVM models of above two same datasets. Using our method, it is possible to predict the protein block letters for any query protein sequence with ~53% accuracy, when the SP1577 dataset and predicted secondary structure from NPS@ server were used. The SVM-PB-Pred server can be freely accessed through http://bioinfo.bdu.ac.in/~svmpbpred. PMID:23855661

  16. RPI-Pred: predicting ncRNA-protein interaction using sequence and structural information.

    PubMed

    Suresh, V; Liu, Liang; Adjeroh, Donald; Zhou, Xiaobo

    2015-02-18

    RNA-protein complexes are essential in mediating important fundamental cellular processes, such as transport and localization. In particular, ncRNA-protein interactions play an important role in post-transcriptional gene regulation like mRNA localization, mRNA stabilization, poly-adenylation, splicing and translation. The experimental methods to solve RNA-protein interaction prediction problem remain expensive and time-consuming. Here, we present the RPI-Pred (RNA-protein interaction predictor), a new support-vector machine-based method, to predict protein-RNA interaction pairs, based on both the sequences and structures. The results show that RPI-Pred can correctly predict RNA-protein interaction pairs with ?94% prediction accuracy when using sequence and experimentally determined protein and RNA structures, and with ?83% when using sequences and predicted protein and RNA structures. Further, our proposed method RPI-Pred was superior to other existing ones by predicting more experimentally validated ncRNA-protein interaction pairs from different organisms. Motivated by the improved performance of RPI-Pred, we further applied our method for reliable construction of ncRNA-protein interaction networks. The RPI-Pred is publicly available at: http://ctsb.is.wfubmc.edu/projects/rpi-pred. PMID:25609700

  17. Predicting forest structure across space and time using lidar and Landsat time series (Invited)

    NASA Astrophysics Data System (ADS)

    Cohen, W. B.; Pflugmacher, D.; Yang, Z.

    2013-12-01

    Lidar is unprecedented in its ability to provide detailed characterizations of forest structure. However, use of lidar is currently limited to relatively small areas associated with specific projects. Moreover, lidar data are even more severely limited historically, which inhibits retrospective analyses of structure change. Landsat data is commonly dismissed when considering a need to map forest structure due to its lack of sensitivity to structural variability. But with the opening of the archive by USGS, Landsat data can now be used in creative ways that take advantage of dense time series to describe historic disturbance and recovery. Because the condition and state of a forest at any given location is largely a function of its disturbance history, this provides an opportunity to use Landsat time series to inform statistical models that predict current forest structure. Additionally, because Landsat time series go back to 1972, it becomes possible to extend those models back in time to derive structure trajectories for retrospective analyses. We will present the results from one or two studies in the Pacific Northwest, USA that use disturbance history metrics derived from Landsat time series to demonstrate the new power of Landsat to predict forest structure (e.g., aboveground live biomass, height). The primary metrics used relate to the magnitude of the greatest disturbance, pre- and post- disturbance spectral trends, and current spectral properties. This is accomplished using a limited field dataset to translate a lidar coverage into the structure measures of interest, and then sampling the lidar data to build a robust statistical relationship between lidar-derived structure and disturbance history. We examined the effect of number of years of history on prediction strength and found that R2 increases and RMSE decreases for a period of ~20 years. This means we can predict forest structure as far back as 1992, using the 20 years of history information contained the MSS to TM data from 1972-1992. Because the time series data are highly calibrated through time, we can apply the model developed for the current period directly to the Landsat time series from 1972-1992 to predict 1992 forest structure. Results compare well to re-measured field data such that change in forest structure between 1992 and the present could be reliably calculated directly from a difference in the two predictions.

  18. Large-scale model quality assessment for improving protein tertiary structure prediction

    PubMed Central

    Cao, Renzhi; Bhattacharya, Debswapna; Adhikari, Badri; Li, Jilong; Cheng, Jianlin

    2015-01-01

    Motivation: Sampling structural models and ranking them are the two major challenges of protein structure prediction. Traditional protein structure prediction methods generally use one or a few quality assessment (QA) methods to select the best-predicted models, which cannot consistently select relatively better models and rank a large number of models well. Results: Here, we develop a novel large-scale model QA method in conjunction with model clustering to rank and select protein structural models. It unprecedentedly applied 14 model QA methods to generate consensus model rankings, followed by model refinement based on model combination (i.e. averaging). Our experiment demonstrates that the large-scale model QA approach is more consistent and robust in selecting models of better quality than any individual QA method. Our method was blindly tested during the 11th Critical Assessment of Techniques for Protein Structure Prediction (CASP11) as MULTICOM group. It was officially ranked third out of all 143 human and server predictors according to the total scores of the first models predicted for 78 CASP11 protein domains and second according to the total scores of the best of the five models predicted for these domains. MULTICOM’s outstanding performance in the extremely competitive 2014 CASP11 experiment proves that our large-scale QA approach together with model clustering is a promising solution to one of the two major problems in protein structure modeling. Availability and implementation: The web server is available at: http://sysbio.rnet.missouri.edu/multicom_cluster/human/. Contact: chengji@missouri.edu PMID:26072473

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

    PubMed Central

    Spencer, Matt; Eickholt, Jesse; Cheng, Jianlin

    2014-01-01

    Ab initio protein secondary structure (SS) predictions are utilized to generate tertiary structure predictions, which are increasingly demanded due to the rapid discovery of proteins. Although recent developments have slightly exceeded previous methods of SS prediction, accuracy has stagnated around 80% and many wonder if prediction cannot be advanced beyond this ceiling. Disciplines that have traditionally employed neural networks are experimenting with novel deep learning techniques in attempts to stimulate progress. Since neural networks have historically played an important role in SS prediction, we wanted to determine whether deep learning could contribute to the advancement of this field as well. We developed an SS predictor that makes use of the position-specific scoring matrix generated by PSI-BLAST and deep learning network architectures, which we call DNSS. Graphical processing units and CUDA software optimize the deep network architecture and efficiently train the deep networks. Optimal parameters for the training process were determined, and a workflow comprising three separately trained deep networks was constructed in order to make refined predictions. This deep learning network approach was used to predict SS for a fully independent test data set of 198 proteins, achieving a Q3 accuracy of 80.7% and a Sov accuracy of 74.2%. PMID:25750595

  20. Towards Practical Carbonation Prediction and Modelling for Service Life Design of Reinforced Concrete Structures

    NASA Astrophysics Data System (ADS)

    Ekolu, O. S.

    2015-11-01

    Amongst the scientific community, the interest in durability of concrete structures has been high for quite a long time of over 40 years. Of the various causes of degradation of concrete structures, corrosion is the most widespread durability problem and carbonation is one of the two causes of steel reinforcement corrosion. While much scientific understanding has been gained from the numerous carbonation studies undertaken over the past years, it is still presently not possible to accurately predict carbonation and apply it in design of structures. This underscores the complex nature of the mechanisms as influenced by several interactive factors. Based on critical literature and some experience of the author, it is found that there still exist major challenges in establishing a mathematical constitutive relation for realistic carbonation prediction. While most current models employ permeability /diffusion as the main model property, analysis shows that the most practical material property would be compressive strength, which has a low coefficient of variation of 20% compared to 30 to 50% for permeability. This important characteristic of compressive strength, combined with its merit of simplicity and data availability at all stages of a structure's life, promote its potential use in modelling over permeability. By using compressive strength in carbonation prediction, the need for accelerated testing and permeability measurement can be avoided. This paper attempts to examine the issues associated with carbonation prediction, which could underlie the current lack of a sound established prediction method. Suggestions are then made for possible employment of different or alternative approaches.

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

    PubMed Central

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

    2011-01-01

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

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

    SciTech Connect

    Caputo, Riccarda; Tekin, Adem

    2011-07-15

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

  3. Prediction of service life of aircraft structural components using the half-cycle method

    NASA Technical Reports Server (NTRS)

    Ko, William L.

    1987-01-01

    The service life of aircraft structural components undergoing random stress cycling was analyzed by the application of fracture mechanics. The initial crack sizes at the critical stress points for the fatigue-crack growth analysis were established through proof load tests. The fatigue-crack growth rates for random stress cycles were calculated using the half-cycle method. A new equation was developed for calculating the number of remaining flights for the structural components. The number of remaining flights predicted by the new equation is much lower than that predicted by the conventional equation.

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

    DOEpatents

    Agarwal, Pratul Kumar (Knoxville, TN)

    2011-07-19

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

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

    NASA Technical Reports Server (NTRS)

    Knight, Norman F.

    1998-01-01

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

  6. Patterned defect structures predicted for graphene are observed on single-layer silica films.

    PubMed

    Yang, Bing; Boscoboinik, Jorge Anibal; Yu, Xin; Shaikhutdinov, Shamil; Freund, Hans-Joachim

    2013-09-11

    Topological defects in two-dimensional materials such as graphene are considered as a tool for tailoring their physical properties. Here, we studied defect structures on a single-layer silica (silicatene) supported on Ru(0001) using a low energy electron diffraction, scanning tunneling microscopy, infrared reflection-absorption spectroscopy, and photoelectron spectroscopy. The results revealed easy formation of periodic defect structures, which were previously predicted for graphene on a theoretical ground, yet experimentally unrealized. The structural similarities between single-layer materials (graphene, silicene, silicatene) open a new playground for deeper understanding and tailoring structural, electronic, and chemical properties of the truly two-dimensional systems. PMID:23937399

  7. Small-molecule 3D Structure Prediction Using Open Crystallography Data

    PubMed Central

    Sadowski, Peter; Baldi, Pierre

    2014-01-01

    Predicting the 3D structures of small molecules is a common problem in chemoinformatics. Even the best methods are inaccurate for complex molecules, and there is a large gap in accuracy between proprietary and free algorithms. Previous work presented COSMOS, a novel, data-driven algorithm that uses knowledge of known structures from the Cambridge Structural Database, and demonstrated performance that was competitive with proprietary algorithms. However, dependence on the Cambridge Structural Database prevented its widespread use. Here we present an updated version of the COSMOS structure predictor, complete with a free structure library derived from open data sources. We demonstrate that COSMOS performs better than other freely-available methods, with a mean RMSD of 1.16 Ĺ and 1.68 Ĺ for organic and metal-organic structures, and a mean prediction time of 60 ms per molecule. This is a 17% and 20% reduction in RMSD compared to the free predictor provided by Open Babel, and ten times faster. The ChemDB webportal provides a COSMOS prediction webserver, as well as downloadable copies of the COSMOS executable and the library of molecular substructures. PMID:24261562

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

    PubMed Central

    Messih, Mario Abdel; Lepore, Rosalba; Tramontano, Anna

    2015-01-01

    Motivation: Predicting the structure of protein loops is very challenging, mainly because they are not necessarily subject to strong evolutionary pressure. This implies that, unlike the rest of the protein, standard homology modeling techniques are not very effective in modeling their structure. However, loops are often involved in protein function, hence inferring their structure is important for predicting protein structure as well as function. Results: We describe a method, LoopIng, based on the Random Forest automated learning technique, which, given a target loop, selects a structural template for it from a database of loop candidates. Compared to the most recently available methods, LoopIng is able to achieve similar accuracy for short loops (4–10 residues) and significant enhancements for long loops (11–20 residues). The quality of the predictions is robust to errors that unavoidably affect the stem regions when these are modeled. The method returns a confidence score for the predicted template loops and has the advantage of being very fast (on average: 1?min/loop). Availability and implementation: www.biocomputing.it/looping Contact: anna.tramontano@uniroma1.it Supplementary information: Supplementary data are available at Bioinformatics online. PMID:26249814

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

    PubMed Central

    Hossain, Md. Anowar

    2014-01-01

    beta-D-N-Acetylhexosaminidase, a family 20 glycosyl hydrolase, catalyzes the removal of ?-1,4-linked N-acetylhexosamine residues from oligosaccharides and their conjugates. We constructed phylogenetic tree of ?-hexosaminidases to analyze the evolutionary history and predicted functions of plant hexosaminidases. Phylogenetic analysis reveals the complex history of evolution of plant ?-hexosaminidase that can be described by gene duplication events. The 3D structure of tomato ?-hexosaminidase (?-Hex-Sl) was predicted by homology modeling using 1now as a template. Structural conformity studies of the best fit model showed that more than 98% of the residues lie inside the favoured and allowed regions where only 0.9% lie in the unfavourable region. Predicted 3D structure contains 531 amino acids residues with glycosyl hydrolase20b domain-I and glycosyl hydrolase20 superfamily domain-II including the (?/?)8 barrel in the central part. The ? and ? contents of the modeled structure were found to be 33.3% and 12.2%, respectively. Eleven amino acids were found to be involved in ligand-binding site; Asp(330) and Glu(331) could play important roles in enzyme-catalyzed reactions. The predicted model provides a structural framework that can act as a guide to develop a hypothesis for ?-Hex-Sl mutagenesis experiments for exploring the functions of this class of enzymes in plant kingdom. PMID:25165734

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

    NASA Astrophysics Data System (ADS)

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

    2015-05-01

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

  11. Hidden Markov models that use predicted local structure for fold recognition: alphabets of backbone geometry.

    PubMed

    Karchin, Rachel; Cline, Melissa; Mandel-Gutfreund, Yael; Karplus, Kevin

    2003-06-01

    An important problem in computational biology is predicting the structure of the large number of putative proteins discovered by genome sequencing projects. Fold-recognition methods attempt to solve the problem by relating the target proteins to known structures, searching for template proteins homologous to the target. Remote homologs that may have significant structural similarity are often not detectable by sequence similarities alone. To address this, we incorporated predicted local structure, a generalization of secondary structure, into two-track profile hidden Markov models (HMMs). We did not rely on a simple helix-strand-coil definition of secondary structure, but experimented with a variety of local structure descriptions, following a principled protocol to establish which descriptions are most useful for improving fold recognition and alignment quality. On a test set of 1298 nonhomologous proteins, HMMs incorporating a 3-letter STRIDE alphabet improved fold recognition accuracy by 15% over amino-acid-only HMMs and 23% over PSI-BLAST, measured by ROC-65 numbers. We compared two-track HMMs to amino-acid-only HMMs on a difficult alignment test set of 200 protein pairs (structurally similar with 3-24% sequence identity). HMMs with a 6-letter STRIDE secondary track improved alignment quality by 62%, relative to DALI structural alignments, while HMMs with an STR track (an expanded DSSP alphabet that subdivides strands into six states) improved by 40% relative to CE. PMID:12784210

  12. Struct2Net: a web service to predict protein–protein interactions using a structure-based approach

    E-print Network

    Singh, Rohit

    Struct2Net is a web server for predicting interactions between arbitrary protein pairs using a structure-based approach. Prediction of protein–protein interactions (PPIs) is a central area of interest and successful ...

  13. PiDNA: Predicting protein-DNA interactions with structural models.

    PubMed

    Lin, Chih-Kang; Chen, Chien-Yu

    2013-07-01

    Predicting binding sites of a transcription factor in the genome is an important, but challenging, issue in studying gene regulation. In the past decade, a large number of protein-DNA co-crystallized structures available in the Protein Data Bank have facilitated the understanding of interacting mechanisms between transcription factors and their binding sites. Recent studies have shown that both physics-based and knowledge-based potential functions can be applied to protein-DNA complex structures to deliver position weight matrices (PWMs) that are consistent with the experimental data. To further use the available structural models, the proposed Web server, PiDNA, aims at first constructing reliable PWMs by applying an atomic-level knowledge-based scoring function on numerous in silico mutated complex structures, and then using the PWM constructed by the structure models with small energy changes to predict the interaction between proteins and DNA sequences. With PiDNA, the users can easily predict the relative preference of all the DNA sequences with limited mutations from the native sequence co-crystallized in the model in a single run. More predictions on sequences with unlimited mutations can be realized by additional requests or file uploading. Three types of information can be downloaded after prediction: (i) the ranked list of mutated sequences, (ii) the PWM constructed by the favourable mutated structures, and (iii) any mutated protein-DNA complex structure models specified by the user. This study first shows that the constructed PWMs are similar to the annotated PWMs collected from databases or literature. Second, the prediction accuracy of PiDNA in detecting relatively high-specificity sites is evaluated by comparing the ranked lists against in vitro experiments from protein-binding microarrays. Finally, PiDNA is shown to be able to select the experimentally validated binding sites from 10,000 random sites with high accuracy. With PiDNA, the users can design biological experiments based on the predicted sequence specificity and/or request mutated structure models for further protein design. As well, it is expected that PiDNA can be incorporated with chromatin immunoprecipitation data to refine large-scale inference of in vivo protein-DNA interactions. PiDNA is available at: http://dna.bime.ntu.edu.tw/pidna. PMID:23703214

  14. External validation of structure-biodegradation relationship (SBR) models for predicting the biodegradability of xenobiotics.

    PubMed

    Devillers, J; Pandard, P; Richard, B

    2013-01-01

    Biodegradation is an important mechanism for eliminating xenobiotics by biotransforming them into simple organic and inorganic products. Faced with the ever growing number of chemicals available on the market, structure-biodegradation relationship (SBR) and quantitative structure-biodegradation relationship (QSBR) models are increasingly used as surrogates of the biodegradation tests. Such models have great potential for a quick and cheap estimation of the biodegradation potential of chemicals. The Estimation Programs Interface (EPI) Suite™ includes different models for predicting the potential aerobic biodegradability of organic substances. They are based on different endpoints, methodologies and/or statistical approaches. Among them, Biowin 5 and 6 appeared the most robust, being derived from the largest biodegradation database with results obtained only from the Ministry of International Trade and Industry (MITI) test. The aim of this study was to assess the predictive performances of these two models from a set of 356 chemicals extracted from notification dossiers including compatible biodegradation data. Another set of molecules with no more than four carbon atoms and substituted by various heteroatoms and/or functional groups was also embodied in the validation exercise. Comparisons were made with the predictions obtained with START (Structural Alerts for Reactivity in Toxtree). Biowin 5 and Biowin 6 gave satisfactorily prediction results except for the prediction of readily degradable chemicals. A consensus model built with Biowin 1 allowed the diminution of this tendency. PMID:24313438

  15. Multiscale model for predicting shear zone structure and permeability in deforming rock

    NASA Astrophysics Data System (ADS)

    Cleary, Paul W.; Pereira, Gerald G.; Lemiale, Vincent; Piane, Claudio Delle; Clennell, M. Ben

    2015-10-01

    A novel multiscale model is proposed for the evolution of faults in rocks, which predicts their internal properties and permeability as strain increases. The macroscale model, based on smoothed particle hydrodynamics (SPH), predicts system scale deformation by a pressure-dependent elastoplastic representation of the rock and shear zone. Being a continuum method, SPH contains no intrinsic information on the grain scale structure or behaviour of the shear zone, so a series of discrete element method microscale shear cell models are embedded into the macroscale model at specific locations. In the example used here, the overall geometry and kinematics of a direct shear test on a block of intact rock is simulated. Deformation is imposed by a macroscale model where stresses and displacement rates are applied at the shear cell walls in contact with the rock. Since the microscale models within the macroscale block of deforming rock now include representations of the grains, the structure of the shear zone, the evolution of the size and shape distribution of these grains, and the dilatancy of the shear zone can all be predicted. The microscale dilatancy can be used to vary the macroscale model dilatancy both spatially and temporally to give a full two-way coupling between the spatial scales. The ability of this model to predict shear zone structure then allows the prediction of the shear zone permeability using the Lattice-Boltzmann method.

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

    SciTech Connect

    Wang, Hui; Department of Physics and Engineering Physics, University of Saskatchewan, Saskatoon, Saskatchewan S7N 5E2 ; LeBlanc, K. A.; Gao, Bo; Yao, Yansun; Canadian Light Source, Saskatoon, Saskatchewan S7N 0X4

    2014-01-28

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

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

    PubMed

    Burbrink, Frank T; McKelvy, Alexander D; Pyron, R Alexander; Myers, Edward A

    2015-11-22

    Predicting species presence and richness on islands is important for understanding the origins of communities and how likely it is that species will disperse and resist extinction. The equilibrium theory of island biogeography (ETIB) and, as a simple model of sampling abundances, the unified neutral theory of biodiversity (UNTB), predict that in situations where mainland to island migration is high, species-abundance relationships explain the presence of taxa on islands. Thus, more abundant mainland species should have a higher probability of occurring on adjacent islands. In contrast to UNTB, if certain groups have traits that permit them to disperse to islands better than other taxa, then phylogeny may be more predictive of which taxa will occur on islands. Taking surveys of 54 island snake communities in the Eastern Nearctic along with mainland communities that have abundance data for each species, we use phylogenetic assembly methods and UNTB estimates to predict island communities. Species richness is predicted by island area, whereas turnover from the mainland to island communities is random with respect to phylogeny. Community structure appears to be ecologically neutral and abundance on the mainland is the best predictor of presence on islands. With regard to young and proximate islands, where allopatric or cladogenetic speciation is not a factor, we find that simple neutral models following UNTB and ETIB predict the structure of island communities. PMID:26609083

  18. Inferential protein structure determination and refinement using fast, electronic structure based backbone amide chemical shift predictions

    E-print Network

    Christensen, Anders S

    2015-01-01

    This report covers the development of a new, fast method for calculating the backbone amide proton chemical shifts in proteins. Through quantum chemical calculations, structure-based forudsiglese the chemical shift for amidprotonen in protein has been parameterized. The parameters are then implemented in a computer program called Padawan. The program has since been implemented in protein folding program Phaistos, wherein the method andvendes to de novo folding of the protein structures and to refine the existing protein structures.

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

    NASA Astrophysics Data System (ADS)

    Mahmood, Zakaria N.; Mahmuddin, Massudi; Mahmood, Mohammed Nooraldeen

    Encoding proteins of amino acid sequence to predict classified into their respective families and subfamilies is important research area. However for a given protein, knowing the exact action whether hormonal, enzymatic, transmembranal or nuclear receptors does not depend solely on amino acid sequence but on the way the amino acid thread folds as well. This study provides a prototype system that able to predict a protein tertiary structure. Several methods are used to develop and evaluate the system to produce better accuracy in protein 3D structure prediction. The Bees Optimization algorithm which inspired from the honey bees food foraging method, is used in the searching phase. In this study, the experiment is conducted on short sequence proteins that have been used by the previous researches using well-known tools. The proposed approach shows a promising result.

  20. A Branch-and-Bound Constraint Optimization Approach to the HPNX Structure Prediction Problem

    E-print Network

    Will, Sebastian

    - bets is the HPNX-alphabet 3], which considers hydrophobic amino acids as well as positively are interesting if one wants to design arti cial proteins (for drug design). For the time being, one problem with lattice proteins. Examples of how lattice proteins can be used for predicting the native structure

  1. Structure Prediction in an HP-type Lattice with an Extended Alphabet

    E-print Network

    Will, Sebastian

    of these alphabets is the HPNX-alphabet 6], which considers hydrophobic amino acids as well as positive and negative wants to design arti cial proteins (for drug design). For the time being, one problem there is 1 #12 with lattice proteins. Examples of how lattice proteins can be used for predicting the native structure

  2. Predicting Crystal Structures with Data Mining of Quantum Calculations Stefano Curtarolo,1

    E-print Network

    Curtarolo, Stefano

    Predicting Crystal Structures with Data Mining of Quantum Calculations Stefano Curtarolo,1 Dane studying a new system. This can be contrasted with data-centered methods, which mine existing data obtained from results already collected on other systems. We refer to this approach as data-mining

  3. Relative Packing Groups in Template-Based Structure Prediction: Cooperative Effects of True Positive Constraints

    PubMed Central

    Day, Ryan; Qu, Xiaotao; Swanson, Rosemarie; Bohannan, Zach; Bliss, Robert

    2011-01-01

    Abstract Most current template-based structure prediction methods concentrate on finding the correct backbone conformation and then packing sidechains within that backbone. Our packing-based method derives distance constraints from conserved relative packing groups (RPGs). In our refinement approach, the RPGs provide a level of resolution that restrains global topology while allowing conformational sampling. In this study, we test our template-based structure prediction method using 51 prediction units from CASP7 experiments. RPG-based constraints are able to substantially improve approximately two-thirds of starting templates. Upon deeper investigation, we find that true positive spatial constraints, especially those non-local in sequence, derived from the RPGs were important to building nearer native models. Surprisingly, the fraction of incorrect or false positive constraints does not strongly influence the quality of the final candidate. This result indicates that our RPG-based true positive constraints sample the self-consistent, cooperative interactions of the native structure. The lack of such reinforcing cooperativity explains the weaker effect of false positive constraints. Generally, these findings are encouraging indications that RPGs will improve template-based structure prediction. PMID:21210729

  4. Structure prediction for the down state of a potassium channel voltage sensor

    E-print Network

    Grabe, Michael

    LETTERS Structure prediction for the down state of a potassium channel voltage sensor Michael Grabe1 *{, Helen C. Lai1,2 *{, Monika Jain1 , Yuh Nung Jan1 & Lily Yeh Jan1 Voltage-gated potassium (Kv) channels, essential for regulating potassium uptake and cell volume in plants and electrical excitabil- ity

  5. Space Weather Forecasting Identifying periodic block-structured models to predict

    E-print Network

    Space Weather Forecasting Identifying periodic block-structured models to predict magnetic storms, can attain speeds of over 900 km/s. A typical cross-sectional solar wind distribution is shown lines, which can overwhelm and destroy transform- ers and electrical networks [2]. Figure 7 shows damage

  6. Alignment and Structure Prediction of Divergent Protein Families: Periplasmic and Outer Membrane

    E-print Network

    Church, George M.

    and Department of Genetics, Harvard Medical School, 200 Longwood Ave Boston, MA 02115, USA Broad-speci®city ef as detailed examples. Gibbs sampling, hidden Markov models, and other analysis techniques were used to locate prediction algorithms in order to identify conserved structural features in protein families. This process

  7. HYPROSP: A Hybrid Protein Secondary Structure Prediction Algorithm --A Knowledge-Based Approach*

    E-print Network

    Chu, Hao-hua

    . This comprises our hybrid protein structure prediction (HYPROSP) approach. We use the DSSP and EVA data as our at least 80%, the average Q3 of PROSP is 3.96 and 7.2 better than that of PSIPRED on DSSP and EVA data

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

    EPA Science Inventory

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

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

    E-print Network

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

  10. Ocean circulation model predicts high genetic structure observed in a long-lived pelagic developer.

    PubMed

    Sunday, J M; Popovic, I; Palen, W J; Foreman, M G G; Hart, M W

    2014-10-01

    Understanding the movement of genes and individuals across marine seascapes is a long-standing challenge in marine ecology and can inform our understanding of local adaptation, the persistence and movement of populations, and the spatial scale of effective management. Patterns of gene flow in the ocean are often inferred based on population genetic analyses coupled with knowledge of species' dispersive life histories. However, genetic structure is the result of time-integrated processes and may not capture present-day connectivity between populations. Here, we use a high-resolution oceanographic circulation model to predict larval dispersal along the complex coastline of western Canada that includes the transition between two well-studied zoogeographic provinces. We simulate dispersal in a benthic sea star with a 6-10 week pelagic larval phase and test predictions of this model against previously observed genetic structure including a strong phylogeographic break within the zoogeographical transition zone. We also test predictions with new genetic sampling in a site within the phylogeographic break. We find that the coupled genetic and circulation model predicts the high degree of genetic structure observed in this species, despite its long pelagic duration. High genetic structure on this complex coastline can thus be explained through ocean circulation patterns, which tend to retain passive larvae within 20-50 km of their parents, suggesting a necessity for close-knit design of Marine Protected Area networks. PMID:25231198

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

    E-print Network

    Goddard III, William A.

    receptor (GLP1R) is a G protein-coupled receptor (GPCR) involved in insulin synthesis and regulation; therePredicted structure of agonist-bound glucagon-like peptide 1 receptor, a class B G protein the MembStruk method for scanning TM bundle conformations. We used protein­protein docking methods

  12. Predicting Protein Complex Structures: A Review of the Docking Process BIOC218 Final Project

    E-print Network

    Predicting Protein Complex Structures: A Review of the Docking Process Adam Perez BIOC218 Final Project 12/11/2011 Introduction Proteins carry out enzymatic reactions and participate in cellular proteins' functional roles is their interaction with other proteins. Indeed, identification of all

  13. Geometry-inspired Optimization Methods for Structural Motifs for Protein Function Prediction

    E-print Network

    Moll, Mark

    Geometry-inspired Optimization Methods for Structural Motifs for Protein Function Prediction Brian Kimmel3 , Olivier Lichtarge4 , Lydia E. Kavraki1 * 1 Department of Computer Science, Rice University, Houston, TX 77005, USA 2 Department of Bioengineering, Rice University. Houston, TX 77005, USA 3

  14. A Constraint-Based Approach to Structure Prediction for Simpli ed Protein Models that

    E-print Network

    Will, Sebastian

    of amino acids (i.e., proteins) can be generated. The peptide bond itself (indicated with a grey rectangleA Constraint-Based Approach to Structure Prediction for Simpli#28;ed Protein Models, or there is no e#30;cient method known for #28;nding native conformations. We present a constraint-based method

  15. Prediction of structure and function of G protein-coupled receptors

    E-print Network

    Goddard III, William A.

    (bovine rhodopsin), making it difficult to use structure-based methods to design drugs and muta- tion experiments. We have recently developed first principles meth- ods (MembStruk and HierDock) for predicting involve malfunction of these receptors (2), making them important targets for drug develop- ment

  16. Training Set Reduction Methods for Protein Secondary Structure Prediction in Single-Sequence Condition

    E-print Network

    Erdogan, Hakan

    Training Set Reduction Methods for Protein Secondary Structure Prediction in Single accuracy. One way to improve the performance of a single-sequence algorithm is to perform re-training. In this approach, first, the models used by the algorithm are trained by a representative set of proteins

  17. Automated prediction of protein function and detection of functional sites from structure

    E-print Network

    Pazos, Florencio

    , an automatic method for structure-based function prediction using automatically extracted functional sites are extracted from the Gene Ontology (GO) database. The method extracts these features from the entire GO problematic (6, 7). Today, an extensively used functional classification is derived from the Gene Ontology (GO

  18. Relative packing groups in template-based structure prediction: cooperative effects of true positive constraints.

    PubMed

    Day, Ryan; Qu, Xiaotao; Swanson, Rosemarie; Bohannan, Zach; Bliss, Robert; Tsai, Jerry

    2011-01-01

    Most current template-based structure prediction methods concentrate on finding the correct backbone conformation and then packing sidechains within that backbone. Our packing-based method derives distance constraints from conserved relative packing groups (RPGs). In our refinement approach, the RPGs provide a level of resolution that restrains global topology while allowing conformational sampling. In this study, we test our template-based structure prediction method using 51 prediction units from CASP7 experiments. RPG-based constraints are able to substantially improve approximately two-thirds of starting templates. Upon deeper investigation, we find that true positive spatial constraints, especially those non-local in sequence, derived from the RPGs were important to building nearer native models. Surprisingly, the fraction of incorrect or false positive constraints does not strongly influence the quality of the final candidate. This result indicates that our RPG-based true positive constraints sample the self-consistent, cooperative interactions of the native structure. The lack of such reinforcing cooperativity explains the weaker effect of false positive constraints. Generally, these findings are encouraging indications that RPGs will improve template-based structure prediction. PMID:21210729

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

    E-print Network

    Moreira, Bruno Contreras

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

  20. Learning to predict the phonological structure of English loanwords in Japanese

    E-print Network

    Blair, Alan

    Learning to predict the phonological structure of English loanwords in Japanese Alan D. Blair School of Computer Science & Engineering University of New South Wales 2052, Australia blair, or the search for some suitable hybrid, continues. In an earlier study (Blair & Ingram, 1998) we showed how

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

    EPA Science Inventory

    DSSTox Website Launch: Improving Public Access to Databases for Building Structure-Toxicity Prediction Models
    Ann M. Richard
    US Environmental Protection Agency, Research Triangle Park, NC, USA

    Distributed: Decentralized set of standardized, field-delimited databases,...

  2. Modified Structural Model for Predicting Particle Size in the Microemulsion and Emulsion Polymerization of

    E-print Network

    Wu, Chi

    Modified Structural Model for Predicting Particle Size in the Microemulsion and Emulsion and emulsion polymerization of styrene at 70 °C in the presence of sodium dodecyl sulfate (SDS, surfactant.Laserlightscatteringwasusedtocharacterizetheresultantpolystyrenelatexparticles formed at different polymerization stages. The influence of the initial emulsion composition, that is

  3. Appears in the SIGGRAPH 2012 Proceedings. Structure-aware Synthesis for Predictive Woven Fabric Appearance

    E-print Network

    Bala, Kavita

    Appears in the SIGGRAPH 2012 Proceedings. Structure-aware Synthesis for Predictive Woven Fabric: We synthesize volumetric appearance models of fabrics with complex designs using a small set of exemplars: (a) density information of exemplars obtained using micro CT imaging; (b) fabric designs

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

    NASA Technical Reports Server (NTRS)

    Ko, William L.; Fleischer, Van Tran

    2012-01-01

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

  5. Predictions of Crystal Structure Based on Radius Ratio: How Reliable Are They?

    ERIC Educational Resources Information Center

    Nathan, Lawrence C.

    1985-01-01

    Discussion of crystalline solids in undergraduate curricula often includes the use of radius ratio rules as a method for predicting which type of crystal structure is likely to be adopted by a given ionic compound. Examines this topic, establishing more definitive guidelines for the use and reliability of the rules. (JN)

  6. Body Vigilance in Nonclinical and Anxiety Disorder Samples: Structure, Correlates, and Prediction of Health Concerns

    ERIC Educational Resources Information Center

    Olatunji, Bunmi O.; Deacon, Brett J.; Abramowitz, Jonathan S.; Valentiner, David P.

    2007-01-01

    The Body Vigilance Scale (BVS) is a measure developed to assess one's conscious attendance to internal cues. The present report investigated the structure, correlates, and predictive utility of the BVS in nonclinical (N=442) and anxiety (N=135) disorder samples. The findings of Study 1 suggest that the BVS is 1-dimensional in a nonclinical sample,…

  7. Constitutive model for predicting dynamic interactions between soil ejecta and structural panels

    E-print Network

    Wadley, Haydn

    Constitutive model for predicting dynamic interactions between soil ejecta and structural panels V form 24 April 2009 Accepted 2 May 2009 Keywords: Granular material Shock waves Constitutive Behaviour Blast loading a b s t r a c t A constitutive model is developed for the high-rate deformation

  8. De novo prediction of protein folding pathways and structure using the principle of sequential stabilization.

    PubMed

    Adhikari, Aashish N; Freed, Karl F; Sosnick, Tobin R

    2012-10-23

    Motivated by the relationship between the folding mechanism and the native structure, we develop a unified approach for predicting folding pathways and tertiary structure using only the primary sequence as input. Simulations begin from a realistic unfolded state devoid of secondary structure and use a chain representation lacking explicit side chains, rendering the simulations many orders of magnitude faster than molecular dynamics simulations. The multiple round nature of the algorithm mimics the authentic folding process and tests the effectiveness of sequential stabilization (SS) as a search strategy wherein 2° structural elements add onto existing structures in a process of progressive learning and stabilization of structure found in prior rounds of folding. Because no a priori knowledge is used, we can identify kinetically significant non-native interactions and intermediates, sometimes generated by only two mutations, while the evolution of contact matrices is often consistent with experiments. Moreover, structure prediction improves substantially by incorporating information from prior rounds. The success of our simple, homology-free approach affirms the validity of our description of the primary determinants of folding pathways and structure, and the effectiveness of SS as a search strategy. PMID:23045636

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

    PubMed

    Zhang, Yang

    2009-01-01

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

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

    PubMed Central

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

    2015-01-01

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

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

    PubMed

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

    2015-06-16

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

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

    PubMed Central

    Barker, Jane E.; Semenov, Andrei D.; Michaelson, Laura; Provan, Lindsay S.; Snyder, Hannah R.; Munakata, Yuko

    2014-01-01

    Executive functions (EFs) in childhood predict important life outcomes. Thus, there is great interest in attempts to improve EFs early in life. Many interventions are led by trained adults, including structured training activities in the lab, and less-structured activities implemented in schools. Such programs have yielded gains in children's externally-driven executive functioning, where they are instructed on what goal-directed actions to carry out and when. However, it is less clear how children's experiences relate to their development of self-directed executive functioning, where they must determine on their own what goal-directed actions to carry out and when. We hypothesized that time spent in less-structured activities would give children opportunities to practice self-directed executive functioning, and lead to benefits. To investigate this possibility, we collected information from parents about their 6–7 year-old children's daily, annual, and typical schedules. We categorized children's activities as “structured” or “less-structured” based on categorization schemes from prior studies on child leisure time use. We assessed children's self-directed executive functioning using a well-established verbal fluency task, in which children generate members of a category and can decide on their own when to switch from one subcategory to another. The more time that children spent in less-structured activities, the better their self-directed executive functioning. The opposite was true of structured activities, which predicted poorer self-directed executive functioning. These relationships were robust (holding across increasingly strict classifications of structured and less-structured time) and specific (time use did not predict externally-driven executive functioning). We discuss implications, caveats, and ways in which potential interpretations can be distinguished in future work, to advance an understanding of this fundamental aspect of growing up. PMID:25071617

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

    PubMed Central

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

    2010-01-01

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

  14. A Fully Bayesian Approach to Improved Calibration and Prediction of Groundwater Models With Structure Error

    NASA Astrophysics Data System (ADS)

    Xu, T.; Valocchi, A. J.

    2014-12-01

    Effective water resource management typically relies on numerical models to analyse groundwater flow and solute transport processes. These models are usually subject to model structure error due to simplification and/or misrepresentation of the real system. As a result, the model outputs may systematically deviate from measurements, thus violating a key assumption for traditional regression-based calibration and uncertainty analysis. On the other hand, model structure error induced bias can be described statistically in an inductive, data-driven way based on historical model-to-measurement misfit. We adopt a fully Bayesian approach that integrates a Gaussian process error model to account for model structure error to the calibration, prediction and uncertainty analysis of groundwater models. The posterior distributions of parameters of the groundwater model and the Gaussian process error model are jointly inferred using DREAM, an efficient Markov chain Monte Carlo sampler. We test the usefulness of the fully Bayesian approach towards a synthetic case study of surface-ground water interaction under changing pumping conditions. We first illustrate through this example that traditional least squares regression without accounting for model structure error yields biased parameter estimates due to parameter compensation as well as biased predictions. In contrast, the Bayesian approach gives less biased parameter estimates. Moreover, the integration of a Gaussian process error model significantly reduces predictive bias and leads to prediction intervals that are more consistent with observations. The results highlight the importance of explicit treatment of model structure error especially in circumstances where subsequent decision-making and risk analysis require accurate prediction and uncertainty quantification. In addition, the data-driven error modelling approach is capable of extracting more information from observation data than using a groundwater model alone.

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

    PubMed Central

    Moriguchi, I; Hirano, H; Hirono, S

    1996-01-01

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

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

    SciTech Connect

    Moriguchi, Ikuo; Hirono, Shuichi; Hirano, Hiroyuki

    1996-10-01

    Fuzzy adaptive least-squares (FALS), a pattern recognition method recently developed in our laboratory for correlating structure with activity rating, was used to generate quantitative structure-activity relationship (QSAR) models on the carcinogenicity of organic compounds of several chemical classes. Using the predictive models obtained from the chemical class-based FALS QSAR approach, the rodent carcinogenicity or noncarcinogenicity of a group of organic chemicals currently being tested by the U.S. National Toxicology Program was estimated from their chemical structures. 12 refs., 4 tabs.

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

    SciTech Connect

    Jiang, Deen

    2011-01-01

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

  18. Ligand-Target Prediction by Structural Network Biology Using nAnnoLyze

    PubMed Central

    Martínez-Jiménez, Francisco; Marti-Renom, Marc A.

    2015-01-01

    Target identification is essential for drug design, drug-drug interaction prediction, dosage adjustment and side effect anticipation. Specifically, the knowledge of structural details is essential for understanding the mode of action of a compound on a target protein. Here, we present nAnnoLyze, a method for target identification that relies on the hypothesis that structurally similar binding sites bind similar ligands. nAnnoLyze integrates structural information into a bipartite network of interactions and similarities to predict structurally detailed compound-protein interactions at proteome scale. The method was benchmarked on a dataset of 6,282 pairs of known interacting ligand-target pairs reaching a 0.96 of area under the Receiver Operating Characteristic curve (AUC) when using the drug names as an input feature for the classifier, and a 0.70 of AUC for “anonymous” compounds or compounds not present in the training set. nAnnoLyze resulted in higher accuracies than its predecessor, AnnoLyze. We applied the method to predict interactions for all the compounds in the DrugBank database with each human protein structure and provide examples of target identification for known drugs against human diseases. The accuracy and applicability of our method to any compound indicate that a comparative docking approach such as nAnnoLyze enables large-scale annotation and analysis of compound–protein interactions and thus may benefit drug development. PMID:25816344

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

    PubMed Central

    Ellington, Roni; Wachira, James

    2010-01-01

    The focus of this Research Experience for Undergraduates (REU) project was on RNA secondary structure prediction by using a lattice walk approach. The lattice walk approach is a combinatorial and computational biology method used to enumerate possible secondary structures and predict RNA secondary structure from RNA sequences. The method uses discrete mathematical techniques and identifies specified base pairs as parameters. The goal of the REU was to introduce upper-level undergraduate students to the principles and challenges of interdisciplinary research in molecular biology and discrete mathematics. At the beginning of the project, students from the biology and mathematics departments of a mid-sized university received instruction on the role of secondary structure in the function of eukaryotic RNAs and RNA viruses, RNA related to combinatorics, and the National Center for Biotechnology Information resources. The student research projects focused on RNA secondary structure prediction on a regulatory region of the yellow fever virus RNA genome and on an untranslated region of an mRNA of a gene associated with the neurological disorder epilepsy. At the end of the project, the REU students gave poster and oral presentations, and they submitted written final project reports to the program director. The outcome of the REU was that the students gained transferable knowledge and skills in bioinformatics and an awareness of the applications of discrete mathematics to biological research problems. PMID:20810968

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

    PubMed

    Ellington, Roni; Wachira, James; Nkwanta, Asamoah

    2010-01-01

    The focus of this Research Experience for Undergraduates (REU) project was on RNA secondary structure prediction by using a lattice walk approach. The lattice walk approach is a combinatorial and computational biology method used to enumerate possible secondary structures and predict RNA secondary structure from RNA sequences. The method uses discrete mathematical techniques and identifies specified base pairs as parameters. The goal of the REU was to introduce upper-level undergraduate students to the principles and challenges of interdisciplinary research in molecular biology and discrete mathematics. At the beginning of the project, students from the biology and mathematics departments of a mid-sized university received instruction on the role of secondary structure in the function of eukaryotic RNAs and RNA viruses, RNA related to combinatorics, and the National Center for Biotechnology Information resources. The student research projects focused on RNA secondary structure prediction on a regulatory region of the yellow fever virus RNA genome and on an untranslated region of an mRNA of a gene associated with the neurological disorder epilepsy. At the end of the project, the REU students gave poster and oral presentations, and they submitted written final project reports to the program director. The outcome of the REU was that the students gained transferable knowledge and skills in bioinformatics and an awareness of the applications of discrete mathematics to biological research problems. PMID:20810968

  1. Prediction of Spontaneous Protein Deamidation from Sequence-Derived Secondary Structure and Intrinsic Disorder

    PubMed Central

    Lorenzo, J. Ramiro; Alonso, Leonardo G.; Sánchez, Ignacio E.

    2015-01-01

    Asparagine residues in proteins undergo spontaneous deamidation, a post-translational modification that may act as a molecular clock for the regulation of protein function and turnover. Asparagine deamidation is modulated by protein local sequence, secondary structure and hydrogen bonding. We present NGOME, an algorithm able to predict non-enzymatic deamidation of internal asparagine residues in proteins in the absence of structural data, using sequence-based predictions of secondary structure and intrinsic disorder. Compared to previous algorithms, NGOME does not require three-dimensional structures yet yields better predictions than available sequence-only methods. Four case studies of specific proteins show how NGOME may help the user identify deamidation-prone asparagine residues, often related to protein gain of function, protein degradation or protein misfolding in pathological processes. A fifth case study applies NGOME at a proteomic scale and unveils a correlation between asparagine deamidation and protein degradation in yeast. NGOME is freely available as a webserver at the National EMBnet node Argentina, URL: http://www.embnet.qb.fcen.uba.ar/ in the subpage “Protein and nucleic acid structure and sequence analysis”. PMID:26674530

  2. Using neural network predicted secondary structure information in automatic protein NMR assignment.

    PubMed

    Choy, W Y; Sanctuary, B C; Zhu, G

    1997-01-01

    In CAPRI, an automated NMR assignment software package that was developed in our laboratory, both chemical shift values and coupling topologies of spin patterns are used in a procedure for amino acids recognition. By using a knowledge base of chemical shift distributions of the 20 amino acid types, fuzzy mathematics, and pattern recognition theory, the spin coupling topological graphs are mapped onto specific amino acid residues. In this work, we investigated the feasibility of using secondary structure information of proteins as predicted by neural networks in the automated NMR assignment. As the 1H and 13C chemical shifts of proteins are known to correlate to their secondary structures, secondary structure information is useful in improving the amino acid recognition. In this study, the secondary structures of proteins predicted by the PHD protein server and our own trained neural networks are used in the amino acid type recognition. The results show that the predicted secondary structure information can help to improve the accuracy of the amino acid recognition. PMID:9392858

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

    SciTech Connect

    DiMaio, Frank

    2013-11-01

    Modeling advances using Rosetta structure prediction to aid in solving difficult molecular-replacement problems are discussed. Recent work has shown the effectiveness of structure-prediction methods in solving difficult molecular-replacement problems. The Rosetta protein structure modeling suite can aid in the solution of difficult molecular-replacement problems using templates from 15 to 25% sequence identity; Rosetta refinement guided by noisy density has consistently led to solved structures where other methods fail. In this paper, an overview of the use of Rosetta for these difficult molecular-replacement problems is provided and new modeling developments that further improve model quality are described. Several variations to the method are introduced that significantly reduce the time needed to generate a model and the sampling required to improve the starting template. The improvements are benchmarked on a set of nine difficult cases and it is shown that this improved method obtains consistently better models in less running time. Finally, strategies for best using Rosetta to solve difficult molecular-replacement problems are presented and future directions for the role of structure-prediction methods in crystallography are discussed.

  4. Development and application of vibroacoustic structural data banks in predicting vibration design and test criteria for rocket vehicle structures

    NASA Technical Reports Server (NTRS)

    Bandgren, H. J.; Smith, W. C.

    1973-01-01

    A method of predicting broadband random vibration criteria for components on space vehicles is presented. Large amounts of vibration and acoustic data obtained from flights and static firing tests of space vehicle were formulated into vibroacoustic data banks for structural categories of ring frame, skin stringer, and honeycomb. The vibration spectra with their associated acoustic spectra are normalized to a reference acoustic spectrum. The individual normalized spectra are grouped according to definite structural characteristics and statistically analyzed to form the vibroacoustic data banks described in this report. These data banks represent the reference vibration criteria available for determining the new vehicle vibration criteria.

  5. Displacement Theories for In-Flight Deformed Shape Predictions of Aerospace Structures

    NASA Technical Reports Server (NTRS)

    Ko, William L.; Richards, W. L.; Tran, Van t.

    2007-01-01

    Displacement theories are developed for a variety of structures with the goal of providing real-time shape predictions for aerospace vehicles during flight. These theories are initially developed for a cantilever beam to predict the deformed shapes of the Helios flying wing. The main structural configuration of the Helios wing is a cantilever wing tubular spar subjected to bending, torsion, and combined bending and torsion loading. The displacement equations that are formulated are expressed in terms of strains measured at multiple sensing stations equally spaced on the surface of the wing spar. Displacement theories for other structures, such as tapered cantilever beams, two-point supported beams, wing boxes, and plates also are developed. The accuracy of the displacement theories is successfully validated by finite-element analysis and classical beam theory using input-strains generated by finite-element analysis. The displacement equations and associated strain-sensing system (such as fiber optic sensors) create a powerful means for in-flight deformation monitoring of aerospace structures. This method serves multiple purposes for structural shape sensing, loads monitoring, and structural health monitoring. Ultimately, the calculated displacement data can be visually displayed to the ground-based pilot or used as input to the control system to actively control the shape of structures during flight.

  6. Prediction Accuracy of a Novel Dynamic Structure–Function Model for Glaucoma Progression

    PubMed Central

    Hu, Rongrong; Marín-Franch, Iván; Racette, Lyne

    2014-01-01

    Purpose. To assess the prediction accuracy of a novel dynamic structure–function (DSF) model to monitor glaucoma progression. Methods. Longitudinal data of paired rim area (RA) and mean sensitivity (MS) from 220 eyes with ocular hypertension or primary open-angle glaucoma enrolled in the Diagnostic Innovations in Glaucoma Study or the African Descent and Glaucoma Evaluation Study were included. Rim area and MS were expressed as percent of mean normal based on an independent dataset of 91 healthy eyes. The DSF model uses centroids as estimates of the current state of the disease and velocity vectors as estimates of direction and rate of change over time. The first three visits were used to predict the fourth visit; the first four visits were used to predict the fifth visit, and so on up to the 11th visit. The prediction error (PE) was compared to that of ordinary least squares linear regression (OLSLR) using Wilcoxon signed-rank test. Results. For predictions at visit 4 to visit 7, the average PE for the DSF model was significantly lower than OLSLR by 1.19% to 3.42% of mean normal. No significant difference was observed for the predictions at visit 8 to visit 11. The DSF model had lower PE than OLSLR for 70% of eyes in predicting visit 4 and approximately 60% in predicting visits 5, 6, and 7. Conclusions. The two models had similar prediction capabilities, and the DSF model performed better in shorter time series. The DSF model could be clinically useful when only limited follow-ups are available. (ClinicalTrials.gov numbers, NCT00221923, NCT00221897.) PMID:25358735

  7. Clinical prediction from structural brain MRI scans: a large-scale empirical study.

    PubMed

    Sabuncu, Mert R; Konukoglu, Ender

    2015-01-01

    Multivariate pattern analysis (MVPA) methods have become an important tool in neuroimaging, revealing complex associations and yielding powerful prediction models. Despite methodological developments and novel application domains, there has been little effort to compile benchmark results that researchers can reference and compare against. This study takes a significant step in this direction. We employed three classes of state-of-the-art MVPA algorithms and common types of structural measurements from brain Magnetic Resonance Imaging (MRI) scans to predict an array of clinically relevant variables (diagnosis of Alzheimer's, schizophrenia, autism, and attention deficit and hyperactivity disorder; age, cerebrospinal fluid derived amyloid-? levels and mini-mental state exam score). We analyzed data from over 2,800 subjects, compiled from six publicly available datasets. The employed data and computational tools are freely distributed ( https://www.nmr.mgh.harvard.edu/lab/mripredict), making this the largest, most comprehensive, reproducible benchmark image-based prediction experiment to date in structural neuroimaging. Finally, we make several observations regarding the factors that influence prediction performance and point to future research directions. Unsurprisingly, our results suggest that the biological footprint (effect size) has a dramatic influence on prediction performance. Though the choice of image measurement and MVPA algorithm can impact the result, there was no universally optimal selection. Intriguingly, the choice of algorithm seemed to be less critical than the choice of measurement type. Finally, our results showed that cross-validation estimates of performance, while generally optimistic, correlate well with generalization accuracy on a new dataset. PMID:25048627

  8. Predicting IQ change from brain structure: A cross-validation study

    PubMed Central

    Price, C.J.; Ramsden, S.; Hope, T.M.H.; Friston, K.J.; Seghier, M.L.

    2013-01-01

    Procedures that can predict cognitive abilities from brain imaging data are potentially relevant to educational assessments and studies of functional anatomy in the developing brain. Our aim in this work was to quantify the degree to which IQ change in the teenage years could be predicted from structural brain changes. Two well-known k-fold cross-validation analyses were applied to data acquired from 33 healthy teenagers – each tested at Time 1 and Time 2 with a 3.5 year interval. One approach, a Leave-One-Out procedure, predicted IQ change for each subject on the basis of structural change in a brain region that was identified from all other subjects (i.e., independent data). This approach predicted 53% of verbal IQ change and 14% of performance IQ change. The other approach used half the sample, to identify regions for predicting IQ change in the other half (i.e., a Split half approach); however – unlike the Leave-One-Out procedure – regions identified using half the sample were not significant. We discuss how these out-of-sample estimates compare to in-sample estimates; and draw some recommendations for k-fold cross-validation procedures when dealing with small datasets that are typical in the neuroimaging literature. PMID:23567505

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

    PubMed

    Johnston, Blair A; Steele, J Douglas; Tolomeo, Serenella; Christmas, David; Matthews, Keith

    2015-01-01

    The application of machine learning techniques to psychiatric neuroimaging offers the possibility to identify robust, reliable and objective disease biomarkers both within and between contemporary syndromal diagnoses that could guide routine clinical practice. The use of quantitative methods to identify psychiatric biomarkers is consequently important, particularly with a view to making predictions relevant to individual patients, rather than at a group-level. Here, we describe predictions of treatment-refractory depression (TRD) diagnosis using structural T1-weighted brain scans obtained from twenty adult participants with TRD and 21 never depressed controls. We report 85% accuracy of individual subject diagnostic prediction. Using an automated feature selection method, the major brain regions supporting this significant classification were in the caudate, insula, habenula and periventricular grey matter. It was not, however, possible to predict the degree of 'treatment resistance' in individual patients, at least as quantified by the Massachusetts General Hospital (MGH-S) clinical staging method; but the insula was again identified as a region of interest. Structural brain imaging data alone can be used to predict diagnostic status, but not MGH-S staging, with a high degree of accuracy in patients with TRD. PMID:26186455

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

    PubMed

    Cuff, J A; Barton, G J

    2000-08-15

    The effect of training a neural network secondary structure prediction algorithm with different types of multiple sequence alignment profiles derived from the same sequences, is shown to provide a range of accuracy from 70.5% to 76.4%. The best accuracy of 76.4% (standard deviation 8.4%), is 3.1% (Q(3)) and 4.4% (SOV2) better than the PHD algorithm run on the same set of 406 sequence non-redundant proteins that were not used to train either method. Residues predicted by the new method with a confidence value of 5 or greater, have an average Q(3) accuracy of 84%, and cover 68% of the residues. Relative solvent accessibility based on a two state model, for 25, 5, and 0% accessibility are predicted at 76.2, 79.8, and 86. 6% accuracy respectively. The source of the improvements obtained from training with different representations of the same alignment data are described in detail. The new Jnet prediction method resulting from this study is available in the Jpred secondary structure prediction server, and as a stand-alone computer program from: http://barton.ebi.ac.uk/. Proteins 2000;40:502-511. PMID:10861942

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

    PubMed Central

    Johnston, Blair A.; Steele, J. Douglas; Tolomeo, Serenella; Christmas, David; Matthews, Keith

    2015-01-01

    The application of machine learning techniques to psychiatric neuroimaging offers the possibility to identify robust, reliable and objective disease biomarkers both within and between contemporary syndromal diagnoses that could guide routine clinical practice. The use of quantitative methods to identify psychiatric biomarkers is consequently important, particularly with a view to making predictions relevant to individual patients, rather than at a group-level. Here, we describe predictions of treatment-refractory depression (TRD) diagnosis using structural T1-weighted brain scans obtained from twenty adult participants with TRD and 21 never depressed controls. We report 85% accuracy of individual subject diagnostic prediction. Using an automated feature selection method, the major brain regions supporting this significant classification were in the caudate, insula, habenula and periventricular grey matter. It was not, however, possible to predict the degree of ‘treatment resistance’ in individual patients, at least as quantified by the Massachusetts General Hospital (MGH-S) clinical staging method; but the insula was again identified as a region of interest. Structural brain imaging data alone can be used to predict diagnostic status, but not MGH-S staging, with a high degree of accuracy in patients with TRD. PMID:26186455

  12. Predicting US Infants' and Toddlers' TV/Video Viewing Rates: Mothers' Cognitions and Structural Life Circumstances

    PubMed Central

    Vaala, Sarah E.; Hornik, Robert C.

    2014-01-01

    There has been rising international concern over media use with children under two. As little is known about the factors associated with more or less viewing among very young children, this study examines maternal factors predictive of TV/video viewing rates among American infants and toddlers. Guided by the Integrative Model of Behavioral Prediction, this survey study examines relationships between children's rates of TV/video viewing and their mothers' structural life circumstances (e.g., number of children in the home; mother's screen use), and cognitions (e.g., attitudes; norms). Results suggest that mothers' structural circumstances and cognitions respectively contribute independent explanatory power to the prediction of children's TV/video viewing. Influence of structural circumstances is partially mediated through cognitions. Mothers' attitudes as well as their own TV/video viewing behavior were particularly predictive of children's viewing. Implications of these findings for international efforts to understand and reduce infant/toddler TV/video exposure are discussed. PMID:25489335

  13. Enhanced hybrid search algorithm for protein structure prediction using the 3D-HP lattice model.

    PubMed

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

    2013-09-01

    The problem of protein structure prediction in the hydrophobic-polar (HP) lattice model is the prediction of protein tertiary structure. This problem is usually referred to as the protein folding problem. This paper presents a method for the application of an enhanced hybrid search algorithm to the problem of protein folding prediction, using the three dimensional (3D) HP lattice model. The enhanced hybrid search algorithm is a combination of the particle swarm optimizer (PSO) and tabu search (TS) algorithms. Since the PSO algorithm entraps local minimum in later evolution extremely easily, we combined PSO with the TS algorithm, which has properties of global optimization. Since the technologies of crossover and mutation are applied many times to PSO and TS algorithms, so enhanced hybrid search algorithm is called the MCMPSO-TS (multiple crossover and mutation PSO-TS) algorithm. Experimental results show that the MCMPSO-TS algorithm can find the best solutions so far for the listed benchmarks, which will help comparison with any future paper approach. Moreover, real protein sequences and Fibonacci sequences are verified in the 3D HP lattice model for the first time. Compared with the previous evolutionary algorithms, the new hybrid search algorithm is novel, and can be used effectively to predict 3D protein folding structure. With continuous development and changes in amino acids sequences, the new algorithm will also make a contribution to the study of new protein sequences. PMID:23824509

  14. Prediction of clathrate structure type and guest position by molecular mechanics.

    PubMed

    Fleischer, Everly B; Janda, Kenneth C

    2013-05-16

    The clathrate hydrates occur in various types in which the number, size, and shape of the various cages differ. Usually the clathrate type of a specific guest is predicted by the size and shape of the molecular guest. We have developed a methodology to determine the clathrate type employing molecular mechanics with the MMFF force field employing a strategy to calculate the energy of formation of the clathrate from the sum of the guest/cage energies. The clathrate type with the most negative (most stable) energy of formation would be the type predicted (we mainly focused on type I, type II, or bromine type). This strategy allows for a calculation to predict the clathrate type for any cage guest in a few minutes on a laptop computer. It proved successful in predicting the clathrate structure for 46 out of 47 guest molecules. The molecular mechanics calculations also provide a prediction of the guest position within the cage and clathrate structure. These predictions are generally consistent with the X-ray and neutron diffraction studies. By supplementing the diffraction study with molecular mechanics, we gain a more detailed insight regarding the details of the structure. We have also compared MM calculations to studies of the multiple occupancy of the cages. Finally, we present a density functional calculation that demonstrates that the inside of the clathrates cages have a relatively uniform and low electrostatic potential in comparison with the outside oxygen and hydrogen atoms. This implies that van der Waals forces will usually be dominant in the guest-cage interactions. PMID:23600658

  15. RosettaHoles: rapid assessment of protein core packing for structure prediction, refinement, design, and validation.

    PubMed

    Sheffler, Will; Baker, David

    2009-01-01

    We present a novel method called RosettaHoles for visual and quantitative assessment of underpacking in the protein core. RosettaHoles generates a set of spherical cavity balls that fill the empty volume between atoms in the protein interior. For visualization, the cavity balls are aggregated into contiguous overlapping clusters and small cavities are discarded, leaving an uncluttered representation of the unfilled regions of space in a structure. For quantitative analysis, the cavity ball data are used to estimate the probability of observing a given cavity in a high-resolution crystal structure. RosettaHoles provides excellent discrimination between real and computationally generated structures, is predictive of incorrect regions in models, identifies problematic structures in the Protein Data Bank, and promises to be a useful validation tool for newly solved experimental structures. PMID:19177366

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

    PubMed Central

    Gorodkin, Jan; Hofacker, Ivo L.

    2011-01-01

    Non-coding RNAs (ncRNAs) are receiving more and more attention not only as an abundant class of genes, but also as regulatory structural elements (some located in mRNAs). A key feature of RNA function is its structure. Computational methods were developed early for folding and prediction of RNA structure with the aim of assisting in functional analysis. With the discovery of more and more ncRNAs, it has become clear that a large fraction of these are highly structured. Interestingly, a large part of the structure is comprised of regular Watson-Crick and GU wobble base pairs. This and the increased amount of available genomes have made it possible to employ structure-based methods for genomic screens. The field has moved from folding prediction of single sequences to computational screens for ncRNAs in genomic sequence using the RNA structure as the main characteristic feature. Whereas early methods focused on energy-directed folding of single sequences, comparative analysis based on structure preserving changes of base pairs has been efficient in improving accuracy, and today this constitutes a key component in genomic screens. Here, we cover the basic principles of RNA folding and touch upon some of the concepts in current methods that have been applied in genomic screens for de novo RNA structures in searches for novel ncRNA genes and regulatory RNA structure on mRNAs. We discuss the strengths and weaknesses of the different strategies and how they can complement each other. PMID:21829340

  17. Structure classification and melting temperature prediction in octet AB solids via machine learning

    NASA Astrophysics Data System (ADS)

    Pilania, G.; Gubernatis, J. E.; Lookman, T.

    2015-06-01

    Machine learning methods are being increasingly used in condensed matter physics and materials science to classify crystals structures and predict material properties. However, the reliability of these methods for a given problem, especially when large data sets are unavailable, has not been well studied. By addressing the tasks of classifying crystal structure and predicting melting temperatures of the octet subset of AB solids, we performed such a study and found potential problems with using machine learning methods on relatively small data sets. At the same time, however, we can reaffirm the potential power of such methods for these tasks. In particular, we uncovered an important new material feature, the excess Born effective charge, that significantly increased the accuracy of the predictions for the classification problem we defined. This discovery leads us to propose a new scale for the degree of ionicity and covalency in these solids. More specifically, we partitioned the crystal structures of a set of 75 octet solids into those that are ionic and covalent bonded and thus performed a binary classification task. We found that using the standard indices (r?,r?) , suggested by St. John and Bloch several decades ago, enabled an average success in classification of 92 % . Using just r? and the excess Born effective charge ? ZA of the A atom enabled an average success of 97 % , but we also found relatively large variations about these averages that were dependent on how certain machine learning methods were used and for which a standard deviation was not a proper measure of the degree of confidence we can place in either average. Instead, we calculated and report with 95 % confidence that the traditional classification pair predicts an accuracy in the interval [89 %,95 %] and the accuracy of the new pair lies in the interval [96 %,99 %] . For melting temperature predictions, the size of our data set was 46. We estimate the root-mean-squared error of our resulting model to be 11 % of the mean melting temperature of the data, but we note that if the accuracy of this predicted error is itself measured, our estimated fitting error itself has a root-mean-square error of 50 % . In short, what we illustrate is that classification and regression predictions can vary significantly, depending on the details of how machine learning methods are applied to small data sets. This variation makes it important, if not essential, to average the predictions and compute confidence intervals about these averages to report results meaningfully. However, when properly used, these statistical methods can advance our understanding and improve predictions of material properties even for small data sets.

  18. Correlation of pp data with predictions of improved six-quark structure models

    NASA Astrophysics Data System (ADS)

    González, P.; Lafrance, P.; Lomon, E. L.

    1987-04-01

    Recent experimental data indicate a structure in ??L corresponding to a pp mass of 2.7 GeV/c2, as earlier predicted for a six-quark 1S0 state by an R-matrix treatment of the cloudy-bag-model quark degrees of freedom interior to a coupled-isobar-channel system. The 1S0 model is improved to agree with 2? production data at 800 MeV laboratory energy. The resulting 1S0 partial wave and recently improved models of the background partial waves as well as older versions of the phase parameters predict experimental observables in the resonance region. The predicted width and inelasticity are consistent with the data. Detailed energy and angular dependence of the model are in agreement with ??L, CLL, and CNN data in the resonance energy region. More data on these observables are needed to confirm the structure and its characteristics. Measurable aspects of the structure in other observables are displayed. Another six-quark resonance structure, in the 1D2 state, is described.

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

    PubMed

    Liu, Taigang; Qin, Yufang; Wang, Yongjie; Wang, Chunhua

    2015-01-01

    The prior knowledge of protein structural class may offer useful clues on understanding its functionality as well as its tertiary structure. Though various significant efforts have been made to find a fast and effective computational approach to address this problem, it is still a challenging topic in the field of bioinformatics. The position-specific score matrix (PSSM) profile has been shown to provide a useful source of information for improving the prediction performance of protein structural class. However, this information has not been adequately explored. To this end, in this study, we present a feature extraction technique which is based on gapped-dipeptides composition computed directly from PSSM. Then, a careful feature selection technique is performed based on support vector machine-recursive feature elimination (SVM-RFE). These optimal features are selected to construct a final predictor. The results of jackknife tests on four working datasets show that our method obtains satisfactory prediction accuracies by extracting features solely based on PSSM and could serve as a very promising tool to predict protein structural class. PMID:26712737

  20. Predicting Successful Memes using Network and Community Structure Lilian Weng and Filippo Menczer and Yong-Yeol Ahn

    E-print Network

    Ahn, Yong-Yeol

    Predicting Successful Memes using Network and Community Structure Lilian Weng and Filippo Menczer Indiana University, Bloomington, USA Abstract We investigate the predictability of successful memes using of fea- tures and develop an accurate model to predict future popu- larity of a meme given its early

  1. On an economic prediction of the finer resolution level wavelet coefficients in electron structure calculations

    E-print Network

    Szilvia Nagy; János Pipek

    2015-02-28

    In wavelet based electron structure calculations introducing a new, finer resolution level is usually an expensive task, this is why often a two-level approximation is used with very fine starting resolution level. This process results in large matrices to calculate with and a large number of coefficients to be stored. In our previous work we have developed an adaptively refining solution scheme that determines the indices, where refined basis functions are to be included, and later a method for predicting the next, finer resolution coefficients in a very economic way. In the present contribution we would like to determine, whether the method can be applied for predicting not only the first, but also the other, higher resolution level coefficients. Also the energy expectation values of the predicted wave functions are studied, as well as the scaling behaviour of the coefficients in the fine resolution limit.

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

    NASA Astrophysics Data System (ADS)

    McCune, Matthew; Shafiee, Ashkan; Forgacs, Gabor; Kosztin, Ioan

    2014-03-01

    Cellular Particle Dynamics (CPD) is an effective computational method for describing and predicting the time evolution of biomechanical relaxation processes of multicellular systems. A typical example is the fusion of spheroidal bioink particles during post bioprinting structure formation. In CPD cells are modeled as an ensemble of cellular particles (CPs) that interact via short-range contact interactions, characterized by an attractive (adhesive interaction) and a repulsive (excluded volume interaction) component. The time evolution of the spatial conformation of the multicellular system is determined by following the trajectories of all CPs through integration of their equations of motion. CPD was successfully applied to describe and predict the fusion of 3D tissue construct involving identical spherical aggregates. Here, we demonstrate that CPD can also predict tissue formation involving uneven spherical aggregates whose volumes decrease during the fusion process. Work supported by NSF [PHY-0957914]. Computer time provided by the University of Missouri Bioinformatics Consortium.

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

    PubMed

    Nagy, Szilvia; Pipek, János

    2015-11-25

    In wavelet based electronic structure calculations, introducing a new, finer resolution level is usually an expensive task, this is why often a two-level approximation is used with very fine starting resolution level. This process results in large matrices to calculate with and a large number of coefficients to be stored. In our previous work we have developed an adaptively refined solution scheme that determines the indices, where the refined basis functions are to be included, and later a method for predicting the next, finer resolution coefficients in a very economic way. In the present contribution, we would like to determine whether the method can be applied for predicting not only the first, but also the other, higher resolution level coefficients. Also the energy expectation values of the predicted wave functions are studied, as well as the scaling behaviour of the coefficients in the fine resolution limit. PMID:26176200

  4. Quantitative structure activity relationship modeling for predicting radiosensitization effectiveness of nitroimidazole compounds.

    PubMed

    Long, Wei; Liu, Peixun

    2010-01-01

    This paper provides quantitative structure activity relationship (QSAR) models for predicting the radiosensitization effectiveness of nitroimidazole compounds. A new method, combining a heuristic method and projection pursuit regression, was used to build an advanced QSAR model. Compared to the conventional multi-linear regression model, this model showed better predictive ability and reliability, with the values of regression coefficient (R(2)) and root mean square error (RMSE) 0.92 and 0.18 for the training set and 0.90 and 0.17 for the test set, respectively. The provided models were useful tools to predict the radiosensitization effectiveness of nitroimidazole compounds. Also, the new finding descriptors derived from this study will help us to facilitate the design of new radiation sensitizers with better activities. PMID:20921823

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

    NASA Astrophysics Data System (ADS)

    Schuster, Peter

    2006-05-01

    RNA secondary structures are derived from RNA sequences, which are strings built form the natural four letter nucleotide alphabet, {AUGC}. These coarse-grained structures, in turn, are tantamount to constrained strings over a three letter alphabet. Hence, the secondary structures are discrete objects and the number of sequences always exceeds the number of structures. The sequences built from two letter alphabets form perfect structures when the nucleotides can form a base pair, as is the case with {GC} or {AU}, but the relation between the sequences and structures differs strongly from the four letter alphabet. A comprehensive theory of RNA structure is presented, which is based on the concepts of sequence space and shape space, being a space of structures. It sets the stage for modelling processes in ensembles of RNA molecules like evolutionary optimization or kinetic folding as dynamical phenomena guided by mappings between the two spaces. The number of minimum free energy (mfe) structures is always smaller than the number of sequences, even for two letter alphabets. Folding of RNA molecules into mfe energy structures constitutes a non-invertible mapping from sequence space onto shape space. The preimage of a structure in sequence space is defined as its neutral network. Similarly the set of suboptimal structures is the preimage of a sequence in shape space. This set represents the conformation space of a given sequence. The evolutionary optimization of structures in populations is a process taking place in sequence space, whereas kinetic folding occurs in molecular ensembles that optimize free energy in conformation space. Efficient folding algorithms based on dynamic programming are available for the prediction of secondary structures for given sequences. The inverse problem, the computation of sequences for predefined structures, is an important tool for the design of RNA molecules with tailored properties. Simultaneous folding or cofolding of two or more RNA molecules can be modelled readily at the secondary structure level and allows prediction of the most stable (mfe) conformations of complexes together with suboptimal states. Cofolding algorithms are important tools for efficient and highly specific primer design in the polymerase chain reaction (PCR) and help to explain the mechanisms of small interference RNA (si-RNA) molecules in gene regulation. The evolutionary optimization of RNA structures is illustrated by the search for a target structure and mimics aptamer selection in evolutionary biotechnology. It occurs typically in steps consisting of short adaptive phases interrupted by long epochs of little or no obvious progress in optimization. During these quasi-stationary epochs the populations are essentially confined to neutral networks where they search for sequences that allow a continuation of the adaptive process. Modelling RNA evolution as a simultaneous process in sequence and shape space provides answers to questions of the optimal population size and mutation rates. Kinetic folding is a stochastic process in conformation space. Exact solutions are derived by direct simulation in the form of trajectory sampling or by solving the master equation. The exact solutions can be approximated straightforwardly by Arrhenius kinetics on barrier trees, which represent simplified versions of conformational energy landscapes. The existence of at least one sequence forming any arbitrarily chosen pair of structures is granted by the intersection theorem. Folding kinetics is the key to understanding and designing multistable RNA molecules or RNA switches. These RNAs form two or more long lived conformations, and conformational changes occur either spontaneously or are induced through binding of small molecules or other biopolymers. RNA switches are found in nature where they act as elements in genetic and metabolic regulation. The reliability of RNA secondary structure prediction is limited by the accuracy with which the empirical parameters can be determined and by principal deficiencies, for example by the lack o

  6. Protein Engineering vol.15 no.8 pp.669675, 2002 Predicting the structure of protein cavities created by mutation

    E-print Network

    Sancho, Javier

    Protein Engineering vol.15 no.8 pp.669­675, 2002 Predicting the structure of protein cavities design of protein cavities, we have developed a minimization strategy that can predict with accuracy the fate of cavities created by mutation. We first modelled, under different conditions, the structures

  7. Framingham Coronary Heart Disease Risk Score Can be Predicted from Structural Brain Images in Elderly Subjects

    PubMed Central

    Rondina, Jane Maryam; Squarzoni, Paula; Souza-Duran, Fabio Luis; Tamashiro-Duran, Jaqueline Hatsuko; Scazufca, Marcia; Menezes, Paulo Rossi; Vallada, Homero; Lotufo, Paulo A.; de Toledo Ferraz Alves, Tania Correa; Busatto Filho, Geraldo

    2014-01-01

    Recent literature has presented evidence that cardiovascular risk factors (CVRF) play an important role on cognitive performance in elderly individuals, both those who are asymptomatic and those who suffer from symptoms of neurodegenerative disorders. Findings from studies applying neuroimaging methods have increasingly reinforced such notion. Studies addressing the impact of CVRF on brain anatomy changes have gained increasing importance, as recent papers have reported gray matter loss predominantly in regions traditionally affected in Alzheimer’s disease (AD) and vascular dementia in the presence of a high degree of cardiovascular risk. In the present paper, we explore the association between CVRF and brain changes using pattern recognition techniques applied to structural MRI and the Framingham score (a composite measure of cardiovascular risk largely used in epidemiological studies) in a sample of healthy elderly individuals. We aim to answer the following questions: is it possible to decode (i.e., to learn information regarding cardiovascular risk from structural brain images) enabling individual predictions? Among clinical measures comprising the Framingham score, are there particular risk factors that stand as more predictable from patterns of brain changes? Our main findings are threefold: (i) we verified that structural changes in spatially distributed patterns in the brain enable statistically significant prediction of Framingham scores. This result is still significant when controlling for the presence of the APOE 4 allele (an important genetic risk factor for both AD and cardiovascular disease). (ii) When considering each risk factor singly, we found different levels of correlation between real and predicted factors; however, single factors were not significantly predictable from brain images when considering APOE4 allele presence as covariate. (iii) We found important gender differences, and the possible causes of that finding are discussed. PMID:25520654

  8. Ligand and Structure-Based Classification Models for Prediction of P-Glycoprotein Inhibitors

    PubMed Central

    2013-01-01

    The ABC transporter P-glycoprotein (P-gp) actively transports a wide range of drugs and toxins out of cells, and is therefore related to multidrug resistance and the ADME profile of therapeutics. Thus, development of predictive in silico models for the identification of P-gp inhibitors is of great interest in the field of drug discovery and development. So far in silico P-gp inhibitor prediction was dominated by ligand-based approaches because of the lack of high-quality structural information about P-gp. The present study aims at comparing the P-gp inhibitor/noninhibitor classification performance obtained by docking into a homology model of P-gp, to supervised machine learning methods, such as Kappa nearest neighbor, support vector machine (SVM), random fores,t and binary QSAR, by using a large, structurally diverse data set. In addition, the applicability domain of the models was assessed using an algorithm based on Euclidean distance. Results show that random forest and SVM performed best for classification of P-gp inhibitors and noninhibitors, correctly predicting 73/75% of the external test set compounds. Classification based on the docking experiments using the scoring function ChemScore resulted in the correct prediction of 61% of the external test set. This demonstrates that ligand-based models currently remain the methods of choice for accurately predicting P-gp inhibitors. However, structure-based classification offers information about possible drug/protein interactions, which helps in understanding the molecular basis of ligand-transporter interaction and could therefore also support lead optimization. PMID:24050383

  9. NNvPDB: Neural Network based Protein Secondary Structure Prediction with PDB Validation

    PubMed Central

    Sakthivel, Seethalakshmi; S.K.M, Habeeb

    2015-01-01

    The predicted secondary structural states are not cross validated by any of the existing servers. Hence, information on the level of accuracy for every sequence is not reported by the existing servers. This was overcome by NNvPDB, which not only reported greater Q3 but also validates every prediction with the homologous PDB entries. NNvPDB is based on the concept of Neural Network, with a new and different approach of training the network every time with five PDB structures that are similar to query sequence. The average accuracy for helix is 76%, beta sheet is 71% and overall (helix, sheet and coil) is 66%. Availability http://bit.srmuniv.ac.in/cgi-bin/bit/cfpdb/nnsecstruct.pl PMID:26420924

  10. Prediction of Protein Structural Features from Sequence Data Based on Shannon Entropy and Kolmogorov Complexity

    PubMed Central

    Bywater, Robert Paul

    2015-01-01

    While the genome for a given organism stores the information necessary for the organism to function and flourish it is the proteins that are encoded by the genome that perhaps more than anything else characterize the phenotype for that organism. It is therefore not surprising that one of the many approaches to understanding and predicting protein folding and properties has come from genomics and more specifically from multiple sequence alignments. In this work I explore ways in which data derived from sequence alignment data can be used to investigate in a predictive way three different aspects of protein structure: secondary structures, inter-residue contacts and the dynamics of switching between different states of the protein. In particular the use of Kolmogorov complexity has identified a novel pathway towards achieving these goals. PMID:25856073

  11. Low amplitude insult project: Structural analysis and prediction of low order reaction

    SciTech Connect

    Scammon, R.J.; Browning, R.V.; Middleditch, J.; Dienes, J.K.; Haberman, K.S.; Bennett, J.G.

    1998-12-31

    The low velocity impact sensitivity of PBX 9501 has been investigated through a series of experiments based on the Steven Test targets and a set of Shear Impact experiments. The authors describe calculations done using DYNA2D, SPRONTO and DYNA3D to support these, and other, low amplitude insult experiments. The calculations allow them to study pressure and strain rate variables, to investigate structural aspects of the experiment, and to predict velocities required for reaction. Structural analyses have played an active role in this project beginning with the original target design and continuing through analyses of the experimental results. Alternative designs and various ideas for active instrumentation were examined as part of the experiment evolution process. Predictions of reaction are used to guide these design studies, even though the authors do not yet have enough experimental data to fully calibrate any of the models.

  12. Investigation and prediction of the severity of p53 mutants using parameters from structural calculations

    PubMed Central

    Carlsson, Jonas; Soussi, Thierry; Persson, Bengt

    2009-01-01

    A method has been developed to predict the effects of mutations in the p53 cancer suppressor gene. The new method uses novel parameters combined with previously established parameters. The most important parameter is the stability measure of the mutated structure calculated using molecular modelling. For each mutant, a severity score is reported, which can be used for classification into deleterious and nondeleterious. Both structural features and sequence properties are taken into account. The method has a prediction accuracy of 77% on all mutants and 88% on breast cancer mutations affecting WAF1 promoter binding. When compared with earlier methods, using the same dataset, our method clearly performs better. As a result of the severity score calculated for every mutant, valuable knowledge can be gained regarding p53, a protein that is believed to be involved in over 50% of all human cancers. PMID:19558493

  13. A new method for failure prediction of SR-200 beryllium sheet structures

    NASA Technical Reports Server (NTRS)

    Papados, P. P.; Roschke, P. N.

    1994-01-01

    Contemporary applications of failure criteria frequently incorporate two-dimensional or simplified three-dimensional methodologies for prediction of stresses. Motivation behind the development of a new multi-dimensional failure criterion is due mainly to the lack of a sufficiently accurate mathematical tool that accounts for the behavior of brittle material with anisotropic properties. Such a criterion should be able to provide a reliable maximum load estimate so that design of the structure is not penalized in terms of excessive weight requirements. The failure criterion developed is represented by a fracture surface in a six-dimensional stress space. The criterion is applied for failure prediction of SR-200 beryllium sheet structures, a non-homogeneous orthotropic material used widely in space applications. Two experiments are used to verify the criterion.

  14. Fast reconstruction and prediction of frozen flow turbulence based on structured Kalman filtering.

    PubMed

    Fraanje, Rufus; Rice, Justin; Verhaegen, Michel; Doelman, Niek

    2010-11-01

    Efficient and optimal prediction of frozen flow turbulence using the complete observation history of the wavefront sensor is an important issue in adaptive optics for large ground-based telescopes. At least for the sake of error budgeting and algorithm performance, the evaluation of an accurate estimate of the optimal performance of a particular adaptive optics configuration is important. However, due to the large number of grid points, high sampling rates, and the non-rationality of the turbulence power spectral density, the computational complexity of the optimal predictor is huge. This paper shows how a structure in the frozen flow propagation can be exploited to obtain a state-space innovation model with a particular sparsity structure. This sparsity structure enables one to efficiently compute a structured Kalman filter. By simulation it is shown that the performance can be improved and the computational complexity can be reduced in comparison with auto-regressive predictors of low order. PMID:21045884

  15. Importance of structural information in predicting human acute toxicity from in vitro cytotoxicity data

    SciTech Connect

    Lee, Soyoung; Park, Keunwan; Ahn, Hee-Sung; Kim, Dongsup

    2010-07-15

    In this study, we tried to assess the utility of the structural information of drugs for predicting human acute toxicity from in vitro basal cytotoxicity, and to interpret the informative quality and the pharmacokinetic meaning of each structural descriptor. For this, human acute toxicity data of 67 drugs were taken from literature with their basal cytotoxicity data, and used to develop predictive models. A series of multiple linear regression analyses were performed to construct feasible regression models by combining molecular descriptors and cytotoxicity data. We found that although the molecular descriptors alone had only moderate correlation with human acute toxicity, they were highly useful for explaining the discrepancy between in vitro cytotoxicity and human acute toxicity. Among many possible models, we selected the most explanatory models by changing the number and the type of combined molecular descriptors. The results showed that our selected models had high predictive power (R{sup 2}: between 0.7 and 0.87). Our analysis indicated that those successful models increased the prediction accuracies by providing the information on human pharmacokinetic parameters which are the major reason for the difference between human acute toxicity and cytotoxicity. In addition, we performed a clustering analysis on selected molecular descriptors to assess their informative qualities. The results indicated that the number of single bonds, the number of hydrogen bond donors and valence connectivity indices are closely related to linking cytotoxicity to acute toxicity, which provides insightful explanation about human toxicity beyond cytotoxicity.

  16. Analytic prediction of baryonic effects from the EFT of large scale structures

    NASA Astrophysics Data System (ADS)

    Lewandowski, Matthew; Perko, Ashley; Senatore, Leonardo

    2015-05-01

    The large scale structures of the universe will likely be the next leading source of cosmological information. It is therefore crucial to understand their behavior. The Effective Field Theory of Large Scale Structures provides a consistent way to perturbatively predict the clustering of dark matter at large distances. The fact that baryons move distances comparable to dark matter allows us to infer that baryons at large distances can be described in a similar formalism: the backreaction of short-distance non-linearities and of star-formation physics at long distances can be encapsulated in an effective stress tensor, characterized by a few parameters. The functional form of baryonic effects can therefore be predicted. In the power spectrum the leading contribution goes as propto k2 P(k), with P(k) being the linear power spectrum and with the numerical prefactor depending on the details of the star-formation physics. We also perform the resummation of the contribution of the long-wavelength displacements, allowing us to consistently predict the effect of the relative motion of baryons and dark matter. We compare our predictions with simulations that contain several implementations of baryonic physics, finding percent agreement up to relatively high wavenumbers such as k simeq 0.3 hMpc?1 or k simeq 0.6 hMpc?1, depending on the order of the calculation. Our results open a novel way to understand baryonic effects analytically, as well as to interface with simulations.

  17. A two-layer structure prediction framework for microscopy cell detection.

    PubMed

    Xu, Yan; Wu, Weiying; Chang, Eric I-Chao; Chen, Danny; Mu, Jian; Lee, Peter P; Blenman, Kim R M; Tu, Zhuowen

    2015-04-01

    The task of microscopy cell detection is of great biological and clinical importance. However, existing algorithms for microscopy cell detection usually ignore the large variations of cells and only focus on the shape feature/descriptor design. Here we propose a new two-layer model for cell centre detection by a two-layer structure prediction framework, which is respectively built on classification for the cell centres implicitly using rich appearances and contextual information and explicit structural information for the cells. Experimental results demonstrate the efficiency and effectiveness of the proposed method over competing state-of-the-art methods, providing a viable alternative for microscopy cell detection. PMID:25082065

  18. Large-Deformation Displacement Transfer Functions for Shape Predictions of Highly Flexible Slender Aerospace Structures

    NASA Technical Reports Server (NTRS)

    Ko, William L.; Fleischer, Van Tran

    2013-01-01

    Large deformation displacement transfer functions were formulated for deformed shape predictions of highly flexible slender structures like aircraft wings. In the formulation, the embedded beam (depth wise cross section of structure along the surface strain sensing line) was first evenly discretized into multiple small domains, with surface strain sensing stations located at the domain junctures. Thus, the surface strain (bending strains) variation within each domain could be expressed with linear of nonlinear function. Such piecewise approach enabled piecewise integrations of the embedded beam curvature equations [classical (Eulerian), physical (Lagrangian), and shifted curvature equations] to yield closed form slope and deflection equations in recursive forms.

  19. Prediction and Observation of the bcc Structure in Pure Copper at a ?3 Grain Boundary

    NASA Astrophysics Data System (ADS)

    Schmidt, C.; Ernst, F.; Finnis, M. W.; Vitek, V.

    1995-09-01

    We have used molecular dynamics and simulated annealing to study an asymmetrical ?3 tilt grain boundary with <211> rotation axis in Cu. The boundary plane was inclined at 84° with respect to the 111 plane. A simple central force N-body interatomic potential was used. The most stable configuration shows a broad band of predominantly bcc structure in the boundary region. Samples of the bicrystal with the same misorientation and inclination of the boundary plane were observed in a 1250 kV transmission electron microscope, confirming the predicted structure with atomic resolution.

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

    SciTech Connect

    O`Donoghue, S.; Rost, B.

    1995-12-31

    This tutorial was one of eight tutorials selected to be presented at the Third International Conference on Intelligent Systems for Molecular Biology which was held in the United Kingdom from July 16 to 19, 1995. The authors intend to review the state of the art in the experimental determination of protein 3D structure (focus on nuclear magnetic resonance), and in the theoretical prediction of protein function and of protein structure in 1D, 2D and 3D from sequence. All the atomic resolution structures determined so far have been derived from either X-ray crystallography (the majority so far) or Nuclear Magnetic Resonance (NMR) Spectroscopy (becoming increasingly more important). The authors briefly describe the physical methods behind both of these techniques; the major computational methods involved will be covered in some detail. They highlight parallels and differences between the methods, and also the current limitations. Special emphasis will be given to techniques which have application to ab initio structure prediction. Large scale sequencing techniques increase the gap between the number of known proteins sequences and that of known protein structures. They describe the scope and principles of methods that contribute successfully to closing that gap. Emphasis will be given on the specification of adequate testing procedures to validate such methods.

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

    E-print Network

    Vlachos, Andreas; Craven, Mark

    2012-06-26

    :297-325. 5. Vlachos A, Craven M: Search-based structured prediction applied to biomedical event extraction. Proceedings of the Fifteenth Conference on Computational Natural Language Learning Association for Computational Linguistics; 2011, 49-57. 6. Domingos... Adaptation. Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Association for Computational Linguistics; 2007, 256-263. 8. Nédellec C: Learning Language in Logic - Genic Interaction Extraction Challenge. Proceedings...

  2. Structure-based constitutive model can accurately predict planar biaxial properties of aortic wall tissue.

    PubMed

    Polzer, S; Gasser, T C; Novak, K; Man, V; Tichy, M; Skacel, P; Bursa, J

    2015-03-01

    Structure-based constitutive models might help in exploring mechanisms by which arterial wall histology is linked to wall mechanics. This study aims to validate a recently proposed structure-based constitutive model. Specifically, the model's ability to predict mechanical biaxial response of porcine aortic tissue with predefined collagen structure was tested. Histological slices from porcine thoracic aorta wall (n=9) were automatically processed to quantify the collagen fiber organization, and mechanical testing identified the non-linear properties of the wall samples (n=18) over a wide range of biaxial stretches. Histological and mechanical experimental data were used to identify the model parameters of a recently proposed multi-scale constitutive description for arterial layers. The model predictive capability was tested with respect to interpolation and extrapolation. Collagen in the media was predominantly aligned in circumferential direction (planar von Mises distribution with concentration parameter bM=1.03 ± 0.23), and its coherence decreased gradually from the luminal to the abluminal tissue layers (inner media, b=1.54 ± 0.40; outer media, b=0.72 ± 0.20). In contrast, the collagen in the adventitia was aligned almost isotropically (bA=0.27 ± 0.11), and no features, such as families of coherent fibers, were identified. The applied constitutive model captured the aorta biaxial properties accurately (coefficient of determination R(2)=0.95 ± 0.03) over the entire range of biaxial deformations and with physically meaningful model parameters. Good predictive properties, well outside the parameter identification space, were observed (R(2)=0.92 ± 0.04). Multi-scale constitutive models equipped with realistic micro-histological data can predict macroscopic non-linear aorta wall properties. Collagen largely defines already low strain properties of media, which explains the origin of wall anisotropy seen at this strain level. The structure and mechanical properties of adventitia are well designed to protect the media from axial and circumferential overloads. PMID:25458466

  3. 1 Brain maturation: Predicting individual BrainAGE in children and adolescents using 2 structural MRI

    E-print Network

    Gaser, Christian

    -born adolescents resulted in a significantly lower estimated brain age than chronological age 42in subjects whoU N C O R R E C T E D P R O O F 1 Brain maturation: Predicting individual BrainAGE in children and adolescents using 2 structural MRI 3 KatjaQ1 Franke a, , Eileen Luders b , Arne May c , Marko Wilke d

  4. Structure- and Sequence-Based Function Prediction for Non-Homologous Proteins

    PubMed Central

    Sael, Lee; Chitale, Meghana; Kihara, Daisuke

    2012-01-01

    The structural genomics projects have been accumulating an increasing number of protein structures, many of which remain functionally unknown. In parallel effort to experimental methods, computational methods are expected to make a significant contribution for functional elucidation of such proteins. However, conventional computational methods that transfer functions from homologous proteins do not help much for these uncharacterized protein structures because they do not have apparent structural or sequence similarity with the known proteins. Here, we briefly review two avenues of computational function prediction methods, i.e. structure-based methods and sequence-based methods. The focus is on our recently developments of local structure-based methods and sequence-based methods, which can effectively extract function information from distantly related proteins. Two structure-based methods, Pocket-Surfer and Patch-Surfer, identify similar known ligand binding sites for pocket regions in a query protein without using global protein fold similarity information. Two sequence-based methods, PFP and ESG, make use of weakly similar sequences that are conventionally discarded in homology based function annotation. Combined together with experimental methods we hope that computational methods will make leading contribution in functional elucidation of the protein structures. PMID:22270458

  5. Particle swarm optimization approach for protein structure prediction in the 3D HP model.

    PubMed

    Mansour, Nashat; Kanj, Fatima; Khachfe, Hassan

    2012-09-01

    The primary structure of proteins consists of a linear chain of amino acids that can vary in length. Proteins fold, under the influence of several chemical and physical factors, into their 3D structures, which determine their biological functions and properties. Misfolding occurs when the protein folds into a 3D structure that does not represent its native structure, which can lead to diseases. Due to the importance of this problem and since laboratory techniques are not always feasible, computational methods for characterizing protein structures have been proposed. In this paper, we present a particle swarm optimization (PSO) based algorithm for predicting protein structures in the 3D hydrophobic polar model. Starting from a small set of candidate solutions, our algorithm efficiently explores the search space and returns 3D protein structures with minimal energy. To test our algorithm, we used two sets of benchmark sequences of different lengths and compared our results to published results. Our algorithm performs better than previous algorithms by finding lower energy structures or by performing fewer numbers of energy evaluations. PMID:23292692

  6. Striking similarities in diverse telomerase proteins revealed by combining structure prediction and machine learning approaches.

    PubMed

    Lee, Jae-Hyung; Hamilton, Michael; Gleeson, Colin; Caragea, Cornelia; Zaback, Peter; Sander, Jeffry D; Li, Xue; Wu, Feihong; Terribilini, Michael; Honavar, Vasant; Dobbs, Drena

    2008-01-01

    Telomerase is a ribonucleoprotein enzyme that adds telomeric DNA repeat sequences to the ends of linear chromosomes. The enzyme plays pivotal roles in cellular senescence and aging, and because it provides a telomere maintenance mechanism for approximately 90% of human cancers, it is a promising target for cancer therapy. Despite its importance, a high-resolution structure of the telomerase enzyme has been elusive, although a crystal structure of an N-terminal domain (TEN) of the telomerase reverse transcriptase subunit (TERT) from Tetrahymena has been reported. In this study, we used a comparative strategy, in which sequence-based machine learning approaches were integrated with computational structural modeling, to explore the potential conservation of structural and functional features of TERT in phylogenetically diverse species. We generated structural models of the N-terminal domains from human and yeast TERT using a combination of threading and homology modeling with the Tetrahymena TEN structure as a template. Comparative analysis of predicted and experimentally verified DNA and RNA binding residues, in the context of these structures, revealed significant similarities in nucleic acid binding surfaces of Tetrahymena and human TEN domains. In addition, the combined evidence from machine learning and structural modeling identified several specific amino acids that are likely to play a role in binding DNA or RNA, but for which no experimental evidence is currently available. PMID:18229711

  7. Development of quantitative structure property relationships for predicting the melting point of energetic materials.

    PubMed

    Morrill, Jason A; Byrd, Edward F C

    2015-11-01

    The accurate prediction of the melting temperature of organic compounds is a significant problem that has eluded researchers for many years. The most common approach used to develop predictive models entails the derivation of quantitative structure-property relationships (QSPRs), which are multivariate linear relationships between calculated quantities that are descriptors of molecular or electronic features and a property of interest. In this report the derivation of QSPRs to predict melting temperatures of energetic materials based on descriptors calculated using the AM1 semiempirical quantum mechanical method are described. In total, the melting points and experimental crystal structures of 148 energetic materials were analyzed. Principal components analysis was performed in order to assess the relative importance and roles of the descriptors in our QSPR models. Also described are the results of k means cluster analysis, performed in order to identify natural groupings within our study set of structures. The QSPR models resulting from these analyses gave training set R(2) values of 0.6085 (RMSE=±15.7°C) and 0.7468 (RMSE=±13.2°C). The test sets for these clusters had R(2) values of 0.9428 (RMSE=±7.0°C) and 0.8974 (RMSE=±8.8°C), respectively. These models are among the best melting point QSPRs yet published for energetic materials. PMID:26473455

  8. Modeling workplace contact networks: The effects of organizational structure, architecture, and reporting errors on epidemic predictions

    PubMed Central

    Potter, Gail E.; Smieszek, Timo; Sailer, Kerstin

    2015-01-01

    Face-to-face social contacts are potentially important transmission routes for acute respiratory infections, and understanding the contact network can improve our ability to predict, contain, and control epidemics. Although workplaces are important settings for infectious disease transmission, few studies have collected workplace contact data and estimated workplace contact networks. We use contact diaries, architectural distance measures, and institutional structures to estimate social contact networks within a Swiss research institute. Some contact reports were inconsistent, indicating reporting errors. We adjust for this with a latent variable model, jointly estimating the true (unobserved) network of contacts and duration-specific reporting probabilities. We find that contact probability decreases with distance, and that research group membership, role, and shared projects are strongly predictive of contact patterns. Estimated reporting probabilities were low only for 0–5 min contacts. Adjusting for reporting error changed the estimate of the duration distribution, but did not change the estimates of covariate effects and had little effect on epidemic predictions. Our epidemic simulation study indicates that inclusion of network structure based on architectural and organizational structure data can improve the accuracy of epidemic forecasting models. PMID:26634122

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

    NASA Technical Reports Server (NTRS)

    Grosveld, Ferdinand W.; Schiller, Noah H.; Cabell, Randolph H.

    2010-01-01

    Comet Enflow is a commercially available, high frequency vibroacoustic analysis software based on the Energy Finite Element Analysis (EFEA). In this method the same finite element mesh used for structural and acoustic analysis can be employed for the high frequency solutions. Comet Enflow is being validated for a floor-equipped composite cylinder by comparing the EFEA vibroacoustic response predictions with Statistical Energy Analysis (SEA) results from the commercial software program VA One from ESI Group. Early in this program a number of discrepancies became apparent in the Enflow predicted response for the power flow from an acoustic space to a structural subsystem. The power flow anomalies were studied for a simple cubic, a rectangular and a cylindrical structural model connected to an acoustic cavity. The current investigation focuses on three specific discrepancies between the Comet Enflow and the VA One predictions: the Enflow power transmission coefficient relative to the VA One coupling loss factor; the importance of the accuracy of the acoustic modal density formulation used within Enflow; and the recommended use of fast solvers in Comet Enflow. The frequency region of interest for this study covers the one-third octave bands with center frequencies from 16 Hz to 4000 Hz.

  10. Quantitative structure-activity relationship to predict acute fish toxicity of organic solvents.

    PubMed

    Levet, A; Bordes, C; Clément, Y; Mignon, P; Chermette, H; Marote, P; Cren-Olivé, C; Lantéri, P

    2013-10-01

    REACH regulation requires ecotoxicological data to characterize industrial chemicals. To limit in vivo testing, Quantitative Structure-Activity Relationships (QSARs) are advocated to predict toxicity of a molecule. In this context, the topic of this work was to develop a reliable QSAR explaining the experimental acute toxicity of organic solvents for fish trophic level. Toxicity was expressed as log(LC50), the concentration in mmol.L(-1) producing the 50% death of fish. The 141 chemically heterogeneous solvents of the dataset were described by physico-chemical descriptors and quantum theoretical parameters calculated via Density Functional Theory. The best subsets of solvent descriptors for LC50 prediction were chosen both through the Kubinyi function associated with Enhanced Replacement Method and a stepwise forward multiple linear regressions. The 4-parameters selected in the model were the octanol-water partition coefficient, LUMO energy, dielectric constant and surface tension. The predictive power and robustness of the QSAR developed were assessed by internal and external validations. Several techniques for training sets selection were evaluated: a random selection, a LC50-based selection, a balanced selection in terms of toxic and non-toxic solvents, a solvent profile-based selection with a space filling technique and a D-optimality onions-based selection. A comparison with fish LC50 predicted by ECOSAR model validated for neutral organics confirmed the interest of the QSAR developed for the prediction of organic solvent aquatic toxicity regardless of the mechanism of toxic action involved. PMID:23866172

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

    SciTech Connect

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

    2011-06-01

    An energy based fatigue life prediction framework has been developed for calculation of remaining fatigue life of in service gas turbine materials. The purpose of the life prediction framework is to account aging effect caused by cyclic loadings on fatigue strength of gas turbine engines structural components which are usually designed for very long life. Previous studies indicate the total strain energy dissipated during a monotonic fracture process and a cyclic process is a material property that can be determined by measuring the area underneath the monotonic true stress-strain curve and the sum of the area within each hysteresis loop in the cyclic process, respectively. The energy-based fatigue life prediction framework consists of the following entities: (1) development of a testing procedure to achieve plastic energy dissipation per life cycle and (2) incorporation of an energy-based fatigue life calculation scheme to determine the remaining fatigue life of in-service gas turbine materials. The accuracy of the remaining fatigue life prediction method was verified by comparison between model approximation and experimental results of Aluminum 6061-T6. The comparison shows promising agreement, thus validating the capability of the framework to produce accurate fatigue life prediction.

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

    PubMed Central

    Jia, Lei; Yarlagadda, Ramya; Reed, Charles C.

    2015-01-01

    Thermostability issue of protein point mutations is a common occurrence in protein engineering. An application which predicts the thermostability of mutants can be helpful for guiding decision making process in protein design via mutagenesis. An in silico point mutation scanning method is frequently used to find “hot spots” in proteins for focused mutagenesis. ProTherm (http://gibk26.bio.kyutech.ac.jp/jouhou/Protherm/protherm.html) is a public database that consists of thousands of protein mutants’ experimentally measured thermostability. Two data sets based on two differently measured thermostability properties of protein single point mutations, namely the unfolding free energy change (ddG) and melting temperature change (dTm) were obtained from this database. Folding free energy change calculation from Rosetta, structural information of the point mutations as well as amino acid physical properties were obtained for building thermostability prediction models with informatics modeling tools. Five supervised machine learning methods (support vector machine, random forests, artificial neural network, naďve Bayes classifier, K nearest neighbor) and partial least squares regression are used for building the prediction models. Binary and ternary classifications as well as regression models were built and evaluated. Data set redundancy and balancing, the reverse mutations technique, feature selection, and comparison to other published methods were discussed. Rosetta calculated folding free energy change ranked as the most influential features in all prediction models. Other descriptors also made significant contributions to increasing the accuracy of the prediction models. PMID:26361227

  13. Polarized Raman spectra of oriented fibers of A DNA and B DNA: anisotropic and isotropic local Raman tensors of base and backbone vibrations.

    PubMed Central

    Thomas, G J; Benevides, J M; Overman, S A; Ueda, T; Ushizawa, K; Saitoh, M; Tsuboi, M

    1995-01-01

    Polarized Raman spectra of oriented fibers of calf thymus DNA in the A and B conformations have been obtained by use of a Raman microscope operating in the 180 degrees back-scattering geometry. The following polarized Raman intensities in the spectral interval 200-1800 cm-1 were measured with both 514.5 and 488.0 nm laser excitations: (1) Icc, in which the incident and scattered light are polarized parallel to the DNA helical axis (c axis); (2) Ibb, in which the incident and scattered light are polarized perpendicular to c; and (3) Ibc and Icb, in which the incident and scattered light are polarized in mutually perpendicular directions. High degrees of structural homogeneity and unidirectional orientation were confirmed for both the A and B form fibers, as judged by comparison of the observed Raman markers and intensity anisotropies with measurements reported previously for oligonucleotide single crystals of known three-dimensional structures. The fiber Raman anisotropies have been combined with solution Raman depolarization ratios to evaluate the local tensors corresponding to key conformation-sensitive Raman bands of the DNA bases and sugar-phosphate backbone. The present study yields novel vibrational assignments for both A DNA and BDNA conformers and also confirms many previously proposed Raman vibrational assignments. Among the significant new findings are the demonstration of complex patterns of A form and B form indicator bands in the spectral intervals 750-900 and 1050-1100 cm-1, the identification of highly anisotropic tensors corresponding to vibrations of base, deoxyribose, and phosphate moieties, and the determination of relatively isotropic Raman tensors for the symmetrical stretching mode of phosphodioxy groups in A and B DNA. The present fiber results provide a basis for exploitation of polarized Raman spectroscopy to determine DNA helix orientation as well as to probe specific nucleotide residue orientations in nucleoproteins, viruses, and other complex biological assemblies. Images FIGURE 2 PMID:7756527

  14. FPGA accelerator for protein secondary structure prediction based on the GOR algorithm

    PubMed Central

    2011-01-01

    Background Protein is an important molecule that performs a wide range of functions in biological systems. Recently, the protein folding attracts much more attention since the function of protein can be generally derived from its molecular structure. The GOR algorithm is one of the most successful computational methods and has been widely used as an efficient analysis tool to predict secondary structure from protein sequence. However, the execution time is still intolerable with the steep growth in protein database. Recently, FPGA chips have emerged as one promising application accelerator to accelerate bioinformatics algorithms by exploiting fine-grained custom design. Results In this paper, we propose a complete fine-grained parallel hardware implementation on FPGA to accelerate the GOR-IV package for 2D protein structure prediction. To improve computing efficiency, we partition the parameter table into small segments and access them in parallel. We aggressively exploit data reuse schemes to minimize the need for loading data from external memory. The whole computation structure is carefully pipelined to overlap the sequence loading, computing and back-writing operations as much as possible. We implemented a complete GOR desktop system based on an FPGA chip XC5VLX330. Conclusions The experimental results show a speedup factor of more than 430x over the original GOR-IV version and 110x speedup over the optimized version with multi-thread SIMD implementation running on a PC platform with AMD Phenom 9650 Quad CPU for 2D protein structure prediction. However, the power consumption is only about 30% of that of current general-propose CPUs. PMID:21342582

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

    PubMed

    Fattebert, Julien; Robinson, Hugh S; Balme, Guy; Slotow, Rob; Hunter, Luke

    2015-10-01

    Natal dispersal promotes inter-population linkage, and is key to spatial distribution of populations. Degradation of suitable landscape structures beyond the specific threshold of an individual's ability to disperse can therefore lead to disruption of functional landscape connectivity and impact metapopulation function. Because it ignores behavioral responses of individuals, structural connectivity is easier to assess than functional connectivity and is often used as a surrogate for landscape connectivity modeling. However using structural resource selection models as surrogate for modeling functional connectivity through dispersal could be erroneous. We tested how well a second-order resource selection function (RSF) models (structural connectivity), based on GPS telemetry data from resident adult leopard (Panthera pardus L.), could predict subadult habitat use during dispersal (functional connectivity). We created eight non-exclusive subsets of the subadult data based on differing definitions of dispersal to assess the predictive ability of our adult-based RSF model extrapolated over a broader landscape. Dispersing leopards used habitats in accordance with adult selection patterns, regardless of the definition of dispersal considered. We demonstrate that, for a wide-ranging apex carnivore, functional connectivity through natal dispersal corresponds to structural connectivity as modeled by a second-order RSF. Mapping of the adult-based habitat classes provides direct visualization of the potential linkages between populations, without the need to model paths between a priori starting and destination points. The use of such landscape scale RSFs may provide insight into predicting suitable dispersal habitat peninsulas in human-dominated landscapes where mitigation of human-wildlife conflict should be focused. We recommend the use of second-order RSFs for landscape conservation planning and propose a similar approach to the conservation of other wide-ranging large carnivore species where landscape-scale resource selection data already exist. PMID:26591456

  16. Structural conversion of the transformer protein RfaH: new insights derived from protein structure prediction and molecular dynamics simulations.

    PubMed

    Balasco, Nicole; Barone, Daniela; Vitagliano, Luigi

    2015-01-01

    Recent structural investigations have shown that the C-terminal domain (CTD) of the transcription factor RfaH undergoes unique structural modifications that have a profound impact into its functional properties. These modifications cause a complete change in RfaH(CTD) topology that converts from an ?-hairpin to a ?-barrel fold. To gain insights into the determinants of this major structural conversion, we here performed computational studies (protein structure prediction and molecular dynamics simulations) on RfaH(CTD). Although these analyses, in line with literature data, suggest that the isolated RfaH(CTD) has a strong preference for the ?-barrel fold, they also highlight that a specific region of the protein is endowed with a chameleon conformational behavior. In particular, the Leu-rich region (residues 141-145) has a good propensity to adopt both ?-helical and ?-structured states. Intriguingly, in the RfaH homolog NusG, whose CTD uniquely adopts the ?-barrel fold, the corresponding region is rich in residues as Val or Ile that present a strong preference for the ?-structure. On this basis, we suggest that the presence of this Leu-rich element in RfaH(CTD) may be responsible for the peculiar structural behavior of the domain. The analysis of the sequences of RfaH family (PfamA code PF02357) unraveled that other members potentially share the structural properties of RfaH(CTD). These observations suggest that the unusual conformational behavior of RfaH(CTD) may be rare but not unique. PMID:25483894

  17. Automated protein motif generation in the structure-based protein function prediction tool ProMOL.

    PubMed

    Osipovitch, Mikhail; Lambrecht, Mitchell; Baker, Cameron; Madha, Shariq; Mills, Jeffrey L; Craig, Paul A; Bernstein, Herbert J

    2015-12-01

    ProMOL, a plugin for the PyMOL molecular graphics system, is a structure-based protein function prediction tool. ProMOL includes a set of routines for building motif templates that are used for screening query structures for enzyme active sites. Previously, each motif template was generated manually and required supervision in the optimization of parameters for sensitivity and selectivity. We developed an algorithm and workflow for the automation of motif building and testing routines in ProMOL. The algorithm uses a set of empirically derived parameters for optimization and requires little user intervention. The automated motif generation algorithm was first tested in a performance comparison with a set of manually generated motifs based on identical active sites from the same 112 PDB entries. The two sets of motifs were equally effective in identifying alignments with homologs and in rejecting alignments with unrelated structures. A second set of 296 active site motifs were generated automatically, based on Catalytic Site Atlas entries with literature citations, as an expansion of the library of existing manually generated motif templates. The new motif templates exhibited comparable performance to the existing ones in terms of hit rates against native structures, homologs with the same EC and Pfam designations, and randomly selected unrelated structures with a different EC designation at the first EC digit, as well as in terms of RMSD values obtained from local structural alignments of motifs and query structures. This research is supported by NIH grant GM078077. PMID:26573864

  18. Can benthic community structure be used to predict the process of bioturbation in real ecosystems?

    NASA Astrophysics Data System (ADS)

    Queirós, Ana M.; Stephens, Nicholas; Cook, Richard; Ravaglioli, Chiara; Nunes, Joana; Dashfield, Sarah; Harris, Carolyn; Tilstone, Gavin H.; Fishwick, James; Braeckman, Ulrike; Somerfield, Paul J.; Widdicombe, Stephen

    2015-09-01

    Disentangling the roles of environmental change and natural environmental variability on biologically mediated ecosystem processes is paramount to predict future marine ecosystem functioning. Bioturbation, the biogenic mixing of sediments, has a regulating role in marine biogeochemical processes. However, our understanding of bioturbation as a community level process and of its environmental drivers is still limited by loose use of terminology, and a lack of consensus about what bioturbation is. To help resolve these challenges, this empirical study investigated the links between four different attributes of bioturbation (bioturbation depth, activity and distance, and biodiffusive transport); the ability of an index of bioturbation (BPc) to predict each of them; and their relation to seasonality, in a shallow coastal system - the Western Channel Observatory, UK. Bioturbation distance depended on changes in benthic community structure, while the other three attributes were more directly influenced by seasonality in food availability. In parallel, BPc successfully predicted bioturbation distance but not the other attributes of bioturbation. This study therefore highlights that community bioturbation results from this combination of processes responding to environmental variability at different time-scales. However, community level measurements of bioturbation across environmental variability are still scarce, and BPc is calculated using commonly available data on benthic community structure and the functional classification of invertebrates. Therefore, BPc could be used to support the growth of landscape scale bioturbation research, but future uses of the index need to consider which bioturbation attributes the index actually predicts. As BPc predicts bioturbation distance, estimated here using a random-walk model applicable to community settings, studies using either of the metrics should be directly comparable and contribute to a more integrated future for bioturbation research.

  19. Structure-based identification of MHC binding peptides: Benchmarking of prediction accuracy.

    PubMed

    Kumar, Narendra; Mohanty, Debasisa

    2010-12-01

    Identification of MHC binding peptides is essential for understanding the molecular mechanism of immune response. However, most of the prediction methods use motifs/profiles derived from experimental peptide binding data for specific MHC alleles, thus limiting their applicability only to those alleles for which such data is available. In this work we have developed a structure-based method which does not require experimental peptide binding data for training. Our method models MHC-peptide complexes using crystal structures of 170 MHC-peptide complexes and evaluates the binding energies using two well known residue based statistical pair potentials, namely Betancourt-Thirumalai (BT) and Miyazawa-Jernigan (MJ) matrices. Extensive benchmarking of prediction accuracy on a data set of 1654 epitopes from class I and class II alleles available in the SYFPEITHI database indicate that BT pair-potential can predict more than 60% of the known binders in case of 14 MHC alleles with AUC values for ROC curves ranging from 0.6 to 0.9. Similar benchmarking on 29,522 class I and class II MHC binding peptides with known IC(50) values in the IEDB database showed AUC values higher than 0.6 for 10 class I alleles and 9 class II alleles in predictions involving classification of a peptide to be binder or non-binder. Comparison with recently available benchmarking studies indicated that, the prediction accuracy of our method for many of the class I and class II MHC alleles was comparable to the sequence based methods, even if it does not use any experimental data for training. It is also encouraging to note that the ranks of true binding peptides could further be improved, when high scoring peptides obtained from pair potential were re-ranked using all atom forcefield and MM/PBSA method. PMID:20953500

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

    PubMed

    Goel, Purva; Bapat, Sanket; Vyas, Renu; Tambe, Amruta; Tambe, Sanjeev S

    2015-11-13

    The development of quantitative structure-retention relationships (QSRR) aims at constructing an appropriate linear/nonlinear model for the prediction of the retention behavior (such as Kovats retention index) of a solute on a chromatographic column. Commonly, multi-linear regression and artificial neural networks are used in the QSRR development in the gas chromatography (GC). In this study, an artificial intelligence based data-driven modeling formalism, namely genetic programming (GP), has been introduced for the development of quantitative structure based models predicting Kovats retention indices (KRI). The novelty of the GP formalism is that given an example dataset, it searches and optimizes both the form (structure) and the parameters of an appropriate linear/nonlinear data-fitting model. Thus, it is not necessary to pre-specify the form of the data-fitting model in the GP-based modeling. These models are also less complex, simple to understand, and easy to deploy. The effectiveness of GP in constructing QSRRs has been demonstrated by developing models predicting KRIs of light hydrocarbons (case study-I) and adamantane derivatives (case study-II). In each case study, two-, three- and four-descriptor models have been developed using the KRI data available in the literature. The results of these studies clearly indicate that the GP-based models possess an excellent KRI prediction accuracy and generalization capability. Specifically, the best performing four-descriptor models in both the case studies have yielded high (>0.9) values of the coefficient of determination (R(2)) and low values of root mean squared error (RMSE) and mean absolute percent error (MAPE) for training, test and validation set data. The characteristic feature of this study is that it introduces a practical and an effective GP-based method for developing QSRRs in gas chromatography that can be gainfully utilized for developing other types of data-driven models in chromatography science. PMID:26460075

  1. Predicted structures of new Vitamin D Receptor agonists based on available X-ray structures.

    PubMed

    Malinska, Maura; Kutner, Andrzej; Wo?niak, Krzysztof

    2015-12-01

    Current efforts in the field of vitamin D are to develop 1,25(OH)2D3 analogs that exhibit equal or even increased anti-proliferative activity while possessing a reduced tendency to cause hypercalcemia. The study proposes a new, rational design of vitamin D analogs based on data available in the Protein Data Bank. Undertaken approach was to minimize the electrostatic interaction energies available after the reconstruction of charge density with the aid of the pseudoatom databank, namely the University at Buffalo Pseudoatom Databank (UBDB). Analysis of 24 vitamin D analogs, bearing similar molecular structures complexed with Vitamin D Receptor enabled the design of new agonists forming all advantageous interaction to the receptor, coded TB1, TB2, TB3 and TB4. PMID:26476188

  2. Predicting physical-chemical properties of compounds from molecular structures by recursive neural networks.

    PubMed

    Bernazzani, Luca; Duce, Celia; Micheli, Alessio; Mollica, Vincenzo; Sperduti, Alessandro; Starita, Antonina; Tiné, Maria Rosaria

    2006-01-01

    In this paper, we report on the potential of a recently developed neural network for structures applied to the prediction of physical chemical properties of compounds. The proposed recursive neural network (RecNN) model is able to directly take as input a structured representation of the molecule and to model a direct and adaptive relationship between the molecular structure and target property. Therefore, it combines in a learning system the flexibility and general advantages of a neural network model with the representational power of a structured domain. As a result, a completely new approach to quantitative structure-activity relationship/quantitative structure-property relationship (QSPR/QSAR) analysis is obtained. An original representation of the molecular structures has been developed accounting for both the occurrence of specific atoms/groups and the topological relationships among them. Gibbs free energy of solvation in water, Delta(solv)G degrees , has been chosen as a benchmark for the model. The different approaches proposed in the literature for the prediction of this property have been reconsidered from a general perspective. The advantages of RecNN as a suitable tool for the automatization of fundamental parts of the QSPR/QSAR analysis have been highlighted. The RecNN model has been applied to the analysis of the Delta(solv)G degrees in water of 138 monofunctional acyclic organic compounds and tested on an external data set of 33 compounds. As a result of the statistical analysis, we obtained, for the predictive accuracy estimated on the test set, correlation coefficient R = 0.9985, standard deviation S = 0.68 kJ mol(-1), and mean absolute error MAE = 0.46 kJ mol(-1). The inherent ability of RecNN to abstract chemical knowledge through the adaptive learning process has been investigated by principal components analysis of the internal representations computed by the network. It has been found that the model recognizes the chemical compounds on the basis of a nontrivial combination of their chemical structure and target property. PMID:16995734

  3. Bioinformatic Screening of Autoimmune Disease Genes and Protein Structure Prediction with FAMS for Drug Discovery

    PubMed Central

    Ishida, Shigeharu; Umeyama, Hideaki; Iwadate, Mitsuo; Y-h, Taguchi

    2014-01-01

    Autoimmune diseases are often intractable because their causes are unknown. Identifying which genes contribute to these diseases may allow us to understand the pathogenesis, but it is difficult to determine which genes contribute to disease. Recently, epigenetic information has been considered to activate/deactivate disease-related genes. Thus, it may also be useful to study epigenetic information that differs between healthy controls and patients with autoimmune disease. Among several types of epigenetic information, promoter methylation is believed to be one of the most important factors. Here, we propose that principal component analysis is useful to identify specific gene promoters that are differently methylated between the normal healthy controls and patients with autoimmune disease. Full Automatic Modeling System (FAMS) was used to predict the three-dimensional structures of selected proteins and successfully inferred relatively confident structures. Several possibilities of the application to the drug discovery based on obtained structures are discussed. PMID:23855671

  4. Performance of corrosion inhibiting admixtures for structural concrete -- assessment methods and predictive modeling

    SciTech Connect

    Yunovich, M.; Thompson, N.G.

    1998-12-31

    During the past fifteen years corrosion inhibiting admixtures (CIAs) have become increasingly popular for protection of reinforced components of highway bridges and other structures from damage induced by chlorides. However, there remains considerable debate about the benefits of CIAs in concrete. A variety of testing methods to assess the performance of CIA have been reported in the literature, ranging from tests in simulated pore solutions to long-term exposures of concrete slabs. The paper reviews the published techniques and recommends the methods which would make up a comprehensive CIA effectiveness testing program. The results of this set of tests would provide the data which can be used to rank the presently commercially available CIA and future candidate formulations utilizing a proposed predictive model. The model is based on relatively short-term laboratory testing and considers several phases of a service life of a structure (corrosion initiation, corrosion propagation without damage, and damage to the structure).

  5. Conformation-family Monte Carlo: A new method for crystal structure prediction

    PubMed Central

    Pillardy, Jaroslaw; Arnautova, Yelena A.; Czaplewski, Cezary; Gibson, Kenneth D.; Scheraga, Harold A.

    2001-01-01

    A new global optimization method, Conformation-family Monte Carlo, has been developed recently for searching the conformational space of macromolecules. In the present paper, we adapted this method for prediction of crystal structures of organic molecules without assuming any symmetry constraints except the number of molecules in the unit cell. This method maintains a database of low energy structures that are clustered into families. The structures in this database are improved iteratively by a Metropolis-type Monte Carlo procedure together with energy minimization, in which the search is biased toward the regions of the lowest energy families. The Conformation-family Monte Carlo method is applied to a set of nine rigid and flexible organic molecules by using two popular force fields, AMBER and W99. The method performed well for the rigid molecules and reasonably well for the molecules with torsional degrees of freedom. PMID:11606783

  6. Fold recognition and ab initio structure predictions using hidden Markov models and beta-strand pair potentials.

    PubMed

    Hubbard, T J; Park, J

    1995-11-01

    Protein structure predictions were submitted for 9 of the target sequences in the competition that ran during 1994. Targets sequences were selected that had no known homology with any sequence of known structure and were members of a reasonably sized family of related but divergent sequences. The objective was either to recognize a compatible fold for the target sequence in the database of known structures or to predict ab initio its rough 3D topology. The main tools used were Hidden Markov models (HMM) for fold recognition, a beta-strand pair potential to predict beta-sheet topology, and the PHD server for secondary structure prediction. Compatible folds were correctly identified in a number of cases and the beta-strand pair potential was shown to be a useful tool for ab initio topology prediction. PMID:8710832

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

    PubMed

    Liu, Jie; Mansouri, Kamel; Judson, Richard S; Martin, Matthew T; Hong, Huixiao; Chen, Minjun; Xu, Xiaowei; Thomas, Russell S; Shah, Imran

    2015-04-20

    The U.S. Tox21 and EPA ToxCast program screen thousands of environmental chemicals for bioactivity using hundreds of high-throughput in vitro assays to build predictive models of toxicity. We represented chemicals based on bioactivity and chemical structure descriptors, then used supervised machine learning to predict in vivo hepatotoxic effects. A set of 677 chemicals was represented by 711 in vitro bioactivity descriptors (from ToxCast assays), 4,376 chemical structure descriptors (from QikProp, OpenBabel, PaDEL, and PubChem), and three hepatotoxicity categories (from animal studies). Hepatotoxicants were defined by rat liver histopathology observed after chronic chemical testing and grouped into hypertrophy (161), injury (101) and proliferative lesions (99). Classifiers were built using six machine learning algorithms: linear discriminant analysis (LDA), Naďve Bayes (NB), support vector machines (SVM), classification and regression trees (CART), k-nearest neighbors (KNN), and an ensemble of these classifiers (ENSMB). Classifiers of hepatotoxicity were built using chemical structure descriptors, ToxCast bioactivity descriptors, and hybrid descriptors. Predictive performance was evaluated using 10-fold cross-validation testing and in-loop, filter-based, feature subset selection. Hybrid classifiers had the best balanced accuracy for predicting hypertrophy (0.84 ± 0.08), injury (0.80 ± 0.09), and proliferative lesions (0.80 ± 0.10). Though chemical and bioactivity classifiers had a similar balanced accuracy, the former were more sensitive, and the latter were more specific. CART, ENSMB, and SVM classifiers performed the best, and nuclear receptor activation and mitochondrial functions were frequently found in highly predictive classifiers of hepatotoxicity. ToxCast and ToxRefDB provide the largest and richest publicly available data sets for mining linkages between the in vitro bioactivity of environmental chemicals and their adverse histopathological outcomes. Our findings demonstrate the utility of high-throughput assays for characterizing rodent hepatotoxicants, the benefit of using hybrid representations that integrate bioactivity and chemical structure, and the need for objective evaluation of classification performance. PMID:25697799

  8. Toxicity of ionic liquids: database and prediction via quantitative structure-activity relationship method.

    PubMed

    Zhao, Yongsheng; Zhao, Jihong; Huang, Ying; Zhou, Qing; Zhang, Xiangping; Zhang, Suojiang

    2014-08-15

    A comprehensive database on toxicity of ionic liquids (ILs) is established. The database includes over 4000 pieces of data. Based on the database, the relationship between IL's structure and its toxicity has been analyzed qualitatively. Furthermore, Quantitative Structure-Activity relationships (QSAR) model is conducted to predict the toxicities (EC50 values) of various ILs toward the Leukemia rat cell line IPC-81. Four parameters selected by the heuristic method (HM) are used to perform the studies of multiple linear regression (MLR) and support vector machine (SVM). The squared correlation coefficient (R(2)) and the root mean square error (RMSE) of training sets by two QSAR models are 0.918 and 0.959, 0.258 and 0.179, respectively. The prediction R(2) and RMSE of QSAR test sets by MLR model are 0.892 and 0.329, by SVM model are 0.958 and 0.234, respectively. The nonlinear model developed by SVM algorithm is much outperformed MLR, which indicates that SVM model is more reliable in the prediction of toxicity of ILs. This study shows that increasing the relative number of O atoms of molecules leads to decrease in the toxicity of ILs. PMID:24996150

  9. Steady-state and transient eddy current predictions using surface impedances in shell structures

    NASA Astrophysics Data System (ADS)

    Davey, Kent; Turner, Larry

    1989-09-01

    Surface impedance techniques are useful means of predicting fields in eddy current problems since they circumvent the need to model the conducting regions themselves. Thus, with their use, two-and three-dimensional field predictions can be made using only scalar potentials. Their use is normally confined to (1) problems where the skin depth is small relative to the other dimensions of the problem and (2) steady-state problems where the skin depth itself is well defined. In this regard, the technique is approximate at best. Presented here is a formulation which can realize an exact prediction of the field, both in steady-state and transient problems. The technique is exact for those problems where knowledge is known as to the nature of the field variation tangential to the conductor shell interface; otherwise, an iterative numerical scheme must be employed to converge on the correct tangential variation. Surface impedances are determined generically and expressed in terms of transfer functions for shell-type structures in three different geometries. The surface impedances happen to be trigometric functions, Bessel functions, and spherical Bessel functions in planar, cylindrical, and spherical shell structures, respectively. Their use is easily implemented in finite difference, finite element, and boundary integral formulations; in this paper, the surface impedances are coupled into a boundary integral approach to verify their use in both two-dimensional cylindrical and a three-dimensional spherical problem. The results are compared to analytical expressions and are shown to disagree by no more than 0.01%.

  10. Prediction of compounds in different local structure-activity relationship environments using emerging chemical patterns.

    PubMed

    Namasivayam, Vigneshwaran; Gupta-Ostermann, Disha; Balfer, Jenny; Heikamp, Kathrin; Bajorath, Jürgen

    2014-05-27

    Active compounds can participate in different local structure-activity relationship (SAR) environments and introduce different degrees of local SAR discontinuity, depending on their structural and potency relationships in data sets. Such SAR features have thus far mostly been analyzed using descriptive approaches, in particular, on the basis of activity landscape modeling. However, compounds in different local SAR environments have not yet been predicted. Herein, we adapt the emerging chemical patterns (ECP) method, a machine learning approach for compound classification, to systematically predict compounds with different local SAR characteristics. ECP analysis is shown to accurately assign many compounds to different local SAR environments across a variety of activity classes covering the entire range of observed local SARs. Control calculations using random forests and multiclass support vector machines were carried out and a variety of statistical performance measures were applied. In all instances, ECP calculations yielded comparable or better performance than controls. The approach presented herein can be applied to predict compounds that complement local SARs or prioritize compounds with different SAR characteristics. PMID:24803014

  11. Predicting adsorption of aromatic compounds by carbon nanotubes based on quantitative structure property relationship principles

    NASA Astrophysics Data System (ADS)

    Rahimi-Nasrabadi, Mehdi; Akhoondi, Reza; Pourmortazavi, Seied Mahdi; Ahmadi, Farhad

    2015-11-01

    Quantitative structure property relationship (QSPR) models were developed to predict the adsorption of aromatic compounds by carbon nanotubes (CNTs). Five descriptors chosen by combining self-organizing map and stepwise multiple linear regression (MLR) techniques were used to connect the structure of the studied chemicals with their adsorption descriptor (K?) using linear and nonlinear modeling techniques. Correlation coefficient (R2) of 0.99 and root-mean square error (RMSE) of 0.29 for multilayered perceptron neural network (MLP-NN) model are signs of the superiority of the developed nonlinear model over MLR model with R2 of 0.93 and RMSE of 0.36. The results of cross-validation test showed the reliability of MLP-NN to predict the K? values for the aromatic contaminants. Molar volume and hydrogen bond accepting ability were found to be the factors much influencing the adsorption of the compounds. The developed QSPR, as a neural network based model, could be used to predict the adsorption of organic compounds by CNTs.

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

    SciTech Connect

    Rabern, D.A. ); Bannister, K.A. . Ballistics Research Lab.)

    1990-01-01

    Numerical modeling techniques in two- and three-dimensions were used to predict the structural and mechanical behavior of sabot/rod systems while inbore and just after muzzle exit. Three-dimensional transient numerical simulations were used to predict the rod deformations and states of stress and strain caused by axial and lateral accelerations during launch. The numerical models include the launch tube, recoil motion, and sabot/rod system modeled as it transits the launch tube and exits. The simulated rod leaves the muzzle of the gun, and exit parameters, including transverse displacement, transverse velocity, pitch, and pitch rate are extracted from the analysis results. Results from the inbore numerical simulations were compared with previous full-scale experiments. The results of the comparisons indicated a predictive capability to model inbore three-dimensional phenomena. Two-dimensional analyses were used to model details of the structural behavior caused by the axial load environment. Methodology and results are presented for several launch environments. 7 refs., 16 figs., 5 tabs.

  13. In silico prediction and screening of modular crystal structures via a high-throughput genomic approach

    PubMed Central

    Li, Yi; Li, Xu; Liu, Jiancong; Duan, Fangzheng; Yu, Jihong

    2015-01-01

    High-throughput computational methods capable of predicting, evaluating and identifying promising synthetic candidates with desired properties are highly appealing to today's scientists. Despite some successes, in silico design of crystalline materials with complex three-dimensionally extended structures remains challenging. Here we demonstrate the application of a new genomic approach to ABC-6 zeolites, a family of industrially important catalysts whose structures are built from the stacking of modular six-ring layers. The sequences of layer stacking, which we deem the genes of this family, determine the structures and the properties of ABC-6 zeolites. By enumerating these gene-like stacking sequences, we have identified 1,127 most realizable new ABC-6 structures out of 78 groups of 84,292 theoretical ones, and experimentally realized 2 of them. Our genomic approach can extract crucial structural information directly from these gene-like stacking sequences, enabling high-throughput identification of synthetic targets with desired properties among a large number of candidate structures. PMID:26395233

  14. In silico prediction and screening of modular crystal structures via a high-throughput genomic approach.

    PubMed

    Li, Yi; Li, Xu; Liu, Jiancong; Duan, Fangzheng; Yu, Jihong

    2015-01-01

    High-throughput computational methods capable of predicting, evaluating and identifying promising synthetic candidates with desired properties are highly appealing to today's scientists. Despite some successes, in silico design of crystalline materials with complex three-dimensionally extended structures remains challenging. Here we demonstrate the application of a new genomic approach to ABC-6 zeolites, a family of industrially important catalysts whose structures are built from the stacking of modular six-ring layers. The sequences of layer stacking, which we deem the genes of this family, determine the structures and the properties of ABC-6 zeolites. By enumerating these gene-like stacking sequences, we have identified 1,127 most realizable new ABC-6 structures out of 78 groups of 84,292 theoretical ones, and experimentally realized 2 of them. Our genomic approach can extract crucial structural information directly from these gene-like stacking sequences, enabling high-throughput identification of synthetic targets with desired properties among a large number of candidate structures. PMID:26395233

  15. In silico prediction and screening of modular crystal structures via a high-throughput genomic approach

    NASA Astrophysics Data System (ADS)

    Li, Yi; Li, Xu; Liu, Jiancong; Duan, Fangzheng; Yu, Jihong

    2015-09-01

    High-throughput computational methods capable of predicting, evaluating and identifying promising synthetic candidates with desired properties are highly appealing to today's scientists. Despite some successes, in silico design of crystalline materials with complex three-dimensionally extended structures remains challenging. Here we demonstrate the application of a new genomic approach to ABC-6 zeolites, a family of industrially important catalysts whose structures are built from the stacking of modular six-ring layers. The sequences of layer stacking, which we deem the genes of this family, determine the structures and the properties of ABC-6 zeolites. By enumerating these gene-like stacking sequences, we have identified 1,127 most realizable new ABC-6 structures out of 78 groups of 84,292 theoretical ones, and experimentally realized 2 of them. Our genomic approach can extract crucial structural information directly from these gene-like stacking sequences, enabling high-throughput identification of synthetic targets with desired properties among a large number of candidate structures.

  16. Protein structure prediction using global optimization by basin-hopping with NMR shift restraints

    NASA Astrophysics Data System (ADS)

    Hoffmann, Falk; Strodel, Birgit

    2013-01-01

    Computational methods that utilize chemical shifts to produce protein structures at atomic resolution have recently been introduced. In the current work, we exploit chemical shifts by combining the basin-hopping approach to global optimization with chemical shift restraints using a penalty function. For three peptides, we demonstrate that this approach allows us to find near-native structures from fully extended structures within 10 000 basin-hopping steps. The effect of adding chemical shift restraints is that the ? and ? secondary structure elements form within 1000 basin-hopping steps, after which the orientation of the secondary structure elements, which produces the tertiary contacts, is driven by the underlying protein force field. We further show that our chemical shift-restraint BH approach also works for incomplete chemical shift assignments, where the information from only one chemical shift type is considered. For the proper implementation of chemical shift restraints in the basin-hopping approach, we determined the optimal weight of the chemical shift penalty energy with respect to the CHARMM force field in conjunction with the FACTS solvation model employed in this study. In order to speed up the local energy minimization procedure, we developed a function, which continuously decreases the width of the chemical shift penalty function as the minimization progresses. We conclude that the basin-hopping approach with chemical shift restraints is a promising method for protein structure prediction.

  17. Current Progress of a Finite Element Computational Fluid Dynamics Prediction of Flutter for the AeroStructures Test Wing

    NASA Technical Reports Server (NTRS)

    Arena, Andrew S., Jr.

    2002-01-01

    This progress report focuses on the use of the STructural Analysis RoutineS suite program, SOLIDS, input for the AeroStructures Test Wing. The AeroStructures Test Wing project as a whole is described. The use of the SOLIDS code to find the mode shapes of a structure is discussed. The frequencies, and the structural dynamics to which they relate are examined. The results of the CFD predictions are compared to experimental data from a Ground Vibration Test.

  18. Temporal structure of motor variability is dynamically regulated and predicts motor learning ability

    PubMed Central

    Wu, Howard G; Miyamoto, Yohsuke R; Castro, Luis Nicolas Gonzalez; Ölveczky, Bence P; Smith, Maurice A

    2015-01-01

    Individual differences in motor learning ability are widely acknowledged, yet little is known about the factors that underlie them. Here we explore whether movement-to-movement variability in motor output, a ubiquitous if often unwanted characteristic of motor performance, predicts motor learning ability. Surprisingly, we found that higher levels of task-relevant motor variability predicted faster learning both across individuals and across tasks in two different paradigms, one relying on reward-based learning to shape specific arm movement trajectories and the other relying on error-based learning to adapt movements in novel physical environments. We proceeded to show that training can reshape the temporal structure of motor variability, aligning it with the trained task to improve learning. These results provide experimental support for the importance of action exploration, a key idea from reinforcement learning theory, showing that motor variability facilitates motor learning in humans and that our nervous systems actively regulate it to improve learning. PMID:24413700

  19. The initial single chiral particle emission mechanism and its predictions of charmonium-like structures

    NASA Astrophysics Data System (ADS)

    Chen, Dian-Yong

    2014-04-01

    We propose a new mechanism, named as the initial single chiral particle emission mechanism, to explain the charged Zb(10610) and Zb(10650) observed in the ?(5S) ? ?+?-?(nS), ?+?-hb(mP), (n = 1 3, m = 1, 2) processes. After successfully interpreting the charge Zb structures, we extend this mechanism to study the process of the dipion transitions between the higher charmonia/charmonium-like state and the lower charmonia. In these transitions process, we predict two charmonium analogs of the charged Zb. After the observations of Zc(3900) by the BESIII and the Belle Collaborations, we reproduce the dipion and the J/?? invariant mass distributions simultaneously with the initial single chiral particle emission mechanism. In addition, we predict some enhancements in the J/?K invariant mass spectra in the dikaon decay of the higher charmonium and the charmonium like states.

  20. Structural Dynamics Modeling of HIRENASD in Support of the Aeroelastic Prediction Workshop

    NASA Technical Reports Server (NTRS)

    Wieseman, Carol; Chwalowski, Pawel; Heeg, Jennifer; Boucke, Alexander; Castro, Jack

    2013-01-01

    An Aeroelastic Prediction Workshop (AePW) was held in April 2012 using three aeroelasticity case study wind tunnel tests for assessing the capabilities of various codes in making aeroelasticity predictions. One of these case studies was known as the HIRENASD model that was tested in the European Transonic Wind Tunnel (ETW). This paper summarizes the development of a standardized enhanced analytical HIRENASD structural model for use in the AePW effort. The modifications to the HIRENASD finite element model were validated by comparing modal frequencies, evaluating modal assurance criteria, comparing leading edge, trailing edge and twist of the wing with experiment and by performing steady and unsteady CFD analyses for one of the test conditions on the same grid, and identical processing of results.

  1. APL: An angle probability list to improve knowledge-based metaheuristics for the three-dimensional protein structure prediction.

    PubMed

    Borguesan, Bruno; E Silva, Mariel Barbachan; Grisci, Bruno; Inostroza-Ponta, Mario; Dorn, Márcio

    2015-12-01

    Tertiary protein structure prediction is one of the most challenging problems in structural bioinformatics. Despite the advances in algorithm development and computational strategies, predicting the folded structure of a protein only from its amino acid sequence remains as an unsolved problem. We present a new computational approach to predict the native-like three-dimensional structure of proteins. Conformational preferences of amino acid residues and secondary structure information were obtained from protein templates stored in the Protein Data Bank and represented as an Angle Probability List. Two knowledge-based prediction methods based on Genetic Algorithms and Particle Swarm Optimization were developed using this information. The proposed method has been tested with twenty-six case studies selected to validate our approach with different classes of proteins and folding patterns. Stereochemical and structural analysis were performed for each predicted three-dimensional structure. Results achieved suggest that the Angle Probability List can improve the effectiveness of metaheuristics used to predicted the three-dimensional structure of protein molecules by reducing its conformational search space. PMID:26495908

  2. Crystal Structure Prediction and its Application in Earth and Materials Sciences

    NASA Astrophysics Data System (ADS)

    Zhu, Qiang

    First of all, we describe how to predict crystal structure by evolutionary approach, and extend this method to study the packing of organic molecules, by our specially designed constrained evolutionary algorithm. The main feature of this new approach is that each unit or molecule is treated as a whole body, which drastically reduces the search space and improves the efficiency. The improved method is possibly to be applied in the fields of (1) high pressure phase of simple molecules (H2O, NH3, CH4, etc); (2) pharmaceutical molecules (glycine, aspirin, etc); (3) complex inorganic crystals containing cluster or molecular unit, (Mg(BH4)2, Ca(BH4)2, etc). One application of the constrained evolutionary algorithm is given by the study of (Mg(BH4)2, which is a promising materials for hydrogen storage. Our prediction does not only reproduce the previous work on Mg(BH4)2 at ambient condition, but also yields two new tetragonal structures at high pressure, with space groups P4 and I41/acd are predicted to be lower in enthalpy, by 15.4 kJ/mol and 21.2 kJ/mol, respectively, than the earlier proposed P42nm phase. We have simulated X-ray diffraction spectra, lattice dynamics, and equations of state of these phases. The density, volume contraction, bulk modulus, and the simulated XRD patterns of P4 and I41/acd structures are in excellent agreement with the experimental results. Two kinds of oxides (Xe-O and Mg-O) have been studied under megabar pressures. For XeO, we predict the existence of thermodynamically stable Xe-O compounds at high pressures (XeO, XeO2 and XeO3 become stable at pressures of 83, 102 and 114 GPa, respectively). For Mg-O, our calculations find that two extraordinary compounds MgO2 and Mg3O 2 become thermodynamically stable at 116 GPa and 500 GPa, respectively. Our calculations indicate large charge transfer in these oxides for both systems, suggesting that large electronegativity difference and pressure are the key factors favouring their formations. We also discuss if these oxides might exist at earth and planetary conditions. If the target properties are set as the global fitness functions while structure relaxations are energy/enthalpy minimization, such hybrid optimization technique could effectively explore the landscape of properties for the given systems. Here we illustrate this function by the case of searching for superdense carbon allotropes. We find three structures (hP3, tI12, and tP12) that have significantly greater density. Furthermore, we find a collection of other superdense structures based on different ways of packing carbon tetrahedral. Superdense carbon allotropes are predicted to have remarkably high refractive indices and strong dispersion of light. Apart from evolutionary approach, there also exist some other methods for structural prediction. One can also combine the features from different methods. We develop a novel method for crystal structure prediction, based on metadynamics and evolutionary algorithms. This technique can be used to produce efficiently both the ground state and metastable states easily reachable from a reasonable initial structure. We use the cell shape as collective variable and evolutionary variation operators developed in the context of the USPEX method to equilibrate the system as a function of the collective variables. We illustrate how this approach helps one to find stable and metastable states for Al2SiO5, SiO2, MgSiO3. Apart from predicting crystal structures, the new method can also provide insight into mechanisms of phase transitions. This method is especially powerful in sampling the metastable structures from a given configuration. Experiments on cold compression indicated the existence of a new superhard carbon allotrope. Numerous metastable candidate structures featuring different topologies have been proposed for this allotrope. We use evolutionary metadynamics to systematically search for possible candidates which could be accessible from graphite. (Abstract shortened by UMI.)

  3. AGGRESCAN3D (A3D): server for prediction of aggregation properties of protein structures

    PubMed Central

    Zambrano, Rafael; Jamroz, Michal; Szczasiuk, Agata; Pujols, Jordi; Kmiecik, Sebastian; Ventura, Salvador

    2015-01-01

    Protein aggregation underlies an increasing number of disorders and constitutes a major bottleneck in the development of therapeutic proteins. Our present understanding on the molecular determinants of protein aggregation has crystalized in a series of predictive algorithms to identify aggregation-prone sites. A majority of these methods rely only on sequence. Therefore, they find difficulties to predict the aggregation properties of folded globular proteins, where aggregation-prone sites are often not contiguous in sequence or buried inside the native structure. The AGGRESCAN3D (A3D) server overcomes these limitations by taking into account the protein structure and the experimental aggregation propensity scale from the well-established AGGRESCAN method. Using the A3D server, the identified aggregation-prone residues can be virtually mutated to design variants with increased solubility, or to test the impact of pathogenic mutations. Additionally, A3D server enables to take into account the dynamic fluctuations of protein structure in solution, which may influence aggregation propensity. This is possible in A3D Dynamic Mode that exploits the CABS-flex approach for the fast simulations of flexibility of globular proteins. The A3D server can be accessed at http://biocomp.chem.uw.edu.pl/A3D/. PMID:25883144

  4. Probabilistic and predictive performance-based approach for assessing reinforced concrete structures lifetime: The applet project

    NASA Astrophysics Data System (ADS)

    Cremona, C.; Adélaide, L.; Berthaud, Y.; Bouteiller, V.; L'Hostis, V.; Poyet, S.; Torrenti, J.-M.

    2011-04-01

    Concrete deterioration results in different damage extents, from cracking to concrete spalling, from losses of reinforcement cross-sections to bond losses. A relevant prediction of this performance is the basis for a successful management of the concrete structures. Conversely, the large amount of uncertainties related to parameters and models require a specific analysis in order to provide relevant results. The APPLET project intends to develop a probabilistic and predictive performance-based approach by quantifying the various sources of variability (material and structure), studying the interaction between environmental aggressive agents and the concrete material, ensuring a transfer of the physical-chemical models at the material scale towards models at the structure level, including and understanding in a better manner the corrosion process, integrating interface models between reinforcement and concrete, proposing relevant numerical models, integrating know-how from monitoring or inspection. To provide answers, a consortium of 19 partners has been established and has promoted a research project funded by the French Research Science Agency (ANR). Started in May 2007, the project has ended in November 2010. This paper will resume the most significant advances targeted by this research project.

  5. Timing Rhythms: Perceived Duration Increases with a Predictable Temporal Structure of Short Interval Fillers

    PubMed Central

    Horr, Ninja K.; Di Luca, Massimiliano

    2015-01-01

    Variations in the temporal structure of an interval can lead to remarkable differences in perceived duration. For example, it has previously been shown that isochronous intervals, that is, intervals filled with temporally regular stimuli, are perceived to last longer than intervals left empty or filled with randomly timed stimuli. Characterizing the extent of such distortions is crucial to understanding how duration perception works. One account to explain effects of temporal structure is a non-linear accumulator-counter mechanism reset at the beginning of every subinterval. An alternative explanation based on entrainment to regular stimulation posits that the neural response to each filler stimulus in an isochronous sequence is amplified and a higher neural response may lead to an overestimation of duration. If entrainment is the key that generates response amplification and the distortions in perceived duration, then any form of predictability in the temporal structure of interval fillers should lead to the perception of an interval that lasts longer than a randomly filled one. The present experiments confirm that intervals filled with fully predictable rhythmically grouped stimuli lead to longer perceived duration than anisochronous intervals. No general over- or underestimation is registered for rhythmically grouped compared to isochronous intervals. However, we find that the number of stimuli in each group composing the rhythm also influences perceived duration. Implications of these findings for a non-linear clock model as well as a neural response magnitude account of perceived duration are discussed. PMID:26474047

  6. Rich stoichiometries of stable Ca-Bi system: Structure prediction and superconductivity

    PubMed Central

    Dong, Xu; Fan, Changzeng

    2015-01-01

    Using a variable-composition ab initio evolutionary algorithm implemented in the USPEX code, we have performed a systematic search for stable compounds in the Ca-Bi system at different pressures. In addition to the well-known tI12-Ca2Bi and oS12-CaBi2, a few more structures were found by our calculations, among which phase transitions were also predicted in Ca2Bi (tI12 ? oI12 ? hP6), Ca3Bi2 (hP5 ? mC20 ? aP5) and CaBi (tI2 ? tI8), as well as a new phase (Ca3Bi) with a cF4 structure. All the newly predicted structures can be both dynamically and thermodynamically stable with increasing pressure. The superconductive properties of cF4-CaBi3, tI2-CaBi and cF4-Ca3Bi were studied and the superconducting critical temperature Tc can be as high as 5.16, 2.27 and 5.25?K, respectively. Different superconductivity behaviors with pressure increasing have been observed by further investigations. PMID:25790859

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

    NASA Technical Reports Server (NTRS)

    Ko, William L.; Fleischer, Van Tran

    2009-01-01

    The Ko displacement theory previously formulated for deformed shape predictions of nonuniform beam structures is further developed mathematically. The further-developed displacement equations are expressed explicitly in terms of geometrical parameters of the beam and bending strains at equally spaced strain-sensing stations along the multiplexed fiber-optic sensor line installed on the bottom surface of the beam. The bending strain data can then be input into the displacement equations for calculations of local slopes, deflections, and cross-sectional twist angles for generating the overall deformed shapes of the nonuniform beam. The further-developed displacement theory can also be applied to the deformed shape predictions of nonuniform two-point supported beams, nonuniform panels, nonuniform aircraft wings and fuselages, and so forth. The high degree of accuracy of the further-developed displacement theory for nonuniform beams is validated by finite-element analysis of various nonuniform beam structures. Such structures include tapered tubular beams, depth-tapered unswept and swept wing boxes, width-tapered wing boxes, and double-tapered wing boxes, all under combined bending and torsional loads. The Ko displacement theory, combined with the fiber-optic strain-sensing system, provide a powerful tool for in-flight deformed shape monitoring of unmanned aerospace vehicles by ground-based pilots to maintain safe flights.

  8. Assessment of ten DFT methods in predicting structures of sheet silicates: Importance of dispersion corrections

    NASA Astrophysics Data System (ADS)

    Tunega, Daniel; Bu?ko, Tomáš; Zaoui, Ali

    2012-09-01

    The performance of ten density functional theory (DFT) methods in a prediction of the structure of four clay minerals, in which non-bonding interactions dominate in the layer stacking (dispersive forces in talc and pyrophyllite, and hydrogen bonds in lizardite and kaolinite), is reported. In a set of DFT methods following functionals were included: standard local and semi-local (LDA, PW91, PBE, and RPBE), dispersion corrected (PW91-D2, PBE-D2, RPBE-D2, and vdW-TS), and functionals developed specifically for solids and solid surfaces (PBEsol and AM05). We have shown that the standard DFT functionals fail in the correct prediction of the structural parameters, for which non-bonding interactions are important. The remarkable improvement leading to very good agreement with experimental structures is achieved if the dispersion corrections are included in the DFT calculations. In such cases the relative error for the most sensitive lattice vector c dropped below 1%. Very good performance was also observed for both DFT functionals developed for solids. Especially, the results achieved with the PBEsol are qualitatively similar to those with DFT-D2.

  9. Ab initio prediction of pressure-induced structural phase transition of superconducting FeSe.

    PubMed

    Rahman, Gul; Kim, In Gee; Freeman, Arthur J

    2012-03-01

    External pressure driven phase transitions of FeSe are predicted using ab initio calculations. The calculations reveal that ?-FeSe makes transitions to NiAs-type, MnP-type, and CsCl-type FeSe. Transitions from NiAs-type to MnP-type and CsCl-type FeSe are also predicted. MnP-type FeSe is also found to be able to transform to CsCl-type FeSe, which is easier from ?-FeSe than the transition to MnP-type FeSe, but comparable to the transition from NiAs-type FeSe. The calculated electronic structures show that all phases of FeSe are metallic, but the ionic interaction between Fe-Se bonds becomes stronger and the covalent interaction becomes weaker when the structural phase transition occurs from ?-FeSe to the other phases of FeSe. The experimentally observed decrease in T(c) of superconducting ?-FeSe at high pressure may be due to a structural/magnetic instability, which exists at high pressure. The results suggest an increase of the T(c) of ?-FeSe if such phase transitions are frustrated by suitable methods. PMID:22317746

  10. Mammalian MicroRNA Prediction through a Support Vector Machine Model of Sequence and Structure

    PubMed Central

    Sheng, Ying; Engström, Pär G.; Lenhard, Boris

    2007-01-01

    Background MicroRNAs (miRNAs) are endogenous small noncoding RNA gene products, on average 22 nt long, found in a wide variety of organisms. They play important regulatory roles by targeting mRNAs for degradation or translational repression. There are 377 known mouse miRNAs and 475 known human miRNAs in the May 2007 release of the miRBase database, the majority of which are conserved between the two species. A number of recent reports imply that it is likely that many mammalian miRNAs remain to be discovered. The possibility that there are more of them expressed at lower levels or in more specialized expression contexts calls for the exploitation of genome sequence information to accelerate their discovery. Methodology/Principal Findings In this article, we describe a computational method-mirCoS-that uses three support vector machine models sequentially to discover new miRNA candidates in mammalian genomes based on sequence, secondary structure, and conservation. mirCoS can efficiently detect the majority of known miRNAs and predicts an extensive set of hairpin structures based on human-mouse comparisons. In total, 3476 mouse candidates and 3441 human candidates were found. These hairpins are more similar to known miRNAs than to negative controls in several aspects not considered by the prediction algorithm. A significant fraction of predictions is supported by existing expression evidence. Conclusions/Significance Using a novel approach, mirCoS performs comparably to or better than existing miRNA prediction methods, and contributes a significant number of new candidate miRNAs for experimental verification. PMID:17895987

  11. Multiscale Modeling of Advanced Materials for Damage Prediction and Structural Health Monitoring

    NASA Astrophysics Data System (ADS)

    Borkowski, Luke

    Advanced aerospace materials, including fiber reinforced polymer and ceramic matrix composites, are increasingly being used in critical and demanding applications, challenging the current damage prediction, detection, and quantification methodologies. Multiscale computational models offer key advantages over traditional analysis techniques and can provide the necessary capabilities for the development of a comprehensive virtual structural health monitoring (SHM) framework. Virtual SHM has the potential to drastically improve the design and analysis of aerospace components through coupling the complementary capabilities of models able to predict the initiation and propagation of damage under a wide range of loading and environmental scenarios, simulate interrogation methods for damage detection and quantification, and assess the health of a structure. A major component of the virtual SHM framework involves having micromechanics-based multiscale composite models that can provide the elastic, inelastic, and damage behavior of composite material systems under mechanical and thermal loading conditions and in the presence of microstructural complexity and variability. Quantification of the role geometric and architectural variability in the composite microstructure plays in the local and global composite behavior is essential to the development of appropriate scale-dependent unit cells and boundary conditions for the multiscale model. Once the composite behavior is predicted and variability effects assessed, wave-based SHM simulation models serve to provide knowledge on the probability of detection and characterization accuracy of damage present in the composite. The research presented in this dissertation provides the foundation for a comprehensive SHM framework for advanced aerospace materials. The developed models enhance the prediction of damage formation as a result of ceramic matrix composite processing, improve the understanding of the effects of architectural and geometric variability in polymer matrix composites, and provide an accurate and computational efficient modeling scheme for simulating guided wave excitation, propagation, interaction with damage, and sensing in a range of materials. The methodologies presented in this research represent substantial progress toward the development of an accurate and generalized virtual SHM framework.

  12. Structured Set Intra Prediction With Discriminative Learning in a Max-Margin Markov Network for High Efficiency Video Coding

    PubMed Central

    Dai, Wenrui; Xiong, Hongkai; Jiang, Xiaoqian; Chen, Chang Wen

    2014-01-01

    This paper proposes a novel model on intra coding for High Efficiency Video Coding (HEVC), which simultaneously predicts blocks of pixels with optimal rate distortion. It utilizes the spatial statistical correlation for the optimal prediction based on 2-D contexts, in addition to formulating the data-driven structural interdependences to make the prediction error coherent with the probability distribution, which is desirable for successful transform and coding. The structured set prediction model incorporates a max-margin Markov network (M3N) to regulate and optimize multiple block predictions. The model parameters are learned by discriminating the actual pixel value from other possible estimates to maximize the margin (i.e., decision boundary bandwidth). Compared to existing methods that focus on minimizing prediction error, the M3N-based model adaptively maintains the coherence for a set of predictions. Specifically, the proposed model concurrently optimizes a set of predictions by associating the loss for individual blocks to the joint distribution of succeeding discrete cosine transform coefficients. When the sample size grows, the prediction error is asymptotically upper bounded by the training error under the decomposable loss function. As an internal step, we optimize the underlying Markov network structure to find states that achieve the maximal energy using expectation propagation. For validation, we integrate the proposed model into HEVC for optimal mode selection on rate-distortion optimization. The proposed prediction model obtains up to 2.85% bit rate reduction and achieves better visual quality in comparison to the HEVC intra coding. PMID:25505829

  13. Predicting Performance and Plasticity in the Development of Respiratory Structures and Metabolic Systems

    PubMed Central

    Montooth, Kristi L.; Helm, Bryan R.

    2014-01-01

    The scaling laws governing metabolism suggest that we can predict metabolic rates across taxonomic scales that span large differences in mass. Yet, scaling relationships can vary with development, body region, and environment. Within species, there is variation in metabolic rate that is independent of mass and which may be explained by genetic variation, the environment or their interaction (i.e., metabolic plasticity). Additionally, some structures, such as the insect tracheal respiratory system, change throughout development and in response to the environment to match the changing functional requirements of the organism. We discuss how study of the development of respiratory function meets multiple challenges set forth by the NSF Grand Challenges Workshop. Development of the structure and function of respiratory and metabolic systems (1) is inherently stable and yet can respond dynamically to change, (2) is plastic and exhibits sensitivity to environments, and (3) can be examined across multiple scales in time and space. Predicting respiratory performance and plasticity requires quantitative models that integrate information across scales of function from the expression of metabolic genes and mitochondrial biogenesis to the building of respiratory structures. We present insect models where data are available on the development of the tracheal respiratory system and of metabolic physiology and suggest what is needed to develop predictive models. Incorporating quantitative genetic data will enable mapping of genetic and genetic-by-environment variation onto phenotypes, which is necessary to understand the evolution of respiratory and metabolic systems and their ability to enable respiratory homeostasis as organisms walk the tightrope between stability and change. PMID:24812329

  14. Improving prediction of secondary structure, local backbone angles, and solvent accessible surface area of proteins by iterative deep learning

    PubMed Central

    Heffernan, Rhys; Paliwal, Kuldip; Lyons, James; Dehzangi, Abdollah; Sharma, Alok; Wang, Jihua; Sattar, Abdul; Yang, Yuedong; Zhou, Yaoqi

    2015-01-01

    Direct prediction of protein structure from sequence is a challenging problem. An effective approach is to break it up into independent sub-problems. These sub-problems such as prediction of protein secondary structure can then be solved independently. In a previous study, we found that an iterative use of predicted secondary structure and backbone torsion angles can further improve secondary structure and torsion angle prediction. In this study, we expand the iterative features to include solvent accessible surface area and backbone angles and dihedrals based on C? atoms. By using a deep learning neural network in three iterations, we achieved 82% accuracy for secondary structure prediction, 0.76 for the correlation coefficient between predicted and actual solvent accessible surface area, 19° and 30° for mean absolute errors of backbone ? and ? angles, respectively, and 8° and 32° for mean absolute errors of C?-based ? and ? angles, respectively, for an independent test dataset of 1199 proteins. The accuracy of the method is slightly lower for 72 CASP 11 targets but much higher than those of model structures from current state-of-the-art techniques. This suggests the potentially beneficial use of these predicted properties for model assessment and ranking. PMID:26098304

  15. Mixing Energy Models in Genetic Algorithms for On-Lattice Protein Structure Prediction

    PubMed Central

    Rashid, Mahmood A.; Newton, M. A. Hakim; Hoque, Md. Tamjidul; Sattar, Abdul

    2013-01-01

    Protein structure prediction (PSP) is computationally a very challenging problem. The challenge largely comes from the fact that the energy function that needs to be minimised in order to obtain the native structure of a given protein is not clearly known. A high resolution 20 × 20 energy model could better capture the behaviour of the actual energy function than a low resolution energy model such as hydrophobic polar. However, the fine grained details of the high resolution interaction energy matrix are often not very informative for guiding the search. In contrast, a low resolution energy model could effectively bias the search towards certain promising directions. In this paper, we develop a genetic algorithm that mainly uses a high resolution energy model for protein structure evaluation but uses a low resolution HP energy model in focussing the search towards exploring structures that have hydrophobic cores. We experimentally show that this mixing of energy models leads to significant lower energy structures compared to the state-of-the-art results. PMID:24224180

  16. Sex differences in structural brain asymmetry predict overt aggression in early adolescents.

    PubMed

    Visser, Troy A W; Ohan, Jeneva L; Whittle, Sarah; Yücel, Murat; Simmons, Julian G; Allen, Nicholas B

    2014-04-01

    The devastating social, emotional and economic consequences of human aggression are laid bare nightly on newscasts around the world. Aggression is principally mediated by neural circuitry comprising multiple areas of the prefrontal cortex and limbic system, including the orbitofrontal cortex (OFC), anterior cingulate cortex (ACC), amygdala and hippocampus. A striking characteristic of these regions is their structural asymmetry about the midline (i.e. left vs right hemisphere). Variations in these asymmetries have been linked to clinical disorders characterized by aggression and the rate of aggressive behavior in psychiatric patients. Here, we show for the first time that structural asymmetries in prefrontal cortical areas are also linked to aggression in a normal population of early adolescents. Our findings indicate a relationship between parent reports of aggressive behavior in adolescents and structural asymmetries in the limbic and paralimbic ACC and OFC, and moreover, that this relationship varies by sex. Furthermore, while there was no relationship between aggression and structural asymmetries in the amygdala or hippocampus, hippocampal volumes did predict aggression in females. Taken together, the results suggest that structural asymmetries in the prefrontal cortex may influence human aggression, and that the anatomical basis of aggression varies substantially by sex. PMID:23446839

  17. Computational prediction of RNA structural motifs involved in posttranscriptional regulatory processes

    PubMed Central

    Rabani, Michal; Kertesz, Michael; Segal, Eran

    2008-01-01

    Messenger RNA molecules are tightly regulated, mostly through interactions with proteins and other RNAs, but the mechanisms that confer the specificity of such interactions are poorly understood. It is clear, however, that this specificity is determined by both the nucleotide sequence and secondary structure of the mRNA. Here, we develop RNApromo, an efficient computational tool for identifying structural elements within mRNAs that are involved in specifying posttranscriptional regulations. By analyzing experimental data on mRNA decay rates, we identify common structural elements in fast-decaying and slow-decaying mRNAs and link them with binding preferences of several RNA binding proteins. We also predict structural elements in sets of mRNAs with common subcellular localization in mouse neurons and fly embryos. Finally, by analyzing pre-microRNA stem–loops, we identify structural differences between pre-microRNAs of animals and plants, which provide insights into the mechanism of microRNA biogenesis. Together, our results reveal unexplored layers of posttranscriptional regulations in groups of RNAs and are therefore an important step toward a better understanding of the regulatory information conveyed within RNA molecules. Our new RNA motif discovery tool is available online. PMID:18815376

  18. Performance assessment of different constraining potentials in computational structure prediction for disulfide-bridged proteins.

    PubMed

    Kondov, Ivan; Verma, Abhinav; Wenzel, Wolfgang

    2011-08-10

    The presence of disulfide bonds in proteins has very important implications on the three-dimensional structure and folding of proteins. An adequate treatment of disulfide bonds in de-novo protein simulations is therefore very important. Here we present a computational study of a set of small disulfide-bridged proteins using an all-atom stochastic search approach and including various constraining potentials to describe the disulfide bonds. The proposed potentials can easily be implemented in any code based on all-atom force fields and employed in simulations to achieve an improved prediction of protein structure. Exploring different potential parameters and comparing the structures to those from unconstrained simulations and to experimental structures by means of a scoring function we demonstrate that the inclusion of constraining potentials improves the quality of final structures significantly. For some proteins (1KVG and 1PG1) the native conformation is visited only in simulations in presence of constraints. Overall, we found that the Morse potential has optimal performance, in particular for the ?-sheet proteins. PMID:21864792

  19. Coupling continuous damage and debris fragmentation for energy absorption prediction by cfrp structures during crushing

    NASA Astrophysics Data System (ADS)

    Espinosa, Christine; Lachaud, Frédéric; Limido, Jérome; Lacome, Jean-Luc; Bisson, Antoine; Charlotte, Miguel

    2015-05-01

    Energy absorption during crushing is evaluated using a thermodynamic based continuum damage model inspired from the Matzenmiller-Lubliner-Taylors model. It was found that for crash-worthiness applications, it is necessary to couple the progressive ruin of the material to a representation of the matter openings and debris generation. Element kill technique (erosion) and/or cohesive elements are efficient but not predictive. A technique switching finite elements into discrete particles at rupture is used to create debris and accumulated mater during the crushing of the structure. Switching criteria are evaluated using the contribution of the different ruin modes in the damage evolution, energy absorption, and reaction force generation.

  20. Predicting Electrocatalytic Properties: Modeling Structure-Activity Relationships of Nitroxyl Radicals.

    PubMed

    Hickey, David P; Schiedler, David A; Matanovic, Ivana; Doan, Phuong Vy; Atanassov, Plamen; Minteer, Shelley D; Sigman, Matthew S

    2015-12-30

    Stable nitroxyl radical-containing compounds, such as 2,2,6,6-tetramethylpiperidine-N-oxyl (TEMPO) and its derivatives, are capable of electrocatalytically oxidizing a wide range of alcohols under mild and environmentally friendly conditions. Herein, we examine the structure-function relationships that determine the catalytic activity of a diverse range of water-soluble nitroxyl radical compounds. A strong correlation is described between the difference in the electrochemical oxidation potentials of a compound and its electrocatalytic activity. Additionally, we construct a simple computational model that is able to accurately predict the electrochemical potential and catalytic activity of a wide range of nitroxyl radical derivatives. PMID:26635089

  1. Structural invariants for the prediction of relative toxicities of polychloro dibenzo-p-dioxins and dibenzofurans.

    PubMed

    Luco, J M; Gálvez, J; García-Domenech, R; de Julián-Ortiz, J V

    2004-01-01

    Multivariate models are reported that can predict the relative toxicity of compounds with severe environmental impact, namely polychloro dibenzo-p-dioxins (PCDDs) and dibenzofurans (PCDFs). Multiple linear regression analysis (MLR) and partial least square projections of latent variables (PLS) show the usefulness of graph-theoretical descriptors, mainly topological charge indices (TCIs), in these series. The general trends of the group are correctly reproduced and better results are presented than have previously been published. In general, the more toxic compounds exhibit more symmetric molecular structures. PMID:15612637

  2. XTALOPT version r7: An open-source evolutionary algorithm for crystal structure prediction

    NASA Astrophysics Data System (ADS)

    Lonie, David C.; Zurek, Eva

    2011-10-01

    A new version of XTALOPT, a user-friendly GPL-licensed evolutionary algorithm for crystal structure prediction, is available for download from the CPC library or the XTALOPT website, http://xtalopt.openmolecules.net. The new version now supports four external geometry optimization codes (VASP, GULP, PWSCF, and CASTEP), as well as three queuing systems: PBS, SGE, SLURM, and “Local”. The local queuing system allows the geometry optimizations to be performed on the user's workstation if an external computational cluster is unavailable. Support for the Windows operating system has been added, and a Windows installer is provided. Numerous bugfixes and feature enhancements have been made in the new release as well.

  3. Predictions of charged charmoniumlike structures with hidden-charm and open-strange channels.

    PubMed

    Chen, Dian-Yong; Liu, Xiang; Matsuki, Takayuki

    2013-06-01

    We propose the initial single chiral particle emission mechanism, with which the hidden-charm dikaon decays of higher charmonia and charmoniumlike states are studied. Calculating the distributions of differential decay width, we obtain the line shape of the J/?K(+) invariant mass spectrum of ?(i)?J/?K(+)K(-), where ?(i)=?(4415), Y(4660), and ?(4790). Our numerical results show that there exist enhancement structures with both hidden-charm and open-strange channels, which are near the D?(s)(*)/D(*)D?(s) and D(*)D?(s)(*)/D?(*)D(s)(*) thresholds. These charged charmoniumlike structures predicted in this Letter can be accessible in future experiments, especially BESIII, BelleII, and SuperB. PMID:25167483

  4. Prediction of a two-dimensional crystalline structure of nitrogen atoms

    NASA Astrophysics Data System (ADS)

    Özçelik, V. Ongun; Aktürk, O. Üzengi; Durgun, E.; Ciraci, S.

    2015-09-01

    Based on first-principles density functional calculations, we predict that nitrogen atoms can form a single-layer, buckled honeycomb structure called nitrogene, which is rigid and stable even above room temperature. This 2D crystalline phase of nitrogen, which corresponds to a local minimum in the Born-Oppenheimer surface, is a nonmagnetic insulator with saturated ? bonds. When grown on a substrate like Al(111) surface and graphene, nitrogene binds weakly to substrates and hence preserves its free-standing properties, but it can easily be pealed off. Zigzag and armchair nanoribbons of nitrogene have fundamental band gaps derived from reconstructed edge states. These band gaps are tunable with size and suitable for the emerging field of 2D electronics. Nitrogene forms not only bilayer, but also 3D graphitic multilayer structures. Single-layer nitrogene can nucleate and grow on the armchair edges of hexagonal boron nitride.

  5. Function and Regulation of Fungal Amino Acid Transporters: Insights from Predicted Structure.

    PubMed

    Gournas, Christos; Prévost, Martine; Krammer, Eva-Maria; André, Bruno

    2016-01-01

    Amino acids constitute a major nutritional source for probably all fungi. Studies of model species such as the yeast Saccharomyces cerevisiae and the filamentous fungus Aspergillus nidulans have shown that they possess multiple amino acid transporters. These proteins belong to a limited number of superfamilies, now defined according to protein fold in addition to sequence criteria, and differ in subcellular location, substrate specificity range, and regulation. Structural models of several of these transporters have recently been built, and the detailed molecular mechanisms of amino acid recognition and translocation are now being unveiled. Furthermore, the particular conformations adopted by some of these transporters in response to amino acid binding appear crucial to promoting their ubiquitin-dependent endocytosis and/or to triggering signaling responses. We here summarize current knowledge, derived mainly from studies on S. cerevisiae and A. nidulans, about the transport activities, regulation, and sensing role of fungal amino acid transporters, in relation to predicted structure. PMID:26721271

  6. Prediction of Shock Wave Structure in Weakly Ionized Gas Flow by Solving MGD Equation

    NASA Technical Reports Server (NTRS)

    Deng, Z. T.; Oviedo-Rojas, Ruben; Chow, Alan; Litchford, Ron J.; Cook, Stephen (Technical Monitor)

    2002-01-01

    This paper reports the recent research results of shockwave structure predictions using a new developed code. The modified Rankine-Hugoniot relations across a standing normal shock wave are discussed and adopted to obtain jump conditions. Coupling a electrostatic body force to the Burnett equations, the weakly ionized flow field across the shock wave was solved. Results indicated that the Modified Rankine-Hugoniot equations for shock wave are valid for a wide range of ionization fraction. However, this model breaks down with small free stream Mach number and with large ionization fraction. The jump conditions also depend on the value of free stream pressure, temperature and density. The computed shock wave structure with ionization provides results, which indicated that shock wave strength may be reduced by existence of weakly ionized gas.

  7. Correlation of predicted and measured thermal stresses on an advanced aircraft structure with similar materials

    NASA Technical Reports Server (NTRS)

    Jenkins, J. M.

    1979-01-01

    A laboratory heating test simulating hypersonic heating was conducted on a heat-sink type structure to provide basic thermal stress measurements. Six NASTRAN models utilizing various combinations of bar, shear panel, membrane, and plate elements were used to develop calculated thermal stresses. Thermal stresses were also calculated using a beam model. For a given temperature distribution there was very little variation in NASTRAN calculated thermal stresses when element types were interchanged for a given grid system. Thermal stresses calculated for the beam model compared similarly to the values obtained for the NASTRAN models. Calculated thermal stresses compared generally well to laboratory measured thermal stresses. A discrepancy of signifiance occurred between the measured and predicted thermal stresses in the skin areas. A minor anomaly in the laboratory skin heating uniformity resulted in inadequate temperature input data for the structural models.

  8. Features of Large Hinge-Bending Conformational Transitions. Prediction of Closed Structure from Open State

    PubMed Central

    Uyar, Arzu; Kantarci-Carsibasi, Nigar; Haliloglu, Turkan; Doruker, Pemra

    2014-01-01

    We performed a detailed analysis of conformational transition pathways for a set of 10 proteins, which undergo large hinge-bending-type motions with 4–12 Ĺ RMSD (root mean-square distance) between open and closed crystal structures. Anisotropic network model-Monte Carlo (ANM-MC) algorithm generates a targeted pathway between two conformations, where the collective modes from the ANM are used for deformation at each iteration and the conformational energy of the deformed structure is minimized via an MC algorithm. The target structure was approached successfully with an RMSD of 0.9–4.1 Ĺ when a relatively low cutoff radius of 10 Ĺ was used in ANM. Even though one predominant mode (first or second) directed the open-to-closed conformational transition, changes in the dominant mode character were observed for most cases along the transition. By imposing radius of gyration constraint during mode selection, it was possible to predict the closed structure for eight out of 10 proteins (with initial 4.1–7.1 Ĺ and final 1.7–2.9 Ĺ RMSD to target). Deforming along a single mode leads to most successful predictions. Based on the previously reported free energy surface of adenylate kinase, deformations along the first mode produced an energetically favorable path, which was interestingly facilitated by a change in mode shape (resembling second and third modes) at key points. Pathway intermediates are provided in our database of conformational transitions (http://safir.prc.boun.edu.tr/anmmc/method/1). PMID:24940783

  9. HC-Search for Structured Prediction in Computer Vision Michael Lam, Janardhan Rao Doppa, Sinisa Todorovic, and Thomas G. Dietterich

    E-print Network

    Todorovic, Sinisa

    that function. At prediction time, this approach must solve an often-challenging optimization problem. Search problems; it has already shown the state-of-the-art performance on structured prediction prob- lems global energy function and then solving a global optimization problem, as is done in these approaches, HC

  10. DEVELOPMENT OF QUANTITATIVE STRUCTURE ACTIVITY RELATIONSHIPS (QSARS) TO PREDICT TOXICITY FOR A VARIETY OF HUMAN AND ECOLOGICAL ENDPOINTS

    EPA Science Inventory

    A web accessible software tool is being developed to predict the toxicity of unknown chemicals for a wide variety of endpoints. The tool will enable a user to easily predict the toxicity of a query compound by simply entering its structure in a 2-dimensional (2-D) chemical sketc...

  11. Prediction of Structures and Atomization Energies of Small Silver Clusters, (Ag)n, n < 100

    SciTech Connect

    Chen, Mingyang; Dyer, Jason E.; Li, Keijing; Dixon, David A.

    2013-07-24

    Neutral silver clusters, Agn, were studied using density functional theory (DFT) followed by high level coupled cluster CCSD(T) calculations to determine the low energy isomers for each cluster size for small clusters. The normalized atomization energy, heats of formation, and average bond lengths were calculated for each of the different isomeric forms of the silver clusters. For n = 2?6, the preferred geometry is planar, and the larger n = 7?8 clusters prefer higher symmetry, three-dimensional geometries. The low spin state is predicted to be the ground state for every cluster size. A number of new low energy isomers for the heptamer and octamer were found. Additional larger Agn structures, n < 100, were initially optimized using a tree growth-hybrid genetic algorithm with an embedded atom method (EAM) potential. For n ? 20, DFT was used to optimize the geometries. DFT with benchmarked functionals were used to predict that the normalized atomization energies (?AE?s) for Agn start to converge slowly to the bulk at n = 55. The ?AE? for Ag99 is predicted to be ?50 kcal/mol.

  12. Predicting normal densities of amines using quantitative structure-property relationship (QSPR).

    PubMed

    Stec, M; Spietz, T; Wi?c?aw-Solny, L; Tatarczuk, A; Krótki, A

    2015-11-01

    This paper reports on a quantitative structure-property relationship (QSPR) approach applied to predict the normal density of amines. A heuristic method was applied to a dataset of experimental densities measured for more than 140 amines extracted from the literature. Statistical processing was performed to find the best correlations. The resulting model was validated successfully based on an external test set. The QSPR analysis showed the importance of intrinsic density, obtained by dividing molecular weight by calculated molecular volume. Further improvement of the model's predictive ability was achieved by introducing descriptors quantifying the influence of intermolecular volume. It has been shown that a simple correlation equation is sufficient to predict the density of amines with satisfactory accuracy. The equation requires only three descriptors: intrinsic density, overall or summation solute hydrogen bond acidity and a sum of intrinsic state values, each of which can be easily calculated using the Simplified Molecular Input Line Entry System Specification (SMILES). The equation provided may be applied to determine the density of amines or their derivatives which are unavailable or unknown. PMID:26524227

  13. Predictions of Tertiary Structures of ?-Helical Membrane Proteins by Replica-Exchange Method with Consideration of Helix Deformations

    NASA Astrophysics Data System (ADS)

    Urano, Ryo; Kokubo, Hironori; Okamoto, Yuko

    2015-08-01

    We propose an improved prediction method of the tertiary structures of ?-helical membrane proteins based on the replica-exchange method by taking into account helix deformations. Our method has wide applications because transmembrane helices of native membrane proteins are often distorted. In order to test the effectiveness of the present method, we applied it to the structure predictions of glycophorin A and phospholamban. The results were in good agreement with experiments.

  14. Fabrication of 3D nanostructures by multidirectional UV lithography and predictive structural modeling

    NASA Astrophysics Data System (ADS)

    Kim, Jungkwun; Kim, Cheolbok; Allen, Mark G.; ‘YK' Yoon, Yong-Kyu

    2015-02-01

    This paper presents the fabrication and modeling of three-dimensional (3D) nanostructures by automated multidirectional ultraviolet (UV) lithography, which is a fast, cost-effective, manufacturable fabrication method. Multidirectional UV exposure is performed using a static UV light source equipped with a tilt-rotational substrate holder. A glass substrate with a nanopatterned chrome layer is utilized as both a photomask and a substrate, for which a backside UV exposure scheme is used. For the analytical modeling of the shape of fabricated nanostructures, UV exposure dosage, diffraction and refraction effects, and absorption rate are taken into account. For more accurate process predictive models, a commercially available multiphysics simulation tool is used. The structural shapes predicted from analytical calculation and simulation are compared with the fabricated ones for which various 3D nanoscale test structures are fabricated such as an inclined nanopillar array and a vertical triangular slab. Also, nanostructures with multiple heights are successfully implemented from single layer photoresist by controlling the UV exposure dosage and tilt angles. A tripod embedded horn and a triangular-slab embedded horn are demonstrated.

  15. Measured and predicted structural behavior of the HiMAT tailored composite wing

    NASA Technical Reports Server (NTRS)

    Nelson, Lawrence H.

    1987-01-01

    A series of load tests was conducted on the HiMAT tailored composite wing. Coupon tests were also run on a series of unbalanced laminates, including the ply configuration of the wing, the purpose of which was to compare the measured and predicted behavior of unbalanced laminates, including - in the case of the wing - a comparison between the behavior of the full scale structure and coupon tests. Both linear and nonlinear finite element (NASTRAN) analyses were carried out on the wing. Both linear and nonlinear point-stress analyses were performed on the coupons. All test articles were instrumented with strain gages, and wing deflections measured. The leading and trailing edges were found to have no effect on the response of the wing to applied loads. A decrease in the stiffness of the wing box was evident over the 27-test program. The measured load-strain behavior of the wing was found to be linear, in contrast to coupon tests of the same laminate, which were nonlinear. A linear NASTRAN analysis of the wing generally correlated more favorably with measurements than did a nonlinear analysis. An examination of the predicted deflections in the wing root region revealed an anomalous behavior of the structural model that cannot be explained. Both hysteresis and creep appear to be less significant in the wing tests than in the corresponding laminate coupon tests.

  16. Evaluating factors that predict the structure of a commensalistic epiphyte–phorophyte network

    PubMed Central

    Sáyago, Roberto; Lopezaraiza-Mikel, Martha; Quesada, Mauricio; Álvarez-Ańorve, Mariana Yolotl; Cascante-Marín, Alfredo; Bastida, Jesus Ma.

    2013-01-01

    A central issue in ecology is the understanding of the establishment of biotic interactions. We studied the factors that affect the assembly of the commensalistic interactions between vascular epiphytes and their host plants. We used an analytical approach that considers all individuals and species of epiphytic bromeliads and woody hosts and non-hosts at study plots. We built models of interaction probabilities among species to assess if host traits and abundance and spatial overlap of species predict the quantitative epiphyte–host network. Species abundance, species spatial overlap and host size largely predicted pairwise interactions and several network metrics. Wood density and bark texture of hosts also contributed to explain network structure. Epiphytes were more common on large hosts, on abundant woody species, with denser wood and/or rougher bark. The network had a low level of specialization, although several interactions were more frequent than expected by the models. We did not detect a phylogenetic signal on the network structure. The effect of host size on the establishment of epiphytes indicates that mature forests are necessary to preserve diverse bromeliad communities. PMID:23407832

  17. Efficient Prediction Structures for H.264 Multi View Coding Using Temporal Scalability

    NASA Astrophysics Data System (ADS)

    Guruvareddiar, Palanivel; Joseph, Biju K.

    2014-03-01

    Prediction structures with "disposable view components based" hierarchical coding have been proven to be efficient for H.264 multi view coding. Though these prediction structures along with the QP cascading schemes provide superior compression efficiency when compared to the traditional IBBP coding scheme, the temporal scalability requirements of the bit stream could not be met to the fullest. On the other hand, a fully scalable bit stream, obtained by "temporal identifier based" hierarchical coding, provides a number of advantages including bit rate adaptations and improved error resilience, but lacks in compression efficiency when compared to the former scheme. In this paper it is proposed to combine the two approaches such that a fully scalable bit stream could be realized with minimal reduction in compression efficiency when compared to state-of-the-art "disposable view components based" hierarchical coding. Simulation results shows that the proposed method enables full temporal scalability with maximum BDPSNR reduction of only 0.34 dB. A novel method also has been proposed for the identification of temporal identifier for the legacy H.264/AVC base layer packets. Simulation results also show that this enables the scenario where the enhancement views could be extracted at a lower frame rate (1/2nd or 1/4th of base view) with average extraction time for a view component of only 0.38 ms.

  18. Significance of ligand tails for interaction with the minor groove of B-DNA.

    PubMed Central

    Wellenzohn, B; Flader, W; Winger, R H; Hallbrucker, A; Mayer, E; Liedl, K R

    2001-01-01

    Minor groove binding ligands are of great interest due to their extraordinary importance as transcription controlling drugs. We performed three molecular dynamics simulations of the unbound d(CGCGAATTCGCG)(2) dodecamer and its complexes with Hoechst33258 and Netropsin. The structural behavior of the piperazine tail of Hoechst33258, which has already been shown to be a contributor in sequence-specific recognition, was analyzed. The simulations also reveal that the tails of the ligands are able to influence the width of the minor groove. The groove width is even sensitive for conformational transitions of these tails, indicating a high adaptability of the minor groove. Furthermore, the ligands also exert an influence on the B(I)/B(II) backbone conformational substate behavior. All together these results are important for the understanding of the binding process of sequence-specific ligands. PMID:11509372

  19. McGenus: A Monte Carlo algorithm to predict RNA secondary structures with pseudoknots

    E-print Network

    M. Bon; C. Micheletti; H. Orland

    2013-02-14

    We present McGenus, an algorithm to predict RNA secondary structures with pseudoknots. The method is based on a classification of RNA structures according to their topological genus. McGenus can treat sequences of up to 1000 bases and performs an advanced stochastic search of their minimum free energy structure allowing for non trivial pseudoknot topologies. Specifically, McGenus employs a multiple Markov chain scheme for minimizing a general scoring function which includes not only free energy contributions for pair stacking, loop penalties, etc. but also a phenomenological penalty for the genus of the pairing graph. The good performance of the stochastic search strategy was successfully validated against TT2NE which uses the same free energy parametrization and performs exhaustive or partially exhaustive structure search, albeit for much shorter sequences (up to 200 bases). Next, the method was applied to other RNA sets, including an extensive tmRNA database, yielding results that are competitive with existing algorithms. Finally, it is shown that McGenus highlights possible limitations in the free energy scoring function. The algorithm is available as a web-server at http://ipht.cea.fr/rna/mcgenus.php .

  20. In vitro anti-Mycobacterium avium activities of quinolones: predicted active structures and mechanistic considerations.

    PubMed Central

    Klopman, G; Li, J Y; Wang, S; Pearson, A J; Chang, K; Jacobs, M R; Bajaksouzian, S; Ellner, J J

    1994-01-01

    The relationship between the structures of quinolones and their anti-Mycobacterium avium activities has been previously derived by using the Multiple Computer-Automated Structure Evaluation program. A number of substructural constraints required to overcome the resistance of most of the strains have been identified. Nineteen new quinolones which qualify under these substructural requirements were identified by the program and subsequently tested. The results show that the substructural attributes identified by the program produced a successful a priori prediction of the anti-M. avium activities of the new quinolones. All 19 quinolones were found to be active, and 4 of them are as active or better than ciprofloxacin. With these new quinolones, the updated multiple computer-automated structure evaluation program structure-activity relationship analysis has helped to uncover additional information about the nature of the substituents at the C5 and C7 positions needed for optimal inhibitory activity. A possible explanation of drug resistance based on the observation of suicide inactivation of bacterial cytochrome P-450 by the cyclopropylamine moiety has also been proposed and is discussed in this report. Furthermore, we confirm the view that the amount of the uncharged form present in a neutral pH solution plays a crucial role in the drug's penetration ability. PMID:7986010