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Sample records for predicting b-dna structure

  1. Predicting B-DNA structure from sequence

    SciTech Connect

    Tung, Chang-Shun; Hummer, G.; Soumpasis, D.M.

    1995-12-31

    This project developed a reliable method that is capable of predicting B-DNA duplex structure from sequence. From any given sequence, the method predicts a complete double helical structure at the atomic level. Tetramers are used as a basic unit for the study to include the sequence effects from the neighboring base pairs. The equilibrium structures of the 136 distinct Tetramers are deduced from Monte Carlo simulations on a set of reduced coordinates developed at LANL. The prediction methods by this project can be used for searching and defining structural motifs in the functional regions of the genes. We have constructed an atomic modeled structure of a 17 base-pair DNA operator (cro, from phage lambda) with the phosphorus structures solved by x-ray crystallography. With this predicted DNA structure and modeled structures of the alpha-3 helix based on the C- alpha atoms solved by x-ray crystallography, we were able to predict two specific interactions between the cro protein and the DNA (Ser-28 to Gua-14, Lys-32 and Gua-12). These interactions were partially verified by NMR using N-15 labeled DNA operator.

  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. NMR proton chemical shift prediction of CC 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 CC 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 CC mismatches can be used interchangeably with those for TT mismatches. As a result, we have generalized a set of correction factors for predicting the flanking residue chemical shifts of pyrimidinepyrimidine mismatches.

  4. The genome-wide distribution of non-B DNA motifs is shaped by operon structure and suggests the transcriptional importance of non-B DNA structures in Escherichia coli.

    PubMed

    Du, Xiangjun; Wojtowicz, Damian; Bowers, Albert A; Levens, David; Benham, Craig J; Przytycka, Teresa M

    2013-07-01

    Although the right-handed double helical B-form DNA is most common under physiological conditions, DNA is dynamic and can adopt a number of alternative structures, such as the four-stranded G-quadruplex, left-handed Z-DNA, cruciform and others. Active transcription necessitates strand separation and can induce such non-canonical forms at susceptible genomic sequences. Therefore, it has been speculated that these non-B DNA motifs can play regulatory roles in gene transcription. Such conjecture has been supported in higher eukaryotes by direct studies of several individual genes, as well as a number of large-scale analyses. However, the role of non-B DNA structures in many lower organisms, in particular proteobacteria, remains poorly understood and incompletely documented. In this study, we performed the first comprehensive study of the occurrence of B DNA-non-B DNA transition-susceptible sites (non-B DNA motifs) within the context of the operon structure of the Escherichia coli genome. We compared the distributions of non-B DNA motifs in the regulatory regions of operons with those from internal regions. We found an enrichment of some non-B DNA motifs in regulatory regions, and we show that this enrichment cannot be simply explained by base composition bias in these regions. We also showed that the distribution of several non-B DNA motifs within intergenic regions separating divergently oriented operons differs from the distribution found between convergent ones. In particular, we found a strong enrichment of cruciforms in the termination region of operons; this enrichment was observed for operons with Rho-dependent, as well as Rho-independent terminators. Finally, a preference for some non-B DNA motifs was observed near transcription factor-binding sites. Overall, the conspicuous enrichment of transition-susceptible sites in these specific regulatory regions suggests that non-B DNA structures may have roles in the transcriptional regulation of specific operons within the E. coli genome. PMID:23620297

  5. The genome-wide distribution of non-B DNA motifs is shaped by operon structure and suggests the transcriptional importance of non-B DNA structures in Escherichia coli

    PubMed Central

    Du, Xiangjun; Wojtowicz, Damian; Bowers, Albert A.; Levens, David; Benham, Craig J.; Przytycka, Teresa M.

    2013-01-01

    Although the right-handed double helical B-form DNA is most common under physiological conditions, DNA is dynamic and can adopt a number of alternative structures, such as the four-stranded G-quadruplex, left-handed Z-DNA, cruciform and others. Active transcription necessitates strand separation and can induce such non-canonical forms at susceptible genomic sequences. Therefore, it has been speculated that these non-B DNA motifs can play regulatory roles in gene transcription. Such conjecture has been supported in higher eukaryotes by direct studies of several individual genes, as well as a number of large-scale analyses. However, the role of non-B DNA structures in many lower organisms, in particular proteobacteria, remains poorly understood and incompletely documented. In this study, we performed the first comprehensive study of the occurrence of B DNAnon-B DNA transition-susceptible sites (non-B DNA motifs) within the context of the operon structure of the Escherichia coli genome. We compared the distributions of non-B DNA motifs in the regulatory regions of operons with those from internal regions. We found an enrichment of some non-B DNA motifs in regulatory regions, and we show that this enrichment cannot be simply explained by base composition bias in these regions. We also showed that the distribution of several non-B DNA motifs within intergenic regions separating divergently oriented operons differs from the distribution found between convergent ones. In particular, we found a strong enrichment of cruciforms in the termination region of operons; this enrichment was observed for operons with Rho-dependent, as well as Rho-independent terminators. Finally, a preference for some non-B DNA motifs was observed near transcription factor-binding sites. Overall, the conspicuous enrichment of transition-susceptible sites in these specific regulatory regions suggests that non-B DNA structures may have roles in the transcriptional regulation of specific operons within the E. coli genome. PMID:23620297

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

    PubMed Central

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

    1997-01-01

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

  7. 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.

  8. The N(2)-Furfuryl-deoxyguanosine Adduct Does Not Alter the Structure of B-DNA.

    PubMed

    Ghodke, Pratibha P; Gore, Kiran R; Harikrishna, S; Samanta, Biswajit; Kottur, Jithesh; Nair, Deepak T; Pradeepkumar, P I

    2016-01-15

    N(2)-Furfuryl-deoxyguanosine (fdG) is carcinogenic DNA adduct that originates from furfuryl alcohol. It is also a stable structural mimic of the damage induced by the nitrofurazone family of antibiotics. For the structural and functional studies of this model N(2)-dG adduct, reliable and rapid access to fdG-modified DNAs are warranted. Toward this end, here we report the synthesis of fdG-modified DNAs using phosphoramidite chemistry involving only three steps. The functional integrity of the modified DNA has been verified by primer extension studies with DNA polymerases I and IV from E. coli. Introduction of fdG into a DNA duplex decreases the Tm by ∼1.6 °C/modification. Molecular dynamics simulations of a DNA duplex bearing the fdG adduct revealed that though the overall B-DNA structure is maintained, this lesion can disrupt W-C H-bonding, stacking interactions, and minor groove hydrations to some extent at the modified site, and these effects lead to slight variations in the local base pair parameters. Overall, our studies show that fdG is tolerated at the minor groove of the DNA to a better extent compared with other bulky DNA damages, and this property will make it difficult for the DNA repair pathways to detect this adduct. PMID:26650891

  9. Mg2+ in the major groove modulates B-DNA structure and dynamics.

    PubMed

    Guroult, Marc; Boittin, Olivier; Mauffret, Oliver; Etchebest, Catherine; Hartmann, Brigitte

    2012-01-01

    This study investigates the effect of Mg(2+) bound to the DNA major groove on DNA structure and dynamics. The analysis of a comprehensive dataset of B-DNA crystallographic structures shows that divalent cations are preferentially located in the DNA major groove where they interact with successive bases of (A/G)pG and the phosphate group of 5'-CpA or TpG. Based on this knowledge, molecular dynamics simulations were carried out on a DNA oligomer without or with Mg(2+) close to an ApG step. These simulations showed that the hydrated Mg(2+) forms a stable intra-strand cross-link between the two purines in solution. ApG generates an electrostatic potential in the major groove that is particularly attractive for cations; its intrinsic conformation is well-adapted to the formation of water-mediated hydrogen bonds with Mg(2+). The binding of Mg(2+) modulates the behavior of the 5'-neighboring step by increasing the BII (?-?>0) population of its phosphate group. Additional electrostatic interactions between the 5'-phosphate group and Mg(2+) strengthen both the DNA-cation binding and the BII character of the 5'-step. Cation binding in the major groove may therefore locally influence the DNA conformational landscape, suggesting a possible avenue for better understanding how strong DNA distortions can be stabilized in protein-DNA complexes. PMID:22844516

  10. Mg2+ in the Major Groove Modulates B-DNA Structure and Dynamics

    PubMed Central

    Guroult, Marc; Boittin, Olivier; Mauffret, Oliver; Etchebest, Catherine; Hartmann, Brigitte

    2012-01-01

    This study investigates the effect of Mg2+ bound to the DNA major groove on DNA structure and dynamics. The analysis of a comprehensive dataset of B-DNA crystallographic structures shows that divalent cations are preferentially located in the DNA major groove where they interact with successive bases of (A/G)pG and the phosphate group of 5?-CpA or TpG. Based on this knowledge, molecular dynamics simulations were carried out on a DNA oligomer without or with Mg2+ close to an ApG step. These simulations showed that the hydrated Mg2+ forms a stable intra-strand cross-link between the two purines in solution. ApG generates an electrostatic potential in the major groove that is particularly attractive for cations; its intrinsic conformation is well-adapted to the formation of water-mediated hydrogen bonds with Mg2+. The binding of Mg2+ modulates the behavior of the 5?-neighboring step by increasing the BII (?-?>0) population of its phosphate group. Additional electrostatic interactions between the 5?-phosphate group and Mg2+ strengthen both the DNA-cation binding and the BII character of the 5?-step. Cation binding in the major groove may therefore locally influence the DNA conformational landscape, suggesting a possible avenue for better understanding how strong DNA distortions can be stabilized in protein-DNA complexes. PMID:22844516

  11. Statistical mechanical treatment of the structural hydration of biological macromolecules: Results for [ital B]-DNA

    SciTech Connect

    Hummer, G. ); Soumpasis, D.M. )

    1994-12-01

    We constructed an efficient and accurate computational tool based on the potentials-of-mean-force approach for computing the detailed hydrophilic hydration of complex molecular structures in aqueous environments. Using the pair and triplet correlation functions database previously obtained from computer simulations of the simple point charge model of water, we computed the detailed structural organization of water around two [ital B]-DNA molecules with sequences d(AATT)[sub 3][center dot]d(AATT)[sub 3] and d(CCGG)[sub 3][center dot]d(CCGG)[sub 3], and canonical structure. [[ital A], T, C, and G denote adenine, thymine, cytosine, and guanine, respectively, and d(...) denotes the deoxyribose in the sugar-phosphate backbone.] The results obtained are in agreement with the experimental observations. A[center dot]T base-pair stretches are found to support the marked minor-groove spines of hydration'' observed in x-ray crystal structures. The hydrophilic hydration of the minor groove of the molecule d(CCGG)[sub 3][center dot]d(CCGG)[sub 3] exhibits a double ribbon of high water density, which is also in agreement with x-ray crystallography observations of C[center dot]G base-pair regions. The major grooves, on the other hand, do not show a comparably strong localization of water molecules. The quantitative results are compared with a computer simulation study of Forester [ital et] [ital al]. [Mol. Phys. 72, 643 (1991)]. We find good agreement for the hydration of the -NH[sub 2] groups, the cylindrically averaged water density distributions, and the overall hydration number. The agreement is less satisfactory for the phosphate groups. However, by refining the treatment of the anionic oxygens on the phosphate groups, almost full quantitative agreement is achieved.

  12. Statistical mechanical treatment of the structural hydration of biological macromolecules: Results for B-DNA

    NASA Astrophysics Data System (ADS)

    Hummer, Gerhard; Soumpasis, Dikeos Mario

    1994-12-01

    We constructed an efficient and accurate computational tool based on the potentials-of-mean-force approach for computing the detailed hydrophilic hydration of complex molecular structures in aqueous environments. Using the pair and triplet correlation functions database previously obtained from computer simulations of the simple point charge model of water, we computed the detailed structural organization of water around two B-DNA molecules with sequences d(AATT)3.d(AATT)3 and d(CCGG)3.d(CCGG)3, and canonical structure. [A, T, C, and G denote adenine, thymine, cytosine, and guanine, respectively, and d(...) denotes the deoxyribose in the sugar-phosphate backbone.] The results obtained are in agreement with the experimental observations. A.T base-pair stretches are found to support the marked minor-groove ``spines of hydration'' observed in x-ray crystal structures. The hydrophilic hydration of the minor groove of the molecule d(CCGG)3.d(CCGG)3 exhibits a double ribbon of high water density, which is also in agreement with x-ray crystallography observations of C.G base-pair regions. The major grooves, on the other hand, do not show a comparably strong localization of water molecules. The quantitative results are compared with a computer simulation study of Forester et al. [Mol. Phys. 72, 643 (1991)]. We find good agreement for the hydration of the -NH2 groups, the cylindrically averaged water density distributions, and the overall hydration number. The agreement is less satisfactory for the phosphate groups. However, by refining the treatment of the anionic oxygens on the phosphate groups, almost full quantitative agreement is achieved.

  13. 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.

  14. 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.

  15. 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

  16. 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

  17. 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.

  18. Insight into F plasmid DNA segregation revealed by structures of SopB and SopB-DNA complexes.

    PubMed

    Schumacher, Maria A; Piro, Kevin M; Xu, Weijun

    2010-07-01

    Accurate DNA segregation is essential for genome transmission. Segregation of the prototypical F plasmid requires the centromere-binding protein SopB, the NTPase SopA and the sopC centromere. SopB displays an intriguing range of DNA-binding properties essential for partition; it binds sopC to form a partition complex, which recruits SopA, and it also coats DNA to prevent non-specific SopA-DNA interactions, which inhibits SopA polymerization. To understand the myriad functions of SopB, we determined a series of SopB-DNA crystal structures. SopB does not distort its DNA site and our data suggest that SopB-sopC forms an extended rather than wrapped partition complex with the SopA-interacting domains aligned on one face. SopB is a multidomain protein, which like P1 ParB contains an all-helical DNA-binding domain that is flexibly attached to a compact (beta(3)-alpha)(2) dimer-domain. Unlike P1 ParB, the SopB dimer-domain does not bind DNA. Moreover, SopB contains a unique secondary dimerization motif that bridges between DNA duplexes. Both specific and non-specific SopB-DNA bridging structures were observed. This DNA-linking function suggests a novel mechanism for in trans DNA spreading by SopB, explaining how it might mask DNA to prevent DNA-mediated inhibition of SopA polymerization. PMID:20236989

  19. Study of Electronic Structures of Nucleobases and Associated Nuclear Quandrupole Interactions for ^14N, ^17O and ^2H in A-DNA and B-DNA

    NASA Astrophysics Data System (ADS)

    Scheicher, R. H.; Mahato, Dip N.; Pink, R. H.; Huang, M. B.; Das, T. P.; Dubey, Archana; Saha, H. P.; Chow, Lee

    2007-03-01

    As part of a research program for first-principles investigation of electronic structures of A-DNA and B-DNA systems we have previously carried out studies of the magnetic hyperfine interactions for the spin-label[1] muonium attached to A-DNA and B-DNA. The present work involves the nuclear quadrupole interactions (NQI) of ^14N, ^17O and ^2H in these two systems. We will present the results of our investigations of the NQI properties using the Hartree-Fock-Roothaan procedure with many-electron correlations included using many-body perturbation theory. For the A-DNA and B-DNA systems we are using available structural data for the four nucleobases. For the free nucleobases, the geometry from the energy optimization procedure is being employed. Comparisons will be made with available experimental NQI data and planned future improvements will be discussed. [1] R.H. Scheicher, E. Torikai, F.L. Pratt, K Nagamine, and T.P. Das, Hyperfine Interactions,158, 53 (2004); Physica B, Physics of Condensed Matter, 374, 448 (2006).

  20. 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

    Tari, L W; Secco, A S

    1995-06-11

    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. PMID:7596838

  1. 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

  2. Genetic and topological analyses of the bop promoter of Halobacterium halobium: stimulation by DNA supercoiling and non-B-DNA structure.

    PubMed Central

    Yang, C F; Kim, J M; Molinari, E; DasSarma, S

    1996-01-01

    The bop gene of wild-type Halobacterium halobium NRC-1 is transcriptionally induced more than 20-fold under microaerobic conditions. bop transcription is inhibited by novobiocin, a DNA gyrase inhibitor, at concentrations subinhibitory for growth. The exposure of NRC-1 cultures to novobiocin concentrations inhibiting bop transcription was found to partially relax plasmid DNA supercoiling, indicating the requirement of high DNA supercoiling for bop transcription. Next, the bop promoter region was cloned on an H. halobium plasmid vector and introduced into NRC-1 and S9, a bop overproducer strain. The cloned promoter was active in both H. halobium strains, but at a higher level in the overproducer than in the wild type. Transcription from the bop promoter on the plasmid was found to be inhibited by novobiocin to a similar extent as was transcription from the chromosome. When the cloned promoter was introduced into S9 mutant strains with insertions in either of two putative regulatory genes, brp and bat, no transcription was detectable, indicating that these genes serve to activate transcription from the bop promoter in trans. Deletion analysis of the cloned bop promoter from a site approximately 480 bp upstream of bop showed that a 53-bp region 5' to the transcription start site is sufficient for transcription, but a 28-bp region is not. An 11-bp alternating purine-pyrimidine sequence within the functional promoter region, centered 23 bp 5' to the transcription start point, was found to display DNA supercoiling-dependent sensitivity to S1 nuclease and OsO4, which is consistent with a non-B-DNA conformation similar to that of left-handed Z-DNA and suggests the involvement of unusual DNA structure in supercoiling-stimulated bop gene transcription. PMID:8550521

  3. 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.

  4. Protein structure prediction.

    PubMed

    Westhead, D R; Thornton, J M

    1998-08-01

    Genome sequencing projects continue to provide a flood of new protein sequences, and prediction methods remain an important means of adding structural information. Recently, there have been advances in secondary structure prediction, which feed, in turn, into improved fold recognition algorithms. Finally, there have been technical improvements in comparative modelling, and studies of the expected accuracy of three-dimensional structural models built by this method. PMID:9751638

  5. 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

  6. Searching for non-B DNA-forming motifs using nBMST (non-B DNA motif search tool).

    PubMed

    Cer, R Z; Bruce, K H; Donohue, D E; Temiz, N A; Mudunuri, U S; Yi, M; Volfovsky, N; Bacolla, A; Luke, B T; Collins, J R; Stephens, R M

    2012-04-01

    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-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 A-phased repeats (static bending). 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 an aggregate of 20 megabasepairs (Mbp). PMID:22470144

  7. 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

  8. De Novo Protein Structure Prediction

    NASA Astrophysics Data System (ADS)

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

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

  9. 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

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

    PubMed

    Yksel, 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

  11. 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.

  12. Computational Prediction of RNA Tertiary Structure

    NASA Astrophysics Data System (ADS)

    Zhao, Yunjie; Gong, Zhou; Chen, Changjun; Xiao, Yi

    2012-02-01

    RNAs have been found to be involved in the biological processes. The large RNA usually consists of two basic elements: RNA hairpins and duplex. Due to the experimental determination difficulties, the few RNA tertiary structures limit our understanding of the specific regulation mechanisms and functions. Therefore, RNA tertiary structure prediction is very important for understanding RNA biological functions. Since RNA often folds hierarchically, one of the possible RNA structure prediction approaches is through the hierarchical steps. Here, we focus on the prediction method of RNA tertiary hairpin and duplex structures in which assembles the small tertiary structure fragments from well-defined RNA structural motifs. In a benchmark test with known experiment structures, more than half of the cases agree with the experimental structure better than 3 RMSD over all the heavy atoms. The prediction results also reproduce the native like complementary base pairs of the secondary structures. Most importantly, the method performs the atomic accuracy of tertiary structures by about several minutes. We expect that the method will be a useful resource for RNA tertiary structure prediction and helpful to the biological research community.

  13. 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

  14. Transmembrane beta-barrel protein structure prediction

    NASA Astrophysics Data System (ADS)

    Randall, Arlo; Baldi, Pierre

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

  15. Interface Structure Prediction from First-Principles

    SciTech Connect

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

    2014-05-08

    Information about the atomic structures at solid–solid interfaces is crucial for understanding and predicting the performance of materials. Due to the complexity of the interfaces, it is very challenging to resolve their atomic structures using either experimental techniques or computer simulations. In this paper, we present an efficient first-principles computational method for interface structure prediction based on an adaptive genetic algorithm. This approach significantly reduces the computational cost, while retaining the accuracy of first-principles prediction. The method is applied to the investigation of both stoichiometric and nonstoichiometric SrTiO3 Σ3(112)[1̅10] 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.

  16. Interval prediction in structural dynamic analysis

    NASA Technical Reports Server (NTRS)

    Hasselman, Timothy K.; Chrostowski, Jon D.; Ross, Timothy J.

    1992-01-01

    Methods for assessing the predictive accuracy of structural dynamic models are examined with attention given to the effects of modal mass, stiffness, and damping uncertainties. The methods are based on a nondeterministic analysis called 'interval prediction' in which interval variables are used to describe parameters and responses that are unknown. Statistical databases for generic modeling uncertainties are derived from experimental data and incorporated analytically to evaluate responses. Covariance matrices of modal mass, stiffness, and damping parameters are propagated numerically in models of large space structures by means of three methods. The test data tend to fall within the predicted intervals of uncertainty determined by the statistical databases. The present findings demonstrate the suitability of using data from previously analyzed and tested space structures for assessing the predictive accuracy of an analytical model.

  17. 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.

  18. 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

  19. 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.

  20. New approaches in molecular structure prediction.

    PubMed

    Böhm, G

    1996-03-01

    In the past years, much effort has been put on the development of new methodologies and algorithms for the prediction of protein secondary and tertiary structures from (sequence) data; this is reviewed in detail. New approaches for these predictions such as neural network methods, genetic algorithms, machine learning, and graph theoretical methods are discussed. Secondary structure prediction algorithms were improved mostly by considering families of related proteins; however, for the reliable tertiary structure modeling of proteins, knowledge-based techniques are still preferred. Methods and examples with more or less successful results are described. Also, programs and parameterizations for energy minimisations, molecular dynamics, and electrostatic interactions have been improved, especially with respect to their former limits of applicability. Other topics discussed in this review include the use of traditional and on-line databases, the docking problem and surface properties of biomolecules, packing of protein cores, de novo design and protein engineering, prediction of membrane protein structures, the verification and reliability of model structures, and progress made with currently available software and computer hardware. In summary, the prediction of the structure, function, and other properties of a protein is still possible only within limits, but these limits continue to be moved. PMID:8867324

  1. 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

  2. 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.

  3. Predicting Odor Perceptual Similarity from Odor Structure

    PubMed Central

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

    2013-01-01

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

  4. 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.

  5. 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

  6. Protein Structure Prediction with Evolutionary Algorithms

    SciTech Connect

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

    1999-02-08

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

  7. Predict7, a program for protein structure prediction.

    PubMed

    Crmenes, R S; Freije, J P; Molina, M M; Martn, J M

    1989-03-15

    We describe a program for protein sequence analysis which runs in IBM PC computers. Protein sequences are loaded from files in Mount-Conrad and Lipman-Pearson format. Seven features are analyzed: hydrophilicity, hydropathy, surface probability, side chain flexibility, antigenicity, secondary structure and N-glycosylation sites. Numeric results can be shown, printed or stored in files exportable to other programs. Graphics of up to four predictions can be displayed on the screen, printed out or plotted, with several definable options. This program has been designed to be fast, user-friendly and to be shared with the scientific community. PMID:2539121

  8. 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.

  9. 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

  10. Protein complex compositions predicted by structural similarity

    PubMed Central

    Davis, Fred P.; Braberg, Hannes; Shen, Min-Yi; Pieper, Ursula; Sali, Andrej; Madhusudhan, M.S.

    2006-01-01

    Proteins function through interactions with other molecules. Thus, the network of physical interactions among proteins is of great interest to both experimental and computational biologists. Here we present structure-based predictions of 3387 binary and 1234 higher order protein complexes in Saccharomyces cerevisiae involving 924 and 195 proteins, respectively. To generate candidate complexes, comparative models of individual proteins were built and combined together using complexes of known structure as templates. These candidate complexes were then assessed using a statistical potential, derived from binary domain interfaces in PIBASE (). The statistical potential discriminated a benchmark set of 100 interface structures from a set of sequence-randomized negative examples with a false positive rate of 3% and a true positive rate of 97%. Moreover, the predicted complexes were also filtered using functional annotation and sub-cellular localization data. The ability of the method to select the correct binding mode among alternates is demonstrated for three camelid VHH domainporcine ?amylase interactions. We also highlight the prediction of co-complexed domain superfamilies that are not present in template complexes. Through integration with MODBASE, the application of the method to proteomes that are less well characterized than that of S.cerevisiae will contribute to expansion of the structural and functional coverage of protein interaction space. The predicted complexes are deposited in MODBASE (). PMID:16738133

  11. RNA secondary structure prediction using soft computing.

    PubMed

    Ray, Shubhra Sankar; Pal, Sankar K

    2013-01-01

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

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

    PubMed

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

    2014-11-10

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

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

    PubMed Central

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

    2014-01-01

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

  14. A-DNA and B-DNA: Comparing Their Historical X-ray Fiber Diffraction Images

    NASA Astrophysics Data System (ADS)

    Lucas, Amand A.

    2008-05-01

    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 Gosling and by Franklin and Gosling. Their corresponding historical diffraction diagrams played an equally crucial role in the discovery of the primary double-helical structure of the DNA molecule by Watson and Crick in 1953. This paper provides a comparative explanation of the structural content of the two diagrams treated on the same footing. The analysis of the diagrams is supported by the optical transform method with which both A-DNA and B-DNA X-ray images can be simulated optically. The simulations use a simple laser pointer and a dozen optical diffraction gratings, all held on a single diffraction slide. The gratings have been specially designed to pinpoint just which of the structural elements of the molecule is responsible for each of the revealing features of the fiber diffraction images.

  15. 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

  16. 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-Garca, Guillermo Ivn; Olvera de la Cruz, Mnica; 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.

  17. 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.

  18. Structure of allergens and structure based epitope predictions?

    PubMed Central

    DallAntonia, Fabio; Pavkov-Keller, Tea; Zangger, Klaus; Keller, Walter

    2014-01-01

    The structure determination of major allergens is a prerequisite for analyzing surface exposed areas of the allergen and for mapping conformational epitopes. These may be determined by experimental methods including crystallographic and NMR-based approaches or predicted by computational methods. In this review we summarize the existing structural information on allergens and their classification in protein fold families. The currently available allergen-antibody complexes are described and the experimentally obtained epitopes compared. Furthermore we discuss established methods for linear and conformational epitope mapping, putting special emphasis on a recently developed approach, which uses the structural similarity of proteins in combination with the experimental cross-reactivity data for epitope prediction. PMID:23891546

  19. 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

  20. Protein structure prediction using basin-hopping

    NASA Astrophysics Data System (ADS)

    Prentiss, Michael C.; Wales, David J.; Wolynes, Peter G.

    2008-06-01

    Associative memory Hamiltonian structure prediction potentials are not overly rugged, thereby suggesting their landscapes are like those of actual proteins. In the present contribution we show how basin-hopping global optimization can identify low-lying minima for the corresponding mildly frustrated energy landscapes. For small systems the basin-hopping algorithm succeeds in locating both lower minima and conformations closer to the experimental structure than does molecular dynamics with simulated annealing. For large systems the efficiency of basin-hopping decreases for our initial implementation, where the steps consist of random perturbations to the Cartesian coordinates. We implemented umbrella sampling using basin-hopping to further confirm when the global minima are reached. We have also improved the energy surface by employing bioinformatic techniques for reducing the roughness or variance of the energy surface. Finally, the basin-hopping calculations have guided improvements in the excluded volume of the Hamiltonian, producing better structures. These results suggest a novel and transferable optimization scheme for future energy function development.

  1. Structure prediction of magnetosome-associated proteins.

    PubMed

    Nudelman, Hila; Zarivach, Raz

    2014-01-01

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

  2. Restriction versus guidance in protein structure prediction.

    PubMed

    Hegler, Joseph A; Lätzer, Joachim; Shehu, Amarda; Clementi, Cecilia; Wolynes, Peter G

    2009-09-01

    Conformational restriction by fragment assembly and guidance in molecular dynamics are alternate conformational search strategies in protein structure prediction. We examine both approaches using a version of the associative memory Hamiltonian that incorporates the influence of water-mediated interactions (AMW). For short proteins (<70 residues), fragment assembly, while searching a restricted space, compares well to molecular dynamics and is often sufficient to fold such proteins to near-native conformations (4A) via simulated annealing. Longer proteins encounter kinetic sampling limitations in fragment assembly not seen in molecular dynamics which generally samples more native-like conformations. We also present a fragment enriched version of the standard AMW energy function, AMW-FME, which incorporates the local sequence alignment derived fragment libraries from fragment assembly directly into the energy function. This energy function, in which fragment information acts as a guide not a restriction, is found by molecular dynamics to improve on both previous approaches. PMID:19706384

  3. Predicting missing links via structural similarity

    NASA Astrophysics Data System (ADS)

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

    2015-04-01

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

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

    PubMed

    Sksd, Zsuzsanna; Andersen, Ebbe S; Lyngs, 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

  5. Helix Geometry, Hydration, and G\\cdot A Mismatch in a B-DNA Decamer

    NASA Astrophysics Data System (ADS)

    Prive, Gilbert G.; Heinemann, Udo; Chandrasegaran, Srinivasan; Kan, Lou-Sing; Kopka, Mary L.; Dickerson, Richard E.

    1987-10-01

    The DNA double helix is not a regular, featureless barberpole molecule. Different base sequences have their own special signature, in the way that they influence groove width, helical twist, bending, and mechanical rigidity or resistance to bending. These special features probably help other molecules such as repressors to read and recognize one base sequence in preference to another. Single crystal x-ray structure analysis is beginning to show us the various structures possible in the B-DNA family. The DNA decamer C-C-A-A-G-A-T-T-G-G appears to be a better model for mixed-sequence B-DNA than was the earlier C-G-C-G-A-A-T-T-C-G-C-G, which is more akin to regions of poly (dA)\\cdot poly(dT). The G\\cdot A mismatch base pairs at the center of the decamer are in the anti-anti conformation about their bonds from base to sugar, in agreement with nuclear magnetic resonance evidence on this and other sequences, and in contrast to the anti-syn geometry reported for G\\cdot A pairs in C-G-C-G-A-A-T-T-A-G-C-G. The ordered spine of hydration seen earlier in the narrow-grooved dodecamer has its counterpart, in this wide-grooved decamer, in two strings of water molecules lining the walls of the minor groove, bridging from purine N3 or pyrimidine O2, to the following sugar O4'. The same strings of hydration are present in the phosphorothioate analog of G-C-G-C-G-C. Unlike the spine, which is broken up by the intrusion of amine groups at guanines, these water strings are found in general, mixed-sequence DNA because they can pass by unimpeded to either side of a guanine N2 amine. The spine and strings are perceived as two extremes of a general pattern of hydration of the minor groove, which probably is the dominant factor in making B-DNA the preferred form at high hydration.

  6. Helix geometry, hydration, and G.A mismatch in a B-DNA decamer.

    PubMed

    Privé, G G; Heinemann, U; Chandrasegaran, S; Kan, L S; Kopka, M L; Dickerson, R E

    1987-10-23

    The DNA double helix is not a regular, featureless barberpole molecule. Different base sequences have their own special signature, in the way that they influence groove width, helical twist, bending, and mechanical rigidity or resistance to bending. These special features probably help other molecules such as repressors to read and recognize one base sequence in preference to another. Single crystal x-ray structure analysis is beginning to show us the various structures possible in the B-DNA family. The DNA decamer C-C-A-A-G-A-T-T-G-G appears to be a better model for mixed-sequence B-DNA than was the earlier C-G-C-G-A-A-T-T-C-G-C-G, which is more akin to regions of poly(dA).poly(dT). The G.A mismatch base pairs at the center of the decamer are in the anti-anti conformation about their bonds from base to sugar, in agreement with nuclear magnetic resonance evidence on this and other sequences, and in contrast to the anti-syn geometry reported for G.A pairs in C-G-C-G-A-A-T-T-A-G-C-G. The ordered spine of hydration seen earlier in the narrow-grooved dodecamer has its counterpart, in this wide-grooved decamer, in two strings of water molecules lining the walls of the minor groove, bridging from purine N3 or pyrimidine O2, to the following sugar O4'. The same strings of hydration are present in the phosphorothioate analog of G-C-G-C-G-C. Unlike the spine, which is broken up by the intrusion of amine groups at guanines, these water strings are found in general, mixed-sequence DNA because they can pass by unimpeded to either side of a guanine N2 amine. The spine and strings are perceived as two extremes of a general pattern of hydration of the minor groove, which probably is the dominant factor in making B-DNA the preferred form at high hydration. PMID:3310237

  7. Structure of nonevaporating sprays - Measurements and predictions

    NASA Technical Reports Server (NTRS)

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

    1984-01-01

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

  8. A comprehensive comparison of comparative RNA structure prediction approaches

    PubMed Central

    Gardner, Paul P; Giegerich, Robert

    2004-01-01

    Background An increasing number of researchers have released novel RNA structure analysis and prediction algorithms for comparative approaches to structure prediction. Yet, independent benchmarking of these algorithms is rarely performed as is now common practice for protein-folding, gene-finding and multiple-sequence-alignment algorithms. Results Here we evaluate a number of RNA folding algorithms using reliable RNA data-sets and compare their relative performance. Conclusions We conclude that comparative data can enhance structure prediction but structure-prediction-algorithms vary widely in terms of both sensitivity and selectivity across different lengths and homologies. Furthermore, we outline some directions for future research. PMID:15458580

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

    PubMed

    Geourjon, C; Delage, 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

  10. 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

  11. 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…

  12. 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 residueresidue 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

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

    PubMed

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

    2015-07-01

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

  14. 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

  15. Genome-wide Membrane Protein Structure Prediction

    PubMed Central

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

    2013-01-01

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

  16. 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.

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

    PubMed

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

    2016-02-01

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

  18. Predicting Conformational Flexibility in Protein Structure

    NASA Astrophysics Data System (ADS)

    Jacobs, Donald J.; Kuhn, Leslie A.; Thorpe, Michael F.

    1999-04-01

    The microstructure of a protein is represented as a generic bar-joint truss framework, where hard covalent forces and strong hydrogen bonds are modeled as distance constraints. The mechanical stability is analyzed using graph theoretical techniques with the aid of the FIRST program that determines the Floppy Inclusion and Rigid Substructure Topography. FIRST provides a real-time tool for evaluating intrinsic flexibility in protein structure. Unlike many methods for parsing protein folds, this approach calculates exact mechanical properties of a protein structure (and other macromolecules) under a given set of distance constraints. These properties include: counting the number of independent degrees of freedom, locating overconstrained regions where internal strain arises, partitioning the protein structure into rigid clusters and identifying underconstrained regions where continuous deformations can take place. We quantify the degree of conformational flexibility in HIV protease, and find that the characterization correlates well with mobility and conformational changes observed crystallographically.

  19. Quantifying variances in comparative RNA secondary structure prediction

    PubMed Central

    2013-01-01

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

  20. 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

  1. Ensemble-based prediction of RNA secondary structures

    PubMed Central

    2013-01-01

    Background Accurate structure prediction methods play an important role for the understanding of RNA function. Energy-based, pseudoknot-free secondary structure prediction is one of the most widely used and versatile approaches, and improved methods for this task have received much attention over the past five years. Despite the impressive progress that as been achieved in this area, existing evaluations of the prediction accuracy achieved by various algorithms do not provide a comprehensive, statistically sound assessment. Furthermore, while there is increasing evidence that no prediction algorithm consistently outperforms all others, no work has been done to exploit the complementary strengths of multiple approaches. Results In this work, we present two contributions to the area of RNA secondary structure prediction. Firstly, we use state-of-the-art, resampling-based statistical methods together with a previously published and increasingly widely used dataset of high-quality RNA structures to conduct a comprehensive evaluation of existing RNA secondary structure prediction procedures. The results from this evaluation clarify the performance relationship between ten well-known existing energy-based pseudoknot-free RNA secondary structure prediction methods and clearly demonstrate the progress that has been achieved in recent years. Secondly, we introduce AveRNA, a generic and powerful method for combining a set of existing secondary structure prediction procedures into an ensemble-based method that achieves significantly higher prediction accuracies than obtained from any of its component procedures. Conclusions Our new, ensemble-based method, AveRNA, improves the state of the art for energy-based, pseudoknot-free RNA secondary structure prediction by exploiting the complementary strengths of multiple existing prediction procedures, as demonstrated using a state-of-the-art statistical resampling approach. In addition, AveRNA allows an intuitive and effective control of the trade-off between false negative and false positive base pair predictions. Finally, AveRNA can make use of arbitrary sets of secondary structure prediction procedures and can therefore be used to leverage improvements in prediction accuracy offered by algorithms and energy models developed in the future. Our data, MATLAB software and a web-based version of AveRNA are publicly available at http://www.cs.ubc.ca/labs/beta/Software/AveRNA. PMID:23617269

  2. Effect of Natural and Semisynthetic Pseudoguianolides on the Stability of NF-?B:DNA Complex Studied by Agarose Gel Electrophoresis

    PubMed Central

    Villagomez, Rodrigo; Hatti-Kaul, Rajni; Sterner, Olov; Almanza, Giovanna; Linares-Pastn, Javier A.

    2015-01-01

    The nuclear factor ?B (NF-?B) is a promising target for drug discovery. NF-?B is a heterodimeric complex of RelA and p50 subunits that interact with the DNA, regulating the expression of several genes; its dysregulation can trigger diverse diseases including inflammation, immunodeficiency, and cancer. There is some experimental evidence, based on whole cells studies, that natural sesquiterpene lactones (Sls) can inhibit the interaction of NF-?B with DNA, by alkylating the RelA subunit via a Michael addition. In the present work, 28 natural and semisynthetic pseudoguianolides were screened as potential inhibitors of NF-?B in a biochemical assay that was designed using pure NF-?B heterodimer, pseudoguianolides and a ~1000 bp palindromic DNA fragment harboring two NF-?B recognition sequences. By comparing the relative amount of free DNA fragment to the NF-?B DNA complex, in a routine agarose gel electrophoresis, the destabilizing effect of a compound on the complex is estimated. The results of the assay and the following structure-activity relationship study, allowed the identification of several relevant structural features in the pseudoguaianolide skeleton, which are necessary to enhance the dissociating capacity of NF-?BDNA complex. The most active compounds are substituted at C-3 (?-carbonyl), in addition to having the ?-methylene-?-lactone moiety which is essential for the alkylation of RelA. PMID:25615602

  3. Effect of natural and semisynthetic pseudoguianolides on the stability of NF-?B:DNA complex studied by agarose gel electrophoresis.

    PubMed

    Villagomez, Rodrigo; Hatti-Kaul, Rajni; Sterner, Olov; Almanza, Giovanna; Linares-Pastn, Javier A

    2015-01-01

    The nuclear factor ?B (NF-?B) is a promising target for drug discovery. NF-?B is a heterodimeric complex of RelA and p50 subunits that interact with the DNA, regulating the expression of several genes; its dysregulation can trigger diverse diseases including inflammation, immunodeficiency, and cancer. There is some experimental evidence, based on whole cells studies, that natural sesquiterpene lactones (Sls) can inhibit the interaction of NF-?B with DNA, by alkylating the RelA subunit via a Michael addition. In the present work, 28 natural and semisynthetic pseudoguianolides were screened as potential inhibitors of NF-?B in a biochemical assay that was designed using pure NF-?B heterodimer, pseudoguianolides and a ~1000 bp palindromic DNA fragment harboring two NF-?B recognition sequences. By comparing the relative amount of free DNA fragment to the NF-?B - DNA complex, in a routine agarose gel electrophoresis, the destabilizing effect of a compound on the complex is estimated. The results of the assay and the following structure-activity relationship study, allowed the identification of several relevant structural features in the pseudoguaianolide skeleton, which are necessary to enhance the dissociating capacity of NF-?B-DNA complex. The most active compounds are substituted at C-3 (?-carbonyl), in addition to having the ?-methylene-?-lactone moiety which is essential for the alkylation of RelA. PMID:25615602

  4. 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.)

  5. Are predicted protein structures of any value for binding site prediction and virtual ligand screening?

    PubMed Central

    Skolnick, Jeffrey; Zhou, Hongyi; Gao, Mu

    2013-01-01

    The recently developed field of ligand homology modeling, LHM, that extends the ideas of protein homology modeling to the prediction of ligand binding sites and for use in virtual ligand screening has emerged as a powerful new approach. Unlike traditional docking methodologies, LHM can be applied to low-to-moderate resolution predicted as well as experimental structures with little if any diminution in performance; thereby enabling ~75% of an average proteome to have potentially significant virtual screening predictions. In large scale benchmarking, LHM is able to predict off-target ligand binding. Thus, despite the widespread belief to the contrary, low-to-moderate resolution predicted structures have considerable utility for biochemical function prediction. PMID:23415854

  6. WeFold: a coopetition for protein structure prediction.

    PubMed

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

    2014-09-01

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

  7. WeFold: A Coopetition for Protein Structure Prediction

    PubMed Central

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

    2014-01-01

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

  8. 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

  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. 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.

  11. Gogny HFB prediction of nuclear structure properties

    SciTech Connect

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

    2011-10-28

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

  12. OPTIMIZATION BIAS IN ENERGY-BASED STRUCTURE PREDICTION

    PubMed Central

    Petrella, Robert J.

    2014-01-01

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

  13. 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

  14. A predictive structural model for bulk metallic glasses.

    PubMed

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

    2015-01-01

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

  15. A predictive structural model for bulk metallic glasses

    NASA Astrophysics Data System (ADS)

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

    2015-09-01

    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.

  16. 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.

  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 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 C? RMSD for 60100 residue proteins (or large fragments of a protein), with a prediction accuracy of 4.0 C? RMSD for residues 180 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. 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

  19. 3D protein structure prediction using Imperialist Competitive algorithm and half sphere exposure prediction.

    PubMed

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

    2016-02-21

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

  20. 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

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

    PubMed Central

    Li, Qiwei; Dahl, David B.; Vannucci, Marina; Hyun Joo; Tsai, Jerry W.

    2014-01-01

    Determining the primary structure (i.e., amino acid sequence) of a protein has become cheaper, faster, and more accurate. Higher order protein structure provides insight into a proteins function in the cell. Understanding a proteins secondary structure is a first step towards this goal. Therefore, a number of computational prediction methods have been developed to predict secondary structure from just the primary amino acid sequence. The most successful methods use machine learning approaches that are quite accurate, but do not directly incorporate structural information. As a step towards improving secondary structure reduction given the primary structure, we propose a Bayesian model based on the knob-socket model of protein packing in secondary structure. The method considers the packing influence of residues on the secondary structure determination, including those packed close in space but distant in sequence. By performing an assessment of our method on 2 test sets we show how incorporation of multiple sequence alignment data, similarly to PSIPRED, provides balance and improves the accuracy of the predictions. Software implementing the methods is provided as a web application and a stand-alone implementation. PMID:25314659

  2. Bayesian model of protein primary sequence for secondary structure prediction.

    PubMed

    Li, Qiwei; Dahl, David B; Vannucci, Marina; Hyun Joo; Tsai, Jerry W

    2014-01-01

    Determining the primary structure (i.e., amino acid sequence) of a protein has become cheaper, faster, and more accurate. Higher order protein structure provides insight into a protein's function in the cell. Understanding a protein's secondary structure is a first step towards this goal. Therefore, a number of computational prediction methods have been developed to predict secondary structure from just the primary amino acid sequence. The most successful methods use machine learning approaches that are quite accurate, but do not directly incorporate structural information. As a step towards improving secondary structure reduction given the primary structure, we propose a Bayesian model based on the knob-socket model of protein packing in secondary structure. The method considers the packing influence of residues on the secondary structure determination, including those packed close in space but distant in sequence. By performing an assessment of our method on 2 test sets we show how incorporation of multiple sequence alignment data, similarly to PSIPRED, provides balance and improves the accuracy of the predictions. Software implementing the methods is provided as a web application and a stand-alone implementation. PMID:25314659

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

    PubMed Central

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

    2009-01-01

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

  4. Modular prediction of protein structural classes from sequences of twilight-zone identity with predicting sequences

    PubMed Central

    2009-01-01

    Background Knowledge of structural class is used by numerous methods for identification of structural/functional characteristics of proteins and could be used for the detection of remote homologues, particularly for chains that share twilight-zone similarity. In contrast to existing sequence-based structural class predictors, which target four major classes and which are designed for high identity sequences, we predict seven classes from sequences that share twilight-zone identity with the training sequences. Results The proposed MODular Approach to Structural class prediction (MODAS) method is unique as it allows for selection of any subset of the classes. MODAS is also the first to utilize a novel, custom-built feature-based sequence representation that combines evolutionary profiles and predicted secondary structure. The features quantify information relevant to the definition of the classes including conservation of residues and arrangement and number of helix/strand segments. Our comprehensive design considers 8 feature selection methods and 4 classifiers to develop Support Vector Machine-based classifiers that are tailored for each of the seven classes. Tests on 5 twilight-zone and 1 high-similarity benchmark datasets and comparison with over two dozens of modern competing predictors show that MODAS provides the best overall accuracy that ranges between 80% and 96.7% (83.5% for the twilight-zone datasets), depending on the dataset. This translates into 19% and 8% error rate reduction when compared against the best performing competing method on two largest datasets. The proposed predictor provides accurate predictions at 58% accuracy for membrane proteins class, which is not considered by majority of existing methods, in spite that this class accounts for only 2% of the data. Our predictive model is analyzed to demonstrate how and why the input features are associated with the corresponding classes. Conclusions The improved predictions stem from the novel features that express collocation of the secondary structure segments in the protein sequence and that combine evolutionary and secondary structure information. Our work demonstrates that conservation and arrangement of the secondary structure segments predicted along the protein chain can successfully predict structural classes which are defined based on the spatial arrangement of the secondary structures. A web server is available at http://biomine.ece.ualberta.ca/MODAS/. PMID:20003388

  5. 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.

  6. 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. PMID:26664034

  7. Exploring polymorphisms in B-DNA helical conformations

    PubMed Central

    Dans, Pablo D.; Prez, 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. 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

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

    PubMed

    Kajn, Lszl; Yachdav, Guy; Vicedo, Esmeralda; Steinegger, Martin; Mirdita, Milot; Angermller, Christof; Bhm, Ariane; Domke, Simon; Ertl, Julia; Mertes, Christian; Reisinger, Eva; Staniewski, Cedric; Rost, Burkhard

    2013-01-01

    We report the release of PredictProtein for the Debian operating system and derivatives, such as Ubuntu, Bio-Linux, and Cloud BioLinux. The PredictProtein suite is available as a standard set of open source Debian packages. The release covers the most popular prediction methods from the Rost Lab, including methods for the prediction of secondary structure and solvent accessibility (profphd), nuclear localization signals (predictnls), and intrinsically disordered regions (norsnet). We also present two case studies that successfully utilize PredictProtein packages for high performance computing in the cloud: the first analyzes protein disorder for whole organisms, and the second analyzes the effect of all possible single sequence variants in protein coding regions of the human genome. PMID:23971032

  10. 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

  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. PredictProteinan 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; Hnigschmid, 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), proteinprotein binding sites (ISIS2), proteinpolynucleotide 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

  13. 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.

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

    PubMed

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

    2015-10-21

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

  15. Sizing Structures and Predicting Weight of a Spacecraft

    NASA Technical Reports Server (NTRS)

    Cerro, Jeffrey; Shore, C. P.

    2006-01-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2013-12-01

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

  17. 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

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

    PubMed

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

    2004-01-01

    Protein secondary structure prediction has a fundamental influence on today's bioinformatics research. In this work, binary and tertiary classifiers of protein secondary structure prediction are implemented on Denoeux belief neural network (DBNN) architecture. Hydrophobicity matrix, orthogonal matrix, BLOSUM62 and PSSM (position specific scoring matrix) are experimented separately as the encoding schemes for DBNN. The experimental results contribute to the design of new encoding schemes. New binary classifier for Helix versus not Helix ( approximately H) for DBNN produces prediction accuracy of 87% when PSSM is used for the input profile. The performance of DBNN binary classifier is comparable to other best prediction methods. The good test results for binary classifiers open a new approach for protein structure prediction with neural networks. Due to the time consuming task of training the neural networks, Pthread and OpenMP are employed to parallelize DBNN in the hyperthreading enabled Intel architecture. Speedup for 16 Pthreads is 4.9 and speedup for 16 OpenMP threads is 4 in the 4 processors shared memory architecture. Both speedup performance of OpenMP and Pthread is superior to that of other research. With the new parallel training algorithm, thousands of amino acids can be processed in reasonable amount of time. Our research also shows that hyperthreading technology for Intel architecture is efficient for parallel biological algorithms. PMID:17270901

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

    PubMed Central

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

    2015-01-01

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

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

    NASA Technical Reports Server (NTRS)

    1995-01-01

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

  1. Process for predicting structural performance of mechanical systems

    DOEpatents

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

    1998-01-01

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

  2. 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.

  3. High-resolution crystal structures leverage protein binding affinity predictions.

    PubMed

    Marillet, Simon; Boudinot, Pierre; Cazals, Frdric

    2016-01-01

    Predicting protein binding affinities from structural data has remained elusive, a difficulty owing to the variety of protein binding modes. Using the structure-affinity-benchmark (SAB, 144 cases with bound/unbound crystal structures and experimental affinity measurements), prediction has been undertaken either by fitting a model using a handfull of predefined variables, or by training a complex model from a large pool of parameters (typically hundreds). The former route unnecessarily restricts the model space, while the latter is prone to overfitting. We design models in a third tier, using 12 variables describing enthalpic and entropic variations upon binding, and a model selection procedure identifying the best sparse model built from a subset of these variables. Using these models, we report three main results. First, we present models yielding a marked improvement of affinity predictions. For the whole dataset, we present a model predicting Kd within 1 and 2 orders of magnitude for 48% and 79% of cases, respectively. These statistics jump to 62% and 89% respectively, for the subset of the SAB consisting of high resolution structures. Second, we show that these performances owe to a new parameter encoding interface morphology and packing properties of interface atoms. Third, we argue that interface flexibility and prediction hardness do not correlate, and that for flexible cases, a performance matching that of the whole SAB can be achieved. Overall, our work suggests that the affinity prediction problem could be partly solved using databases of high resolution complexes whose affinity is known. Proteins 2016; 84:9-20. 2015 Wiley Periodicals, Inc. PMID:26471944

  4. A dynamic Bayesian network approach to protein secondary structure prediction

    PubMed Central

    Yao, Xin-Qiu; Zhu, Huaiqiu; She, Zhen-Su

    2008-01-01

    Background Protein secondary structure prediction method based on probabilistic models such as hidden Markov model (HMM) appeals to many because it provides meaningful information relevant to sequence-structure relationship. However, at present, the prediction accuracy of pure HMM-type methods is much lower than that of machine learning-based methods such as neural networks (NN) or support vector machines (SVM). Results In this paper, we report a new method of probabilistic nature for protein secondary structure prediction, based on dynamic Bayesian networks (DBN). The new method models the PSI-BLAST profile of a protein sequence using a multivariate Gaussian distribution, and simultaneously takes into account the dependency between the profile and secondary structure and the dependency between profiles of neighboring residues. In addition, a segment length distribution is introduced for each secondary structure state. Tests show that the DBN method has made a significant improvement in the accuracy compared to other pure HMM-type methods. Further improvement is achieved by combining the DBN with an NN, a method called DBNN, which shows better Q3 accuracy than many popular methods and is competitive to the current state-of-the-arts. The most interesting feature of DBN/DBNN is that a significant improvement in the prediction accuracy is achieved when combined with other methods by a simple consensus. Conclusion The DBN method using a Gaussian distribution for the PSI-BLAST profile and a high-ordered dependency between profiles of neighboring residues produces significantly better prediction accuracy than other HMM-type probabilistic methods. Owing to their different nature, the DBN and NN combine to form a more accurate method DBNN. Future improvement may be achieved by combining DBNN with a method of SVM type. PMID:18218144

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

    PubMed Central

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

    2011-01-01

    Evaluating other individuals with respect to personality characteristics plays a crucial role in human relations and it is the focus of attention for research in diverse fields such as psychology and interactive computer systems. In psychology, face perception has been recognized as a key component of this evaluation system. Multiple studies suggest that observers use face information to infer personality characteristics. Interactive computer systems are trying to take advantage of these findings and apply them to increase the natural aspect of interaction and to improve the performance of interactive computer systems. Here, we experimentally test whether the automatic prediction of facial trait judgments (e.g. dominance) can be made by using the full appearance information of the face and whether a reduced representation of its structure is sufficient. We evaluate two separate approaches: a holistic representation model using the facial appearance information and a structural model constructed from the relations among facial salient points. State of the art machine learning methods are applied to a) derive a facial trait judgment model from training data and b) predict a facial trait value for any face. Furthermore, we address the issue of whether there are specific structural relations among facial points that predict perception of facial traits. Experimental results over a set of labeled data (9 different trait evaluations) and classification rules (4 rules) suggest that a) prediction of perception of facial traits is learnable by both holistic and structural approaches; b) the most reliable prediction of facial trait judgments is obtained by certain type of holistic descriptions of the face appearance; and c) for some traits such as attractiveness and extroversion, there are relationships between specific structural features and social perceptions. PMID:21858069

  6. Assessment of structural integrity in pressure vessels predictions and verification

    SciTech Connect

    Loushin, L.L.

    1996-12-01

    Methods to assess the structural integrity of pressure vessels, piping, and storage tankage have been developed by a wide variety of sources. Of these efforts, the Materials Properties Council Program on Fitness-for-Service Evaluation Procedures for Operating Pressure Vessels, Tanks, and Piping in Refinery and Chemical Service is one of the most noteworthy. This fitness-for-service evaluation methodology is applied to real scenarios where the continued service of carbon and stainless steel pressure vessels was in question. How such assessments of structural integrity, fitness-for-service, remaining life, and failure modes would be made by an owner/user engineering specialist are described. The conclusions derived from this full-scale testing program demonstrate that technically sound and economically viable predictions are well within acceptable bounds of structural integrity. The real life behavior of pressure vessels tested to failure were far more resistant to catastrophic failure than was predicted.

  7. Combining Sequence and Structural Profiles for Protein Solvent Accessibility Prediction

    PubMed Central

    Bondugula, Rajkumar

    2009-01-01

    Solvent accessibility is an important structural feature for a protein. We propose a new method for solvent accessibility prediction that uses known structure and sequence information more efficiently. We first estimate the relative solvent accessibility of the query protein using fuzzy mean operator from the solvent accessibilities of known structure fragments that have similar sequences to the query protein. We then integrate the estimated solvent accessibility and the position specific scoring matrix of the query protein using a neural network. We tested our method on a large data set consisting of 3386 non-redundant proteins. The comparison with other methods show slightly improved prediction accuracies with our method. The resulting system does need not be re-trained when new data is available. We incorporated our method into the MUPRED system, which is available as a web server at http://digbio.missouri.edu/mupred. PMID:19642280

  8. 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 acidnucleotide 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

  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. Structure-Based Predictive Models for Allosteric Hot Spots

    PubMed Central

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

    2009-01-01

    In allostery, a binding event at one site in a protein modulates the behavior of a distant site. Identifying residues that relay the signal between sites remains a challenge. We have developed predictive models using support-vector machines, a widely used machine-learning method. The training data set consisted of residues classified as either hotspots or non-hotspots based on experimental characterization of point mutations from a diverse set of allosteric proteins. Each residue had an associated set of calculated features. Two sets of features were used, one consisting of dynamical, structural, network, and informatic measures, and another of structural measures defined by Daily and Gray [1]. The resulting models performed well on an independent data set consisting of hotspots and non-hotspots from five allosteric proteins. For the independent data set, our top 10 models using Feature Set 1 recalled 6881% of known hotspots, and among total hotspot predictions, 5867% were actual hotspots. Hence, these models have precision P?=?5867% and recall R?=?6881%. The corresponding models for Feature Set 2 had P?=?5559% and R?=?8192%. We combined the features from each set that produced models with optimal predictive performance. The top 10 models using this hybrid feature set had R?=?7381% and P?=?6471%, the best overall performance of any of the sets of models. Our methods identified hotspots in structural regions of known allosteric significance. Moreover, our predicted hotspots form a network of contiguous residues in the interior of the structures, in agreement with previous work. In conclusion, we have developed models that discriminate between known allosteric hotspots and non-hotspots with high accuracy and sensitivity. Moreover, the pattern of predicted hotspots corresponds to known functional motifs implicated in allostery, and is consistent with previous work describing sparse networks of allosterically important residues. PMID:19816556

  11. Methods for predicting crack growth in advanced structures

    NASA Technical Reports Server (NTRS)

    Van Stone, R. H.; Kim, K. S.

    1990-01-01

    Damage tolerance design and analysis is widely used in fracture critical military aircraft engine components. Linear elastic fracture mechanics techniques have been developed and verified. These are used to predict the crack propagation lives of complex geometries under mission cycling conditions. Research on methods necessary for the prediction of elevated temperature crack growth in advanced structures is reviewed. These include environmentally assisted time-dependent crack growth, non-linear fracture mechanics parameters for thermal mechanical fatigue crack growth, and finite element modeling of crack growth in composites.

  12. Predictability of the polymorphs of small organic compounds: crystal structure predictions of four benchmark blind test molecules.

    PubMed

    Chan, H C Stephen; Kendrick, John; Leusen, Frank J J

    2011-12-01

    Predicting the crystal structure of an organic molecule from first principles has been a major challenge in physical chemistry. Recently, the application of Density Functional Theory including a dispersive energy correction (the DFT(d) method) has been shown to be a reliable method for predicting experimental structures based purely on their ranking according to lattice energy. Further validation results of the application of the DFT(d) method to four organic molecules are presented here. The compounds were targets (labelled molecule II, VI, VII and XI) in previous blind tests of crystal structure prediction, and their structures proved difficult to predict. However, this study shows that the DFT(d) approach is capable of predicting the solid state structures of these small molecules. For molecule VII, the most stable (rank 1) predicted crystal structure corresponds to the experimentally observed structure. For molecule VI, the rank 1, 2 and 3 predicted structures correspond to the three experimental polymorphs, forms I, III and II, respectively. For molecules II and XI, their rank 1 predicted structures are energetically more stable than those corresponding to the experimental crystal structures, and were not found amongst the structures submitted by the participants in the blind tests. The rank 1 structure of molecule II is predicted to exist under high pressure, whilst the rank 1 structure predicted for molecule XI has the same space group and hydrogen bonding pattern as observed in the crystal of 1-amino-1-methyl-cyclopropane, which is structurally related to molecule XI. The experimental crystal structure of molecule II corresponds to the rank 4 prediction, 0.8 kJ mol(-1) above the global minimum structure, and the experimental structure of molecule XI corresponds to the rank 2 prediction, 0.4 kJ mol(-1) above the global minimum. PMID:21993855

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

    PubMed

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

    2016-01-01

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

  14. Protein Secondary Structure Prediction Using Deep Convolutional Neural Fields

    PubMed Central

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

    2016-01-01

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

  15. Protein Secondary Structure Prediction Using Deep Convolutional Neural Fields

    NASA Astrophysics Data System (ADS)

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

    2016-01-01

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

  16. Prediction of the structure of symmetrical protein assemblies

    PubMed Central

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

    2007-01-01

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

  17. Predicting oxygen uptake and VOC emissions at enclosed drop structures

    SciTech Connect

    Rahme, Z.G.; Zytner, R.G.; Madani-Isfahani, M.; Corsi, R.L.

    1997-01-01

    Drop structures used during wastewater collection and treatment are sources for volatile organic compound (VOC) emissions. To assist in the reduction of such emissions, pilot-scale experiments were completed using municipal wastewater to study the effects of drop height, liquid flow rate, and tailwater depth on oxygen transfer, and to evaluate the effects of the same parameters on the stripping of 10 VOC tracers. Results were used to develop predictive models for oxygen and VOC transfer. Oxygen uptake at the pilot drop structure suggests that the drop height is the most important parameter influencing oxygen uptake at enclosed drop structures. Tailwater depth had little effect on oxygen transfer at the drop structure. Stripping of VOCs at drop structures was seen to be a strong function of Henry`s law coefficient. This sensitivity was related to gas-phase resistance in mass-transfer and/or VOC accumulation in the air bubbles. Incorporating gas-phase resistance and an appropriate {alpha} factor for wastewater into the model allowed the prediction of VOC deficit ratios and estimation of VOC stripping at drop structures for both clean water and wastewater.

  18. 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.

  19. 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

  20. Predicting olfactory receptor neuron responses from odorant structure

    PubMed Central

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

    2007-01-01

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

  1. 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 buildings 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.

  2. Effects of scale in predicting global structural response

    NASA Technical Reports Server (NTRS)

    Deo, R. B.; Kan, H. P.

    1991-01-01

    Analytical techniques for scale-up effects were reviewed. The advantages and limitations of applying the principles of similitude to composite structures is summarized and illustrated by simple examples. An analytical procedure was formulated to design scale models of an axially compressed composite cylinder. A building-block approach was outlined where each structural detail is analyzed independently and the probable failure sequence of a selected component is predicted, taking into account load redistribution subsequent to first element failure. Details of this building-block approach are under development.

  3. Fragment-HMM: a new approach to protein structure prediction.

    PubMed

    Li, Shuai Cheng; Bu, Dongbo; Xu, Jinbo; Li, Ming

    2008-11-01

    We designed a simple position-specific hidden Markov model to predict protein structure. Our new framework naturally repeats itself to converge to a final target, conglomerating fragment assembly, clustering, target selection, refinement, and consensus, all in one process. Our initial implementation of this theory converges to within 6 A of the native structures for 100% of decoys on all six standard benchmark proteins used in ROSETTA (discussed by Simons and colleagues in a recent paper), which achieved only 14%-94% for the same data. The qualities of the best decoys and the final decoys our theory converges to are also notably better. PMID:18723665

  4. Predicting structure/property relations in polymeric photovoltaic devices.

    NASA Astrophysics Data System (ADS)

    Buxton, Gavin; Clarke, Nigel

    2007-03-01

    Plastic solar cells are attractive candidates for providing cheap, clean and renewable energy. However, such devices are critically dependent on the internal structure, or morphology, of the polymer constituents. We have developed a model that enables us to predict photovoltaic behaviour for arbitrary morphologies, which we also generate from numerical simulations. We illustrate the model by showing how diblock copolymer morphologies can be manipulated to optimise the photovoltaic effect in plastic solar cells. In this manner, we can correlate photovoltaic properties with device structure and hence guide experiments to optimise polymer morphologies to meet photovoltaic needs.

  5. Predicting structure and property relations in polymeric photovoltaic devices

    NASA Astrophysics Data System (ADS)

    Buxton, Gavin A.; Clarke, Nigel

    2006-08-01

    Plastic solar cells are attractive candidates for providing cheap, clean, and renewable energy. However, such devices are critically dependent on the internal structure, or morphology, of the polymer constituents. We have developed a model that enables us to predict photovoltaic behavior for arbitrary morphologies, which we also generate from numerical simulations. We illustrate the model by showing how diblock copolymer morphologies can be manipulated to optimize the photovoltaic effect in plastic solar cells. In this manner, we can correlate photovoltaic properties with device structure and hence guide experiments to optimize polymer morphologies to meet photovoltaic needs.

  6. 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.

  7. Fiber composite structural durability and damage tolerance - Simplified predictive methods

    NASA Technical Reports Server (NTRS)

    Chamis, Christos C.; Ginty, Carol A.

    1989-01-01

    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.

  8. Structure and stoichiometry prediction of surfaces reacting with multicomponent gases.

    PubMed

    Herrmann, Philipp; Heimel, Georg

    2015-01-14

    Reactive interactions of molecules with solid surfaces are of key interest for catalysis and surface functionalization. Here, conceptual shortcomings of previous theoretical methods for the prediction of steady-state surface structures and stoichiometries from first-principles thermodynamics are identified. An extension is then proposed, which now enables the unconstrained description of an arbitrary number of mutually reacting gas-phase species. PMID:25382305

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

    PubMed Central

    Bryan, Allen W; O’Donnell, Charles W; Menke, Matthew; Cowen, Lenore J; Lindquist, Susan; Berger, Bonnie

    2012-01-01

    The supersecondary structure of amyloids and prions, proteins of intense clinical and biological interest, are difficult to determine by standard experimental or computational means. In addition, significant conformational heterogeneity is known or suspected to exist in many amyloid fibrils. Previous work has demonstrated that probability-based prediction of discrete β-strand pairs can offer insight into these structures. Here, we devise a system of energetic rules that can be used to dynamically assemble these discrete β-strand pairs into complete amyloid β-structures. The STITCHER algorithm progressively ‘stitches’ strand-pairs into full β-sheets based on a novel free-energy model, incorporating experimentally observed amino-acid side-chain stacking contributions, entropic estimates, and steric restrictions for amyloidal parallel β-sheet construction. A dynamic program computes the top 50 structures and returns both the highest scoring structure and a consensus structure taken by polling this list for common discrete elements. Putative structural heterogeneity can be inferred from sequence regions that compose poorly. Predictions show agreement with experimental models of Alzheimer’s amyloid beta peptide and the Podospora anserina Het-s prion. Predictions of the HET-s homolog HET-S also reflect experimental observations of poor amyloid formation. We put forward predicted structures for the yeast prion Sup35, suggesting N-terminal structural stability enabled by tyrosine ladders, and C-terminal heterogeneity. Predictions for the Rnq1 prion and alpha-synuclein are also given, identifying a similar mix of homogenous and heterogeneous secondary structure elements. STITCHER provides novel insight into the energetic basis of amyloid structure, provides accurate structure predictions, and can help guide future experimental studies. Proteins 2012. © 2011 Wiley Periodicals, Inc. PMID:22095906

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

    PubMed

    Bryan, Allen W; O'Donnell, Charles W; Menke, Matthew; Cowen, Lenore J; Lindquist, Susan; Berger, Bonnie

    2012-02-01

    The supersecondary structure of amyloids and prions, proteins of intense clinical and biological interest, are difficult to determine by standard experimental or computational means. In addition, significant conformational heterogeneity is known or suspected to exist in many amyloid fibrils. Previous work has demonstrated that probability-based prediction of discrete β-strand pairs can offer insight into these structures. Here, we devise a system of energetic rules that can be used to dynamically assemble these discrete β-strand pairs into complete amyloid β-structures. The STITCHER algorithm progressively 'stitches' strand-pairs into full β-sheets based on a novel free-energy model, incorporating experimentally observed amino-acid side-chain stacking contributions, entropic estimates, and steric restrictions for amyloidal parallel β-sheet construction. A dynamic program computes the top 50 structures and returns both the highest scoring structure and a consensus structure taken by polling this list for common discrete elements. Putative structural heterogeneity can be inferred from sequence regions that compose poorly. Predictions show agreement with experimental models of Alzheimer's amyloid beta peptide and the Podospora anserina Het-s prion. Predictions of the HET-s homolog HET-S also reflect experimental observations of poor amyloid formation. We put forward predicted structures for the yeast prion Sup35, suggesting N-terminal structural stability enabled by tyrosine ladders, and C-terminal heterogeneity. Predictions for the Rnq1 prion and alpha-synuclein are also given, identifying a similar mix of homogenous and heterogeneous secondary structure elements. STITCHER provides novel insight into the energetic basis of amyloid structure, provides accurate structure predictions, and can help guide future experimental studies. PMID:22095906

  11. Predicting ion binding properties for RNA tertiary structures.

    PubMed

    Tan, Zhi-Jie; Chen, Shi-Jie

    2010-09-01

    Recent experiments pointed to the potential importance of ion correlation for multivalent ions such as Mg(2+) ions in RNA folding. In this study, we develop an all-atom model to predict the ion electrostatics in RNA folding. The model can treat ion correlation effects explicitly by considering an ensemble of discrete ion distributions. In contrast to the previous coarse-grained models that can treat ion correlation, this new model is based on all-atom nucleic acid structures. Thus, unlike the previous coarse-grained models, this new model allows us to treat complex tertiary structures such as HIV-1 DIS type RNA kissing complexes. Theory-experiment comparisons for a variety of tertiary structures indicate that the model gives improved predictions over the Poisson-Boltzmann theory, which underestimates the Mg(2+) binding in the competition with Na(+). Further systematic theory-experiment comparisons for a series of tertiary structures lead to a set of analytical formulas for Mg(2+)/Na(+) ion-binding to various RNA and DNA structures over a wide range of Mg(2+) and Na(+) concentrations. PMID:20816069

  12. 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.

  13. 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

  14. Factors influencing protein tyrosine nitration structure-based predictive models

    PubMed Central

    Bayden, Alexander S.; Yakovlev, Vasily A.; Graves, Paul R.; Mikkelsen, Ross B.; Kellogg, Glen E.

    2010-01-01

    Models for exploring tyrosine nitration in proteins have been created based on 3D structural features of 20 proteins for which high resolution X-ray crystallographic or NMR data are available and for which nitration of 35 total tyrosines has been experimentally proven under oxidative stress. Factors suggested in previous work to enhance nitration were examined with quantitative structural descriptors. The role of neighboring acidic and basic residues is complex: for the majority of tyrosines that are nitrated the distance to the heteroatom of the closest charged sidechain corresponds to the distance needed for suspected nitrating species to form hydrogen bond bridges between the tyrosine and that charged amino acid. This suggests that such bridges play a very important role in tyrosine nitration. Nitration is generally hindered for tyrosines that are buried and for those tyrosines where there is insufficient space for the nitro group. For in vitro nitration, closed environments with nearby heteroatoms or unsaturated centers that can stabilize radicals are somewhat favored. Four quantitative structure-based models, depending on the conditions of nitration, have been developed for predicting site-specific tyrosine nitration. The best model, relevant for both in vitro and in vivo cases predicts 30 of 35 tyrosine nitrations (positive predictive value) and has a sensitivity of 60/71 (11 false positives). PMID:21172423

  15. 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

  16. GTOP: a database of protein structures predicted from genome sequences

    PubMed Central

    Kawabata, Takeshi; Fukuchi, Satoshi; Homma, Keiichi; Ota, Motonori; Araki, Jiro; Ito, Takehiko; Ichiyoshi, Nobuyuki; Nishikawa, Ken

    2002-01-01

    Large-scale genome projects generate an unprecedented number of protein sequences, most of them are experimentally uncharacterized. Predicting the 3D structures of sequences provides important clues as to their functions. We constructed the Genomes TO Protein structures and functions (GTOP) database, containing protein fold predictions of a huge number of sequences. Predictions are mainly carried out with the homology search program PSI-BLAST, currently the most popular among high-sensitivity profile search methods. GTOP also includes the results of other analyses, e.g. homology and motif search, detection of transmembrane helices and repetitive sequences. We have completed analyzing the sequences of 41 organisms, with the number of proteins exceeding 120 000 in total. GTOP uses a graphical viewer to present the analytical results of each ORF in one page in a color-bar format. The assigned 3D structures are presented by Chime plug-in or RasMol. The binding sites of ligands are also included, providing functional information. The GTOP server is available at http://spock.genes.nig.ac.jp/~genome/gtop.html. PMID:11752318

  17. 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

  18. Sequence-only evolutionary and predicted structural features for the prediction of stability changes in protein mutants

    PubMed Central

    2013-01-01

    Background Even a single amino acid substitution in a protein sequence may result in significant changes in protein stability, structure, and therefore in protein function as well. In the post-genomic era, computational methods for predicting stability changes from only the sequence of a protein are of importance. While evolutionary relationships of protein mutations can be extracted from large protein databases holding millions of protein sequences, relevant evolutionary features for the prediction of stability changes have not been proposed. Also, the use of predicted structural features in situations when a protein structure is not available has not been explored. Results We proposed a number of evolutionary and predicted structural features for the prediction of stability changes and analysed which of them capture the determinants of protein stability the best. We trained and evaluated our machine learning method on a non-redundant data set of experimentally measured stability changes. When only the direction of the stability change was predicted, we found that the best performance improvement can be achieved by the combination of the evolutionary features mutation likelihood and SIFTscore in conjunction with the predicted structural feature secondary structure. The same two evolutionary features in the combination with the predicted structural feature accessible surface area achieved the lowest error when the prediction of actual values of stability changes was assessed. Compared to similar studies, our method achieved improvements in prediction performance. Conclusion Although the strongest feature for the prediction of stability changes appears to be the vector of amino acid identities in the sequential neighbourhood of the mutation, the most relevant combination of evolutionary and predicted structural features further improves prediction performance. Even the predicted structural features, which did not perform well on their own, turn out to be beneficial when appropriately combined with evolutionary features. We conclude that a high prediction accuracy can be achieved knowing only the sequence of a protein when the right combination of both structural and evolutionary features is used. PMID:23369338

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

    PubMed Central

    Vacic, Vladimir; Iakoucheva, Lilia M.

    2010-01-01

    Abstract We introduce a novel graph-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. Each vertex in the graph is then represented as a vector of counts of labeled non?isomorphic subgraphs (graphlets), centered on the vertex of interest. A similarity measure between two vertices is expressed as the inner product of their respective count vectors and is used in a supervised learning framework to classify protein residues. We evaluated our method on two function prediction problems: identification of catalytic residues in proteins, which is a well-studied problem suitable for benchmarking, and a much less explored problem of predicting phosphorylation sites in protein structures. The performance of the graphlet kernel approach was then compared against two alternative methods, a sequence?based predictor and our implementation of the FEATURE framework. On both tasks, the graphlet kernel performed favorably; however, the margin of difference was considerably higher on the problem of phosphorylation site prediction. While there is data that phosphorylation sites are preferentially positioned in intrinsically disordered regions, we provide evidence that for the sites that are located in structured regions, neither the surface accessibility alone nor the averaged measures calculated from the residue microenvironments utilized by FEATURE were sufficient to achieve high accuracy. The key benefit of the graphlet representation is its ability to capture neighborhood similarities in protein structures via enumerating the patterns of local connectivity in the corresponding labeled graphs. PMID:20078397

  20. SVM-based method for protein structural class prediction using secondary structural content and structural information of amino acids.

    PubMed

    Mohammad, Tabrez Anwar Shamim; Nagarajaram, Hampapathalu Adimurthy

    2011-08-01

    The knowledge collated from the known protein structures has revealed that the proteins are usually folded into the four structural classes: all-α, all-β, α/β and α + β. A number of methods have been proposed to predict the protein's structural class from its primary structure; however, it has been observed that these methods fail or perform poorly in the cases of distantly related sequences. In this paper, we propose a new method for protein structural class prediction using low homology (twilight-zone) protein sequences dataset. Since protein structural class prediction is a typical classification problem, we have developed a Support Vector Machine (SVM)-based method for protein structural class prediction that uses features derived from the predicted secondary structure and predicted burial information of amino acid residues. The examination of different individual as well as feature combinations revealed that the combination of secondary structural content, secondary structural and solvent accessibility state frequencies of amino acids gave rise to the best leave-one-out cross-validation accuracy of ~81% which is comparable to the best accuracy reported in the literature so far. PMID:21776605

  1. 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

  2. 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

  3. 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

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

    PubMed Central

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

    2016-01-01

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

  5. Functional Structure of Biological Communities Predicts Ecosystem Multifunctionality

    PubMed Central

    Mouillot, David; Villger, Sbastien; Scherer-Lorenzen, Michael; Mason, Norman W. H.

    2011-01-01

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

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

    PubMed

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

    2016-01-01

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

  7. EVOEvolutionary algorithm for crystal structure prediction

    NASA Astrophysics Data System (ADS)

    Bahmann, Silvia; Kortus, Jens

    2013-06-01

    We present EVOan 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.

  8. Prediction of protein continuum secondary structure with probabilistic models based on NMR solved structures

    PubMed Central

    Bodn, Mikael; Yuan, Zheng; Bailey, Timothy L

    2006-01-01

    Background The structure of proteins may change as a result of the inherent flexibility of some protein regions. We develop and explore probabilistic machine learning methods for predicting a continuum secondary structure, i.e. assigning probabilities to the conformational states of a residue. We train our methods using data derived from high-quality NMR models. Results Several probabilistic models not only successfully estimate the continuum secondary structure, but also provide a categorical output on par with models directly trained on categorical data. Importantly, models trained on the continuum secondary structure are also better than their categorical counterparts at identifying the conformational state for structurally ambivalent residues. Conclusion Cascaded probabilistic neural networks trained on the continuum secondary structure exhibit better accuracy in structurally ambivalent regions of proteins, while sustaining an overall classification accuracy on par with standard, categorical prediction methods. PMID:16478545

  9. Prediction of protease substrates using sequence and structure features

    PubMed Central

    Barkan, David T.; Hostetter, Daniel R.; Mahrus, Sami; Pieper, Ursula; Wells, James A.; Craik, Charles S.; Sali, Andrej

    2010-01-01

    Motivation:Granzyme B (GrB) and caspases cleave specific protein substrates to induce apoptosis in virally infected and neoplastic cells. While substrates for both types of proteases have been determined experimentally, there are many more yet to be discovered in humans and other metazoans. Here, we present a bioinformatics method based on support vector machine (SVM) learning that identifies sequence and structural features important for protease recognition of substrate peptides and then uses these features to predict novel substrates. Our approach can act as a convenient hypothesis generator, guiding future experiments by high-confidence identification of peptide-protein partners. Results:The method is benchmarked on the known substrates of both protease types, including our literature-curated GrB substrate set (GrBah). On these benchmark sets, the method outperforms a number of other methods that consider sequence only, predicting at a 0.87 true positive rate (TPR) and a 0.13 false positive rate (FPR) for caspase substrates, and a 0.79 TPR and a 0.21 FPR for GrB substrates. The method is then applied to ?25 000 proteins in the human proteome to generate a ranked list of predicted substrates of each protease type. Two of these predictions, AIF-1 and SMN1, were selected for further experimental analysis, and each was validated as a GrB substrate. Availability: All predictions for both protease types are publically available at http://salilab.org/peptide. A web server is at the same site that allows a user to train new SVM models to make predictions for any protein that recognizes specific oligopeptide ligands. Contact: craik@cgl.ucsf.edu; sali@salilab.org Supplementary information: Supplementary data are available at Bioinformatics online PMID:20505003

  10. Prediction of three-dimensional transmembrane helical protein structures

    NASA Astrophysics Data System (ADS)

    Barth, Patrick

    Membrane proteins are critical to living cells and their dysfunction can lead to serious diseases. High-resolution structures of these proteins would provide very valuable information for designing eficient therapies but membrane protein crystallization is a major bottleneck. As an important alternative approach, methods for predicting membrane protein structures have been developed in recent years. This chapter focuses on the problem of modeling the structure of transmembrane helical proteins, and describes recent advancements, current limitations, and future challenges facing de novo modeling, modeling with experimental constraints, and high-resolution comparative modeling of these proteins. Abbreviations: MP, membrane protein; SP, water-soluble protein; RMSD, root-mean square deviation; C? RMSD, root-mean square deviation over C? atoms; TM, transmembrane; TMH, transmembrane helix; GPCR, G protein-coupled receptor; 3D, three dimensional; NMR, nuclear magnetic resonance spectroscopy; EPR, electron paramagnetic resonance spectroscopy; FTIR, Fourier transform infrared spectroscopy.

  11. Structure Prediction and Validation of the ERK8 Kinase Domain

    PubMed Central

    Strambi, Angela; Mori, Mattia; Rossi, Matteo; Colecchia, David; Manetti, Fabrizio; Carlomagno, Francesca; Botta, Maurizio; Chiariello, Mario

    2013-01-01

    Extracellular signal-regulated kinase 8 (ERK8) has been already implicated in cell transformation and in the protection of genomic integrity and, therefore, proposed as a novel potential therapeutic target for cancer. In the absence of a crystal structure, we developed a three-dimensional model for its kinase domain. To validate our model we applied a structure-based virtual screening protocol consisting of pharmacophore screening and molecular docking. Experimental characterization of the hit compounds confirmed that a high percentage of the identified scaffolds was able to inhibit ERK8. We also confirmed an ATP competitive mechanism of action for the two best-performing molecules. Ultimately, we identified an ERK8 drug-resistant “gatekeeper” mutant that corroborated the predicted molecular binding mode, confirming the reliability of the generated structure. We expect that our model will be a valuable tool for the development of specific ERK8 kinase inhibitors. PMID:23326322

  12. 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

  13. 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

  14. Structural class tendency of polypeptide: A new conception in predicting protein structural class

    NASA Astrophysics Data System (ADS)

    Yu, Tao; Sun, Zhi-Bo; Sang, Jian-Ping; Huang, Sheng-You; Zou, Xian-Wu

    2007-12-01

    Prediction of protein domain structural classes is an important topic in protein science. In this paper, we proposed a new conception: structural class tendency of polypeptides (SCTP), which is based on the fact that a given amino acid fragment tends to be presented in certain type of proteins. The SCTP is obtained from an available training data set PDB40-B. When using the SCTP to predict protein structural classes by Intimate Sorting predictive method, we got the predictive accuracy (jackknife test) with 93.7%, 96.5%, and 78.6% for the testing data set PDB40-j, Chou&Maggiora and CHOU. These results indicate that the SCTP approach is quite encouraging and promising. This new conception provides an effective tool to extract valuable information from protein sequences.

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

    PubMed

    Fonteneau, Elisabeth

    2013-02-01

    The current study used event-related potentials (ERPs) to investigate how and when argument structure information is used during the processing of sentences with a filler-gap dependency. We hypothesize that one specific property - animacy (living vs. non-living) - is used by the parser during the building of the syntactic structure. Participants heard sentences that were rated off-line as having an expected noun (Who did the Lion King chase the caravan with?) or an unexpected noun (Who did Lion King chase the animal with?). This prediction is based on the animacy properties relation between the wh-word and the noun in the object position. ERPs from the noun in the unexpected condition (animal) elicited a typical Early Left Anterior Negativity (ELAN)/P600 complex compared to the noun in the expected condition (caravan). Firstly, these results demonstrate that the ELAN reflects not only grammatical category violation but also animacy property expectations in filler-gap dependency. Secondly, our data suggests that the language comprehension system is able to make detailed predictions about aspects of the upcoming words to build up the syntactic structure. PMID:23262082

  16. Structural brain MRI trait polygenic score prediction of cognitive abilities

    PubMed Central

    Luciano, Michelle; Marioni, Riccardo E; Hernández, Maria Valdés; Maniega, Susana Munoz; Hamilton, Iona F; Royle, Natalie A.; Scotland, Generation; Chauhan, Ganesh; Bis, Joshua C.; Debette, Stephanie; DeCarli, Charles; Fornage, Myriam; Schmidt, Reinhold; Ikram, M. Arfan; Launer, Lenore J.; Seshadri, Sudha; Bastin, Mark E.; Porteous, David J.; Wardlaw, Joanna; Deary, Ian J

    2016-01-01

    Structural brain magnetic resonance imaging (MRI) traits share part of their genetic variance with cognitive traits. Here, we use genetic association results from large meta-analytic studies of genome-wide association for brain infarcts, white matter hyperintensities, intracranial, hippocampal and total brain volumes to estimate polygenic scores for these traits in three Scottish samples: Generation Scotland: Scottish Family Health Study (GS:SFHS), and the Lothian Birth Cohorts of 1936 (LBC1936) and 1921 (LBC1921). These five brain MRI trait polygenic scores were then used to 1) predict corresponding MRI traits in the LBC1936 (numbers ranged 573 to 630 across traits) and 2) predict cognitive traits in all three cohorts (in 8,115 to 8,250 persons). In the LBC1936, all MRI phenotypic traits were correlated with at least one cognitive measure; and polygenic prediction of MRI traits was observed for intracranial volume. Meta-analysis of the correlations between MRI polygenic scores and cognitive traits revealed a significant negative correlation (maximal r=0.08) between the hippocampal volume polygenic score and measures of global cognitive ability collected in childhood and in old age in the Lothian Birth Cohorts. The lack of association to a related general cognitive measure when including the GS:SFHS points to either type 1 error or the importance of using prediction samples that closely match the demographics of the genome-wide association samples from which prediction is based. Ideally, these analyses should be repeated in larger samples with data on both MRI and cognition, and using MRI GWA results from even larger meta-analysis studies. PMID:26427786

  17. Structural Brain MRI Trait Polygenic Score Prediction of Cognitive Abilities.

    PubMed

    Luciano, Michelle; Marioni, Riccardo E; Valds Hernndez, Maria; Muoz Maniega, Susana; Hamilton, Iona F; Royle, Natalie A; Chauhan, Ganesh; Bis, Joshua C; Debette, Stephanie; DeCarli, Charles; Fornage, Myriam; Schmidt, Reinhold; Ikram, M Arfan; Launer, Lenore J; Seshadri, Sudha; Bastin, Mark E; Porteous, David J; Wardlaw, Joanna; Deary, Ian J

    2015-12-01

    Structural brain magnetic resonance imaging (MRI) traits share part of their genetic variance with cognitive traits. Here, we use genetic association results from large meta-analytic studies of genome-wide association (GWA) for brain infarcts (BI), white matter hyperintensities, intracranial, hippocampal, and total brain volumes to estimate polygenic scores for these traits in three Scottish samples: Generation Scotland: Scottish Family Health Study (GS:SFHS), and the Lothian Birth Cohorts of 1936 (LBC1936) and 1921 (LBC1921). These five brain MRI trait polygenic scores were then used to: (1) predict corresponding MRI traits in the LBC1936 (numbers ranged 573 to 630 across traits), and (2) predict cognitive traits in all three cohorts (in 8,115-8,250 persons). In the LBC1936, all MRI phenotypic traits were correlated with at least one cognitive measure, and polygenic prediction of MRI traits was observed for intracranial volume. Meta-analysis of the correlations between MRI polygenic scores and cognitive traits revealed a significant negative correlation (maximal r = 0.08) between the HV polygenic score and measures of global cognitive ability collected in childhood and in old age in the Lothian Birth Cohorts. The lack of association to a related general cognitive measure when including the GS:SFHS points to either type 1 error or the importance of using prediction samples that closely match the demographics of the GWA samples from which prediction is based. Ideally, these analyses should be repeated in larger samples with data on both MRI and cognition, and using MRI GWA results from even larger meta-analysis studies. PMID:26427786

  18. 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.

  19. The sequential structure of brain activation predicts skill.

    PubMed

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

    2016-01-29

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

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

    PubMed

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

    2014-01-01

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

  1. De novo prediction of structured RNAs from genomic sequences

    PubMed Central

    Gorodkin, Jan; Hofacker, Ivo L.; Torarinsson, Elfar; Yao, Zizhen; Havgaard, Jakob H.; Ruzzo, Walter L.

    2015-01-01

    Growing recognition of the numerous, diverse and important roles played by non-coding RNA in all organisms motivates better elucidation of these cellular components. Comparative genomics is a powerful tool for this task and is arguably preferable to any high-throughput experimental technology currently available because evolutionary conservation highlights functionally important regions. Conserved secondary structure, rather than primary sequence, is the hallmark of many functionally important RNAs, since compensatory substitutions in base-paired regions preserve structure. Unfortunately, such substitutions also obscure sequence identity and confound alignment algorithms, greatly complicating analysis. This paper surveys recent computational advances in this difficult arena, which have enabled genome-scale prediction of cross-species conserved RNA elements, suggesting that a wealth of these elements indeed exist. PMID:19942311

  2. 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.

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

    NASA Technical Reports Server (NTRS)

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

    1990-01-01

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

  4. 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.

  5. Exploiting Homology Information in Nontemplate Based Prediction of Protein Structures.

    PubMed

    Iacoangeli, Alfredo; Marcatili, Paolo; Tramontano, Anna

    2015-10-13

    In this paper we describe a novel strategy for exploring the conformational space of proteins and show that this leads to better models for proteins the structure of which is not amenable to template based methods. Our strategy is based on the assumption that the energy global minimum of homologous proteins must correspond to similar conformations, while the precise profiles of their energy landscape, and consequently the positions of the local minima, are likely to be different. In line with this hypothesis, we apply a replica exchange Monte Carlo simulation protocol that, rather than using different parameters for each parallel simulation, uses the sequences of homologous proteins. We show that our results are competitive with respect to alternative methods, including those producing the best model for each of the analyzed targets in the CASP10 (10th Critical Assessment of techniques for protein Structure Prediction) experiment free modeling category. PMID:26574289

  6. Importance of coulombic end effects on cation accumulation near oligoelectrolyte B-DNA: a demonstration using 23Na NMR.

    PubMed

    Stein, V M; Bond, J P; Capp, M W; Anderson, C F; Record, M T

    1995-03-01

    The local cation concentration at the surface of oligomeric or polymeric B-DNA is expected, on the basis of MC simulations (Olmsted, M. C., C. F. Anderson, and M. T. Record, Jr. 1989. Proc. Natl. Acad. Sci. USA. 86:7766-7770), to decrease sharply as either end of the molecule is approached. In this paper we report 23Na NMR measurements indicating the importance of this "coulombic" end effect on the average extent of association of Na+ with oligomeric duplex DNA. In solutions containing either 20-bp synthetic DNA or 160-bp mononucleosomal calf thymus DNA at phosphate monomer concentrations [P] of 4-10 mM, measurements were made over the range of ratios 1 < or = [Na]/[LP] < or = 20, corresponding to Na+ concentrations of 4-200 nM. The longitudinal 23Na NMR relaxation rates measured in these NaDNA solutions, Robs, are interpreted as population-weighted averages of contributions from "bound" (RB) and "free" (RF) 23Na relaxation rates. The observed enhancements of Robs indicate that RB significantly exceeds RF, which is approximately equal to the 23Na relaxation rate in an aqueous solution containing only NaCl. Under salt-fre-tconditions ([Na]/[P] = 1), where the enhancement in Robs is maximal, we find that Robs--RF in the solution containing 160-bp DNA is approximately 1.8 times that observed for the 20-bp DNA. For the 160-bp oligomer (which theoretical calculations predict to be effectively polyion-like), we find that a plot of Robs v. [P]/[Na] is linear, as observed previously for sonicated (approximately 700 bp) DNA samples. For the 20-bp oligonucleotide this plot exhibits a marked departure from linearity that can be fitted to a quadratic function of [P]/[Na]. Monte Carlo simulations based on a simplified model are capable of reproducing the qualitative trends in the 23Na NMR measurements analyzed here. In particular, the dependences of Robs--RF on DNA charge magnitude of Z(320 vs. 38 phosphates) and (for the 20-bp oligomer) on [Na]/[P] are well correlated with the calculated average surface concentration of Na+. Thus, effects of sodium concentration on RB appear to be of secondary importance. We conclude that 23Na NMR relaxation measurements are a sensitive probe of the effects of oligomer charge on the extent of ion accumulation near B-DNA oligonucleotides, as a function of [Na] and [P]. PMID:7756526

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

    NASA Astrophysics Data System (ADS)

    Kasahara, Naoto

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

  8. 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

  9. Prediction of Alzheimer's disease using individual structural connectivity networks.

    PubMed

    Shao, Junming; Myers, Nicholas; Yang, Qinli; Feng, Jing; Plant, Claudia; Böhm, Christian; Förstl, Hans; Kurz, Alexander; Zimmer, Claus; Meng, Chun; Riedl, Valentin; Wohlschläger, Afra; Sorg, Christian

    2012-12-01

    Alzheimer's disease (AD) progressively degrades the brain's gray and white matter. Changes in white matter reflect changes in the brain's structural connectivity pattern. Here, we established individual structural connectivity networks (ISCNs) to distinguish predementia and dementia AD from healthy aging in individual scans. Diffusion tractography was used to construct ISCNs with a fully automated procedure for 21 healthy control subjects (HC), 23 patients with mild cognitive impairment and conversion to AD dementia within 3 years (AD-MCI), and 17 patients with mild AD dementia. Three typical pattern classifiers were used for AD prediction. Patients with AD and AD-MCI were separated from HC with accuracies greater than 95% and 90%, respectively, irrespective of prediction approach and specific fiber properties. Most informative connections involved medial prefrontal, posterior parietal, and insular cortex. Patients with mild AD were separated from those with AD-MCI with an accuracy of approximately 85%. Our finding provides evidence that ISCNs are sensitive to the impact of earliest stages of AD. ISCNs may be useful as a white matter-based imaging biomarker to distinguish healthy aging from AD. PMID:22405045

  10. Prediction of Alzheimer's disease using individual structural connectivity networks

    PubMed Central

    Shao, Junming; Myers, Nicholas; Yang, Qinli; Feng, Jing; Plant, Claudia; Bhm, Christian; Frstl, Hans; Kurz, Alexander; Zimmer, Claus; Meng, Chun; Riedl, Valentin; Wohlschlger, Afra; Sorg, Christian

    2012-01-01

    Alzheimer's disease (AD) progressively degrades the brain's gray and white matter. Changes in white matter reflect changes in the brain's structural connectivity pattern. Here, we established individual structural connectivity networks (ISCNs) to distinguish predementia and dementia AD from healthy aging in individual scans. Diffusion tractography was used to construct ISCNs with a fully automated procedure for 21 healthy control subjects (HC), 23 patients with mild cognitive impairment and conversion to AD dementia within 3 years (AD-MCI), and 17 patients with mild AD dementia. Three typical pattern classifiers were used for AD prediction. Patients with AD and AD-MCI were separated from HC with accuracies greater than 95% and 90%, respectively, irrespective of prediction approach and specific fiber properties. Most informative connections involved medial prefrontal, posterior parietal, and insular cortex. Patients with mild AD were separated from those with AD-MCI with an accuracy of approximately 85%. Our finding provides evidence that ISCNs are sensitive to the impact of earliest stages of AD. ISCNs may be useful as a white matter-based imaging biomarker to distinguish healthy aging from AD. PMID:22405045

  11. Phylogenetic structure of soil bacterial communities predicts ecosystem functioning.

    PubMed

    Prez-Valera, Eduardo; Goberna, Marta; Verd, Miguel

    2015-05-01

    Quantifying diversity with phylogeny-informed metrics helps understand the effects of diversity on ecosystem functioning (EF). The sign of these effects remains controversial because phylogenetic diversity and taxonomic identity may interactively influence EF. Positive relationships, traditionally attributed to complementarity effects, seem unimportant in natural soil bacterial communities. Negative relationships could be attributed to fitness differences leading to the overrepresentation of few productive clades, a mechanism recently invoked to assemble soil bacteria communities. We tested in two ecosystems contrasting in terms of environmental heterogeneity whether two metrics of phylogenetic community structure, a simpler measure of phylogenetic diversity (NRI) and a more complex metric incorporating taxonomic identity (PCPS), correctly predict microbially mediated EF. We show that the relationship between phylogenetic diversity and EF depends on the taxonomic identity of the main coexisting lineages. Phylogenetic diversity was negatively related to EF in soils where a marked fertility gradient exists and a single and productive clade (Proteobacteria) outcompete other clades in the most fertile plots. However, phylogenetic diversity was unrelated to EF in soils where the fertility gradient is less marked and Proteobacteria coexist with other abundant lineages. Including the taxonomic identity of bacterial lineages in metrics of phylogenetic community structure allows the prediction of EF in both ecosystems. PMID:25873469

  12. 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.

  13. Experimental assessment of structure and property predictions during hot working

    NASA Astrophysics Data System (ADS)

    Lalli, L. A.; Deardo, A. J.

    1990-12-01

    Uniaxial compression experiments were conducted in the hot-working range for a commercial purity aluminum alloy using constant strain-rate tests and strain-rate drop tests producing strain hardening, strain softening, and steady-state deformation behaviors. The structure of the deformed material was characterized by microhardness and grain shape. A single internal state variable constitutive model for flow stress was developed using the microhardness data to quantify the state variable. The change in the grain aspect ratio was related to the imposed bulk strain in the samples. The constitutive model was incorporated into a finite element program. A critical experimental assessment of predictions of the spatial variation in structure and properties throughout a workpiece was then made using a tapered compression specimen. Comparisons with experimental results indicated that the load was underpredicted by 10 pct and the microhardness by 6 pct, while the severity of the strain gradients was overpredicted. This was concluded to be due to an underprediction of the work-hardening rate at low strains. Additional calculations made with alternative constitutive models showed that the internal state variable model predicted the applied force much more accurately than alternative models.

  14. Hybrid Global Optimization Algorithms for Protein Structure Prediction: Alternating Hybrids

    PubMed Central

    Klepeis, J. L.; Pieja, M. J.; Floudas, C. A.

    2003-01-01

    Hybrid global optimization methods attempt to combine the beneficial features of two or more algorithms, and can be powerful methods for solving challenging nonconvex optimization problems. In this paper, novel classes of hybrid global optimization methods, termed alternating hybrids, are introduced for application as a tool in treating the peptide and protein structure prediction problems. In particular, these new optimization methods take the form of hybrids between a deterministic global optimization algorithm, the αBB, and a stochastically based method, conformational space annealing (CSA). The αBB method, as a theoretically proven global optimization approach, exhibits consistency, as it guarantees convergence to the global minimum for twice-continuously differentiable constrained nonlinear programming problems, but can benefit from computationally related enhancements. On the other hand, the independent CSA algorithm is highly efficient, though the method lacks theoretical guarantees of convergence. Furthermore, both the αBB method and the CSA method are found to identify ensembles of low-energy conformers, an important feature for determining the true free energy minimum of the system. The proposed hybrid methods combine the desirable features of efficiency and consistency, thus enabling the accurate prediction of the structures of larger peptides. Computational studies for met-enkephalin and melittin, employing sequential and parallel computing frameworks, demonstrate the promise for these proposed hybrid methods. PMID:12547770

  15. 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

  16. HU protein employs similar mechanisms of minor-groove recognition in binding to different B-DNA sites: demonstration by Raman spectroscopy.

    PubMed

    Serban, Doinita; Benevides, James M; Thomas, George J

    2003-06-24

    The sequence isomers d(CGCAAATTTGCG) and d(TCAAGGCCTTGA) form self-complementary duplexes that present distinct targets for binding of the homodimeric architectural protein HU of Bacillus stearothermophilus (HUBst). Raman spectroscopy shows that although each duplex structure is of the B-DNA type, there are subtle conformational dissimilarities between them, involving torsion angles of the phosphodiester backbone and the arrangements of stacked bases. Each DNA duplex forms a stable stoichiometric (1:1) complex with HUBst, in which the structure of the HUBst dimer is largely conserved. However, the Raman signature of each DNA duplex is perturbed significantly and similarly with HUBst binding, as reflected in marker bands assigned to localized vibrations of the phosphodiester moieties and base residues. The spectral perturbations identify a reorganization of the DNA backbone and partial unstacking of bases with HUBst binding, which is consistent with non-sequence-specific minor-groove recognition. Prominent among the HUBst-induced perturbations of B-DNA are a conversion of approximately one-third of the alpha/beta/gamma torsions from the canonical g(-)/t/g(+) conformation to an alternative conformation, an equivalent conversion of deoxyadenosyl moieties from the C2'-endo/anti to the C3'-endo/anti conformation, and appreciable unstacking of purines. The results imply that each solution complex is characterized by structural perturbations extending throughout the 12-bp sequence. Comparison with previously studied protein/DNA complexes suggests that binding of HUBst bends DNA by approximately 70 degrees. PMID:12809494

  17. 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.

  18. Predicting DNA-Binding Proteins and Binding Residues by Complex Structure Prediction and Application to Human Proteome

    PubMed Central

    Zhao, Huiying; Wang, Jihua; Zhou, Yaoqi; Yang, Yuedong

    2014-01-01

    As more and more protein sequences are uncovered from increasingly inexpensive sequencing techniques, an urgent task is to find their functions. This work presents a highly reliable computational technique for predicting DNA-binding function at the level of protein-DNA complex structures, rather than low-resolution two-state prediction of DNA-binding as most existing techniques do. The method first predicts protein-DNA complex structure by utilizing the template-based structure prediction technique HHblits, followed by binding affinity prediction based on a knowledge-based energy function (Distance-scaled finite ideal-gas reference state for protein-DNA interactions). A leave-one-out cross validation of the method based on 179 DNA-binding and 3797 non-binding protein domains achieves a Matthews correlation coefficient (MCC) of 0.77 with high precision (94%) and high sensitivity (65%). We further found 51% sensitivity for 82 newly determined structures of DNA-binding proteins and 56% sensitivity for the human proteome. In addition, the method provides a reasonably accurate prediction of DNA-binding residues in proteins based on predicted DNA-binding complex structures. Its application to human proteome leads to more than 300 novel DNA-binding proteins; some of these predicted structures were validated by known structures of homologous proteins in APO forms. The method [SPOT-Seq (DNA)] is available as an on-line server at http://sparks-lab.org. PMID:24792350

  19. 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.

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

    PubMed

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

    2015-09-01

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

  1. 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.

  2. 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)

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

    PubMed Central

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

    2015-01-01

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

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

    PubMed

    Laland, Kevin N; Uller, Tobias; Feldman, Marcus W; Sterelny, Kim; Mller, 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 waysone that preserves the internal structure of contemporary evolutionary theory and one that points towards an alternative conceptual framework. The latter, which we label the 'extended evolutionary synthesis' (EES), retains the fundaments of evolutionary theory, but differs in its emphasis on the role of constructive processes in development and evolution, and reciprocal portrayals of causation. In the EES, developmental processes, operating through developmental bias, inclusive inheritance and niche construction, share responsibility for the direction and rate of evolution, the origin of character variation and organism-environment complementarity. We spell out the structure, core assumptions and novel predictions of the EES, and show how it can be deployed to stimulate and advance research in those fields that study or use evolutionary biology. PMID:26246559

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

    NASA Technical Reports Server (NTRS)

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

    2005-01-01

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

  6. Leveraging structure for enzyme function prediction: methods, opportunities and challenges

    PubMed Central

    Jacobson, Matthew P.; Kalyanaraman, Chakrapani; Zhao, Suwen; Tian, Boxue

    2014-01-01

    The rapid growth of the number of protein sequences that can be inferred from sequenced genomes presents challenges for function assignment, as only a small fraction (currently <%) of have been experimentally characterized. Bioinformatics tools are commonly used to predict functions of uncharacterized proteins. Recently there has been significant progress in using protein structures as an additional source of information to infer aspects of enzyme function, which is the focus of this review. Successful application of these approaches has led to the identification of novel metabolites, enzyme activities, and biochemical pathways. We discuss opportunities to systematically elucidate protein domains of unknown function, orphan enzyme activities, dead-end metabolites, and pathways in secondary metabolism. PMID:24998033

  7. 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.

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

    NASA Astrophysics Data System (ADS)

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

    2005-09-01

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

  9. The predictive power of structural MRI in Autism diagnosis.

    PubMed

    Katuwal, Gajendra J; Cahill, Nathan D; Baum, Stefi A; Michael, Andrew M

    2015-08-01

    Diagnosis of Autism Spectrum Disorder (ASD) using structural magnetic resonance imaging (sMRI) of the brain has been a topic of significant research interest. Previous studies using small datasets with well-matched Typically Developing Controls (TDC) report high classification accuracies (80-96%) but studies using the large heterogeneous ABIDE dataset report accuracies less than 60%. In this study we investigate the predictive power of sMRI in ASD using 373 ASD and 361 TDC male subjects from the ABIDE. Brain morphometric features were derived using FreeSurfer and classification was performed using three different techniques: Random Forest (RF), Support Vector Machine (SVM) and Gradient Boosting Machine (GBM). Although high classification accuracies were possible in individual sites (with a maximum of 97% in Caltech), the highest classification accuracy across all sites was only 60% (sensitivity = 57%, specificity = 64%). However, the accuracy across all sites improved to 67% when IQ and age information were added to morphometric features. Across all three classifiers, volume and surface area had more discriminative power. In general, important features for classification were present in the frontal and temporal regions and these regions have been implicated in ASD. This study also explores the effect of demographics and behavioral measures on the predictive power of sMRI. Results show that classification accuracy increases with autism severity and that ASD detection with sMRI is easier before the age of 10 years. PMID:26737238

  10. 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.

  11. 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

  12. High Precision Prediction of Functional Sites in Protein Structures

    PubMed Central

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

    2014-01-01

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

  13. 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) polymeraseDNA 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

  14. 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

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

    PubMed Central

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

    2015-01-01

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

  16. How to predict very large and complex crystal structures

    NASA Astrophysics Data System (ADS)

    Lyakhov, Andriy O.; Oganov, Artem R.; Valle, Mario

    2010-09-01

    Evolutionary crystal structure prediction proved to be a powerful approach in discovering new materials. Certain limitations are encountered for systems with a large number of degrees of freedom ("large systems") and complex energy landscapes ("complex systems"). We explore the nature of these limitations and address them with a number of newly developed tools. For large systems a major problem is the lack of diversity: any randomly produced population consists predominantly of high-energy disordered structures, offering virtually no routes toward the ordered ground state. We offer two solutions: first, modified variation operators that favor atoms with higher local order (a function we introduce here), and, second, construction of the first generation non-randomly, using pseudo-subcells with, in general, fractional atomic occupancies. This enhances order and diversity and improves energies of the structures. We introduce an additional variation operator, coordinate mutation, which applies preferentially to low-order ("badly placed") atoms. Biasing other variation operators by local order is also found to produce improved results. One promising version of coordinate mutation, explored here, displaces atoms along the eigenvector of the lowest-frequency vibrational mode. For complex energy landscapes, the key problem is the possible existence of several energy funnels - in this situation it is possible to get trapped in one funnel (not necessarily containing the ground state). To address this problem, we develop an algorithm incorporating the ideas of abstract "distance" between structures. These new ingredients improve the performance of the evolutionary algorithm USPEX, in terms of efficiency and reliability, for large and complex systems.

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

    PubMed

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

    2015-07-01

    Biological models have typically framed sexual orientation in terms of effects of variation in fetal androgen signaling on sexual differentiation, although other biological models exist. Despite marked sex differences in facial structure, the relationship between sexual orientation and facial structure is understudied. A total of 52 lesbian women, 134 heterosexual women, 77 gay men, and 127 heterosexual men were recruited at a Canadian campus and various Canadian Pride and sexuality events. We found that facial structure differed depending on sexual orientation; substantial variation in sexual orientation was predicted using facial metrics computed by a facial modelling program from photographs of White faces. At the univariate level, lesbian and heterosexual women differed in 17 facial features (out of 63) and four were unique multivariate predictors in logistic regression. Gay and heterosexual men differed in 11 facial features at the univariate level, of which three were unique multivariate predictors. Some, but not all, of the facial metrics differed between the sexes. Lesbian women had noses that were more turned up (also more turned up in heterosexual men), mouths that were more puckered, smaller foreheads, and marginally more masculine face shapes (also in heterosexual men) than heterosexual women. Gay men had more convex cheeks, shorter noses (also in heterosexual women), and foreheads that were more tilted back relative to heterosexual men. Principal components analysis and discriminant functions analysis generally corroborated these results. The mechanisms underlying variation in craniofacial structure--both related and unrelated to sexual differentiation--may thus be important in understanding the development of sexual orientation. PMID:25550146

  18. 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.

  19. 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 proteins 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

  20. Protein structure prediction provides comparable performance to crystallographic structures in docking-based virtual screening.

    PubMed

    Du, Hongying; Brender, Jeffrey R; Zhang, Jian; Zhang, Yang

    2015-01-01

    Structure based virtual screening has largely been limited to protein targets for which either an experimental structure is available or a strongly homologous template exists so that a high-resolution model can be constructed. The performance of state of the art protein structure predictions in virtual screening in systems where only weakly homologous templates are available is largely untested. Using the challenging DUD database of structural decoys, we show here that even using templates with only weak sequence homology (<30% sequence identity) structural models can be constructed by I-TASSER which achieve comparable enrichment rates to using the experimental bound crystal structure in the majority of the cases studied. For 65% of the targets, the I-TASSER models, which are constructed essentially in the apo conformations, reached 70% of the virtual screening performance of using the holo-crystal structures. A correlation was observed between the success of I-TASSER in modeling the global fold and local structures in the binding pockets of the proteins versus the relative success in virtual screening. The virtual screening performance can be further improved by the recognition of chemical features of the ligand compounds. These results suggest that the combination of structure-based docking and advanced protein structure modeling methods should be a valuable approach to the large-scale drug screening and discovery studies, especially for the proteins lacking crystallographic structures. PMID:25220914

  1. [A method for prediction of conserved RNA secondary structures].

    PubMed

    Mironov, A A

    2007-01-01

    The RNA secondary structure prediction is a classical problem in bioinformatics. The most efficient approach to this problem is based on the idea of a comparative analysis. In this approach the algorithms utilize multiple alignment of the RNA sequences and find common RNA structure. This paper describes a new algorithm for this task. This algorithm does not require predefined multiple alignment. The main idea of the algorithm is based on MEME-like iterative searching of abstract profile on different levels. On the first level the algorithm searches the common blocks in the RNA sequences and creates chain of this blocks. On the next step the algorithm refines the chain of common blocks. On the last stage the algorithm searches sets of common helices that have consistent locations relative to common blocks. The algorithm was tested on sets of tRNA with a subset of junk sequences and on RFN riboswitches. The algorithm is implemented as a web server (http://bioinf.fbb.msu.ru/RNAAlign/). PMID:17936993

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

    SciTech Connect

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

    1997-06-25

    During the second quarter of this project, progress was made on both major technical tasks. Three parallel efforts were initiated on the modeling of carbon structural evolution. Structural ordering during carbonization was studied by a numerical simulation scheme proposed by Alan Kerstein involving molecular weight growth and rotational mobility. Work was also initiated to adapt a model of carbonaceous mesophase formation, originally developed under parallel NSF funding, to the prediction of coke texture. 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. Boston University has initiated molecular dynamics simulations of carbonization processes and Ohio State has begun theoretical treatment of surface reactions. Experimental work has also begun on model compound studies at Brown and on pilot-scale combustion systems with widely varying flame types at OSE. The work on mobility / growth models shows great promise and is discussed in detail in the body of the report.

  3. Detection of Non-B-DNA Secondary Structures by S1 Nuclease Digestion

    NASA Astrophysics Data System (ADS)

    Del Olmo, Marcel. Li; Aranda, Agustin; Perez-Ortin, Jose E.; Tordera, Vicente

    1998-06-01

    In nature, almost all DNA strands are supercoiled in both prokaryotic and eukaryotic cells. Here, we present two cheap and simple laboratory experiments to analyze the different topological states of DNA and, simultaneously, to detect denatured regions and cruciforms in vitro, using the single-strand specific S1 nuclease. A natural (A+T)-rich region of the 3' region of Saccharomyces cerevisiae FBP1 gene and a DNA (A+T)-rich region in pUC plasmids around the terminator of the ampicillin resistance gene (both capable of undergoing supercoiling-dependent denaturation and therefore sensitive to S1 nuclease) have been used in the experiments. Experimental costs are low, and the small amounts of chemicals and the laboratory equipment used are available in every laboratory.

  4. Structure prediction of the solid forms of methanol: an ab initio random structure searching approach.

    PubMed

    Lin, Tzu-Jen; Hsing, Cheng-Rong; Wei, Ching-Ming; Kuo, Jer-Lai

    2016-01-20

    Liquid methanol and methanol clusters have been comprehensively studied to reveal their local structure and hydrogen bond networks. However, our understanding of the crystal forms of methanol is rather limited. The known crystal structures of solid methanol, ?, ?, and ?, are composed of infinite hydrogen bond chains in their unit cell. The structural diversity of solid methanol is much less than that of liquid methanol, in which both chain and ring structures exist and have been confirmed by experiments. In this study, we employed ab initio random structure searching (AIRSS) to study possible solid methanol structures. AIRSS predicted known solid methanol phases as well as various ring structures that have not been considered. A new possible candidate structure for the ? phase was also discovered. The relative stability of known solid methanol phases and our newly discovered structures were also investigated through dispersion corrected density functional theory. The density functional calculation provides reliable phase transition pressures between the known phases and the searched structures compared with experimental suggestions. In addition, the simulation result indicated that CHO hydrogen bonds play a major role in stabilizing the methanol crystals under high pressures. PMID:26725921

  5. Structural Time Series Model for El Nio 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 Nio/EN) has long been a subject of intense research and improvement. Thus, the present study explores a novel method for the prediction of the Nio 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 Nio event; and a southern Pacific temperature-difference tracer, the Rossbell dipole, leading EN by about nine months (Ballester, 2011).

  6. 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.

  7. Tertiary structure prediction of RNA-RNA complexes using a secondary structure and fragment-based method.

    PubMed

    Yamasaki, Satoshi; Hirokawa, Takatsugu; Asai, Kiyoshi; Fukui, Kazuhiko

    2014-02-24

    A method has been developed for predicting the tertiary structures of RNA-RNA complex structures using secondary structure information and a fragment assembly algorithm. The linker base pair and secondary structure potential derived from the secondary structure information are particularly useful for prediction. Application of this method to several kinds of RNA-RNA complex structures, including kissing loops, hammerhead ribozymes, and other functional RNAs, produced promising results. Use of the secondary structure potential effectively restrained the conformational search space, leading to successful prediction of kissing loop structures, which mainly consist of common structural elements. The failure to predict more difficult targets had various causes but should be overcome through such measures as tuning the balance of the energy contributions from the Watson-Crick and non- Watson-Crick base pairs, by obtaining knowledge about a wider variety of RNA structures. PMID:24479711

  8. Chromatin structure predicts epigenetic therapy responsiveness in sarcoma

    PubMed Central

    Mills, Joslyn; Hricik, Todd; Siddiqi, Sara; Matushansky, Igor

    2010-01-01

    To formally explore the potential therapeutic effect of histone deacetylase inhibitors (HDACIs) and DNA-methyltransferase inhibitors (DNA-MIs) on sarcomas, we treated a large sarcoma cell line panel with five different HDACIs in the absence and presence of the DNA-MI decitabine. We observed that the IC50 of each HDACI was consistent for all cell lines while decitabine as a single agent showed no growth inhibition at standard doses. Combination HDACI/DNA-MI therapy showed a preferential synergism for specific sarcoma cell lines. Subsequently we identified and validated (in vitro and in vivo) a two gene set signature (high CUGBP2; low RHOJ) that associated with the synergistic phenotype. We further uncover that the epigenetic synergism leading to specific upregulation of CDKI p21 in specific cell lines is a function of the differences in the degree of baseline chromatin modification. Finally, we show that these chromatin and gene expression patterns are similarly present in the majority of high grade primary sarcomas. Our results provide the first demonstration of a gene set that can predict responsiveness to HDACI/DNA-MI and links this responsiveness mechanistically to the baseline chromatin structure. PMID:21216937

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

    SciTech Connect

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

    2013-10-15

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

  10. 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.

  11. 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.

  12. 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.

  13. 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; Malmstrm, 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

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

    PubMed

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

    2015-12-01

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

  15. 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…

  16. 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

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

    PubMed

    Tsang, Herbert H; Wiese, Kay C

    2015-01-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2016-03-01

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

  19. APC targeted micelle for enhanced intradermal delivery of hepatitis B DNA vaccine.

    PubMed

    Layek, Buddhadev; Lipp, Lindsey; Singh, Jagdish

    2015-06-10

    Chronic hepatitis B is a serious liver disease and puts people at high risk of death from cirrhosis and liver cancer. Although DNA vaccination has been emerged as a potential immunotherapeutic strategy for the treatment of chronic hepatitis B, the efficiencies were not adequate in clinical trials. Here we describe the design, synthesis, and evaluation of mannosylated phenylalanine grafted chitosan (Man-CS-Phe) as a DNA delivery vector for direct transfection of antigen presenting cells to improve cellular and humoral immunity to plasmid-coded antigen. The cationic Man-CS-Phe micelles condense plasmid DNA into nanoscale polyplexes and provide efficient protection of complexed DNA from nuclease degradation. The mannose receptor-mediated enhanced cell uptake and high in vitro transfection efficiency of the polyplexes were demonstrated in RAW 264.7 and DC 2.4 cells using GFP-expressing plasmid DNA. Furthermore, intradermal immunization of BALB/c mice indicated that hepatitis B DNA vaccine/Man-CS-Phe polyplexes not only induced multi-fold higher serum antibody titer in comparison to all other formulations including FuGENE HD, but also significantly stimulated T-cell proliferation and skewed T helper toward Th1 polarization. These results illustrate that the Man-CS-Phe can serve as a promising DNA delivery vector to harness both cellular and humoral arms of immune system. PMID:25886704

  20. 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.

  1. Structural synthetic biotechnology: from molecular structure to predictable design for industrial strain development.

    PubMed

    Chen, Zhen; Wilmanns, Matthias; Zeng, An-Ping

    2010-10-01

    The future of industrial biotechnology requires efficient development of highly productive and robust strains of microorganisms. Present praxis of strain development cannot adequately fulfill this requirement, primarily owing to the inability to control reactions precisely at a molecular level, or to predict reliably the behavior of cells upon perturbation. Recent developments in two areas of biology are changing the situation rapidly: structural biology has revealed details about enzymes and associated bioreactions at an atomic level; and synthetic biology has provided tools to design and assemble precisely controllable modules for re-programming cellular metabolic circuitry. However, because of different emphases, to date, these two areas have developed separately. A linkage between them is desirable to harness their concerted potential. We therefore propose structural synthetic biotechnology as a new field in biotechnology, specifically for application to the development of industrial microbial strains. PMID:20727604

  2. 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.

  3. 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.

  4. 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.

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

    EPA Science Inventory

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

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

    EPA Science Inventory

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

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

  7. 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

  8. Automated Detection of Eruptive Structures for Solar Eruption Prediction

    NASA Astrophysics Data System (ADS)

    Georgoulis, Manolis K.

    2012-07-01

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

  9. Predicting emergency evacuation and sheltering behavior: a structured analytical approach.

    PubMed

    Dombroski, Matt; Fischhoff, Baruch; Fischbeck, Paul

    2006-12-01

    We offer a general approach to predicting public compliance with emergency recommendations. It begins with a formal risk assessment of an anticipated emergency, whose parameters include factors potentially affecting and affected by behavior, as identified by social science research. Standard procedures are used to elicit scientific experts' judgments regarding these behaviors and dependencies, in the context of an emergency scenario. Their judgments are used to refine the model and scenario, enabling local emergency coordinators to predict the behavior of citizens in their area. The approach is illustrated with a case study involving a radiological dispersion device (RDD) exploded in downtown Pittsburgh, PA. Both groups of experts (national and local) predicted approximately 80-90% compliance with an order to evacuate workplaces and 60-70% compliance with an order to shelter in place at home. They predicted 10% lower compliance for people asked to shelter at the office or to evacuate their homes. They predicted 10% lower compliance should the media be skeptical, rather than supportive. They also identified preparatory policies that could improve public compliance by 20-30%. We consider the implications of these results for improving emergency risk assessment models and for anticipating and improving preparedness for disasters, using Hurricane Katrina as a further case in point. PMID:17184405

  10. 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

  11. Predicting Gene Structures from Multiple RT-PCR Tests

    NASA Astrophysics Data System (ADS)

    Kováč, Jakub; Vinař, Tomáš; Brejová, Broňa

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

  12. Probabilistic predictions of penetrating injury to anatomic structures.

    PubMed Central

    Ogunyemi, O.; Webber, B.; Clarke, J. R.

    1997-01-01

    This paper presents an interactive 3D graphical system which allows the user to visualize different bullet path hypotheses and stab wound paths and computes the probability that an anatomical structure associated with a given penetration path is injured. Probabilities can help to identify those anatomical structures which have potentially critical damage from penetrating trauma and differentiate these from structures that are not seriously injured. Images Figure 3 Figure 4 PMID:9357718

  13. Computational Approaches to RNA Structure Prediction, Analysis and Design

    PubMed Central

    Laing, Christian; Schlick, Tamar

    2011-01-01

    RNA molecules are important cellular components involved in many fundamental biological processes. Understanding the mechanisms behind their functions requires RNA tertiary structure knowledge. While modeling approaches for the study of RNA structures and dynamics lag behind efforts in protein folding, much progress has been achieved in the past two years. Here, we review recent advances in RNA folding algorithms, RNA tertiary motif discovery, applications of graph theory approaches to RNA structure and function, and in silico generation of RNA sequence pools for aptamer design. Advances within each area can be combined to impact many problems in RNA structure and function. PMID:21514143

  14. 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.

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

    NASA Astrophysics Data System (ADS)

    Cronin, Mark T. D.

    There is a great need to assess the harmful effects of chemicals to which man is exposed. Various in silico techniques including chemical grouping and category formation, as well as the use of (Q)SARs can be applied to predict the toxicity of chemicals for a number of toxicological effects. This chapter provides an overview of the state of the art of the prediction of the harmful effects of chemicals to human health. A variety of existing data can be used to obtain information; many such data are formalized into freely available and commercial databases. (Q)SARs can be developed (as illustrated with reference to skin sensitization) for local and global data sets. In addition, chemical grouping techniques can be applied on "similar" chemicals to allow for read-across predictions. Many "expert systems" are now available that incorporate these approaches. With these in silico approaches available, the techniques to apply them successfully have become essential. Integration of different in silico approaches with each other, as well as with other alternative approaches, e.g., in vitro and -omics through the development of integrated testing strategies, will assist in the more efficient prediction of the harmful health effects of chemicals

  16. Superfamily Assignments for the Yeast Proteome through Integration of Structure Prediction with the Gene Ontology

    PubMed Central

    Malmström, Lars; Riffle, Michael; Strauss, Charlie E. M; Chivian, Dylan; Davis, Trisha N; Bonneau, Richard; Baker, David

    2007-01-01

    Saccharomyces cerevisiae is one of the best-studied model organisms, yet the three-dimensional structure and molecular function of many yeast proteins remain unknown. Yeast proteins were parsed into 14,934 domains, and those lacking sequence similarity to proteins of known structure were folded using the Rosetta de novo structure prediction method on the World Community Grid. This structural data was integrated with process, component, and function annotations from the Saccharomyces Genome Database to assign yeast protein domains to SCOP superfamilies using a simple Bayesian approach. We have predicted the structure of 3,338 putative domains and assigned SCOP superfamily annotations to 581 of them. We have also assigned structural annotations to 7,094 predicted domains based on fold recognition and homology modeling methods. The domain predictions and structural information are available in an online database at http://rd.plos.org/10.1371_journal.pbio.0050076_01. PMID:17373854

  17. Predicting human resting-state functional connectivity from structural connectivity

    PubMed Central

    Honey, C. J.; Sporns, O.; Cammoun, L.; Gigandet, X.; Thiran, J. P.; Meuli, R.; Hagmann, P.

    2009-01-01

    In the cerebral cortex, the activity levels of neuronal populations are continuously fluctuating. When neuronal activity, as measured using functional MRI (fMRI), is temporally coherent across 2 populations, those populations are said to be functionally connected. Functional connectivity has previously been shown to correlate with structural (anatomical) connectivity patterns at an aggregate level. In the present study we investigate, with the aid of computational modeling, whether systems-level properties of functional networksincluding their spatial statistics and their persistence across timecan be accounted for by properties of the underlying anatomical network. We measured resting state functional connectivity (using fMRI) and structural connectivity (using diffusion spectrum imaging tractography) in the same individuals at high resolution. Structural connectivity then provided the couplings for a model of macroscopic cortical dynamics. In both model and data, we observed (i) that strong functional connections commonly exist between regions with no direct structural connection, rendering the inference of structural connectivity from functional connectivity impractical; (ii) that indirect connections and interregional distance accounted for some of the variance in functional connectivity that was unexplained by direct structural connectivity; and (iii) that resting-state functional connectivity exhibits variability within and across both scanning sessions and model runs. These empirical and modeling results demonstrate that although resting state functional connectivity is variable and is frequently present between regions without direct structural linkage, its strength, persistence, and spatial statistics are nevertheless constrained by the large-scale anatomical structure of the human cerebral cortex. PMID:19188601

  18. 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.

  19. Automated 3D RNA structure prediction using the RNAComposer method for riboswitches.

    PubMed

    Purzycka, K J; Popenda, M; Szachniuk, M; Antczak, M; Lukasiak, P; Blazewicz, J; Adamiak, R W

    2015-01-01

    Understanding the numerous functions of RNAs depends critically on the knowledge of their three-dimensional (3D) structure. In contrast to the protein field, a much smaller number of RNA 3D structures have been assessed using X-ray crystallography, NMR spectroscopy, and cryomicroscopy. This has led to a great demand to obtain the RNA 3D structures using prediction methods. The 3D structure prediction, especially of large RNAs, still remains a significant challenge and there is still a great demand for high-resolution structure prediction methods. In this chapter, we describe RNAComposer, a method and server for the automated prediction of RNA 3D structures based on the knowledge of secondary structure. Its applications are supported by other automated servers: RNA FRABASE and RNApdbee, developed to search and analyze secondary and 3D structures. Another method, RNAlyzer, offers new way to analyze and visualize quality of RNA 3D models. Scope and limitations of RNAComposer in application for an automated prediction of riboswitches' 3D structure will be presented and discussed. Analysis of the cyclic di-GMP-II riboswitch from Clostridium acetobutylicum (PDB ID 3Q3Z) as an example allows for 3D structure prediction of related riboswitches from Clostridium difficile 4, Bacillus halodurans 1, and Thermus aquaticus Y5.1 of yet unknown structures. PMID:25726459

  20. Vfold: A Web Server for RNA Structure and Folding Thermodynamics Prediction

    PubMed Central

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

    2014-01-01

    Background The ever increasing discovery of non-coding RNAs leads to unprecedented demand for the accurate modeling of RNA folding, including the predictions of two-dimensional (base pair) and three-dimensional all-atom structures and folding stabilities. Accurate modeling of RNA structure and stability has far-reaching impact on our understanding of RNA functions in human health and our ability to design RNA-based therapeutic strategies. Results The Vfold server offers a web interface to predict (a) RNA two-dimensional structure from the nucleotide sequence, (b) three-dimensional structure from the two-dimensional structure and the sequence, and (c) folding thermodynamics (heat capacity melting curve) from the sequence. To predict the two-dimensional structure (base pairs), the server generates an ensemble of structures, including loop structures with the different intra-loop mismatches, and evaluates the free energies using the experimental parameters for the base stacks and the loop entropy parameters given by a coarse-grained RNA folding model (the Vfold model) for the loops. To predict the three-dimensional structure, the server assembles the motif scaffolds using structure templates extracted from the known PDB structures and refines the structure using all-atom energy minimization. Conclusions The Vfold-based web server provides a user friendly tool for the prediction of RNA structure and stability. The web server and the source codes are freely accessible for public use at “http://rna.physics.missouri.edu”. PMID:25215508

  1. Principles for Predicting RNA Secondary Structure Design Difficulty.

    PubMed

    Anderson-Lee, Jeff; Fisker, Eli; Kosaraju, Vineet; Wu, Michelle; Kong, Justin; Lee, Jeehyung; Lee, Minjae; Zada, Mathew; Treuille, Adrien; Das, Rhiju

    2016-02-27

    Designing RNAs that form specific secondary structures is enabling better understanding and control of living systems through RNA-guided silencing, genome editing and protein organization. Little is known, however, about which RNA secondary structures might be tractable for downstream sequence design, increasing the time and expense of design efforts due to inefficient secondary structure choices. Here, we present insights into specific structural features that increase the difficulty of finding sequences that fold into a target RNA secondary structure, summarizing the design efforts of tens of thousands of human participants and three automated algorithms (RNAInverse, INFO-RNA and RNA-SSD) in the Eterna massive open laboratory. Subsequent tests through three independent RNA design algorithms (NUPACK, DSS-Opt and MODENA) confirmed the hypothesized importance of several features in determining design difficulty, including sequence length, mean stem length, symmetry and specific difficult-to-design motifs such as zigzags. Based on these results, we have compiled an Eterna100 benchmark of 100 secondary structure design challenges that span a large range in design difficulty to help test future efforts. Our in silico results suggest new routes for improving computational RNA design methods and for extending these insights to assess "designability" of single RNA structures, as well as of switches for in vitro and in vivo applications. PMID:26902426

  2. Statistical potential for assessment and prediction of protein structures.

    PubMed

    Shen, Min-Yi; Sali, Andrej

    2006-11-01

    Protein structures in the Protein Data Bank provide a wealth of data about the interactions that determine the native states of proteins. Using the probability theory, we derive an atomic distance-dependent statistical potential from a sample of native structures that does not depend on any adjustable parameters (Discrete Optimized Protein Energy, or DOPE). DOPE is based on an improved reference state that corresponds to noninteracting atoms in a homogeneous sphere with the radius dependent on a sample native structure; it thus accounts for the finite and spherical shape of the native structures. The DOPE potential was extracted from a nonredundant set of 1472 crystallographic structures. We tested DOPE and five other scoring functions by the detection of the native state among six multiple target decoy sets, the correlation between the score and model error, and the identification of the most accurate non-native structure in the decoy set. For all decoy sets, DOPE is the best performing function in terms of all criteria, except for a tie in one criterion for one decoy set. To facilitate its use in various applications, such as model assessment, loop modeling, and fitting into cryo-electron microscopy mass density maps combined with comparative protein structure modeling, DOPE was incorporated into the modeling package MODELLER-8. PMID:17075131

  3. Titanium ? -? phase transformation pathway and a predicted metastable structure

    NASA Astrophysics Data System (ADS)

    Zarkevich, N. A.; Johnson, D. D.

    2016-01-01

    As titanium is a highly utilized metal for structural lightweighting, its phases, transformation pathways (transition states), and structures have scientific and industrial importance. Using a proper solid-state nudged elastic band method employing two climbing images combined with density functional theory DFT + U methods for accurate energetics, we detail the pressure-induced ? (ductile) to ? (brittle) transformation at the coexistence pressure. We find two transition states along the minimal-enthalpy path and discover a metastable body-centered orthorhombic structure, with stable phonons, a lower density than the end-point phases, and decreasing stability with increasing pressure.

  4. 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; Rhle, 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.

  5. μABC: 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; Petkevičiūtė, Daiva; Spackova, Nada; Sponer, Jiri; Zakrzewska, Krystyna; Lavery, Richard

    2014-01-01

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

  6. 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

  7. A method for WD40 repeat detection and secondary structure prediction.

    PubMed

    Wang, Yang; Jiang, Fan; Zhuo, Zhu; Wu, Xian-Hui; Wu, Yun-Dong

    2013-01-01

    WD40-repeat proteins (WD40s), as one of the largest protein families in eukaryotes, play vital roles in assembling protein-protein/DNA/RNA complexes. WD40s fold into similar β-propeller structures despite diversified sequences. A program WDSP (WD40 repeat protein Structure Predictor) has been developed to accurately identify WD40 repeats and predict their secondary structures. The method is designed specifically for WD40 proteins by incorporating both local residue information and non-local family-specific structural features. It overcomes the problem of highly diversified protein sequences and variable loops. In addition, WDSP achieves a better prediction in identifying multiple WD40-domain proteins by taking the global combination of repeats into consideration. In secondary structure prediction, the average Q3 accuracy of WDSP in jack-knife test reaches 93.7%. A disease related protein LRRK2 was used as a representive example to demonstrate the structure prediction. PMID:23776530

  8. 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

  9. 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.

  10. 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

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

    PubMed Central

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

    2015-01-01

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

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

    PubMed

    Miao, Zhichao; Adamiak, Ryszard W; Blanchet, Marc-Frdrick; Boniecki, Michal; Bujnicki, Janusz M; Chen, Shi-Jie; Cheng, Clarence; Chojnowski, Grzegorz; Chou, Fang-Chieh; Cordero, Pablo; Cruz, Jos Almeida; Ferr-D'Amar, Adrian R; Das, Rhiju; Ding, Feng; Dokholyan, Nikolay V; Dunin-Horkawicz, Stanislaw; Kladwang, Wipapat; Krokhotin, Andrey; Lach, Grzegorz; Magnus, Marcin; Major, Franois; Mann, Thomas H; Masquida, Benot; Matelska, Dorota; Meyer, Mlanie; 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 , 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

  13. 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

  14. 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.

  15. Prediction of protein structural class using novel evolutionary collocation-based sequence representation.

    PubMed

    Chen, Ke; Kurgan, Lukasz A; Ruan, Jishou

    2008-07-30

    Knowledge of structural classes is useful in understanding of folding patterns in proteins. Although existing structural class prediction methods applied virtually all state-of-the-art classifiers, many of them use a relatively simple protein sequence representation that often includes amino acid (AA) composition. To this end, we propose a novel sequence representation that incorporates evolutionary information encoded using PSI-BLAST profile-based collocation of AA pairs. We used six benchmark datasets and five representative classifiers to quantify and compare the quality of the structural class prediction with the proposed representation. The best, classifier support vector machine achieved 61-96% accuracy on the six datasets. These predictions were comprehensively compared with a wide range of recently proposed methods for prediction of structural classes. Our comprehensive comparison shows superiority of the proposed representation, which results in error rate reductions that range between 14% and 26% when compared with predictions of the best-performing, previously published classifiers on the considered datasets. The study also shows that, for the benchmark dataset that includes sequences characterized by low identity (i.e., 25%, 30%, and 40%), the prediction accuracies are 20-35% lower than for the other three datasets that include sequences with a higher degree of similarity. In conclusion, the proposed representation is shown to substantially improve the accuracy of the structural class prediction. A web server that implements the presented prediction method is freely available at http://biomine.ece.ualberta.ca/Structural_Class/SCEC.html. PMID:18293306

  16. 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

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

    PubMed

    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

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

    PubMed Central

    Seeliger, Daniel; de Groot, Bert L.

    2010-01-01

    Biological function of proteins is frequently associated with the formation of complexes with small-molecule ligands. Experimental structure determination of such complexes at atomic resolution, however, can be time-consuming and costly. Computational methods for structure prediction of protein/ligand complexes, particularly docking, are as yet restricted by their limited consideration of receptor flexibility, rendering them not applicable for predicting protein/ligand complexes if large conformational changes of the receptor upon ligand binding are involved. Accurate receptor models in the ligand-bound state (holo structures), however, are a prerequisite for successful structure-based drug design. Hence, if only an unbound (apo) structure is available distinct from the ligand-bound conformation, structure-based drug design is severely limited. We present a method to predict the structure of protein/ligand complexes based solely on the apo structure, the ligand and the radius of gyration of the holo structure. The method is applied to ten cases in which proteins undergo structural rearrangements of up to 7.1 backbone RMSD upon ligand binding. In all cases, receptor models within 1.6 backbone RMSD to the target were predicted and close-to-native ligand binding poses were obtained for 8 of 10 cases in the top-ranked complex models. A protocol is presented that is expected to enable structure modeling of protein/ligand complexes and structure-based drug design for cases where crystal structures of ligand-bound conformations are not available. PMID:20066034

  19. 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.

  20. Memoir: template-based structure prediction for membrane proteins.

    PubMed

    Ebejer, Jean-Paul; Hill, Jamie R; Kelm, Sebastian; Shi, Jiye; Deane, Charlotte M

    2013-07-01

    Membrane proteins are estimated to be the targets of 50% of drugs that are currently in development, yet we have few membrane protein crystal structures. As a result, for a membrane protein of interest, the much-needed structural information usually comes from a homology model. Current homology modelling software is optimized for globular proteins, and ignores the constraints that the membrane is known to place on protein structure. Our Memoir server produces homology models using alignment and coordinate generation software that has been designed specifically for transmembrane proteins. Memoir is easy to use, with the only inputs being a structural template and the sequence that is to be modelled. We provide a video tutorial and a guide to assessing model quality. Supporting data aid manual refinement of the models. These data include a set of alternative conformations for each modelled loop, and a multiple sequence alignment that incorporates the query and template. Memoir works with both α-helical and β-barrel types of membrane proteins and is freely available at http://opig.stats.ox.ac.uk/webapps/memoir. PMID:23640332

  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. A Structural Equation Model for Predicting Business Student Performance

    ERIC Educational Resources Information Center

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

    2008-01-01

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

  3. Acoustic fatigue life prediction for non-linear structures

    NASA Technical Reports Server (NTRS)

    Sun, J. Q.; Miles, R. N.

    1991-01-01

    Using an approach based on a time domain analysis, a method of equivalent linearization is applied for an estimation of the strain response of complex nonlinear structures having nearly arbitrary complexity. Fatigue lives estimated for a nonlinear beam with random excitation using the approximate method were compared with results obtained using a conventional numerical simulation, yielding nearly identical results.

  4. 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.

  5. 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."

  6. Protein structure prediction by global optimization of a potential energy function

    PubMed Central

    Liwo, Adam; Lee, Jooyoung; Ripoll, Daniel R.; Pillardy, Jaroslaw; Scheraga, Harold A.

    1999-01-01

    An approach based exclusively on finding the global minimum of an appropriate potential energy function has been used to predict the unknown structures of five globular proteins with sizes ranging from 89 to 140 amino acid residues. Comparison of the computed lowest-energy structures of two of them (HDEA and MarA) with the crystal structures, released by the Protein Data Bank after the predictions were made, shows that large fragments (61 residues) of both proteins were predicted with rms deviations of 4.2 and 6.0 ? for the C? atoms, for HDEA and MarA, respectively. This represents 80% and 53% of the observed structures of HDEA and MarA, respectively. Similar rms deviations were obtained for ?60-residue fragments of the other three proteins. These results constitute an important step toward the prediction of protein structure based solely on global optimization of a potential energy function for a given amino acid sequence. PMID:10318909

  7. 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.

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

    NASA Astrophysics Data System (ADS)

    Suzuki, Tomonori; Miyazaki, Satoru

    2011-01-01

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

  9. 3D protein structure prediction of influenza A virus based on optimization genetic algorithm.

    PubMed

    Gao, Jie; Jin, Pei-Xuan; Xu, Hong-xing

    2014-05-01

    The 3D structure of close polymer is constituted by the interaction of close contact couples among amino acid residues. In this paper, 3D protein structure of influenza A virus was predicted. Twenty kinds of amino acid residues were divided into four categories according to the number of close contact couples. The stable structure with minimum energy was obtained by using optimization genetic algorithm. The HNXP 3D lattice model was established to predict the 3D protein structure. It can be concluded that the two kinds of structures are significantly similar by computing the similarity. PMID:24816711

  10. 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.

  11. Structure of evaporating and combusting sprays: measurements and predictions

    SciTech Connect

    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.

  12. 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.

  13. 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.

  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. 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.

  16. Structure of Evaporating and Combusting Sprays: Measurements and Predictions

    NASA Technical Reports Server (NTRS)

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

    1983-01-01

    Complete measurements of the structure of nonevaporating, evaporating and combusting sprays for sufficiently well defined boundary conditions to allow evaluation of models of these processes were obtained. The development of rational design methods for aircraft combustion chambers and other devices involving spray combustion were investigated. Three methods for treating the discrete phase are being considered: a locally homogeneous flow (LHF) model, a deterministic separated flow (DSF) model, and a stochastic separated flow (SSF) model. The main properties of these models are summarized.

  17. Design of ?-amyloid aggregation inhibitors from a predicted structural motif

    PubMed Central

    Novick, Paul A.; Lopes, Dahabada H.; Branson, Kim M.; Esteras-Chopo, Alexandra; Graef, Isabella A.; Bitan, Gal; Pande, Vijay S.

    2012-01-01

    Drug design studies targeting one of the primary toxic agents in Alzheimers Disease, soluble oligomers of amyloid ?-protein (A?i), have been complicated by the rapid, heterogeneous aggregation of A? and the resulting difficulty to structurally characterize the peptide. To address this, we have developed [Nle35, D-Pro37]A?42, a substituted peptide inspired from molecular dynamics simulations which forms structures stable enough to be analyzed by NMR. We report herein that [Nle35, D-Pro37]A?42 stabilizes the trimer, and prevents mature fibril and ?-sheet formation. Further, [Nle35, D-Pro37]A?42 interacts with WT A?42 and reduces aggregation levels and fibril formation in mixtures. Using ligand-based drug design based on [Nle35, D-Pro37]A?42, a lead compound was identified with effects on inhibition similar to the peptide. The ability of [Nle35, D-Pro37]A?42 and the compound to inhibit the aggregation of A?42 provides a novel tool to study the structure of A? oligomers. More broadly, our data demonstrate how molecular dynamics simulation can guide experiment for further research into AD. PMID:22420626

  18. 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.

  19. 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

  20. 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

  1. Genomic-scale comparison of sequence- and structure-based methods of function prediction: Does structure provide additional insight?

    PubMed Central

    Fetrow, Jacquelyn S.; Siew, Naomi; Di Gennaro, Jeannine A.; Martinez-Yamout, Maria; Dyson, H. Jane; Skolnick, Jeffrey

    2001-01-01

    A function annotation method using the sequence-to-structure-to-function paradigm is applied to the identification of all disulfide oxidoreductases in the Saccharomyces cerevisiae genome. The method identifies 27 sequences as potential disulfide oxidoreductases. All previously known thioredoxins, glutaredoxins, and disulfide isomerases are correctly identified. Three of the 27 predictions are probable false-positives. Three novel predictions, which subsequently have been experimentally validated, are presented. Two additional novel predictions suggest a disulfide oxidoreductase regulatory mechanism for two subunits (OST3 and OST6) of the yeast oligosaccharyltransferase complex. Based on homology, this prediction can be extended to a potential tumor suppressor gene, N33, in humans, whose biochemical function was not previously known. Attempts to obtain a folded, active N33 construct to test the prediction were unsuccessful. The results show that structure prediction coupled with biochemically relevant structural motifs is a powerful method for the function annotation of genome sequences and can provide more detailed, robust predictions than function prediction methods that rely on sequence comparison alone. PMID:11316881

  2. STRUCTURE BASED PREDICTIVE MODEL FOR COAL CHAR COMBUSTION

    SciTech Connect

    Robert Hurt; Joseph Calo; Robert Essenhigh; Christopher Hadad

    2001-06-15

    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, the project was in a period no-cost extension and discussions were held about the end phase of the project and possible continuations. The technical tasks were essentially dormant this period, but presentations of results were made, and plans were formulated for renewed activity in the fiscal year 2001.

  3. RNA secondary structure prediction from sequence alignments using a network of k-nearest neighbor classifiers.

    PubMed

    Bindewald, Eckart; Shapiro, Bruce A

    2006-03-01

    We present a machine learning method (a hierarchical network of k-nearest neighbor classifiers) that uses an RNA sequence alignment in order to predict a consensus RNA secondary structure. The input to the network is the mutual information, the fraction of complementary nucleotides, and a novel consensus RNAfold secondary structure prediction of a pair of alignment columns and its nearest neighbors. Given this input, the network computes a prediction as to whether a particular pair of alignment columns corresponds to a base pair. By using a comprehensive test set of 49 RFAM alignments, the program KNetFold achieves an average Matthews correlation coefficient of 0.81. This is a significant improvement compared with the secondary structure prediction methods PFOLD and RNAalifold. By using the example of archaeal RNase P, we show that the program can also predict pseudoknot interactions. PMID:16495232

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

    PubMed

    Haselhuhn, Michael P; Wong, Elaine M

    2012-02-01

    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

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

    PubMed Central

    Haselhuhn, Michael P.; Wong, Elaine M.

    2012-01-01

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

  6. 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.

  7. Combining Evolutionary Information and an Iterative Sampling Strategy for Accurate Protein Structure Prediction

    PubMed Central

    Braun, Tatjana; Koehler Leman, Julia; Lange, Oliver F.

    2015-01-01

    Recent work has shown that the accuracy of ab initio structure prediction can be significantly improved by integrating evolutionary information in form of intra-protein residue-residue contacts. Following this seminal result, much effort is put into the improvement of contact predictions. However, there is also a substantial need to develop structure prediction protocols tailored to the type of restraints gained by contact predictions. Here, we present a structure prediction protocol that combines evolutionary information with the resolution-adapted structural recombination approach of Rosetta, called RASREC. Compared to the classic Rosetta ab initio protocol, RASREC achieves improved sampling, better convergence and higher robustness against incorrect distance restraints, making it the ideal sampling strategy for the stated problem. To demonstrate the accuracy of our protocol, we tested the approach on a diverse set of 28 globular proteins. Our method is able to converge for 26 out of the 28 targets and improves the average TM-score of the entire benchmark set from 0.55 to 0.72 when compared to the top ranked models obtained by the EVFold web server using identical contact predictions. Using a smaller benchmark, we furthermore show that the prediction accuracy of our method is only slightly reduced when the contact prediction accuracy is comparatively low. This observation is of special interest for protein sequences that only have a limited number of homologs. PMID:26713437

  8. 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

  9. A Pascal microcomputer program for prediction of protein secondary structure and hydropathic segments.

    PubMed

    Parrilla, A; Domnech, A; Querol, E

    1986-09-01

    This paper describes a simple Pascal microcomputer program for prediction of protein secondary structure according to the Chou and Fasman algorithm. In addition, it performs an analysis of the hydropathic character of the residues for prediction of external/internal regions of the polypeptide chain. Also it searches for probable glycosylation and phosphorylation sites. PMID:3507244

  10. Web applet for predicting structure and thermodynamics of complex fluids

    NASA Astrophysics Data System (ADS)

    Popp, Theodore R.; Hollingshead, Kyle B.; Truskett, Thomas M.

    2015-03-01

    Based on a recently introduced analytical strategy [Hollingshead et al., J. Chem. Phys. 139, 161102 (2013)], we present a web applet that can quickly and semi-quantitatively estimate the equilibrium radial distribution function and related thermodynamic properties of a fluid from knowledge of its pair interaction. We describe the applet's features and present two (of many possible) examples of how it can be used to illustrate concepts of interest for introductory statistical mechanics courses: the transition from ideal gas-like behavior to correlated-liquid behavior with increasing density, and the tradeoff between dominant length scales with changing temperature in a system with ramp-shaped repulsions. The latter type of interaction qualitatively captures distinctive thermodynamic properties of liquid water, because its energetic bias toward locally open structures mimics that of water's hydrogen-bond network.

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

    NASA Technical Reports Server (NTRS)

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

    1990-01-01

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

  12. 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.

  13. 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.

  14. Predicting protein-protein interface residues using local surface structural similarity

    PubMed Central

    2012-01-01

    Background Identification of the residues in protein-protein interaction sites has a significant impact in problems such as drug discovery. Motivated by the observation that the set of interface residues of a protein tend to be conserved even among remote structural homologs, we introduce PrISE, a family of local structural similarity-based computational methods for predicting protein-protein interface residues. Results We present a novel representation of the surface residues of a protein in the form of structural elements. Each structural element consists of a central residue and its surface neighbors. The PrISE family of interface prediction methods uses a representation of structural elements that captures the atomic composition and accessible surface area of the residues that make up each structural element. Each of the members of the PrISE methods identifies for each structural element in the query protein, a collection of similar structural elements in its repository of structural elements and weights them according to their similarity with the structural element of the query protein. PrISEL relies on the similarity between structural elements (i.e. local structural similarity). PrISEG relies on the similarity between protein surfaces (i.e. general structural similarity). PrISEC, combines local structural similarity and general structural similarity to predict interface residues. These predictors label the central residue of a structural element in a query protein as an interface residue if a weighted majority of the structural elements that are similar to it are interface residues, and as a non-interface residue otherwise. The results of our experiments using three representative benchmark datasets show that the PrISEC outperforms PrISEL and PrISEG; and that PrISEC is highly competitive with state-of-the-art structure-based methods for predicting protein-protein interface residues. Our comparison of PrISEC with PredUs, a recently developed method for predicting interface residues of a query protein based on the known interface residues of its (global) structural homologs, shows that performance superior or comparable to that of PredUs can be obtained using only local surface structural similarity. PrISEC is available as a Web server at http://prise.cs.iastate.edu/ Conclusions Local surface structural similarity based methods offer a simple, efficient, and effective approach to predict protein-protein interface residues. PMID:22424103

  15. Predicting multi-wall structural response to hypervelocity impact using the hull code

    NASA Technical Reports Server (NTRS)

    Schonberg, William P.

    1993-01-01

    Previously, multi-wall structures have been analyzed extensively, primarily through experiment, as a means of increasing the meteoroid/space debris impact protection of spacecraft. As structural configurations become more varied, the number of tests required to characterize their response increases dramatically. As an alternative to experimental testing, numerical modeling of high-speed impact phenomena is often being used to predict the response of a variety of structural systems under different impact loading conditions. The results of comparing experimental tests to Hull Hydrodynamic Computer Code predictions are reported. Also, the results of a numerical parametric study of multi-wall structural response to hypervelocity cylindrical projectile impact are presented.

  16. Prediction of structural response to large earthquakes by using recordings from smaller earthquakes

    USGS Publications Warehouse

    Safak, Erdal

    1994-01-01

    The feasibility of predicting structural response to large earthquakes by using recorded responses from collocated smaller earthquakes is investigated. Records from large earthquakes can be approximated as linear combinations of records from smaller earthquakes. Two methods are introduced to predict structural response to a large earthquake by using the recorded response to a smaller earthquake. The accuracy of the methods are tested by applying them to data from a highway overpass.

  17. Synconset waves and chains: spiking onsets in synchronous populations predict and are predicted by network structure.

    PubMed

    Raghavan, Mohan; Amrutur, Bharadwaj; Narayanan, Rishikesh; Sikdar, Sujit Kumar

    2013-01-01

    Synfire waves are propagating spike packets in synfire chains, which are feedforward chains embedded in random networks. Although synfire waves have proved to be effective quantification for network activity with clear relations to network structure, their utilities are largely limited to feedforward networks with low background activity. To overcome these shortcomings, we describe a novel generalisation of synfire waves, and define 'synconset wave' as a cascade of first spikes within a synchronisation event. Synconset waves would occur in 'synconset chains', which are feedforward chains embedded in possibly heavily recurrent networks with heavy background activity. We probed the utility of synconset waves using simulation of single compartment neuron network models with biophysically realistic conductances, and demonstrated that the spread of synconset waves directly follows from the network connectivity matrix and is modulated by top-down inputs and the resultant oscillations. Such synconset profiles lend intuitive insights into network organisation in terms of connection probabilities between various network regions rather than an adjacency matrix. To test this intuition, we develop a Bayesian likelihood function that quantifies the probability that an observed synfire wave was caused by a given network. Further, we demonstrate it's utility in the inverse problem of identifying the network that caused a given synfire wave. This method was effective even in highly subsampled networks where only a small subset of neurons were accessible, thus showing it's utility in experimental estimation of connectomes in real neuronal-networks. Together, we propose synconset chains/waves as an effective framework for understanding the impact of network structure on function, and as a step towards developing physiology-driven network identification methods. Finally, as synconset chains extend the utilities of synfire chains to arbitrary networks, we suggest utilities of our framework to several aspects of network physiology including cell assemblies, population codes, and oscillatory synchrony. PMID:24116018

  18. 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.

  19. STRUCTURE-BASED PREDICTIVE MODEL FOR COAL CHAR COMBUSTION

    SciTech Connect

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

    1999-01-13

    Significant progress continued to be made during the past reporting quarter on both major technical tasks. During the reporting period at OSU, computational investigations were conducted of addition vs. abstraction reactions of H, O(3 P), and OH with monocyclic aromatic hydrocarbons. The potential energy surface for more than 80 unique reactions of H, O ( 3 P), and OH with aromatic hydrocarbons were determined at the B3LYP/6-31G(d) level of theory. The calculated transition state barriers and reaction free energies indicate that the addition channel is preferred at 298K, but that the abstraction channel becomes dominant at high temperatures. The thermodynamic preference for reactivity with aromatic hydrocarbons increases in the order O(3 P) < H < OH. Abstraction from six-membered aromatic rings is more facile than abstraction from five-membered aromatic rings. However, addition to five-membered rings is thermodynamically more favorable than addition to six-membered rings. The free energies for the abstraction and addition reactions of H, O, and OH with aromatic hydrocarbons and the characteristics of the respective transition states can be used to calculate the reaction rate constants for these important combustion reactions. Experimental work at Brown University on the effect of reaction on the structural evolution of different chars (i.e., phenolic resin char and chars produced from three different coals) have been investigated in a TGA/TPD-MS system. It has been found that samples of different age of these chars appeared to lose their "memory" concerning their initial structures at high burn-offs. During the reporting period, thermal desorption experiments of selected samples were conducted. These spectra show that the population of low temperature oxygen surface complexes, which are primarily responsible for reactivity, are more similar for the high burn-off than for the low burn-off samples of different ages; i.e., the population of active sites are more similar for the ?younger? and ?older? chars at high burn-offs. Progress continued on experimental work at OSU. Another furnace run was conducted with a Pittsburgh seam coal. Temperature profiles were obtained, as well as char samples from three sampling ports. Nonisothermal TGA reactivities were also obtained for these samples. Work is continuing on final ?fine-tuning? of the gas analysis section.

  20. Evaluation of several lightweight stochastic context-free grammars for RNA secondary structure prediction

    PubMed Central

    Dowell, Robin D; Eddy, Sean R

    2004-01-01

    Background RNA secondary structure prediction methods based on probabilistic modeling can be developed using stochastic context-free grammars (SCFGs). Such methods can readily combine different sources of information that can be expressed probabilistically, such as an evolutionary model of comparative RNA sequence analysis and a biophysical model of structure plausibility. However, the number of free parameters in an integrated model for consensus RNA structure prediction can become untenable if the underlying SCFG design is too complex. Thus a key question is, what small, simple SCFG designs perform best for RNA secondary structure prediction? Results Nine different small SCFGs were implemented to explore the tradeoffs between model complexity and prediction accuracy. Each model was tested for single sequence structure prediction accuracy on a benchmark set of RNA secondary structures. Conclusions Four SCFG designs had prediction accuracies near the performance of current energy minimization programs. One of these designs, introduced by Knudsen and Hein in their PFOLD algorithm, has only 21 free parameters and is significantly simpler than the others. PMID:15180907

  1. Predicting structure and stability for RNA complexes with intermolecular loop-loop base-pairing.

    PubMed

    Cao, Song; Xu, Xiaojun; Chen, Shi-Jie

    2014-06-01

    RNA loop-loop interactions are essential for genomic RNA dimerization and regulation of gene expression. In this article, a statistical mechanics-based computational method that predicts the structures and thermodynamic stabilities of RNA complexes with loop-loop kissing interactions is described. The method accounts for the entropy changes for the formation of loop-loop interactions, which is a notable advancement that other computational models have neglected. Benchmark tests with several experimentally validated systems show that the inclusion of the entropy parameters can indeed improve predictions for RNA complexes. Furthermore, the method can predict not only the native structures of RNA/RNA complexes but also alternative metastable structures. For instance, the model predicts that the SL1 domain of HIV-1 RNA can form two different dimer structures with similar stabilities. The prediction is consistent with experimental observation. In addition, the model predicts two different binding sites for hTR dimerization: One binding site has been experimentally proposed, and the other structure, which has a higher stability, is structurally feasible and needs further experimental validation. PMID:24751648

  2. Predicting structure and stability for RNA complexes with intermolecular loop–loop base-pairing

    PubMed Central

    Cao, Song; Xu, Xiaojun; Chen, Shi-Jie

    2014-01-01

    RNA loop–loop interactions are essential for genomic RNA dimerization and regulation of gene expression. In this article, a statistical mechanics-based computational method that predicts the structures and thermodynamic stabilities of RNA complexes with loop–loop kissing interactions is described. The method accounts for the entropy changes for the formation of loop–loop interactions, which is a notable advancement that other computational models have neglected. Benchmark tests with several experimentally validated systems show that the inclusion of the entropy parameters can indeed improve predictions for RNA complexes. Furthermore, the method can predict not only the native structures of RNA/RNA complexes but also alternative metastable structures. For instance, the model predicts that the SL1 domain of HIV-1 RNA can form two different dimer structures with similar stabilities. The prediction is consistent with experimental observation. In addition, the model predicts two different binding sites for hTR dimerization: One binding site has been experimentally proposed, and the other structure, which has a higher stability, is structurally feasible and needs further experimental validation. PMID:24751648

  3. Prediction of complex super-secondary structure ??? motifs based on combined features

    PubMed Central

    Sun, Lixia; Hu, Xiuzhen; Li, Shaobo; Jiang, Zhuo; Li, Kun

    2015-01-01

    Prediction of a complex super-secondary structure is a key step in the study of tertiary structures of proteins. The strand-loop-helix-loop-strand (???) motif is an important complex super-secondary structure in proteins. Many functional sites and active sites often occur in polypeptides of ??? motifs. Therefore, the accurate prediction of ??? motifs is very important to recognizing protein tertiary structure and the study of protein function. In this study, the ??? motif dataset was first constructed using the DSSP package. A statistical analysis was then performed on ??? motifs and non-??? motifs. The target motif was selected, and the length of the loop-?-loop varies from 10 to 26 amino acids. The ideal fixed-length pattern comprised 32 amino acids. A Support Vector Machine algorithm was developed for predicting ??? motifs by using the sequence information, the predicted structure and function information to express the sequence feature. The overall predictive accuracy of 5-fold cross-validation and independent test was 81.7% and 76.7%, respectively. The Matthews correlation coefficient of the 5-fold cross-validation and independent test are 0.63 and 0.53, respectively. Results demonstrate that the proposed method is an effective approach for predicting ??? motifs and can be used for structure and function studies of proteins. PMID:26858540

  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. Structural Damage Prediction and Analysis for Hypervelocity Impact

    NASA Technical Reports Server (NTRS)

    Elfer, Norman

    1995-01-01

    It is necessary to integrate a wide variety of technical disciplines to provide an analysis of structural damage to a spacecraft due to hypervelocity impact. There are many uncertainties, and more detailed investigation is warranted, in each technical discipline. However, a total picture of the debris and meteoroid hazard is required to support manned spaceflight in general, and the international Space Station in particular. In the performance of this contract, besides producing a handbook, research and development was conducted in several different areas. The contract was broken into six separate tasks. Each task objectives and accomplishments will be reviewed in the following sections. The Handbook and separate task reports are contained as attachments to the final report. The final section summarizes all of the recommendations coming out of this study. The analyses and comments are general design guidelines and not necessarily applicable to final Space Station designs since several configuration and detailed design changes were being made during the course of this contract. Rather, the analyses and comments may indicate either a point-in-time concept analysis, available test data, or desirable protection goals, not hindered by the design and operation constraints faced by Space Station designers.

  6. 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.

  7. 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.

  8. Predictability of gene ontology slim-terms from primary structure information in Embryophyta plant proteins

    PubMed Central

    2013-01-01

    Background Proteins are the key elements on the path from genetic information to the development of life. The roles played by the different proteins are difficult to uncover experimentally as this process involves complex procedures such as genetic modifications, injection of fluorescent proteins, gene knock-out methods and others. The knowledge learned from each protein is usually annotated in databases through different methods such as the proposed by The Gene Ontology (GO) consortium. Different methods have been proposed in order to predict GO terms from primary structure information, but very few are available for large-scale functional annotation of plants, and reported success rates are much less than the reported by other non-plant predictors. This paper explores the predictability of GO annotations on proteins belonging to the Embryophyta group from a set of features extracted solely from their primary amino acid sequence. Results High predictability of several GO terms was found for Molecular Function and Cellular Component. As expected, a lower degree of predictability was found on Biological Process ontology annotations, although a few biological processes were easily predicted. Proteins related to transport and transcription were particularly well predicted from primary structure information. The most discriminant features for prediction were those related to electric charges of the amino-acid sequence and hydropathicity derived features. Conclusions An analysis of GO-slim terms predictability in plants was carried out, in order to determine single categories or groups of functions that are most related with primary structure information. For each highly predictable GO term, the responsible features of such successfulness were identified and discussed. In addition to most published studies, focused on few categories or single ontologies, results in this paper comprise a complete landscape of GO predictability from primary structure encompassing 75 GO terms at molecular, cellular and phenotypical level. Thus, it provides a valuable guide for researchers interested on further advances in protein function prediction on Embryophyta plants. PMID:23441934

  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. 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, Jørgen

    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

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

    PubMed

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

    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

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

    NASA Astrophysics Data System (ADS)

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

    2014-12-01

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

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

    PubMed Central

    DeLeonardis, Eleonora; Lutz, Benjamin; Ratz, Sebastian; Cocco, Simona; Monasson, Rmi; 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

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

    PubMed

    De Leonardis, Eleonora; Lutz, Benjamin; Ratz, Sebastian; Cocco, Simona; Monasson, Rmi; 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

  15. 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

  16. 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

  17. 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 strandstrand 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

  18. 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.

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

    NASA Technical Reports Server (NTRS)

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

    1977-01-01

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

  20. Combinatorial docking approach for structure prediction of large proteins and multi-molecular assemblies

    NASA Astrophysics Data System (ADS)

    Inbar, Yuval; Benyamini, Hadar; Nussinov, Ruth; Wolfson, Haim J.

    2005-12-01

    Protein folding and protein binding are similar processes. In both, structural units combinatorially associate with each other. In the case of folding, we mostly handle relatively small units, building blocks or domains, that are covalently linked. In the case of multi-molecular binding, the subunits are relatively large and are associated only by non-covalent bonds. Experimentally, the difficulty in the determination of the structures of such large assemblies increases with the complex size and the number of components it contains. Computationally, the prediction of the structures of multi-molecular complexes has largely not been addressed, probably owing to the magnitude of the combinatorial complexity of the problem. Current docking algorithms mostly target prediction of pairwise interactions. Here our goal is to predict the structures of multi-unit associations, whether these are chain-connected as in protein folding, or separate disjoint molecules in the assemblies. We assume that the structures of the single units are known, either through experimental determination or modeling. Our aim is to combinatorially assemble these units to predict their structure. To address this problem we have developed CombDock. CombDock is a combinatorial docking algorithm for the structural units assembly problem. Below, we briefly describe the algorithm and present examples of its various applications to folding and to multi-molecular assemblies. To test the robustness of the algorithm, we use inaccurate models of the structural units, derived either from crystal structures of unbound molecules or from modeling of the target sequences. The algorithm has been able to predict near-native arrangements of the input structural units in almost all of the cases, suggesting that a combinatorial approach can overcome the imperfect shape complementarity caused by the inaccuracy of the models. In addition, we further show that through a combinatorial docking strategy it is possible to enhance the predictions of pairwise interactions involved in a multi-molecular assembly.

  1. Predicting faunal fire responses in heterogeneous landscapes: the role of habitat structure.

    PubMed

    Swan, Matthew; Christie, Fiona; Sitters, Holly; York, Alan; Di Stefano, Julian

    2015-12-01

    Predicting the effects of fire on biota is important for biodiversity conservation in fire-prone landscapes. Time since fire is often used to predict the occurrence of fauna, yet for many species, it is a surrogate variable and it is temporal change in resource availability to which animals actually respond. Therefore prediction of fire-fauna relationships will be uncertain if time since fire is not strongly related to resources. In this study, we used a space-for-time substitution across a large diverse landscape to investigate interrelationships between the occurrence of ground-dwelling mammals, time since fire, and structural resources. We predicted that much variation in habitat structure would remain unexplained by time since fire and that habitat structure would predict species' occurrence better than time since fire. In line with predictions, we found that time since fire was moderately correlated with habitat structure yet was a poor surrogate for mammal occurrence. Variables representing habitat structure were better predictors of occurrence than time since fire for all species considered. Our results suggest that time since fire is unlikely to be a useful surrogate for ground-dwelling mammals in heterogeneous landscapes. Faunal conservation in fire-prone landscapes will benefit from a combined understanding of fauna-resource relationships and the ways in which fire (including planned fires and wildfires) alters the spatial and temporal distribution of faunal resources. PMID:26910956

  2. RNA secondary structure prediction based on SHAPE data in helix regions.

    PubMed

    Lotfi, Mohadeseh; Zare-Mirakabad, Fatemeh; Montaseri, Soheila

    2015-09-01

    RNA molecules play important and fundamental roles in biological processes. Frequently, the functional form of single-stranded RNA molecules requires a specific tertiary structure. Classically, RNA structure determination has mostly been accomplished by X-Ray crystallography or Nuclear Magnetic Resonance approaches. These experimental methods are time consuming and expensive. In the past two decades, some computational methods and algorithms have been developed for RNA secondary structure prediction. In these algorithms, minimum free energy is known as the best criterion. However, the results of algorithms show that minimum free energy is not a sufficient criterion to predict RNA secondary structure. These algorithms need some additional knowledge about the structure, which has to be added in the methods. Recently, the information obtained from some experimental data, called SHAPE, can greatly improve the consistency between the native and predicted RNA secondary structure. In this paper, we investigate the influence of SHAPE data on four types of RNA substructures, helices, loops, base pairs from the start and end of helices and two base pairs from the start and end of helices. The results show that SHAPE data in helix regions can improve the prediction. We represent a new method to apply SHAPE data in helix regions for finding RNA secondary structure. Finally, we compare the results of the method on a set of RNAs to predict minimum free energy structure based on considering all SHAPE data and only SHAPE data in helix regions as pseudo free energy and without SHAPE data (without any pseudo free energy). The results show that RNA secondary structure prediction based on considering only SHAPE data in helix regions is more successful than not considering SHAPE data and it provides competitive results in comparison with considering all SHAPE data. PMID:26037307

  3. Recent improvements in prediction of protein structure by global optimization of a potential energy function

    PubMed Central

    Pillardy, Jaros?aw; Czaplewski, Cezary; Liwo, Adam; Lee, Jooyoung; Ripoll, Daniel R.; Ka?mierkiewicz, Rajmund; O?dziej, Stanis?aw; Wedemeyer, William J.; Gibson, Kenneth D.; Arnautova, Yelena A.; Saunders, Jeff; Ye, Yuan-Jie; Scheraga, Harold A.

    2001-01-01

    Recent improvements of a hierarchical ab initio or de novo approach for predicting both ? and ? structures of proteins are described. The united-residue energy function used in this procedure includes multibody interactions from a cumulant expansion of the free energy of polypeptide chains, with their relative weights determined by Z-score optimization. The critical initial stage of the hierarchical procedure involves a search of conformational space by the conformational space annealing (CSA) method, followed by optimization of an all-atom model. The procedure was assessed in a recent blind test of protein structure prediction (CASP4). The resulting lowest-energy structures of the target proteins (ranging in size from 70 to 244 residues) agreed with the experimental structures in many respects. The entire experimental structure of a cyclic ?-helical protein of 70 residues was predicted to within 4.3 ? ?-carbon (C?) rms deviation (rmsd) whereas, for other ?-helical proteins, fragments of roughly 60 residues were predicted to within 6.0 ? C? rmsd. Whereas ? structures can now be predicted with the new procedure, the success rate for ?/?- and ?-proteins is lower than that for ?-proteins at present. For the ? portions of ?/? structures, the C? rmsd's are less than 6.0 ? for contiguous fragments of 3040 residues; for one target, three fragments (of length 10, 23, and 28 residues, respectively) formed a compact part of the tertiary structure with a C? rmsd less than 6.0 ?. Overall, these results constitute an important step toward the ab initio prediction of protein structure solely from the amino acid sequence. PMID:11226239

  4. 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.

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

    PubMed Central

    Yang, Jianyi; Zhang, Yang

    2015-01-01

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

  6. A probabilistic model for secondary structure prediction from protein chemical shifts.

    PubMed

    Mechelke, Martin; Habeck, Michael

    2013-06-01

    Protein chemical shifts encode detailed structural information that is difficult and computationally costly to describe at a fundamental level. Statistical and machine learning approaches have been used to infer correlations between chemical shifts and secondary structure from experimental chemical shifts. These methods range from simple statistics such as the chemical shift index to complex methods using neural networks. Notwithstanding their higher accuracy, more complex approaches tend to obscure the relationship between secondary structure and chemical shift and often involve many parameters that need to be trained. We present hidden Markov models (HMMs) with Gaussian emission probabilities to model the dependence between protein chemical shifts and secondary structure. The continuous emission probabilities are modeled as conditional probabilities for a given amino acid and secondary structure type. Using these distributions as outputs of first- and second-order HMMs, we achieve a prediction accuracy of 82.3%, which is competitive with existing methods for predicting secondary structure from protein chemical shifts. Incorporation of sequence-based secondary structure prediction into our HMM improves the prediction accuracy to 84.0%. Our findings suggest that an HMM with correlated Gaussian distributions conditioned on the secondary structure provides an adequate generative model of chemical shifts. PMID:23292699

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

    PubMed

    Yang, Jianyi; Zhang, Yang

    2015-07-01

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

  8. Mimicking the folding pathway to improve homology-free protein structure prediction

    NASA Astrophysics Data System (ADS)

    Freed, Karl; Debartolo, Joe; Colubri, Andres; Jha, Abhishek; Fitzgerald, James; Sosnick, Tobin

    2010-03-01

    Since demonstrating that a protein's sequence encodes its structure, the prediction of structure from sequence remains an outstanding problem that impacts numerous scientific disciplines including many genome projects. By iteratively fixing secondary structure assignments of residues during Monte Carlo simulations of folding, our coarse grained model without information concerning homology or explicit side chains outperforms current homology-based secondary structure prediction methods for many proteins. The computationally rapid algorithm using only single residue (phi, psi) dihedral angle moves also generates tertiary structures of comparable accuracy to existing all-atom methods for many small proteins, particularly ones with low homology. Hence, given appropriate search strategies and scoring functions, reduced representations can be used for accurately predicting secondary structure as well as providing three-dimensional structures, thereby increasing the size of proteins approachable by homology-free methods and the accuracy of template methods whose accuracy depends on the quality of the input secondary structure. Inclusion of information from evolutionarily related sequences enhances the statistics and the accuracy of the predictions.

  9. 3DLigandSite: predicting ligand-binding sites using similar structures.

    PubMed

    Wass, Mark N; Kelley, Lawrence A; Sternberg, Michael J E

    2010-07-01

    3DLigandSite is a web server for the prediction of ligand-binding sites. It is based upon successful manual methods used in the eighth round of the Critical Assessment of techniques for protein Structure Prediction (CASP8). 3DLigandSite utilizes protein-structure prediction to provide structural models for proteins that have not been solved. Ligands bound to structures similar to the query are superimposed onto the model and used to predict the binding site. In benchmarking against the CASP8 targets 3DLigandSite obtains a Matthew's correlation co-efficient (MCC) of 0.64, and coverage and accuracy of 71 and 60%, respectively, similar results to our manual performance in CASP8. In further benchmarking using a large set of protein structures, 3DLigandSite obtains an MCC of 0.68. The web server enables users to submit either a query sequence or structure. Predictions are visually displayed via an interactive Jmol applet. 3DLigandSite is available for use at http://www.sbg.bio.ic.ac.uk/3dligandsite. PMID:20513649

  10. 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

  11. A novel method for structure-based prediction of ion channel conductance properties.

    PubMed Central

    Smart, O S; Breed, J; Smith, G R; Sansom, M S

    1997-01-01

    A rapid and easy-to-use method of predicting the conductance of an ion channel from its three-dimensional structure is presented. The method combines the pore dimensions of the channel as measured in the HOLE program with an Ohmic model of conductance. An empirically based correction factor is then applied. The method yielded good results for six experimental channel structures (none of which were included in the training set) with predictions accurate to within an average factor of 1.62 to the true values. The predictive r2 was equal to 0.90, which is indicative of a good predictive ability. The procedure is used to validate model structures of alamethicin and phospholamban. Two genuine predictions for the conductance of channels with known structure but without reported conductances are given. A modification of the procedure that calculates the expected results for the effect of the addition of nonelectrolyte polymers on conductance is set out. Results for a cholera toxin B-subunit crystal structure agree well with the measured values. The difficulty in interpreting such studies is discussed, with the conclusion that measurements on channels of known structure are required. Images FIGURE 1 FIGURE 3 FIGURE 4 FIGURE 6 FIGURE 10 PMID:9138559

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

    PubMed Central

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

    2015-01-01

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

  13. 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 ). The median rmsd of TM domains containing 75-222 residues between predicted and measured structures is 3.9 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

  14. 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.

  15. 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

  16. Towards crystal structure prediction of complex organic compounds – a report on the fifth blind test

    PubMed Central

    Bardwell, David A.; Adjiman, Claire S.; Arnautova, Yelena A.; Bartashevich, Ekaterina; Boerrigter, Stephan X. M.; Braun, Doris E.; Cruz-Cabeza, Aurora J.; Day, Graeme M.; Della Valle, Raffaele G.; Desiraju, Gautam R.; van Eijck, Bouke P.; Facelli, Julio C.; Ferraro, Marta B.; Grillo, Damian; Habgood, Matthew; Hofmann, Detlef W. M.; Hofmann, Fridolin; Jose, K. V. Jovan; Karamertzanis, Panagiotis G.; Kazantsev, Andrei V.; Kendrick, John; Kuleshova, Liudmila N.; Leusen, Frank J. J.; Maleev, Andrey V.; Misquitta, Alston J.; Mohamed, Sharmarke; Needs, Richard J.; Neumann, Marcus A.; Nikylov, Denis; Orendt, Anita M.; Pal, Rumpa; Pantelides, Constantinos C.; Pickard, Chris J.; Price, Louise S.; Price, Sarah L.; Scheraga, Harold A.; van de Streek, Jacco; Thakur, Tejender S.; Tiwari, Siddharth; Venuti, Elisabetta; Zhitkov, Ilia K.

    2011-01-01

    Following on from the success of the previous crystal structure prediction blind tests (CSP1999, CSP2001, CSP2004 and CSP2007), a fifth such collaborative project (CSP2010) was organized at the Cambridge Crystallographic Data Centre. A range of methodologies was used by the participating groups in order to evaluate the ability of the current computational methods to predict the crystal structures of the six organic molecules chosen as targets for this blind test. The first four targets, two rigid molecules, one semi-flexible molecule and a 1:1 salt, matched the criteria for the targets from CSP2007, while the last two targets belonged to two new challenging categories – a larger, much more flexible molecule and a hydrate with more than one polymorph. Each group submitted three predictions for each target it attempted. There was at least one successful prediction for each target, and two groups were able to successfully predict the structure of the large flexible molecule as their first place submission. The results show that while not as many groups successfully predicted the structures of the three smallest molecules as in CSP2007, there is now evidence that methodologies such as dispersion-corrected density functional theory (DFT-D) are able to reliably do so. The results also highlight the many challenges posed by more complex systems and show that there are still issues to be overcome. PMID:22101543

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

    NASA Astrophysics Data System (ADS)

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

    2015-12-01

    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 will further develop the model by improving the implicit-salt electrostatic potential and involving a sequence-dependent coaxial stacking potential to enable the model to simulate RNA 3D structure folding in divalent/monovalent ion solutions. As compared with the experimental data, the present model can predict 3D structures of RNA hairpins with bulge/internal loops (<77nt) from their sequences at the corresponding experimental ion conditions with an overall improved accuracy, and the model also makes reliable predictions for the flexibility of RNA hairpins with bulge loops of different length at extensive divalent/monovalent ion conditions. In addition, the model successfully predicts the stability of RNA hairpins with various loops/stems in divalent/monovalent ion solutions.

  18. 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

  19. 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

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

    PubMed Central

    2014-01-01

    Background Improving accuracy and efficiency of computational methods that predict pseudoknotted RNA secondary structures is an ongoing challenge. Existing methods based on free energy minimization tend to be very slow and are limited in the types of pseudoknots that they can predict. Incorporating known structural information can improve prediction accuracy; however, there are not many methods for prediction of pseudoknotted structures that can incorporate structural information as input. There is even less understanding of the relative robustness of these methods with respect to partial information. Results We present a new method, Iterative HFold, for pseudoknotted RNA secondary structure prediction. Iterative HFold takes as input a pseudoknot-free structure, and produces a possibly pseudoknotted structure whose energy is at least as low as that of any (density-2) pseudoknotted structure containing the input structure. Iterative HFold leverages strengths of earlier methods, namely the fast running time of HFold, a method that is based on the hierarchical folding hypothesis, and the energy parameters of HotKnots V2.0. Our experimental evaluation on a large data set shows that Iterative HFold is robust with respect to partial information, with average accuracy on pseudoknotted structures steadily increasing from roughly 54% to 79% as the user provides up to 40% of the input structure. Iterative HFold is much faster than HotKnots V2.0, while having comparable accuracy. Iterative HFold also has significantly better accuracy than IPknot on our HK-PK and IP-pk168 data sets. Conclusions Iterative HFold is a robust method for prediction of pseudoknotted RNA secondary structures, whose accuracy with more than 5% information about true pseudoknot-free structures is better than that of IPknot, and with about 35% information about true pseudoknot-free structures compares well with that of HotKnots V2.0 while being significantly faster. Iterative HFold and all data used in this work are freely available at http://www.cs.ubc.ca/~hjabbari/software.php. PMID:24884954

  1. 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.

  2. Prediction of biodegradability from chemical structure: Modeling or ready biodegradation test data

    SciTech Connect

    Loonen, H.; Lindgren, F.; Hansen, B.

    1999-08-01

    Biodegradation data were collected and evaluated for 894 substances with widely varying chemical structures. All data were determined according to the Japanese Ministry of International Trade and Industry (MITI) I test protocol. The MITI I test is a screening test for ready biodegradability and has been described by Organization for Economic Cooperation and Development (OECD) test guideline 301 C and European Union (EU) test guideline C4F. The chemicals were characterized by a set of 127 predefined structural fragments. This data set was used to develop a model for the prediction of the biodegradability of chemicals under standardized OECD and EU ready biodegradation test conditions. Partial least squares (PLS) discriminant analysis was used for the model development. The model was evaluated by means of internal cross-validation and repeated external validation. The importance of various structural fragments and fragment interactions was investigated. The most important fragments include the presence of a long alkyl chain; hydroxy, ester, and acid groups (enhancing biodegradation); and the presence of one or more aromatic rings and halogen substituents (regarding biodegradation). More than 85% of the model predictions were correct for using the complete data set. The not readily biodegradable predictions were slightly better than the readily biodegradable predictions (86 vs 84%). The average percentage of correct predictions from four external validation studies was 83%. Model optimization by including fragment interactions improve the model predicting capabilities to 89%. It can be concluded that the PLS model provides predictions of high reliability for a diverse range of chemical structures. The predictions conform to the concept of readily biodegradable (or not readily biodegradable) as defined by OECD and EU test guidelines.

  3. 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.

  4. 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

  5. Antibodies: Computer-Aided Prediction of Structure and Design of Function.

    PubMed

    Sevy, Alexander M; Meiler, Jens

    2014-12-01

    With the advent of high-throughput sequencing, and the increased availability of experimental structures of antibodies and antibody-antigen complexes, comes the improvement of computational approaches to predict the structure and design the function of antibodies and antibody-antigen complexes. While antibodies pose formidable challenges for protein structure prediction and design due to their large size and highly flexible loops in the complementarity-determining regions, they also offer exciting opportunities: the central importance of antibodies for human health results in a wealth of structural and sequence information that-as a knowledge base-can drive the modeling algorithms by limiting the conformational and sequence search space to likely regions of success. Further, efficient experimental platforms exist to test predicted antibody structure or designed antibody function, thereby leading to an iterative feedback loop between computation and experiment. We briefly review the history of computer-aided prediction of structure and design of function in the antibody field before we focus on recent methodological developments and the most exciting application examples. PMID:26104439

  6. 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

  7. 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.

  8. A Bayesian approach to improved calibration and prediction of groundwater models with structural error

    NASA Astrophysics Data System (ADS)

    Xu, Tianfang; Valocchi, Albert J.

    2015-11-01

    Numerical groundwater flow and solute transport models are usually subject to model structural error due to simplification and/or misrepresentation of the real system, which raises questions regarding the suitability of conventional least squares regression-based (LSR) calibration. We present a new framework that explicitly describes the model structural error statistically in an inductive, data-driven way. We adopt a fully Bayesian approach that integrates Gaussian process error models into the calibration, prediction, and uncertainty analysis of groundwater flow models. We test the usefulness of the fully Bayesian approach with a synthetic case study of the impact of pumping on surface-ground water interaction. We illustrate through this example that the Bayesian parameter posterior distributions differ significantly from parameters estimated by conventional LSR, which does not account for model structural error. For the latter method, parameter compensation for model structural error leads to biased, overconfident prediction under changing pumping condition. In contrast, integrating Gaussian process error models significantly reduces predictive bias and leads to prediction intervals that are more consistent with validation data. Finally, we carry out a generalized LSR recalibration step to assimilate the Bayesian prediction while preserving mass conservation and other physical constraints, using a full error covariance matrix obtained from Bayesian results. It is found that the recalibrated model achieved lower predictive bias compared to the model calibrated using conventional LSR. The results highlight the importance of explicit treatment of model structural error especially in circumstances where subsequent decision-making and risk analysis require accurate prediction and uncertainty quantification.

  9. The application of nonlinear FE analysis for capacity prediction of offshore concrete structures

    SciTech Connect

    Waagaard, K.; Askheim, D.; Johansen, H.; Egeland, S.

    1994-12-31

    Offshore concrete structures are today designed using the best available techniques. These include predictions of the force resultants in the structure using FE analysis with up to 1.5 million degrees of freedom, code checking of FE results using special dedicated post-processing software, local capacity evaluations in areas where the linear FE analysis is known to be inaccurate. Further, special analyses are carried out for prediction of response from earthquake, wave induced dynamic action and geometric non-linear action. This paper addresses the application of non-linear finite element analysis for the prediction of the capacity of reinforced concrete offshore structures in geometric complex areas and with non-conservative loading from external water pressure. The water pressure will enter the opening cracks as failure is approaching.

  10. 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

  11. PAIRpred: partner-specific prediction of interacting residues from sequence and structure.

    PubMed

    Minhas, Fayyaz ul Amir Afsar; Geiss, Brian J; Ben-Hur, Asa

    2014-07-01

    We present a novel partner-specific protein-protein interaction site prediction method called PAIRpred. Unlike most existing machine learning binding site prediction methods, PAIRpred uses information from both proteins in a protein complex to predict pairs of interacting residues from the two proteins. PAIRpred captures sequence and structure information about residue pairs through pairwise kernels that are used for training a support vector machine classifier. As a result, PAIRpred presents a more detailed model of protein binding, and offers state of the art accuracy in predicting binding sites at the protein level as well as inter-protein residue contacts at the complex level. We demonstrate PAIRpred's performance on Docking Benchmark 4.0 and recent CAPRI targets. We present a detailed performance analysis outlining the contribution of different sequence and structure features, together with a comparison to a variety of existing interface prediction techniques. We have also studied the impact of binding-associated conformational change on prediction accuracy and found PAIRpred to be more robust to such structural changes than existing schemes. As an illustration of the potential applications of PAIRpred, we provide a case study in which PAIRpred is used to analyze the nature and specificity of the interface in the interaction of human ISG15 protein with NS1 protein from influenza A virus. Python code for PAIRpred is available at http://combi.cs.colostate.edu/supplements/pairpred/. PMID:24243399

  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. 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

  14. 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.

  15. 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. MULTICOMs 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

  16. Phonon anomalies predict superconducting T(c) for AlB2-type structures.

    PubMed

    Alarco, Jose A; Talbot, Peter C; Mackinnon, Ian D R

    2015-10-14

    We show that the well-known Kohn anomaly predicts Tc for ordered AlB2-type structures. We use ab initio density functional theory to calculate phonon dispersions for Mg1-xAlxB2 compositions and identify a phonon anomaly with magnitude that predicts experimental values of Tc for all x. Key features of these anomalies correlate with the electronic structure of Mg1-xAlxB2. This approach predicts Tc for other known AlB2-type structures as well as new compositions. We predict that Mg0.5Ba0.5B2 will show Tc = 63.6 6.6 K. Other forms of the Mg1-xBaxB2 series will also be superconductors when successfully synthesised. Our calculations predict that the end-member composition, BaB2, is likely to show a Tc significantly higher than currently achieved by other diborides although an applied pressure ?16 GPa may be required to stabilise the structure. PMID:26348839

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

    PubMed Central

    Guturu, Harendra; Doxey, Andrew C.; Wenger, Aaron M.; Bejerano, Gill

    2013-01-01

    Mapping the DNA-binding preferences of transcription factor (TF) complexes is critical for deciphering the functions of cis-regulatory elements. Here, we developed a computational method that compares co-occurring motif spacings in conserved versus unconserved regions of the human genome to detect evolutionarily constrained binding sites of rigid TF complexes. Structural data were used to estimate TF complex physical plausibility, explore overlapping motif arrangements seldom tackled by non-structure-aware methods, and generate and analyse three-dimensional models of the predicted complexes bound to DNA. Using this approach, we predicted 422 physically realistic TF complex motifs at 18% false discovery rate, the majority of which (326, 77%) contain some sequence overlap between binding sites. The set of mostly novel complexes is enriched in known composite motifs, predictive of binding site configurations in TF–TF–DNA crystal structures, and supported by ChIP-seq datasets. Structural modelling revealed three cooperativity mechanisms: direct protein–protein interactions, potentially indirect interactions and ‘through-DNA’ interactions. Indeed, 38% of the predicted complexes were found to contain four or more bases in which TF pairs appear to synergize through overlapping binding to the same DNA base pairs in opposite grooves or strands. Our TF complex and associated binding site predictions are available as a web resource at http://bejerano.stanford.edu/complex. PMID:24218641

  18. 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.

  19. 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 principlesbased 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

  20. Template-based C8-SCORPION: a protein 8-state secondary structure prediction method using structural information and context-based features

    PubMed Central

    2014-01-01

    Background Secondary structures prediction of proteins is important to many protein structure modeling applications. Correct prediction of secondary structures can significantly reduce the degrees of freedom in protein tertiary structure modeling and therefore reduces the difficulty of obtaining high resolution 3D models. Methods In this work, we investigate a template-based approach to enhance 8-state secondary structure prediction accuracy. We construct structural templates from known protein structures with certain sequence similarity. The structural templates are then incorporated as features with sequence and evolutionary information to train two-stage neural networks. In case of structural templates absence, heuristic structural information is incorporated instead. Results After applying the template-based 8-state secondary structure prediction method, the 7-fold cross-validated Q8 accuracy is 78.85%. Even templates from structures with only 20%~30% sequence similarity can help improve the 8-state prediction accuracy. More importantly, when good templates are available, the prediction accuracy of less frequent secondary structures, such as 3-10 helices, turns, and bends, are highly improved, which are useful for practical applications. Conclusions Our computational results show that the templates containing structural information are effective features to enhance 8-state secondary structure predictions. Our prediction algorithm is implemented on a web server named "C8-SCORPION" available at: http://hpcr.cs.odu.edu/c8scorpion. PMID:25080939

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

    PubMed Central

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

    2009-01-01

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

  2. Atomic accuracy in predicting and designing non-canonical RNA structure

    PubMed Central

    Das, Rhiju; Karanicolas, John; Baker, David

    2010-01-01

    We present a Rosetta full-atom framework for predicting and designing the non-canonical motifs that define RNA tertiary structure, called FARFAR (Fragment Assembly of RNA with Full Atom Refinement). For a test set of thirty-two 6-to-20-nucleotide motifs, the method recapitulated 50% of the experimental structures at near-atomic accuracy. Additionally, design calculations recovered the native sequence at the majority of RNA residues engaged in non-canonical interactions, and mutations predicted to stabilize a signal recognition particle domain were experimentally validated. PMID:20190761

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

    DOEpatents

    Agarwal, Pratul Kumar

    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.

  4. 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.

  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. 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 (410 residues) and significant enhancements for long loops (1120 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

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

    PubMed

    Hossain, Md Anowar; Roslan, Hairul Azman

    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

  8. Predicting RNA-binding sites from the protein structure based on electrostatics, evolution and geometry.

    PubMed

    Chen, Yao Chi; Lim, Carmay

    2008-03-01

    An RNA-binding protein places a surface helix, beta-ribbon, or loop in an RNA helix groove and/or uses a cavity to accommodate unstacked bases. Hence, our strategy for predicting RNA-binding residues is based on detecting a surface patch and a disparate cleft. These were generated and scored according to the gas-phase electrostatic energy change upon mutating each residue to Asp(-)/Glu(-) and each residue's relative conservation. The method requires as input the protein structure and sufficient homologous sequences to define each residue's relative conservation. It yields as output a priority list of surface patch residues followed by a backup list of surface cleft residues distant from the patch residues for experimental testing of RNA binding. Among the 69 structurally non-homologous proteins tested, 81% possess a RNA-binding site with at least 70% of the maximum number of true positives in randomly generated patches of the same size as the predicted site; only two proteins did not contain any true RNA-binding residues in both predicted regions. Regardless of the protein conformational changes upon RNA-binding, the prediction accuracies based on the RNA-free/bound protein structures were found to be comparable and their binding sites overlapped as long as there are no disordered RNA-binding regions in the free structure that are ordered in the corresponding RNA-bound protein structure. PMID:18276647

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

  10. The four ingredients of single-sequence RNA secondary structure prediction. A unifying perspective

    PubMed Central

    Rivas, Elena

    2013-01-01

    Any method for RNA secondary structure prediction is determined by four ingredients. The architecture is the choice of features implemented by the model (such as stacked basepairs, loop length distributions, etc.). The architecture determines the number of parameters in the model. The scoring scheme is the nature of those parameters (whether thermodynamic, probabilistic, or weights). The parameterization stands for the specific values assigned to the parameters. These three ingredients are referred to as “the model.” The fourth ingredient is the folding algorithms used to predict plausible secondary structures given the model and the sequence of a structural RNA. Here, I make several unifying observations drawn from looking at more than 40 years of methods for RNA secondary structure prediction in the light of this classification. As a final observation, there seems to be a performance ceiling that affects all methods with complex architectures, a ceiling that impacts all scoring schemes with remarkable similarity. This suggests that modeling RNA secondary structure by using intrinsic sequence-based plausible “foldability” will require the incorporation of other forms of information in order to constrain the folding space and to improve prediction accuracy. This could give an advantage to probabilistic scoring systems since a probabilistic framework is a natural platform to incorporate different sources of information into one single inference problem. PMID:23695796

  11. M3Ag17(SPh)12 Nanoparticles and Their Structure Prediction.

    PubMed

    Wickramasinghe, Sameera; Atnagulov, Aydar; Yoon, Bokwon; Barnett, Robert N; Griffith, Wendell P; Landman, Uzi; Bigioni, Terry P

    2015-09-16

    Although silver nanoparticles are of great fundamental and practical interest, only one structure has been determined thus far: M4Ag44(SPh)30, where M is a monocation, and SPh is an aromatic thiolate ligand. This is in part due to the fact that no other molecular silver nanoparticles have been synthesized with aromatic thiolate ligands. Here we report the synthesis of M3Ag17(4-tert-butylbenzene-thiol)12, which has good stability and an unusual optical spectrum. We also present a rational strategy for predicting the structure of this molecule. First-principles calculations support the structural model, predict a HOMO-LUMO energy gap of 1.77 eV, and predict a new "monomer mount" capping motif, Ag(SR)3, for Ag nanoparticles. The calculated optical absorption spectrum is in good correspondence with the measured spectrum. Heteroatom substitution was also used as a structural probe. First-principles calculations based on the structural model predicted a strong preference for a single Au atom substitution in agreement with experiment. PMID:26301320

  12. Toward a structure determination method for biomineral-associated protein using combined solid-state NMR and computational structure prediction

    PubMed Central

    Masica, David L.; Ash, Jason T.; Ndao, Moise; Drobny, Gary P.; Gray, Jeffrey J

    2010-01-01

    Summary Protein-biomineral interactions are paramount to materials production in biology, including the mineral phase of hard tissue. Unfortunately, the structure of biomineral-associated proteins cannot be determined by X-ray crystallography or solution NMR. Here we report a method for determining the structure of biomineral-associated proteins. The method combines solid-state NMR (ssNMR) and ssNMR-biased computational structure prediction. In addition, the algorithm is able to identify lattice geometries most compatible with ssNMR constraints, representing a quantitative, novel method for investigating crystal-face binding specificity. We use this new method to determine most of the structure of human salivary statherin interacting with the mineral phase of tooth enamel. Computation and experiment converge on an ensemble of related structures and identify preferential binding at three crystal surfaces. The work represents a significant advance toward determining structure of biomineral-adsorbed protein using experimentally biased structure prediction. This method is generally applicable to proteins that can be chemically synthesized. PMID:21134646

  13. Carbohydrate-binding protein identification by coupling structural similarity searching with binding affinity prediction.

    PubMed

    Zhao, Huiying; Yang, Yuedong; von Itzstein, Mark; Zhou, Yaoqi

    2014-11-15

    Carbohydrate-binding proteins (CBPs) are potential biomarkers and drug targets. However, the interactions between carbohydrates and proteins are challenging to study experimentally and computationally because of their low binding affinity, high flexibility, and the lack of a linear sequence in carbohydrates as exists in RNA, DNA, and proteins. Here, we describe a structure-based function-prediction technique called SPOT-Struc that identifies carbohydrate-recognizing proteins and their binding amino acid residues by structural alignment program SPalign and binding affinity scoring according to a knowledge-based statistical potential based on the distance-scaled finite-ideal gas reference state (DFIRE). The leave-one-out cross-validation of the method on 113 carbohydrate-binding domains and 3442 noncarbohydrate binding proteins yields a Matthews correlation coefficient of 0.56 for SPalign alone and 0.63 for SPOT-Struc (SPalign?+?binding affinity scoring) for CBP prediction. SPOT-Struc is a technique with high positive predictive value (79% correct predictions in all positive CBP predictions) with a reasonable sensitivity (52% positive predictions in all CBPs). The sensitivity of the method was changed slightly when applied to 31 APO (unbound) structures found in the protein databank (14/31 for APO versus 15/31 for HOLO). The result of SPOT-Struc will not change significantly if highly homologous templates were used. SPOT-Struc predicted 19 out of 2076 structural genome targets as CBPs. In particular, one uncharacterized protein in Bacillus subtilis (1oq1A) was matched to galectin-9 from Mus musculus. Thus, SPOT-Struc is useful for uncovering novel carbohydrate-binding proteins. SPOT-Struc is available at http://sparks-lab.org. PMID:25220682

  14. ENTPRISE: An Algorithm for Predicting Human Disease-Associated Amino Acid Substitutions from Sequence Entropy and Predicted Protein Structures

    PubMed Central

    Zhou, Hongyi; Gao, Mu; Skolnick, Jeffrey

    2016-01-01

    The advance of next-generation sequencing technologies has made exome sequencing rapid and relatively inexpensive. A major application of exome sequencing is the identification of genetic variations likely to cause Mendelian diseases. This requires processing large amounts of sequence information and therefore computational approaches that can accurately and efficiently identify the subset of disease-associated variations are needed. The accuracy and high false positive rates of existing computational tools leave much room for improvement. Here, we develop a boosted tree regression machine-learning approach to predict human disease-associated amino acid variations by utilizing a comprehensive combination of protein sequence and structure features. On comparing our method, ENTPRISE, to the state-of-the-art methods SIFT, PolyPhen-2, MUTATIONASSESSOR, MUTATIONTASTER, FATHMM, ENTPRISE exhibits significant improvement. In particular, on a testing dataset consisting of only proteins with balanced disease-associated and neutral variations defined as having the ratio of neutral/disease-associated variations between 0.3 and 3, the Mathews Correlation Coefficient by ENTPRISE is 0.493 as compared to 0.432 by PPH2-HumVar, 0.406 by SIFT, 0.403 by MUTATIONASSESSOR, 0.402 by PPH2-HumDiv, 0.305 by MUTATIONTASTER, and 0.181 by FATHMM. ENTPRISE is then applied to nucleic acid binding proteins in the human proteome. Disease-associated predictions are shown to be highly correlated with the number of protein-protein interactions. Both these predictions and the ENTPRISE server are freely available for academic users as a web service at http://cssb.biology.gatech.edu/entprise/. PMID:26982818

  15. Evaluation of a universal flow-through model for predicting and designing phosphorus removal structures.

    PubMed

    Penn, Chad; Bowen, James; McGrath, Joshua; Nairn, Robert; Fox, Garey; Brown, Glenn; Wilson, Stuart; Gill, Clinton

    2016-05-01

    Phosphorus (P) removal structures have been shown to decrease dissolved P loss from agricultural and urban areas which may reduce the threat of eutrophication. In order to design or quantify performance of these structures, the relationship between discrete and cumulative removal with cumulative P loading must be determined, either by individual flow-through experiments or model prediction. A model was previously developed for predicting P removal with P sorption materials (PSMs) under flow-through conditions, as a function of inflow P concentration, retention time (RT), and PSM characteristics. The objective of this study was to compare model results to measured P removal data from several PSM under a range of conditions (P concentrations and RT) and scales ranging from laboratory to field. Materials tested included acid mine drainage residuals (AMDRs), treated and non-treated electric arc furnace (EAF) steel slag at different size fractions, and flue gas desulfurization (FGD) gypsum. Equations for P removal curves and cumulative P removed were not significantly different between predicted and actual values for any of the 23 scenarios examined. However, the model did tend to slightly over-predict cumulative P removal for calcium-based PSMs. The ability of the model to predict P removal for various materials, RTs, and P concentrations in both controlled settings and field structures validate its use in design and quantification of these structures. This ability to predict P removal without constant monitoring is vital to widespread adoption of P removal structures, especially for meeting discharge regulations and nutrient trading programs. PMID:26950026

  16. An improved hybrid global optimization method for protein tertiary structure prediction

    PubMed Central

    McAllister, Scott R.

    2009-01-01

    First principles approaches to the protein structure prediction problem must search through an enormous conformational space to identify low-energy, near-native structures. In this paper, we describe the formulation of the tertiary structure prediction problem as a nonlinear constrained minimization problem, where the goal is to minimize the energy of a protein conformation subject to constraints on torsion angles and interatomic distances. The core of the proposed algorithm is a hybrid global optimization method that combines the benefits of the ?BB deterministic global optimization approach with conformational space annealing. These global optimization techniques employ a local minimization strategy that combines torsion angle dynamics and rotamer optimization to identify and improve the selection of initial conformations and then applies a sequential quadratic programming approach to further minimize the energy of the protein conformations subject to constraints. The proposed algorithm demonstrates the ability to identify both lower energy protein structures, as well as larger ensembles of low-energy conformations. PMID:20357906

  17. 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.

  18. A comparison of laboratory measured temperatures with predictions for a spar/skin type aircraft structure

    NASA Technical Reports Server (NTRS)

    Jenkins, J. M.

    1981-01-01

    A typical spar/skin aircraft structure was heated nonuniformly in a laboratory and the resulting temperatures were measured. The heat transfer NASTRAN computer program was used to provide predictions. Calculated temperatures based on a thermal model with conduction, radiation, and convection features compared closely to measured spar temperatures. Results were obtained without the thermal conductivity, specific heat, or emissivity with temperature. All modes of heat transfer (conduction, radiation, and convection) show to affect the magnitude and distribution of structural temperatures.

  19. 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

  20. 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.

  1. Interaction site prediction by structural similarity to neighboring clusters in protein-protein interaction networks

    PubMed Central

    2011-01-01

    Background Recently, revealing the function of proteins with protein-protein interaction (PPI) networks is regarded as one of important issues in bioinformatics. With the development of experimental methods such as the yeast two-hybrid method, the data of protein interaction have been increasing extremely. Many databases dealing with these data comprehensively have been constructed and applied to analyzing PPI networks. However, few research on prediction interaction sites using both PPI networks and the 3D protein structures complementarily has explored. Results We propose a method of predicting interaction sites in proteins with unknown function by using both of PPI networks and protein structures. For a protein with unknown function as a target, several clusters are extracted from the neighboring proteins based on their structural similarity. Then, interaction sites are predicted by extracting similar sites from the group of a protein cluster and the target protein. Moreover, the proposed method can improve the prediction accuracy by introducing repetitive prediction process. Conclusions The proposed method has been applied to small scale dataset, then the effectiveness of the method has been confirmed. The challenge will now be to apply the method to large-scale datasets. PMID:21342570

  2. 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

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

    PubMed Central

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

    2012-01-01

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

  4. 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.

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

    PubMed

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

    2014-08-01

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

  6. Flow-induced vibration prediction using fluid-structure interaction method

    SciTech Connect

    Oyamada, O.; Kawahata, J.; Ono, S.; Murayama, K.; Takamura, N.

    1995-12-01

    An FEM analysis program which can predict FIV (flow-induced vibration) in piping system solving fluid-structure interaction is developed. This program solves the relationship between fluid vibration source (pump, orifice, etc.) and piping vibration by coupling fluid and pipe structure together. A present evaluation method of flow-induced vibration is based on conventional design methods with excessive margin or on the rules having been cumulated in the past plant experiences. Therefore the countermeasures based on the measurement of vibrations in start-up tests are very important. In such methods, the phenomena of flow-induced vibrations are not accurately predicted but sometimes conservatively evaluated. This newly developed program provides the function to predict the influence of fluid pulsations on piping vibrations. In this paper, the development and application result of the program are presented.

  7. 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.

  8. The structural integrity of an amygdala-prefrontal pathway predicts trait anxiety

    PubMed Central

    Kim, M. Justin; Whalen, Paul J.

    2009-01-01

    Here we used diffusion tensor imaging (DTI) and showed that the strength of an axonal pathway identified between the amygdala and prefrontal cortex predicted individual differences in trait anxiety. A functional magnetic resonance imaging (fMRI) functional localizer that has been shown to produce reliable amygdala activation was collected in twenty psychiatrically healthy subjects. Voxelwise regression analyses using this fMRI amygdala reactivity as a regressor were performed on fractional anisotropy (FA) images derived from DTI. This analysis identified a white matter pathway between the amygdala and ventromedial prefrontal cortex. Individual differences in the structural integrity of this putative amygdala-prefrontal pathway were inversely correlated with trait anxiety levels (i.e., higher pathway strength predicted lower anxiety). More generally, this study illustrates a strategy for combining fMRI and DTI to identify individual differences in structural pathways that predict behavioral outcomes. PMID:19759308

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

    PubMed

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

    2012-06-26

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

  10. 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,...

  11. Aircraft interior noise prediction using a structural-acoustic analogy in NASTRAN modal synthesis

    NASA Technical Reports Server (NTRS)

    Grosveld, Ferdinand W.; Sullivan, Brenda M.; Marulo, Francesco

    1988-01-01

    The noise induced inside a cylindrical fuselage model by shaker excitation is investigated theoretically and experimentally. The NASTRAN modal-synthesis program is used in the theoretical analysis, and the predictions are compared with experimental measurements in extensive graphs. Good general agreement is obtained, but the need for further refinements to account for acoustic-cavity damping and structural-acoustic interaction is indicated.

  12. 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.

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

    ERIC Educational Resources Information Center

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

    2007-01-01

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

  14. The Prediction of Process Quality from Structural Features of Child Care.

    ERIC Educational Resources Information Center

    Phillipsen, Leslie C.; Burchinal, Margaret R.; Howes, Carollee; Cryer, Debby

    1997-01-01

    This study examined the structure of child care classrooms and centers to predict process quality. Costs and quality of early childhood center-based care in four states with varying levels of regulation were analyzed to identify characteristics of the teacher, classroom, director, and center related to child care quality. Findings suggest the need

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

    PubMed Central

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

    1996-01-01

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

  16. 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 ...

  17. 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,…

  18. DEVELOPMENT OF QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIPS FOR PREDICTING BIODEGRADATION KINETICS

    EPA Science Inventory

    Results have been presented on the development of a structure-activity relationship for biodegradation using a group contribution approach. sing this approach, reported results of the kinetic rate constant agree within 20% with the predicted values. dditional compound studies are...

  19. X-ray crystallographic validation of structure predictions used in computational design for protein stabilization.

    PubMed

    Floor, Robert J; Wijma, Hein J; Jekel, Peter A; Terwisscha van Scheltinga, Anke C; Dijkstra, Bauke W; Janssen, Dick B

    2015-05-01

    Protein engineering aimed at enhancing enzyme stability is increasingly supported by computational methods for calculation of mutant folding energies and for the design of disulfide bonds. To examine the accuracy of mutant structure predictions underlying these computational methods, crystal structures of thermostable limonene epoxide hydrolase variants obtained by computational library design were determined. Four different predicted effects indeed contributed to the obtained stabilization: (i) enhanced interactions between a flexible loop close to the N-terminus and the rest of the protein; (ii) improved interactions at the dimer interface; (iii) removal of unsatisfied hydrogen bonding groups; and (iv) introduction of additional positively charged groups at the surface. The structures of an eightfold and an elevenfold mutant showed that most mutations introduced the intended stabilizing interactions, and side-chain conformations were correctly predicted for 72-88% of the point mutations. However, mutations that introduced a disulfide bond in a flexible region had a larger influence on the backbone conformation than predicted. The enzyme active sites were unaltered, in agreement with the observed preservation of catalytic activities. The structures also revealed how a c-Myc tag, which was introduced for facile detection and purification, can reduce access to the active site and thereby lower the catalytic activity. Finally, sequence analysis showed that comprehensive mutant energy calculations discovered stabilizing mutations that are not proposed by the consensus or B-FIT methods. PMID:25739581

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

    PubMed Central

    Rttig, Marc; Rausch, Christian; Kohlbacher, Oliver

    2010-01-01

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

  1. 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)

  2. 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,

  3. Comparison of Algorithms for Prediction of Protein Structural Features from Evolutionary Data

    PubMed Central

    Bywater, Robert P.

    2016-01-01

    Proteins have many functions and predicting these is still one of the major challenges in theoretical biophysics and bioinformatics. Foremost amongst these functions is the need to fold correctly thereby allowing the other genetically dictated tasks that the protein has to carry out to proceed efficiently. In this work, some earlier algorithms for predicting protein domain folds are revisited and they are compared with more recently developed methods. In dealing with intractable problems such as fold prediction, when different algorithms show convergence onto the same result there is every reason to take all algorithms into account such that a consensus result can be arrived at. In this work it is shown that the application of different algorithms in protein structure prediction leads to results that do not converge as such but rather they collude in a striking and useful way that has never been considered before. PMID:26963911

  4. 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

  5. Facing the challenges of structure-based target prediction by inverse virtual screening.

    PubMed

    Schomburg, Karen T; Bietz, Stefan; Briem, Hans; Henzler, Angela M; Urbaczek, Sascha; Rarey, Matthias

    2014-06-23

    Computational target prediction for bioactive compounds is a promising field in assessing off-target effects. Structure-based methods not only predict off-targets, but, simultaneously, binding modes, which are essential for understanding the mode of action and rationally designing selective compounds. Here, we highlight the current open challenges of computational target prediction methods based on protein structures and show why inverse screening rather than sequential pairwise protein-ligand docking methods are needed. A new inverse screening method based on triangle descriptors is introduced: iRAISE (inverse Rapid Index-based Screening Engine). A Scoring Cascade considering the reference ligand as well as the ligand and active site coverage is applied to overcome interprotein scoring noise of common protein-ligand scoring functions. Furthermore, a statistical evaluation of a score cutoff for each individual protein pocket is used. The ranking and binding mode prediction capabilities are evaluated on different datasets and compared to inverse docking and pharmacophore-based methods. On the Astex Diverse Set, iRAISE ranks more than 35% of the targets to the first position and predicts more than 80% of the binding modes with a root-mean-square deviation (RMSD) accuracy of <2.0 Å. With a median computing time of 5 s per protein, large amounts of protein structures can be screened rapidly. On a test set with 7915 protein structures and 117 query ligands, iRAISE predicts the first true positive in a ranked list among the top eight ranks (median), i.e., among 0.28% of the targets. PMID:24851945

  6. Use of tiling array data and RNA secondary structure predictions to identify noncoding RNA genes

    PubMed Central

    Weile, Christian; Gardner, Paul P; Hedegaard, Mads M; Vinther, Jeppe

    2007-01-01

    Background Within the last decade a large number of noncoding RNA genes have been identified, but this may only be the tip of the iceberg. Using comparative genomics a large number of sequences that have signals concordant with conserved RNA secondary structures have been discovered in the human genome. Moreover, genome wide transcription profiling with tiling arrays indicate that the majority of the genome is transcribed. Results We have combined tiling array data with genome wide structural RNA predictions to search for novel noncoding and structural RNA genes that are expressed in the human neuroblastoma cell line SK-N-AS. Using this strategy, we identify thousands of human candidate RNA genes. To further verify the expression of these genes, we focused on candidate genes that had a stable hairpin structures or a high level of covariance. Using northern blotting, we verify the expression of 2 out of 3 of the hairpin structures and 3 out of 9 high covariance structures in SK-N-AS cells. Conclusion Our results demonstrate that many human noncoding, structured and conserved RNA genes remain to be discovered and that tissue specific tiling array data can be used in combination with computational predictions of sequences encoding structural RNAs to improve the search for such genes. PMID:17645787

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

    PubMed Central

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

    2012-01-01

    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

  8. Predicted structures for kappa opioid G-protein coupled receptor bound to selective agonists.

    PubMed

    Li, Quanjie; Kim, Soo-Kyung; Goddard, William A; Chen, Guangju; Tan, Hongwei

    2015-03-23

    Human kappa opioid receptor (?-OR), a G protein-coupled receptor (GPCR), has been identified as a drug target for treatment of such human disorders as pain perception, neuroendocrine physiology, affective behavior, and cognition. In order to find more selective and active agonists, one would like to do structure based drug design. Indeed, there is an X-ray structure for an antagonist bound to ?-OR, but structures for activated GPCRs are quite different from those for the inactive GPCRs. Here we predict the ensemble of 24 low-energy structures of human kappa opioid receptor (?-OR), obtained by application of the GEnSeMBLE (GPCR Ensemble of Structures in Membrane Bilayer Environment) complete sampling method, which evaluates 13 trillion combinations of tilt and rotation angles for ?-OR to select the best 24. To validate these structures, we used the DarwinDock complete sampling method to predict the binding sites for five known agonists (ethylketocyclazocine, bremazocine, pentazocine, nalorphine, and morphine) bound to all 24 ?-OR conformations. We find that some agonists bind selectively to receptor conformations that lack the salt bridge between transmembrane domains 3 and 6 as expected for active conformations. These 3D structures for ?-OR provide a structural basis for understanding ligand binding and activation of ?-OR, which should be useful for guiding subtype specific drug design. PMID:25642595

  9. Local structure based method for prediction of the biochemical function of proteins: Applications to glycoside hydrolases.

    PubMed

    Parasuram, Ramya; Mills, Caitlyn L; Wang, Zhouxi; Somasundaram, Saroja; Beuning, Penny J; Ondrechen, Mary Jo

    2016-01-15

    Thousands of protein structures of unknown or uncertain function have been reported as a result of high-throughput structure determination techniques developed by Structural Genomics (SG) projects. However, many of the putative functional assignments of these SG proteins in the Protein Data Bank (PDB) are incorrect. While high-throughput biochemical screening techniques have provided valuable functional information for limited sets of SG proteins, the biochemical functions for most SG proteins are still unknown or uncertain. Therefore, computational methods for the reliable prediction of protein function from structure can add tremendous value to the existing SG data. In this article, we show how computational methods may be used to predict the function of SG proteins, using examples from the six-hairpin glycosidase (6-HG) and the concanavalin A-like lectin/glucanase (CAL/G) superfamilies. Using a set of predicted functional residues, obtained from computed electrostatic and chemical properties for each protein structure, it is shown that these superfamilies may be sorted into functional families according to biochemical function. Within these superfamilies, a total of 18 SG proteins were analyzed according to their predicted, local functional sites: 13 from the 6-HG superfamily, five from the CAL/G superfamily. Within the 6-HG superfamily, an uncharacterized protein BACOVA_03626 from Bacteroides ovatus (PDB 3ON6) and a hypothetical protein BT3781 from Bacteroides thetaiotaomicron (PDB 2P0V) are shown to have very strong active site matches with exo-?-1,6-mannosidases, thus likely possessing this function. Also in this superfamily, it is shown that protein BH0842, a putative glycoside hydrolase from Bacillus halodurans (PDB 2RDY), has a predicted active site that matches well with a known ?-l-galactosidase. In the CAL/G superfamily, an uncharacterized glycosyl hydrolase family 16 protein from Mycobacterium smegmatis (PDB 3RQ0) is shown to have local structural similarity at the predicted active site with the known members of the GH16 family, with the closest match to the endoglucanase subfamily. The method discussed herein can predict whether an SG protein is correctly or incorrectly annotated and can sometimes provide a reliable functional annotation. Examples of application of the method across folds, comparing active sites between two proteins of different structural folds, are also given. PMID:26564235

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

    PubMed

    Micsonai, Andrs; Wien, Frank; Kernya, Linda; Lee, Young-Ho; Goto, Yuji; Rfrgiers, Matthieu; Kardos, Jzsef

    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

  11. Predicting inactive conformations of protein kinases using active structures: conformational selection of type-II inhibitors.

    PubMed

    Xu, Min; Yu, Lu; Wan, Bo; Yu, Long; Huang, Qiang

    2011-01-01

    Protein kinases have been found to possess two characteristic conformations in their activation-loops: the active DFG-in conformation and the inactive DFG-out conformation. Recently, it has been very interesting to develop type-II inhibitors which target the DFG-out conformation and are more specific than the type-I inhibitors binding to the active DFG-in conformation. However, solving crystal structures of kinases with the DFG-out conformation remains a challenge, and this seriously hampers the application of the structure-based approaches in development of novel type-II inhibitors. To overcome this limitation, here we present a computational approach for predicting the DFG-out inactive conformation using the DFG-in active structures, and develop related conformational selection protocols for the uses of the predicted DFG-out models in the binding pose prediction and virtual screening of type-II ligands. With the DFG-out models, we predicted the binding poses for known type-II inhibitors, and the results were found in good agreement with the X-ray crystal structures. We also tested the abilities of the DFG-out models to recognize their specific type-II inhibitors by screening a database of small molecules. The AUC (area under curve) results indicated that the predicted DFG-out models were selective toward their specific type-II inhibitors. Therefore, the computational approach and protocols presented in this study are very promising for the structure-based design and screening of novel type-II kinase inhibitors. PMID:21818358

  12. 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.

  13. Better prediction of sub-cellular localization by combining evolutionary and structural information.

    PubMed

    Nair, Rajesh; Rost, Burkhard

    2003-12-01

    The native sub-cellular compartment of a protein is one aspect of its function. Thus, predicting localization is an important step toward predicting function. Short zip code-like sequence fragments regulate some of the shuttling between compartments. Cataloguing and predicting such motifs is the most accurate means of determining localization in silico. However, only few motifs are currently known, and not all the trafficking appears regulated in this way. The amino acid composition of a protein correlates with its localization. All general prediction methods employed this observation. Here, we explored the evolutionary information contained in multiple alignments and aspects of protein structure to predict localization in absence of homology and targeting motifs. Our final system combined statistical rules and a variety of neural networks to achieve an overall four-state accuracy above 65%, a significant improvement over systems using only composition. The system was at its best for extra-cellular and nuclear proteins; it was significantly less accurate than TargetP for mitochondrial proteins. Interestingly, all methods that were developed on SWISS-PROT sequences failed grossly when fed with sequences from proteins of known structures taken from PDB. We therefore developed two separate systems: one for proteins of known structure and one for proteins of unknown structure. Finally, we applied the PDB-based system along with homology-based inferences and automatic text analysis to annotate all eukaryotic proteins in the PDB (http://cubic.bioc.columbia.edu/db/LOC3D). We imagine that this pilot method-certainly in combination with similar tools-may be valuable target selection in structural genomics. PMID:14635133

  14. Prediction of structures and infrared spectra of the candidate circumstellar molecules SinOm

    NASA Astrophysics Data System (ADS)

    Liu, Na; Zhao, Hui-Yan; Zheng, Li-Jia; Qin, Sheng-Li; Liu, Ying

    2016-01-01

    A systematic study of the geometric structures of steady states and metastable states of silicon oxide clusters has been performed using density functional theory. We find that silicon-rich and oxygen-rich clusters have different characteristics. Oxygen-rich clusters usually have oxygen atoms on the edges of the clusters, but separated from others by Si atoms. However, silicon-rich clusters tend to have rings nested within each other. The spectra for the structures have been calculated to compare with observed spectra. The predicted structures and spectroscopic properties are expected to be useful for the identification of silicon oxide species in the interstellar medium.

  15. Model Predictive Control for Deflection-Limiting Maneuver of Flexible Structure

    NASA Astrophysics Data System (ADS)

    Kojima, Hirohisa; Tomikawa, Masataka

    An application of a model predictive control (MPC) for a deflection limiting maneuver of a flexible structure is studied. To limit the deflection of a flexible structure to within an allowable range during maneuver, deflection-limiting control (DLC) is employed as the primary control, and MPC is combined with DLC to overcome the disadvantages of DLC, including low robustness to modeling errors and disturbances. Moreover, a simple adaptive method is used to estimate the first modal frequency that dominates the vibration motion of the flexible structure. The effectiveness of the proposed algorithm is evaluated by numerical simulations.

  16. 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

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

    PubMed Central

    Lorenzo, J. Ramiro; Alonso, Leonardo G.; Snchez, 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

  19. Microbes as Engines of Ecosystem Function: When Does Community Structure Enhance Predictions of Ecosystem Processes?

    PubMed

    Graham, Emily B; Knelman, Joseph E; Schindlbacher, Andreas; Siciliano, Steven; Breulmann, Marc; Yannarell, Anthony; Beman, J M; Abell, Guy; Philippot, Laurent; Prosser, James; Foulquier, Arnaud; Yuste, Jorge C; Glanville, Helen C; Jones, Davey L; Angel, Roey; Salminen, Janne; Newton, Ryan J; Bürgmann, Helmut; Ingram, Lachlan J; Hamer, Ute; Siljanen, Henri M P; Peltoniemi, Krista; Potthast, Karin; Bañeras, Lluís; Hartmann, Martin; Banerjee, Samiran; Yu, Ri-Qing; Nogaro, Geraldine; Richter, Andreas; Koranda, Marianne; Castle, Sarah C; Goberna, Marta; Song, Bongkeun; Chatterjee, Amitava; Nunes, Olga C; Lopes, Ana R; Cao, Yiping; Kaisermann, Aurore; Hallin, Sara; Strickland, Michael S; Garcia-Pausas, Jordi; Barba, Josep; Kang, Hojeong; Isobe, Kazuo; Papaspyrou, Sokratis; Pastorelli, Roberta; Lagomarsino, Alessandra; Lindström, Eva S; Basiliko, Nathan; Nemergut, Diana R

    2016-01-01

    Microorganisms are vital in mediating the earth's biogeochemical cycles; yet, despite our rapidly increasing ability to explore complex environmental microbial communities, the relationship between microbial community structure and ecosystem processes remains poorly understood. Here, we address a fundamental and unanswered question in microbial ecology: 'When do we need to understand microbial community structure to accurately predict function?' We present a statistical analysis investigating the value of environmental data and microbial community structure independently and in combination for explaining rates of carbon and nitrogen cycling processes within 82 global datasets. Environmental variables were the strongest predictors of process rates but left 44% of variation unexplained on average, suggesting the potential for microbial data to increase model accuracy. Although only 29% of our datasets were significantly improved by adding information on microbial community structure, we observed improvement in models of processes mediated by narrow phylogenetic guilds via functional gene data, and conversely, improvement in models of facultative microbial processes via community diversity metrics. Our results also suggest that microbial diversity can strengthen predictions of respiration rates beyond microbial biomass parameters, as 53% of models were improved by incorporating both sets of predictors compared to 35% by microbial biomass alone. Our analysis represents the first comprehensive analysis of research examining links between microbial community structure and ecosystem function. Taken together, our results indicate that a greater understanding of microbial communities informed by ecological principles may enhance our ability to predict ecosystem process rates relative to assessments based on environmental variables and microbial physiology. PMID:26941732

  20. 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

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

    PubMed Central

    Martnez-Jimnez, 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 compoundprotein interactions and thus may benefit drug development. PMID:25816344

  2. 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.

  3. 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.

  4. Protein subcellular localization prediction based on compartment-specific features and structure conservation

    PubMed Central

    Su, Emily Chia-Yu; Chiu, Hua-Sheng; Lo, Allan; Hwang, Jenn-Kang; Sung, Ting-Yi; Hsu, Wen-Lian

    2007-01-01

    Background Protein subcellular localization is crucial for genome annotation, protein function prediction, and drug discovery. Determination of subcellular localization using experimental approaches is time-consuming; thus, computational approaches become highly desirable. Extensive studies of localization prediction have led to the development of several methods including composition-based and homology-based methods. However, their performance might be significantly degraded if homologous sequences are not detected. Moreover, methods that integrate various features could suffer from the problem of low coverage in high-throughput proteomic analyses due to the lack of information to characterize unknown proteins. Results We propose a hybrid prediction method for Gram-negative bacteria that combines a one-versus-one support vector machines (SVM) model and a structural homology approach. The SVM model comprises a number of binary classifiers, in which biological features derived from Gram-negative bacteria translocation pathways are incorporated. In the structural homology approach, we employ secondary structure alignment for structural similarity comparison and assign the known localization of the top-ranked protein as the predicted localization of a query protein. The hybrid method achieves overall accuracy of 93.7% and 93.2% using ten-fold cross-validation on the benchmark data sets. In the assessment of the evaluation data sets, our method also attains accurate prediction accuracy of 84.0%, especially when testing on sequences with a low level of homology to the training data. A three-way data split procedure is also incorporated to prevent overestimation of the predictive performance. In addition, we show that the prediction accuracy should be approximately 85% for non-redundant data sets of sequence identity less than 30%. Conclusion Our results demonstrate that biological features derived from Gram-negative bacteria translocation pathways yield a significant improvement. The biological features are interpretable and can be applied in advanced analyses and experimental designs. Moreover, the overall accuracy of combining the structural homology approach is further improved, which suggests that structural conservation could be a useful indicator for inferring localization in addition to sequence homology. The proposed method can be used in large-scale analyses of proteomes. PMID:17825110

  5. 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 β, α + β, and α/β proteins. The total energy of a protein is expressed as sum of a Cα – Cα 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 β-sheet alignments. These β-sheet alignments are used as constraints for contacts between residues of β-sheets. This model was tested on three independent protein test sets and CASP8 test proteins consisting of β, α + β, α/β proteins and was found to perform very well. The average accuracy of the predictions (separated by at least six residues) was approximately 61%. The average true positive and false positive distances were also calculated for each of the test sets and they are 7.58 Å and 15.88 Å, respectively. Residue contact prediction can be directly used to facilitate the protein tertiary structure prediction. This proposed residue contact prediction model is incorporated into the first principles protein tertiary structure prediction approach, ASTRO-FOLD. The effectiveness of the contact prediction model was further demonstrated by the improvement in the quality of the protein structure ensemble generated using the predicted residue contacts for a test set of 10 proteins. PMID:20225257

  6. Prediction Accuracy of a Novel Dynamic StructureFunction Model for Glaucoma Progression

    PubMed Central

    Hu, Rongrong; Marn-Franch, Ivn; Racette, Lyne

    2014-01-01

    Purpose. To assess the prediction accuracy of a novel dynamic structurefunction (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. 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.

  8. Using crystallographic water properties for the analysis and prediction of lectin-carbohydrate complex structures.

    PubMed

    Modenutti, C; Gauto, D; Radusky, L; Blanco, J; Turjanski, A; Hajos, S; Marti, Ma

    2015-02-01

    Understanding protein-ligand interactions is a fundamental question in basic biochemistry, and the role played by the solvent along this process is not yet fully understood. This fact is particularly relevant in lectins, proteins that mediate a large variety of biological processes through the recognition of specific carbohydrates. In the present work, we have thoroughly analyzed a nonredundant and well-curated set of lectin structures looking for a potential relationship between the structural water properties in the apo-structures and the corresponding protein-ligand complex structures. Our results show that solvent structure adjacent to the binding sites mimics the ligand oxygen structural framework in the resulting protein-ligand complex, allowing us to develop a predictive method using a Naive Bayes classifier. We also show how these properties can be used to improve docking predictions of lectin-carbohydrate complex structures in terms of both accuracy and precision, thus developing a solid strategy for the rational design of glycomimetic drugs. Overall our results not only contribute to the understanding of protein-ligand complexes, but also underscore the role of the water solvent in the ligand recognition process. Finally, we discuss our findings in the context of lectin specificity and ligand recognition properties. PMID:25267604

  9. Structure based function prediction of proteins using fragment library frequency vectors.

    PubMed

    Yadav, Akshay; Jayaraman, Valadi Krishnamoorthy

    2012-01-01

    The function of the protein is primarily dictated by its structure. Therefore it is far more logical to find the functional clues of the protein in its overall 3-dimensional fold or its global structure. In this paper, we have developed a novel Support Vector Machines (SVM) based prediction model for functional classification and prediction of proteins using features extracted from its global structure based on fragment libraries. Fragment libraries have been previously used for abintio modelling of proteins and protein structure comparisons. The query protein structure is broken down into a collection of short contiguous backbone fragments and this collection is discretized using a library of fragments. The input feature vector is frequency vector that counts the number of each library fragment in the collection of fragments by all-to-all fragment comparisons. SVM models were trained and optimised for obtaining the best 10-fold Cross validation accuracy for classification. As an example, this method was applied for prediction and classification of Cell Adhesion molecules (CAMs). Thirty-four different fragment libraries with sizes ranging from 4 to 400 and fragment lengths ranging from 4 to 12 were used for obtaining the best prediction model. The best 10-fold CV accuracy of 95.25% was obtained for library of 400 fragments of length 10. An accuracy of 87.5% was obtained on an unseen test dataset consisting of 20 CAMs and 20 NonCAMs. This shows that protein structure can be accurately and uniquely described using 400 representative fragments of length 10. PMID:23144557

  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 α/β-mixed or β-structure–rich proteins. The problem arises from the spectral diversity of β-structures, which has hitherto been considered as an intrinsic limitation of the technique. The predictions are less reliable for proteins of unusual β-structures such as membrane proteins, protein aggregates, and amyloid fibrils. Here, we show that the parallel/antiparallel orientation and the twisting of the β-sheets account for the observed spectral diversity. We have developed a method called β-structure selection (BeStSel) for the secondary structure estimation that takes into account the twist of β-structures. This method can reliably distinguish parallel and antiparallel β-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. 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

  12. Predictions of Native American Population Structure Using Linguistic Covariates in a Hidden Regression Framework

    PubMed Central

    Jay, Flora; Franois, Olivier; Blum, Michael G. B.

    2011-01-01

    Background The mainland of the Americas is home to a remarkable diversity of languages, and the relationships between genes and languages have attracted considerable attention in the past. Here we investigate to which extent geography and languages can predict the genetic structure of Native American populations. Methodology/Principal Findings Our approach is based on a Bayesian latent cluster regression model in which cluster membership is explained by geographic and linguistic covariates. After correcting for geographic effects, we find that the inclusion of linguistic information improves the prediction of individual membership to genetic clusters. We further compare the predictive power of Greenberg's and The Ethnologue classifications of Amerindian languages. We report that The Ethnologue classification provides a better genetic proxy than Greenberg's classification at the stock and at the group levels. Although high predictive values can be achieved from The Ethnologue classification, we nevertheless emphasize that Choco, Chibchan and Tupi linguistic families do not exhibit a univocal correspondence with genetic clusters. Conclusions/Significance The Bayesian latent class regression model described here is efficient at predicting population genetic structure using geographic and linguistic information in Native American populations. PMID:21305006

  13. 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

  14. 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

  15. 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

  16. 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

  17. SAHG, a comprehensive database of predicted structures of all human proteins

    PubMed Central

    Motono, Chie; Nakata, Junichi; Koike, Ryotaro; Shimizu, Kana; Shirota, Matsuyuki; Amemiya, Takayuki; Tomii, Kentaro; Nagano, Nozomi; Sakaya, Naofumi; Misoo, Kiyotaka; Sato, Miwa; Kidera, Akinori; Hiroaki, Hidekazu; Shirai, Tsuyoshi; Kinoshita, Kengo; Noguchi, Tamotsu; Ota, Motonori

    2011-01-01

    Most proteins from higher organisms are known to be multi-domain proteins and contain substantial numbers of intrinsically disordered (ID) regions. To analyse such protein sequences, those from human for instance, we developed a special protein-structure-prediction pipeline and accumulated the products in the Structure Atlas of Human Genome (SAHG) database at http://bird.cbrc.jp/sahg. With the pipeline, human proteins were examined by local alignment methods (BLAST, PSI-BLAST and Smith–Waterman profile–profile alignment), global–local alignment methods (FORTE) and prediction tools for ID regions (POODLE-S) and homology modeling (MODELLER). Conformational changes of protein models upon ligand-binding were predicted by simultaneous modeling using templates of apo and holo forms. When there were no suitable templates for holo forms and the apo models were accurate, we prepared holo models using prediction methods for ligand-binding (eF-seek) and conformational change (the elastic network model and the linear response theory). Models are displayed as animated images. As of July 2010, SAHG contains 42 581 protein-domain models in approximately 24 900 unique human protein sequences from the RefSeq database. Annotation of models with functional information and links to other databases such as EzCatDB, InterPro or HPRD are also provided to facilitate understanding the protein structure-function relationships. PMID:21051360

  18. Prediction of HIV drug resistance from genotype with encoded three-dimensional protein structure

    PubMed Central

    2014-01-01

    Background Drug resistance has become a severe challenge for treatment of HIV infections. Mutations accumulate in the HIV genome and make certain drugs ineffective. Prediction of resistance from genotype data is a valuable guide in choice of drugs for effective therapy. Results In order to improve the computational prediction of resistance from genotype data we have developed a unified encoding of the protein sequence and three-dimensional protein structure of the drug target for classification and regression analysis. The method was tested on genotype-resistance data for mutants of HIV protease and reverse transcriptase. Our graph based sequence-structure approach gives high accuracy with a new sparse dictionary classification method, as well as support vector machine and artificial neural networks classifiers. Cross-validated regression analysis with the sparse dictionary gave excellent correlation between predicted and observed resistance. Conclusion The approach of encoding the protein structure and sequence as a 210-dimensional vector, based on Delaunay triangulation, has promise as an accurate method for predicting resistance from sequence for drugs inhibiting HIV protease and reverse transcriptase. PMID:25081370

  19. 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

  20. Predictive grain yield models based on canopy structure and structural plasticity

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Structural dimensions, digitally measured on stems and leaves of soybean plants during the first six reproductive growth stages (R1-R6), were used to assess the impact of five management strategies including cropping systems (conventional (C) vs. organic, (O)), tillage (conventional moldboard (C) vs...

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

    PubMed

    Singh, Kunwar P; Gupta, Shikha; Rai, Premanjali

    2013-09-01

    The research aims to develop global modeling tools capable of categorizing structurally diverse chemicals in various toxicity classes according to the EEC and European Community directives, and to predict their acute toxicity in fathead minnow using set of selected molecular descriptors. Accordingly, artificial intelligence approach based classification and regression models, such as probabilistic neural networks (PNN), generalized regression neural networks (GRNN), multilayer perceptron neural network (MLPN), radial basis function neural network (RBFN), support vector machines (SVM), gene expression programming (GEP), and decision tree (DT) were constructed using the experimental toxicity data. Diversity and non-linearity in the chemicals' data were tested using the Tanimoto similarity index and Brock-Dechert-Scheinkman statistics. Predictive and generalization abilities of various models constructed here were compared using several statistical parameters. PNN and GRNN models performed relatively better than MLPN, RBFN, SVM, GEP, and DT. Both in two and four category classifications, PNN yielded a considerably high accuracy of classification in training (95.85 percent and 90.07 percent) and validation data (91.30 percent and 86.96 percent), respectively. GRNN rendered a high correlation between the measured and model predicted -log LC50 values both for the training (0.929) and validation (0.910) data and low prediction errors (RMSE) of 0.52 and 0.49 for two sets. Efficiency of the selected PNN and GRNN models in predicting acute toxicity of new chemicals was adequately validated using external datasets of different fish species (fathead minnow, bluegill, trout, and guppy). The PNN and GRNN models showed good predictive and generalization abilities and can be used as tools for predicting toxicities of structurally diverse chemical compounds. PMID:23764236

  2. Tertiary structure-based prediction of conformational B-cell epitopes through B factors

    PubMed Central

    Ren, Jing; Liu, Qian; Ellis, John; Li, Jinyan

    2014-01-01

    Motivation: B-cell epitope is a small area on the surface of an antigen that binds to an antibody. Accurately locating epitopes is of critical importance for vaccine development. Compared with wet-lab methods, computational methods have strong potential for efficient and large-scale epitope prediction for antigen candidates at much lower cost. However, it is still not clear which features are good determinants for accurate epitope prediction, leading to the unsatisfactory performance of existing prediction methods. Method and results: We propose a much more accurate B-cell epitope prediction method. Our method uses a new feature B factor (obtained from X-ray crystallography), combined with other basic physicochemical, statistical, evolutionary and structural features of each residue. These basic features are extended by a sequence window and a structure window. All these features are then learned by a two-stage random forest model to identify clusters of antigenic residues and to remove isolated outliers. Tested on a dataset of 55 epitopes from 45 tertiary structures, we prove that our method significantly outperforms all three existing structure-based epitope predictors. Following comprehensive analysis, it is found that features such as B factor, relative accessible surface area and protrusion index play an important role in characterizing B-cell epitopes. Our detailed case studies on an HIV antigen and an influenza antigen confirm that our second stage learning is effective for clustering true antigenic residues and for eliminating self-made prediction errors introduced by the first-stage learning. Availability and implementation: Source codes are available on request. Contact: jinyan.li@uts.edu.au Supplementary information: Supplementary data are available at Bioinformatics online. PMID:24931993

  3. Structural predictions of neurobiologically relevant G-protein coupled receptors and intrinsically disordered proteins.

    PubMed

    Rossetti, Giulia; Dibenedetto, Domenica; Calandrini, Vania; Giorgetti, Alejandro; Carloni, Paolo

    2015-09-15

    G protein coupled receptors (GPCRs) and intrinsic disordered proteins (IDPs) are key players for neuronal function and dysfunction. Unfortunately, their structural characterization is lacking in most cases. From one hand, no experimental structure has been determined for the two largest GPCRs subfamilies, both key proteins in neuronal pathways. These are the odorant (450 members out of 900 human GPCRs) and the bitter taste receptors (25 members) subfamilies. On the other hand, also IDPs structural characterization is highly non-trivial. They exist as dynamic, highly flexible structural ensembles that undergo conformational conversions on a wide range of timescales, spanning from picoseconds to milliseconds. Computational methods may be of great help to characterize these neuronal proteins. Here we review recent progress from our lab and other groups to develop and apply in silico methods for structural predictions of these highly relevant, fascinating and challenging systems. PMID:25797436

  4. 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

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

    PubMed Central

    Al-Khatib, Ra’ed M.; Abdullah, Rosni; Rashid, Nur’Aini Abdul

    2010-01-01

    RNA molecules have been discovered playing crucial roles in numerous biological and medical procedures and processes. RNA structures determination have become a major problem in the biology context. Recently, computer scientists have empowered the biologists with RNA secondary structures that ease an understanding of the RNA functions and roles. Detecting RNA secondary structure is an NP-hard problem, especially in pseudoknotted RNA structures. The detection process is also time-consuming; as a result, an alternative approach such as using parallel architectures is a desirable option. The main goal in this paper is to do an intensive investigation of parallel methods used in the literature to solve the demanding issues, related to the RNA secondary structure prediction methods. Then, we introduce a new taxonomy for the parallel RNA folding methods. Based on this proposed taxonomy, a systematic and scientific comparison is performed among these existing methods. PMID:20458364

  6. Prediction of common secondary structures of RNAs: a genetic algorithm approach

    PubMed Central

    Chen, Jih-H.; Le, Shu-Yun; Maizel, Jacob V.

    2000-01-01

    In this study we apply a genetic algorithm to a set of RNA sequences to find common RNA secondary structures. Our method is a three-step procedure. At the first stage of the procedure for each sequence, a genetic algorithm is used to optimize the structures in a population to a certain degree of stability. In this step, the free energy of a structure is the fitness criterion for the algorithm. Next, for each structure, we define a measure of structural conservation with respect to those in other sequences. We use this measure in a genetic algorithm to improve the structural similarity among sequences for the structures in the population of a sequence. Finally, we select those structures satisfying certain conditions of structural stability and similarity as predicted common structures for a set of RNA sequences. We have obtained satisfactory results from a set of tRNA, 5S rRNA, rev response elements (RRE) of HIV-1 and RRE of HIV-2/SIV, respectively. PMID:10648793

  7. Predicting the life of high-temperature structural components in power plants

    SciTech Connect

    Liaw, P.K. ); Saxena, A. ); Schaefer, J. )

    1992-02-01

    This paper reports on the concept of time-dependent fracture mechanics that has been used to develop the quantitative life-prediction methodology and inspection criteria for high-temperature structural components. As an example, the methodology was applied to steam pipes. Leak-before-break analyses were utilized to determine the flaw inspection criteria of steam pipes. Both static and cyclic loading conditions were included in the life-prediction analyses. Increasing the frequency of shut-downs was found to decrease the remaining life. The effects of operating pressures and temperatures and material properties on the life of steam pipes were quantified.

  8. Predicting the life of high-temperature structural components in power plants

    NASA Astrophysics Data System (ADS)

    Liaw, P. K.; Saxena, A.; Schaefer, J.

    1992-02-01

    The concept of time-dependent fracture mechanics has been used to develop the quantitative life-prediction methodology and inspection criteria for high-temperature structural components. As an example, the methodology was applied to steam pipes. Leak-before-break analyses were utilized to determine the flaw inspection criteria of steam pipes. Both static and cyclic loading conditions were included in the life-prediction analyses. Increasing the frequency of shutdowns was found to decrease the remaining life. The effects of operating pressures and temperatures and material properties on the life of steam pipes were quantified.

  9. Synthesized variable structure control and gray prediction for a class of perturbed systems.

    PubMed

    Chou, Chien-Hsin

    2003-04-01

    Based on the Lyapunov stability theorem, we apply a gray prediction scheme to eliminate the "chattering" disadvantage of the traditional variable structure control. By using a moving window of recent past data, we can directly identify the system dynamics and unknown perturbations via the gray prediction scheme. Therefore there is no need for the information of the upper bound of the perturbations in advance. In addition, the presented control scheme ensures the property of the globally uniformly ultimate boundness for the overall controlled system. Finally, a numerical example is given to illustrate the feasibility of the proposed control scheme. PMID:12708542

  10. 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 (rσ,rπ) , suggested by St. John and Bloch several decades ago, enabled an average success in classification of 92 % . Using just rσ and the excess Born effective charge Δ 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.

  11. 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

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

    PubMed Central

    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

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

    PubMed Central

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

    2008-01-01

    With many genomes sequenced, a pressing challenge in biology is predicting the function of the proteins that the genes encode. When proteins are unrelated to others of known activity, bioinformatics inference for function becomes problematic. It would thus be useful to interrogate protein structures for function directly. Here, we predict the function of an enzyme of unknown activity, Tm0936 from Thermotoga maritima, by docking high-energy intermediate forms of thousands of candidate metabolites. The docking hit list was dominated by adenine analogues, which appeared to undergo C6-deamination. Four of these, including 5-methylthioadenosine and S-adenosylhomocysteine (SAH), were tested as substrates, and three had substantial catalytic rate constants (105 M?1s?1). The X-ray crystal structure of the complex between Tm0936 and the product resulting from the deamination of SAH, S-inosylhomocysteine, was determined, and it corresponded closely to the predicted structure. The deaminated products can be further metabolized by T. maritima in a previously uncharacterized SAH degradation pathway. Structure-based docking with high-energy forms of potential substrates may be a useful tool to annotate enzymes for function. PMID:17603473

  14. Structure-Based Activity Prediction for an Enzyme of Unknown Function

    SciTech Connect

    Hermann,J.; Marti-Arbona, R.; Fedorov, A.; Fedorov, E.; Almo, S.; Shoichet, B.; Raushel, F.

    2007-01-01

    With many genomes sequenced, a pressing challenge in biology is predicting the function of the proteins that the genes encode. When proteins are unrelated to others of known activity, bioinformatics inference for function becomes problematic. It would thus be useful to interrogate protein structures for function directly. Here, we predict the function of an enzyme of unknown activity, Tm0936 from Thermotoga maritima, by docking high-energy intermediate forms of thousands of candidate metabolites. The docking hit list was dominated by adenine analogues, which appeared to undergo C6-deamination. Four of these, including 5-methylthioadenosine and S-adenosylhomocysteine (SAH), were tested as substrates, and three had substantial catalytic rate constants (10{sup 5} M{sup -1} s{sup -1}). The X-ray crystal structure of the complex between Tm0936 and the product resulting from the deamination of SAH, S-inosylhomocysteine, was determined, and it corresponded closely to the predicted structure. The deaminated products can be further metabolized by T. maritima in a previously uncharacterized SAH degradation pathway. Structure-based docking with high-energy forms of potential substrates may be a useful tool to annotate enzymes for function.

  15. 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

  16. The importance of larger data sets for protein secondary structure prediction with neural networks.

    PubMed Central

    Chandonia, J. M.; Karplus, M.

    1996-01-01

    A neural network algorithm is applied to secondary structure and structural class prediction for a database of 318 nonhomologous protein chains. Significant improvement in accuracy is obtained as compared with performance on smaller databases. A systematic study of the effects of network topology shows that, for the larger database, better results are obtained with more units in the hidden layer. In a 32-fold cross validated test, secondary structure prediction accuracy is 67.0%, relative to 62.6% obtained previously, without any evolutionary information on the sequence. Introduction of sequence profiles increases this value to 72.9%, suggesting that the two types of information are essentially independent. Tertiary structural class is predicted with 80.2% accuracy, relative to 73.9% obtained previously. The use of a larger database is facilitated by the introduction of a scaled conjugate gradient algorithm for optimizing the neural network. This algorithm is about 10-20 times as fast as the standard steepest descent algorithm. PMID:8845767

  17. 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

  18. Fast assessment of structural models of ion channels based on their predicted current-voltage characteristics.

    PubMed

    Dyrka, Witold; Kurczy?ska, Monika; Konopka, Bogumi? M; Kotulska, Ma?gorzata

    2016-02-01

    Computational prediction of protein structures is a difficult task, which involves fast and accurate evaluation of candidate model structures. We propose to enhance single-model quality assessment with a functionality evaluation phase for proteins whose quantitative functional characteristics are known. In particular, this idea can be applied to evaluation of structural models of ion channels, whose main function - conducting ions - can be quantitatively measured with the patch-clamp technique providing the current-voltage characteristics. The study was performed on a set of KcsA channel models obtained from complete and incomplete contact maps. A fast continuous electrodiffusion model was used for calculating the current-voltage characteristics of structural models. We found that the computed charge selectivity and total current were sensitive to structural and electrostatic quality of models. In practical terms, we show that evaluating predicted conductance values is an appropriate method to eliminate models with an occluded pore or with multiple erroneously created pores. Moreover, filtering models on the basis of their predicted charge selectivity results in a substantial enrichment of the candidate set in highly accurate models. Tests on three other ion channels indicate that, in addition to being a proof of the concept, our function-oriented single-model quality assessment method can be directly applied to evaluation of structural models of some classes of protein channels. Finally, our work raises an important question whether a computational validation of functionality should be included in the evaluation process of structural models, whenever possible. Proteins 2016; 84:217-231. 2015 Wiley Periodicals, Inc. PMID:26650347

  19. Identification and prediction of transitional vertebrae on imaging studies: anatomical significance of paraspinal structures.

    PubMed

    Lee, Chang Hee; Park, Cheol Min; Kim, Kyeong Ah; Hong, Suk Joo; Seol, Hae Young; Kim, Baek Hyun; Kim, Jung Hyuk

    2007-11-01

    The aim of our study was to examine the locational distribution of paraspinal structures on MRI and to determine any predictable parameters that may be used for the identification of transitional vertebra (TV). We enrolled 534 patients who underwent MRI of their lumbosacral spine. The locations of the paraspinal structures, such as aortic bifurcation (AB), IVC confluence (IC), right renal artery (RRA), celiac trunk (CT), SMA root (SR), and iliolumbar ligament (ILL), were determined using "cross link" in PACS. We also assessed the morphology of the TV. The MRI showed that the most common site of the paraspinal structures in the normal group was AB at the lower L4, IC at the L4-5 disc space, RRA at the L1-2 disc space, CT at the T12-L1 disc space, SR at the upper L1, and ILL at the L5. The frequency of TV was 23.8% (lumbarization, 9.9%; sacralization, 13.9%). The paraspinal structures of the S1 lumbarization were positioned more toward the caudal location, whereas the paraspinal structures of the L5 sacralization were positioned more toward the cephalic location (P < 0.01). In conclusion, AB, IC, RRA, CT, SR, and ILL are useful landmarks for predicting the presence of TV on MRI. TV is possible when these paraspinal structures are in positions outside of the frequent locations. PMID:17879307

  20. Chemical shift prediction for protein structure calculation and quality assessment using an optimally parameterized force field

    PubMed Central

    Nielsen, Jakob T.; Eghbalnia, Hamid R.; Nielsen, Niels Chr.

    2011-01-01

    The exquisite sensitivity of chemical shifts as reporters of structural information, and the ability to measure them routinely and accurately, gives great import to formulations that elucidate the structure-chemical-shift relationship. Here we present a new and highly accurate, precise, and robust formulation for the prediction of NMR chemical shifts from protein structures. Our approach, shAIC (shift prediction guided by Akaikes Information Criterion), capitalizes on mathematical ideas and an information-theoretic principle, to represent the functional form of the relationship between structure and chemical shift as a parsimonious sum of smooth analytical potentials which optimally takes into account short-, medium-, and long-range parameters in a nuclei-specific manner to capture potential chemical shift perturbations caused by distant nuclei. shAIC outperforms the state-of-the-art methods that use analytical formulations. Moreover, for structures derived by NMR or structures with novel folds, shAIC delivers better overall results; even when it is compared to sophisticated machine learning approaches. shAIC provides for a computationally lightweight implementation that is unimpeded by molecular size, making it an ideal for use as a force field. PMID:22293396

  1. 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

  2. High quality protein sequence alignment by combining structural profile prediction and profile alignment using SABERTOOTH

    PubMed Central

    2010-01-01

    Background Protein align