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1

Non-B DB: a database of predicted non-B DNA-forming motifs in mammalian genomes  

PubMed Central

Although the capability of DNA to form a variety of non-canonical (non-B) structures has long been recognized, the overall significance of these alternate conformations in biology has only recently become accepted en masse. In order to provide access to genome-wide locations of these classes of predicted structures, we have developed non-B DB, a database integrating annotations and analysis of non-B DNA-forming sequence motifs. The database provides the most complete list of alternative DNA structure predictions available, including Z-DNA motifs, quadruplex-forming motifs, inverted repeats, mirror repeats and direct repeats and their associated subsets of cruciforms, triplex and slipped structures, respectively. The database also contains motifs predicted to form static DNA bends, short tandem repeats and homo(purine•pyrimidine) tracts that have been associated with disease. The database has been built using the latest releases of the human, chimp, dog, macaque and mouse genomes, so that the results can be compared directly with other data sources. In order to make the data interpretable in a genomic context, features such as genes, single-nucleotide polymorphisms and repetitive elements (SINE, LINE, etc.) have also been incorporated. The database is accessed through query pages that produce results with links to the UCSC browser and a GBrowse-based genomic viewer. It is freely accessible at http://nonb.abcc.ncifcrf.gov.

Cer, Regina Z.; Bruce, Kevin H.; Mudunuri, Uma S.; Yi, Ming; Volfovsky, Natalia; Luke, Brian T.; Bacolla, Albino; Collins, Jack R.; Stephens, Robert M.

2011-01-01

2

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

PubMed Central

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

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

1997-01-01

3

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

SciTech Connect

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.

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

2012-10-23

4

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

PubMed Central

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

Wang, Tingting; Wang, Jinke

2014-01-01

5

Structural insights into VirB-DNA complexes reveal mechanism of transcriptional activation of virulence genes  

PubMed Central

VirB activates transcription of virulence genes in Shigella flexneri by alleviating heat-stable nucleoid-structuring protein-mediated promoter repression. VirB is unrelated to the conventional transcriptional regulators, but homologous to the plasmid partitioning proteins. We determined the crystal structures of VirB HTH domain bound by the cis-acting site containing the inverted repeat, revealing that the VirB-DNA complex is related to ParB-ParS-like complexes, presenting an example that a ParB-like protein acts exclusively in transcriptional regulation. The HTH domain of VirB docks DNA major groove and provides multiple contacts to backbone and bases, in which the only specific base readout is mediated by R167. VirB only recognizes one half site of the inverted repeats containing the most matches to the consensus for VirB binding. The binding of VirB induces DNA conformational changes and introduces a bend at an invariant A-tract segment in the cis-acting site, suggesting a role of DNA remodeling. VirB exhibits positive cooperativity in DNA binding that is contributed by the C-terminal domain facilitating VirB oligomerization. The isolated HTH domain only confers partial DNA specificity. Additional determinants for sequence specificity may reside in N- or C-terminal domains. Collectively, our findings support and extend a previously proposed model for relieving heat-stable nucleoid-structuring protein-mediated repression by VirB.

Gao, Xiaopan; Zou, Tingting; Mu, Zhixia; Qin, Bo; Yang, Jian; Waltersperger, Sandro; Wang, Meitian; Cui, Sheng; Jin, Qi

2013-01-01

6

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

PubMed Central

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.

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

2012-01-01

7

An ensemble of B-DNA dinucleotide geometries lead to characteristic nucleosomal DNA structure and provide plasticity required for gene expression  

PubMed Central

Background A nucleosome is the fundamental repeating unit of the eukaryotic chromosome. It has been shown that the positioning of a majority of nucleosomes is primarily controlled by factors other than the intrinsic preference of the DNA sequence. One of the key questions in this context is the role, if any, that can be played by the variability of nucleosomal DNA structure. Results In this study, we have addressed this question by analysing the variability at the dinucleotide and trinucleotide as well as longer length scales in a dataset of nucleosome X-ray crystal structures. We observe that the nucleosome structure displays remarkable local level structural versatility within the B-DNA family. The nucleosomal DNA also incorporates a large number of kinks. Conclusions Based on our results, we propose that the local and global level versatility of B-DNA structure may be a significant factor modulating the formation of nucleosomes in the vicinity of high-plasticity genes, and in varying the probability of binding by regulatory proteins. Hence, these factors should be incorporated in the prediction algorithms and there may not be a unique 'template' for predicting putative nucleosome sequences. In addition, the multimodal distribution of dinucleotide parameters for some steps and the presence of a large number of kinks in the nucleosomal DNA structure indicate that the linear elastic model, used by several algorithms to predict the energetic cost of nucleosome formation, may lead to incorrect results.

2011-01-01

8

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

NASA Technical Reports Server (NTRS)

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.

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

1986-01-01

9

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

PubMed Central

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

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

2013-01-01

10

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

PubMed

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

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

2013-11-01

11

Structural insights into VirB-DNA complexes reveal mechanism of transcriptional activation of virulence genes.  

PubMed

VirB activates transcription of virulence genes in Shigella flexneri by alleviating heat-stable nucleoid-structuring protein-mediated promoter repression. VirB is unrelated to the conventional transcriptional regulators, but homologous to the plasmid partitioning proteins. We determined the crystal structures of VirB HTH domain bound by the cis-acting site containing the inverted repeat, revealing that the VirB-DNA complex is related to ParB-ParS-like complexes, presenting an example that a ParB-like protein acts exclusively in transcriptional regulation. The HTH domain of VirB docks DNA major groove and provides multiple contacts to backbone and bases, in which the only specific base readout is mediated by R167. VirB only recognizes one half site of the inverted repeats containing the most matches to the consensus for VirB binding. The binding of VirB induces DNA conformational changes and introduces a bend at an invariant A-tract segment in the cis-acting site, suggesting a role of DNA remodeling. VirB exhibits positive cooperativity in DNA binding that is contributed by the C-terminal domain facilitating VirB oligomerization. The isolated HTH domain only confers partial DNA specificity. Additional determinants for sequence specificity may reside in N- or C-terminal domains. Collectively, our findings support and extend a previously proposed model for relieving heat-stable nucleoid-structuring protein-mediated repression by VirB. PMID:23985969

Gao, Xiaopan; Zou, Tingting; Mu, Zhixia; Qin, Bo; Yang, Jian; Waltersperger, Sandro; Wang, Meitian; Cui, Sheng; Jin, Qi

2013-12-01

12

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

PubMed

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

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

2014-06-11

13

Structure and mechanism of the UvrA?UvrB DNA damage sensor  

SciTech Connect

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.

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

2012-04-17

14

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

PubMed Central

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

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

2014-01-01

15

Accurate representation of B-DNA double helical structure with implicit solvent and counterions.  

PubMed Central

High-resolution nuclear magnetic resonance (NMR) and crystallographic data have been taken to refine the force field used in the torsion angle space nucleic acids molecular mechanics program DUPLEX. The population balance deduced from NMR studies of two carcinogen-modified DNA conformers in equilibrium was used to fine tune a sigmoidal, distance-dependent dielectric function so that reasonable relative energies could be obtained. In addition, the base-pair and backbone geometry from high-resolution crystal structures of the Dickerson-Drew dodecamer was used to re-evaluate the deoxyribose pseudorotation profile and the Lennard-Jones nonbonded energy terms. With a modified dielectric function that assumes a very steep distance-dependent form, a deoxyribose pseudorotation profile with reduced energy barriers between C2'- and C3'-endo minima, and a shift of the Lennard-Jones potential energy minimum to a distance approximately 0.4 A greater than the sum of the van der Waals' radii, the sequence-dependent conformational features of the Dickerson-Drew dodecamer in both the solid state and the aqueous liquid crystalline phase are well reproduced. The robust performance of the revised force field, in conjunction with its efficiency through implicit treatment of solvent and counterions, provides a valuable tool for elucidating conformations and structure-function relationships of DNA, including those of molecules modified by carcinogens and other ligands.

Wang, Lihua; Hingerty, Brian E; Srinivasan, A R; Olson, Wilma K; Broyde, Suse

2002-01-01

16

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

PubMed Central

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

Tari, L W; Secco, A S

1995-01-01

17

Sequence-dependent crossed helix packing in the crystal structure of a B-DNA decamer yields a detailed model for the Holliday junction.  

PubMed

The structure of the B-DNA decamer d(CGCAATTGCG)2 has been determined by X-ray diffraction analysis to a resolution of 2.3 A and an R-factor of 17.7%. The decamer crystallises in the monoclinic space group C2 and packs with a crossed arrangement of helices and a unique crossing contact distinct from all other decamer structures. This is believed to be a direct result of the sequence-dependent minor groove width of the duplex. Crossed helix structures of DNA are valuable starting points for modelling studies of the Holliday junction. Two unique sites are observed at the cross-over junction where strand exchange may occur. A Holliday junction model has been constructed for each case and modelled using molecular mechanics and dynamics techniques. One of these models was found to be fully consistent with the available physical data. PMID:9223644

Wood, A A; Nunn, C M; Trent, J O; Neidle, S

1997-06-27

18

The DnaB?DnaC complex: a structure based on dimers assembled around an occluded channel  

PubMed Central

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

Barcena, Montserrat; Ruiz, Teresa; Donate, Luis Enrique; Brown, Susan E.; Dixon, Nicholas E.; Radermacher, Michael; Carazo, Jose Maria

2001-01-01

19

Protein Structure Prediction Center  

NSDL National Science Digital Library

Our goal is to help advance the methods of identifying protein structure from sequence. The Center has been organized to provide the means of objective testing of these methods via the process of blind prediction. In addition to support of the CASP meetings our goal is to promote an objective evaluation of prediction methods on a continuing basis.

20

Prediction of Modes with Dominant Base Roll and Propeller Twist in B-DNA poly(dA)-poly(dT).  

National Technical Information Service (NTIS)

The polymer poly(dA)-poly(dT) is a one-dimensional infinite lattice with the nucleotide pair adenine (A)-thymine (T) for a unit cell. Both bases are planar ringed structures.The orientation of these planar structures is described in terms of two parameter...

L. Young V. V. Prabhu E. W. Prohofsky G. S. Edwards

1989-01-01

21

Local conformational variations observed in B-DNA crystals do not improve base stacking: computational analysis of base stacking in a d(CATGGGCCCATG)(2) B<-->A intermediate crystal structure.  

PubMed

The crystal structure of d(CATGGGCCCATG)(2) shows unique stacking patterns of a stable B<-->A-DNA intermediate. We evaluated intrinsic base stacking energies in this crystal structure using an ab initio quantum mechanical method. We found that all crystal base pair steps have stacking energies close to their values in the standard and crystal B-DNA geometries. Thus, naturally occurring stacking geometries were essentially isoenergetic while individual base pair steps differed substantially in the balance of intra-strand and inter-strand stacking terms. Also, relative dispersion, electrostatic and polarization contributions to the stability of different base pair steps were very sensitive to base composition and sequence context. A large stacking flexibility is most apparent for the CpA step, while the GpG step is characterized by weak intra-strand stacking. Hydration effects were estimated using the Langevin dipoles solvation model. These calculations showed that an aqueous environment efficiently compensates for electrostatic stacking contributions. Finally, we have carried out explicit solvent molecular dynamics simulation of the d(CATGGGCCCATG)(2) duplex in water. Here the DNA conformation did not retain the initial crystal geometry, but moved from the B<-->A intermediate towards the B-DNA structure. The base stacking energy improved in the course of this simulation. Our findings indicate that intrinsic base stacking interactions are not sufficient to stabilize the local conformational variations in crystals. PMID:11121480

Poner, J; Florián, J; Ng, H L; Poner, J E; Packová, N

2000-12-15

22

The relative flexibility of B-DNA and ARNA duplexes: database analysis  

Microsoft Academic Search

An extensive analysis of structural databases is carried out to investigate the relative flexibility of B-DNA and A-RNA duplexes in crystal form. Our results show that the general anisotropic concept of flexibility is not very useful to compare the deform- ability of B-DNA and A-RNA duplexes, since the flexibility patterns of B-DNA and A-RNA are quite dif- ferent. In other

Alberto Perez; Agnes Noy; Filip Lankas; F. Javier Luque; Modesto Orozco

2004-01-01

23

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

PubMed Central

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

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

1997-01-01

24

Learning to Predict Combinatorial Structures  

NASA Astrophysics Data System (ADS)

The major challenge in designing a discriminative learning algorithm for predicting structured data is to address the computational issues arising from the exponential size of the output space. Existing algorithms make different assumptions to ensure efficient, polynomial time estimation of model parameters. For several combinatorial structures, including cycles, partially ordered sets, permutations and other graph classes, these assumptions do not hold. In this thesis, we address the problem of designing learning algorithms for predicting combinatorial structures by introducing two new assumptions: (i) The first assumption is that a particular counting problem can be solved efficiently. The consequence is a generalisation of the classical ridge regression for structured prediction. (ii) The second assumption is that a particular sampling problem can be solved efficiently. The consequence is a new technique for designing and analysing probabilistic structured prediction models. These results can be applied to solve several complex learning problems including but not limited to multi-label classification, multi-category hierarchical classification, and label ranking.

Vembu, Shankar

2009-12-01

25

Protein structure prediction using threading.  

PubMed

This chapter discusses the protocol for computational protein structure prediction by protein threading. First, we present a general procedure and summarize some typical ideas for each step of protein threading. Then, we describe the design and implementation of RAPTOR, a protein structure prediction program based on threading. The major focuses are three key components of RAPTOR: a linear programming approach to protein threading, two machine learning approaches (SVM and Gradient Boosting) to fold recognition, and evaluation of the statistical significance of the prediction results. The first part of this chapter is a brief review of protein threading, and the second part contains original research results. Some key ideas and results have been previously published. PMID:18075163

Xu, Jinbo; Jiao, Feng; Yu, Libo

2008-01-01

26

TRITIUM RESERVOIR STRUCTURAL PERFORMANCE PREDICTION  

SciTech Connect

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

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

2005-11-10

27

Local protein structure prediction using discriminative models  

PubMed Central

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

Sander, Oliver; Sommer, Ingolf; Lengauer, Thomas

2006-01-01

28

Predicting protein structure using only sequence information  

Microsoft Academic Search

Abstract: This paper presents results of blind predictions submittedto the CASP3 protein structure prediction experiment.We made predictions using the SAM-T98 method,an iterative hidden Markov model based method for constructingprotein family proles. The method is purely sequencebased|using no structural information|and yetwas able to predict structures as well as all butve of thestructure-based methods in CASP3

Kevin Karplus; Christian Barrett; Melissa Cline; Mark Diekhans; Leslie Grate; Richard Hughey

1999-01-01

29

The human specialized DNA polymerases and non-B DNA: vital relationships to preserve genome integrity.  

PubMed

In addition to the canonical right-handed double helix, DNA molecule can adopt several other non-B DNA structures. Readily formed in the genome at specific DNA repetitive sequences, these secondary conformations present a distinctive challenge for progression of DNA replication forks. Impeding normal DNA synthesis, cruciforms, hairpins, H DNA, Z DNA and G4 DNA considerably impact the genome stability and in some instances play a causal role in disease development. Along with previously discovered dedicated DNA helicases, the specialized DNA polymerases emerge as major actors performing DNA synthesis through these distorted impediments. In their new role, they are facilitating DNA synthesis on replication stalling sites formed by non-B DNA structures and thereby helping the completion of DNA replication, a process otherwise crucial for preserving genome integrity and concluding normal cell division. This review summarizes the evidence gathered describing the function of specialized DNA polymerases in replicating DNA through non-B DNA structures. PMID:24095858

Boyer, Anne-Sophie; Grgurevic, Srdana; Cazaux, Christophe; Hoffmann, Jean-Sébastien

2013-11-29

30

Computational prediction of RNA secondary structure.  

PubMed

The purpose of this section is to detail methods for the computational prediction of RNA secondary structure. This protocol is intended to provide an easy entry into the field of RNA structure prediction for those wishing to utilize it in their research and to suggest 'best practices' for going from sequence to secondary structure depending on the available data. PMID:24034313

Moss, Walter N

2013-01-01

31

The relative flexibility of B-DNA and A-RNA duplexes: database analysis  

PubMed Central

An extensive analysis of structural databases is carried out to investigate the relative flexibility of B-DNA and A-RNA duplexes in crystal form. Our results show that the general anisotropic concept of flexibility is not very useful to compare the deformability of B-DNA and A-RNA duplexes, since the flexibility patterns of B-DNA and A-RNA are quite different. In other words, ‘flexibility’ is a dangerous word for describing macromolecules, unless it is clearly defined. A few soft essential movements explain most of the natural flexibility of A-RNA, whereas many are necessary for B-DNA. Essential movements occurring in naked B-DNAs are identical to those necessary to deform DNA in DNA–protein complexes, which suggest that evolution has designed DNA–protein complexes so that B-DNA is deformed according to its natural tendency. DNA is generally more flexible, but for some distortions A-RNA is easier to deform. Local stiffness constants obtained for naked B-DNAs and DNA complexes are very close, demonstrating that global distortions in DNA necessary for binding to proteins are the result of the addition of small concerted deformations at the base-pair level. Finally, it is worth noting that in general the picture of the relative deformability of A-RNA and DNA derived from database analysis agrees very well with that derived from state of the art molecular dynamics (MD) simulations.

Perez, Alberto; Noy, Agnes; Lankas, Filip; Luque, F. Javier; Orozco, Modesto

2004-01-01

32

Tree Adjoining Grammars for RNA Structure Prediction  

Microsoft Academic Search

In this paper, we are concerned with identifying a subclass of tree adjoining grammars (TAGs) that is suitable for the application to modeling and predicting RNA secondary structures. The goal of this paper is twofold: For the purpose of applying to the RNA secondary structure prediction problem, we first introduce a special subclass of TAGs and develop a fast parsing

Yasuo Uemura; Aki Hasegawa; Satoshi Kobayashi; Takashi Yokomori

1999-01-01

33

Neural networks for secondary structure and structural class predictions  

Microsoft Academic Search

A pair of neural network-based algorithms is presented for predicting the tertiary structural class and the second- ary structure of proteins. Each algorithm realizes improvements in accuracy based on information provided by the other. Structural class prediction of proteins nonhomologous to any in the training set is improved signifi- cantly, from 62.3% to 73.9%, and secondary structure prediction accuracy improves

JOHN-MARC CHANDONIA; MARTIN KARPLUS

1995-01-01

34

Prediction of RNA Secondary Structure  

Microsoft Academic Search

Calculations of the free energy required to close single-strand loops by formation of a base pair in double-helical nucleic acids are reported. These results can be used to estimate the free energy of particular secondary structures for a given RNA molecule under conditions of high-salt concentration.

Charles Delisi; Donald M. Crothers

1971-01-01

35

Transmembrane beta-barrel protein structure prediction  

NASA Astrophysics Data System (ADS)

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.

Randall, Arlo; Baldi, Pierre

36

Semi-Supervised Discriminative Structured Prediction.  

National Technical Information Service (NTIS)

The proposed research develops cutting-edge machine learning techniques to improve the performance of a wide spectrum of robust and intelligent classification tasks. Structured prediction, one of major challenges in statistical machine learning, is a clas...

S. Wang

2013-01-01

37

Accurate Prediction of Protein Secondary Structural Content  

Microsoft Academic Search

An improved multiple linear regression (MLR) method is proposed to predict a protein's secondary structural content based on its primary sequence. The amino acid composition, the autocorrelation function, and the interaction function of side-chain mass derived from the primary sequence are taken into account. The average absolute errors of prediction over 704 unrelated proteins with the jackknife test are 0.088,

Zong Lin; Xian-Ming Pan

2001-01-01

38

Impact damage prediction in carbon composite structures  

Microsoft Academic Search

This paper describes a strategy for predicting the extent of internal damage in a brittle carbon fibre laminated composite stucture, when subjected to low velocity impact by a single mass. The success of the predictions, which avoid expensive three-dimensional analysis, is validated by test for a wide range of structures from small stiff plates through to large flexible stiffened compression

G. A. O. Davies; X. Zhang

1995-01-01

39

SCRATCH: a protein structure and structural feature prediction server  

PubMed Central

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

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

2005-01-01

40

Prediction of protein tertiary structures using MUFOLD.  

PubMed

There have been steady improvements in protein structure prediction during the past two decades. However, current methods are still far from consistently predicting structural models accurately with computing power accessible to common users. To address this challenge, we developed MUFOLD, a hybrid method of using whole and partial template information along with new computational techniques for protein tertiary structure prediction. MUFOLD covers both template-based and ab initio predictions using the same framework and aims to achieve high accuracy and fast computing. Two major novel contributions of MUFOLD are graph-based model generation and molecular dynamics ranking (MDR). By formulating a prediction as a graph realization problem, we apply an efficient optimization approach of Multidimensional Scaling (MDS) to speed up the prediction dramatically. In addition, under this framework, we enhance the predictions consistently by iteratively using the information from generated models. MDR, in contrast to widely used static scoring functions, exploits dynamics properties of structures to evaluate their qualities, which can often identify best structures from a pool more effectively. PMID:22130979

Zhang, Jingfen; He, Zhiquan; Wang, Qingguo; Barz, Bogdan; Kosztin, Ioan; Shang, Yi; Xu, Dong

2012-01-01

41

Geometric prediction structure for multiview video coding  

NASA Astrophysics Data System (ADS)

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

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

2010-02-01

42

Data Mining for Protein Secondary Structure Prediction  

Microsoft Academic Search

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

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

43

Protein secondary structure prediction using local alignments.  

PubMed

The accuracy of secondary structure prediction methods has been improved significantly by the use of aligned protein sequences. The PHD method and the NNSSP method reach 71 to 72% of sustained overall three-state accuracy when multiple sequence alignments are with neural networks and nearest-neighbor algorithms, respectively. We introduce a variant of the nearest-neighbor approach that can achieve similar accuracy using a single sequence as the query input. We compute the 50 best non-intersecting local alignments of the query sequence with each sequence from a set of proteins with known 3D structures. Each position of the query sequence is aligned with the database amino acids in alpha-helical, beta-strand or coil states. The prediction type of secondary structure is selected as the type of aligned position with the maximal total score. On the dataset of 124 non-membrane non-homologous proteins, used earlier as a benchmark for secondary structure predictions, our method reaches an overall three-state accuracy of 71.2%. The performance accuracy is verified by an additional test on 461 non-homologous proteins giving an accuracy of 71.0%. The main strength of the method is the high level of prediction accuracy for proteins without any known homolog. Using multiple sequence alignments as input the method has a prediction accuracy of 73.5%. Prediction of secondary structure by the SSPAL method is available via Baylor College of Medicine World Wide Web server. PMID:9149139

Salamov, A A; Solovyev, V V

1997-04-25

44

Accurate prediction of protein structural classes using functional domains and predicted secondary structure sequences.  

PubMed

Protein structural class prediction is one of the challenging problems in bioinformatics. Previous methods directly based on the similarity of amino acid (AA) sequences have been shown to be insufficient for low-similarity protein data-sets. To improve the prediction accuracy for such low-similarity proteins, different methods have been recently proposed that explore the novel feature sets based on predicted secondary structure propensities. In this paper, we focus on protein structural class prediction using combinations of the novel features including secondary structure propensities as well as functional domain (FD) features extracted from the InterPro signature database. Our comprehensive experimental results based on several benchmark data-sets have shown that the integration of new FD features substantially improves the accuracy of structural class prediction for low-similarity proteins as they capture meaningful relationships among AA residues that are far away in protein sequence. The proposed prediction method has also been tested to predict structural classes for partially disordered proteins with the reasonable prediction accuracy, which is a more difficult problem comparing to structural class prediction for commonly used benchmark data-sets and has never been done before to the best of our knowledge. In addition, to avoid overfitting with a large number of features, feature selection is applied to select discriminating features that contribute to achieve high prediction accuracy. The selected features have been shown to achieve stable prediction performance across different benchmark data-sets. PMID:22545993

Ahmadi Adl, Amin; Nowzari-Dalini, Abbas; Xue, Bin; Uversky, Vladimir N; Qian, Xiaoning

2012-01-01

45

Predicting structure in nonsymmetric sparse matrix factorizations  

SciTech Connect

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.

Gilbert, J.R. (Xerox Palo Alto Research Center, CA (United States)); Ng, E.G. (Oak Ridge National Lab., TN (United States))

1992-10-01

46

Facilitating RNA Structure Prediction with Microarrays†  

PubMed Central

Determining RNA secondary structure is important for understanding structure-function relationships and identifying potential drug targets. This paper reports the use of microarrays with heptamer 2?-O-methyl oligoribonucleotides to probe the secondary structure of an RNA and thereby improve prediction of that secondary structure. When experimental constraints from hybridization results are added to a free energy minimization algorithm, the prediction of the secondary structure of E. coli 5S rRNA improves from 27% to 92% of the known canonical base pairs. Optimization of buffer conditions for hybridization and application of 2?-O-methyl-2-thiouridine to enhance binding and improve discrimination between AU and GU pairs are also described. The results suggest that probing RNA with oligonucleotide microarrays can facilitate determination of secondary structure.

Kierzek, Elzbieta; Kierzek, Ryszard; Turner, Douglas H.; Catrina, Irina E.

2014-01-01

47

Protein Structure Prediction by Protein Threading  

Microsoft Academic Search

\\u000a The seminal work of Bowie, Lüthy, and Eisenberg (Bowie et al., 1991) on “the inverse protein folding problem” laid the foundation of protein structure prediction by protein threading. By using\\u000a simple measures for fitness of different amino acid types to local structural environments defined in terms of solvent accessibility\\u000a and protein secondary structure, the authors derived a simple and yet

Ying Xu; Zhijie Liu; Liming Cai; Dong Xu

2007-01-01

48

SHAPE-Directed RNA Secondary Structure Prediction  

PubMed Central

The diverse functional roles of RNA are determined by its underlying structure. Accurate and comprehensive knowledge of RNA structure would inform a broader understanding of RNA biology and facilitate exploiting RNA as a biotechnological tool and therapeutic target. Determining the pattern of base pairing, or secondary structure, of RNA is a first step in these endeavors. Advances in experimental, computational, and comparative analysis approaches for analyzing secondary structure have yielded accurate structures for many small RNAs, but only a few large (>500 nts) RNAs. In addition, most current methods for determining a secondary structure require considerable effort, analytical expertise, and technical ingenuity. In this review, we outline an efficient strategy for developing accurate secondary structure models for RNAs of arbitrary length. This approach melds structural information obtained using SHAPE chemistry with structure prediction using nearest-neighbor rules and the dynamic programming algorithm implemented in the RNAstructure program. Prediction accuracies reach ?95% for RNAs on the kilobase scale. This approach facilitates both development of new models and refinement of existing RNA structure models, which we illustrate using the Gag-Pol frameshift element in an HIV-1 M-group genome. Most promisingly, integrated experimental and computational refinement brings closer the ultimate goal of efficiently and accurately establishing the secondary structure for any RNA sequence.

Low, Justin T.; Weeks, Kevin M.

2010-01-01

49

Ko Displacement Theory for Structural Shape Predictions  

NASA Technical Reports Server (NTRS)

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.

Ko, William L.

2010-01-01

50

FPGA Accelerator for Protein Structure Prediction Algorithms  

Microsoft Academic Search

Bioinformatics applications are computationally very expensive programs. They work with large data sets and also consume a lot of CPU cycles and often require high degrees of precision. An important application in this area is tertiary structure prediction of proteins. This paper reports a codesign methodology to build hardware accelerators to minimize the running time of a protein energy minimization

Advait Jain; Pulkit Gambhir; Kolin Paul

51

Effective energy functions for protein structure prediction  

Microsoft Academic Search

Protein structure prediction, fold recognition, homology modeling and design rely mainly on statistical effective energy functions. Although the theoretical foundation of such functions is not clear, their usefulness has been demonstrated in many applications. Molecular mechanics force fields, particularly when augmented by implicit solvation models, provide physical effective energy functions that are beginning to play a role in this area.

Themis Lazaridis; Martin Karplus

2000-01-01

52

Gene structure prediction by linguistic methods  

SciTech Connect

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

Dong, S.; Searls, D.B. [Univ. of Pennsylvania School of Medicine, Philadelphia, PA (United States)] [Univ. of Pennsylvania School of Medicine, Philadelphia, PA (United States)

1994-10-01

53

Structure of allergens and structure based epitope predictions?  

PubMed Central

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.

Dall'Antonia, Fabio; Pavkov-Keller, Tea; Zangger, Klaus; Keller, Walter

2014-01-01

54

FRMSDPred: Predicting Local RMSD Between Structural Fragments Using Sequence Information.  

National Technical Information Service (NTIS)

The effectiveness of comparative modeling approaches for protein structure prediction can be substantially improved by incorporating predicted structural information in the initial sequence-structure alignment. Motivated by the approaches used to align pr...

G. Karypis H. Rangwala

2007-01-01

55

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

PubMed

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

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

2012-07-01

56

Gene structure prediction in syntenic DNA segments  

PubMed Central

The accurate prediction of higher eukaryotic gene structures and regulatory elements directly from genomic sequences is an important early step in the understanding of newly assembled contigs and finished genomes. As more new genomes are sequenced, comparative approaches are becoming increasingly practical and valuable for predicting genes and regulatory elements. We demonstrate the effectiveness of a comparative method called pattern filtering; it utilizes synteny between two or more genomic segments for the annotation of genomic sequences. Pattern filtering optimally detects the signatures of conserved functional elements despite the stochastic noise inherent in evolutionary processes, allowing more accurate annotation of gene models. We anticipate that pattern filtering will facilitate sequence annotation and the discovery of new functional elements by the genetics and genomics communities.

Moore, Jonathan E.; Lake, James A.

2003-01-01

57

Structure of allergens and structure based epitope predictions.  

PubMed

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

Dall'antonia, Fabio; Pavkov-Keller, Tea; Zangger, Klaus; Keller, Walter

2014-03-01

58

Service life prediction of reinforced concrete structures  

SciTech Connect

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

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

1999-09-01

59

Protein structure based prediction of catalytic residues  

PubMed Central

Background Worldwide structural genomics projects continue to release new protein structures at an unprecedented pace, so far nearly 6000, but only about 60% of these proteins have any sort of functional annotation. Results We explored a range of features that can be used for the prediction of functional residues given a known three-dimensional structure. These features include various centrality measures of nodes in graphs of interacting residues: closeness, betweenness and page-rank centrality. We also analyzed the distance of functional amino acids to the general center of mass (GCM) of the structure, relative solvent accessibility (RSA), and the use of relative entropy as a measure of sequence conservation. From the selected features, neural networks were trained to identify catalytic residues. We found that using distance to the GCM together with amino acid type provide a good discriminant function, when combined independently with sequence conservation. Using an independent test set of 29 annotated protein structures, the method returned 411 of the initial 9262 residues as the most likely to be involved in function. The output 411 residues contain 70 of the annotated 111 catalytic residues. This represents an approximately 14-fold enrichment of catalytic residues on the entire input set (corresponding to a sensitivity of 63% and a precision of 17%), a performance competitive with that of other state-of-the-art methods. Conclusions We found that several of the graph based measures utilize the same underlying feature of protein structures, which can be simply and more effectively captured with the distance to GCM definition. This also has the added the advantage of simplicity and easy implementation. Meanwhile sequence conservation remains by far the most influential feature in identifying functional residues. We also found that due the rapid changes in size and composition of sequence databases, conservation calculations must be recalibrated for specific reference databases.

2013-01-01

60

Structure prediction of magnetosome-associated proteins  

PubMed Central

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.

Nudelman, Hila; Zarivach, Raz

2014-01-01

61

A scoring framework for predicting protein structures  

NASA Astrophysics Data System (ADS)

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

Zou, Xiaoqin

2013-03-01

62

Protein secondary structure. Studies on the limits of prediction accuracy.  

PubMed

A secondary structure prediction technique is proposed which includes nucleation site determination through multiplication of conformational preference parameters as well as weighting factors to represent structurally stabilizing short range interactions. The prediction accuracy of the method is calculated using data bases categorized according to the four protein structural classes and with differing assignments of secondary structural regions. The results indicate that nearest neighbor prediction techniques (a) are insensitive to various assignment criteria for the secondary structural spans, (b) have nearly achieved their upper limit of prediction accuracy, and (c) can be somewhat improved through the use of stereochemical weighting factors and conformational parameters derived from the four structural groups. PMID:7118409

Palau, J; Argos, P; Puigdomenech, P

1982-04-01

63

Protein secondary structure prediction with a neural network  

SciTech Connect

A method is presented for protein secondary structure prediction based on a neural network. A training phase was used to teach the network to recognize the relation between secondary structure and amino acid sequences on a sample set of 48 proteins of known structure. On a separate test set of 14 proteins of known structure, the method achieved a maximum overall predictive accuracy of 63% for three states: helix, sheet, and coil. A numerical measure of helix and sheet tendency for each residue was obtained from the calculations. When predictions were filtered to include only the strongest 31% of predictions, the predictive accuracy rose to 79%.

Holley, L.H.; Karplus, M. (Harvard Univ., Cambridge, MA (United States))

1989-01-01

64

Structure of nonevaporating sprays - Measurements and predictions  

NASA Technical Reports Server (NTRS)

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.

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

1984-01-01

65

Using the RNAstructure Software Package to Predict Conserved RNA Structures.  

PubMed

The structures of many non-coding RNA (ncRNA) are conserved by evolution to a greater extent than their sequences. By predicting the conserved structure of two or more homologous sequences, the accuracy of secondary structure prediction can be improved as compared to structure prediction for a single sequence. This unit provides protocols for the use of four programs in the RNAstructure suite for prediction of conserved structures, Multilign, TurboFold, Dynalign, and PARTS. These programs can be run via Web servers, on the command line, or with graphical interfaces. Curr. Protoc. Bioinform. 46:12.4.1-12.4.22. © 2014 by John Wiley & Sons, Inc. PMID:24939126

Mathews, David H

2014-01-01

66

RNA-SSPT: RNA Secondary Structure Prediction Tools.  

PubMed

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

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

2013-01-01

67

Multistrand RNA secondary structure prediction and nanostructure design including pseudoknots.  

PubMed

We are presenting NanoFolder, a method for the prediction of the base pairing of potentially pseudoknotted multistrand RNA nanostructures. We show that the method outperforms several other structure prediction methods when applied to RNA complexes with non-nested base pairs. We extended this secondary structure prediction capability to allow RNA sequence design. Using native PAGE, we experimentally confirm that four in silico designed RNA strands corresponding to a triangular RNA structure form the expected stable complex. PMID:22067111

Bindewald, Eckart; Afonin, Kirill; Jaeger, Luc; Shapiro, Bruce A

2011-12-27

68

Evolutionary Crystal Structure Prediction and Novel High-Pressure Phases  

Microsoft Academic Search

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

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

2010-01-01

69

Predicting local quality of a sequence-structure alignment.  

PubMed

Although protein structure prediction has made great progress in recent years, a protein model derived from automated prediction methods is subject to various errors. As methods for structure prediction develop, a continuing problem is how to evaluate the quality of a protein model, especially to identify some well-predicted regions of the model, so that the structural biology community can benefit from the automated structure prediction. It is also important to identify badly-predicted regions in a model so that some refinement measurements can be applied to it. We present two complementary techniques, FragQA and PosQA, to accurately predict local quality of a sequence-structure (i.e. sequence-template) alignment generated by comparative modeling (i.e. homology modeling and threading). FragQA and PosQA predict local quality from two different perspectives. Different from existing methods, FragQA directly predicts cRMSD between a continuously aligned fragment determined by an alignment and the corresponding fragment in the native structure, while PosQA predicts the quality of an individual aligned position. Both FragQA and PosQA use an SVM (Support Vector Machine) regression method to perform prediction using similar information extracted from a single given alignment. Experimental results demonstrate that FragQA performs well on predicting local fragment quality, and PosQA outperforms two top-notch methods, ProQres and ProQprof. Our results indicate that (1) local quality can be predicted well; (2) local sequence evolutionary information (i.e. sequence similarity) is the major factor in predicting local quality; and (3) structural information such as solvent accessibility and secondary structure helps to improve the prediction performance. PMID:19785046

Gao, Xin; Xu, Jinbo; Li, Shuai Cheng; Li, Ming

2009-10-01

70

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

PubMed

The internal ribosomal entry site (IRES) functions as cap-independent translation initiation sites in eukaryotic cells. IRES elements have been applied as useful tools for bi-cistronic expression vectors. Current RNA structure prediction programs are unable to predict precisely the potential IRES element. We have designed a viral IRES prediction system (VIPS) to perform the IRES secondary structure prediction. In order to obtain better results for the IRES prediction, the VIPS can evaluate and predict for all four different groups of IRESs with a higher accuracy. RNA secondary structure prediction, comparison, and pseudoknot prediction programs were implemented to form the three-stage procedure for the VIPS. The backbone of VIPS includes: the RNAL fold program, aimed to predict local RNA secondary structures by minimum free energy method; the RNA Align program, intended to compare predicted structures; and pknotsRG program, used to calculate the pseudoknot structure. VIPS was evaluated by using UTR database, IRES database and Virus database, and the accuracy rate of VIPS was assessed as 98.53%, 90.80%, 82.36% and 80.41% for IRES groups 1, 2, 3, and 4, respectively. This advance useful search approach for IRES structures will facilitate IRES related studies. The VIPS on-line website service is available at http://140.135.61.250/vips/. PMID:24223923

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

2013-01-01

71

Assessing the Accuracy of Template-Based Structure Prediction Metaservers by Comparison with Structural Genomics Structures  

PubMed Central

The explosion of the size of the universe of known protein sequences has stimulated two complementary approaches to structural mapping of these sequences: theoretical structure prediction and experimental determination by structural genomics (SG). In this work, we assess the accuracy of structure prediction by two automated template-based structure prediction metaservers (genesilico.pl and bioinfo.pl) by measuring the structural similarity of the predicted models to corresponding experimental models determined a posteriori. Of 199 targets chosen from SG programs, the metaservers predicted the structures of about a fourth of them “correctly.” (In this case, “correct” was defined as placing more than 70% of the alpha carbon atoms in the model within 2 Å of the experimentally determined positions.) Almost all of the targets that could be modeled to this accuracy were those with an available template in the Protein Data Bank (PDB) with more than 25% sequence identity. The majority of those SG targets with lower sequence identity to structures in the PDB were not predicted by the metaservers with this accuracy. We also compared metaserver results to CASP8 results, finding that the models obtained by participants in the CASP competition were significantly better than those produced by the metaservers.

Gront, Dominik; Grabowski, Marek; Raynor, John; Tkaczuk, Karolina L.; Minor, Wladek

2014-01-01

72

Assessing the accuracy of template-based structure prediction metaservers by comparison with structural genomics structures.  

PubMed

The explosion of the size of the universe of known protein sequences has stimulated two complementary approaches to structural mapping of these sequences: theoretical structure prediction and experimental determination by structural genomics (SG). In this work, we assess the accuracy of structure prediction by two automated template-based structure prediction metaservers (genesilico.pl and bioinfo.pl) by measuring the structural similarity of the predicted models to corresponding experimental models determined a posteriori. Of 199 targets chosen from SG programs, the metaservers predicted the structures of about a fourth of them "correctly." (In this case, "correct" was defined as placing more than 70 % of the alpha carbon atoms in the model within 2 Å of the experimentally determined positions.) Almost all of the targets that could be modeled to this accuracy were those with an available template in the Protein Data Bank (PDB) with more than 25 % sequence identity. The majority of those SG targets with lower sequence identity to structures in the PDB were not predicted by the metaservers with this accuracy. We also compared metaserver results to CASP8 results, finding that the models obtained by participants in the CASP competition were significantly better than those produced by the metaservers. PMID:23086054

Gront, Dominik; Grabowski, Marek; Zimmerman, Matthew D; Raynor, John; Tkaczuk, Karolina L; Minor, Wladek

2012-12-01

73

HFold: RNA Pseudoknotted Secondary Structure Prediction Using Hierarchical Folding  

Microsoft Academic Search

Improving the accuracy and efficiency of computational RNA secondary structure prediction is an important challenge, particularly\\u000a for pseudoknotted secondary structures. We propose a new approach for prediction of pseudoknotted structures, motivated by\\u000a the hypothesis that RNA structures fold hierarchically, with pseudoknot free pairs forming initially, and pseudoknots forming\\u000a later so as to minimize energy relative to the initial pseudoknot free

Hosna Jabbari; Anne Condon; Ana Pop; Cristina Pop; Yinglei Zhao

2007-01-01

74

Contact order and ab initio protein structure prediction  

PubMed Central

Although much of the motivation for experimental studies of protein folding is to obtain insights for improving protein structure prediction, there has been relatively little connection between experimental protein folding studies and computational structural prediction work in recent years. In the present study, we show that the relationship between protein folding rates and the contact order (CO) of the native structure has implications for ab initio protein structure prediction. Rosetta ab initio folding simulations produce a dearth of high CO structures and an excess of low CO structures, as expected if the computer simulations mimic to some extent the actual folding process. Consistent with this, the majority of failures in ab initio prediction in the CASP4 (critical assessment of structure prediction) experiment involved high CO structures likely to fold much more slowly than the lower CO structures for which reasonable predictions were made. This bias against high CO structures can be partially alleviated by performing large numbers of additional simulations, selecting out the higher CO structures, and eliminating the very low CO structures; this leads to a modest improvement in prediction quality. More significant improvements in predictions for proteins with complex topologies may be possible following significant increases in high–performance computing power, which will be required for thoroughly sampling high CO conformations (high CO proteins can take six orders of magnitude longer to fold than low CO proteins). Importantly for such a strategy, simulations performed for high CO structures converge much less strongly than those for low CO structures, and hence, lack of simulation convergence can indicate the need for improved sampling of high CO conformations. The parallels between Rosetta simulations and folding in vivo may extend to misfolding: The very low CO structures that accumulate in Rosetta simulations consist primarily of local up–down ?–sheets that may resemble precursors to amyloid formation.

Bonneau, Richard; Ruczinski, Ingo; Tsai, Jerry; Baker, David

2002-01-01

75

A novel protein structural classes prediction method based on predicted secondary structure.  

PubMed

Knowledge of structural classes plays an important role in understanding protein folding patterns. In this paper, features based on the predicted secondary structure sequence and the corresponding E-H sequence are extracted. Then, an 11-dimensional feature vector is selected based on a wrapper feature selection algorithm and a support vector machine (SVM). Among the 11 selected features, 4 novel features are newly designed to model the differences between ?/? class and ? + ? class, and other 7 rational features are proposed by previous researchers. To examine the performance of our method, a total of 5 datasets are used to design and test the proposed method. The results show that competitive prediction accuracies can be achieved by the proposed method compared to existing methods (SCPRED, RKS-PPSC and MODAS), and 4 new features are demonstrated essential to differentiate ?/? and ? + ? classes. Standalone version of the proposed method is written in JAVA language and it can be downloaded from http://web.xidian.edu.cn/slzhang/paper.html. PMID:22353242

Ding, Shuyan; Zhang, Shengli; Li, Yang; Wang, Tianming

2012-05-01

76

Prediction of protein structural classes using hybrid properties  

Microsoft Academic Search

In this paper, amino acid compositions are combined with some protein sequence properties (physiochemical properties) to predict\\u000a protein structural classes. We are able to predict protein structural classes using a mathematical model that combines the\\u000a nearest neighbor algorithm (NNA), mRMR (minimum redundancy, maximum relevance), and feature forward searching strategy. Jackknife\\u000a cross-validation is used to evaluate the prediction accuracy. As a

Wenjin Li; Kao Lin; Kaiyan Feng; Yudong Cai

2008-01-01

77

Protein Secondary Structure Prediction with a Neural Network  

Microsoft Academic Search

A method is presented for protein secondary structure prediction based on a neural network. A training phase was used to teach the network to recognize the relation between secondary structure and amino acid sequences on a sample set of 48 proteins of known structure. On a separate test set of 14 proteins of known structure, the method achieved a maximum

L. Howard Holley; Martin Karplus

1989-01-01

78

RNAstructure: software for RNA secondary structure prediction and analysis  

PubMed Central

Background To understand an RNA sequence's mechanism of action, the structure must be known. Furthermore, target RNA structure is an important consideration in the design of small interfering RNAs and antisense DNA oligonucleotides. RNA secondary structure prediction, using thermodynamics, can be used to develop hypotheses about the structure of an RNA sequence. Results RNAstructure is a software package for RNA secondary structure prediction and analysis. It uses thermodynamics and utilizes the most recent set of nearest neighbor parameters from the Turner group. It includes methods for secondary structure prediction (using several algorithms), prediction of base pair probabilities, bimolecular structure prediction, and prediction of a structure common to two sequences. This contribution describes new extensions to the package, including a library of C++ classes for incorporation into other programs, a user-friendly graphical user interface written in JAVA, and new Unix-style text interfaces. The original graphical user interface for Microsoft Windows is still maintained. Conclusion The extensions to RNAstructure serve to make RNA secondary structure prediction user-friendly. The package is available for download from the Mathews lab homepage at http://rna.urmc.rochester.edu/RNAstructure.html.

2010-01-01

79

Designing and benchmarking the MULTICOM protein structure prediction system  

PubMed Central

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

2013-01-01

80

Improving the accuracy of protein secondary structure prediction using structural alignment  

Microsoft Academic Search

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

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

2006-01-01

81

Prediction of protein structural classes using hybrid properties.  

PubMed

In this paper, amino acid compositions are combined with some protein sequence properties (physiochemical properties) to predict protein structural classes. We are able to predict protein structural classes using a mathematical model that combines the nearest neighbor algorithm (NNA), mRMR (minimum redundancy, maximum relevance), and feature forward searching strategy. Jackknife cross-validation is used to evaluate the prediction accuracy. As a result, the prediction success rate improves to 68.8%, which is better than the 62.2% obtained when using only amino acid compositions. Therefore, we conclude that the physiochemical properties are factors that contribute to the protein folding phenomena and the most contributing features are found to be the amino acid composition. We expect that prediction accuracy will improve further as more sequence information comes to light. A web server for predicting the protein structural classes is available at http://app3.biosino.org:8080/liwenjin/index.jsp. PMID:18953662

Li, Wenjin; Lin, Kao; Feng, Kaiyan; Cai, Yudong

2008-01-01

82

Predicted structures of two proteins involved in human diseases  

Microsoft Academic Search

Structures of 79 proteins involved in human diseases were predicted by sequence alignments with structural templates. The\\u000a predicted structures for ALDP and CSA, proteins responsible for adrenoleukodystrophy and the Cockayne syndrome, respectively,\\u000a were analyzed to elucidate the molecular basis of disease mutations. In particular we positioned residue P484 of ALDP in the\\u000a homodimer interface. This positioning is consistent with a

Huan-Xiang Zhou; Guoli Wang

2001-01-01

83

Quantifying variances in comparative RNA secondary structure prediction  

PubMed Central

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.

2013-01-01

84

SARNA-Predict: accuracy improvement of RNA secondary structure prediction using permutation-based simulated annealing.  

PubMed

Ribonucleic acid (RNA), a single-stranded linear molecule, is essential to all biological systems. Different regions of the same RNA strand will fold together via base pair interactions to make intricate secondary and tertiary structures that guide crucial homeostatic processes in living organisms. Since the structure of RNA molecules is the key to their function, algorithms for the prediction of RNA structure are of great value. In this article, we demonstrate the usefulness of SARNA-Predict, an RNA secondary structure prediction algorithm based on Simulated Annealing (SA). A performance evaluation of SARNA-Predict in terms of prediction accuracy is made via comparison with eight state-of-the-art RNA prediction algorithms: mfold, Pseudoknot (pknotsRE), NUPACK, pknotsRG-mfe, Sfold, HotKnots, ILM, and STAR. These algorithms are from three different classes: heuristic, dynamic programming, and statistical sampling techniques. An evaluation for the performance of SARNA-Predict in terms of prediction accuracy was verified with native structures. Experiments on 33 individual known structures from eleven RNA classes (tRNA, viral RNA, antigenomic HDV, telomerase RNA, tmRNA, rRNA, RNaseP, 5S rRNA, Group I intron 23S rRNA, Group I intron 16S rRNA, and 16S rRNA) were performed. The results presented in this paper demonstrate that SARNA-Predict can out-perform other state-of-the-art algorithms in terms of prediction accuracy. Furthermore, there is substantial improvement of prediction accuracy by incorporating a more sophisticated thermodynamic model (efn2). PMID:21030739

Tsang, Herbert H; Wiese, Kay C

2010-01-01

85

Fatigue Life Prediction of Served Aircraft Aluminum Alloy Structure  

Microsoft Academic Search

In this paper, the life prediction method of corroded aircraft aluminum alloy structure was proposed. The stress multiplication of aluminum alloy structure subjected to pitting corrosion damage was simulated by ANSYS software. Based upon the simulation results, the AFGROW software was used to carry out the crack growth analyses, the life cycles of corroded structure were estimated, and a very

Youhong Zhang; Shengli Zhou; Jupeng Liu; Enyi Chu; Rui Zhang

2009-01-01

86

Prediction of Protein Structural Classes by Support Vector Machines  

Microsoft Academic Search

In this paper, we apply a new machine learning method which is called support vector machine to approach the prediction of protein structural class. The support vector machine method is performed based on the database derived from SCOP which is based upon domains of known structure and the evolutionary relationships and the principles that govern their 3D structure. As a

Yu-dong Cai; Xiao-jun Liu; Xue-biao Xu; Kuo-chen Chou

2002-01-01

87

Ensemble-based prediction of RNA secondary structures  

PubMed Central

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.

2013-01-01

88

Local Structure Prediction with Local Structure-based Sequence Profiles  

Microsoft Academic Search

Motivation: A large body of experimental and theoretical evidence suggests that local structural determinants are frequently encoded in short segments of protein sequence. Although the local structural information, once recognized, is particularly useful in protein structural and functional analyses, it remains a difficult problem to identify embed- ded local structural codes based solely on sequence infor- mation. Results: In this

An-suei Yang; Lu-yong Wang

2003-01-01

89

Predicting protein structure using hidden Markov models  

Microsoft Academic Search

We discuss how methods based on hidden Markov models performed in the fold-recognition sectionof the CASP2 experiment. Hidden Markov models were built for a representative set of just over onethousand structures from the Protein Data Bank (pdb). Each CASP2 target sequence was scored againstthis library of hmms. In addition, an hmm was built for each of the target sequences, and

Kevin Karplus; Kimmen Sjölander; Christian Barrett; Melissa Cline; David Haussler; Richard Hughey; Liisa Holm; Chris Sander

1997-01-01

90

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

PubMed Central

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.

Skolnick, Jeffrey; Zhou, Hongyi; Gao, Mu

2013-01-01

91

Bimetric structure formation: Non-Gaussian predictions  

SciTech Connect

The minimal bimetric theory employing a disformal transformation between matter and gravity metrics is known to produce exactly scale-invariant fluctuations. It has a purely equilateral non-Gaussian signal, with an amplitude smaller than that of Dirac Born Infeld inflation (with opposite sign) but larger than standard inflation. We consider nonminimal bimetric models, where the coupling B appearing in the disformal transformation g-circumflex{sub {mu}{nu}}=g{sub {mu}{nu}}-B{partial_derivative}{sub {mu}{phi}{partial_derivative}{nu}{phi}} can run with {phi}. For power-law B({phi}) these models predict tilted spectra. For each value of the spectral index, a distinctive distortion to the equilateral property can be found. The constraint between this distortion and the spectral index can be seen as a 'consistency relation' for nonminimal bimetric models.

Magueijo, Joao; Noller, Johannes [Theoretical Physics, Blackett Laboratory, Imperial College, London, SW7 2BZ (United Kingdom); Piazza, Federico [Perimeter Institute for Theoretical Physics, Waterloo, Ontario, N2L 2Y5 (Canada)

2010-08-15

92

Unraveling the mechanism of protein disaggregation through a ClpB-DnaK interaction.  

PubMed

HSP-100 protein machines, such as ClpB, play an essential role in reactivating protein aggregates that can otherwise be lethal to cells. Although the players involved are known, including the DnaK/DnaJ/GrpE chaperone system in bacteria, details of the molecular interactions are not well understood. Using methyl-transverse relaxation-optimized nuclear magnetic resonance spectroscopy, we present an atomic-resolution model for the ClpB-DnaK complex, which we verified by mutagenesis and functional assays. ClpB and GrpE compete for binding to the DnaK nucleotide binding domain, with GrpE binding inhibiting disaggregation. DnaK, in turn, plays a dual role in both disaggregation and subsequent refolding of polypeptide chains as they emerge from the aggregate. On the basis of a combined structural-biochemical analysis, we propose a model for the mechanism of protein aggregate reactivation by ClpB. PMID:23393091

Rosenzweig, Rina; Moradi, Shoeib; Zarrine-Afsar, Arash; Glover, John R; Kay, Lewis E

2013-03-01

93

Aurine tricarboxylic acid, a potent metal-chelating inhibitor of NFkappaB-DNA binding.  

PubMed

The metal-interaction of aurine tricarboxylic acid (ATA) and its inhibitory effect on the DNA binding of NFkappaB were studied. Chemical speciation and spectroscopic studies have shown the strong interaction of ATA with metal ions present in the biological systems. EPR, FTIR and electronic spectral studies indicated the square planar structure of the metal-binding carboxylic and hydroxyl groups of ATA indicating the ground state 2B1g. Electrophoretic mobility shift assay using NFkappaB and 32P labeled DNA has shown that ATA was inhibitory against the DNA-NFkappaB binding at 30 microM. This activity was the strongest among the metal-chelating inhibitors of NFkappaB-DNA binding reported so far. PMID:10976530

Sharma, R K; Garg, B S; Kurosaki, H; Goto, M; Otsuka, M; Yamamoto, T; Inoue, J

2000-07-01

94

Inhibition and enhancement of eukaryotic gene expression by potential non-B DNA sequences.  

PubMed

In a transient or constitutive expression assay we have examined the effect of non-B DNA sequences d(CA)40 and d(CAAAAATGCC)n on gene expression in eukaryotic cells. These sequences were cloned adjacent to the weak eukaryotic promoter (CGTATTTATTTG) and located upstream from the coding sequence of galactokinase enzyme. Recombinants were micro-injected in cultured cells (Chinese hamster fibroblasts R1610, mutant gal-K-) and expression levels have been determined. The alternating purine-pyrimidine tract found in d(CA)40 able to assume the Z-DNA conformation shows an inhibitory effect on gene expression. In addition, our results suggest a new potential role of Z-DNA motifs in vivo to stimulate recombination. The sequences d(CAAAAATGCC)n able to adopt another non-B structure, corresponding to curved (or bended) helix conformation, strongly enhance gene expression and this enhancement depends on sequence redundancy. PMID:1755861

Delic, J; Onclercq, R; Moisan-Coppey, M

1991-12-16

95

Improved free energy parameters for RNA pseudoknotted secondary structure prediction.  

PubMed

Accurate prediction of RNA pseudoknotted secondary structures from the base sequence is a challenging computational problem. Since prediction algorithms rely on thermodynamic energy models to identify low-energy structures, prediction accuracy relies in large part on the quality of free energy change parameters. In this work, we use our earlier constraint generation and Boltzmann likelihood parameter estimation methods to obtain new energy parameters for two energy models for secondary structures with pseudoknots, namely, the Dirks-Pierce (DP) and the Cao-Chen (CC) models. To train our parameters, and also to test their accuracy, we create a large data set of both pseudoknotted and pseudoknot-free secondary structures. In addition to structural data our training data set also includes thermodynamic data, for which experimentally determined free energy changes are available for sequences and their reference structures. When incorporated into the HotKnots prediction algorithm, our new parameters result in significantly improved secondary structure prediction on our test data set. Specifically, the prediction accuracy when using our new parameters improves from 68% to 79% for the DP model, and from 70% to 77% for the CC model. PMID:19933322

Andronescu, Mirela S; Pop, Cristina; Condon, Anne E

2010-01-01

96

Protein secondary structure prediction using nearest-neighbor methods.  

PubMed

We have studied the use of nearest-neighbor classifiers to predict the secondary structure of proteins. The nearest-neighbor rule states that a test instance is classified according to the classifications of "nearby" training examples from a database of known structures. In the context of secondary structure prediction, the test instances are windows of n consecutive residues, and the label is the secondary structure type (alpha-helix, beta-strand, or coil) of the center position of the window. To define the neighborhood of a test instance, we employed a novel similarity metric based on the local structural environment scoring scheme of Bowie et al. In this manner, we have attempted to exploit the underlying structural similarity between segments of different proteins to aid in the prediction of secondary structure. Furthermore, in addition to using neighborhoods of fixed radius, we explored a modification of the standard nearest-neighbor algorithm that involved defining an "effective radius" for each exemplar by measuring its performance on a training set. Using these ideas, we achieved a peak prediction accuracy of 68%. Finally, we sought to improve the biological utility of secondary structure prediction by identifying the subset of the predictions that are most likely to be correct. Toward this end, we developed a nearest-neighbor estimator that produced not the traditional "one-state" prediction (alpha-helix, beta-strand, or coil) but rather a probability distribution over the three states. It should be emphasized that this scheme estimates true probability values and that the resulting numbers are not pseudo-probability scores generated by simple normalization of the raw output of the predictor. Applying the mutual information statistic, we found that these probability triplets possess 58% more information than the one-state predictions. Furthermore, the probability estimates allow one to assign an a priori confidence level to the prediction at each residue. Using this approach, we found that the top 28% of the predictions were 86% accurate and the top 43% of the predictions were 81% accurate. These results indicate that, notwithstanding the limitations on overall accuracy of secondary structure prediction, a substantial proportion of a protein can be predicted with considerable accuracy. PMID:8371270

Yi, T M; Lander, E S

1993-08-20

97

Structural Acoustic Prediction and Interior Noise Control Technology.  

National Technical Information Service (NTIS)

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 fro...

C. L. Chin G. P. Mathur J. T. Lee M. A. Simpson

2001-01-01

98

Multiobjective Approach Applied to the Protein Structure Prediction Problem.  

National Technical Information Service (NTIS)

Interest in discovering a methodology for solving the Protein Structure Prediction problem extends into many fields of study including biochemistry, medicine, biology, and numerous engineering and science disciplines. Experimental approaches, such as, x-r...

R. O. Day

2002-01-01

99

PREDICTING MODES OF TOXIC ACTION FROM CHEMICAL STRUCTURE: AN OVERVIEW  

EPA Science Inventory

In the field of environmental toxicology, and especially aquatic toxicology, quantitative structure activity relationships (QSARS) have developed as scientifically-credible tools for predicting the toxicity of chemicals when little or no empirical data are available. asic and fun...

100

A comparative study on filtering protein secondary structure prediction.  

PubMed

Filtering of Protein Secondary Structure Prediction (PSSP) aims to provide physicochemically realistic results, while it usually improves the predictive performance. We performed a comparative study on this challenging problem, utilizing both machine learning techniques and empirical rules and we found that combinations of the two lead to the highest improvement. PMID:22291162

Kountouris, Petros; Agathocleous, Michalis; Promponas, Vasilis J; Christodoulou, Georgia; Hadjicostas, Simos; Vassiliades, Vassilis; Christodoulou, Chris

2012-01-01

101

A comprehensive comparison of comparative RNA structure prediction approaches  

Microsoft Academic Search

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

Paul P. Gardner; Robert Giegerich

2004-01-01

102

Erosion prediction for exploration and production structures in the Arctic  

SciTech Connect

Exploration and production structures in the Southern Beaufort Sea will be subjected to wave and current action during the open water period of about 3 months each year. Two simulation models were developed to predict the severity of erosion problems associated with the structures. The first model predicts beach erosion caused by waves breaking on sand islands with gentle slopes. The second model predicts erosion and accretion patterns due to the wave and current action around large vertical bodies of revolution with steep slopes. Example computations for hypothetical sacrificial beach islands and fixed cones are performed using the wave and current characteristics associated with storms in the Beaufort Sea. 18 refs.

Kobayashi, N.; Vivatrat, V.; Madsen, O.S.; Boaz, I.B.

1981-01-01

103

Prediction of the secondary structure of HIV-1 gp120.  

PubMed

The secondary structure of HIV-1 gp120 was predicted using multiple alignment and a combination of two independent methods based on neural network and nearest-neighbor algorithms. The methods agreed on the secondary structure for 80% of the residues in BH10 gp120. Six helices were predicted in HIV strain BH10 gp120, as well as in 27 other HIV-1 strains examined. Two helical segments were predicted in regions displaying profound sequence variation, one in a region suggested to be critical for CD4 binding. The predicted content of helix, beta-strand, and coil was consistent with estimates from Fourier transform infrared spectroscopy. The predicted secondary structure of gp120 compared well with data from NMR analysis of synthetic peptides from the V3 loop and the C4 region. As a first step towards modeling the tertiary structure of gp120, the predicted secondary structure may guide the design of future HIV subunit vaccine candidates. PMID:8727315

Hansen, J E; Lund, O; Nielsen, J O; Brunak, S; Hansen, J E

1996-05-01

104

The Prediction of Membrane Protein Structure and Genome Structural Annotation  

PubMed Central

New methods, essentially based on hidden Markov models (HMM) and neural networks (NN), can predict the topography of both ?-barrel and all-? membrane proteins with high accuracy and a low rate of false positives and false negatives. These methods have been integrated in a suite of programs to filter proteomes of Gram-negative bacteria, searching for new membrane proteins.

Martelli, Pier Luigi; Fariselli, Piero; Tasco, Gianluca

2003-01-01

105

Brain structure predicts risk for obesity.  

PubMed

The neurobiology of obesity is poorly understood. Here we report findings of a study designed to examine the differences in brain regional gray matter volume in adults recruited as either Obese Prone or Obese Resistant based on self-identification, body mass index, and personal/family weight history. Magnetic resonance imaging was performed in 28 Obese Prone (14 male, 14 female) and 25 Obese Resistant (13 male, 12 female) healthy adults. Voxel-based morphometry was used to identify gray matter volume differences between groups. Gray matter volume was found to be lower in the insula, medial orbitofrontal cortex and cerebellum in Obese Prone, as compared to Obese Resistant individuals. Adjusting for body fat mass did not impact these results. Insula gray matter volume was negatively correlated with leptin concentration and measures of hunger. These findings suggest that individuals at risk for weight gain have structural differences in brain regions known to be important in energy intake regulation, and that these differences, particularly in the insula, may be related to leptin. PMID:22963736

Smucny, Jason; Cornier, Marc-Andre; Eichman, Lindsay C; Thomas, Elizabeth A; Bechtell, Jamie L; Tregellas, Jason R

2012-12-01

106

Predicted structure of two adenovirus tumor antigens.  

PubMed Central

Early adenovirus type 2(Ad2) mRNA sequences have been cloned by using the pBR322 plasmid as a vector. Two clones that include sequences from region E1B were identified and their DNAs were characterized by hybridization, restriction enzyme cleavage, and DNA sequence analysis. The results showed that the clones were derived from two different spliced mRNAs. By combining our results with the established DNA sequence for region E1B of the closely related adenovirus type 5[Maat, J., van Beveren, C.P. & van Ormondt, H. (1980) Gene, in press] it was possible to deduce the structure of a 13S and a 22S mRNA. The two mRNAs differ from each other by the size of their intervening sequences. If translation starts at the first AUG following the cap, the 22S mRNA encodes a Mr 67,000 polypeptide that is terminated by a UGA stop codon located immediately before the splice, whereas the 13S mRNA encodes a Mr 20,000 polypeptide that is translated in different reading frames before and after the splice. The Mr 20,000 and 67,000 polypeptides correspond in molecular weight to two proteins that invariably are precipitated from infected cell extracts by antisera from animals carrying adenovirus-induced tumors. Images

Perricaudet, M; Le Moullec, J M; Pettersson, U

1980-01-01

107

Brain structure predicts risk for obesity ?  

PubMed Central

The neurobiology of obesity is poorly understood. Here we report findings of a study designed to examine the differences in brain regional gray matter volume in adults recruited as either Obese Prone or Obese Resistant based on self-identification, body mass index, and personal/family weight history. Magnetic resonance imaging was performed in 28 Obese Prone (14 male, 14 female) and 25 Obese Resistant (13 male, 12 female) healthy adults. Voxel-based morphometry was used to identify gray matter volume differences between groups. Gray matter volume was found to be lower in the insula, medial orbitofrontal cortex and cerebellum in Obese Prone, as compared to Obese Resistant individuals. Adjusting for body fat mass did not impact these results. Insula gray matter volume was negatively correlated with leptin concentration and measures of hunger. These findings suggest that individuals at risk for weight gain have structural differences in brain regions known to be important in energy intake regulation, and that these differences, particularly in the insula, may be related to leptin.

Smucny, Jason; Cornier, Marc-Andre; Eichman, Lindsay C.; Thomas, Elizabeth A.; Bechtell, Jamie L.; Tregellas, Jason R.

2014-01-01

108

Alternative target functions for protein structure prediction with neural networks  

NASA Astrophysics Data System (ADS)

The prediction and modeling of protein structure is a central problem in bioinformatics. Neural networks have been used extensively to predict the secondary structure of proteins. While significant progress has been made by using multiple sequence data, the ability to predict secondary structure from a single sequence and a single prediction network has stagnated with an accuracy of about 75%. This implies that there is some limit to the accuracy of the prediction. In order to understand this behavior we asked the question of what happens as we change the target function for the prediction. Instead of predicting a derived quantity, such as whether a given chain is a helix, sheet or turn, we tested whether a more directly observed quantity such as the distance between a pair of ?-carbon atoms could be predicted with reasonable accuracy. The ?-carbon atom position is central to each residue in the protein and the distances between them in sequence define the backbone of protein. Knowledge of the distances between the ?-carbon atoms is sufficient to determine the three dimensional structure of the protein. We have trained on distance data derived from the complete protein structure database (pdb) using a multi-layered perceptron feedforward neural network with back propagation. It shows that the root of mean square error is 0.4 Å with orthogonal coding of protein primary sequence. This is comparable to the experimental error in the structures used to form the database. The effects of exploring other encoding schemes, and different complexities of neural networks as well as related target functions such as distance thresholds will be presented.

Deng, Hai; Harrison, Robert; Pan, Yi; Tai, Phang C.

2004-04-01

109

Prediction of Protein Structural Classes by Modified Mahalanobis Discriminant Algorithm  

Microsoft Academic Search

We first discuss quantitative rules for determining the protein structural classes based on their secondary structures. Then we propose a modification of the least Mahalanobis distance method for prediction of protein classes. It is a generalization of a quadratic discriminant function to the case of degenerate covariance matrices. The resubstitution tests and leave-one-out tests are carried out to compare several

Wei-min Liu; Kuo-Chen Chou

1998-01-01

110

Prediction of Delamination in Wind Turbine Blade Structural Details  

Microsoft Academic Search

Delamination between plies is the root cause of many failures of composite materials structures such as wind turbine blades. Design methodologies to prevent such failures have not been widely available for the materials and processes used in blades. This paper presents simplified methodologies for the prediction of delamination under both static and fatigue loading at typical structural details in blades.

John F. Mandell; Douglas S. Cairns; Daniel D. Samborsky; Robert B. Morehead; Darrin J. Haugen

2003-01-01

111

Predicting Protein Structural Class with AdaBoost Learner  

Microsoft Academic Search

The structural class is an important feature in characterizing the overall topological folding type of a protein or the domains therein. Prediction of protein structural classification has attracted the attention and efforts from many inves- tigators. In this paper a novel predictor, the AdaBoost Learner, was introduced to deal with this problem. The essence of the AdaBoost Learner is that

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

2006-01-01

112

A life prediction model for laminated composite structural components  

NASA Technical Reports Server (NTRS)

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.

Allen, David H.

1990-01-01

113

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

PubMed

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

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

2014-07-01

114

Predict-2nd: a tool for generalized protein local structure prediction  

PubMed Central

Motivation: Predictions of protein local structure, derived from sequence alignment information alone, provide visualization tools for biologists to evaluate the importance of amino acid residue positions of interest in the absence of X-ray crystal/NMR structures or homology models. They are also useful as inputs to sequence analysis and modeling tools, such as hidden Markov models (HMMs), which can be used to search for homology in databases of known protein structure. In addition, local structure predictions can be used as a component of cost functions in genetic algorithms that predict protein tertiary structure. We have developed a program (predict-2nd) that trains multilayer neural networks and have applied it to numerous local structure alphabets, tuning network parameters such as the number of layers, the number of units in each layer and the window sizes of each layer. We have had the most success with four-layer networks, with gradually increasing window sizes at each layer. Results: Because the four-layer neural nets occasionally get trapped in poor local optima, our training protocol now uses many different random starts, with short training runs, followed by more training on the best performing networks from the short runs. One recent addition to the program is the option to add a guide sequence to the profile inputs, increasing the number of inputs per position by 20. We find that use of a guide sequence provides a small but consistent improvement in the predictions for several different local-structure alphabets. Availability: Local structure prediction with the methods described here is available for use online at http://www.soe.ucsc.edu/compbio/SAM_T08/T08-query.html. The source code and example networks for PREDICT-2ND are available at http://www.soe.ucsc.edu/~karplus/predict-2nd/ A required C++ library is available at http://www.soe.ucsc.edu/~karplus/ultimate/ Contact: karplus@soe.ucsc.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Katzman, Sol; Barrett, Christian; Thiltgen, Grant; Karchin, Rachel; Karplus, Kevin

2008-01-01

115

Computational prediction of organic crystal structures and polymorphism  

NASA Astrophysics Data System (ADS)

The development of a robust manufacturing process for solid organic materials, such as pharmaceuticals, can be complicated when the molecules crystallize in different solid forms, including polymorphs. The diverse challenges to computational chemistry in computing the relative thermodynamic stability of different potential crystal structures for a range of organic molecules are outlined. Once the crystal structures which are thermodynamically feasible have been obtained, then comparison with the experimentally known polymorphs can provide interesting insights into crystallization behaviour. Although the computational prediction of polymorphism requires modelling the kinetic factors that can influence crystallization, the computational prediction of the crystal energy landscape is already a valuable complement to experimental searches for polymorphs.

Price, S. L.

116

PCI-SS: MISO dynamic nonlinear protein secondary structure prediction  

PubMed Central

Background Since the function of a protein is largely dictated by its three dimensional configuration, determining a protein's structure is of fundamental importance to biology. Here we report on a novel approach to determining the one dimensional secondary structure of proteins (distinguishing ?-helices, ?-strands, and non-regular structures) from primary sequence data which makes use of Parallel Cascade Identification (PCI), a powerful technique from the field of nonlinear system identification. Results Using PSI-BLAST divergent evolutionary profiles as input data, dynamic nonlinear systems are built through a black-box approach to model the process of protein folding. Genetic algorithms (GAs) are applied in order to optimize the architectural parameters of the PCI models. The three-state prediction problem is broken down into a combination of three binary sub-problems and protein structure classifiers are built using 2 layers of PCI classifiers. Careful construction of the optimization, training, and test datasets ensures that no homology exists between any training and testing data. A detailed comparison between PCI and 9 contemporary methods is provided over a set of 125 new protein chains guaranteed to be dissimilar to all training data. Unlike other secondary structure prediction methods, here a web service is developed to provide both human- and machine-readable interfaces to PCI-based protein secondary structure prediction. This server, called PCI-SS, is available at . In addition to a dynamic PHP-generated web interface for humans, a Simple Object Access Protocol (SOAP) interface is added to permit invocation of the PCI-SS service remotely. This machine-readable interface facilitates incorporation of PCI-SS into multi-faceted systems biology analysis pipelines requiring protein secondary structure information, and greatly simplifies high-throughput analyses. XML is used to represent the input protein sequence data and also to encode the resulting structure prediction in a machine-readable format. To our knowledge, this represents the only publicly available SOAP-interface for a protein secondary structure prediction service with published WSDL interface definition. Conclusion Relative to the 9 contemporary methods included in the comparison cascaded PCI classifiers perform well, however PCI finds greatest application as a consensus classifier. When PCI is used to combine a sequence-to-structure PCI-based classifier with the current leading ANN-based method, PSIPRED, the overall error rate (Q3) is maintained while the rate of occurrence of a particularly detrimental error is reduced by up to 25%. This improvement in BAD score, combined with the machine-readable SOAP web service interface makes PCI-SS particularly useful for inclusion in a tertiary structure prediction pipeline.

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

2009-01-01

117

Distance matrix-based approach to protein structure prediction.  

PubMed

Much structural information is encoded in the internal distances; a distance matrix-based approach can be used to predict protein structure and dynamics, and for structural refinement. Our approach is based on the square distance matrix D = [r(ij)(2)] containing all square distances between residues in proteins. This distance matrix contains more information than the contact matrix C, that has elements of either 0 or 1 depending on whether the distance r (ij) is greater or less than a cutoff value r (cutoff). We have performed spectral decomposition of the distance matrices D = sigma lambda(k)V(k)V(kT), in terms of eigenvalues lambda kappa and the corresponding eigenvectors v kappa and found that it contains at most five nonzero terms. A dominant eigenvector is proportional to r (2)--the square distance of points from the center of mass, with the next three being the principal components of the system of points. By predicting r (2) from the sequence we can approximate a distance matrix of a protein with an expected RMSD value of about 7.3 A, and by combining it with the prediction of the first principal component we can improve this approximation to 4.0 A. We can also explain the role of hydrophobic interactions for the protein structure, because r is highly correlated with the hydrophobic profile of the sequence. Moreover, r is highly correlated with several sequence profiles which are useful in protein structure prediction, such as contact number, the residue-wise contact order (RWCO) or mean square fluctuations (i.e. crystallographic temperature factors). We have also shown that the next three components are related to spatial directionality of the secondary structure elements, and they may be also predicted from the sequence, improving overall structure prediction. We have also shown that the large number of available HIV-1 protease structures provides a remarkable sampling of conformations, which can be viewed as direct structural information about the dynamics. After structure matching, we apply principal component analysis (PCA) to obtain the important apparent motions for both bound and unbound structures. There are significant similarities between the first few key motions and the first few low-frequency normal modes calculated from a static representative structure with an elastic network model (ENM) that is based on the contact matrix C (related to D), strongly suggesting that the variations among the observed structures and the corresponding conformational changes are facilitated by the low-frequency, global motions intrinsic to the structure. Similarities are also found when the approach is applied to an NMR ensemble, as well as to atomic molecular dynamics (MD) trajectories. Thus, a sufficiently large number of experimental structures can directly provide important information about protein dynamics, but ENM can also provide a similar sampling of conformations. Finally, we use distance constraints from databases of known protein structures for structure refinement. We use the distributions of distances of various types in known protein structures to obtain the most probable ranges or the mean-force potentials for the distances. We then impose these constraints on structures to be refined or include the mean-force potentials directly in the energy minimization so that more plausible structural models can be built. This approach has been successfully used by us in 2006 in the CASPR structure refinement (http://predictioncenter.org/caspR). PMID:19224393

Kloczkowski, Andrzej; Jernigan, Robert L; Wu, Zhijun; Song, Guang; Yang, Lei; Kolinski, Andrzej; Pokarowski, Piotr

2009-03-01

118

Pairwise covariance adds little to secondary structure prediction but improves the prediction of non-canonical local structure  

SciTech Connect

Amino acid sequence probability distributions, or profiles, have been used successfully to predict secondary structure and local structure in proteins. Profile models assume the statistical independence of each position in the sequence, but the energetics of protein folding is better captured in a scoring function that is based on pairwise interactions, like a force field. I-sites motifs are short sequence/structure motifs that populate the protein structure database due to energy-driven convergent evolution. Here we show that a pairwise covariant sequence model does not predict alpha helix or beta strand significantly better overall than a profile-based model, but it does improve the prediction of certain loop motifs. The finding is best explained by considering secondary structure profiles as multivariant, all-or-none models, which subsume covariant models. Pairwise covariance is nonetheless present and energetically rational. Examples of negative design are present, where the covariances disfavor non-native structures. Measured pairwise covariances are shown to be statistically robust in cross-validation tests, as long as the amino acid alphabet is reduced to nine classes. We present an updated I-sites local structure motif library and web server that provide sequence covariance information for all types of local structure in globular proteins.

Bystroff, Christopher; Webb-Robertson, Bobbie-Jo M.

2009-05-06

119

Improvements of the hierarchical approach for predicting RNA tertiary structure.  

PubMed

Computational prediction of RNA tertiary structures is a significant challenge, especially for longer RNA and pseudoknots. At present it is still difficult to do this by pure all-atom molecular dynamics simulation. One of possible approaches is through hierarchical steps: from sequence to secondary structure and then to tertiary structure. Here we present improvements of two key steps of this approach, the manual adjustment of atom clashes and bond stretches and molecular dynamics refinement. We provide an energy function to find the locations of atom clashes and bond stretches and to guide their manual adjustment and a new scheme of molecular dynamics refinement using a tested combination of solvent model and the ff98 Amber force field suitable for RNA. We predicted with higher accuracy the tertiary structures of nine typical RNA molecules of lengths from 12 to 52, including hairpins, duplex helices and pseudoknots. PMID:21294592

Zhao, Yunjie; Gong, Zhou; Xiao, Yi

2011-04-01

120

CONTRAfold: RNA secondary structure prediction without physics-based models  

Microsoft Academic Search

Motivation: For several decades, free energy minimization methods have been the dominant strategy for single sequence RNA secondary structure prediction. More recently, stochastic context-free grammars (SCFGs)have emergedas an alternative probabilisticmethodology for modeling RNA structure. Unlike physics-based methods, which rely on thousands of experimentally-measured thermodynamic parameters, SCFGs use fully-automated statistical learning algorithms to derive model parameters. Despite this advantage, however, probabilistic

Chuong B. Do; Daniel A. Woods; Serafim Batzoglou

2006-01-01

121

Improved Chou-Fasman method for protein secondary structure prediction  

PubMed Central

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

Chen, Hang; Gu, Fei; Huang, Zhengge

2006-01-01

122

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

NASA Astrophysics Data System (ADS)

Whether the static microstructural order information is strongly correlated with the subsequent structural rearrangement (SR) and their predicting power for SR are investigated experimentally in the quenched dusty plasma liquid with microheterogeneities. The poor local structural order is found to be a good alarm to identify the soft spot and predict the short term SR. For the site with good structural order, the persistent time for sustaining the structural memory until SR has a large mean value but a broad distribution. The deviation of the local structural order from that averaged over nearest neighbors serves as a good second alarm to further sort out the short time SR sites. It has the similar sorting power to that using the temporal fluctuation of the local structural order over a small time interval.

Su, Yen-Shuo; Liu, Yu-Hsuan; I, Lin

2012-11-01

123

Extracting Physicochemical Features to Predict Protein Secondary Structure  

PubMed Central

We propose a protein secondary structure prediction method based on position-specific scoring matrix (PSSM) profiles and four physicochemical features including conformation parameters, net charges, hydrophobic, and side chain mass. First, the SVM with the optimal window size and the optimal parameters of the kernel function is found. Then, we train the SVM using the PSSM profiles generated from PSI-BLAST and the physicochemical features extracted from the CB513 data set. Finally, we use the filter to refine the predicted results from the trained SVM. For all the performance measures of our method, Q3 reaches 79.52, SOV94 reaches 86.10, and SOV99 reaches 74.60; all the measures are higher than those of the SVMpsi method and the SVMfreq method. This validates that considering these physicochemical features in predicting protein secondary structure would exhibit better performances.

Chen, Shu-Ying

2013-01-01

124

Protein secondary structure prediction using NMR chemical shift data.  

PubMed

Accurate determination of protein secondary structure from the chemical shift information is a key step for NMR tertiary structure determination. Relatively few work has been done on this subject. There needs to be a systematic investigation of algorithms that are (a) robust for large datasets; (b) easily extendable to (the dynamic) new databases; and (c) approaching to the limit of accuracy. We introduce new approaches using k-nearest neighbor algorithm to do the basic prediction and use the BCJR algorithm to smooth the predictions and combine different predictions from chemical shifts and based on sequence information only. Our new system, SUCCES, improves the accuracy of all existing methods on a large dataset of 805 proteins (at 86% Q(3) accuracy and at 92.6% accuracy when the boundary residues are ignored), and it is easily extendable to any new dataset without requiring any new training. The software is publicly available at http://monod.uwaterloo.ca/nmr/succes. PMID:20981892

Zhao, Yuzhong; Alipanahi, Babak; Li, Shuai Cheng; Li, Ming

2010-10-01

125

A statistical sampling algorithm for RNA secondary structure prediction  

PubMed Central

An RNA molecule, particularly a long-chain mRNA, may exist as a population of structures. Further more, multiple structures have been demonstrated to play important functional roles. Thus, a representation of the ensemble of probable structures is of interest. We present a statistical algorithm to sample rigorously and exactly from the Boltzmann ensemble of secondary structures. The forward step of the algorithm computes the equilibrium partition functions of RNA secondary structures with recent thermodynamic parameters. Using conditional probabilities computed with the partition functions in a recursive sampling process, the backward step of the algorithm quickly generates a statistically representative sample of structures. With cubic run time for the forward step, quadratic run time in the worst case for the sampling step, and quadratic storage, the algorithm is efficient for broad applicability. We demonstrate that, by classifying sampled structures, the algorithm enables a statistical delineation and representation of the Boltzmann ensemble. Applications of the algorithm show that alternative biological structures are revealed through sampling. Statistical sampling provides a means to estimate the probability of any structural motif, with or without constraints. For example, the algorithm enables probability profiling of single-stranded regions in RNA secondary structure. Probability profiling for specific loop types is also illustrated. By overlaying probability profiles, a mutual accessibility plot can be displayed for predicting RNA:RNA interactions. Boltzmann probability-weighted density of states and free energy distributions of sampled structures can be readily computed. We show that a sample of moderate size from the ensemble of an enormous number of possible structures is sufficient to guarantee statistical reproducibility in the estimates of typical sampling statistics. Our applications suggest that the sampling algorithm may be well suited to prediction of mRNA structure and target accessibility. The algorithm is applicable to the rational design of small interfering RNAs (siRNAs), antisense oligonucleotides, and trans-cleaving ribozymes in gene knock-down studies.

Ding, Ye; Lawrence, Charles E.

2003-01-01

126

Fast evaluation of internal loops in RNA secondary structure prediction  

Microsoft Academic Search

Motivation: Though not as abundant in known biologicalprocesses as proteins, RNA molecules serve as morethan mere intermediaries between DNA and proteins.Research in the last 15 years demonstrates that RNAmolecules serve in many roles, including catalysis. Furthermore,RNA secondary structure prediction based onfree energy rules for stacking and loop formation remainsone of the few major breakthroughs in the eld of structureprediction, as

Rune B. Lyngsø; Michael Zuker; Christian N. S. Pedersen

1999-01-01

127

Process for predicting structural performance of mechanical systems  

DOEpatents

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.

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

1998-05-19

128

The development of life prediction techniques for structural ceramics  

Microsoft Academic Search

One of the major challenges involved in the use of ceramic materials in advanced vehicular heat engines is ensuring adequate strength and durability. This Department of Energy supported activity has developed methodologies to predict the structural behavior of ceramic components. The effort involved the characterization of injection-molded and hot isostatic pressed PY6 silicon nitride and the development of analytical life

P. K. Khandelwal; N. J. Provenzano; W. E. Schneider

1996-01-01

129

Prediction of Temperature at the Outlet of Stormwater Management Structures.  

National Technical Information Service (NTIS)

The primary objective of the project was to create a physics-based computer model of the current BMP stormwater management structures that allows prediction of outlet temperature as a function of time. The approach is physics based, depending on energy an...

D. Bretall K. E. Herold M. Kapelanczyk S. Desai

2008-01-01

130

Structural Damage Prediction and Analysis for Hypervelocity Impact: Consulting  

NASA Technical Reports Server (NTRS)

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.

1995-01-01

131

Structural prediction of peptides bound to MHC class I.  

PubMed

An ab initio structure prediction approach adapted to the peptide-major histocompatibility complex (MHC) class I system is presented. Based on structure comparisons of a large set of peptide-MHC class I complexes, a molecular dynamics protocol is proposed using simulated annealing (SA) cycles to sample the conformational space of the peptide in its fixed MHC environment. A set of 14 peptide-human leukocyte antigen (HLA) A0201 and 27 peptide-non-HLA A0201 complexes for which X-ray structures are available is used to test the accuracy of the prediction method. For each complex, 1000 peptide conformers are obtained from the SA sampling. A graph theory clustering algorithm based on heavy atom root-mean-square deviation (RMSD) values is applied to the sampled conformers. The clusters are ranked using cluster size, mean effective or conformational free energies, with solvation free energies computed using Generalized Born MV 2 (GB-MV2) and Poisson-Boltzmann (PB) continuum models. The final conformation is chosen as the center of the best-ranked cluster. With conformational free energies, the overall prediction success is 83% using a 1.00 Angstroms crystal RMSD criterion for main-chain atoms, and 76% using a 1.50 Angstroms RMSD criterion for heavy atoms. The prediction success is even higher for the set of 14 peptide-HLA A0201 complexes: 100% of the peptides have main-chain RMSD values < or =1.00 Angstroms and 93% of the peptides have heavy atom RMSD values < or =1.50 Angstroms. This structure prediction method can be applied to complexes of natural or modified antigenic peptides in their MHC environment with the aim to perform rational structure-based optimizations of tumor vaccines. PMID:16368108

Fagerberg, Theres; Cerottini, Jean-Charles; Michielin, Olivier

2006-02-17

132

Predicting PDZ domain mediated protein interactions from structure  

PubMed Central

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

2013-01-01

133

Symmetry building Monte Carlo-based crystal structure prediction  

NASA Astrophysics Data System (ADS)

Methods are presented that allow for the automatic increase and preservation of symmetry during global optimization of crystal structures. This systematic building of symmetry allows for its incorporation into structure prediction simulations even when the space group information is not known a priori. It is shown that simulations that build and maintain symmetry converge much more rapidly to ground state crystal structures than when symmetry is ignored, allowing for the treatment of unit cells much larger than would otherwise be possible, especially when beginning from the P1 space group.

Michel, Kyle Jay; Wolverton, C.

2014-05-01

134

RNA secondary structure prediction using high-throughput SHAPE.  

PubMed

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

Lusvarghi, Sabrina; Sztuba-Solinska, Joanna; Purzycka, Katarzyna J; Rausch, Jason W; Le Grice, Stuart F J

2013-01-01

135

Ab initio crystal structure prediction-I. Rigid molecules.  

PubMed

A new methodology for the prediction of molecular crystal structures using only the atomic connectivity of the molecule under consideration is presented. The approach is based on the global minimization of the lattice enthalpy of the crystal. The modeling of the electrostatic interactions is accomplished through a set of distributed charges that are optimally and automatically selected and positioned based on results of quantum mechanical calculations. A four-step global optimization algorithm is used for the identification of the local minima of the lattice enthalpy surface. A parallelized implementation of the algorithm permits a much more extensive search of the solution space than has hitherto been possible, allowing the identification of crystal structures in less frequently occurring space groups and with more than one molecule in the asymmetric unit. The algorithm has been applied successfully to the prediction of the crystal structures of 3-aza-bicyclo(3.3.1)nonane-2,4-dione (P2(1)/a, Z' = 1), allopurinol (P2(1)/c, Z' = 1), 1,3,4,6,7,9-hexa-azacycl(3.3.3)azine (Pbca, Z' = 2), and triethylenediamine (P6(3)/m, Z' = 1). In all cases, the experimentally known structure is among the most stable predicted structures, but not necessarily the global minimum. PMID:15622548

Karamertzanis, Panagiotis G; Pantelides, Constantinos C

2005-02-01

136

Protein structure prediction using sparse dipolar coupling data  

PubMed Central

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

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

2004-01-01

137

Predicting gene ontology functions from protein's regional surface structures  

PubMed Central

Background Annotation of protein functions is an important task in the post-genomic era. Most early approaches for this task exploit only the sequence or global structure information. However, protein surfaces are believed to be crucial to protein functions because they are the main interfaces to facilitate biological interactions. Recently, several databases related to structural surfaces, such as pockets and cavities, have been constructed with a comprehensive library of identified surface structures. For example, CASTp provides identification and measurements of surface accessible pockets as well as interior inaccessible cavities. Results A novel method was proposed to predict the Gene Ontology (GO) functions of proteins from the pocket similarity network, which is constructed according to the structure similarities of pockets. The statistics of the networks were presented to explore the relationship between the similar pockets and GO functions of proteins. Cross-validation experiments were conducted to evaluate the performance of the proposed method. Results and codes are available at: . Conclusion The computational results demonstrate that the proposed method based on the pocket similarity network is effective and efficient for predicting GO functions of proteins in terms of both computational complexity and prediction accuracy. The proposed method revealed strong relationship between small surface patterns (or pockets) and GO functions, which can be further used to identify active sites or functional motifs. The high quality performance of the prediction method together with the statistics also indicates that pockets play essential roles in biological interactions or the GO functions. Moreover, in addition to pockets, the proposed network framework can also be used for adopting other protein spatial surface patterns to predict the protein functions.

Liu, Zhi-Ping; Wu, Ling-Yun; Wang, Yong; Chen, Luonan; Zhang, Xiang-Sun

2007-01-01

138

CyloFold: secondary structure prediction including pseudoknots.  

PubMed

Computational RNA secondary structure prediction approaches differ by the way RNA pseudoknot interactions are handled. For reasons of computational efficiency, most approaches only allow a limited class of pseudoknot interactions or are not considering them at all. Here we present a computational method for RNA secondary structure prediction that is not restricted in terms of pseudoknot complexity. The approach is based on simulating a folding process in a coarse-grained manner by choosing helices based on established energy rules. The steric feasibility of the chosen set of helices is checked during the folding process using a highly coarse-grained 3D model of the RNA structures. Using two data sets of 26 and 241 RNA sequences we find that this approach is competitive compared to the existing RNA secondary structure prediction programs pknotsRG, HotKnots and UnaFold. The key advantages of the new method are that there is no algorithmic restriction in terms of pseudoknot complexity and a test is made for steric feasibility. Availability: The program is available as web server at the site: http://cylofold.abcc.ncifcrf.gov. PMID:20501603

Bindewald, Eckart; Kluth, Tanner; Shapiro, Bruce A

2010-07-01

139

Predicting protein structures with a multiplayer online game.  

PubMed

People exert large amounts of problem-solving effort playing computer games. Simple image- and text-recognition tasks have been successfully 'crowd-sourced' through games, but it is not clear if more complex scientific problems can be solved with human-directed computing. Protein structure prediction is one such problem: locating the biologically relevant native conformation of a protein is a formidable computational challenge given the very large size of the search space. Here we describe Foldit, a multiplayer online game that engages non-scientists in solving hard prediction problems. Foldit players interact with protein structures using direct manipulation tools and user-friendly versions of algorithms from the Rosetta structure prediction methodology, while they compete and collaborate to optimize the computed energy. We show that top-ranked Foldit players excel at solving challenging structure refinement problems in which substantial backbone rearrangements are necessary to achieve the burial of hydrophobic residues. Players working collaboratively develop a rich assortment of new strategies and algorithms; unlike computational approaches, they explore not only the conformational space but also the space of possible search strategies. The integration of human visual problem-solving and strategy development capabilities with traditional computational algorithms through interactive multiplayer games is a powerful new approach to solving computationally-limited scientific problems. PMID:20686574

Cooper, Seth; Khatib, Firas; Treuille, Adrien; Barbero, Janos; Lee, Jeehyung; Beenen, Michael; Leaver-Fay, Andrew; Baker, David; Popovi?, Zoran; Players, Foldit

2010-08-01

140

Simplified model for predicting impulsive loads on submerged structures to account for fluid-structure interaction  

Microsoft Academic Search

A simplified method for accounting for the effects of fluid-structure interaction (FSI) in sandwich structures subjected to dynamic underwater loads is developed. The method provides quite accurate predictions of the impulse on submerged structures for a large range of loads and core yield strengths. It is a simple model with two lumped masses, one of which is subjected to an

Timon Rabczuk; Esteban Samaniego; Ted Belytschko

2007-01-01

141

Prediction of the structure of symmetrical protein assemblies  

PubMed Central

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.

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

2007-01-01

142

Generalized Pattern Search Algorithm for Peptide Structure Prediction  

PubMed Central

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

Nicosia, Giuseppe; Stracquadanio, Giovanni

2008-01-01

143

Predicting loop-helix tertiary structural contacts in RNA pseudoknots.  

PubMed

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

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

2010-03-01

144

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

PubMed

Although residue-residue contact maps dictate the topology of proteins, sequence-based ab initio contact predictions have been found little use in actual structure prediction due to the low accuracy. We developed a composite set of nine SVM-based contact predictors that are used in I-TASSER simulation in combination with sparse template contact restraints. When testing the strategy on 273 nonhomologous targets, remarkable improvements of I-TASSER models were observed for both easy and hard targets, with p value by Student's t test<0.00001 and 0.001, respectively. In several cases, template modeling score increases by >30%, which essentially converts "nonfoldable" targets into "foldable" ones. In CASP9, I-TASSER employed ab initio contact predictions, and generated models for 26 FM targets with a GDT-score 16% and 44% higher than the second and third best servers from other groups, respectively. These findings demonstrate a new avenue to improve the accuracy of protein structure prediction especially for free-modeling targets. PMID:21827953

Wu, Sitao; Szilagyi, Andras; Zhang, Yang

2011-08-10

145

Approximation algorithms for predicting RNA secondary structures with arbitrary pseudoknots.  

PubMed

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

Jiang, Minghui

2010-01-01

146

Virality prediction and community structure in social networks.  

PubMed

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

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

2013-01-01

147

Constraint Logic Programming approach to protein structure prediction  

PubMed Central

Background The protein structure prediction problem is one of the most challenging problems in biological sciences. Many approaches have been proposed using database information and/or simplified protein models. The protein structure prediction problem can be cast in the form of an optimization problem. Notwithstanding its importance, the problem has very seldom been tackled by Constraint Logic Programming, a declarative programming paradigm suitable for solving combinatorial optimization problems. Results Constraint Logic Programming techniques have been applied to the protein structure prediction problem on the face-centered cube lattice model. Molecular dynamics techniques, endowed with the notion of constraint, have been also exploited. Even using a very simplified model, Constraint Logic Programming on the face-centered cube lattice model allowed us to obtain acceptable results for a few small proteins. As a test implementation their (known) secondary structure and the presence of disulfide bridges are used as constraints. Simplified structures obtained in this way have been converted to all atom models with plausible structure. Results have been compared with a similar approach using a well-established technique as molecular dynamics. Conclusions The results obtained on small proteins show that Constraint Logic Programming techniques can be employed for studying protein simplified models, which can be converted into realistic all atom models. The advantage of Constraint Logic Programming over other, much more explored, methodologies, resides in the rapid software prototyping, in the easy way of encoding heuristics, and in exploiting all the advances made in this research area, e.g. in constraint propagation and its use for pruning the huge search space.

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

2004-01-01

148

Protein family comparison using statistical models and predicted structural information  

PubMed Central

Background This paper presents a simple method to increase the sensitivity of protein family comparisons by incorporating secondary structure (SS) information. We build upon the effective information theory approach towards profile-profile comparison described in [Yona & Levitt 2002]. Our method augments profile columns using PSIPRED secondary structure predictions and assesses statistical similarity using information theoretical principles. Results Our tests show that this tool detects more similarities between protein families of distant homology than the previous primary sequence-based method. A very significant improvement in performance is observed when the real secondary structure is used. Conclusions Integration of primary and secondary structure information can substantially improve detection of relationships between remotely related protein families.

Chung, Richard; Yona, Golan

2004-01-01

149

A dynamic programming algorithm for RNA structure prediction including pseudoknots  

Microsoft Academic Search

We describe a dynamic programming algorithm for predicting optimal RNA secondary structure, including pseudoknots. The algorithm has a worst case complexity of ${\\\\cal O}(N^6)$ in time and ${\\\\cal O}(N^4)$ in storage. The description of the algorithm is complex, which led us to adopt a useful graphical representation (Feynman diagrams) borrowed from quantum field theory. We present an implementation of the

Elena Rivas; Sean R. Eddy

1998-01-01

150

DFS Based Partial Pathways in GA for Protein Structure Prediction  

Microsoft Academic Search

Nondeterministic conformational search techniques, such as Genetic Algorithms (GAs) are promising for solving protein structure\\u000a prediction (PSP) problem. The crossover operator of a GA can underpin the formation of potential conformations by exchanging\\u000a and sharing potential sub-conformations, which is promising for solving PSP. However, the usual nature of an optimum PSP conformation\\u000a being compact can produce many invalid conformations (by

Tamjidul Hoque; Madhu Chetty; Andrew Lewis; Abdul Sattar

2008-01-01

151

A tool for the prediction of structures of complex sugars.  

PubMed

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

Xia, Junchao; Margulis, Claudio

2008-12-01

152

Ab Initio Protein Structure Prediction Using Chunk-TASSER  

PubMed Central

We have developed an ab initio protein structure prediction method called chunk-TASSER that uses ab initio folded supersecondary structure chunks of a given target as well as threading templates for obtaining contact potentials and distance restraints. The predicted chunks, selected on the basis of a new fragment comparison method, are folded by a fragment insertion method. Full-length models are built and refined by the TASSER methodology, which searches conformational space via parallel hyperbolic Monte Carlo. We employ an optimized reduced force field that includes knowledge-based statistical potentials and restraints derived from the chunks as well as threading templates. The method is tested on a dataset of 425 hard target proteins ?250 amino acids in length. The average TM-scores of the best of top five models per target are 0.266, 0.336, and 0.362 by the threading algorithm SP3, original TASSER and chunk-TASSER, respectively. For a subset of 80 proteins with predicted ?-helix content ?50%, these averages are 0.284, 0.356, and 0.403, respectively. The percentages of proteins with the best of top five models having TM-score ?0.4 (a statistically significant threshold for structural similarity) are 3.76, 20.94, and 28.94% by SP3, TASSER, and chunk-TASSER, respectively, overall, while for the subset of 80 predominantly helical proteins, these percentages are 2.50, 23.75, and 41.25%. Thus, chunk-TASSER shows a significant improvement over TASSER for modeling hard targets where no good template can be identified. We also tested chunk-TASSER on 21 medium/hard targets <200 amino-acids-long from CASP7. Chunk-TASSER is ?11% (10%) better than TASSER for the total TM-score of the first (best of top five) models. Chunk-TASSER is fully automated and can be used in proteome scale protein structure prediction.

Zhou, Hongyi; Skolnick, Jeffrey

2007-01-01

153

Effects of scale in predicting global structural response  

NASA Technical Reports Server (NTRS)

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.

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

1991-01-01

154

MUSE: Multi-algorithm collaborative crystal structure prediction  

NASA Astrophysics Data System (ADS)

The algorithm and testing of the Multi-algorithm-collaborative Universal Structure-prediction Environment (MUSE) are detailed. Presently, in MUSE I combined the evolutionary, the simulated annealing, and the basin hopping algorithms to realize high-efficiency structure predictions of materials under certain conditions. MUSE is kept open and other algorithms can be added in future. I introduced two new operators, slip and twist, to increase the diversity of structures. In order to realize the self-adaptive evolution of structures, I also introduced the competition scheme among the ten variation operators, as is proved to further increase the diversity of structures. The symmetry constraints in the first generation, the multi-algorithm collaboration, the ten variation operators, and the self-adaptive scheme are all key to enhancing the performance of MUSE. To study the search ability of MUSE, I performed extensive tests on different systems, including the metallic, covalent, and ionic systems. All these present tests show that MUSE has very high efficiency and 100% success rate.

Liu, Zhong-Li

2014-07-01

155

Residual Strength Prediction of Fuselage Structures with Multiple Site Damage  

NASA Technical Reports Server (NTRS)

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.

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

1999-01-01

156

Factors influencing protein tyrosine nitration - structure-based predictive models  

PubMed Central

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).

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

2010-01-01

157

Numerical predictions of tuned liquid tank structural systems  

NASA Astrophysics Data System (ADS)

A fully nonlinear 2-D ?-transformed finite difference solver has been developed based on inviscid flow equations in rectangular tanks. The fluid equations are coupled to a linear elastic support structure. Nonoverturning sloshing motions are simulated during structural vibration cycles at and outside resonance. The wave tank acts as a tuned liquid damper (TLD). The TLD response is highly nonlinear when large liquid sloshing occurs. The solver is valid at any water depth except for small depth when shallow water waves and viscous effects would become important. Results of liquid sloshing induced by horizontal base excitations are presented for small to steep nonbreaking waves at tank aspect ratios, depth to length, h/b of 0.5, 0.25 and 0.125, representing deep to near shallow water cases. The effectiveness of the TLD is discussed through predictions of coupling frequencies and response of the tank-structural system for different tank sizes and mass ratios between fluid and structure. An effective tank-structural system typically displays two distinct frequencies with reduced structural response (e.g., h/b=0.5). These eigenfrequencies differ considerably from their noninteracting values. Hardening or softening spring behavior of the fluid, known to be present in solutions of pure sloshing motion in tanks, does not exists in the coupled system response. Strongest interactions occur with only one dominating sloshing mode when the nth sloshing frequency is close to the natural frequency of the structure, as the mass ratio between fluid and structure ??0. Inclusion of higher modes reduces the efficiency of the TLD. Good agreement is achieved between the numerical model and a first-order potential theory approximation outside the resonance region when the unsteady sloshing motions remain small. When the free-surface amplitudes become large in the coupled system, the numerical peaks are larger and the troughs become lower as time evolves (typical nonlinear effects) compared to the linear solution. Nonlinearities were found to reduce the system displacement significantly, e.g., system resonance shifted to beating response, compared to linear predictions. It was also found that the system response is extremely sensitive to small changes in forcing frequency. In conclusion, if strong interaction exists, the coupled system exhibits nonlinearity in structural and free-surface response, but the coupled eigenfrequencies compare well with the linear predictions. Furthermore, the solver removes the need for free-surface smoothing for the cases considered herein (maximum wave steepness of 1.2). The numerical model provides a quick and accurate way of determining system eigenfrequencies which can be hard to identify and interpret in physical experiments.

Frandsen, J. B.

2005-04-01

158

How Good Are Simplified Models for Protein Structure Prediction?  

PubMed Central

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.

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

2014-01-01

159

Predictive strategy for the rapid structure elucidation of drug degradants.  

PubMed

Structural information on drug degradants and impurities can serve to accelerate the drug discovery and development cycle. Traditional structure elucidation methodologies for obtaining this information are often slow and resource-consuming; therefore, LC/MS profiling and LC/MS/MS substructural analysis methodologies have been developed to rapidly and accurately elucidate structures of impurities and degradants. This work is a further development of methodologies used for the elucidation of degradation products of paclitaxel [K.J. Volk et al., Proc. 9th AAPS Ann. Meeting, 1994, p.29]. In this study cefadroxil was used as a model compound for the evaluation of a predictive strategy for the production and elucidation of impurities and degradants induced by acid, base, and heat, using LC/MS and LC/MS/MS profiling methodology, resulting in an LC/MS degradant database which includes information on molecular structures, chromatographic behavior, molecular weight, UV data, and MS/MS substructural information. Furthermore, libraries such as this can provide a predictive foundation for pre-clinical development work involving drug stability, synthesis, and monitoring. PMID:8887722

Rourick, R A; Volk, K J; Klohr, S E; Spears, T; Kerns, E H; Lee, M S

1996-09-01

160

Exploiting intrastructure information for secondary structure prediction with multifaceted pipelines.  

PubMed

Predicting the secondary structure of proteins is still a typical step in several bioinformatic tasks, in particular, for tertiary structure prediction. Notwithstanding the impressive results obtained so far, mostly due to the advent of sequence encoding schemes based on multiple alignment, in our view the problem should be studied from a novel perspective, in which understanding how available information sources are dealt with plays a central role. After revisiting a well-known secondary structure predictor viewed from this perspective (with the goal of identifying which sources of information have been considered and which have not), we propose a generic software architecture designed to account for all relevant information sources. To demonstrate the validity of the approach, a predictor compliant with the proposed generic architecture has been implemented and compared with several state-of-the-art secondary structure predictors. Experiments have been carried out on standard data sets, and the corresponding results confirm the validity of the approach. The predictor is available at http://iasc.diee.unica.it/ssp2/ through the corresponding web application or as downloadable stand-alone portable unpack-and-run bundle. PMID:22201070

Armano, Giuliano; Ledda, Filippo

2012-01-01

161

EVO—Evolutionary algorithm for crystal structure prediction  

NASA Astrophysics Data System (ADS)

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

Bahmann, Silvia; Kortus, Jens

2013-06-01

162

Multiclass Fuzzy Clustering Support Vector Machines for Protein Local Structure Prediction  

Microsoft Academic Search

Local protein structure prediction is a central task in bioinformatics research. Local protein structure prediction can be transformed into the multiclass problem for huge datasets. In previous study, multiclass clustering support vector machines (CSVMs) was proposed for local protein structure prediction. The greedy algorithm is utilized to select the next closest class if CSVM modeled for the assigned class predicts

Wei Zhong; Jieyue He; Yi Pan

2007-01-01

163

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

NASA Astrophysics Data System (ADS)

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.

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

2007-12-01

164

Pcons.net: protein structure prediction meta server  

PubMed Central

The Pcons.net Meta Server (http://pcons.net) provides improved automated tools for protein structure prediction and analysis using consensus. It essentially implements all the steps necessary to produce a high quality model of a protein. The whole process is fully automated and a potential user only submits the protein sequence. For PSI-BLAST detectable targets, an accurate model is generated within minutes of submission. For more difficult targets the sequence is automatically submitted to publicly available fold-recognition servers that use more advanced approaches to find distant structural homologs. The results from these servers are analyzed and assessed for structural correctness using Pcons and ProQ; and the user is presented with a ranked list of possible models. In addition, if the protein sequence contains more than one domain, these are automatically parsed out and resubmitted to the server as individual queries.

Wallner, Bjorn; Larsson, Per; Elofsson, Arne

2007-01-01

165

Electrostatic potential of B-DNA: effect of interionic correlations.  

PubMed Central

Modified Poisson-Boltzmann (MPB) equations have been numerically solved to study ionic distributions and mean electrostatic potentials around a macromolecule of arbitrarily complex shape and charge distribution. Results for DNA are compared with those obtained by classical Poisson-Boltzmann (PB) calculations. The comparisons were made for 1:1 and 2:1 electrolytes at ionic strengths up to 1 M. It is found that ion-image charge interactions and interionic correlations, which are neglected by the PB equation, have relatively weak effects on the electrostatic potential at charged groups of the DNA. The PB equation predicts errors in the long-range electrostatic part of the free energy that are only approximately 1.5 kJ/mol per nucleotide even in the case of an asymmetrical electrolyte. In contrast, the spatial correlations between ions drastically affect the electrostatic potential at significant separations from the macromolecule leading to a clearly predicted effect of charge overneutralization.

Gavryushov, S; Zielenkiewicz, P

1998-01-01

166

Integrating Chemical Footprinting Data into RNA Secondary Structure Prediction  

PubMed Central

Chemical and enzymatic footprinting experiments, such as shape (selective 2?-hydroxyl acylation analyzed by primer extension), yield important information about RNA secondary structure. Indeed, since the -hydroxyl is reactive at flexible (loop) regions, but unreactive at base-paired regions, shape yields quantitative data about which RNA nucleotides are base-paired. Recently, low error rates in secondary structure prediction have been reported for three RNAs of moderate size, by including base stacking pseudo-energy terms derived from shape data into the computation of minimum free energy secondary structure. Here, we describe a novel method, RNAsc (RNA soft constraints), which includes pseudo-energy terms for each nucleotide position, rather than only for base stacking positions. We prove that RNAsc is self-consistent, in the sense that the nucleotide-specific probabilities of being unpaired in the low energy Boltzmann ensemble always become more closely correlated with the input shape data after application of RNAsc. From this mathematical perspective, the secondary structure predicted by RNAsc should be ‘correct’, in as much as the shape data is ‘correct’. We benchmark RNAsc against the previously mentioned method for eight RNAs, for which both shape data and native structures are known, to find the same accuracy in 7 out of 8 cases, and an improvement of 25% in one case. Furthermore, we present what appears to be the first direct comparison of shape data and in-line probing data, by comparing yeast asp-tRNA shape data from the literature with data from in-line probing experiments we have recently performed. With respect to several criteria, we find that shape data appear to be more robust than in-line probing data, at least in the case of asp-tRNA.

Zarringhalam, Kourosh; Meyer, Michelle M.; Dotu, Ivan; Chuang, Jeffrey H.; Clote, Peter

2012-01-01

167

A Probabilistic Fragment-Based Protein Structure Prediction Algorithm  

PubMed Central

Conformational sampling is one of the bottlenecks in fragment-based protein structure prediction approaches. They generally start with a coarse-grained optimization where mainchain atoms and centroids of side chains are considered, followed by a fine-grained optimization with an all-atom representation of proteins. It is during this coarse-grained phase that fragment-based methods sample intensely the conformational space. If the native-like region is sampled more, the accuracy of the final all-atom predictions may be improved accordingly. In this work we present EdaFold, a new method for fragment-based protein structure prediction based on an Estimation of Distribution Algorithm. Fragment-based approaches build protein models by assembling short fragments from known protein structures. Whereas the probability mass functions over the fragment libraries are uniform in the usual case, we propose an algorithm that learns from previously generated decoys and steers the search toward native-like regions. A comparison with Rosetta AbInitio protocol shows that EdaFold is able to generate models with lower energies and to enhance the percentage of near-native coarse-grained decoys on a benchmark of proteins. The best coarse-grained models produced by both methods were refined into all-atom models and used in molecular replacement. All atom decoys produced out of EdaFold’s decoy set reach high enough accuracy to solve the crystallographic phase problem by molecular replacement for some test proteins. EdaFold showed a higher success rate in molecular replacement when compared to Rosetta. Our study suggests that improving low resolution coarse-grained decoys allows computational methods to avoid subsequent sampling issues during all-atom refinement and to produce better all-atom models. EdaFold can be downloaded from http://www.riken.jp/zhangiru/software/.

Simoncini, David; Berenger, Francois; Shrestha, Rojan; Zhang, Kam Y. J.

2012-01-01

168

Failure prediction of thin beryllium sheets used in spacecraft structures  

NASA Technical Reports Server (NTRS)

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.

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

1991-01-01

169

Measurements and predictions of the structure of evaporating sprays  

NASA Technical Reports Server (NTRS)

In the present experimental and theoretical study of turbulent, evaporating sprays, round, Freon-11 sprays produced by an air-atomizing injector that is directed vertically downward in still air were subjected to structure measurements for mean and fluctuating gas velocities, total Freon-11 concentration, drop size and velocity contributions, mean gas temperature, and liquid flux distributions. An evaluation was then conducted of three spray models: (1) locally homogeneous flow, (2) deterministic separated flow, and (3) stochastic separated flow. The first two of these were found to perform poorly; the stochastic model yielded the best agreement between predictions and measurements.

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

1985-01-01

170

Mapping Monomeric Threading to Protein-Protein Structure Prediction  

PubMed Central

The key step of template-based protein–protein structure prediction is the recognition of complexes from experimental structure libraries that have similar quaternary fold. Maintaining two monomer and dimer structure libraries is however laborious, and inappropriate library construction can degrade template recognition coverage. We propose a novel strategy SPRING to identify complexes by mapping monomeric threading alignments to protein–protein interactions based on the original oligomer entries in the PDB, which does not rely on library construction and increases the efficiency and quality of complex template recognitions. SPRING is tested on 1838 nonhomologous protein complexes which can recognize correct quaternary template structures with a TM score >0.5 in 1115 cases after excluding homologous proteins. The average TM score of the first model is 60% and 17% higher than that by HHsearch and COTH, respectively, while the number of targets with an interface RMSD <2.5 Å by SPRING is 134% and 167% higher than these competing methods. SPRING is controlled with ZDOCK on 77 docking benchmark proteins. Although the relative performance of SPRING and ZDOCK depends on the level of homology filters, a combination of the two methods can result in a significantly higher model quality than ZDOCK at all homology thresholds. These data demonstrate a new efficient approach to quaternary structure recognition that is ready to use for genome-scale modeling of protein–protein interactions due to the high speed and accuracy.

Guerler, Aysam; Govindarajoo, Brandon; Zhang, Yang

2014-01-01

171

Simulating regime structures in weather and climate prediction models  

NASA Astrophysics Data System (ADS)

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

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

2012-11-01

172

Predicting fracture in micron-scale polycrystalline silicon MEMS structures.  

SciTech Connect

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.

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

2010-09-01

173

Predicting protein secondary structure with a nearest-neighbor algorithm.  

PubMed

We have developed a new method for protein secondary structure prediction that achieves accuracies as high as 71.0%, the highest value yet reported. The main component of our method is a nearest-neighbor algorithm that uses a more sophisticated treatment of the feature space than standard nearest-neighbor methods. It calculates distance tables that allow it to produce real-valued distances between amino acid residues, and attaches weights to the instances to further modify the the structure of feature space. The algorithm, which is closely related to the memory-based reasoning method of Zhang et al., is simple and easy to train, and has also been applied with excellent results to the problem of identifying DNA promoter sequences. PMID:1404357

Salzberg, S; Cost, S

1992-09-20

174

Methods for evaluating the predictive accuracy of structural dynamic models  

NASA Technical Reports Server (NTRS)

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.

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

1990-01-01

175

Factors Influencing Progressive Failure Analysis Predictions for Laminated Composite Structure  

NASA Technical Reports Server (NTRS)

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.

Knight, Norman F., Jr.

2008-01-01

176

Ab initio crystal structure prediction. II. Flexible molecules  

NASA Astrophysics Data System (ADS)

An extension of a recently reported algorithm (J. Comput. Chem., 26, 304 (2005)) for the prediction of the crystal structure of organic materials to molecules whose conformation is likely to be significantly affected by the packing forces is presented. The molecule is modelled as a set of rigid fragments connected by flexible torsion angles whose values in the crystalline environment are determined by the balance of intra- and inter-molecular forces. The intramolecular energy is initially computed using quantum mechanics over a grid in the space of flexible torsion angles. This allows the intramolecular energy to be expressed as a continuous and differentiable function of the torsion angles using multidimensional interpolants based on restricted cubic Hermite polynomials. The intermolecular electrostatic interactions are treated with a conformation-dependent atomic charge model, with the charges also being expressed as functions of the flexible torsion angles using restricted Hermite interpolants. Overall, this approach allows the computationally efficient, yet accurate, evaluation of the parts of the lattice enthalpy that depend on the molecular conformation. The proposed algorithm requires only the atomic connectivity of the molecule under consideration and performs an extensive search for local minima of the lattice enthalpy surface by using deterministic low-discrepancy sequences to ensure an optimal coverage of the search space. Candidate structures can be generated in the 59 most common space groups with one or more crystallographically independent entities. These are then used as starting points of local optimization calculations performed with a sequential quadratic programming algorithm, which makes use of analytical partial derivatives with respect to all inter and intra-molecular degrees of freedom. A parallelized implementation of the algorithm allows minimizations from several hundreds of thousands of initial guesses to be carried out in reasonable time. The algorithm is used to predict the crystal structures of the polymorphic system p-chlorobenzamide and to study the relative stability of the crystal structures of the diastereomeric salt pair (R)-1-phenylethylammonium, (R&S)-2-phenylbutyrate.

Karamertzanis, P. G.; Pantelides, C. C.

177

Generic eukaryotic core promoter prediction using structural features of DNA.  

PubMed

Despite many recent efforts, in silico identification of promoter regions is still in its infancy. However, the accurate identification and delineation of promoter regions is important for several reasons, such as improving genome annotation and devising experiments to study and understand transcriptional regulation. Current methods to identify the core region of promoters require large amounts of high-quality training data and often behave like black box models that output predictions that are difficult to interpret. Here, we present a novel approach for predicting promoters in whole-genome sequences by using large-scale structural properties of DNA. Our technique requires no training, is applicable to many eukaryotic genomes, and performs extremely well in comparison with the best available promoter prediction programs. Moreover, it is fast, simple in design, and has no size constraints, and the results are easily interpretable. We compared our approach with 14 current state-of-the-art implementations using human gene and transcription start site data and analyzed the ENCODE region in more detail. We also validated our method on 12 additional eukaryotic genomes, including vertebrates, invertebrates, plants, fungi, and protists. PMID:18096745

Abeel, Thomas; Saeys, Yvan; Bonnet, Eric; Rouzé, Pierre; Van de Peer, Yves

2008-02-01

178

Incorporating structure to predict microRNA targets.  

PubMed

MicroRNAs (miRNAs) are a recently discovered set of regulatory genes that constitute up to an estimated 1% of the total number of genes in animal genomes, including Caenorhabditis elegans, Drosophila, mouse, and humans [Lagos-Quintana, M., Rauhut, R., Lendeckel, W. & Tuschl, T. (2001) Science 294, 853-858; Lai, E. C., Tomancak, P., Williams, R. W. & Rubin, G.M. (2003) Genome Biol. 4, R42; Lau, N. C., Lim, L. P., Weinstein, E. G. & Bartel, D. P. (2001) Science 294, 858-862; Lee, R. C. & Ambros, V. (2001) Science 294, 862-8644; and Lee, R. C., Feinbaum, R. L. & Ambros, V. (1993) Cell 115, 787-798]. In animals, miRNAs regulate genes by attenuating protein translation through imperfect base pair binding to 3' UTR sequences of target genes. A major challenge in understanding the regulatory role of miRNAs is to accurately predict regulated targets. We have developed an algorithm for predicting targets that does not rely on evolutionary conservation. As one of the features of this algorithm, we incorporate the folded structure of mRNA. By using Drosophila miRNAs as a test case, we have validated our predictions in 10 of 15 genes tested. One of these validated genes is mad as a target for bantam. Furthermore, our computational and experimental data suggest that miRNAs have fewer targets than previously reported. PMID:15738385

Robins, Harlan; Li, Ying; Padgett, Richard W

2005-03-15

179

Incorporating structure to predict microRNA targets  

PubMed Central

MicroRNAs (miRNAs) are a recently discovered set of regulatory genes that constitute up to an estimated 1% of the total number of genes in animal genomes, including Caenorhabditis elegans, Drosophila, mouse, and humans [Lagos-Quintana, M., Rauhut, R., Lendeckel, W. & Tuschl, T. (2001) Science 294, 853–858; Lai, E. C., Tomancak, P., Williams, R. W. & Rubin, G.M. (2003) Genome Biol. 4, R42; Lau, N. C., Lim, L. P., Weinstein, E. G. & Bartel, D. P. (2001) Science 294, 858–862; Lee, R. C. & Ambros, V. (2001) Science 294, 862-8644; and Lee, R. C., Feinbaum, R. L. & Ambros, V. (1993) Cell 115, 787–798]. In animals, miRNAs regulate genes by attenuating protein translation through imperfect base pair binding to 3? UTR sequences of target genes. A major challenge in understanding the regulatory role of miRNAs is to accurately predict regulated targets. We have developed an algorithm for predicting targets that does not rely on evolutionary conservation. As one of the features of this algorithm, we incorporate the folded structure of mRNA. By using Drosophila miRNAs as a test case, we have validated our predictions in 10 of 15 genes tested. One of these validated genes is mad as a target for bantam. Furthermore, our computational and experimental data suggest that miRNAs have fewer targets than previously reported.

Robins, Harlan; Li, Ying; Padgett, Richard W.

2005-01-01

180

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

NASA Astrophysics Data System (ADS)

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.

Kasahara, Naoto

181

Prediction of Alzheimer's disease using individual structural connectivity networks  

PubMed Central

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.

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

2012-01-01

182

Predicting the bifurcation structure of localized snaking patterns  

NASA Astrophysics Data System (ADS)

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

Makrides, Elizabeth; Sandstede, Björn

2014-02-01

183

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

PubMed Central

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.

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

2014-01-01

184

Connecting protein structure with predictions of regulatory sites  

PubMed Central

A common task posed by microarray experiments is to infer the binding site preferences for a known transcription factor from a collection of genes that it regulates and to ascertain whether the factor acts alone or in a complex. The converse problem can also be posed: Given a collection of binding sites, can the regulatory factor or complex of factors be inferred? Both tasks are substantially facilitated by using relatively simple homology models for protein–DNA interactions, as well as the rapidly expanding protein structure database. For budding yeast, we are able to construct reliable structural models for 67 transcription factors and with them redetermine factor binding sites by using a Bayesian Gibbs sampling algorithm and an extensive protein localization data set. For 49 factors in common with a prior analysis of this data set (based largely on phylogenetic conservation), we find that half of the previously predicted binding motifs are in need of some revision. We also solve the inverse problem of ascertaining the factors from the binding sites by assigning a correct protein fold to 25 of the 49 cases from a previous study. Our approach is easily extended to other organisms, including higher eukaryotes. Our study highlights the utility of enlarging current structural genomics projects that exhaustively sample fold structure space to include all factors with significantly different DNA-binding specificities.

Morozov, Alexandre V.; Siggia, Eric D.

2007-01-01

185

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

PubMed Central

Structure mapping experiments (using probes such as dimethyl sulfate [DMS], kethoxal, and T1 and V1 RNases) are used to determine the secondary structures of RNA molecules. The process is iterative, combining the results of several probes with constrained minimum free-energy calculations to produce a model of the structure. We aim to evaluate whether particular probes provide more structural information, and specifically, how noise in the data affects the predictions. Our approach involves generating “decoy” RNA structures (using the sFold Boltzmann sampling procedure) and evaluating whether we are able to identify the correct structure from this ensemble of structures. We show that with perfect information, we are always able to identify the optimal structure for five RNAs of known structure. We then collected orthogonal structure mapping data (DMS and RNase T1 digest) under several solution conditions using our high-throughput capillary automated footprinting analysis (CAFA) technique on two group I introns of known structure. Analysis of these data reveals the error rates in the data under optimal (low salt) and suboptimal solution conditions (high MgCl2). We show that despite these errors, our computational approach is less sensitive to experimental noise than traditional constraint-based structure prediction algorithms. Finally, we propose a novel approach for visualizing the interaction of chemical and enzymatic mapping data with RNA structure. We project the data onto the first two dimensions of a multidimensional scaling of the sFold-generated decoy structures. We are able to directly visualize the structural information content of structure mapping data and reconcile multiple data sets.

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

2010-01-01

186

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

PubMed Central

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

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

2012-01-01

187

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

ERIC Educational Resources Information Center

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…

Lucas, Amand A.

2008-01-01

188

Flexibility of the B-DNA backbone: effects of local and neighbouring sequences on pyrimidine-purine steps.  

PubMed Central

The structurally correlated dihedral angles epsilon and zeta are known for their large variability within the B-DNA backbone. We have used molecular modelling to study both energetic and mechanical features of these variations which can produce BI/BII transitions. Calculations were carried out on DNA oligomers containing either YpR or RpY dinucleotides steps within various sequence environments. The results indicate that CpA and CpG steps favour the BI/BII transition more than TpA or any RpY step. The stacking energy and its intra- and inter-strand components explain these effects. Analysis of neighbouring base pairs reveals that BI/BII transitions of CpG and CpA are easiest within (Y)n(R)n sequences. These can also induce a large vibrational amplitude for TpA steps within the BI conformation.

Bertrand, H; Ha-Duong, T; Fermandjian, S; Hartmann, B

1998-01-01

189

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

PubMed

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

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

2014-06-01

190

Structural Acoustic Prediction and Interior Noise Control Technology  

NASA Technical Reports Server (NTRS)

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.

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

2001-01-01

191

PUDGE: a flexible, interactive server for protein structure prediction  

PubMed Central

The construction of a homology model for a protein can involve a number of decisions requiring the integration of different sources of information and the application of different modeling tools depending on the particular problem. Functional information can be especially important in guiding the modeling process, but such information is not generally integrated into modeling pipelines. Pudge is a flexible, interactive protein structure prediction server, which is designed with these issues in mind. By dividing the modeling into five stages (template selection, alignment, model building, model refinement and model evaluation) and providing various tools to visualize, analyze and compare the results at each stage, we enable a flexible modeling strategy that can be tailored to the needs of a given problem. Pudge is freely available at http://wiki.c2b2.columbia.edu/honiglab_public/index.php/Software:PUDGE.

Norel, Raquel; Petrey, Donald; Honig, Barry

2010-01-01

192

Evolutionary computer programming of protein folding and structure predictions.  

PubMed

In order to understand the mechanism of protein folding and to assist the rational de-novo design of fast-folding, non-aggregating and stable artificial enzymes it is very helpful to be able to simulate protein folding reactions and to predict the structures of proteins and other biomacromolecules. Here, we use a method of computer programming called "evolutionary computer programming" in which a program evolves depending on the evolutionary pressure exerted on the program. In the case of the presented application of this method on a computer program for folding simulations, the evolutionary pressure exerted was towards faster finding deep minima in the energy landscape of protein folding. Already after 20 evolution steps, the evolved program was able to find deep minima in the energy landscape more than 10 times faster than the original program prior to the evolution process. PMID:15178181

Nölting, Bengt; Jülich, Dennis; Vonau, Winfried; Andert, Karl

2004-07-01

193

Applications of tree-structured regression for regional precipitation prediction  

NASA Astrophysics Data System (ADS)

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

Li, Xiangshang

2000-11-01

194

Exploring polymorphisms in B-DNA helical conformations  

PubMed Central

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.

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

2012-01-01

195

Numerical Prediction of Interior and Structural Response of Buildings to Sonic Boom Overflights.  

National Technical Information Service (NTIS)

The report describes a procedure for predicting the structural and acoustic response of full scale architectural structures to sonic booms using laboratory techniques. It is shown that the essential acoustic properties of a full scale structure located in...

L. Arnold S. Slutsky

1972-01-01

196

Failure prediction of thin beryllium sheets used in spacecraft structures  

NASA Technical Reports Server (NTRS)

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.

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

1991-01-01

197

Towards a molecular dynamics consensus view of B-DNA flexibility  

PubMed Central

We present a systematic study of B-DNA flexibility in aqueous solution using long-scale molecular dynamics simulations with the two more recent versions of nucleic acids force fields (CHARMM27 and parmbsc0) using four long duplexes designed to contain several copies of each individual base pair step. Our study highlights some differences between pambsc0 and CHARMM27 families of simulations, but also extensive agreement in the representation of DNA flexibility. We also performed additional simulations with the older AMBER force fields parm94 and parm99, corrected for non-canonical backbone flips. Taken together, the results allow us to draw for the first time a consensus molecular dynamics picture of B-DNA flexibility.

Perez, Alberto; Lankas, Filip; Luque, F. Javier; Orozco, Modesto

2008-01-01

198

Decision tree-based formation of consensus protein secondary structure prediction  

Microsoft Academic Search

Motivation: Prediction of protein secondary structure provides information that is useful for other prediction methods like fold recognition and ab initio 3D prediction. A consensus prediction constructed from the output of several methods should yield more reliable results than each of the individual methods. Method: We present an approach that reveals subtle but systematic differences in the output of different

Joachim Selbig; Heinz-theodor Mevissen; Thomas Lengauer

1999-01-01

199

A Parallel, Out-of-Core Algorithm for RNA Secondary Structure Prediction  

Microsoft Academic Search

RNA pseudoknot prediction is an algorithm for RNA se- quence search and alignment. An important building block towards pseudoknot prediction is RNA secondary structure prediction. The difculty of extending the secondary struc- ture prediction algorithm to a parallel program is (1) it has complicated data dependences, and (2) it has a large data set that typically cannot t completely in

Wenduo Zhou; David K. Lowenthal

2006-01-01

200

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

PubMed Central

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.

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; Malmstrom, Lars; Bonneau, Richard

2011-01-01

201

Predictive modeling of pedestal structure in KSTAR using EPED model  

SciTech Connect

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.

Han, Hyunsun; Kim, J. Y. [National Fusion Research Institute, Daejeon 305-806 (Korea, Republic of)] [National Fusion Research Institute, Daejeon 305-806 (Korea, Republic of); Kwon, Ohjin [Department of Physics, Daegu University, Gyeongbuk 712-714 (Korea, Republic of)] [Department of Physics, Daegu University, Gyeongbuk 712-714 (Korea, Republic of)

2013-10-15

202

Structural and performance predictions for an active composite panel  

NASA Astrophysics Data System (ADS)

Successful use of smart materials or derives requires an understanding of the performance characteristics of the combination of smart and passive components. Synergistic material and geometric interactions often produce complex performance characteristics. Computational simulations which explicitly include material components are often required for accurate performance predictions. A performance evaluation is completed for a multilayered composite panel designed for acoustic suppression. The panel contains multiple pistons. Each piston consists of an array of actuators which support an alumina head mass plate. The alumina plate is designed to deliver uniform displacement with movement of the actuators. The panel design features a graphite composite box which functions as both a base plate for the active panel and as housing for the power electronics required for plate activation. ABAQUS, a commercial finite element code, is used for deformation and structural integrity analyses. Performance issues addressed are: (1)Final surface displacement and flatness of the top surface. (2) Loss of actuator authority resulting from intermediate layers of passive materials. (3) Changes in performance due to changes in interfacial layer characteristics. (4) Changes in performance due to changes in operating temperatures. (5) Internal stress and strain levels in each of the component materials.

DeGiorgi, Virginia G.; McDermott, S. H.

1997-05-01

203

Chromatin structure predicts epigenetic therapy responsiveness in sarcoma  

PubMed Central

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.

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

2010-01-01

204

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

NASA Astrophysics Data System (ADS)

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.

Talha, Mohammad; Ashokkumar, Chimpalthradi R.

2014-05-01

205

The prediction of a protein and nucleic acid structure: Problems and prospects  

Microsoft Academic Search

Recent advances in DNA and protein-sequencing technologies have made an increasing number of primary structures available for theoretical investigations. The prediction of a higher-order protein, and nucleic acid structure in particular, is an area where computational approaches will be able to complement the lack of experimental observations. We review some of the problems related to structure predictions: sequence homology searches,

Minoru Kanehisa; Charles DeLisi

1985-01-01

206

Predicting the secondary structure of globular proteins using neural network models  

Microsoft Academic Search

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

Ning Qian; Terrence J. Sejnowski

1988-01-01

207

A Combination of Support Vector Machines and Bidirectional Recurrent Neural Networks for Protein Secondary Structure Prediction  

Microsoft Academic Search

Predicting the secondary structure of a protein is a main topic in bioinformatics. A reliable predictor is needed by threading meth- ods to improve the prediction of tertiary structure. Moreover,the pre- dicted secondary structure content of a protein can be used to assign the protein to a specific folding class and thus estimate its function. We discuss here the use

Alessio Ceroni; Paolo Frasconi; Andrea Passerini; Alessandro Vullo

2003-01-01

208

Assessment of Prediction Confidence and Domain Extrapolation of Two Structure-Activity Relationship Models for Predicting Estrogen Receptor Binding Activity  

PubMed Central

Quantitative structure–activity relationship (QSAR) methods have been widely applied in drug discovery, lead optimization, toxicity prediction, and regulatory decisions. Despite major advances in algorithms and software, QSAR models have inherent limitations associated with a size and chemical-structure diversity of the training set, experimental error, and many characteristics of structure representation and correlation algorithms. Whereas excellent fit to the training data may be readily attainable, often models fail to predict accurately chemicals that are outside their domain of applicability. A QSAR’s utility and, in the case of regulatory decisions, justification for usage increasingly depend on the ability to quantify a model’s potential for predicting unknown chemicals with some known degree of certainty. It is never possible to predict an unknown chemical with absolute certainty. Here we report on two QSAR models based on different data sets for classification of chemicals according to their ability to bind to the estrogen receptor. The models were developed by using a novel QSAR method, Decision Forest, which combines the results of multiple heterogeneous but comparable Decision Tree models to produce a consensus prediction. We used an extensive cross-validation process to define an applicability domain for model predictions based on two quantitative measures: prediction confidence and domain extrapolation. Together, these measures quantify the accuracy of each prediction within and outside of the training domain. Despite being based on large and diverse training sets, both QSAR models had poor accuracy for chemicals within the domain of low confidence, whereas good accuracy was obtained for those within the domain of high confidence. For prediction in the high confidence domain, accuracy was inversely proportional to the degree of domain extrapolation. The model with a larger training set of 1,092, compared with 232 for the other, was more accurate in predicting chemicals at larger domain extrapolation, and could be particularly useful for rapidly prioritizing potential endocrine disruptors from large chemical universe.

Tong, Weida; Xie, Qian; Hong, Huixiao; Shi, Leming; Fang, Hong; Perkins, Roger

2004-01-01

209

Assessment of prediction confidence and domain extrapolation of two structure-activity relationship models for predicting estrogen receptor binding activity.  

PubMed

Quantitative structure-activity relationship (QSAR) methods have been widely applied in drug discovery, lead optimization, toxicity prediction, and regulatory decisions. Despite major advances in algorithms and software, QSAR models have inherent limitations associated with a size and chemical-structure diversity of the training set, experimental error, and many characteristics of structure representation and correlation algorithms. Whereas excellent fit to the training data may be readily attainable, often models fail to predict accurately chemicals that are outside their domain of applicability. A QSAR's utility and, in the case of regulatory decisions, justification for usage increasingly depend on the ability to quantify a model's potential for predicting unknown chemicals with some known degree of certainty. It is never possible to predict an unknown chemical with absolute certainty. Here we report on two QSAR models based on different data sets for classification of chemicals according to their ability to bind to the estrogen receptor. The models were developed by using a novel QSAR method, Decision Forest, which combines the results of multiple heterogeneous but comparable Decision Tree models to produce a consensus prediction. We used an extensive cross-validation process to define an applicability domain for model predictions based on two quantitative measures: prediction confidence and domain extrapolation. Together, these measures quantify the accuracy of each prediction within and outside of the training domain. Despite being based on large and diverse training sets, both QSAR models had poor accuracy for chemicals within the domain of low confidence, whereas good accuracy was obtained for those within the domain of high confidence. For prediction in the high confidence domain, accuracy was inversely proportional to the degree of domain extrapolation. The model with a larger training set of 1,092, compared with 232 for the other, was more accurate in predicting chemicals at larger domain extrapolation, and could be particularly useful for rapidly prioritizing potential endocrine disruptors from large chemical universe. PMID:15345371

Tong, Weida; Xie, Qian; Hong, Huixiao; Shi, Leming; Fang, Hong; Perkins, Roger

2004-08-01

210

An application software for protein secondary structure prediction based on peptide triplet units and artificial neural networks: Protein secondary structure prediction from amino acid sequences  

Microsoft Academic Search

On the basis of a bank of tendentious factors of tripeptide units, a protein secondary structure prediction system (PSSP) was built. Our research results revealed that PSSP represents a higher prediction accuracy of alpha-helix up to 89.45% on average, even the mean correct rate of alpha-helix also achieved 67.78% for all-beta proteins. PSSP only achieved a whole prediction accuracy of

Jie Yang; Tong-Yang Zhu; Xian-Chi Dong

2010-01-01

211

Towards fully automated structure-based function prediction in structural genomics: a case study.  

PubMed

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

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

2007-04-13

212

3D-Jury: A Simple Approach to Improve Protein Structure Predictions  

Microsoft Academic Search

Motivation: Consensus structure prediction methods (meta-predictors) have higher accuracy than individual structure prediction algorithms (their components). The goal for the development of the 3D-Jury system is to create a simple but powerful procedure for generating meta-predictions using variable sets of models obtained from diverse sources. The resulting protocol should help to improve the quality of structural annotations of novel proteins.

Krzysztof Ginalski; Arne Elofsson; Daniel Fischer; Leszek Rychlewski

2003-01-01

213

Highly Predictive Structural Cell for Particulate Polymeric Composites  

Microsoft Academic Search

A unit cell of a specified shape under specified loading conditions has been offered for predicting some basic properties of particulate polymeric composites. The stress-strain state of the bonded and debonded cell has been calculated in the framework of the large deformation approach within a wide range of filler volume concentrations. Effective modulus-filler concentration curves have been predicted on the

V. V. Moshev; L. L. Kozhevnikova

1997-01-01

214

A new prediction strategy for long local protein structures using an original description  

PubMed Central

A relevant and accurate description of three-dimensional (3D) protein structures can be achieved by characterising recurrent local structures. In a previous study, we developed a library of 120 3D structural prototypes encompassing all known 11- residues long local protein structures and ensuring a good quality of structural approximation. A local structure prediction method was also proposed. Here, overlapping properties of local protein structures in global ones are taken into account in order to characterize frequent local networks. At the same time, we propose a new long local structure prediction strategy which involves the use of evolutionary information coupled with Support Vector Machines (SVMs). Our prediction is evaluated by a stringent geometrical assessment. Every local structure prediction with a C? RMSD less than 2.5 Å from the true local structure is considered as correct. A global prediction rate of 63.1% is then reached, corresponding to an improvement of 7.7 points compared to the previous strategy. In the same way, the prediction of 88.33% of the 120 structural classes is improved with 8.65 % mean gain. And 85.33% of proteins have better prediction results with a 9.43 % average gain. An analysis of prediction rate per local network also supports the global improvement and gives insights into the potential of our method for predicting super local structures. Moreover, a confidence index for the direct estimation of prediction quality is proposed. Finally, our method is proved to be very competitive with cutting-edge strategies encompassing three categories of local structure predictions.

Bornot, Aurelie; Etchebest, Catherine; De Brevern, Alexandre G.

2009-01-01

215

Impact of model error structure on predictability of linear stochastic dynamics  

NASA Astrophysics Data System (ADS)

A system is predictable on those times-scales where prediction errors do not exceed climate variability. Predictability, therefore, depends on both the physical system and on the prediction system. Here, we consider the predictability of physical systems described by linear stochastic dynamics and prediction systems with perfect initial conditions and temporally uncorrelated model error. We investigate the impact of model error structure on global measures of predictability. Decomposing the model error in the normal modes of the dynamics, we find that predictability is independent of model error spectrum when model error is uncorrelated in normal-mode space. Predictability is increased when model error is correlated in normal-mode space. Therefore normal-mode analysis gives lower bounds for predictability. These results are illustrated in some theoretical examples that identify factors leading to enhanced predictability.

Tippett, M. K.; Chang, P.

2001-12-01

216

Impact of residue accessible surface area on the prediction of protein secondary structures  

PubMed Central

Background The problem of accurate prediction of protein secondary structure continues to be one of the challenging problems in Bioinformatics. It has been previously suggested that amino acid relative solvent accessibility (RSA) might be an effective factor for increasing the accuracy of protein secondary structure prediction. Previous studies have either used a single constant threshold to classify residues into discrete classes (buries vs. exposed), or used the real-value predicted RSAs in their prediction method. Results We studied the effect of applying different RSA threshold types (namely, fixed thresholds vs. residue-dependent thresholds) on a variety of secondary structure prediction methods. With the consideration of DSSP-assigned RSA values we realized that improvement in the accuracy of prediction strictly depends on the selected threshold(s). Furthermore, we showed that choosing a single threshold for all amino acids is not the best possible parameter. We therefore used residue-dependent thresholds and most of residues showed improvement in prediction. Next, we tried to consider predicted RSA values, since in the real-world problem, protein sequence is the only available information. We first predicted the RSA classes by RVP-net program and then used these data in our method. Using this approach, improvement in prediction was also obtained. Conclusion The success of applying the RSA information on different secondary structure prediction methods suggest that prediction accuracy can be improved independent of prediction approaches. Thus, solvent accessibility can be considered as a rich source of information to help the improvement of these methods.

Momen-Roknabadi, Amir; Sadeghi, Mehdi; Pezeshk, Hamid; Marashi, Sayed-Amir

2008-01-01

217

Physics-based de novo prediction of RNA 3D structures.  

PubMed

Current experiments on structural determination cannot keep up the pace with the steadily emerging RNA sequences and new functions. This underscores the request for an accurate model for RNA three-dimensional (3D) structural prediction. Although considerable progress has been made in mechanistic studies, accurate prediction for RNA tertiary folding from sequence remains an unsolved problem. The first and most important requirement for the prediction of RNA structure from physical principles is an accurate free energy model. A recently developed three-vector virtual bond-based RNA folding model ("Vfold") has allowed us to compute the chain entropy and predict folding free energies and structures for RNA secondary structures and simple pseudoknots. Here we develop a free energy-based method to predict larger more complex RNA tertiary folds. The approach is based on a multiscaling strategy: from the nucleotide sequence, we predict the two-dimensional (2D) structures (defined by the base pairs and tertiary contacts); based on the 2D structure, we construct a 3D scaffold; with the 3D scaffold as the initial state, we combine AMBER energy minimization and PDB-based fragment search to predict the all-atom structure. A key advantage of the approach is the statistical mechanical calculation for the conformational entropy of RNA structures, including those with cross-linked loops. Benchmark tests show that the model leads to significant improvements in RNA 3D structure prediction. PMID:21413701

Cao, Song; Chen, Shi-Jie

2011-04-14

218

A statistical sampling algorithm for RNA secondary structure prediction  

Microsoft Academic Search

An RNA molecule, particularly a long-chain mRNA, may exist as a population of structures. Further- more, multiple structures have been demonstrated to play important functional roles. Thus, a repre- sentation of the ensemble of probable structures is of interest. We present a statistical algorithm to sample rigorously and exactly from the Boltzmann ensemble of secondary structures. The forward step of

Ye Ding; Charles E. Lawrence

2003-01-01

219

ProbKnot: fast prediction of RNA secondary structure including pseudoknots.  

PubMed

It is a significant challenge to predict RNA secondary structures including pseudoknots. Here, a new algorithm capable of predicting pseudoknots of any topology, ProbKnot, is reported. ProbKnot assembles maximum expected accuracy structures from computed base-pairing probabilities in O(N(2)) time, where N is the length of the sequence. The performance of ProbKnot was measured by comparing predicted structures with known structures for a large database of RNA sequences with fewer than 700 nucleotides. The percentage of known pairs correctly predicted was 69.3%. Additionally, the percentage of predicted pairs in the known structure was 61.3%. This performance is the highest of four tested algorithms that are capable of pseudoknot prediction. The program is available for download at: http://rna.urmc.rochester.edu/RNAstructure.html. PMID:20699301

Bellaousov, Stanislav; Mathews, David H

2010-10-01

220

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

NASA Astrophysics Data System (ADS)

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.

Davidson, Noel E.; Ma, Yimin

2012-07-01

221

Validation of Finite Element and Boundary Element Methods for Predicting Structural Vibration and Radiated Noise.  

National Technical Information Service (NTIS)

Analytical and experimental validation of methods to predict structural vibration and radiated noise is presented in this paper. A rectangular box excited by a mechanical shaker was used as a vibrating structure. Combined finite element method (FEM) and b...

A. F. Seybert X. F. Wu F. B. Oswald

1992-01-01

222

Validation of Finite Element and Boundary Element Methods for Predicting Structural Vibration and Radiated Noise.  

National Technical Information Service (NTIS)

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 eleme...

A. F. Seybert X. F. Wu F. B. Oswald

1992-01-01

223

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

EPA Science Inventory

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

224

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...

225

MaxSub: an automated measure for the assessment of protein structure prediction quality  

Microsoft Academic Search

Motivation: Evaluating the accuracy of predicted models is critical for assessing structure prediction methods. Because this problem is not trivial, a large number of different assessment measures have been proposed by various authors, and it has already become an active subfield of research (Moultet al., 1999). The CASP (Moult et al., 1997, 1999) and CAFASP (Fischeret al., 1999) prediction experiments

Naomi Siew; Arne Elofsson; Leszek Rychlewski; Daniel Fischer

2000-01-01

226

Predicting the Operon Structure of Bacillus subtilis Using Operon Length, Intergene Distance, and Gene Expression Information  

Microsoft Academic Search

We predict the operon structure of the Bacillus subtilis genome using the average operon length, the distance between genes in base pairs, and the similarity in gene expression measured in time course and gene disruptant experiments. By expressing the operon prediction for each method as a Bayesian probability, we are able to combine the four prediction methods into a Bayesian

Michiel J. L. De Hoon; Seiya Imoto; Kazuo Kobayashi; Naotake Ogasawara; Satoru Miyano

2004-01-01

227

Measurement and Prediction of Vibration Response of Deepwater Offshore Structures.  

National Technical Information Service (NTIS)

Good engineering practice for the design of very large deepwater structures leads to structures with lower resonance frequencies. These natural resonance frequencies are within a range at which waves from small storms are quite energetic. The dynamic ampl...

N. Doelling

1981-01-01

228

Aircraft Structural Mass Property Prediction Using Conceptual-Level Structural Analysis  

NASA Technical Reports Server (NTRS)

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.

Sexstone, Matthew G.

1998-01-01

229

Aircraft Structural Mass Property Prediction Using Conceptual-Level Structural Analysis  

NASA Technical Reports Server (NTRS)

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.

Sexstone, Matthew G.

1998-01-01

230

High-resolution structure prediction and the crystallographic phase problem  

Microsoft Academic Search

The energy-based refinement of low-resolution protein structure models to atomic-level accuracy is a major challenge for computational structural biology. Here we describe a new approach to refining protein structure models that focuses sampling in regions most likely to contain errors while allowing the whole structure to relax in a physically realistic all-atom force field. In applications to models produced using

Bin Qian; Srivatsan Raman; Rhiju Das; Philip Bradley; Airlie J. McCoy; Randy J. Read; David Baker

2007-01-01

231

Alternative structure predicted for lithium at ambient pressure  

NASA Astrophysics Data System (ADS)

We perform a global search for possible structures of bulk lithium, where we employ simulated annealing with ab initio energies in all steps. We find the A15 structure as a different promising local minimum at ambient pressure. Its energy is competitive with the already known structures, thus making it an interesting target for synthesis.

Kulkarni, A.; Doll, K.; Prasad, D. L. V. K.; Schön, J. C.; Jansen, M.

2011-11-01

232

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

NASA Astrophysics Data System (ADS)

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.

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

2013-12-01

233

Prediction of protein secondary structure using Large Margin Nearest Neighbour classification.  

PubMed

In this paper, we introduce a novel method for protein secondary structure prediction by using Position-Specific Scoring Matrices (PSSM) profiles and Large Margin Nearest Neighbour (LMNN) classification. Since the PSSM profiles are not specifically designed for protein secondary structure prediction, the traditional nearest neighbour method could not achieve satisfactory prediction accuracy. To address this problem, we first use a LMNN model to learn a Mahalanobis distance metric for nearest neighbour classification. Then, an energy-based rule is invoked to assign secondary structure. Tests show that the proposed method obtains better prediction accuracy when compared with previous nearest neighbour methods. PMID:23467064

Yang, Wei; Wang, Kuanquan; Zuo, Wangmeng

2013-01-01

234

Potential for assessing quality of protein structure based on contact number prediction.  

PubMed

We developed a novel knowledge-based residue environment potential for assessing the quality of protein structures in protein structure prediction. The potential uses the contact number of residues in a protein structure and the absolute contact number of residues predicted from its amino acid sequence using a new prediction method based on a support vector regression (SVR). The contact number of an amino acid residue in a protein structure is defined by the number of residues around a given residue. First, the contact number of each residue is predicted using SVR from an amino acid sequence of a target protein. Then, the potential of the protein structure is calculated from the probability distribution of the native contact numbers corresponding to the predicted ones. The performance of this potential is compared with other score functions using decoy structures to identify both native structure from other structures and near-native structures from nonnative structures. This potential improves not only the ability to identify native structures from other structures but also the ability to discriminate near-native structures from nonnative structures. PMID:16788993

Ishida, Takashi; Nakamura, Shugo; Shimizu, Kentaro

2006-09-01

235

Preferential binding of the chemical carcinogen N-hydroxy-2-aminofluorene to B-DNA as compared to Z-DNA.  

PubMed Central

The reaction between the chemical carcinogen N-hydroxy-2-aminofluorene and poly (dG-dC) . poly (dG-dC) (B-form), poly (dG-m5dC) . poly (dG-m5dC) (B-or Z-form), poly(dG-br5dC) . poly (dG-br5dC) (Z-form) has been studied. The carcinogen binds covalently to B-DNA but does not bind significantly to Z-DNA. These results are discussed as related to the accessibility, the electrostatic potential and the dynamic structure of DNA. The accessibility and the electrostatic potential of DNA do not explain the difference in reactivity of the carcinogen since a related carcinogen N-acetoxy-N-acetyl-2-aminofluorene binds equally well to both B and Z-DNA. On the other hand, poly (dG-dC) . poly(dG-dC) and poly (dG-br5dC) . poly(dG-br5dC), in presence of ethidium bromide binds equally well to N-hydroxy-2-aminofluorene. It is suggested that the very low binding of this carcinogen to Z-DNA as compared to B-DNA is due to differences in the dynamic structures of these two forms of DNA.

Rio, P; Leng, M

1983-01-01

236

PreSSAPro: A software for the prediction of secondary structure by amino acid properties  

Microsoft Academic Search

PreSSAPro is a software, available to the scientific community as a free web service designed to provide predictions of secondary structures starting from the amino acid sequence of a given protein. Predictions are based on our recently published work on the amino acid propensities for secondary structures in either large but not homogeneous protein data sets, as well as in

Susan Costantini; Giovanni Colonna; Angelo M. Facchiano

2007-01-01

237

A structure-motivated hybrid machine for prediction of biological activity of chemical compounds  

Microsoft Academic Search

In this work we propose a hybrid learning machine, combining artificial neural networks (ANNs) and binary decision trees, to predict quantitative structure activity relationships (QSARs). This approach directly uses the structural cues from chemical compounds and has been validated for the two significant prediction problems, viz. regression and classification. For regression analysis we show the utility of the algorithm in

Amit Kumar Mishra; O. P. Patri

2010-01-01

238

The Predictive Validity of the Structured Assessment of Violence Risk in Youth in Secondary Educational Settings  

ERIC Educational Resources Information Center

Current developments in violence risk assessment warrant consideration for use within educational settings. Using a structured professional judgment (SPJ) model, the present study investigated the predictive validity of the Structured Assessment of Violence in Youth (SAVRY) within educational settings. The predictive accuracy of the SAVRY scales…

McGowan, Mark R.; Horn, Robert A.; Mellott, Ramona N.

2011-01-01

239

Amide Pyramidalization in Carbamazepine: a Flexibility Problem in Crystal Structure Prediction?  

Microsoft Academic Search

Carbamazepine is known to exist in various polymorphic forms. Here we report on crystal structure prediction calculations for carbamazepine in an attempt to examine the predictability and relative stability of the various polymorphs. Hypothetical crystal structures have been generated in ten of the most common space groups and these were compared to the four known polymorphs. Particular attention was given

Aurora J. Cruz-Cabeza; W. D. Sam

240

Exploiting structural and topological information to improve prediction of RNA-protein binding sites  

Microsoft Academic Search

BACKGROUND: RNA-protein interactions are important for a wide range of biological processes. Current computational methods to predict interacting residues in RNA-protein interfaces predominately rely on sequence data. It is, however, known that interface residue propensity is closely correlated with structural properties. In this paper we systematically study information obtained from sequences and structures and compare their contributions in this prediction

Stefan R Maetschke; Zheng Yuan

2009-01-01

241

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

PubMed

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

Dombroski, Matt; Fischhoff, Baruch; Fischbeck, Paul

2006-12-01

242

CentroidHomfold-LAST: accurate prediction of RNA secondary structure using automatically collected homologous sequences  

PubMed Central

Although secondary structure predictions of an individual RNA sequence have been widely used in a number of sequence analyses of RNAs, accuracy is still limited. Recently, we proposed a method (called ‘CentroidHomfold’), which includes information about homologous sequences into the prediction of the secondary structure of the target sequence, and showed that it substantially improved the performance of secondary structure predictions. CentroidHomfold, however, forces users to prepare homologous sequences of the target sequence. We have developed a Web application (CentroidHomfold-LAST) that predicts the secondary structure of the target sequence using automatically collected homologous sequences. LAST, which is a fast and sensitive local aligner, and CentroidHomfold are employed in the Web application. Computational experiments with a commonly-used data set indicated that CentroidHomfold-LAST substantially outperformed conventional secondary structure predictions including CentroidFold and RNAfold.

Hamada, Michiaki; Yamada, Koichiro; Sato, Kengo; Frith, Martin C.; Asai, Kiyoshi

2011-01-01

243

Prediction of Four Kinds of Simple Supersecondary Structures in Protein by Using Chemical Shifts  

PubMed Central

Knowledge of supersecondary structures can provide important information about its spatial structure of protein. Some approaches have been developed for the prediction of protein supersecondary structure. However, the feature used by these approaches is primarily based on amino acid sequences. In this study, a novel model is presented to predict protein supersecondary structure by use of chemical shifts (CSs) information derived from nuclear magnetic resonance (NMR) spectroscopy. Using these CSs as inputs of the method of quadratic discriminant analysis (QD), we achieve the overall prediction accuracy of 77.3%, which is competitive with the same method for predicting supersecondary structures from amino acid compositions in threefold cross-validation. Moreover, our finding suggests that the combined use of different chemical shifts will influence the accuracy of prediction.

Yonge, Feng

2014-01-01

244

Predicting the protein-protein interactions using primary structures with predicted protein surface  

Microsoft Academic Search

BACKGROUND: Many biological functions involve various protein-protein interactions (PPIs). Elucidating such interactions is crucial for understanding general principles of cellular systems. Previous studies have shown the potential of predicting PPIs based on only sequence information. Compared to approaches that require other auxiliary information, these sequence-based approaches can be applied to a broader range of applications. RESULTS: This study presents a

Darby Tien-Hao Chang; Yu-Tang Syu; Po-Chang Lin

2010-01-01

245

Comparison of Stability Predictions and Simulated Unfolding of Rhodopsin Structures  

Microsoft Academic Search

Developing a 1 better mechanistic understanding of membrane protein folding is urgently needed because of the discovery of an increasing number of human diseases, where membrane protein instability and misfolding is involved. Towards this goal, we investigated folding and stability of 7-transmembrane (TM) helical bundles by computational methods. We compared the results of three different algorithms for predicting changes in

Oznur Tastan; Esther Yu; Madhavi Ganapathiraju; Anes Aref; AJ Rader; Judith Klein-Seetharaman

246

Learning structured prediction models: a large margin approach  

Microsoft Academic Search

We consider large margin estimation in a broad range of prediction models where infer- ence involves solving combinatorial optimiza- tion problems, for example, weighted graph- cuts or matchings. Our goal is to learn pa- rameters such that inference using the model reproduces correct answers on the training data. Our method relies on the expressive power of convex optimization problems to

Benjamin Taskar; Vassil Chatalbashev; Daphne Koller; Carlos Guestrin

2005-01-01

247

OPTIMAL CHARACTERIZATION OF STRUCTURE FOR PREDICTION OF PROPERTIES  

EPA Science Inventory

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

248

Prediction of Harmful Human Health Effects of Chemicals from Structure  

NASA Astrophysics Data System (ADS)

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

Cronin, Mark T. D.

249

No-Regret Algorithms for Structured Prediction Problems.  

National Technical Information Service (NTIS)

No-regret algorithms are a popular class of online learning rules. Unfortunately, most no-regret algorithms assume that the set Y of allowable hypotheses is small and discrete. Instead, the authors consider prediction problems where Y has internal structu...

G. J. Gordon

2005-01-01

250

Gene structure conservation aids similarity based gene prediction  

Microsoft Academic Search

One of the primary tasks in deciphering the func- tional contents of a newly sequenced genome is the identification of its protein coding genes. Existing computational methods for gene prediction include ab initio methods which use the DNA sequence itself as the only source of information, comparative methods using multiple genomic sequences, and similarity based methods which employ the cDNA

Irmtraud M. Meyer; Richard Durbin

2004-01-01

251

IPknot: fast and accurate prediction of RNA secondary structures with pseudoknots using integer programming  

PubMed Central

Motivation: Pseudoknots found in secondary structures of a number of functional RNAs play various roles in biological processes. Recent methods for predicting RNA secondary structures cover certain classes of pseudoknotted structures, but only a few of them achieve satisfying predictions in terms of both speed and accuracy. Results: We propose IPknot, a novel computational method for predicting RNA secondary structures with pseudoknots based on maximizing expected accuracy of a predicted structure. IPknot decomposes a pseudoknotted structure into a set of pseudoknot-free substructures and approximates a base-pairing probability distribution that considers pseudoknots, leading to the capability of modeling a wide class of pseudoknots and running quite fast. In addition, we propose a heuristic algorithm for refining base-paring probabilities to improve the prediction accuracy of IPknot. The problem of maximizing expected accuracy is solved by using integer programming with threshold cut. We also extend IPknot so that it can predict the consensus secondary structure with pseudoknots when a multiple sequence alignment is given. IPknot is validated through extensive experiments on various datasets, showing that IPknot achieves better prediction accuracy and faster running time as compared with several competitive prediction methods. Availability: The program of IPknot is available at http://www.ncrna.org/software/ipknot/. IPknot is also available as a web server at http://rna.naist.jp/ipknot/. Contact: satoken@k.u-tokyo.ac.jp; ykato@is.naist.jp Supplementary information: Supplementary data are available at Bioinformatics online.

Sato, Kengo; Kato, Yuki; Hamada, Michiaki; Akutsu, Tatsuya; Asai, Kiyoshi

2011-01-01

252

Elasticity theory of the B-DNA to S-DNA transition.  

PubMed Central

We propose in this note a simple model--the two-state Worm Like Chain--to describe the elasticity of the recently discovered stress-induced transformation from B-DNA to S-DNA. The model reduces for low tractions to the well-known Worm Like chain theory, which is used to describe the elastic properties of B-DNA, while in the limit of high chain-bending moduli it reduces to the two-state Ising model proposed by Cluzel et al. for the B-S transition [Cluzel, P., A. Lebrun, C. Heller, R. Lavery, J-L. Viovy, D. Chatenay, and F. Caron. 1996. DNA: an extensible molecule. Science. 271:792-794]. Our model can be treated analytically to produce an explicit form of the force-extension relationship which agrees reasonably with the observations. We use the model to show that conformational fluctuations of the chain play a role also for the B to S transformation.

Ahsan, A; Rudnick, J; Bruinsma, R

1998-01-01

253

A method for WD40 repeat detection and secondary structure prediction.  

PubMed

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 ?-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

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

2013-01-01

254

Observed & Predicted Debris Disks Structures Beyond the Reach of Kepler  

NASA Astrophysics Data System (ADS)

Over the last several years our theoretical understanding of debris disks has evolved significantly. A number of new computational advances, in the realms of disk modeling and data analysis, have deepened our knowledge of structures in debris disks. More than ever, we are acutely aware of the many sources of structures--be they gravitational perturbations by planets, other external perturbations, or more subtle non-perturbative sources. At the same time, new observatories, instruments, and observation strategies have provided a rich data set for debris disk theorists to test and constrain their models. I will discuss our current understanding of structures in debris disks. I will show the wide array of structures that planets can dynamically sculpt and comment on how imaging of these structures with future missions may constrain the underlying planetary system. I will also present a cautionary tale of interpreting debris disk structures as planetary perturbations, show how our appreciation of alternative sources of structures has grown, and present new methods for disentangling true density structures from projection and scattering effects.

Stark, Christopher C.

2014-06-01

255

Prediction of airblast loads on structures behind a protective barrier  

Microsoft Academic Search

Experimental results and numerical simulations have demonstrated that a protective barrier can effectively reduce blast load and, therefore, protect structures from an external explosion. However, there are no formulae in the open literature that can be used to estimate the blast loads on a structure behind a barrier. In this paper, pseudo-analytical formulae based on numerical results are derived to

X. Q. Zhou; H. Hao

2008-01-01

256

Finite Element Based HWB Centerbody Structural Optimization and Weight Prediction  

NASA Technical Reports Server (NTRS)

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.

Gern, Frank H.

2012-01-01

257

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

PubMed Central

Since the demonstration that the sequence of a protein 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 can outperform current homology-based secondary structure prediction methods for many proteins. The computationally rapid algorithm using only single (?,?) dihedral angle moves also generates tertiary structures of accuracy comparable with existing all-atom methods for many small proteins, particularly those with low homology. Hence, given appropriate search strategies and scoring functions, reduced representations can be used for accurately predicting secondary structure and providing 3D structures, thereby increasing the size of proteins approachable by homology-free methods and the accuracy of template methods that depend on a high-quality input secondary structure.

DeBartolo, Joe; Colubri, Andres; Jha, Abhishek K.; Fitzgerald, James E.; Freed, Karl F.; Sosnick, Tobin R.

2009-01-01

258

Proteinase inhibitors and dendrotoxins. Sequence classification, structural prediction and structure/activity.  

PubMed

The amino acid sequences of four presynaptically active toxins from mamba snake venom (termed 'dendrotoxins') were compared systematically with homologous sequences of members of the proteinase inhibitor family (Kunitz). A comparison based on the complete sequences revealed that relatively few amino acid changes are necessary to abolish antiprotease activity and convert a proteinase inhibitor into a dendrotoxin. When comparison centred only on the sequence segments known to comprise the antiprotease site of bovine pancreatic trypsin inhibitor, the dendrotoxins were clearly classified apart from all the known inhibitors. Since the mode of action of the bovine pancreatic trypsin/kallikrein inhibitor involves beta sheet formation with the enzyme, predictions were obtained for this secondary structure in the region of the 'antiprotease site' throughout the homologues. Again, the dendrotoxins were clearly distinguished from the inhibitors. Structure/activity analyses, based on the crystal structures of inhibitor/enzyme complexes, suggest that unlike proteinase inhibitors, dendrotoxins might specifically co-ordinate the active-site 'catalytic' histidine residues of serine proteases. Although the significance of this remains to be studied, the presynaptic target is expected to involve an as yet uncharacterised member of the serine protease family. PMID:4076193

Dufton, M J

1985-12-16

259

Prediction of Harmful Human Health Effects of Chemicals from Structure  

Microsoft Academic Search

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

Mark T. D. Cronin

260

Predicting life satisfaction of the Angolan elderly: a structural model.  

PubMed

Satisfaction with life is of particular interest in the study of old age well-being because it has arisen as an important component of old age. A considerable amount of research has been done to explain life satisfaction in the elderly, and there is growing empirical evidence on best predictors of life satisfaction. This research evaluates the predictive power of some aging process variables, on Angolan elderly people's life satisfaction, while including perceived health into the model. Data for this research come from a cross-sectional survey of elderly people living in the capital of Angola, Luanda. A total of 1003 Angolan elderly were surveyed on socio-demographic information, perceived health, active engagement, generativity, and life satisfaction. A Multiple Indicators Multiple Causes model was built to test variables' predictive power on life satisfaction. The estimated theoretical model fitted the data well. The main predictors were those related to active engagement with others. Perceived health also had a significant and positive effect on life satisfaction. Several processes together may predict life satisfaction in the elderly population of Angola, and the variance accounted for it is large enough to be considered relevant. The key factor associated to life satisfaction seems to be active engagement with others. PMID:22793686

Gutiérrez, M; Tomás, J M; Galiana, L; Sancho, P; Cebrià, M A

2013-01-01

261

Quad-PRE: A Hybrid Method to Predict Protein Quaternary Structure Attributes.  

PubMed

The protein quaternary structure is very important to the biological process. Predicting their attributes is an essential task in computational biology for the advancement of the proteomics. However, the existing methods did not consider sufficient properties of amino acid. To end this, we proposed a hybrid method Quad-PRE to predict protein quaternary structure attributes using the properties of amino acid, predicted secondary structure, predicted relative solvent accessibility, and position-specific scoring matrix profiles and motifs. Empirical evaluation on independent dataset shows that Quad-PRE achieved higher overall accuracy 81.7%, especially higher accuracy 92.8%, 93.3%, and 90.6% on discrimination for trimer, hexamer, and octamer, respectively. Our model also reveals that six features sets are all important to the prediction, and a hybrid method is an optimal strategy by now. The results indicate that the proposed method can classify protein quaternary structure attributes effectively. PMID:24963340

Sheng, Yajun; Qiu, Xingye; Zhang, Chen; Xu, Jun; Zhang, Yanping; Zheng, Wei; Chen, Ke

2014-01-01

262

Quad-PRE: A Hybrid Method to Predict Protein Quaternary Structure Attributes  

PubMed Central

The protein quaternary structure is very important to the biological process. Predicting their attributes is an essential task in computational biology for the advancement of the proteomics. However, the existing methods did not consider sufficient properties of amino acid. To end this, we proposed a hybrid method Quad-PRE to predict protein quaternary structure attributes using the properties of amino acid, predicted secondary structure, predicted relative solvent accessibility, and position-specific scoring matrix profiles and motifs. Empirical evaluation on independent dataset shows that Quad-PRE achieved higher overall accuracy 81.7%, especially higher accuracy 92.8%, 93.3%, and 90.6% on discrimination for trimer, hexamer, and octamer, respectively. Our model also reveals that six features sets are all important to the prediction, and a hybrid method is an optimal strategy by now. The results indicate that the proposed method can classify protein quaternary structure attributes effectively.

Sheng, Yajun; Qiu, Xingye; Zhang, Chen; Xu, Jun; Zhang, Yanping; Zheng, Wei; Chen, Ke

2014-01-01

263

Prediction of structure and function of G protein-coupled receptors  

PubMed Central

G protein-coupled receptors (GPCRs) mediate our sense of vision, smell, taste, and pain. They are also involved in cell recognition and communication processes, and hence have emerged as a prominent superfamily for drug targets. Unfortunately, the atomic-level structure is available for only one GPCR (bovine rhodopsin), making it difficult to use structure-based methods to design drugs and mutation experiments. We have recently developed first principles methods (MembStruk and HierDock) for predicting structure of GPCRs, and for predicting the ligand binding sites and relative binding affinities. Comparing to the one case with structural data, bovine rhodopsin, we find good accuracy in both the structure of the protein and of the bound ligand. We report here the application of MembStruk and HierDock to ?1-adrenergic receptor, endothelial differential gene 6, mouse and rat I7 olfactory receptors, and human sweet receptor. We find that the predicted structure of ?1-adrenergic receptor leads to a binding site for epinephrine that agrees well with the mutation experiments. Similarly the predicted binding sites and affinities for endothelial differential gene 6, mouse and rat I7 olfactory receptors, and human sweet receptor are consistent with the available experimental data. These predicted structures and binding sites allow the design of mutation experiments to validate and improve the structure and function prediction methods. As these structures are validated they can be used as targets for the design of new receptor-selective antagonists or agonists for GPCRs.

Vaidehi, Nagarajan; Floriano, Wely B.; Trabanino, Rene; Hall, Spencer E.; Freddolino, Peter; Choi, Eun Jung; Zamanakos, Georgios; Goddard, William A.

2002-01-01

264

Structure-Based Predictive Model For Coal Char Combustion.  

National Technical Information Service (NTIS)

Progress was made at OSU on advancing the application of computational chemistry to oxidative attack on model polyaromatic hydrocarbons (PAHs) and graphitic structures. This work is directed at the application of quantitative ab initio molecular orbital t...

Christopher Hadad Eugene Stanley J.M. Calo Robert Essenhigh Robert Hurt

1998-01-01

265

Modular Approach to Structural Simulation for Vehicle Crashworthiness Prediction.  

National Technical Information Service (NTIS)

A modular formulation for simulation of the structural deformation and deceleration of a vehicle for crashworthiness and collision compatibility is presented. This formulation includes three dimensional beam elements, various spring elements, rigid body e...

P. Tong J. N. Rossettos

1975-01-01

266

ITScorePro: an efficient scoring program for evaluating the energy scores of protein structures for structure prediction.  

PubMed

One important component in protein structure prediction is to evaluate the free energy of a given conformation. Given the enormous number of possible conformations for a sequence, it is extremely challenging to quickly and accurately score the energies of these conformations and predict a reasonable structure within a practical computational time. Here, we describe an efficient program for energy evaluation, referred to as ITScorePro (Copyright © 2012). The energy scoring function in the ITScorePro program is based on the distance-dependent, pairwise atomic potentials for protein structure prediction that we recently derived by using statistical mechanics principles (Huang and Zou, Proteins 79:2648-2661, 2011). ITScorePro is a stand-alone program and can also be easily implemented in other software suites for protein structure prediction. PMID:24573475

Huang, Sheng-You; Zou, Xiaoqin

2014-01-01

267

Dynamic programming algorithms for RNA secondary structure prediction with pseudoknots  

Microsoft Academic Search

This paper shows simple dynamic programming algorithms for RNA secondary structure pre- diction with pseudoknots. For a basic version of the problem (i.e., maximizing the number of base pairs), this paper presents an O(n4) time exact algorithm and an O(n4 ) time approxima- tion algorithm. The latter one outputs, for most RNA sequences, a secondary structure in which the number

Tatsuya Akutsu

2000-01-01

268

RNAG: a new Gibbs sampler for predicting RNA secondary structure for unaligned sequences  

PubMed Central

Motivation: RNA secondary structure plays an important role in the function of many RNAs, and structural features are often key to their interaction with other cellular components. Thus, there has been considerable interest in the prediction of secondary structures for RNA families. In this article, we present a new global structural alignment algorithm, RNAG, to predict consensus secondary structures for unaligned sequences. It uses a blocked Gibbs sampling algorithm, which has a theoretical advantage in convergence time. This algorithm iteratively samples from the conditional probability distributions P(Structure | Alignment) and P(Alignment | Structure). Not surprisingly, there is considerable uncertainly in the high-dimensional space of this difficult problem, which has so far received limited attention in this field. We show how the samples drawn from this algorithm can be used to more fully characterize the posterior space and to assess the uncertainty of predictions. Results: Our analysis of three publically available datasets showed a substantial improvement in RNA structure prediction by RNAG over extant prediction methods. Additionally, our analysis of 17 RNA families showed that the RNAG sampled structures were generally compact around their ensemble centroids, and at least 11 families had at least two well-separated clusters of predicted structures. In general, the distance between a reference structure and our predicted structure was large relative to the variation among structures within an ensemble. Availability: The Perl implementation of the RNAG algorithm and the data necessary to reproduce the results described in Sections 3.1 and 3.2 are available at http://ccmbweb.ccv.brown.edu/rnag.html Contact: charles_lawrence@brown.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Wei, Donglai; Alpert, Lauren V.; Lawrence, Charles E.

2011-01-01

269

Rtips: fast and accurate tools for RNA 2D structure prediction using integer programming.  

PubMed

We present a web-based tool set Rtips for fast and accurate prediction of RNA 2D complex structures. Rtips comprises two computational tools based on integer programming, IPknot for predicting RNA secondary structures with pseudoknots and RactIP for predicting RNA-RNA interactions with kissing hairpins. Both servers can run much faster than existing services with the same purpose on large data sets as well as being at least comparable in prediction accuracy. The Rtips web server along with the stand-alone programs is freely accessible at http://rna.naist.jp/. PMID:22600734

Kato, Yuki; Sato, Kengo; Asai, Kiyoshi; Akutsu, Tatsuya

2012-07-01

270

Human Parietal Cortex Structure Predicts Individual Differences in Perceptual Rivalry  

PubMed Central

Summary When visual input has conflicting interpretations, conscious perception can alternate spontaneously between competing interpretations [1]. There is a large amount of unexplained variability between individuals in the rate of such spontaneous alternations in perception [2–5]. We hypothesized that variability in perceptual rivalry might be reflected in individual differences in brain structure, because brain structure can exhibit systematic relationships with an individual's cognitive experiences and skills [6–9]. To test this notion, we examined in a large group of individuals how cortical thickness, local gray-matter density, and local white-matter integrity correlate with individuals' alternation rate for a bistable, rotating structure-from-motion stimulus [10]. All of these macroscopic measures of brain structure consistently revealed that the structure of bilateral superior parietal lobes (SPL) could account for interindividual variability in perceptual alternation rate. Furthermore, we examined whether the bilateral SPL regions play a causal role in the rate of perceptual alternations by using transcranial magnetic stimulation (TMS) and found that transient disruption of these areas indeed decreases the rate of perceptual alternations. These findings demonstrate a direct relationship between structure of SPL and individuals' perceptual switch rate.

Kanai, Ryota; Bahrami, Bahador; Rees, Geraint

2010-01-01

271

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

NASA Technical Reports Server (NTRS)

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.

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

1992-01-01

272

Multiple classifier integration for the prediction of protein structural classes.  

PubMed

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

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

2009-11-15

273

FREAD revisited: Accurate loop structure prediction using a database search algorithm.  

PubMed

Loops are the most variable regions of protein structure and are, in general, the least accurately predicted. Their prediction has been approached in two ways, ab initio and database search. In recent years, it has been thought that ab initio methods are more powerful. In light of the continued rapid expansion in the number of known protein structures, we have re-evaluated FREAD, a database search method and demonstrate that the power of database search methods may have been underestimated. We found that sequence similarity as quantified by environment specific substitution scores can be used to significantly improve prediction. In fact, FREAD performs appreciably better for an identifiable subset of loops (two thirds of shorter loops and half of the longer loops tested) than the ab initio methods of MODELLER, PLOP, and RAPPER. Within this subset, FREAD's predictive ability is length independent, in general, producing results within 2A RMSD, compared to an average of over 10A for loop length 20 for any of the other tested methods. We also benchmarked the prediction protocols on a set of 212 loops from the model structures in CASP 7 and 8. An extended version of FREAD is able to make predictions for 127 of these, it gives the best prediction of the methods tested in 61 of these cases. In examining FREAD's ability to predict in the model environment, we found that whole structure quality did not affect the quality of loop predictions. PMID:20034110

Choi, Yoonjoo; Deane, Charlotte M

2010-05-01

274

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

PubMed Central

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.

2014-01-01

275

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

NASA Astrophysics Data System (ADS)

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

Sebastijanovic, Nebojsa

276

An adaptive genetic algorithm for crystal structure prediction.  

PubMed

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

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

2014-01-22

277

Use of SEA to predict structure-borne noise in aircraft  

NASA Technical Reports Server (NTRS)

A Statistical Energy Analysis is used to predict aircraft noise from the structural components. Structural-borne noise is vibration: (1) generated at one location; (2) transmitted by the structure to other locations; and (3) radiated into the cabin as noise. All results are presented in viewgraph format.

Manning, Jerome E.

1992-01-01

278

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

Microsoft Academic Search

RNA secondary structures are important in many biological processes and efficient structure predic- tion can give vital directions for experimental investigations. Many available programs for RNA secondary structure prediction only use a single sequence at a time. This may be sufficient in some applications, but often it is possible to obtain related RNA sequences with conserved secondary struc- ture. These

Bjarne Knudsen; Jotun Hein

2003-01-01

279

An APL-programmed genetic algorithm for the prediction of RNA secondary structure  

Microsoft Academic Search

The possibilities of using a genetic algorithm for the prediction of RNA secondary structure were investigated. The algorithm, using the procedure of stepwise selection of the most fit structures (similarly to natural evolution), allows different models of fitness or driving forces determining RNA structure to be easily introduced. This can be used for simulation of the RNA folding process and

F. H. D. Van Batenburg; A. P. Gultyaev; C. W. A. Pleij

1995-01-01

280

Hidden Markov models that use predicted local structure for fold recognition: Alphabets of backbone geometry  

Microsoft Academic Search

An important problem in computa- tional biology is predicting the structure of the large number of putative proteins discovered by genome sequencing projects. Fold-recognition meth- ods attempt to solve the problem by relating the target proteins to known structures, searching for template proteins homologous to the target. Remote homologs that may have significant structural simi- larity are often not detectable

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

2003-01-01

281

Use of SEA to predict structure-borne noise in aircraft  

NASA Astrophysics Data System (ADS)

A Statistical Energy Analysis is used to predict aircraft noise from the structural components. Structural-borne noise is vibration: (1) generated at one location; (2) transmitted by the structure to other locations; and (3) radiated into the cabin as noise. All results are presented in viewgraph format.

Manning, Jerome E.

1992-07-01

282

Predicting protein function from sequence and structural data.  

PubMed

When a protein's function cannot be experimentally determined, it can often be inferred from sequence similarity. Should this process fail, analysis of the protein structure can provide functional clues or confirm tentative functional assignments inferred from the sequence. Many structure-based approaches exist (e.g. fold similarity, three-dimensional templates), but as no single method can be expected to be successful in all cases, a more prudent approach involves combining multiple methods. Several automated servers that integrate evidence from multiple sources have been released this year and particular improvements have been seen with methods utilizing the Gene Ontology functional annotation schema. PMID:15963890

Watson, James D; Laskowski, Roman A; Thornton, Janet M

2005-06-01

283

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

PubMed Central

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

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

1985-01-01

284

Prediction of alpha-turns in proteins using PSI-BLAST profiles and secondary structure information.  

PubMed

In this paper a systematic attempt has been made to develop a better method for predicting alpha-turns in proteins. Most of the commonly used approaches in the field of protein structure prediction have been tried in this study, which includes statistical approach "Sequence Coupled Model" and machine learning approaches; i) artificial neural network (ANN); ii) Weka (Waikato Environment for Knowledge Analysis) Classifiers and iii) Parallel Exemplar Based Learning (PEBLS). We have also used multiple sequence alignment obtained from PSIBLAST and secondary structure information predicted by PSIPRED. The training and testing of all methods has been performed on a data set of 193 non-homologous protein X-ray structures using five-fold cross-validation. It has been observed that ANN with multiple sequence alignment and predicted secondary structure information outperforms other methods. Based on our observations we have developed an ANN-based method for predicting alpha-turns in proteins. The main components of the method are two feed-forward back-propagation networks with a single hidden layer. The first sequence-structure network is trained with the multiple sequence alignment in the form of PSI-BLAST-generated position specific scoring matrices. The initial predictions obtained from the first network and PSIPRED predicted secondary structure are used as input to the second structure-structure network to refine the predictions obtained from the first net. The final network yields an overall prediction accuracy of 78.0% and MCC of 0.16. A web server AlphaPred (http://www.imtech.res.in/raghava/alphapred/) has been developed based on this approach. PMID:14997542

Kaur, Harpreet; Raghava, G P S

2004-04-01

285

Social Structure Predicts Genital Morphology in African Mole-Rats  

PubMed Central

Background African mole-rats (Bathyergidae, Rodentia) exhibit a wide range of social structures, from solitary to eusocial. We previously found a lack of sex differences in the external genitalia and morphology of the perineal muscles associated with the phallus in the eusocial naked mole-rat. This was quite surprising, as the external genitalia and perineal muscles are sexually dimorphic in all other mammals examined. We hypothesized that the lack of sex differences in naked mole-rats might be related to their unusual social structure. Methodology/Principal Findings We compared the genitalia and perineal muscles in three African mole-rat species: the naked mole-rat, the solitary silvery mole-rat, and the Damaraland mole-rat, a species considered to be eusocial, but with less reproductive skew than naked mole-rats. Our findings support a relationship between social structure, mating system, and sexual differentiation. Naked mole-rats lack sex differences in genitalia and perineal morphology, silvery mole-rats exhibit sex differences, and Damaraland mole-rats are intermediate. Conclusions/Significance The lack of sex differences in naked mole-rats is not an attribute of all African mole-rats, but appears to have evolved in relation to their unusual social structure and reproductive biology.

Seney, Marianne L.; Kelly, Diane A.; Goldman, Bruce D.; Sumbera, Radim; Forger, Nancy G.

2009-01-01

286

The prediction and measurement of sound radiated by structures  

NASA Technical Reports Server (NTRS)

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

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

1976-01-01

287

De novo prediction of structured RNAs from genomic sequences  

Microsoft Academic Search

Growing recognition of the numerous, diverse and important roles played by non-coding RNA in all organ- isms motivates better elucidation of these cellular com- ponents. Comparative genomics is a powerful tool for this task and is arguably preferable to any high-through- put experimental technology currently available, because evolutionary conservation highlights function- ally important regions. Conserved secondary structure, rather than primary

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

2009-01-01

288

Prediction of Complete Gene Structures in Human Genomic DNA  

Microsoft Academic Search

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

Christopher B. Burge; Samuel Karlin

1997-01-01

289

Predicted crater morphologies on Ceres: Probing internal structure and evolution  

NASA Astrophysics Data System (ADS)

The detailed internal structure of the dwarf planet Ceres, target of NASA's Dawn mission, has not been unequivocally determined from ground-based data. Whereas Ceres is most likely differentiated with a near surface ice layer tens to one-hundred kilometers thick, the possibility of a homogenous, ice-poor interior structure cannot be completely eliminated. These two internal structural end-members have profoundly different implications for Ceres' origin and evolution. Here we demonstrate that observations of Ceres' impact craters by the Dawn spacecraft will permit unambiguous distinction between the two internal structural models. Using finite element simulations of crater relaxation, we show that if Ceres does contain a water ice layer, its relatively warm diurnally-averaged surface temperature ensures extensive viscous relaxation of even small impact craters. At likely equatorial temperatures, craters as small as 4-km in diameter can be relaxed to the point where complete crater erasure is plausible, thus decreasing the overall crater density in the equatorial region. At mid-latitudes, crater relaxation is less extensive, but still sufficient to completely relax all craters older than 10 Ma and larger than ˜16 km in diameter, as well as smaller, ancient craters. Only in Ceres' cold polar regions are some crater morphologies expected to be pristine. In contrast, if Ceres is primarily a rocky body, we expect crater relaxation to be negligible. These basic conclusions are generally independent of ice grain size, salt/dust contamination of the ice, the presence of a thin, undifferentiated ice/rock crust, and the total thickness of the ice layer, all of which produce second-order modifications to the relaxation process that can be used to better constrain such ice layer properties. Thus, the morphology of impact craters on Ceres, as revealed by the Dawn spacecraft, will provide direct insight into the internal structure and evolution of the dwarf planet.

Bland, Michael T.

2013-09-01

290

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

PubMed Central

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.

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

2001-01-01

291

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

PubMed

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

Bindewald, Eckart; Shapiro, Bruce A

2006-03-01

292

The structure of evaporating and combusting sprays: Measurements and predictions  

NASA Astrophysics Data System (ADS)

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.

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

1984-07-01

293

The structure of evaporating and combusting sprays: Measurements and predictions  

NASA Technical Reports Server (NTRS)

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.

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

1982-01-01

294

A new approach to assess and predict the functional roles of proteins across all known structures.  

PubMed

The three dimensional atomic structures of proteins provide information regarding their function; and codified relationships between structure and function enable the assessment of function from structure. In the current study, a new data mining tool was implemented that checks current gene ontology (GO) annotations and predicts new ones across all the protein structures available in the Protein Data Bank (PDB). The tool overcomes some of the challenges of utilizing large amounts of protein annotation and measurement information to form correspondences between protein structure and function. Protein attributes were extracted from the Structural Biology Knowledgebase and open source biological databases. Based on the presence or absence of a given set of attributes, a given protein's functional annotations were inferred. The results show that attributes derived from the three dimensional structures of proteins enhanced predictions over that using attributes only derived from primary amino acid sequence. Some predictions reflected known but not completely documented GO annotations. For example, predictions for the GO term for copper ion binding reflected used information a copper ion was known to interact with the protein based on information in a ligand interaction database. Other predictions were novel and require further experimental validation. These include predictions for proteins labeled as unknown function in the PDB. Two examples are a role in the regulation of transcription for the protein AF1396 from Archaeoglobus fulgidus and a role in RNA metabolism for the protein psuG from Thermotoga maritima. PMID:21445639

Julfayev, Elchin S; McLaughlin, Ryan J; Tao, Yi-Ping; McLaughlin, William A

2011-03-01

295

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

NASA Astrophysics Data System (ADS)

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

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

2004-04-01

296

Disulfide Connectivity Prediction by Expanding Template Proteins and Encoding Global Protein Secondary Structure  

Microsoft Academic Search

Disulfide connectivity prediction from protein sequence helps determine protein three dimensional structure. The methods for predicting disulfide connectivity generally fall into two categories: pair-wise and pattern-wise methods. Previously, the accuracy rates of the predictions were up to 52%, because (1) pair-wise methods feature mainly on each individual bond but neglect the global influence among disulfide bonds in each protein; (2)

Guantao Chen; Hui Liu; Hai Deng

2008-01-01

297

Structural Damage Prediction and Analysis for Hypervelocity Impacts: Handbook  

NASA Technical Reports Server (NTRS)

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.

Elfer, N. C.

1996-01-01

298

Social Structure Predicts Genital Morphology in African Mole-Rats  

Microsoft Academic Search

BackgroundAfrican mole-rats (Bathyergidae, Rodentia) exhibit a wide range of social structures, from solitary to eusocial. We previously found a lack of sex differences in the external genitalia and morphology of the perineal muscles associated with the phallus in the eusocial naked mole-rat. This was quite surprising, as the external genitalia and perineal muscles are sexually dimorphic in all other mammals

Marianne L. Seney; Diane A. Kelly; Bruce D. Goldman; Radim Sumbera; Nancy G. Forger; Anna Dornhaus

2009-01-01

299

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

PubMed Central

Drug design studies targeting one of the primary toxic agents in Alzheimer’s 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.

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

2012-01-01

300

Revealing hidden karst structures: From geophysical measurements towards predictive modelling  

NASA Astrophysics Data System (ADS)

Shallow caves and karst structures in soluble rocks can be detected from the surface with a variety of geophysical methods. Gravity reveals cavities through the negative Bouguer-anomalies associated with the air- or sediment-filled voids. Electrical resistivity tomography reflects the different infill of cavities, either high resistivities from air-filled voids or dry soft sediments, or low resistivities from saturated sediments. Georadar measurements reveal structural information from the boundaries between cave and rock, and from layered sediment filling the passages. We have surveyed several sites above known shallow karst caves in dolomite and in gypsum with a combination of gravimetry, electrical resistivity tomography, and georadar surveys to detect the signal of the underlying karst objects. We successfully located the caves with at least two of the geophysical methods. Additional information on the structure of the caves could then be revealed by simple two-dimensional forward modelling of selected caves. We further improved the modelling perspective with the development of a new three-dimensional program tool PREDICTOR, which is able to simulate geophysical signatures of the sub-surface voids below a realistic surface located in an aquifer. Results from a first version of this tool will be presented.

Kaufmann, G.; Romanov, D.; Jahn, G.; Galindo Guerreros, J.

2012-04-01

301

Tools for improving predictive capabilities of environmental impact assessments: Structured hypotheses, audits, and monitoring.  

National Technical Information Service (NTIS)

In this paper we recommend three tools for improving impact predictions: (1) structured impact hypotheses, (2) environmental audits, and (3) environmental monitoring. Use of these tools in combination, and formally integrating them into the environmental ...

D. P. Bernard D. R. Marmorek D. B. Hunsaker

1989-01-01

302

Conformational properties of phospholipases A2. Secondary-structure prediction, circular dichroism and relative interface hydrophobicity.  

PubMed

The sequences of 32 phospholipases A2 (EC 3.1.1.4) were analysed by secondary-structure prediction and the results were compared with the available crystallographic data. Good agreement is evident between prediction and experiment, especially for helical structure. Circular dichroic spectra were also determined for six enzymes from Elapid snake venom and these, in association with previously published spectra, confirm the main implication of the predictions, namely that all the homologues have qualitatively similar tertiary structures. Consideration was then given to possible structure/activity relationships in the light of the above findings. The relative hydrophobicity/hydrophilicity of the area of the enzyme thought to interact with lipid/water interfaces was predicted and certain correlations were noted with relative penetrating power, species of origin and the presence of beta-neurotoxic properties. PMID:6662109

Dufton, M J; Eaker, D; Hider, R C

1983-12-15

303

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,...

304

Phase noise characteristic of an Yb-doped fiber amplifier predicted from structure function  

Microsoft Academic Search

From a measurement of the noise power spectral density of the fiber amplifier by using multi-dithering technique, we predict and verify the frequency bandwidth for a feedback loop using the noise structure function.

Jianhua Wang; Lei Si

2009-01-01

305

Neural network structure optimization and its application for passenger flow predicting of comprehensive transportation between cities  

Microsoft Academic Search

The modeling and predicting for passenger flow of comprehensive transportation between cities are studied Passenger flow for different transportation mode is concerned with both social and economic characteristics of passengers, and also concerned with personal preference of every passenger. This is a modeling and predicting problem for complex systems. First, a 3-layer neural network is structured according to Kolmogorov theorem,

Chen Senfa; Tang Changbao

2007-01-01

306

An Iterated loop matching approach to the prediction of RNA secondary structures with pseudoknots  

Microsoft Academic Search

Motivation: Pseudoknots have generally been excluded from the prediction of RNA secondary structures due to its diffi- culty in modeling. Although, several dynamic programming algorithms exist for the prediction of pseudoknots using thermodynamic approaches, they are neither reliable nor effi- cient. On the other hand, comparative methods are more reliable, but are often done in an ad hoc manner and

Jianhua Ruan; Gary D. Stormo; Weixiong Zhang

2004-01-01

307

RNA secondary structure prediction using stochastic context-free grammars and evolutionary history  

Microsoft Academic Search

Motivation: Many computerized methods for RNA secondarystructure prediction have been developed. Few of thesemethods, however, employ an evolutionary model, thusrelevant information is often left out from the structuredetermination. This paper introduces a method whichincorporates evolutionary history into RNA secondarystructure prediction. The method reported here is based onstochastic context-free grammars (SCFGs) to give a priorprobability distribution of structures.

Bjarne Knudsen; Jotun Hein

1999-01-01

308

Endogenous term premia and anomalies in the term structure of interest rates: Explaining the predictability smile  

Microsoft Academic Search

Numerous studies have documented a `predictability smile’ in the post-war term structure of interest rates: spreads between long rates and short rates predict subsequent movements in short rates provided the long horizon is less than three months or greater than two years, but not for intermediate maturities. Proposed explanations of the smile involve interest rate smoothing by the Fed, time-varying

William Roberds; Charles H. Whiteman

1999-01-01

309

Critical assessment of methods of protein structure prediction (CASP): Round III  

Microsoft Academic Search

This article is an introduction to the special issue of the journal Proteins, dedicated to the sixth CASP experiment to assess the state of the art in protein structure prediction. The article describes the conduct of the experiment and the categories of prediction included, and outlines the evaluation and assessment procedures. A brief sum- mary of progress over the decade

John Moult; Tim Hubbard; Krzysztof Fidelis; Jan T. Pedersen

1999-01-01

310

Structure Based Predictive Model for Coal Char Combustion  

SciTech Connect

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.

Robert Hurt; Joseph Calo; Robert Essenhigh; Christopher Hadad

2000-12-30

311

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

Microsoft Academic Search

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

Daniel Seeliger; Bert L. de Groot

2010-01-01

312

Conformational Transitions Upon Ligand Binding: Holo Structure Prediction from Apo Conformations  

Microsoft Academic Search

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

Daniel Seeliger; Bert L. de Groot; Thomas Lengauer

2010-01-01

313

Solution- and Adsorbed-State Structural Ensembles Predicted for the Statherin-Hydroxyapatite System  

PubMed Central

Abstract We have developed a multiscale structure prediction technique to study solution- and adsorbed-state ensembles of biomineralization proteins. The algorithm employs a Metropolis Monte Carlo-plus-minimization strategy that varies all torsional and rigid-body protein degrees of freedom. We applied the technique to fold statherin, starting from a fully extended peptide chain in solution, in the presence of hydroxyapatite (HAp) (001), (010), and (100) monoclinic crystals. Blind (unbiased) predictions capture experimentally observed macroscopic and high-resolution structural features and show minimal statherin structural change upon adsorption. The dominant structural difference between solution and adsorbed states is an experimentally observed folding event in statherin's helical binding domain. Whereas predicted statherin conformers vary slightly at three different HAp crystal faces, geometric and chemical similarities of the surfaces allow structurally promiscuous binding. Finally, we compare blind predictions with those obtained from simulation biased to satisfy all previously published solid-state NMR (ssNMR) distance and angle measurements (acquired from HAp-adsorbed statherin). Atomic clashes in these structures suggest a plausible, alternative interpretation of some ssNMR measurements as intermolecular rather than intramolecular. This work demonstrates that a combination of ssNMR and structure prediction could effectively determine high-resolution protein structures at biomineral interfaces.

Masica, David L.; Gray, Jeffrey J.

2009-01-01

314

Protein structure prediction using mutually orthogonal Latin squares and a genetic algorithm.  

PubMed

We combine a new, extremely fast technique to generate a library of low energy structures of an oligopeptide (by using mutually orthogonal Latin squares to sample its conformational space) with a genetic algorithm to predict protein structures. The protein sequence is divided into oligopeptides, and a structure library is generated for each. These libraries are used in a newly defined mutation operator that, together with variation, crossover, and diversity operators, is used in a modified genetic algorithm to make the prediction. Application to five small proteins has yielded near native structures. PMID:16487483

Arunachalam, J; Kanagasabai, V; Gautham, N

2006-04-01

315

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

NASA Technical Reports Server (NTRS)

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.

Schonberg, William P.

1993-01-01

316

Dispersal differences predict population genetic structure in Mormon crickets.  

PubMed

Research investigating the geographical context of speciation has primarily focused on abiotic factors such as the role of Pleistocene glacial cycles, or geotectonic events. Few study systems allow a direct comparison of how biological differences, such as dispersal behaviour, affect population genetic structure of organisms that were subdivided during the Pleistocene. Mormon crickets exist in solitary and gregarious 'phases', which broadly correspond with an east-west mtDNA division across the Rocky Mountains. Gregarious individuals form bands that can move up to 2 km daily. This study assessed whether population genetic structure results mainly from deep Pleistocene vicariance or if we can also detect more recent genetic patterns due to phase and dispersal differences superimposed on the older, deeper divisions. We found that separation in refugia was a more important influence on genetic divergence than phase, with the Rockies acting as a barrier that separated Mormon cricket populations into eastern and western refugia during Pleistocene glacial cycles. However, patterns of isolation by distance differ between eastern and western clades for both mitochondrial and nuclear DNA, with greater divergence within the eastern, solitary clade. An mtDNA haplotype mismatch distribution is compatible with historical population expansion in the western clade but not in the eastern clade. A persistent (and possibly sex-biased) difference in dispersal ability has most likely influenced the greater population genetic structure seen in the eastern clade, emphasizing the importance of the interaction of Quaternary climate fluctuations and geography with biotic factors in producing the patterns of genetic subdivision observed today. PMID:17498233

Bailey, Nathan W; Gwynne, Darryl T; Ritchie, Michael G

2007-05-01

317

Bad to the bone: facial structure predicts unethical behaviour  

PubMed Central

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.

Haselhuhn, Michael P.; Wong, Elaine M.

2012-01-01

318

Bad to the bone: facial structure predicts unethical behaviour.  

PubMed

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

Haselhuhn, Michael P; Wong, Elaine M

2012-02-01

319

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

PubMed

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

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

2014-06-01

320

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

PubMed Central

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.

Dowell, Robin D; Eddy, Sean R

2004-01-01

321

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

NASA Technical Reports Server (NTRS)

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.

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

1994-01-01

322

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

PubMed Central

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.

2013-01-01

323

Prediction of hydrocarbon-bearing structures based on remote sensing  

SciTech Connect

The technology we developed is based on the use of remotely sensed data and has proved to be effective for identification of structures that appear promising for oil and gas, in particular, reefs in the hydrocarbon-bearing basin of central Asia (Turkmenistan and Uzbekistan). It implements the [open quotes]geoindication[close quotes] concept, the main idea being that landscape components (geoindicators) and subsurface geological features are correlated and depend on each other. Subsurface features (uplifts, depressions, faults, reefs, and other lithological and structural heterogeneities) cause physical and chemical alterations in overlying rocks up to the land surface; thus, they are reflected in distribution of landscape components and observed on airborne and satellite images as specific patterns. The following identified geoindicators are related to different subsurface geological features: definite formations, anticlines, and reefs (barrier, atoll, and bioherm). The geoindicators are extracted from images either visually or by using computer systems. Specially developed software is applied to analyze geoindicator distribution and calculate their characteristics. In the course of processing, it is possible to distinguish folds from reefs. Distribution of geoindicator characteristics is examined on the well studied reefs, and from the regularities, established promising areas with reefs are revealed. When applying the technology in central Asia, the results were successfully verified by field works, seismic methods, and drilling.

Smirnova, I.; Gololobov, Yu.; Rusanova, A. (Institute of Remote Sensing Methods for Geology, St. Petersburg (Russian Federation))

1993-09-01

324

Prediction  

NSDL National Science Digital Library

Students must be guided to state not only what they think will happen, but also a reason or explanation for what will happen based upon their prior knowledge. Therefore, the predictions students write should activate prior knowledge, relate to their focus

Klentschy, Michael P.

2008-04-01

325

Synconset Waves and Chains: Spiking Onsets in Synchronous Populations Predict and Are Predicted by Network Structure  

PubMed Central

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.

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

2013-01-01

326

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

PubMed

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

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

2013-01-01

327

Geometric programming prediction of design trends for OMV protective structures  

NASA Technical Reports Server (NTRS)

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.

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

1990-01-01

328

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

NASA Technical Reports Server (NTRS)

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.

Balmes, Etienne

1993-01-01

329

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

NASA Technical Reports Server (NTRS)

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.

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

1977-01-01

330

Rapid protein domain assignment from amino acid sequence using predicted secondary structure  

PubMed Central

The elucidation of the domain content of a given protein sequence in the absence of determined structure or significant sequence homology to known domains is an important problem in structural biology. Here we address how successfully the delineation of continuous domains can be accomplished in the absence of sequence homology using simple baseline methods, an existing prediction algorithm (Domain Guess by Size), and a newly developed method (DomSSEA). The study was undertaken with a view to measuring the usefulness of these prediction methods in terms of their application to fully automatic domain assignment. Thus, the sensitivity of each domain assignment method was measured by calculating the number of correctly assigned top scoring predictions. We have implemented a new continuous domain identification method using the alignment of predicted secondary structures of target sequences against observed secondary structures of chains with known domain boundaries as assigned by Class Architecture Topology Homology (CATH). Taking top predictions only, the success rate of the method in correctly assigning domain number to the representative chain set is 73.3%. The top prediction for domain number and location of domain boundaries was correct for 24% of the multidomain set (±20 residues). These results have been put into context in relation to the results obtained from the other prediction methods assessed.

Marsden, Russell L.; McGuffin, Liam J.; Jones, David T.

2002-01-01

331

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

PubMed Central

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

He, Yi; Mozolewska, Magdalena A.; Krupa, Pawel; Sieradzan, Adam K.; Wirecki, Tomasz K.; Liwo, Adam; Kachlishvili, Khatuna; Rackovsky, Shalom; Jagiela, Dawid; Slusarz, Rafal; Czaplewski, Cezary R.; Oldziej, Stanislaw; Scheraga, Harold A.

2013-01-01

332

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

PubMed

The prediction of adverse drug reactions (ADRs) has become increasingly important, due to the rising concern on serious ADRs that can cause drugs to fail to reach or stay in the market. We proposed a framework for predicting ADR profiles by integrating protein-protein interaction (PPI) networks with drug structures. We compared ADR prediction performances over 18 ADR categories through four feature groups-only drug targets, drug targets with PPI networks, drug structures, and drug targets with PPI networks plus drug structures. The results showed that the integration of PPI networks and drug structures can significantly improve the ADR prediction performance. The median AUC values for the four groups were 0.59, 0.61, 0.65, and 0.70. We used the protein features in the best two models, "Cardiac disorders" (median-AUC: 0.82) and "Psychiatric disorders" (median-AUC: 0.76), to build ADR-specific PPI networks with literature supports. For validation, we examined 30 drugs withdrawn from the U.S. market to see if our approach can predict their ADR profiles and explain why they were withdrawn. Except for three drugs having ADRs in the categories we did not predict, 25 out of 27 withdrawn drugs (92.6%) having severe ADRs were successfully predicted by our approach. PMID:23184540

Huang, Liang-Chin; Wu, Xiaogang; Chen, Jake Y

2013-01-01

333

Prediction of immobilised artificial membrane chromatography retention factors using theoretical molecular fragments and structural features.  

PubMed

Many in silico alternatives to aquatic toxicity tests rely on hydrophobicity-based quantitative structure-activity relationships (QSARs). Hydrophobicity is often estimated as log P, where P is the octanol-water partition coefficient. Immobilised artificial membrane (IAM) high performance liquid chromatography (HPLC) may be a more biologically relevant alternative to log P. The aim of this study was to investigate the applicability of a theoretical structural fragment and feature-based method to predict log k IAM (the logarithm of the retention index determined by IAM-HPLC) values. This will allow the prediction of log k IAM based on chemical structure alone. The use of structural fragment values to predict log P was first proposed in the 1970s. The application of a similar method using fragment values to predict log k IAM is a novel approach. Values of log k IAM were determined for 22 aliphatic and 42 aromatic compounds using an optimised and robust IAM-HPLC assay. The method developed shows good predictive performance using leave-one-out cross validation and application to an external validation set not seen a priori by the training set also generated good predictive values. The ability to predict log k IAM without the need for practical measurement will allow for the increased use of QSARs based on this descriptor. PMID:23724974

Ledbetter, M R; Gutsell, S; Hodges, G; O'Connor, S; Madden, J C; Rowe, P H; Cronin, M T D

2013-08-01

334

A protein secondary structure prediction scheme for the IBM PC and compatibles.  

PubMed

A prediction scheme has been developed for the IBM PC and compatibles containing computer programs which make use of the protein secondary structure prediction algorithms of Nagano (1977a,b), Garnier et al. (1978), Burgess et al. (1974), Chou and Fasman (1974a,b), Lim (1974) and Dufton and Hider (1977). The results of the individual prediction methods are combined as described by Hamodrakas et al. (1982) by the program PLOTPROG to produce joint prediction histograms for a protein, for three types of secondary structure: alpha-helix, beta-sheet and beta-turns. The scheme requires uniform input for the prediction programs, produced by any word processor, spreadsheet, editor or database program and produces uniform output on a printer, a graphics screen or a file. The scheme is independent of any additional software and runs under DOS 2.0 or later releases. PMID:3208182

Hamodrakas, S J

1988-11-01

335

STRUCTURE-BASED PREDICTIVE MODEL FOR COAL CHAR COMBUSTION  

SciTech Connect

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.

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

1999-01-13

336

Assessment of template-based protein structure predictions in CASP10.  

PubMed

Template-based modeling (TBM) is a major component of the critical assessment of protein structure prediction (CASP). In CASP10, some 41,740 predicted models submitted by 150 predictor groups were assessed as TBM predictions. The accuracy of protein structure prediction was assessed by geometric comparison with experimental X-ray crystal and NMR structures using a composite score that included both global alignment metrics and distance-matrix-based metrics. These included GDT-HA and GDC-all global alignment scores, and the superimposition-independent LDDT distance-matrix-based score. In addition, a superimposition-independent RPF metric, similar to that described previously for comparing protein models against experimental NMR data, was used for comparing predicted protein structure models against experimental protein structures. To score well on all four of these metrics, models must feature accurate predictions of both backbone and side-chain conformations. Performance rankings were determined independently for server and the combined server plus human-curated predictor groups. Final rankings were made using paired head-to-head Student's t-test analysis of raw metric scores among the top 25 performing groups in each category. PMID:24323734

Huang, Yuanpeng J; Mao, Binchen; Aramini, James M; Montelione, Gaetano T

2014-02-01

337

Structural Damage Prediction and Analysis for Hypervelocity Impact  

NASA Technical Reports Server (NTRS)

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.

Elfer, Norman

1995-01-01

338

Inference and updating of probabilistic structural life prediction models  

NASA Astrophysics Data System (ADS)

Aerospace design requirements mandate acceptable levels of structural failure risk. Probabilistic fatigue models enable estimation of the likelihood of fatigue failure. A key step in the development of these models is the accurate inference of the probability distributions for dominant parameters. Since data sets for these inferences are of limited size, the fatigue model parameter distributions are themselves uncertain. A hierarchical Bayesian approach is adopted to account for the uncertainties in both the parameters and their distribution. Variables specifying the distribution of the fatigue model parameters are cast as hyperparameters whose uncertainty is modeled with a hyperprior distribution. Bayes' rule is used to determine the posterior hyperparameter distribution, given available data, thus specifying the probabilistic model. The Bayesian formulation provides an additional advantage by allowing the posterior distribution to be updated as new data becomes available through inspections. By updating the probabilistic model, uncertainty in the hyperparameters can be reduced, and the appropriate level of conservatism can be achieved. In this work, techniques for Bayesian inference and updating of fatigue models for metallic components are developed. Both safe-life and damage-tolerant methods are considered. Uncertainty in damage rates, crack growth behavior, damage, and initial flaws are quantified. Efficient computational techniques are developed to perform the inference and updating analyses. The developed capabilities are demonstrated through a series of case studies.

Cross, Richard J.

339

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

SciTech Connect

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.

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

340

A Protocol for Computer-Based Protein Structure and Function Prediction  

PubMed Central

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

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

2011-01-01

341

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

Microsoft Academic Search

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

Robin D. Dowell; Sean R. Eddy

2004-01-01

342

Mapping of conserved RNA secondary structures predicts thousands of functional noncoding RNAs in the human genome  

Microsoft Academic Search

In contrast to the fairly reliable and complete annotation of the protein coding genes in the human genome, comparable information is lacking for noncoding RNAs (ncRNAs). We present a comparative screen of vertebrate genomes for structural noncoding RNAs, which evaluates conserved genomic DNA sequences for signatures of structural conservation of base-pairing patterns and exceptional thermodynamic stability. We predict more than

Stefan Washietl; Ivo L Hofacker; Melanie Lukasser; Alexander Hüttenhofer; Peter F Stadler

2005-01-01

343

A domain-based model for predicting large and complex pseudoknotted structures  

PubMed Central

Pseudoknotted structures play important structural and functional roles in RNA cellular functions at the level of transcription, splicing and translation. However, the problem of computational prediction for large pseudoknotted folds remains. Here we develop a domain-based method for predicting complex and large pseudoknotted structures from RNA sequences. The model is based on the observation that large RNAs can be separated into different structural domains. The basic idea is to first identify the domains and then predict the structures for each domain. Assembly of the domain structures gives the full structure. The use of the domain-based approach leads to a reduction of computational time by a factor of about ~N2 for an N-nt sequence. As applications of the model, we predict structures for a variety of RNA systems, such as regions in human telomerase RNA (hTR), internal ribosome entry site (IRES) and HIV genome. The lengths of these sequences range from 200-nt to 400-nt. The results show good agreements with the experiments.

Cao, Song; Chen, Shi-Jie

2012-01-01

344

Correlation of ground motion intensity parameters used for predicting structural and geotechnical response  

Microsoft Academic Search

Bycombiningprobabilisticdescriptionsofgroundmotionintensitywithpredictionsofstructural or geo-technical response as a function of that intensity, it is possible to compute the seismic reliability of engineeringsystems.Thisapproachhasbeenusedforassessmentofstructuralreliabilityconsideringseismically- induced collapse, as well as geotechnical reliability considering liquefaction failures. But reliability assessments that simultaneously consider both structural and geotechnical failures are currently not possible using this approach, because structural and geotechnical responses are generally predicted using different ground motion

Jack W. Baker

2007-01-01

345

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

Microsoft Academic Search

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

Juergen Thun; Markus Schoeffel; Josef Breu

2008-01-01

346

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

NASA Astrophysics Data System (ADS)

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.

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

2010-03-01

347

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

PubMed

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

Mechelke, Martin; Habeck, Michael

2013-06-01

348

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

PubMed Central

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

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

1997-01-01

349

A multi-layer evaluation approach for protein structure prediction and model quality assessment  

PubMed Central

Protein tertiary structures are essential for studying functions of proteins at molecular level. An indispensable approach for protein structure solution is computational prediction. Most protein structure prediction methods generate candidate models first and select the best candidates by model quality assessment (QA). In many cases, good models can be produced but the QA tools fail to select the best ones from the candidate model pool. Because of incomplete understanding of protein folding, each QA method only reflects partial facets of a structure model, and thus, has limited discerning power with no one consistently outperforming others. In this paper, we developed a set of new QA methods, including two QA methods for target/template alignments, a molecular dynamics (MD) based QA method, and three consensus QA methods with selected references to reveal new facets of protein structures complementary to the existing methods. Moreover, the underlying relationship among different QA methods were analyzed and then integrated into a multi-layer evaluation approach to guide the model generation and model selection in prediction. All methods are integrated and implemented into an innovative and improved prediction system hereafter referred to as MUFOLD. In CASP8 and CASP9 MUFOLD has demonstrated the proof of the principles in terms of both QA discerning power and structure prediction accuracy.

Zhang, Jingfen; Wang, Qingguo; Vantasin, Kittinun; Zhang, Jiong; He, Zhiquan; Kosztin, Ioan; Shang, Yi; Xu, Dong

2011-01-01

350

CRNPRED: highly accurate prediction of one-dimensional protein structures by large-scale critical random networks  

PubMed Central

Background One-dimensional protein structures such as secondary structures or contact numbers are useful for three-dimensional structure prediction and helpful for intuitive understanding of the sequence-structure relationship. Accurate prediction methods will serve as a basis for these and other purposes. Results We implemented a program CRNPRED which predicts secondary structures, contact numbers and residue-wise contact orders. This program is based on a novel machine learning scheme called critical random networks. Unlike most conventional one-dimensional structure prediction methods which are based on local windows of an amino acid sequence, CRNPRED takes into account the whole sequence. CRNPRED achieves, on average per chain, Q3 = 81% for secondary structure prediction, and correlation coefficients of 0.75 and 0.61 for contact number and residue-wise contact order predictions, respectively. Conclusion CRNPRED will be a useful tool for computational as well as experimental biologists who need accurate one-dimensional protein structure predictions.

Kinjo, Akira R; Nishikawa, Ken

2006-01-01

351

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

PubMed Central

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

2014-01-01

352

Prediction of molecular crystal structures by Monte Carlo simulated annealing without reference to diffraction data  

NASA Astrophysics Data System (ADS)

The first ab initio method to predict crystal structures is presented. The packing problem is solved by attempting to find the global minimum of the potential energy function of the crystal, using Monte Carlo simulated annealing. To prevent the crystal from evaporating at high temperatures, the parameters which govern the spatial extension of the crystal (e.g. a, b, and c) are optimized for each Monte Carlo-trial configuration. Without anticipation of symmetry, the crystal structures of hexamethylbenzene and ethene are predicted to an accuracy of better than 0.02 Å and 0.1°, when compared to the optimized experimental structure.

Gdanitz, Robert J.

1992-03-01

353

Variability and predictability of Antarctic krill swarm structure  

NASA Astrophysics Data System (ADS)

Swarming is a fundamental part of the life of Euphausia superba, yet we still know very little about what drives the considerable variability in swarm shape, size and biomass. We examined swarms across the Scotia Sea in January and February 2003 using a Simrad EK60 (38 and 120 kHz) echosounder, concurrent with net sampling. The acoustic data were analysed through applying a swarm-identification algorithm and then filtering out all non-krill targets. The area, length, height, depth, packing-concentration and inter-swarm distance of 4525 swarms was derived by this method. Hierarchical clustering revealed 2 principal swarm types, which differed in both their dimensions and packing-concentrations. Type 1 swarms were generally small (<50 m long) and were not very tightly packed (<10 ind. m -3), whereas type 2 swarms were an order of magnitude larger and had packing concentrations up to 10 times greater. Further sub-divisions of these types identified small and standard swarms within the type 1 group and large and superswarms within the type 2 group. A minor group (swarm type 3) was also found, containing swarms that were isolated (>100 km away from the next swarm). The distribution of swarm types over the survey grid was examined with respect to a number of potential explanatory variables describing both the environment and the internal-state of krill (namely maturity, body length, body condition). Most variables were spatially averaged over scales of ˜100 km and so mainly had a mesoscale perspective. The exception was the level of light (photosynthetically active radiation (PAR)) for which measurements were specific to each swarm. A binary logistic model was constructed from four variables found to have significant explanatory power ( P<0.05): surface fluorescence, PAR, krill maturity and krill body length. Larger (type 2) swarms were more commonly found during nighttime or when it was overcast during the day, when surface fluorescence was low, and when the krill were small and immature. A strong pattern of diel vertical migration was not observed although the larger and denser swarms tended to occur more often at night than during the day. The vast majority of krill were contained within a minor fraction of the total number of swarms. These krill-rich swarms were more common in areas dominated by small and immature krill. We propose that, at the mesoscale level, the structure of swarms switches from being predominantly large and tightly packed to smaller and more diffuse as krill grow and mature. This pattern is further modulated according to feeding conditions and then level of light.

Tarling, Geraint A.; Klevjer, Thor; Fielding, Sophie; Watkins, Jon; Atkinson, Angus; Murphy, Eugene; Korb, Rebecca; Whitehouse, Mick; Leaper, Russell

2009-11-01

354

Manual for the prediction of blast and fragment loadings on structures  

SciTech Connect

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.

Not Available

1980-11-01

355

TT2NE: a novel algorithm to predict RNA secondary structures with pseudoknots.  

PubMed

We present TT2NE, a new algorithm to predict RNA secondary structures with pseudoknots. The method is based on a classification of RNA structures according to their topological genus. TT2NE is guaranteed to find the minimum free energy structure regardless of pseudoknot topology. This unique proficiency is obtained at the expense of the maximum length of sequences that can be treated, but comparison with state-of-the-art algorithms shows that TT2NE significantly improves the quality of predictions. Analysis of TT2NE's incorrect predictions sheds light on the need to study how sterical constraints limit the range of pseudoknotted structures that can be formed from a given sequence. An implementation of TT2NE on a public server can be found at http://ipht.cea.fr/rna/tt2ne.php. PMID:21593129

Bon, Michaël; Orland, Henri

2011-08-01

356

Ion and solvent density distributions around canonical B-DNA from integral equations  

PubMed Central

We calculate the water and ion spatial distributions around charged oligonucleotides using a renormalized three-dimensional reference interaction site theory coupled with the HNC closure. Our goal is to understand the balance between inter-DNA strand forces and solvation forces as a function of oligonucleotide length in the short strand limit. The DNA is considered in aqueous electrolyte solutions of 1 M KCl, 0.1 M KCl or 0.1 M NaCl. The current theoretical results are compared to MD simulations and experiments. It is found that the IE theory replicates the MD and the experimental results for the base-specific hydration patterns in both the major and minor grooves. We are also able to discern characteristic structural pattern differences between Na+ and K+ ions. When compared to Poisson-Boltzmann methods the IE theory, like simulation, predicts a richly structured ion environment which is better described as multi-layer rather than double-layer.

Howard, Jesse J.; Lynch, Gillian C.; Pettitt, B. Montgomery

2011-01-01

357

Predicting absolute contact numbers of native protein structure from amino acid sequence.  

PubMed

The contact number of an amino acid residue in a protein structure is defined by the number of C(beta) atoms around the C(beta) atom of the given residue, a quantity similar to, but different from, solvent accessible surface area. We present a method to predict the contact numbers of a protein from its amino acid sequence. The method is based on a simple linear regression scheme and predicts the absolute values of contact numbers. When single sequences are used for both parameter estimation and cross-validation, the present method predicts the contact numbers with a correlation coefficient of 0.555 on average. When multiple sequence alignments are used, the correlation increases to 0.627, which is a significant improvement over previous methods. In terms of discrete states prediction, the accuracies for 2-, 3-, and 10-state predictions are, respectively, 71.4%, 54.1%, and 18.9% with residue type-dependent unbiased thresholds, and 76.3%, 59.2%, and 21.8% with residue type-independent unbiased thresholds. The difference between accessible surface area and contact number from a prediction viewpoint and the application of contact number prediction to three-dimensional structure prediction are discussed. PMID:15523668

Kinjo, Akira R; Horimoto, Katsuhisa; Nishikawa, Ken

2005-01-01

358

Smoothing Protein Energy Landscapes by Integrating Folding Models with Structure Prediction  

PubMed Central

Decades of work has investigated the energy landscapes of simple protein models, but what do the landscapes of real, large, atomically detailed proteins look like? We explore an approach to this problem that systematically extracts simple funnel models of actual proteins using ensembles of structure predictions and physics-based atomic force fields and sampling. Central to our effort are calculations of a quantity called the relative entropy, which quantifies the extent to which a given set of structure decoys and a putative native structure can be projected onto a theoretical funnel description. We examine 86 structure prediction targets and one coupled folding-binding system, and find that in a majority of cases the relative entropy robustly signals which structures are nearest to native (i.e., which appear to lie closest to a funnel bottom). Importantly, the landscape model improves substantially upon purely energetic measures in scoring decoys. Our results suggest that physics-based models—including both folding theories and all-atom force fields—may be successfully integrated with structure prediction efforts. Conversely, detailed predictions of structures and the relative entropy approach enable one to extract coarse topographic features of protein landscapes that may enhance the development and application of simpler folding models.

Pritchard-Bell, Ari; Shell, M. Scott

2011-01-01

359

Antibody structure determination using a combination of homology modeling, energy-based refinement, and loop prediction.  

PubMed

We present the blinded prediction results in the Second Antibody Modeling Assessment (AMA-II) using a fully automatic antibody structure prediction method implemented in the programs BioLuminate and Prime. We have developed a novel knowledge based approach to model the CDR loops, using a combination of sequence similarity, geometry matching, and the clustering of database structures. The homology models are further optimized with a physics-based energy function (VSGB2.0), which improves the model quality significantly. H3 loop modeling remains the most challenging task. Our ab initio loop prediction performs well for the H3 loop in the crystal structure context, and allows improved results when refining the H3 loops in the context of homology models. For the 10 human and mouse derived antibodies in this assessment, the average RMSDs for the homology model Fv and framework regions are 1.19 Å and 0.74 Å, respectively. The average RMSDs for five non-H3 CDR loops range from 0.61 Å to 1.05 Å, and the H3 loop average RMSD is 2.91 Å using our knowledge-based loop prediction approach. The ab initio H3 loop predictions yield an average RMSD of 1.28 Å when performed in the context of the crystal structure and 2.67 Å in the context of the homology modeled structure. Notably, our method for predicting the H3 loop in the crystal structure environment ranked first among the seven participating groups in AMA-II, and our method made the best prediction among all participants for seven of the ten targets. Proteins 2014; 82:1646-1655. © 2014 Wiley Periodicals, Inc. PMID:24619874

Zhu, Kai; Day, Tyler; Warshaviak, Dora; Murrett, Colleen; Friesner, Richard; Pearlman, David

2014-08-01

360

Evaluating our ability to predict the structural disruption of RNA by SNPs  

PubMed Central

The structure of RiboNucleic Acid (RNA) has the potential to be altered by a Single Nucleotide Polymorphism (SNP). Disease-associated SNPs mapping to non-coding regions of the genome that are transcribed into RiboNucleic Acid (RNA) can potentially affect cellular regulation (and cause disease) by altering the structure of the transcript. We performed a large-scale meta-analysis of Selective 2'-Hydroxyl Acylation analyzed by Primer Extension (SHAPE) data, which probes the structure of RNA. We found that several single point mutations exist that significantly disrupt RNA secondary structure in the five transcripts we analyzed. Thus, every RNA that is transcribed has the potential to be a “RiboSNitch;” where a SNP causes a large conformational change that alters regulatory function. Predicting the SNPs that will have the largest effect on RNA structure remains a contemporary computational challenge. We therefore benchmarked the most popular RNA structure prediction algorithms for their ability to identify mutations that maximally affect structure. We also evaluated metrics for rank ordering the extent of the structural change. Although no single algorithm/metric combination dramatically outperformed the others, small differences in AUC (Area Under the Curve) values reveal that certain approaches do provide better agreement with experiment. The experimental data we analyzed nonetheless show that multiple single point mutations exist in all RNA transcripts that significantly disrupt structure in agreement with the predictions.

2012-01-01

361

Prediction of structures of zinc-binding proteins through explicit modeling of metal coordination geometry  

PubMed Central

Metal ions play an essential role in stabilizing protein structures and contributing to protein function. Ions such as zinc have well-defined coordination geometries, but it has not been easy to take advantage of this knowledge in protein structure prediction efforts. Here, we present a computational method to predict structures of zinc-binding proteins given knowledge of the positions of zinc-coordinating residues in the amino acid sequence. The method takes advantage of the “atom-tree” representation of molecular systems and modular architecture of the Rosetta3 software suite to incorporate explicit metal ion coordination geometry into previously developed de novo prediction and loop modeling protocols. Zinc cofactors are tethered to their interacting residues based on coordination geometries observed in natural zinc-binding proteins. The incorporation of explicit zinc atoms and their coordination geometry in both de novo structure prediction and loop modeling significantly improves sampling near the native conformation. The method can be readily extended to predict protein structures bound to other metal and/or small chemical cofactors with well-defined coordination or ligation geometry.

Wang, Chu; Vernon, Robert; Lange, Oliver; Tyka, Michael; Baker, David

2010-01-01

362

Prediction of protein secondary structure from circular dichroism using theoretically derived spectra.  

PubMed

Circular dichroism (CD) is a spectroscopic technique commonly used to investigate the structure of proteins. Major secondary structure types, alpha-helices and beta-strands, produce distinctive CD spectra. Thus, by comparing the CD spectrum of a protein of interest to a reference set consisting of CD spectra of proteins of known structure, predictive methods can estimate the secondary structure of the protein. Currently available methods, including K2D2, use such experimental CD reference sets, which are very small in size when compared to the number of tertiary structures available in the Protein Data Bank (PDB). Conversely, given a PDB structure, it is possible to predict a theoretical CD spectrum from it. The methodological framework for this calculation was established long ago but only recently a convenient implementation called DichroCalc has been developed. In this study, we set to determine whether theoretically derived spectra could be used as reference set for accurate CD based predictions of secondary structure. We used DichroCalc to calculate the theoretical CD spectra of a nonredundant set of structures representing most proteins in the PDB, and applied a straightforward approach for predicting protein secondary structure content using these theoretical CD spectra as reference set. We show that this method improves the predictions, particularly for the wavelength interval between 200 and 240 nm and for beta-strand content. We have implemented this method, called K2D3, in a publicly accessible web server at http://www. ogic.ca/projects/k2d3. Proteins 2011. © 2011 Wiley Periodicals, Inc. PMID:22095872

Louis-Jeune, Caroline; Andrade-Navarro, Miguel A; Perez-Iratxeta, Carol

2011-09-14

363

Multiobjective evolutionary algorithm with many tables for purely ab initio protein structure prediction.  

PubMed

This article focuses on the development of an approach for ab initio protein structure prediction (PSP) without using any earlier knowledge from similar protein structures, as fragment-based statistics or inference of secondary structures. Such an approach is called purely ab initio prediction. The article shows that well-designed multiobjective evolutionary algorithms can predict relevant protein structures in a purely ab initio way. One challenge for purely ab initio PSP is the prediction of structures with ?-sheets. To work with such proteins, this research has also developed procedures to efficiently estimate hydrogen bond and solvation contribution energies. Considering van der Waals, electrostatic, hydrogen bond, and solvation contribution energies, the PSP is a problem with four energetic terms to be minimized. Each interaction energy term can be considered an objective of an optimization method. Combinatorial problems with four objectives have been considered too complex for the available multiobjective optimization (MOO) methods. The proposed approach, called "Multiobjective evolutionary algorithms with many tables" (MEAMT), can efficiently deal with four objectives through the combination thereof, performing a more adequate sampling of the objective space. Therefore, this method can better map the promising regions in this space, predicting structures in a purely ab initio way. In other words, MEAMT is an efficient optimization method for MOO, which explores simultaneously the search space as well as the objective space. MEAMT can predict structures with one or two domains with RMSDs comparable to values obtained by recently developed ab initio methods (GAPFCG , I-PAES, and Quark) that use different levels of earlier knowledge. PMID:23666867

Brasil, Christiane Regina Soares; Delbem, Alexandre Claudio Botazzo; da Silva, Fernando Luís Barroso

2013-07-30

364

Protein secondary structure prediction using support vector machine with advanced encoding schemes  

NASA Astrophysics Data System (ADS)

Over the decades, many studies have been done for the prediction of the protein structure. Since the protein secondary structure is closely related to the protein tertiary structure, many approaches begin with the prediction of secondary structure and apply the results to predict the tertiary structure. The recent trend of secondary structure prediction studies is mostly based on the neural network or the support vector machine (SVM). In this study, SVM is used as a machine learning tool for the prediction of secondary structure and several new encoding schemes, including orthogonal matrix, hydrophobicity matrix, BLOSUM62 substitution matrix and combined matrix of these, are developed and optimized to improve the prediction accuracy. Based on the best encoding scheme, each protein sequence is expressed as consecutive sliding windows and each amino acid inside a window is represented with 20 different matrix values. Once the optimal window length for six SVM binary classifiers is chosen to be 13 through many experiments, the new encoding scheme is tested based on this optimal window size with the 7-fold cross validation tests. The results show 2% increase in the accuracy of the binary classifiers when compared with the instances in which the classical orthogonal matrix is used. For the training and testing of the SVM binary classifiers, RS126 data sets is used since this is the common set adopted by the previous research groups. Finally, to combine the results of the six SVM binary classifiers, several existing tertiary classifiers are applied and the efficiency of each tertiary classifier is compared.

Hu, Hae-Jin; Pan, Yi; Harrison, Robert; Tai, Phang C.

2004-04-01

365

Network properties of decoys and CASP predicted models: a comparison with native protein structures.  

PubMed

Protein structure space is believed to consist of a finite set of discrete folds, unlike the protein sequence space which is astronomically large, indicating that proteins from the available sequence space are likely to adopt one of the many folds already observed. In spite of extensive sequence-structure correlation data, protein structure prediction still remains an open question with researchers having tried different approaches (experimental as well as computational). One of the challenges of protein structure prediction is to identify the native protein structures from a milieu of decoys/models. In this work, a rigorous investigation of Protein Structure Networks (PSNs) has been performed to detect native structures from decoys/models. Ninety four parameters obtained from network studies have been optimally combined with Support Vector Machines (SVM) to derive a general metric to distinguish decoys/models from the native protein structures with an accuracy of 94.11%. Recently, for the first time in the literature we had shown that PSN has the capability to distinguish native proteins from decoys. A major difference between the present work and the previous study is to explore the transition profiles at different strengths of non-covalent interactions and SVM has indeed identified this as an important parameter. Additionally, the SVM trained algorithm is also applied to the recent CASP10 predicted models. The novelty of the network approach is that it is based on general network properties of native protein structures and that a given model can be assessed independent of any reference structure. Thus, the approach presented in this paper can be valuable in validating the predicted structures. A web-server has been developed for this purpose and is freely available at . PMID:23694935

Chatterjee, S; Ghosh, S; Vishveshwara, S

2013-07-01

366

Predicting the Structure of the Solar Corona During the December 4, 2002 Total Solar Eclipse  

NASA Astrophysics Data System (ADS)

We describe the application of a three-dimensional magnetohydrodynamic (MHD) model to the prediction of the structure of the corona during the total solar eclipse that is expected to occur on 4 December 2002. The calculation uses the observed photospheric radial magnetic field as a boundary condition. This model makes it possible to determine the large-scale structure of the magnetic field in the corona, as well as the distribution of the solar wind velocity, plasma density, and temperature. We will use magnetic fields observed on the solar disk prior to eclipse day to predict what the corona will look like during the eclipse. The estimated coronal density and temperature will be used to predict the plane-of-sky polarization brightness and emission of UV radiation prior to the eclipse. The prediction will be posted on our web site (http://haven.saic.com) prior to the eclipse.

Mikic, Z.; Linker, J. A.; Lionello, R.; Riley, P.

2002-12-01

367

A benchmark server using high resolution protein structure data, and benchmark results for membrane helix predictions  

PubMed Central

Background Helical membrane proteins are vital for the interaction of cells with their environment. Predicting the location of membrane helices in protein amino acid sequences provides substantial understanding of their structure and function and identifies membrane proteins in sequenced genomes. Currently there is no comprehensive benchmark tool for evaluating prediction methods, and there is no publication comparing all available prediction tools. Current benchmark literature is outdated, as recently determined membrane protein structures are not included. Current literature is also limited to global assessments, as specialised benchmarks for predicting specific classes of membrane proteins were not previously carried out. Description We present a benchmark server at http://sydney.edu.au/pharmacy/sbio/software/TMH_benchmark.shtml that uses recent high resolution protein structural data to provide a comprehensive assessment of the accuracy of existing membrane helix prediction methods. The server further allows a user to compare uploaded predictions generated by novel methods, permitting the comparison of these novel methods against all existing methods compared by the server. Benchmark metrics include sensitivity and specificity of predictions for membrane helix location and orientation, and many others. The server allows for customised evaluations such as assessing prediction method performances for specific helical membrane protein subtypes. We report results for custom benchmarks which illustrate how the server may be used for specialised benchmarks. Which prediction method is the best performing method depends on which measure is being benchmarked. The OCTOPUS membrane helix prediction method is consistently one of the highest performing methods across all measures in the benchmarks that we performed. Conclusions The benchmark server allows general and specialised assessment of existing and novel membrane helix prediction methods. Users can employ this benchmark server to determine the most suitable method for the type of prediction the user needs to perform, be it general whole-genome annotation or the prediction of specific types of helical membrane protein. Creators of novel prediction methods can use this benchmark server to evaluate the performance of their new methods. The benchmark server will be a valuable tool for researchers seeking to extract more sophisticated information from the large and growing protein sequence databases.

2013-01-01

368

A simplified method for creep-fatigue life prediction for structures subjected to thermal loadings  

Microsoft Academic Search

A simplified creep-fatigue failure prevention-life prediction method (TTSDS) for pressure vessels and pipings subjected to thermal expansion-thermal transient loadings is presented. The method was constructed based on numerous SUS304 material-structure test data, and incorporates a linear cumulative damage rule with an instrinsic life reduction factor. When the TTSDS method was applied using no safety factors, good life predictions for piping

Katsumi Watashi

1995-01-01

369

Prediction of consensus structural motifs in a family of coregulated RNA sequences  

PubMed Central

Given a set of homologous or functionally related RNA sequences, the consensus motifs may represent the binding sites of RNA regulatory proteins. Unlike DNA motifs, RNA motifs are more conserved in structures than in sequences. Knowing the structural motifs can help us gain a deeper insight of the regulation activities. There have been various studies of RNA secondary structure prediction, but most of them are not focused on finding motifs from sets of functionally related sequences. Although recent research shows some new approaches to RNA motif finding, they are limited to finding relatively simple structures, e.g. stem–loops. In this paper, we propose a novel genetic programming approach to RNA secondary structure prediction. It is capable of finding more complex structures than stem–loops. To demonstrate the performance of our new approach as well as to keep the consistency of our comparative study, we first tested it on the same data sets previously used to verify the current prediction systems. To show the flexibility of our new approach, we also tested it on a data set that contains pseudoknot motifs which most current systems cannot identify. A web-based user interface of the prediction system is set up at http://bioinfo. cis.nctu.edu.tw/service/gprm/.

Hu, Yuh-Jyh

2002-01-01

370

Structural Models of Protein-DNA Complexes Based on Interface Prediction and Docking  

PubMed Central

Protein-DNA interactions are the physical basis of gene expression and DNA modification. Structural models that reveal these interactions are essential for their understanding. As only a limited number of structures for protein-DNA complexes have been determined by experimental methods, computation methods provide a potential way to fill the need. We have developed the DISPLAR method to predict DNA binding sites on proteins. Predicted binding sites have been used to assist the building of structural models by docking, either by guiding the docking or by selecting near-native candidates from the docked poses. Here we applied the DISPLAR method to predict the DNA binding sites for 20 DNA-binding proteins, which have had their DNA binding sites characterized by NMR chemical shift perturbation. For two of these proteins, the structures of their complexes with DNA have also been determined. With the help of the DISPLAR predictions, we built structural models for these two complexes. Evaluations of both the 20 DNA binding sites and the structural models of the two protein-DNA complexes against experimental results demonstrate the significant promise of our model-building approach.

Qin, Sanbo; Zhou, Huan-Xiang

2012-01-01

371

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

NASA Technical Reports Server (NTRS)

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.

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

1996-01-01

372

Protein design by fusion: implications for protein structure prediction and evolution.  

PubMed

Domain fusion is a useful tool in protein design. Here, the structure of a fusion of the heterodimeric flagella-assembly proteins FliS and FliC is reported. Although the ability of the fusion protein to maintain the structure of the heterodimer may be apparent, threading-based structural predictions do not properly fuse the heterodimer. Additional examples of naturally occurring heterodimers that are homologous to full-length proteins were identified. These examples highlight that the designed protein was engineered by the same tools as used in the natural evolution of proteins and that heterodimeric structures contain a wealth of information, currently unused, that can improve structural predictions. PMID:24311586

Skorupka, Katarzyna; Han, Seong Kyu; Nam, Hyun-Jun; Kim, Sanguk; Faham, Salem

2013-12-01

373

Ab Initio structure prediction for Escherichia coli: towards genome-wide protein structure modeling and fold assignment  

PubMed Central

Genome-wide protein structure prediction and structure-based function annotation have been a long-term goal in molecular biology but not yet become possible due to difficulties in modeling distant-homology targets. We developed a hybrid pipeline combining ab initio folding and template-based modeling for genome-wide structure prediction applied to the Escherichia coli genome. The pipeline was tested on 43 known sequences, where QUARK-based ab initio folding simulation generated models with TM-score 17% higher than that by traditional comparative modeling methods. For 495 unknown hard sequences, 72 are predicted to have a correct fold (TM-score > 0.5) and 321 have a substantial portion of structure correctly modeled (TM-score > 0.35). 317 sequences can be reliably assigned to a SCOP fold family based on structural analogy to existing proteins in PDB. The presented results, as a case study of E. coli, represent promising progress towards genome-wide structure modeling and fold family assignment using state-of-the-art ab initio folding algorithms.

Xu, Dong; Zhang, Yang

2013-01-01

374

Ab Initio structure prediction for Escherichia coli: towards genome-wide protein structure modeling and fold assignment.  

PubMed

Genome-wide protein structure prediction and structure-based function annotation have been a long-term goal in molecular biology but not yet become possible due to difficulties in modeling distant-homology targets. We developed a hybrid pipeline combining ab initio folding and template-based modeling for genome-wide structure prediction applied to the Escherichia coli genome. The pipeline was tested on 43 known sequences, where QUARK-based ab initio folding simulation generated models with TM-score 17% higher than that by traditional comparative modeling methods. For 495 unknown hard sequences, 72 are predicted to have a correct fold (TM-score > 0.5) and 321 have a substantial portion of structure correctly modeled (TM-score > 0.35). 317 sequences can be reliably assigned to a SCOP fold family based on structural analogy to existing proteins in PDB. The presented results, as a case study of E. coli, represent promising progress towards genome-wide structure modeling and fold family assignment using state-of-the-art ab initio folding algorithms. PMID:23719418

Xu, Dong; Zhang, Yang

2013-01-01

375

Probable novel PSEN2 Val214Leu mutation in Alzheimer's disease supported by structural prediction  

PubMed Central

Background PSEN2 mutations are rare variants, and fewer than 30 different PSEN2 mutations have been found. So far, it has not been reported in Asia. Case presentation PSEN2 mutation at codon 214 for predicting a valine to leucine substitution was found in a 70-year-old woman, who showed a dementia of the Alzheimer type. We did not find the mutation in 614 control chromosomes. We also predicted the structures of presenilin 2 protein with native Val 214 residue and Leu 214 mutation, which revealed significant structural changes in the region. Conclusion It could be a novel mutation verified with structural prediction in a patient with Alzheimer’s disease.

2014-01-01

376

Efficient method for predicting crystal structures at finite temperature: variable box shape simulations.  

PubMed

We present an efficient and robust method based on Monte Carlo simulations for predicting crystal structures at finite temperature. We apply this method, which is surprisingly easy to implement, to a variety of systems, demonstrating its effectiveness for hard, attractive, and anisotropic interactions, binary mixtures, semi-long-range soft interactions, and truly long-range interactions where the truly long-range interactions are treated using Ewald sums. In the case of binary hard-sphere mixtures, star polymers, and binary Lennard-Jones mixtures, the crystal structures predicted by this algorithm are consistent with literature, providing confidence in the method. Finally, we predict new crystal structures for hard asymmetric dumbbell particles, bowl-like particles and hard oblate cylinders and present the phase diagram for the oblate cylinders based on full free energy calculations. PMID:19905838

Filion, Laura; Marechal, Matthieu; van Oorschot, Bas; Pelt, Daniël; Smallenburg, Frank; Dijkstra, Marjolein

2009-10-30

377

Statistical properties of thermodynamically predicted RNA secondary structures in viral genomes  

NASA Astrophysics Data System (ADS)

By performing a comprehensive study on 1832 segments of 1212 complete genomes of viruses, we show that in viral genomes the hairpin structures of thermodynamically predicted RNA secondary structures are more abundant than expected under a simple random null hypothesis. The detected hairpin structures of RNA secondary structures are present both in coding and in noncoding regions for the four groups of viruses categorized as dsDNA, dsRNA, ssDNA and ssRNA. For all groups, hairpin structures of RNA secondary structures are detected more frequently than expected for a random null hypothesis in noncoding rather than in coding regions. However, potential RNA secondary structures are also present in coding regions of dsDNA group. In fact, we detect evolutionary conserved RNA secondary structures in conserved coding and noncoding regions of a large set of complete genomes of dsDNA herpesviruses.

Spanò, M.; Lillo, F.; Miccichè, S.; Mantegna, R. N.

2008-10-01

378

Statistical prediction of single-stranded regions in RNA secondary structure and application to predicting effective antisense target sites and beyond  

Microsoft Academic Search

Single-stranded regions in RNA secondary structure are important for RNA-RNA and RNA-protein inter- actions. We present a probability profile approach for the prediction of these regions based on a statistical algorithm for sampling RNA secondary structures. For the prediction of phylogenetically-determined single-stranded regions in secondary structures of representative RNA sequences, the probability profile offers substantial improvement over the minimum free

Ye Ding; Charles E. Lawrence

2001-01-01

379

Prediction of protein secondary structure with a reliability score estimated by local sequence clustering.  

PubMed

Most algorithms for protein secondary structure prediction are based on machine learning techniques, e.g. neural networks. Good architectures and learning methods have improved the performance continuously. The introduction of profile methods, e.g. PSI-BLAST, has been a major breakthrough in increasing the prediction accuracy to close to 80%. In this paper, a brute-force algorithm is proposed and the reliability of each prediction is estimated by a z-score based on local sequence clustering. This algorithm is intended to perform well for those secondary structures in a protein whose formation is mainly dominated by the neighboring sequences and short-range interactions. A reliability z-score has been defined to estimate the goodness of a putative cluster found for a query sequence in a database. The database for prediction was constructed by experimentally determined, non-redundant protein structures with <25% sequence homology, a list maintained by PDBSELECT. Our test results have shown that this new algorithm, belonging to what is known as nearest neighbor methods, performed very well within the expectation of previous methods and that the reliability z-score as defined was correlated with the reliability of prediction. This led to the possibility of making very accurate predictions for a few selected residues in a protein with an accuracy measure of Q3 > 80%. The further development of this algorithm, and a nucleation mechanism for protein folding are suggested. PMID:14560050

Jiang, Fan

2003-09-01

380

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

PubMed

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/. Proteins 2014; 82:1142-1155. © 2013 Wiley Periodicals, Inc. PMID:24243399

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

2014-07-01

381

Structural descriptor database: a new tool for sequence-based functional site prediction  

PubMed Central

Background The Structural Descriptor Database (SDDB) is a web-based tool that predicts the function of proteins and functional site positions based on the structural properties of related protein families. Structural alignments and functional residues of a known protein set (defined as the training set) are used to build special Hidden Markov Models (HMM) called HMM descriptors. SDDB uses previously calculated and stored HMM descriptors for predicting active sites, binding residues, and protein function. The database integrates biologically relevant data filtered from several databases such as PDB, PDBSUM, CSA and SCOP. It accepts queries in fasta format and predicts functional residue positions, protein-ligand interactions, and protein function, based on the SCOP database. Results To assess the SDDB performance, we used different data sets. The Trypsion-like Serine protease data set assessed how well SDDB predicts functional sites when curated data is available. The SCOP family data set was used to analyze SDDB performance by using training data extracted from PDBSUM (binding sites) and from CSA (active sites). The ATP-binding experiment was used to compare our approach with the most current method. For all evaluations, significant improvements were obtained with SDDB. Conclusion SDDB performed better when trusty training data was available. SDDB worked better in predicting active sites rather than binding sites because the former are more conserved than the latter. Nevertheless, by using our prediction method we obtained results with precision above 70%.

Bernardes, Juliana S; Fernandez, Jorge H; Vasconcelos, Ana Tereza R

2008-01-01

382

Prediction of RNA secondary structure by maximizing pseudo-expected accuracy  

PubMed Central

Background Recent studies have revealed the importance of considering the entire distribution of possible secondary structures in RNA secondary structure predictions; therefore, a new type of estimator is proposed including the maximum expected accuracy (MEA) estimator. The MEA-based estimators have been designed to maximize the expected accuracy of the base-pairs and have achieved the highest level of accuracy. Those methods, however, do not give the single best prediction of the structure, but employ parameters to control the trade-off between the sensitivity and the positive predictive value (PPV). It is unclear what parameter value we should use, and even the well-trained default parameter value does not, in general, give the best result in popular accuracy measures to each RNA sequence. Results Instead of using the expected values of the popular accuracy measures for RNA secondary structure prediction, which is difficult to be calculated, the pseudo-expected accuracy, which can easily be computed from base-pairing probabilities, is introduced. It is shown that the pseudo-expected accuracy is a good approximation in terms of sensitivity, PPV, MCC, or F-score. The pseudo-expected accuracy can be approximately maximized for each RNA sequence by stochastic sampling. It is also shown that well-balanced secondary structures between sensitivity and PPV can be predicted with a small computational overhead by combining the pseudo-expected accuracy of MCC or F-score with the ?-centroid estimator. Conclusions This study gives not only a method for predicting the secondary structure that balances between sensitivity and PPV, but also a general method for approximately maximizing the (pseudo-)expected accuracy with respect to various evaluation measures including MCC and F-score.

2010-01-01

383

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

PubMed

Disulfide bridges strongly constrain the native structure of many proteins and predicting their formation is therefore a key sub-problem of protein structure and function inference. Most recently proposed approaches for this prediction problem adopt the following pipeline: first they enrich the primary sequence with structural annotations, second they apply a binary classifier to each candidate pair of cysteines to predict disulfide bonding probabilities and finally, they use a maximum weight graph matching algorithm to derive the predicted disulfide connectivity pattern of a protein. In this paper, we adopt this three step pipeline and propose an extensive study of the relevance of various structural annotations and feature encodings. In particular, we consider five kinds of structural annotations, among which three are novel in the context of disulfide bridge prediction. So as to be usable by machine learning algorithms, these annotations must be encoded into features. For this purpose, we propose four different feature encodings based on local windows and on different kinds of histograms. The combination of structural annotations with these possible encodings leads to a large number of possible feature functions. In order to identify a minimal subset of relevant feature functions among those, we propose an efficient and interpretable feature function selection scheme, designed so as to avoid any form of overfitting. We apply this scheme on top of three supervised learning algorithms: k-nearest neighbors, support vector machines and extremely randomized trees. Our results indicate that the use of only the PSSM (position-specific scoring matrix) together with the CSP (cysteine separation profile) are sufficient to construct a high performance disulfide pattern predictor and that extremely randomized trees reach a disulfide pattern prediction accuracy of [Formula: see text] on the benchmark dataset SPX[Formula: see text], which corresponds to [Formula: see text] improvement over the state of the art. A web-application is available at http://m24.giga.ulg.ac.be:81/x3CysBridges. PMID:23533562

Becker, Julien; Maes, Francis; Wehenkel, Louis

2013-01-01

384

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

NASA Astrophysics Data System (ADS)

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.

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

2013-12-01

385

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

PubMed Central

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

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

2011-01-01

386

Artificial Intelligence in Prediction of Secondary Protein Structure Using CB513 Database  

PubMed Central

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.

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

2009-01-01

387

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

DOEpatents

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.

Agarwal, Pratul Kumar (Knoxville, TN)

2011-07-19

388

Structure prediction and activity analysis of human heme oxygenase-1 and its mutant  

Microsoft Academic Search

AIM: To predict wild human heme oxygenase-1 (whHO-1) and hHO-1 His25Ala mutant (hHO-1) structures, to clone and express them and analyze their activities. METHODS: Swiss-PdbViewer and Antheprot 5.0 were used for the prediction of structure diversity and physical- chemical changes between wild and mutant hHO-1. hHO- 1 His25Ala mutant cDNA was constructed by site-directed mutagenesis in two plasmids of E.

Zhen-Wei Xia; Wen-Pu Zhou; Wen-Jun Cui; Xue-Hong Zhang; Qing-Xiang Shen; Yun-Zhu Li; Shan-Chang Yu

2004-01-01

389

Atomic accuracy in predicting and designing non-canonical RNA structure  

PubMed Central

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.

Das, Rhiju; Karanicolas, John; Baker, David

2010-01-01

390

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

SciTech Connect

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.

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

2011-07-15

391

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

PubMed Central

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.

Gorodkin, Jan; Hofacker, Ivo L.

2011-01-01

392

Analysis and Design of Fuselage Structures Including Residual Strength Prediction Methodology  

NASA Technical Reports Server (NTRS)

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.

Knight, Norman F.

1998-01-01

393

Thermodynamic Properties of Asphaltenes: A Predictive Approach Based On Computer Assisted Structure Elucidation and Atomistic Simulations  

SciTech Connect

The authors describe a new methodology for predicting the thermodynamic properties of petroleum geomacromolecules (asphaltenes and resins). This methodology combines computer assisted structure elucidation (CASE) with atomistic simulations (molecular mechanics and molecular dynamics and statistical mechanics). They use quantitative and qualitative structural data as input to a CASE program (SIGNATURE) to generate a sample of ten asphaltene model structures for a Saudi crude oil (Arab Berri). MM calculations and MD simulations are used to estimate selected volumetric and thermal properties of the model structures.

Diallo, Mamadou S.; Cagin, Tahir; Faulon, Jean Loup; Goddard, William A.

2000-08-01

394

Small-molecule 3D structure prediction using open crystallography data.  

PubMed

Predicting the 3D structures of small molecules is a common problem in chemoinformatics. Even the best methods are inaccurate for complex molecules, and there is a large gap in accuracy between proprietary and free algorithms. Previous work presented COSMOS, a novel data-driven algorithm that uses knowledge of known structures from the Cambridge Structural Database and demonstrates performance that was competitive with proprietary algorithms. However, dependence on the Cambridge Structural Database prevented its widespread use. Here, we present an updated version of the COSMOS structure predictor, complete with a free structure library derived from open data sources. We demonstrate that COSMOS performs better than other freely available methods, with a mean RMSD of 1.16 and 1.68 Å for organic and metal-organic structures, respectively, and a mean prediction time of 60 ms per molecule. This is a 17% and 20% reduction, respectively, in RMSD compared to the free predictor provided by Open Babel, and it is 10 times faster. The ChemDB Web portal provides a COSMOS prediction Web server, as well as downloadable copies of the COSMOS executable and library of molecular substructures. PMID:24261562

Sadowski, Peter; Baldi, Pierre

2013-12-23

395

Combined computational metabolite prediction and automated structure-based analysis of mass spectrometric data.  

PubMed

ABSTRACT As high-throughput technologies have developed in the pharmaceutical industry, the demand for identification of possible metabolites using predominantly liquid chromatographic/mass spectrometry-mass spectrometry/mass spectrometry (LC/MS-MS/MS) for a large number of molecules in drug discovery has also increased. In parallel, computational technologies have also been developed to generate predictions for metabolites alongside methods to predict MS spectra and score the quality of the match with experimental spectra. The goal of the current study was to generate metabolite predictions from molecular structure with a software product, MetaDrug. In vitro microsomal incubations were used to ultimately produce MS data that could be used to verify the predictions with Apex, which is a new software tool that can predict the molecular ion spectrum and a fragmentation spectrum, automating the detailed examination of both MS and MS/MS spectra. For the test molecule imipramine used to illustrate the combined in vitro/in silico process proposed, MetaDrug predicts 16 metabolites. Following rat microsomal incubations with imipramine and analysis of the MS(n) data using the Apex software, strong evidence was found for imipramine and five metabolites and weaker evidence for five additional metabolites. This study suggests a new approach to streamline MS data analysis using a combination of predictive computational approaches with software capable of comparing the predicted metabolite output with empirical data when looking at drug metabolites. PMID:20020918

Stranz, David D; Miao, Shichang; Campbell, Scott; Maydwell, George; Ekins, Sean

2008-01-01

396

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

PubMed Central

Current structural genomics projects are yielding structures for proteins whose functions are unknown. Accordingly, there is a pressing requirement for computational methods for function prediction. Here we present phunctioner, an automatic method for structure-based function prediction using automatically extracted functional sites (residues associated to functions). The method relates proteins with the same function through structural alignments and extracts 3D profiles of conserved residues. Functional features to train the method are extracted from the Gene Ontology (GO) database. The method extracts these features from the entire GO hierarchy and hence is applicable across the whole range of function specificity. 3D profiles associated with 121 GO annotations were extracted. We tested the power of the method both for the prediction of function and for the extraction of functional sites. The success of function prediction by our method was compared with the standard homology-based method. In the zone of low sequence similarity (?15%), our method assigns the correct GO annotation in 90% of the protein structures considered, ?20% higher than inheritance of function from the closest homologue.

Pazos, Florencio; Sternberg, Michael J. E.

2004-01-01

397

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

PubMed Central

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.

Rivas, Elena

2013-01-01

398

RNA folding with soft constraints: reconciliation of probing data and thermodynamic secondary structure prediction  

PubMed Central

Thermodynamic folding algorithms and structure probing experiments are commonly used to determine the secondary structure of RNAs. Here we propose a formal framework to reconcile information from both prediction algorithms and probing experiments. The thermodynamic energy parameters are adjusted using ‘pseudo-energies’ to minimize the discrepancy between prediction and experiment. Our framework differs from related approaches that used pseudo-energies in several key aspects. (i) The energy model is only changed when necessary and no adjustments are made if prediction and experiment are consistent. (ii) Pseudo-energies remain biophysically interpretable and hold positional information where experiment and model disagree. (iii) The whole thermodynamic ensemble of structures is considered thus allowing to reconstruct mixtures of suboptimal structures from seemingly contradicting data. (iv) The noise of the energy model and the experimental data is explicitly modeled leading to an intuitive weighting factor through which the problem can be seen as folding with ‘soft’ constrain