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1

Structural correlations and melting of B-DNA fibers  

SciTech Connect

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

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

2011-06-15

2

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. PMID:21097885

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

3

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

PubMed

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. PMID:21097885

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

4

Structural change in a B-DNA helix with hydrostatic pressure  

PubMed Central

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

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

2008-01-01

5

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

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. PMID:22844516

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

2012-01-01

7

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

PubMed

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

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

2012-01-01

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

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

PubMed Central

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

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

2013-01-01

10

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

PubMed

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

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

2013-01-01

11

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. PMID:23985969

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

2013-01-01

12

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. PMID:23963698

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

2013-01-01

13

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

NASA Astrophysics Data System (ADS)

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

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

2014-08-01

14

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

15

The right-handed double helical structure of Bform DNA (B-DNA) has been known since 1953 (REF. 1). However,  

E-print Network

. Moreover, recent work suggests that failure to resolve non-canonical DNA structures makes the sequenceThe right-handed double helical structure of Bform DNA (B-DNA) has been known since 1953 (REF. 1). However, it has become increasingly clear that DNA can adopt a variety of alternative conformations based

Dever, Jennifer A.

16

The non-B-DNA structure of d(CA/TG)n differs from that of Z-DNA.  

PubMed Central

Chemical probing of two predominantly alternating purine-pyrimidine d(CA/TG)n repeats led us to propose previously that in supercoiled plasmids these elements adopt a non-B-DNA structure distinct from that of Z-DNA formed by d(CG)n sequences. Here, we present further evidence supporting this contention. Reactivity with the conformation-sensitive reagent chloroacetaldehyde, which reacts with unpaired adenines and cytosines, was confined strictly to adenines in the d(CA/TG)n repeat. In contrast, only bases outside the d(CG)n repeat exhibited chloroacetaldehyde reactivity. Two-dimensional gel analysis of topoisomers containing d(CA/TG)n tracts with bases out of strict purine-pyrimidine alteration revealed multiple superhelical-dependent transitions to an alternative left-handed structure. Within individual plasmid molecules, these multiple transitions resulted from the stepwise conversion of contiguous segments of alternating purine-pyrimidine sequence, which are delimited by bases out of alternation, to the full-length alternative conformation. When the left-handed helices increased in length to include more bases out of alternation, the average helical pitch changed substantially to produce a less tightly wound left-handed helix. Overall, these data indicate that d(CA/TG)n tracts adopt a left-handed conformation significantly different from that of the canonical Z-DNA structure of d(CG)n sequences. Images PMID:8127902

Kladde, M P; Kohwi, Y; Kohwi-Shigematsu, T; Gorski, J

1994-01-01

17

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

PubMed

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

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

18

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

PubMed Central

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

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

2012-01-01

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

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

PubMed

This unit describes basic protocols on using the non-B DNA Motif Search Tool (nBMST) to search for sequence motifs predicted to form alternative DNA conformations that differ from the canonical right-handed Watson-Crick double-helix, collectively known as non-B DNA, and on using the associated PolyBrowse, a GBrowse-based genomic browser. The nBMST is a Web-based resource that allows users to submit one or more DNA sequences to search for inverted repeats (cruciform DNA), mirror repeats (triplex DNA), direct/tandem repeats (slipped/hairpin structures), G4 motifs (tetraplex, G-quadruplex DNA), alternating purine-pyrimidine tracts (left-handed Z-DNA), and A-phased repeats (static bending). The nBMST is versatile, simple to use, does not require bioinformatics skills, and can be applied to any type of DNA sequences, including viral and bacterial genomes, up to an aggregate of 20 megabasepairs (Mbp). PMID:22470144

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

2012-04-01

21

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

NASA Technical Reports Server (NTRS)

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

Gruskin, E. A.; Rich, A.

1993-01-01

22

Structured Prediction in Computer Vision  

E-print Network

Structured Prediction in Computer Vision Tiberio Caetano and Richard Hartley NICTA Tiberio Caetano and Richard Hartley: Structured Prediction in Computer Vision 1 / 71 nicta-logo #12;ThanksAuley, Yu, NIPS '09 Tiberio Caetano and Richard Hartley: Structured Prediction in Computer Vision 2 / 71

Caetano, Tiberio

23

Understanding and predicting protein structure  

SciTech Connect

Protein structure prediction from sequence remains a premiere computational problem for modern molecular biology. Just as protein structure prediction may be divided into sub-problems of main-chain and side-chain placement, so the protein structure prediction track this year has been divided into sub-tracks of protein threading (organized by Daniel Fischer and Adam Godzik) and side-chain packing (organized by Su Chung and S. Subbiah). The result is an unusually rich tour through different levels of protein structure prediction, from coarse-grained prediction of the tertiary fold to the fine-grained atomic detail of individual side-chains. 8 refs.

Fischer, D. [Univ. of California, Los Angeles, CA (United States); Godzik, A. [Scripps Research Institute, La Jolla, CA (United States); Chung, S. [Uniformed Services Univ. of the Health Sciences, Bethesda, MD (United States)] [and others

1996-12-31

24

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

PubMed Central

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

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

2012-01-01

25

Protein Structure Prediction and Structural Genomics  

NSDL National Science Digital Library

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

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

2001-10-05

26

De Novo Protein Structure Prediction  

NASA Astrophysics Data System (ADS)

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

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

27

PROTEIN STRUCTURE PREDICTION CENTER IN CASP8  

PubMed Central

We present an outline of the Critical Assessment of Protein Structure Prediction (CASP) infrastructure implemented at the University of California, Davis, Protein Structure Prediction Center. The infrastructure supports selection and validation of prediction targets, collection of predictions, standard evaluation of submitted predictions, and presentation of results. The Center also supports information exchange relating to CASP experiments and structure prediction in general. Technical aspects of conducting the CASP8 experiment and relevant statistics are also provided. PMID:19722263

Kryshtafovych, Andriy; Krysko, Oleh; Daniluk, Pawel; Dmytriv, Zinovii; Fidelis, Krzysztof

2009-01-01

28

Protein structure prediction center in CASP8.  

PubMed

We present an outline of the Critical Assessment of Protein Structure Prediction (CASP) infrastructure implemented at the University of California, Davis, Protein Structure Prediction Center. The infrastructure supports selection and validation of prediction targets, collection of predictions, standard evaluation of submitted predictions, and presentation of results. The Center also supports information exchange relating to CASP experiments and structure prediction in general. Technical aspects of conducting the CASP8 experiment and relevant statistics are also provided. PMID:19722263

Kryshtafovych, Andriy; Krysko, Oleh; Daniluk, Pawel; Dmytriv, Zinovii; Fidelis, Krzysztof

2009-01-01

29

Sequence-Specific B-DNA Flexibility Modulates Z-DNA Formation  

PubMed Central

Conversion of right-handed B-DNA into left-handed Z-DNA is one of the largest structural transitions in biology that plays fundamental roles in gene expression and regulation. Z-DNA segments must form within genomes surrounded by a sea of B-DNA and require creation of energetically costly B/Z junctions. Here, we show using a combination of natural abundance NMR R1? carbon relaxation measurements and CD spectroscopy that sequence-specific B-DNA flexibility modulates the thermodynamic propensity to form Z-DNA and the location of B/Z junctions. We observe sequence-specific flexibility in B-DNA spanning fast (ps-ns) and slow (µsms) timescales localized at the site of B/Z junction formation. Further, our studies show that CG-repeats play an active role tuning this intrinsic B-DNA flexibility. Taken together, our results suggest that sequence-specific B-DNA flexibility may provide a mechanism for defining the length and location of Z-DNA in genomes. PMID:21275369

Bothe, Jameson R.; Lowenhaupt, Ky; Al-Hashimi, Hashim M.

2012-01-01

30

EVA: evaluation of protein structure prediction servers  

Microsoft Academic Search

EVA (http:\\/\\/cubic.bioc.columbia.edu\\/eva\\/) is a web server for evaluation of the accuracy of automated protein structure prediction methods. The evaluation is updated automatically each week, to cope with the large number of existing prediction servers and the constant changes in the prediction methods. EVA currently assesses servers for secondary structure prediction, contact prediction, comparative protein structure modelling and threading\\/fold recognition. Every

Ingrid Y. Y. Koh; Volker A. Eyrich; Marc A. Martí-renom; Dariusz Przybylski; Mallur S. Madhusudhan; Narayanan Eswar; Osvaldo Graña; Florencio Pazos; Alfonso Valencia; Andrej Sali; Burkhard Rost

2003-01-01

31

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

32

Prediction of protein secondary structure by mining structural fragment database  

E-print Network

-sheet-rich structures, responsible for Alzheimer's and Parkinson's diseases [5]. Secondary structure prediction can vector machines (SVM) to improve the accuracy of predictions and found that SVM gave the best performance patterns like a-helices and b-sheets. Many have shown that predicting secondary structure can be a first

Margaritis, Dimitris

33

Towards structured output prediction of enzyme function  

PubMed Central

Background In this paper we describe work in progress in developing kernel methods for enzyme function prediction. Our focus is in developing so called structured output prediction methods, where the enzymatic reaction is the combinatorial target object for prediction. We compared two structured output prediction methods, the Hierarchical Max-Margin Markov algorithm (HM3) and the Maximum Margin Regression algorithm (MMR) in hierarchical classification of enzyme function. As sequence features we use various string kernels and the GTG feature set derived from the global alignment trace graph of protein sequences. Results In our experiments, in predicting enzyme EC classification we obtain over 85% accuracy (predicting the four digit EC code) and over 91% microlabel F1 score (predicting individual EC digits). In predicting the Gold Standard enzyme families, we obtain over 79% accuracy (predicting family correctly) and over 89% microlabel F1 score (predicting superfamilies and families). In the latter case, structured output methods are significantly more accurate than nearest neighbor classifier. A polynomial kernel over the GTG feature set turned out to be a prerequisite for accurate function prediction. Combining GTG with string kernels boosted accuracy slightly in the case of EC class prediction. Conclusion Structured output prediction with GTG features is shown to be computationally feasible and to have accuracy on par with state-of-the-art approaches in enzyme function prediction. PMID:19091049

Astikainen, Katja; Holm, Liisa; Pitkänen, Esa; Szedmak, Sandor; Rousu, Juho

2008-01-01

34

Protein structure prediction and structure-based protein function annotation  

E-print Network

of predicted EC numbers........................................................................................... 1014.2.2.2 Analysis of predicted GO terms.................................................................................................104 4... ..............................................................................................................................119 5.1.1 Protein structure predictions using the I-TASSER server ............................................. 119 5.1.2 Detection of functional sites in protein using the COFACTOR algorithm .............. 120 5.1.3 Prediction of EC number and Gene...

Roy, Ambrish

2011-12-31

35

Protein structure prediction using hybrid AI methods  

SciTech Connect

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

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

1993-11-01

36

Protein structure Predictive methods and  

E-print Network

Comparative Modeling folding evolution Andras Fiser, Albert Einstein College of Medicine #12;4 Protein Andras Fiser, Albert Einstein College of Medicine Structural Genomics Definition: The aim of structural the rest of the family members using comparative modeling Andras Fiser, Albert Einstein College of Medicine

Sjölander, Kimmen

37

Bayesian Nonparametric Methods for Protein Structure Prediction  

E-print Network

BAYESIAN NONPARAMETRIC METHODS FOR PROTEIN STRUCTURE PREDICTION A Dissertation by KRISTIN PATRICIA LENNOX Submitted to the Office of Graduate Studies of Texas A&M University in partial fulfillment of the requirements for the degree of DOCTOR... OF PHILOSOPHY August 2010 Major Subject: Statistics BAYESIAN NONPARAMETRIC METHODS FOR PROTEIN STRUCTURE PREDICTION A Dissertation by KRISTIN PATRICIA LENNOX Submitted to the Office of Graduate Studies of Texas A&M University in partial fulfillment...

Lennox, Kristin Patricia

2011-10-21

38

Protein Structure Prediction and Structural Genomics  

E-print Network

, building a model, and assessing the model (1). The templates for modeling may be found by sequence comparison methods, such as PSI-BLAST (2), or by sequence-structure threading methods (3) that can sometimes building in- cludes either sequential or simultaneous mod- eling of the core of the protein, loops

Batzoglou, Serafim

39

Interface Structure Prediction from First-Principles  

SciTech Connect

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

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

2014-05-08

40

Characteristics and Prediction of RNA Structure  

PubMed Central

RNA secondary structures with pseudoknots are often predicted by minimizing free energy, which is NP-hard. Most RNAs fold during transcription from DNA into RNA through a hierarchical pathway wherein secondary structures form prior to tertiary structures. Real RNA secondary structures often have local instead of global optimization because of kinetic reasons. The performance of RNA structure prediction may be improved by considering dynamic and hierarchical folding mechanisms. This study is a novel report on RNA folding that accords with the golden mean characteristic based on the statistical analysis of the real RNA secondary structures of all 480 sequences from RNA STRAND, which are validated by NMR or X-ray. The length ratios of domains in these sequences are approximately 0.382L, 0.5L, 0.618L, and L, where L is the sequence length. These points are just the important golden sections of sequence. With this characteristic, an algorithm is designed to predict RNA hierarchical structures and simulate RNA folding by dynamically folding RNA structures according to the above golden section points. The sensitivity and number of predicted pseudoknots of our algorithm are better than those of the Mfold, HotKnots, McQfold, ProbKnot, and Lhw-Zhu algorithms. Experimental results reflect the folding rules of RNA from a new angle that is close to natural folding. PMID:25110687

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

2014-01-01

41

New approaches in molecular structure prediction.  

PubMed

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

Böhm, G

1996-03-01

42

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

43

Particle-swarm structure prediction on clusters  

NASA Astrophysics Data System (ADS)

We have developed an efficient method for cluster structure prediction based on the generalization of particle swarm optimization (PSO). A local version of PSO algorithm was implemented to utilize a fine exploration of potential energy surface for a given non-periodic system. We have specifically devised a technique of so-called bond characterization matrix (BCM) to allow the proper measure on the structural similarity. The BCM technique was then employed to eliminate similar structures and define the desirable local search spaces. We find that the introduction of point group symmetries into generation of cluster structures enables structural diversity and apparently avoids the generation of liquid-like (or disordered) clusters for large systems, thus considerably improving the structural search efficiency. We have incorporated Metropolis criterion into our method to further enhance the structural evolution towards low-energy regimes of potential energy surfaces. Our method has been extensively benchmarked on Lennard-Jones clusters with different sizes up to 150 atoms and applied into prediction of new structures of medium-sized Lin (n = 20, 40, 58) clusters. High search efficiency was achieved, demonstrating the reliability of the current methodology and its promise as a major method on cluster structure prediction.

Lv, Jian; Wang, Yanchao; Zhu, Li; Ma, Yanming

2012-08-01

44

Particle-swarm structure prediction on clusters.  

PubMed

We have developed an efficient method for cluster structure prediction based on the generalization of particle swarm optimization (PSO). A local version of PSO algorithm was implemented to utilize a fine exploration of potential energy surface for a given non-periodic system. We have specifically devised a technique of so-called bond characterization matrix (BCM) to allow the proper measure on the structural similarity. The BCM technique was then employed to eliminate similar structures and define the desirable local search spaces. We find that the introduction of point group symmetries into generation of cluster structures enables structural diversity and apparently avoids the generation of liquid-like (or disordered) clusters for large systems, thus considerably improving the structural search efficiency. We have incorporated Metropolis criterion into our method to further enhance the structural evolution towards low-energy regimes of potential energy surfaces. Our method has been extensively benchmarked on Lennard-Jones clusters with different sizes up to 150 atoms and applied into prediction of new structures of medium-sized Li(n) (n = 20, 40, 58) clusters. High search efficiency was achieved, demonstrating the reliability of the current methodology and its promise as a major method on cluster structure prediction. PMID:22938215

Lv, Jian; Wang, Yanchao; Zhu, Li; Ma, Yanming

2012-08-28

45

Predicting Odor Perceptual Similarity from Odor Structure  

PubMed Central

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

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

2013-01-01

46

Predicting polymeric crystal structures by evolutionary algorithms.  

PubMed

The recently developed evolutionary algorithm USPEX proved to be a tool that enables accurate and reliable prediction of structures. Here we extend this method to predict the crystal structure of polymers by constrained evolutionary search, where each monomeric unit is treated as a building block with fixed connectivity. This greatly reduces the search space and allows the initial structure generation with different sequences and packings of these blocks. The new constrained evolutionary algorithm is successfully tested and validated on a diverse range of experimentally known polymers, namely, polyethylene, polyacetylene, poly(glycolic acid), poly(vinyl chloride), poly(oxymethylene), poly(phenylene oxide), and poly (p-phenylene sulfide). By fixing the orientation of polymeric chains, this method can be further extended to predict the structures of complex linear polymers, such as all polymorphs of poly(vinylidene fluoride), nylon-6 and cellulose. The excellent agreement between predicted crystal structures and experimentally known structures assures a major role of this approach in the efficient design of the future polymeric materials. PMID:25338876

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

2014-10-21

47

Predicting polymeric crystal structures by evolutionary algorithms  

NASA Astrophysics Data System (ADS)

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

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

2014-10-01

48

Dynamic matching algorithm for viral structure prediction.  

PubMed

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

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

2014-07-01

49

Protein Structure Prediction with Evolutionary Algorithms  

SciTech Connect

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

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

1999-02-08

50

Genome-Wide Protein Structure Prediction  

Microsoft Academic Search

\\u000a The post-genomic era has witnessed an explosion of protein sequences in the public databases; but this has not been complemented\\u000a by the availability of genome-wide structure and function information, due to the technical difficulties and labor expenses\\u000a incurred by existing experimental techniques. The rapid advancements in computer-based protein structure prediction methods\\u000a have enabled automated and yet reliable methods for generating

Srayanta Mukherjee; Andras Szilagyi; Ambrish Roy; Yang Zhang

51

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

52

Protein Structure Prediction with Lattice Models  

E-print Network

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

Newman, Alantha

53

CALYPSO: A method for crystal structure prediction  

NASA Astrophysics Data System (ADS)

We have developed a software package CALYPSO (Crystal structure AnaLYsis by Particle Swarm Optimization) to predict the energetically stable/metastable crystal structures of materials at given chemical compositions and external conditions (e.g., pressure). The CALYPSO method is based on several major techniques (e.g. particle-swarm optimization algorithm, symmetry constraints on structural generation, bond characterization matrix on elimination of similar structures, partial random structures per generation on enhancing structural diversity, and penalty function, etc.) for global structural minimization from scratch. All of these techniques have been demonstrated to be critical to the prediction of global stable structure. We have implemented these techniques into the CALYPSO code. Testing of the code on many known and unknown systems shows high efficiency and the highly successful rate of this CALYPSO method [Y. Wang, J. Lv, L. Zhu, Y. Ma, Phys. Rev. B 82 (2010) 094116] [29]. In this paper, we focus on descriptions of the implementation of CALYPSO code and why it works.

Wang, Yanchao; Lv, Jian; Zhu, Li; Ma, Yanming

2012-10-01

54

RNA secondary structure prediction using soft computing.  

PubMed

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

Ray, Shubhra Sankar; Pal, Sankar K

2013-01-01

55

Predictive modeling of post bioprinting structure formation.  

PubMed

Cellular particle dynamics (CPD) is an effective computational method to describe the shape evolution and biomechanical relaxation processes in systems composed of micro tissues such as multicellular aggregates. Therefore, CPD is a useful tool to predict the outcome of postprinting structure formation in bioprinting. The predictive power of CPD has been demonstrated for multicellular systems composed of identical volume-conserving spherical and cylindrical bioink units. Experiments and computer simulations were related through an independently developed theoretical formalism based on continuum mechanics. Here we generalize the CPD formalism to (i) include non-identical bioink particles often used in specific bioprinting applications, (ii) describe the more realistic experimental situation in which during the post-printing structure formation via the fusion of spherical bioink units the volume of the system decreases, and (iii) directly connect CPD simulations to the corresponding experiments without the need of the intermediate continuum theory inherently based on simplifying assumptions. PMID:24800270

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

2014-03-21

56

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

PubMed

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

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

2014-02-01

57

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

PubMed Central

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

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

2014-01-01

58

Predicting crystal structure by merging data mining with quantum mechanics  

E-print Network

ARTICLES Predicting crystal structure by merging data mining with quantum mechanics CHRISTOPHER C crystal structures will form in an alloy system. Crystal structure can only be predicted effectively the stable crystal structure of materials. C rystal structure occupies a central and often critical role

Ceder, Gerbrand

59

Protein Secondary Structure Prediction Method Based on Neural Networks  

Microsoft Academic Search

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

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

2008-01-01

60

Protein structure prediction and analysis using the Robetta server  

E-print Network

Protein structure prediction and analysis using the Robetta server David E. Kim, Dylan Chivian The Robetta server (http://robetta.bakerlab.org) pro- vides automated tools for protein structure prediction and analysis. For structure prediction, sequences submitted to the server are parsed into putative domains

Baker, David

61

STRUCTURE PREDICTION CASP AND OTHER COMMUNITY-WIDE  

E-print Network

Section VI STRUCTURE PREDICTION #12;#12;28 CASP AND OTHER COMMUNITY-WIDE ASSESSMENTS TO ADVANCE THE FIELD OF STRUCTURE PREDICTION Jenny Gu and Philip E. Bourne A MEASURE FOR SUCCESS In the early 1990s to the biological community and to measure the progress in this developing field of structure prediction. In part

Bourne, Philip E.

62

Structure prediction of magnetosome-associated proteins.  

PubMed

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

Nudelman, Hila; Zarivach, Raz

2014-01-01

63

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

PubMed

The ionic atmosphere around a nucleic acid regulates its stability in aqueous salt solutions. One major source of complexity in biological activities involving nucleic acids arises from the strong influence of the surrounding ions and water molecules on their structural and thermodynamic properties. Here, we implement a classical density functional theory for cylindrical polyelectrolytes embedded in aqueous electrolytes containing explicit (neutral hard sphere) water molecules at experimental solvent concentrations. Our approach allows us to include ion correlations as well as solvent and ion excluded volume effects for studying the structural and thermodynamic properties of highly charged cylindrical polyelectrolytes. Several models of size and charge asymmetric mixtures of aqueous electrolytes at physiological concentrations are studied. Our results are in good agreement with Monte Carlo simulations. Our numerical calculations display significant differences in the ion density profiles for the different aqueous electrolyte models studied. However, similar results regarding the excess number of ions adsorbed to the B-DNA molecule are predicted by our theoretical approach for different aqueous electrolyte models. These findings suggest that ion counting experimental data should not be used alone to validate the performance of aqueous DNA-electrolyte models. PMID:25494770

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

2014-12-14

64

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

NASA Astrophysics Data System (ADS)

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

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

2014-12-01

65

Optimizing nondecomposable loss functions in structured prediction.  

PubMed

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

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

2013-04-01

66

Structure Prediction for Multicomponent Materials Using Biminima  

NASA Astrophysics Data System (ADS)

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

Schebarchov, D.; Wales, D. J.

2014-10-01

67

Protein Structure Prediction Using Bee Colony Optimization Metaheuristic  

Microsoft Academic Search

Predicting the native structure of proteins is one of the most challenging prob- lems in molecular biology. The goal is to determine the three-dimensional structure from the one-dimensional amino acid sequence. De novo prediction algorithms seek to do this by devel- oping a representation of the proteins structure, an energy potential and some optimization algorithm that finds the structure with

Rasmus Fonseca; Martin Paluszewski; Pawel Winter

2010-01-01

68

I-TASSER server for protein 3D structure prediction  

E-print Network

Background: Prediction of 3-dimensional protein structures from amino acid sequences represents one of the most important problems in computational structural biology. The community-wide Critical Assessment of Structure ...

Zhang, Yang

2008-01-23

69

A Coprocessor Architecture for Fast Protein Structure Prediction  

E-print Network

A Coprocessor Architecture for Fast Protein Structure Prediction M. Marolia, R. Khoja, T. Acharya, C. Chakrabarti Department of Electrical Engineering Arizona State University, Tempe, USA. Abstract--Predicting for fast protein structure prediction based on the PSIPRED algorithm. The architecture consists of systolic

Kambhampati, Subbarao

70

EVA: continuous automatic evaluation of protein structure prediction servers  

Microsoft Academic Search

words; Text 1129 words; 5 References Abstract Summary: Evaluation of protein structure prediction methods is difficult and time- consuming. Here, we describe EVA, a web server for assessing protein structure prediction methods, in an automated, continuous and large-scale fashion. Currently, EVA evaluates the performance of a variety of prediction methods available through the internet. Every week, the sequences of the

Volker A. Eyrich; Marc A. Martí-renom; Dariusz Przybylski; Mallur S. Madhusudhan; András Fiser; Florencio Pazos; Alfonso Valencia; Andrej Sali; Burkhard Rost

2001-01-01

71

Role of sequence encoded ?B DNA geometry in gene regulation by Dorsal  

PubMed Central

Many proteins of the Rel family can act as both transcriptional activators and repressors. However, mechanism that discerns the ‘activator/repressor’ functions of Rel-proteins such as Dorsal (Drosophila homologue of mammalian NF?B) is not understood. Using genomic, biophysical and biochemical approaches, we demonstrate that the underlying principle of this functional specificity lies in the ‘sequence-encoded structure’ of the ?B-DNA. We show that Dorsal-binding motifs exist in distinct activator and repressor conformations. Molecular dynamics of DNA-Dorsal complexes revealed that repressor ?B-motifs typically have A-tract and flexible conformation that facilitates interaction with co-repressors. Deformable structure of repressor motifs, is due to changes in the hydrogen bonding in A:T pair in the ‘A-tract’ core. The sixth nucleotide in the nonameric ?B-motif, ‘A’ (A6) in the repressor motifs and ‘T’ (T6) in the activator motifs, is critical to confer this functional specificity as A6???T6 mutation transformed flexible repressor conformation into a rigid activator conformation. These results highlight that ‘sequence encoded ?B DNA-geometry’ regulates gene expression by exerting allosteric effect on binding of Rel proteins which in turn regulates interaction with co-regulators. Further, we identified and characterized putative repressor motifs in Dl-target genes, which can potentially aid in functional annotation of Dorsal gene regulatory network. PMID:21890896

Mrinal, Nirotpal; Tomar, Archana; Nagaraju, Javaregowda

2011-01-01

72

Neural network definitions of highly predictable protein secondary structure classes  

Microsoft Academic Search

We use two co-evolving neural networks to determine new classes of protein secondary structure which are significantly more predictable from local amino sequence than the conventional secondary structure classification. Accurate prediction of the conventional secondary structure classes: alpha helix, beta strand, and coil, from primary sequence has long been an important problem in computational molecular biology. Neural networks have been

A. Lapedes; E. Steeg; R. Farber

1994-01-01

73

The prediction of protein structural class using averaged chemical shifts  

Microsoft Academic Search

Knowledge of protein structural class can provide important information about its folding patterns. Many approaches have been developed for the prediction of protein structural classes. However, the information used by these approaches is primarily based on amino acid sequences. In this study, a novel method is presented to predict protein structural classes by use of chemical shift (CS) information derived

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

2012-01-01

74

Transition between B-DNA and Z-DNA: Free Energy Landscape for the B?Z Junction Propagation  

Microsoft Academic Search

Canonical, right-handed B-DNA can be transformed into noncanonical, left-handed Z-DNA in vitro at high salt concentrations or in vivo under physiological conditions. The molecular mechanism of this drastic conformational transition is still unknown despite numerous studies. Inspired by the crystal structure of a B-Z junction and the previous zipper model, we show here, with the aid of molecular dynamics simulations,

Juyong Lee; Yang-Gyun Kim; Kyeong Kyu Kim; Chaok Seok

2010-01-01

75

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

E-print Network

- 1 - A new prediction strategy for long local protein structures using an original description Prediction Keywords: library of fragments, structural networks, local structure prediction, support vector approximation. A local structure prediction method was also proposed. Here, overlapping properties of local

Paris-Sud XI, Université de

76

Prediction of binary hard-sphere crystal structures  

NASA Astrophysics Data System (ADS)

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

Filion, Laura; Dijkstra, Marjolein

2009-04-01

77

Prediction of binary hard-sphere crystal structures.  

PubMed

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

Filion, Laura; Dijkstra, Marjolein

2009-04-01

78

RNAstructure: software for RNA secondary structure prediction and analysis  

Microsoft Academic Search

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

Jessica S. Reuter; David H. Mathews

2010-01-01

79

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

E-print Network

Ligand docking Protein structure prediction a b s t r a c t Structure based virtual screening has largely-based docking and advanced protein structure modeling methods should be a valuable approach to the largeProtein structure prediction provides comparable performance to crystallographic structures

Zhang, Yang

80

Neural network definitions of highly predictable protein secondary structure classes  

SciTech Connect

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

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

1994-02-01

81

Predicting Career Advancement with Structural Equation Modelling  

ERIC Educational Resources Information Center

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

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

2012-01-01

82

Multipass Membrane Protein Structure Prediction Using Rosetta  

E-print Network

-resolution crystals have proven to be very significant obstacles to determine atomic level structures of membrane­13 and that small side-chain amino acids, such as glycine, alanine, serine, and threonine favor helix

Baker, David

83

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

84

An Atomic Environment Potential for use in Protein Structure Prediction  

E-print Network

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

Summa, Christopher M.

85

Aligning Protein Sequences with Predicted Secondary Structure  

E-print Network

the amino acid sequence alone often does not provide enough information to obtain accurate alignments under in regions that may be in the structural core, and are employed by CLUSTAL W (Thompson et al., 1994), T-Coffee favors matches in the alignment that have high support, and is employed by T-Coffee, MAFFT, Prob

Wheeler, Travis

86

Aligning protein sequences with predicted secondary structure  

E-print Network

. Since the amino acid sequence alone often does not provide enough information to obtain accurate be in the structural core, and are employed by CLUSTAL W (Thompson et al. 1994), T-Coffee (Notredame et al. 2000 high support, and is employed by T-Coffee, MAFFT, ProbCons, SPEM (Zhou and Zhou 2005), PROMALS (Pei

Kececioglu, John

87

A machine learning approach to crystal structure prediction  

E-print Network

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

Fischer, Christopher Carl

2007-01-01

88

Predicting structures of cross-linked condensation polymers  

NASA Technical Reports Server (NTRS)

Mathematical procedure is used to predict structure of cross-linked condensation polymer differentiated from an additional polymer resulting from specific reaction. Procedure will greatly reduce amount of empirical formulation and testing needed to produce desired product.

Marsh, H. E.

1979-01-01

89

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

E-print Network

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

Paris-Sud XI, Université de

90

WeFold: a coopetition for protein structure prediction.  

PubMed

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

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

2014-09-01

91

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...

92

IMPORTANCE OF SECONDARY STRUCTURE ELEMENTS FOR PREDICTION OF GO ANNOTATIONS  

E-print Network

for the proteins of human, mouse, rat, arabidop- sis, zebra fish, chicken and cow. It also provides a Protein Data: bioinfo.ce.itu.edu.tr ABSTRACT Predicted or actual protein secondary structure, in addition to amino acid: loop) for protein function prediction is investi- gated. Smith-Waterman alignment similarity scores

Cataltepe, Zehra

93

proteinsSTRUCTURE O FUNCTION O BIOINFORMATICS FUNCTION: PREDICTIONS  

E-print Network

proteinsSTRUCTURE O FUNCTION O BIOINFORMATICS FUNCTION: PREDICTIONS Protein-binding site prediction interaction by docking.2 Recently, much attention is drawn to the atomistic description of protein based on three-dimensional protein modeling Mina Oh, Keehyoung Joo, and Jooyoung Lee* School

Lee, Jooyoung

94

OPTIMIZATION BIAS IN ENERGY-BASED STRUCTURE PREDICTION  

PubMed Central

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

Petrella, Robert J.

2014-01-01

95

Computational methods in sequence and structure prediction  

NASA Astrophysics Data System (ADS)

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

Lang, Caiyi

96

Locating monovalent cations in the grooves of B-DNA.  

PubMed

Here we demonstrate that monovalent cations can localize around B-DNA in geometrically regular, sequence-specific sites in oligonucleotide crystals. Positions of monovalent ions were determined from high-resolution X-ray diffraction of DNA crystals grown in the presence of thallium(I) cations (Tl(+)). Tl(+) has previously been shown to be a useful K(+) mimic. Tl(+) positions determined by refinement of model to data are consistent with positions determined using isomorphous F(Tl) - F(K) difference Fouriers and anomalous difference Fouriers. None of the observed Tl(+) sites surrounding CGCGAATTCGCG are fully occupied by Tl(+) ions. The most highly occupied sites, located within the G-tract major groove, have estimated occupancies ranging from 20% to 35%. The occupancies of the minor groove sites are estimated to be around 10%. The Tl(+) positions in general are not in direct proximity to phosphate groups. The A-tract major groove appears devoid of localized cations. The majority of the observed Tl(+) ions interact with a single duplex and so are not engaged in lattice interactions or crystal packing. The locations of the cation sites are dictated by coordination geometry, electronegative potential, avoidance of electropositive amino groups, and cation-pi interactions. It appears that partially dehydrated monovalent cations, hydrated divalent cations, and polyamines compete for a common binding region on the floor of the G-tract major groove. PMID:11513580

Howerton, S B; Sines, C C; VanDerveer, D; Williams, L D

2001-08-28

97

Prediction and Observation of Crystal Structures of Oppositely Charged Colloids  

NASA Astrophysics Data System (ADS)

We studied crystal structures in mixtures of large and small oppositely charged spherical colloids with size ratio 0.31 using Monte Carlo simulations and confocal microscopy. We developed an interactive method based on simulated annealing to predict new binary crystal structures with stoichiometries from 1 to 8. Employing these structures in Madelung energy calculations using a screened Coulomb potential, we constructed a ground-state phase diagram, which shows a remarkably rich variety of crystals. Our phase diagram displays colloidal analogs of simple-salt structures and of the doped fullerene C60 structures, but also novel structures that do not have an atomic or molecular analog. We found three of the predicted structures experimentally, which provides confidence that our method yields reliable results.

Hynninen, A.-P.; Christova, C. G.; van Roij, R.; van Blaaderen, A.; Dijkstra, M.

2006-04-01

98

Prediction of structure and density for organic nitramines  

Microsoft Academic Search

An approach to ab initio crystal structure prediction by packing optimization is developed for organic nitramines, an important class of energetic materials. The principal features of the search method are: use of statistical data on the organic crystal structural classes to select typical space groups and site symmetries for further search; accounting for the energy-hypersurface symmetry to determine the unique

A. V. Dzyabchenko; T. S. Pivina; E. A. Arnautova

1996-01-01

99

Application of expert networks for predicting proteins secondary structure  

Microsoft Academic Search

The present study utilizes expert neural networks for the prediction of proteins secondary structure. We use three independent networks, one for each structure (alpha, beta and coil) as the first-level processing unit; decision upon the chosen structure for each residue is carried out by a second-level, post-processing unit, which utilizes the Chou and Fasman frequency values F? and F? in

Sarit Sivan; Orna Filo; Hava Siegelmann

2007-01-01

100

Protein structure prediction enhanced with evolutionary diversity: SPEED  

PubMed Central

For naturally occurring proteins, similar sequence implies similar structure. Consequently, multiple sequence alignments (MSAs) often are used in template-based modeling of protein structure and have been incorporated into fragment-based assembly methods. Our previous homology-free structure prediction study introduced an algorithm that mimics the folding pathway by coupling the formation of secondary and tertiary structure. Moves in the Monte Carlo procedure involve only a change in a single pair of ?,? backbone dihedral angles that are obtained from a Protein Data Bank-based distribution appropriate for each amino acid, conditional on the type and conformation of the flanking residues. We improve this method by using MSAs to enrich the sampling distribution, but in a manner that does not require structural knowledge of any protein sequence (i.e., not homologous fragment insertion). In combination with other tools, including clustering and refinement, the accuracies of the predicted secondary and tertiary structures are substantially improved and a global and position-resolved measure of confidence is introduced for the accuracy of the predictions. Performance of the method in the Critical Assessment of Structure Prediction (CASP8) is discussed. PMID:20066664

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

2010-01-01

101

Protein structure prediction enhanced with evolutionary diversity : SPEED.  

SciTech Connect

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

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

2010-03-01

102

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

PubMed

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

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

2015-01-01

103

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

104

Predicting protein secondary structure by cascade-correlation neural networks  

Microsoft Academic Search

Summary: The back-propagation neural network algorithm is a commonly used method for predicting the secondary structure of proteins. Whilst popular, this method can be slow to learn and here we compare it with an alternative: the cascade-correlation architecture. Using a constructive algorithm, cascade-correlation achieves predictive accuracies comparable to those obtained by back-propagation, in shorter time. Availability: A web server is

Matthew J. Wood; Jonathan D. Hirst

2004-01-01

105

MUFOLD: A new solution for protein 3D structure prediction  

PubMed Central

There have been steady improvements in protein structure prediction during the past 2 decades. However, current methods are still far from consistently predicting structural models accurately with computing power accessible to common users. Toward achieving more accurate and efficient structure prediction, we developed a number of novel methods and integrated them into a software package, MUFOLD. First, a systematic protocol was developed to identify useful templates and fragments from Protein Data Bank for a given target protein. Then, an efficient process was applied for iterative coarse-grain model generation and evaluation at the C? or backbone level. In this process, we construct models using interresidue spatial restraints derived from alignments by multidimensional scaling, evaluate and select models through clustering and static scoring functions, and iteratively improve the selected models by integrating spatial restraints and previous models. Finally, the full-atom models were evaluated using molecular dynamics simulations based on structural changes under simulated heating. We have continuously improved the performance of MUFOLD by using a benchmark of 200 proteins from the Astral database, where no template with >25% sequence identity to any target protein is included. The average root-mean-square deviation of the best models from the native structures is 4.28 Å, which shows significant and systematic improvement over our previous methods. The computing time of MUFOLD is much shorter than many other tools, such as Rosetta. MUFOLD demonstrated some success in the 2008 community-wide experiment for protein structure prediction CASP8. PMID:19927325

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

2010-01-01

106

A new protein structure representation for efficient protein function prediction.  

PubMed

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

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

2014-12-01

107

Conformational flexibility of B-DNA at 0.74 A resolution: d(CCAGTACTGG)(2).  

PubMed

The affinity and specificity of a ligand for its DNA site is a function of the conformational changes between the isolated and complexed states. Although the structures of a hydroxypyrrole-imidazole-pyrrole polyamide dimer with 5'-CCAGTACTGG-3' and the trp repressor recognizing the sequence 5'-GTACT-3' are known, the baseline conformation of the DNA site would contribute to our understanding of DNA recognition by these ligands. The 0.74 A resolution structure of a B-DNA double helix, 5'-CCAGTACTGG-3', has been determined by X-ray crystallography. Six of the nine phosphates, two of four bound calcium ions and networks of water molecules hydrating the oligonucleotide have alternate conformations. By contrast, nine of the ten bases have a single, unique conformation with hydrogen atoms visible in most cases. The polyamide molecules alter the geometry of the phosphodiester backbone, and the water molecules mediating contacts in the trp repressor/operator complex are conserved in the unliganded DNA. Furthermore, the multiple conformational states, ions and hydration revealed by this ultrahigh resolution structure of a B-form oligonucleotide are potentially general considerations for understanding DNA-binding affinity and specificity by ligands. PMID:10677281

Kielkopf, C L; Ding, S; Kuhn, P; Rees, D C

2000-02-25

108

Sequence based residue depth prediction using evolutionary information and predicted secondary structure  

PubMed Central

Background Residue depth allows determining how deeply a given residue is buried, in contrast to the solvent accessibility that differentiates between buried and solvent-exposed residues. When compared with the solvent accessibility, the depth allows studying deep-level structures and functional sites, and formation of the protein folding nucleus. Accurate prediction of residue depth would provide valuable information for fold recognition, prediction of functional sites, and protein design. Results A new method, RDPred, for the real-value depth prediction from protein sequence is proposed. RDPred combines information extracted from the sequence, PSI-BLAST scoring matrices, and secondary structure predicted with PSIPRED. Three-fold/ten-fold cross validation based tests performed on three independent, low-identity datasets show that the distance based depth (computed using MSMS) predicted by RDPred is characterized by 0.67/0.67, 0.66/0.67, and 0.64/0.65 correlation with the actual depth, by the mean absolute errors equal 0.56/0.56, 0.61/0.60, and 0.58/0.57, and by the mean relative errors equal 17.0%/16.9%, 18.2%/18.1%, and 17.7%/17.6%, respectively. The mean absolute and the mean relative errors are shown to be statistically significantly better when compared with a method recently proposed by Yuan and Wang [Proteins 2008; 70:509–516]. The results show that three-fold cross validation underestimates the variability of the prediction quality when compared with the results based on the ten-fold cross validation. We also show that the hydrophilic and flexible residues are predicted more accurately than hydrophobic and rigid residues. Similarly, the charged residues that include Lys, Glu, Asp, and Arg are the most accurately predicted. Our analysis reveals that evolutionary information encoded using PSSM is characterized by stronger correlation with the depth for hydrophilic amino acids (AAs) and aliphatic AAs when compared with hydrophobic AAs and aromatic AAs. Finally, we show that the secondary structure of coils and strands is useful in depth prediction, in contrast to helices that have relatively uniform distribution over the protein depth. Application of the predicted residue depth to prediction of buried/exposed residues shows consistent improvements in detection rates of both buried and exposed residues when compared with the competing method. Finally, we contrasted the prediction performance among distance based (MSMS and DPX) and volume based (SADIC) depth definitions. We found that the distance based indices are harder to predict due to the more complex nature of the corresponding depth profiles. Conclusion The proposed method, RDPred, provides statistically significantly better predictions of residue depth when compared with the competing method. The predicted depth can be used to provide improved prediction of both buried and exposed residues. The prediction of exposed residues has implications in characterization/prediction of interactions with ligands and other proteins, while the prediction of buried residues could be used in the context of folding predictions and simulations. PMID:18803867

Zhang, Hua; Zhang, Tuo; Chen, Ke; Shen, Shiyi; Ruan, Jishou; Kurgan, Lukasz

2008-01-01

109

Sizing Structures and Predicting Weight of a Spacecraft  

NASA Technical Reports Server (NTRS)

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

Cerro, Jeffrey; Shore, C. P.

2006-01-01

110

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

111

Chiroptical Detection Of Condensed Nickel(II)-Z-DNA In The Presence Of The B-DNA Via Porphyrin Exciton Coupled Circular Dichroism  

PubMed Central

Here, we report a highly sensitive and specific chiroptical detection method of condensed left-handed ZDNA in the presence of canonical right-handed B-DNA. The selective formation of a left-handed cytosine-guanine oligonucleotide (CG ODN) in the presence of a right-handed adenine-thymine oligonucleotide (AT ODN) was induced by millimolar concentrations of NiCl2 and confirmed by electronic circular dichroism. The nickel(II) induced B-to-Z DNA transition of the CG ODN was accompanied by the concurrent condensation of the Ni(II)-Z-DNA as confirmed by resonance light scattering, transmission spectroscopy, and centrifugation. The selective condensation of the CG ODN allowed its separation from the AT ODN using centrifugation. No structural changes were observed for the AT ODN upon addition of Ni(II). Anionic nickel(II) meso-tetra(4-sulfonatophenyl) porphyrin (NiTPPS) spectroscopically detected the left-handed Z-DNA in the Z-DNA/B-DNA mixture via a strong exciton coupled circular dichroism (ECCD) signal induced in the porphyrin Soret band absorption region. The bisignate ECCD signal originates from the assembly of achiral porphyrins into helical arrays by intermolecular interactions with the condensed Z-DNA scaffold. No induced CD signal was observed for the Ni(II)-B-DNA-NiTPPS complex. Hence, an unambiguous spectroscopic recognition of Ni(II) induced condensed Z-DNA in the presence of B-DNA is possible. The sensitivity of this chiroptical method was as low as 5% of the Z-DNA (4.4 ?mol base pair concentration) in the presence of 95% of B-DNA (80 ?mol). Thus, NiTPPS is a highly sensitive probe for applications in bio-sensing via the CD signal amplification. PMID:21774503

Choi, Jung Kyu; Sargsyan, Gevorg; Shabbir-Hussain, Murtaza; Holmes, Andrea E.; Balaz, Milan

2011-01-01

112

Fast learning optimized prediction methodology (FLOPRED) for protein secondary structure prediction  

PubMed Central

Computational methods are rapidly gaining importance in the field of structural biology, mostly due to the explosive progress in genome sequencing projects and the large disparity between the number of sequences and the number of structures. There has been an exponential growth in the number of available protein sequences and a slower growth in the number of structures. There is therefore an urgent need to develop computational methods to predict structures and identify their functions from the sequence. Developing methods that will satisfy these needs both efficiently and accurately is of paramount importance for advances in many biomedical fields, including drug development and discovery of biomarkers. A novel method called Fast Learning Optimized PREDiction Methodology (FLOPRED) is proposed for predicting protein secondary structure, using knowledge-based potentials combined with structure information from the CATH database. A Neural Network-based Extreme Learning Machine (ELM) and advanced Particle Swarm Optimization (PSO) are used with this data that yield better and faster convergence to produce more accurate results. Protein secondary structures are predicted efficiently, reliably, more efficiently and more accurately using FLOPRED. These techniques yield superior classification of secondary structure elements, with a training accuracy ranging between 83% and 87% over a wide range of hidden neurons and a cross-validated testing accuracy ranging between 81% and 84% and a Segment OVerlap (SOV) score of 78% that are obtained with different sets of proteins. These results are comparable to other recently published studies, but are obtained with greater efficiencies, in terms of time and cost. PMID:22562230

Saraswathi, Saras; Fernández-Martínez, Juan Luis; Kolinski, Andrzej; Jernigan, Robert L.; Kloczkowski, Andrzej

2013-01-01

113

Improving the accuracy of protein secondary structure prediction using structural alignment  

PubMed Central

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

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

2006-01-01

114

Algorithm independent properties of RNA secondary structure predictions  

Microsoft Academic Search

Algorithms predicting RNA secondary structures based on different folding criteria – minimum free energies (mfe), kinetic\\u000a folding (kin), maximum matching (mm) – and different parameter sets are studied systematically. Two base pairing alphabets\\u000a were used: the binary GC and the natural four-letter AUGC alphabet. Computed structures and free energies depend strongly on both the algorithm and the parameter set. Statistical

Manfred Tacker; Peter F. Stadler; Erich G. Bornberg-Bauer; Ivo L. Hofacker; Peter Schuster

1996-01-01

115

Computational Methods for Protein Structure Prediction and Fold Recognition  

Microsoft Academic Search

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

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

116

CRISPR revisited: structure prediction of CRISPR repeats Sita Lange1  

E-print Network

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

Will, Sebastian

117

Predicting protein structures with a multiplayer online game  

E-print Network

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

Baker, David

118

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

119

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

120

Identifying Predictive Structures in Relational Data Using Multiple Instance Learning  

E-print Network

will be nominated for academy awards every year. The Internet Movie Database (IMDb) con- tains about one hundred movies that were nominated for academy awards in the time period 1970 to 2000 and thou- sands of movies for an academy award. Such structures are useful not only for classification and prediction tasks but also

McGovern, Amy

121

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

PubMed Central

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

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

2015-01-01

122

Ab Initio Prediction of Transcription Factor Targets Using Structural Knowledge  

PubMed Central

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

Kaplan, Tommy; Friedman, Nir; Margalit, Hanah

2005-01-01

123

Characterization and sequence prediction of structural variations in ?-helix  

PubMed Central

Background The structure conservation in various ?-helix subclasses reveals the sequence and context dependent factors causing distortions in the ?-helix. The sequence-structure relationship in these subclasses can be used to predict structural variations in ?-helix purely based on its sequence. We train support vector machine(SVM) with dot product kernel function to discriminate between regular ?-helix and non-regular ?-helices purely based on the sequences, which are represented with various overall and position specific propensities of amino acids. Results We characterize the structural distortions in five ?-helix subclasses. The sequence structure correlation in the subclasses reveals that the increased propensity of proline, histidine, serine, aspartic acid and aromatic amino acids are responsible for the distortions in regular ?-helix. The N-terminus of regular ?-helix prefers neutral and acidic polar amino acids, while the C-terminus prefers basic polar amino acid. Proline is preferred in the first turn of regular ?-helix , while it is preferred to produce kinked and curved subclasses. The SVM discriminates between regular ?-helix and the rest with precision of 80.97% and recall of 88.05%. Conclusions The correlation between structural variation in helices and their sequences is manifested by the performance of SVM based on sequence features. The results presented here are useful for computational design of helices. The results are also useful for prediction of structural perturbations in helix sequence purely based on its sequence. PMID:21342550

2011-01-01

124

Predicting inclusion behaviour and framework structures in organic crystals.  

PubMed

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

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

2009-12-01

125

Structure-Based Predictive Models for Allosteric Hot Spots  

PubMed Central

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

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

2009-01-01

126

Methods for predicting crack growth in advanced structures  

NASA Technical Reports Server (NTRS)

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

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

1990-01-01

127

Binding of Hoechst 33258 to the minor groove of B-DNA.  

PubMed

An X-ray crystallographic structure analysis has been carried out on the complex between the antibiotic and DNA fluorochrome Hoechst 33258 and a synthetic B-DNA dodecamer of sequence C-G-C-G-A-A-T-T-C-G-C-G. The drug molecule, which can be schematized as: phenol-benzimidazole-benzimidazole-piperazine, sits within the minor groove in the A-T-T-C region of the DNA double helix, displacing the spine of hydration that is found in drug-free DNA. The NH groups of the benzimidazoles make bridging three-center hydrogen bonds between adenine N-3 and thymine O-2 atoms on the edges of base-pairs, in a manner both mimicking the spine of hydration and calling to mind the binding of the auti-tumor drug netropsin. Two conformers of Hoechst are seen in roughly equal populations, related by 180 degrees rotation about the central benzimidazole-benzimidazole bond: one form in which the piperazine ring extends out from the surface of the double helix, and another in which it is buried deep within the minor groove. Steric clash between the drug and DNA dictates that the phenol-benzimidazole-benzimidazole portion of Hoechst 33258 binds only to A.T regions of DNA, whereas the piperazine ring demands the wider groove characteristic of G.C regions. Hence, the piperazine ring suggests a possible G.C-reading element for synthetic DNA sequence-reading drug analogs. PMID:2445998

Pjura, P E; Grzeskowiak, K; Dickerson, R E

1987-09-20

128

Benchmark data sets for structure-based computational target prediction.  

PubMed

Structure-based computational target prediction methods identify potential targets for a bioactive compound. Methods based on protein-ligand docking so far face many challenges, where the greatest probably is the ranking of true targets in a large data set of protein structures. Currently, no standard data sets for evaluation exist, rendering comparison and demonstration of improvements of methods cumbersome. Therefore, we propose two data sets and evaluation strategies for a meaningful evaluation of new target prediction methods, i.e., a small data set consisting of three target classes for detailed proof-of-concept and selectivity studies and a large data set consisting of 7992 protein structures and 72 drug-like ligands allowing statistical evaluation with performance metrics on a drug-like chemical space. Both data sets are built from openly available resources, and any information needed to perform the described experiments is reported. We describe the composition of the data sets, the setup of screening experiments, and the evaluation strategy. Performance metrics capable to measure the early recognition of enrichments like AUC, BEDROC, and NSLR are proposed. We apply a sequence-based target prediction method to the large data set to analyze its content of nontrivial evaluation cases. The proposed data sets are used for method evaluation of our new inverse screening method iRAISE. The small data set reveals the method's capability and limitations to selectively distinguish between rather similar protein structures. The large data set simulates real target identification scenarios. iRAISE achieves in 55% excellent or good enrichment a median AUC of 0.67 and RMSDs below 2.0 Å for 74% and was able to predict the first true target in 59 out of 72 cases in the top 2% of the protein data set of about 8000 structures. PMID:25084060

Schomburg, Karen T; Rarey, Matthias

2014-08-25

129

A Structure-Based Model for Predicting Serum Albumin Binding  

PubMed Central

One of the many factors involved in determining the distribution and metabolism of a compound is the strength of its binding to human serum albumin. While experimental and QSAR approaches for determining binding to albumin exist, various factors limit their ability to provide accurate binding affinity for novel compounds. Thus, to complement the existing tools, we have developed a structure-based model of serum albumin binding. Our approach for predicting binding incorporated the inherent flexibility and promiscuity known to exist for albumin. We found that a weighted combination of the predicted logP and docking score most accurately distinguished between binders and nonbinders. This model was successfully used to predict serum albumin binding in a large test set of therapeutics that had experimental binding data. PMID:24691448

Lexa, Katrina W.; Dolghih, Elena; Jacobson, Matthew P.

2014-01-01

130

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

131

Virality Prediction and Community Structure in Social Networks  

NASA Astrophysics Data System (ADS)

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.

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

2013-08-01

132

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

PubMed

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

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

2015-01-01

133

Predicting olfactory receptor neuron responses from odorant structure  

PubMed Central

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

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

2007-01-01

134

Structural Prediction of Dynamic Bayesian Network With Partial Prior Information.  

PubMed

The prediction of the structure of a hidden Dynamic Bayesian Network (DBN) from a noisy dataset is an important and challenging task. This work presents a generalized framework to infer the DBN network structure with partial prior information. In the proposed framework, the partial information about the network structure is provided in the form of prior. The proposed method makes use of the prior information regarding the presence and as well as absence of some of the edges. Using the noisy dataset and partial prior information, this method is able to infer nearly accurate structure of the network. The proposed method is validated using simulated datasets. In addition, two real biological datasets are used to infer hidden biological interaction networks. PMID:25314704

Maiti, Aniruddha; Reddy, Ramakanth; Mukherjee, Anirban

2014-10-13

135

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

136

Structure and stoichiometry prediction of surfaces reacting with multicomponent gases.  

PubMed

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

Herrmann, Philipp; Heimel, Georg

2015-01-01

137

Protein Structure Prediction by Applying an Evolutionary Algorithm  

Microsoft Academic Search

Interest in protein structure prediction is wide-spread, and has been previously addressed using evolutionary al- gorithms, such as the Simple genetic algorithm (GA), messy GA (mga), fast messy GA (fmGA), and Linkage Learning GA (LLGA). However, past research used off the shelf soft- ware such as GENOCOP, GENESIS, and mGA. In this study we report results of a modified fmGA,

Richard O. Day; Gary B. Lamont; Ruth Pachter

2003-01-01

138

Structure-based phenotyping predicts HIV-1 protease inhibitor resistance.  

PubMed

Mutations in HIV-1 drug targets lead to resistance and consequent therapeutic failure of antiretroviral drugs. Phenotypic resistance assays are time-consuming and costly, and genotypic rules-based interpretations may fail to predict the effects of multiple mutations. We have developed a computational procedure that rapidly evaluates changes in the binding energy of inhibitors to mutant HIV-1 PR variants. Models of WT complexes were produced from crystal structures. Mutant complexes were built by amino acid substitutions in the WT complexes with subsequent energy minimization of the ligand and PR binding site residues. Accuracy of the models was confirmed by comparison with available crystal structures and by prediction of known resistance-related mutations. PR variants from clinical isolates were modeled in complex with six FDA-approved PIs, and changes in the binding energy (DeltaE(bind)) of mutant versus WT complexes were correlated with the ratios of phenotypic 50% inhibitory concentration (IC(50)) values. The calculated DeltaE(bind) of five PIs showed significant correlations (R(2) = 0.7-0.8) with IC(50) ratios from the Virco Antivirogram assay, and the DeltaE(bind) of six PIs showed good correlation (R(2) = 0.76-0.85) with IC(50) ratios from the Virologic PhenoSense assay. DeltaE(bind) cutoffs corresponding to a four-fold increase in IC(50) were used to define the structure-based phenotype as susceptible, resistant, or equivocal. Blind predictions for 78 PR variants gave overall agreement of 92% (kappa = 0.756) and 86% (kappa = 0.666) with PhenoSense and Antivirogram phenotypes, respectively. The structural phenotyping predicted drug resistance of clinical HIV-1 PR variants with an accuracy approaching that of frequently used cell-based phenotypic assays. PMID:12876320

Shenderovich, Mark D; Kagan, Ron M; Heseltine, Peter N R; Ramnarayan, Kal

2003-08-01

139

Structure-based phenotyping predicts HIV-1 protease inhibitor resistance  

PubMed Central

Mutations in HIV-1 drug targets lead to resistance and consequent therapeutic failure of antiretroviral drugs. Phenotypic resistance assays are time-consuming and costly, and genotypic rules-based interpretations may fail to predict the effects of multiple mutations. We have developed a computational procedure that rapidly evaluates changes in the binding energy of inhibitors to mutant HIV-1 PR variants. Models of WT complexes were produced from crystal structures. Mutant complexes were built by amino acid substitutions in the WT complexes with subsequent energy minimization of the ligand and PR binding site residues. Accuracy of the models was confirmed by comparison with available crystal structures and by prediction of known resistance-related mutations. PR variants from clinical isolates were modeled in complex with six FDA-approved PIs, and changes in the binding energy (?Ebind) of mutant versus WT complexes were correlated with the ratios of phenotypic 50% inhibitory concentration (IC50) values. The calculated ?Ebind of five PIs showed significant correlations (R2 = 0.7–0.8) with IC50 ratios from the Virco Antivirogram assay, and the ?Ebind of six PIs showed good correlation (R2 = 0.76–0.85) with IC50 ratios from the Virologic PhenoSense assay. ?Ebind cutoffs corresponding to a four-fold increase in IC50 were used to define the structure-based phenotype as susceptible, resistant, or equivocal. Blind predictions for 78 PR variants gave overall agreement of 92% (kappa = 0.756) and 86% (kappa = 0.666) with PhenoSense and Antivirogram phenotypes, respectively. The structural phenotyping predicted drug resistance of clinical HIV-1 PR variants with an accuracy approaching that of frequently used cell-based phenotypic assays. PMID:12876320

Shenderovich, Mark D.; Kagan, Ron M.; Heseltine, Peter N.R.; Ramnarayan, Kal

2003-01-01

140

Crystal structure prediction for cyclotrimethylene trinitramine (RDX) from first principles.  

PubMed

Crystal structure prediction and molecular dynamics methods were applied to the cyclotrimethylene trinitramine (RDX) crystal to explore the stability rankings of various polymorphs using a recently developed nonempirical potential energy function that describes the RDX dimer interactions. The energies of 500 high-density structures resulting from molecular packing were minimized and the 14 lowest-energy structures were subjected to isothermal-isostress molecular dynamics (NsT-MD) simulations. For both crystal structure prediction methods and molecular dynamics simulations, the lowest-energy polymorph corresponded to the experimental structure; furthermore, the lattice energy of this polymorph was lower than that of the other polymorphs by at least 1.1 kcal mol(-1). Crystal parameters and densities of the low-energy crystal produced by the NsT-MD simulations matched those of the experimental crystal to within 1% of density and cell edge lengths and 0.01 degrees of the cell angle. The arrangement of the molecules within the time-averaged unit cell were in equally outstanding agreement with experiment, with the largest deviation of the location of the molecular mass centers being less than 0.07 A and the largest deviation in molecular orientation being less than 2.8 degrees . NsT-MD simulations were also used to calculate crystallographic parameters as functions of temperature and pressure and the results were in a reasonable agreement with experiment. PMID:19551222

Podeszwa, Rafal; Rice, Betsy M; Szalewicz, Krzysztof

2009-07-14

141

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

142

Improved hybrid optimization algorithm for 3D protein structure prediction.  

PubMed

A new improved hybrid optimization algorithm - PGATS algorithm, which is based on toy off-lattice model, is presented for dealing with three-dimensional protein structure prediction problems. The algorithm combines the particle swarm optimization (PSO), genetic algorithm (GA), and tabu search (TS) algorithms. Otherwise, we also take some different improved strategies. The factor of stochastic disturbance is joined in the particle swarm optimization to improve the search ability; the operations of crossover and mutation that are in the genetic algorithm are changed to a kind of random liner method; at last tabu search algorithm is improved by appending a mutation operator. Through the combination of a variety of strategies and algorithms, the protein structure prediction (PSP) in a 3D off-lattice model is achieved. The PSP problem is an NP-hard problem, but the problem can be attributed to a global optimization problem of multi-extremum and multi-parameters. This is the theoretical principle of the hybrid optimization algorithm that is proposed in this paper. The algorithm combines local search and global search, which overcomes the shortcoming of a single algorithm, giving full play to the advantage of each algorithm. In the current universal standard sequences, Fibonacci sequences and real protein sequences are certified. Experiments show that the proposed new method outperforms single algorithms on the accuracy of calculating the protein sequence energy value, which is proved to be an effective way to predict the structure of proteins. PMID:25069136

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

2014-07-01

143

Evaluating predictive performance of network biomarkers with network structures.  

PubMed

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

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

2014-10-01

144

Graphlet Kernels for Prediction of Functional Residues in Protein Structures  

PubMed Central

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

Vacic, Vladimir; Iakoucheva, Lilia M.

2010-01-01

145

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

146

Predicted structure model of Bungarotoxin from Bungarus fasciatus snake  

PubMed Central

Snake venoms are cocktails comprising combinations of different proteins, peptides, enzymes and toxins. Snake toxins have diverse characteristics having different molecular configuration, structure and mode of action. Many toxins derived from snake venom have distinct pharmacological activities. Venom from Bungarus fasciatus (commonly known as banded krait) is a species of elapid snake found on the South East Asia and Indian sub-continent, mainly contains neurotoxins. Beta bungartotoxin is the major fraction of Bungarus venom and particularly act pre-synaptically by obstructing neurotransmitter release. This toxin in other snake species functionally forms a heterodimer containing two different subunits (A and B). Dimerization of these two chains is a pre-requisite for the proper functionality of this protein. However, B. fasciatus bungartotoxin contains only B chain and their structural orientation in yet to be resolved. Therefore, it is of interest to describe the predicted structure model of the toxin for functional insights. In this work we analyzed the neurotoxic nature, their alignments, secondary and three dimensional structures, functions, active sites and stability with the help of different bioinformatical tools. A comprehensive analysis of the predicted model provides approaching to the functional interpretation of its molecular action. PMID:25489170

Roly, Zahida Yesmin; Hasan, SM Naimul; Ferdaus, KMKB; Reza, Md Abu

2014-01-01

147

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

148

Structural Bioinformatics Prediction of Membrane-Binding Proteins  

PubMed Central

Summary Membrane-binding peripheral proteins play important roles in many biological processes, including cell signaling and membrane trafficking. Unlike integral membrane proteins, these proteins bind the membrane mostly in a reversible manner. Since peripheral proteins do not have canonical transmembrane segments, it is difficult to identify them from their amino acid sequences. As a first step toward genome-scale identification of membrane-binding peripheral proteins, we built a kernel-based machine learning protocol. Key features of known membrane-binding proteins, including electrostatic properties and amino acid composition, were calculated from their amino acid sequences and tertiary structures, which were then incorporated into the support vector machine to perform the classification. A data set of 40 membrane-binding proteins and 230 non-membrane-binding ones was used to construct and validate the protocol. Cross-validation and holdout evaluation of the protocol showed that the accuracy of the prediction reached up to 93.7% and 91.6%, respectively. The protocol was applied to the prediction of membrane binding properties of four C2 domains from novel protein kinases C. Although these C2 domains have 50% sequence identity, only one of them was predicted to bind the membrane, which was verified experimentally with surface plasmon resonance analysis. These results suggest that our protocol can be used for predicting membrane-binding properties of a wide variety of modular domains and may be further extended to genome-scale identification of membrane-binding peripheral proteins. PMID:16626739

Bhardwaj, Nitin; Stahelin, Robert V.; Langlois, Robert E.; Cho, Wonhwa; Lu, Hui

2009-01-01

149

Incorporating secondary structural features into sequence information for predicting protein structural class.  

PubMed

Knowledge of structural classes is applied in numerous important predictive tasks that address structural and functional features of proteins, although the prediction accuracy of the protein structural classes is not high. In this study, 45 different features were rationally designed to model the differences between protein structural classes, among which, 30 of them reflect the combined protein sequence information. In terms of correlation function, the protein sequence can be converted to a digital signal sequence, from which we can generate 20 discrete Fourier spectrum numbers. According to the segments of amino with different characteristics occurring in protein sequences, the frequencies of the 10 kinds of segments of amino acid (motifs) in protein are calculated. Other features include the secondary structural information :10 features were proposed to model the strong adjacent correlations in the secondary structural elements and capture the long-range spatial interactions between secondary structures, other 5 features were designed to differentiate ?/? from ?+? classes , which is a major problem of the existing algorithm. The methods were proposed based on a large set of low-identity sequences for which secondary structure is predicted from their sequence (based on PSI-PRED). By means of this method, the overall prediction accuracy of four benchmark datasets were all improved. Especially for the dataset FC699, 25PDB and D1189 which are 1.26%, 1% and 0.85% higher than the best previous method respectively. PMID:23688152

Liao, Bo; Peng, Ting; Chen, Haowen; Lin, Yaping

2013-10-01

150

Structural syntactic prediction measured with ELAN: evidence from ERPs.  

PubMed

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

Fonteneau, Elisabeth

2013-02-01

151

Tailor-made force fields for crystal-structure prediction.  

PubMed

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

Neumann, Marcus A

2008-08-14

152

Structure prediction and targeted synthesis: a new NanN2 diazenide crystalline structure  

SciTech Connect

Significant progress in theoretical and computational techniques for predicting stable crystal structures has recently begun to stimulate targeted synthesis of such predicted structures. Using a global space-group optimization (GSGO) approach that locates ground-statestructures and stable stoichiometries from first-principles energy functionals by objectively starting from randomly selected lattice vectors and random atomic positions, we predict the first alkali diazenide compound Na{sub n} N{sub 2} , manifesting homopolar N–N bonds. The previously predicted Na{sub 3} N structure manifests only heteropolar Na–N bonds and has positive formation enthalpy. It was calculated based on local Hartree–Fock relaxation of a fixed-structure type (Li{sub 3} P -type) found by searching an electrostatic point-ion model. Synthesis attempts of this positive ?H compound using activated nitrogen yielded another structure (anti-ReO{sub 3} -type). The currently predicted (negative formation enthalpy) diazenide Na{sub 2} N{sub 2} completes the series of previously known BaN{sub 2} and SrN{sub 2} diazenides where the metal sublattice transfers charge into the empty N{sub 2} ?{sub g} orbital. This points to a new class of alkali nitrides with fundamentally different bonding, i.e., homopolar rather than heteropolar bonds and, at the same time, illustrates some of the crucial subtleties and pitfalls involved in structure predictions versus planned synthesis. Attempts at synthesis of the stable Na{sub 2} N{sub 2} predicted here will be interesting.

Zhang, Xiuwen; Zunger, Alex; Trimarchi, Giancarlo

2010-01-01

153

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

154

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. PMID:23413988

Guerler, Aysam; Govindarajoo, Brandon; Zhang, Yang

2014-01-01

155

Effects of scale in predicting global structural response  

NASA Technical Reports Server (NTRS)

In the course of previous composite structures test programs, the need for and the feasibility of developing analyses for scale-up effects has been demonstrated. The analysis techniques for scale-up effects fall into two categories. The first category pertains to developing analysis methods independently for a single, unique failure mode in composites, and using this compendium of analysis methods together with a global structural model to identify and predict the response and failure mode of full-scale built-up structures. The second category of scale-up effects pertains to similitude in structural validation testing. In this latter category, dimensional analysis is used to develop scale-up laws that enable extrapolation of sub-scale component test data to full-scale structures. This viewgraph presentation describes the approach taken and some developments accomplished in the first category of analysis for scale-up effects. Layup dependence of composite material properties severely limits the use of the dimensional analysis approach and these limitations are illustrated by examples.

Kan, Han-Pin; Deo, R. B.

1994-01-01

156

PREDICTING EXPLOSIBILITY PROPERTIES OF CHEMICALS FROM QUANTITATIVE STRUCTURE-PROPERTY RELATIONSHIPS  

E-print Network

PREDICTING EXPLOSIBILITY PROPERTIES OF CHEMICALS FROM QUANTITATIVE STRUCTURE-PROPERTY RELATIONSHIPS to predict physico- chemical properties is a growing interest. In this context, an original approach associating QSPR methods and quantum chemical calculations for the prediction of chemicals explosibility

Paris-Sud XI, Université de

157

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

158

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

159

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

160

The systematic structure and predictability of urban business diversity  

E-print Network

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

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

2014-01-01

161

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

162

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

PubMed

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

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

2012-04-01

163

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

164

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

165

Triple recognition of B-DNA Bert Willis, Dev P. Arya *  

E-print Network

provide us with the ability to regulate cellular events. For example, a better under- standing of DNATriple recognition of B-DNA Bert Willis, Dev P. Arya * Laboratory of Medicinal Chemistry: Received 11 July 2009 Accepted 14 July 2009 Available online 19 July 2009 Keywords: Neomycin DNA

Stuart, Steven J.

166

Generic eukaryotic core promoter prediction using structural features of DNA  

PubMed Central

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

2008-01-01

167

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

PubMed Central

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

2014-01-01

168

Structure-Based Predictive model for Coal Char Combustion.  

SciTech Connect

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

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

1997-09-24

169

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

170

Two-stage support vector machines for protein structure and solvent prediction.  

E-print Network

??We propose Two-Stage Support Vector Machines (TSSVM) for the prediction of structural properties of amino acid residues, namely, relative solvent accessibilities and protein secondary structure… (more)

Nguyen, Ngoc Minh.

2008-01-01

171

Protein structure prediction with local adjust tabu search algorithm  

PubMed Central

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

2014-01-01

172

An Assessment of Lattice Energy Minimization for the Prediction of Molecular Organic Crystal Structures  

E-print Network

in the predicted lists are used to determine the efficacy of lattice energy minimization in crystal structure Computational methods of predicting the most likely crystal structures of a molecule from its atomic con in the pro- cessing cycle of a pharmaceutical molecule. The field of crystal structure prediction (CSP) has

de Gispert, Adrià

173

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

E-print Network

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

Das, Rhiju

174

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

NASA Technical Reports Server (NTRS)

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

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

1995-01-01

175

Lifetime Reliability Prediction of Ceramic Structures Under Transient Thermomechanical Loads  

NASA Technical Reports Server (NTRS)

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

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

2005-01-01

176

Optimizing Non-Decomposable Loss Functions in Structured Prediction  

PubMed Central

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

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

2012-01-01

177

Simple neural substrate predicts complex rhythmic structure in duetting birds  

NASA Astrophysics Data System (ADS)

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

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

2005-09-01

178

Protein–DNA binding specificity predictions with structural models  

PubMed Central

Protein–DNA interactions play a central role in transcriptional regulation and other biological processes. Investigating the mechanism of binding affinity and specificity in protein–DNA complexes is thus an important goal. Here we develop a simple physical energy function, which uses electrostatics, solvation, hydrogen bonds and atom-packing terms to model direct readout and sequence-specific DNA conformational energy to model indirect readout of DNA sequence by the bound protein. The predictive capability of the model is tested against another model based only on the knowledge of the consensus sequence and the number of contacts between amino acids and DNA bases. Both models are used to carry out predictions of protein–DNA binding affinities which are then compared with experimental measurements. The nearly additive nature of protein–DNA interaction energies in our model allows us to construct position-specific weight matrices by computing base pair probabilities independently for each position in the binding site. Our approach is less data intensive than knowledge-based models of protein–DNA interactions, and is not limited to any specific family of transcription factors. However, native structures of protein–DNA complexes or their close homologs are required as input to the model. Use of homology modeling can significantly increase the extent of our approach, making it a useful tool for studying regulatory pathways in many organisms and cell types. PMID:16246914

Morozov, Alexandre V.; Havranek, James J.; Baker, David; Siggia, Eric D.

2005-01-01

179

Engineering Property Prediction Tools for Tailored Polymer Composite Structures  

SciTech Connect

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

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

2009-12-23

180

PRT-HMM: A Novel Hidden Markov Model for Protein Secondary Structure Prediction  

Microsoft Academic Search

Protein secondary structure prediction is one of the most important and challenging problems in structural bioinformatics, which has been an essential task in determining the structure and function of the proteins. Despite significant progress made in recent years, protein structure prediction maintains its status as one of the prime unsolved problems in computational biology. A novel probability revise table based

Wang Ding; Dongbo Dai; Jiang Xie; Huiran Zhang; Wu Zhang; Hao Xie

2012-01-01

181

De Novo Prediction of Three-dimensional Structures for Major Protein Families  

Microsoft Academic Search

We use the Rosetta de novo structure prediction method to produce three-dimensional structure models for all Pfam-A sequence families with average length under 150 residues and no link to any protein of known structure. To estimate the reliability of the predictions, the method was calibrated on 131 proteins of known structure. For approximately 60% of the proteins one of the

Richard Bonneau; Charlie E. M. Strauss; Carol A. Rohl; Dylan Chivian; Phillip Bradley; Lars Malmström; Tim Robertson; David Baker

2002-01-01

182

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

Microsoft Academic Search

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

Florencio Pazos; Michael J. E. Sternberg

2004-01-01

183

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

PubMed

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

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

2015-01-01

184

Structure-Based Predictive model for Coal Char Combustion.  

SciTech Connect

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

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

1997-06-25

185

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

PubMed Central

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

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

2014-01-01

186

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

PubMed

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

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

2014-10-29

187

Arby: automatic protein structure prediction using profile-profile alignment and confidence measures  

Microsoft Academic Search

Motivation: Arby is a new server for protein structure prediction that combines several homology-based methods for predicting the three-dimensional structure of a protein, given its sequence.The methods used include a threading approach, which makes use of structural information, and a profile-profile alignment approach that incorporates secondary structure predictions. The combination of the different methods with the help of empirically derived

Niklas Von Öhsen; Ingolf Sommer; Ralf Zimmer; Thomas Lengauer

2004-01-01

188

Rosetta in CASP4: Progress in ab initio protein structure prediction  

Microsoft Academic Search

Rosetta ab initio protein structure predictions in CASP4 were considerably more con- sistent and more accurate than previous ab initio structure predictions. Large segments were cor- rectly predicted (>50 residues superimposed within an RMSD of 6.5 Å) for 16 of the 21 domains under 300 residues for which models were submitted. Models with the global fold largely correct were produced

Richard Bonneau; Jerry Tsai; Ingo Ruczinski; Dylan Chivian; Carol Rohl; Charlie E. M. Strauss; David Baker

2001-01-01

189

Beyond the Isotropic Atom Model in Crystal Structure Prediction of Rigid Molecules: Atomic Multipoles versus  

E-print Network

Beyond the Isotropic Atom Model in Crystal Structure Prediction of Rigid Molecules: Atomic ABSTRACT: The lattice energies of predicted and known crystal structures for 50 small organic molecules is described by atom-centered multipoles. In comparison to previous predictions using atomic point charge

de Gispert, Adrià

190

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

191

Failure prediction of thin beryllium sheets used in spacecraft structures  

NASA Astrophysics Data System (ADS)

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.

1991-12-01

192

A comparison study on feature selection of DNA structural properties for promoter prediction  

PubMed Central

Background Promoter prediction is an integrant step for understanding gene regulation and annotating genomes. Traditional promoter analysis is mainly based on sequence compositional features. Recently, many kinds of structural features have been employed in promoter prediction. However, considering the high-dimensionality and overfitting problems, it is unfeasible to utilize all available features for promoter prediction. Thus it is necessary to choose some appropriate features for the prediction task. Results This paper conducts an extensive comparison study on feature selection of DNA structural properties for promoter prediction. Firstly, to examine whether promoters possess some special structures, we carry out a systematical comparison among the profiles of thirteen structural features on promoter and non-promoter sequences. Secondly, we investigate the correlations between these structural features and promoter sequences. Thirdly, both filter and wrapper methods are utilized to select appropriate feature subsets from thirteen different kinds of structural features for promoter prediction, and the predictive power of the selected feature subsets is evaluated. Finally, we compare the prediction performance of the feature subsets selected in this paper with nine existing promoter prediction approaches. Conclusions Experimental results show that the structural features are differentially correlated to promoters. Specifically, DNA-bending stiffness, DNA denaturation and energy-related features are highly correlated with promoters. The predictive power for promoter sequences differentiates greatly among different structural features. Selecting the relevant features can significantly improve the accuracy of promoter prediction. PMID:22226192

2012-01-01

193

nature | methods Predicting free energy changes using structural ensembles  

E-print Network

new monomeric insulins obtained by alanine scanning the dimer­ forming surface of the insulin molecule structures Crystal structures were taken as input structures. In some cases, the input structures were

Caflisch, Amedeo

194

Space group selection for crystal structure prediction of solvates{ Aurora J. Cruz Cabeza,a  

E-print Network

Space group selection for crystal structure prediction of solvates{ Aurora J. Cruz Cabeza,a Elna with common solvents are presented to assist crystal structure prediction calculations on these complex systems. Introduction Many programs for the computer generation of crystal structures (e.g. UPACK,1

de Gispert, Adrià

195

Realizing Predicted Crystal Structures at Extreme Conditions: The Low-Temperature and High-Pressure  

E-print Network

Realizing Predicted Crystal Structures at Extreme Conditions: The Low-Temperature and High-Pressure Crystal Structures of 2-Chlorophenol and 4-Fluorophenol Iain D. H. Oswald, David R. Allan, Graeme M. Day but a small group at high pressure. We show that Crystal Structure Prediction methodologies reproduce all four

de Gispert, Adrià

196

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

Microsoft Academic Search

Orphan proteins are characterized by the lack of significant sequence similarity to database proteins. To infer the functional properties of the orphans, more elaborate techniques that utilize structural information are required. In this regard, the protein structure prediction gains considerable importance. Secondary structure prediction algorithms designed for orphan proteins (also known as single-sequence algorithms) cannot utilize multiple alignments or alignment

Zafer Aydin; Yucel Altunbasak; Isa Kemal Pakatci; Hakan Erdogan

2007-01-01

197

Training Set Reduction Methods for Single Sequence Protein Secondary Structure Prediction  

Microsoft Academic Search

Orphan proteins are characterized by the lack of significant sequence similarity to almost all proteins in the database. To infer the functional properties of the orphans, more elaborate techniques that utilize structural information are required. In this regard, the protein structure prediction gains considerable importance. Secondary structure prediction algorithms designed for orphan proteins (also known as single-sequence algorithms) cannot utilize

I. K. Pakatci; Z. Aydin; H. Erdogan; Y. Altunbasak

2007-01-01

198

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

E-print Network

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

199

A Web-Accessible Protein Structure Prediction Pipeline Michael S. Lee  

E-print Network

A Web-Accessible Protein Structure Prediction Pipeline Michael S. Lee Biotechnology HPC Software-throughput protein structure prediction pipeline (dubbed "PSPP"), which given input protein sequences infers their 3D atomic structures. The pipeline was designed to be used with high performance computing clusters

200

Prediction of Structures and Properties for Organic Superconductors  

NASA Astrophysics Data System (ADS)

The main contributions of this thesis to the field of organic superconductors are basically (a) the band structure calculations for the investigations of the conduction properties of kappa-(BEDT-TTF)_2Cu(NCS)_2 using 2-D Hubbard Model with Unrestricted Hartree -Fock (UHF) theory, (b) ab initio quantum mechanical calculations for the structural characterizations and the properties of the donors of the organic superconductors, (c) electron -transfer boat-vibration (ET-BV) mechanism for the superconductivity of these materials, (d) developing force fields for BEDT -TTF and BEDT-TTF^+.. To provide a basis for understanding the puzzling electronic properties of the organic superconductor kappa-(BEDT-TTF)_2Cu(NCS)_2 (with T_{c} = 10.4 K), we carried out band calculations using the 2-D Hubbard Model with Unrestricted Hartree-Fock (UHF) theory. The calculations lead to a two-band semi-metal with a momentum gap separating the electron and the hole bands. The anomalous experimental observations are explained in terms of BEDT -TTF related phonons coupling these two bands (lower temperature) and by anion related phonons (higher temperature). The donors of all known one- or two-dimensional organic superconductors, X, are based on a core organic molecule that is either tetrathiafulvalene (denoted as TTF) or tetraselenafulvalene (denoted as TSeF) or some mixture of these two molecules. Coupling X, with appropriate acceptors, Y, leads to superconductivity. The oxidized form of X may be X^+ or X _sp{2}{+}^ecies in the crystal. Using ab initio Hartree-Fock (HF) calculations (6-31G** basis set), we find that all known organic superconductors involve an X that deforms to a boat structure while X ^+ is planar. This leads to a coupling between charge transfer and the boat deformation phonon modes. We propose that this electron-phonon coupling is responsible for the superconductivity and predict the isotope shifts (delta T_{c}) for experimental tests of the electron-transfer boat-vibration (ET-BV) mechanism. We suggest that new higher temperature organic donors can be sought by finding modifications that change the frequency and stability of this boat distortion mode. Based on this idea we have developed similar organic donors having the same properties and have suggested that with appropriate electron acceptors they will also lead to superconductivity.

Demiralp, Ersan

201

STRUCTURE-BASED PREDICTIVE MODEL FOR COAL CHAR COMBUSTION  

SciTech Connect

Progress was made this period on a number of tasks. A significant advance was made in the incorporation of macrostructural ideas into high temperature combustion models. Work at OSU by R. Essenhigh in collaboration with the University of Stuttgart has led to a theory that the zone I / II transition in char combustion lies within the range of conditions of interest for pulverized char combustion. The group has presented evidence that some combustion data, previously interpreted with zone II models, in fact takes place in the transition from zone II to zone 1. This idea was used at Brown to make modifications to the CBK model (a char kinetics package specially designed for carbon burnout prediction, currently used by a number of research and furnace modeling groups in academia and industry). The resulting new model version, CBK8, shows improved ability to predict extinction behavior in the late stages of combustion, especially for particles with low ash content. The full development and release of CBK8, along with detailed descriptions of the role of the zone 1/2 transition will be reported on in subsequent reports. ABB-CE is currently implementing CBK7 into a special version of the CFD code Fluent for use in the modeling and design of their boilers. They have been appraised of the development, and have expressed interest in incorporating the new feature, realizing full CBK8 capabilities into their combustion codes. The computational chemistry task at OSU continued to study oxidative pathways for PAH, with emphasis this period on heteroatom containing ring compounds. Preliminary XPS studies were also carried out. Combustion experiments were also carried out at OSU this period, leading to the acquisition of samples at various residence times and the measurement of their oxidation reactivity by nonisothermal TGA techniques. Several members of the project team attended the Carbon Conference this period and made contacts with representatives from the new FETC Consortium for Premium Carbon Products from Coal. Possibilities for interactions with this new center will be explored. Also this period, an invited review paper was prepared for the 27th International Symposium on Combustion, to be held in Boulder, Colorado in August. The paper is entitled; "Structure, Properties, and Reactivity of Solid Fuels," and reports on a number of advances made in this collaborative project.

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

1998-09-11

202

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  

NASA Astrophysics Data System (ADS)

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 biomolecule B-DNA are then studied as examples, where the model systems have been constructed extracting from the perfect or a 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, di-Thy) and a surrounding physical/chemical environment of interest (e.g. backbone, adjacent stacking nucleobases) and examine how the excitation properties of the core vary in response to the environment. We find that the orbitals contributed from DNA backbone and surrounding nucleobases often participate in a transition of Thy-related excitations affecting their composition, absorption energy, and oscillator strength. 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 in cells may from time to time exhibit very different photo-induced physical and/or chemical processes.

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

203

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

204

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...

205

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

EPA Science Inventory

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

206

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...

207

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

E-print Network

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 ...

Mag Washietl, Stefan

208

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. PMID:6878038

Rio, P; Leng, M

1983-01-01

209

Protein secondary structure prediction for a single-sequence using hidden semi-Markov models  

Microsoft Academic Search

Background: The accuracy of protein secondary structure prediction has been improving steadily towards the 88% estimated theoretical limit. There are two types of prediction algorithms: Single- sequence prediction algorithms imply that information about other (homologous) proteins is not available, while algorithms of the second type imply that information about homologous proteins is available, and use it intensively. The single-sequence algorithms

Zafer Aydin; Yucel Altunbasak; Mark Borodovsky

2006-01-01

210

Prediction of Peptide Ion Collision Cross Sections from Topological Molecular Structure and Amino  

E-print Network

Prediction of Peptide Ion Collision Cross Sections from Topological Molecular Structure and Amino-property relationships (QSPRs) have been developed to predict the ion mobility spec- trometry (IMS) collision cross to accurately predict the collision cross section. The models were built using multiple linear regression (MLR

Clemmer, David E.

211

Incomplete gene structure prediction with almost 100% specificity  

E-print Network

Bank. The proposed gene prediction algorithm is a similarity based algorithm which capitalizes on the fact that similar sequences bear similar functions. The proposed algorithm, like most other similarity based algorithms, is based on dynamic programming. Given a...

Chin, See Loong

2004-09-30

212

Using molecular structure for reliable predicting enthalpy of melting of nitroaromatic energetic compounds.  

PubMed

In this work, a reliable simple method has been introduced for predicting enthalpy of melting of nitroaromatic energetic compounds through their molecular structures. This method can be used for a wide range of nitroaromatics including halogenated nitroaromatic compounds. The contribution of hydrogen bonding and polar groups as well as structural parameters can be used to improve the predicted values on the basis of the number of carbon, nitrogen and oxygen atoms. The predicted results show that this method gives reliable prediction of standard enthalpy of melting with respect to the best available methods for different nitroaromatic compounds including high explosives with complex molecular structures. PMID:20117881

Semnani, Abolfazl; Keshavarz, Mohammad Hossein

2010-06-15

213

Database guided conformation selection in crystal structure prediction of Timothy G. Cooper,a  

E-print Network

Database guided conformation selection in crystal structure prediction of alanine Timothy G. Cooper March 2007 First published as an Advance Article on the web 4th April 2007 DOI: 10.1039/b702136d Crystal structure prediction calculations have been performed for the a-amino acid alanine with the intention

de Gispert, Adrià

214

APPLICATION OF THE STRIP-YIELD CRACK CLOSURE MODEL TO CRACK GROWTH PREDICTIONS FOR STRUCTURAL STEEL  

Microsoft Academic Search

Though the strip yield (SY) type models for crack growth predictions are currently a widely used tool to simulate fatigue crack growth in aircraft materials, their adequacy for structural steel remains unknown. In this paper, the SY model ability to simulate crack growth observed in fatigue tests on a structural steel is explored. It is shown first that the predictions

M. Skorupa; T. Machniewicz; A. Skorupa; S. Beretta; M. Carboni

215

GeneSeqer@PlantGDB: gene structure prediction in plant genomes  

E-print Network

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

Brendel, Volker

216

CAFASP2: The second critical assessment of fully automated structure prediction methods  

Microsoft Academic Search

The results of the second Critical Assessment of Fully Automated Structure Predic- tion (CAFASP2) are presented. The goals of CAFASP are to (i) assess the performance of fully automatic web servers for structure prediction, by using the same blind prediction targets as those used at CASP4, (ii) inform the community of users about the capabilities of the servers, (iii) allow

Daniel Fischer; Arne Elofsson; Leszek Rychlewski; Florencio Pazos; Alfonso Valencia; Burkhard Rost; Angel R. Ortiz; Roland L. Dunbrack

2001-01-01

217

PCI-SS: MISO dynamic nonlinear protein secondary structure prediction  

Microsoft Academic Search

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

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

2009-01-01

218

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. PMID:25050407

Yonge, Feng

2014-01-01

219

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

PubMed Central

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

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

2013-01-01

220

Automated Detection of Eruptive Structures for Solar Eruption Prediction  

NASA Astrophysics Data System (ADS)

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

Georgoulis, Manolis K.

2012-07-01

221

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

222

Polymorphism of Scyllo-Inositol: Joining Crystal Structure Prediction with Experiment to Elucidate the Structures of Two  

E-print Network

Polymorphism of Scyllo-Inositol: Joining Crystal Structure Prediction with Experiment to Elucidate, 2006; ReVised Manuscript ReceiVed July 19, 2006 ABSTRACT: We report on the crystal structures of two in parallel with the crystallization experiments. When a single crystal was finally grown, its structure

de Gispert, Adrià

223

Predicting Gene Structures from Multiple RT-PCR Tests  

NASA Astrophysics Data System (ADS)

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

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

224

A Method for WD40 Repeat Detection and Secondary Structure Prediction  

PubMed Central

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

225

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

PubMed

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

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

2013-11-01

226

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...

227

Predicting Emergency Evacuation and Sheltering Behavior: A Structured Analytical Approach  

Microsoft Academic Search

We offer a general approach to predicting public compliance with emergency recommenda- tions. It begins with a formal risk assessment of an anticipated emergency, whose parame- ters include factors potentially affecting and affected by behavior, as identified by social sci- ence research. Standard procedures are used to elicit scientific experts' judgments regarding these behaviors and dependencies, in the context of

Matt Dombroski; Baruch Fischhoff; Paul Fischbeck

2006-01-01

228

Predicting total clearance in humans from chemical structure.  

PubMed

A conceptually simple, fully in silico model to predict total clearance of new compounds in humans is described. Based on the premise that similar molecules will exhibit similar pharmacokinetic properties, we used a k-nearest-neighbors (kNN) technique to predict total clearance by comparison with known reference agents. Molecular similarity was defined using readily calculated one- and two-dimensional molecular descriptors, and the reference set was obtained by combining the Obach and Berellini sets of human pharmacokinetic data. Neutral molecules and drugs whose biological activity is associated with a metal center were removed from the combined set. The remaining 462 compounds were partitioned into a training and external test set of 370 and 92 compounds, respectively. For acids, bases, zwitterions, and quaternary ammonium/pyridinium ions, average prediction accuracy was within two-fold of observed for the external test set (n = 92). Using a collection of 20 drugs from the literature with > or =3 preclinical animal species allometric scaling data, accuracy of the in silico kNN model was not as good as the rule of exponents, but better than simple allometry (SA), and approached that of combination multiexponential allometry (ME) as defined by the number of predictions with < or =50% error. For a collection of 18 drugs with two species (rat-dog) data, the kNN model outperformed both SA and combination ME using the same performance standard. Since the model is fully in silico and, therefore, capable of generating total clearance predictions in the absence of any experimental data, it can be used to help guide early drug discovery research efforts, such as virtual compound library screening, and analogue prioritization prior to chemical synthesis and biological evaluation. Model validation was accomplished using the external test set, three- and five-fold cross-validation and two different y-randomization techniques (y-shuffling and random number pseudodescriptors). PMID:20617831

Yu, Melvin J

2010-07-26

229

A set of nearest neighbor parameters for predicting the enthalpy change of RNA secondary structure formation  

PubMed Central

A complete set of nearest neighbor parameters to predict the enthalpy change of RNA secondary structure formation was derived. These parameters can be used with available free energy nearest neighbor parameters to extend the secondary structure prediction of RNA sequences to temperatures other than 37°C. The parameters were tested by predicting the secondary structures of sequences with known secondary structure that are from organisms with known optimal growth temperatures. Compared with the previous set of enthalpy nearest neighbor parameters, the sensitivity of base pair prediction improved from 65.2 to 68.9% at optimal growth temperatures ranging from 10 to 60°C. Base pair probabilities were predicted with a partition function and the positive predictive value of structure prediction is 90.4% when considering the base pairs in the lowest free energy structure with pairing probability of 0.99 or above. Moreover, a strong correlation is found between the predicted melting temperatures of RNA sequences and the optimal growth temperatures of the host organism. This indicates that organisms that live at higher temperatures have evolved RNA sequences with higher melting temperatures. PMID:16982646

Lu, Zhi John; Turner, Douglas H.; Mathews, David H.

2006-01-01

230

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

231

Bayesian Model of Protein Primary Sequence for Secondary Structure Prediction  

E-print Network

into a protein's function in the cell. Understanding a protein's secondary structure is a first step towards of residues on the secondary structure determination, including those packed close in space but distant sequences relatively cheap, accurate and fast, in comparison to the costly and involved approaches

Dahl, David B.

232

Bayesian Model of Protein Primary Sequence for Secondary Structure Prediction  

E-print Network

into a protein's function in the cell. Understanding a protein's secondary structure is a first step towards influence of residues on the secondary structure determination, including those packed close in space made obtaining protein sequences relatively cheap, accurate and fast, in comparison to the costly

Vannucci, Marina

233

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

234

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. PMID:21788211

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

2011-01-01

235

Prediction of protein structural classes for low-similarity sequences using reduced PSSM and position-based secondary structural features.  

PubMed

Many efficient methods have been proposed to advance protein structural class prediction, but there are still some challenges where additional insight or technology is needed for low-similarity sequences. In this work, we schemed out a new prediction method for low-similarity datasets using reduced PSSM and position-based secondary structural features. We evaluated the proposed method with four experiments and compared it with the available competing prediction methods. The results indicate that the proposed method achieved the best performance among the evaluated methods, with overall accuracy 3-5% higher than the existing best-performing method. This paper also found that the reduced alphabets with size 13 simplify PSSM structures efficiently while reserving its maximal information. This understanding can be used to design more powerful prediction methods for protein structural class. PMID:25445293

Wang, Junru; Wang, Cong; Cao, Jiajia; Liu, Xiaoqing; Yao, Yuhua; Dai, Qi

2015-01-10

236

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

237

Structure-Based Predictive Models for Allosteric Hot Spots  

Microsoft Academic Search

In allostery, a binding event at one site in a protein modulates the behavior of a distant site. Identifying residues that relay the signal between sites remains a challenge. We have developed predictive models using support-vector machines, a widely used machine-learning method. The training data set consisted of residues classified as either hotspots or non-hotspots based on experimental characterization of

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

2009-01-01

238

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

NASA Astrophysics Data System (ADS)

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

Suzuki, Tomonori; Miyazaki, Satoru

2011-01-01

239

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

240

Prediction of gene expression in embryonic structures of Drosophila melanogaster.  

PubMed

Understanding how sets of genes are coordinately regulated in space and time to generate the diversity of cell types that characterise complex metazoans is a major challenge in modern biology. The use of high-throughput approaches, such as large-scale in situ hybridisation and genome-wide expression profiling via DNA microarrays, is beginning to provide insights into the complexities of development. However, in many organisms the collection and annotation of comprehensive in situ localisation data is a difficult and time-consuming task. Here, we present a widely applicable computational approach, integrating developmental time-course microarray data with annotated in situ hybridisation studies, that facilitates the de novo prediction of tissue-specific expression for genes that have no in vivo gene expression localisation data available. Using a classification approach, trained with data from microarray and in situ hybridisation studies of gene expression during Drosophila embryonic development, we made a set of predictions on the tissue-specific expression of Drosophila genes that have not been systematically characterised by in situ hybridisation experiments. The reliability of our predictions is confirmed by literature-derived annotations in FlyBase, by overrepresentation of Gene Ontology biological process annotations, and, in a selected set, by detailed gene-specific studies from the literature. Our novel organism-independent method will be of considerable utility in enriching the annotation of gene function and expression in complex multicellular organisms. PMID:17658945

Samsonova, Anastasia A; Niranjan, Mahesan; Russell, Steven; Brazma, Alvis

2007-07-01

241

Practical theories for service life prediction of critical aerospace structural components  

NASA Technical Reports Server (NTRS)

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

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

1992-01-01

242

Computational prediction of coiled-coil interaction structure specificity  

E-print Network

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

Gutwin, Karl N. (Karl Nickolai)

2009-01-01

243

The application of transfer functions in the prediction of structural response to blast induced ground vibration  

SciTech Connect

This paper outlines the theory behind transfer functions and illustrates their application in the prediction of structural response to blast induced ground vibration. The ability to predict the response of a structure to a recorded ground vibration is desirable for a number of reasons: structural or cosmetic damage often occurs, or is alleged to have occurred, away from the location at which vibration has been monitored, and humans or sensitive equipment likely to be affected by vibration are also often some distance from the monitoring location. Existing techniques used for structural response prediction include: (1) PPV ratio/amplification -- This approach is overly simplistic and only predicts the PPV and not the whole vibration response, (2) Lumped Mass Models -- These techniques assume structures act as lumped mass systems and also require a good deal of expertise to produce the full vibration response. The technique of transfer functions overcomes these problems by measuring the actual response of each structural monitoring point relative to a fixed location outside of the structure. The application of this technique to structural response prediction is illustrated with three case studies associated with vibrations from surface mine blasting: A simple shaking table model; soil/foundation response; and typical U.K. domestic house. These case studies are used to illustrate how transfer functions are calculated and their limitations in use by comparing actual and predicted responses.

Farnfield, R.A. [Univ. of Leeds (United Kingdom). Dept. of Mining and Mineral Engineering

1994-12-31

244

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

E-print Network

materials structures such as wind turbine blades. Design methodologies to prevent such failures have1 PREDICTION OF DELAM INATION IN WIND TURBINE BLADE STRUCTURAL DETAILS John F. Mandell, Douglas S static and fatigue loading. INTRODUCTION Composite material structures such as wind turbine blades

245

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

246

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

PubMed Central

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

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

2002-01-01

247

Learning models for aligning protein sequences with predicted secondary structure  

E-print Network

that may be in the structural core, and are employed by CLUSTAL W [34], T-Coffee [28], and MUSCLE [10]; (b in the alignment that have high support, and is employed by T-Coffee, MAFFT, ProbCons, SPEM [39], PROMALS [29 the input residues with profiles of amino acid exchanges from closely related sequences found through

Kececioglu, John

248

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

249

Predicting protein-protein interactions from primary structure  

Microsoft Academic Search

Motivation: An ambitious goal of proteomics is to eluci- date the structure, interactions and functions of all proteins within cells and organisms. The expectation is that this will provide a fuller appreciation of cellular processes and net- works at the protein level, ultimately leading to a better un- derstanding of disease mechanisms and suggesting new means for intervention. This paper

Joel R. Bock; David A. Gough

2001-01-01

250

Dispersal differences predict population genetic structure in Mormon crickets  

Microsoft Academic Search

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 beha- viour, affect population genetic structure of organisms that were subdivided during the Pleistocene. Mormon crickets exist in solitary and gregarious 'phases',

NATHAN W. B AILEY; DARRYL T. G WYNNE; MICHAEL G. R ITCHIE

2007-01-01

251

Deleterious mutation prediction in the secondary structure of RNAs  

E-print Network

transcription termination in the thiamin pyrophosphate and S- adenosyl-methionine induced riboswitches. Ribo- switches are mRNA structures that have recently been found to regulate transcription termination or translation initiation in bacteria by conformation rearrangement in response to direct metabolite binding

Barash, Danny

252

Structure of Jupiter's upper atmosphere: Predictions for Galileo  

Microsoft Academic Search

The Voyager mission of the outer solar system discovered that the thermospheres of all the giant planets are remarkably hot. To date, no convincing explanation for this phenomenon has been offered; however, there are a number of recent observational results which provide new information on the thermal structure of Jupiter's upper atmosphere that bear on his outstanding problem. We present

Roger V. Yelle; Leslie A. Young; Ronald J. Vervack; Richard Young; Leonard Pfister; Bill R. Sandel

1996-01-01

253

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

NASA Technical Reports Server (NTRS)

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

Ko, William L.; Chen, Tony

2006-01-01

254

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

E-print Network

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

Zhang, Yang

255

Comput Biol Chem . Author manuscript Protein short loop prediction in terms of a structural alphabet  

E-print Network

to insights to flexible docking approach.ab initio MESH Keywords Computer Simulation ; Databases, Protein ; protein loops ; secondary structure ; protein function ; bioinformatics ; biophysics. IntroductionComput Biol Chem . Author manuscript Page /1 6 Protein short loop prediction in terms

Paris-Sud XI, Université de

256

Montane refugia predict population genetic structure in the Large-blotched Ensatina salamander  

E-print Network

Montane refugia predict population genetic structure in the Large-blotched Ensatina salamander in the Large-blotched Ensatina (Ensatina eschscholtzii klauberi), a plethodon- tid salamander endemic to middle and northern Baja California. A compos- ite SDM representing the range through time predicts two disjunct

McGuire, Jimmy A.

257

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

ERIC Educational Resources Information Center

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

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

2000-01-01

258

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

E-print Network

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

Cheng, Jianlin Jack

259

Learning to predict the phonological structure of English loanwords in Japanese  

E-print Network

Learning to predict the phonological structure of English loanwords in Japanese Alan D. Blair a feedforward neural network can be trained to predict the phonology of loanwords bor- rowed from English@cse.unsw.edu.au John Ingram Department of English University of Queensland 4072, Australia jingram

Blair, Alan

260

StoneHinge: Hinge prediction by network analysis of individual protein structures  

E-print Network

, and knowledge of their location can guide the sampling of protein conformations for docking. Predicting domainsStoneHinge: Hinge prediction by network analysis of individual protein structures Kevin S. Keating in Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut 2 Department of Physics, Yale

Gerstein, Mark

261

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. PMID:19383454

Masica, David L.; Gray, Jeffrey J.

2009-01-01

262

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

Microsoft Academic Search

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

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

2004-01-01

263

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

264

A seqlet-based maximum entropy Markov approach for protein secondary structure prediction  

Microsoft Academic Search

A novel method for predicting the secondary structures of proteins from amino acid sequence has been presented. The protein\\u000a secondary structure seqlets that are analogous to the words in natural language have been extracted. These seqlets will capture\\u000a the relationship between amino acid sequence and the secondary structures of proteins and further form the protein secondary\\u000a structure dictionary. To be

Qiwen Dong; Xiaolong Wang; Lei Lin; Yi Guan

2005-01-01

265

Free energy minimization to predict RNA secondary structures and computational RNA design.  

PubMed

Determining the RNA secondary structure from sequence data by computational predictions is a long-standing problem. Its solution has been approached in two distinctive ways. If a multiple sequence alignment of a collection of homologous sequences is available, the comparative method uses phylogeny to determine conserved base pairs that are more likely to form as a result of billions of years of evolution than by chance. In the case of single sequences, recursive algorithms that compute free energy structures by using empirically derived energy parameters have been developed. This latter approach of RNA folding prediction by energy minimization is widely used to predict RNA secondary structure from sequence. For a significant number of RNA molecules, the secondary structure of the RNA molecule is indicative of its function and its computational prediction by minimizing its free energy is important for its functional analysis. A general method for free energy minimization to predict RNA secondary structures is dynamic programming, although other optimization methods have been developed as well along with empirically derived energy parameters. In this chapter, we introduce and illustrate by examples the approach of free energy minimization to predict RNA secondary structures. PMID:25577369

Churkin, Alexander; Weinbrand, Lina; Barash, Danny

2015-01-01

266

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

267

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

268

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

269

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

270

Revisiting the blind tests in crystal structure prediction: accurate energy ranking of molecular crystals.  

PubMed

In the 2007 blind test of crystal structure prediction hosted by the Cambridge Crystallographic Data Centre (CCDC), a hybrid DFT/MM method correctly ranked each of the four experimental structures as having the lowest lattice energy of all the crystal structures predicted for each molecule. The work presented here further validates this hybrid method by optimizing the crystal structures (experimental and submitted) of the first three CCDC blind tests held in 1999, 2001, and 2004. Except for the crystal structures of compound IX, all structures were reminimized and ranked according to their lattice energies. The hybrid method computes the lattice energy of a crystal structure as the sum of the DFT total energy and a van der Waals (dispersion) energy correction. Considering all four blind tests, the crystal structure with the lowest lattice energy corresponds to the experimentally observed structure for 12 out of 14 molecules. Moreover, good geometrical agreement is observed between the structures determined by the hybrid method and those measured experimentally. In comparison with the correct submissions made by the blind test participants, all hybrid optimized crystal structures (apart from compound II) have the smallest calculated root mean squared deviations from the experimentally observed structures. It is predicted that a new polymorph of compound V exists under pressure. PMID:19950907

Asmadi, Aldi; Neumann, Marcus A; Kendrick, John; Girard, Pascale; Perrin, Marc-Antoine; Leusen, Frank J J

2009-12-24

271

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

SciTech Connect

The machine learning program Progol was applied to the problem of forming the structure-activity relationship (SAR) for a set of compounds tested for carcinogenicity in rodent bioassays by the U.S. National Toxicology Program (NTP). Progol is the first inductive logic programming (ILP) algorithm to use a fully relational method for describing chemical structure in SARs, based on using atoms and their bond connectivities. Progol is well suited to forming SARs for carcinogenicity as it is designed to produce easily understandable rules (structural alerts) for sets of noncongeneric compounds. The Progol SAR method was tested by prediction of a set of compounds that have been widely predicted by other SAR methods (the compounds used in the NTP`s first round of carcinogenesis predictions). For these compounds no method (human or machine) was significantly more accurate than Progol. Progol was the most accurate method that did not use data from biological tests on rodents (however, the difference in accuracy is not significant). The Progol predictions were based solely on chemical structure and the results of tests for Salmonella mutagenicity. Using the full NTP database, the prediction accuracy of Progol was estimated to be 63% ({+-}3%) using 5-fold cross validation. A set of structural alerts for carcinogenesis was automatically generated and the chemical rationale for them investigated-these structural alerts are statistically independent of the Salmonella mutagenicity. Carcinogenicity is predicted for the compounds used in the NTP`s second round of carcinogenesis predictions. The results for prediction of carcinogenesis, taken together with the previous successful applications of predicting mutagenicity in nitroaromatic compounds, and inhibition of angiogenesis by suramin analogues, show that Progol has a role to play in understanding the SARs of cancer-related compounds. 29 refs., 2 figs., 4 tabs.

King, R.D. [Biomolecular Modelling Lab., London (United Kingdom); Srinivasan, A. [Univ. of Oxford (United Kingdom)

1996-10-01

272

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

PubMed Central

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

King, R D; Srinivasan, A

1996-01-01

273

Prediction of rodent carcinogenicity bioassays from molecular structure using inductive logic programming. Environmental Health Perspectives  

E-print Network

The machine learning program Progol was applied to the problem of forming the structure-activity relationship (SAR) for a set of compounds tested for carcinogenicity in rodent bioassays by the U.S. National Toxicology Program (NTP). Progol is the first inductive logic programming (ILP) algorithm to use a fully relational method for describing chemical structure in SARs, based on using atoms and their bond connectivities. Progol is well suited to forming SARs for carcinogenicity as it is designed to produce easily understandable rules (structural alerts) for sets of noncongeneric compounds. The Progol SAR method was tested by prediction of a set of compounds that have been widely predicted by other SAR methods (the compounds used in the NTP's first round of carcinogenesis predictions). For these compounds no method (human or machine) was significantly more accurate than Progol. Progol was the most accurate method that did not use data from biological tests on rodents (however, the difference in accuracy is not significant). tests for Salmonella mutagenicity. Using the full NTP database, the prediction accuracy of Progol was estimated to be 63 % (±3%) using 5-fold cross validation. A set of structural alerts for carcinogenesis was automatically generated and the chemical rationale for them investigatedthese structural alerts are statistically independent of the Salmonella mutagenicity. Carcinogenicity is predicted for the compounds used in the NTP's second round of carcinogenesis predictions. The results for prediction of carcinogenesis, taken together with the previous successful applications of predicting mutagenicity in nitroaromatic compounds, and inhibition of angiogenesis by suramin analogues, show that Progol has a role to play in understanding the SARs of cancer-related compounds. Environ Health Perspect 104(Suppl 5):1031-1040 (1996) The Progol predictions were based solely on chemical structure and the results of

Ross D. King; Ashwin Srinivasan

1996-01-01

274

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

275

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

PubMed Central

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

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

2014-01-01

276

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

NASA Astrophysics Data System (ADS)

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

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

2014-12-01

277

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

PubMed

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

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

2014-01-01

278

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

279

STRUCTURE BASED PREDICTIVE MODEL FOR COAL CHAR COMBUSTION  

SciTech Connect

This report is part on the ongoing effort at Brown University and Ohio State University to develop structure based models of coal combustion. A very fundamental approach is taken to the description of coal chars and their reaction processes, and the results are therefore expected to have broad applicability to the spectrum of carbon materials of interest in energy technologies. This quarter, the project was in a period no-cost extension and discussions were held about the end phase of the project and possible continuations. The technical tasks were essentially dormant this period, but presentations of results were made, and plans were formulated for renewed activity in the fiscal year 2001.

Robert Hurt; Joseph Calo; Robert Essenhigh; Christopher Hadad

2001-06-15

280

Lithium dihydroborate: first-principles structure prediction of LiBH2.  

PubMed

We report a first-principles structure prediction of the LiBH(2), which structures are modeled by using four formula units per unit cell without symmetry restrictions. The computational methodology combines a simulated annealing approach and density functional total energy calculations for crystalline solid structures. The predicted lowest energy structure shows the formation of linear anionic chains, (?)(1)[BH(2)], enthalpy of formation at 0 K equal to -90.07 kJ/mol. Ring structures, in particular with butterfly and planar square topologies, are found to be stable but well above the ground state by 20.26 and 12.92 kJ/mol, respectively. All convergent structures fall in the symmetry families monoclinic, tetragonal, and orthorhombic. For the representative structures of each family group, simulated X-ray diffraction patterns and infrared spectra are reported. PMID:22928952

Caputo, Riccarda; Tekin, Adem

2012-09-17

281

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. PMID:23980156

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

2013-01-01

282

Transition between B-DNA and Z-DNA: Free Energy Landscape for the B-Z Junction Propagation  

E-print Network

be transformed into noncanonical, left-handed Z-DNA in vitro at high salt concentrations or in vivo under in 1979 was a noncanonical, left-handed double helical form of DNA, Z-DNA.1 It was later revealed that Z-DNATransition between B-DNA and Z-DNA: Free Energy Landscape for the B-Z Junction Propagation Juyong

Seok, Chaok

283

Tertiary structure prediction and identification of druggable pocket in the cancer biomarker – Osteopontin-c  

PubMed Central

Background Osteopontin (Eta, secreted sialoprotein 1, opn) is secreted from different cell types including cancer cells. Three splice variant forms namely osteopontin-a, osteopontin-b and osteopontin-c have been identified. The main astonishing feature is that osteopontin-c is found to be elevated in almost all types of cancer cells. This was the vital point to consider it for sequence analysis and structure predictions which provide ample chances for prognostic, therapeutic and preventive cancer research. Methods Osteopontin-c gene sequence was determined from Breast Cancer sample and was translated to protein sequence. It was then analyzed using various software and web tools for binding pockets, docking and druggability analysis. Due to the lack of homological templates, tertiary structure was predicted using ab-initio method server – I-TASSER and was evaluated after refinement using web tools. Refined structure was compared with known bone sialoprotein electron microscopic structure and docked with CD44 for binding analysis and binding pockets were identified for drug designing. Results Signal sequence of about sixteen amino acid residues was identified using signal sequence prediction servers. Due to the absence of known structures of similar proteins, three dimensional structure of osteopontin-c was predicted using I-TASSER server. The predicted structure was refined with the help of SUMMA server and was validated using SAVES server. Molecular dynamic analysis was carried out using GROMACS software. The final model was built and was used for docking with CD44. Druggable pockets were identified using pocket energies. Conclusions The tertiary structure of osteopontin-c was predicted successfully using the ab-initio method and the predictions showed that osteopontin-c is of fibrous nature comparable to firbronectin. Docking studies showed the significant similarities of QSAET motif in the interaction of CD44 and osteopontins between the normal and splice variant forms of osteopontins and binding pockets analyses revealed several pockets which paved the way to the identification of a druggable pocket. PMID:24401206

2014-01-01

284

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

285

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

286

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

PubMed

The cell-specific and long-term expression of therapeutic transgenes often requires a full array of native gene control elements including distal enhancers, regulatory introns and chromatin organisation sequences. The delivery of such extended gene expression modules to human cells can be accomplished with non-viral high-molecular-weight DNA vectors, in particular with several classes of linear DNA vectors. All high-molecular-weight DNA vectors are susceptible to damage by shear stress, and while for some of the vectors the harmful impact of shear stress can be minimised through the transformation of the vectors to compact topological configurations by supercoiling and/or knotting, linear DNA vectors with terminal loops or covalently attached terminal proteins cannot be self-compacted in this way. In this case, the only available self-compacting option is self-entangling, which can be defined as the folding of single DNA molecules into a configuration with mutual restriction of molecular motion by the individual segments of bent DNA. A negatively charged phosphate backbone makes DNA self-repulsive, so it is reasonable to assume that a certain number of 'sticky points' dispersed within DNA could facilitate the entangling by bringing DNA segments into proximity and by interfering with the DNA slipping away from the entanglement. I propose that the spontaneous entanglement of vector DNA can be enhanced by the interlacing of the DNA with sites capable of mutual transient attachment through the formation of non-B-DNA forms, such as interacting cruciform structures, inter-segment triplexes, slipped-strand DNA, left-handed duplexes (Z-forms) or G-quadruplexes. It is expected that the non-B-DNA based entanglement of the linear DNA vectors would consist of the initial transient and co-operative non-B-DNA mediated binding events followed by tight self-ensnarement of the vector DNA. Once in the nucleoplasm of the target human cells, the DNA can be disentangled by type II topoisomerases. The technology for such self-entanglement can be an avenue for the improvement of gene delivery with high-molecular-weight naked DNA using therapeutically important methods associated with considerable shear stress. Priority applications include in vivo muscle electroporation and sonoporation for Duchenne muscular dystrophy patients, aerosol inhalation to reach the target lung cells of cystic fibrosis patients and bio-ballistic delivery to skin melanomas with the vector DNA adsorbed on gold or tungsten projectiles. PMID:22356834

Tolmachov, Oleg E

2012-05-01

287

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

288

Computational Mechanics of Fatigue and Life Predictions for Composite Materials and Structures  

E-print Network

are introduced to model the fatigue damage. The degradation of material response under cyclic loading initiation, propaga- tion and overall structural failure under cyclic loading. When applying the CDM based1 Computational Mechanics of Fatigue and Life Predictions for Composite Materials and Structures

Fish, Jacob

289

Expanded sequence dependence of thermodynamic parameters improves prediction of RNA secondary structure1  

Microsoft Academic Search

An improved dynamic programming algorithm is reported for RNA secondary structure prediction by free energy minimization. Thermodyn- amic parameters for the stabilities of secondary structure motifs are revised to include expanded sequence dependence as revealed by recent experiments. Additional algorithmic improvements include reduced search time and storage for multibranch loop free energies and improved imposition of folding constraints. An extended

David H. Mathews; Jeffrey Sabina; Michael Zuker; Douglas H. Turner

1999-01-01

290

Predicting Singapore Students' Achievement Goals in Their English Study: Self-Construal and Classroom Goal Structure  

ERIC Educational Resources Information Center

This study examined the role of self-construal and classroom goal structure in predicting Singapore secondary students' achievement goals in their English study. Students from 104 classes were administered surveys of achievement goals, classroom goal structure, English self-concept, and self-construal. The results of two-level hierarchical linear…

Luo, Wenshu; Hogan, David; Paris, Scott G.

2011-01-01

291

A parallel hybrid genetic algorithm for protein structure prediction on the computational grid  

Microsoft Academic Search

Solving the structure prediction problem for complex proteins is difficult and computationally expensive. In this paper, we propose a bicriterion parallel hybrid genetic algorithm (GA) in order to efficiently deal with the problem using the computational grid. The use of a near-optimal metaheuristic, such as a GA, allows a significant reduction in the number of explored potential structures. However, the

Alexandru-adrian Tantar; Nouredine Melab; El-ghazali Talbi; Benjamin Parent; Dragos Horvath

2007-01-01

292

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

E-print Network

Three-dimensional structure of a halotolerant algal carbonic anhydrase predicts halotolerance molecular adaptation to drastically shifting salinities was studied in dCA II, an -type carbonic anhydrase structure of dCA II, determined at 1.86-Ã? resolution, is globally similar to other -type carbonic anhydrases

Sussman, Joel L.

293

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

E-print Network

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

Curtarolo, Stefano

294

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

E-print Network

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

Jones, James Holland

295

CASP6 data processing and automatic evaluation at the protein structure prediction center.  

PubMed

We present a short overview of the system governing data processing and automatic evaluation of predictions in CASP6, implemented at the Livermore Protein Structure Prediction Center. The system incorporates interrelated facilities for registering participants, collecting prediction targets from crystallographers and NMR spectroscopists and making them available to the CASP6 participants, accepting predictions and providing their preliminary evaluation, and finally, storing and visualizing results. We have automatically evaluated predictions submitted to CASP6 using criteria and methods developed over the successive CASP experiments. Also, we have tested a new evaluation technique based on non-rigid-body type superpositions. Approximately the same number of predictions has been submitted to CASP6 as to all previous CASPs combined, making navigation through and understanding of the data particularly challenging. To facilitate this, we have substantially modernized all data handling procedures, including implementation of a dedicated relational database. An overview of our redesigned website is also presented (http://predictioncenter.org/casp6/). PMID:16187343

Kryshtafovych, Andriy; Milostan, Maciej; Szajkowski, Lukasz; Daniluk, Pawel; Fidelis, Krzysztof

2005-01-01

296

TASSER_WT: a protein structure prediction algorithm with accurate predicted contact restraints for difficult protein targets.  

PubMed

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

Lee, Seung Yup; Skolnick, Jeffrey

2010-11-01

297

Rotor Airloads Prediction Using Loose Aerodynamic Structural Coupling  

NASA Technical Reports Server (NTRS)

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

Potsdam, Mark; Yeo, Hyeonsoo; Johnson, Wayne

2004-01-01

298

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

299

Predicting the thermal/structural performance of the atmospheric trace molecules spectroscopy /ATMOS/ Fourier transform spectrometer  

NASA Technical Reports Server (NTRS)

ATMOS is a Fourier transform spectrometer to measure atmospheric trace molecules over a spectral range of 2-16 microns. Assessment of the system performance of ATMOS includes evaluations of optical system errors induced by thermal and structural effects. In order to assess the optical system errors induced from thermal and structural effects, error budgets are assembled during system engineering tasks and line of sight and wavefront deformations predictions (using operational thermal and vibration environments and computer models) are subsequently compared to the error budgets. This paper discusses the thermal/structural error budgets, modelling and analysis methods used to predict thermal/structural induced errors and the comparisons that show that predictions are within the error budgets.

Miller, J. M.

1980-01-01

300

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

PubMed

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

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

2011-12-01

301

Structure prediction, disorder and dynamics in a DMSO solvate of carbamazepine.  

PubMed

We have applied crystal structure prediction methods to understand and predict the formation of a DMSO solvate of the anti-convulsant drug carbamazepine (CBZ), in which the DMSO molecules are disordered. Crystal structure prediction calculations on the 1:1 CBZ:DMSO solvate revealed the generation of two similar low energy structures which differ only in the orientation of the DMSO molecules. Analysis of crystal energy landscapes generated at 0 K suggests the possibility of solvent disorder. A combined computational and experimental study of the changes in the orientation of the DMSO within the crystal structure revealed that the nature of the disorder changes with temperature. At low temperature, the DMSO disorder is static whilst at high temperature the DMSO configurations can interconvert by a 180° rotation of the DMSO molecules within the lattice. This 180° rotation of the DMSO molecules drives a phase change from a high temperature dynamically disordered phase to a low temperature phase with static disorder. Crystallisation of a DMSO solvate of the related molecule epoxycarbamazepine resulted in a different degree of DMSO disorder in the crystal structure, despite the similarity of the carbamazepine and epoxycarbamazepine molecules. We believe consideration of disorder and its contribution to entropy and crystal free energies at temperature other than 0 K is fundamental for the accuracy of future energy rankings in crystal structure prediction calculations of similar solvated structures. PMID:21670828

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

2011-07-28

302

CSSP2: an improved method for predicting contact-dependent secondary structure propensity.  

PubMed

The calculation of contact-dependent secondary structure propensity (CSSP) has been reported to sensitively detect non-native beta-strand propensities in the core sequences of amyloidogenic proteins. Here we describe a noble energy-based CSSP method implemented on dual artificial neural networks that rapidly and accurately estimate the potential for the non-native secondary structure formation in local regions of protein sequences. In this method, we attempted to quantify long-range interaction patterns in diverse secondary structures by potential energy calculations and decomposition on a pairwise per-residue basis. The calculated energy parameters and seven-residue sequence information were used as inputs for artificial neural networks (ANNs) to predict sequence potential for secondary structure conversion. The trained single ANN using the >(i, i+/-4) interaction energy parameter exhibited 74% accuracy in predicting the secondary structure of test sequences in their native energy state, while the dual ANN-based predictor using (i, i+/-4) and >(i, i+/-4) interaction energies showed 83% prediction accuracy. The present method provides a simple and accurate tool for predicting sequence potential for secondary structure conversions without using 3D structural information. PMID:17644485

Yoon, Sukjoon; Welsh, William J; Jung, Heeyoung; Yoo, Young Do

2007-10-01

303

Hydrogen-bond coordination in organic crystal structures: statistics, predictions and applications.  

PubMed

Statistical models to predict the number of hydrogen bonds that might be formed by any donor or acceptor atom in a crystal structure have been derived using organic structures in the Cambridge Structural Database. This hydrogen-bond coordination behaviour has been uniquely defined for more than 70 unique atom types, and has led to the development of a methodology to construct hypothetical hydrogen-bond arrangements. Comparing the constructed hydrogen-bond arrangements with known crystal structures shows promise in the assessment of structural stability, and some initial examples of industrially relevant polymorphs, co-crystals and hydrates are described. PMID:24441132

Galek, Peter T A; Chisholm, James A; Pidcock, Elna; Wood, Peter A

2014-02-01

304

Training set reduction methods for protein secondary structure prediction in single-sequence condition.  

PubMed

Orphan proteins are characterized by the lack of significant sequence similarity to database proteins. To infer the functional properties of the orphans, more elaborate techniques that utilize structural information are required. In this regard, the protein structure prediction gains considerable importance. Secondary structure prediction algorithms designed for orphan proteins (also known as single-sequence algorithms) cannot utilize multiple alignments or alignment profiles, which are derived from similar proteins. This is a limiting factor for the prediction accuracy. One way to improve the performance of a single-sequence algorithm is to perform re-training. In this approach, first, the models used by the algorithm are trained by a representative set of proteins and a secondary structure prediction is computed. Then, using a distance measure, the original training set is refined by removing proteins that are dissimilar to the given protein. This step is followed by the re-estimation of the model parameters and the prediction of the secondary structure. In this paper, we compare training set reduction methods that are used to re-train the hidden semi-Markov models employed by the IPSSP algorithm [1]. We found that the composition based reduction method has the highest performance compared to the alignment based and the Chou-Fasman based reduction methods. In addition, threshold-based reduction performed better than the reduction technique that selects the first 80% of the dataset proteins. PMID:18003135

Aydin, Zafer; Altunbasak, Yucel; Pakatci, Isa Kemal; Erdogan, Hakan

2007-01-01

305

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. PMID:24884954

2014-01-01

306

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

307

Structural link prediction based on ant colony approach in social networks  

NASA Astrophysics Data System (ADS)

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

Sherkat, Ehsan; Rahgozar, Maseud; Asadpour, Masoud

2015-02-01

308

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

309

Indirect readout of the trp-repressor-operator complex by B-DNA's backbone conformation transitions.  

PubMed

Although the trp-repressor-operator complex is one of the best studied transcriptional controlling systems, some questions regarding the specific recognition of the operator by the repressor remain. We performed a 2.35 ns long molecular dynamics simulation to clarify the influence of the two B-DNA backbone conformational substates B(I) and B(II) on complexation. The trp-repressor-operator is an ideal biological system for this study because experimental results have already figured out that the interaction between the internucleotide phosphates and the protein is essential for the formation of the high affinity complex. Our simulation supports these results, but more important it shows a strong correlation between the B(I)/B(II) phosphate substate and the number of interactions with this phosphate. In particular the B(I) <==> B(II) transitions occur synchronous to hydrogen bond breaking or formation. To the best of our knowledge, this was observed for the first time. Thus, we conclude that the sequence specific B(I)/B(II) behavior contributes via indirect readout to sequence specific recognition. These results have implication for the design of transcription-controlling drugs in view of the recently published influence of minor groove binders on the B(I)/B(II) pattern. The simulation also agrees with crystallographically observed hydration sites. This is consistent with experimental results and indicates the correctness of the model used. PMID:11900552

Wellenzohn, Bernd; Flader, Wolfgang; Winger, Rudolf H; Hallbrucker, Andreas; Mayer, Erwin; Liedl, Klaus R

2002-03-26

310

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

311

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

Microsoft Academic Search

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

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

2011-01-01

312

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

PubMed Central

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

2014-01-01

313

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

314

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/. PMID:24243399

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

2014-07-01

315

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

316

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

PubMed

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

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

2012-05-01

317

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

318

New tools and expanded data analysis capabilities at the protein structure prediction center  

PubMed Central

We outline the main tasks performed by the Protein Structure Prediction Center in support of the CASP7 experiment and provide a brief review of the major measures used in the automatic evaluation of predictions. We describe in more detail the software developed to facilitate analysis of modeling success over and beyond the available templates and the adopted Java-based tool enabling visualization of multiple structural superpositions between target and several models/templates. We also give an overview of the CASP infrastructure provided by the Center and discuss the organization of the results web pages available through http://predictioncenter.org PMID:17705273

Kryshtafovych, Andriy; Prlic, Andreas; Dmytriv, Zinoviy; Daniluk, Pawel; Milostan, Maciej; Eyrich, Volker; Hubbard, Tim; Fidelis, Krzysztof

2007-01-01

319

New tools and expanded data analysis capabilities at the Protein Structure Prediction Center.  

PubMed

We outline the main tasks performed by the Protein Structure Prediction Center in support of the CASP7 experiment and provide a brief review of the major measures used in the automatic evaluation of predictions. We describe in more detail the software developed to facilitate analysis of modeling success over and beyond the available templates and the adopted Java-based tool enabling visualization of multiple structural superpositions between target and several models/templates. We also give an overview of the CASP infrastructure provided by the Center and discuss the organization of the results web pages available through http://predictioncenter.org. PMID:17705273

Kryshtafovych, Andriy; Prlic, Andreas; Dmytriv, Zinoviy; Daniluk, Pawel; Milostan, Maciej; Eyrich, Volker; Hubbard, Tim; Fidelis, Krzysztof

2007-01-01

320

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

321

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

322

Computational prediction of N-linked glycosylation incorporating structural properties and patterns  

PubMed Central

Motivation: N-linked glycosylation occurs predominantly at the N-X-T/S motif, where X is any amino acid except proline. Not all N-X-T/S sequons are glycosylated, and a number of web servers for predicting N-linked glycan occupancy using sequence and/or residue pattern information have been developed. None of the currently available servers, however, utilizes protein structural information for the prediction of N-glycan occupancy. Results: Here, we describe a novel classifier algorithm, NGlycPred, for the prediction of glycan occupancy at the N-X-T/S sequons. The algorithm utilizes both structural as well as residue pattern information and was trained on a set of glycosylated protein structures using the Random Forest algorithm. The best predictor achieved a balanced accuracy of 0.687 under 10-fold cross-validation on a curated dataset of 479 N-X-T/S sequons and outperformed sequence-based predictors when evaluated on the same dataset. The incorporation of structural information, including local contact order, surface accessibility/composition and secondary structure thus improves the prediction accuracy of glycan occupancy at the N-X-T/S consensus sequon. Availability and Implementation: NGlycPred is freely available to non-commercial users as a web-based server at http://exon.niaid.nih.gov/nglycpred/. Contact: ivelin.georgiev@nih.gov Supplementary Information: Supplementary data are available at Bioinformatics online. PMID:22782545

Chuang, Gwo-Yu; Boyington, Jeffrey C.; Joyce, M. Gordon; Zhu, Jiang; Nabel, Gary J.; Kwong, Peter D.; Georgiev, Ivelin

2012-01-01

323

Sound Insulation of DOORS—PART 1: Prediction Models for Structural and Leak Transmission  

NASA Astrophysics Data System (ADS)

Examination of sound insulation of doors presupposes two separate transmission paths to be considered: the structural transmission through the door leaf and the leak transmission through the slits. In this paper, simple prediction models for both transmission paths are presented which are applicable for most types of passage doors. The practicability of the selected models are of great concern to obtain a high degree of utilization in product development. Most doors are designed nowadays as double-panel structures with sound absorbing and fire-resistant materials in the air cavity. Strong interpanel connections are often present at least in the edges of the door. Sharp's double-panel prediction model was found appropriate for modelling both single- and double-panel doors. The slit transmission can be estimated at least by two different theories. The simple model assumes perfect transmission through the apertures. The more profound Gomperts model enables the evaluation of structurally regular slits. The total sound reduction index of doors is predicted from the area-weighted sum of the structural transmission and the slit transmission. Acoustical structure and airtightness of the door shall be developed hand-in-hand to obtain the optimum performance of the door. The prediction models presented in this paper are verified in the second part for 18 steel doors and timber doors [1].

HONGISTO, V.

2000-02-01

324

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

PubMed Central

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

Chen, Yao Chi; Lim, Carmay

2008-01-01

325

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. PMID:23695796

Rivas, Elena

2013-01-01

326

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

PubMed

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

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

2014-11-15

327

Development of a phospholipidosis database and predictive quantitative structure-activity relationship (QSAR) models.  

PubMed

ABSTRACT Drug-induced phospholipidosis (PL) is a condition characterized by the accumulation of phospholipids and drug in lysosomes, and is found in a variety of tissue types. PL is frequently manifested in preclinical studies and may delay or prevent the development of pharmaceuticals. This report describes the construction of a database of PL findings in a variety of animal species and its use as a training data set for computational toxicology software. PL data and chemical structures were compiled from the published literature, existing pharmaceutical databases, and Food and Drug Administration (FDA) internal reports yielding a total of 583 compounds suitable for modeling. The database contained 190 (33%) positive drugs and 393 (77%) negative drugs, of which 39 were electron microscopy-confirmed negative compounds and 354 were classified as negatives due to the absence of positive reported data. Of the 190 positive findings, 76 were electron microscopy confirmed and 114 were considered positive based on other evidence. Quantitative structure-activity relationship (QSAR) models were constructed using two commercially available software programs, MC4PC and MDL-QSAR, and internal cross-validation (10 x 10%) experiments were performed to assess their predictive performance. Performance parameters for the MC4PC model were specificity 92%, sensitivity 50%, concordance 78%, positive predictivity 76%, and negative predictivity 78%. For MDL-QSAR, predictive performance was similar: specificity 80%, sensitivity 76%, concordance 79%, positive predictivity 65%, and negative predictivity 87%. By combining the output of the two QSAR programs, the overall predictive performance was vastly improved and sensitivity could be optimized to 81% without significant loss of specificity (79%). Many of the structural alerts and significant molecular descriptors obtained from the QSAR software were found to be associated with parts of active molecules known for their cationic amphiphilic drug (CAD) properties supporting the hypothesis that the endpoint of PL is statistically correlated with chemical structure. QSAR models can be useful tools for screening drug candidate molecules for potential PL. PMID:20020916

Kruhlak, Naomi L; Choi, Sydney S; Contrera, Joseph F; Weaver, James L; Willard, James M; Hastings, Kenneth L; Sancilio, Lawrence F

2008-01-01

328

CARES\\/LIFE Ceramics Analysis and Reliability Evaluation of Structures Life Prediction Program  

Microsoft Academic Search

This manual describes the Ceramics Analysis and Reliability Evaluation of Structures Life Prediction (CARES\\/LIFE) computer program. The program calculates the time-dependent reliability of monolithic ceramic components subjected to thermomechanical and\\/or proof test loading. CARES\\/LIFE is an extension of the CARES (Ceramic Analysis and Reliability Evaluation of Structures) computer program. The program uses results from MSC\\/NASTRAN, ABAQUS, and ANSYS finite element

Noel N. Nemeth; Lynn M. Powers; Lesley A. Janosik; John P. Gyekenyesi

2003-01-01

329

Predictions of electronic, structural, and elastic properties of cubic InN  

Microsoft Academic Search

We present theoretical predictions of electronic, structural, and elastic properties of cubic indium nitride in the zine-blende structure (c-InN). Our ab initio, self-consistent calculations employed a local density approximation potential and the Bagayoko, Zhao, and Williams implementation of the linear combination of atomic orbitals. The theoretical equilibrium lattice constant is 5.017 A˚, the band gap is 0.65 eV, and the

D. Bagayoko; L. Franklin; G. L. Zhao

2004-01-01

330

Prediction of composition for stable half-Heusler phases from electronic-band-structure analyses  

Microsoft Academic Search

This report describes a procedure to predict the frequently occurring non-stoichiometry of the half-Heusler XYZ alloys (viz. deviations from the equiatomic 1:1:1 composition and the usually accompanied narrow homogeneity regions) from ab initio calculated electronic-band-structure characteristics. The essential feature of this approach is to utilize the valence electron content (VEC) and the calculated electronic band structure to expose factors that

L. Offernes; P. Ravindran; C. W. Seim; A. Kjekshus

2008-01-01

331

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

PubMed

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

Devillers, J; Pandard, P; Richard, B

2013-01-01

332

Prediction of protein secondary structure using probability based features and a hybrid system.  

PubMed

In this paper, we propose some co-occurrence probability-based features for prediction of protein secondary structure. The features are extracted using occurrence/nonoccurrence of secondary structures in the protein sequences. We explore two types of features: position-specific (based on position of amino acid on fragments of protein sequences) as well as position-independent (independent of amino acid position on fragments of protein sequences). We use a hybrid system, NEUROSVM, consisting of neural networks and support vector machines for classification of secondary structures. We propose two schemes NSVMps and NSVM for protein secondary structure prediction. The NSVMps uses position-specific probability-based features and NEUROSVM classifier whereas NSVM uses the same classifier with position-independent probability-based features. The proposed method falls in the single-sequence category of methods because it does not use any sequence profile information such as position specific scoring matrices (PSSM) derived from PSI-BLAST. Two widely used datasets RS126 and CB513 are used in the experiments. The results obtained using the proposed features and NEUROSVM classifier are better than most of the existing single-sequence prediction methods. Most importantly, the results using NSVMps that are obtained using lower dimensional features, are comparable to those by other existing methods. The NSVMps and NSVM are finally tested on target proteins of the critical assessment of protein structure prediction experiment-9 (CASP9). A larger dataset is used to compare the performance of the proposed methods with that of two recent single-sequence prediction methods. We also investigate the impact of presence of different amino acid residues (in protein sequences) that are responsible for the formation of different secondary structures. PMID:24131056

Ghanty, Pradip; Pal, Nikhil R; Mudi, Rajani K

2013-10-01

333

De novo structure prediction and experimental characterization of folded peptoid oligomers  

PubMed Central

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

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

2012-01-01

334

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

E-print Network

Hydrogen constitutes nearly half of all atoms in proteins and their positions are essential for analyzing hydrogen-bonding interactions and refining atomic-level structures. However, most protein structures determined by experiments or computer prediction lack hydrogen coordinates. We present a new algorithm, HAAD, to predict the positions of hydrogen atoms based on the positions of heavy atoms. The algorithm is built on the basic rules of orbital hybridization followed by the optimization of steric repulsion and electrostatic interactions. We tested the algorithm using three independent data sets: ultra-high-resolution X-ray structures, structures determined by neutron diffraction, and NOE proton-proton distances. Compared with the widely used programs CHARMM and REDUCE, HAAD has a significantly higher accuracy, with the average RMSD of the predicted hydrogen atoms to the X-ray and neutron diffraction structures decreased by 26 % and 11%, respectively. Furthermore, hydrogen atoms placed by HAAD have more matches with the NOE restraints and fewer clashes with heavy atoms. The average CPU cost by HAAD is 18 and 8 times lower than that of CHARMM and REDUCE, respectively. The significant advantage of HAAD in both the accuracy and the speed of the hydrogen additions should make HAAD a useful tool for the detailed study of protein structure and function. Both an executable and the source code of HAAD are

Yunqi Li; Ambrish Roy; Yang Zhang

2009-01-01

335

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

SciTech Connect

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

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

2014-01-28

336

Evolutionary crystal structure prediction: discovering new minerals in the deep Earth.  

NASA Astrophysics Data System (ADS)

Experimental determination of crystal structures at high pressure is often extremely difficult; given this and the strengths of quantum-mechanical simulations, theory presents an attractive tool to investigate matter at extreme conditions. However, crystal structure prediction on the basis of just the chemical formula has long been considered a formidable or even insoluble problem. Solving it would enable structural studies of planetary materials at extreme conditions [1,2] and probe changing chemistry at high pressure, solve structures where experimental data are insufficient, and design new materials entirely on the computer (once the structure is known, it is relatively easy to predict many of its properties e.g., [3]). Recently, we addressed this problem and devised a new method based on an ab initio evolutionary algorithm, which we implemented in the USPEX code (Universal Structure Predictor: Evolutionary Xtallography, [4-6]). USPEX uses ab initio free energy as evaluation function and features local optimization and spatial heredity, as well as further operators such as mutation and permutation. At given P-T conditions, USPEX finds the stable structure and a set of robust metastable structures, using no experimental information except the chemical composition. This method has been widely tested and applied to solve a number of important problems. In this talk I will discuss some of the applications of this method to a number of interesting materials at high pressure (C, O, S, MgSiO3, CO2, CaCO3, MgCO3). 1. Oganov A.R. & Ono S. (2004). Theoretical and experimental evidence for a post-perovskite phase of MgSiO3 in Earth's D" layer. Nature 430, 445-448. 2. Oganov A.R., Ono S. (2005). The high pressure phase of alumina and implications for Earth's D" layer. Proc. Natl. Acad. Sci. 102, 10828-10831. 3. Oganov A.R., Brodholt J.P., Price G.D. (2001). The elastic constants of MgSiO3 perovskite at pressures and temperatures of the Earth's mantle. Nature 411, 934-937. 4. Oganov A.R., Glass C.W., Ono S. (2006). High-pressure phases of CaCO3: crystal structure prediction and experiment. Earth Planet. Sci. Lett. 241, 95-103. 5. Oganov A.R. & Glass C.W. (2006). Crystal Structure Prediction using ab initio evolutionary techniques: principles and applications. J. Chem. Phys, 124, art. 244704. 6. Glass C.W., Oganov A.R. & Hansen N. (2006). USPEX - evolutionary crystal structure prediction. Comp. Phys. Comm., in press.

Oganov, A. R.; Glass, C. W.

2006-12-01

337

Polypeptide structure prediction: real-value versus binary hybrid genetic algorithms  

Microsoft Academic Search

Abstract Energy,minimization,efforts,to,predict,polypeptide structures,assuule,their,native,conformation,corre- sponds,to the global,minimum,free energy,state. Given this assumption, the problem becomes that of develop- ing,efficient global,optinfization,techniques,applicable to polypeptide,energy,models.,This general,structure prediction,objective,is also known,as the,protein,fold- ing problem. Our prediction algorithms, based on gen- eral fifil-atom potential energy models, are expanded to incorporate,domain,knowledge,into the,search,pro- cess. Specifically. we evaluate,the effectiveness,of a real- valued,genetic,algorithm,exploiting,domain,knowledge about,certain,dihedral,angle,values,inorder,to limit the search space.,We contrast,this approach,with our hybrid

Charles E. Kaiser Jr.; Gary B. Lamont; Laurence D. Merkle; George H. Gates Jr.; Ruth Pachter

1997-01-01

338

Rational Design of Temperature-Sensitive Alleles Using Computational Structure Prediction  

PubMed Central

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

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

2011-01-01

339

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

NASA Astrophysics Data System (ADS)

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

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

340

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

ERIC Educational Resources Information Center

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

Nathan, Lawrence C.

1985-01-01

341

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

PubMed

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

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

2014-11-21

342

Protein Structure Prediction by Threading. Why it Works and Why it Does Not  

E-print Network

, Cambridge, MA 02138 USA We developed a novel Monte Carlo threading algorithm which allows gaps to the optimal one; (iii) as Monte Carlo temperature decreases a sharp cooperative tran- sition to the optimal¯y. # 1998 Academic Press Keywords: threading; Monte Carlo procedure; protein structure prediction

Mirny, Leonid

343

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

PubMed Central

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

Röttig, Marc; Rausch, Christian; Kohlbacher, Oliver

2010-01-01

344

Prediction of membrane protein structures with complex topologies using limited constraints  

E-print Network

Prediction of membrane protein structures with complex topologies using limited constraints P tested the method on 12 membrane proteins of diverse topologies and functions with lengths ranging between 190 and 300 residues. Enforcing a single constraint during the folding simulations en- riched

Baker, David

345

Neural Network Prediction of Reduced Ion Mobility of Chemical Compound Based on Molecular Structure  

Microsoft Academic Search

We present a user-friendly hardware learning algorithm called the cascade error projection (CEP) that was developed at JPL and was equipped with a new input feature mapping technique. This new technique is based on Riemannian metric tensor to enhance the learning capability for predicting the reduced ion mobility based on the molecular structure. Our simulation results are reported and compared

Tuan A. Duong; De-ling Liu; Isik Kanik

2006-01-01

346

Fatigue analysis and life prediction of bridges with structural health monitoring data — Part II: application  

Microsoft Academic Search

This paper is a continuation of the paper titled “FATIGUE ANALYSIS AND LIFE PREDICTION OF BRIDGES WITH STRUCTURAL HEALTH MONITORING DATA — PART I: METHODOLOGY AND STRATEGY” with the emphasis on application of the developed method to the fatigue damage assessment of the Tsing Ma Bridge. Based on the methodology and strategy of the fatigue analysis presented in Part I,

T. H. T. Chan; Z. X. Li; J. M. Ko

2001-01-01

347

15 Structure Prediction of Protein Complexes Brian Pierce, Andrew T. Phillips, and Zhiping Weng  

E-print Network

Docking: Definition Protein­protein docking can be defined as the determination of the complex struc- ture15 Structure Prediction of Protein Complexes Brian Pierce, Andrew T. Phillips, and Zhiping Weng 15.1 Introduction Protein­protein interactions are critical for biological function. They directly and indirectly

Weng, Zhiping

348

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

E-print Network

Loading and Response of Offshore Wind Turbine Support Structures: Prediction with Comparison, offshore wind support platforms differ from oil platforms is several important ways: First, wind platforms turbines, combined with the relatively slender profit margins in the offshore wind business, makes cost

Sweetman, Bert

349

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

E-print Network

(secretin-like), class B2 (adhesion- like), class C (glutamate-like), and Frizzled/Taste2 (12). Class B1Predicted structure of agonist-bound glucagon-like peptide 1 receptor, a class B G protein-coupled receptor Andrea Kirkpatrick, Jiyoung Heo1 , Ravinder Abrol2 , and William A. Goddard III2 Materials

Goddard III, William A.

350

proteinsSTRUCTURE O FUNCTION O BIOINFORMATICS Computationally-predicted CB1 cannabinoid  

E-print Network

proteinsSTRUCTURE O FUNCTION O BIOINFORMATICS Computationally-predicted CB1 cannabinoid receptor, including neurotransmitters, hormones, and light, across the cell membrane. The cannabinoid receptor one (CB active mutant; CB1, cannabinoid 1 receptor; CP55940, (1R,3R,4R)-3-[2-hydroxy-4-(1,1-dimethyl- heptyl

Goddard III, William A.

351

Fragility curves for service life prediction of deteriorating structures based on monitoring simulation  

E-print Network

Fragility curves for service life prediction of deteriorating structures based on monitoring based on fragility curves is proposed. It is firstly applied on sets of experimental data concerning. The computa- tion of the Pf(t) for different damage levels is then performed and the "fragility curves

Garavaglia, Elsa

352

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

NASA Technical Reports Server (NTRS)

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

Ko, William L.; Fleischer, Van Tran

2012-01-01

353

FLUTTER PREDICTION OF A TRANSONIC FAN WITH TRAVELLING WAVE USING FULLY COUPLED FLUID/STRUCTURE INTERACTION  

E-print Network

FLUTTER PREDICTION OF A TRANSONIC FAN WITH TRAVELLING WAVE USING FULLY COUPLED FLUID/structure interac- tion (FSI) to investigate the flutter mechanism of a modern transonic fan rotor with a forward at the circumferential boundaries. The present FSI simulations show that the shock instability causes the flutter. When

Zha, Gecheng

354

Automated Alphabet Reduction Method with Evolutionary Algorithms for Protein Structure Prediction Biological Applications Track  

Microsoft Academic Search

This paper focuses on automated procedures to reduce the dimensionality of protein structure prediction datasets by simplifying the way in which the primary sequence of a pro- tein is represented. The potential benefits of this proce- dure are faster and easier learning process and generation of more compact and human-readable solutions. This sim- plification consists of an alphabet reduction procedure

Jaume Bacardit; Michael Stout; Jonathan D. Hirst; Kumara Sastry; Xavier Llor; Natalio Krasnogor

355

Using Lidar and Radar measurements to constrain predictions of forest2 ecosystem structure and function3  

E-print Network

#12;1 1 Using Lidar and Radar measurements to constrain predictions of forest2 ecosystem structure in their spatial extent. Lidar and Synthetic Aperture Radar are promising remote47 sensing-based techniques investigate how Lidar-derived forest heights and49 Radar-derived above-ground biomass can be used to constrain

Moorcroft, Paul R.

356

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

PubMed

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

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

2014-10-01

357

HYDRONMR: Prediction of NMR Relaxation of Globular Proteins from Atomic-Level Structures and Hydrodynamic Calculations  

Microsoft Academic Search

The heteronuclear NMR relaxation of globular proteins depends on the anisotropic rotational diffusion tensor. Using our previous developments for prediction of hydrodynamic properties of arbitrarily shaped particles, by means of bead models, we have constructed a computational procedure to calculate the rotational diffusion tensor and other properties of proteins from their detailed, atomic-level structure. From the atomic coordinates file used

J Garc??a de la Torre; M. L. Huertas; B. Carrasco

2000-01-01

358

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

E-print Network

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

Erdogan, Hakan

359

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

PubMed Central

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

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

2014-01-01

360

Engineering a software tool for gene structure prediction in higher organisms Gordon Gremme a  

E-print Network

the same genes and pathways in the context of all the genes of an organism, mapped onto the chromosomesEngineering a software tool for gene structure prediction in higher organisms Gordon Gremme.V. All rights reserved. Keywords: Computational biology; Genome annotation; Similarity-based gene

Brendel, Volker

361

Protein Secondary Structure Prediction Based on Position-specific Scoring Matrices  

Microsoft Academic Search

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

David T. Jones

1999-01-01

362

Behavioral/Systems/Cognitive The Statistical Structure of Human Speech Sounds Predicts  

E-print Network

The similarity of musical scales and consonance judgments across human populations has no generally accepted by the physical parameters of the stimulus per se. Key words: audition; auditory system; perception; music; scalesBehavioral/Systems/Cognitive The Statistical Structure of Human Speech Sounds Predicts Musical

Purves, Dale

363

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

E-print Network

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

Moreira, Bruno Contreras

364

Modeling and Prediction of Structure-Borne Seek Noise of Hard Disk Drives  

Microsoft Academic Search

A numerical approach is presented for modeling and predicting the structure-borne seek noise in hard disk drives (HDDs) in time-domain. Rayleigh integral is adopted to relate the transient acceleration of top cover to its radiated sound pressure. A finite element modeling and simulation technique is employed to arrive at the transient vibration response, which is further used as the input

H. Zheng; J. Q. Mou; W. Z. Lin; E. H. Ong

2009-01-01

365

Phylogenetic hypotheses of gorgoniid octocorals according to ITS2 and their predicted RNA secondary structures  

Microsoft Academic Search

Gorgoniid octocorals taxonomy (Cnidaria; Octocorallia; Gorgoniidae) includes diagnostic characters not well defined at the generic level, and based on the family diagnosis some species could be classified in either Gorgoniidae or Plexauridae. In this study, we used sequences from the Internal Transcribed Spacer 2 (ITS2) and their predicted RNA secondary structure to both correct the alignment and reconstruct phylogenies using

Catalina Aguilar; Juan Armando Sánchez

2007-01-01

366

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

PubMed Central

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

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

2013-01-01

367

Probabilistic Graphical Model for Protein Structure Prediction If we know the primary sequence of a protein, can we predict its three  

E-print Network

for mainlyalpha proteins. Short Bio: Dr. Jinbo Xu currently is an assistant professor at the Toyota TechnologicalX/RAPTOR programs have been ranked very top in several CASP (Critical Assessment of Structure Prediction) events

Goldwasser, Shafi

368

Prediction of protein structure by evaluation of sequence-structure fitness. Aligning sequences to contact profiles derived from three-dimensional structures.  

PubMed

The problem of protein structure prediction is formulated here as that of evaluating how well an amino acid sequence fits a hypothetical structure. The simplest and most complicated approaches, secondary structure prediction and all-atom free energy calculations, can be viewed as sequence-structure fitness problems. Here, an approach of intermediate complexity is described, which involves; (1) description of a protein structure in terms of contact interface vectors, with both intra-protein and protein-solvent contacts counted, (2) derivation of sequence preferences for 2 up to 29 contact interface types, (3) generation of numerous hypothetical model structures by placing the input sequence into a large set of known three-dimensional structures in all possible alignments, (4) evaluation of these models by summing the sequence preferences over all structural positions and (5) choice of predicted three-dimensional structure as that with the best sequence-structure fitness. Evolutionary information is incorporated by using position-dependent core weights derived from multiple sequence alignments. A number of tests of the method are performed: (1) evaluation of cyclic shifts of a sequence in its native structure; (2) alignment of a sequence in its native structure, allowing gaps; (3) alignment search with a sequence or sequence fragment in a database of structures; and (4) alignment search with a structure in a database of sequences. The main results are: (1) a native sequence can very well find its native structure among a large number of alternatives, in correct alignment; (2) substructures, such as (beta alpha)n units, can be detected in spite of very low sequence similarity; (3) remote homologous can be detected, with some dependence on the set of parameters used; (4) contact interface parameters are clearly superior to classical secondary structure parameters; (5) a simple interface description in terms of just two states, protein-protein and protein-water contacts, performs surprisingly well; (6) the use of core weights considerably improves accuracy in detection of remote homologues; (7) based on a sequence database search with a myoglobin contact profile, the C-terminal domain of a viral origin of replication binding protein is predicted to have an all-helical fold. The sequence-structure fitness concept is sufficiently general to accommodate a large variety of protein structure prediction methods, including new models of intermediate complexity currently being developed. PMID:8355272

Ouzounis, C; Sander, C; Scharf, M; Schneider, R

1993-08-01

369

Predicted and experimental crystal structures of ethyl-tert-butyl ether.  

PubMed

Possible crystal structures of ethyl-tert-butyl ether (ETBE) were predicted by global lattice-energy minimizations using the force-field approach. 33 structures were found within an energy range of 2 kJmol(-1) above the global minimum. Low-temperature crystallization experiments were carried out at 80-160 K. The crystal structure was determined from X-ray powder data. ETBE crystallizes in C2/m, Z = 4, with molecules on mirror planes. The ETBE molecule adopts a trans conformation with a (CH(3))(3)C-O-C-C torsion angle of 180°. The experimental structure corresponds with high accuracy to the predicted structure with energy rank 2, which has an energy of 0.54 kJmol(-1) above the global minimum and is the most dense low-energy structure. In some crystallization experiments a second polymorph was observed, but the quality of the powder data did not allow the determination of the crystal structure. Possibilities and limitations are discussed for solving crystal structures from powder diffraction data by real-space methods and lattice-energy minimizations. PMID:21422614

Hammer, Sonja M; Alig, Edith; Fink, Lothar; Schmidt, Martin U

2011-04-01

370

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

PubMed

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

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

2014-06-23

371

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

E-print Network

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

Bryan, Allen W.

372

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

NASA Astrophysics Data System (ADS)

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

Ashwill, Thomas D.; Veers, Paul S.

373

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

PubMed Central

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

Moriguchi, I; Hirano, H; Hirono, S

1996-01-01

374

Uncertainties in predicting structure-borne sound power input into buildings.  

PubMed

There has been a steady development of methods of measurement and prediction of structure-borne noise in buildings, particularly over the last two decades. In proposing and evaluating these methods, a major consideration has been the likely trade-off between accuracy and simplicity. Structure-borne sound transmission is a more complicated process than airborne sound transmission, but practitioners seek methods of prediction for the former, which are as straightforward as for the latter. In this paper a description is given of a study of multi-contact sources in buildings. The study concentrates on measurement and calculation procedures for sources and calculation procedures for receiver structures, particularly lightweight building elements. Although the study is not exhaustive, the findings point to the limitations of simplified methods, specifically the uncertainties likely as a result of reducing the data sets and computational effort, and the discrepancies resulting from simplifying assumptions. PMID:23654376

Gibbs, B M

2013-05-01

375

A protein structural classes prediction method based on PSI-BLAST profile.  

PubMed

Knowledge of protein structural classes plays an important role in understanding protein folding patterns. Prediction of protein structural class based solely on sequence data remains to be a challenging problem. In this study, we extract the long-range correlation information and linear correlation information from position-specific score matrix (PSSM). A total of 3600 features are extracted, then, 278 features are selected by a filter feature selection method based on 1189 dataset. To verify the performance of our method (named by LCC-PSSM), jackknife tests are performed on three widely used low similarity benchmark datasets. Comparison of our results with the existing methods shows that our method provides the favorable performance for protein structural class prediction. Stand-alone version of the proposed method (LCC-PSSM) is written in MATLAB language and it can be downloaded from http://bioinfo.zstu.edu.cn/LCC-PSSM/. PMID:24607742

Ding, Shuyan; Yan, Shoujiang; Qi, Shuhua; Li, Yan; Yao, Yuhua

2014-07-21

376

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

NASA Technical Reports Server (NTRS)

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

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

2007-01-01

377

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

PubMed

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

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

2015-02-01

378

A global machine learning based scoring function for protein structure prediction.  

PubMed

We present a knowledge-based function to score protein decoys based on their similarity to native structure. A set of features is constructed to describe the structure and sequence of the entire protein chain. Furthermore, a qualitative relationship is established between the calculated features and the underlying electromagnetic interaction that dominates this scale. The features we use are associated with residue-residue distances, residue-solvent distances, pairwise knowledge-based potentials and a four-body potential. In addition, we introduce a new target to be predicted, the fitness score, which measures the similarity of a model to the native structure. This new approach enables us to obtain information both from decoys and from native structures. It is also devoid of previous problems associated with knowledge-based potentials. These features were obtained for a large set of native and decoy structures and a back-propagating neural network was trained to predict the fitness score. Overall this new scoring potential proved to be superior to the knowledge-based scoring functions used as its inputs. In particular, in the latest CASP (CASP10) experiment our method was ranked third for all targets, and second for freely modeled hard targets among about 200 groups for top model prediction. Ours was the only method ranked in the top three for all targets and for hard targets. This shows that initial results from the novel approach are able to capture details that were missed by a broad spectrum of protein structure prediction approaches. Source codes and executable from this work are freely available at http://mathmed.org/#Software and http://mamiris.com/. PMID:24264942

Faraggi, Eshel; Kloczkowski, Andrzej

2014-05-01

379

Prediction of sub-cavity binding preferences using an adaptive physicochemical structure representation  

PubMed Central

Motivation: The ability to predict binding profiles for an arbitrary protein can significantly improve the areas of drug discovery, lead optimization and protein function prediction. At present, there are no successful algorithms capable of predicting binding profiles for novel proteins. Existing methods typically rely on manually curated templates or entire active site comparison. Consequently, they perform best when analyzing proteins sharing significant structural similarity with known proteins (i.e. proteins resulting from divergent evolution). These methods fall short when used to characterize the binding profile of a novel active site or one for which a template is not available. In contrast to previous approaches, our method characterizes the binding preferences of sub-cavities within the active site by exploiting a large set of known protein–ligand complexes. The uniqueness of our approach lies not only in the consideration of sub-cavities, but also in the more complete structural representation of these sub-cavities, their parametrization and the method by which they are compared. By only requiring local structural similarity, we are able to leverage previously unused structural information and perform binding inference for proteins that do not share significant structural similarity with known systems. Results: Our algorithm demonstrates the ability to accurately cluster similar sub-cavities and to predict binding patterns across a diverse set of protein–ligand complexes. When applied to two high-profile drug targets, our algorithm successfully generates a binding profile that is consistent with known inhibitors. The results suggest that our algorithm should be useful in structure-based drug discovery and lead optimization. Contact: izharw@cs.toronto.edu; lilien@cs.toronto.edu PMID:19478002

Wallach, Izhar; Lilien, Ryan H.

2009-01-01

380

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

PubMed Central

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

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

2011-01-01

381

Clinical Prediction from Structural Brain MRI Scans: A Large-Scale Empirical Study.  

PubMed

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

Sabuncu, Mert R; Konukoglu, Ender

2014-07-22

382

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

PubMed

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

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

2013-09-01

383

Use of quantitative structural analysis to predict fish bioconcentration factors for pesticides.  

PubMed

The focus of this research was to develop a model based solely on molecular descriptors capable of predicting fish bioconcentration factors (BCF). A fish BCF database was developed from high-quality, regulatory agency reviewed studies for pesticides based on the same laboratory protocol and the same fish species, Lepomis macrochirus. A commercially available software program was used to create a quantitative structure-activity relationship (QSAR) from 93 BCF studies based on unique molecules. An additional 16 molecules were used to test the accuracy of QSAR model predictions for a variety of pesticide classes. Regression of the measured versus predicted log BCF values yielded a regression coefficient of 0.88 for the validation data set. On the basis of the results from this research, the ability to predict BCF by a QSAR regression model is improved using a fully structurally derived model based solely on structural data such as the number of atoms for a given group (e.g., -CH3) or the local topology of each atom as derived from electron counts. Such descriptors provide insightful information on a molecule's potential BCF behavior in aquatic systems. PMID:19138085

Jackson, Scott H; Cowan-Ellsberry, Christina E; Thomas, Gareth

2009-02-11

384

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

PubMed Central

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

2014-01-01

385

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

PubMed

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

Fleischer, Everly B; Janda, Kenneth C

2013-05-16

386

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

PubMed

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

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

2013-09-01

387

Flow structure generated by perpendicular blade vortex interaction and implications for helicopter noise predictions  

NASA Technical Reports Server (NTRS)

Activities carried out in support of research on flow structure generated by perpendicular blade vortex interaction and implications for helicopter noise prediction are summarized. Progress in the following areas is described: (1) construction of 8 inch-chord NACA 0012 full-span blade; (2) Acquisition of two full-span blades; (3) preparation for hot wire measurements; (4) related work on a modified Betz's theory; and (5) work related to helicopter noise prediction. In addition, a list of publications based on the results of prior experimentation is presented.

Devenport, William J.; Glegg, Stewart A. L.

1994-01-01

388

Automated antibody structure prediction using Accelrys tools: Results and best practices  

PubMed Central

We describe the methodology and results from our participation in the second Antibody Modeling Assessment experiment. During the experiment we predicted the structure of eleven unpublished antibody Fv fragments. Our prediction methods centered on template-based modeling; potential templates were selected from an antibody database based on their sequence similarity to the target in the framework regions. Depending on the quality of the templates, we constructed models of the antibody framework regions either using a single, chimeric or multiple template approach. The hypervariable loop regions in the initial models were rebuilt by grafting the corresponding regions from suitable templates onto the model. For the H3 loop region, we further refined models using ab initio methods. The final models were subjected to constrained energy minimization to resolve severe local structural problems. The analysis of the models submitted show that Accelrys tools allow for the construction of quite accurate models for the framework and the canonical CDR regions, with RMSDs to the X-ray structure on average below 1 Å for most of these regions. The results show that accurate prediction of the H3 hypervariable loops remains a challenge. Furthermore, model quality assessment of the submitted models show that the models are of quite high quality, with local geometry assessment scores similar to that of the target X-ray structures. Proteins 2014; 82:1583–1598. © 2014 The Authors. Proteins published by Wiley Periodicals, Inc. PMID:24833271

Fasnacht, Marc; Butenhof, Ken; Goupil-Lamy, Anne; Hernandez-Guzman, Francisco; Huang, Hongwei; Yan, Lisa

2014-01-01

389

Structure-Based Activity Prediction for an Enzyme of Unknown Function  

SciTech Connect

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

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

2007-01-01

390

The Yin and Yang of Repair Mechanisms in DNA Structure-induced Genetic Instability  

PubMed Central

DNA can adopt a variety of secondary structures that deviate from the canonical Watson-Crick B-DNA form. More than 10 types of non-canonical or non-B DNA secondary structures have been characterized, and the sequences that have the capacity to adopt such structures are very abundant in the human genome. Non-B DNA structures have been implicated in many important biological processes and can serve as sources of genetic instability, implicating them in disease and evolution. Non-B DNA conformations interact with a wide variety of proteins involved in replication, transcription, DNA repair, and chromatin architectural regulation. In this review, we will focus on the interactions of DNA repair proteins with non-B DNA and their roles in genetic instability, as the proteins and DNA involved in such interactions may represent plausible targets for selective therapeutic intervention. PMID:23219604

Vasquez, Karen M.; Wang, Guliang

2013-01-01

391

A model for predicting damage induced fatigue life of laminated composite structural components  

NASA Technical Reports Server (NTRS)

This paper presents a model for predicting the life of laminated composite structural components subjected to fatigue induced microstructural damage. The model uses the concept of continuum damage mechanics, wherein the effects of microcracks are incorporated into a damage dependent lamination theory instead of treating each crack as an internal boundary. Internal variables are formulated to account for the effects of both matrix cracks and internal delaminations. Evolution laws for determining the damage variables as functions of ply stresses are proposed, and comparisons of predicted damage evolution are made to experiment. In addition, predicted stiffness losses, as well as ply stresses are shown as functions of damage state for a variety of stacking sequences.

Allen, David H.; Lo, David C.; Georgiou, Ioannis T.; Harris, Charles E.

1990-01-01

392

Fatigue life prediction of welded structures containing non-planar flaws using Quality Category under non-constant amplitude loading  

Microsoft Academic Search

The prediction of fatigue life is important for welded structures. A new fatigue life prediction method of welded structures containing non-planar flaws was developed in this article. The new method is based on the Quality Category of PD6493 and Miner's liner damage rule. It accounts for the maximum height or width of slag inclusions, and the percentage of projected area

Jin Xing; Q. P. Zhong; Y. J. Hong; J. F. Tian

1998-01-01

393

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

PubMed Central

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

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

2014-01-01

394

Predicted Structures of Agonist and Antagonist Bound Complexes of Adenosine A3 Receptor  

PubMed Central

We used the GEnSeMBLE Monte Carlo method to predict ensemble of the 20 best packings (helix rotations and tilts) based on the neutral total energy (E) from a vast number (10 trillion) of potential packings for each of the 4 subtypes of the adenosine G protein-coupled receptors (GPCRs), which are involved in many cytoprotective functions. We then used the DarwinDock Monte Carlo methods to predict the binding pose for the human A3 adenosine receptor (hAA3R) for subtype selective agonists and antagonists. We find that all four A3 agonists stabilize the 15th lowest conformation of apo-hAA3R while also binding strongly to the 1st and 3rd. In contrast the four A3 antagonists stabilize the 2nd or 3rd lowest conformation. These results show that different ligands can stabilize different GPCR conformations, which will likely affect function, complicating the design of functionally unique ligands. Interestingly all agonists lead to a trans ?1 angle for W6.48 that experiments on other GPCRs associate with G-protein activation while all 20 apo-AA3R conformations have a W6.48 gauche+ ?1 angle associated experimentally with inactive GPCRs for other systems. Thus docking calculations have identified critical ligand-GPCR structures involved with activation. We find that the predicted binding site for selective agonist Cl-IB-MECA to the predicted structure of hAA3R shows favorable interactions to three subtype variable residues, I2536.58, V169EL2, and Q167EL2, while the predicted structure for hAA2AR shows weakened to the corresponding amino acids: T2566.58, E169EL2, and L167EL2, explaining the observed subtype selectivity. PMID:21488099

Kim, Soo-Kyung; Riley, Lindsay; Abrol, Ravinder; Jacobson, Kenneth A.; Goddard, William A.

2011-01-01

395

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

NASA Astrophysics Data System (ADS)

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

Schuster, Peter

2006-05-01

396

Enhancement of accuracy and efficiency for RNA secondary structure prediction by sequence segmentation and MapReduce  

PubMed Central

Background Ribonucleic acid (RNA) molecules play important roles in many biological processes including gene expression and regulation. Their secondary structures are crucial for the RNA functionality, and the prediction of the secondary structures is widely studied. Our previous research shows that cutting long sequences into shorter chunks, predicting secondary structures of the chunks independently using thermodynamic methods, and reconstructing the entire secondary structure from the predicted chunk structures can yield better accuracy than predicting the secondary structure using the RNA sequence as a whole. The chunking, prediction, and reconstruction processes can use different methods and parameters, some of which produce more accurate predictions than others. In this paper, we study the prediction accuracy and efficiency of three different chunking methods using seven popular secondary structure prediction programs that apply to two datasets of RNA with known secondary structures, which include both pseudoknotted and non-pseudoknotted sequences, as well as a family of viral genome RNAs whose structures have not been predicted before. Our modularized MapReduce framework based on Hadoop allows us to study the problem in a parallel and robust environment. Results On average, the maximum accuracy retention values are larger than one for our chunking methods and the seven prediction programs over 50 non-pseudoknotted sequences, meaning that the secondary structure predicted using chunking is more similar to the real structure than the secondary structure predicted by using the whole sequence. We observe similar results for the 23 pseudoknotted sequences, except for the NUPACK program using the centered chunking method. The performance analysis for 14 long RNA sequences from the Nodaviridae virus family outlines how the coarse-grained mapping of chunking and predictions in the MapReduce framework exhibits shorter turnaround times for short RNA sequences. However, as the lengths of the RNA sequences increase, the fine-grained mapping can surpass the coarse-grained mapping in performance. Conclusions By using our MapReduce framework together with statistical analysis on the accuracy retention results, we observe how the inversion-based chunking methods can outperform predictions using the whole sequence. Our chunk-based approach also enables us to predict secondary structures for very long RNA sequences, which is not feasible with traditional methods alone. PMID:24564983

2013-01-01

397

Prediction of stable hafnium carbides: Stoichiometries, mechanical properties, and electronic structure  

NASA Astrophysics Data System (ADS)

We have performed a search for stable compounds in the hafnium-carbon (Hf-C) system at ambient pressure using a variable-composition ab initio evolutionary algorithm implemented in the uspex code. In addition to the well-known HfC, we predicted two additional thermodynamically stable compounds Hf3C2 and Hf6C5. The structure of Hf6C5 with space group C2/m contains 22 atoms in the conventional cell, and this prediction revives the earlier proposal by Gusev and Rempel [Phys. Status Solidi A 135, 15 (1993), 10.1002/pssa.2211350102]. The stable structure of Hf3C2 also has space group C2/m and is more energetically favorable than the Immm ,P3¯m1,P2, and C2221 structures put forward by Gusev and Rempel [Phys. Status Solidi A 135, 15 (1993), 10.1002/pssa.2211350102]. The dynamical and mechanical stabilities of the newly predicted structures have been verified by calculations of their phonons and elastic constants. Structural vacancies are found in the ordered defective rock-salt-type HfC. Chemical bonding, band structure, and Bader charges are presented and are discussed. All three compounds are weak metals with increasing metallicity as the vacancy concentration increases. The mechanical properties of the hafnium carbides nonlinearly decrease with increasing vacancy concentration, indicating the defect tolerance of this refractory compound. It is, therefore, possible to tune the hardness, ductility, and electrical conductivity by varying the stoichiometry of the hafnium carbides.

Zeng, Qingfeng; Peng, Junhui; Oganov, Artem R.; Zhu, Qiang; Xie, Congwei; Zhang, Xiaodong; Dong, Dong; Zhang, Litong; Cheng, Laifei

2013-12-01

398

LiveBench-1: continuous benchmarking of protein structure prediction servers.  

PubMed

We present a novel, continuous approach aimed at the large-scale assessment of the performance of available fold-recognition servers. Six popular servers were investigated: PDB-Blast, FFAS, T98-lib, GenTHREADER, 3D-PSSM, and INBGU. The assessment was conducted using as prediction targets a large number of selected protein structures released from October 1999 to April 2000. A target was selected if its sequence showed no significant similarity to any of the proteins previously available in the structural database. Overall, the servers were able to produce structurally similar models for one-half of the targets, but significantly accurate sequence-structure alignments were produced for only one-third of the targets. We further classified the targets into two sets: easy and hard. We found that all servers were able to find the correct answer for the vast majority of the easy targets if a structurally similar fold was present in the server's fold libraries. However, among the hard targets--where standard methods such as PSI-BLAST fail--the most sensitive fold-recognition servers were able to produce similar models for only 40% of the cases, half of which had a significantly accurate sequence-structure alignment. Among the hard targets, the presence of updated libraries appeared to be less critical for the ranking. An "ideally combined consensus" prediction, where the results of all servers are considered, would increase the percentage of correct assignments by 50%. Each server had a number of cases with a correct assignment, where the assignments of all the other servers were wrong. This emphasizes the benefits of considering more than one server in difficult prediction tasks. The LiveBench program (http://BioInfo.PL/LiveBench) is being continued, and all interested developers are cordially invited to join. PMID:11266621

Bujnicki, J M; Elofsson, A; Fischer, D; Rychlewski, L

2001-02-01

399

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

SciTech Connect

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

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

1998-12-31

400

Data quality in predictive toxicology: identification of chemical structures and calculation of chemical properties.  

PubMed Central

Every technique for toxicity prediction and for the detection of structure-activity relationships relies on the accurate estimation and representation of chemical and toxicologic properties. In this paper we discuss the potential sources of errors associated with the identification of compounds, the representation of their structures, and the calculation of chemical descriptors. It is based on a case study where machine learning techniques were applied to data from noncongeneric compounds and a complex toxicologic end point (carcinogenicity). We propose methods applicable to the routine quality control of large chemical datasets, but our main intention is to raise awareness about this topic and to open a discussion about quality assurance in predictive toxicology. The accuracy and reproducibility of toxicity data will be reported in another paper. PMID:11102292

Helma, C; Kramer, S; Pfahringer, B; Gottmann, E

2000-01-01

401

Reported and predicted structures of Ba(Co,Nb)(1-?)O? hexagonal perovskite phases.  

PubMed

The Extended Module Materials Assembly computational method for structure solution and prediction has been implemented for close-packed lattices. Exploring the family of B-site deficient materials in hexagonal perovskite barium cobalt niobates, it is found that the EMMA procedure returns the experimental structures as the most stable for the known compositions of Ba3CoNb2O9, Ba5Nb4O15 and Ba8CoNb6O24. The unknown compositions Ba11Co2Nb8O33 and Ba13CoNb10O39, having longer stacking sequences, are predicted to form as intergrowths of Ba3CoNb2O9 and Ba5Nb4O15, and are found to have similar stability to pure Ba3CoNb2O9 and Ba5Nb4O15, indicating that it is likely they can be synthesised. PMID:24871400

Bradley, Kathryn A; Collins, Christopher; Dyer, Matthew S; Claridge, John B; Darling, George R; Rosseinsky, Matthew J

2014-10-21

402

A multi-objective evolutionary approach to the protein structure prediction problem  

PubMed Central

The protein structure prediction (PSP) problem is concerned with the prediction of the folded, native, tertiary structure of a protein given its sequence of amino acids. It is a challenging and computationally open problem, as proven by the numerous methodological attempts and the research effort applied to it in the last few years. The potential energy functions used in the literature to evaluate the conformation of a protein are based on the calculations of two different interaction energies: local (bond atoms) and non-local (non-bond atoms). In this paper, we show experimentally that those types of interactions are in conflict, and do so by using the potential energy function Chemistry at HARvard Macromolecular Mechanics. A multi-objective formulation of the PSP problem is introduced and its applicability studied. We use a multi-objective evolutionary algorithm as a search procedure for exploring the conformational space of the PSP problem. PMID:16849226

Cutello, Vincenzo; Narzisi, Giuseppe; Nicosia, Giuseppe

2005-01-01

403

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

NASA Technical Reports Server (NTRS)

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

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

1994-01-01

404

Computational Analysis of structure-based interactions and ligand properties can predict efflux effects on antibiotics  

PubMed Central

AcrA-AcrB-TolC efflux pumps extrude drugs of multiple classes from bacterial cells and are a leading cause for antimicrobial resistance. Thus, they are of paramount interest to those engaged in antibiotic discovery. Accurate prediction of antibiotic efflux has been elusive, despite several studies aimed at this purpose. Minimum inhibitory concentration (MIC) ratios of 32 ?-lactam antibiotics were collected from literature. 3-Dimensional Quantitative Structure Activity Relationship on the ?-lactam antibiotic structures revealed seemingly predictive models (q2 = 0.53), but the lack of a general superposition rule does not allow its use on antibiotics that lack the ?-lactam moiety. Since MIC ratios must depend on interactions of antibiotics with lipid membranes and transport proteins during influx, capture and extrusion of antibiotics from the bacterial cell, descriptors representing these factors were calculated and used in building mathematical models that quantitatively classify antibiotics as having high/low efflux (>93% accuracy). Our models provide preliminary evidence that it is possible to predict the effects of antibiotic efflux if the passage of antibiotics into, and out of, bacterial cells is taken into account – something descriptor and field-based QSAR models cannot do. While the paucity of data in the public domain remains the limiting factor in such studies, these models show significant improvements in predictions over simple LogP-based regression models and should pave the path towards further work in this field. This method should also be extensible to other pharmacologically and biologically relevant transport proteins. PMID:22483632

Sarkar, Aurijit; Anderson, Kelcey C.; Kellogg, Glen E.

2012-01-01

405

Quantitative structure-ion intensity relationship strategy to the prediction of absolute levels without authentic standards.  

PubMed

The lack of authentic standards represents a major bottleneck in the quantitative analysis of complex samples. Here we propose a quantitative structure and ionization intensity relationship (QSIIR) approach to predict the absolute levels of compounds in complex matrixes. An absolute quantitative method for simultaneous quantification of 25 organic acids was firstly developed and validated. Napierian logarithm (LN) of the relative slope rate derived from the calibration curves was applied as an indicator of the relative ionization intensity factor (RIIF) and serves as the dependent variable for building a QSIIR model via a multiple linear regression (MLR) approach. Five independent variables representing for hydrogen bond acidity, HOMO energy, the number of hydrogen bond donating group, the ratio of organic phase, and the polar solvent accessible surface area were found as the dominant contributors to the RIIF of organic acids. This QSIIR model was validated to be accurate and robust, with the correlation coefficients (R(2)), R(2) adjusted, and R(2) prediction at 0.945, 0.925, and 0.89, respectively. The deviation of accuracy between the predicted and experimental value in analyzing a real complex sample was less than 20% in most cases (15/18). Furthermore, the high adaptability of this model was validated one year later in another LC/MS system. The QSIIR approach is expected to provide better understanding of quantitative structure and ionization efficiency relationship of analogous compounds, and also to be useful in predicting the absolute levels of analogous analytes in complex mixtures. PMID:23972977

Wu, Liang; Wu, Yuzheng; Shen, Hanyuan; Gong, Ping; Cao, Lijuan; Wang, Guangji; Hao, Haiping

2013-09-10

406

Using the RosettaSurface algorithm to predict protein structure at mineral surfaces.  

PubMed

Determination of protein structure on mineral surfaces is necessary to understand biomineralization processes toward better treatment of biomineralization diseases and design of novel protein-synthesized materials. To date, limited atomic-resolution data have hindered experimental structure determination for proteins on mineral surfaces. Molecular simulation represents a complementary approach. In this chapter, we review RosettaSurface, a computational structure prediction-based algorithm designed to broadly sample conformational space to identify low-energy structures. We summarize the computational approaches, the published applications, and the new releases of the code in the Rosetta 3 framework. In addition, we provide a protocol capture to demonstrate the practical steps to employ RosettaSurface. As an example, we provide input files and output data analysis for a previously unstudied mineralization protein, osteocalcin. Finally, we summarize ongoing challenges in energy function optimization and conformational searching and suggest that the fusion between experiment and calculation is the best route forward. PMID:24188775

Pacella, Michael S; Koo, Da Chen Emily; Thottungal, Robin A; Gray, Jeffrey J

2013-01-01

407

Finite Element Prediction of Acoustic Scattering and Radiation from Submerged Elastic Structures  

NASA Technical Reports Server (NTRS)

A finite element formulation is derived for the scattering and radiation of acoustic waves from submerged elastic structures. The formulation uses as fundamental unknowns the displacement in the structure and a velocity potential in the field. Symmetric coefficient matrices result. The outer boundary of the fluid region is terminated with an approximate local wave-absorbing boundary condition which assumes that outgoing waves are locally planar. The finite element model is capable of predicting only the near-field acoustic pressures. Far-field sound pressure levels may be determined by integrating the surface pressures and velocities over the wet boundary of the structure using the Helmholtz integral. Comparison of finite element results with analytic results show excellent agreement. The coupled fluid-structure problem may be solved with general purpose finite element codes by using an analogy between the equations of elasticity and the wave equation of linear acoustics.

Everstine, G. C.; Henderson, F. M.; Lipman, R. R.

1984-01-01

408

Centenary Award and Sir Frederick Gowland Hopkins Memorial Lecture. Protein folding, structure prediction and design.  

PubMed

I describe how experimental studies of protein folding have led to advances in protein structure prediction and protein design. I describe the finding that protein sequences are not optimized for rapid folding, the contact order-protein folding rate correlation, the incorporation of experimental insights into protein folding into the Rosetta protein structure production methodology and the use of this methodology to determine structures from sparse experimental data. I then describe the inverse problem (protein design) and give an overview of recent work on designing proteins with new structures and functions. I also describe the contributions of the general public to these efforts through the Rosetta@home distributed computing project and the FoldIt interactive protein folding and design game. PMID:24646222

Baker, David

2014-04-01

409

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

NASA Technical Reports Server (NTRS)

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

Ko, William L.; Fleischer, Van Tran

2013-01-01

410

A modified Marriage in Honey Bee Optimisation (MBO) algorithm for protein structure prediction  

Microsoft Academic Search

This paper refines and introduces a modification to the Marriage in Honey Bee Optimisation algorithm (MBO). The modified algorithm was applied to predict the structure of Met-enkphaline using torsion angles representation and ECEPP\\/3 energy function. The results showed the significance of the modification. The algorithm was able to find the lowest reported free energy conformation of value -12.43 kcal\\/mol.

H. A. A. Bahamish; R. Abdullah; M. A. Abu-Hashem

2010-01-01

411

PREDICTION OF FATIGUE LIFE AND CRACK PATH IN GENERIC 2D STRUCTURAL COMPONENTS UNDER COMPLEX LOADING  

Microsoft Academic Search

A reliable and cost effective two-phase methodology is proposed to predict crack propagation in generic two-dimensional structural components under complex fatigue loading. First, the fatigue crack path and its stress intensity factors are calculated in a specialized finite element software, using small crack increments. At each crack propagation step, the mesh is automatically redefined based on a self-adaptive strategy that

Oliveira Miranda; Luiz Fernando Martha; Tulio N. Bittencourt

412

Application of Multiple Sequence Alignment Profiles to Improve Protein Secondary Structure Prediction  

E-print Network

, and as a stand-alone computer program from: http://barton.ebi.ac.uk/. Proteins 2000; 40:502­511. © 2000 Wiley algorithms.1­5 Secondary structure predictions may also be used to guide the design of site directed,13 machine learning,14,15 neural networks,16 ­22 k-way nearest neighbors,23­28 evolutionary trees,29

Barton, Geoffrey J.

413

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

PubMed

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

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

2015-03-01

414

Improved hybrid wavelet neural network methodology for time-varying behavior prediction of engineering structures  

Microsoft Academic Search

An improved neuro-wavelet modeling (NWM) methodology is presented, and it aims at improving prediction precision of time-varying\\u000a behavior of engineering structures. The proposed methodology distinguishes from the existing NWM methodology by featuring\\u000a the distinctive capabilities of constructing optimally uncoupled dynamic subsystems in light of the redundant Haar wavelet\\u000a transform (RHWT) and optimizing neural network. In particular, two techniques of imitating

Maosen Cao; Pizhong Qiao; Qingwen Ren

2009-01-01

415

SPEM: improving multiple sequence alignment with sequence profiles and predicted secondary structures  

Microsoft Academic Search

Motivation: Multiple sequence alignment is an essential part of bioinformatics tools for a genome-scale study of genes and their evolution relations. However, making an accurate alignment between remote homologs is challenging. Here, we develop a method, called SPEM, that aligns multiple sequences using pre-processed sequence profiles and predicted secondary structures for pairwise alignment, consistency-based scoring for refinement of the pairwise

Hongyi Zhou; Yaoqi Zhou

2005-01-01

416

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

Microsoft Academic Search

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

David L. Masica; Jeffrey J. Gray

2009-01-01

417

Computational tools for experimental determination and theoretical prediction of protein structure  

SciTech Connect

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

O`Donoghue, S.; Rost, B.

1995-12-31

418

Prediction and experimental validation of enzyme substrate specificity in protein structures  

PubMed Central

Structural Genomics aims to elucidate protein structures to identify their functions. Unfortunately, the variation of just a few residues can be enough to alter activity or binding specificity and limit the functional resolution of annotations based on sequence and structure; in enzymes, substrates are especially difficult to predict. Here, large-scale controls and direct experiments show that the local similarity of five or six residues selected because they are evolutionarily important and on the protein surface can suffice to identify an enzyme activity and substrate. A motif of five residues predicted that a previously uncharacterized Silicibacter sp. protein was a carboxylesterase for short fatty acyl chains, similar to hormone-sensitive-lipase–like proteins that share less than 20% sequence identity. Assays and directed mutations confirmed this activity and showed that the motif was essential for catalysis and substrate specificity. We conclude that evolutionary and structural information may be combined on a Structural Genomics scale to create motifs of mixed catalytic and noncatalytic residues that identify enzyme activity and substrate specificity. PMID:24145433

Amin, Shivas R.; Erdin, Serkan; Ward, R. Matthew; Lua, Rhonald C.; Lichtarge, Olivier

2013-01-01

419

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

SciTech Connect

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

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

2011-06-01

420

Improved Bevirimat resistance prediction by combination of structural and sequence-based classifiers  

PubMed Central

Background Maturation inhibitors such as Bevirimat are a new class of antiretroviral drugs that hamper the cleavage of HIV-1 proteins into their functional active forms. They bind to these preproteins and inhibit their cleavage by the HIV-1 protease, resulting in non-functional virus particles. Nevertheless, there exist mutations in this region leading to resistance against Bevirimat. Highly specific and accurate tools to predict resistance to maturation inhibitors can help to identify patients, who might benefit from the usage of these new drugs. Results We tested several methods to improve Bevirimat resistance prediction in HIV-1. It turned out that combining structural and sequence-based information in classifier ensembles led to accurate and reliable predictions. Moreover, we were able to identify the most crucial regions for Bevirimat resistance computationally, which are in line with experimental results from other studies. Conclusions Our analysis demonstrated the use of machine learning techniques to predict HIV-1 resistance against maturation inhibitors such as Bevirimat. New maturation inhibitors are already under development and might enlarge the arsenal of antiretroviral drugs in the future. Thus, accurate prediction tools are very useful to enable a personalized therapy. PMID:22082002

2011-01-01

421

Improving the prediction of disease-related variants using protein three-dimensional structure  

PubMed Central

Background Single Nucleotide Polymorphisms (SNPs) are an important source of human genome variability. Non-synonymous SNPs