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1

Predicting B-DNA structure from sequence  

SciTech Connect

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

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

1995-12-31

2

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

NASA Astrophysics Data System (ADS)

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

Kwok, Chun Kit; Lam, Sik Lok

2013-09-01

3

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

4

The structure of a stable intermediate in the A <-> B DNA helix transition  

PubMed Central

The DNA dodecamer CATGGGCCCATG in a crystal structure of resolution 1.3 ? has a conformation intermediate between A and B DNA. This trapping of a stable intermediate suggests that the A and B DNA families are not discrete, as previously believed. The structure supports a base-centered rather than a backbone-centered mechanism for the A ? B transition mediated by guanine tracts. Interconversion between A and B DNA provides another means for regulating protein–DNA recognition.

Ng, Ho-Leung; Kopka, Mary L.; Dickerson, Richard E.

2000-01-01

5

An A-DNA Triplet Code: Thermodynamic Rules for Predicting A- and B-DNA  

Microsoft Academic Search

The ability to predict macromolecular conformations from sequence and thermodynamic principles has long been coveted but generally has not been achieved. We show that differences in the hydration of DNA surfaces can be used to distinguish between sequences that form A- and B-DNA. From this, a \\

Beth Basham; Gary P. Schroth; P. Shing Ho

1995-01-01

6

Models for chromosomal replication-independent non-B DNA structure-induced genetic instability  

PubMed Central

Regions of genomic DNA containing repetitive nucleotide sequences can adopt a number of different structures in addition to the canonical B-DNA form: many of these non-B DNA structures are causative factors in genetic instability and human disease. Although chromosomal DNA replication through such repetitive sequences has been considered a major cause of non-B form DNA structure-induced genetic instability, it is also observed in non-proliferative tissues. In this review, we discuss putative mechanisms responsible for the mutagenesis induced by non-B DNA structures in the absence of chromosomal DNA replication.

Wang, Guliang; Vasquez, Karen M.

2009-01-01

7

Structure of a B-DNA Dodecamer: Conformation and Dynamics  

NASA Astrophysics Data System (ADS)

The crystal structure of the synthetic DNA dodecamer d(CpGpCpGpApApTpTpCpGpCpG) has been refined to a residual error of R = 17.8% at 1.9- angstrom resolution (two-? data). The molecule forms slightly more than one complete turn of righthanded double-stranded B helix. The two ends of the helix overlap and interlock minor grooves with neighboring molecules up and down a 21 screw axis, producing a 19 degrees bend in helix axis over the 11-base-pair steps of the dodecamer. In the center of the molecule, where perturbation is least, the helix has a mean rotation of 36.9 degrees per step, or 9.8 base pairs per turn. The mean propeller twist (total dihedral angle between base planes) between A\\cdot T base pairs in the center of the molecule is 17.3 degrees, and that between C\\cdot G pairs on the two ends averages 11.5 degrees. Individual deoxyribose ring conformations as measured by the C5'-C4'-C3'-O3' torsion angle ? , exhibit an approximately Gaussian distribution centered around the C1'-exo position with ? avg=123 degrees and a range of 79 degrees to 157 degrees. Purine sugars cluster at high ? values, and pyrimidine sugars cluster at lower ? . A tendency toward 2-fold symmetry in sugar conformation about the center of the molecule is detectable in spite of the destruction of ideal 2-fold symmetry by the molecular bending. More strikingly, sugar conformations of paired bases appear to follow a ``principle of anticorrelation,'' with ? values lying approximately the same distance to either side of the center value, ? =123 degrees. This same anticorrelation is also observed in other DNA and DNA\\cdot RNA structures.

Drew, Horace R.; Wing, Richard M.; Takano, Tsunehiro; Broka, Christopher; Tanaka, Shoji; Itakura, Keiichi; Dickerson, Richard E.

1981-04-01

8

Structure of a B-DNA dodecamer: conformation and dynamics.  

PubMed

The crystal structure of the synthetic DNA dodecamer d(CpGpCpGpApApTpTpCpGpCpG) has been refined to a residual error of R = 17.8% at 1.9-A resolution (two-sigma data). The molecule forms slightly more than one complete turn of right-handed double-stranded B helix. The two ends of the helix overlap and interlock minor grooves with neighboring molecules up and down a 2(1) screw axis, producing a 19 degrees bend in helix axis over the 11-base-pair steps of the dodecamer. In the center of the molecule, where perturbation is least, the helix has a mean rotation of 36.9 degrees per step, or 9.8 base pairs per turn. The mean propeller twist (total dihedral angle between base planes) between A . T base pairs in the center of the molecule is 17.3 degrees, and that between C . G pairs on the two ends averages 11.5 degrees. Individual deoxyribose ring conformations as measured by the C5'-C4'-C3'-O3' torsion angle delta, exhibit an approximately Gaussian distribution centered around the C1'-exo position with delta avg = 123 degrees and a range of 79 degrees to 157 degrees. Purine sugars cluster at high delta values, and pyrimidine sugars cluster at lower delta. A tendency toward 2-fold symmetry in sugar conformation about the center of the molecule is detectable in spite of the destruction of ideal 2-fold symmetry by the molecular bending. More strikingly, sugar conformations of paired based appear to follow a "principle of anticorrelation," with delta values lying approximately the same distance to either side of the center value, delta = 123 degrees. This same anticorrelation is also observed in other DNA and DNA . RNA structures. PMID:6941276

Drew, H R; Wing, R M; Takano, T; Broka, C; Tanaka, S; Itakura, K; Dickerson, R E

1981-04-01

9

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

PubMed Central

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

Sharma, Sudha

2011-01-01

10

B-DNA to Zip-DNA: Simulating a DNA Transition to a Novel Structure with Enhanced Charge-Transport Characteristics  

PubMed Central

The forced extension of a DNA segment is studied in a series of steered molecular dynamics simulations, employing a broad range of pulling forces. Throughout the entire force range, the formation of a zipper-like (zip-) DNA structure is observed. In that structure, first predicted by Lohikoski et al., the bases of the DNA strands interdigitate with each other and form a single-base aromatic stack. Similar motifs, albeit only a few base pairs in extent, have been observed in experimental crystal structures. Analysis of the dynamics of structural changes in pulled DNA shows that S-form DNA, thought to be adopted by DNA under applied force, serves as an intermediate between B-DNA and zip-DNA. Therefore, the phase transition plateau observed in force–extension curves of DNA is suggested to reflect the B-DNA to zip-DNA structural transition. Electronic structure analysis of purine bases in zip-DNA indicates a several-fold to order of magnitude increase in the ?–? electronic coupling among nearest-neighbor nucleobases, compared to B-DNA. We further observe that zip-DNA does not require base pair complementarity between DNA strands, and we predict that the increased electronic coupling in zip-DNA will result in a much higher rate of charge transfer through an all-purine zip-DNA compared to B-DNA of equal length.

Balaeff, Alexander; Craig, Stephen L.; Beratan, David N.

2013-01-01

11

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.

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

2008-01-01

12

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

PubMed

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

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

2013-04-25

13

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

PubMed Central

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

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

2013-01-01

14

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

PubMed Central

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; Kruger Woods, Kristen; Moulaei, Tinoush; Hud, Nicholas V.; Dean Williams, Loren

2012-01-01

15

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

16

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

PubMed Central

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

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

2012-01-01

17

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

NASA Astrophysics Data System (ADS)

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

Hummer, Gerhard; Soumpasis, Dikeos Mario

1994-12-01

18

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

2012-11-03

19

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.

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

20

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

21

Double-helical --> ladder structural transition in the B-DNA is induced by a loss of dispersion energy.  

PubMed

The role of the dispersion energy and electrostatic energy on the geometry and stability of the B-DNA helix was investigated. Both molecular dynamics simulations with empirical force field and hybrid quantum mechanical/molecular mechanics molecular dynamics simulations, where the dispersion or electrostatics term is suppressed/increased, on the one hand and an ab initio minimization procedure on the other have shown that the lack of the dispersion term leads to an increase of the vertical separation of the bases as well as to a loss of helicity, thus resulting in a ladder-like structure. A decrease of the electrostatic term produces a separation of the DNA strands. The biological consequences of both electrostatic and dispersion forces in DNA are enormous, and without either of them, DNA would become unstable and unable to provide the storage and transfer of genetic information. PMID:18975944

Cerný, Jirí; Kabelác, Martin; Hobza, Pavel

2008-11-26

22

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

PubMed Central

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

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

2002-01-01

23

Solution Structure of the Dodecamer d-(CATGGGCC-CATG)2 is B-DNA. Experimental and Molecular Dynamics Study  

Microsoft Academic Search

The DNA duplex d-(CATGGGCCCATG)2 has been studied in solution by FTIR, NMR and CD. The experimental approaches have been complemented by series of large-scale unrestrained molecular dynamics simulation with explicit inclusion of solvent and counterions. Typical proton-proton distances extracted from the NMR spectra and the CD spectra are completely in agreement with slightly modified B-DNA. By molecular dynamics simulation, starting

Utz Dornberger; Nadezda Spacková; Axel Walter; Friedrich A. Gollmick; Jirí Sponer; Hartmut Fritzsche

2001-01-01

24

Crystal structures of B-DNA with incorporated 2'-deoxy-2'-fluoro-arabino-furanosyl thymines: implications of conformational preorganization for duplex stability.  

PubMed Central

The fundamental conformational states of right-handed double helical DNA, the A- and B-forms, are associated with distinct puckers of the sugar moieties. The furanose conformation itself is affected by the steric and electronic nature of the ring substituents. For example, a strongly electronegative substituent at the C2' position, such as in the 2'-deoxy-2'-fluoro ribo furanosyl analogue, will drive the conformational equilibrium towards the C3'- endo type (north). Conversely, the 2'-deoxy-2'-fluoro arabino furanosyl modification with opposite stereochemistry at C2' appears to have a preference for a C2'- endo type pucker (south). Incorporation of 2'-fluoroarabinofuranosyl thymines was previously shown to enhance the thermodynamic stability of B-DNA duplexes. We have determined the crystal structures of the B-DNA dodecamer duplexes [d(CGCGAASSCGCG)]2and [d(CGCGAASTCGCG)]2with incorporated 2'-deoxy-2'-fluoroarabinofuranosyl thymines S (south) at 1.55 A resolution. In the crystal structures, all S residues adopt an O4'- endo conformation (east), well compatible with an overall B-form duplex geometry. In addition to the increased rigidity of S nucleosides, a clathrate-like ordered water structure around the 2'-fluorines may account for the observed larger thermodynamic stability of DNA duplexes containing 2'-deoxy-2'-fluoroarabino thymidines.

Berger, I; Tereshko, V; Ikeda, H; Marquez, V E; Egli, M

1998-01-01

25

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).

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

2012-01-01

26

Use of axial Pattersons in Assessment of Compatibility of alternative B-DNA structures with fibre X-ray data  

Microsoft Academic Search

Results are reported for an analysis of the compatibility of various models for duplex DNA with available X-ray data for moist DNA fibres using axial Pattersons. The models considered are the Arnott refined double helix, the Levitt modified double helix and a stereochemically refined version of the side-by-side structure. This relatively simple test of gross structural features circumvents difficulties associated

G. A. Rodley; R. P. Millane; G. C. McKinnon; R. H. T. Bates

1984-01-01

27

Molecular topography of the MED12-deleted region in smooth muscle tumors: a possible link between non-B DNA structures and hypermutability  

PubMed Central

Background Deletions of the gene encoding mediator subcomplex 12 (MED12) in human smooth muscle tumors rank among the most frequent genomic alterations in human tumors at all. In a minority of these cases, small deletions are found. In an attempt to delineate key features of the deletions aimed at a better understanding of the molecular pathogenesis of uterine smooth muscle tumors we have analyzed 70 MED12 deletions including 46 cases from the literature and 24 own unpublished cases. Results The average length of the deletions was 18.7 bp ranging between 2 bp and 43 bp. While in general multitudes of 3 clearly dominated leaving the transcript in frame, deletions of 21, 24, 30, and 33 nucleotides were clearly underrepresented. Within the DNA segment affected deletion breakpoints were not randomly distributed. Most breakpoints clustered within the center of the segment where two peaks of breakpoint clusters could be distinguished. Interestingly, one of these clusters coincides with the loop of a putative folded non-B DNA structure whereas a much lower number of breaks noted in the 5? and 3? stem of the structure forming an intramolecular B-helix. The second cluster mainly consisting of 3? breaks was located in a region downstream adjacent to the stem. Conclusion The present study describes for the first time main characteristics of MED12 deletions occurring in smooth muscle tumors. Interestingly, the non-random distribution of breakpoints within the deletion hotspot region may point to a role of non-canonical DNA structures for the occurrence of these mutations and the molecular pathogenesis of uterine smooth muscle tumors, respectively.

2013-01-01

28

Structured Prediction with Reinforcement Learning  

Microsoft Academic Search

We formalize the problem of Structured Prediction as a Reinforcement Learning task. We first define a Structured Prediction Markov Decision Process (SP-MDP), an instantiation of Markov Decision Processes for Structured Prediction and show that learning an optimal policy\\u000a for this SP-MDP is equivalent to minimizing the empirical loss. This link between the supervised learning formulation of structured\\u000a prediction and reinforcement

Francis Maes; Ludovic Denoyer; Patrick Gallinari

2009-01-01

29

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.

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

2012-01-01

30

B DNA Twisting Correlates with Base-pair Morphology  

Microsoft Academic Search

The observed sequence dependence of the mean twist angles in 38B-DNA crystal structures can be understood in terms of simple geometrical features of the constituent base-pairs. Structures with low twist appear to unwind in response to severe steric clashes of large exocyclic groups (such as NH2—NH2) in the major and minor grooves, while those with high twist are subjected to

Andrey A. Gorin; Victor B. Zhurkin

1995-01-01

31

Structure of catabolite gene activator protein at 2.9 Å resolution suggests binding to left-handed B-DNA  

Microsoft Academic Search

The 2.9 Å resolution crystal structure of Escherichia coli catabolite gene activator protein (CAP) completed with cyclic AMP reveals two distinct structural domains separated by a cleft. The smaller carboxy-terminal domain is presumed to bind DNA while the amino-terminal domain is seen to bind cyclic AMP. Model building studies suggest that CAP binds to left-handed B-type DNA, contacting its major

David B. McKay; Thomas A. Steitz

1981-01-01

32

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

33

Predicting delamination in composite structures  

Microsoft Academic Search

The mesoscale composite damage model (MCDM) and a new component damage indicator are incorporated into a finite element code to predict the initiation and evolution of damage, and then final failure of fibrous composites structure subjected to combined loadings. The veracity of the method is demonstrated through applications to a stiffened, composite panel that is subjected to various combinations of

Nathan D. Flesher; Carl T. Herakovich

2006-01-01

34

Water in protein structure prediction  

PubMed Central

Proteins have evolved to use water to help guide folding. A physically motivated, nonpairwise-additive model of water-mediated interactions added to a protein structure prediction Hamiltonian yields marked improvement in the quality of structure prediction for larger proteins. Free energy profile analysis suggests that long-range water-mediated potentials guide folding and smooth the underlying folding funnel. Analyzing simulation trajectories gives direct evidence that water-mediated interactions facilitate native-like packing of supersecondary structural elements. Long-range pairing of hydrophilic groups is an integral part of protein architecture. Specific water-mediated interactions are a universal feature of biomolecular recognition landscapes in both folding and binding.

Papoian, Garegin A.; Ulander, Johan; Eastwood, Michael P.; Luthey-Schulten, Zaida; Wolynes, Peter G.

2004-01-01

35

TRITIUM RESERVOIR STRUCTURAL PERFORMANCE PREDICTION  

SciTech Connect

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

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

2005-11-10

36

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

PubMed Central

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

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

1997-01-01

37

Practical lessons from protein structure prediction  

PubMed Central

Despite recent efforts to develop automated protein structure determination protocols, structural genomics projects are slow in generating fold assignments for complete proteomes, and spatial structures remain unknown for many protein families. Alternative cheap and fast methods to assign folds using prediction algorithms continue to provide valuable structural information for many proteins. The development of high-quality prediction methods has been boosted in the last years by objective community-wide assessment experiments. This paper gives an overview of the currently available practical approaches to protein structure prediction capable of generating accurate fold assignment. Recent advances in assessment of the prediction quality are also discussed.

Ginalski, Krzysztof; Grishin, Nick V.; Godzik, Adam; Rychlewski, Leszek

2005-01-01

38

Refined Genetic Algorithms for Polypeptide Structure Prediction.  

National Technical Information Service (NTIS)

Accurate and reliable prediction of macromolecular structures has eluded researchers for nearly 40 years. Prediction via energy minimization assumes the native conformation has the globally minimal energy potential. An exhaustive search is impossible sinc...

C. E. Kaiser

1996-01-01

39

Predicting protein flexibility through the prediction of local structures  

PubMed Central

Protein structures are valuable tools for understanding protein function. However, protein dynamics is also considered a key element in protein function. Therefore, in addition to structural analysis, fully understanding protein function at the molecular level now requires accounting for flexibility. However, experimental techniques that produce both types of information simultaneously are still limited. Prediction approaches are useful alternative tools for obtaining otherwise unavailable data. It has been shown that protein structure can be described by a limited set of recurring local structures. In this context, we previously established a library composed of 120 overlapping long structural prototypes (LSPs) representing fragments of 11 residues in length and covering all known local protein structures. Based on the close sequence-structure relationship observed in LSPs, we developed a novel prediction method that proposes structural candidates in terms of LSPs along a given sequence. The prediction accuracy rate was high given the number of structural classes. In this study, we utilise this methodology to predict protein flexibility. We first examine flexibility according two different descriptors, the B-factor and root mean square fluctuations from molecular dynamics simulations. We then show the relevance of using both descriptors together. We define three flexibility classes and propose a method based on the LSP prediction method for predicting flexibility along the sequence. The prediction rate reaches 49.6%. This method competes rather efficiently with the most recent, cutting-edge methods based on true flexibility data learning with sophisticated algorithms, Accordingly, flexibility information should be taken into account in structural prediction assessments.

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

2011-01-01

40

Computational prediction of RNA secondary structure.  

PubMed

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

Moss, Walter N

2013-01-01

41

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.

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

2008-01-01

42

Computational prediction of secondary and supersecondary structures.  

PubMed

The sequence-based prediction of the secondary and supersecondary structures enjoys strong interest and finds applications in numerous areas related to the characterization and prediction of protein structure and function. Substantial efforts in these areas over the last three decades resulted in the development of accurate predictors, which take advantage of modern machine learning models and availability of evolutionary information extracted from multiple sequence alignment. In this chapter, we first introduce and motivate both prediction areas and introduce basic concepts related to the annotation and prediction of the secondary and supersecondary structures, focusing on the ? hairpin, coiled coil, and ?-turn-? motifs. Next, we overview state-of-the-art prediction methods, and we provide details for 12 modern secondary structure predictors and 4 representative supersecondary structure predictors. Finally, we provide several practical notes for the users of these prediction tools. PMID:22987347

Chen, Ke; Kurgan, Lukasz

2013-01-01

43

Computational Approach for Protein Structure Prediction  

PubMed Central

Objectives To predict the structure of protein, which dictates the function it performs, a newly designed algorithm is developed which blends the concept of self-organization and the genetic algorithm. Methods Among many other approaches, genetic algorithm is found to be a promising cooperative computational method to solve protein structure prediction in a reasonable time. To automate the right choice of parameter values the influence of self-organization is adopted to design a new genetic operator to optimize the process of prediction. Torsion angles, the local structural parameters which define the backbone of protein are considered to encode the chromosome that enhances the quality of the confirmation. Newly designed self-configured genetic operators are used to develop self-organizing genetic algorithm to facilitate the accurate structure prediction. Results Peptides are used to gauge the validity of the proposed algorithm. As a result, the structure predicted shows clear improvements in the root mean square deviation on overlapping the native indicates the overall performance of the algorithm. In addition, the Ramachandran plot results implies that the conformations of phi-psi angles in the predicted structure are better as compared to native and also free from steric hindrances. Conclusions The proposed algorithm is promising which contributes to the prediction of a native-like structure by eliminating the time constraint and effort demand. In addition, the energy of the predicted structure is minimized to a greater extent, which proves the stability of protein.

Gopal, Jeyakodi; Candavelou, Manimozhi; Gollapalli, Sowjanya; Karthikeyan, Kayathri

2013-01-01

44

Protein structure prediction. Implications for the biologist  

Microsoft Academic Search

Recent improvements in the prediction of protein secondary structure are described, particularly those methods using the information contained into multiple alignments. In this respect, the prediction accuracy has been checked and methods that take into account multiple alignments are 70% correct for a three-state description of secondary structure. This quality is obtained by a ‘leave-one out’ procedure on a reference

G. Deléage; C. Blanchet; C. Geourjon

1997-01-01

45

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

46

Computational Prediction of RNA Tertiary Structure  

NASA Astrophysics Data System (ADS)

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

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

2012-02-01

47

(PS)2: protein structure prediction server.  

PubMed

Protein structure prediction provides valuable insights into function, and comparative modeling is one of the most reliable methods to predict 3D structures directly from amino acid sequences. However, critical problems arise during the selection of the correct templates and the alignment of query sequences therewith. We have developed an automatic protein structure prediction server, (PS)2, which uses an effective consensus strategy both in template selection, which combines PSI-BLAST and IMPALA, and target-template alignment integrating PSI-BLAST, IMPALA and T-Coffee. (PS)2 was evaluated for 47 comparative modeling targets in CASP6 (Critical Assessment of Techniques for Protein Structure Prediction). For the benchmark dataset, the predictive performance of (PS)2, based on the mean GTD_TS score, was superior to 10 other automatic servers. Our method is based solely on the consensus sequence and thus is considerably faster than other methods that rely on the additional structural consensus of templates. Our results show that (PS)2, coupled with suitable consensus strategies and a new similarity score, can significantly improve structure prediction. Our approach should be useful in structure prediction and modeling. The (PS)2 is available through the website at http://ps2.life.nctu.edu.tw/. PMID:16844981

Chen, Chih-Chieh; Hwang, Jenn-Kang; Yang, Jinn-Moon

2006-07-01

48

Transmembrane beta-barrel protein structure prediction  

NASA Astrophysics Data System (ADS)

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

Randall, Arlo; Baldi, Pierre

49

Impact damage prediction in carbon composite structures  

Microsoft Academic Search

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

G. A. O. Davies; X. Zhang

1995-01-01

50

Multipass membrane protein structure prediction using Rosetta  

Microsoft Academic Search

We describe the adaptation of the Rosetta de novo structure prediction method for prediction of helical transmembrane protein struc- tures. The membrane environment is modeled by embedding the protein chain into a model mem- brane represented by parallel planes defining hydro- phobic, interface, and polar membrane layers for each energy evaluation. The optimal embedding is determined by maximizing the exposure

Vladimir Yarov-Yarovoy; Jack Schonbrun; David Baker

2006-01-01

51

Geometric prediction structure for multiview video coding  

NASA Astrophysics Data System (ADS)

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

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

2010-02-01

52

Predicting RNA structure: advances and limitations.  

PubMed

RNA secondary structures can be predicted using efficient algorithms. A widely used software package implementing a large number of computational methods is the ViennaRNA Package. This chapter describes how to use programs from the ViennaRNA Package to perform common tasks such as prediction of minimum free-energy structures, suboptimal structures, or base pairing probabilities, and generating secondary structure plots with reliability annotation. Moreover, we present recent methods to assess the folding kinetics of an RNA via 2D projections of the energy landscape, identification of local minima and energy barriers, or simulation of RNA folding as a Markov process. PMID:24136595

Hofacker, Ivo L; Lorenz, Ronny

2014-01-01

53

Data Mining for Protein Secondary Structure Prediction  

Microsoft Academic Search

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

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

54

Gene duplications in evolution of archaeal family B DNA polymerases.  

PubMed Central

All archaeal DNA-dependent DNA polymerases sequenced to date are homologous to family B DNA polymerases from eukaryotes and eubacteria. Presently, representatives of the euryarchaeote division of archaea appear to have a single family B DNA polymerase, whereas two crenarchaeotes, Pyrodictium occultum and Sulfolobus solfataricus, each possess two family B DNA polymerases. We have found the gene for yet a third family B DNA polymerase, designated B3, in the crenarchaeote S. solfataricus P2. The encoded protein is highly divergent at the amino acid level from the previously characterized family B polymerases in S. solfataricus P2 and contains a number of nonconserved amino acid substitutions in catalytic domains. We have cloned and sequenced the ortholog of this gene from the closely related Sulfolobus shibatae. It is also highly divergent from other archaeal family B DNA polymerases and, surprisingly, from the S. solfataricus B3 ortholog. Phylogenetic analysis using all available archaeal family B DNA polymerases suggests that the S. solfataricus P2 B3 and S. shibatae B3 paralogs are related to one of the two DNA polymerases of P. occultum. These sequences are members of a group which includes all euryarchaeote family B homologs, while the remaining crenarchaeote sequences form another distinct group. Archaeal family B DNA polymerases together constitute a monophyletic subfamily whose evolution has been characterized by a number of gene duplication events.

Edgell, D R; Klenk, H P; Doolittle, W F

1997-01-01

55

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

56

Protein Structure Prediction: Is It Useful?  

PubMed Central

Summary Computationally predicted three-dimensional structure of protein molecules has demonstrated the usefulness in many areas of biomedicine, ranging from approximate family assignments to precise drug screening. For nearly 40 years, however, the accuracy of the predicted models has been dictated by the availability of close structural templates. Progress has recently been achieved in refining low-resolution models closer to the native ones; this has been made possible by combining knowledge-based information from multiple sources of structural templates as well as by improving the energy funnel of physics-based force fields. Unfortunately, there has been no essential progress in the development of techniques for detecting remotely homologous templates and for predicting novel protein structures.

2009-01-01

57

Predicting odor perceptual similarity from odor structure.  

PubMed

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

Snitz, Kobi; Yablonka, Adi; Weiss, Tali; Frumin, Idan; Khan, Rehan M; Sobel, Noam

2013-09-12

58

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.

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

2013-01-01

59

Hybrid system for protein secondary structure prediction.  

PubMed

We have developed a hybrid system to predict the secondary structures (alpha-helix, beta-sheet and coil) of proteins and achieved 66.4% accuracy, with correlation coefficients of C(coil) = 0.429, C alpha = 0.470 and C beta = 0.387. This system contains three subsystems ("experts"): a neural network module, a statistical module and a memory-based reasoning module. First, the three experts independently learn the mapping between amino acid sequences and secondary structures from the known protein structures, then a Combiner learns to combine automatically the outputs of the experts to make final predictions. The hybrid system was tested with 107 protein structures through k-way cross-validation. Its performance was better than each expert and all previously reported methods with greater than 0.99 statistical significance. It was observed that for 20% of the residues, all three experts produced the same but wrong predictions. This may suggest an upper bound on the accuracy of secondary structure predictions based on local information from the currently available protein structures, and indicate places where non-local interactions may play a dominant role in conformation. For 64% of the residues, at least two experts were the same and correct, which shows that the Combiner performed better than majority vote. For 77% of the residues, at least one expert was correct, thus there may still be room for improvement in this hybrid approach. Rigorous evaluation procedures were used in testing the hybrid system, and statistical significance measures were developed in analyzing the differences among different methods. When measured in terms of the number of secondary structures (rather than the number of residues) that were predicted correctly, the prediction produced by the hybrid system was also better than those of individual experts. PMID:1613789

Zhang, X; Mesirov, J P; Waltz, D L

1992-06-20

60

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

61

A Hoogsteen base pair embedded in undistorted B-DNA  

PubMed Central

Hoogsteen base pairs within duplex DNA typically are only observed in regions containing significant distortion or near sites of drug intercalation. We report here the observation of a Hoogsteen base pair embedded within undistorted, unmodified B-DNA. The Hoogsteen base pair, consisting of a syn adenine base paired with an anti thymine base, is found in the 2.1 ? resolution structure of the MAT?2 homeodomain bound to DNA in a region where a specifically and a non-specifically bound homeodomain contact overlapping sites. NMR studies of the free DNA show no evidence of Hoogsteen base pair formation, suggesting that protein binding favors the transition from a Watson–Crick to a Hoogsteen base pair. Molecular dynamics simulations of the homeodomain–DNA complex support a role for the non-specifically bound protein in favoring Hoogsteen base pair formation. The presence of a Hoogsteen base pair in the crystal structure of a protein–DNA complex raises the possibility that Hoogsteen base pairs could occur within duplex DNA and play a hitherto unrecognized role in transcription, replication and other cellular processes.

Aishima, Jun; Gitti, Rossitza K.; Noah, Joyce E.; Gan, Hin Hark; Schlick, Tamar; Wolberger, Cynthia

2002-01-01

62

A Hoogsteen base pair embedded in undistorted B-DNA.  

PubMed

Hoogsteen base pairs within duplex DNA typically are only observed in regions containing significant distortion or near sites of drug intercalation. We report here the observation of a Hoogsteen base pair embedded within undistorted, unmodified B-DNA. The Hoogsteen base pair, consisting of a syn adenine base paired with an anti thymine base, is found in the 2.1 A resolution structure of the MATalpha2 homeodomain bound to DNA in a region where a specifically and a non-specifically bound homeodomain contact overlapping sites. NMR studies of the free DNA show no evidence of Hoogsteen base pair formation, suggesting that protein binding favors the transition from a Watson-Crick to a Hoogsteen base pair. Molecular dynamics simulations of the homeodomain-DNA complex support a role for the non-specifically bound protein in favoring Hoogsteen base pair formation. The presence of a Hoogsteen base pair in the crystal structure of a protein-DNA complex raises the possibility that Hoogsteen base pairs could occur within duplex DNA and play a hitherto unrecognized role in transcription, replication and other cellular processes. PMID:12466549

Aishima, Jun; Gitti, Rossitza K; Noah, Joyce E; Gan, Hin Hark; Schlick, Tamar; Wolberger, Cynthia

2002-12-01

63

Protein Structure Prediction by Protein Threading  

NASA Astrophysics Data System (ADS)

The seminal work of Bowie, Lüthy, and Eisenberg (Bowie et al., 1991) on "the inverse protein folding problem" laid the foundation of protein structure prediction by protein threading. By using simple measures for fitness of different amino acid types to local structural environments defined in terms of solvent accessibility and protein secondary structure, the authors derived a simple and yet profoundly novel approach to assessing if a protein sequence fits well with a given protein structural fold. Their follow-up work (Elofsson et al., 1996; Fischer and Eisenberg, 1996; Fischer et al., 1996a,b) and the work by Jones, Taylor, and Thornton (Jones et al., 1992) on protein fold recognition led to the development of a new brand of powerful tools for protein structure prediction, which we now term "protein threading." These computational tools have played a key role in extending the utility of all the experimentally solved structures by X-ray crystallography and nuclear magnetic resonance (NMR), providing structural models and functional predictions for many of the proteins encoded in the hundreds of genomes that have been sequenced up to now.

Xu, Ying; Liu, Zhijie; Cai, Liming; Xu, Dong

64

Integrating genomic homology into gene structure prediction  

Microsoft Academic Search

TWINSCAN is a new gene-structure prediction system that directly extends the probability model of GENSCAN, allowing it to exploit homology between two related genomes. Separate probability models are used for conservation in exons, introns, splice sites, and UTRs, reflecting the differences among their patterns of evolu- tionary conservation. TWINSCAN is specifically designed for the analysis of high-throughput genomic sequences containing

Ian Korf; Paul Flicek; Daniel Duan; Michael R. Brent

2001-01-01

65

Predicting Failure Initiation in Structural Adhesive Joints.  

National Technical Information Service (NTIS)

The aim of this project is to bring a better knowledge of the phenomena involved in the failure of structural adhesive joints and to develop new tools to predict the initiation of this failure. First, a constitutive model of the adhesive was developed by ...

A. Diaz Diaz

2012-01-01

66

Twin Gaussian Processes for Structured Prediction  

Microsoft Academic Search

We describe twin Gaussian processes (TGP), a generic structured prediction method that uses Gaussian process (GP) priors on both covariates and responses, both multivariate, and estimates outputs by minimizing the Kullback-Leibler divergence between two GP modeled as normal distributions over finite index sets of training and testing examples, emphasizing the goal that similar inputs should produce similar percepts and this

Liefeng Bo; Cristian Sminchisescu

2010-01-01

67

TRITIUM RESERVOIR STRUCTURAL PERFORMANCE PREDICTION (U)  

Microsoft Academic Search

The burst test is used to assess the material performance of tritium reservoirs in the surveillance program in which reservoirs have been in service for extended periods of time. A materials system model and finite element procedure were developed under a Savannah River Site Plant-Directed Research and Development (PDRD) program to predict the structural response under a full range of

P. S. Lam; M. J. Morgan

2005-01-01

68

Seizure prediction using EEG spatiotemporal correlation structure.  

PubMed

A seizure prediction algorithm is proposed that combines novel multivariate EEG features with patient-specific machine learning. The algorithm computes the eigenspectra of space-delay correlation and covariance matrices from 15-s blocks of EEG data at multiple delay scales. The principal components of these features are used to classify the patient's preictal or interictal state. This is done using a support vector machine (SVM), whose outputs are averaged using a running 15-minute window to obtain a final prediction score. The algorithm was tested on 19 of 21 patients in the Freiburg EEG data set who had three or more seizures, predicting 71 of 83 seizures, with 15 false predictions and 13.8 h in seizure warning during 448.3 h of interictal data. The proposed algorithm scales with the number of available EEG signals by discovering the variations in correlation structure among any given set of signals that correlate with seizure risk. PMID:23041171

Williamson, James R; Bliss, Daniel W; Browne, David W; Narayanan, Jaishree T

2012-10-02

69

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

70

A Structured Approach to Sediment Transport Prediction  

NASA Astrophysics Data System (ADS)

There are two types of sediment transport problem. One, flow competence, concerns the conditions that initiate motion of grains on the bed surface. The other, transport capacity, concerns the rate at which sediment is transported and involves sediment found locally on the bed as well as sediment delivered from upstream. The two problems can be linked by the critical stress for incipient motion. A model for critical stress is used directly to predict flow competence. The Ashida/Parker similarity hypothesis provides a useful approximation of transport rates and incorporates local sediment effects entirely via the reference stress, a surrogate for critical stress. Although critical stress is key to both predictions, its application is quite different. The difficult problem of wash load - sizes found in transport in quantities much larger than would be predicted by their presence in the bed - makes the distinction clear and challenges any attempt to predict transport rate from a competence-like approach based on hydraulics and bed material alone. The Shields Diagram and a hiding function provide models for critical stress for uni-size and mixed-size sediment. In addition to grain size - both absolute and relative - other factors alter the critical stress of bed material. These include the proportion of fine-grained material, the aging or freshening of bed material via biologically mediated processes, and the development of bed structure at flows close to the critical stress. Although these factors directly influence the prediction of competent flows, their effect on transport rate is less clear. As flow increases, to what extent does bed strengthening through structuring and other mechanisms persist in dampening transport rate? The answer involves the condition of partial transport in which some grains in a size fraction are active and others remain inactive. Tracing of grains in the flume and field provide guidance on the domain of partial transport and thus on the influence on transport rates of bed strengthening. A surface-based transport model can be used with a bed-sorting algorithm to predict the evolution of the bed surface under active transport. The same transport model can be used in inverse form to predict the combination of flow, transport, and bed surface grain size under steady-state conditions. These formulas provide a useful starting point for documenting the effect of bed structuring on sediment transport rate. Careful (although not complex) consideration of the type of transport problem - competence or capacity - and the nature of the time-varying boundary conditions are needed to make accurate predictions of sediment transport.

Wilcock, Peter

2013-04-01

71

Structure prediction of AlnOm clusters  

NASA Astrophysics Data System (ADS)

Genetic algorithm simulations, using Buckingham potential to represent the anion-anion and cation-anion short-range interactions, were performed in order to predict the equilibrium positions of the Al and O ions in AlnOm clusters. In order to find the equilibrium structures of compounds a self-organizing genetic algorithm were constructed. The calculation were carried out for several clusters AlnOm, with different numbers of aluminium and oxygen atoms.

Smok, P.

2011-04-01

72

Service life prediction of reinforced concrete structures  

SciTech Connect

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

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

1999-09-01

73

Phylogenetic approaches to natural product structure prediction.  

PubMed

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

Ziemert, Nadine; Jensen, Paul R

2012-01-01

74

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

PubMed

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

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

2013-04-24

75

Neural network definitions of highly predictable protein secondary structure classes.  

National Technical Information Service (NTIS)

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 conventi...

A. Lapedes E. Steeg R. Farber

1994-01-01

76

Prediction of biodegradability from structure: imidazoles.  

PubMed

A project for the development of Structure-Activity Relationship for Biodegradation is presented. The aim of the project is to assemble sets of structural rules governing the potential microbial degradability of (classes of) chemicals. These rules will provide tools to take into account the biodegradation aspects of a product--and all precursors in the production process--early in the product development. The modeling concept is to take all experimental biodegradation data available and combine structural trends in the data with mechanistical information from degradation pathways. The rules that are derived should give insight into the possibility of biodegradation for specific classes of chemicals, thereby revealing why a compound is biodegradable or not. For the class of imidazole derivatives such rules are derived, and a model degradation mechanism is proposed in analogy to the urocanate-hydratase mechanism from histidine metabolism. The model is validated using 12 imidazole-compounds, which are all predicted correctly to be poorly biodegradable. It is demonstrated that both data analysis and information on enzymatic reaction mechanisms are necessary to yield valid Structure-Biodegradation Relationship. PMID:12074388

Rorije, E; Germa, F; Philipp, B; Schink, B; Beimborn, D B

2002-03-01

77

Protein structure based prediction of catalytic residues  

PubMed Central

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

2013-01-01

78

Structure Prediction of Partial-Length Protein Sequences  

PubMed Central

Protein structure information is essential to understand protein function. Computational methods to accurately predict protein structure from the sequence have primarily been evaluated on protein sequences representing full-length native proteins. Here, we demonstrate that top-performing structure prediction methods can accurately predict the partial structures of proteins encoded by sequences that contain approximately 50% or more of the full-length protein sequence. We hypothesize that structure prediction may be useful for predicting functions of proteins whose corresponding genes are mapped expressed sequence tags (ESTs) that encode partial-length amino acid sequences. Additionally, we identify a confidence score representing the quality of a predicted structure as a useful means of predicting the likelihood that an arbitrary polypeptide sequence represents a portion of a foldable protein sequence (“foldability”). This work has ramifications for the prediction of protein structure with limited or noisy sequence information, as well as genome annotation.

Laurenzi, Adrian; Hung, Ling-Hong; Samudrala, Ram

2013-01-01

79

Protein structure prediction using basin-hopping  

PubMed Central

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

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

2008-01-01

80

Integrating genomic homology into gene structure prediction.  

PubMed

TWINSCAN is a new gene-structure prediction system that directly extends the probability model of GENSCAN, allowing it to exploit homology between two related genomes. Separate probability models are used for conservation in exons, introns, splice sites, and UTRs, reflecting the differences among their patterns of evolutionary conservation. TWINSCAN is specifically designed for the analysis of high-throughput genomic sequences containing an unknown number of genes. In experiments on high-throughput mouse sequences, using homologous sequences from the human genome, TWINSCAN shows notable improvement over GENSCAN in exon sensitivity and specificity and dramatic improvement in exact gene sensitivity and specificity. This improvement can be attributed entirely to modeling the patterns of evolutionary conservation in genomic sequence. PMID:11473003

Korf, I; Flicek, P; Duan, D; Brent, M R

2001-01-01

81

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

82

Pneumococcal Capsular Polysaccharide Structure Predicts Serotype Prevalence  

PubMed Central

There are 91 known capsular serotypes of Streptococcus pneumoniae. The nasopharyngeal carriage prevalence of particular serotypes is relatively stable worldwide, but the host and bacterial factors that maintain these patterns are poorly understood. Given the possibility of serotype replacement following vaccination against seven clinically important serotypes, it is increasingly important to understand these factors. We hypothesized that the biochemical structure of the capsular polysaccharides could influence the degree of encapsulation of different serotypes, their susceptibility to killing by neutrophils, and ultimately their success during nasopharyngeal carriage. We sought to measure biological differences among capsular serotypes that may account for epidemiological patterns. Using an in vitro assay with both isogenic capsule-switch variants and clinical carriage isolates, we found an association between increased carriage prevalence and resistance to non-opsonic neutrophil-mediated killing, and serotypes that were resistant to neutrophil-mediated killing tended to be more heavily encapsulated, as determined by FITC-dextran exclusion. Next, we identified a link between polysaccharide structure and carriage prevalence. Significantly, non-vaccine serotypes that have become common in vaccinated populations tend to be those with fewer carbons per repeat unit and low energy expended per repeat unit, suggesting a novel biological principle to explain patterns of serotype replacement. More prevalent serotypes are more heavily encapsulated and more resistant to neutrophil-mediated killing, and these phenotypes are associated with the structure of the capsular polysaccharide, suggesting a direct relationship between polysaccharide biochemistry and the success of a serotype during nasopharyngeal carriage and potentially providing a method for predicting serotype replacement.

Weinberger, Daniel M.; Trzcinski, Krzysztof; Lu, Ying-Jie; Bogaert, Debby; Brandes, Aaron; Galagan, James; Anderson, Porter W.; Malley, Richard; Lipsitch, Marc

2009-01-01

83

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

NASA Astrophysics Data System (ADS)

A-DNA and B-DNA are two secondary molecular conformations (among other allomorphs) that double-stranded DNA drawn into a fiber can assume, depending on the relative water content and other chemical parameters of the fiber. They were the first two forms to be observed by X-ray fiber diffraction in the early 1950s, respectively by Wilkins and Gosling and by Franklin and Gosling. Their corresponding historical diffraction diagrams played an equally crucial role in the discovery of the primary double-helical structure of the DNA molecule by Watson and Crick in 1953. This paper provides a comparative explanation of the structural content of the two diagrams treated on the same footing. The analysis of the diagrams is supported by the optical transform method with which both A-DNA and B-DNA X-ray images can be simulated optically. The simulations use a simple laser pointer and a dozen optical diffraction gratings, all held on a single diffraction slide. The gratings have been specially designed to pinpoint just which of the structural elements of the molecule is responsible for each of the revealing features of the fiber diffraction images.

Lucas, Amand A.

2008-05-01

84

Behavior predicts genetic structure in a wild primate group  

Microsoft Academic Search

The predictability of genetic structure from social structure and differential mating success was tested in wild baboons. Baboon populations are subdivided into cohe- sive social groups that include multiple adults of both sexes. As in many mammals, males are the dispersing sex. Social structure and behavior successfully predicted molecular ge- netic measures of relatedness and variance in reproductive success. In

JEANNE ALTMANN; USAN C. ALBERTS; S USAN A. HAINES; J EAN DUBACH; P HILIP MURUTHI; TREVOR COOTE; E LI GEFFEN; DAVID J. CHEESMAN; R APHAEL S. MUTUTUA; ERAH N. SAIYALEL; ROBERT K. WAYNE; R OBERT C. LACY; MICHAEL W. BRUFORD

85

Progress of 1D protein structure prediction at last.  

PubMed

Accuracy of predicting protein secondary structure and solvent accessibility from sequence information has been improved significantly by using information contained in multiple sequence alignments as input to a neural network system. For the Asilomar meeting, predictions for 13 proteins were generated automatically using the publicly available prediction method PHD. The results confirm the estimate of 72% three-state prediction accuracy. The fairly accurate predictions of secondary structure segments made the tool useful as a starting point for modeling of higher dimensional aspects of protein structure. PMID:8710823

Rost, B; Sander, C

1995-11-01

86

A comprehensive comparison of comparative RNA structure prediction approaches  

PubMed Central

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

Gardner, Paul P; Giegerich, Robert

2004-01-01

87

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.

Mrinal, Nirotpal; Tomar, Archana; Nagaraju, Javaregowda

2011-01-01

88

Linear Predictive Coding with Modified Filter Structures.  

National Technical Information Service (NTIS)

In conventional one-step forward linear prediction an estimate for the current sample value is formed as a linear combination of previous sample values. In this paper, a generalized form of this scheme is studied. Here, the prediction is not based simply ...

A. Haermae

2001-01-01

89

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

PubMed

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

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

2012-10-20

90

New developments in evolutionary structure prediction algorithm USPEX  

NASA Astrophysics Data System (ADS)

We present new developments of the evolutionary algorithm USPEX for crystal structure prediction and its adaptation to cluster structure prediction. We show how to generate randomly symmetric structures, and how to introduce 'smart' variation operators, learning about preferable local environments. These and other developments substantially improve the efficiency of the algorithm and allow reliable prediction of structures with up to ˜200 atoms in the unit cell. We show that an advanced version of the Particle Swarm Optimization (PSO) can be created on the basis of our method, but PSO is strongly outperformed by USPEX. We also show how ideas from metadynamics can be used in the context of evolutionary structure prediction for escaping from local minima. Our cluster structure prediction algorithm, using the ideas initially developed for crystals, also shows excellent performance and outperforms other state-of-the-art algorithms.

Lyakhov, Andriy O.; Oganov, Artem R.; Stokes, Harold T.; Zhu, Qiang

2013-04-01

91

Simultaneous prediction of protein secondary structure and transmembrane spans.  

PubMed

Prediction of transmembrane spans and secondary structure from the protein sequence is generally the first step in the structural characterization of (membrane) proteins. Preference of a stretch of amino acids in a protein to form secondary structure and being placed in the membrane are correlated. Nevertheless, current methods predict either secondary structure or individual transmembrane states. We introduce a method that simultaneously predicts the secondary structure and transmembrane spans from the protein sequence. This approach not only eliminates the necessity to create a consensus prediction from possibly contradicting outputs of several predictors but bears the potential to predict conformational switches, i.e., sequence regions that have a high probability to change for example from a coil conformation in solution to an ?-helical transmembrane state. An artificial neural network was trained on databases of 177 membrane proteins and 6048 soluble proteins. The output is a 3 × 3 dimensional probability matrix for each residue in the sequence that combines three secondary structure types (helix, strand, coil) and three environment types (membrane core, interface, solution). The prediction accuracies are 70.3% for nine possible states, 73.2% for three-state secondary structure prediction, and 94.8% for three-state transmembrane span prediction. These accuracies are comparable to state-of-the-art predictors of secondary structure (e.g., Psipred) or transmembrane placement (e.g., OCTOPUS). The method is available as web server and for download at www.meilerlab.org. PMID:23349002

Leman, Julia Koehler; Mueller, Ralf; Karakas, Mert; Woetzel, Nils; Meiler, Jens

2013-04-10

92

CAFASP?1: Critical assessment of fully automated structure prediction methods  

Microsoft Academic Search

The results of the first Critical As- sessment of Fully Automated Structure Prediction (CAFASP-1) are presented. The objective was to evaluate the success rates of fully automatic web servers for fold recognition which are available to the community. This study was based on the targets used in the third meeting on the Critical Assessment of Techniques for Protein Structure Prediction

Daniel Fischer; Christian Barret; Kevin Bryson; Arne Elofsson; Adam Godzik; David Jones; Kevin J. Karplus; Lawrence A. Kelley; Robert M. MacCallum; Krzysztof Pawowski; Burkhard Rost; Leszek Rychlewski; Michael Sternberg

1999-01-01

93

Protein Structure Prediction with EPSO in Toy Model  

Microsoft Academic Search

Predicting the structure of protein through its sequence of amino acids is a complex and challenging problem in computational biology. Though toy model is one of the simplest and effective models, it is still extremely difficult to predict its structure as the increase of amino acids. Particle swarm optimization (PSO) is a swarm intelligence algorithm, has been successfully applied to

Hongbing Zhu; Chengdong Pu; Xiaoli Lin; Jinguang Gu; Shanjun Zhang; Mengsi Su

2009-01-01

94

Predicting crystal structure by merging data mining with quantum mechanics  

Microsoft Academic Search

Modern methods of quantum mechanics have proved to be effective tools to understand and even predict materials properties. An essential element of the materials design process, relevant to both new materials and the optimization of existing ones, is knowing which crystal structures will form in an alloy system. Crystal structure can only be predicted effectively with quantum mechanics if an

Christopher C. Fischer; Kevin J. Tibbetts; Dane Morgan; Gerbrand Ceder

2006-01-01

95

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

96

Protein Tertiary Structure Prediction Using Artificial Bee Colony Algorithm  

Microsoft Academic Search

Proteins are essential for the biological processes in the human body. They can only perform their functions when they fold into their tertiary structure. Protein structure can be determined experimentally and computationally. Experimental methods are time consuming and high-priced and it is not always feasible to identify the protein structure experimentally. In order to predict the protein structure using computational

Hesham Awadh Abdallah Bahamish; Rosni Abdullah; Rosalina Abdul Salam

2009-01-01

97

Structure-based function prediction: approaches and applications  

Microsoft Academic Search

The ever increasing number of protein structures determined by structural genomic projects has spurred much interest in the development of methods for structure-based function prediction. Existing methods can be roughly classified in two groups: some use a comparative approach looking for the presence of structural motifs possibly associated with a known biochemical function. Other methods try to identify functional patches

Pier Federico Gherardini; Manuela Helmer-Citterich

2008-01-01

98

RNAstructure: software for RNA secondary structure prediction and analysis  

PubMed Central

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

2010-01-01

99

Designing and benchmarking the MULTICOM protein structure prediction system  

PubMed Central

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

2013-01-01

100

Improving the accuracy of protein secondary structure prediction using structural alignment  

Microsoft Academic Search

Background: The accuracy of protein secondary structure prediction has steadily improved over the past 30 years. Now many secondary structure prediction methods routinely achieve an accuracy (Q3) of about 75%. We believe this accuracy could be further improved by including structure (as opposed to sequence) database comparisons as part of the prediction process. Indeed, given the large size of the

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

2006-01-01

101

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

102

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

103

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

104

Prediction of protein structural classes using hybrid properties.  

PubMed

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

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

2008-10-25

105

An improved prediction of catalytic residues in enzyme structures  

Microsoft Academic Search

edu.cn The protein databases contain a huge number of function unknown proteins, including many proteins with newly determined 3D structures resulted from the Structural Genomics Projects. To accelerate experiment-based assignment of function, de novo prediction of protein functional sites, like active sites in enzymes, becomes increasingly important. Here, we attempted to improve the prediction of catalytic residues in enzyme structures

Yu-Rong Tang; Zhi-Ya Sheng; Yong-Zi Chen; Ziding Zhang

2008-01-01

106

Improving protein secondary structure prediction with aligned homologous sequences.  

PubMed Central

Most recent protein secondary structure prediction methods use sequence alignments to improve the prediction quality. We investigate the relationship between the location of secondary structural elements, gaps, and variable residue positions in multiple sequence alignments. We further investigate how these relationships compare with those found in structurally aligned protein families. We show how such associations may be used to improve the quality of prediction of the secondary structure elements, using the Quadratic-Logistic method with profiles. Furthermore, we analyze the extent to which the number of homologous sequences influences the quality of prediction. The analysis of variable residue positions shows that surprisingly, helical regions exhibit greater variability than do coil regions, which are generally thought to be the most common secondary structure elements in loops. However, the correlation between variability and the presence of helices does not significantly improve prediction quality. Gaps are a distinct signal for coil regions. Increasing the coil propensity for those residues occurring in gap regions enhances the overall prediction quality. Prediction accuracy increases initially with the number of homologues, but changes negligibly as the number of homologues exceeds about 14. The alignment quality affects the prediction more than other factors, hence a careful selection and alignment of even a small number of homologues can lead to significant improvements in prediction accuracy.

Di Francesco, V.; Garnier, J.; Munson, P. J.

1996-01-01

107

Quantifying variances in comparative RNA secondary structure prediction  

PubMed Central

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

2013-01-01

108

Protein Structure Prediction Using String Kernels.  

National Technical Information Service (NTIS)

With recent advances in large-scale sequencing technologies, there has been an exponential growth in protein sequence information. Currently, the ability to produce sequence information far out-paces the rate at which one can produce structural and functi...

H. Rangwala K. DeRonne G. Karypis

2006-01-01

109

Gene structure prediction by linguistic methods  

Microsoft Academic Search

The higher-order structure of genes and other features of biological sequences can be described by means of formal grammars. These grammars can then be used by general-purpose parsers to detect and to assemble such structures by means of syntactic pattern recognition. We describe a grammar and parser for eukaryotic protein-encoding genes, which by some measures is as effective as current

Shan Dong; David B. Searls

1994-01-01

110

Can Morphing Methods Predict Intermediate Structures?  

PubMed Central

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

Weiss, Dahlia R.; Levitt, Michael

2009-01-01

111

MSACompro: Improving Multiple Protein Sequence Alignment by Predicted Structural Features.  

PubMed

Multiple Sequence Alignment (MSA) is an essential tool in protein structure modeling, gene and protein function prediction, DNA motif recognition, phylogenetic analysis, and many other bioinformatics tasks. Therefore, improving the accuracy of multiple sequence alignment is an important long-term objective in bioinformatics. We designed and developed a new method MSACompro to incorporate predicted secondary structure, relative solvent accessibility, and residue-residue contact information into the currently most accurate posterior probability-based MSA methods to improve the accuracy of multiple sequence alignments. Different from the multiple sequence alignment methods that use the tertiary structure information of some sequences, our method uses the structural information purely predicted from sequences. In this chapter, we first introduce some background and related techniques in the field of multiple sequence alignment. Then, we describe the detailed algorithm of MSACompro. Finally, we show that integrating predicted protein structural information improved the multiple sequence alignment accuracy. PMID:24170409

Deng, Xin; Cheng, Jianlin

2014-01-01

112

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

113

Ensemble-based prediction of RNA secondary structures  

PubMed Central

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

2013-01-01

114

Predicting Conformational Flexibility in Protein Structure  

NASA Astrophysics Data System (ADS)

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

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

1999-04-01

115

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

PubMed

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

Skolnick, Jeffrey; Zhou, Hongyi; Gao, Mu

2013-02-14

116

Predicting Protein Structures Using Springs and Traveling Salesmen  

NASA Astrophysics Data System (ADS)

We describe a new method for predicting protein structures from their amino acid sequences. Amino acids are beads and their covalent and noncovalent interactions are represented by a matrix of Hooke's law springs. Combined with the elastic net solution to the Traveling Salesman Problem, this method converges to predicted solutions very quickly - proteins having lengths greater than 100 monomers can be predicted (although not yet with high accuracy) in minutes to hours.

Dill, Ken

2000-03-01

117

Predicting Modes of Toxic Action from Chemical Structure: An Overview.  

National Technical Information Service (NTIS)

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 not empirical data a...

S. P. Bradbury

1994-01-01

118

SVM learning of IP address structure for latency prediction  

Microsoft Academic Search

We examine the ability to exploit the hierarchical struc- ture of Internet addresses in order to endow network agents with predictive capabilities. Specifically, we consider Sup- port Vector Machines (SVMs) for prediction of round-trip latency to random network destinations the agent has not previously interacted with. We use kernel functions to trans- form the structured, yet fragmented and discontinuous, IP

Robert Beverly; Karen R. Sollins; Arthur Berger

2006-01-01

119

Predicting Protein Structures Using Springs and Traveling Salesmen  

Microsoft Academic Search

We describe a new method for predicting protein structures from their amino acid sequences. Amino acids are beads and their covalent and noncovalent interactions are represented by a matrix of Hooke's law springs. Combined with the elastic net solution to the Traveling Salesman Problem, this method converges to predicted solutions very quickly - proteins having lengths greater than 100 monomers

Ken Dill

2000-01-01

120

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

Microsoft Academic Search

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

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

2008-01-01

121

Prediction of protein structural class for the twilight zone sequences  

Microsoft Academic Search

Structural class characterizes the overall folding type of a protein or its domain. This paper develops an accurate method for in silico prediction of structural classes from low homology (twilight zone) protein sequences. The proposed LLSC-PRED method applies linear logistic regression classifier and a custom-designed, feature-based sequence representation to provide predictions. The main advantages of the LLSC-PRED are the comprehensive

Lukasz Kurgan; Ke Chen

2007-01-01

122

Ab initio protein structure prediction using a combined hierarchical approach  

Microsoft Academic Search

As part of the third Critical Assess- ment of Structure Prediction meeting (CASP3), we predict the three-dimensional structures for 13 pro- teins using a hierarchical approach. First, all pos- sible compact conformations of a protein sequence are enumerated using a highly simplified tetrahe- dral lattice model. We select a large subset of these conformations using a lattice-based scoring func- tion

Ram Samudrala; Yu Xia; Enoch Huang; Michael Levitt

1999-01-01

123

Improving structure-based function prediction using molecular dynamics  

PubMed Central

Summary The number of molecules with solved three-dimensional structure but unknown function is increasing rapidly. Particularly problematic are novel folds with little detectable similarity to molecules of known function. Experimental assays can determine the functions of such molecules, but are time-consuming and expensive. Computational approaches can identify potential functional sites; however, these approaches generally rely on single static structures and do not use information about dynamics. In fact, structural dynamics can enhance function prediction: we coupled molecular dynamics simulations with structure-based function prediction algorithms that identify Ca2+ binding sites. When applied to 11 challenging proteins, both methods showed substantial improvement in performance, revealing 22 more sites in one case and 12 more in the other, with a modest increase in apparent false positives. Thus, we show that treating molecules as dynamic entities improves the performance of structure-based function prediction methods.

Glazer, Dariya S.; Radmer, Randall J.; Altman, Russ B.

2009-01-01

124

Prediction of replication origins by calculating DNA structural properties.  

PubMed

In this study, we introduced two DNA structural characteristics, namely, bendability and hydroxyl radical cleavage intensity to analyze origin of replication (ORI) in the Saccharomyces cerevisiae genome. We found that both DNA bendability and cleavage intensity in core replication regions were significantly lower than in the linker regions. By using these two DNA structural characteristics, we developed a computational model for ORI prediction and evaluated the model in a benchmark dataset. The predictive performance of the jackknife cross-validation indicates that DNA bendability and cleavage intensity have the ability to describe core replication regions and our model is effective in ORI prediction. PMID:22449982

Chen, Wei; Feng, Pengmian; Lin, Hao

2012-02-28

125

Molecular dynamics of a ?B DNA element: base flipping via cross-strand intercalative stacking in a microsecond-scale simulation  

PubMed Central

The sequence-dependent structural variability and conformational dynamics of DNA play pivotal roles in many biological milieus, such as in the site-specific binding of transcription factors to target regulatory elements. To better understand DNA structure, function, and dynamics in general, and protein···DNA recognition in the ‘?B’ family of genetic regulatory elements in particular, we performed molecular dynamics simulations of a 20-bp DNA encompassing a cognate ?B site recognized by the proto-oncogenic ‘c-Rel’ subfamily of NF-?B transcription factors. Simulations of the ?B DNA in explicit water were extended to microsecond duration, providing a broad, atomically detailed glimpse into the structural and dynamical behavior of double helical DNA over many timescales. Of particular note, novel (and structurally plausible) conformations of DNA developed only at the long times sampled in this simulation—including a peculiar state arising at ?0.7 ?s and characterized by cross-strand intercalative stacking of nucleotides within a longitudinally sheared base pair, followed (at ?1 ?s) by spontaneous base flipping of a neighboring thymine within the A-rich duplex. Results and predictions from the microsecond-scale simulation include implications for a dynamical NF-?B recognition motif, and are amenable to testing and further exploration via specific experimental approaches that are suggested herein.

Mura, Cameron; McCammon, J. Andrew

2008-01-01

126

Gogny HFB prediction of nuclear structure properties  

NASA Astrophysics Data System (ADS)

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

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

2011-10-01

127

Structure prediction of loops with fixed and flexible stems.  

PubMed

The prediction of loop structures is considered one of the main challenges in the protein folding problem. Regardless of the dependence of the overall algorithm on the protein data bank, the flexibility of loop regions dictates the need for special attention to their structures. In this article, we present algorithms for loop structure prediction with fixed stem and flexible stem geometry. In the flexible stem geometry problem, only the secondary structure of three stem residues on either side of the loop is known. In the fixed stem geometry problem, the structure of the three stem residues on either side of the loop is also known. Initial loop structures are generated using a probability database for the flexible stem geometry problem, and using torsion angle dynamics for the fixed stem geometry problem. Three rotamer optimization algorithms are introduced to alleviate steric clashes between the generated backbone structures and the side chain rotamers. The structures are optimized by energy minimization using an all-atom force field. The optimized structures are clustered using a traveling salesman problem-based clustering algorithm. The structures in the densest clusters are then utilized to refine dihedral angle bounds on all amino acids in the loop. The entire procedure is carried out for a number of iterations, leading to improved structure prediction and refined dihedral angle bounds. The algorithms presented in this article have been tested on 3190 loops from the PDBSelect25 data set and on targets from the recently concluded CASP9 community-wide experiment. PMID:22352982

Subramani, A; Floudas, C A

2012-03-02

128

Intrinsic flexibility of B-DNA: the experimental TRX scale  

PubMed Central

B-DNA flexibility, crucial for DNA–protein recognition, is sequence dependent. Free DNA in solution would in principle be the best reference state to uncover the relation between base sequences and their intrinsic flexibility; however, this has long been hampered by a lack of suitable experimental data. We investigated this relationship by compiling and analyzing a large dataset of NMR 31P chemical shifts in solution. These measurements reflect the BI ? BII equilibrium in DNA, intimately correlated to helicoidal descriptors of the curvature, winding and groove dimensions. Comparing the ten complementary DNA dinucleotide steps indicates that some steps are much more flexible than others. This malleability is primarily controlled at the dinucleotide level, modulated by the tetranucleotide environment. Our analyses provide an experimental scale called TRX that quantifies the intrinsic flexibility of the ten dinucleotide steps in terms of Twist, Roll, and X-disp (base pair displacement). Applying the TRX scale to DNA sequences optimized for nucleosome formation reveals a 10 base-pair periodic alternation of stiff and flexible regions. Thus, DNA flexibility captured by the TRX scale is relevant to nucleosome formation, suggesting that this scale may be of general interest to better understand protein-DNA recognition.

Oguey, Christophe; Lavelle, Christophe; Foloppe, Nicolas; Hartmann, Brigitte

2010-01-01

129

Investigation of crack prediction in reinforced concrete liquid containing structures  

Microsoft Academic Search

Cracking in liquid containing structures, if it is not properly controlled, can have serious detrimental effects on the overall system functionality. Having a consistent knowledge of concrete cracking characteristics is essential for a designer to ensure serviceability requirements of the structure. In spite of several proposed crack prediction models that have been used as the base for design codes, still

Armin Zyarishalmani

2007-01-01

130

A Bayesian Statistical Algorithm for RNA Secondary Structure Prediction  

Microsoft Academic Search

A Bayesian approach for predicting RNA secondary structure that addresses the following three open issues is described: (1) the need for a representation of the full ensemble of probable structures; (2) the need to specify a fixed set of energy parameters; (3) the desire to make statistical inferences on all variables in the problem. It has recently been shown that

Ye Ding; Charles E. Lawrence

1999-01-01

131

Analytical Predictions of the Air Gap Response of Floating Structures  

Microsoft Academic Search

Two separate studies are presented here that deal with analytical predictions of the air gap for floating structures. (1) To obtain an understanding of the importance of first- and second-order incident and diffracted wave effects as well as to determine the influence of the structure's motions on the instantaneous air gap, statistics of the air gap response are studied under

Lance Manuel; Bert Sweetman; Steven R. Winterstein

2001-01-01

132

Predicting secondary structures of membrane proteins with neural networks.  

PubMed

Back-propagation, feed-forward neural networks are used to predict the secondary structures of membrane proteins whose structures are known to atomic resolution. These networks are trained on globular proteins and can predict globular protein structures having no homology to those of the training set with correlation coefficients (Ci) of 0.45, 0.32 and 0.43 for alpha-helix, beta-strand and random coil structures, respectively. When tested on membrane proteins, neural networks trained on globular proteins do, on average, correctly predict (Qi) 62%, 38% and 69% of the residues in the alpha-helix, beta-strand and random coil structures. These scores rank higher than those obtained with the currently used statistical methods and are comparable to those obtained with the joint approaches tested so far on membrane proteins. The lower success score for beta-strand as compared to the other structures suggests that the sample of beta-strand patterns contained in the training set is less representative than those of alpha-helix and random coil. Our analysis, which includes the effects of the network parameters and of the structural composition of the training set on the prediction, shows that regular patterns of secondary structures can be successfully extrapolated from globular to membrane proteins. PMID:8513752

Fariselli, P; Compiani, M; Casadio, R

1993-01-01

133

Predicted structure of two adenovirus tumor antigens.  

PubMed Central

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

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

1980-01-01

134

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.

Saraswathi, Saras; Fernandez-Martinez, Juan Luis; Kolinski, Andrzej; Jernigan, Robert L.; Kloczkowski, Andrzej

2013-01-01

135

Sampling bottlenecks in de novo protein structure prediction.  

PubMed

The primary obstacle to de novo protein structure prediction is conformational sampling: the native state generally has lower free energy than nonnative structures but is exceedingly difficult to locate. Structure predictions with atomic level accuracy have been made for small proteins using the Rosetta structure prediction method, but for larger and more complex proteins, the native state is virtually never sampled, and it has been unclear how much of an increase in computing power would be required to successfully predict the structures of such proteins. In this paper, we develop an approach to determining how much computer power is required to accurately predict the structure of a protein, based on a reformulation of the conformational search problem as a combinatorial sampling problem in a discrete feature space. We find that conformational sampling for many proteins is limited by critical "linchpin" features, often the backbone torsion angles of individual residues, which are sampled very rarely in unbiased trajectories and, when constrained, dramatically increase the sampling of the native state. These critical features frequently occur in less regular and likely strained regions of proteins that contribute to protein function. In a number of proteins, the linchpin features are in regions found experimentally to form late in folding, suggesting a correspondence between folding in silico and in reality. PMID:19646450

Kim, David E; Blum, Ben; Bradley, Philip; Baker, David

2009-07-28

136

Crystal structure prediction using the minima hopping method  

NASA Astrophysics Data System (ADS)

A structure prediction method is presented based on the minima hopping method. To escape local minima, moves on the configurational enthalpy surface are performed by variable cell shape molecular dynamics. To optimize the escape steps the initial atomic and cell velocities are aligned to low curvature directions of the current local minimum. The method is applied to both silicon crystals and well-studied binary Lennard-Jones mixtures. For the latter new putative ground state structures are presented. It is shown that a high success rate is achieved and a reliable prediction of unknown ground state structures is possible.

Amsler, Maximilian; Goedecker, Stefan

2010-12-01

137

Protein structural similarities predicted by a sequence-structure compatibility method.  

PubMed Central

A method for protein structure prediction has been developed, which evaluates the compatibility of an amino acid sequence with known 3-dimensional structures and identifies the most likely structure. The method was applied to a large number of sequences in a database, and the structures of the following proteins were predicted: (1) shikimate kinase (SKase), (2) the hydrophilic subunit of mannose permease (IIABMan), (3) rat tyrosine aminotransferase (Tyr AT), and (4) threonine dehydratase (TDH). The functional and evolutionary implications of the predictions are discussed. (1) The structural similarity between SKase and adenylate kinase was predicted. Alignment of their sequences reveals that the ATP-binding type A sequence motif and 2 ATP-binding arginine residues are conserved. The prediction suggests a similarity in their functional mechanisms as well as an evolutionary relationship. (2) The structural similarity between IIABMan and galactose/glucose-binding protein (GGBP) was predicted. The IIA and IIB domains are aligned with the N- and C-terminal domains of GGBP, respectively. The 2 phosphorylated residues, His 10 and His 175, of IIABMan are threaded onto loops located in the substrate-binding cleft of GGBP. The prediction accounts for the phosphoryl transfer from His 10 to His 175, and to the sugar substrate. (3) The structural similarity between rat Tyr AT and Escherichia coli aspartate AT was predicted, as well as (4) the structural similarity between TDH and the tryptophan synthase beta subunit. Predictions (3) and (4) support the previous predictions based on observations of the functional similarities between the proteins.

Matsuo, Y.; Nishikawa, K.

1994-01-01

138

Predicting Secondary Structural Folding Kinetics for Nucleic Acids  

PubMed Central

Abstract We report a new computational approach to the prediction of RNA secondary structure folding kinetics. In this approach, each elementary kinetic step is represented as the transformation between two secondary structures that differ by a helix. Based on the free energy landscape analysis, we identify three types of dominant pathways and the rate constants for the kinetic steps: 1), formation; 2), disruption of a helix stem; and 3), helix formation with concomitant partial melting of a competing (incompatible) helix. The third pathway, termed the tunneling pathway, is the low-barrier dominant pathway for the conversion between two incompatible helices. Comparisons with experimental data indicate that this new method is quite reliable in predicting the kinetics for RNA secondary structural folding and structural rearrangements. The approach presented here may provide a robust first step for further systematic development of a predictive theory for the folding kinetics for large RNAs.

Zhao, Peinan; Zhang, Wen-Bing; Chen, Shi-Jie

2010-01-01

139

Predicting secondary structural folding kinetics for nucleic acids.  

PubMed

We report a new computational approach to the prediction of RNA secondary structure folding kinetics. In this approach, each elementary kinetic step is represented as the transformation between two secondary structures that differ by a helix. Based on the free energy landscape analysis, we identify three types of dominant pathways and the rate constants for the kinetic steps: 1), formation; 2), disruption of a helix stem; and 3), helix formation with concomitant partial melting of a competing (incompatible) helix. The third pathway, termed the tunneling pathway, is the low-barrier dominant pathway for the conversion between two incompatible helices. Comparisons with experimental data indicate that this new method is quite reliable in predicting the kinetics for RNA secondary structural folding and structural rearrangements. The approach presented here may provide a robust first step for further systematic development of a predictive theory for the folding kinetics for large RNAs. PMID:20409482

Zhao, Peinan; Zhang, Wen-Bing; Chen, Shi-Jie

2010-04-21

140

Coarse-Grained Prediction of RNA Loop Structures  

PubMed Central

One of the key issues in the theoretical prediction of RNA folding is the prediction of loop structure from the sequence. RNA loop free energies are dependent on the loop sequence content. However, most current models account only for the loop length-dependence. The previously developed “Vfold” model (a coarse-grained RNA folding model) provides an effective method to generate the complete ensemble of coarse-grained RNA loop and junction conformations. However, due to the lack of sequence-dependent scoring parameters, the method is unable to identify the native and near-native structures from the sequence. In this study, using a previously developed iterative method for extracting the knowledge-based potential parameters from the known structures, we derive a set of dinucleotide-based statistical potentials for RNA loops and junctions. A unique advantage of the approach is its ability to go beyond the the (known) native structures by accounting for the full free energy landscape, including all the nonnative folds. The benchmark tests indicate that for given loop/junction sequences, the statistical potentials enable successful predictions for the coarse-grained 3D structures from the complete conformational ensemble generated by the Vfold model. The predicted coarse-grained structures can provide useful initial folds for further detailed structural refinement.

Liu, Liang; Chen, Shi-Jie

2012-01-01

141

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

PubMed Central

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

2009-01-01

142

Prediction of shock structure using the bimodal distribution function  

Microsoft Academic Search

A modification of Mott-Smith method for predicting the one-dimensional shock wave solution is presented. Mott-Smith distribution function is used to construct the system of moment equations to study the steady-state structure of shock wave in a gas of Maxwell molecules and in argon. The predicted shock solutions using the newly proposed formalism are compared to the experimental data, direct-simulation Monte

Maxim A. Solovchuk; Tony W. H. Sheu

2010-01-01

143

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.

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

2010-01-01

144

Confidence-Guided Local Structure Prediction with HHfrag.  

PubMed

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

Kalev, Ivan; Habeck, Michael

2013-10-16

145

Confidence-Guided Local Structure Prediction with HHfrag  

PubMed Central

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

Kalev, Ivan; Habeck, Michael

2013-01-01

146

Servers for sequence-structure relationship analysis and prediction  

PubMed Central

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

Dosztanyi, Zsuzsanna; Magyar, Csaba; Tusnady, Gabor E.; Cserzo, Miklos; Fiser, Andras; Simon, Istvan

2003-01-01

147

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.

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

2008-01-01

148

Prediction of structure-borne sound transmission in large welded ship structures using statistical energy analysis  

Microsoft Academic Search

An efficient method is presented for the prediction of structure-borne sound transmission in large welded ship structures. SEA (Statistical Energy Analysis) is used, and the equations used for the SEA parameters are also presented. Traditionally, the SEA method requires a great deal of work when steel structures are modelled. It is almost impossible to prepare models manually for large structures

P. Hynná; P. Klinge; J. Vuoksinen

1995-01-01

149

Correlating structural order with structural rearrangement in dusty plasma liquids: can structural rearrangement be predicted by static structural information?  

PubMed

Whether the static microstructural order information is strongly correlated with the subsequent structural rearrangement (SR) and their predicting power for SR are investigated experimentally in the quenched dusty plasma liquid with microheterogeneities. The poor local structural order is found to be a good alarm to identify the soft spot and predict the short term SR. For the site with good structural order, the persistent time for sustaining the structural memory until SR has a large mean value but a broad distribution. The deviation of the local structural order from that averaged over nearest neighbors serves as a good second alarm to further sort out the short time SR sites. It has the similar sorting power to that using the temporal fluctuation of the local structural order over a small time interval. PMID:23215390

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

2012-11-07

150

Distance matrix-based approach to protein structure prediction.  

PubMed

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

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

2009-02-18

151

Predictive Fault Detection for Missile Defense Mission Equipment and Structures  

Microsoft Academic Search

\\u000a Equipment failures in defense systems result in loss or reduction of operational capability, impacting system readiness. Faults\\u000a in critical equipment can impact system performance and reliability, as can structural failures. Predictive fault detection\\u000a (PFD) provides prognostic capability to identify components and internal structures that exhibit either degradation or increased\\u000a variability of parameters, in advance of actual faults occurrences. It has

Jeffrey S. Yalowitz; Roger K. Youree; Aaron Corder; Teng K. Ooi

152

Ab Initio Prediction of Transcription Factor Targets Using Structural Knowledge  

Microsoft Academic Search

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

Tommy Kaplan; Nir Friedman; Hanah Margalit

2005-01-01

153

An improved prediction of catalytic residues in enzyme structures.  

PubMed

The protein databases contain a huge number of function unknown proteins, including many proteins with newly determined 3D structures resulted from the Structural Genomics Projects. To accelerate experiment-based assignment of function, de novo prediction of protein functional sites, like active sites in enzymes, becomes increasingly important. Here, we attempted to improve the prediction of catalytic residues in enzyme structures by seeking and refining different encodings (i.e. residue properties) as well as employing new machine learning algorithms. In particular, considering that catalytic residues can often reveal specific network centrality when representing enzyme structure as a residue contact network, the corresponding measurement (i.e. closeness centrality) was used as one of the most important encodings in our new predictor. Meanwhile, a genetic algorithm integrated neural network (GANN) was also employed. Thanks to the above strategies, our GANN predictor demonstrated a high accuracy of 91.2% in the prediction of catalytic residues based on balanced datasets (i.e. the 1:1 ratio of catalytic to non-catalytic residues). When the GANN method was optimally applied to real enzyme structures, 73.9% of the tested structures had the active site correctly located. Compared with two existing methods, the proposed GANN method also demonstrated a better performance. PMID:18287176

Tang, Yu-Rong; Sheng, Zhi-Ya; Chen, Yong-Zi; Zhang, Ziding

2008-02-20

154

Automated RNA Tertiary Structure Prediction from Secondary Structure and Low-Resolution Restraints  

PubMed Central

A novel protocol for all-atom RNA tertiary structure prediction is presented that employs restrained molecular mechanics and simulated annealing. The restraints are from secondary structure, co-variation analysis, coaxial stacking predictions for helices in junctions, and, when available, cross-linking data. Results are demonstrated on the Alu domain of the mammalian signal recognition particle RNA, the Saccharomyces cerevisiae phenylalanine tRNA, the hammerhead ribozyme, the hepatitis C virus internal ribosomal entry site, and the P4-P6 domain of the Tetrahymena thermophila group I intron. The predicted structure is selected from a pool of decoy structures with a score that maximizes radius of gyration and base-base contacts, which was empirically found to select higher quality decoys. This simple ab initio approach is sufficient to make good predictions of the structure of RNAs compared to current crystal structures using both root mean square deviation and the accuracy of base-base contacts.

Seetin, Matthew G.; Mathews, David H.

2011-01-01

155

Extracting physicochemical features to predict protein secondary structure.  

PubMed

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

Huang, Yin-Fu; Chen, Shu-Ying

2013-05-14

156

Extracting Physicochemical Features to Predict Protein Secondary Structure  

PubMed Central

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

Chen, Shu-Ying

2013-01-01

157

Exploiting structural information in patent specifications for key compound prediction.  

PubMed

Patent specifications are one of many information sources needed to progress drug discovery projects. Understanding compound prior art and novelty checking, validation of biological assays, and identification of new starting points for chemical explorations are a few areas where patent analysis is an important component. Cheminformatics methods can be used to facilitate the identification of so-called key compounds in patent specifications. Such methods, relying on structural information extracted from documents by expert curation or text mining, can complement or in some cases replace the traditional manual approach of searching for clues in the text. This paper describes and compares three different methods for the automatic prediction of key compounds in patent specifications using structural information alone. For this data set, the cluster seed analysis described by Hattori et al. (Hattori, K.; Wakabayashi, H.; Tamaki, K. Predicting key example compounds in competitors' patent applications using structural information alone. J. Chem. Inf. Model.2008, 48, 135-142) is superior in terms of prediction accuracy with 26 out of 48 drugs (54%) correctly predicted from their corresponding patents. Nevertheless, the two new methods, based on frequency of R-groups (FOG) and maximum common substructure (MCS) similarity measures, show significant advantages due to their inherent ability to visualize relevant structural features. The results of the FOG method can be enhanced by manual selection of the scaffolds used in the analysis. Finally, a successful example of applying FOG analysis for designing potent ATP-competitive AXL kinase inhibitors with improved properties is described. PMID:22639789

Tyrchan, Christian; Boström, Jonas; Giordanetto, Fabrizio; Winter, Jon; Muresan, Sorel

2012-06-11

158

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.

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

1998-01-01

159

Prediction of shock structure using the bimodal distribution function.  

PubMed

A modification of Mott-Smith method for predicting the one-dimensional shock wave solution is presented. Mott-Smith distribution function is used to construct the system of moment equations to study the steady-state structure of shock wave in a gas of Maxwell molecules and in argon. The predicted shock solutions using the newly proposed formalism are compared to the experimental data, direct-simulation Monte Carlo (DSMC) solution, and the solutions predicted by other existing theories for Mach numbers M<11 . The density, temperature, heat flux profiles, and shock thickness calculated at different Mach numbers have been shown to have good agreement with the experimental and DSMC solutions. In addition, the predicted shock thickness is in good agreement with the DSMC simulation result at low Mach numbers. PMID:20866329

Solovchuk, Maxim A; Sheu, Tony W H

2010-05-14

160

High-resolution structure prediction and the crystallographic phase problem.  

PubMed

The energy-based refinement of low-resolution protein structure models to atomic-level accuracy is a major challenge for computational structural biology. Here we describe a new approach to refining protein structure models that focuses sampling in regions most likely to contain errors while allowing the whole structure to relax in a physically realistic all-atom force field. In applications to models produced using nuclear magnetic resonance data and to comparative models based on distant structural homologues, the method can significantly improve the accuracy of the structures in terms of both the backbone conformations and the placement of core side chains. Furthermore, the resulting models satisfy a particularly stringent test: they provide significantly better solutions to the X-ray crystallographic phase problem in molecular replacement trials. Finally, we show that all-atom refinement can produce de novo protein structure predictions that reach the high accuracy required for molecular replacement without any experimental phase information and in the absence of templates suitable for molecular replacement from the Protein Data Bank. These results suggest that the combination of high-resolution structure prediction with state-of-the-art phasing tools may be unexpectedly powerful in phasing crystallographic data for which molecular replacement is hindered by the absence of sufficiently accurate previous models. PMID:17934447

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

2007-10-14

161

Predicting PDZ domain mediated protein interactions from structure  

PubMed Central

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

2013-01-01

162

Local Duality Predictions for x approximately 1 Structure Functions.  

PubMed

Recent data on the proton F2 structure function in the resonance region suggest that local quark-hadron duality works remarkably well for each of the low-lying resonances, including the elastic, to rather low values of Q2. We derive model-independent relations between structure functions at x approximately 1 and elastic electromagnetic form factors, and predict the x-->1 behavior of nucleon polarization asymmetries and the neutron to proton structure function ratios from available data on nucleon electric and magnetic form factors. PMID:11136087

Melnitchouk

2001-01-01

163

Automatic prediction of catalytic residues by modeling residue structural neighborhood  

PubMed Central

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

2010-01-01

164

Structural analysis of heme proteins: implications for design and prediction  

PubMed Central

Background Heme is an essential molecule and plays vital roles in many biological processes. The structural determination of a large number of heme proteins has made it possible to study the detailed chemical and structural properties of heme binding environment. Knowledge of these characteristics can provide valuable guidelines in the design of novel heme proteins and help us predict unknown heme binding proteins. Results In this paper, we constructed a non-redundant dataset of 125 heme-binding protein chains and found that these heme proteins encompass at least 31 different structural folds with all-? class as the dominating scaffold. Heme binding pockets are enriched in aromatic and non-polar amino acids with fewer charged residues. The differences between apo and holo forms of heme proteins in terms of the structure and the binding pockets have been investigated. In most cases the proteins undergo small conformational changes upon heme binding. We also examined the CP (cysteine-proline) heme regulatory motifs and demonstrated that the conserved dipeptide has structural implications in protein-heme interactions. Conclusions Our analysis revealed that heme binding pockets show special features and that most of the heme proteins undergo small conformational changes after heme binding, suggesting the apo structures can be used for structure-based heme protein prediction and as scaffolds for future heme protein design.

2011-01-01

165

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.

2011-01-01

166

Fast prediction algorithm of adaptive GOP structure for SVC  

NASA Astrophysics Data System (ADS)

Adaptive group-of-picture (GOP) structure is an important encoding tool in multi-level motion-compensated temporal filtering coding scheme. Compared to conventional fixed-GOP scheme, it can dynamically adapt the GOP size to enhance the coding performance based on each sequence's characteristics. But the existing adaptive GOP structure (AGS) algorithm proposed in JSVM requires huge computation complexity. In this paper, a fast AGS prediction algorithm is proposed. At first, based on the relationship among coding performance, GOP size and corresponding intra block ratio, a sub-GOP size prediction model for different decomposition levels is developed based on the encoded intra block ratio. Then, a prediction scheme is proposed to implement AGS by the sub-GOP size prediction model. It can predict the following sub-GOP size by current sub-GOP's information instead of searching all possible sub-GOP composition. The experimental results show that the proposed algorithm with linear threshold has almost equivalent coding performance as AGS in JSVM but only one-fourth computation complexity for 4-level interframe coding scheme is required.

Chen, Yi-Hau; Lin, Chia-Hua; Chen, Ching-Yeh; Chen, Liang-Gee

2007-01-01

167

Prediction of shock structure using the bimodal distribution function  

Microsoft Academic Search

A modification of Mott-Smith method for predicting the one-dimensional shock\\u000awave solution is presented. Mott-Smith distribution function is used to\\u000aconstruct the system of moment equations to study the steady-state structure of\\u000ashock wave in a gas of Maxwell molecules and in argon. The predicted shock\\u000asolutions using the newly proposed formalism are compared with the experimental\\u000adata, direct-simulation Monte

Maxim A. Solovchuk; Tony W. H. Sheu

2010-01-01

168

Performance of secondary structure prediction methods on proteins containing structurally ambivalent sequence fragments.  

PubMed

Several approaches for predicting secondary structures from sequences have been developed and reached a fair accuracy. One of the most rigorous tests for these prediction methods is their ability to correctly predict identical fragments of protein sequences adopting different secondary structures in unrelated proteins. In our previous work, we obtained 30 identical octapeptide sequence fragments adopting different backbone conformations. It is of interest to find whether the presence of structurally ambivalent fragments in proteins will affect the accuracy of secondary structure prediction methods or not. Hence, in this work, we have made a systematic comparative analysis on secondary structure prediction results of 30 identical octapeptide pairs and 52 identical heptapeptide pairs adopting different conformations with the aid of segment overlap measure. The results reveal the better performance of profile-based methods such as PSIpred and JPred and misprediction by classical rule-based methods such as Garnier Osguthorpe Robson Method and Double Prediction Method. The results discussed here insist that modern secondary structure prediction methods are able to better discriminate conformationally ambivalent peptide fragments. © 2012 Wiley Periodicals, Inc. Biopolymers (Pept Sci) 100: 148-153, 2013. PMID:23616098

Saravanan, K Mani; Selvaraj, Samuel

2013-04-01

169

Active site prediction using evolutionary and structural information  

PubMed Central

Motivation: The identification of catalytic residues is a key step in understanding the function of enzymes. While a variety of computational methods have been developed for this task, accuracies have remained fairly low. The best existing method exploits information from sequence and structure to achieve a precision (the fraction of predicted catalytic residues that are catalytic) of 18.5% at a corresponding recall (the fraction of catalytic residues identified) of 57% on a standard benchmark. Here we present a new method, Discern, which provides a significant improvement over the state-of-the-art through the use of statistical techniques to derive a model with a small set of features that are jointly predictive of enzyme active sites. Results: In cross-validation experiments on two benchmark datasets from the Catalytic Site Atlas and CATRES resources containing a total of 437 manually curated enzymes spanning 487 SCOP families, Discern increases catalytic site recall between 12% and 20% over methods that combine information from both sequence and structure, and by ?50% over methods that make use of sequence conservation signal only. Controlled experiments show that Discern's improvement in catalytic residue prediction is derived from the combination of three ingredients: the use of the INTREPID phylogenomic method to extract conservation information; the use of 3D structure data, including features computed for residues that are proximal in the structure; and a statistical regularization procedure to prevent overfitting. Contact: kimmen@berkeley.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Sankararaman, Sriram; Sha, Fei; Kirsch, Jack F.; Jordan, Michael I.; Sjolander, Kimmen

2010-01-01

170

Predicting protein structures with a multiplayer online game  

PubMed Central

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

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

2010-01-01

171

3D-Fun: predicting enzyme function from structure  

PubMed Central

The ‘omics’ revolution is causing a flurry of data that all needs to be annotated for it to become useful. Sequences of proteins of unknown function can be annotated with a putative function by comparing them with proteins of known function. This form of annotation is typically performed with BLAST or similar software. Structural genomics is nowadays also bringing us three dimensional structures of proteins with unknown function. We present here software that can be used when sequence comparisons fail to determine the function of a protein with known structure but unknown function. The software, called 3D-Fun, is implemented as a server that runs at several European institutes and is freely available for everybody at all these sites. The 3D-Fun servers accept protein coordinates in the standard PDB format and compare them with all known protein structures by 3D structural superposition using the 3D-Hit software. If structural hits are found with proteins with known function, these are listed together with their function and some vital comparison statistics. This is conceptually very similar in 3D to what BLAST does in 1D. Additionally, the superposition results are displayed using interactive graphics facilities. Currently, the 3D-Fun system only predicts enzyme function but an expanded version with Gene Ontology predictions will be available soon. The server can be accessed at http://3dfun.bioinfo.pl/ or at http://3dfun.cmbi.ru.nl/.

von Grotthuss, Marcin; Plewczynski, Dariusz; Vriend, Gert; Rychlewski, Leszek

2008-01-01

172

Generalized Pattern Search Algorithm for Peptide Structure Prediction  

PubMed Central

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

Nicosia, Giuseppe; Stracquadanio, Giovanni

2008-01-01

173

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

PubMed

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

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

2011-09-23

174

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

PubMed

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

Gonnelli, Giulia; Rooman, Marianne; Dehouck, Yves

2012-07-08

175

Predicting loop-helix tertiary structural contacts in RNA pseudoknots  

PubMed Central

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

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

2010-01-01

176

Constraint Logic Programming approach to protein structure prediction  

PubMed Central

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

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

2004-01-01

177

Evolutionary Computer Programming of Protein Folding and Structure Predictions  

Microsoft Academic Search

In order to understand the mechanism of protein folding and to assist the rational de-novo design of fast-folding, non-aggregating and stable artificial enzymes it is very helpful to be able to simulate protein folding reactions and to predict the structures of proteins and other biomacromolecules. Here, we use a method of computer programming called “evolutionary computer programming” in which a

Bengt Nölting; Dennis Jülich; Winfried Vonau; Karl Andert

2004-01-01

178

Blind Test of Physics-Based Prediction of Protein Structures  

Microsoft Academic Search

We report here a multiprotein blind test of a computer method to predict native protein structures based solely on an all-atom physics-based force field. We use the AMBER 96 potential function with an implicit (GB\\/SA) model of solvation, combined with replica-exchange molecular-dynamics simulations. Coarse conformational sampling is performed using the zipping and assembly method (ZAM), an approach that is designed

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

2009-01-01

179

An RNA Base Discrete State Model toward Tertiary Structure Prediction  

NASA Astrophysics Data System (ADS)

We report a new ribonucleic acid (RNA) base discrete state model, which was first developed in our lab and designed to provide an efficient and accurate way of representing RNA structures toward RNA three-dimensional structure predictions. Since RNA free energy is largely determined by base pairs and base stackings instead of backbone trajectories, we directly model the RNA base configurations with respect to its previous one along the sequence. This is in sharp contrast with all previous works where the backbone trace was represented. To test how faithfully the discrete model can reproduce the chain trace in continuous space, we randomly select partial chains from the native structure of 23S ribosome RNA and re-grow them. The rms distance of the re-grown structures from the native ones is ~ 1.7 Å for an optimized 16-state discrete model and gradually increases to ~ 3.3 Å for long chains of length 50. The efficiency is also good, e.g. the program will finish within several tens of second for long loops of length 50. Our model may facilitate the RNA three-dimensional structure predictions in the near future when combined with appropriate free energy evaluation methods.

Zhang, Jian; Zhang, Yu-Jie; Wang, Wei

2010-11-01

180

Predicting Ion Binding Properties for RNA Tertiary Structures  

PubMed Central

Recent experiments pointed to the potential importance of ion correlation for multivalent ions such as Mg2+ ions in RNA folding. In this study, we develop an all-atom model to predict the ion electrostatics in RNA folding. The model can treat ion correlation effects explicitly by considering an ensemble of discrete ion distributions. In contrast to the previous coarse-grained models that can treat ion correlation, this new model is based on all-atom nucleic acid structures. Thus, unlike the previous coarse-grained models, this new model allows us to treat complex tertiary structures such as HIV-1 DIS type RNA kissing complexes. Theory-experiment comparisons for a variety of tertiary structures indicate that the model gives improved predictions over the Poisson-Boltzmann theory, which underestimates the Mg2+ binding in the competition with Na+. Further systematic theory-experiment comparisons for a series of tertiary structures lead to a set of analytical formulas for Mg2+/Na+ ion-binding to various RNA and DNA structures over a wide range of Mg2+ and Na+ concentrations.

Tan, Zhi-Jie; Chen, Shi-Jie

2010-01-01

181

Towards genome-scale structure prediction for transmembrane proteins  

PubMed Central

In this paper we briefly review some of the recent progress made by ourselves and others in developing methods for predicting the structures of transmembrane proteins from amino acid sequence. Transmembrane proteins are an important class of proteins involved in many diverse biological functions, many of which have great impact in terms of disease mechanism and drug discovery. Despite their biological importance, it has proven very difficult to solve the structures of these proteins by experimental techniques, and so there is a great deal of pressure to develop effective methods for predicting their structure. The methods we discuss range from methods for transmembrane topology prediction to new methods for low resolution folding simulations in a knowledge-based force field. This potential is designed to reproduce the properties of the lipid bilayer. Our eventual aim is to apply these methods in tandem so that useful three-dimensional models can be built for a large fraction of the transmembrane protein domains in whole proteomes.

Hurwitz, Naama; Pellegrini-Calace, Marialuisa; Jones, David T

2006-01-01

182

Factors influencing protein tyrosine nitration - structure-based predictive models  

PubMed Central

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

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

2010-01-01

183

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

PubMed

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

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

2010-12-21

184

Prediction of complete gene structures in human genomic DNA.  

PubMed

We introduce a general probabilistic model of the gene structure of human genomic sequences which incorporates descriptions of the basic transcriptional, translational and splicing signals, as well as length distributions 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 splice signals are described which capture potentially important dependencies between signal positions. The model is applied to the problem of gene identification in a computer program, GENSCAN, which identifies complete exon/intron structures of genes in genomic DNA. Novel features of the program include the capacity to predict multiple genes in a sequence, to deal with partial as well as complete genes, and to predict consistent sets of genes occurring on either or both DNA strands. GENSCAN is shown to have substantially higher accuracy than existing methods when tested on standardized sets of human and vertebrate genes, with 75 to 80% of exons identified exactly. The program is also capable of indicating fairly accurately the reliability of each predicted exon. Consistently high levels of accuracy are observed for sequences of differing C + G content and for distinct groups of vertebrates. PMID:9149143

Burge, C; Karlin, S

1997-04-25

185

Functional Structure of Biological Communities Predicts Ecosystem Multifunctionality  

PubMed Central

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

Mouillot, David; Villeger, Sebastien; Scherer-Lorenzen, Michael; Mason, Norman W. H.

2011-01-01

186

Prediction of protease substrates using sequence and structure features  

PubMed Central

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

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

2010-01-01

187

Structure Prediction and Validation of the ERK8 Kinase Domain  

PubMed Central

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

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

2013-01-01

188

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

189

Shape Prediction of Large Deployable Antenna Structure on Orbit  

NASA Astrophysics Data System (ADS)

For the worst-case analysis of a space structure's shape on orbit, various factors such as effects of thermal deformation, aged deterioration, and material hysteresis should be considered. Furthermore, the parameters of each factor have some uncertainty, such as in material properties. Therefore, in shape prediction, the consideration of these various factors and their parameters' uncertainties leads to a combinatorial explosion. To solve this problem, the factors are classified by the mode shape of deformation. If the mode shapes of some factors have high correlation, those factors are categorized in the same group. Within each group, maximum and minimum deformations are analyzed considering the uncertainty in the parameters. Among the groups with low correlation, deformations are evaluated using a combination of the maximum and minimum deformations from each group. As a result, the combinations of factors and parameters are drastically reduced. Such a shape prediction method was applied to a large deployable antenna structure of ASTRO-G. In this study, the performance of this antenna is evaluated using GRASP analysis for the predicted antenna shapes.

Ishimura, Kosei; Kii, Tsuneo; Komatsu, Keiji; Goto, Ken; Higuchi, Ken; Matsumoto, Kazuro; Iikura, Shoichi; Yoshihara, Makoto; Tsuchiya, Masaharu

190

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

PubMed Central

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

Gavryushov, S; Zielenkiewicz, P

1998-01-01

191

Prediction of common folding structures of homologous RNAs.  

PubMed Central

We have developed an algorithm and a computer program for simultaneously folding homologous RNA sequences. Given an alignment of M homologous sequences of length N, the program performs phylogenetic comparative analysis and predicts a common secondary structure conserved in the sequences. When the structure is not uniquely determined, it infers multiple structures which appear most plausible. This method is superior to energy minimization methods in the sense that it is not sensitive to point mutation of a sequence. It is also superior to usual phylogenetic comparative methods in that it does not require manual scrutiny for covariation or secondary structures. The most plausible 1-5 structures are produced in O(MN2 + N3) time and O(N2) space, which are the same requirements as those of widely used dynamic programs based on energy minimization for folding a single sequence. This is the first algorithm probably practical both in terms of time and space for finding secondary structures of homologous RNA sequences. The algorithm has been implemented in C on a Sun SparcStation, and has been verified by testing on tRNAs, 5S rRNAs, 16S rRNAs, TAR RNAs of human immunodeficiency virus type 1 (HIV-1), and RRE RNAs of HIV-1. We have also applied the program to cis-acting packaging sequences of HIV-1, for which no generally accepted structures yet exist, and propose potentially stable structures. Simulation of the program with random sequences with the same base composition and the same degree of similarity as the above sequences shows that structures common to homologous sequences are very unlikely to occur by chance in random sequences.

Han, K; Kim, H J

1993-01-01

192

FOURIER ANALYSIS OF EXTENDED FINE STRUCTURE WITH AUTOREGRESSIVE PREDICTION  

SciTech Connect

Autoregressive prediction is adapted to double the resolution of Angle-Resolved Photoemission Extended Fine Structure (ARPEFS) Fourier transforms. Even with the optimal taper (weighting function), the commonly used taper-and-transform Fourier method has limited resolution: it assumes the signal is zero beyond the limits of the measurement. By seeking the Fourier spectrum of an infinite extent oscillation consistent with the measurements but otherwise having maximum entropy, the errors caused by finite data range can be reduced. Our procedure developed to implement this concept applies autoregressive prediction to extrapolate the signal to an extent controlled by a taper width. Difficulties encountered when processing actual ARPEFS data are discussed. A key feature of this approach is the ability to convert improved measurements (signal-to-noise or point density) into improved Fourier resolution.

Barton, J.; Shirley, D.A.

1985-01-01

193

Protein Structure Prediction: From Recognition of Matches with Known Structures to Recombination of Fragments  

Microsoft Academic Search

\\u000a The field of protein structure prediction has been revolutionized by the application of “mix-and-match” methods both in template-based\\u000a homology modeling, as well as in template-free, “de novo” folding. Automated generation of models that are closer to the native\\u000a structure of the target protein than the structure of its closest homolog is currently possible by recombination of fragments\\u000a copied from known

Michal J. Gajda; Marcin Pawlowski; Janusz M. Bujnicki

194

Cortical structure predicts success in performing musical transformation judgments.  

PubMed

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

Foster, Nicholas E V; Zatorre, Robert J

2010-06-23

195

Brain white matter structural properties predict transition to chronic pain.  

PubMed

Neural mechanisms mediating the transition from acute to chronic pain remain largely unknown. In a longitudinal brain imaging study, we followed up patients with a single sub-acute back pain (SBP) episode for more than 1year as their pain recovered (SBPr), or persisted (SBPp) representing a transition to chronic pain. We discovered brain white matter structural abnormalities (n=24 SBP patients; SBPp=12 and SBPr=12), as measured by diffusion tensor imaging (DTI), at entry into the study in SBPp in comparison to SBPr. These white matter fractional anisotropy (FA) differences accurately predicted pain persistence over the next year, which was validated in a second cohort (n=22 SBP patients; SBPp=11 and SBPr=11), and showed no further alterations over a 1-year period. Tractography analysis indicated that abnormal regional FA was linked to differential structural connectivity to medial vs lateral prefrontal cortex. Local FA was correlated with functional connectivity between medial prefrontal cortex and nucleus accumbens in SBPr. As we have earlier shown that the latter functional connectivity accurately predicts transition to chronic pain, we can conclude that brain structural differences, most likely existing before the back pain-inciting event and independent of the back pain, predispose subjects to pain chronification. PMID:24040975

Mansour, Ali R; Baliki, Marwan N; Huang, Lejian; Torbey, Souraya; Herrmann, Kristi M; Schnitzer, Thomas J; Apkarian, A Vania

2013-10-01

196

Integrating Chemical Footprinting Data into RNA Secondary Structure Prediction  

PubMed Central

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

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

2012-01-01

197

A Probabilistic Fragment-Based Protein Structure Prediction Algorithm  

PubMed Central

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

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

2012-01-01

198

Accurate prediction of scorpion toxin functional properties from primary structures.  

PubMed

Scorpion toxins are common experimental tools for studies of biochemical and pharmacological properties of ion channels. The number of functionally annotated scorpion toxins is steadily growing, but the number of identified toxin sequences is increasing at much faster pace. With an estimated 100,000 different variants, bioinformatic analysis of scorpion toxins is becoming a necessary tool for their systematic functional analysis. Here, we report a bioinformatics-driven system involving scorpion toxin structural classification, functional annotation, database technology, sequence comparison, nearest neighbour analysis, and decision rules which produces highly accurate predictions of scorpion toxin functional properties. PMID:15950506

Tan, Paul T J; Srinivasan, K N; Seah, Seng Hong; Koh, Judice L Y; Tan, Tin Wee; Ranganathan, Shoba; Brusic, Vladimir

2005-09-01

199

Offspring social network structure predicts fitness in families.  

PubMed

Social structures such as families emerge as outcomes of behavioural interactions among individuals, and can evolve over time if families with particular types of social structures tend to leave more individuals in subsequent generations. The social behaviour of interacting individuals is typically analysed as a series of multiple dyadic (pair-wise) interactions, rather than a network of interactions among multiple individuals. However, in species where parents feed dependant young, interactions within families nearly always involve more than two individuals simultaneously. Such social networks of interactions at least partly reflect conflicts of interest over the provision of costly parental investment. Consequently, variation in family network structure reflects variation in how conflicts of interest are resolved among family members. Despite its importance in understanding the evolution of emergent properties of social organization such as family life and cooperation, nothing is currently known about how selection acts on the structure of social networks. Here, we show that the social network structure of broods of begging nestling great tits Parus major predicts fitness in families. Although selection at the level of the individual favours large nestlings, selection at the level of the kin-group primarily favours families that resolve conflicts most effectively. PMID:23097505

Royle, Nick J; Pike, Thomas W; Heeb, Philipp; Richner, Heinz; Kölliker, Mathias

2012-10-24

200

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

201

Prediction of halocarbon toxicity from structure: a hierarchical QSAR approach.  

PubMed

Mathematical structural invariants and quantum theoretical descriptors have been used extensively in quantitative structure-activity relationships (QSARs) for the estimation of pharmaceutical activities, biological properties, physicochemical properties, and the toxicities of chemicals. Recently our research team has explored the relative importance of various levels of chemodescriptors, i.e. topostructural (TS), topochemical (TC), geometrical (3D), and quantum theoretical descriptors, in property estimation. This study examines the contribution of chemodescriptors ranging from topostructural to quantum theoretic calculations, up to the Gaussian STO-3G level, in predicting the results of six indicators of oxidative stress for a set of 20 halocarbons. Using quantum theoretical calculations in this study is of particular interest as molecular energetics is related to the likelihood of electron attachment and free radical formation, the mechanism of toxicity for these chemicals and should aid in modeling their potential for oxidative stress. PMID:21782698

Gute, Brian D; Balasubramanian, K; Geiss, K T; Basak, S C

2004-03-01

202

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.

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

2008-01-01

203

Generic eukaryotic core promoter prediction using structural features of DNA.  

PubMed

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

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

2007-12-20

204

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

PubMed Central

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

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

2010-01-01

205

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

PubMed

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

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

2010-04-22

206

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

PubMed Central

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

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

2012-01-01

207

RNAalifold: improved consensus structure prediction for RNA alignments  

PubMed Central

Background The prediction of a consensus structure for a set of related RNAs is an important first step for subsequent analyses. RNAalifold, which computes the minimum energy structure that is simultaneously formed by a set of aligned sequences, is one of the oldest and most widely used tools for this task. In recent years, several alternative approaches have been advocated, pointing to several shortcomings of the original RNAalifold approach. Results We show that the accuracy of RNAalifold predictions can be improved substantially by introducing a different, more rational handling of alignment gaps, and by replacing the rather simplistic model of covariance scoring with more sophisticated RIBOSUM-like scoring matrices. These improvements are achieved without compromising the computational efficiency of the algorithm. We show here that the new version of RNAalifold not only outperforms the old one, but also several other tools recently developed, on different datasets. Conclusion The new version of RNAalifold not only can replace the old one for almost any application but it is also competitive with other approaches including those based on SCFGs, maximum expected accuracy, or hierarchical nearest neighbor classifiers.

Bernhart, Stephan H; Hofacker, Ivo L; Will, Sebastian; Gruber, Andreas R; Stadler, Peter F

2008-01-01

208

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

209

How evolutionary crystal structure prediction works--and why.  

PubMed

Once the crystal structure of a chemical substance is known, many properties can be predicted reliably and routinely. Therefore if researchers could predict the crystal structure of a material before it is synthesized, they could significantly accelerate the discovery of new materials. In addition, the ability to predict crystal structures at arbitrary conditions of pressure and temperature is invaluable for the study of matter at extreme conditions, where experiments are difficult. Crystal structure prediction (CSP), the problem of finding the most stable arrangement of atoms given only the chemical composition, has long remained a major unsolved scientific problem. Two problems are entangled here: search, the efficient exploration of the multidimensional energy landscape, and ranking, the correct calculation of relative energies. For organic crystals, which contain a few molecules in the unit cell, search can be quite simple as long as a researcher does not need to include many possible isomers or conformations of the molecules; therefore ranking becomes the main challenge. For inorganic crystals, quantum mechanical methods often provide correct relative energies, making search the most critical problem. Recent developments provide useful practical methods for solving the search problem to a considerable extent. One can use simulated annealing, metadynamics, random sampling, basin hopping, minima hopping, and data mining. Genetic algorithms have been applied to crystals since 1995, but with limited success, which necessitated the development of a very different evolutionary algorithm. This Account reviews CSP using one of the major techniques, the hybrid evolutionary algorithm USPEX (Universal Structure Predictor: Evolutionary Xtallography). Using recent developments in the theory of energy landscapes, we unravel the reasons evolutionary techniques work for CSP and point out their limitations. We demonstrate that the energy landscapes of chemical systems have an overall shape and explore their intrinsic dimensionalities. Because of the inverse relationships between order and energy and between the dimensionality and diversity of an ensemble of crystal structures, the chances that a random search will find the ground state decrease exponentially with increasing system size. A well-designed evolutionary algorithm allows for much greater computational efficiency. We illustrate the power of evolutionary CSP through applications that examine matter at high pressure, where new, unexpected phenomena take place. Evolutionary CSP has allowed researchers to make unexpected discoveries such as a transparent phase of sodium, a partially ionic form of boron, complex superconducting forms of calcium, a novel superhard allotrope of carbon, polymeric modifications of nitrogen, and a new class of compounds, perhydrides. These methods have also led to the discovery of novel hydride superconductors including the "impossible" LiH(n) (n=2, 6, 8) compounds, and CaLi(2). We discuss extensions of the method to molecular crystals, systems of variable composition, and the targeted optimization of specific physical properties. PMID:21361336

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

2011-03-01

210

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

211

Electric field of a charged dielectric cylinder with counterions: application to B-DNA  

Microsoft Academic Search

An analytical model, based on the full nonlinear Poisson-Boltzmann equation, for the electric field and the space distribution of the counterions around a rod with a surface charge density a function of the polar angle is presented. The model is applied for dissolved B-DNA, which is modelled as a dielectric cylinder with a surface charge density consisting of ten vertical

D. Ouroushev

2000-01-01

212

Electric field of a charged dielectric cylinder with counterions: application to B-DNA  

Microsoft Academic Search

An analytical model, based on the fuli nonlinear Poisson-Boltzmann equation, for the electric field and the space distribution of the counterions around a rod with a surface charge density a function of the polar angle is presented. The model is applied for dissolved B-DNA, which is modelled as a dielectric cylinder with a surface charge density consisting of ten vertical

D. Ouroushev

2000-01-01

213

RNA 3D structure prediction: (1) assessing rna 3D structure similarity from 2D structure similarity.  

PubMed

Computational techniques for 3D structure prediction of proteins, the holy grail of bioinformatics, have undergone major developments in recent years, geared by international cooperation and competition with CASP (Critical Assessment of Structure Prediction Techniques) like contests to improve and refine them. Although straightforward extrapolation of these methodologies for the prediction of the 3D structures of other similarly relevant bio macromolecules may not be too compelling due mostly to the intrinsic differences in constitution, nature, and function between them, the conceptual framework underlying most of those techniques applied to the development of similar computational techniques in structural biology can lead to efficient systems for prediction of the 3D structure of other bio-macromolecules. One of them is the development of rational methodologies to model RNA 3D structures from the sequence of nucleotides composing them. In this paper we establish the fundamentals of a methodology to thread a sequence of nucleotides into a set of 3D fragments extracted from a data base expressly developed for this purpose. The technique is based on a newly implemented algorithm for extraction of 3D fragments by comparison of secondary structures of RNA. The result is a highly efficient system to produce a set of fragments from which entire RNA structure for the given nucleotide sequence can be built. PMID:15706497

Barreda D C, Jaime E; Shigenobu, Yoshimitsu; Ichiishi, Eiichiro; Del Carpio M, Carlos A

2004-01-01

214

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

PubMed Central

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

2013-01-01

215

Automatic measurement of voice onset time using discriminative structured prediction.  

PubMed

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

Sonderegger, Morgan; Keshet, Joseph

2012-12-01

216

Prediction of solar wind structures between Venus and Mars orbits  

NASA Astrophysics Data System (ADS)

We performed a detailed study of the temporal evolution and spatial variation of the solar wind on different scales during the recent long solar activity minimum. We use STEREO Ahead and Behind, Venus Express and Mars Express in-situ plasma measurements to infer the solar wind properties and structures at any heliospheric in-ecliptic positions. We test the range of validity of these predictions by comparing their results. We find that our predictions are valid at radial spacecraft separations as far as the Mars and Venus orbits and even at 60 degrees longitudinal separation due to the steadiness of the solar wind at this time. Our results prove that two spacecraft positioned at the L4 and L5 Lagrangian points would be indeed suitable for terrestrial space weather forecasting of solar wind features such as high speed streams and stream interaction regions. Since 2010 the frequency of transients increases as the solar cycle proceeds towards activity maximum, so in this time period we expect lower correlations between the datasets of two widely separated spacecraft.

Opitz, A.; Fedorov, A.; Wurz, P.; Sauvaud, J.; Luhmann, J. G.

2011-12-01

217

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

PubMed Central

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

2010-01-01

218

How to predict very large and complex crystal structures  

NASA Astrophysics Data System (ADS)

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

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

2010-09-01

219

Structure activity relationships: their function in biological prediction  

SciTech Connect

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

Schultz, T.W.

1982-01-01

220

HSRNAFold: A harmony search algorithm for RNA secondary structure prediction based on minimum free energy  

Microsoft Academic Search

Current physical methods for RNA structure determination are time consuming and expensive; thus the methods for the computational prediction of structure are necessary. Various algorithms have been used for RNA structure prediction including dynamic programming and meta-heuristic algorithms. This paper proposes a meta-heuristic harmony search algorithm (HSRNAFold) for finding RNA secondary structure with minimum free energy and similarity to the

Abdulqader M. Mohsen; Ahamad Tajudin Khader; Dhanesh Ramachandram

2008-01-01

221

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

222

Prediction of grain structures in various solidification processes  

NASA Astrophysics Data System (ADS)

Grain structure formation during solidification can be simulated via the use of stochastic models providing the physical mechanisms of nucleation and dendrite growth are accounted for. With this goal in mind, a physically based cellular automaton (CA) model has been coupled with finite element (FE) heat flow computations and implemented into the code 3- MOS. The CA enmeshment of the solidifying domain with small square cells is first generated automatically from the FE mesh. Within each time-step, the variation of enthalpy at each node of the FE mesh is calculated using an implicit scheme and a Newton-type linearization method. After interpolation of the explicit temperature and of the enthalpy variation at the cell location, the nucleation and growth of grains are simulated using the CA algorithm. This algorithm accounts for the heterogeneous nucleation in the bulk and at the surface of the ingot, for the growth and preferential growth directions of the dendrites, and for microsegregation. The variations of volume fraction of solid at the cell location are then summed up at the FE nodes in order to find the new temperatures. This CAFE model, which allows the prediction and the visualization of grain structures during and after solidification, is applied to various solidification processes: the investment casting of turbine blades, the continuous casting of rods, and the laser remelting or welding of plates. Because the CAFE model is yet two-dimensional (2-D), the simulation results are compared in a qualitative way with experimental findings.

Rappaz, M.; Gandin, Ch A.; Desbiolles, J. L.; Thévoz, Ph.

1996-03-01

223

Ionic distribution around simple B-DNA models II. Deviations from cylindrical symmetry  

NASA Astrophysics Data System (ADS)

The structure of the ions around two B-DNA models with added monovalent salt at the continuum solvent level is investigated by computer simulation. The salt concentrations cover a wide range, from 0.05 to 4.5 M. The simplicity of the so-called grooved primitive model (unit electron charges at the phosphate positions of canonical DNA and the grooves shape approximated by means of simple geometric elements) enables a detailed study of the counterion and coion distributions with a very small statistical noise. The inhomogeneity of the ionic distributions is noticeable along the axial direction up to distances of about 20 Å from the DNA axis. The counterions deeply penetrate into the DNA grooves even at very low added salt concentrations. In the minor groove, the counterions are preferentially located in its center whereas they lie at the sides of the major groove, close to the phosphate positions. The coions also enter within the major groove, especially in the systems at high added salt concentrations for which regions of absolute negative charge can be found within the groove. This can be explained in terms of an arrangement of ions with alternating charges. The grooved primitive model has also been solved in the context of the finite difference Poisson-Boltzmann theory. The theory accurately describes the ionic structure around DNA at low salt concentrations but the results deteriorate with increasing salt missing important qualitative features at or above molar concentrations. The other model investigated differs from the more detailed one in that the shape of DNA is not taken into account; a soft cylinder is used instead. The counterions accumulate in this model in front of the phosphates and the axial inhomogeneity of the distribution quickly vanishes. These results together with those of previous investigations lead to the conclusion that the coupling of the discrete description of the DNA charge with the steric effects due to the presence of the grooves is the primary determinant of the final ionic distribution, especially at high salt concentrations. This effect may play a decisive role in those DNA properties which are strongly dependent on the salt concentration, like the B- to Z-DNA conformational transition.

Montoro, Juan Carlos Gil; Abascal, José L. F.

1998-10-01

224

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

225

Structure prediction for CASP7 targets using extensive all-atom refinement with Rosetta@home  

Microsoft Academic Search

We describe predictions made using the Rosetta structure prediction methodology for both tem- plate-based modeling and free modeling catego- ries in the Seventh Critical Assessment of Tech- niques for Protein Structure Prediction. For the first time, aggressive sampling and all-atom refinement could be carried out for the majority of targets, an advance enabled by the Rosetta@ home distributed computing network.

Rhiju Das; Bin Qian; Srivatsan Raman; Robert Vernon; James Thompson; Philip Bradley; Sagar Khare; Michael D. Tyka; Divya Bhat; Dylan Chivian; David E. Kim; William H. Sheffler; Lars Malmström; Andrew M. Wollacott; Chu Wang; Ingemar Andre; David Baker

2007-01-01

226

Structure classification-based assessment of CASP3 predictions for the fold recognition targets.  

PubMed

The sequences of at least 23 of the 43 CASP3 targets showed no significant similarity to the sequences of known structures. The experimental structures of all but three of these 23 targets revealed substantial similarities to known structures, with at least eleven of the target structures likely being distantly homologous to known structures. Nineteen of the 23 target structures were available at the time of the final CASP3 meeting in Asilomar in December 1998, whereas the experimental data on the protein folds of the remaining four targets were obtained afterwards. The predicted three-dimensional structures for each of the 23 targets were analyzed to select those predictions sharing with the experimental structures a similar overall fold and/or having correctly folded a substantial fraction of the target sequence. Initially, predicted models were numerically evaluated and the evaluation results aided the selection process. Each target structure was then classified to identify a minimal set of structural features characteristic to its protein fold and evolutionary superfamily. The predictions containing this set were assessed comparatively to find the best predictions for each target. The predictions of new folds were assessed separately. The total number of the selected 'correct' predictions and the quality of these predictions were used to compare the performance of different predictor teams and different prediction methods in the fold prediction/recognition category. PMID:10526357

Murzin, A G

1999-01-01

227

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

PubMed

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

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

2011-08-08

228

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

PubMed Central

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

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

2011-01-01

229

Towards a molecular dynamics consensus view of B-DNA flexibility  

PubMed Central

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

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

2008-01-01

230

Anticentromere-protein-B-DNA complex activities in anticentromere antibody-positive patients  

Microsoft Academic Search

Centromere protein B (CENP-B), which is an alphoid DNA binding protein, is the target antigen in autoimmune disease patients (often those with scleroderma). In this study, we analysed activities of anti-CENP-B-DNA complex in anticentromere antibody (ACA)-positive patients using DNA immunoprecipitation with purified CENP-B. The activities correlated with ACA titres and were closely associated with Raynaud's phenomenon. Patients with CREST symptoms

Y. Muro; Y. Matsumoto; M. Ohashi

1992-01-01

231

STRUCTURE-BASED PREDICTIVE MODEL FOR COAL CHAR COMBUSTION  

SciTech Connect

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

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

1998-06-04

232

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

ERIC Educational Resources Information Center

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

Ellington, Roni; Wachira, James; Nkwanta, Asamoah

2010-01-01

233

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

234

Multi-Strand RNA Secondary Structure Prediction and Nanostructure Design including Pseudoknots  

PubMed Central

We are presenting NanoFolder, a method for the prediction of the base pairing of potentially pseudoknotted multi-strand RNA nanostructures. We show that the method outperforms several other structure prediction methods when applied to RNA complexes with non-nested base pairs. We extended this secondary structure prediction capability to allow RNA sequence design. Using native PAGE, we experimentally confirm that 4 in silico designed RNA strands corresponding to a triangular RNA structure form the expected stable complex.

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

2011-01-01

235

Analysis of an optimal hidden Markov model for secondary structure prediction  

Microsoft Academic Search

Background  Secondary structure prediction is a useful first step toward 3D structure prediction. A number of successful secondary structure\\u000a prediction methods use neural networks, but unfortunately, neural networks are not intuitively interpretable. On the contrary,\\u000a hidden Markov models are graphical interpretable models. Moreover, they have been successfully used in many bioinformatic\\u000a applications. Because they offer a strong statistical background and allow

Juliette Martin; Jean-François Gibrat; François Rodolphe

2006-01-01

236

"Well-determined" regions in RNA secondary structure prediction: analysis of small subunit ribosomal RNA.  

PubMed Central

Recent structural analyses of genomic RNAs from RNA coliphages suggest that both well-determined base paired helices and well-determined structural domains that are identified by "energy dot plot" analysis using the RNA folding package mfold, are likely to be predicted correctly. To test these observations with another group of large RNAs, we have analyzed 15 ribosomal RNAs. Published secondary structure models that were derived by comparative sequence analysis were used to evaluate the predicted structures. Both the optimal predicted fold and the predicted "energy dot plot" of each sequence were examined. Each prediction was obtained from a single computer run on an entire ribosomal RNA sequence. All predicted base pairs in optimal foldings were examined for agreement with proven base pairs in the comparative models. Our analyses show that the overall correspondence between the predicted and comparative models varied for different RNAs and ranges from a low of 27% to high of 70%, with a mean value of 49%. The correspondence improves to a mean value of 81% when the analysis is limited to well-determined helices. In addition to well-determined helices, large well-determined structural domains can be observed in "energy dot plots" of some 16S ribosomal RNAs. The predicted domains correspond closely with structural domains that are found by the comparative method in the same RNAs. Our analyses also show that measuring the agreement between predicted and comparative secondary structure models underestimates the reliability of structural prediction by mfold.

Zuker, M; Jacobson, A B

1995-01-01

237

Physics-based de novo prediction of RNA 3D structures  

PubMed Central

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

Cao, Song; Chen, Shi-Jie

2011-01-01

238

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

PubMed

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

Cao, Song; Chen, Shi-Jie

2011-03-17

239

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

240

Prediction of corrosion rates in marine and offshore structures  

Microsoft Academic Search

The corrosion rate of structural steels in the hostile environments of the coastal, harbour or ocean zones effects the economic interest of offshore structures since both the loss of steel and pitting may have significant impacts on structural safety and performance. With the increasing emphasis to maintain existing structures in service for longer periods of time and hence to defer

Ong Shiou Ting; Narayanan Sambu Potty; M. Shahir Liew

2011-01-01

241

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...

242

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...

243

A Feature Selection Algorithm Based on Graph Theory and Random Forests for Protein Secondary Structure Prediction  

Microsoft Academic Search

Protein secondary structure prediction problem is one of the widely studied problems in bioinformatics. Predicting the secondary\\u000a structure of a protein is an important step for determining its tertiary structure and thus its function. This paper explores\\u000a the protein secondary structure problem using a novel feature selection algorithm combined with a machine learning approach\\u000a based on random forests. For feature

Gulsah Altun; Hae-jin Hu; Stefan Gremalschi; Robert W. Harrison; Yi Pan

2007-01-01

244

Selection of antisense oligonucleotides based on multiple predicted target mRNA structures  

Microsoft Academic Search

Background: Local structures of target mRNAs play a significant role in determining the efficacies of antisense oligonucleotides (ODNs), but some structure-based target site selection methods are limited by uncertainties in RNA secondary structure prediction. If all the predicted structures of a given mRNA within a certain energy limit could be used simultaneously, target site selection would obviously be improved in

Xiaochen Bo; Shaoke Lou; Daochun Sun; Wenjie Shu; Jing Yang; Shengqi Wang

2006-01-01

245

Theoretical prediction and direct observation of the 9R structure in Ag  

Microsoft Academic Search

Molecular-dynamics simulations of the Sigma3(211) twin boundary in Ag predict a thin (1 nm) boundary phase of the 9R (alpha-Sm) structure. High-resolution electron microscopy shows the presence of the predicted structure. We also calculate the energy ab initio for several hypothetical structures of Cu and Ag. Low energies of the 9R structure and other polytypes, low experimental stacking-fault energies, and

F. Ernst; M. W. Finnis; D. Hofmann; T. Muschik; U. Schönberger; U. Wolf; M. Methfessel

1992-01-01

246

Two structural intensity prediction methods in plates excited by turbulent boundary layers  

Microsoft Academic Search

Structural intensity (S-I) fields may be used to identify energy flow paths through a vibrating structure, as well as energy source and sink regions. Boundary layer excitation of structures occurs in numerous aerospace and underwater applications. This study describes two methods of predicting S-I fields in structures excited by turbulent boundary layers. The first prediction method combines well known multiple-input\\/multiple-output

Michael J. Daley; Stephen A. Hambric

2002-01-01

247

Prediction of strong shock structure using the bimodal distribution function  

Microsoft Academic Search

A modified Mott-Smith method for predicting the one-dimensional shock wave\\u000asolution at very high Mach numbers is constructed by developing a system of\\u000afluid dynamic equations. The predicted shock solutions in a gas of Maxwell\\u000amolecules, a hard sphere gas and in argon using the newly proposed formalism\\u000aare compared with the experimental data, direct-simulation Monte Carlo (DSMC)\\u000asolution and

Maxim A. Solovchuk; Tony W. H. Sheu

2010-01-01

248

PSPP: A Protein Structure Prediction Pipeline for Computing Clusters.  

National Technical Information Service (NTIS)

Background: Protein structures are critical for understanding the mechanisms of biological systems and, subsequently, for drug and vaccine design. Unfortunately, protein sequence data exceed structural data by a factor of more than 200 to 1. This gap can ...

I. Yeh M. S. Lee N. Zavaljevski R. Bondugula V. Desai

2009-01-01

249

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

Microsoft Academic Search

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

Amit Kumar Mishra; O. P. Patri

2010-01-01

250

Strand-Invasion of Extended, Mixed-Sequence B-DNA by ?PNAs  

PubMed Central

In this Communication we show that peptide nucleic acids (PNAs), 15 to 20nt in length, when preorganized into a right-handed helix, can invade mixed-sequence double helical B-form DNA (B-DNA). Strand-invasion occurs in a highly sequence-specific manner through direct Watson-Crick base-pairing. Unlike the previously developed double-duplex invasion strategy that requires simultaneous binding of two strands of pseudocomplementary PNAs to DNA, only a single strand of ?PNA is required for invasion in this case, and no nucleobase substitution is needed.

He, Gaofei; Rapireddy, Srinivas; Bahal, Raman; Sahu, Bichismita; Ly, Danith H.

2009-01-01

251

Prediction of Musical Affect Using a Combination of Acoustic Structural Cues  

Microsoft Academic Search

This study explores whether musical affect attribution can be predicted by a linear combination of acoustical structural cues. To that aim, a database of sixty musical audio excerpts was compiled and analyzed at three levels: judgments of affective content by subjects; judgments of structural content by musicological experts (i.e., “manual structural cues”), and extraction of structural content by an auditory-based

Marc Leman; Valery Vermeulen; Liesbeth De Voogdt; Dirk Moelants; Micheline Lesaffre

2005-01-01

252

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

253

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

PubMed Central

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

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

2011-01-01

254

The prediction of EEG signals using a feedback-structured adaptive rational function filter  

Microsoft Academic Search

.  ?In this article, we present a feedback-structured adaptive rational function filter based on a recursive modified Gram-Schmidt\\u000a algorithm and apply it to the prediction of an EEG signal that has nonlinear and nonstationary characteristics. For the evaluation\\u000a of the prediction performance, the proposed filter is compared with other methods, where a single-step prediction and a multi-step\\u000a prediction are considered for

Hyun-Sool Kim; Taek-Soo Kim; Yoon-Ho Choi; Sang-Hui Park

2000-01-01

255

Probabilistic predictions of penetrating injury to anatomic structures.  

PubMed Central

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

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

1997-01-01

256

Predicting RNA secondary structures with pseudoknots by MCMC sampling  

Microsoft Academic Search

The most probable secondary structure of an RNA molecule, given the nucleotide sequence, can be computed efficiently if a\\u000a stochastic context-free grammar (SCFG) is used as the prior distribution of the secondary structure. The structures of some\\u000a RNA molecules contain so-called pseudoknots. Allowing all possible configurations of pseudoknots is not compatible with context-free\\u000a grammar models and makes the search for

Dirk Metzler; Markus E. Nebel

2008-01-01

257

Selection of antisense oligonucleotides based on multiple predicted target mRNA structures  

PubMed Central

Background Local structures of target mRNAs play a significant role in determining the efficacies of antisense oligonucleotides (ODNs), but some structure-based target site selection methods are limited by uncertainties in RNA secondary structure prediction. If all the predicted structures of a given mRNA within a certain energy limit could be used simultaneously, target site selection would obviously be improved in both reliability and efficiency. In this study, some key problems in ODN target selection on the basis of multiple predicted target mRNA structures are systematically discussed. Results Two methods were considered for merging topologically different RNA structures into integrated representations. Several parameters were derived to characterize local target site structures. Statistical analysis on a dataset with 448 ODNs against 28 different mRNAs revealed 9 features quantitatively associated with efficacy. Features of structural consistency seemed to be more highly correlated with efficacy than indices of the proportion of bases in single-stranded or double-stranded regions. The local structures of the target site 5' and 3' termini were also shown to be important in target selection. Neural network efficacy predictors using these features, defined on integrated structures as inputs, performed well in "minus-one-gene" cross-validation experiments. Conclusion Topologically different target mRNA structures can be merged into integrated representations and then used in computer-aided ODN design. The results of this paper imply that some features characterizing multiple predicted target site structures can be used to predict ODN efficacy.

Bo, Xiaochen; Lou, Shaoke; Sun, Daochun; Shu, Wenjie; Yang, Jing; Wang, Shengqi

2006-01-01

258

A method for WD40 repeat detection and secondary structure prediction.  

PubMed

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

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

2013-06-11

259

Structure analysis, fatigue testing, and lifetime prediction of composite steels  

Microsoft Academic Search

Composite steels prepared by technology of powder metallurgy are widely used as low cost parts with good resistance to wear, fracture, and corrosion. The development of powder composite steels is directly related to strength under vibration, fatigue stabilizing, and accurate lifetime prediction for actual composite topology. The fatigue behavior of powder steels was studied by experimental and numerical methods of

Yu. V. Sokolkin; A. A. Chekalkin; A. V. Babushkin

1998-01-01

260

Developing Geological Structural Criteria for Predicting Unstable Mine Roof Rocks.  

National Technical Information Service (NTIS)

This study was designed to investigate roof falls in room-and-pillar drift coal mines and to determine geologic methods for predicting unstable roof conditions. The study area was located in Harlan County, Ky., and the Bailey Creek (Harlan coal bed) and H...

D. K. Hylbert

1977-01-01

261

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

262

Prediction of Harmful Human Health Effects of Chemicals from Structure  

Microsoft Academic Search

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

Mark T. D. Cronin

2010-01-01

263

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

264

Evaluating the accuracy of SHAPE-directed RNA secondary structure predictions.  

PubMed

Recent advances in RNA structure determination include using data from high-throughput probing experiments to improve thermodynamic prediction accuracy. We evaluate the extent and nature of improvements in data-directed predictions for a diverse set of 16S/18S ribosomal sequences using a stochastic model of experimental SHAPE data. The average accuracy for 1000 data-directed predictions always improves over the original minimum free energy (MFE) structure. However, the amount of improvement varies with the sequence, exhibiting a correlation with MFE accuracy. Further analysis of this correlation shows that accurate MFE base pairs are typically preserved in a data-directed prediction, whereas inaccurate ones are not. Thus, the positive predictive value of common base pairs is consistently higher than the directed prediction accuracy. Finally, we confirm sequence dependencies in the directability of thermodynamic predictions and investigate the potential for greater accuracy improvements in the worst performing test sequence. PMID:23325843

Sükösd, Zsuzsanna; Swenson, M Shel; Kjems, Jørgen; Heitsch, Christine E

2013-01-15

265

Prediction of Harmful Human Health Effects of Chemicals from Structure  

Microsoft Academic Search

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

Mark T. D. Cronin

266

Prediction of Zeolite Types Based on Structural Data.  

NASA Astrophysics Data System (ADS)

Application of knowledge discovery methods in the search of information contained in databases is an emerging field in materials science that plays an important role on facilitating data analysis. In this study we propose a model for identification of the zeolite mineral type based on the topological analysis of the underlying crystal structure. High-throughput generation of topological descriptors is derived from the Delaunay tessellation of zeolite supercells. Based on these descriptors, our Zeolite-Structure-Predictor is trained for classifying zeolite crystals into twenty two different types of minerals and is based on a random forest model constructed with attributes that include tetrahedrality index, in-sphere volume, average edge, frequency of occurrence and probability of oxygen rich selected simplices. The underlying crystal structure data used for this study are included in the Inorganic Crystal Structural Database (ICSD).

Lach-Hab, M.; Carr, D. A.; Vaisman, I.; Blaisten-Barojas, E.

2008-03-01

267

Prediction of Structures and Properties for Organic Superconductors  

Microsoft Academic Search

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

Ersan Demiralp

1996-01-01

268

Crystal structure prediction of flexible molecules using parallel genetic algorithms with a standard force field.  

PubMed

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

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

2009-10-01

269

COFOLD: an RNA secondary structure prediction method that takes co-transcriptional folding into account.  

PubMed

Existing state-of-the-art methods that take a single RNA sequence and predict the corresponding RNA secondary structure are thermodynamic methods. These aim to predict the most stable RNA structure. There exists by now ample experimental and theoretical evidence that the process of structure formation matters and that sequences in vivo fold while they are being transcribed. None of the thermodynamic methods, however, consider the process of structure formation. Here, we present a conceptually new method for predicting RNA secondary structure, called CoFold, that takes effects of co-transcriptional folding explicitly into account. Our method significantly improves the state-of-art in terms of prediction accuracy, especially for long sequences of >1000 nt in length. PMID:23511969

Proctor, Jeff R; Meyer, Irmtraud M

2013-03-19

270

Transient RNA structure features are evolutionarily conserved and can be computationally predicted.  

PubMed

Functional RNA structures tend to be conserved during evolution. This finding is, for example, exploited by comparative methods for RNA secondary structure prediction that currently provide the state-of-art in terms of prediction accuracy. We here provide strong evidence that homologous RNA genes not only fold into similar final RNA structures, but that their folding pathways also share common transient structural features that have been evolutionarily conserved. For this, we compile and investigate a non-redundant data set of 32 sequences with known transient and final RNA secondary structures and devise a dedicated computational analysis pipeline. PMID:23625966

Zhu, Jing Yun A; Steif, Adi; Proctor, Jeff R; Meyer, Irmtraud M

2013-04-26

271

Transient RNA structure features are evolutionarily conserved and can be computationally predicted  

PubMed Central

Functional RNA structures tend to be conserved during evolution. This finding is, for example, exploited by comparative methods for RNA secondary structure prediction that currently provide the state-of-art in terms of prediction accuracy. We here provide strong evidence that homologous RNA genes not only fold into similar final RNA structures, but that their folding pathways also share common transient structural features that have been evolutionarily conserved. For this, we compile and investigate a non-redundant data set of 32 sequences with known transient and final RNA secondary structures and devise a dedicated computational analysis pipeline.

Zhu, Jing Yun A.; Steif, Adi; Proctor, Jeff R.; Meyer, Irmtraud M.

2013-01-01

272

PARTS: Probabilistic Alignment for RNA joinT Secondary structure prediction  

PubMed Central

A novel method is presented for joint prediction of alignment and common secondary structures of two RNA sequences. The joint consideration of common secondary structures and alignment is accomplished by structural alignment over a search space defined by the newly introduced motif called matched helical regions. The matched helical region formulation generalizes previously employed constraints for structural alignment and thereby better accommodates the structural variability within RNA families. A probabilistic model based on pseudo free energies obtained from precomputed base pairing and alignment probabilities is utilized for scoring structural alignments. Maximum a posteriori (MAP) common secondary structures, sequence alignment and joint posterior probabilities of base pairing are obtained from the model via a dynamic programming algorithm called PARTS. The advantage of the more general structural alignment of PARTS is seen in secondary structure predictions for the RNase P family. For this family, the PARTS MAP predictions of secondary structures and alignment perform significantly better than prior methods that utilize a more restrictive structural alignment model. For the tRNA and 5S rRNA families, the richer structural alignment model of PARTS does not offer a benefit and the method therefore performs comparably with existing alternatives. For all RNA families studied, the posterior probability estimates obtained from PARTS offer an improvement over posterior probability estimates from a single sequence prediction. When considering the base pairings predicted over a threshold value of confidence, the combination of sensitivity and positive predictive value is superior for PARTS than for the single sequence prediction. PARTS source code is available for download under the GNU public license at http://rna.urmc.rochester.edu.

Harmanci, Arif Ozgun; Sharma, Gaurav; Mathews, David H.

2008-01-01

273

Hill-Climbing search and diversification within an evolutionary approach to protein structure prediction  

PubMed Central

Proteins are complex structures made of amino acids having a fundamental role in the correct functioning of living cells. The structure of a protein is the result of the protein folding process. However, the general principles that govern the folding of natural proteins into a native structure are unknown. The problem of predicting a protein structure with minimum-energy starting from the unfolded amino acid sequence is a highly complex and important task in molecular and computational biology. Protein structure prediction has important applications in fields such as drug design and disease prediction. The protein structure prediction problem is NP-hard even in simplified lattice protein models. An evolutionary model based on hill-climbing genetic operators is proposed for protein structure prediction in the hydrophobic - polar (HP) model. Problem-specific search operators are implemented and applied using a steepest-ascent hill-climbing approach. Furthermore, the proposed model enforces an explicit diversification stage during the evolution in order to avoid local optimum. The main features of the resulting evolutionary algorithm - hill-climbing mechanism and diversification strategy - are evaluated in a set of numerical experiments for the protein structure prediction problem to assess their impact to the efficiency of the search process. Furthermore, the emerging consolidated model is compared to relevant algorithms from the literature for a set of difficult bidimensional instances from lattice protein models. The results obtained by the proposed algorithm are promising and competitive with those of related methods.

2011-01-01

274

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

Microsoft Academic Search

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

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

2010-01-01

275

MASTR: multiple alignment and structure prediction of non-coding RNAs using simulated annealing  

Microsoft Academic Search

Motivation: As more non-coding RNAs are discovered, the import- ance of methods for RNA analysis increases. Since the structure of ncRNA is intimately tied to the function of the molecule, programs for RNA structure prediction are necessary tools in this growing field of research. Furthermore, it is known that RNA structure is often evolutionarily more conserved than sequence. However, few

Stinus Lindgreen; Paul P. Gardner; Anders Krogh

2007-01-01

276

Quantitative Structure-Based Modeling Applied to Characterization and Prediction of Chemical Toxicity  

Microsoft Academic Search

Quantitative modeling methods, relating aspects of chemical structure to biological activity, have long been applied to the prediction and characterization of chemical toxicity. The early linear free-energy approaches of Hansch and Free Wilson provided a fundamental scientific framework for the quantitative correlation of chemical structure with biological activity and spurred many developments in the field of quantitative structure–activity relationships (QSARs).

Romualdo Benigni; Ann M. Richard

1998-01-01

277

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

NASA Astrophysics Data System (ADS)

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

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

2004-04-01

278

Prediction of geometrically feasible three-dimensional structures of pseudoknotted RNA through free energy estimation  

PubMed Central

Accurate free energy estimation is essential for RNA structure prediction. The widely used Turner's energy model works well for nested structures. For pseudoknotted RNAs, however, there is no effective rule for estimation of loop entropy and free energy. In this work we present a new free energy estimation method, termed the pseudoknot predictor in three-dimensional space (pk3D), which goes beyond Turner's model. Our approach treats nested and pseudoknotted structures alike in one unifying physical framework, regardless of how complex the RNA structures are. We first test the ability of pk3D in selecting native structures from a large number of decoys for a set of 43 pseudoknotted RNA molecules, with lengths ranging from 23 to 113. We find that pk3D performs slightly better than the Dirks and Pierce extension of Turner's rule. We then test pk3D for blind secondary structure prediction, and find that pk3D gives the best sensitivity and comparable positive predictive value (related to specificity) in predicting pseudoknotted RNA secondary structures, when compared with other methods. A unique strength of pk3D is that it also generates spatial arrangement of structural elements of the RNA molecule. Comparison of three-dimensional structures predicted by pk3D with the native structure measured by nuclear magnetic resonance or X-ray experiments shows that the predicted spatial arrangement of stems and loops is often similar to that found in the native structure. These close-to-native structures can be used as starting points for further refinement to derive accurate three-dimensional structures of RNA molecules, including those with pseudoknots.

Zhang, Jian; Dundas, Joseph; Lin, Ming; Chen, Rong; Wang, Wei; Liang, Jie

2009-01-01

279

Efficient pairwise RNA structure prediction using probabilistic alignment constraints in Dynalign  

PubMed Central

Background Joint alignment and secondary structure prediction of two RNA sequences can significantly improve the accuracy of the structural predictions. Methods addressing this problem, however, are forced to employ constraints that reduce computation by restricting the alignments and/or structures (i.e. folds) that are permissible. In this paper, a new methodology is presented for the purpose of establishing alignment constraints based on nucleotide alignment and insertion posterior probabilities. Using a hidden Markov model, posterior probabilities of alignment and insertion are computed for all possible pairings of nucleotide positions from the two sequences. These alignment and insertion posterior probabilities are additively combined to obtain probabilities of co-incidence for nucleotide position pairs. A suitable alignment constraint is obtained by thresholding the co-incidence probabilities. The constraint is integrated with Dynalign, a free energy minimization algorithm for joint alignment and secondary structure prediction. The resulting method is benchmarked against the previous version of Dynalign and against other programs for pairwise RNA structure prediction. Results The proposed technique eliminates manual parameter selection in Dynalign and provides significant computational time savings in comparison to prior constraints in Dynalign while simultaneously providing a small improvement in the structural prediction accuracy. Savings are also realized in memory. In experiments over a 5S RNA dataset with average sequence length of approximately 120 nucleotides, the method reduces computation by a factor of 2. The method performs favorably in comparison to other programs for pairwise RNA structure prediction: yielding better accuracy, on average, and requiring significantly lesser computational resources. Conclusion Probabilistic analysis can be utilized in order to automate the determination of alignment constraints for pairwise RNA structure prediction methods in a principled fashion. These constraints can reduce the computational and memory requirements of these methods while maintaining or improving their accuracy of structural prediction. This extends the practical reach of these methods to longer length sequences. The revised Dynalign code is freely available for download.

2007-01-01

280

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

281

Structural Models for Predicting the Difficulty of Multiplication Problems  

ERIC Educational Resources Information Center

Difficulty levels of 168 randomly generated multiplication problems were obtained by testing 238 fifth-graders. Variables concerning the number of operations, digits carried, and magnitude of the digits were defined, and a least-squares procedure was used to construct structural models accounting for 60-70 per cent of observed problem difficulty…

Cromer, Fred Eugene

1974-01-01

282

Memoir: template-based structure prediction for membrane proteins  

PubMed Central

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

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

2013-01-01

283

Predicting actuation efficiency of structurally integrated active materials  

Microsoft Academic Search

A method for estimating the actuation efficiency of a structurally integrated active material is presented. A background literature search revealed many different expressions for efficiency depending upon the application and discipline of interest. Following the review of the literature, an efficiency expression was developed for a piezoelectric actuator in the frequency domain. The actuation efficiency of the piezoceramic actuator was

Christopher L. Davis; Frederick T. Calkins; Tamara J. Leeks; Donald G. Morris

1999-01-01

284

Edge strands in protein structure prediction and aggregation  

Microsoft Academic Search

It is well established that recognition between exposed edges of -sheets is an important mode of protein- protein interaction and can have pathological consequences; for instance, it has been linked to the aggre- gation of proteins into a fibrillar structure, which is associated with a number of predominantly neurode- generative disorders. A number of protective mechanisms have evolved in the

JENNIFER A. SIEPEN; SHEENA E. RADFORD; DAVID R. WESTHEAD

2008-01-01

285

Crystal structures of drugs: advances in determination, prediction and engineering  

Microsoft Academic Search

Most marketed pharmaceuticals consist of molecular crystals. The arrangement of the molecules in a crystal determines its physical properties and, in certain cases, its chemical properties, and so greatly influences the processing and formulation of solid pharmaceuticals, as well as key drug properties such as dissolution rate and stability. A thorough understanding of the relationships between physical structures and the

Sharmistha Datta; David J. W. Grant

2004-01-01

286

Predicted low frequency structures in the wake of elliptical cylinders  

Microsoft Academic Search

The vortex structures in the wake of 2D elliptical cylinders at low Reynolds numbers are investigated for a Reynolds numbers range of 75 to 175 using direct numerical simulation. By varying the aspect ratio of an elliptical cylinder, the geometry is varied between the extremes of a circular cylinder and a flat plate normal to the flow. The power spectrum

Shaun A. Johnson; Mark C. Thompson; Kerry Hourigan

2004-01-01

287

Prediction of grain structures in various solidification processes  

Microsoft Academic Search

Grain structure formation during solidification can be simulated via the use of stochastic models providing the physical mechanisms of nucleation and dendrite growth are accounted for. With this goal in mind, a physically based cellular automaton (CA) model has been coupled with finite element (FE) heat flow computations and implemented into the code 3- MOS. The CA enmeshment of the

M. Rappaz; Ch A. Gandin; J. L. Desbiolles; Ph. Thévoz

1996-01-01

288

A Structural Equation Model for Predicting Business Student Performance  

ERIC Educational Resources Information Center

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

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

2008-01-01

289

A Structural Equation Model for Predicting Business Student Performance  

ERIC Educational Resources Information Center

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

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

2008-01-01

290

A Historical Perspective and Overview of Protein Structure Prediction  

NASA Astrophysics Data System (ADS)

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

Wooley, John C.; Ye, Yuzhen

291

Predicted crater morphologies on Ceres: Probing internal structure and evolution  

NASA Astrophysics Data System (ADS)

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

Bland, Michael T.

2013-09-01

292

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

2013-10-01

293

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

National Technical Information Service (NTIS)

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 ...

C. E. Harris J. C. Newman R. S. Piascik J. H. Starnes

1996-01-01

294

PPfold 3.0: fast RNA secondary structure prediction using phylogeny and auxiliary data.  

PubMed

PPfold is a multi-threaded implementation of the Pfold algorithm for RNA secondary structure prediction. Here we present a new version of PPfold, which extends the evolutionary analysis with a flexible probabilistic model for incorporating auxiliary data, such as data from structure probing experiments. Our tests show that the accuracy of single-sequence secondary structure prediction using experimental data in PPfold 3.0 is comparable to RNAstructure. Furthermore, alignment structure prediction quality is improved even further by the addition of experimental data. PPfold 3.0 therefore has the potential of producing more accurate predictions than it was previously possible. Availability and implementation: PPfold 3.0 is available as a platform-independent Java application and can be downloaded from http://birc.au.dk/software/ppfold. PMID:22877864

Sükösd, Zsuzsanna; Knudsen, Bjarne; Kjems, Jørgen; Pedersen, Christian N S

2012-08-09

295

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

EPA Science Inventory

DSSTox Website Launch: Improving Public Access to Databases for Building Structure-Toxicity Prediction Models Ann M. Richard US Environmental Protection Agency, Research Triangle Park, NC, USA Distributed: Decentralized set of standardized, field-delimited databases,...

296

Systems and Methods for Predicting the Structure and Function of Multipass Transmembrane Proteins.  

National Technical Information Service (NTIS)

The invention provides computer-implemented methods and apparatus implementing a hierarchical protocol using multiscale molecular dynamics and molecular modeling methods to predict the structure of transmembrane proteins such as G-Protein Coupled Receptor...

N. Vaidehi R. J. Trabanino S. E. Hall W. Floriano W. A. Goddard

2004-01-01

297

Building Research Translation: The Behavior of Concrete Structures in Fire - A Method for Prediction by Calculation.  

National Technical Information Service (NTIS)

This method provides a means for predicting, by calculation, the resistance to fire of a reinforced or prestressed concrete element of construction, in accordance with 1959 French directives. The method is useful in allowing builders to design structures ...

S. G. Weber

1978-01-01

298

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

PubMed Central

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

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

1985-01-01

299

Structure-based activity prediction for an enzyme of unknown function  

Microsoft Academic Search

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

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

2007-01-01

300

A structural-feature-based computational approach for toxicity prediction of water-soluble arsenicals  

Microsoft Academic Search

We have used the density functional theory to make a toxicity prediction model of water-soluble arsenicals (WSA). The structures have been optimised for the minimum energy of the Schrödinger equation. In the present work, the usefulness of electrophilicity and charge transfer in predicting the toxicity of WSA, namely, monomethylarsenic acid (MMA) (III), dimethylarsenic acid (DMA) (III), arsenic acid, arsenous acid,

M. Abdus Salam; C. G. Jesudason; Keshav N. Shrivastava; M. Aminul Islam

2012-01-01

301

A structural-feature-based computational approach for toxicity prediction of water-soluble arsenicals  

Microsoft Academic Search

We have used the density functional theory to make a toxicity prediction model of water-soluble arsenicals (WSA). The structures have been optimised for the minimum energy of the Schrödinger equation. In the present work, the usefulness of electrophilicity and charge transfer in predicting the toxicity of WSA, namely, monomethylarsenic acid (MMA) (III), dimethylarsenic acid (DMA) (III), arsenic acid, arsenous acid,

M. Abdus Salam; C. G. Jesudason; Keshav N. Shrivastava; M. Aminul Islam

2011-01-01

302

Crystal structure prediction of flexible molecules using parallel genetic algorithms with a standard force field  

Microsoft Academic Search

This article describes the application of our distributed computing framework for crystal structure pre- diction (CSP) the modified genetic algorithms for crystal and cluster prediction (MGAC), to predict the crystal struc- ture of flexible molecules using the general Amber force field (GAFF) and the CHARMM program. The MGAC dis- tributed computing framework includes a series of tightly integrated computer programs

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

2009-01-01

303

Prediction of corrosion-induced cover cracking in reinforced concrete structures  

Microsoft Academic Search

Usually, the time of repair\\/replacement of reinforced concrete structures due to corrosion is controlled by cracking of the concrete cover. Thus, it is important to be able to predict with sufficient accuracy the time from corrosion initiation to crack formation in the concrete cover. The paper presents a critical overview of existing empirical, analytical and numerical models for predicting the

Leon Chernin; Dimitri V. Val

2011-01-01

304

Gypsy moth response to landscape structure differs from neutral model predictions: implications for invasion monitoring  

Microsoft Academic Search

Simulations of dispersal across computer-generated neutral landscapes have generated testable predictions about the relation- ship between dispersal success and landscape structure. Models predict a threshold response in dispersal success with increasing habitat frag- mentation. A threshold is defined as an abrupt, disproportionate decline in dispersal success at a certain proportion of habitat in the landscape. To identify potential empirical threshold

Genevieve M. Nesslage; Brian A. Maurer; Stuart H. Gage

2006-01-01

305

Predicting protein-protein interface residues using local surface structural similarity  

PubMed Central

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

2012-01-01

306

Parental meta-emotion structure predicts family and child outcomes  

Microsoft Academic Search

Fifty-six families with a preschool child whose parents varied widely in parental marital satisfaction were studied at two time points: at time-I when the children were 5 years old and again at time-2 when the children were 8 years old. At time-1 each parent was separately interviewed about their “meta-emotion structure”, that is, their feelings about their own emotions, and

Carole Hooven; John Mordechai Gottman; Lynn Fainsilber Katz

1995-01-01

307

Use of discriminant analysis to predict arson-prone structures  

Microsoft Academic Search

Summary  The technique of discriminant analysis is widely used for discriminating arson from nonarson structures. Since in this particular\\u000a case only two groups are involved viz., the arson and the match groups, the computations required for obtaining a discriminant\\u000a function are relatively simple. Because of this, it is possible to delete a variable by examining the value of a\\u000a i\\u000a d

Hari Shiledar Baxi

1984-01-01

308

Social Structure Predicts Genital Morphology in African Mole-Rats  

Microsoft Academic Search

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

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

2009-01-01

309

Prediction of grain structures in various solidification processes  

Microsoft Academic Search

Grain structure formation during solidification can be simulatedvia the use of stochastic models providing the physical mechanisms of nucleation and dendrite growth are accounted for. With\\u000a this goal in mind, a physically based cellular automaton (CA) model has been coupled with finite element (FE) heat flow computations\\u000a and implemented into the code3- MOS. The CA enmeshment of the solidifying domain

M. Rappaz; Ch A. Gandin; J. L. Desbiolles; Ph. Thévoz

1996-01-01

310

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

NASA Astrophysics Data System (ADS)

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

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

2009-04-01

311

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

312

Prediction of membrane protein structures with complex topologies using limited constraints  

PubMed Central

Reliable structure-prediction methods for membrane proteins are important because the experimental determination of high-resolution membrane protein structures remains very difficult, especially for eukaryotic proteins. However, membrane proteins are typically longer than 200 aa and represent a formidable challenge for structure prediction. We have developed a method for predicting the structures of large membrane proteins by constraining helix–helix packing arrangements at particular positions predicted from sequence or identified by experiments. We 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 enriched the population of near-native models for 9 proteins. In 4 of the cases in which the constraint was predicted from the sequence, 1 of the 5 lowest energy models was superimposable within 4 ? on the native structure. Near-native structures could also be selected for heme-binding and pore-forming domains from simulations in which pairs of conserved histidine-chelating hemes and one experimentally determined salt bridge were constrained, respectively. These results suggest that models within 4 ? of the native structure can be achieved for complex membrane proteins if even limited information on residue-residue interactions can be obtained from protein structure databases or experiments.

Barth, P.; Wallner, B.; Baker, D.

2009-01-01

313

Predicting RNA Secondary Structure Using Profile Stochastic Context-Free Grammars and Phylogenic Analysis  

Microsoft Academic Search

Stochastic context-free grammars (SCFGs) have been applied to predicting RNA secondary structure. The prediction of RNA secondary\\u000a structure can be facilitated by incorporating with comparative sequence analysis. However, most of existing SCFG-based methods\\u000a lack explicit phylogenic analysis of homologous RNA sequences, which is probably the reason why these methods are not ideal\\u000a in practical application. Hence, we present a new

Xiao-Yong Fang; Zhi-Gang Luo; Zheng-hua Wang

2008-01-01

314

Exploiting the past and the future in protein secondary structure prediction  

Microsoft Academic Search

Motivation: Predicting the secondary structure of a protein (alpha-helix, beta-sheet, coil) is an important step towards elucidating its three-dimensional structure, as well as its function. Presently, the best predictors are based on machine learning approaches, in particular neural network architectures with a fixed, and relatively short, input window of amino acids, centered at the prediction site. Although a fixed small

Pierre Baldi; Søren Brunak; Paolo Frasconi; Giovanni Soda; Gianluca Pollastri

1999-01-01

315

The predicted secondary structures of class I fructose-bisphosphate aldolases.  

PubMed

The results of several secondary-structure prediction programs were combined to produce an estimate of the regions of alpha-helix, beta-sheet and reverse turns for fructose-bisphosphate aldolases from human and rat muscle and liver, from Trypanosoma brucei and from Drosophila melanogaster. All the aldolase sequences gave essentially the same pattern of secondary-structure predictions despite having sequences up to 50% different. One exception to this pattern was an additional strongly predicted helix in the rat liver and Drosophila enzymes. Regions of relatively high sequence variation generally were predicted as reverse turns, and probably occur as surface loops. Most of the positions corresponding to exon boundaries are located between regions predicted to have secondary-structural elements consistent with a compact structure. The predominantly alternating alpha/beta structure predicted is consistent with the alpha/beta-barrel structure indicated by preliminary high-resolution X-ray diffraction studies on rabbit muscle aldolase [Sygusch, Beaudry & Allaire (1986) Biophys. J. 49, 287a]. PMID:3128269

Sawyer, L; Fothergill-Gilmore, L A; Freemont, P S

1988-02-01

316

Global or local? Predicting secondary structure and accessibility in mRNAs  

PubMed Central

Determining the structural properties of mRNA is key to understanding vital post-transcriptional processes. As experimental data on mRNA structure are scarce, accurate structure prediction is required to characterize RNA regulatory mechanisms. Although various structure prediction approaches are available, it is often unclear which to choose and how to set their parameters. Furthermore, no standard measure to compare predictions of local structure exists. We assessed the performance of different methods using two types of data: transcriptome-wide enzymatic probing information and a large, curated set of cis-regulatory elements. To compare the approaches, we introduced structure accuracy, a measure that is applicable to both global and local methods. Our results showed that local folding was more accurate than the classic global approach. We investigated how the locality parameters, maximum base pair span and window size, influenced the prediction performance. A span of 150 provided a reasonable balance between maximizing the number of accurately predicted base pairs, while minimizing effects of incorrect long-range predictions. We characterized the error at artificial sequence ends, which we reduced by setting the window size sufficiently greater than the maximum span. Our method, LocalFold, diminished all border effects and produced the most robust performance.

Lange, Sita J.; Maticzka, Daniel; Mohl, Mathias; Gagnon, Joshua N.; Brown, Chris M.; Backofen, Rolf

2012-01-01

317

Global or local? Predicting secondary structure and accessibility in mRNAs.  

PubMed

Determining the structural properties of mRNA is key to understanding vital post-transcriptional processes. As experimental data on mRNA structure are scarce, accurate structure prediction is required to characterize RNA regulatory mechanisms. Although various structure prediction approaches are available, it is often unclear which to choose and how to set their parameters. Furthermore, no standard measure to compare predictions of local structure exists. We assessed the performance of different methods using two types of data: transcriptome-wide enzymatic probing information and a large, curated set of cis-regulatory elements. To compare the approaches, we introduced structure accuracy, a measure that is applicable to both global and local methods. Our results showed that local folding was more accurate than the classic global approach. We investigated how the locality parameters, maximum base pair span and window size, influenced the prediction performance. A span of 150 provided a reasonable balance between maximizing the number of accurately predicted base pairs, while minimizing effects of incorrect long-range predictions. We characterized the error at artificial sequence ends, which we reduced by setting the window size sufficiently greater than the maximum span. Our method, LocalFold, diminished all border effects and produced the most robust performance. PMID:22373926

Lange, Sita J; Maticzka, Daniel; Möhl, Mathias; Gagnon, Joshua N; Brown, Chris M; Backofen, Rolf

2012-02-28

318

Protein structure validation by generalized linear model root-mean-square deviation prediction  

PubMed Central

Large-scale initiatives for obtaining spatial protein structures by experimental or computational means have accentuated the need for the critical assessment of protein structure determination and prediction methods. These include blind test projects such as the critical assessment of protein structure prediction (CASP) and the critical assessment of protein structure determination by nuclear magnetic resonance (CASD-NMR). An important aim is to establish structure validation criteria that can reliably assess the accuracy of a new protein structure. Various quality measures derived from the coordinates have been proposed. A universal structural quality assessment method should combine multiple individual scores in a meaningful way, which is challenging because of their different measurement units. Here, we present a method based on a generalized linear model (GLM) that combines diverse protein structure quality scores into a single quantity with intuitive meaning, namely the predicted coordinate root-mean-square deviation (RMSD) value between the present structure and the (unavailable) “true” structure (GLM-RMSD). For two sets of structural models from the CASD-NMR and CASP projects, this GLM-RMSD value was compared with the actual accuracy given by the RMSD value to the corresponding, experimentally determined reference structure from the Protein Data Bank (PDB). The correlation coefficients between actual (model vs. reference from PDB) and predicted (model vs. “true”) heavy-atom RMSDs were 0.69 and 0.76, for the two datasets from CASD-NMR and CASP, respectively, which is considerably higher than those for the individual scores (?0.24 to 0.68). The GLM-RMSD can thus predict the accuracy of protein structures more reliably than individual coordinate-based quality scores.

Bagaria, Anurag; Jaravine, Victor; Huang, Yuanpeng J; Montelione, Gaetano T; Guntert, Peter

2012-01-01

319

Dispersal differences predict population genetic structure in Mormon crickets.  

PubMed

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

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

2007-05-01

320

Failure/leakage predictions of concrete structures containing cracks  

SciTech Connect

An approach is presented for studying the cracking and radioactive release of a reactor containment during severe accidents and extreme environments. The cracking of concrete is modeled as the blunt crack. The initiation and propagation of a crack are determined by using the maximum strength and the J-integral criteria. Furthermore, the extent of cracking is related to the leakage calculation by using a model developed by Rizkalla, Lau and Simmonds. Numerical examples are given for a three-point bending problem and a hypothetical case of a concrete containment structure subjected to high internal pressure during an accident.

Pan, Y.C.; Marchertas, A.H.; Kennedy, J.M.

1984-06-01

321

Correcting pervasive errors in RNA crystallography through enumerative structure prediction.  

PubMed

Three-dimensional RNA models fitted into crystallographic density maps exhibit pervasive conformational ambiguities, geometric errors and steric clashes. To address these problems, we present enumerative real-space refinement assisted by electron density under Rosetta (ERRASER), coupled to Python-based hierarchical environment for integrated 'xtallography' (PHENIX) diffraction-based refinement. On 24 data sets, ERRASER automatically corrects the majority of MolProbity-assessed errors, improves the average R(free) factor, resolves functionally important discrepancies in noncanonical structure and refines low-resolution models to better match higher-resolution models. PMID:23202432

Chou, Fang-Chieh; Sripakdeevong, Parin; Dibrov, Sergey M; Hermann, Thomas; Das, Rhiju

2012-12-02

322

Computational predictions of pulmonary blood flow gradients: gravity versus structure.  

PubMed

A computational model of blood flow through the human pulmonary arterial tree has been developed to investigate the mechanisms contributing to regional pulmonary perfusion in the isolated network when the lung is in different orientations. The arterial geometric model was constructed using a combination of computed tomography and a volume-filling branching algorithm. Equations governing conservation of mass, momentum, and vessel distension, incorporating gravity, were solved to predict pressure, flow, and vessel radius. Analysis of results in the upright posture, with and without gravity, and in the inverted, prone, and supine postures reveals significant flow heterogeneity and a persistent decrease in flow in the cranial and caudal regions for all postures suggesting that vascular geometry makes a major contribution to regional flow with gravity having a lesser role. Results in the isolated arterial tree demonstrate that the vascular path lengths and therefore the positioning of the pulmonary trunk relative to the rest of the network play a significant role in the determination of flow. PMID:16386472

Burrowes, Kelly S; Tawhai, Merryn H

2005-12-28

323

Comparison of intrinsic stacking energies of ten unique dinucleotide steps in A-RNA and B-DNA duplexes. Can we determine correct order of stability by quantum-chemical calculations?  

PubMed

High level ab initio methods have been used to study stacking interactions in ten unique base pair steps both in A-RNA and in B-DNA duplexes. The protocol for selection of geometries based on molecular dynamics (MD) simulations is proposed, and its suitability is demonstrated by comparison with stacking in steps at fiber diffraction geometries. It is shown that fiber diffraction geometries are not sufficiently accurate for interaction energy calculations. In addition, the protocol for selection of geometries based on MD simulations allows for the evaluation of the variability of the intrinsic stacking energies along the MD trajectories. The uncertainty in stacking energies (difference between the most and least stable geometry) due to the dynamical nature of systems can be, in some cases, as large as 3.0 kcal x mol(-1), which is almost 50% of the actual sequence dependence of base stacking energies (the energy difference between the most and least stable sequences). Thus, assessing the relative magnitude of the gas phase stacking energy using a single geometry for each sequence is insufficient to obtain an unambiguous order of gas phase stacking energies in canonical double helices. Though the ordering of ten unique dinucleotide steps cannot be definitive, some general conclusions were drawn. The stacking energies of base pair steps in A-RNA are more evenly separated compared to B-DNA, and their ordering is less sensitive to the dynamics of the system compared to be B-DNA. The most stable step both in B-DNA and A-RNA is the GC/GC [corrected] step that is well separated from the second most stable step CG/CG. [corrected] Also the least stable step (the CC/GG step) is well separated from the rest of the structures. The calculations further show that B-DNA stacking is favorable only marginally (on average by 1.14 kcal x mol(-1) per base pair step) over A-RNA stacking, and this difference vanishes after subtracting the stabilizing van der Waals effect of the thymine 5-methyl group that is absent in RNA. Basically, no correlation between the sequence dependence of gas phase stacking energies and the sequence dependence of DeltaG degrees(37) free energies used in nearest-neighbor models was found either for B-DNA or for A-RNA. This reflects the complexity of the balance of forces that are responsible for the sequence dependence of thermodynamics stability of nucleic acids, which masks the effect of the intrinsic interactions between the stacked base pairs. PMID:20000584

Svozil, Daniel; Hobza, Pavel; Sponer, Jirí

2010-01-21

324

Prediction of hydrocarbon-bearing structures based on remote sensing  

SciTech Connect

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

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

1993-09-01

325

How good is prediction of protein structural class by the component-coupled method?  

PubMed

Proteins of known structures are usually classified into four structural classes: all-alpha, all-beta, alpha+beta, and alpha/beta type of proteins. A number of methods to predicting the structural class of a protein based on its amino acid composition have been developed during the past few years. Recently, a component-coupled method was developed for predicting protein structural class according to amino acid composition. This method is based on the least Mahalanobis distance principle, and yields much better predicted results in comparison with the previous methods. However, the success rates reported for structural class prediction by different investigators are contradictory. The highest reported accuracies by this method are near 100%, but the lowest one is only about 60%. The goal of this study is to resolve this paradox and to determine the possible upper limit of prediction rate for structural classes. In this paper, based on the normality assumption and the Bayes decision rule for minimum error, a new method is proposed for predicting the structural class of a protein according to its amino acid composition. The detailed theoretical analysis indicates that if the four protein folding classes are governed by the normal distributions, the present method will yield the optimum predictive result in a statistical sense. A non-redundant data set of 1,189 protein domains is used to evaluate the performance of the new method. Our results demonstrate that 60% correctness is the upper limit for a 4-type class prediction from amino acid composition alone for an unknown query protein. The apparent relatively high accuracy level (more than 90%) attained in the previous studies was due to the preselection of test sets, which may not be adequately representative of all unrelated proteins. PMID:10656263

Wang, Z X; Yuan, Z

2000-02-01

326

Critical assessment of methods of protein structure prediction--Round VII  

PubMed Central

This paper is an introduction to the supplemental issue of the journal PROTEINS, dedicated to the seventh CASP experiment to assess the state of the art in protein structure prediction. The paper describes the conduct of the experiment, the categories of prediction included, and outlines the evaluation and assessment procedures. Highlights are improvements in model accuracy relative to that obtainable from knowledge of a single best template structure; convergence of the accuracy of models produced by automatic servers toward that produced by human modeling teams; the emergence of methods for predicting the quality of models; and rapidly increasing practical applications of the methods.

Moult, John; Fidelis, Krzysztof; Kryshtafovych, Andriy; Rost, Burkhard; Hubbard, Tim; Tramontano, Anna

2007-01-01

327

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

NASA Astrophysics Data System (ADS)

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

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

2005-12-01

328

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

PubMed

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

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

2013-06-03

329

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

PubMed

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

Hamodrakas, S J

1988-11-01

330

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

PubMed

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

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

2011-08-30

331

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

Robert H. Hurt; Eric M. Suuberg

2000-05-03

332

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

PubMed Central

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

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

2013-01-01

333

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

2013-10-08

334

Side-chain repacking calculations for predicting structures and stabilities of heterodimeric coiled coils  

PubMed Central

An important goal in biology is to predict from sequence data the high-resolution structures of proteins and the interactions that occur between them. In this paper, we describe a computational approach that can make these types of predictions for a series of coiled-coil dimers. Our method comprises a dual strategy that augments extensive conformational sampling with molecular mechanics minimization. To test the performance of the method, we designed six heterodimeric coiled coils with a range of stabilities and solved x-ray crystal structures for three of them. The stabilities and structures predicted by the calculations agree very well with experimental data: the average error in unfolding free energies is <1 kcal/mol, and nonhydrogen atoms in the predicted structures superimpose onto the experimental structures with rms deviations <0.7 ?. We have also tested the method on a series of homodimers derived from vitellogenin-binding protein. The predicted relative stabilities of the homodimers show excellent agreement with previously published experimental measurements. A critical step in our procedure is to use energy minimization to relax side-chain geometries initially selected from a rotamer library. Our results show that computational methods can predict interaction specificities that are in good agreement with experimental data.

Keating, Amy E.; Malashkevich, Vladimir N.; Tidor, Bruce; Kim, Peter S.

2001-01-01

335

Structure-based methods for predicting mutagenicity and carcinogenicity: are we there yet?  

PubMed

There is a great deal of current interest in the use of commercial, automated programs for the prediction of mutagenicity and carcinogenicity based on chemical structure. However, the goal of accurate and reliable toxicity prediction for any chemical, based solely on structural information remains elusive. The toxicity prediction challenge is global in its objective, but limited in its solution, to within local domains of chemicals acting according to similar mechanisms of action in the biological system; to predict, we must be able to generalize based on chemical structure, but the biology fundamentally limits our ability to do so. Available commercial systems for mutagenicity and/or carcinogenicity prediction differ in their specifics, yet most fall in two major categories: (1) automated approaches that rely on the use of statistics for extracting correlations between structure and activity; and (2) knowledge-based expert systems that rely on a set of programmed rules distilled from available knowledge and human expert judgement. These two categories of approaches differ in the ways that they represent, process, and generalize chemical-biological activity information. An application of four commercial systems (TOPKAT, CASE/MULTI-CASE, DEREK, and OncoLogic) to mutagenicity and carcinogenicity prediction for a particular class of chemicals-the haloacetic acids (HAs)-is presented to highlight these differences. Some discussion is devoted to the issue of gauging the relative performance of commercial prediction systems, as well as to the role of prospective prediction exercises in this effort. And finally, an alternative approach that stops short of delivering a prediction to a user, involving structure-searching and data base exploration, is briefly considered. PMID:9685707

Richard, A M

1998-05-25

336

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

337

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

PubMed

G protein-coupled receptors (GPCRs) mediate our sense of vision, smell, taste, and pain. They are also involved in cell recognition and communication processes, and hence have emerged as a prominent superfamily for drug targets. Unfortunately, the atomic-level structure is available for only one GPCR (bovine rhodopsin), making it difficult to use structure-based methods to design drugs and mutation experiments. We have recently developed first principles methods (MembStruk and HierDock) for predicting structure of GPCRs, and for predicting the ligand binding sites and relative binding affinities. Comparing to the one case with structural data, bovine rhodopsin, we find good accuracy in both the structure of the protein and of the bound ligand. We report here the application of MembStruk and HierDock to beta1-adrenergic receptor, endothelial differential gene 6, mouse and rat I7 olfactory receptors, and human sweet receptor. We find that the predicted structure of beta1-adrenergic receptor leads to a binding site for epinephrine that agrees well with the mutation experiments. Similarly the predicted binding sites and affinities for endothelial differential gene 6, mouse and rat I7 olfactory receptors, and human sweet receptor are consistent with the available experimental data. These predicted structures and binding sites allow the design of mutation experiments to validate and improve the structure and function prediction methods. As these structures are validated they can be used as targets for the design of new receptor-selective antagonists or agonists for GPCRs. PMID:12351677

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

2002-09-26

338

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

339

Energy-based classification and structure prediction of transmembrane beta-barrel proteins  

Microsoft Academic Search

Transmembrane ?? -barrel (TMB) proteins are a special class of transmembrane proteins which play several key roles in human body and diseases. Due to experimental difficulties, the number of TMB proteins with known structures is very small. Over the years, a number of learning-based methods have been introduced for recognition and structure prediction of TMB proteins. Most of these methods

Van Du Tran; Philippe Chassignet; Saad Sheikh; Jean-Marc Steyaert

2011-01-01

340

Accurate Prediction of Protein Disordered Regions by Mining Protein Structure Data  

Microsoft Academic Search

Intrinsically disordered regions in proteins are relatively frequent and important for our understanding of molecular recognition and assembly, and protein structure and function. From an algorithmic standpoint, agging large disordered regions is also imortant for ab inito protein structure prediction methods. Here we rst extract a curated, non-redundant, data set of protein disordered regions from the Protein Data Bank and

Jianlin Cheng; Michael J. Sweredoski; Pierre Baldi

2005-01-01

341

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

Microsoft Academic Search

A neural network algorithm is applied to secondary structure and structural class prediction for a database of 318 nonhomologous protein chains. Significant improvement in accuracy is obtained as compared with performance on smaller databases. A systematic study of the effects of network topology shows that, for the larger database, better results are obtained with more units in the hidden layer.

John-Marc Chandonia; Martin Karplus

2008-01-01

342

Experimental results and predictions on fatigue crack growth in structural steel  

Microsoft Academic Search

Fatigue crack growth, the methodology of estimating crack closure from compliance records, and calibrating a strip yield type model for crack growth predictions using constraint factors are studied for structural steel. Trends in fatigue crack growth observed for two structural steels with distinct mechanical properties under constant amplitude and variable amplitude load histories of various types are outlined. An algorithm

Ma?gorzata Skorupa; Andrzej Skorupa

2005-01-01

343

Analytical model for predicting nonlinear reversed cyclic behaviour of reinforced concrete structural walls  

Microsoft Academic Search

This paper summarizes the results of an investigation on postpeak modelling and nonlinear performance prediction of reinforced concrete (RC) structural walls. The RC walls are designed for seismic loads using the capacity design method. We begin with a review of the proposed inelastic multilayer flexibility-based finite element with multilayer interfaces. An analytical structural engineering model for simulating the nonlinear hysteretic

Youssef Belmouden; Pierino Lestuzzi

2007-01-01

344

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

PubMed Central

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

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

2012-01-01

345

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

PubMed Central

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

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

2011-01-01

346

Manual for the prediction of blast and fragment loadings on structures  

SciTech Connect

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

Not Available

1980-11-01

347

Ab initio structure prediction of the antibody hypervariable H3 loop.  

PubMed

Antibodies have the capability of binding a wide range of antigens due to the diversity of the six loops constituting the complementarity determining region (CDR). Among the six loops, the H3 loop is the most diverse in structure, length, and sequence identity. Prediction of the three-dimensional structures of antibodies, especially the CDR loops, is an important step in the computational design and engineering of novel antibodies for improved affinity and specificity. Although it has been demonstrated that the conformation of the five non-H3 loops can be accurately predicted by comparing their sequences against databases of canonical loop conformations, no such connection has been established for H3 loops. In this work, we present the results for ab initio structure prediction of the H3 loop using conformational sampling and energy calculations with the program Prime on a dataset of 53 loops ranging in length from 4 to 22 residues. When the prediction is performed in the crystal environment and including symmetry mates, the median backbone root mean square deviation (RMSD) is 0.5 Å to the crystal structure, with 91% of cases having an RMSD of less than 2.0 Å. When the prediction is performed in a noncrystallographic environment, where the scaffold is constructed by swapping the H3 loops between homologous antibodies, 70% of cases have an RMSD below 2.0 Å. These results show promise for ab initio loop predictions applied to modeling of antibodies. PMID:23255066

Zhu, Kai; Day, Tyler

2013-05-02

348

Predicted structure and phyletic distribution of the RNA-binding protein Hfq  

Microsoft Academic Search

Hfq, a bacterial RNA-binding protein, was recently shown to contain the Sm1 motif, a characteristic of Sm and LSm proteins that function in RNA process- ing events in archaea and eukaryotes. In this report, comparative structural modeling was used to pre- dict a three-dimensional structure of the Hfq core sequence. The predicted structure aligns with most major features of the

Xueguang Sun; Igor Zhulin; Roger M. Wartell

2002-01-01

349

CompaRNA: a server for continuous benchmarking of automated methods for RNA secondary structure prediction  

PubMed Central

We present a continuous benchmarking approach for the assessment of RNA secondary structure prediction methods implemented in the CompaRNA web server. As of 3 October 2012, the performance of 28 single-sequence and 13 comparative methods has been evaluated on RNA sequences/structures released weekly by the Protein Data Bank. We also provide a static benchmark generated on RNA 2D structures derived from the RNAstrand database. Benchmarks on both data sets offer insight into the relative performance of RNA secondary structure prediction methods on RNAs of different size and with respect to different types of structure. According to our tests, on the average, the most accurate predictions obtained by a comparative approach are generated by CentroidAlifold, MXScarna, RNAalifold and TurboFold. On the average, the most accurate predictions obtained by single-sequence analyses are generated by CentroidFold, ContextFold and IPknot. The best comparative methods typically outperform the best single-sequence methods if an alignment of homologous RNA sequences is available. This article presents the results of our benchmarks as of 3 October 2012, whereas the rankings presented online are continuously updated. We will gladly include new prediction methods and new measures of accuracy in the new editions of CompaRNA benchmarks.

Puton, Tomasz; Kozlowski, Lukasz P.; Rother, Kristian M.; Bujnicki, Janusz M.

2013-01-01

350

Protein secondary structure pattern discovery and its application in secondary structure prediction  

Microsoft Academic Search

A method of protein secondary structure pattern discovery is presented. The TEIRESIAS algorithm has been improved to discover protein secondary structure patterns. Four protein secondary structure pattern dictionaries have been built for four organisms. The distribution of patterns and common patterns' structure in different dictionaries is different. Different organism's proteins represent different biological language. Based on the organism-specific dictionary, a

Ming-Hui Li; Xiao-Long Wang; Lei Lin; Yi Guan

2004-01-01

351

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

PubMed

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

Chatterjee, S; Ghosh, S; Vishveshwara, S

2013-05-22

352

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

PubMed Central

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

2013-01-01

353

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

PubMed Central

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

Xu, Dong; Zhang, Yang

2013-01-01

354

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

PubMed

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

Xu, Dong; Zhang, Yang

2013-01-01

355

Protein secondary structure prediction using modular reciprocal bidirectional recurrent neural networks.  

PubMed

The supervised learning of recurrent neural networks well-suited for prediction of protein secondary structures from the underlying amino acids sequence is studied. Modular reciprocal recurrent neural networks (MRR-NN) are proposed to model the strong correlations between adjacent secondary structure elements. Besides, a multilayer bidirectional recurrent neural network (MBR-NN) is introduced to capture the long-range intramolecular interactions between amino acids in formation of the secondary structure. The final modular prediction system is devised based on the interactive integration of the MRR-NN and the MBR-NN structures to arbitrarily engage the neighboring effects of the secondary structure types concurrent with memorizing the sequential dependencies of amino acids along the protein chain. The advanced combined network augments the percentage accuracy (Q?) to 79.36% and boosts the segment overlap (SOV) up to 70.09% when tested on the PSIPRED dataset in three-fold cross-validation. PMID:20472322

Babaei, Sepideh; Geranmayeh, Amir; Seyyedsalehi, Seyyed Ali

2010-05-15

356

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

Microsoft Academic Search

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

Katsumi Watashi

1995-01-01

357

Gypsy moth response to landscape structure differs from neutral model predictions: implications for invasion monitoring  

Microsoft Academic Search

Simulations of dispersal across computer-generated neutral landscapes have generated testable predictions about the relationship\\u000a between dispersal success and landscape structure. Models predict a threshold response in dispersal success with increasing\\u000a habitat fragmentation. A threshold is defined as an abrupt, disproportionate decline in dispersal success at a certain proportion\\u000a of habitat in the landscape. To identify potential empirical threshold responses in

Geneviève M. Nesslage; Brian A. Maurer; Stuart H. Gage

2007-01-01

358

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

PubMed Central

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

Hu, Yuh-Jyh

2002-01-01

359

PSP_MCSVM: brainstorming consensus prediction of protein secondary structures using two-stage multiclass support vector machines  

Microsoft Academic Search

Secondary structure prediction is a crucial task for understanding the variety of protein structures and performed biological\\u000a functions. Prediction of secondary structures for new proteins using their amino acid sequences is of fundamental importance\\u000a in bioinformatics. We propose a novel technique to predict protein secondary structures based on position-specific scoring\\u000a matrices (PSSMs) and physico-chemical properties of amino acids. It is

Piyali Chatterjee; Subhadip Basu; Mahantapas Kundu; Mita Nasipuri; Dariusz Plewczynski

360

A 2-D orientation-adaptive prediction filter in lifting structures for image coding.  

PubMed

Lifting-style implementations of wavelets are widely used in image coders. A two-dimensional (2-D) edge adaptive lifting structure, which is similar to Daubechies 5/3 wavelet, is presented. The 2-D prediction filter predicts the value of the next polyphase component according to an edge orientation estimator of the image. Consequently, the prediction domain is allowed to rotate +/-45 degrees in regions with diagonal gradient. The gradient estimator is computationally inexpensive with additional costs of only six subtractions per lifting instruction, and no multiplications are required. PMID:16435541

Gerek, Omer N; Cetin, A Enis

2006-01-01

361

Boosting Algorithm with Sequence-Loss Cost Function for Structured Prediction  

NASA Astrophysics Data System (ADS)

The problem of sequence prediction i.e. annotating sequences appears in many problems across a variety of scientific disciplines, especially in computational biology, natural language processing, speech recognition, etc. The paper investigates a boosting approach to structured prediction, AdaBoostSTRUCT, based on proposed sequence-loss balancing function, combining advantages of boosting scheme with the efficiency of dynamic programming method. In the paper the method's formalism for modeling and predicting label sequences is introduced as well as examined, presenting its validity and competitiveness.

Kajdanowicz, Tomasz; Kazienko, Przemys?aw; Kraszewski, Jan

362

Evaluation of protein 3-D structure prediction: comparison of modelled and X-ray structure of an alkaline serine protease.  

PubMed

We describe the modelling of the structure of the highly alkaline subtilisin protease OPTICLEAN from Bacillus alcalophilus. The model was developed through modelling by homology. We used the structure of subtilisin Carlsberg from the Brookhaven protein databank (entry 1CSE) as start structure. Amino acid changes and deletions were performed with the graphic protein design program BRAGI. Force field calculations and molecular dynamic simulations were made with AMBER 3.0 on a Multiflow TRACE 14/300. The comparison of the model and the later solved X-ray structure of OPTICLEAN shows a high similarity between the two structures, but there were also remarkable deviations between the two structures in some loop regions. The comparison shows that the deviations are due to difficulties in the prediction of correct main chain torsion angles of the prolines and the selection of correct loops in deletion or insertion regions. Strategies to avoid these mistakes are discussed. PMID:7654351

Aehle, W; Sobek, H; Schomburg, D

1995-07-31

363

On the Relevance of Sophisticated Structural Annotations for Disulfide Connectivity Pattern Prediction  

PubMed Central

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

Becker, Julien; Maes, Francis; Wehenkel, Louis

2013-01-01

364

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

PubMed

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

Becker, Julien; Maes, Francis; Wehenkel, Louis

2013-02-15

365

Atomic accuracy in predicting and designing non-canonical RNA structure  

PubMed Central

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

Das, Rhiju; Karanicolas, John; Baker, David

2010-01-01

366

Systematic prediction of crystal structures: An application to s p3 -hybridized carbon polymorphs  

NASA Astrophysics Data System (ADS)

A general systematic method of predicting hypothetical crystal structures could enable important advances in many areas of science. We describe a recently developed approach based on graph theory and density functional theory and apply it to enumerate systematically a number of sp3 -hybridized carbon polymorphs with four atoms per unit cell. The calculations predict three unknown structures that are potentially metastable under appropriate pressure and temperature conditions. The theoretical properties of these hypothetical polymorphs and their relative stability with respect to diamond are discussed.

Strong, Rachel T.; Pickard, Chris J.; Milman, Victor; Thimm, Georg; Winkler, Björn

2004-07-01

367

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

368

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

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

2007-01-01

369

Aircraft Structural Mass Property Prediction Using Conceptual-Level Structural Analysis  

Microsoft Academic Search

This paper describes a methodology that extends the use of the Equivalent LAminated Plate Solution (ELAPS) structural analysis code from conceptual-level aircraft structural analysis to conceptual-level aircraft mass property analysis. Mass property analysis in aircraft structures has historically depended upon parametric we ight equations at the conceptual design level and Finite Element Analysis (FEA) at the detailed design level. ELAPS

Matthew G. Sexstone

1998-01-01

370

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

SciTech Connect

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

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

2000-08-01

371

An effective structure prediction method for layered materials based on 2D particle swarm optimization algorithm.  

PubMed

A structure prediction method for layered materials based on two-dimensional (2D) particle swarm optimization algorithm is developed. The relaxation of atoms in the perpendicular direction within a given range is allowed. Additional techniques including structural similarity determination, symmetry constraint enforcement, and discretization of structure constructions based on space gridding are implemented and demonstrated to significantly improve the global structural search efficiency. Our method is successful in predicting the structures of known 2D materials, including single layer and multi-layer graphene, 2D boron nitride (BN) compounds, and some quasi-2D group 6 metals(VIB) chalcogenides. Furthermore, by use of this method, we predict a new family of mono-layered boron nitride structures with different chemical compositions. The first-principles electronic structure calculations reveal that the band gap of these N-rich BN systems can be tuned from 5.40 eV to 2.20 eV by adjusting the composition. PMID:23248988

Wang, Yanchao; Miao, Maosheng; Lv, Jian; Zhu, Li; Yin, Ketao; Liu, Hanyu; Ma, Yanming

2012-12-14

372

An effective structure prediction method for layered materials based on 2D particle swarm optimization algorithm  

NASA Astrophysics Data System (ADS)

A structure prediction method for layered materials based on two-dimensional (2D) particle swarm optimization algorithm is developed. The relaxation of atoms in the perpendicular direction within a given range is allowed. Additional techniques including structural similarity determination, symmetry constraint enforcement, and discretization of structure constructions based on space gridding are implemented and demonstrated to significantly improve the global structural search efficiency. Our method is successful in predicting the structures of known 2D materials, including single layer and multi-layer graphene, 2D boron nitride (BN) compounds, and some quasi-2D group 6 metals(VIB) chalcogenides. Furthermore, by use of this method, we predict a new family of mono-layered boron nitride structures with different chemical compositions. The first-principles electronic structure calculations reveal that the band gap of these N-rich BN systems can be tuned from 5.40 eV to 2.20 eV by adjusting the composition.

Wang, Yanchao; Miao, Maosheng; Lv, Jian; Zhu, Li; Yin, Ketao; Liu, Hanyu; Ma, Yanming

2012-12-01

373

Enriching the human apoptosis pathway by predicting the structures of protein-protein complexes  

PubMed Central

Apoptosis is a matter of life and death for cells and both inhibited and enhanced apoptosis may be involved in the pathogenesis of human diseases. The structures of protein-protein complexes in the apoptosis signaling pathway are important as the structural pathway helps in understanding the mechanism of the regulation and information transfer, and in identifying targets for drug design. Here, we aim to predict the structures toward a more informative pathway than currently available. Based on the 3D structures of complexes in the target pathway and a protein-protein interaction modeling tool which allows accurate and proteome-scale applications, we modeled the structures of 29 interactions, 21 of which were previously unknown. Next, 27 interactions which were not listed in the KEGG apoptosis pathway were predicted and subsequently validated by the experimental data in the literature. Additional interactions are also predicted. The multi-partner hub proteins are analyzed and interactions that can and cannot co-exist are identified. Overall, our results enrich the understanding of the pathway with interactions and provide structural details for the human apoptosis pathway. They also illustrate that computational modeling of protein-protein interactions on a large scale can help validate experimental data and provide accurate, structural atom-level detail of signaling pathways in the human cell.

Acuner Ozbabacan, Saliha Ece; Keskin, Ozlem; Nussinov, Ruth; Gursoy, Attila

2012-01-01

374

Prediction and analysis of the modular structure of cytochrome P450 monooxygenases  

PubMed Central

Background Cytochrome P450 monooxygenases (CYPs) form a vast and diverse family of highly variable sequences. They catalyze a wide variety of oxidative reactions and are therefore of great relevance in drug development and biotechnological applications. Despite their differences in sequence and substrate specificity, the structures of CYPs are highly similar. Although being in research focus for years, factors mediating selectivity and activity remain vague. Description This systematic comparison of CYPs based on the Cytochrome P450 Engineering Database (CYPED) involved sequence and structure analysis of more than 8000 sequences. 31 structures have been applied to generate a reliable structure-based HMM profile in order to predict structurally conserved regions. Therefore, it was possible to automatically transfer these modules on CYP sequences without any secondary structure information, to analyze substrate interacting residues and to compare interaction sites with redox partners. Conclusions Functionally relevant structural sites of CYPs were predicted. Regions involved in substrate binding were analyzed in all sequences among the CYPED. For all CYPs that require a reductase, two reductase interaction sites were identified and classified according to their length. The newly gained insights promise an improvement of engineered enzyme properties for potential biotechnological application. The annotated sequences are accessible on the current version of the CYPED. The prediction tool can be applied to any CYP sequence via the web interface at http://www.cyped.uni-stuttgart.de/cgi-bin/strpred/dosecpred.pl.

2010-01-01

375

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.

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

2012-01-01

376

Automated RNA structure prediction uncovers a kink-turn linker in double glycine riboswitches.  

PubMed

The tertiary structures of functional RNA molecules remain difficult to decipher. A new generation of automated RNA structure prediction methods may help address these challenges but have not yet been experimentally validated. Here we apply four prediction tools to a class of double glycine riboswitches that can bind two ligands cooperatively. A novel method (BPPalign), RMdetect, JAR3D, and Rosetta 3D modeling give consistent predictions for a new stem P0 and a kink-turn motif. These elements structure the linker between the RNAs' double aptamers. Chemical mapping on the Fusobacterium nucleatum riboswitch with N-methylisatoic anhydride, dimethyl sulfate and 1-cyclohexyl-3-(2-morpholinoethyl)carbodiimide metho-p-toluenesulfonate probing, mutate-and-map studies, and mutation/rescue experiments all provide strong evidence for the structured linker. Under solution conditions that permit rigorous thermodynamic analysis, disrupting this helix-junction-helix structure gives 120- and 6-30-fold poorer dissociation constants for the RNA's two glycine-binding transitions, corresponding to an overall energetic impact of 4.3 ± 0.5 kcal/mol. Prior biochemical and crystallography studies did not include this critical element due to over-truncation of the RNA. We speculate that several further undiscovered elements are likely to exist in the flanking regions of this and other functional RNAs, and automated prediction tools can play a useful role in their detection and dissection. PMID:22192063

Kladwang, Wipapat; Chou, Fang-Chieh; Das, Rhiju

2012-01-12

377

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

PubMed Central

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

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

2010-01-01

378

Predicting the behaviour of structures under impact loads using geometrically distorted scaled models  

NASA Astrophysics Data System (ADS)

When a scaled structure (model or replica) is used to predict the response of a full-size compound (prototype), the model geometric dimensions should relate to the corresponding prototype dimensions by a single scaling factor. However, owing to manufacturing technical restrictions, this condition cannot be accomplished for some of the dimensions in real structures. Accordingly, the distorted geometry will not comply with the overall geometric scaling factor, infringing the ? theorem requirements for complete dynamic similarity. In the present study, a method which takes geometrical distortions into account is introduced, leading to a model similar to the prototype. As a means to infer the performance of this method, three analytical problems of structures subjected to dynamic loads are analysed. It is shown that the replica developed applying this technique is able to accurately predict the full-size structure behaviour even when the studied models have some of their dimensions severely distorted.

Oshiro, R. E.; Alves, M.

2012-07-01

379

A Non-parametric Bayesian Approach for Predicting RNA Secondary Structures  

NASA Astrophysics Data System (ADS)

Since many functional RNAs form stable secondary structures which are related to their functions, RNA secondary structure prediction is a crucial problem in bioinformatics. We propose a novel model for generating RNA secondary structures based on a non-parametric Bayesian approach, called hierarchical Dirichlet processes for stochastic context-free grammars (HDP-SCFGs). Here non-parametric means that some meta-parameters, such as the number of non-terminal symbols and production rules, do not have to be fixed. Instead their distributions are inferred in order to be adapted (in the Bayesian sense) to the training sequences provided. The results of our RNA secondary structure predictions show that HDP-SCFGs are more accurate than the MFE-based and other generative models.

Sato, Kengo; Hamada, Michiaki; Mituyama, Toutai; Asai, Kiyoshi; Sakakibara, Yasubumi

380

Predicting Singapore students' achievement goals in their English study: Self-construal and classroom goal structure  

Microsoft Academic Search

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 modeling showed that after controlling for gender, previous English achievement, and English self-concept, interdependent self-construal

Wenshu Luo; David Hogan; Scott G. Paris

2011-01-01

381

Predictive modeling of an energy-absorbing sandwich structural concept using the building block approach  

Microsoft Academic Search

An energy-absorbing composite sandwich structural concept, comprised of a deep honeycomb core with carbon\\/epoxy facesheets, is subject to through-thickness crushing and penetration using a cylindrical pole. With the aid of the building block approach, the response of the structure is predicted by analysis supported by test evidence. Experiments are conducted at various levels of complexity, from the coupon level used

Paolo Feraboli; Francesco Deleo; Bonnie Wade; Mostafa Rassaian; Mark Higgins; Alan Byar; Maurizio Reggiani; Andrea Bonfatti; Luciano DeOto; Attilio Masini

2010-01-01

382

Structure-dynamics relationships in bursting neuronal networks revealed using a prediction framework.  

PubMed

The question of how the structure of a neuronal network affects its functionality has gained a lot of attention in neuroscience. However, the vast majority of the studies on structure-dynamics relationships consider few types of network structures and assess limited numbers of structural measures. In this in silico study, we employ a wide diversity of network topologies and search among many possibilities the aspects of structure that have the greatest effect on the network excitability. The network activity is simulated using two point-neuron models, where the neurons are activated by noisy fluctuation of the membrane potential and their connections are described by chemical synapse models, and statistics on the number and quality of the emergent network bursts are collected for each network type. We apply a prediction framework to the obtained data in order to find out the most relevant aspects of network structure. In this framework, predictors that use different sets of graph-theoretic measures are trained to estimate the activity properties, such as burst count or burst length, of the networks. The performances of these predictors are compared with each other. We show that the best performance in prediction of activity properties for networks with sharp in-degree distribution is obtained when the prediction is based on clustering coefficient. By contrast, for networks with broad in-degree distribution, the maximum eigenvalue of the connectivity graph gives the most accurate prediction. The results shown for small ([Formula: see text]) networks hold with few exceptions when different neuron models, different choices of neuron population and different average degrees are applied. We confirm our conclusions using larger ([Formula: see text]) networks as well. Our findings reveal the relevance of different aspects of network structure from the viewpoint of network excitability, and our integrative method could serve as a general framework for structure-dynamics studies in biosciences. PMID:23935998

Mäki-Marttunen, Tuomo; A?imovi?, Jugoslava; Ruohonen, Keijo; Linne, Marja-Leena

2013-07-25

383

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

Microsoft Academic Search

Previously, multi-wall structures have been analyzed extensively, primarily through experiment, as a means of increasing the meteoroid\\/space debris impact protection of spacecraft. As structural configurations become more varied, the number of tests required to characterize their response increases dramatically. As an alternative to experimental testing, numerical modeling of high-speed impact phenomena is often being used to predict the response of

William P. Schonberg

1993-01-01

384

Protein Structure Prediction Based on HP Model Using an Improved Hybrid EDA  

Microsoft Academic Search

\\u000a Protein structure prediction (PSP) is one of the most important problems in computational biology. This chapter introduces\\u000a a novel hybrid Estimation of Distribution Algorithm (EDA) to solve the PSP problem on HP model. Firstly, a composite fitness\\u000a function containing the information of folding structure core (H-Core) is introduced to replace the traditional fitness function\\u000a of HP model. The new fitness

Benhui Chen; Jinglu Hu

385

Structure-Dynamics Relationships in Bursting Neuronal Networks Revealed Using a Prediction Framework  

PubMed Central

The question of how the structure of a neuronal network affects its functionality has gained a lot of attention in neuroscience. However, the vast majority of the studies on structure-dynamics relationships consider few types of network structures and assess limited numbers of structural measures. In this in silico study, we employ a wide diversity of network topologies and search among many possibilities the aspects of structure that have the greatest effect on the network excitability. The network activity is simulated using two point-neuron models, where the neurons are activated by noisy fluctuation of the membrane potential and their connections are described by chemical synapse models, and statistics on the number and quality of the emergent network bursts are collected for each network type. We apply a prediction framework to the obtained data in order to find out the most relevant aspects of network structure. In this framework, predictors that use different sets of graph-theoretic measures are trained to estimate the activity properties, such as burst count or burst length, of the networks. The performances of these predictors are compared with each other. We show that the best performance in prediction of activity properties for networks with sharp in-degree distribution is obtained when the prediction is based on clustering coefficient. By contrast, for networks with broad in-degree distribution, the maximum eigenvalue of the connectivity graph gives the most accurate prediction. The results shown for small () networks hold with few exceptions when different neuron models, different choices of neuron population and different average degrees are applied. We confirm our conclusions using larger () networks as well. Our findings reveal the relevance of different aspects of network structure from the viewpoint of network excitability, and our integrative method could serve as a general framework for structure-dynamics studies in biosciences.

Maki-Marttunen, Tuomo; Acimovic, Jugoslava; Ruohonen, Keijo; Linne, Marja-Leena

2013-01-01

386

Surface pressure profiles, vortex structure and initialization for hurricane prediction. Part I: analysis of observed and synthetic structures  

NASA Astrophysics Data System (ADS)

Without detailed reconnaissance, consistent representation of hurricane-like vortices in initial conditions for operational prediction and research simulations still remains elusive. It is thus often necessary, particularly for high-resolution intensity forecasting, to use synthetic tropical cyclone circulations to initialize forecast models. Variants on three commonly used surface pressure profiles are evaluated for possible use. Enhancements to the original profiles are proposed that allows definition of both the inner-core and outer circulation. The latter improvement creates a vortex more consistent with the estimated outer structure which sometimes appears to be crucial to the evolving intensity of the storm. It also allows smoother merging of the synthetic vortex with the environment. Comparisons of the profiles against (a) structure estimates, (b) each other, (c) structures obtained via conservation of angular momentum, and (d) observed vorticity structures, suggest that a new enhanced Fujita profile best represents real TC structures. Student- t tests indicate that improved fitting to the observations is statistically significant.

Ma, Yimin; Kafatos, Menas; Davidson, Noel E.

2012-07-01

387

corRna: a web server for predicting multiple-point deleterious mutations in structural RNAs  

PubMed Central

RNA molecules can achieve a broad range of regulatory functions through specific structures that are in turn determined by their sequence. The prediction of mutations changing the structural properties of RNA sequences (a.k.a. deleterious mutations) is therefore useful for conducting mutagenesis experiments and synthetic biology applications. While brute force approaches can be used to analyze single-point mutations, this strategy does not scale well to multiple mutations. In this article, we present corRna a web server for predicting the multiple-point deleterious mutations in structural RNAs. corRna uses our RNAmutants framework to efficiently explore the RNA mutational landscape. It also enables users to apply search heuristics to improve the quality of the predictions. We show that corRna predictions correlate with mutagenesis experiments on the hepatitis C virus cis-acting replication element as well as match the accuracy of previous approaches on a large test-set in a much lower execution time. We illustrate these new perspectives offered by corRna by predicting five-point deleterious mutations—an insight that could not be achieved by previous methods. corRna is available at: http://corrna.cs.mcgill.ca.

Lam, Edmund; Kam, Alfred; Waldispuhl, Jerome

2011-01-01

388

Prediction of human clearance from animal data and molecular structural parameters using multivariate regression analysis.  

PubMed

The aim of the study reported here was to develop a method for predicting human clearance that can be applied to various kinds of drugs using clearance values for rats and dogs and some molecular structural parameters. The clearance data for rats, dogs, and humans of 68 drugs were obtained from literature. The compounds have various structures, pharmacological activities, and pharmacokinetic characteristics. In addition, molecular weight, c log P, and the number of hydrogen bond acceptors were used as possible descriptors related to the human clearance value for each drug. Three types of regression methods, multiple linear regression (MLR) analysis, partial least squares (PLS) method, and artificial neural network (ANN), were used to predict human clearance, and their predictive performances were compared with allometric approaches, which have been widely used in interspecies scaling. In MLR and PLS analyses, interaction terms were introduced to evaluate the nonlinear relationships. For the data sets used in the present study, MLR and PLS with quadratic terms gave the same equation and the best predictive performance. The value of the squared cross-validated correlation coefficient (q(2)) was 0.682. In conclusion, the MLR method using animal clearance data from only two species and using easily calculated structural parameters can generally predict human clearance better than allometric methods. This approach can be applied to drugs with various characteristics. PMID:12434392

Wajima, Toshihiro; Fukumura, Kazuya; Yano, Yoshitaka; Oguma, Takayoshi

2002-12-01

389

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

PubMed Central

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 freely available at http://zhang.bioinformatics.ku.edu/HAAD.

Li, Yunqi; Roy, Ambrish; Zhang, Yang

2009-01-01

390

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

391

Binding site detection and druggability prediction of protein targets for structure-based drug design.  

PubMed

Assessing whether a protein structure is a good target or not before actually doing structure-based drug design on it is an important step to speed up the ligand discovery process. This is known as the "druggability" or "ligandability" assessment problem that has attracted increasing interest in recent years. The assessment typically includes the detection of ligand-binding sites on the protein surface and the prediction of their abilities to bind drug-like small molecules. A brief summary of the established methods of binding sites detection and druggability(ligandability) prediction, as well as a detailed description of the CAVITY approach developed in the authors' group was given. CAVITY showed good performance on ligand-binding site detection, and was successfully used to predict both the ligandabilities and druggabilities of the detected binding sites. PMID:23082974

Yuan, Yaxia; Pei, Jianfeng; Lai, Luhua

2013-01-01

392

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.

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

2011-01-01

393

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

394

Quantitative structure-property relationship modeling of skin sensitization: a quantitative prediction.  

PubMed

A quantitative structure-property relationship (QSPR) model for predicting the skin sensitization effects of chemical compounds has been developed. An extensive database of test results from three exclusive test procedures was used for QSPR model development. Since the experimental procedure and end-point ranking of data for local lymph node assay (LLNA), guinea pig maximization test (GPMT), and Federal Institute for Health Protection of Consumers and Veterinary Medicine (BgVV) are different, three separate QSPR models were developed. Effective non-linear regression models were used for QSPR model development. The predictive capability of the final QSPR models was further improved by using a combination of literature-recommended and structural descriptors. The resultant QSPR models are capable of predicting skin sensitization of the diverse set of molecules considered with accuracies of 90%, 95%, and 90% for the LLNA, GPMT, and BgVV datasets, respectively. PMID:19162165

Golla, Sharath; Madihally, Sundar; Robinson, Robert L; Gasem, Khaled A M

2009-01-08

395

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

396

Prediction of C-peptide structure using artificial bee colony algorithm  

Microsoft Academic Search

Artificial bee colony algorithm (ABC) is a swarm intelligence based algorithm. It is inspired by the foraging behavior of honey bee colony. In this paper, the ABC algorithm was utilized to predict the tertiary structure of C-peptide of ribonuclease A by searching the conformational search space to locate the lowest free energy conformation. Conformations were represented using torsion angles representation

H. A. A. Bahamish; R. Abdullah

2010-01-01

397

Exploring protein fold space by secondary structure prediction using data distribution method on Grid platform  

Microsoft Academic Search

Motivation: Since the newly developed Grid platform has been considered as a powerful tool to share resources in the Internet environment, it is of interest to demonstrate an efficient methodology to process massive biological data on the Grid environments at a low cost. This paper presents an efficient and economical method based on a Grid platform to predict secondary structures

Soojin Lee; Min-kyu Cho; Jin-won Jung; Jai-hoon Kim; Weontae Lee

2004-01-01

398

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

399

Using Quantitative Structure–Activity Relationships (QSAR) to Predict Toxic Endpoints for Polycyclic Aromatic Hydrocarbons (PAH)  

Microsoft Academic Search

Quantitative structure–activity relationships (QSAR) offer a reliable, cost-effective alternative to the time, money, and animal lives necessary to determine chemical toxicity by traditional methods. Additionally, humans are exposed to tens of thousands of chemicals in their lifetimes, necessitating the determination of chemical toxicity and screening for those posing the greatest risk to human health. This study developed models to predict

Erica D. Bruce; Robin L. Autenrieth; Robert C. Burghardt; K. C. Donnelly; Thomas J. McDonald

2008-01-01

400

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