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1

Predicting B-DNA structure from sequence  

Microsoft Academic Search

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

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

1995-01-01

2

Statistical Mechanical Approach for Predicting the Transition to Non-B DNA Structures in Supercoiled DNA  

Microsoft Academic Search

Supercoiling causes global twist of DNA structure and the supercoiled state has wide influence on conformational transition. A statistical mechanical approach was made for prediction of the transition probability to non-B DNA structures under torsional stress. A conditional partition function was defined as the sum over all possible states of the DNA sequence with basepair 1 and basepair n being

Shinji Katsura; Fusao Makishima; Hajime Nishimura

1993-01-01

3

Positive supercoiling affiliated with nucleosome formation repairs non-B DNA structures.  

PubMed

It is demonstrated that positive supercoiling affiliated with nucleosome formation can act as the driving force to repair the G-quadruplex, cruciform as well as a stable non-B DNA structure caused by peptide nucleic acid. PMID:25075997

Li, Dawei; Lv, Bei; Zhang, Hao; Lee, Jasmine Yiqin; Li, Tianhu

2014-09-21

4

Non-B DNA structure-induced genetic instability and evolution  

Microsoft Academic Search

Repetitive DNA motifs are abundant in the genomes of various species and have the capacity to adopt non-canonical (i.e., non-B)\\u000a DNA structures. Several non-B DNA structures, including cruciforms, slipped structures, triplexes, G-quadruplexes, and Z-DNA,\\u000a have been shown to cause mutations, such as deletions, expansions, and translocations in both prokaryotes and eukaryotes.\\u000a Their distributions in genomes are not random and often

Junhua Zhao; Albino Bacolla; Guliang Wang; Karen M. Vasquez

2010-01-01

5

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. PMID:21598926

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

2013-01-01

6

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. PMID:21977309

Sharma, Sudha

2011-01-01

7

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

PubMed Central

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

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

1997-01-01

8

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. PMID:22180536

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

2012-01-01

9

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

PubMed Central

In this study, we established a single nucleotide mutation matrix (SNMM) model based on the relative binding affinities of NF-?B p50 homodimer to a wild-type binding site (GGGACTTTCC) and its all single-nucleotide mutants detected with the double-stranded DNA microarray. We evaluated this model by scoring different groups of 10-bp DNA sequences with this model and analyzing the correlations between the scores and the relative binding affinities detected with three wet experiments, including the electrophoresis mobility shift assay (EMSA), the protein-binding microarray (PBM) and the systematic evolution of ligands by exponential enrichment-sequencing (SELEX-Seq). The results revealed that the SNMM scores were strongly correlated with the detected binding affinities. We also scored the DNA sequences with other three models, including the principal coordinate (PC) model, the position weight matrix scoring algorithm (PWMSA) model and the Match model, and analyzed the correlations between the scores and the detected binding affinities. In comparison with these models, the SNMM model achieved reliable results. We finally determined 0.747 as the optimal threshold for predicting the NF-?B DNA-binding sites with the SNMM model. The SNMM model thus provides a new alternative model for scoring the relative binding affinities of NF-?B to the 10-bp DNA sequences and predicting the NF-?B DNA-binding sites. PMID:24992458

Wang, Tingting; Wang, Jinke

2014-01-01

10

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

NASA Technical Reports Server (NTRS)

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

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

1986-01-01

11

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

PubMed

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

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

2013-01-01

12

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

13

Structural variability and the nature of intermolecular interactions in Watson-Crick B-DNA base pairs.  

PubMed

A set of nearly 100 crystallographic structures was analyzed using ab initio methods in order to verify the effect of the conformational variability of Watson-Crick guanine-cytosine and adenine-thymine base pairs on the intermolecular interaction energy and its components. Furthermore, for the representative structures, a potential energy scan of the structural parameters describing mutual orientation of the base pairs was carried out. The results were obtained using the hybrid variational-perturbational interaction energy decomposition scheme. The electron correlation effects were estimated by means of the second-order Møller-Plesset perturbation theory and coupled clusters with singles and doubles method adopting AUG-cc-pVDZ basis set. Moreover, the characteristics of hydrogen bonds in complexes, mimicking those appearing in B-DNA, were evaluated using topological analysis of the electron density. Although the first-order electrostatic energy is usually the largest stabilizing component, it is canceled out by the associated exchange repulsion in majority of the studied crystallographic structures. Therefore, the analyzed complexes of the nucleic acid bases appeared to be stabilized mainly by the delocalization component of the intermolecular interaction energy which, in terms of symmetry adapted perturbation theory, encompasses the second- and higher-order induction and exchange-induction terms. Furthermore, it was found that the dispersion contribution, albeit much smaller in terms of magnitude, is also a vital stabilizing factor. It was also revealed that the intermolecular interaction energy and its components are strongly influenced by four (out of six) structural parameters describing mutual orientation of bases in Watson-Crick pairs, namely shear, stagger, stretch, and opening. Finally, as a part of a model study, much of the effort was devoted to an extensive testing of the UBDB databank. It was shown that the databank quite successfully reproduces the electrostatic energy determined with the aid of ab initio methods. PMID:20604521

Czyznikowska, Z; Góra, R W; Zale?ny, R; Lipkowski, P; Jarzembska, K N; Dominiak, P M; Leszczynski, J

2010-07-29

14

Breakpoints of gross deletions coincide with non-B DNA conformations  

Microsoft Academic Search

Genomic rearrangements are a frequent source of instability, but the mechanisms involved are poorly understood. A 2.5-kbp poly(purine·pyrimidine) sequence from the human PKD1 gene, known to form non-B DNA structures, induced long deletions and other instabilities in plasmids that were mediated by mismatch repair and, in some cases, transcription. The breakpoints occurred at predicted non-B DNA structures. Distance measurements also

Albino Bacolla; Adam Jaworski; Jacquelynn E. Larson; John P. Jakupciak; Nadia Chuzhanova; Shaun S. Abeysinghe; Catherine D. O'Connell; David N. Cooper; Robert D. Wells

2004-01-01

15

Protein secondary structure prediction.  

PubMed

While the prediction of a native protein structure from sequence continues to remain a challenging problem, over the past decades computational methods have become quite successful in exploiting the mechanisms behind secondary structure formation. The great effort expended in this area has resulted in the development of a vast number of secondary structure prediction methods. Especially the combination of well-optimized/sensitive machine-learning algorithms and inclusion of homologous sequence information has led to increased prediction accuracies of up to 80%. In this chapter, we will first introduce some basic notions and provide a brief history of secondary structure prediction advances. Then a comprehensive overview of state-of-the-art prediction methods will be given. Finally, we will discuss open questions and challenges in this field and provide some practical recommendations for the user. PMID:20221928

Pirovano, Walter; Heringa, Jaap

2010-01-01

16

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

NASA Technical Reports Server (NTRS)

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

Gruskin, E. A.; Rich, A.

1993-01-01

17

Understanding and predicting protein structure  

SciTech Connect

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

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

1996-12-31

18

From the double-stranded helix to the chiral nematic phase of B-DNA: A molecular model  

NASA Astrophysics Data System (ADS)

B-DNA solutions of suitable concentration form left-handed chiral nematic phases (cholesterics). Such phases have also been observed in solutions of other stiff or semiflexible chiral polymers; magnitude and handedness of the cholesteric pitch are uniquely related to the molecular features. In this work we present a theoretical method and a numerical procedure which, starting from the structure of polyelectrolytes, lead to the prediction of the cholesteric pitch. Molecular expressions for the free energy of the system are obtained on the basis of steric and electrostatic interactions between polymers; the former are described in terms of excluded volume, while a mean field approximation is used for the latter. Calculations have been performed for 130 base pair fragments of B-DNA. The theoretical predictions provide an explanation for the experimental behavior, by showing the counteracting role played by shape and charge chirality of the molecule.

Tombolato, Fabio; Ferrarini, Alberta

2005-02-01

19

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

PubMed Central

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

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

2012-01-01

20

EVA: evaluation of protein structure prediction servers  

Microsoft Academic Search

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

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

2003-01-01

21

Non-B DNA-forming sequences and WRN deficiency independently increase the frequency of base substitution in human cells.  

PubMed

Although alternative DNA secondary structures (non-B DNA) can induce genomic rearrangements, their associated mutational spectra remain largely unknown. The helicase activity of WRN, which is absent in the human progeroid Werner syndrome, is thought to counteract this genomic instability. We determined non-B DNA-induced mutation frequencies and spectra in human U2OS osteosarcoma cells and assessed the role of WRN in isogenic knockdown (WRN-KD) cells using a supF gene mutation reporter system flanked by triplex- or Z-DNA-forming sequences. Although both non-B DNA and WRN-KD served to increase the mutation frequency, the increase afforded by WRN-KD was independent of DNA structure despite the fact that purified WRN helicase was found to resolve these structures in vitro. In U2OS cells, ?70% of mutations comprised single-base substitutions, mostly at G·C base-pairs, with the remaining ?30% being microdeletions. The number of mutations at G·C base-pairs in the context of NGNN/NNCN sequences correlated well with predicted free energies of base stacking and ionization potentials, suggesting a possible origin via oxidation reactions involving electron loss and subsequent electron transfer (hole migration) between neighboring bases. A set of ?40,000 somatic mutations at G·C base pairs identified in a lung cancer genome exhibited similar correlations, implying that hole migration may also be involved. We conclude that alternative DNA conformations, WRN deficiency and lung tumorigenesis may all serve to increase the mutation rate by promoting, through diverse pathways, oxidation reactions that perturb the electron orbitals of neighboring bases. It follows that such "hole migration" is likely to play a much more widespread role in mutagenesis than previously anticipated. PMID:21285356

Bacolla, Albino; Wang, Guliang; Jain, Aklank; Chuzhanova, Nadia A; Cer, Regina Z; Collins, Jack R; Cooper, David N; Bohr, Vilhelm A; Vasquez, Karen M

2011-03-25

22

Non-B DNA-forming Sequences and WRN Deficiency Independently Increase the Frequency of Base Substitution in Human Cells*  

PubMed Central

Although alternative DNA secondary structures (non-B DNA) can induce genomic rearrangements, their associated mutational spectra remain largely unknown. The helicase activity of WRN, which is absent in the human progeroid Werner syndrome, is thought to counteract this genomic instability. We determined non-B DNA-induced mutation frequencies and spectra in human U2OS osteosarcoma cells and assessed the role of WRN in isogenic knockdown (WRN-KD) cells using a supF gene mutation reporter system flanked by triplex- or Z-DNA-forming sequences. Although both non-B DNA and WRN-KD served to increase the mutation frequency, the increase afforded by WRN-KD was independent of DNA structure despite the fact that purified WRN helicase was found to resolve these structures in vitro. In U2OS cells, ?70% of mutations comprised single-base substitutions, mostly at G·C base-pairs, with the remaining ?30% being microdeletions. The number of mutations at G·C base-pairs in the context of NGNN/NNCN sequences correlated well with predicted free energies of base stacking and ionization potentials, suggesting a possible origin via oxidation reactions involving electron loss and subsequent electron transfer (hole migration) between neighboring bases. A set of ?40,000 somatic mutations at G·C base pairs identified in a lung cancer genome exhibited similar correlations, implying that hole migration may also be involved. We conclude that alternative DNA conformations, WRN deficiency and lung tumorigenesis may all serve to increase the mutation rate by promoting, through diverse pathways, oxidation reactions that perturb the electron orbitals of neighboring bases. It follows that such “hole migration” is likely to play a much more widespread role in mutagenesis than previously anticipated. PMID:21285356

Bacolla, Albino; Wang, Guliang; Jain, Aklank; Chuzhanova, Nadia A.; Cer, Regina Z.; Collins, Jack R.; Cooper, David N.; Bohr, Vilhelm A.; Vasquez, Karen M.

2011-01-01

23

NF-?B DNA binding activity of sesquiterpene lactones from Anthemis arvensis and Anthemis cotula  

Microsoft Academic Search

Previous investigations of Anthemis arvensis L. and Anthemis cotula L., species growing wild in Serbia, revealed linear sesquiterpene lactones (SLs) with unusual types of skeleton, named antheindurolides and anthecotuloides. The NF-?B DNA binding activity of four of those SLs as well as from compounds representing structural parts of the SLs is reported in this article. The relationship between their structure

Ivan Vu?kovi?; Ljubodrag Vujisi?; Christoph A. Klaas; Irmgard Merfort; Slobodan Milosavljevi?

2011-01-01

24

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

PubMed Central

An extensive analysis of structural databases is carried out to investigate the relative flexibility of B-DNA and A-RNA duplexes in crystal form. Our results show that the general anisotropic concept of flexibility is not very useful to compare the deformability of B-DNA and A-RNA duplexes, since the flexibility patterns of B-DNA and A-RNA are quite different. In other words, ‘flexibility’ is a dangerous word for describing macromolecules, unless it is clearly defined. A few soft essential movements explain most of the natural flexibility of A-RNA, whereas many are necessary for B-DNA. Essential movements occurring in naked B-DNAs are identical to those necessary to deform DNA in DNA–protein complexes, which suggest that evolution has designed DNA–protein complexes so that B-DNA is deformed according to its natural tendency. DNA is generally more flexible, but for some distortions A-RNA is easier to deform. Local stiffness constants obtained for naked B-DNAs and DNA complexes are very close, demonstrating that global distortions in DNA necessary for binding to proteins are the result of the addition of small concerted deformations at the base-pair level. Finally, it is worth noting that in general the picture of the relative deformability of A-RNA and DNA derived from database analysis agrees very well with that derived from state of the art molecular dynamics (MD) simulations. PMID:15562006

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

2004-01-01

25

RNA structure prediction: progress and perspective  

E-print Network

Many recent exciting discoveries have revealed the versatility of RNAs and their importance in a variety of cellular functions which are strongly coupled to RNA structures. To understand the functions of RNAs, some structure prediction models have been developed in recent years. In this review, the progress in computational models for RNA structure prediction is introduced and the distinguishing features of many outstanding algorithms are discussed, emphasizing three dimensional (3D) structure prediction. A promising coarse-grained model for predicting RNA 3D structure, stability and salt effect is also introduced briefly. Finally, we discuss the major challenges in the RNA 3D structure modeling.

Shi, Ya-Zhou; Wang, Feng-Hua; Tan, Zhi-Jie

2014-01-01

26

Predicting Chemical Carcinogenesis using Structural Information Only?  

E-print Network

into the mechanistic paths and features that gov- ern chemical toxicity, since the solutions produced are readilyPredicting Chemical Carcinogenesis using Structural Information Only? Claire J. Kennedy1 of predicting the carcinogenic activity of chemical com- pounds from their molecular structure and the outcomes

Fernandez, Thomas

27

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. PMID:9098062

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

1997-01-01

28

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

29

Confidence Estimation in Structured Prediction  

Microsoft Academic Search

Structured classification tasks such as sequence labeling and dependency parsing have seen much interest by the Natural Language Processing and the machine learning communities. Several online learning algorithms were adapted for structured tasks such as Perceptron, Passive- Aggressive and the recently introduced Confidence-Weighted learning . These online algorithms are easy to implement, fast to train and yield state-of-the-art performance. However,

Avihai Mejer; Koby Crammer

2011-01-01

30

Secondary Structure Prediction of Proposed RNAi  

E-print Network

Secondary Structure Prediction of Proposed RNAi Targets: Can Current Energy Minimization Algorithms structures such as pseudoknots has proved a challenge, relatively recent advances in modeling algorithms have allowed for the development of several web-based secondary structure modeling programs. Until now, most

31

Prediction of three dimensional structure of calmodulin.  

PubMed

Calmodulin (CaM) is an important human protein, which has multiple structures. Numerous researchers studied the CaM structures in the past, and about 50 different structures in complex with fragments derived from CaM-regulated proteins have been discovered. Discovery and analysis of existing and new CaM structures is difficult due to the inherent complexity, i.e. flexibility of 6 loops and a central linker that constitute part of the CaM structure. The extensive interest in CaM structure analysis and discovery calls for a comprehensive study, which based on the accumulated expertise would design a method for prediction and analysis of future and existing CaM structures. It is also important to find the mechanisms by which the protein adjusts its structure with respect to various factors. To this end, this paper analyzes the known CaM structures and finds four factors that influence CaM structure, which include existence of Ca2+ binding, different binding segments, measuring surroundings, and sequence mutation. The degree of influence of specific factors on different structural regions is also investigated. Based on the analysis of the relation between the four factors and the corresponding CaM structure a novel method for prediction of the CaM structure in complex with novel segments, given that the surroundings of the complex, is developed. The developed prediction method is tested on a set aside, newest CaM structure. The prediction results provide useful and accurate information about the structure verifying high quality of the proposed prediction method and performed structural analysis. PMID:16721661

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

2006-01-01

32

NF-?B DNA binding activity of sesquiterpene lactones from Anthemis arvensis and Anthemis cotula.  

PubMed

Previous investigations of Anthemis arvensis L. and Anthemis cotula L., species growing wild in Serbia, revealed linear sesquiterpene lactones (SLs) with unusual types of skeleton, named antheindurolides and anthecotuloides. The NF-?B DNA binding activity of four of those SLs as well as from compounds representing structural parts of the SLs is reported in this article. The relationship between their structure and NF-?B inhibition potency is briefly discussed. PMID:20603773

Vuckovi?, Ivan; Vujisi?, Ljubodrag; Klaas, Christoph A; Merfort, Irmgard; Milosavljevi?, Slobodan

2011-04-01

33

Bayesian Nonparametric Methods for Protein Structure Prediction  

E-print Network

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

Lennox, Kristin Patricia

2011-10-21

34

Structured Prediction, Dual Extragradient and Bregman Projections  

E-print Network

Structured Prediction, Dual Extragradient and Bregman Projections Ben Taskar taskar a simple and scalable algorithm for maximum-margin estimation of structured output models, including-concave saddle-point problem that allows us to use simple projection methods based on the dual extragradient

Plotkin, Joshua B.

35

Global Optimization and Protein Structure Prediction  

E-print Network

of protein structure, SIAM Rev. 39 1997, 407-460. Here I shall discuss the mathematical formulation on the statistical analysis of the data in the Brookhaven Protein Data Bank. Topics discussed are background fromGlobal Optimization and Protein Structure Prediction Arnold Neumaier Institut fur Mathematik

Neumaier, Arnold

36

Predicting Odor Perceptual Similarity from Odor Structure  

PubMed Central

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

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

2013-01-01

37

Predicting polymeric crystal structures by evolutionary algorithms.  

PubMed

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

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

2014-10-21

38

The Jpred 3 secondary structure prediction server  

PubMed Central

Jpred (http://www.compbio.dundee.ac.uk/jpred) is a secondary structure prediction server powered by the Jnet algorithm. Jpred performs over 1000 predictions per week for users in more than 50 countries. The recently updated Jnet algorithm provides a three-state (?-helix, ?-strand and coil) prediction of secondary structure at an accuracy of 81.5%. Given either a single protein sequence or a multiple sequence alignment, Jpred derives alignment profiles from which predictions of secondary structure and solvent accessibility are made. The predictions are presented as coloured HTML, plain text, PostScript, PDF and via the Jalview alignment editor to allow flexibility in viewing and applying the data. The new Jpred 3 server includes significant usability improvements that include clearer feedback of the progress or failure of submitted requests. Functional improvements include batch submission of sequences, summary results via email and updates to the search databases. A new software pipeline will enable Jnet/Jpred to continue to be updated in sync with major updates to SCOP and UniProt and so ensures that Jpred 3 will maintain high-accuracy predictions. PMID:18463136

Cole, Christian; Barber, Jonathan D.; Barton, Geoffrey J.

2008-01-01

39

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

40

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

41

Accurate prediction of protein structural class.  

PubMed

Because of the increasing gap between the data from sequencing and structural genomics, the accurate prediction of the structural class of a protein domain solely from the primary sequence has remained a challenging problem in structural biology. Traditional sequence-based predictors generally select several sequence features and then feed them directly into a classification program to identify the structural class. The current best sequence-based predictor achieved an overall accuracy of 74.1% when tested on a widely used, non-homologous benchmark dataset 25PDB. In the present work, we built a multiple linear regression (MLR) model to convert the 440-dimensional (440D) sequence feature vector extracted from the Position Specific Scoring Matrix (PSSM) of a protein domain to a 4-dimensinal (4D) structural feature vector, which could then be used to predict the four major structural classes. We performed 10-fold cross-validation and jackknife tests of the method on a large non-homologous dataset containing 8,244 domains distributed among the four major classes. The performance of our approach outperformed all of the existing sequence-based methods and had an overall accuracy of 83.1%, which is even higher than the results of those predicted secondary structure-based methods. PMID:22723837

Xia, Xia-Yu; Ge, Meng; Wang, Zhi-Xin; Pan, Xian-Ming

2012-01-01

42

Predicting Polymeric Crystal Structures by Evolutionary Algorithms  

E-print Network

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

Qiang Zhu; Vinit Sharma; Artem R Oganov; Rampi Ramprasad

2014-06-05

43

Ko Displacement Theory for Structural Shape Predictions  

NASA Technical Reports Server (NTRS)

The development of the Ko displacement theory for predictions of structure deformed shapes was motivated in 2003 by the Helios flying wing, which had a 247-ft (75-m) wing span with wingtip deflections reaching 40 ft (12 m). The Helios flying wing failed in midair in June 2003, creating the need to develop new technology to predict in-flight deformed shapes of unmanned aircraft wings for visual display before the ground-based pilots. Any types of strain sensors installed on a structure can only sense the surface strains, but are incapable to sense the overall deformed shapes of structures. After the invention of the Ko displacement theory, predictions of structure deformed shapes could be achieved by feeding the measured surface strains into the Ko displacement transfer functions for the calculations of out-of-plane deflections and cross sectional rotations at multiple locations for mapping out overall deformed shapes of the structures. The new Ko displacement theory combined with a strain-sensing system thus created a revolutionary new structure- shape-sensing technology.

Ko, William L.

2010-01-01

44

Taxonomic Prediction with Tree-Structured Covariances  

E-print Network

Taxonomic Prediction with Tree-Structured Covariances Matthew B. Blaschko1,2,3 , Wojciech Zaremba1Tech, France 4 Gatsby Computational Neuroscience Unit, University College London, UK Abstract. Taxonomies have classes. Such taxonomies have been used to improve classification results by increasing the statistical

Boyer, Edmond

45

Fractal structure enables temporal prediction in music.  

PubMed

1/f serial correlations and statistical self-similarity (fractal structure) have been measured in various dimensions of musical compositions. Musical performances also display 1/f properties in expressive tempo fluctuations, and listeners predict tempo changes when synchronizing. Here the authors show that the 1/f structure is sufficient for listeners to predict the onset times of upcoming musical events. These results reveal what information listeners use to anticipate events in complex, non-isochronous acoustic rhythms, and this will entail innovative models of temporal synchronization. This finding could improve therapies for Parkinson's and related disorders and inform deeper understanding of how endogenous neural rhythms anticipate events in complex, temporally structured communication signals. PMID:25324107

Rankin, Summer K; Fink, Philip W; Large, Edward W

2014-10-01

46

Protein structure prediction and model quality assessment  

PubMed Central

Protein structures have proven to be a crucial piece of information for biomedical research. Of the millions of currently sequenced proteins only a small fraction is experimentally solved for structure and the only feasible way to bridge the gap between sequence and structure data is computational modeling. Half a century has passed since it was shown that the amino acid sequence of a protein determines its shape, but a method to translate the sequence code reliably into the 3D structure still remains to be developed. This review summarizes modern protein structure prediction techniques with the emphasis on comparative modeling, and describes the recent advances in methods for theoretical model quality assessment. PMID:19100336

Fidelis, Krzysztof

2009-01-01

47

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

48

Predictive modeling of post bioprinting structure formation.  

PubMed

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

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

2014-03-21

49

Conformation of B-DNA containing O6-ethyl-G-C base pairs stabilized by minor groove binding drugs: molecular structure of d(CGC[e6G]AATTCGCG complexed with Hoechst 33258 or Hoechst 33342.  

PubMed Central

O6-ethyl-G (e6G) is an important DNA lesion, caused by the exposure of cells to alkylating agents such as N-ethyl-N-nitrosourea. A strong correlation exists between persistence of e6G lesion and subsequent carcinogenic conversion. We have determined the three-dimensional structure of a DNA molecule incorporating the e6G lesion by X-ray crystallography. The DNA dodecamer d(CGC[e6G]AATTCGCG), complexed to minor groove binding drugs Hoechst 33258 or Hoechst 33342, has been crystallized in the space group P212121, isomorphous to other related dodecamer DNA crystals. In addition, the native dodecamer d(CGCGAATTCGCG) was crystallized with Hoechst 33342. All three new structures were solved by the molecular replacement method and refined by the constrained least squares procedure to R-factors of approximately 16% at approximately 2.0 A resolution. In the structure of three Hoechst drug-dodecamer complexes in addition to the one published earlier [Teng et al. (1988) Nucleic Acids Res., 16, 2671-2690], the Hoechst molecule lies squarely at the central AATT site with the ends approaching the G4-C21 and the G16-C9 base pairs, consistent with other spectroscopic data, but not with another crystal structure reported [Pjura et al. (1987) J. Mol. Biol., 197, 257-271]. The two independent e6G-C base pairs in the DNA duplex adopt different base pairing schemes. The e6G4-C21 base pair has a configuration similar to a normal Watson-Crick base pair, except with bifurcated hydrogen bonds between e6G4 and C21, and the ethyl group is in the proximal orientation. In contrast, the e6G16-C9 base pair adopts a wobble configuration and the ethyl group is in the distal orientation.(ABSTRACT TRUNCATED AT 250 WORDS) Images PMID:1371249

Sriram, M; van der Marel, G A; Roelen, H L; van Boom, J H; Wang, A H

1992-01-01

50

Antibody structural modeling with prediction of immunoglobulin structure (PIGS).  

PubMed

Antibodies (or immunoglobulins) are crucial for defending organisms from pathogens, but they are also key players in many medical, diagnostic and biotechnological applications. The ability to predict their structure and the specific residues involved in antigen recognition has several useful applications in all of these areas. Over the years, we have developed or collaborated in developing a strategy that enables researchers to predict the 3D structure of antibodies with a very satisfactory accuracy. The strategy is completely automated and extremely fast, requiring only a few minutes (?10 min on average) to build a structural model of an antibody. It is based on the concept of canonical structures of antibody loops and on our understanding of the way light and heavy chains pack together. PMID:25375991

Marcatili, Paolo; Olimpieri, Pier Paolo; Chailyan, Anna; Tramontano, Anna

2014-12-01

51

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

PubMed Central

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

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

2014-01-01

52

Incomplete gene structure prediction with almost 100% specificity  

E-print Network

The goals of gene prediction using computational approaches are to determine gene location and the corresponding functionality of the coding region. A subset of gene prediction is the gene structure prediction problem, which is to define the exon...

Chin, See Loong

2004-09-30

53

Structure Prediction for Multicomponent Materials Using Biminima  

NASA Astrophysics Data System (ADS)

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

Schebarchov, D.; Wales, D. J.

2014-10-01

54

Theory for the hydrodynamic and electrophoretic stretch of tethered B-DNA.  

PubMed Central

We have developed a theory for the extension and force of B-DNA tethered at a fixed point in a uniform hydrodynamic flow or in a uniform applied electric field. The chain tethered in an electric field is considered to be subject to free electrophoresis compensated by free sedimentation in the opposite direction. This allows the use of results of free electrophoresis for including the effects of small ions. The force on the chain is derived for a sequence of ellipsoidal segments, each twice the persistence length of the wormlike chain. Hydrodynamic interaction between these segments is based on the long-range limit of flow around the prolate ellipsoids, as derived from equivalent Stokes spheres. The chain extension is derived by applying the entropic elasticity relation of Marko and Siggia (1995 Macromolecules. 28:8759-8770) to each segment for polymer chains under constant tension. We justify this procedure by comparing with extension results based on the Boltzmann averaged orientation of straight, freely jointed segments. Predicted results agree well with recent extension-flow experiments by Perkins et al., 1995. Science. 258:83-87, and with electrophoretic stretch experiments by Smith and Bendich (1990 Biopolymers. 29:1167-1173) on fluorescently stained B-DNA. We find that the equivalence of hydrodynamic and electrophoretic stretch, proposed by Long et al. (1996 Phys. Rev. Lett. 76:3858-3861; 1996 Biopolymers 39:755-759), is valid only for very small chain deformations, but not in general. PMID:9726922

Stigter, D; Bustamante, C

1998-01-01

55

Structure-based function prediction: approaches and applications  

E-print Network

Structure-based function prediction: approaches and applications Pier Federico Gherardini 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

Cheng, Jianlin Jack

56

I-TASSER server for protein 3D structure prediction  

E-print Network

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

Zhang, Yang

2008-01-23

57

Structure of nonevaporating sprays - Measurements and predictions  

NASA Technical Reports Server (NTRS)

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

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

1984-01-01

58

Prediction of structure-borne vibration for an assembly of three structures in series  

E-print Network

Prediction of structure-borne vibration for an assembly of three structures in series M.-H. Mouleta 2012, Nantes, France 3455 #12;The prediction of structure-borne sound and vibration in an assembly of two structures needs the prediction of the forces applied by the source structure on the host

Boyer, Edmond

59

STRUCTURE PREDICTION CASP AND OTHER COMMUNITY-WIDE  

E-print Network

Section VI STRUCTURE PREDICTION #12;#12;28 CASP AND OTHER COMMUNITY-WIDE ASSESSMENTS TO ADVANCE THE FIELD OF STRUCTURE PREDICTION Jenny Gu and Philip E. Bourne A MEASURE FOR SUCCESS In the early 1990s, the community recognized that methods for structure determination from sequence information have been

Bourne, Philip E.

60

Current approaches to predicting molecular organic crystal structures  

Microsoft Academic Search

Considerable effort has been invested in developing methods for predicting the crystalline structure(s) of a given compound, ideally starting from no more than a structural formula of the molecule. Reliable computational predictions would be of great value in many areas of materials chemistry, from the design of materials with novel properties to the avoidance of an undesirable change of form

Graeme M. Day

2011-01-01

61

Agent-based Protein Structure Prediction Luca Bortolussi  

E-print Network

Agent-based Protein Structure Prediction Luca Bortolussi Dept. of Mathematics and Computer Science of Udine ffogolari@makek.dstb.uniud.it Abstract A protein is identified by a finite sequence of amino acids, each of them chosen from a set of 20 elements. The Protein Structure Prediction Problem is the problem

Bortolussi, Luca

62

Cascaded Bidirectional Recurrent Neural Networks for Protein Secondary Structure Prediction  

Microsoft Academic Search

Protein secondary structure (PSS) prediction is an important topic in bioinformatics. Our study on a large set of non-homologous proteins shows that long-range interactions commonly exist and negatively affect PSS prediction. Besides, we also reveal strong correlations between secondary structure (SS) elements. In order to take into account the long-range interactions and SS-SS correlations, we propose a novel prediction system

Jinmiao Chen; Narendra S. Chaudhari

2007-01-01

63

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

64

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

65

Prediction of protein structural classes using hybrid properties.  

PubMed

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

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

2008-01-01

66

Network for Protein Secondary Structure Prediction  

Microsoft Academic Search

The formation of protein secondary structure especially the regions of fl-sheets involves long-range interactions between amino acids. We propose a novel recurrent neural network architecture called Segmented-Memory Recurrent Neural Network (SMRNN) and present experimental results showing that SMRNN outperforms conventional recurrent neu- ral networks on long-term dependency problems. In order to capture long-term dependencies in protein sequences for secondary structure

Jinmiao Chen; Narendra S. Chaudhari

67

Lewis Structures Are Models for Predicting Molecular Structure, Not Electronic Structure  

NASA Astrophysics Data System (ADS)

This article argues against a close relationship between Lewis dot structures and electron structure obtained from quantum mechanical calculations. Lewis structures are a powerful tool for structure prediction, though they are classical models of bonding and do not predict electronic structure. The "best" Lewis structures are those that, when combined with the VSEPR model, allow the accurate prediction of molecular properties, such as polarity, bond length, bond angle, and bond strength. These structures are achieved by minimizing formal charges within the molecule, even if it requires an expanded octet on atoms beyond the second period. Lewis structures that show an expanded octet do not imply full d-orbital involvement in the bonding. They suggest that the presence of low-lying d-orbitals is important in producing observed molecular structures. Based on this work, the presence of electron density, not a large separation in charge, is responsible for the short bond lengths and large angles in species containing nonmetal atoms from beyond the second period. This result contradicts results obtained from natural population analysis, a method that attempts to derive Lewis structures from molecular orbital calculations.

Purser, Gordon H.

1999-07-01

68

Protein structure prediction and analysis using the Robetta server  

E-print Network

initiatives to help accelerate struc- ture determination and gain structural insight for targeted open reading) constraints data can also be sub- mittedwithaquerysequenceforRosettaNMR denovo structure determination. Other information from genomic data. The server uses the first fully automated structure prediction procedure

Baker, David

69

PredyFlexy: flexibility and local structure prediction from sequence  

E-print Network

PredyFlexy: flexibility and local structure prediction from sequence Alexandre G. de Brevern1 at a molecular level. Dynamics and flexibility of protein structures are also key elements of protein function. So, we have proposed to look at protein flexibility using novel methods: (i) using a structural

Paris-Sud XI, Université de

70

Crowdsourced Comprehension: Predicting Prerequisite Structure in Wikipedia  

E-print Network

impact of documents on an individual reader's state of knowledge. Experimental re- sults using Wikipedia regarding prerequisite structure, and then gen- eralizing these labels using a learned classifier which-generated content from Twitter and Facebook is mainly comprised of short, shallow snippets of information. Most

Murphy, Robert F.

71

Structure prediction of the RPE65 protein  

Microsoft Academic Search

The RPE65 protein is located in the retinal pigment epithelial cells and plays an important role in the visual cycle. Although numerous experimental results demonstrate that it participates in the visual cycle, its detailed structure and function are not clear yet because of difficulties in isolation and crystallization. This paper describes a computational modeling study to propose a three-dimensional (3D)

Hao Guo; Chong Zheng; Elizabeth R. Gaillard

2006-01-01

72

A machine learning approach to crystal structure prediction  

E-print Network

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

Fischer, Christopher Carl

2007-01-01

73

PREDICTING MODES OF TOXIC ACTION FROM CHEMICAL STRUCTURE: AN OVERVIEW  

EPA Science Inventory

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

74

Predicting Secondary Structure of All-Helical Proteins Using  

E-print Network

methods in use today [10,14], delivering a pointwise prediction accuracy (Q3) of about 77% and a segment structure pre- diction, it likely will be necessary to develop a cost model that mirrors the underlying

Gifford, David K.

75

Protein Secondary Structure Prediction Using Sigmoid Belief Networks to Parameterize  

E-print Network

of these methods have utilized neural networks. A major improvement in the prediction accuracy of these methodsProtein Secondary Structure Prediction Using Sigmoid Belief Networks to Parameterize Segmental Semi-Markov Models Wei Chu , Zoubin Ghahramani Gatsby Computational Neuroscience Unit, University College London

Ghahramani, Zoubin

76

Structured Prediction for Object Detection in Deep Neural Networks  

E-print Network

,behnke}@ais.uni-bonn.de Abstract. Deep convolutional neural networks are currently applied to computer vision tasks, especiallyStructured Prediction for Object Detection in Deep Neural Networks Hannes Schulz and Sven Behnke for neural network training which di- rectly maximizes overlap of the prediction with ground truth bounding

Behnke, Sven

77

Computational methods in sequence and structure prediction  

NASA Astrophysics Data System (ADS)

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

Lang, Caiyi

78

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

79

Gogny HFB prediction of nuclear structure properties  

SciTech Connect

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. [IAA-ULB, Campus de la Plaine, CP 226 1050 Brussels (Belgium); Hilaire, S.; Girod, M. [CEA, DAM, DIF, F-91297 Arpajon (France)

2011-10-28

80

A classifier system for predicting RNA secondary structure.  

PubMed

Finding the secondary structures of ribonucleic acid sequences is a very important task. The secondary structure helps determine their functionalities which in turn plays a role in the proteins production. Manual laboratory methods use X-ray diffraction to predict secondary structures but it is complex, slow and expensive. Therefore, different computational approaches are used to predict RNA secondary structure in order to reduce the time and cost associated with the manual process. We propose a system called IsRNA to predict a single element, internal loop, of the RNA secondary structure. IsRNA experiments with different classifiers such as SVM, KNN, Naive Bayes and Simple K means to find the most accurate classifier. We present a through experimental evaluation of 24 features, classified into five groups, to determine the most relevant feature groups. The system is evaluated using Rfam sequences and achieves an overall sensitivity, selectivity, and accuracy of 96.1%, 98%, and 96.1%, respectively. PMID:24794072

Aldwairi, Monther; Al-Hajasad, Bashar; Khamayseh, Yaser

2014-01-01

81

[RNA secondary structure prediction based on support vector machine classification].  

PubMed

The comparative sequence analysis is the most reliable method for RNA secondary structure prediction, and many algorithms based on it have been developed in last several decades. This paper considers RNA structure prediction as a 2-classes classification problem: given a sequence alignment, to decide whether or not two columns of alignment form a base pair. We employed Support Vector Machine (SVM) to predict potential paired sites, and selected co-variation information, thermodynamic information and the fraction of complementary bases as feature vectors. Considering the effect of sequence similarity upon co-variation score, we introduced a similarity weight factor, which could adjust the contribution of co-variation and thermodynamic information toward prediction according to sequence similarity. The test on 49 Rfam-seed alignments showed the effectiveness of our method, and the accuracy was better than many similar algorithms. Furthermore, this method could predict simple pseudoknot. PMID:18837386

Zhao, Yingjie; Wang, Zhengzhi

2008-07-01

82

A neural network structure for prediction of chemical agent fate  

NASA Astrophysics Data System (ADS)

This work presents the development of a multi-input, multi-output neural network structure to predict the time dependent concentration of chemical agents as they participate in chemical reaction with environmental substrates or moisture content within these substrates. The neural network prediction is based on a computationally or experimentally produced database that includes the concentration of all chemicals presents (reactants and products) as a function of the chemical agent droplet size, wind speed, temperature, and turbulence. The utilization of this prediction structure is made userfriendly via an easy-to-use graphical user interface. Furthermore, upon the knowledge of the time-varying environmental parameters (wind speed and temperature that are usually recorded and available), the time varying concentration of all chemicals can be predicted almost instantaneously by recalling the previously trained network. The network prediction was compared with actual open air test data and the results were found to match.

Navaz, H. K.; Kehtarnavaz, N.; Jovic, Zoran

2014-05-01

83

RNA structure prediction from evolutionary patterns of nucleotide composition  

PubMed Central

Structural elements in RNA molecules have a distinct nucleotide composition, which changes gradually over evolutionary time. We discovered certain features of these compositional patterns that are shared between all RNA families. Based on this information, we developed a structure prediction method that evaluates candidate structures for a set of homologous RNAs on their ability to reproduce the patterns exhibited by biological structures. The method is named SPuNC for ‘Structure Prediction using Nucleotide Composition’. In a performance test on a diverse set of RNA families we demonstrate that the SPuNC algorithm succeeds in selecting the most realistic structures in an ensemble. The average accuracy of top-scoring structures is significantly higher than the average accuracy of all ensemble members (improvements of more than 20% observed). In addition, a consensus structure that includes the most reliable base pairs gleaned from a set of top-scoring structures is generally more accurate than a consensus derived from the full structural ensemble. Our method achieves better accuracy than existing methods on several RNA families, including novel riboswitches and ribozymes. The results clearly show that nucleotide composition can be used to reveal the quality of RNA structures and thus the presented technique should be added to the set of prediction tools. PMID:19129237

Smit, S.; Knight, R.; Heringa, J.

2009-01-01

84

Alternative target functions for protein structure prediction with neural networks  

NASA Astrophysics Data System (ADS)

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

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

2004-04-01

85

Sampling bottlenecks in de novo protein structure prediction  

PubMed Central

Summary The primary obstacle to de novo protein structure prediction is conformational sampling: the native state generally has lower free energy than non-native structures but is exceedingly difficult to locate. Structure predictions with atomic level accuracy have been made for small proteins using the Rosetta structure prediction methodology, 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

2009-01-01

86

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. PMID:20409482

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

2010-01-01

87

Prediction of residual strength of impact damaged aerospace composite structures  

SciTech Connect

The importance of composites for aerospace structures is well known and therefore its increased use is being made for such structural components. However, these structures may be damaged as a result of various causes. One of the important causes is the impact damage either during manufacture or service. The amount of damage by impact created in the structure depends on several parameters such as impactor mass and velocity (impact energy), the structure material and support conditions. Since the magnitude of damage depends on impact energy, the residual strength may be expressed as a function of impact energy. Using a three parametric approach, a model is proposed to predict the residual strength behavior of impact damaged structure. The predicted behavior is shown to compare favorably with the available test data.

Garg, A.C. [Aerospace Technologies of Australia, Port Melbourne (Australia)

1993-12-31

88

Structure prediction of polyglutamine disease proteins: comparison of methods  

PubMed Central

Background The expansion of polyglutamine (poly-Q) repeats in several unrelated proteins is associated with at least ten neurodegenerative diseases. The length of the poly-Q regions plays an important role in the progression of the diseases. The number of glutamines (Q) is inversely related to the onset age of these polyglutamine diseases, and the expansion of poly-Q repeats has been associated with protein misfolding. However, very little is known about the structural changes induced by the expansion of the repeats. Computational methods can provide an alternative to determine the structure of these poly-Q proteins, but it is important to evaluate their performance before large scale prediction work is done. Results In this paper, two popular protein structure prediction programs, I-TASSER and Rosetta, have been used to predict the structure of the N-terminal fragment of a protein associated with Huntington's disease with 17 glutamines. Results show that both programs have the ability to find the native structures, but I-TASSER performs better for the overall task. Conclusions Both I-TASSER and Rosetta can be used for structure prediction of proteins with poly-Q repeats. Knowledge of poly-Q structure may significantly contribute to development of therapeutic strategies for poly-Q diseases. PMID:25080018

2014-01-01

89

Bayesian Model of Protein Primary Sequence for Secondary Structure Prediction  

PubMed Central

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

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

2014-01-01

90

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

PubMed

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

Kaján, László; Yachdav, Guy; Vicedo, Esmeralda; Steinegger, Martin; Mirdita, Milot; Angermüller, Christof; Böhm, Ariane; Domke, Simon; Ertl, Julia; Mertes, Christian; Reisinger, Eva; Staniewski, Cedric; Rost, Burkhard

2013-01-01

91

Adaptive modelling of structured molecular representations for toxicity prediction  

NASA Astrophysics Data System (ADS)

We investigated the possibility of modelling structure-toxicity relationships by direct treatment of the molecular structure (without using descriptors) through an adaptive model able to retain the appropriate structural information. With respect to traditional descriptor-based approaches, this provides a more general and flexible way to tackle prediction problems that is particularly suitable when little or no background knowledge is available. Our method employs a tree-structured molecular representation, which is processed by a recursive neural network (RNN). To explore the realization of RNN modelling in toxicological problems, we employed a data set containing growth impairment concentrations (IGC50) for Tetrahymena pyriformis.

Bertinetto, Carlo; Duce, Celia; Micheli, Alessio; Solaro, Roberto; Tiné, Maria Rosaria

2012-12-01

92

Sizing Structures and Predicting Weight of a Spacecraft  

NASA Technical Reports Server (NTRS)

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

Cerro, Jeffrey; Shore, C. P.

2006-01-01

93

Structure-based Methods for Computational Protein Functional Site Prediction  

PubMed Central

Due to the advent of high throughput sequencing techniques and structural genomic projects, the number of gene and protein sequences has been ever increasing. Computational methods to annotate these genes and proteins are even more indispensable. Proteins are important macromolecules and study of the function of proteins is an important problem in structural bioinformatics. This paper discusses a number of methods to predict protein functional site especially focusing on protein ligand binding site prediction. Initially, a short overview is presented on recent advances in methods for selection of homologous sequences. Furthermore, a few recent structural based approaches and sequence-and-structure based approaches for protein functional sites are discussed in details. PMID:24688745

Dukka, B KC

2013-01-01

94

Prediction of the secondary structure content of globular proteins based on structural classes  

Microsoft Academic Search

The prediction of the secondary structure content (a-helix and?-strand content) of a globular protein may play an important complementary role in the prediction of the protein's structure. We propose a new prediction algorithm based on Chou's database [Chou (1995),Proteins Struct. Fund Genet.21, 319]. The new algorithm is an improved multiple linear regression method, taking the nonlinear and coupling terms of

Chun-Ting Zhang; Ziding Zhang; Zhimin He

1996-01-01

95

Influence of sequence on the conformation of the B-DNA helix.  

PubMed Central

We have tried to ascertain whether the variability found in the conformational features of the 10 base steps in B-DNA is mainly due to the flanking sequences or to interactions with the environment. From an analysis of the twist parameter of the base-pair steps available from crystals of oligonucleotides and protein/oligonucleotide complexes, we conclude that in most cases the flanking sequences show little influence: the conformation of a DNA region results from the combination of the independent intrinsic features of each base step (average conformation and intrinsic variability), modulated by their interactions with the environment. Only in some cases (YR steps, in particular CG and CA/TG) does it appear that flanking sequences have an influence on the conformation of the central base step. The values obtained allow an approximation to the parameters expected for repetitive DNA sequences. In particular, it is found that poly[d(AG/CT)] should have a strongly alternating conformation, in agreement with recently reported oligonucleotide structures. PMID:9199797

Subirana, J A; Faria, T

1997-01-01

96

Workshop—Predicting the Structure of Biological Molecules  

PubMed Central

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

2004-01-01

97

Fast learning optimized prediction methodology for protein secondary structure prediction, relative solvent accessibility prediction and phosphorylation prediction  

Microsoft Academic Search

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

Saraswathi Sundararajan

2011-01-01

98

Prediction of reactive hazards based on molecular structure.  

PubMed

There is considerable interest in prediction of reactive hazards based on chemical structure. Calorimetric measurements to determine reactivity can be resource consuming, so computational methods to predict reactivity hazards present an attractive option. This paper reviews some of the commonly employed theoretical hazard evaluation techniques, including the oxygen-balance method, ASTM CHETAH, and calculated adiabatic reaction temperature (CART). It also discusses the development of a study table to correlate and predict calorimetric properties of pure compounds. Quantitative structure-property relationships (QSPR) based on quantum mechanical calculations can be employed to correlate calorimetrically measured onset temperatures, T(o), and energies of reaction, -deltaH, with molecular properties. To test the feasibility of this approach, the QSPR technique is used to correlate differential scanning calorimeter (DSC) data, T(o) and -deltaH, with molecular properties for 19 nitro compounds. PMID:12628775

Saraf, S R; Rogers, W J; Mannan, M S

2003-03-17

99

Structural Damage Prediction and Analysis for Hypervelocity Impact: Consulting  

NASA Technical Reports Server (NTRS)

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

1995-01-01

100

Computational Predictions of Structures of Multichromosomes of Budding Yeast  

E-print Network

Computational Predictions of Structures of Multichromosomes of Budding Yeast (Accepted, Conf Proc of budding yeast nucleus. We successfully generated a large number of model genomes of yeast with appropriate yeast genome realistically. The model developed here provides a general computational framework

Liang, Jie

101

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

102

CRISPR revisited: structure prediction of CRISPR repeats Sita Lange1  

E-print Network

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

Will, Sebastian

103

Structurally adaptive RBF network in nonstationary time series prediction  

Microsoft Academic Search

A sequentially adaptive radial basis function (RBF) network is applied to the nonstationary, time series prediction. Sequential adaptation of parameters and structure is achieved using an extended Kalman filter criterion for network growing is obtained from the Kalman filter's consistency test. The Optimal Brain Surgeon and Optimal Brain Damage pruning methods are derived for networks which parameters are estimated by

B. Todorovic; M. Stankovic; S. Todorovic-Zarkula

2000-01-01

104

Predicting protein structures with a multiplayer online game  

E-print Network

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

Baker, David

105

I-TASSER server for protein 3D structure prediction  

PubMed Central

Background Prediction of 3-dimensional protein structures from amino acid sequences represents one of the most important problems in computational structural biology. The community-wide Critical Assessment of Structure Prediction (CASP) experiments have been designed to obtain an objective assessment of the state-of-the-art of the field, where I-TASSER was ranked as the best method in the server section of the recent 7th CASP experiment. Our laboratory has since then received numerous requests about the public availability of the I-TASSER algorithm and the usage of the I-TASSER predictions. Results An on-line version of I-TASSER is developed at the KU Center for Bioinformatics which has generated protein structure predictions for thousands of modeling requests from more than 35 countries. A scoring function (C-score) based on the relative clustering structural density and the consensus significance score of multiple threading templates is introduced to estimate the accuracy of the I-TASSER predictions. A large-scale benchmark test demonstrates a strong correlation between the C-score and the TM-score (a structural similarity measurement with values in [0, 1]) of the first models with a correlation coefficient of 0.91. Using a C-score cutoff > -1.5 for the models of correct topology, both false positive and false negative rates are below 0.1. Combining C-score and protein length, the accuracy of the I-TASSER models can be predicted with an average error of 0.08 for TM-score and 2 Å for RMSD. Conclusion The I-TASSER server has been developed to generate automated full-length 3D protein structural predictions where the benchmarked scoring system helps users to obtain quantitative assessments of the I-TASSER models. The output of the I-TASSER server for each query includes up to five full-length models, the confidence score, the estimated TM-score and RMSD, and the standard deviation of the estimations. The I-TASSER server is freely available to the academic community at . PMID:18215316

Zhang, Yang

2008-01-01

106

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

2007-11-01

107

Protein structure prediction: assembly of secondary structure elements by basin-hopping.  

PubMed

The prediction of protein tertiary structure from primary structure remains a challenging task. One possible approach to this problem is the application of basin-hopping global optimization combined with an all-atom force field. In this work, the efficiency of basin-hopping is improved by introducing an approach that derives tertiary structures from the secondary structure assignments of individual residues. This approach is termed secondary-to-tertiary basin-hopping and benchmarked for three miniproteins: trpzip, trp-cage and ER-10. For each of the three miniproteins, the secondary-to-tertiary basin-hopping approach successfully and reliably predicts their three-dimensional structure. When it is applied to larger proteins, correctly folded structures are obtained. It can be concluded that the assembly of secondary structure elements using basin-hopping is a promising tool for de novo protein structure prediction. PMID:25056272

Hoffmann, Falk; Vancea, Ioan; Kamat, Sanjay G; Strodel, Birgit

2014-10-20

108

Automatic prediction of facial trait judgments: appearance vs. structural models.  

PubMed

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

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

2011-01-01

109

Structure-based prediction of bZIP partnering specificity.  

PubMed

Predicting protein interaction specificity from sequence is an important goal in computational biology. We present a model for predicting the interaction preferences of coiled-coil peptides derived from bZIP transcription factors that performs very well when tested against experimental protein microarray data. We used only sequence information to build atomic-resolution structures for 1711 dimeric complexes, and evaluated these with a variety of functions based on physics, learned empirical weights or experimental coupling energies. A purely physical model, similar to those used for protein design studies, gave reasonable performance. The results were improved significantly when helix propensities were used in place of a structurally explicit model to represent the unfolded reference state. Further improvement resulted upon accounting for residue-residue interactions in competing states in a generic way. Purely physical structure-based methods had difficulty capturing core interactions accurately, especially those involving polar residues such as asparagine. When these terms were replaced with weights from a machine-learning approach, the resulting model was able to correctly order the stabilities of over 6000 pairs of complexes with greater than 90% accuracy. The final model is physically interpretable, and suggests specific pairs of residues that are important for bZIP interaction specificity. Our results illustrate the power and potential of structural modeling as a method for predicting protein interactions and highlight obstacles that must be overcome to reach quantitative accuracy using a de novo approach. Our method shows unprecedented performance in predicting protein-protein interaction specificity accurately using structural modeling and suggests that predicting coiled-coil interactions generally may be within reach. PMID:16359704

Grigoryan, Gevorg; Keating, Amy E

2006-02-01

110

Symmetry building Monte Carlo-based crystal structure prediction  

NASA Astrophysics Data System (ADS)

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

Michel, Kyle Jay; Wolverton, C.

2014-05-01

111

Ab Initio Prediction of Transcription Factor Targets Using Structural Knowledge  

PubMed Central

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

Kaplan, Tommy; Friedman, Nir; Margalit, Hanah

2005-01-01

112

Structure prediction and targeted synthesis: a new Na(n)N2 diazenide crystalline structure.  

PubMed

Significant progress in theoretical and computational techniques for predicting stable crystal structures has recently begun to stimulate targeted synthesis of such predicted structures. Using a global space-group optimization (GSGO) approach that locates ground-state structures and stable stoichiometries from first-principles energy functionals by objectively starting from randomly selected lattice vectors and random atomic positions, we predict the first alkali diazenide compound Na(n)N(2), manifesting homopolar N-N bonds. The previously predicted Na(3)N structure manifests only heteropolar Na-N bonds and has positive formation enthalpy. It was calculated based on local Hartree-Fock relaxation of a fixed-structure type (Li(3)P-type) found by searching an electrostatic point-ion model. Synthesis attempts of this positive ?H compound using activated nitrogen yielded another structure (anti-ReO(3)-type). The currently predicted (negative formation enthalpy) diazenide Na(2)N(2) completes the series of previously known BaN(2) and SrN(2) diazenides where the metal sublattice transfers charge into the empty N(2) ?(g) orbital. This points to a new class of alkali nitrides with fundamentally different bonding, i.e., homopolar rather than heteropolar bonds and, at the same time, illustrates some of the crucial subtleties and pitfalls involved in structure predictions versus planned synthesis. Attempts at synthesis of the stable Na(2)N(2) predicted here will be interesting. PMID:21090865

Zhang, Xiuwen; Zunger, Alex; Trimarchi, Giancarlo

2010-11-21

113

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?. PMID:18487293

Nicosia, Giuseppe; Stracquadanio, Giovanni

2008-01-01

114

PDBalert: automatic, recurrent remote homology tracking and protein structure prediction  

PubMed Central

Background During the last years, methods for remote homology detection have grown more and more sensitive and reliable. Automatic structure prediction servers relying on these methods can generate useful 3D models even below 20% sequence identity between the protein of interest and the known structure (template). When no homologs can be found in the protein structure database (PDB), the user would need to rerun the same search at regular intervals in order to make timely use of a template once it becomes available. Results PDBalert is a web-based automatic system that sends an email alert as soon as a structure with homology to a protein in the user's watch list is released to the PDB database or appears among the sequences on hold. The mail contains links to the search results and to an automatically generated 3D homology model. The sequence search is performed with the same software as used by the very sensitive and reliable remote homology detection server HHpred, which is based on pairwise comparison of Hidden Markov models. Conclusion PDBalert will accelerate the information flow from the PDB database to all those who can profit from the newly released protein structures for predicting the 3D structure or function of their proteins of interest. PMID:19025670

Agarwal, Vatsal; Remmert, Michael; Biegert, Andreas; Soding, Johannes

2008-01-01

115

Virality prediction and community structure in social networks.  

PubMed

How does network structure affect diffusion? Recent studies suggest that the answer depends on the type of contagion. Complex contagions, unlike infectious diseases (simple contagions), are affected by social reinforcement and homophily. Hence, the spread within highly clustered communities is enhanced, while diffusion across communities is hampered. A common hypothesis is that memes and behaviors are complex contagions. We show that, while most memes indeed spread like complex contagions, a few viral memes spread across many communities, like diseases. We demonstrate that the future popularity of a meme can be predicted by quantifying its early spreading pattern in terms of community concentration. The more communities a meme permeates, the more viral it is. We present a practical method to translate data about community structure into predictive knowledge about what information will spread widely. This connection contributes to our understanding in computational social science, social media analytics, and marketing applications. PMID:23982106

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

2013-01-01

116

Structural transitions in hypersphere fluids: predictions of Kirkwood's approximation  

E-print Network

We use an analytic criterion for vanishing of exponential damping of correlations developed previously (Piasecki et al, J. Chem. Phys., 133, 164507, 2010) to determine the threshold volume fractions for structural transitions in hard sphere systems in dimensions D=3,4,5 and 6, proceeding from the YBG hierarchy and using the Kirkwood superposition approximation. We conclude that the theory does predict phase transitions in qualitative agreement with numerical studies. We also derive, within the superposition approximation, the asymptotic form of the analytic condition for occurence of a structural transition in the D->Infinity limit .

Jaroslaw Piasecki; Piotr Szymczak; John J. Kozak

2011-08-15

117

Fiber composite structural durability and damage tolerance - Simplified predictive methods  

NASA Technical Reports Server (NTRS)

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

Chamis, Christos C.; Ginty, Carol A.

1989-01-01

118

Fiber composite structural durability and damage tolerance: Simplified predictive methods  

NASA Technical Reports Server (NTRS)

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

Chamis, Christos C.; Ginty, Carol A.

1987-01-01

119

Numerical predictions of tuned liquid tank structural systems  

NASA Astrophysics Data System (ADS)

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

Frandsen, J. B.

2005-04-01

120

Can structure predict function in the human brain?  

PubMed

Over the past decade, scientific interest in the properties of large-scale spontaneous neural dynamics has intensified. Concurrently, novel technologies have been developed for characterizing the connective anatomy of intra-regional circuits and inter-regional fiber pathways. It will soon be possible to build computational models that incorporate these newly detailed structural network measurements to make predictions of neural dynamics at multiple scales. Here, we review the practicality and the value of these efforts, while at the same time considering in which cases and to what extent structure does determine neural function. Studies of the healthy brain, of neural development, and of pathology all yield examples of direct correspondences between structural linkage and dynamical correlation. Theoretical arguments further support the notion that brain network topology and spatial embedding should strongly influence network dynamics. Although future models will need to be tested more quantitatively and against a wider range of empirical neurodynamic features, our present large-scale models can already predict the macroscopic pattern of dynamic correlation across the brain. We conclude that as neuroscience grapples with datasets of increasing completeness and complexity, and attempts to relate the structural and functional architectures discovered at different neural scales, the value of computational modeling will continue to grow. PMID:20116438

Honey, Christopher J; Thivierge, Jean-Philippe; Sporns, Olaf

2010-09-01

121

Functional structure of biological communities predicts ecosystem multifunctionality.  

PubMed

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

Mouillot, David; Villéger, Sébastien; Scherer-Lorenzen, Michael; Mason, Norman W H

2011-01-01

122

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 PMID:20505003

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

2010-01-01

123

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

NASA Astrophysics Data System (ADS)

In this paper, an atomistic geometrical model for the B-DNA configuration is explained. This model accounts for five organization levels of the DNA, up to the 30 nm chromatin fiber. However, fragments of this fiber can be used to construct the whole genome. The algorithm developed in this work is capable to determine which is the closest atom with respect to an arbitrary point in space. It can be used in any application in which a DNA geometrical model is needed, for instance, in investigations related to the effects of ionizing radiations on the human genetic material. Successful consistency checks were carried out to test the proposed model. Catalogue identifier: AEPZ_v1_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEPZ_v1_0.html Program obtainable from: CPC Program Library, Queen’s University, Belfast, N. Ireland Licensing provisions: Standard CPC licence, http://cpc.cs.qub.ac.uk/licence/licence.html No. of lines in distributed program, including test data, etc.: 1245 No. of bytes in distributed program, including test data, etc.: 6574 Distribution format: tar.gz Programming language: FORTRAN. Computer: Any. Operating system: Multi-platform. RAM: 2 Gb Classification: 3. Nature of problem: The Monte Carlo method is used to simulate the interaction of ionizing radiation with the human genetic material in order to determine DNA damage yields per unit absorbed dose. To accomplish this task, an algorithm to determine if a given energy deposition lies within a given target is needed. This target can be an atom or any other structure of the genetic material. Solution method: This is a stand-alone subroutine describing an atomic-resolution geometrical model of the B-DNA configuration. It is able to determine the closest atom to an arbitrary point in space. This model accounts for five organization levels of the human genetic material, from the nucleotide pair up to the 30 nm chromatin fiber. This subroutine carries out a series of coordinate transformations to find which is the closest atom containing an arbitrary point in space. Atom sizes are according to the corresponding van der Waals radii. Restrictions: The geometrical model presented here does not include the chromosome organization level but it could be easily build up by using fragments of the 30 nm chromatin fiber. Unusual features: To our knowledge, this is the first open source atomic-resolution DNA geometrical model developed for DNA-radiation interaction Monte Carlo simulations. In our tests, the current model took into account the explicit position of about 56×106 atoms, although the user may enhance this amount according to the necessities. Running time: This subroutine can process about 2 million points within a few minutes in a typical current computer.

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

2013-12-01

124

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

125

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

E-print Network

Transition between B-DNA and Z-DNA: Free Energy Landscape for the B-Z Junction Propagation Juyong, and Department of Molecular Cell Biology, Sungkyunkwan UniVersity School of Medicine, Suwon 440-746, Korea ReceiVed: April 16, 2010; ReVised Manuscript ReceiVed: May 31, 2010 Canonical, right-handed B-DNA can

Seok, Chaok

126

Recognition of B-DNA by Neomycin-Hoechst 33258 Conjugates Bert Willis and Dev P. Arya*  

E-print Network

, an aminoglycoside antibiotic, with the B-DNA minor groove binding ligand Hoechst 33258. Described herein are novel of the spatial differences that define B-DNA binding. Spectroscopic studies such as ultraviolet (UV) melting stabilization of A,T rich duplexes when compared to Hoechst 33258. Neomycin is an aminoglycoside antibiotic

Stuart, Steven J.

127

Improved thermodynamic parameters for prediction of structure H hydrate equilibria  

SciTech Connect

An improved set of all the thermodynamic and molecular properties required for the prediction of the existing 20 systems of structure H (sH) hydrate phase equilibrium data is presented. The statistical thermodynamics model was based on the van der Waals and Platteeuw theory, and the spherical core Kihara potential was used for guest-water interactions. Optimized Kihara parameters and reference thermodynamic properties were derived from experimental data of over 20 sH hydrate forming systems. The model could fit all the existing sH hydrate data within an accuracy of {+-}6%. Inhibitor predictions were also shown to fit recent data with no adjustable parameters. The feasibility of using hydrate cage occupancies to derive refined Kihara parameters of the guest molecules was investigated. Possible existence of sH hydrates at cryogenic temperatures was also established based on the model.

Mehta, A.P.; Sloan, E.D. [Colorado School of Mines, Golden, CO (United States)] [Colorado School of Mines, Golden, CO (United States)

1996-07-01

128

Brain white matter structural properties predict transition to chronic pain  

PubMed Central

Neural mechanisms mediating the transition from acute to chronic pain remain largely unknown. In a longitudinal brain imaging study, we followed patients with a single subacute back pain (SBP) episode for over one year as their pain subsided (SBPr), or persisted (SBPp) representing a transition to chronic pain. We discovered brain white-matter structural abnormalities (in n=24 SBP; 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 (in n=22 SBP; SBPp=11 and SBPr=11), and showed no further alterations over a one-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 to 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 prior to the back pain inciting event and independent of the back pain, predisposes subjects to pain chronification. PMID:24040975

Mansour, Ali; Baliki, Marwan N.; Huang, Lejian; Torbey, Souraya; Herrmann, K.; Schnitzer, Thomas J.; Apkarian, A. Vania

2013-01-01

129

Understanding and Predicting Protein Assemblies With 3D Structures  

PubMed Central

Protein interactions are central to most biological processes, and are currently the subject of great interest. Yet despite the many recently developed methods for interaction discovery, little attention has been paid to one of the best sources of data: complexes of known three-dimensional (3D) structure. Here we discuss how such complexes can be used to study and predict protein interactions and complexes, and to interrogate interaction networks proposed by methods such as two-hybrid screens or affinity purifications. PMID:18629088

Aloy, Patrick

2003-01-01

130

Failure prediction of thin beryllium sheets used in spacecraft structures  

NASA Technical Reports Server (NTRS)

In an attempt to predict failure for cross-rolled beryllium sheet structures, high order macroscopic failure criteria are used. These require the knowledge of in-plane uniaxial and shear strengths. Test results are included for in-plane biaxial tension, uniaxial compression for two different material orientations, and shear. All beryllium specimens have the same chemical composition. In addition, all experimental work was performed in a controlled laboratory environment. Numerical simulation complements these tests. A brief bibliography supplements references listed in a previous report.

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

1991-01-01

131

Predicting fracture in micron-scale polycrystalline silicon MEMS structures.  

SciTech Connect

Designing reliable MEMS structures presents numerous challenges. Polycrystalline silicon fractures in a brittle manner with considerable variability in measured strength. Furthermore, it is not clear how to use a measured tensile strength distribution to predict the strength of a complex MEMS structure. To address such issues, two recently developed high throughput MEMS tensile test techniques have been used to measure strength distribution tails. The measured tensile strength distributions enable the definition of a threshold strength as well as an inferred maximum flaw size. The nature of strength-controlling flaws has been identified and sources of the observed variation in strength investigated. A double edge-notched specimen geometry was also tested to study the effect of a severe, micron-scale stress concentration on the measured strength distribution. Strength-based, Weibull-based, and fracture mechanics-based failure analyses were performed and compared with the experimental results.

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

2010-09-01

132

Methods for evaluating the predictive accuracy of structural dynamic models  

NASA Technical Reports Server (NTRS)

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

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

1990-01-01

133

Simulating regime structures in weather and climate prediction models  

NASA Astrophysics Data System (ADS)

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

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

2012-11-01

134

The systematic structure and predictability of urban business diversity  

E-print Network

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

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

2014-01-01

135

Generic eukaryotic core promoter prediction using structural features of DNA  

PubMed Central

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

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

2008-01-01

136

Nonequilibrium Structure Factor for Diffusive Kinetics: New Predictions for Disordering.  

NASA Astrophysics Data System (ADS)

We have recently obtained^2thanksJ.H. Luscombe and M. Luban, submitted to Physical Review E.an exact expression for the nonequilibrium elastic-scattering structure factor, S(q,t), for the one-dimensional spin-conserving kinetic Ising model subjected to a sudden temperature increase. Although our results pertain explicitly to one dimension, as we will discuss, owing to the common constraint of the conserved number of particles, many of the qualitative features we have obtained for S(q,t) should generalize to two dimensions and be observable in the disordering of adsorbed particles. For example, we predict that for antiferromagnetic interactions, an initial Bragg peak will decay exponentially in t while approximately preserving its shape in q-space, before giving way to a t-1 decay. After the Bragg peak has decayed sufficiently, we predict that S(q,t)?\\chi(q)F(t), where \\chi(q) is the final, equilibrium structure factor and F slowly approaches unity, as t-1 for long times.

Luscombe, J. H.; Luban, M.

1996-03-01

137

PREDICTED STRUCTURE AND BINDING MOTIFS OF COLLAGEN ?1(XI)  

PubMed Central

The amino propeptide of collagen ?1(XI) (NPP) has been shown to bind glycosaminoglycans and to form a dimer. While these are independent biochemical events, it is likely that dimerization facilitates the interaction with glycosaminoglycans or alternatively, that glycosaminoglycan interaction facilitates the formation of an NPP:NPP dimer. The computer program MODELLER was used to generate a homology model of the collagen ?1(XI) NPP monomer using the crystal structure of the closely related noncollagenous-4 (NC4) domain of collagen ?1(IX) (PDB:2UUR) as the template. Additionally, a dimer model of collagen ?1(XI) NPP domain was created based upon the thrombospondin dimer template (PDB:1Z78). The structure of the dimer created in MODELLER was validated by comparison to a dimer model generated by docking two monomers of PDB:2UUR using ClusPro. Calculations of relative binding energy for the interaction between each collagen ?1(XI) NPP model and glycosaminoglycans as ligands was performed using AutoDock4. Computational results support a higher affinity between heparan sulfate and a dimer compared to a monomer. These findings are supported by affinity chromatography experiments in which distinct monomer and dimer peaks were observed. Sequential point mutation studies of the putative binding site (147-KKKITK-152) indicated the importance of the basic lysine residue for binding to heparan sulfate. Two orders of magnitude change in binding affinity was predicted when comparing wild type to the mutation K152A. Experimental data supports the predicted change in affinity. PMID:25309886

McDougal, Owen M.; Warner, Lisa R.; Mallory, Chris; Oxford, Julia Thom

2013-01-01

138

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

PubMed Central

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

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

2014-01-01

139

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

PubMed Central

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

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

2014-01-01

140

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

PubMed

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

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

2014-10-29

141

Molecular dynamics simulations of B-DNA: an analysis of the role of initial molecular configuration, randomly assigned velocity distribution, long integration times, and nonconstrained termini.  

PubMed

Molecular dynamics simulations of three DNA sequences using the AMBER 3.0 force field were performed with implicit inclusion of water through a distance-dependent dielectric constant and solvated counterions. Simulations of the self-complementary DNA dodecamer d(CGCGAATTCGCG) were started from a regular B-DNA structure and the x-ray single crystal B-DNA structure. Although mean convergence during the 89-ps calculation was confirmed, localized differences in backbone torsionals and base-pair helicoidals were observed. A nanosecond simulation of the nonself-complementary 14 base-pair DNA d(GGCGGAATTGGCGG) indicates that most structural parameters stabilize within the first 100-200 ps, while isolated features show low-frequency oscillations throughout the calculation. The lack of harmonic constraints on the ends of the molecules was shown not to perturb the structural dynamics of the internal oligonucleotide beyond the external 2 base pairs. Comparison of three simulations of the nonself-complementary 14 base-pair DNA d(GGCGAAATTCGCGG), identical in all respects other than the assignment of initial Maxwellian atomic velocity distributions, revealed the inherent systematic variability. The three calculations result in nearly superimposable global structures, with localized variations in torsionals and helicoidals. Our results provide a basis for performing a comparative analysis of the effect of DNA sequence on localized structure. PMID:8461453

Falsafi, S; Reich, N O

1993-03-01

142

The experimental search for new predicted binary-alloy structures  

NASA Astrophysics Data System (ADS)

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

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

2010-10-01

143

Alpha-helical topology and tertiary structure prediction in globular proteins  

Microsoft Academic Search

Within the field of protein structure prediction, the packing of alpha-helical proteins has been one of the more difficult problems. Distance constraints and topology predictions can be highly useful for reducing the conformational space that must be searched to find a protein structure of minimum conformational energy. We present a novel first principles framework to predict the structure of alpha-helical

Scott R. McAllister; Christodoulos A. Floudas

2007-01-01

144

New Secondary Structure Prediction software package using automatically trained Bayesian Networks  

E-print Network

New Secondary Structure Prediction software package using automatically trained Bayesian Networks software package for prediction of secondary structure of proteins. Two main contribution are presented: A novel approach to protein secondary structure prediction based on the usage of Bayesian Networks whose

145

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

E-print Network

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

Sperduti, Alessandro

146

The Experimental Search for New Predicted Binary-alloy Structures  

NASA Astrophysics Data System (ADS)

Predicting new ordered phases in metallic alloys is a productive line of inquiry because configurational ordering in an alloy can dramatically alter their useful material properties. One is able to infer the existence of an ordered phase in an alloy using first-principles calculated formation enthalpies.ootnotetextG. L. W. Hart, ``Where are Nature's missing structures?'' Nature Materials 6 941-945 2007 Using this approach, we have been able to identify stable (i.e. lowest energy) orderings in a variety of binary Pt-based alloys, many of which have never before been observed experimentally. After preparing alloys of the desired composition, we performed synchrotron x-ray powder diffraction experiments to prove or disprove the expected orderings.

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

2011-10-01

147

Structural Acoustic Prediction and Interior Noise Control Technology  

NASA Technical Reports Server (NTRS)

This report documents the results of Task 14, "Structural Acoustic Prediction and Interior Noise Control Technology". The task was to evaluate the performance of tuned foam elements (termed Smart Foam) both analytically and experimentally. Results taken from a three-dimensional finite element model of an active, tuned foam element are presented. Measurements of sound absorption and sound transmission loss were taken using the model. These results agree well with published data. Experimental performance data were taken in Boeing's Interior Noise Test Facility where 12 smart foam elements were applied to a 757 sidewall. Several configurations were tested. Noise reductions of 5-10 dB were achieved over the 200-800 Hz bandwidth of the controller. Accelerometers mounted on the panel provided a good reference for the controller. Configurations with far-field error microphones outperformed near-field cases.

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

2001-01-01

148

Prediction of silicon-based layered structures for optoelectronic applications.  

PubMed

A method based on the particle swarm optimization algorithm is presented to design quasi-two-dimensional materials. With this development, various single-layer and bilayer materials of C, Si, Ge, Sn, and Pb were predicted. A new Si bilayer structure is found to have a more favored energy than the previously widely accepted configuration. Both single-layer and bilayer Si materials have small band gaps, limiting their usages in optoelectronic applications. Hydrogenation has therefore been used to tune the electronic and optical properties of Si layers. We discover two hydrogenated materials of layered Si8H2 and Si6H2 possessing quasidirect band gaps of 0.75 and 1.59 eV, respectively. Their potential applications for light-emitting diode and photovoltaics are proposed and discussed. Our study opened up the possibility of hydrogenated Si layered materials as next-generation optoelectronic devices. PMID:25314126

Luo, Wei; Ma, Yanming; Gong, Xingao; Xiang, Hongjun

2014-11-12

149

Optimizing Non-Decomposable Loss Functions in Structured Prediction  

PubMed Central

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

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

2012-01-01

150

High Precision Prediction of Functional Sites in Protein Structures  

PubMed Central

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

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

2014-01-01

151

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

152

Engineering Property Prediction Tools for Tailored Polymer Composite Structures  

SciTech Connect

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

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

2009-12-23

153

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

154

RNA secondary structure prediction using dynamic programming algorithm — A review and proposed work  

Microsoft Academic Search

Ribonucleic acid (RNA) plays a fundamental and important role in cellular life forms and their function is directly related to their structure. RNA secondary structure prediction is a significant area of study for many scientists seeking insights into potential drug interactions or innovative new treatment methodologies. Predicting structure can overcome many issues related with physical structure determination and their study

Mohd Nizam Osman; Rosni Abdullah; Nuraini AbdulRashid

2010-01-01

155

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

E-print Network

Protein Structure Prediction using String Kernels Huzefa Rangwala, Kevin DeRonne, George Karypis produce structural and functional information. Consequently, researchers increasingly rely on computational techniques to extract useful information from known structures contained in large databases

Minnesota, University of

156

Predictive modeling of pedestal structure in KSTAR using EPED model  

SciTech Connect

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

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

2013-10-15

157

Predictive modeling of pedestal structure in KSTAR using EPED model  

NASA Astrophysics Data System (ADS)

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

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

2013-10-01

158

Prediction of a Structural Transition in the Hard Disk Fluid  

E-print Network

Starting from the second equilibrium equation in the BBGKY hierarchy under the Kirkwood superposition closure, we implement a new method for studying the asymptotic decay of correlations in the hard disk fluid in the high density regime. From our analysis and complementary numerical studies, we find that exponentially damped oscillations can occur only up to a packing fraction {\\eta}*~0.718, a value which is in substantial agreement with the packing fraction, {\\eta}~0.723, believed to characterize the transition from the ordered solid phase to a dense fluid phase, as inferred from Mak's Monte Carlo simulations [Phys. Rev. E 73, 065104 (2006)]. We next show that the same method of analysis predicts that exponential damping of oscillations in the hard sphere fluid becomes impossible when \\lambda = 4n\\pi {\\sigma}^3 [1 + H(1)]>/- 34.81, where H(1) is the contact value of the correlation function, n is the number density and {\\sigma} is the sphere diameter, in exact agreement with the condition, \\lambda >/- 34.8, first reported in a numerical study of the Kirkwood equation by Kirkwood et al. [J. Chem. Phys. 18, 1040 (1950)]. Finally, we show that our method confirms the absence of any structural transition in hard rods for the entire range of densities below close packing.

Jaroslaw Piasecki; Piotr Szymczak; John J. Kozak

2010-09-16

159

LocARNA-P: Accurate boundary prediction and improved detection of structural RNAs  

E-print Network

Current genomic screens for noncoding RNAs (ncRNAs) predict a large number of genomic regions containing potential structural ncRNAs. The analysis of these data requires highly accurate prediction of ncRNA boundaries and ...

Joshi, T.

160

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

161

Structure-borne sound Why analyse and predict vibro-acoustic behaviour  

E-print Network

Mechanics Thermo-dyn. Fluid-dyn. Hydraulics Dynamics Acoustics Dynamics Acoustics Acoustics Toolboxes1 Structure-borne sound · Why analyse and predict vibro-acoustic behaviour Structure-borne sound · Why analyse and predict vibro-acoustic behaviour ­ Structural strength · Displacement ~ Strain ­ Sound

Berlin,Technische Universität

162

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

Microsoft Academic Search

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

Alessio Ceroni; Paolo Frasconi; Andrea Passerini; Alessandro Vullo

2003-01-01

163

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

E-print Network

Data Mining Approach to Ab-Initio Prediction of Crystal Structure Dane Morgan, Gerbrand Ceder are a powerful tool for predicting crystal structure, but are too slow to explore the extremely large space of possible structures for new alloys. Here we describe ongoing work on a novel method (Data Mining of Quantum

Curtarolo, Stefano

164

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

165

Towards Fully Automated Structure-Based Function Prediction In Structural Genomics: A Case Study  

PubMed Central

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

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

2007-01-01

166

A Dynamic Programming Algorithm for Circular Single-stranded DNA Tiles Secondary Structure Prediction  

E-print Network

DNA computing, and PCR-based applications. DNA secondary structure prediction is the key part for these DNA nanotechnologies. In this paper, we present a dynamic programming algorithm to predict the secondary structure of single-stranded DNA tiles. The algorithm calculates all possible maximum matches based on the nearest-neighbour model and global energy minimization. Experimental results show that the algorithm performers significantly to predict secondary structures for single-stranded DNA tiles.

Zhang Kai; Huang Xinquan; Shi Xiaolong; Qiang Xiaoli; Song Tao; Shi Xinzhu; Chen Zhihua

2013-01-01

167

Improvement of RNA secondary structure prediction using RNase H cleavage and randomized oligonucleotides  

PubMed Central

RNA secondary structure prediction using free energy minimization is one method to gain an approximation of structure. Constraints generated by enzymatic mapping or chemical modification can improve the accuracy of secondary structure prediction. We report a facile method that identifies single-stranded regions in RNA using short, randomized DNA oligonucleotides and RNase H cleavage. These regions are then used as constraints in secondary structure prediction. This method was used to improve the secondary structure prediction of Escherichia coli 5S rRNA. The lowest free energy structure without constraints has only 27% of the base pairs present in the phylogenetic structure. The addition of constraints from RNase H cleavage improves the prediction to 100% of base pairs. The same method was used to generate secondary structure constraints for yeast tRNAPhe, which is accurately predicted in the absence of constraints (95%). Although RNase H mapping does not improve secondary structure prediction, it does eliminate all other suboptimal structures predicted within 10% of the lowest free energy structure. The method is advantageous over other single-stranded nucleases since RNase H is functional in physiological conditions. Moreover, it can be used for any RNA to identify accessible binding sites for oligonucleotides or small molecules. PMID:19596816

Kauffmann, Andrew D.; Campagna, Ryan J.; Bartels, Chantal B.; Childs-Disney, Jessica L.

2009-01-01

168

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

169

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...

170

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

E-print Network

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

Mag Washietl, Stefan

171

Predicting the electronic structure of weakly interacting hybrid systems: The example of nanosized peapod structures  

NASA Astrophysics Data System (ADS)

We provide a simple scheme for predicting the electronic structure of van der Waals bound systems, based on the mere knowledge of the electronic structure of the subunits. We demonstrate this with the example of nanopeapods, consisting of polythiophene encapsulated in single-wall carbon nanotubes. Using density functional theory we disentangle the contributions to the level alignment. The main contribution is shown to be given by the ionization potential of the polymer inside the host, which in turn is determined by the curvature of the tube. Only a small correction arises from charge redistributions within the domains of the constituents. Polarization effects turn out to be minor due to the cylindrical geometry of the peapods and their dielectric characteristics. Our findings open the possibility of designing optoelectronic properties of such complex materials.

Milko, Matus; Puschnig, Peter; Draxl, Claudia

2012-10-01

172

Aircraft Structural Mass Property Prediction Using Conceptual-Level Structural Analysis  

NASA Technical Reports Server (NTRS)

This paper describes a methodology that extends the use of the Equivalent LAminated Plate Solution (ELAPS) structural analysis code from conceptual-level aircraft structural analysis to conceptual-level aircraft mass property analysis. Mass property analysis in aircraft structures has historically depended upon parametric weight equations at the conceptual design level and Finite Element Analysis (FEA) at the detailed design level. ELAPS allows for the modeling of detailed geometry, metallic and composite materials, and non-structural mass coupled with analytical structural sizing to produce high-fidelity mass property analyses representing fully configured vehicles early in the design process. This capability is especially valuable for unusual configuration and advanced concept development where existing parametric weight equations are inapplicable and FEA is too time consuming for conceptual design. This paper contrasts the use of ELAPS relative to empirical weight equations and FEA. ELAPS modeling techniques are described and the ELAPS-based mass property analysis process is detailed. Examples of mass property stochastic calculations produced during a recent systems study are provided. This study involved the analysis of three remotely piloted aircraft required to carry scientific payloads to very high altitudes at subsonic speeds. Due to the extreme nature of this high-altitude flight regime, few existing vehicle designs are available for use in performance and weight prediction. ELAPS was employed within a concurrent engineering analysis process that simultaneously produces aerodynamic, structural, and static aeroelastic results for input to aircraft performance analyses. The ELAPS models produced for each concept were also used to provide stochastic analyses of wing structural mass properties. The results of this effort indicate that ELAPS is an efficient means to conduct multidisciplinary trade studies at the conceptual design level.

Sexstone, Matthew G.

1998-01-01

173

Aircraft Structural Mass Property Prediction Using Conceptual-Level Structural Analysis  

NASA Technical Reports Server (NTRS)

This paper describes a methodology that extends the use of the Equivalent LAminated Plate Solution (ELAPS) structural analysis code from conceptual-level aircraft structural analysis to conceptual-level aircraft mass property analysis. Mass property analysis in aircraft structures has historically depended upon parametric weight equations at the conceptual design level and Finite Element Analysis (FEA) at the detailed design level ELAPS allows for the modeling of detailed geometry, metallic and composite materials, and non-structural mass coupled with analytical structural sizing to produce high-fidelity mass property analyses representing fully configured vehicles early in the design process. This capability is especially valuable for unusual configuration and advanced concept development where existing parametric weight equations are inapplicable and FEA is too time consuming for conceptual design. This paper contrasts the use of ELAPS relative to empirical weight equations and FEA. ELAPS modeling techniques are described and the ELAPS-based mass property analysis process is detailed Examples of mass property stochastic calculations produced during a recent systems study are provided This study involved the analysis of three remotely piloted aircraft required to carry scientific payloads to very high altitudes at subsonic speeds. Due to the extreme nature of this high-altitude flight regime,few existing vehicle designs are available for use in performance and weight prediction. ELAPS was employed within a concurrent engineering analysis process that simultaneously produces aerodynamic, structural, and static aeroelastic results for input to aircraft performance analyses. The ELAPS models produced for each concept were also used to provide stochastic analyses of wing structural mass properties. The results of this effort indicate that ELAPS is an efficient means to conduct multidisciplinary trade studies at the conceptual design level.

Sexstone, Matthew G.

1998-01-01

174

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

Microsoft Academic Search

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

Naomi Siew; Arne Elofsson; Leszek Rychlewski; Daniel Fischer

2000-01-01

175

Metabolism site prediction based on xenobiotic structural formulas and PASS prediction algorithm.  

PubMed

A new ligand-based method for the prediction of sites of metabolism (SOMs) for xenobiotics has been developed on the basis of the LMNA (labeled multilevel neighborhoods of atom) descriptors and the PASS (prediction of activity spectra for substances) algorithm and applied to predict the SOMs of the 1A2, 2C9, 2C19, 2D6, and 3A4 isoforms of cytochrome P450. An average IAP (invariant accuracy of prediction) of SOMs calculated by the leave-one-out cross-validation procedure was 0.89 for the developed method. The external validation was made with evaluation sets containing data on biotransformations for 57 cardiovascular drugs. An average IAP of regioselectivity for evaluation sets was 0.83. It was shown that the proposed method exceeds accuracy of SOM prediction by RS-Predictor for CYP 1A2, 2D6, 2C9, 2C19, and 3A4 and is comparable to or better than SMARTCyp for CYP 2C9 and 2D6. PMID:24417355

Rudik, Anastasia V; Dmitriev, Alexander V; Lagunin, Alexey A; Filimonov, Dmitry A; Poroikov, Vladimir V

2014-02-24

176

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

177

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

E-print Network

structure prediction; Intron cutout technique; Incremental updates 1. Introduction Modern biology research of this paper is the intron cutout technique, which allows prediction of gene structures stretching over large. The intron cutout technique consists of an efficient filtering step and a dynamic programming step, and we

Brendel, Volker

178

Protein Secondary Structure Prediction using Bayesian Inference method on Decision fusion algorithms  

Microsoft Academic Search

Prediction of protein secondary structure (alpha-helix, beta-sheet, coil) from primary sequence of amino acids is a very challenging task, and the problem has been approached from several angles. Previously research was performed in this field using several techniques such as neural networks, simulated annealing (SA) and genetic algorithms (GA) for improving the protein secondary structure prediction accuracy. Decision fusion methods

Somasheker Akkaladevi; Ajay K. Katangur

2007-01-01

179

A STOCHASTIC MODEL OF LANGUAGE CHANGE THROUGH SOCIAL STRUCTURE AND PREDICTION-DRIVEN  

E-print Network

A STOCHASTIC MODEL OF LANGUAGE CHANGE THROUGH SOCIAL STRUCTURE AND PREDICTION-DRIVEN INSTABILITY W. GARRETT MITCHENER ABSTRACT. We develop a new stochastic model of language learning and change that incorporates variable speech and age structure. Children detect correlations between age and speech, predict

Mitchener, W. Garrett

180

P-SLAM: Simultaneous Localization and Mapping With Environmental-Structure Prediction  

Microsoft Academic Search

Traditionally, simultaneous localization and mapping (SLAM) algorithms solve the localization and mapping problem in explored regions. This paper presents a prediction-based SLAM al- gorithm (called P-SLAM), which has an environmental-structure predictor to predict the structure inside an unexplored region (i.e., look-ahead mapping). The prediction process is based on the ob- servation of the surroundings of an unexplored region and com-

H. Jacky Chang; C. S. George Lee; Yung-hsiang Lu; Y. Charlie Hu

2007-01-01

181

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

E-print Network

An Overview of Protein Structure Prediction: From Homology to Ab Initio Final Project For Bioc218 structure. Thus there is enormous benefit in knowing the three dimensional structures of all the proteins. Although more and more structures are determined experimentally at an accelerated rate, it is simply

182

Global or local? Predicting secondary structure and accessibility in mRNAs  

E-print Network

RNA. This is surprising, since a vast number of cis-regulatory structures (4), e.g. riboswitches (5), iron response structure prediction is required to characterize RNA regulatory mechan- isms. Although various structure conformation. For example, local structures in messenger RNA (mRNA) can regulate protein gene expression

Will, Sebastian

183

Intermolecular interactions and water structure in a condensed phase B-DNA crystal  

Microsoft Academic Search

By controlled dehydration, the unit cells of dodecamer DNA-drug crystals have been shrunk from 68 000 (normal state) to 60 000 (partially dehydrated intermediate state) to 51 000 Å3 (fully dehydrated state), beyond which no further solvent loss occurs. The total solvent content in the normal crystals is ~40% by volume, reducing to ~20% in the fully dehy- drated phase.

George R. Clark; Christopher J. Squire; L. J. Baker; Roger F. Martin; Jonathan White

2000-01-01

184

Electronic polarization stabilizes tertiary structure prediction of HP-36.  

PubMed

Molecular dynamic (MD) simulations with both implicit and explicit solvent models have been carried out to study the folding dynamics of HP-36 protein. Starting from the extended conformation, the secondary structure of all three helices in HP-36 was formed in about 50 ns and remained stable in the remaining simulation. However, the formation of the tertiary structure was difficult. Although some intermediates were close to the native structure, the overall conformation was not stable. Further analysis revealed that the large structure fluctuation of loop and hydrophobic core regions was devoted mostly to the instability of the structure during MD simulation. The backbone root-mean-square deviation (RMSD) of the loop and hydrophobic core regions showed strong correlation with the backbone RMSD of the whole protein. The free energy landscape indicated that the distribution of main chain torsions in loop and turn regions was far away from the native state. Starting from an intermediate structure extracted from the initial AMBER simulation, HP-36 was found to generally fold to the native state under the dynamically adjusted polarized protein-specific charge (DPPC) simulation, while the peptide did not fold into the native structure when AMBER force filed was used. The two best folded structures were extracted and taken into further simulations in water employing AMBER03 charge and DPPC for 25 ns. Result showed that introducing polarization effect into interacting potential could stabilize the near-native protein structure. PMID:24715046

Duan, Li L; Zhu, Tong; Zhang, Qing G; Tang, Bo; Zhang, John Z H

2014-04-01

185

OPTIMAL CHARACTERIZATION OF STRUCTURE FOR PREDICTION OF PROPERTIES  

EPA Science Inventory

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

186

Prediction of Harmful Human Health Effects of Chemicals from Structure  

NASA Astrophysics Data System (ADS)

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

Cronin, Mark T. D.

187

Observed & Predicted Debris Disks Structures Beyond the Reach of Kepler  

NASA Astrophysics Data System (ADS)

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

Stark, Christopher C.

2014-06-01

188

SUPPLEMENTARY MATERIAL FOR Predicting RNA Secondary Structures with Pseudoknots by  

E-print Network

Structures for Treponema Pallidum pre-tmRNA . . . . . . . . . . . . . . . . . 3 2.2 PPV and Sensitivity the evaluations shown below. 2 #12;2.1 Sampled Structures for Treponema Pallidum pre-tmRNA McQFold samples from of the following 20 panels show the samples for the pre-tmRNA sequence of Treponema Pallid

Metzler, Dirk

189

Finite Element Based HWB Centerbody Structural Optimization and Weight Prediction  

NASA Technical Reports Server (NTRS)

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

Gern, Frank H.

2012-01-01

190

I-TASSER: a unified platform for automated protein structure and function prediction  

PubMed Central

The I-TASSER server is an integrated platform for automated protein structure and function prediction based on the sequence-to-structure-to-function paradigm. Starting from an amino acid sequence, I-TASSER first generates three-dimensional atomic models from multiple threading alignments and iterative structural assembly simulations. The function of the protein is then inferred by structurally matching the 3D models with other known proteins. The output from a typical server run contains full-length secondary and tertiary structure predictions, and functional annotations on ligand-binding sites, Enzyme Commission numbers and Gene Ontology terms. An estimate of accuracy of the predictions is provided based on the confidence score of the modeling. This protocol provides new insights and guidelines for designing of on-line server systems for the state-of-the-art protein structure and function predictions. The server is available at http://zhang.bioinformatics.ku.edu/I-TASSER. PMID:20360767

Roy, Ambrish; Kucukural, Alper; Zhang, Yang

2009-01-01

191

Predicting wave run-up on rubble-mound structures using M5 model tree  

Microsoft Academic Search

Prediction of run-up level is a key task in design of the coastal structures. For the design of the crest level of coastal structures, the wave run-up level with a 2% exceedance probability, Ru2%, is most commonly used. In this study, the performance of M5 model tree for prediction of the wave run-up on rubble-mound structures was investigated. The main

Lisham Bonakdar; Amir Etemad-Shahidi

2011-01-01

192

Evaluating the accuracy of SHAPE-directed RNA secondary structure predictions  

PubMed Central

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

Sukosd, Zsuzsanna; Swenson, M. Shel; Kjems, J?rgen; Heitsch, Christine E.

2013-01-01

193

Inflated contours for extreme response prediction in complex structural systems  

E-print Network

This study investigates the technique of environmental contour inflation to account for statistical uncertainties in either the loading or the response. Complex structural systems are treated as simple systems and the additional loadings are treated...

Van De Lindt, John Willem

2012-06-07

194

GTfold: A Scalable Multicore Code for RNA Secondary Structure Prediction  

E-print Network

, and the secondary structure of viruses like dengue [3], ebola [16], and HIV [17] is known to have functional significance. Thus, disrupting functionally significant base pairings in RNA viral genomes is one potential

Bader, David A.

195

Computational prediction of coiled-coil interaction structure specificity  

E-print Network

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

Gutwin, Karl N. (Karl Nickolai)

2009-01-01

196

Sampling Bottlenecks in De novo Protein Structure Prediction  

E-print Network

the native structure and typical nonnative conformation, the driving force for fold- ing, must be quite large to overcome the very large entropic barrier to folding (here and throughout the text, we use "energy" to refer

Baker, David

197

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

PubMed Central

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

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

2014-01-01

198

Quad-PRE: a hybrid method to predict protein quaternary structure attributes.  

PubMed

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

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

2014-01-01

199

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

NASA Technical Reports Server (NTRS)

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

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

1992-01-01

200

Multiple classifier integration for the prediction of protein structural classes.  

PubMed

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

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

2009-11-15

201

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

PubMed Central

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

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

2009-01-01

202

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

NASA Astrophysics Data System (ADS)

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

Suzuki, Tomonori; Miyazaki, Satoru

2011-01-01

203

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 PMID:2981343

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

1985-01-01

204

PREDICTS  

NASA Technical Reports Server (NTRS)

PREDICTS is a computer program that predicts the frequencies, as functions of time, of signals to be received by a radio science receiver in this case, a special-purpose digital receiver dedicated to analysis of signals received by an antenna in NASA s Deep Space Network (DSN). Unlike other software used in the DSN, PREDICTS does not use interpolation early in the calculations; as a consequence, PREDICTS is more precise and more stable. The precision afforded by the other DSN software is sufficient for telemetry; the greater precision afforded by PREDICTS is needed for radio-science experiments. In addition to frequencies as a function of time, PREDICTS yields the rates of change and interpolation coefficients for the frequencies and the beginning and ending times of reception, transmission, and occultation. PREDICTS is applicable to S-, X-, and Ka-band signals and can accommodate the following link configurations: (1) one-way (spacecraft to ground), (2) two-way (from a ground station to a spacecraft to the same ground station), and (3) three-way (from a ground transmitting station to a spacecraft to a different ground receiving station).

Zhou, Hanying

2007-01-01

205

Volume 172, number 2 FEBS 1537 July 1984 Internal mobility in a double-stranded B DNA hexamer and  

E-print Network

include base pair propeller twisting, rolling and buckling, coupled Abbreviations: NOE, nuclear OverhauserVolume 172, number 2 FEBS 1537 July 1984 Internal mobility in a double-stranded B DNA hexamer IAA, England Received 4 April 1984 The internal mobility of the deoxyribose H2'-H2' ' and base C(H5)-C

Clore, G. Marius

206

Poster EXPERIMENTAL STUDY AND MONTE CARLO SIMULATION ON OVERSTRETCHING TRANSITION OF B-DNA INTERACTING WITH MAGNESIUM IONS  

E-print Network

The elastic properties of a DNA molecule are very important to its physiological behavior, such as DNA wrapping around nucleosomes, packing inside bacteriophage capsids, and interacting with proteina, etc. When single B-form DNA (B-DNA) molecule is stretched over its contour length, it shows a highly overstretching transition to S-form DNA (S-DNA) which is about 70 % longer than B-DNA [1]. Since DNA molecule has negatively charged phosphate groups along the double helix, the cations in the surroundings will affect the overstretching transition behavior. B-DNA interacting with sodium ions has been measured by some researchers [2]. In this paper, the effects of different magnesium salt concentrations on the overstretching transition of single B-DNA molecule are studied by experimental and numerical methods. In the experimental study using optical tweezers, DNA molecule was stretched at physiological temperature (37?C). As the magnesium salt concentration was decreased from 50mM to 50 µ M, the overstretching transition force decreased from 89.02 to 72.01pN. There is a natural logarithmic relationship between the force and ionic strength. Magnesium ions, even in much lower concentration than sodium ions, can interact strongly with DNA and have

Hongxia Fu; Chan Ghee Koh; Hu Chen; Chwee Teck Lim; Tay Chee Siang

207

A Critical Review of Computational Methods for RNA Secondary Structure Prediction  

E-print Network

A Critical Review of Computational Methods for RNA Secondary Structure Prediction Adam Silverman Biochem218 Submitted June 6, 2003 Introduction The three-dimensional structure of RNA molecules is crucial to their function. The primary structure is determined by the sequence of G, A, C, and U bases in a strand

208

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

E-print Network

Conformational Transitions upon Ligand Binding: Holo- Structure Prediction from Apo Conformations design. Hence, if only an unbound (apo) structure is available distinct from the ligand of protein/ligand complexes based solely on the apo structure, the ligand and the radius of gyration

de Groot, Bert

209

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

E-print Network

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

210

GTfold: A Scalable Multicore Code for RNA Secondary Structure Prediction  

E-print Network

our website. Keywords Computational Biology, Parallel Algorithms, Ribosomal and Viral RNA College structure of viruses like dengue [3], ebola [18], and HIV [19] is known to have func- tional significance. Thus, disrupting functionally significant base pairings in RNA viral genomes is one potential method

Bader, David A.

211

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

212

Prediction of Complete Gene Structures in Human Genomic DNA  

Microsoft Academic Search

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

Christopher B. Burge; Samuel Karlin

1997-01-01

213

Dispersal differences predict population genetic structure in Mormon crickets  

Microsoft Academic Search

Research investigating the geographical context of speciation has primarily focused on abiotic factors such as the role of Pleistocene glacial cycles, or geotectonic events. Few study systems allow a direct comparison of how biological differences, such as dispersal beha- viour, affect population genetic structure of organisms that were subdivided during the Pleistocene. Mormon crickets exist in solitary and gregarious 'phases',

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

2007-01-01

214

Predicting human resting-state functional connectivity from structural connectivity  

E-print Network

Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405; bSignal Processing Laboratory 5 pop- ulations, those populations are said to be functionally connected. Functional connectivity has structure of the human cerebral cortex. computational model diffusion MRI neuroanatomy cerebral cortex brain

Dickerson, Brad

215

De novo prediction of structured RNAs from genomic sequences  

Microsoft Academic Search

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

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

2009-01-01

216

A Structural Approach to Latency Prediction Harsha V. Madhyastha  

E-print Network

. These techniques treat the network as an unknown "black box." Black-box approaches are desirable if they can obtain the Internet as a black-box, ignoring its internal structure. While these models are simple, they can often, the most widely used black-box model. Fur- thermore, unlike metric embeddings, our approach successfully

Venkataramani, Arun

217

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

218

Evolving stochastic context--free grammars for RNA secondary structure prediction  

PubMed Central

Background Stochastic Context–Free Grammars (SCFGs) were applied successfully to RNA secondary structure prediction in the early 90s, and used in combination with comparative methods in the late 90s. The set of SCFGs potentially useful for RNA secondary structure prediction is very large, but a few intuitively designed grammars have remained dominant. In this paper we investigate two automatic search techniques for effective grammars – exhaustive search for very compact grammars and an evolutionary algorithm to find larger grammars. We also examine whether grammar ambiguity is as problematic to structure prediction as has been previously suggested. Results These search techniques were applied to predict RNA secondary structure on a maximal data set and revealed new and interesting grammars, though none are dramatically better than classic grammars. In general, results showed that many grammars with quite different structure could have very similar predictive ability. Many ambiguous grammars were found which were at least as effective as the best current unambiguous grammars. Conclusions Overall the method of evolving SCFGs for RNA secondary structure prediction proved effective in finding many grammars that had strong predictive accuracy, as good or slightly better than those designed manually. Furthermore, several of the best grammars found were ambiguous, demonstrating that such grammars should not be disregarded. PMID:22559985

2012-01-01

219

Predicting evolutionary site variability from structure in viral proteins: buriedness, flexibility, and design  

E-print Network

Several recent works have shown that protein structure can predict site-specific evolutionary sequence variation. In particular, sites that are buried and/or have many contacts with other sites in a structure have been shown to evolve more slowly, on average, than surface sites with few contacts. Here, we present a comprehensive study of the extent to which numerous structural properties can predict sequence variation. The structural properties we considered include buriedness (relative solvent accessibility and contact number), structural flexibility (B factors, root-mean-square fluctuations, and variation in dihedral angles), and variability in designed structures. We obtained structural flexibility measures both from molecular dynamics simulations performed on 9 non-homologous viral protein structures and from variation in homologous variants of those proteins, where available. We obtained measures of variability in designed structures from flexible-backbone design in the Rosetta software. We found that mo...

Shahmoradi, Amir; Spielman, Stephanie J; Jackson, Eleisha L; Dawson, Eric T; Meyer, Austin G; Wilke, Claus O

2014-01-01

220

The structure of evaporating and combusting sprays: Measurements and predictions  

NASA Astrophysics Data System (ADS)

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

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

1984-07-01

221

The structure of evaporating and combusting sprays: Measurements and predictions  

NASA Technical Reports Server (NTRS)

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

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

1984-01-01

222

The structure of evaporating and combusting sprays: Measurements and predictions  

NASA Technical Reports Server (NTRS)

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

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

1982-01-01

223

Structural Damage Prediction and Analysis for Hypervelocity Impacts: Handbook  

NASA Technical Reports Server (NTRS)

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

Elfer, N. C.

1996-01-01

224

method estimate certain losses related identification assessment ground acceleration structural parameters improve prediction mortality tall buildings method estimate certain losses related identification assessment ground acceleration structural parameters improve prediction mortality tall buildings Skol  

EPA Pesticide Factsheets

Search instead for method estimate certain losses related identification assessment ground acceleration structural parameters improve prediction mortality tall buildings method estimate certain losses related identification assessment ground acceleration structural parameters improve prediction mortality tall buildings Skol ?

225

Computational methods for predicting impact damage in composite structures  

Microsoft Academic Search

This paper describes recent progress in materials modelling and numerical simulation of the impact response of fibre-reinforced composite structures. A continuum damage-mechanics (CDM) model for fabric-reinforced composites is developed as a framework within which both in-ply and delamination failure may be modelled during impact loading. Damage-development equations are derived and appropriate materials parameters determined from experiments. The CDM model for

A. F Johnson; A. K Pickett; P Rozycki

2001-01-01

226

Predicting the Structural Design of Metabolic Pathways: An Evolutionary Approach  

Microsoft Academic Search

\\u000a Metabolic systems are characterized by two different types of data, describing first their structural properties (stoicheiometric\\u000a relations of the chemical conversions, regulatory couplings, kinetic parameter of the enzymes), and second their variables\\u000a (metabolite concentrations, fluxes). The former are generally fixed during the life span of a cell or an organism, whereas\\u000a the latter may change in a short time scale,

Reinhart Heinrich

227

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

228

In silico predicted structural and functional robustness of piscine steroidogenesis.  

PubMed

Assessments of metabolic robustness or susceptibility are inherently dependent on quantitative descriptions of network structure and associated function. In this paper a stoichiometric model of piscine steroidogenesis was constructed and constrained with productions of selected steroid hormones. Structural and flux metrics of this in silico model were quantified by calculating extreme pathways and optimal flux distributions (using linear programming). Extreme pathway analysis showed progestin and corticosteroid synthesis reactions to be highly participant in extreme pathways. Furthermore, reaction participation in extreme pathways also fitted a power law distribution (degree exponent ?=2.3), which suggested that progestin and corticosteroid reactions act as 'hubs' capable of generating other functionally relevant pathways required to maintain steady-state functionality of the network. Analysis of cofactor usage (O2 and NADPH) showed progestin synthesis reactions to exhibit high robustness, whereas estrogen productions showed highest energetic demands with low associated robustness to maintain such demands. Linear programming calculated optimal flux distributions showed high heterogeneity of flux values with a near-random power law distribution (degree exponent ??2.7). Subsequently, network robustness was tested by assessing maintenance of metabolite flux-sum subject to targeted deletions of rank-ordered (low to high metric) extreme pathway participant and optimal flux reactions. Network robustness was susceptible to deletions of extreme pathway participant reactions, whereas minimal impact of high flux reaction deletion was observed. This analysis shows that the steroid network is susceptible to perturbation of structurally relevant (extreme pathway) reactions rather than those carrying high flux. PMID:24333207

Hala, D; Huggett, D B

2014-03-21

229

Direct comparison of Neural Network, Fuzzy Logic and Model Prediction Variable Structure vortex flow controllers  

E-print Network

Predictive Variable Structure and Fuzzy Logic based controllers for the same benchmark problem. Evaluation criteria consist of closed-loop system performance, activity level of the VFC nozzles, ease of controller synthesis, time required to synthesize...

Joshi, Praveen Sudhakar

2012-06-07

230

Computational prediction of the lifetime of self-healing CMC structures M. Geneta,1,  

E-print Network

EADS Foundation Chair "Advanced Computational Structural Mechanics" cSAFRAN­Snecma Propulsion Solide, Les cinq the simulations were carried out using Abaqus/Standard. The main interest of the model is its ability to predict

Boyer, Edmond

231

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

232

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

NASA Technical Reports Server (NTRS)

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

Ko, William L.; Chen, Tony

2006-01-01

233

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,...

234

Resummed predictions for the structure function F2 at small x  

Microsoft Academic Search

We report the results of including resummed splitting functions in the QCD evolution equations at small x, and discuss the predictions that follow for the deep inelastic structure functions. *Contribution at XXX Rencontres de Moriond, Les Arcs, March 1995

F. Hautmann

1995-01-01

235

A Molecular Mechanics Knowledge Base Applied to Template Based Structure Prediction  

E-print Network

Predicting protein structure using its primary sequence has always been a challenging topic in biochemistry. Although it seems as simple as finding the minimal energy conformation, it has been quite difficult to provide an accurate yet reliable...

Qu, Xiaotao

2011-02-22

236

Computer Science and Artificial Intelligence Laboratory Secondary Structure Prediction of All-Helical  

E-print Network

Computer Science and Artificial Intelligence Laboratory Secondary Structure Prediction of All Dijk, S. Devadas Technical Report massachusetts institute of technology, cambridge, ma 02139 usa -- www Dijk and S. Devadas Computer Science and Artificial Intelligence Laboratory (CSAIL) Massachusetts

Gifford, David K.

237

Energy predictions of turbulent boundary layer induced mid-high frequency structural vibrations  

Microsoft Academic Search

This paper addresses the study of vibrations induced by turbulent boundary layers (TBLs) flowing over plate-like structures. The aim is here to propose a predictive method in order to evaluate the vibroacoustical behaviour of a plate excited by TBL pressure fluctuations. The ultimate goal is to develop tools for predicting internal cabin noise in an aircraft during flight. Hence TBL

M. N. Ichchou; O. Bareille; Y. Jacques

2009-01-01

238

Predicting termination of atrial fibrillation based on the structure and quantification of the recurrence plot  

Microsoft Academic Search

Predicting the spontaneous termination of the atrial fibrillation (AF) leads to not only better understanding of mechanisms of the arrhythmia but also the improved treatment of the sustained AF. A novel method is proposed to characterize the AF based on structure and the quantification of the recurrence plot (RP) to predict the termination of the AF. The RP of the

Rongrong Sun; Yuanyuan Wang

2008-01-01

239

An integral predictive\\/nonlinear Hinfinity control structure for a quadrotor helicopter  

Microsoft Academic Search

This paper presents an integral predictive and nonlinear robust control strategy to solve the path following problem for a quadrotor helicopter. The dynamic motion equations are obtained by the Lagrange–Euler formalism. The proposed control structure is a hierarchical scheme consisting of a model predictive controller (mpc) to track the reference trajectory together with a nonlinear H? controller to stabilize the

Guilherme V. Raffo; Manuel G. Ortega; Francisco R. Rubio

2010-01-01

240

A Graphical Model for Protein Secondary Structure Prediction Wei Chu chuwei@gatsby.ucl.ac.uk  

E-print Network

used in neural network prediction methods to achieve fur- ther improvements (Jones, 1999; Cuff & BartonA Graphical Model for Protein Secondary Structure Prediction Wei Chu chuwei@gatsby.ucl.ac.uk Zoubin Ghahramani zoubin@gatsby.ucl.ac.uk Gatsby Computational Neuroscience Unit, University College London, London

Ghahramani, Zoubin

241

A Graphical Model for Protein Secondary Structure Prediction Wei Chu chuwei@gatsby.ucl.ac.uk  

E-print Network

in neural network prediction methods to achieve fur- ther improvements (Jones, 1999; Cuff & Barton, 2000A Graphical Model for Protein Secondary Structure Prediction Wei Chu chuwei@gatsby.ucl.ac.uk Zoubin Ghahramani zoubin@gatsby.ucl.ac.uk Gatsby Computational Neuroscience Unit, University College London, London

Wei, Chu

242

A Graphical Model for Protein Secondary Structure Prediction Wei Chu chuwei@gatsby.ucl.ac.uk  

E-print Network

in neural network prediction methods to achieve fur­ ther improvements (Jones, 1999; Cu# & Barton, 2000A Graphical Model for Protein Secondary Structure Prediction Wei Chu chuwei@gatsby.ucl.ac.uk Zoubin Ghahramani zoubin@gatsby.ucl.ac.uk Gatsby Computational Neuroscience Unit, University College London, London

Wei, Chu

243

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

SciTech Connect

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

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

2004-10-01

244

Correcting pervasive errors in RNA crystallography through enumerative structure prediction  

PubMed Central

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 PHENIX (Python-based Hierarchical Environment for Integrated Xtallography) diffraction-based refinement. On 24 datasets, ERRASER automatically corrects the majority of MolProbity-assessed errors, improves average Rfree factor, resolves functionally important discrepancies in non-canonical 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

2012-01-01

245

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

246

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

Microsoft Academic Search

Biological function of proteins is frequently associated with the formation of complexes with small-molecule ligands. Experimental structure determination of such complexes at atomic resolution, however, can be time-consuming and costly. Computational methods for structure prediction of protein\\/ligand complexes, particularly docking, are as yet restricted by their limited consideration of receptor flexibility, rendering them not applicable for predicting protein\\/ligand complexes if

Daniel Seeliger; Bert L. de Groot

2010-01-01

247

Variability in anger intensity profiles: Structure and predictive basis.  

PubMed

The aim of this study is to describe variability in the shape and amplitude of intensity profiles of anger episodes and how it relates to duration, and to investigate whether this variability can be predicted on the basis of appraisals and emotion regulation strategies used. Participants were asked to report on a wide range of recollected anger episodes. By means of K-spectral centroid clustering, two prototypical shapes of anger intensity profiles were identified: early- and late-blooming episodes. Early-blooming episodes are relatively short and reach their peak immediately. These profiles are associated with low-importance events and adaptive regulation. Late-blooming episodes last longer and reach their peak (relatively) late in the episode. These profiles are related to high-importance events and maladaptive regulation. For both early- and late-blooming profiles, overall amplitude is positively associated with event importance and the use of maladaptive regulation strategies and negatively with the use of adaptive ones. PMID:24641250

Heylen, Joke; Verduyn, Philippe; Van Mechelen, Iven; Ceulemans, Eva

2015-01-01

248

Finite element prediction of wave motion in structural waveguides  

NASA Astrophysics Data System (ADS)

A method is presented by which the wavenumbers for a one-dimensional waveguide can be predicted from a finite element (FE) model. The method involves postprocessing a conventional, but low order, FE model, the mass and stiffness matrices of which are typically found using a conventional FE package. This is in contrast to the most popular previous waveguide/FE approach, sometimes termed the spectral finite element approach, which requires new spectral element matrices to be developed. In the approach described here, a section of the waveguide is modeled using conventional FE software and the dynamic stiffness matrix formed. A periodicity condition is applied, the wavenumbers following from the eigensolution of the resulting transfer matrix. The method is described, estimation of wavenumbers, energy, and group velocity discussed, and numerical examples presented. These concern wave propagation in a beam and a simply supported plate strip, for which analytical solutions exist, and the more complex case of a viscoelastic laminate, which involves postprocessing an ANSYS FE model. The method is seen to yield accurate results for the wavenumbers and group velocities of both propagating and evanescent waves. .

Mace, Brian R.; Duhamel, Denis; Brennan, Michael J.; Hinke, Lars

2005-05-01

249

Finite element prediction of wave motion in structural waveguides.  

PubMed

A method is presented by which the wavenumbers for a one-dimensional waveguide can be predicted from a finite element (FE) model. The method involves postprocessing a conventional, but low order, FE model, the mass and stiffness matrices of which are typically found using a conventional FE package. This is in contrast to the most popular previous waveguide/FE approach, sometimes termed the spectral finite element approach, which requires new spectral element matrices to be developed. In the approach described here, a section of the waveguide is modeled using conventional FE software and the dynamic stiffness matrix formed. A periodicity condition is applied, the wavenumbers following from the eigensolution of the resulting transfer matrix. The method is described, estimation of wavenumbers, energy, and group velocity discussed, and numerical examples presented. These concern wave propagation in a beam and a simply supported plate strip, for which analytical solutions exist, and the more complex case of a viscoelastic laminate, which involves postprocessing an ANSYS FE model. The method is seen to yield accurate results for the wavenumbers and group velocities of both propagating and evanescent waves. PMID:15957754

Mace, Brian R; Duhamel, Denis; Brennan, Michael J; Hinke, Lars

2005-05-01

250

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

251

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

NASA Technical Reports Server (NTRS)

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

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

1978-01-01

252

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

PubMed

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

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

2014-06-01

253

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

NASA Technical Reports Server (NTRS)

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

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

1994-01-01

254

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

PubMed Central

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

2013-01-01

255

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

PubMed Central

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

King, R D; Srinivasan, A

1996-01-01

256

Prediction  

Microsoft Academic Search

This chapter first presents a rather personal view of some different aspects of predictability, going in crescendo from simple linear systems to high-dimensional nonlinear systems with stochastic forcing, which exhibit emergent properties such as phase transitions and regime shifts. Then, a detailed correspondence between the phenomenology of earthquakes, financial crashes and epileptic seizures is offered. The presented statistical evidence provides

Didier Sornette; Ivan Osorio

2010-01-01

257

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

NASA Technical Reports Server (NTRS)

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

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

1977-01-01

258

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. PMID:17918729

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

2007-01-01

259

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

PubMed Central

The performance of the physics-based protocol, whose main component is the United Residue (UNRES) physics-based coarse-grained force field, developed in our laboratory for the prediction of protein structure from amino acid sequence, is illustrated. Candidate models are selected, based on probabilities of the conformational families determined by multiplexed replica-exchange simulations, from the 10th Community Wide Experiment on the Critical Assessment of Techniques for Protein Structure Prediction (CASP10). For target T0663, classified as a new fold, which consists of two domains homologous to those of known proteins, UNRES predicted the correct symmetry of packing, in which the domains are rotated with respect to each other by 180° in the experimental structure. By contrast, models obtained by knowledge-based methods, in which each domain is modeled very accurately but not rotated, resulted in incorrect packing. Two UNRES models of this target were featured by the assessors. Correct domain packing was also predicted by UNRES for the homologous target T0644, which has a similar structure to that of T0663, except that the two domains are not rotated. Predictions for two other targets, T0668 and T0684_D2, are among the best ones by global distance test score. These results suggest that our physics-based method has substantial predictive power. In particular, it has the ability to predict domain–domain orientations, which is a significant advance in the state of the art. PMID:23980156

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

2013-01-01

260

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

PubMed Central

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

2014-01-01

261

Rotor Airloads Prediction Using Loose Aerodynamic Structural Coupling  

NASA Technical Reports Server (NTRS)

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

Potsdam, Mark; Yeo, Hyeonsoo; Johnson, Wayne

2004-01-01

262

Inference and updating of probabilistic structural life prediction models  

NASA Astrophysics Data System (ADS)

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

Cross, Richard J.

263

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

E-print Network

Predicting Protein Complex Structures: A Review of the Docking Process Adam Perez BIOC218 Final Project 12/11/2011 Introduction Proteins carry out enzymatic reactions and participate in cellular, and ultimately the processes within a cell, requires knowledge of the three- dimensional structure

264

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

ERIC Educational Resources Information Center

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

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

2011-01-01

265

Using pseudo-amino acid composition and support vector machine to predict protein structural class  

Microsoft Academic Search

As a result of genome and other sequencing projects, the gap between the number of known protein sequences and the number of known protein structural classes is widening rapidly. In order to narrow this gap, it is vitally important to develop a computational prediction method for fast and accurately determining the protein structural class. In this paper, a novel predictor

Chao Chen; Yuan-Xin Tian; Xiao-Yong Zou; Pei-Xiang Cai; Jin-Yuan Mo

2006-01-01

266

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

E-print Network

Realizing Predicted Crystal Structures at Extreme Conditions: The Low-Temperature and High-Pressure Crystal Structures of 2-Chlorophenol and 4-Fluorophenol Iain D. H. Oswald, David R. Allan, Graeme M. Day Road, Cambridge CB2 1EZ, England Received October 14, 2004 ABSTRACT: A crystal of 2-chlorophenol

de Gispert, Adrià

267

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

Microsoft Academic Search

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

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

2005-01-01

268

Analysis of the role of predicted RNA secondary structures in Ebola virus replication  

Microsoft Academic Search

Thermodynamic modeling of Ebola viral RNA predicts the formation of RNA stem-loop structures at the 3? and 5? termini and panhandle structures between the termini of the genomic (or antigenomic) RNAs. Sequence analysis showed a high degree of identity among Ebola Zaire, Sudan, Reston, and Cote d’Ivoire subtype viruses in their 3? and 5? termini (18 nucleotides in length) and

Sharon M Crary; Jonathan S Towner; Jessica E Honig; Trevor R Shoemaker; Stuart T Nichol

2003-01-01

269

Predictive Validity of the Structured Assessment for Violence Risk in Youth (SAVRY) With Juvenile Offenders  

Microsoft Academic Search

Violence is a serious social problem that is often encountered in the youth justice system. Identifying those adolescents who are at the highest risk for future violence is an important step toward effective rehabilitation. The current study examined the predictive validity of the Structured Assessment for Violence Risk in Youth (SAVRY), a structured professional judgment risk tool, in a sample

Joanna R. Meyers; Fred Schmidt

2008-01-01

270

Wavelet Support Vector Machine and Particle Swarm Optimizer for Prediction of Protein Structural Class  

Microsoft Academic Search

Determination of protein structural class is a quite meaningful topic in protein science. In this paper a wavelet support vector machine (WSVM) coupled with particle swarm optimizer (PSO) is presented for prediction of protein structural class, which is featured by introducing wavelet as a kernel and using PSO to optimize kernel parameters. As a demonstration, the rigorous jackknife cross-validation test

Chao Chen; Xiao-yong Zou

2011-01-01

271

Multilign: an algorithm to predict secondary structures conserved in multiple RNA sequences  

PubMed Central

Motivation: With recent advances in sequencing, structural and functional studies of RNA lag behind the discovery of sequences. Computational analysis of RNA is increasingly important to reveal structure–function relationships with low cost and speed. The purpose of this study is to use multiple homologous sequences to infer a conserved RNA structure. Results: A new algorithm, called Multilign, is presented to find the lowest free energy RNA secondary structure common to multiple sequences. Multilign is based on Dynalign, which is a program that simultaneously aligns and folds two sequences to find the lowest free energy conserved structure. For Multilign, Dynalign is used to progressively construct a conserved structure from multiple pairwise calculations, with one sequence used in all pairwise calculations. A base pair is predicted only if it is contained in the set of low free energy structures predicted by all Dynalign calculations. In this way, Multilign improves prediction accuracy by keeping the genuine base pairs and excluding competing false base pairs. Multilign has computational complexity that scales linearly in the number of sequences. Multilign was tested on extensive datasets of sequences with known structure and its prediction accuracy is among the best of available algorithms. Multilign can run on long sequences (> 1500 nt) and an arbitrarily large number of sequences. Availability: The algorithm is implemented in ANSI C++ and can be downloaded as part of the RNAstructure package at: http://rna.urmc.rochester.edu Contact: david_mathews@urmc.rochester.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:21193521

Xu, Zhenjiang; Mathews, David H.

2011-01-01

272

Ab initio protein structure prediction on a genomic scale: Application to the Mycoplasma  

E-print Network

Ab initio protein structure prediction on a genomic scale: Application to the Mycoplasma genitalium of the Mycoplasma genitalium genome. TOUCHSTONE is based on a Monte Carlo refinement of a lattice model of proteins structure of all the small proteins in the Mycoplasma genitalium genome. Methods The TOUCHSTONE Procedure

Kihara, Daisuke

273

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

274

Variability and predictability of Antarctic krill swarm structure  

NASA Astrophysics Data System (ADS)

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

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

2009-11-01

275

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

PubMed

?-Barrel membrane proteins are found in the outer membrane of gram-negative bacteria, mitochondria, and chloroplasts. They are important for pore formation, membrane anchoring, and enzyme activity. These proteins are also often responsible for bacterial virulence. Due to difficulties in experimental structure determination, they are sparsely represented in the protein structure databank. We have developed a computational method for predicting structures of the transmembrane (TM) domains of ?-barrel membrane proteins. Based on physical principles, our method can predict structures of the TM domain of ?-barrel membrane proteins of novel topology, including those from eukaryotic mitochondria. Our method is based on a model of physical interactions, a discrete conformational state space, an empirical potential function, as well as a model to account for interstrand loop entropy. We are able to construct three-dimensional atomic structure of the TM domains from sequences for a set of 23 nonhomologous proteins (resolution 1.8-3.0 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. PMID:22148174

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

2012-01-25

276

Structure prediction of the second extracellular loop in G-protein-coupled receptors.  

PubMed

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

Kmiecik, Sebastian; Jamroz, Michal; Kolinski, Michal

2014-06-01

277

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

278

ProFunc: a server for predicting protein function from 3D structure  

Microsoft Academic Search

ProFunc (http:\\/\\/www.ebi.ac.uk\\/thornton-srv\\/data- bases\\/ProFunc) is a web server for predicting the likely function of proteins whose 3D structure is known but whose function is not. Users submit the coordinates of their structure to the server in PDB format. ProFunc makes use of both existing and novel methods to analyse the protein's sequence and structure identifying functional motifs or close relationships to

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

2005-01-01

279

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

PubMed Central

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

2014-01-01

280

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

PubMed

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

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

2011-09-14

281

Predicting evolutionary site variability from structure in viral proteins: buriedness, packing, flexibility, and design.  

PubMed

Several recent works have shown that protein structure can predict site-specific evolutionary sequence variation. In particular, sites that are buried and/or have many contacts with other sites in a structure have been shown to evolve more slowly, on average, than surface sites with few contacts. Here, we present a comprehensive study of the extent to which numerous structural properties can predict sequence variation. The quantities we considered include buriedness (as measured by relative solvent accessibility), packing density (as measured by contact number), structural flexibility (as measured by B factors, root-mean-square fluctuations, and variation in dihedral angles), and variability in designed structures. We obtained structural flexibility measures both from molecular dynamics simulations performed on nine non-homologous viral protein structures and from variation in homologous variants of those proteins, where they were available. We obtained measures of variability in designed structures from flexible-backbone design in the Rosetta software. We found that most of the structural properties correlate with site variation in the majority of structures, though the correlations are generally weak (correlation coefficients of 0.1-0.4). Moreover, we found that buriedness and packing density were better predictors of evolutionary variation than structural flexibility. Finally, variability in designed structures was a weaker predictor of evolutionary variability than buriedness or packing density, but it was comparable in its predictive power to the best structural flexibility measures. We conclude that simple measures of buriedness and packing density are better predictors of evolutionary variation than the more complicated predictors obtained from dynamic simulations, ensembles of homologous structures, or computational protein design. PMID:25217382

Shahmoradi, Amir; Sydykova, Dariya K; Spielman, Stephanie J; Jackson, Eleisha L; Dawson, Eric T; Meyer, Austin G; Wilke, Claus O

2014-10-01

282

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

PubMed

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

Chatterjee, S; Ghosh, S; Vishveshwara, S

2013-07-01

283

Prediction of Protein Secondary Structure Content by Using the Concept of Chou's Pseudo Amino Acid Composition and Support Vector Machine  

Microsoft Academic Search

Protein secondary structure carries information about local structural arrangements. Significant majority of suc- cessful methods for predicting the secondary structure is based on multiple sequence alignment. However, the multiple alignment fails to achieve accurate results when a protein sequence is characterized by low homology. To this end, we propose a novel method for prediction of secondary structure content through comprehensive

Chao Chen; Lixuan Chen; Xiaoyong Zou; Peixiang Cai

2009-01-01

284

Colour simulation and prediction of complex nano-structured metal oxide films  

Microsoft Academic Search

An optical modeling procedure is developed to predict and model the colour of electro-coloured anodized aluminium that has been modified in pore structure for the generation of interference colours. The relation between the multi-layered, nano-sized oxide microstructure and the colour is experimentally determined and translated into an optical model that is able to predict the colour as a function of

I. De Graeve; P. Laha; V. Goossens; R. Furneaux; D. Verwimp; E. Stijns; H. Terryn

2011-01-01

285

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

NASA Technical Reports Server (NTRS)

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

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

1996-01-01

286

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

PubMed Central

Background PSEN2 mutations are rare variants, and fewer than 30 different PSEN2 mutations have been found. So far, it has not been reported in Asia. Case presentation PSEN2 mutation at codon 214 for predicting a valine to leucine substitution was found in a 70-year-old woman, who showed a dementia of the Alzheimer type. We did not find the mutation in 614 control chromosomes. We also predicted the structures of presenilin 2 protein with native Val 214 residue and Leu 214 mutation, which revealed significant structural changes in the region. Conclusion It could be a novel mutation verified with structural prediction in a patient with Alzheimer’s disease. PMID:24885952

2014-01-01

287

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

PubMed

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

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

2009-10-30

288

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

PubMed

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

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

2014-07-01

289

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

PubMed Central

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

2014-01-01

290

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

PubMed Central

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

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

2011-01-01

291

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

PubMed

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

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

2012-05-01

292

Target Highlights in CASP9: Experimental Target Structures for the Critical Assessment of Techniques for Protein Structure Prediction  

PubMed Central

One goal of the CASP Community Wide Experiment on the Critical Assessment of Techniques for Protein Structure Prediction is to identify the current state of the art in protein structure prediction and modeling. A fundamental principle of CASP is blind prediction on a set of relevant protein targets, i.e. the participating computational methods are tested on a common set of experimental target proteins, for which the experimental structures are not known at the time of modeling. Therefore, the CASP experiment would not have been possible without broad support of the experimental protein structural biology community. In this manuscript, several experimental groups discuss the structures of the proteins which they provided as prediction targets for CASP9, highlighting structural and functional peculiarities of these structures: the long tail fibre protein gp37 from bacteriophage T4, the cyclic GMP-dependent protein kinase I? (PKGI?) dimerization/docking domain, the ectodomain of the JTB (Jumping Translocation Breakpoint) transmembrane receptor, Autotaxin (ATX) in complex with an inhibitor, the DNA-Binding J-Binding Protein 1 (JBP1) domain essential for biosynthesis and maintenance of DNA base-J (?-D-glucosyl-hydroxymethyluracil) in Trypanosoma and Leishmania, an so far uncharacterized 73 residue domain from Ruminococcus gnavus with a fold typical for PDZ-like domains, a domain from the Phycobilisome (PBS) core-membrane linker (LCM) phycobiliprotein ApcE from Synechocystis, the Heat shock protein 90 (Hsp90) activators PFC0360w and PFC0270w from Plasmodium falciparum, and 2-oxo-3-deoxygalactonate kinase from Klebsiella pneumoniae. PMID:22020785

Kryshtafovych, Andriy; Moult, John; Bartual, Sergio G.; Bazan, J. Fernando; Berman, Helen; Casteel, Darren E.; Christodoulou, Evangelos; Everett, John K.; Hausmann, Jens; Heidebrecht, Tatjana; Hills, Tanya; Hui, Raymond; Hunt, John F.; Jayaraman, Seetharaman; Joachimiak, Andrzej; Kennedy, Michael A.; Kim, Choel; Lingel, Andreas; Michalska, Karolina; Montelione, Gaetano T.; Otero, Jose M.; Perrakis, Anastassis; Pizarro, Juan C.; van Raaij, Mark J.; Ramelot, Theresa A.; Rousseau, Francois; Tong, Liang; Wernimont, Amy K.; Young, Jasmine; Schwede, Torsten

2011-01-01

293

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

NASA Technical Reports Server (NTRS)

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

Ko, William L.

1987-01-01

294

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

PubMed

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

Sadowski, Peter; Baldi, Pierre

2013-12-23

295

A novel protocol for three-dimensional structure prediction of RNA-protein complexes  

PubMed Central

Three-dimensional structures of RNA-protein complexes are crucial for understanding their diverse functions. However, the number of the RNA-protein complex structures solved by experiments is still limited at present. To solve this problem, some computational protocols have been proposed to predict three-dimensional RNA-protein complex structures. But the prediction accuracies of these protocols are lower. The reason may be that these protocols don't fully incorporate the features of RNA-protein interfaces. Here we propose a novel computational protocol for three-dimensional RNA-protein complex structure prediction, 3dRPC, which applies new schemes to the discreteness of molecule and charge in docking algorithm and the construction of the reference state in scoring function in order to take account of the features of RNA-protein interfaces. This protocol achieves a high accuracy comparable to the well-developed algorithms for three-dimensional structure prediction of protein-protein complexes when tested on a RNA-protein docking benchmark. PMID:23712416

Huang, Yangyu; Liu, Shiyong; Guo, Dachuan; Li, Lin; Xiao, Yi

2013-01-01

296

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

PubMed

Carbohydrate-binding proteins (CBPs) are potential biomarkers and drug targets. However, the interactions between carbohydrates and proteins are challenging to study experimentally and computationally because of their low binding affinity, high flexibility, and the lack of a linear sequence in carbohydrates as exists in RNA, DNA, and proteins. Here, we describe a structure-based function-prediction technique called SPOT-Struc that identifies carbohydrate-recognizing proteins and their binding amino acid residues by structural alignment program SPalign and binding affinity scoring according to a knowledge-based statistical potential based on the distance-scaled finite-ideal gas reference state (DFIRE). The leave-one-out cross-validation of the method on 113 carbohydrate-binding domains and 3442 noncarbohydrate binding proteins yields a Matthews correlation coefficient of 0.56 for SPalign alone and 0.63 for SPOT-Struc (SPalign?+?binding affinity scoring) for CBP prediction. SPOT-Struc is a technique with high positive predictive value (79% correct predictions in all positive CBP predictions) with a reasonable sensitivity (52% positive predictions in all CBPs). The sensitivity of the method was changed slightly when applied to 31 APO (unbound) structures found in the protein databank (14/31 for APO versus 15/31 for HOLO). The result of SPOT-Struc will not change significantly if highly homologous templates were used. SPOT-Struc predicted 19 out of 2076 structural genome targets as CBPs. In particular, one uncharacterized protein in Bacillus subtilis (1oq1A) was matched to galectin-9 from Mus musculus. Thus, SPOT-Struc is useful for uncovering novel carbohydrate-binding proteins. SPOT-Struc is available at http://sparks-lab.org. © 2014 Wiley Periodicals, Inc. PMID:25220682

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

2014-11-15

297

Prediction, conservation analysis, and structural characterization of mammalian mucin-type O-glycosylation sites.  

PubMed

O-GalNAc-glycosylation is one of the main types of glycosylation in mammalian cells. No consensus recognition sequence for the O-glycosyltransferases is known, making prediction methods necessary to bridge the gap between the large number of known protein sequences and the small number of proteins experimentally investigated with regard to glycosylation status. From O-GLYCBASE a total of 86 mammalian proteins experimentally investigated for in vivo O-GalNAc sites were extracted. Mammalian protein homolog comparisons showed that a glycosylated serine or threonine is less likely to be precisely conserved than a nonglycosylated one. The Protein Data Bank was analyzed for structural information, and 12 glycosylated structures were obtained. All positive sites were found in coil or turn regions. A method for predicting the location for mucin-type glycosylation sites was trained using a neural network approach. The best overall network used as input amino acid composition, averaged surface accessibility predictions together with substitution matrix profile encoding of the sequence. To improve prediction on isolated (single) sites, networks were trained on isolated sites only. The final method combines predictions from the best overall network and the best isolated site network; this prediction method correctly predicted 76% of the glycosylated residues and 93% of the nonglycosylated residues. NetOGlyc 3.1 can predict sites for completely new proteins without losing its performance. The fact that the sites could be predicted from averaged properties together with the fact that glycosylation sites are not precisely conserved indicates that mucin-type glycosylation in most cases is a bulk property and not a very site-specific one. NetOGlyc 3.1 is made available at www.cbs.dtu.dk/services/netoglyc. PMID:15385431

Julenius, Karin; Mølgaard, Anne; Gupta, Ramneek; Brunak, Søren

2005-02-01

298

De Novo Structure Prediction of Globular Proteins Aided by Sequence Variation-Derived Contacts  

PubMed Central

The advent of high accuracy residue-residue intra-protein contact prediction methods enabled a significant boost in the quality of de novo structure predictions. Here, we investigate the potential benefits of combining a well-established fragment-based folding algorithm – FRAGFOLD, with PSICOV, a contact prediction method which uses sparse inverse covariance estimation to identify co-varying sites in multiple sequence alignments. Using a comprehensive set of 150 diverse globular target proteins, up to 266 amino acids in length, we are able to address the effectiveness and some limitations of such approaches to globular proteins in practice. Overall we find that using fragment assembly with both statistical potentials and predicted contacts is significantly better than either statistical potentials or contacts alone. Results show up to nearly 80% of correct predictions (TM-score ?0.5) within analysed dataset and a mean TM-score of 0.54. Unsuccessful modelling cases emerged either from conformational sampling problems, or insufficient contact prediction accuracy. Nevertheless, a strong dependency of the quality of final models on the fraction of satisfied predicted long-range contacts was observed. This not only highlights the importance of these contacts on determining the protein fold, but also (combined with other ensemble-derived qualities) provides a powerful guide as to the choice of correct models and the global quality of the selected model. A proposed quality assessment scoring function achieves 0.93 precision and 0.77 recall for the discrimination of correct folds on our dataset of decoys. These findings suggest the approach is well-suited for blind predictions on a variety of globular proteins of unknown 3D structure, provided that enough homologous sequences are available to construct a large and accurate multiple sequence alignment for the initial contact prediction step. PMID:24637808

Kosciolek, Tomasz; Jones, David T.

2014-01-01

299

An improved hybrid global optimization method for protein tertiary structure prediction  

PubMed Central

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

McAllister, Scott R.

2009-01-01

300

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

301

Never born proteins as a test case for ab initio protein structures prediction.  

PubMed

The number of natural proteins although large is significantly smaller than the theoretical number of proteins that can be obtained combining the 20 natural amino acids, the so-called "never born proteins" (NBPs). The study of the structure and properties of these proteins allows to investigate the sources of the natural proteins being of unique characteristics or special properties. However the structural study of NPBs can also been intended as an ideal test for evaluating the efficiency of software packages for the ab initio protein structure prediction. In this research, 10.000 three-dimensional structures of proteins of completely random sequence generated according to ROSETTA and FOD model were compared. The results show the limits of these software packages, but at the same time indicate that in many cases there is a significant agreement between the prediction obtained. PMID:19238243

Minervini, Giovanni; Evangelista, Giuseppe; Polticelli, Fabio; Piwowar, Monika; Kochanczyk, Marek; Flis, Lukasz; Malawski, Maciej; Szepieniec, Tomasz; Wi?niowski, Zdzis?aw; Matczy?ska, Ewa; Prymula, Katarzyna; Roterman, Irena

2008-01-01

302

Predicting lidar measured forest vertical structure from multi-angle spectral data  

Microsoft Academic Search

A capability to remotely measure the vertical and spatial distribution of forest structure is required for more accurate modeling of energy, carbon, water, and climate over regional, continental, and global scales. We examined the potential of using a multi-angle spectral sensor to predict forest vertical structure as measured by an airborne lidar system. Data were acquired from AirMISR (Airborne Multi-Angle

D. S. Kimes; K. J. Ranson; G. Sun; J. B. Blair

2006-01-01

303

A graph-theoretic approach for classification and structure prediction of transmembrane ?-barrel proteins  

PubMed Central

Background Transmembrane ?-barrel 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 transmembrane ?-barrel 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 transmembrane ?-barrel proteins. Most of these methods emphasize on homology search rather than any biological or chemical basis. Results We present a novel graph-theoretic model for classification and structure prediction of transmembrane ?-barrel proteins. This model folds proteins based on energy minimization rather than a homology search, avoiding any assumption on availability of training dataset. The ab initio model presented in this paper is the first method to allow for permutations in the structure of transmembrane proteins and provides more structural information than any known algorithm. The model is also able to recognize ?-barrels by assessing the pseudo free energy. We assess the structure prediction on 41 proteins gathered from existing databases on experimentally validated transmembrane ?-barrel proteins. We show that our approach is quite accurate with over 90% F-score on strands and over 74% F-score on residues. The results are comparable to other algorithms suggesting that our pseudo-energy model is close to the actual physical model. We test our classification approach and show that it is able to reject ?-helical bundles with 100% accuracy and ?-barrel lipocalins with 97% accuracy. Conclusions We show that it is possible to design models for classification and structure prediction for transmembrane ?-barrel proteins which do not depend essentially on training sets but on combinatorial properties of the structures to be proved. These models are fairly accurate, robust and can be run very efficiently on PC-like computers. Such models are useful for the genome screening. PMID:22537300

2012-01-01

304

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

NASA Technical Reports Server (NTRS)

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

Jenkins, J. M.

1981-01-01

305

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. PMID:23935998

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

2013-01-01

306

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

2013-01-01

307

PiDNA: Predicting protein-DNA interactions with structural models.  

PubMed

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

Lin, Chih-Kang; Chen, Chien-Yu

2013-07-01

308

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

PubMed

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. PMID:21596778

Lam, Edmund; Kam, Alfred; Waldispühl, Jérôme

2011-07-01

309

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

PubMed

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

Devillers, J; Pandard, P; Richard, B

2013-01-01

310

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

E-print Network

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

Bryan, Allen W.

311

Thermodynamic ground state of MgB6 predicted from first principles structure search methods  

NASA Astrophysics Data System (ADS)

Crystalline structures of magnesium hexaboride, MgB6, were investigated using unbiased structure searching methods combined with first principles density functional calculations. An orthorhombic Cmcm structure was predicted as the thermodynamic ground state of MgB6. The energy of the Cmcm structure is significantly lower than the theoretical MgB6 models previously considered based on a primitive cubic arrangement of boron octahedra. The Cmcm structure is stable against the decomposition to elemental magnesium and boron solids at atmospheric pressure and high pressures up to 18.3 GPa. A unique feature of the predicted Cmcm structure is that the boron atoms are clustered into two forms: localized B6 octahedra and extended B? ribbons. Within the boron ribbons, the electrons are delocalized and this leads to a metallic ground state with vanished electric dipoles. The present prediction is in contrast to the previous proposal that the crystalline MgB6 maintains a semiconducting state with permanent dipole moments. MgB6 is estimated to have much weaker electron-phonon coupling compared with that of MgB2, and therefore it is not expected to be able to sustain superconductivity at high temperatures.

Wang, Hui; LeBlanc, K. A.; Gao, Bo; Yao, Yansun

2014-01-01

312

Vertical structure of predictability and information transport over the Northern Hemisphere  

NASA Astrophysics Data System (ADS)

Based on nonlinear prediction and information theory, vertical heterogeneity of predictability and information loss rate in geopotential height field are obtained over the Northern Hemisphere. On a seasonal-to-interannual time scale, the predictability is low in the lower troposphere and high in the mid-upper troposphere. However, within mid-upper troposphere over the subtropics ocean area, there is a relatively poor predictability. These conclusions also fit the seasonal time scale. Moving to the interannual time scale, the predictability becomes high in the lower troposphere and low in the mid-upper troposphere, contrary to the former case. On the whole the interannual trend is more predictable than the seasonal trend. The average information loss rate is low over the mid-east Pacific, west of North America, Atlantic and Eurasia, and the atmosphere over other places has a relatively high information loss rate on all-time scales. Two channels are found steadily over the Pacific Ocean and Atlantic Ocean in subtropics. There are also unstable channels. The four-season influence on predictability and information communication are studied. The predictability is low, no matter which season data are removed and each season plays an important role in the existence of the channels, except for the winter. The predictability and teleconnections are paramount issues in atmospheric science, and the teleconnections may be established by communication channels. So, this work is interesting since it reveals the vertical structure of predictability distribution, channel locations, and the contributions of different time scales to them and their variations under different seasons.

Feng, Ai-Xia; Wang, Qi-Gang; Gong, Zhi-Qiang; Feng, Guo-Lin

2014-02-01

313

Prediction of protein structural class using a complexity-based distance measure.  

PubMed

Knowledge of structural class plays an important role in understanding protein folding patterns. So it is necessary to develop effective and reliable computational methods for prediction of protein structural class. To this end, we present a new method called NN-CDM, a nearest neighbor classifier with a complexity-based distance measure. Instead of extracting features from protein sequences as done previously, distance between each pair of protein sequences is directly evaluated by a complexity measure of symbol sequences. Then the nearest neighbor classifier is adopted as the predictive engine. To verify the performance of this method, jackknife cross-validation tests are performed on several benchmark datasets. Results show that our approach achieves a high prediction accuracy over some classical methods. PMID:19330425

Liu, Taigang; Zheng, Xiaoqi; Wang, Jun

2010-03-01

314

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

315

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

316

FINDSITE: a combined evolution/structure-based approach to protein function prediction  

PubMed Central

A key challenge of the post-genomic era is the identification of the function(s) of all the molecules in a given organism. Here, we review the status of sequence and structure-based approaches to protein function inference and ligand screening that can provide functional insights for a significant fraction of the ?50% of ORFs of unassigned function in an average proteome. We then describe FINDSITE, a recently developed algorithm for ligand binding site prediction, ligand screening and molecular function prediction, which is based on binding site conservation across evolutionary distant proteins identified by threading. Importantly, FINDSITE gives comparable results when high-resolution experimental structures as well as predicted protein models are used. PMID:19324930

Brylinski, Michal

2009-01-01

317

Rational Design of Temperature-Sensitive Alleles Using Computational Structure Prediction  

PubMed Central

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

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

2011-01-01

318

Predicted velocity and density structure of the exhuming Papua New Guinea ultrahighpressure terrane  

E-print Network

Predicted velocity and density structure of the exhuming Papua New Guinea ultrahighpressure terrane that the Papua New Guinea (PNG) ultrahighpressure (UHP) terrane is dominated by rocks with weakly oriented quartz terrane is in amphibolite at 8% and 7% for VP and VS, respectively. Calculations of seismic velocities

Hacker, Bradley R.

319

DEVELOPMENT OF QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIPS FOR PREDICTING BIODEGRADATION KINETICS  

EPA Science Inventory

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

320

An intelligent neural system for predicting structural response subject to earthquakes  

Microsoft Academic Search

An efficient method is introduced to predict the time history responses of structures subject to earthquakes employing neural network techniques. In order to achieve this purpose, a new intelligent neural system (INS) is designed by combining competitive and radial basis function (RBF) neural networks. In the INS the input space is classified by a competitive neural network (CNN) based on

Saeed Gholizadeh; Javad Salajegheh; Eysa Salajegheh

2009-01-01

321

Hybrid approach in bird strike damage prediction on aeronautical composite structures  

Microsoft Academic Search

This paper deals with the problem of numerical prediction of bird strike induced damage on aeronautical structures. The problem of soft body impacts has been tackled by applying a hybrid Eulerian Lagrangian technique, thereby avoiding numerical difficulties associated with extensive mesh distortion. Eulerian modeling of the bird impactor resulted in a more realistic behavior of bird material during impact, which

D. Ivan?evi?; I. Smojver

2011-01-01

322

The predictive validity of the Structured Assessment of Violence Risk in Youth (SAVRY) among institutionalised adolescents  

Microsoft Academic Search

The aim of this study was to examine the short-term predictive validity of the Structured Assessment of Violence Risk in Youth (SAVRY) in a sample of institutionalised adolescents. Subjects were 208 adolescents in general residential adolescent psychiatry, correctional schools, or adolescent forensic units. Demographic features and the information needed to assess violence risk with SAVRY were retrieved from medical files

Monica Gammelgård; Anna-Maija Koivisto; Markku Eronen; Riittakerttu Kaltiala-Heino

2008-01-01

323

Predictive validity of the Structured Assessment of Violence Risk in Youth (SAVRY) during residential treatment  

Microsoft Academic Search

This prospective study examines the predictive validity of the Dutch version of the Structured Assessment of Violence Risk in Youth (SAVRY) by examining relationships between SAVRY scores and various types of disruptive behavior during residential treatment. The SAVRY, a risk assessment instrument, was coded for 66 male adolescents on the basis of file information and interviews. The adolescents were referred

Henny P. B. Lodewijks; Theo A. H. Doreleijers; Corine de Ruiter; Randy Borum

2008-01-01

324

Osmotic ensemble methods for predicting adsorption-induced structural transitions in nanoporous materials using molecular simulations  

Microsoft Academic Search

Osmotic framework adsorbed solution theory is a useful molecular simulation method to predict the evolution of structural transitions upon adsorption of guest molecules in flexible nanoporous solids. One challenge with previous uses of this approach has been the estimation of free energy differences between the solid phases of interest in the absence of adsorbed molecules. Here we demonstrate that these

Ji Zang; Sankar Nair; David S. Sholl

2011-01-01

325

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

ERIC Educational Resources Information Center

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

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

2007-01-01

326

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

PubMed

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

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

2014-10-01

327

SPACE: a suite of tools for protein structure prediction and analysis based on complementarity and environment  

Microsoft Academic Search

We describe a suite of SPACE tools for analysis and prediction of structures of biomolecules and their complexes. LPC\\/CSU software provides a common definition of inter-atomic contacts and complement- arity of contacting surfaces to analyze protein struc- ture and complexes. In the current version of LPC\\/ CSU, analyses of water molecules and nucleic acids have been added, together with improved

Vladimir Sobolev; Eran Eyal; Sergey Gerzon; Vladimir Potapov; Mariana Babor; Jaime Prilusky; Marvin Edelman

2005-01-01

328

1 INTRODUCTION Fluid flow predictions for ships and marine structures are often significantly more complex  

E-print Network

1 INTRODUCTION Fluid flow predictions for ships and marine structures are often significantly more problem when targeting an in- tegrated approach to ship simulation and optimisation during design aspects of ship hydrodynamic behaviour. Therefore, in order to maintain its competitive edge, the European

Andrzejak, Artur

329

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

E-print Network

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

Erdogan, Hakan

330

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

E-print Network

Institute for Pharmaceutical Materials Science, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge, UK, CB2 1EW, and The Pfizer Institute for Pharmaceutical Materials Science, Cambridge in the pro- cessing cycle of a pharmaceutical molecule. The field of crystal structure prediction (CSP) has

de Gispert, Adrià

331

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

PubMed

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

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

2014-01-01

332

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

PubMed Central

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

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

2014-01-01

333

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

PubMed Central

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

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

2010-01-01

334

Enriched behavioral prediction equation and its impact on structured leaning and the dynamic calculus.  

PubMed

This theoretical note describes an expansion of the behavioral prediction equation, in line with the greater complexity encountered in models of structured learning theory (R. B. Cattell, 1996a). This presents learning theory with a vector substitute for the simpler scalar quantities by which traditional Pavlovian-Skinnerian models have hitherto been represented. Structured learning can be demonstrated by vector changes across a range of intrapersonal psychological variables (ability, personality, motivation, and state constructs). Its use with motivational dynamic trait measures (R. B. Cattell, 1985) should reveal new theoretical possibilities for scientifically monitoring change processes (dynamic calculus model: R. B. Cattell, 1996b), such as encountered within psychotherapeutic settings (R. B. Cattell, 1987). The enhanced behavioral prediction equation suggests that static conceptualizations of personality structure such as the Big Five model are less than optimal. PMID:11863038

Cattell, Raymond B; Boyle, Gregory J; Chant, David

2002-01-01

335

Effective 3D protein structure prediction with local adjustment genetic-annealing algorithm.  

PubMed

The protein folding problem consists of predicting protein tertiary structure from a given amino acid sequence by minimizing the energy function. The protein folding structure prediction is computationally challenging and has been shown to be NP-hard problem when the 3D off-lattice AB model is employed. In this paper, the local adjustment genetic-annealing (LAGA) algorithm was used to search the ground state of 3D offlattice AB model for protein folding structure. The algorithm included an improved crossover strategy and an improved mutation strategy, where a local adjustment strategy was also used to enhance the searching ability. The experiments were carried out with the Fibonacci sequences. The experimental results demonstrate that the LAGA algorithm appears to have better performance and accuracy compared to the previous methods. PMID:20658338

Zhang, Xiao-Long; Lin, Xiao-Li

2010-09-01

336

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

NASA Technical Reports Server (NTRS)

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

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

2007-01-01

337

Computational neural networks in chemistry: Model free mapping devices for predicting chemical reactivity from molecular structure  

SciTech Connect

Computational neural networks (CNNs) are a computational paradigm inspired by the brain's massively parallel network of highly interconnected neurons. The power of computational neural networks derives not so much from their ability to model the brain as from their ability to learn by example and to map highly complex, nonlinear functions, without the need to explicitly specify the functional relationship. Two central questions about CNNs were investigated in the context of predicting chemical reactions: (1) the mapping properties of neural networks and (2) the representation of chemical information for use in CNNs. Chemical reactivity is here considered an example of a complex, nonlinear function of molecular structure. CNN's were trained using modifications of the back propagation learning rule to map a three dimensional response surface similar to those typically observed in quantitative structure-activity and structure-property relationships. The computational neural network's mapping of the response surface was found to be robust to the effects of training sample size, noisy data and intercorrelated input variables. The investigation of chemical structure representation led to the development of a molecular structure-based connection-table representation suitable for neural network training. An extension of this work led to a BE-matrix structure representation that was found to be general for several classes of reactions. The CNN prediction of chemical reactivity and regiochemistry was investigated for electrophilic aromatic substitution reactions, Markovnikov addition to alkenes, Saytzeff elimination from haloalkanes, Diels-Alder cycloaddition, and retro Diels-Alder ring opening reactions using these connectivity-matrix derived representations. The reaction predictions made by the CNNs were more accurate than those of an expert system and were comparable to predictions made by chemists.

Elrod, D.W.

1992-01-01

338

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

PubMed

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

Faraggi, Eshel; Kloczkowski, Andrzej

2014-05-01

339

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

PubMed Central

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

2014-01-01

340

SAND, a New Protein Family: From Nucleic Acid to Protein Structure and Function Prediction  

PubMed Central

As a result of genome, EST and cDNA sequencing projects, there are huge numbers of predicted and/or partially characterised protein sequences compared with a relatively small number of proteins with experimentally determined function and structure. Thus, there is a considerable attention focused on the accurate prediction of gene function and structure from sequence by using bioinformatics. In the course of our analysis of genomic sequence from Fugu rubripes, we identified a novel gene, SAND, with significant sequence identity to hypothetical proteins predicted in Saccharomyces cerevisiae, Schizosaccharomyces pombe, Caenorhabditis elegans, a Drosophila melanogaster gene, and mouse and human cDNAs. Here we identify a further SAND homologue in human and Arabidopsis thaliana by use of standard computational tools. We describe the genomic organisation of SAND in these evolutionarily divergent species and identify sequence homologues from EST database searches confirming the expression of SAND in over 20 different eukaryotes. We confirm the expression of two different SAND paralogues in mammals and determine expression of one SAND in other vertebrates and eukaryotes. Furthermore, we predict structural properties of SAND, and characterise conserved sequence motifs in this protein family. PMID:18628914

Cottage, Amanda; Edwards, Yvonne J. K.

2001-01-01

341

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

PubMed

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

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

2013-09-01

342

I-TASSER: Fully automated protein structure prediction in CASP8  

PubMed Central

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

Zhang, Yang

2009-01-01

343

Prediction of antiprion activity of therapeutic agents with structure-activity models.  

PubMed

We have developed computational structure-activity models for the prediction of antiprion activity of compounds with known molecular structure. The aim is to apply the developed classification and predictive models in further drug design of antiprion therapeutics. The neural network models developed on the counter-propagation reinforcement learning strategy performed better than the linear regression models. The initial data set was composed of 461 compounds representing diverse groups of chemicals (derivatives of acridine, quinolone, Congo red, 2-aminopyridine-3,5-dicarbonitrile, styrylbenzoazole, 2,5-diamino-benzoquinone), which have been tested in comparable cell-screening assay studies for their activity against prion accumulation. Initially, we have designed a classification model for preliminary sorting of compounds into highly active, active, and inactive groups. Further, only the active compounds with IC50 less or equal to 10 ?M were considered as the initial source of data. Altogether, 158 compounds were used to train the artificial neural network model for the estimation of the antiprion activity. The predictive ability of the model was significantly improved after selection of influential variables with genetic algorithm. The root- mean-squared error of the predicted pIC50 values for the external validation set (RMS EV) was slightly above 0.50 log units. A linear regression model, developed for the reasons of comparison, performed with a lower predictive ability (RMS EV 0.92 log units). The applicability domain of the models was assessed by a leverage and distance approach. The set of selected influential structural variables was further studied with the aim to get a better insight into the structural features of compounds potentially involved in disturbing of the prion-prion interactions. PMID:24052197

Venko, Katja; Župerl, Špela; Novi?, Marjana

2014-02-01

344

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

PubMed Central

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

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

2014-01-01

345

A Comparative Taxonomy of Parallel Algorithms for RNA Secondary Structure Prediction  

PubMed Central

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

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

2010-01-01

346

Numerical modeling of cellular/dendritic array growth: Spacing and structure predictions  

SciTech Connect

A numerical model of cellular and dendritic growth has been developed that can predict cellular and dendritic spacings, undercoolings, and the transition between structures. Fully self-consistent solutions are produced for axisymmetric interface shapes. An important feature of the model is that the spacing selection mechanism has been treated. A small, stable range of spacings is predicted for both cells and dendrites, and these agree well with experiment at both low and high velocities. By suitable nondimensionalization, relatively simple analytic expressions can be used to fit the numerical results. These expressions provide an insight into the cellular and dendritic growth processes and are useful for comparing theory with experiment.

Hunt, J.D. [Oxford Univ. (United Kingdom). Dept. of Materials; Lu, S.Z. [Michigan Technological Univ., Houghton, MI (United States)

1996-03-01

347

Structure-Based Function Prediction of Uncharacterized Protein Using Binding Sites Comparison  

PubMed Central

A challenge in structural genomics is prediction of the function of uncharacterized proteins. When proteins cannot be related to other proteins of known activity, identification of function based on sequence or structural homology is impossible and in such cases it would be useful to assess structurally conserved binding sites in connection with the protein's function. In this paper, we propose the function of a protein of unknown activity, the Tm1631 protein from Thermotoga maritima, by comparing its predicted binding site to a library containing thousands of candidate structures. The comparison revealed numerous similarities with nucleotide binding sites including specifically, a DNA-binding site of endonuclease IV. We constructed a model of this Tm1631 protein with a DNA-ligand from the newly found similar binding site using ProBiS, and validated this model by molecular dynamics. The interactions predicted by the Tm1631-DNA model corresponded to those known to be important in endonuclease IV-DNA complex model and the corresponding binding free energies, calculated from these models were in close agreement. We thus propose that Tm1631 is a DNA binding enzyme with endonuclease activity that recognizes DNA lesions in which at least two consecutive nucleotides are unpaired. Our approach is general, and can be applied to any protein of unknown function. It might also be useful to guide experimental determination of function of uncharacterized proteins. PMID:24244144

Konc, Janez; Hodoscek, Milan; Ogrizek, Mitja; Trykowska Konc, Joanna; Janezic, Dusanka

2013-01-01

348

REVIEWS OF TOPICAL PROBLEMS: Prediction and discovery of new structures in spiral galaxies  

NASA Astrophysics Data System (ADS)

A review is given of the last 20 years of published research into the nature, origin mechanisms, and observed features of spiral-vortex structures found in galaxies. The so-called rotating shallow water experiments are briefly discussed, carried out with a facility designed by the present author and built at the Russian Scientific Center 'Kurchatov Institute' to model the origin of galactic spiral structures. The discovery of new vortex-anticyclone structures in these experiments stimulated searching for them astronomically using the RAS Special Astrophysical Observatory's 6-meter BTA optical telescope, formerly the world's and now Europe's largest. Seven years after the pioneering experiments, Afanasyev and the present author discovered the predicted giant anticyclones in the galaxy Mrk 1040 by using BTA. Somewhat later, the theoretical prediction of giant cyclones in spiral galaxies was made, also to be verified by BTA afterwards. To use the observed line-of-sight velocity field for reconstructing the 3D velocity vector distribution in a galactic disk, a method for solving a problem from the class of ill-posed astrophysical problems was developed by the present author and colleagues. In addition to the vortex structure, other new features were discovered — in particular, slow bars (another theoretical prediction), for whose discovery an observational test capable of distinguishing them from their earlier-studied normal (fast) counterparts was designed.

Fridman, Aleksei M.

2007-02-01

349

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

PubMed Central

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

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

2011-01-01

350

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

PubMed Central

Background Protein alignments are an essential tool for many bioinformatics analyses. While sequence alignments are accurate for proteins of high sequence similarity, they become unreliable as they approach the so-called 'twilight zone' where sequence similarity gets indistinguishable from random. For such distant pairs, structure alignment is of much better quality. Nevertheless, sequence alignment is the only choice in the majority of cases where structural data is not available. This situation demands development of methods that extend the applicability of accurate sequence alignment to distantly related proteins. Results We develop a sequence alignment method that combines the prediction of a structural profile based on the protein's sequence with the alignment of that profile using our recently published alignment tool SABERTOOTH. In particular, we predict the contact vector of protein structures using an artificial neural network based on position-specific scoring matrices generated by PSI-BLAST and align these predicted contact vectors. The resulting sequence alignments are assessed using two different tests: First, we assess the alignment quality by measuring the derived structural similarity for cases in which structures are available. In a second test, we quantify the ability of the significance score of the alignments to recognize structural and evolutionary relationships. As a benchmark we use a representative set of the SCOP (structural classification of proteins) database, with similarities ranging from closely related proteins at SCOP family level, to very distantly related proteins at SCOP fold level. Comparing these results with some prominent sequence alignment tools, we find that SABERTOOTH produces sequence alignments of better quality than those of Clustal W, T-Coffee, MUSCLE, and PSI-BLAST. HHpred, one of the most sophisticated and computationally expensive tools available, outperforms our alignment algorithm at family and superfamily levels, while the use of SABERTOOTH is advantageous for alignments at fold level. Our alignment scheme will profit from future improvements of structural profiles prediction. Conclusions We present the automatic sequence alignment tool SABERTOOTH that computes pairwise sequence alignments of very high quality. SABERTOOTH is especially advantageous when applied to alignments of remotely related proteins. The source code is available at http://www.fkp.tu-darmstadt.de/sabertooth_project/, free for academic users upon request. PMID:20470364

2010-01-01

351

Advances in Predictive Capability of Pedestal Structure from FY11 Joint Research Target  

NASA Astrophysics Data System (ADS)

Joint experiment/theory/modeling research, performed as part of a US DOE Joint Research Target in FY2011, has led to improved predictive capability of the H-mode pedestal structure. Comparisons of experiments in C-Mod, DIII-D and NSTX with ELITE and BOUT++ show that the pedestals in the three machines reach the predicted peeling/ballooning (PB) limit at the onset of Type-I ELMs. Studies in all three devices show that the pedestal width scales approximately as the square root of the pedestal beta poloidal. This is expected if the pedestal p^' is limited by kinetic ballooning modes (KBMs). Coherent density fluctuations with characteristics expected for KBMs have been observed in some plasma conditions in DIII-D. The EPED model combines models for bootstrap current, PB modes and KBMs and predicts the pedestal pressure in DIII-D and C-Mod to within ˜20%.

Groebner, R. J.; Snyder, P. B.; Chang, C. S.; Hughes, J. W.; Maingi, R.; Xu, X. Q.

2012-10-01

352

Reported and predicted structures of Ba(Co,Nb)1-?O3 hexagonal perovskite phases.  

PubMed

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

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

2014-10-21

353

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

NASA Technical Reports Server (NTRS)

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

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

1994-01-01

354

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

PubMed Central

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

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

2012-01-01

355

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

PubMed

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

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

2013-09-10

356

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

PubMed

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

Baker, David

2014-04-01

357

Using the RosettaSurface Algorithm to Predict Protein Structure at Mineral Surfaces  

PubMed Central

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

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

2014-01-01

358

First-principles prediction of the equation of state for TcC with rocksalt structure  

NASA Astrophysics Data System (ADS)

The equation of state of TcC with rocksalt structure is investigated by means of first-principles density functional theory calculations combined with the quasi-harmonic Debye model in which the phononic effects are considered. Particular attention is paid to the predictions of the compressibility, the isothermal bulk modulus and its first pressure derivative which play a central role in the formulation of approximate equations of state for the first time. The properties of TcC with rocksalt structure are summarized in the pressure range of 0-80 GPa and the temperature up to 2500 K.

Sun, Xiao-Wei; Chu, Yan-Dong; Liu, Zi-Jiang; Song, Ting; Tian, Jun-Hong; Wei, Xiao-Ping

2014-10-01

359

Achieving luster: prenuptial molt pattern predicts iridescent structural coloration in Blue-black Grassquits  

Microsoft Academic Search

Colors in feathers are produced by pigment deposition or by nanostructures within barbs or barbules. In the absence of pigments\\u000a or nanostructures, light is scattered incoherently, producing white coloration. Honest advertisement models predict that ornamental\\u000a colors evolve if they reliably signal individual properties such as viability, health, or nutritional state. In this study,\\u000a we tested if (1) iridescent structural and

Rafael MaiaRegina; Regina H. Macedo

2011-01-01

360

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

Microsoft Academic Search

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

Maosen Cao; Pizhong Qiao; Qingwen Ren

2009-01-01

361

Analysis of the role of predicted RNA secondary structures in Ebola virus replication.  

PubMed

Thermodynamic modeling of Ebola viral RNA predicts the formation of RNA stem-loop structures at the 3' and 5' termini and panhandle structures between the termini of the genomic (or antigenomic) RNAs. Sequence analysis showed a high degree of identity among Ebola Zaire, Sudan, Reston, and Cote d'Ivoire subtype viruses in their 3' and 5' termini (18 nucleotides in length) and within a second region (internal by approximately 20 nucleotides). While base pairing of the two conserved regions could lead to the formation of the base of the putative stem-loop or panhandle structures, the intervening sequence variation altered the predictions for the rest of the structures. Using an in vivo minigenome replication system, we engineered mutations designed to disrupt potential base pairing in the viral RNA termini. Analysis of these variants by screening for enhanced green fluorescent protein reporter expression and by quantitation of minigenomic RNA levels demonstrated that the upper portions of the putative panhandle and 3' genomic structures can be destabilized without affecting virus replication. PMID:12642094

Crary, Sharon M; Towner, Jonathan S; Honig, Jessica E; Shoemaker, Trevor R; Nichol, Stuart T

2003-02-15

362

Damage Prediction and Estimation in Structural Mechanics Based on Data Mining  

SciTech Connect

Damage in a material includes localized softening or cracks in a structural component due to high operational loads, or the presence of flaws in a structure due to various manufacturing processes. Methods that identify the presence, the location and the severity of damage in the structure are useful for non-destructive evaluation procedures that are typically employed in agile manufacturing and rapid prototyping systems. The current state-of-the art techniques for these inverse problems are computationally intensive or ill conditioned when insufficient data exists. Early work by a number of researchers has shown that data mining techniques can provide a potential solution to this problem. In this paper, they investigate the use of data mining techniques for predicting failure in a variety of 2D and 3D structures using artificial neural networks (ANNs) and decision trees. This work shows that if the correct features are chosen to build the model, and the model is trained on an adequate amount of data, the model can then correctly classify the failure event as well as predict location and severity of the damage in these structures.

Sandhu, S S; Kanapady, R; Tamma, K K; Kamath, C; Kumar, V

2001-07-23

363

Neuroimaging of structural pathology and connectomics in traumatic brain injury: Toward personalized outcome prediction?  

PubMed Central

Recent contributions to the body of knowledge on traumatic brain injury (TBI) favor the view that multimodal neuroimaging using structural and functional magnetic resonance imaging (MRI and fMRI, respectively) as well as diffusion tensor imaging (DTI) has excellent potential to identify novel biomarkers and predictors of TBI outcome. This is particularly the case when such methods are appropriately combined with volumetric/morphometric analysis of brain structures and with the exploration of TBI-related changes in brain network properties at the level of the connectome. In this context, our present review summarizes recent developments on the roles of these two techniques in the search for novel structural neuroimaging biomarkers that have TBI outcome prognostication value. The themes being explored cover notable trends in this area of research, including (1) the role of advanced MRI processing methods in the analysis of structural pathology, (2) the use of brain connectomics and network analysis to identify outcome biomarkers, and (3) the application of multivariate statistics to predict outcome using neuroimaging metrics. The goal of the review is to draw the community's attention to these recent advances on TBI outcome prediction methods and to encourage the development of new methodologies whereby structural neuroimaging can be used to identify biomarkers of TBI outcome. PMID:24179732

Irimia, Andrei; Wang, Bo; Aylward, Stephen R.; Prastawa, Marcel W.; Pace, Danielle F.; Gerig, Guido; Hovda, David A.; Kikinis, Ron; Vespa, Paul M.; Van Horn, John D.

2012-01-01

364

e-RNA: a collection of web servers for comparative RNA structure prediction and visualisation  

PubMed Central

e-RNA offers a free and open-access collection of five published RNA sequence analysis tools, each solving specific problems not readily addressed by other available tools. Given multiple sequence alignments, Transat detects all conserved helices, including those expected in a final structure, but also transient, alternative and pseudo-knotted helices. RNA-Decoder uses unique evolutionary models to detect conserved RNA secondary structure in alignments which may be partly protein-coding. SimulFold simultaneously co-estimates the potentially pseudo-knotted conserved structure, alignment and phylogenetic tree for a set of homologous input sequences. CoFold predicts the minimum-free energy structure for an input sequence while taking the effects of co-transcriptional folding into account, thereby greatly improving the prediction accuracy for long sequences. R-chie is a program to visualise RNA secondary structures as arc diagrams, allowing for easy comparison and analysis of conserved base-pairs and quantitative features. The web site server dispatches user jobs to a cluster, where up to 100 jobs can be processed in parallel. Upon job completion, users can retrieve their results via a bookmarked or emailed link. e-RNA is located at http://www.e-rna.org. PMID:24810851

Lai, Daniel; Meyer, Irmtraud M.

2014-01-01

365

CPU-GPU hybrid accelerating the Zuker algorithm for RNA secondary structure prediction applications  

PubMed Central

Background Prediction of ribonucleic acid (RNA) secondary structure remains one of the most important research areas in bioinformatics. The Zuker algorithm is one of the most popular methods of free energy minimization for RNA secondary structure prediction. Thus far, few studies have been reported on the acceleration of the Zuker algorithm on general-purpose processors or on extra accelerators such as Field Programmable Gate-Array (FPGA) and Graphics Processing Units (GPU). To the best of our knowledge, no implementation combines both CPU and extra accelerators, such as GPUs, to accelerate the Zuker algorithm applications. Results In this paper, a CPU-GPU hybrid computing system that accelerates Zuker algorithm applications for RNA secondary structure prediction is proposed. The computing tasks are allocated between CPU and GPU for parallel cooperate execution. Performance differences between the CPU and the GPU in the task-allocation scheme are considered to obtain workload balance. To improve the hybrid system performance, the Zuker algorithm is optimally implemented with special methods for CPU and GPU architecture. Conclusions Speedup of 15.93× over optimized multi-core SIMD CPU implementation and performance advantage of 16% over optimized GPU implementation are shown in the experimental results. More than 14% of the sequences are executed on CPU in the hybrid system. The system combining CPU and GPU to accelerate the Zuker algorithm is proven to be promising and can be applied to other bioinformatics applications. PMID:22369626

2012-01-01

366

How well can the accuracy of comparative protein structure models be predicted?  

PubMed Central

Comparative structure models are available for two orders of magnitude more protein sequences than are experimentally determined structures. These models, however, suffer from two limitations that experimentally determined structures do not: They frequently contain significant errors, and their accuracy cannot be readily assessed. We have addressed the latter limitation by developing a protocol optimized specifically for predicting the C? root-mean-squared deviation (RMSD) and native overlap (NO3.5Å) errors of a model in the absence of its native structure. In contrast to most traditional assessment scores that merely predict one model is more accurate than others, this approach quantifies the error in an absolute sense, thus helping to determine whether or not the model is suitable for intended applications. The assessment relies on a model-specific scoring function constructed by a support vector machine. This regression optimizes the weights of up to nine features, including various sequence similarity measures and statistical potentials, extracted from a tailored training set of models unique to the model being assessed: If possible, we use similarly sized models with the same fold; otherwise, we use similarly sized models with the same secondary structure composition. This protocol predicts the RMSD and NO3.5Å errors for a diverse set of 580,317 comparative models of 6174 sequences with correlation coefficients (r) of 0.84 and 0.86, respectively, to the actual errors. This scoring function achieves the best correlation compared to 13 other tested assessment criteria that achieved correlations ranging from 0.35 to 0.71. PMID:18832340

Eramian, David; Eswar, Narayanan; Shen, Min-Yi; Sali, Andrej

2008-01-01

367

Validation of Quantitative Structure-Activity Relationship (QSAR) Model for Photosensitizer Activity Prediction  

PubMed Central

Photodynamic therapy is a relatively new treatment method for cancer which utilizes a combination of oxygen, a photosensitizer and light to generate reactive singlet oxygen that eradicates tumors via direct cell-killing, vasculature damage and engagement of the immune system. Most of photosensitizers that are in clinical and pre-clinical assessments, or those that are already approved for clinical use, are mainly based on cyclic tetrapyrroles. In an attempt to discover new effective photosensitizers, we report the use of the quantitative structure-activity relationship (QSAR) method to develop a model that could correlate the structural features of cyclic tetrapyrrole-based compounds with their photodynamic therapy (PDT) activity. In this study, a set of 36 porphyrin derivatives was used in the model development where 24 of these compounds were in the training set and the remaining 12 compounds were in the test set. The development of the QSAR model involved the use of the multiple linear regression analysis (MLRA) method. Based on the method, r2 value, r2 (CV) value and r2 prediction value of 0.87, 0.71 and 0.70 were obtained. The QSAR model was also employed to predict the experimental compounds in an external test set. This external test set comprises 20 porphyrin-based compounds with experimental IC50 values ranging from 0.39 ?M to 7.04 ?M. Thus the model showed good correlative and predictive ability, with a predictive correlation coefficient (r2 prediction for external test set) of 0.52. The developed QSAR model was used to discover some compounds as new lead photosensitizers from this external test set. PMID:22272096

Frimayanti, Neni; Yam, Mun Li; Lee, Hong Boon; Othman, Rozana; Zain, Sharifuddin M.; Rahman, Noorsaadah Abd.

2011-01-01

368

mRNA secondary structure optimization using a correlated stem-loop prediction  

PubMed Central

Secondary structure of messenger RNA plays an important role in the bio-synthesis of proteins. Its negative impact on translation can reduce the yield of protein by slowing or blocking the initiation and movement of ribosomes along the mRNA, becoming a major factor in the regulation of gene expression. Several algorithms can predict the formation of secondary structures by calculating the minimum free energy of RNA sequences, or perform the inverse process of obtaining an RNA sequence for a given structure. However, there is still no approach to redesign an mRNA to achieve minimal secondary structure without affecting the amino acid sequence. Here we present the first strategy to optimize mRNA secondary structures, to increase (or decrease) the minimum free energy of a nucleotide sequence, without changing its resulting polypeptide, in a time-efficient manner, through a simplistic approximation to hairpin formation. Our data show that this approach can efficiently increase the minimum free energy by >40%, strongly reducing the strength of secondary structures. Applications of this technique range from multi-objective optimization of genes by controlling minimum free energy together with CAI and other gene expression variables, to optimization of secondary structures at the genomic level. PMID:23325845

Gaspar, Paulo; Moura, Gabriela; Santos, Manuel A. S.; Oliveira, Jose Luis

2013-01-01

369

Predicting the secondary structures and tertiary interactions of 211 group I introns in IE subgroup  

PubMed Central

The large number of currently available group I intron sequences in the public databases provides opportunity for studying this large family of structurally complex catalytic RNA by large-scale comparative sequence analysis. In this study, the detailed secondary structures of 211 group I introns in the IE subgroup were manually predicted. The secondary structure-favored alignments showed that IE introns contain 14 conserved stems. The P13 stem formed by long-range base-pairing between P2.1 and P9.1 is conserved among IE introns. Sequence variations in the conserved core divide IE introns into three distinct minor subgroups, namely IE1, IE2 and IE3. Co-variation of the peripheral structural motifs with core sequences supports that the peripheral elements function in assisting the core structure folding. Interestingly, host-specific structural motifs were found in IE2 introns inserted at S516 position. Competitive base-pairing is found to be conserved at the junctions of all long-range paired regions, suggesting a possible mechanism of establishing long-range base-pairing during large RNA folding. These findings extend our knowledge of IE introns, indicating that comparative analysis can be a very good complement for deepening our understanding of RNA structure and function in the genomic era. PMID:15843683

Li, Zhijie; Zhang, Yi

2005-01-01

370

A Parallel Framework for Multipoint Spiral Search in ab Initio Protein Structure Prediction.  

PubMed

Protein structure prediction is computationally a very challenging problem. A large number of existing search algorithms attempt to solve the problem by exploring possible structures and finding the one with the minimum free energy. However, these algorithms perform poorly on large sized proteins due to an astronomically wide search space. In this paper, we present a multipoint spiral search framework that uses parallel processing techniques to expedite exploration by starting from different points. In our approach, a set of random initial solutions are generated and distributed to different threads. We allow each thread to run for a predefined period of time. The improved solutions are stored threadwise. When the threads finish, the solutions are merged together and the duplicates are removed. A selected distinct set of solutions are then split to different threads again. In our ab initio protein structure prediction method, we use the three-dimensional face-centred-cubic lattice for structure-backbone mapping. We use both the low resolution hydrophobic-polar energy model and the high-resolution 20 × 20 energy model for search guiding. The experimental results show that our new parallel framework significantly improves the results obtained by the state-of-the-art single-point search approaches for both energy models on three-dimensional face-centred-cubic lattice. We also experimentally show the effectiveness of mixing energy models within parallel threads. PMID:24744779

Rashid, Mahmood A; Shatabda, Swakkhar; Newton, M A Hakim; Hoque, Md Tamjidul; Sattar, Abdul

2014-01-01

371

Prediction and statistics of pseudoknots in RNA structures using exactly clustered stochastic simulations  

NASA Astrophysics Data System (ADS)

Ab initio RNA secondary structure predictions have long dismissed helices interior to loops, so-called pseudoknots, despite their structural importance. Here we report that many pseudoknots can be predicted through long-time-scale RNA-folding simulations, which follow the stochastic closing and opening of individual RNA helices. The numerical efficacy of these stochastic simulations relies on an (n2) clustering algorithm that computes time averages over a continuously updated set of n reference structures. Applying this exact stochastic clustering approach, we typically obtain a 5- to 100-fold simulation speed-up for RNA sequences up to 400 bases, while the effective acceleration can be as high as 105-fold for short, multistable molecules (150 bases). We performed extensive folding statistics on random and natural RNA sequences and found that pseudoknots are distributed unevenly among RNA structures and account for up to 30% of base pairs in G+C-rich RNA sequences (online RNA-folding kinetics server including pseudoknots: http://kinefold.u-strasbg.fr).

Xayaphoummine, A.; Bucher, T.; Thalmann, F.; Isambert, H.

2003-12-01

372

A Parallel Framework for Multipoint Spiral Search in ab Initio Protein Structure Prediction  

PubMed Central

Protein structure prediction is computationally a very challenging problem. A large number of existing search algorithms attempt to solve the problem by exploring possible structures and finding the one with the minimum free energy. However, these algorithms perform poorly on large sized proteins due to an astronomically wide search space. In this paper, we present a multipoint spiral search framework that uses parallel processing techniques to expedite exploration by starting from different points. In our approach, a set of random initial solutions are generated and distributed to different threads. We allow each thread to run for a predefined period of time. The improved solutions are stored threadwise. When the threads finish, the solutions are merged together and the duplicates are removed. A selected distinct set of solutions are then split to different threads again. In our ab initio protein structure prediction method, we use the three-dimensional face-centred-cubic lattice for structure-backbone mapping. We use both the low resolution hydrophobic-polar energy model and the high-resolution 20 × 20 energy model for search guiding. The experimental results show that our new parallel framework significantly improves the results obtained by the state-of-the-art single-point search approaches for both energy models on three-dimensional face-centred-cubic lattice. We also experimentally show the effectiveness of mixing energy models within parallel threads. PMID:24744779

Rashid, Mahmood A.; Newton, M. A. Hakim; Hoque, Md Tamjidul; Sattar, Abdul

2014-01-01

373

Switch Region for Pathogenic Structural Change in Conformational Disease and Its Prediction  

PubMed Central

Many diseases are believed to be related to abnormal protein folding. In the first step of such pathogenic structural changes, misfolding occurs in regions important for the stability of the native structure. This destabilizes the normal protein conformation, while exposing the previously hidden aggregation-prone regions, leading to subsequent errors in the folding pathway. Sites involved in this first stage can be deemed switch regions of the protein, and can represent perfect binding targets for drugs to block the abnormal folding pathway and prevent pathogenic conformational changes. In this study, a prediction algorithm for the switch regions responsible for the start of pathogenic structural changes is introduced. With an accuracy of 94%, this algorithm can successfully find short segments covering sites significant in triggering conformational diseases (CDs) and is the first that can predict switch regions for various CDs. To illustrate its effectiveness in dealing with urgent public health problems, the reason of the increased pathogenicity of H5N1 influenza virus is analyzed; the mechanisms of the pandemic swine-origin 2009 A(H1N1) influenza virus in overcoming species barriers and in infecting large number of potential patients are also suggested. It is shown that the algorithm is a potential tool useful in the study of the pathology of CDs because: (1) it can identify the origin of pathogenic structural conversion with high sensitivity and specificity, and (2) it provides an ideal target for clinical treatment. PMID:20111584

Liu, Xin; Zhao, Ya-Pu

2010-01-01

374

A quantitative structure-activity relationship for predicting metabolic biotransformation rates for organic chemicals in fish.  

PubMed

An evaluated database of whole body in vivo biotransformation rate estimates in fish was used to develop a model for predicting the primary biotransformation half-lives of organic chemicals. The estimated biotransformation rates were converted to half-lives and divided into a model development set (n=421) and an external validation set (n=211) to test the model. The model uses molecular substructures similar to those of other biodegradation models. The biotransformation half-life predictions were calculated based on multiple linear regressions of development set data against counts of 57 molecular substructures, the octanol-water partition coefficient, and molar mass. The coefficient of determination (r2) for the development set was 0.82, the cross-validation (leave-one-out coefficient of determination, q2) was 0.75, and the mean absolute error (MAE) was 0.38 log units (factor of 2.4). Results for the external validation of the model using an independent test set were r2 = 0.73 and MAE = 0.45 log units (factor of 2.8). For the development set, 68 and 95% of the predicted values were within a factor of 3 and a factor of 10 of the expected values, respectively. For the test (or validation) set, 63 and 90% of the predicted values were within a factor of 3 and a factor of 10 of the expected values, respectively. Reasons for discrepancies between model predictions and expected values are discussed and recommendations are made for improving the model. This model can predict biotransformation rate constants from chemical structure for screening level bioaccumulation hazard assessments, exposure and risk assessments, comparisons with other in vivo and in vitro estimates, and as a contribution to testing strategies that reduce animal usage. PMID:19152232

Arnot, Jon A; Meylan, William; Tunkel, Jay; Howard, Phil H; Mackay, Don; Bonnell, Mark; Boethling, Robert S

2009-06-01

375

Bioinformatic Screening of Autoimmune Disease Genes and Protein Structure Prediction with FAMS for Drug Discovery  

PubMed Central

Autoimmune diseases are often intractable because their causes are unknown. Identifying which genes contribute to these diseases may allow us to understand the pathogenesis, but it is difficult to determine which genes contribute to disease. Recently, epigenetic information has been considered to activate/deactivate disease-related genes. Thus, it may also be useful to study epigenetic information that differs between healthy controls and patients with autoimmune disease. Among several types of epigenetic information, promoter methylation is believed to be one of the most important factors. Here, we propose that principal component analysis is useful to identify specific gene promoters that are differently methylated between the normal healthy controls and patients with autoimmune disease. Full Automatic Modeling System (FAMS) was used to predict the three-dimensional structures of selected proteins and successfully inferred relatively confident structures. Several possibilities of the application to the drug discovery based on obtained structures are discussed. PMID:23855671

Ishida, Shigeharu; Umeyama, Hideaki; Iwadate, Mitsuo; Y-h, Taguchi

2014-01-01

376

An All-Atom Force Field for Tertiary Structure Prediction of Helical Proteins  

PubMed Central

We have developed an all-atom free-energy force field (PFF01) for protein tertiary structure prediction. PFF01 is based on physical interactions and was parameterized using experimental structures of a family of proteins believed to span a wide variety of possible folds. It contains empirical, although sequence-independent terms for hydrogen bonding. Its solvent-accessible surface area solvent model was first fit to transfer energies of small peptides. The parameters of the solvent model were then further optimized to stabilize the native structure of a single protein, the autonomously folding villin headpiece, against competing low-energy decoys. Here we validate the force field for five nonhomologous helical proteins with 20–60 amino acids. For each protein, decoys with 2–3 Å backbone root mean-square deviation and correct experimental C?–C? distance constraints emerge as those with the lowest energy. PMID:15507688

Herges, T.; Wenzel, W.

2004-01-01

377

Radio Brightness Temperatures and Angular Dimensions of Recently Predicted Vl-Bi Small-Scale Structures  

NASA Astrophysics Data System (ADS)

RESUMEN. Muestro que analisis recientes publicados de fuentes de radio galacticas y extragalacticas predicen estructuras en pequera escala en fuentes de radio extendidas, remanentes de supernova, vientos protoestelares, nubes moleculares, distorsiones del fondo de 3 K, enanas blancas magnetizadas, estrellas de tipo tardio y el Sol. Discuto las temperatu- ras de brillo de radio de estas estructuras y sus ditnensiones. Muestro que estas estructuras son detectables con las sensibilidades actuales de VLBI (o en el futuro cercano). ABSTRACT. I show that recently published analysis of galactic and extragalactic radio sources make predictions of small-scale structures in extended radio sources, supernovae remnants, protostellar winds, molecu- lar clouds, distortions of the 3 K background, magnetized white dwarf binaries, late-type stars and the sun. I discuss the radio brightness temperatures of these structures and their dimensions. I show that these structures are detectable with present (or near future) VLBI sensitivities. : RADIO SOURCES-EXTENDED

Opher, R.

1990-11-01

378

Critical Assessment of Methods of Protein Structure Prediction (CASP) - Round IX  

PubMed Central

This paper is an introduction to the special issue of the journal PROTEINS, dedicated to the ninth CASP experiment to assess the state of the art in protein structure modeling. The paper describes the conduct of the experiment, the categories of prediction included, and outlines the evaluation and assessment procedures. Methods for modeling protein structure continue to advance, although at a more modest pace than in the early CASP experiments. Developments of note are indications of improvement in model accuracy for some classes of target, an improved ability to choose the most accurate of a set of generated models, and evidence of improvement in accuracy for short ‘new fold’ models. In addition, a new analysis of regions of models not derivable from the most obvious template structure has revealed better performance than expected. PMID:21997831

Moult, John; Fidelis, Krzysztof; Kryshtafovych, Andriy; Tramontano, Anna

2014-01-01

379

Prediction of compounds in different local structure-activity relationship environments using emerging chemical patterns.  

PubMed

Active compounds can participate in different local structure-activity relationship (SAR) environments and introduce different degrees of local SAR discontinuity, depending on their structural and potency relationships in data sets. Such SAR features have thus far mostly been analyzed using descriptive approaches, in particular, on the basis of activity landscape modeling. However, compounds in different local SAR environments have not yet been predicted. Herein, we adapt the emerging chemical patterns (ECP) method, a machine learning approach for compound classification, to systematically predict compounds with different local SAR characteristics. ECP analysis is shown to accurately assign many compounds to different local SAR environments across a variety of activity classes covering the entire range of observed local SARs. Control calculations using random forests and multiclass support vector machines were carried out and a variety of statistical performance measures were applied. In all instances, ECP calculations yielded comparable or better performance than controls. The approach presented herein can be applied to predict compounds that complement local SARs or prioritize compounds with different SAR characteristics. PMID:24803014

Namasivayam, Vigneshwaran; Gupta-Ostermann, Disha; Balfer, Jenny; Heikamp, Kathrin; Bajorath, Jürgen

2014-05-27

380

Structure-based predictions broadly link transcription factor mutations to gene expression changes in cancers  

PubMed Central

Thousands of unique mutations in transcription factors (TFs) arise in cancers, and the functional and biological roles of relatively few of these have been characterized. Here, we used structure-based methods developed specifically for DNA-binding proteins to systematically predict the consequences of mutations in several TFs that are frequently mutated in cancers. The explicit consideration of protein–DNA interactions was crucial to explain the roles and prevalence of mutations in TP53 and RUNX1 in cancers, and resulted in a higher specificity of detection for known p53-regulated genes among genetic associations between TP53 genotypes and genome-wide expression in The Cancer Genome Atlas, compared to existing methods of mutation assessment. Biophysical predictions also indicated that the relative prevalence of TP53 missense mutations in cancer is proportional to their thermodynamic impacts on protein stability and DNA binding, which is consistent with the selection for the loss of p53 transcriptional function in cancers. Structure and thermodynamics-based predictions of the impacts of missense mutations that focus on specific molecular functions may be increasingly useful for the precise and large-scale inference of aberrant molecular phenotypes in cancer and other complex diseases. PMID:25378323

Ashworth, Justin; Bernard, Brady; Reynolds, Sheila; Plaisier, Christopher L.; Shmulevich, Ilya; Baliga, Nitin S.

2014-01-01

381

Predicting translational diffusion of evolutionary conserved RNA structures by the nucleotide number  

PubMed Central

Ribonucleic acids are highly conserved essential parts of cellular life. RNA function is determined to a large extent by its hydrodynamic behaviour. The presented study proposes a strategy to predict the hydrodynamic behaviour of RNA single strands on the basis of the polymer size. By atom-level shell-modelling of high-resolution structures, hydrodynamic radius and diffusion coefficient of evolutionary conserved RNA single strands (ssRNA) were calculated. The diffusion coefficients D of 17–174 nucleotides (nt) containing ssRNA depended on the number of nucleotides N with D?=?4.56?×?10?10 N?0.39 m2 s?1. The hydrodynamic radius RH depended on N with RH?=?5.00?×?10?10 N0.38 m. An average ratio of the radius of gyration and the hydrodynamic radius of 0.98?±?0.08 was calculated in solution. The empirical law was tested by in solution measured hydrodynamic radii and radii of gyration and was found to be highly consistent with experimental data of evolutionary conserved ssRNA. Furthermore, the hydrodynamic behaviour of several evolutionary unevolved ribonucleic acids could be predicted. Based on atom-level shell-modelling of high-resolution structures and experimental hydrodynamic data, empirical models are proposed, which enable to predict the translational diffusion coefficient and molecular size of short RNA single strands solely on the basis of the polymer size. PMID:21068070

Werner, Arne

2011-01-01

382

PackHelix: a tool for helix-sheet packing during protein structure prediction  

PubMed Central

The three-dimensional structure of a protein is organized around the packing of its secondary structure elements. Predicting the topology and constructing the geometry of structural motifs involving ?-helices and/or ?-strands are therefore key steps for accurate prediction of protein structure. While many efforts have focused on how to pack helices and on how to sample exhaustively the topologies and geometries of multiple strands forming a ?-sheet in a protein, there has been little progress on generating native-like packing of helices on sheets. We describe a method that can generate the packing of multiple helices on a given ?-sheet for ??? sandwich type protein folds. This method mines the results of a statistical analysis of the conformations of ??2 motifs in protein structures to provide input values for the geometric attributes of the packing of a helix on a sheet. It then proceeds with a geometric builder that generates multiple arrangements of the helices on the sheet of interest by sampling through these values and performing consistency checks that guarantee proper loop geometry between the helices and the strands, minimal number of collisions between the helices, and proper formation of a hydrophobic core. The method is implemented as a module of ProteinShop. Our results show that it produces structures that are within 4–6 Å RMSD of the native one, regardless of the number of helices that need to be packed, though this number may increase if the protein has several helices between two consecutive strands in the sequence that pack on the sheet formed by these two strands. PMID:21905109

Hu, Chengcheng; Koehl, Patrice; Max, Nelson

2011-01-01

383

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

E-print Network

-relevant DNA sequences could be addressed. Recently, neomycin, an aminoglycoside antibiotic, has been shown as controls in these experiments. All structures of the conjugates used in the study are shown in Figure 1. UV

Stuart, Steven J.

384

Performance of a new atomistic geometrical model of the B-DNA configuration for DNA-radiation interaction simulations  

NASA Astrophysics Data System (ADS)

We have recently developed an atomistic model of the B-DNA configuration, up to the 30-nm chromatin fiber. This model is intended to be used in Monte Carlo simulations of the DNA-radiation interaction, specifically in conjunction with the Geant4-DNA extension of the Geant4 Monte Carlo toolkit. In this work, 11449 parallel chromatin fibers have been arranged within a cube mimicking a cell nucleus containing about 6.5×109 base pairs. Each atom in the model is represented by a sphere with the corresponding van der Waals radius. Direct single, double and total DNA strand break yields due to the impact of protons and alpha particles with LET ranging from 4.57 to 207.1 keV/?m have been determined. Also, the corresponding site-hit probabilities have been calculated.

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

2014-03-01

385

Noise and randomlike behavior in perceptrons: theory and application to protein structure prediction  

NASA Astrophysics Data System (ADS)

In this paper we study the effective behavior of a single-layer perceptron that is forced to learn a noisy mapping (e.g. associations of patterns with classes). The effect of different kinds of noise on the output of the network is discussed as a function of the noise intensity. It is argued that noise induces a random-like component in the overall behavior of the perceptron which we describe in terms of independent biased random flights in the space of the weights. These random processes (one for each class) are ruled by probability distributions specified by the weights themselves. Our model is applied to the real world application of the prediction of protein secondary structures. Several observations made in this task domain are rationalized in terms of the present model that, among others, provides a link between the seeming existence of an upper bound for the prediction efficiency and the amount of noise in the mapping.

Compiani, Mario; Fariselli, Piero; Casadio, Rita

1996-03-01

386

Geometrical analysis of Cys-Cys bridges in proteins and their prediction from incomplete structural information  

NASA Technical Reports Server (NTRS)

Analysis of C-alpha atom positions from cysteines involved in disulphide bridges in protein crystals shows that their geometric characteristics are unique with respect to other Cys-Cys, non-bridging pairs. They may be used for predicting disulphide connections in incompletely determined protein structures, such as low resolution crystallography or theoretical folding experiments. The basic unit for analysis and prediction is the 3 x 3 distance matrix for Cx positions of residues (i - 1), Cys(i), (i +1) with (j - 1), Cys(j), (j + 1). In each of its columns, row and diagonal vector--outer distances are larger than the central distance. This analysis is compared with some analytical models.

Goldblum, A.; Rein, R.

1987-01-01

387

Structural Dynamics Modeling of HIRENASD in Support of the Aeroelastic Prediction Workshop  

NASA Technical Reports Server (NTRS)

An Aeroelastic Prediction Workshop (AePW) was held in April 2012 using three aeroelasticity case study wind tunnel tests for assessing the capabilities of various codes in making aeroelasticity predictions. One of these case studies was known as the HIRENASD model that was tested in the European Transonic Wind Tunnel (ETW). This paper summarizes the development of a standardized enhanced analytical HIRENASD structural model for use in the AePW effort. The modifications to the HIRENASD finite element model were validated by comparing modal frequencies, evaluating modal assurance criteria, comparing leading edge, trailing edge and twist of the wing with experiment and by performing steady and unsteady CFD analyses for one of the test conditions on the same grid, and identical processing of results.

Wieseman, Carol; Chwalowski, Pawel; Heeg, Jennifer; Boucke, Alexander; Castro, Jack

2013-01-01

388

Structural integrity of corticospinal motor fibers predicts motor impairment in chronic stroke  

PubMed Central

Objective: Motor impairment after stroke has been related to infarct size, infarct location, and integrity of motor tracts. To determine the value of diffusion tensor imaging (DTI) as a predictor of motor outcome and its role as a structural surrogate marker of impairment in chronic stroke, we tested correlations between motor impairment and DTI-derived measures of motor tract integrity. Methods: Thirty-five chronic stroke patients with varying degrees of recovery underwent DTI and motor impairment assessments. Fibers originating from the precentral gyrus were traced and separated into pyramidal tract (PT) and alternate motor fibers (aMF). Asymmetry indices of fiber number and regional fractional anisotropy (FA) values comparing lesional with nonlesional hemispheres were correlated with motor impairment scores and compared to an age-matched control group. Results: Fiber number and regional FA value asymmetry significantly differed between the groups with lower values in the patients' lesional hemispheres. Both measures significantly predicted motor impairment with stronger predictions when all motor tracts were combined as compared to predictions using only the PT. The pattern of motor tract damage (PT only vs PT and aMF) led to a classification of mild, moderate, or severe impairment with significant between-group differences in motor impairment scores. Conclusions: Diffusion tensor imaging-derived measures are valid structural markers of motor impairment. The integrity of all descending motor tracts, not merely the pyramidal tract, appears to account for stroke recovery. A 3-tier, hierarchical classification of impairment categories based on the pattern of motor tract damage is proposed that might be helpful in predicting recovery potential. GLOSSARY aMF = alternate motor fibers; DTI = diffusion tensor imaging; FA = fractional anisotropy; FLAIR = fluid-attenuated inversion recovery; MCA = middle cerebral artery; MRC = Medical Research Council; PLIC = posterior limb of the internal capsule; PT = pyramidal tract; ROI = region of interest; TMS = transcranial magnetic stimulation; UE-FM = Upper Extremity Fugl-Meyer assessment; WMFT = Wolf Motor Function Test. PMID:20101033

Lindenberg, R; Renga, V; Zhu, L L.; Betzler, F; Alsop, D; Schlaug, G

2010-01-01

389

A nonlinear viscoelastic approach to durability predictions for polymer based composite structures  

NASA Technical Reports Server (NTRS)

Current industry approaches for the durability assessment of metallic structures are briefly reviewed. For polymer based composite structures, it is suggested that new approaches must be adopted to include memory or viscoelastic effects which could lead to delayed failures that might not be predicted using current techniques. A durability or accelerated life assessment plan for fiber reinforced plastics (FRP) developed and documented over the last decade or so is reviewed and discussed. Limitations to the plan are outlined and suggestions to remove the limitations are given. These include the development of a finite element code to replace the previously used lamination theory code and the development of new specimen geometries to evaluate delamination failures. The new DCB model is reviewed and results are presented. Finally, it is pointed out that new procedures are needed to determine interfacial properties and current efforts underway to determine such properties are reviewed. Suggestions for additional efforts to develop a consistent and accurate durability predictive approach for FRP structures are outlined.

Brinson, Hal F.

1991-01-01

390

Further Development of Ko Displacement Theory for Deformed Shape Predictions of Nonuniform Aerospace Structures  

NASA Technical Reports Server (NTRS)

The Ko displacement theory previously formulated for deformed shape predictions of nonuniform beam structures is further developed mathematically. The further-developed displacement equations are expressed explicitly in terms of geometrical parameters of the beam and bending strains at equally spaced strain-sensing stations along the multiplexed fiber-optic sensor line installed on the bottom surface of the beam. The bending strain data can then be input into the displacement equations for calculations of local slopes, deflections, and cross-sectional twist angles for generating the overall deformed shapes of the nonuniform beam. The further-developed displacement theory can also be applied to the deformed shape predictions of nonuniform two-point supported beams, nonuniform panels, nonuniform aircraft wings and fuselages, and so forth. The high degree of accuracy of the further-developed displacement theory for nonuniform beams is validated by finite-element analysis of various nonuniform beam structures. Such structures include tapered tubular beams, depth-tapered unswept and swept wing boxes, width-tapered wing boxes, and double-tapered wing boxes, all under combined bending and torsional loads. The Ko displacement theory, combined with the fiber-optic strain-sensing system, provide a powerful tool for in-flight deformed shape monitoring of unmanned aerospace vehicles by ground-based pilots to maintain safe flights.

Ko, William L.; Fleischer, Van Tran

2009-01-01

391

Crystal Structure Prediction and its Application in Earth and Materials Sciences  

NASA Astrophysics Data System (ADS)

First of all, we describe how to predict crystal structure by evolutionary approach, and extend this method to study the packing of organic molecules, by our specially designed constrained evolutionary algorithm. The main feature of this new approach is that each unit or molecule is treated as a whole body, which drastically reduces the search space and improves the efficiency. The improved method is possibly to be applied in the fields of (1) high pressure phase of simple molecules (H2O, NH3, CH4, etc); (2) pharmaceutical molecules (glycine, aspirin, etc); (3) complex inorganic crystals containing cluster or molecular unit, (Mg(BH4)2, Ca(BH4)2, etc). One application of the constrained evolutionary algorithm is given by the study of (Mg(BH4)2, which is a promising materials for hydrogen storage. Our prediction does not only reproduce the previous work on Mg(BH4)2 at ambient condition, but also yields two new tetragonal structures at high pressure, with space groups P4 and I41/acd are predicted to be lower in enthalpy, by 15.4 kJ/mol and 21.2 kJ/mol, respectively, than the earlier proposed P42nm phase. We have simulated X-ray diffraction spectra, lattice dynamics, and equations of state of these phases. The density, volume contraction, bulk modulus, and the simulated XRD patterns of P4 and I41/acd structures are in excellent agreement with the experimental results. Two kinds of oxides (Xe-O and Mg-O) have been studied under megabar pressures. For XeO, we predict the existence of thermodynamically stable Xe-O compounds at high pressures (XeO, XeO2 and XeO3 become stable at pressures of 83, 102 and 114 GPa, respectively). For Mg-O, our calculations find that two extraordinary compounds MgO2 and Mg3O 2 become thermodynamically stable at 116 GPa and 500 GPa, respectively. Our calculations indicate large charge transfer in these oxides for both systems, suggesting that large electronegativity difference and pressure are the key factors favouring their formations. We also discuss if these oxides might exist at earth and planetary conditions. If the target properties are set as the global fitness functions while structure relaxations are energy/enthalpy minimization, such hybrid optimization technique could effectively explore the landscape of properties for the given systems. Here we illustrate this function by the case of searching for superdense carbon allotropes. We find three structures (hP3, tI12, and tP12) that have significantly greater density. Furthermore, we find a collection of other superdense structures based on different ways of packing carbon tetrahedral. Superdense carbon allotropes are predicted to have remarkably high refractive indices and strong dispersion of light. Apart from evolutionary approach, there also exist some other methods for structural prediction. One can also combine the features from different methods. We develop a novel method for crystal structure prediction, based on metadynamics and evolutionary algorithms. This technique can be used to produce efficiently both the ground state and metastable states easily reachable from a reasonable initial structure. We use the cell shape as collective variable and evolutionary variation operators developed in the context of the USPEX method to equilibrate the system as a function of the collective variables. We illustrate how this approach helps one to find stable and metastable states for Al2SiO5, SiO2, MgSiO3. Apart from predicting crystal structures, the new method can also provide insight into mechanisms of phase transitions. This method is especially powerful in sampling the metastable structures from a given configuration. Experiments on cold compression indicated the existence of a new superhard carbon allotrope. Numerous metastable candidate structures featuring different topologies have been proposed for this allotrope. We use evolutionary metadynamics to systematically search for possible candidates which could be accessible from graphite. (Abstract shortened by UMI.)

Zhu, Qiang

392

A nonlinear viscoelastic approach to durability predictions for polymer based composite structures  

NASA Technical Reports Server (NTRS)

Current industry approaches for the durability assessment of metallic structures are briefly reviewed. For polymer based composite structures, it is suggested that new approaches must be adopted to include memory or viscoelastic effects which could lead to delayed failures that might not be predicted using current techniques. A durability or accelerated life assessment plan for fiber reinforced plastics (FRP) developed and documented over the last decade or so is reviewed and discussed. Limitations to the plan are outlined and suggestions to remove the limitations are given. These include the development of a finite element code to replace the previously used lamination theory code and the development of new specimen geometries to evaluate delamination failures. The new DCB model is reviewed and results are presented. Finally, it is pointed out that new procedures are needed to determine interfacial properties and current efforts underway to determine such properties are reviewed. Suggestions for additional efforts to develop a consistent and accurate durability predictive approach for FRP structures is outlined.

Brinson, Hal F.; Hiel, C. C.

1990-01-01

393

GeMMA: functional subfamily classification within superfamilies of predicted protein structural domains  

PubMed Central

GeMMA (Genome Modelling and Model Annotation) is a new approach to automatic functional subfamily classification within families and superfamilies of protein sequences. A major advantage of GeMMA is its ability to subclassify very large and diverse superfamilies with tens of thousands of members, without the need for an initial multiple sequence alignment. Its performance is shown to be comparable to the established high-performance method SCI-PHY. GeMMA follows an agglomerative clustering protocol that uses existing software for sensitive and accurate multiple sequence alignment and profile–profile comparison. The produced subfamilies are shown to be equivalent in quality whether whole protein sequences are used or just the sequences of component predicted structural domains. A faster, heuristic version of GeMMA that also uses distributed computing is shown to maintain the performance levels of the original implementation. The use of GeMMA to increase the functional annotation coverage of functionally diverse Pfam families is demonstrated. It is further shown how GeMMA clusters can help to predict the impact of experimentally determining a protein domain structure on comparative protein modelling coverage, in the context of structural genomics. PMID:19923231

Lee, David A.; Rentzsch, Robert; Orengo, Christine

2010-01-01

394

Sampling Multiple Scoring Functions Can Improve Protein Loop Structure Prediction Accuracy  

PubMed Central

Accurately predicting loop structures is important for understanding functions of many proteins. In order to obtain loop models with high accuracy, efficiently sampling the loop conformation space to discover reasonable structures is a critical step. In loop conformation sampling, coarse-grain energy (scoring) functions coupling with reduced protein representations are often used to reduce the number of degrees of freedom as well as sampling computational time. However, due to implicitly considering many factors by reduced representations, the coarse-grain scoring functions may have potential insensitivity and inaccuracy, which can mislead the sampling process and consequently ignore important loop conformations. In this paper, we present a new computational sampling approach to obtain reasonable loop backbone models, so-called the Pareto Optimal Sampling (POS) method. The rationale of the POS method is to sample the function space of multiple, carefully-selected scoring functions to discover an ensemble of diversified structures yielding Pareto optimality to all sampled conformations. POS method can efficiently tolerate insensitivity and inaccuracy in individual scoring functions and thereby lead to significant accuracy improvement in loop structure prediction. We apply the POS method to a set of 4- to 12-residue loop targets using a function space composed of backbone-only Rosetta, DFIRE, and a triplet backbone dihedral potential developed in our lab. Our computational results show that in 501 out of 502 targets, the model sets generated by POS contain structure models are within subangstrom resolution. Moreover, the top-ranked models have Root Mean Square Deviation (RMSD) less than 1A in 96.8%, 84.1%, and 72.2% of the short (4~6 residues), medium (7~9 residues), and long (10~12) targets, respectively, when the all-atom models are generated by local optimization from the backbone models and are ranked by our recently developed Pareto Optimal Consensus (POC) method. Similar sampling effectiveness can also be found in a set of 13-residue loop targets. PMID:21702492

Rata, Ionel; Jakobsson, Eric

2011-01-01

395

Structure prediction of ordered and disordered multiple octahedral cation perovskites using SPuDS.  

PubMed

The software package SPuDS has previously been shown to accurately predict crystal structures of AMX(3) and A(1 - x)A'(x)MX(3) perovskites that have undergone octahedral tilting distortions. This paper describes the extension of this technique and its accuracy for A(2)MM'X(6) ordered double perovskites with the aristotype Fm\\overline 3m cubic structure, as well as those that have undergone octahedral tilting distortions. A survey of the literature shows that roughly 70% of all ordered double perovskites undergo octahedral tilting distortions. Of the 11 distinct types of octahedral tilting that can occur in ordered perovskites, five tilt systems account for approximately 97% of the reported structures. SPuDS can calculate structures for the five dominant tilt systems, Fm\\overline 3m (a(0)a(0)a(0)), I4/m (a(0)a(0)c(-)), R\\overline 3 (a(-)a(-)a(-)), I2/m (a(0)b(-)b(-)) and P2(1)/n (a(-)a(-)b(+)), as well as two additional tilt systems, Pn\\overline 3 (a(+)a(+)a(+)) and P4/mnc (a(0)a(0)c(+)). Comparison with reported crystal structures shows that SPuDS is quite accurate at predicting distortions driven by octahedral tilting. The favored modes of octahedral tilting in ordered double perovskites are compared and contrasted with those in AMX(3) perovskites. Unit-cell pseudosymmetry in Sr- and Ca-containing double perovskites is also examined. Experimentally, Sr(2)MM'O(6) compounds show a much stronger tendency toward pseudosymmetry than do Ca(2)MM'O(6) compounds with similar tolerance factors. PMID:16710058

Lufaso, Michael W; Barnes, Paris W; Woodward, Patrick M

2006-06-01

396

On the prediction of impact noise, VII: The structural damping of machinery  

NASA Astrophysics Data System (ADS)

In earlier parts of this series of papers on the prediction of impact noise, it has been found that in predicting the noise energy radiated from an industrial machine, the only term in the energy accountancy equation which involves the true conversion of vibrational energy into heat is the quantity 10 log ?s; the other terms represent the fraction of impact energy entering the machine and the radiation efficiency change associated with moving this vibrational energy to lower frequencies. Thus the study of the overall damping factor ?s is of crucial importance to the accurate prediction of noise radiated. In spite of the large bibliography available on damping, the practical prediction of this quantity in industrial type machinery is so uncertain that many workers treat the quantity as an unknown "fudge factor" to be obtained from previous similar machines. This forbids the deliberate "designing in" of damping in a new machine, and leads to disappointment if new practices have inadvertently caused a significant loss in ?s, especially when, in fact, the previous versions were relatively highly damped. In this paper a study aimed at improving damping prediction is described. Based upon an investigation of the values of ?s obtained in industrial machinery structures, as opposed to "thin shell" viscoelastically damped structures, a review is presented of the levels of damping which can be obtained by various standard methods. The effects of bolts and fluid sloshing are included, and specific experiments are described on the effects of adding aggregates in cavities, adding close covers and fitting stick-slip springs on drill rods. There is ample evidence that adequate damping may be obtainable only by the additi·n of several of these palliatives to different parts of the machinery structure, and accordingly a possible method of summation is proposed, based upon an analogy with room acoustics. The study has led to a realization of the importance of obtaining a simple method of summating damping, and further work is now being done to validate such a method.

Richards, E. J.; Lenzi, A.

1984-12-01

397

Lost in folding space? Comparing four variants of the thermodynamic model for RNA secondary structure prediction  

PubMed Central

Background Many bioinformatics tools for RNA secondary structure analysis are based on a thermodynamic model of RNA folding. They predict a single, "optimal" structure by free energy minimization, they enumerate near-optimal structures, they compute base pair probabilities and dot plots, representative structures of different abstract shapes, or Boltzmann probabilities of structures and shapes. Although all programs refer to the same physical model, they implement it with considerable variation for different tasks, and little is known about the effects of heuristic assumptions and model simplifications used by the programs on the outcome of the analysis. Results We extract four different models of the thermodynamic folding space which underlie the programs RNAFOLD, RNASHAPES, and RNASUBOPT. Their differences lie within the details of the energy model and the granularity of the folding space. We implement probabilistic shape analysis for all models, and introduce the shape probability shift as a robust measure of model similarity. Using four data sets derived from experimentally solved structures, we provide a quantitative evaluation of the model differences. Conclusions We find that search space granularity affects the computed shape probabilities less than the over- or underapproximation of free energy by a simplified energy model. Still, the approximations perform similar enough to implementations of the full model to justify their continued use in settings where computational constraints call for simpler algorithms. On the side, we observe that the rarely used level 2 shapes, which predict the complete arrangement of helices, multiloops, internal loops and bulges, include the "true" shape in a rather small number of predicted high probability shapes. This calls for an investigation of new strategies to extract high probability members from the (very large) level 2 shape space of an RNA sequence. We provide implementations of all four models, written in a declarative style that makes them easy to be modified. Based on our study, future work on thermodynamic RNA folding may make a choice of model based on our empirical data. It can take our implementations as a starting point for further program development. PMID:22051375

2011-01-01

398

Predicting performance and plasticity in the development of respiratory structures and metabolic systems.  

PubMed

The scaling laws governing metabolism suggest that we can predict metabolic rates across taxonomic scales that span large differences in mass. Yet, scaling relationships can vary with development, body region, and environment. Within species, there is variation in metabolic rate that is independent of mass and which may be explained by genetic variation, the environment or their interaction (i.e., metabolic plasticity). Additionally, some structures, such as the insect tracheal respiratory system, change throughout development and in response to the environment to match the changing functional requirements of the organism. We discuss how study of the development of respiratory function meets multiple challenges set forth by the NSF Grand Challenges Workshop. Development of the structure and function of respiratory and metabolic systems (1) is inherently stable and yet can respond dynamically to change, (2) is plastic and exhibits sensitivity to environments, and (3) can be examined across multiple scales in time and space. Predicting respiratory performance and plasticity requires quantitative models that integrate information across scales of function from the expression of metabolic genes and mitochondrial biogenesis to the building of respiratory structures. We present insect models where data are available on the development of the tracheal respiratory system and of metabolic physiology and suggest what is needed to develop predictive models. Incorporating quantitative genetic data will enable mapping of genetic and genetic-by-environment variation onto phenotypes, which is necessary to understand the evolution of respiratory and metabolic systems and their ability to enable respiratory homeostasis as organisms walk the tightrope between stability and change. PMID:24812329

Greenlee, Kendra J; Montooth, Kristi L; Helm, Bryan R

2014-07-01

399

Video Quality Prediction Using a 3D Dual-Tree Complex Wavelet Structural Similarity Index  

Microsoft Academic Search

\\u000a In this paper, we test the performance of the complex wavelet structural similarity index (?W-SSIM) using the 2D dual-tree\\u000a complex wavelet transform (DT-?WT). Also, we propose using a 3D DT-?WT with the ?W-SSIM algorithm, to predict the quality\\u000a of digital video signals. The 2D algorithm was tested against the LIVE image database and has shown higher correlation with\\u000a the subjective

K. Yonis; Richard M. Dansereau

2010-01-01

400

Real-time identification and prediction of geoeffective solar wind structures  

SciTech Connect

A feature-based classification technique is applied to the analysis of solar wind properties upstream of the Earth to predict the occurrence, duration, and magnitude of magnetic structures that can cause large geomagnetic storms. Because this method is based on identifiable physical features, it is highly upgradable by either analyzing data or physical models. The formulation of the technique is discussed and is then applied to two relatively {open_quotes}simple{close_quotes} solar wind events leading to large storms. The introduction of additional features to classify more {open_quotes}complicated{close_quotes} events is discussed to illustrate the upgradability. 14 refs., 3 fig.

Chen, J.; Cargill, P.J.; Palmadesso, P.J.

1996-03-15

401

Predicted alternative structure for tantalum metal under high pressure and high temperature  

NASA Astrophysics Data System (ADS)

First-principles simulations have been performed to investigate the phase stability of tantalum (Ta) metal under high pressure and high temperature. We searched its low-energy structures globally using our developed multi-algorithm collaborative crystal structure prediction technique. The body-centered cubic (bcc) was found to be stable at pressure up to 300 GPa. The previously reported ? and A15 structures were also reproduced successfully. More interestingly, we observed another phase (space group: Pnma, 62) that is more stable than ? and A15. Its stability is confirmed by its phonon spectra and elastic constants. For ?-Ta, the calculated elastic constants and high-temperature phonon spectra both imply that it is neither mechanically nor dynamically stable. Thus, ? is not the structure to which bcc-Ta transits before melting. On the contrary, the good agreement of Pnma-Ta shear sound velocities with experiment suggests Pnma is the new structure of Ta implied by the discontinuation of shear sound velocities in recent shock experiment [J. Appl. Phys. 111, 033511 (2012)].

Liu, Zhong-Li; Cai, Ling-Cang; Zhang, Xiu-Lu; Xi, Feng

2013-08-01

402

Mixing Energy Models in Genetic Algorithms for On-Lattice Protein Structure Prediction  

PubMed Central

Protein structure prediction (PSP) is computationally a very challenging problem. The challenge largely comes from the fact that the energy function that needs to be minimised in order to obtain the native structure of a given protein is not clearly known. A high resolution 20 × 20 energy model could better capture the behaviour of the actual energy function than a low resolution energy model such as hydrophobic polar. However, the fine grained details of the high resolution interaction energy matrix are often not very informative for guiding the search. In contrast, a low resolution energy model could effectively bias the search towards certain promising directions. In this paper, we develop a genetic algorithm that mainly uses a high resolution energy model for protein structure evaluation but uses a low resolution HP energy model in focussing the search towards exploring structures that have hydrophobic cores. We experimentally show that this mixing of energy models leads to significant lower energy structures compared to the state-of-the-art results. PMID:24224180

Rashid, Mahmood A.; Newton, M. A. Hakim; Hoque, Md. Tamjidul; Sattar, Abdul

2013-01-01

403

Integration of Expressed Sequence Tag Data Flanking Predicted RNA Secondary Structures Facilitates Novel Non-Coding RNA Discovery  

PubMed Central

Many computational methods have been used to predict novel non-coding RNAs (ncRNAs), but none, to our knowledge, have explicitly investigated the impact of integrating existing cDNA-based Expressed Sequence Tag (EST) data that flank structural RNA predictions. To determine whether flanking EST data can assist in microRNA (miRNA) prediction, we identified genomic sites encoding putative miRNAs by combining functional RNA predictions with flanking ESTs data in a model consistent with miRNAs undergoing cleavage during maturation. In both human and mouse genomes, we observed that the inclusion of flanking ESTs adjacent to and not overlapping predicted miRNAs significantly improved the performance of various methods of miRNA prediction, including direct high-throughput sequencing of small RNA libraries. We analyzed the expression of hundreds of miRNAs predicted to be expressed during myogenic differentiation using a customized microarray and identified several known and predicted myogenic miRNA hairpins. Our results indicate that integrating ESTs flanking structural RNA predictions improves the quality of cleaved miRNA predictions and suggest that this strategy can be used to predict other non-coding RNAs undergoing cleavage during maturation. PMID:21698286

Krzyzanowski, Paul M.; Price, Feodor D.; Muro, Enrique M.; Rudnicki, Michael A.; Andrade-Navarro, Miguel A.

2011-01-01

404

A novel method for predicting antioxidant activity based on amino acid structure.  

PubMed

Epidemiological studies show a positive correlation between oxidative stress and chronic disease development such as heart disease and cancer. While several antioxidant compounds with varying physical and chemical characteristics are able to reduce oxidative stress in biological systems, relatively few studies have been performed to examine the structural characteristics that produce potent antioxidants. We examined 20 essential and non-essential amino acids using the ORAC assay and used a simplest-case amino acid model to gather data to make predictions regarding the antioxidant activity of non-amino acid compounds; we also tested our findings on chalcone and nitrone data from the current literature. We observed that the sp(2)-hybridized carbons were the most consistent predictors of antioxidant activity in all groups. Valence electron to carbon ratio and length of conjugated double bond groups also emerged as important structural characteristics. Further testing may help to elucidate more accurate trends, as well as nonlinear relationships. PMID:24731374

Garrett, Andrew R; Weagel, Evita G; Martinez, Andrés D; Heaton, M; Robison, Richard A; O'Neill, Kim L

2014-09-01

405

Prediction of Shock Wave Structure in Weakly Ionized Gas Flow by Solving MGD Equation  

NASA Technical Reports Server (NTRS)

This paper reports the recent research results of shockwave structure predictions using a new developed code. The modified Rankine-Hugoniot relations across a standing normal shock wave are discussed and adopted to obtain jump conditions. Coupling a electrostatic body force to the Burnett equations, the weakly ionized flow field across the shock wave was solved. Results indicated that the Modified Rankine-Hugoniot equations for shock wave are valid for a wide range of ionization fraction. However, this model breaks down with small free stream Mach number and with large ionization fraction. The jump conditions also depend on the value of free stream pressure, temperature and density. The computed shock wave structure with ionization provides results, which indicated that shock wave strength may be reduced by existence of weakly ionized gas.

Deng, Z. T.; Oviedo-Rojas, Ruben; Chow, Alan; Litchford, Ron J.; Cook, Stephen (Technical Monitor)

2002-01-01

406

Prediction of structures and magnetic orientations in solid alpha and beta-O2  

NASA Technical Reports Server (NTRS)

A quasi-harmonic-lattice-dynamics method coupled with a pattern-recognition optimization scheme is used to determine the minimum energy structures and magnetic orientations of solid oxygen. It is shown that the magnetic interaction is responsible for the stability of alpha-O2 with respect to beta-O2 at zero temperature and pressure. The calculated alpha-O2 lattice parameters, magnetic orientations, and sublimation energy are in good agreement with experiment. Phonon dispersion curves are calculated at vector k not equal to zero and the acoustic sound velocities are determined. The rms translational and orientational fluctuations from equilibrium are also calculated. The beta-O2 phase is described by constraining the magnetic moments so that the magnetic Hamiltonian preserves the hexagonal symmetry of the crystal. The calculated lattice parameters are in good agreement with the experiments, and a three-sublattice, quasi-helical magnetic orientation is predicted from structural and energetic considerations.

Etters, R. D.; Helmy, A. A.; Kobashi, K.

1983-01-01

407

Does Adolescent Family Structure Predict Military Enlistment? A Comparison of Post-High School Activities  

PubMed Central

This paper investigates the link between adolescent family structure and the likelihood of military enlistment in young adulthood, as compared to alternative post-high school activities. We use data from the National Longitudinal Study of Adolescent Health and multinomial logistic regression analyses to compare the odds of military enlistment with college attendance or labor force involvement. We find that alternative family structures predict enlistment relative to college attendance. Living in a single-parent household during adolescence increased odds of military enlistment, but the effect is accounted for by socioeconomic status and early feelings of social isolation. Living with a stepparent or with neither biological parent more than doubles the odds of enlistment, independent of socioeconomic status, characteristics of parent-child relationships, or feelings of social isolation. Although college attendance is widely promoted as a valued post-high school activity, military service may offer a route to independence and a greater sense of belonging. PMID:24000268

Spence, Naomi J.; Henderson, Kathryn A.; Elder, Glen H.

2013-01-01

408

Structural relaxation in glassy polymers predicted by soft modes: a quantitative analysis.  

PubMed

We present a quantitative analysis of the correlation between quasi-localized, low energy vibrational modes and structural relaxation events in computer simulations of a quiescent, thermal polymer glass. Our results extend previous studies on glass forming binary mixtures in 2D, and show that the soft modes identify regions that undergo irreversible rearrangements with up to 7 times the average probability. We study systems in the supercooled- and aging-regimes and discuss temperature- as well as age-dependence of the correlation. In addition to the location of rearrangements, we find that soft modes also predict their direction on the molecular level. The soft regions are long lived structural features, and the observed correlations vanish only after >50% of the system has undergone rearrangements. PMID:25241966

Smessaert, Anton; Rottler, Jörg

2014-10-01

409

Structure based model for the prediction of phospholipidosis induction potential of small molecules.  

PubMed

Drug-induced phospholipidosis (PLD), characterized by an intracellular accumulation of phospholipids and formation of concentric lamellar bodies, has raised concerns in the drug discovery community, due to its potential adverse effects. To evaluate the PLD induction potential, 4,161 nonredundant drug-like molecules from the National Institutes of Health Chemical Genomics Center (NCGC) Pharmaceutical Collection (NPC), the Library of Pharmacologically Active Compounds (LOPAC), and the Tocris Biosciences collection were screened in a quantitative high-throughput screening (qHTS) format. The potential of drug-lipid complex formation can be linked directly to the structures of drug molecules, and many PLD inducing drugs were found to share common structural features. Support vector machine (SVM) models were constructed by using customized atom types or Molecular Operating Environment (MOE) 2D descriptors as structural descriptors. Either the compounds from LOPAC or randomly selected from the entire data set were used as the training set. The impact of training data with biased structural features and the impact of molecule descriptors emphasizing whole-molecule properties or detailed functional groups at the atom level on model performance were analyzed and discussed. Rebalancing strategies were applied to improve the predictive power of the SVM models. Using the undersampling method, the consensus model using one-third of the compounds randomly selected from the data set as the training set achieved high accuracy of 0.90 in predicting the remaining two-thirds of the compounds constituting the test set, as measured by the area under the receiver operator characteristic curve (AUC-ROC). PMID:22725677

Sun, Hongmao; Shahane, Sampada; Xia, Menghang; Austin, Christopher P; Huang, Ruili

2012-07-23

410

Population structure in the native range predicts the spread of introduced marine species.  

PubMed

Forecasting invasion success remains a fundamental challenge in invasion biology. The effort to identify universal characteristics that predict which species become invasive has faltered in part because of the diversity of taxa and systems considered. Here, we use an alternative approach focused on the spread stage of invasions. FST, a measure of alternative fixation of alleles, is a common proxy for realized dispersal among natural populations, summarizing the combined influences of life history, behaviour, habitat requirements, population size, history and ecology. We test the hypothesis that population structure in the native range (FST) is negatively correlated with the geographical extent of spread of marine species in an introduced range. An analysis of the available data (29 species, nine phyla) revealed a significant negative correlation (R(2) = 0.245-0.464) between FST and the extent of spread of non-native species. Mode FST among pairwise comparisons between populations in the native range demonstrated the highest predictive power (R(2) = 0.464, p < 0.001). There was significant improvement when marker type was considered, with mtDNA datasets providing the strongest relationship (n = 21, R(2) = 0.333-0.516). This study shows that FST can be used to make qualitative predictions concerning the geographical extent to which a non-native marine species will spread once established in a new area. PMID:23595272

Gaither, Michelle R; Bowen, Brian W; Toonen, Robert J

2013-06-01

411

Structural similarity-based predictions of protein interactions between HIV-1 and Homo sapiens  

PubMed Central

Background In the course of infection, viruses such as HIV-1 must enter a cell, travel to sites where they can hijack host machinery to transcribe their genes and translate their proteins, assemble, and then leave the cell again, all while evading the host immune system. Thus, successful infection depends on the pathogen's ability to manipulate the biological pathways and processes of the organism it infects. Interactions between HIV-encoded and human proteins provide one means by which HIV-1 can connect into cellular pathways to carry out these survival processes. Results We developed and applied a computational approach to predict interactions between HIV and human proteins based on structural similarity of 9 HIV-1 proteins to human proteins having known interactions. Using functional data from RNAi studies as a filter, we generated over 2000 interaction predictions between HIV proteins and 406 unique human proteins. Additional filtering based on Gene Ontology cellular component annotation reduced the number of predictions to 502 interactions involving 137 human proteins. We find numerous known interactions as well as novel interactions showing significant functional relevance based on supporting Gene Ontology and literature evidence. Conclusions Understanding the interplay between HIV-1 and its human host will help in understanding the viral lifecycle and the ways in which this virus is able to manipulate its host. The results shown here provide a potential set of interactions that are amenable to further experimental manipulation as well as potential targets for therapeutic intervention. PMID:20426868

2010-01-01

412

Engineering Property Prediction Tools for Tailored Polymer Composite Structures (FY06 Annual Report)  

SciTech Connect

Recently, long-fiber injection molded thermoplastics (LFTs) have generated great interest within the automotive industry as these materials can be used for structural applications in order to reduce vehicle weight. However, injection-molding of these materials poses a great challenge because of two main reasons: (i) no process models for LFTs have been developed that can be used to predict the processing of an LFT part, and (ii) no experimental characterization methods exist to fully characterize the as-formed LFT microstructure to determine the fiber orientation and length distributions and fiber dispersion that are critical for any process model development. This report summarizes the work conducted during the fiscal year 2006 (FY06) that includes (i) the assessment of current process modeling approaches, (ii) experimental evaluation of LFT microstructure and mechanical properties, and (iii) the computation of thermoelastic properties using the measured and predicted orientation distributions as well as the measured fiber length distribution. Our objective is two-fold. First, it is necessary to assess current process models and characterization techniques in order to determine their capabilities and limitations, and the necessary developments for LFTs. Second, before modeling the nonlinear behaviors of LFTs, it is essential to develop computation tools for predicting the elastic and thermoelastic properties of these materials.

Nguyen, Ba Nghiep; Holbery, Jim; Kunc, Vlastimil

2006-12-31

413

A simple structure-based model for the prediction of HIV-1 co-receptor tropism  

PubMed Central

Background Human Immunodeficiency Virus 1 enters host cells through interaction of its V3 loop (which is part of the gp120 protein) with the host cell receptor CD4 and one of two co-receptors, namely CCR5 or CXCR4. Entry inhibitors binding the CCR5 co-receptor can prevent viral entry. As these drugs are only available for CCR5-using viruses, accurate prediction of this so-called co-receptor tropism is important in order to ensure an effective personalized therapy. With the development of next-generation sequencing technologies, it is now possible to sequence representative subpopulations of the viral quasispecies. Results Here we present T-CUP 2.0, a model for predicting co-receptor tropism. Based on our recently published T-CUP model, we developed a more accurate and even faster solution. Similarly to its predecessor, T-CUP 2.0 models co-receptor tropism using information of the electrostatic potential and hydrophobicity of V3-loops. However, extracting this information from a simplified structural vacuum-model leads to more accurate and faster predictions. The area-under-the-ROC-curve (AUC) achieved with T-CUP 2.0 on the training set is 0.968±0.005 in a leave-one-patient-out cross-validation. When applied to an independent dataset, T-CUP 2.0 has an improved prediction accuracy of around 3% when compared to the original T-CUP. Conclusions We found that it