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Sample records for predicting b-dna structure

  1. Non-B-DNA structures on the interferon-beta promoter?

    PubMed

    Robbe, K; Bonnefoy, E

    1998-01-01

    The high mobility group (HMG) I protein intervenes as an essential factor during the virus induced expression of the interferon-beta (IFN-beta) gene. It is a non-histone chromatine associated protein that has the dual capacity of binding to a non-B-DNA structure such as cruciform-DNA as well as to AT rich B-DNA sequences. In this work we compare the binding affinity of HMGI for a synthetic cruciform-DNA to its binding affinity for the HMGI-binding-site present in the positive regulatory domain II (PRDII) of the IFN-beta promoter. Using gel retardation experiments, we show that HMGI protein binds with at least ten times more affinity to the synthetic cruciform-DNA structure than to the PRDII B-DNA sequence. DNA hairpin sequences are present in both the human and the murine PRDII-DNAs. We discuss in this work the presence of, yet putative, non-B-DNA structures in the IFN-beta promoter.

  2. A molecular mechanical model to predict the helix twist angles of B-DNA.

    PubMed Central

    Tung, C S; Harvey, S C

    1984-01-01

    We present here a model for the prediction of helix twist angles in B-DNA, a model composed of a collection of torsional springs. Statistically averaged conformational energy calculations show that, for a specified basepair step, the basepair-basepair conformational energy is quadratically dependent on the helix twist angle, so the calculations provide the spring parameters for the basepair-basepair interactions. Torsional springs can also be used to model the effects of the backbone on the helix twist, and the parameters for those springs are derived by fitting the model to experimental data. The model predicts a macroscopic torsional stiffness and a longitudinal compressibility (Young's modulus) which are both in good agreement with experiment. One biological consequence of the model is examined, the sequence specificity of the Eco RI restriction endonuclease, and it is shown that the discriminatory power of the enzyme receives a substantial contribution from the energetic cost of torsional deformations of the DNA when wrong sequences are forced into the enzyme binding site. PMID:6326059

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

    PubMed Central

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

    2013-01-01

    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 π–π 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

  4. Structural and thermodynamic studies on the adenine.guanine mismatch in B-DNA.

    PubMed Central

    Leonard, G A; Booth, E D; Brown, T

    1990-01-01

    The structure of the synthetic dodecamer d(CGCAAATTGGCG) has been shown by single crystal X-ray diffraction methods to be that of a B-DNA helix containing two A(anti).G(syn) base pairs. The refinement, based on data to a resolution of 2.25 A shows that the mismatch base pairs are held together by two hydrogen bonds. The syn-conformation of the guanine base of the mismatch is stabilised by hydrogen bonding to a network of solvent molecules in both the major and minor grooves. A pH-dependent ultraviolet melting study indicates that the duplex is stabilised by protonation, suggesting that the bases of the A.G mispair are present in their most common tautomeric forms and that the N(1)-atom of adenine is protonated. The structure refinement shows that there is some disorder in the sugar-phosphate backbone. PMID:2216754

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

    SciTech Connect

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

    2012-10-23

    Increasingly exact measurement of single crystal X-ray diffraction data offers detailed characterization of DNA conformation, hydration and electrostatics. However, instead of providing a more clear and unambiguous image of DNA, highly accurate diffraction data reveal polymorphism of the DNA atomic positions and conformation and hydration. Here we describe an accurate X-ray structure of B-DNA, painstakingly fit to a multistate model that contains multiple competing positions of most of the backbone and of entire base pairs. Two of ten base-pairs of CCAGGCCTGG are in multiple states distinguished primarily by differences in slide. Similarly, all the surrounding ions are seen to fractionally occupy discrete competing and overlapping sites. And finally, the vast majority of water molecules show strong evidence of multiple competing sites. Conventional resolution appears to give a false sense of homogeneity in conformation and interactions of DNA. In addition, conventional resolution yields an average structure that is not accurate, in that it is different from any of the multiple discrete structures observed at high resolution. Because base pair positional heterogeneity has not always been incorporated into model-building, even some high and ultrahigh-resolution structures of DNA do not indicate the full extent of conformational polymorphism.

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

    PubMed

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

    2016-01-15

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

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

    NASA Technical Reports Server (NTRS)

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

    1986-01-01

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

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

    PubMed Central

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

    2013-01-01

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

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

    PubMed

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

    2010-07-29

    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

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

    PubMed Central

    2013-01-01

    Background Deletions of the gene encoding mediator subcomplex 12 (MED12) in human smooth muscle tumors rank among the most frequent genomic alterations in human tumors at all. In a minority of these cases, small deletions are found. In an attempt to delineate key features of the deletions aimed at a better understanding of the molecular pathogenesis of uterine smooth muscle tumors we have analyzed 70 MED12 deletions including 46 cases from the literature and 24 own unpublished cases. Results The average length of the deletions was 18.7 bp ranging between 2 bp and 43 bp. While in general multitudes of 3 clearly dominated leaving the transcript in frame, deletions of 21, 24, 30, and 33 nucleotides were clearly underrepresented. Within the DNA segment affected deletion breakpoints were not randomly distributed. Most breakpoints clustered within the center of the segment where two peaks of breakpoint clusters could be distinguished. Interestingly, one of these clusters coincides with the loop of a putative folded non-B DNA structure whereas a much lower number of breaks noted in the 5′ and 3′ stem of the structure forming an intramolecular B-helix. The second cluster mainly consisting of 3′ breaks was located in a region downstream adjacent to the stem. Conclusion The present study describes for the first time main characteristics of MED12 deletions occurring in smooth muscle tumors. Interestingly, the non-random distribution of breakpoints within the deletion hotspot region may point to a role of non-canonical DNA structures for the occurrence of these mutations and the molecular pathogenesis of uterine smooth muscle tumors, respectively. PMID:23738817

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

    NASA Technical Reports Server (NTRS)

    Gruskin, E. A.; Rich, A.

    1993-01-01

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

  12. Crystal structure and prediction.

    PubMed

    Thakur, Tejender S; Dubey, Ritesh; Desiraju, Gautam R

    2015-04-01

    The notion of structure is central to the subject of chemistry. This review traces the development of the idea of crystal structure since the time when a crystal structure could be determined from a three-dimensional diffraction pattern and assesses the feasibility of computationally predicting an unknown crystal structure of a given molecule. Crystal structure prediction is of considerable fundamental and applied importance, and its successful execution is by no means a solved problem. The ease of crystal structure determination today has resulted in the availability of large numbers of crystal structures of higher-energy polymorphs and pseudopolymorphs. These structural libraries lead to the concept of a crystal structure landscape. A crystal structure of a compound may accordingly be taken as a data point in such a landscape.

  13. Crystal Structure and Prediction

    NASA Astrophysics Data System (ADS)

    Thakur, Tejender S.; Dubey, Ritesh; Desiraju, Gautam R.

    2015-04-01

    The notion of structure is central to the subject of chemistry. This review traces the development of the idea of crystal structure since the time when a crystal structure could be determined from a three-dimensional diffraction pattern and assesses the feasibility of computationally predicting an unknown crystal structure of a given molecule. Crystal structure prediction is of considerable fundamental and applied importance, and its successful execution is by no means a solved problem. The ease of crystal structure determination today has resulted in the availability of large numbers of crystal structures of higher-energy polymorphs and pseudopolymorphs. These structural libraries lead to the concept of a crystal structure landscape. A crystal structure of a compound may accordingly be taken as a data point in such a landscape.

  14. Electronic structure and carrier transfer in B-DNA monomer polymers and dimer polymers: Stationary and time-dependent aspects of a wire model versus an extended ladder model

    NASA Astrophysics Data System (ADS)

    Lambropoulos, K.; Chatzieleftheriou, M.; Morphis, A.; Kaklamanis, K.; Lopp, R.; Theodorakou, M.; Tassi, M.; Simserides, C.

    2016-12-01

    We employ two tight-binding (TB) approaches to systematically study the electronic structure and hole or electron transfer in B-DNA monomer polymers and dimer polymers made up of N monomers (base pairs): (I) at the base-pair level, using the onsite energies of base pairs and the hopping integrals between successive base pairs, i.e., a wire model and (II) at the single-base level, using the onsite energies of the bases and the hopping integrals between neighboring bases, i.e., an extended ladder model since we also include diagonal hoppings. We solve a system of M (matrix dimension) coupled equations [(I) M =N , (II) M =2 N ] for the time-independent problem, and a system of M coupled first order differential equations for the time-dependent problem. We perform a comparative study of stationary and time-dependent aspects of the two TB variants, using realistic sets of parameters. The studied properties include HOMO and LUMO eigenspectra, occupation probabilities, density of states and HOMO-LUMO gaps as well as mean over time probabilities to find the carrier at each site [(I) base pair or (II) base], Fourier spectra, which reflect the frequency content of charge transfer, and pure mean transfer rates from a certain site to another. The two TB approaches give coherent, complementary aspects of electronic properties and charge transfer in B-DNA monomer polymers and dimer polymers.

  15. Electronic structure and carrier transfer in B-DNA monomer polymers and dimer polymers: Stationary and time-dependent aspects of a wire model versus an extended ladder model.

    PubMed

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

    2016-12-01

    We employ two tight-binding (TB) approaches to systematically study the electronic structure and hole or electron transfer in B-DNA monomer polymers and dimer polymers made up of N monomers (base pairs): (I) at the base-pair level, using the onsite energies of base pairs and the hopping integrals between successive base pairs, i.e., a wire model and (II) at the single-base level, using the onsite energies of the bases and the hopping integrals between neighboring bases, i.e., an extended ladder model since we also include diagonal hoppings. We solve a system of M (matrix dimension) coupled equations [(I) M=N, (II) M=2N] for the time-independent problem, and a system of M coupled first order differential equations for the time-dependent problem. We perform a comparative study of stationary and time-dependent aspects of the two TB variants, using realistic sets of parameters. The studied properties include HOMO and LUMO eigenspectra, occupation probabilities, density of states and HOMO-LUMO gaps as well as mean over time probabilities to find the carrier at each site [(I) base pair or (II) base], Fourier spectra, which reflect the frequency content of charge transfer, and pure mean transfer rates from a certain site to another. The two TB approaches give coherent, complementary aspects of electronic properties and charge transfer in B-DNA monomer polymers and dimer polymers.

  16. Mitochondrial DNA deletions are associated with non-B DNA conformations

    PubMed Central

    Damas, Joana; Carneiro, João; Gonçalves, Joana; Stewart, James B.; Samuels, David C.; Amorim, António; Pereira, Filipe

    2012-01-01

    Mitochondrial DNA (mtDNA) deletions are a primary cause of mitochondrial disease and are believed to contribute to the aging process and to various neurodegenerative diseases. Despite strong observational and experimental evidence, the molecular basis of the deletion process remains obscure. In this study, we test the hypothesis that the primary cause of mtDNA vulnerability to breakage resides in the formation of non-B DNA conformations, namely hairpin, cruciform and cloverleaf-like elements. Using the largest database of human mtDNA deletions built thus far (753 different cases), we show that site-specific breakage hotspots exist in the mtDNA. Furthermore, we discover that the most frequent deletion breakpoints occur within or near predicted structures, a result that is supported by data from transgenic mice with mitochondrial disease. There is also a significant association between the folding energy of an mtDNA region and the number of breakpoints that it harbours. In particular, two clusters of hairpins (near the D-loop 3′-terminus and the L-strand origin of replication) are hotspots for mtDNA breakage. Consistent with our hypothesis, the highest number of 5′- and 3′-breakpoints per base is found in the highly structured tRNA genes. Overall, the data presented in this study suggest that non-B DNA conformations are a key element of the mtDNA deletion process. PMID:22661583

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

    PubMed

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

    2013-11-29

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

  18. Practical lessons from protein structure prediction

    PubMed Central

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

    2005-01-01

    Despite recent efforts to develop automated protein structure determination protocols, structural genomics projects are slow in generating fold assignments for complete proteomes, and spatial structures remain unknown for many protein families. Alternative cheap and fast methods to assign folds using prediction algorithms continue to provide valuable structural information for many proteins. The development of high-quality prediction methods has been boosted in the last years by objective community-wide assessment experiments. This paper gives an overview of the currently available practical approaches to protein structure prediction capable of generating accurate fold assignment. Recent advances in assessment of the prediction quality are also discussed. PMID:15805122

  19. Predicting Protein Structure Using Parallel Genetic Algorithms.

    DTIC Science & Technology

    1994-12-01

    By " Predicting rotein Structure D istribticfiar.. ................ Using Parallel Genetic Algorithms ,Avaiu " ’ •"... Dist THESIS I IGeorge H...iiLite-d Approved for public release; distribution unlimited AFIT/ GCS /ENG/94D-03 Predicting Protein Structure Using Parallel Genetic Algorithms ...1-1 1.2 Genetic Algorithms ......... ............................ 1-3 1.3 The Protein Folding Problem

  20. Collective prediction based on community structure

    NASA Astrophysics Data System (ADS)

    Jiang, Yasong; Li, Taisong; Zhang, Yan; Yan, Yonghong

    2017-01-01

    Collective prediction algorithms have been used to improve performances when network structures are involved in prediction tasks. The training dataset of such tasks often contain information of content, links and labels, while the testing dataset have only content and link information. Conventional collective prediction algorithms conduct predictions based on the content of a node and the information of its direct neighbors with a base classifier. However, the information of some direct neighbor nodes may be not consistent with the target one. In addition, the information of indirect neighbors can be helpful when that of direct neighbors is scant. In this paper, instead of using information of direct neighbors, we propose to apply community structures in networks to prediction tasks. A community detection method is aggregated into the collective prediction process to improve prediction performance. Experimental results show that the proposed algorithm outperforms a number of standard prediction algorithms specially under conditions that labeled training dataset are limited.

  1. Protein Structure Prediction with Visuospatial Analogy

    NASA Astrophysics Data System (ADS)

    Davies, Jim; Glasgow, Janice; Kuo, Tony

    We show that visuospatial representations and reasoning techniques can be used as a similarity metric for analogical protein structure prediction. Our system retrieves pairs of α-helices based on contact map similarity, then transfers and adapts the structure information to an unknown helix pair, showing that similar protein contact maps predict similar 3D protein structure. The success of this method provides support for the notion that changing representations can enable similarity metrics in analogy.

  2. Computational Prediction of RNA Tertiary Structure

    NASA Astrophysics Data System (ADS)

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

    2012-02-01

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

  3. Energy-directed RNA structure prediction.

    PubMed

    Hofacker, Ivo L

    2014-01-01

    In this chapter we present the classic dynamic programming algorithms for RNA structure prediction by energy minimization, as well as variations of this approach that allow to compute suboptimal foldings, or even the partition function over all possible secondary structures. The latter are essential in order to deal with the inaccuracy of minimum free energy (MFE) structure prediction, and can be used, for example, to derive reliability measures that assign a confidence value to all or part of a predicted structure. In addition, we discuss recently proposed alternatives to the MFE criterion such as the use of maximum expected accuracy (MEA) or centroid structures. The dynamic programming algorithms implicitly assume that the RNA molecule is in thermodynamic equilibrium. However, especially for long RNAs, this need not be the case. In the last section we therefore discuss approaches for predicting RNA folding kinetics and co-transcriptional folding.

  4. Predicting pseudoknotted structures across two RNA sequences

    PubMed Central

    Sperschneider, Jana; Datta, Amitava; Wise, Michael J.

    2012-01-01

    Motivation: Laboratory RNA structure determination is demanding and costly and thus, computational structure prediction is an important task. Single sequence methods for RNA secondary structure prediction are limited by the accuracy of the underlying folding model, if a structure is supported by a family of evolutionarily related sequences, one can be more confident that the prediction is accurate. RNA pseudoknots are functional elements, which have highly conserved structures. However, few comparative structure prediction methods can handle pseudoknots due to the computational complexity. Results: A comparative pseudoknot prediction method called DotKnot-PW is introduced based on structural comparison of secondary structure elements and H-type pseudoknot candidates. DotKnot-PW outperforms other methods from the literature on a hand-curated test set of RNA structures with experimental support. Availability: DotKnot-PW and the RNA structure test set are available at the web site http://dotknot.csse.uwa.edu.au/pw. Contact: janaspe@csse.uwa.edu.au Supplementary information: Supplementary data are available at Bioinformatics online. PMID:23044552

  5. SCRATCH: a protein structure and structural feature prediction server

    PubMed Central

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

    2005-01-01

    SCRATCH is a server for predicting protein tertiary structure and structural features. The SCRATCH software suite includes predictors for secondary structure, relative solvent accessibility, disordered regions, domains, disulfide bridges, single mutation stability, residue contacts versus average, individual residue contacts and tertiary structure. The user simply provides an amino acid sequence and selects the desired predictions, then submits to the server. Results are emailed to the user. The server is available at . PMID:15980571

  6. Predicting crystal structures of organic compounds.

    PubMed

    Price, Sarah L

    2014-04-07

    Currently, organic crystal structure prediction (CSP) methods are based on searching for the most thermodynamically stable crystal structure, making various approximations in evaluating the crystal energy. The most stable (global minimum) structure provides a prediction of an experimental crystal structure. However, depending on the specific molecule, there may be other structures which are very close in energy. In this case, the other structures on the crystal energy landscape may be polymorphs, components of static or dynamic disorder in observed structures, or there may be no route to nucleating and growing these structures. A major reason for performing CSP studies is as a complement to solid form screening to see which alternative packings to the known polymorphs are thermodynamically feasible.

  7. Predicting RNA structure: advances and limitations.

    PubMed

    Hofacker, Ivo L; Lorenz, Ronny

    2014-01-01

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

  8. Enhanced multi-view prediction structure

    NASA Astrophysics Data System (ADS)

    Liu, Da; Li, Yi; Xiong, Yazhou; Wang, Li; Li, Chunyan; Yin, Fang

    2016-11-01

    In this paper, firstly an extended DFMC Structure is proposed, then HQF jump period in extended DFMC is presented. Considering temporal-view and interview prediction structure, HQF location is determined. From the HQF, an enhance LQF is proposed. Then considering the HQF and enhance LQF, improved interview prediction is proposed. Finally bit allocation in the proposed multi-view is proposed. Experimental results show that the proposed method can achieve better performance than the previous schemes.

  9. Molecular stripping in the NFκB / IκB / DNA genetic regulatory network

    NASA Astrophysics Data System (ADS)

    Potoyan, Davit; Wolynes, Peter

    Genetic switches based on the NFκB / IκB / DNA system are master regulators of an array of cellular responses. Recent kinetic experiments have shown that IκB can actively remove NF κB bound to its genetic sites via a process called ''molecular stripping''. This allows the NFκB / IκB / DNA switch to function under kinetic control rather than the thermodynamic control contemplated in the traditional models of gene switches. Using molecular dynamics simulations of coarse grained predictive energy landscape models for the constituent proteins by themselves and interacting with the DNA we explore the functional motions of the transcription factor NFκB and its various binary and ternary complexes with DNA and the inhibitor I κB. These studies show that the function of the NFκB / IκB / DNA genetic switch is realized via an allosteric mechanism. Molecular stripping occurs through the activation of a domain twist mode by the binding of IκB which occurs through conformational selection. Free energy calculations for DNA binding show that the binding of IκB not only results in a significant decrease of the affinity of the transcription factor for the DNA but also kinetically speeds DNA release. Projections of the

  10. Predicting complex mineral structures using genetic algorithms.

    PubMed

    Mohn, Chris E; Kob, Walter

    2015-10-28

    We show that symmetry-adapted genetic algorithms are capable of finding the ground state of a range of complex crystalline phases including layered- and incommensurate super-structures. This opens the way for the atomistic prediction of complex crystal structures of functional materials and mineral phases.

  11. Characteristics and Prediction of RNA Structure

    PubMed Central

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

    2014-01-01

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

  12. Predicting structure in nonsymmetric sparse matrix factorizations

    SciTech Connect

    Gilbert, J.R. ); Ng, E.G. )

    1992-10-01

    Many computations on sparse matrices have a phase that predicts the nonzero structure of the output, followed by a phase that actually performs the numerical computation. We study structure prediction for computations that involve nonsymmetric row and column permutations and nonsymmetric or non-square matrices. Our tools are bipartite graphs, matchings, and alternating paths. Our main new result concerns LU factorization with partial pivoting. We show that if a square matrix A has the strong Hall property (i.e., is fully indecomposable) then an upper bound due to George and Ng on the nonzero structure of L + U is as tight as possible. To show this, we prove a crucial result about alternating paths in strong Hall graphs. The alternating-paths theorem seems to be of independent interest: it can also be used to prove related results about structure prediction for QR factorization that are due to Coleman, Edenbrandt, Gilbert, Hare, Johnson, Olesky, Pothen, and van den Driessche.

  13. Improved Predictions of Secondary Structures for RNA

    NASA Astrophysics Data System (ADS)

    Jaeger, John A.; Turner, Douglas H.; Zuker, Michael

    1989-10-01

    The accuracy of computer predictions of RNA secondary structure from sequence data and free energy parameters has been increased to roughly 70%. Performance is judged by comparison with structures known from phylogenetic analysis. The algorithm also generates suboptimal structures. On average, the best structure within 10% of the lowest free energy contains roughly 90% of phylogenetically known helixes. The algorithm does not include tertiary interactions or pseudoknots and employs a crude model for single-stranded regions. The only favorable interactions are base pairing and stacking of terminal unpaired nucleotides at the ends of helixes. The excellent performance is consistent with these interactions being the primary interactions determining RNA secondary structure.

  14. Particle-swarm structure prediction on clusters

    NASA Astrophysics Data System (ADS)

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

    2012-08-01

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

  15. Genome-Wide Analyses of Recombination Prone Regions Predict Role of DNA Structural Motif in Recombination

    PubMed Central

    Das, Swapan Kumar; Chowdhury, Shantanu

    2009-01-01

    HapMap findings reveal surprisingly asymmetric distribution of recombinogenic regions. Short recombinogenic regions (hotspots) are interspersed between large relatively non-recombinogenic regions. This raises the interesting possibility of DNA sequence and/or other cis- elements as determinants of recombination. We hypothesized the involvement of non-canonical sequences that can result in local non-B DNA structures and tested this using the G-quadruplex DNA as a model. G-quadruplex or G4 DNA is a unique form of four-stranded non-B DNA structure that engages certain G-rich sequences, presence of such motifs has been noted within telomeres. In support of this hypothesis, genome-wide computational analyses presented here reveal enrichment of potential G4 (PG4) DNA forming sequences within 25618 human hotspots relative to 9290 coldspots (p<0.0001). Furthermore, co-occurrence of PG4 DNA within several short sequence elements that are associated with recombinogenic regions was found to be significantly more than randomly expected. Interestingly, analyses of more than 50 DNA binding factors revealed that co-occurrence of PG4 DNA with target DNA binding sites of transcription factors c-Rel, NF-kappa B (p50 and p65) and Evi-1 was significantly enriched in recombination-prone regions. These observations support involvement of G4 DNA in recombination, predicting a functional model that is consistent with duplex-strand separation induced by formation of G4 motifs in supercoiled DNA and/or when assisted by other cellular factors. PMID:19198658

  16. SHAPE-Directed RNA Secondary Structure Prediction

    PubMed Central

    Low, Justin T.; Weeks, Kevin M.

    2010-01-01

    The diverse functional roles of RNA are determined by its underlying structure. Accurate and comprehensive knowledge of RNA structure would inform a broader understanding of RNA biology and facilitate exploiting RNA as a biotechnological tool and therapeutic target. Determining the pattern of base pairing, or secondary structure, of RNA is a first step in these endeavors. Advances in experimental, computational, and comparative analysis approaches for analyzing secondary structure have yielded accurate structures for many small RNAs, but only a few large (>500 nts) RNAs. In addition, most current methods for determining a secondary structure require considerable effort, analytical expertise, and technical ingenuity. In this review, we outline an efficient strategy for developing accurate secondary structure models for RNAs of arbitrary length. This approach melds structural information obtained using SHAPE chemistry with structure prediction using nearest-neighbor rules and the dynamic programming algorithm implemented in the RNAstructure program. Prediction accuracies reach ≥95% for RNAs on the kilobase scale. This approach facilitates both development of new models and refinement of existing RNA structure models, which we illustrate using the Gag-Pol frameshift element in an HIV-1 M-group genome. Most promisingly, integrated experimental and computational refinement brings closer the ultimate goal of efficiently and accurately establishing the secondary structure for any RNA sequence. PMID:20554050

  17. Predicting polymeric crystal structures by evolutionary algorithms

    NASA Astrophysics Data System (ADS)

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

    2014-10-01

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

  18. Reduced ceria nanofilms from structure prediction.

    PubMed

    Kozlov, Sergey M; Demiroglu, Ilker; Neyman, Konstantin M; Bromley, Stefan T

    2015-03-14

    Experimentally, Ce2O3 films are used to study cerium oxide in its fully or partially reduced state, as present in many applications. We have explored the space of low energy Ce2O3 nanofilms using structure prediction and density functional calculations, yielding more than 30 distinct nanofilm structures. First, our results help to rationalize the roles of thermodynamics and kinetics in the preparation of reduced ceria nanofilms with different bulk crystalline structures (e.g. A-type or bixbyite) depending on the support used. Second, we predict a novel, as yet experimentally unresolved, nanofilm which has a structure that does not correspond to any previously reported bulk A2B3 phase and which has an energetic stability between that of A-type and bixbyite. To assist identification and fabrication of this new Ce2O3 nanofilm we calculate some observable properties and propose supports for its epitaxial growth.

  19. Ko Displacement Theory for Structural Shape Predictions

    NASA Technical Reports Server (NTRS)

    Ko, William L.

    2010-01-01

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

  20. Cascaded multiple classifiers for secondary structure prediction.

    PubMed Central

    Ouali, M.; King, R. D.

    2000-01-01

    We describe a new classifier for protein secondary structure prediction that is formed by cascading together different types of classifiers using neural networks and linear discrimination. The new classifier achieves an accuracy of 76.7% (assessed by a rigorous full Jack-knife procedure) on a new nonredundant dataset of 496 nonhomologous sequences (obtained from G.J. Barton and J.A. Cuff). This database was especially designed to train and test protein secondary structure prediction methods, and it uses a more stringent definition of homologous sequence than in previous studies. We show that it is possible to design classifiers that can highly discriminate the three classes (H, E, C) with an accuracy of up to 78% for beta-strands, using only a local window and resampling techniques. This indicates that the importance of long-range interactions for the prediction of beta-strands has been probably previously overestimated. PMID:10892809

  1. Structure-Based Predictions of Activity Cliffs

    PubMed Central

    Husby, Jarmila; Bottegoni, Giovanni; Kufareva, Irina; Abagyan, Ruben; Cavalli, Andrea

    2015-01-01

    In drug discovery, it is generally accepted that neighboring molecules in a given descriptors' space display similar activities. However, even in regions that provide strong predictability, structurally similar molecules can occasionally display large differences in potency. In QSAR jargon, these discontinuities in the activity landscape are known as ‘activity cliffs’. In this study, we assessed the reliability of ligand docking and virtual ligand screening schemes in predicting activity cliffs. We performed our calculations on a diverse, independently collected database of cliff-forming co-crystals. Starting from ideal situations, which allowed us to establish our baseline, we progressively moved toward simulating more realistic scenarios. Ensemble- and template-docking achieved a significant level of accuracy, suggesting that, despite the well-known limitations of empirical scoring schemes, activity cliffs can be accurately predicted by advanced structure-based methods. PMID:25918827

  2. Predicting continuous local structure and the effect of its substitution for secondary structure in fragment-free protein structure prediction.

    PubMed

    Faraggi, Eshel; Yang, Yuedong; Zhang, Shesheng; Zhou, Yaoqi

    2009-11-11

    Local structures predicted from protein sequences are used extensively in every aspect of modeling and prediction of protein structure and function. For more than 50 years, they have been predicted at a low-resolution coarse-grained level (e.g., three-state secondary structure). Here, we combine a two-state classifier with real-value predictor to predict local structure in continuous representation by backbone torsion angles. The accuracy of the angles predicted by this approach is close to that derived from NMR chemical shifts. Their substitution for predicted secondary structure as restraints for ab initio structure prediction doubles the success rate. This result demonstrates the potential of predicted local structure for fragment-free tertiary-structure prediction. It further implies potentially significant benefits from using predicted real-valued torsion angles as a replacement for or supplement to the secondary-structure prediction tools used almost exclusively in many computational methods ranging from sequence alignment to function prediction.

  3. Gene structure prediction by linguistic methods

    SciTech Connect

    Dong, S.; Searls, D.B.

    1994-10-01

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

  4. RNA Structure: Advances and Assessment of 3D Structure Prediction.

    PubMed

    Miao, Zhichao; Westhof, Eric

    2017-03-30

    Biological functions of RNA molecules are dependent upon sustained specific three-dimensional (3D) structures of RNA, with or without the help of proteins. Understanding of RNA structure is frequently based on 2D structures, which describe only the Watson-Crick (WC) base pairs. Here, we hierarchically review the structural elements of RNA and how they contribute to RNA 3D structure. We focus our analysis on the non-WC base pairs and on RNA modules. Several computer programs have now been designed to predict RNA modules. We describe the RNA-Puzzles initiative, which is a community-wide, blind assessment of RNA 3D structure prediction programs to determine the capabilities and bottlenecks of current predictions. The assessment metrics used in RNA-Puzzles are briefly described. The detection of RNA 3D modules from sequence data and their automatic implementation belong to the current challenges in RNA 3D structure prediction. Expected final online publication date for the Annual Review of Biophysics Volume 46 is May 20, 2017. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

  5. Predicting structured metadata from unstructured metadata

    PubMed Central

    Posch, Lisa; Panahiazar, Maryam; Dumontier, Michel; Gevaert, Olivier

    2016-01-01

    Enormous amounts of biomedical data have been and are being produced by investigators all over the world. However, one crucial and limiting factor in data reuse is accurate, structured and complete description of the data or data about the data—defined as metadata. We propose a framework to predict structured metadata terms from unstructured metadata for improving quality and quantity of metadata, using the Gene Expression Omnibus (GEO) microarray database. Our framework consists of classifiers trained using term frequency-inverse document frequency (TF-IDF) features and a second approach based on topics modeled using a Latent Dirichlet Allocation model (LDA) to reduce the dimensionality of the unstructured data. Our results on the GEO database show that structured metadata terms can be the most accurately predicted using the TF-IDF approach followed by LDA both outperforming the majority vote baseline. While some accuracy is lost by the dimensionality reduction of LDA, the difference is small for elements with few possible values, and there is a large improvement over the majority classifier baseline. Overall this is a promising approach for metadata prediction that is likely to be applicable to other datasets and has implications for researchers interested in biomedical metadata curation and metadata prediction. Database URL: http://www.yeastgenome.org/

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

    PubMed

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

    2014-11-10

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

  7. Reduced ceria nanofilms from structure prediction

    NASA Astrophysics Data System (ADS)

    Kozlov, Sergey M.; Demiroglu, Ilker; Neyman, Konstantin M.; Bromley, Stefan T.

    2015-02-01

    Experimentally, Ce2O3 films are used to study cerium oxide in its fully or partially reduced state, as present in many applications. We have explored the space of low energy Ce2O3 nanofilms using structure prediction and density functional calculations, yielding more than 30 distinct nanofilm structures. First, our results help to rationalize the roles of thermodynamics and kinetics in the preparation of reduced ceria nanofilms with different bulk crystalline structures (e.g. A-type or bixbyite) depending on the support used. Second, we predict a novel, as yet experimentally unresolved, nanofilm which has a structure that does not correspond to any previously reported bulk A2B3 phase and which has an energetic stability between that of A-type and bixbyite. To assist identification and fabrication of this new Ce2O3 nanofilm we calculate some observable properties and propose supports for its epitaxial growth.Experimentally, Ce2O3 films are used to study cerium oxide in its fully or partially reduced state, as present in many applications. We have explored the space of low energy Ce2O3 nanofilms using structure prediction and density functional calculations, yielding more than 30 distinct nanofilm structures. First, our results help to rationalize the roles of thermodynamics and kinetics in the preparation of reduced ceria nanofilms with different bulk crystalline structures (e.g. A-type or bixbyite) depending on the support used. Second, we predict a novel, as yet experimentally unresolved, nanofilm which has a structure that does not correspond to any previously reported bulk A2B3 phase and which has an energetic stability between that of A-type and bixbyite. To assist identification and fabrication of this new Ce2O3 nanofilm we calculate some observable properties and propose supports for its epitaxial growth. Electronic supplementary information (ESI) available: Graph of IP versus DFT relative energies for nanofilms, GGA + U calculated lattice parameters and

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

    NASA Astrophysics Data System (ADS)

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

    2014-12-01

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

  9. Multithreaded parsing for predicting RNA secondary structures.

    PubMed

    Al-Mulhem, Muhammed S

    2010-01-01

    Many computational approaches have been developed for modelling and analysing the RNA secondary structure. These approaches are based on diverse methods such as grammars, dynamic programming, matching and evolutionary algorithms. This paper proposes a new parsing algorithm for the prediction of RNA secondary structures. The proposed algorithm is based on the shift-reduce LR parsing algorithm for programming languages. It has two main contributions: it extends the LR parsing algorithm by using a Stochastic Context-Free Grammar (SCFG) instead of Context-Free Grammar (CFG) for parsing RNA secondary structures; it extends the LR parsing algorithm by using a multithreaded approach to handle the LR parsing conflicts resulting from the use of ambiguous grammars.

  10. Toward structure prediction of cyclic peptides.

    PubMed

    Yu, Hongtao; Lin, Yu-Shan

    2015-02-14

    Cyclic peptides are a promising class of molecules that can be used to target specific protein-protein interactions. A computational method to accurately predict their structures would substantially advance the development of cyclic peptides as modulators of protein-protein interactions. Here, we develop a computational method that integrates bias-exchange metadynamics simulations, a Boltzmann reweighting scheme, dihedral principal component analysis and a modified density peak-based cluster analysis to provide a converged structural description for cyclic peptides. Using this method, we evaluate the performance of a number of popular protein force fields on a model cyclic peptide. All the tested force fields seem to over-stabilize the α-helix and PPII/β regions in the Ramachandran plot, commonly populated by linear peptides and proteins. Our findings suggest that re-parameterization of a force field that well describes the full Ramachandran plot is necessary to accurately model cyclic peptides.

  11. Predicting road accidents: Structural time series approach

    NASA Astrophysics Data System (ADS)

    Junus, Noor Wahida Md; Ismail, Mohd Tahir

    2014-07-01

    In this paper, the model for occurrence of road accidents in Malaysia between the years of 1970 to 2010 was developed and throughout this model the number of road accidents have been predicted by using the structural time series approach. The models are developed by using stepwise method and the residual of each step has been analyzed. The accuracy of the model is analyzed by using the mean absolute percentage error (MAPE) and the best model is chosen based on the smallest Akaike information criterion (AIC) value. A structural time series approach found that local linear trend model is the best model to represent the road accidents. This model allows level and slope component to be varied over time. In addition, this approach also provides useful information on improving the conventional time series method.

  12. Structural network efficiency predicts conversion to dementia

    PubMed Central

    Tuladhar, Anil M.; van Uden, Ingeborg W.M.; Rutten-Jacobs, Loes C.A.; Lawrence, Andrew; van der Holst, Helena; van Norden, Anouk; de Laat, Karlijn; van Dijk, Ewoud; Claassen, Jurgen A.H.R.; Kessels, Roy P.C.; Markus, Hugh S.; Norris, David G.

    2016-01-01

    Objective: To examine whether structural network connectivity at baseline predicts incident all-cause dementia in a prospective hospital-based cohort of elderly participants with MRI evidence of small vessel disease (SVD). Methods: A total of 436 participants from the Radboud University Nijmegen Diffusion Tensor and Magnetic Resonance Cohort (RUN DMC), a prospective hospital-based cohort of elderly without dementia with cerebral SVD, were included in 2006. During follow-up (2011–2012), dementia was diagnosed. The structural network was constructed from baseline diffusion tensor imaging followed by deterministic tractography and measures of efficiency using graph theory were calculated. Cox proportional regression analyses were conducted. Results: During 5 years of follow-up, 32 patients developed dementia. MRI markers for SVD were strongly associated with network measures. Patients with dementia showed lower total network strength and global and local efficiency at baseline as compared with the group without dementia. Lower global network efficiency was independently associated with increased risk of incident all-cause dementia (hazard ratio 0.63, 95% confidence interval 0.42–0.96, p = 0.032); in contrast, individual SVD markers including lacunes, white matter hyperintensities volume, and atrophy were not independently associated. Conclusions: These results support a role of network disruption playing a pivotal role in the genesis of dementia in SVD, and suggest network analysis of the connectivity of white matter has potential as a predictive marker in the disease. PMID:26888983

  13. Predicted and experimental structures of integrins and beta-propellers.

    PubMed

    Springer, Timothy A

    2002-12-01

    Integrins and other cell surface receptors have been fertile grounds for structure prediction experiments. Recently determined structures show remarkable successes, especially with beta-propeller domain predictions, and also reveal how ligand binding by integrins is conformationally regulated.

  14. Evaluation, analysis and prediction of geologic structures

    NASA Astrophysics Data System (ADS)

    Woodward, Nicholas B.

    2012-08-01

    Balanced cross-sections claim to be better because they apply a rigorous set of rules to develop the conceptual model of the structures present in an area. Balanced cross-sections can be further improved and become more useful to understanding real physical problems by collection of additional data such as seismic reflection surveys, collection of additional stratigraphic data, or collection of rock fabric information. The additional information validates the initial model and provides details on deformation conditions and on local rock responses to the deformation. Although individual cross-sections are two dimensional, the objective of evaluation and analysis of deformed regions should be three dimensional whenever possible to recognize the challenges of the real world. Subsurface system analysis derived from the hydrologic community emphasizes conceptual model development through model verification, validation, uncertainty quantification, benchmarking and meta-analysis. Their approach includes many steps informally used by the structural geology community but in a much more explicit way. Newer geological applications of structural geology would benefit from this more rigorous approach for designing and doing performance predictions as technological needs become more socially sensitive such as for carbon storage sites, new areas of energy exploration in higher population density areas, or for nuclear waste storage facilities.

  15. Blind protein structure prediction using accelerated free-energy simulations

    PubMed Central

    Perez, Alberto; Morrone, Joseph A.; Brini, Emiliano; MacCallum, Justin L.; Dill, Ken A.

    2016-01-01

    We report a key proof of principle of a new acceleration method [Modeling Employing Limited Data (MELD)] for predicting protein structures by molecular dynamics simulation. It shows that such Boltzmann-satisfying techniques are now sufficiently fast and accurate to predict native protein structures in a limited test within the Critical Assessment of Structure Prediction (CASP) community-wide blind competition. PMID:27847872

  16. Optimizing nondecomposable loss functions in structured prediction.

    PubMed

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

    2013-04-01

    We develop an algorithm for structured prediction with nondecomposable performance measures. The algorithm learns parameters of Markov Random Fields (MRFs) and can be applied to multivariate performance measures. Examples include performance measures such as Fβ 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.

  17. Predicting missing links via structural similarity

    NASA Astrophysics Data System (ADS)

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

    2015-04-01

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

  18. Structure prediction of magnetosome-associated proteins

    PubMed Central

    Nudelman, Hila; Zarivach, Raz

    2014-01-01

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

  19. Crystal structure prediction of rigid molecules.

    PubMed

    Elking, Dennis M; Fusti-Molnar, Laszlo; Nichols, Anthony

    2016-08-01

    A non-polarizable force field based on atomic multipoles fit to reproduce experimental crystal properties and ab initio gas-phase dimers is described. The Ewald method is used to calculate both long-range electrostatic and 1/r(6) dispersion energies of crystals. The dispersion energy of a crystal calculated by a cutoff method is shown to converge slowly to the exact Ewald result. A method for constraining space-group symmetry during unit-cell optimization is derived. Results for locally optimizing 4427 unit cells including volume, cell parameters, unit-cell r.m.s.d. and CPU timings are given for both flexible and rigid molecule optimization. An algorithm for randomly generating rigid molecule crystals is described. Using the correct experimentally determined space group, the average and maximum number of random crystals needed to find the correct experimental structure is given for 2440 rigid single component crystals. The force field energy rank of the correct experimental structure is presented for the same set of 2440 rigid single component crystals assuming the correct space group. A complete crystal prediction is performed for two rigid molecules by searching over the 32 most probable space groups.

  20. Crystal Structure Prediction for Cyclotrimethylene Trinitramine (RDX) from First Principles

    DTIC Science & Technology

    2009-04-01

    REPORT Crystal structure prediction for cyclotrimethylene trinitramine (RDX) from ?rst principles 14. ABSTRACT 16. SECURITY CLASSIFICATION OF: Crystal... structure prediction and molecular dynamics methods were applied to the cyclotrimethylene trinitramine (RDX) crystal to explore the stability rankings...500 high-density structures resulting from molecular packing were minimized and the 14 lowest-energy structures were subjected to isothermal

  1. Non-B DNA conformations as determinants of mutagenesis and human disease.

    PubMed

    Bacolla, Albino; Wells, Robert D

    2009-04-01

    Repetitive DNA motifs may fold into non-B DNA structures, including cruciforms/hairpins, triplexes, slipped conformations, quadruplexes, and left-handed Z-DNA, thereby representing chromosomal targets for DNA repair, recombination, and aberrant DNA synthesis leading to repeat expansion or genomic rearrangements associated with neurodegenerative and genomic disorders. Hairpins and quadruplexes also determined the relative abundances of simple sequence repeats (SSR) in vertebrate genomes, whereas strong base stacking has permitted the expansion of purine.pyrimidine-rich SSR during evolutionary time. SSR are enriched in regulatory and cancer-related gene classes, where they have been actively recruited to participate in both gene and protein functions. SSR polymorphic alleles in the population are associated with cancer susceptibility, including within genes that appear to share regulatory circuits involving reactive oxygen species.

  2. Statistical energy analysis response prediction methods for structural systems

    NASA Technical Reports Server (NTRS)

    Davis, R. F.

    1979-01-01

    The results of an effort to document methods for accomplishing response predictions for commonly encountered aerospace structural configurations is presented. Application of these methods to specified aerospace structure to provide sample analyses is included. An applications manual, with the structural analyses appended as example problems is given. Comparisons of the response predictions with measured data are provided for three of the example problems.

  3. RNA-SSPT: RNA Secondary Structure Prediction Tools

    PubMed Central

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

    2013-01-01

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

  4. Myotrophin-kappaB DNA interaction in the initiation process of cardiac hypertrophy.

    PubMed

    Gupta, Sudhiranjan; Sen, Subha

    2002-05-08

    To investigate how cardiac hypertrophy and heart failure develop, we isolated and characterized a candidate initiator, the soluble 12-kDa protein myotrophin, from rat and human hearts. Myotrophin stimulates protein synthesis and myocardial cell growth associated with increased levels of hypertrophy marker genes. Recombinant myotrophin from the cloned gene showed structural/functional motifs, including ankyrin repeats and putative phosphorylation sites for protein kinase C (PKC) and casein kinase II. One repeat, homologous with I kappaB, interacts with rel/NF-kappaB in vitro. We analyzed the interaction of recombinant myotrophin and nuclear extracts prepared from neonatal and adult cardiomyocytes; gel mobility shift assay showed that myotrophin bound to kappaB DNA. To define PKC's role in myotrophin-induced myocyte growth, we incubated neonatal rat myocytes (normal and stretch) with specific inhibitors and found that myotrophin inhibits [3H]leucine incorporation into myocytes and different hypertrophic gene expression in neonatal myocytes. Using confocal microscopy, we observed that a basal level of myotrophin was present in both cytoplasm and nucleus under normal conditions, but under cyclic stretch, myotrophin levels became elevated in the nucleus. Myotrophin gene levels were upregulated when myocytes underwent cyclic stretch or were treated with tumor necrosis factor-alpha (TNF-alpha) or interleukin-1beta and also when excised beating hearts were exposed to high pressure. Our data showed that the myotrophin-kappaB interaction was increased with age in spontaneously hypertensive rats (SHRs) only. Our data provide evidence that myotrophin-kappaB DNA interaction may be an important step in initiating cardiac hypertrophy.

  5. Studies of the B-Z transition of DNA: The temperature dependence of the free-energy difference, the composition of the counterion sheath in mixed salt, and the preparation of a sample of the 5'-d[T-(m(5) C-G)12 -T] duplex in pure B-DNA or Z-DNA form.

    PubMed

    Guéron, Maurice; Plateau, Pierre; Filoche, Marcel

    2016-07-01

    It is often envisioned that cations might coordinate at specific sites of nucleic acids and play an important structural role, for instance in the transition between B-DNA and Z-DNA. However, nucleic acid models explicitly devoid of specific sites may also exhibit features previously considered as evidence for specific binding. Such is the case of the "composite cylinder" (or CC) model which spreads out localized features of DNA structure and charge by cylindrical averaging, while sustaining the main difference between the B and Z structures, namely the better immersion of the B-DNA phosphodiester charges in the solution. Here, we analyze the non-electrostatic component of the free-energy difference between B-DNA and Z-DNA. We also compute the composition of the counterion sheath in a wide range of mixed-salt solutions and of temperatures: in contrast with the large difference of composition between the B-DNA and Z-DNA forms, the temperature dependence of sheath composition, previously unknown, is very weak. In order to validate the model, the mixed-salt predictions should be compared to experiment. We design a procedure for future measurements of the sheath composition based on Anomalous Small-Angle X-ray Scattering and complemented by (31) P NMR. With due consideration for the kinetics of the B-Z transition and for the capacity of generating at will the B or Z form in a single sample, the 5'-d[T-(m(5) C-G)12 -T] 26-mer emerges as a most suitable oligonucleotide for this study. Finally, the application of the finite element method to the resolution of the Poisson-Boltzmann equation is described in detail. © 2016 Wiley Periodicals, Inc. Biopolymers 105: 369-384, 2016.

  6. A comprehensive comparison of comparative RNA structure prediction approaches

    PubMed Central

    Gardner, Paul P; Giegerich, Robert

    2004-01-01

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

  7. Both intra- and interstrand charge-transfer excited states in aqueous B-DNA are present at energies comparable to, or just above, the (1)pipi* excitonic bright states.

    PubMed

    Lange, Adrian W; Herbert, John M

    2009-03-25

    Vertical electronic excitations in model systems representing single- and double-stranded B-DNA are characterized using electronic structure theory, including both time-dependent density functional theory (TD-DFT) and correlated wave function techniques. Previous TD-DFT predictions of charge-transfer (CT) states well below the optically bright (1)pipi* states are shown to be artifacts of the improper long-range behavior of standard density-functional exchange approximations, which we rectify here using a long-range correction (LRC) procedure. For nucleobase dimers (hydrogen-bonded or pi-stacked), TD-LRC-DFT affords vertical excitation energies in reasonable agreement with the wave function methods, not only for the (1)npi* and (1)pipi* states but also for the CT states, and qualitatively reproduces well-known base-stacking effects on the absorption spectrum of DNA. The emergence of (1)pipi* Frenkel exciton states, localized on a single strand, is clearly evident, and these states (rather than low-energy CT states) are primarily responsible for the fact that DNA's absorption spectrum exhibits a red tail that is absent in monomer absorption spectra. For B-DNA in aqueous solution, the low-energy tail of the CT band (representing both intra- and interstrand CT states) appears at energies comparable to those of the optically bright (1)pipi* exciton states. In systems with more than one base pair, we also observe the emergence of delocalized, interstrand CT excitations, whose excitation energies may be significantly lower than the lowest CT excitation in a single base pair. Together, these observations suggest that a single Watson-Crick base pair is an inadequate model of the photophysics of B-DNA.

  8. On the significance of an RNA tertiary structure prediction

    PubMed Central

    Hajdin, Christine E.; Ding, Feng; Dokholyan, Nikolay V.; Weeks, Kevin M.

    2010-01-01

    Tertiary structure prediction is important for understanding structure–function relationships for RNAs whose structures are unknown and for characterizing RNA states recalcitrant to direct analysis. However, it is unknown what root-mean-square deviation (RMSD) corresponds to a statistically significant RNA tertiary structure prediction. We use discrete molecular dynamics to generate RNA-like folds for structures up to 161 nucleotides (nt) that have complex tertiary interactions and then determine the RMSD distribution between these decoys. These distributions are Gaussian-like. The mean RMSD increases with RNA length and is smaller if secondary structure constraints are imposed while generating decoys. The compactness of RNA molecules with true tertiary folds is intermediate between closely packed spheres and a freely jointed chain. We use this scaling relationship to define an expression relating RMSD with the confidence that a structure prediction is better than that expected by chance. This is the prediction significance, and corresponds to a P-value. For a 100-nt RNA, the RMSD of predicted structures should be within 25 Å of the accepted structure to reach the P ≤ 0.01 level if the secondary structure is predicted de novo and within 14 Å if secondary structure information is used as a constraint. This significance approach should be useful for evaluating diverse RNA structure prediction and molecular modeling algorithms. PMID:20498460

  9. Predicting crystal structure by merging data mining with quantum mechanics.

    PubMed

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

    2006-08-01

    Modern methods of quantum mechanics have proved to be effective tools to understand and even predict materials properties. An essential element of the materials design process, relevant to both new materials and the optimization of existing ones, is knowing which crystal structures will form in an alloy system. Crystal structure can only be predicted effectively with quantum mechanics if an algorithm to direct the search through the large space of possible structures is found. We present a new approach to the prediction of structure that rigorously mines correlations embodied within experimental data and uses them to direct quantum mechanical techniques efficiently towards the stable crystal structure of materials.

  10. Critical Features of Fragment Libraries for Protein Structure Prediction.

    PubMed

    Trevizani, Raphael; Custódio, Fábio Lima; Dos Santos, Karina Baptista; Dardenne, Laurent Emmanuel

    2017-01-01

    The use of fragment libraries is a popular approach among protein structure prediction methods and has proven to substantially improve the quality of predicted structures. However, some vital aspects of a fragment library that influence the accuracy of modeling a native structure remain to be determined. This study investigates some of these features. Particularly, we analyze the effect of using secondary structure prediction guiding fragments selection, different fragments sizes and the effect of structural clustering of fragments within libraries. To have a clearer view of how these factors affect protein structure prediction, we isolated the process of model building by fragment assembly from some common limitations associated with prediction methods, e.g., imprecise energy functions and optimization algorithms, by employing an exact structure-based objective function under a greedy algorithm. Our results indicate that shorter fragments reproduce the native structure more accurately than the longer. Libraries composed of multiple fragment lengths generate even better structures, where longer fragments show to be more useful at the beginning of the simulations. The use of many different fragment sizes shows little improvement when compared to predictions carried out with libraries that comprise only three different fragment sizes. Models obtained from libraries built using only sequence similarity are, on average, better than those built with a secondary structure prediction bias. However, we found that the use of secondary structure prediction allows greater reduction of the search space, which is invaluable for prediction methods. The results of this study can be critical guidelines for the use of fragment libraries in protein structure prediction.

  11. Critical Features of Fragment Libraries for Protein Structure Prediction

    PubMed Central

    dos Santos, Karina Baptista

    2017-01-01

    The use of fragment libraries is a popular approach among protein structure prediction methods and has proven to substantially improve the quality of predicted structures. However, some vital aspects of a fragment library that influence the accuracy of modeling a native structure remain to be determined. This study investigates some of these features. Particularly, we analyze the effect of using secondary structure prediction guiding fragments selection, different fragments sizes and the effect of structural clustering of fragments within libraries. To have a clearer view of how these factors affect protein structure prediction, we isolated the process of model building by fragment assembly from some common limitations associated with prediction methods, e.g., imprecise energy functions and optimization algorithms, by employing an exact structure-based objective function under a greedy algorithm. Our results indicate that shorter fragments reproduce the native structure more accurately than the longer. Libraries composed of multiple fragment lengths generate even better structures, where longer fragments show to be more useful at the beginning of the simulations. The use of many different fragment sizes shows little improvement when compared to predictions carried out with libraries that comprise only three different fragment sizes. Models obtained from libraries built using only sequence similarity are, on average, better than those built with a secondary structure prediction bias. However, we found that the use of secondary structure prediction allows greater reduction of the search space, which is invaluable for prediction methods. The results of this study can be critical guidelines for the use of fragment libraries in protein structure prediction. PMID:28085928

  12. Protein Structure Prediction Using String Kernels

    DTIC Science & Technology

    2006-03-03

    Prediction using String Kernels 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) 5d. PROJECT NUMBER 5e. TASK NUMBER...consists of 4352 sequences from SCOP version 1.53 extracted from the Astral database, grouped into families and superfamilies. The dataset is processed

  13. Predicting Career Advancement with Structural Equation Modelling

    ERIC Educational Resources Information Center

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

    2012-01-01

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

  14. A Software Pipeline for Protein Structure Prediction

    DTIC Science & Technology

    2006-11-01

    programs, such as GenTHREADER (Jones 1999), FUGUE (Shi, Blundell et al. 2001), and 3D- PSSM (Kelley, MacCallum et al. 2000), use hybrid approaches...annotation using structural profiles in the program 3D- PSSM , J Mol Biol, 299, 499- 520. Kim, D., D. Xu, et al., 2003: PROSPECT II: protein structure

  15. Prediction of Protein Structure Using Surface Accessibility Data

    PubMed Central

    Hartlmüller, Christoph; Göbl, Christoph

    2016-01-01

    Abstract An approach to the de novo structure prediction of proteins is described that relies on surface accessibility data from NMR paramagnetic relaxation enhancements by a soluble paramagnetic compound (sPRE). This method exploits the distance‐to‐surface information encoded in the sPRE data in the chemical shift‐based CS‐Rosetta de novo structure prediction framework to generate reliable structural models. For several proteins, it is demonstrated that surface accessibility data is an excellent measure of the correct protein fold in the early stages of the computational folding algorithm and significantly improves accuracy and convergence of the standard Rosetta structure prediction approach. PMID:27560616

  16. Fatigue Prediction for Composite Materials and Structures

    DTIC Science & Technology

    2005-10-01

    Eugenio OÑATE CIMNE (International Center for Numerical Methods in Engineering) Building C-1, Campus Nord UPC -C/ Gran Capitán s/n 08034 Barcelona...SPAIN * salomon@cimne.upc.edu ABSTRACT The objective of this paper is to present a new computational methodology for predicting the durability of... methodology is validated using experimental data from tests on CFRR composite material samples. 1.0 INTRODUCTION Fatigue is defined as "the process

  17. Neural network definitions of highly predictable protein secondary structure classes

    SciTech Connect

    Lapedes, A. |; Steeg, E.; Farber, R.

    1994-02-01

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

  18. Protein structure prediction from sequence variation

    PubMed Central

    Marks, Debora S; Hopf, Thomas A; Sander, Chris

    2015-01-01

    Genomic sequences contain rich evolutionary information about functional constraints on macromolecules such as proteins. This information can be efficiently mined to detect evolutionary couplings between residues in proteins and address the long-standing challenge to compute protein three-dimensional structures from amino acid sequences. Substantial progress has recently been made on this problem owing to the explosive growth in available sequences and the application of global statistical methods. In addition to three-dimensional structure, the improved understanding of covariation may help identify functional residues involved in ligand binding, protein-complex formation and conformational changes. We expect computation of covariation patterns to complement experimental structural biology in elucidating the full spectrum of protein structures, their functional interactions and evolutionary dynamics. PMID:23138306

  19. Characterization and Prediction of Protein Flexibility Based on Structural Alphabets

    PubMed Central

    Liu, Bin

    2016-01-01

    Motivation. To assist efforts in determining and exploring the functional properties of proteins, it is desirable to characterize and predict protein flexibilities. Results. In this study, the conformational entropy is used as an indicator of the protein flexibility. We first explore whether the conformational change can capture the protein flexibility. The well-defined decoy structures are converted into one-dimensional series of letters from a structural alphabet. Four different structure alphabets, including the secondary structure in 3-class and 8-class, the PB structure alphabet (16-letter), and the DW structure alphabet (28-letter), are investigated. The conformational entropy is then calculated from the structure alphabet letters. Some of the proteins show high correlation between the conformation entropy and the protein flexibility. We then predict the protein flexibility from basic amino acid sequence. The local structures are predicted by the dual-layer model and the conformational entropy of the predicted class distribution is then calculated. The results show that the conformational entropy is a good indicator of the protein flexibility, but false positives remain a problem. The DW structure alphabet performs the best, which means that more subtle local structures can be captured by large number of structure alphabet letters. Overall this study provides a simple and efficient method for the characterization and prediction of the protein flexibility. PMID:27660756

  20. Structural Prediction and Experimental Verification of Three New Ferroelectric Materials

    NASA Astrophysics Data System (ADS)

    Arbogast, D. J.; Foster, M. C.; Nielson, R. M.; Photinos, P. J.; Abrahams, S. C.

    1999-05-01

    Aminoguanidinium hexafluorozirconate, potassium niobyl silicate and fresnoite are typical members of a growing group of materials predicted to be new ferroelectrics by the application of structural criteria. Following prediction, undergraduate chemistry majors prepare and physics majors measure the dielectric properties of each. Experimental results will be presented for the three title materials in verification of their predicted property. In addition to the structural criteria on which the predictions depend, the circuits built locally and other instrumentation used for measuring ac and dc hysteresis, the thermal and frequency dependence of the dielectric permittivity, the thermal dependence of the spontaneous polarization and also the pyroelectric coefficient will be presented.

  1. Protein Structure and Function Prediction Using I-TASSER.

    PubMed

    Yang, Jianyi; Zhang, Yang

    2015-12-17

    I-TASSER is a hierarchical protocol for automated protein structure prediction and structure-based function annotation. Starting from the amino acid sequence of target proteins, I-TASSER first generates full-length atomic structural models from multiple threading alignments and iterative structural assembly simulations followed by atomic-level structure refinement. The biological functions of the protein, including ligand-binding sites, enzyme commission number, and gene ontology terms, are then inferred from known protein function databases based on sequence and structure profile comparisons. I-TASSER is freely available as both an on-line server and a stand-alone package. This unit describes how to use the I-TASSER protocol to generate structure and function prediction and how to interpret the prediction results, as well as alternative approaches for further improving the I-TASSER modeling quality for distant-homologous and multi-domain protein targets.

  2. A Software Pipeline for Protein Structure Prediction

    DTIC Science & Technology

    2006-11-01

    programs, such as GenTHREADER (Jones 1999), FUGUE (Shi, Blundell et al. 2001), and 3D- PSSM (Kelley, MacCallum et al. 2000), use hybrid approaches...2000: Enhanced genome annotation using structural profiles in the program 3D- PSSM , J Mol Biol, 299, 499- 520. Kim, D., D. Xu, et al., 2003: PROSPECT

  3. Structure Prediction and Analysis of Neuraminidase Sequence Variants

    ERIC Educational Resources Information Center

    Thayer, Kelly M.

    2016-01-01

    Analyzing protein structure has become an integral aspect of understanding systems of biochemical import. The laboratory experiment endeavors to introduce protein folding to ascertain structures of proteins for which the structure is unavailable, as well as to critically evaluate the quality of the prediction obtained. The model system used is the…

  4. WeFold: A Coopetition for Protein Structure Prediction

    PubMed Central

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

    2014-01-01

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

  5. Status of research aimed at predicting structural integrity

    SciTech Connect

    Reuter, W.G.

    1997-12-31

    Considerable research has been performed throughout the world on measuring the fracture toughness of metals. The existing capability fills the need encountered when selecting materials, thermal-mechanical treatments, welding procedures, etc., but cannot predict the fracture process of structural components containing cracks. The Idaho National Engineering and Environmental Laboratory and the Massachusetts Institute of Technology have been collaborating for a number of years on developing capabilities for using fracture toughness results to predict structural integrity. Because of the high cost of fabricating and testing structural components, these studies have been limited to predicting the fracture process in specimens containing surface cracks. This paper summarizes the present status of the experimental studies of using fracture toughness data to predict crack growth initiation in specimens (structural components) containing surface cracks. These results are limited to homogeneous base materials.

  6. A predictive structural model for bulk metallic glasses.

    PubMed

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

    2015-09-15

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

  7. OPTIMIZATION BIAS IN ENERGY-BASED STRUCTURE PREDICTION.

    PubMed

    Petrella, Robert J

    2013-12-01

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

  8. Comparative melting and healing of B-DNA and Z-DNA by an infrared laser pulse

    NASA Astrophysics Data System (ADS)

    Man, Viet Hoang; Pan, Feng; Sagui, Celeste; Roland, Christopher

    2016-04-01

    We explore the use of a fast laser melting simulation approach combined with atomistic molecular dynamics simulations in order to determine the melting and healing responses of B-DNA and Z-DNA dodecamers with the same d(5'-CGCGCGCGCGCG-3')2 sequence. The frequency of the laser pulse is specifically tuned to disrupt Watson-Crick hydrogen bonds, thus inducing melting of the DNA duplexes. Subsequently, the structures relax and partially refold, depending on the field strength. In addition to the inherent interest of the nonequilibrium melting process, we propose that fast melting by an infrared laser pulse could be used as a technique for a fast comparison of relative stabilities of same-sequence oligonucleotides with different secondary structures with full atomistic detail of the structures and solvent. This could be particularly useful for nonstandard secondary structures involving non-canonical base pairs, mismatches, etc.

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

    NASA Technical Reports Server (NTRS)

    Hasselman, Timothy K.; Chrostowski, Jon D.

    1991-01-01

    Modeling uncertainty is defined in terms of the difference between predicted and measured eigenvalues and eigenvectors. Data compiled from 22 sets of analysis/test results was used to create statistical databases for large truss-type space structures and both pretest and posttest models of conventional satellite-type space structures. Modeling uncertainty is propagated through the model to produce intervals of uncertainty on frequency response functions, both amplitude and phase. This methodology was used successfully to evaluate the predictive accuracy of several structures, including the NASA CSI Evolutionary Structure tested at Langley Research Center. Test measurements for this structure were within + one-sigma intervals of predicted accuracy for the most part, demonstrating the validity of the methodology and computer code.

  10. Improving structure-based function prediction using molecular dynamics

    PubMed Central

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

    2009-01-01

    Summary The number of molecules with solved three-dimensional structure but unknown function is increasing rapidly. Particularly problematic are novel folds with little detectable similarity to molecules of known function. Experimental assays can determine the functions of such molecules, but are time-consuming and expensive. Computational approaches can identify potential functional sites; however, these approaches generally rely on single static structures and do not use information about dynamics. In fact, structural dynamics can enhance function prediction: we coupled molecular dynamics simulations with structure-based function prediction algorithms that identify Ca2+ binding sites. When applied to 11 challenging proteins, both methods showed substantial improvement in performance, revealing 22 more sites in one case and 12 more in the other, with a modest increase in apparent false positives. Thus, we show that treating molecules as dynamic entities improves the performance of structure-based function prediction methods. PMID:19604472

  11. High-speed prediction of crystal structures for organic molecules

    NASA Astrophysics Data System (ADS)

    Obata, Shigeaki; Goto, Hitoshi

    2015-02-01

    We developed a master-worker type parallel algorithm for allocating tasks of crystal structure optimizations to distributed compute nodes, in order to improve a performance of simulations for crystal structure predictions. The performance experiments were demonstrated on TUT-ADSIM supercomputer system (HITACHI HA8000-tc/HT210). The experimental results show that our parallel algorithm could achieve speed-ups of 214 and 179 times using 256 processor cores on crystal structure optimizations in predictions of crystal structures for 3-aza-bicyclo(3.3.1)nonane-2,4-dione and 2-diazo-3,5-cyclohexadiene-1-one, respectively. We expect that this parallel algorithm is always possible to reduce computational costs of any crystal structure predictions.

  12. Brain structure predicts risk for obesity ☆

    PubMed Central

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

    2014-01-01

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

  13. Ceramide structure predicts tumor ganglioside immunosuppressive activity.

    PubMed Central

    Ladisch, S; Li, R; Olson, E

    1994-01-01

    Molecular determinants of biological activity of gangliosides are generally believed to be carbohydrate in nature. However, our studies of immunomodulation by highly purified naturally occurring tumor gangliosides provide another perspective: while the immunosuppressive activity of gangliosides requires the intact molecule (both carbohydrate and ceramide moieties), ceramide structure strikingly influences ganglioside immunosuppressive activity. Molecular species of human neuroblastoma GD2 ganglioside in which the ceramide contains a shorter fatty acyl chain (C16:0, C18:0) were 6- to 10-fold more active than those with a longer fatty acyl chain (C22:0/C24:1, C24:0). These findings were confirmed in studies of ceramide species of human leukemia sialosylparagloboside and murine lymphoma GalNAcGM1b. Gangliosides that contain shorter-chain fatty acids (and are most immunosuppressive) are known to be preferentially shed by tumor cells. Therefore, the results suggest that the tumor cell is optimized to protect itself from host immune destruction by selective shedding of highly active ceramide species of gangliosides. Images PMID:8127917

  14. Evolving networks-Using past structure to predict the future

    NASA Astrophysics Data System (ADS)

    Shang, Ke-ke; Yan, Wei-sheng; Small, Michael

    2016-08-01

    Many previous studies on link prediction have focused on using common neighbors to predict the existence of links between pairs of nodes. More broadly, research into the structural properties of evolving temporal networks and temporal link prediction methods have recently attracted increasing attention. In this study, for the first time, we examine the use of links between a pair of nodes to predict their common neighbors and analyze the relationship between the weight and the structure in static networks, evolving networks, and in the corresponding randomized networks. We propose both new unweighted and weighted prediction methods and use six kinds of real networks to test our algorithms. In unweighted networks, we find that if a pair of nodes connect to each other in the current network, they will have a higher probability to connect common nodes both in the current and the future networks-and the probability will decrease with the increase of the number of neighbors. Furthermore, we find that the original networks have their particular structure and statistical characteristics which benefit link prediction. In weighted networks, the prediction algorithm performance of networks which are dominated by human factors decrease with the decrease of weight and are in general better in static networks. Furthermore, we find that geographical position and link weight both have significant influence on the transport network. Moreover, the evolving financial network has the lowest predictability. In addition, we find that the structure of non-social networks has more robustness than social networks. The structure of engineering networks has both best predictability and also robustness.

  15. The conformation of a B-DNA decamer is mainly determined by its sequence and not by crystal environment.

    PubMed Central

    Heinemann, U; Alings, C

    1991-01-01

    By comparing the conformations adopted by a double-stranded decameric B-DNA fragment in different crystal environments, we address the question of the degree of deformability of DNA helices. The three-dimensional structure of the self-complementary DNA decamer CCAGGCmeCTGG has been determined from crystals of space group P6 at 2.25 A resolution with an R value of 17.2% for 2407 1 sigma structure amplitudes. The oligonucleotide forms a B-type double helix with a characteristic sequence-dependent conformation closely resembling that of the corresponding unmethylated decamer, the structure of which is known from a high-resolution analysis of crystals of space group C2. Evidently, both the effects of single-site methylation and altered crystal environment on the DNA conformation are small. Therefore, double-helical DNA may possess sequence-determined conformational features that are less deformable than previously thought. Images PMID:1989887

  16. Referent Predictability Is Affected by Syntactic Structure: Evidence from Chinese

    ERIC Educational Resources Information Center

    Cheng, Wei; Almor, Amit

    2017-01-01

    This paper examines the effect of syntactic structures on referent predictability. Focusing on stimulus-experiencer (SE) verbs, we conducted two sentence-completion experiments in Chinese by contrasting SE verbs in three structures (active canonical, active "ba," and passive). The results showed that although verb semantics and discourse…

  17. Data-directed RNA secondary structure prediction using probabilistic modeling.

    PubMed

    Deng, Fei; Ledda, Mirko; Vaziri, Sana; Aviran, Sharon

    2016-08-01

    Structure dictates the function of many RNAs, but secondary RNA structure analysis is either labor intensive and costly or relies on computational predictions that are often inaccurate. These limitations are alleviated by integration of structure probing data into prediction algorithms. However, existing algorithms are optimized for a specific type of probing data. Recently, new chemistries combined with advances in sequencing have facilitated structure probing at unprecedented scale and sensitivity. These novel technologies and anticipated wealth of data highlight a need for algorithms that readily accommodate more complex and diverse input sources. We implemented and investigated a recently outlined probabilistic framework for RNA secondary structure prediction and extended it to accommodate further refinement of structural information. This framework utilizes direct likelihood-based calculations of pseudo-energy terms per considered structural context and can readily accommodate diverse data types and complex data dependencies. We use real data in conjunction with simulations to evaluate performances of several implementations and to show that proper integration of structural contexts can lead to improvements. Our tests also reveal discrepancies between real data and simulations, which we show can be alleviated by refined modeling. We then propose statistical preprocessing approaches to standardize data interpretation and integration into such a generic framework. We further systematically quantify the information content of data subsets, demonstrating that high reactivities are major drivers of SHAPE-directed predictions and that better understanding of less informative reactivities is key to further improvements. Finally, we provide evidence for the adaptive capability of our framework using mock probe simulations.

  18. Structure Prediction of RNA Loops with a Probabilistic Approach

    PubMed Central

    Li, Jun; Zhang, Jian; Wang, Jun; Li, Wenfei; Wang, Wei

    2016-01-01

    The knowledge of the tertiary structure of RNA loops is important for understanding their functions. In this work we develop an efficient approach named RNApps, specifically designed for predicting the tertiary structure of RNA loops, including hairpin loops, internal loops, and multi-way junction loops. It includes a probabilistic coarse-grained RNA model, an all-atom statistical energy function, a sequential Monte Carlo growth algorithm, and a simulated annealing procedure. The approach is tested with a dataset including nine RNA loops, a 23S ribosomal RNA, and a large dataset containing 876 RNAs. The performance is evaluated and compared with a homology modeling based predictor and an ab initio predictor. It is found that RNApps has comparable performance with the former one and outdoes the latter in terms of structure predictions. The approach holds great promise for accurate and efficient RNA tertiary structure prediction. PMID:27494763

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

    PubMed

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

    2016-02-21

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

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

    PubMed Central

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

    2009-01-01

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

  1. Clathrate Structure Determination by Combining Crystal Structure Prediction with Computational and Experimental (129) Xe NMR Spectroscopy.

    PubMed

    Selent, Marcin; Nyman, Jonas; Roukala, Juho; Ilczyszyn, Marek; Oilunkaniemi, Raija; Bygrave, Peter J; Laitinen, Risto; Jokisaari, Jukka; Day, Graeme M; Lantto, Perttu

    2017-01-23

    An approach is presented for the structure determination of clathrates using NMR spectroscopy of enclathrated xenon to select from a set of predicted crystal structures. Crystal structure prediction methods have been used to generate an ensemble of putative structures of o- and m-fluorophenol, whose previously unknown clathrate structures have been studied by (129) Xe NMR spectroscopy. The high sensitivity of the (129) Xe chemical shift tensor to the chemical environment and shape of the crystalline cavity makes it ideal as a probe for porous materials. The experimental powder NMR spectra can be used to directly confirm or reject hypothetical crystal structures generated by computational prediction, whose chemical shift tensors have been simulated using density functional theory. For each fluorophenol isomer one predicted crystal structure was found, whose measured and computed chemical shift tensors agree within experimental and computational error margins and these are thus proposed as the true fluorophenol xenon clathrate structures.

  2. Contingency Table Browser − prediction of early stage protein structure

    PubMed Central

    Kalinowska, Barbara; Krzykalski, Artur; Roterman, Irena

    2015-01-01

    The Early Stage (ES) intermediate represents the starting structure in protein folding simulations based on the Fuzzy Oil Drop (FOD) model. The accuracy of FOD predictions is greatly dependent on the accuracy of the chosen intermediate. A suitable intermediate can be constructed using the sequence-structure relationship information contained in the so-called contingency table − this table expresses the likelihood of encountering various structural motifs for each tetrapeptide fragment in the amino acid sequence. The limited accuracy with which such structures could previously be predicted provided the motivation for a more indepth study of the contingency table itself. The Contingency Table Browser is a tool which can visualize, search and analyze the table. Our work presents possible applications of Contingency Table Browser, among them − analysis of specific protein sequences from the point of view of their structural ambiguity. PMID:26664034

  3. A life prediction model for laminated composite structural components

    NASA Technical Reports Server (NTRS)

    Allen, David H.

    1990-01-01

    A life prediction methodology for laminated continuous fiber composites subjected to fatigue loading conditions was developed. A summary is presented of research completed. A phenomenological damage evolution law was formulated for matrix cracking which is independent of stacking sequence. Mechanistic and physical support was developed for the phenomenological evolution law proposed above. The damage evolution law proposed above was implemented to a finite element computer program. And preliminary predictions were obtained for a structural component undergoing fatigue loading induced damage.

  4. MUFOLD: A new solution for protein 3D structure prediction.

    PubMed

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

    2010-04-01

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

  5. Toward the Prediction of Organic Hydrate Crystal Structures.

    PubMed

    Hulme, Ashley T; Price, Sarah L

    2007-07-01

    Lattice energy minimization studies on four ordered crystal structures of ice and 22 hydrates of approximately rigid organic molecules (along with 11 corresponding anhydrate structures) were used to establish a model potential scheme, based on the use of a distributed multipole electrostatic model, that can reasonably reproduce the crystal structures. Transferring the empirical repulsion-dispersion potentials for organic oxygen and polar hydrogen atoms to water appears more successful for modeling ice phases than using common water potentials derived from liquid properties. Lattice energy differences are reasonable but quite sensitive to the exact conformation of water and the organic molecule used in the rigid molecule modeling. This potential scheme was used to test a new approach of predicting the crystal structure of 5-azauracil monohydrate (an isolated site hydrate) based on seeking dense crystal packings of 66 5-azauracil···water hydrogen-bonded clusters, derived from an analysis of hydrate hydrogen bond geometries involving the carbonyl- and aza-group acceptors in the Cambridge Structural Database. The known structure was found within 5 kJ mol(-1) of the global minimum in static lattice energy and as the third most stable structure, within 1 kJ mol(-1), when thermal effects at ambient temperature were considered. Thus, although the computational prediction of whether an organic molecule will crystallize in a hydrated form poses many challenges, the prediction of plausible structures for hydrogen-bonded monohydrates is now possible.

  6. A new protein structure representation for efficient protein function prediction.

    PubMed

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

    2014-12-01

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

  7. Exploring polymorphisms in B-DNA helical conformations

    PubMed Central

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

    2012-01-01

    The traditional mesoscopic paradigm represents DNA as a series of base-pair steps whose energy response to equilibrium perturbations is elastic, with harmonic oscillations (defining local stiffness) around a single equilibrium conformation. In addition, base sequence effects are often analysed as a succession of independent XpY base-pair steps (i.e. a nearest-neighbour (NN) model with only 10 unique cases). Unfortunately, recent massive simulations carried out by the ABC consortium suggest that the real picture of DNA flexibility may be much more complex. The paradigm of DNA flexibility therefore needs to be revisited. In this article, we explore in detail one of the most obvious violations of the elastic NN model of flexibility: the bimodal distributions of some helical parameters. We perform here an in-depth statistical analysis of a very large set of MD trajectories and also of experimental structures, which lead to very solid evidence of bimodality. We then suggest ways to improve mesoscopic models to account for this deviation from the elastic regime. PMID:23012264

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

    PubMed

    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

    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.

  9. Cloud Prediction of Protein Structure and Function with PredictProtein for Debian

    PubMed Central

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

    2013-01-01

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

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

    PubMed

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

    2012-11-09

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

  11. HOPE: a homotopy optimization method for protein structure prediction.

    PubMed

    Dunlavy, Daniel M; O'Leary, Dianne P; Klimov, Dmitri; Thirumalai, D

    2005-12-01

    We use a homotopy optimization method, HOPE, to minimize the potential energy associated with a protein model. The method uses the minimum energy conformation of one protein as a template to predict the lowest energy structure of a query sequence. This objective is achieved by following a path of conformations determined by a homotopy between the potential energy functions for the two proteins. Ensembles of solutions are produced by perturbing conformations along the path, increasing the likelihood of predicting correct structures. Successful results are presented for pairs of homologous proteins, where HOPE is compared to a variant of Newton's method and to simulated annealing.

  12. Fast and accurate automatic structure prediction with HHpred.

    PubMed

    Hildebrand, Andrea; Remmert, Michael; Biegert, Andreas; Söding, Johannes

    2009-01-01

    Automated protein structure prediction is becoming a mainstream tool for biological research. This has been fueled by steady improvements of publicly available automated servers over the last decade, in particular their ability to build good homology models for an increasing number of targets by reliably detecting and aligning more and more remotely homologous templates. Here, we describe the three fully automated versions of the HHpred server that participated in the community-wide blind protein structure prediction competition CASP8. What makes HHpred unique is the combination of usability, short response times (typically under 15 min) and a model accuracy that is competitive with those of the best servers in CASP8.

  13. PROTEUS2: a web server for comprehensive protein structure prediction and structure-based annotation.

    PubMed

    Montgomerie, Scott; Cruz, Joseph A; Shrivastava, Savita; Arndt, David; Berjanskii, Mark; Wishart, David S

    2008-07-01

    PROTEUS2 is a web server designed to support comprehensive protein structure prediction and structure-based annotation. PROTEUS2 accepts either single sequences (for directed studies) or multiple sequences (for whole proteome annotation) and predicts the secondary and, if possible, tertiary structure of the query protein(s). Unlike most other tools or servers, PROTEUS2 bundles signal peptide identification, transmembrane helix prediction, transmembrane beta-strand prediction, secondary structure prediction (for soluble proteins) and homology modeling (i.e. 3D structure generation) into a single prediction pipeline. Using a combination of progressive multi-sequence alignment, structure-based mapping, hidden Markov models, multi-component neural nets and up-to-date databases of known secondary structure assignments, PROTEUS is able to achieve among the highest reported levels of predictive accuracy for signal peptides (Q2 = 94%), membrane spanning helices (Q2 = 87%) and secondary structure (Q3 score of 81.3%). PROTEUS2's homology modeling services also provide high quality 3D models that compare favorably with those generated by SWISS-MODEL and 3D JigSaw (within 0.2 A RMSD). The average PROTEUS2 prediction takes approximately 3 min per query sequence. The PROTEUS2 server along with source code for many of its modules is accessible a http://wishart.biology.ualberta.ca/proteus2.

  14. Sizing Structures and Predicting Weight of a Spacecraft

    NASA Technical Reports Server (NTRS)

    Cerro, Jeffrey; Shore, C. P.

    2006-01-01

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

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

    ERIC Educational Resources Information Center

    Lucas, Amand A.

    2008-01-01

    A-DNA and B-DNA are two secondary molecular conformations (among other allomorphs) that double-stranded DNA drawn into a fiber can assume, depending on the relative water content and other chemical parameters of the fiber. They were the first two forms to be observed by X-ray fiber diffraction in the early 1950s, respectively by Wilkins and…

  16. Distance matrix-based approach to protein structure prediction.

    PubMed

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

    2009-03-01

    Much structural information is encoded in the internal distances; a distance matrix-based approach can be used to predict protein structure and dynamics, and for structural refinement. Our approach is based on the square distance matrix D = [r(ij)(2)] containing all square distances between residues in proteins. This distance matrix contains more information than the contact matrix C, that has elements of either 0 or 1 depending on whether the distance r (ij) is greater or less than a cutoff value r (cutoff). We have performed spectral decomposition of the distance matrices D = sigma lambda(k)V(k)V(kT), in terms of eigenvalues lambda kappa and the corresponding eigenvectors v kappa and found that it contains at most five nonzero terms. A dominant eigenvector is proportional to r (2)--the square distance of points from the center of mass, with the next three being the principal components of the system of points. By predicting r (2) from the sequence we can approximate a distance matrix of a protein with an expected RMSD value of about 7.3 A, and by combining it with the prediction of the first principal component we can improve this approximation to 4.0 A. We can also explain the role of hydrophobic interactions for the protein structure, because r is highly correlated with the hydrophobic profile of the sequence. Moreover, r is highly correlated with several sequence profiles which are useful in protein structure prediction, such as contact number, the residue-wise contact order (RWCO) or mean square fluctuations (i.e. crystallographic temperature factors). We have also shown that the next three components are related to spatial directionality of the secondary structure elements, and they may be also predicted from the sequence, improving overall structure prediction. We have also shown that the large number of available HIV-1 protease structures provides a remarkable sampling of conformations, which can be viewed as direct structural information about the

  17. Structure-based Methods for Computational Protein Functional Site Prediction

    PubMed Central

    Dukka, B KC

    2013-01-01

    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

  18. PSPP: A Protein Structure Prediction Pipeline for Computing Clusters

    DTIC Science & Technology

    2009-07-01

    Clusters 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) 5d. PROJECT NUMBER 5e. TASK NUMBER 5f. WORK UNIT NUMBER 7...Fold recognition/threading [34] [60] PSIPRED Jones Secondary structure prediction [21] [61] Rosetta Baker Ab initio folder [41] [62] SCOP/ ASTRAL Chothia...the folds of the models that result from the Rosetta code, the structures of the top few models are compared against ASTRAL PDB-style coordinates of

  19. A system structure for predictive relations in penetration mechanics

    NASA Astrophysics Data System (ADS)

    Korjack, Thomas A.

    1992-02-01

    The availability of a software system yielding quick numerical models to predict ballistic behavior is a requisite for any research laboratory engaged in material behavior. What is especially true about accessibility of rapid prototyping for terminal impaction is the enhancement of a system structure which will direct the specific material and impact situation towards a specific predictive model. This is of particular importance when the ranges of validity are at stake and the pertinent constraints associated with the impact are unknown. Hence, a compilation of semiempirical predictive penetration relations for various physical phenomena has been organized into a data structure for the purpose of developing a knowledge-based decision aided expert system to predict the terminal ballistic behavior of projectiles and targets. The ranges of validity and constraints of operation of each model were examined and cast into a decision tree structure to include target type, target material, projectile types, projectile materials, attack configuration, and performance or damage measures. This decision system implements many penetration relations, identifies formulas that match user-given conditions, and displays the predictive relation coincident with the match in addition to a numerical solution. The physical regimes under consideration encompass the hydrodynamic, transitional, and solid; the targets are either semi-infinite or plate, and the projectiles include kinetic and chemical energy. A preliminary databases has been constructed to allow further development of inductive and deductive reasoning techniques applied to ballistic situations involving terminal mechanics.

  20. CENTROIDFOLD: a web server for RNA secondary structure prediction.

    PubMed

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

    2009-07-01

    The CENTROIDFOLD web server (http://www.ncrna.org/centroidfold/) is a web application for RNA secondary structure prediction powered by one of the most accurate prediction engine. The server accepts two kinds of sequence data: a single RNA sequence and a multiple alignment of RNA sequences. It responses with a prediction result shown as a popular base-pair notation and a graph representation. PDF version of the graph representation is also available. For a multiple alignment sequence, the server predicts a common secondary structure. Usage of the server is quite simple. You can paste a single RNA sequence (FASTA or plain sequence text) or a multiple alignment (CLUSTAL-W format) into the textarea then click on the 'execute CentroidFold' button. The server quickly responses with a prediction result. The major advantage of this server is that it employs our original CentroidFold software as its prediction engine which scores the best accuracy in our benchmark results. Our web server is freely available with no login requirement.

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

    NASA Astrophysics Data System (ADS)

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

    2013-12-01

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

  2. Process for predicting structural performance of mechanical systems

    DOEpatents

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

    1998-05-19

    A process for predicting the structural performance of a mechanical system represents the mechanical system by a plurality of surface elements. The surface elements are grouped according to their location in the volume occupied by the mechanical system so that contacts between surface elements can be efficiently located. The process is well suited for efficient practice on multiprocessor computers. 12 figs.

  3. Process for predicting structural performance of mechanical systems

    DOEpatents

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

    1998-01-01

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

  4. A Web-Accessible Protein Structure Prediction Pipeline

    DTIC Science & Technology

    2009-06-01

    almost all of these programs, the input is a protein position-specific substitution matrix ( PSSM ), in addition to the protein sequence itself. We...calculate a single PSSM for each of the input proteins using PSI-BLAST[8] and the NR database. Once the structural properties are predicted using the

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

    NASA Technical Reports Server (NTRS)

    1995-01-01

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

  6. Multidimensional models for predicting muscle structure and fascicle pennation.

    PubMed

    Randhawa, Avleen; Wakeling, James M

    2015-10-07

    Pennation angles change during muscle contraction and must be tracked by muscle models. When muscles contract they can change in depth (distance between the bounding sheets of aponeurosis) or width, and this is related to pennation angle and muscle fascicle length. As a simplification to these relationships, many models of pennate muscle assume a constant distance between aponeuroses during contraction (constant depth). It is possible that these 1D models do not recreate the internal structure of muscles adequately, whereas 2D panel models that assume a constant panel area, or 3D models that assume a constant muscle volume may better predict the structural changes that occur within muscle during contraction. However, these ideas have never been validated in man. The purpose of this study was to test the accuracy with which 1D, 2D or 3D structural models of muscle could predict the pennation and muscle depth within the medial gastrocnemius (MG) and lateral gastrocnemius (LG) in man during ankle plantarflexions. The 1D model, by definition, was unable to account for changes in muscle depth. The 2D model predicted change in depth as the aponeurosis was loaded, but could only allow a decrease in depth as the aponeurosis is stretched. This was not sufficient to predict the increases in depth that occur in the LG during plantarflexion. The 3D model had the ability to predict either increases or decreases in depth during the ankle plantarflexions and predicted opposing changes in depth that occurred between the MG and LG, whilst simultaneously predicting the pennation more accurately than the 1D or 2D models. However, when using mean parameters, the 3D model performed no better than the more simple 1D model, and so if the intent of a model is purely to establish a good relation between fascicle length and pennation then the 1D model is a suitable choice for these muscles.

  7. Predictive modeling of neuroanatomic structures for brain atrophy detection

    NASA Astrophysics Data System (ADS)

    Hu, Xintao; Guo, Lei; Nie, Jingxin; Li, Kaiming; Liu, Tianming

    2010-03-01

    In this paper, we present an approach of predictive modeling of neuroanatomic structures for the detection of brain atrophy based on cross-sectional MRI image. The underlying premise of applying predictive modeling for atrophy detection is that brain atrophy is defined as significant deviation of part of the anatomy from what the remaining normal anatomy predicts for that part. The steps of predictive modeling are as follows. The central cortical surface under consideration is reconstructed from brain tissue map and Regions of Interests (ROI) on it are predicted from other reliable anatomies. The vertex pair-wise distance between the predicted vertex and the true one within the abnormal region is expected to be larger than that of the vertex in normal brain region. Change of white matter/gray matter ratio within a spherical region is used to identify the direction of vertex displacement. In this way, the severity of brain atrophy can be defined quantitatively by the displacements of those vertices. The proposed predictive modeling method has been evaluated by using both simulated atrophies and MRI images of Alzheimer's disease.

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

    NASA Astrophysics Data System (ADS)

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

    2015-12-01

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

  9. Prediction of RNA secondary structure, including pseudoknotting, by computer simulation.

    PubMed Central

    Abrahams, J P; van den Berg, M; van Batenburg, E; Pleij, C

    1990-01-01

    A computer program is presented which determines the secondary structure of linear RNA molecules by simulating a hypothetical process of folding. This process implies the concept of 'nucleation centres', regions in RNA which locally trigger the folding. During the simulation, the RNA is allowed to fold into pseudoknotted structures, unlike all other programs predicting RNA secondary structure. The simulation uses published, experimentally determined free energy values for nearest neighbour base pair stackings and loop regions, except for new extrapolated values for loops larger than seven nucleotides. The free energy value for a loop arising from pseudoknot formation is set to a single, estimated value of 4.2 kcal/mole. Especially in the case of long RNA sequences, our program appears superior to other secondary structure predicting programs described so far, as tests on tRNAs, the LSU intron of Tetrahymena thermophila and a number of plant viral RNAs show. In addition, pseudoknotted structures are often predicted successfully. The program is written in mainframe APL and is adapted to run on IBM compatible PCs, Atari ST and Macintosh personal computers. On an 8 MHz 8088 standard PC without coprocessor, using STSC APL, it folds a sequence of 700 nucleotides in one and a half hour. PMID:1693421

  10. RNA Secondary Structure Prediction Using High-throughput SHAPE

    PubMed Central

    Purzycka, Katarzyna J.; Rausch, Jason W.; Le Grice, Stuart F.J.

    2013-01-01

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

  11. Three-dimensional protein structure prediction: Methods and computational strategies.

    PubMed

    Dorn, Márcio; E Silva, Mariel Barbachan; Buriol, Luciana S; Lamb, Luis C

    2014-10-12

    A long standing problem in structural bioinformatics is to determine the three-dimensional (3-D) structure of a protein when only a sequence of amino acid residues is given. Many computational methodologies and algorithms have been proposed as a solution to the 3-D Protein Structure Prediction (3-D-PSP) problem. These methods can be divided in four main classes: (a) first principle methods without database information; (b) first principle methods with database information; (c) fold recognition and threading methods; and (d) comparative modeling methods and sequence alignment strategies. Deterministic computational techniques, optimization techniques, data mining and machine learning approaches are typically used in the construction of computational solutions for the PSP problem. Our main goal with this work is to review the methods and computational strategies that are currently used in 3-D protein prediction.

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

    PubMed

    Zhang, Xiuwen; Zunger, Alex; Trimarchi, Giancarlo

    2010-11-21

    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 ΔH 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) Π(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.

  13. Application of Functional Use Predictions to Aid in Structure ...

    EPA Pesticide Factsheets

    Humans are potentially exposed to thousands of anthropogenic chemicals in commerce. Recent work has shown that the bulk of this exposure may occur in near-field indoor environments (e.g., home, school, work, etc.). Advances in suspect screening analyses (SSA) now allow an improved understanding of the chemicals present in these environments. However, due to the nature of suspect screening techniques, investigators are often left with chemical formula predictions, with the possibility of many chemical structures matching to each formula. Here, newly developed quantitative structure-use relationship (QSUR) models are used to identify potential exposure sources for candidate structures. Previously, a suspect screening workflow was introduced and applied to house dust samples collected from the U.S. Department of Housing and Urban Development’s American Healthy Homes Survey (AHHS) [Rager, et al., Env. Int. 88 (2016)]. This workflow utilized the US EPA’s Distributed Structure-Searchable Toxicity (DSSTox) Database to link identified molecular features to molecular formulas, and ultimately chemical structures. Multiple QSUR models were applied to support the evaluation of candidate structures. These QSURs predict the likelihood of a chemical having a functional use commonly associated with consumer products having near-field use. For 3,228 structures identified as possible chemicals in AHHS house dust samples, we were able to obtain the required descriptors to appl

  14. Predicting protein structures with a multiplayer online game.

    PubMed

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

    2010-08-05

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

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

    PubMed Central

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

    2008-01-01

    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

  16. Generalized Pattern Search Algorithm for Peptide Structure Prediction

    PubMed Central

    Nicosia, Giuseppe; Stracquadanio, Giovanni

    2008-01-01

    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 Cα. PMID:18487293

  17. 3D-Fun: predicting enzyme function from structure.

    PubMed

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

    2008-07-01

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

  18. Predicting loop-helix tertiary structural contacts in RNA pseudoknots.

    PubMed

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

    2010-03-01

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

  19. Virality Prediction and Community Structure in Social Networks

    NASA Astrophysics Data System (ADS)

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

    2013-08-01

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

  20. One Single Static Measurement Predicts Wave Localization in Complex Structures.

    PubMed

    Lefebvre, Gautier; Gondel, Alexane; Dubois, Marc; Atlan, Michael; Feppon, Florian; Labbé, Aimé; Gillot, Camille; Garelli, Alix; Ernoult, Maxence; Mayboroda, Svitlana; Filoche, Marcel; Sebbah, Patrick

    2016-08-12

    A recent theoretical breakthrough has brought a new tool, called the localization landscape, for predicting the localization regions of vibration modes in complex or disordered systems. Here, we report on the first experiment which measures the localization landscape and demonstrates its predictive power. Holographic measurement of the static deformation under uniform load of a thin plate with complex geometry provides direct access to the landscape function. When put in vibration, this system shows modes precisely confined within the subregions delineated by the landscape function. Also the maxima of this function match the measured eigenfrequencies, while the minima of the valley network gives the frequencies at which modes become extended. This approach fully characterizes the low frequency spectrum of a complex structure from a single static measurement. It paves the way for controlling and engineering eigenmodes in any vibratory system, especially where a structural or microscopic description is not accessible.

  1. Virality prediction and community structure in social networks.

    PubMed

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

    2013-01-01

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

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

    PubMed

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

    2015-01-01

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

  3. Prediction of protein function from protein sequence and structure.

    PubMed

    Whisstock, James C; Lesk, Arthur M

    2003-08-01

    The sequence of a genome contains the plans of the possible life of an organism, but implementation of genetic information depends on the functions of the proteins and nucleic acids that it encodes. Many individual proteins of known sequence and structure present challenges to the understanding of their function. In particular, a number of genes responsible for diseases have been identified but their specific functions are unknown. Whole-genome sequencing projects are a major source of proteins of unknown function. Annotation of a genome involves assignment of functions to gene products, in most cases on the basis of amino-acid sequence alone. 3D structure can aid the assignment of function, motivating the challenge of structural genomics projects to make structural information available for novel uncharacterized proteins. Structure-based identification of homologues often succeeds where sequence-alone-based methods fail, because in many cases evolution retains the folding pattern long after sequence similarity becomes undetectable. Nevertheless, prediction of protein function from sequence and structure is a difficult problem, because homologous proteins often have different functions. Many methods of function prediction rely on identifying similarity in sequence and/or structure between a protein of unknown function and one or more well-understood proteins. Alternative methods include inferring conservation patterns in members of a functionally uncharacterized family for which many sequences and structures are known. However, these inferences are tenuous. Such methods provide reasonable guesses at function, but are far from foolproof. It is therefore fortunate that the development of whole-organism approaches and comparative genomics permits other approaches to function prediction when the data are available. These include the use of protein-protein interaction patterns, and correlations between occurrences of related proteins in different organisms, as

  4. Sixth blind test of organic crystal-structure prediction methods.

    PubMed

    Groom, Colin R; Reilly, Anthony M

    2014-08-01

    Over the past 15 years progress in predicting crystal structures of small organic molecules has been charted by a series of blind tests hosted by the Cambridge Crystallographic Data Centre. This letter announces a sixth blind test to take place between September 2014 and August 2015, giving details of the target systems and the revised procedure. We hope that as many methods as possible will be assessed and benchmarked in this new blind test.

  5. Prediction of interface structures and energies via virtual screening

    PubMed Central

    Kiyohara, Shin; Oda, Hiromi; Miyata, Tomohiro; Mizoguchi, Teruyasu

    2016-01-01

    Interfaces markedly affect the properties of materials because of differences in their atomic configurations. Determining the atomic structure of the interface is therefore one of the most significant tasks in materials research. However, determining the interface structure usually requires extensive computation. If the interface structure could be efficiently predicted, our understanding of the mechanisms that give rise to the interface properties would be significantly facilitated, and this would pave the way for the design of material interfaces. Using a virtual screening method based on machine learning, we demonstrate a powerful technique to determine interface energies and structures. On the basis of the results obtained by a nonlinear regression using training data from 4 interfaces, structures and energies for 13 other interfaces were predicted. Our method achieved an efficiency that is more than several hundred to several tens of thousand times higher than that of the previously reported methods. Because the present method uses geometrical factors, such as bond length and atomic density, as descriptors for the regression analysis, the method presented here is robust and general and is expected to be beneficial to understanding the nature of any interface. PMID:28138517

  6. Prediction of interface structures and energies via virtual screening.

    PubMed

    Kiyohara, Shin; Oda, Hiromi; Miyata, Tomohiro; Mizoguchi, Teruyasu

    2016-11-01

    Interfaces markedly affect the properties of materials because of differences in their atomic configurations. Determining the atomic structure of the interface is therefore one of the most significant tasks in materials research. However, determining the interface structure usually requires extensive computation. If the interface structure could be efficiently predicted, our understanding of the mechanisms that give rise to the interface properties would be significantly facilitated, and this would pave the way for the design of material interfaces. Using a virtual screening method based on machine learning, we demonstrate a powerful technique to determine interface energies and structures. On the basis of the results obtained by a nonlinear regression using training data from 4 interfaces, structures and energies for 13 other interfaces were predicted. Our method achieved an efficiency that is more than several hundred to several tens of thousand times higher than that of the previously reported methods. Because the present method uses geometrical factors, such as bond length and atomic density, as descriptors for the regression analysis, the method presented here is robust and general and is expected to be beneficial to understanding the nature of any interface.

  7. Residual Strength Prediction of Fuselage Structures with Multiple Site Damage

    NASA Technical Reports Server (NTRS)

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

    1999-01-01

    This paper summarizes recent results on simulating full-scale pressure tests of wide body, lap-jointed fuselage panels with multiple site damage (MSD). The crack tip opening angle (CTOA) fracture criterion and the FRANC3D/STAGS software program were used to analyze stable crack growth under conditions of general yielding. The link-up of multiple cracks and residual strength of damaged structures were predicted. Elastic-plastic finite element analysis based on the von Mises yield criterion and incremental flow theory with small strain assumption was used. A global-local modeling procedure was employed in the numerical analyses. Stress distributions from the numerical simulations are compared with strain gage measurements. Analysis results show that accurate representation of the load transfer through the rivets is crucial for the model to predict the stress distribution accurately. Predicted crack growth and residual strength are compared with test data. Observed and predicted results both indicate that the occurrence of small MSD cracks substantially reduces the residual strength. Modeling fatigue closure is essential to capture the fracture behavior during the early stable crack growth. Breakage of a tear strap can have a major influence on residual strength prediction.

  8. Improved hybrid optimization algorithm for 3D protein structure prediction.

    PubMed

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

    2014-07-01

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

  9. Graphlet kernels for prediction of functional residues in protein structures.

    PubMed

    Vacic, Vladimir; Iakoucheva, Lilia M; Lonardi, Stefano; Radivojac, Predrag

    2010-01-01

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

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

    PubMed Central

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

    2016-01-01

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

  11. EVO—Evolutionary algorithm for crystal structure prediction

    NASA Astrophysics Data System (ADS)

    Bahmann, Silvia; Kortus, Jens

    2013-06-01

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

  12. Predicting the stability of large structured food webs.

    PubMed

    Allesina, Stefano; Grilli, Jacopo; Barabás, György; Tang, Si; Aljadeff, Johnatan; Maritan, Amos

    2015-07-22

    The stability of ecological systems has been a long-standing focus of ecology. Recently, tools from random matrix theory have identified the main drivers of stability in ecological communities whose network structure is random. However, empirical food webs differ greatly from random graphs. For example, their degree distribution is broader, they contain few trophic cycles, and they are almost interval. Here we derive an approximation for the stability of food webs whose structure is generated by the cascade model, in which 'larger' species consume 'smaller' ones. We predict the stability of these food webs with great accuracy, and our approximation also works well for food webs whose structure is determined empirically or by the niche model. We find that intervality and broad degree distributions tend to stabilize food webs, and that average interaction strength has little influence on stability, compared with the effect of variance and correlation.

  13. Predicting the stability of large structured food webs

    PubMed Central

    Allesina, Stefano; Grilli, Jacopo; Barabás, György; Tang, Si; Aljadeff, Johnatan; Maritan, Amos

    2015-01-01

    The stability of ecological systems has been a long-standing focus of ecology. Recently, tools from random matrix theory have identified the main drivers of stability in ecological communities whose network structure is random. However, empirical food webs differ greatly from random graphs. For example, their degree distribution is broader, they contain few trophic cycles, and they are almost interval. Here we derive an approximation for the stability of food webs whose structure is generated by the cascade model, in which ‘larger' species consume ‘smaller' ones. We predict the stability of these food webs with great accuracy, and our approximation also works well for food webs whose structure is determined empirically or by the niche model. We find that intervality and broad degree distributions tend to stabilize food webs, and that average interaction strength has little influence on stability, compared with the effect of variance and correlation. PMID:26198207

  14. Structure-based prediction of host-pathogen protein interactions.

    PubMed

    Mariano, Rachelle; Wuchty, Stefan

    2017-03-16

    The discovery, validation, and characterization of protein-based interactions from different species are crucial for translational research regarding a variety of pathogens, ranging from the malaria parasite Plasmodium falciparum to HIV-1. Here, we review recent advances in the prediction of host-pathogen protein interfaces using structural information. In particular, we observe that current methods chiefly perform machine learning on sequence and domain information to produce large sets of candidate interactions that are further assessed and pruned to generate final, highly probable sets. Structure-based studies have also emphasized the electrostatic properties and evolutionary transformations of pathogenic interfaces, supplying crucial insight into antigenic determinants and the ways pathogens compete for host protein binding. Advancements in spectroscopic and crystallographic methods complement the aforementioned techniques, permitting the rigorous study of true positives at a molecular level. Together, these approaches illustrate how protein structure on a variety of levels functions coordinately and dynamically to achieve host takeover.

  15. RNAex: an RNA secondary structure prediction server enhanced by high-throughput structure-probing data

    PubMed Central

    Wu, Yang; Qu, Rihao; Huang, Yiming; Shi, Binbin; Liu, Mengrong; Li, Yang; Lu, Zhi John

    2016-01-01

    Several high-throughput technologies have been developed to probe RNA base pairs and loops at the transcriptome level in multiple species. However, to obtain the final RNA secondary structure, extensive effort and considerable expertise is required to statistically process the probing data and combine them with free energy models. Therefore, we developed an RNA secondary structure prediction server that is enhanced by experimental data (RNAex). RNAex is a web interface that enables non-specialists to easily access cutting-edge structure-probing data and predict RNA secondary structures enhanced by in vivo and in vitro data. RNAex annotates the RNA editing, RNA modification and SNP sites on the predicted structures. It provides four structure-folding methods, restrained MaxExpect, SeqFold, RNAstructure (Fold) and RNAfold that can be selected by the user. The performance of these four folding methods has been verified by previous publications on known structures. We re-mapped the raw sequencing data of the probing experiments to the whole genome for each species. RNAex thus enables users to predict secondary structures for both known and novel RNA transcripts in human, mouse, yeast and Arabidopsis. The RNAex web server is available at http://RNAex.ncrnalab.org/. PMID:27137891

  16. A new hybrid coding for protein secondary structure prediction based on primary structure similarity.

    PubMed

    Li, Zhong; Wang, Jing; Zhang, Shunpu; Zhang, Qifeng; Wu, Wuming

    2017-03-16

    The coding pattern of protein can greatly affect the prediction accuracy of protein secondary structure. In this paper, a novel hybrid coding method based on the physicochemical properties of amino acids and tendency factors is proposed for the prediction of protein secondary structure. The principal component analysis (PCA) is first applied to the physicochemical properties of amino acids to construct a 3-bit-code, and then the 3 tendency factors of amino acids are calculated to generate another 3-bit-code. Two 3-bit-codes are fused to form a novel hybrid 6-bit-code. Furthermore, we make a geometry-based similarity comparison of the protein primary structure between the reference set and the test set before the secondary structure prediction. We finally use the support vector machine (SVM) to predict those amino acids which are not detected by the primary structure similarity comparison. Experimental results show that our method achieves a satisfactory improvement in accuracy in the prediction of protein secondary structure.

  17. Quantitative structure-property relationships for predicting Henry's law constant from molecular structure.

    PubMed

    Dearden, John C; Schüürmann, Gerrit

    2003-08-01

    Various models are available for the prediction of Henry's law constant (H) or the air-water partition coefficient (Kaw), its dimensionless counterpart. Incremental methods are based on structural features such as atom types, bond types, and local structural environments; other regression models employ physicochemical properties, structural descriptors such as connectivity indices, and descriptors reflecting the electronic structure. There are also methods to calculate H from the ratio of vapor pressure (p(v)) and water solubility (S(w)) that in turn can be estimated from molecular structure, and quantum chemical continuum-solvation models to predict H via the solvation-free energy (deltaG(s)). This review is confined to methods that calculate H from molecular structure without experimental information and covers more than 40 methods published in the last 26 years. For a subset of eight incremental methods and four continuum-solvation models, a comparative analysis of their prediction performance is made using a test set of 700 compounds that includes a significant number of more complex and drug-like chemical structures. The results reveal substantial differences in the application range as well as in the prediction capability, a general decrease in prediction performance with decreasing H, and surprisingly large individual prediction errors, which are particularly striking for some quantum chemical schemes. The overall best-performing method appears to be the bond contribution method as implemented in the HENRYWIN software package, yielding a predictive squared correlation coefficient (q2) of 0.87 and a standard error of 1.03 log units for the test set.

  18. Addressing the Role of Conformational Diversity in Protein Structure Prediction

    PubMed Central

    Parisi, Gustavo; Fornasari, Maria Silvina

    2016-01-01

    Computational modeling of tertiary structures has become of standard use to study proteins that lack experimental characterization. Unfortunately, 3D structure prediction methods and model quality assessment programs often overlook that an ensemble of conformers in equilibrium populates the native state of proteins. In this work we collected sets of publicly available protein models and the corresponding target structures experimentally solved and studied how they describe the conformational diversity of the protein. For each protein, we assessed the quality of the models against known conformers by several standard measures and identified those models ranked best. We found that model rankings are defined by both the selected target conformer and the similarity measure used. 70% of the proteins in our datasets show that different models are structurally closest to different conformers of the same protein target. We observed that model building protocols such as template-based or ab initio approaches describe in similar ways the conformational diversity of the protein, although for template-based methods this description may depend on the sequence similarity between target and template sequences. Taken together, our results support the idea that protein structure modeling could help to identify members of the native ensemble, highlight the importance of considering conformational diversity in protein 3D quality evaluations and endorse the study of the variability of the native structure for a meaningful biological analysis. PMID:27159429

  19. StructBoost: Boosting Methods for Predicting Structured Output Variables.

    PubMed

    Chunhua Shen; Guosheng Lin; van den Hengel, Anton

    2014-10-01

    Boosting is a method for learning a single accurate predictor by linearly combining a set of less accurate weak learners. Recently, structured learning has found many applications in computer vision. Inspired by structured support vector machines (SSVM), here we propose a new boosting algorithm for structured output prediction, which we refer to as StructBoost. StructBoost supports nonlinear structured learning by combining a set of weak structured learners. As SSVM generalizes SVM, our StructBoost generalizes standard boosting approaches such as AdaBoost, or LPBoost to structured learning. The resulting optimization problem of StructBoost is more challenging than SSVM in the sense that it may involve exponentially many variables and constraints. In contrast, for SSVM one usually has an exponential number of constraints and a cutting-plane method is used. In order to efficiently solve StructBoost, we formulate an equivalent 1-slack formulation and solve it using a combination of cutting planes and column generation. We show the versatility and usefulness of StructBoost on a range of problems such as optimizing the tree loss for hierarchical multi-class classification, optimizing the Pascal overlap criterion for robust visual tracking and learning conditional random field parameters for image segmentation.

  20. Prediction of common folding structures of homologous RNAs.

    PubMed Central

    Han, K; Kim, H J

    1993-01-01

    We have developed an algorithm and a computer program for simultaneously folding homologous RNA sequences. Given an alignment of M homologous sequences of length N, the program performs phylogenetic comparative analysis and predicts a common secondary structure conserved in the sequences. When the structure is not uniquely determined, it infers multiple structures which appear most plausible. This method is superior to energy minimization methods in the sense that it is not sensitive to point mutation of a sequence. It is also superior to usual phylogenetic comparative methods in that it does not require manual scrutiny for covariation or secondary structures. The most plausible 1-5 structures are produced in O(MN2 + N3) time and O(N2) space, which are the same requirements as those of widely used dynamic programs based on energy minimization for folding a single sequence. This is the first algorithm probably practical both in terms of time and space for finding secondary structures of homologous RNA sequences. The algorithm has been implemented in C on a Sun SparcStation, and has been verified by testing on tRNAs, 5S rRNAs, 16S rRNAs, TAR RNAs of human immunodeficiency virus type 1 (HIV-1), and RRE RNAs of HIV-1. We have also applied the program to cis-acting packaging sequences of HIV-1, for which no generally accepted structures yet exist, and propose potentially stable structures. Simulation of the program with random sequences with the same base composition and the same degree of similarity as the above sequences shows that structures common to homologous sequences are very unlikely to occur by chance in random sequences. PMID:7681944

  1. Brain white matter structural properties predict transition to chronic pain.

    PubMed

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

    2013-10-01

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

  2. Predicted primary and antigenic structure of canine corticotropin releasing hormone.

    PubMed

    Mol, J A; van Wolferen, M; Kwant, M; Meloen, R

    1994-07-01

    Although the dog has been recognized as a useful model for the study of the cerebrospinal and peripheral actions of corticotropin releasing hormone (CRH) the exact amino acid composition of canine CRH is still unknown. In the present study the structure of canine CRH was predicted from the partial sequence of the gene encoding canine CRH. The CRH gene was amplified from genomic DNA obtained from white blood cells by a polymerase chain reaction and subsequently sequenced using the dideoxy method. The likely structure of canine CRH is: SEEPPISLDLTFHLLREVLEMARAEQLAQQAHSNRKLMEII-NH2, which is identical to the structure of human, rat and equine CRH. PEPSCAN analysis of 3 different CRH antisera predicted an antiserum raised against a conjugate of human CRH and CNBr -activated thyroglobulin to be the antiserum of choice for the measurement of CRH in the dog. Preliminary data confirmed the existence of the highest cross-reactivity of a canine hypothalamus extract, known to have a high content of CRH, with antisera directed against human, rat CRH. As a result of the present study immunological tools for CRH estimations are characterized. Also, a homologous DNA probe for in situ hybridizations has become available for further investigations.

  3. Failure prediction of thin beryllium sheets used in spacecraft structures

    NASA Technical Reports Server (NTRS)

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

    1991-01-01

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

  4. Offspring social network structure predicts fitness in families.

    PubMed

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

    2012-12-22

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

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

    PubMed

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

    2014-01-01

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

  6. Offspring social network structure predicts fitness in families

    PubMed Central

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

    2012-01-01

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

  7. Factors Influencing Progressive Failure Analysis Predictions for Laminated Composite Structure

    NASA Technical Reports Server (NTRS)

    Knight, Norman F., Jr.

    2008-01-01

    Progressive failure material modeling methods used for structural analysis including failure initiation and material degradation are presented. Different failure initiation criteria and material degradation models are described that define progressive failure formulations. These progressive failure formulations are implemented in a user-defined material model for use with a nonlinear finite element analysis tool. The failure initiation criteria include the maximum stress criteria, maximum strain criteria, the Tsai-Wu failure polynomial, and the Hashin criteria. The material degradation model is based on the ply-discounting approach where the local material constitutive coefficients are degraded. Applications and extensions of the progressive failure analysis material model address two-dimensional plate and shell finite elements and three-dimensional solid finite elements. Implementation details are described in the present paper. Parametric studies for laminated composite structures are discussed to illustrate the features of the progressive failure modeling methods that have been implemented and to demonstrate their influence on progressive failure analysis predictions.

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

    NASA Technical Reports Server (NTRS)

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

    1990-01-01

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

  9. Prediction of Halocarbon Toxicity from Structure: A Hierarchical QSAR Approach

    SciTech Connect

    Gute, B D; Balasubramanian, K; Geiss, K; Basak, S C

    2003-04-11

    Mathematical structural invariants and quantum theoretical descriptors have been used extensively in quantitative structure-activity relationships (QSARs) for the estimation of pharmaceutical activities, biological properties, physicochemical properties, and the toxicities of chemicals. Recently our research team has explored the relative importance of various levels of chemodescriptors, i.e., topostructural, topochemical, geometrical, and quantum theoretical descriptors, in property estimation. This study examines the contribution of chemodescriptors ranging from topostructural to quantum theoretic calculations up to the Gaussian STO-3G level in the prediction of the toxicity of a set of twenty halocarbons. We also report the results of experimental cell-level toxicity studies on these twenty halocarbons to validate our models.

  10. Universal structural parameter to quantitatively predict metallic glass properties

    NASA Astrophysics Data System (ADS)

    Ding, Jun; Cheng, Yong-Qiang; Sheng, Howard; Asta, Mark; Ritchie, Robert O.; Ma, Evan

    2016-12-01

    Quantitatively correlating the amorphous structure in metallic glasses (MGs) with their physical properties has been a long-sought goal. Here we introduce `flexibility volume' as a universal indicator, to bridge the structural state the MG is in with its properties, on both atomic and macroscopic levels. The flexibility volume combines static atomic volume with dynamics information via atomic vibrations that probe local configurational space and interaction between neighbouring atoms. We demonstrate that flexibility volume is a physically appropriate parameter that can quantitatively predict the shear modulus, which is at the heart of many key properties of MGs. Moreover, the new parameter correlates strongly with atomic packing topology, and also with the activation energy for thermally activated relaxation and the propensity for stress-driven shear transformations. These correlations are expected to be robust across a very wide range of MG compositions, processing conditions and length scales.

  11. Universal structural parameter to quantitatively predict metallic glass properties

    PubMed Central

    Ding, Jun; Cheng, Yong-Qiang; Sheng, Howard; Asta, Mark; Ritchie, Robert O.; Ma, Evan

    2016-01-01

    Quantitatively correlating the amorphous structure in metallic glasses (MGs) with their physical properties has been a long-sought goal. Here we introduce ‘flexibility volume' as a universal indicator, to bridge the structural state the MG is in with its properties, on both atomic and macroscopic levels. The flexibility volume combines static atomic volume with dynamics information via atomic vibrations that probe local configurational space and interaction between neighbouring atoms. We demonstrate that flexibility volume is a physically appropriate parameter that can quantitatively predict the shear modulus, which is at the heart of many key properties of MGs. Moreover, the new parameter correlates strongly with atomic packing topology, and also with the activation energy for thermally activated relaxation and the propensity for stress-driven shear transformations. These correlations are expected to be robust across a very wide range of MG compositions, processing conditions and length scales. PMID:27941922

  12. Predicting fracture in micron-scale polycrystalline silicon MEMS structures.

    SciTech Connect

    Hazra, Siddharth S.; de Boer, Maarten Pieter; Boyce, Brad Lee; Ohlhausen, James Anthony; Foulk, James W., III; Reedy, Earl David, Jr.

    2010-09-01

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

  13. On Predicting the Crystal Structure of Energetic Materials From Quantum Mechanics

    DTIC Science & Technology

    2008-12-01

    ON PREDICTING THE CRYSTAL STRUCTURE OF ENERGETIC MATERIALS FROM QUANTUM MECHANICS Betsy M. Rice* U. S. Army Research Laboratory, AMSRD-ARL-WM...cyclotrimethylenetrinitramine (RDX) is used to predict polymorphic structures of crystalline RDX. The potential was first used in crystal structure prediction...methods which generate polymorphs of RDX and provide a 0 K ordering in energy. The 13 low energy- structures generated by crystal structure prediction

  14. A Predictive Structural Model of the Primate Connectome

    PubMed Central

    Beul, Sarah F.; Barbas, Helen; Hilgetag, Claus C.

    2017-01-01

    Anatomical connectivity imposes strong constraints on brain function, but there is no general agreement about principles that govern its organization. Based on extensive quantitative data, we tested the power of three factors to predict connections of the primate cerebral cortex: architectonic similarity (structural model), spatial proximity (distance model) and thickness similarity (thickness model). Architectonic similarity showed the strongest and most consistent influence on connection features. This parameter was strongly associated with the presence or absence of inter-areal connections and when integrated with spatial distance, the factor allowed predicting the existence of projections with very high accuracy. Moreover, architectonic similarity was strongly related to the laminar pattern of projection origins, and the absolute number of cortical connections of an area. By contrast, cortical thickness similarity and distance were not systematically related to connection features. These findings suggest that cortical architecture provides a general organizing principle for connections in the primate brain, providing further support for the well-corroborated structural model. PMID:28256558

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

    SciTech Connect

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

    1997-09-24

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

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

    PubMed

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

    2015-07-01

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

  17. Crystal Structure Prediction from First Principles: The Crystal Structures of Glycine

    PubMed Central

    Lund, Albert M.; Pagola, Gabriel I.; Orendt, Anita M.; Ferraro, Marta B.; Facelli, Julio C.

    2015-01-01

    Here we present the results of our unbiased searches of glycine polymorphs obtained using the Genetic Algorithms search implemented in Modified Genetic Algorithm for Crystals coupled with the local optimization and energy evaluation provided by Quantum Espresso. We demonstrate that it is possible to predict the crystal structures of a biomedical molecule using solely first principles calculations. We were able to find all the ambient pressure stable glycine polymorphs, which are found in the same energetic ordering as observed experimentally and the agreement between the experimental and predicted structures is of such accuracy that the two are visually almost indistinguishable. PMID:25843964

  18. Crystal structure prediction from first principles: The crystal structures of glycine

    NASA Astrophysics Data System (ADS)

    Lund, Albert M.; Pagola, Gabriel I.; Orendt, Anita M.; Ferraro, Marta B.; Facelli, Julio C.

    2015-04-01

    Here we present the results of our unbiased searches of glycine polymorphs obtained using the genetic algorithms search implemented in MGAC, modified genetic algorithm for crystals, coupled with the local optimization and energy evaluation provided by Quantum Espresso. We demonstrate that it is possible to predict the crystal structures of a biomedical molecule using solely first principles calculations. We were able to find all the ambient pressure stable glycine polymorphs, which are found in the same energetic ordering as observed experimentally and the agreement between the experimental and predicted structures is of such accuracy that the two are visually almost indistinguishable.

  19. Protein structure prediction with local adjust tabu search algorithm

    PubMed Central

    2014-01-01

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

  20. How evolutionary crystal structure prediction works--and why.

    PubMed

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

    2011-03-15

    Once the crystal structure of a chemical substance is known, many properties can be predicted reliably and routinely. Therefore if researchers could predict the crystal structure of a material before it is synthesized, they could significantly accelerate the discovery of new materials. In addition, the ability to predict crystal structures at arbitrary conditions of pressure and temperature is invaluable for the study of matter at extreme conditions, where experiments are difficult. Crystal structure prediction (CSP), the problem of finding the most stable arrangement of atoms given only the chemical composition, has long remained a major unsolved scientific problem. Two problems are entangled here: search, the efficient exploration of the multidimensional energy landscape, and ranking, the correct calculation of relative energies. For organic crystals, which contain a few molecules in the unit cell, search can be quite simple as long as a researcher does not need to include many possible isomers or conformations of the molecules; therefore ranking becomes the main challenge. For inorganic crystals, quantum mechanical methods often provide correct relative energies, making search the most critical problem. Recent developments provide useful practical methods for solving the search problem to a considerable extent. One can use simulated annealing, metadynamics, random sampling, basin hopping, minima hopping, and data mining. Genetic algorithms have been applied to crystals since 1995, but with limited success, which necessitated the development of a very different evolutionary algorithm. This Account reviews CSP using one of the major techniques, the hybrid evolutionary algorithm USPEX (Universal Structure Predictor: Evolutionary Xtallography). Using recent developments in the theory of energy landscapes, we unravel the reasons evolutionary techniques work for CSP and point out their limitations. We demonstrate that the energy landscapes of chemical systems have an

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

    NASA Technical Reports Server (NTRS)

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

    1995-01-01

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

  2. Predicting DNA-binding proteins and binding residues by complex structure prediction and application to human proteome.

    PubMed

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

    2014-01-01

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

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

    PubMed Central

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

    2015-01-01

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

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

    PubMed

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

    2015-08-22

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

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

    NASA Technical Reports Server (NTRS)

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

    2005-01-01

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

  6. Structural Acoustic Prediction and Interior Noise Control Technology

    NASA Technical Reports Server (NTRS)

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

    2001-01-01

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

  7. Detecting and representing predictable structure during auditory scene analysis.

    PubMed

    Sohoglu, Ediz; Chait, Maria

    2016-09-07

    We use psychophysics and MEG to test how sensitivity to input statistics facilitates auditory-scene-analysis (ASA). Human subjects listened to 'scenes' comprised of concurrent tone-pip streams (sources). On occasional trials a new source appeared partway. Listeners were more accurate and quicker to detect source appearance in scenes comprised of temporally-regular (REG), rather than random (RAND), sources. MEG in passive listeners and those actively detecting appearance events revealed increased sustained activity in auditory and parietal cortex in REG relative to RAND scenes, emerging ~400 ms of scene-onset. Over and above this, appearance in REG scenes was associated with increased responses relative to RAND scenes. The effect of temporal structure on appearance-evoked responses was delayed when listeners were focused on the scenes relative to when listening passively, consistent with the notion that attention reduces 'surprise'. Overall, the results implicate a mechanism that tracks predictability of multiple concurrent sources to facilitate active and passive ASA.

  8. Prediction of silicon-based layered structures for optoelectronic applications.

    PubMed

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

    2014-11-12

    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.

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

    NASA Astrophysics Data System (ADS)

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

    2005-09-01

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

  10. Optimizing Non-Decomposable Loss Functions in Structured Prediction

    PubMed Central

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

    2012-01-01

    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 Fβ 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

  11. Can computationally designed protein sequences improve secondary structure prediction?

    PubMed

    Bondugula, Rajkumar; Wallqvist, Anders; Lee, Michael S

    2011-05-01

    Computational sequence design methods are used to engineer proteins with desired properties such as increased thermal stability and novel function. In addition, these algorithms can be used to identify an envelope of sequences that may be compatible with a particular protein fold topology. In this regard, we hypothesized that sequence-property prediction, specifically secondary structure, could be significantly enhanced by using a large database of computationally designed sequences. We performed a large-scale test of this hypothesis with 6511 diverse protein domains and 50 designed sequences per domain. After analysis of the inherent accuracy of the designed sequences database, we realized that it was necessary to put constraints on what fraction of the native sequence should be allowed to change. With mutational constraints, accuracy was improved vs. no constraints, but the diversity of designed sequences, and hence effective size of the database, was moderately reduced. Overall, the best three-state prediction accuracy (Q(3)) that we achieved was nearly a percentage point improved over using a natural sequence database alone, well below the theoretical possibility for improvement of 8-10 percentage points. Furthermore, our nascent method was used to augment the state-of-the-art PSIPRED program by a percentage point.

  12. Engineering Property Prediction Tools for Tailored Polymer Composite Structures

    SciTech Connect

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

    2009-12-23

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

  13. High Precision Prediction of Functional Sites in Protein Structures

    PubMed Central

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

    2014-01-01

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

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

    NASA Astrophysics Data System (ADS)

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

    1998-05-01

    Composite steels prepared by technology of powder metallurgy are widely used as low cost parts with good resistance to wear, fracture, and corrosion. The development of powder composite steels is directly related to strength under vibration, fatigue stabilizing, and accurate lifetime prediction for actual composite topology. The fatigue behavior of powder steels was studied by experimental and numerical methods of composite mechanics and materials sciences. The chemical composition of composite steel is a pure iron powder as the base material and a handful of carbon, chromium, nickel, or phosphorus powders. The powder multi-component mixture is compacted by cold isostatic pressing to a rectangular form. The compactants are sintered in protective atmosphere. The microscale examination of the composite structure included an METAM-RV-21 metallographic optic microscope with a high-resolution ScanNexIIc scanner and an image processing package on the PC platform. The phase composition of powder steels has complex disordered topology with irregular ferrite/austenite grains, iron carbide inclusions, and pores. The microstructure images are treated according to the theory of stochastic processes as ergodic probability functions; statistical moments and a structural covariance function of the composite steels are given. The microscale stress-strain state of the composite steel is analyzed by finite element methods. The stiffness matrix of the composite steel is also presented together with stiffness matrices of ferrite/austenite grains, iron carbide inclusions, and pores as zero matrices. Endurance limits of the microstructural components are described by the Basquin or Coffin-Manson laws, respectively, as high and low cycle fatigue; cumulative microdamage in loading with a variable amplitude is taken from the Palmgren-Miner rule. Planar specimens were tested by console bending. Symmetric fatigue cycling was performed at a stable frequency of 20 Hz with endurance limits up

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

    PubMed

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

    2015-01-01

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

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

    PubMed

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

    2015-01-01

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

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

    PubMed

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

    2013-02-01

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

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

    PubMed Central

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

    2015-01-01

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

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

    SciTech Connect

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

    1997-06-25

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

  20. Prediction of new crystal structure phases in metal borides

    NASA Astrophysics Data System (ADS)

    Kolmogorov, Aleksey

    2006-03-01

    Identification of novel crystal structures is an important step for predicting new stable compounds in alloys, since most theoretical search algorithms are restricted to a given prototype library or a lattice type. Performing ab initio data mining [1] of intermetallic compounds we have discovered that even in such a well-studied class of systems as metal borides there are previously unknown phases comparable in energy to the existing ones [2]. We demonstrate that even though the new structures are relatively simple, their identification is not straightforward. We systematically investigate the stability and electronic properties of the new metal boride phases. Our calculations show that some phases exhibit electronic features similar to those in the famous MgB2 and could be good superconductors. The new phases are likely to have random stacking faults, so they might not be detected with standard x-ray methods. Our results could thus be used as an important guide in the search for new superconducting metal borides. [1] S. Curtarolo et al., Phys. Rev. Lett. 91, 135503 (2003). [2] A.N. Kolmogorov et al., submitted (2005).

  1. Prediction of grain structures in various solidification processes

    SciTech Connect

    Rappaz, M.; Gandin, C.A.; Desbiolles, J.L.; Thevoz, P.

    1996-03-01

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

  2. Topology prediction of Brucella abortus Omp2b and Omp2a porins after critical assessment of transmembrane beta strands prediction by several secondary structure prediction methods.

    PubMed

    Paquet, J Y; Vinals, C; Wouters, J; Letesson, J J; Depiereux, E

    2000-02-01

    In order to propose a reliable model for Brucella porin topology, several structure prediction methods were evaluated in their ability to predict porin topology. Four porins of known structure were selected as test-cases and their secondary structure delineated. The specificity and sensitivity of 11 methods were separately evaluated. Our critical assessment shows that some secondary structure prediction methods (PHD, Dsc, Sopma) originally designed to predict globular protein structure are useful on porin topology prediction. The overall best prediction is obtained by combining these three "generalist" methods with a transmembrane beta strand prediction technique. This "consensus" method was applied to Brucella porins Omp2b and Omp2a, sharing no sequence homology with any other porin. The predicted topology is a 16-stranded antiparallel beta barrel with Omp2a showing a higher number of negatively charged residue in the exposed loops than Omp2b. Experiments are in progress to validate the proposed topology and the functional hypotheses. The ability of the proposed consensus method to predict topology of complex outer membrane protein is briefly discussed.

  3. Influence of packing interactions on the average conformation of B-DNA in crystalline structures.

    PubMed

    Tereshko, V; Subirana, J A

    1999-04-01

    The molecular interactions in crystals of oligonucleotides in the B form have been analysed and in particular the end-to-end interactions. Phosphate-phosphate interactions in dodecamers are also reviewed. A strong influence of packing constraints on the average conformation of the double helix is found. There is a strong relationship between the space group, the end-to-end interactions and the average conformation of DNA. Dodecamers must have a B-form average conformation with 10 +/- 0.1 base pairs per turn in order to crystallize in the P212121 and related space groups usually found. Decamers show a wider range of conformational variation, with 9.7-10. 6 base pairs per turn, depending on the terminal sequence and the space group. The influence of the space group in decamers is quite striking and remains unexplained. Only small variations are allowed in each case. Thus, crystal packing is strongly related to the average DNA conformation in the crystals and deviations from the average are rather limited. The constraints imposed by the crystal lattice explain why the average twist of the DNA in solution (10.6 base pairs per turn) is seldom found in oligonucleotides crystallized in the B form.

  4. Hippocampal structure predicts cortical indices of reactivation of related items.

    PubMed

    Walker, John A; Low, Kathy A; Fletcher, Mark A; Cohen, Neal J; Gratton, Gabriele; Fabiani, Monica

    2017-01-27

    One of the key components of relational memory is the ability to bind together the constituent elements of a memory experience, and this ability is thought to be supported by the hippocampus. Previously we had shown that these relational bindings can be used to reactivate the cortical processors of an absent item in the presence of a relationally bound associate (Walker et al., 2014). Specifically, we recorded the event-related optical signal (EROS) when presenting the scene of a face-scene pair during a preview period immediately preceding a test display, and demonstrated reactivation of a face-processing cortical area (the superior temporal sulcus, STS) for scenes that had been previously paired with faces, relative to scenes that had not. Here we combined the EROS measures during the same preview paradigm with anatomical estimates of hippocampal integrity (structural MRI measures of hippocampal volume and diffusion tensor imaging measures of mean fractional anisotropy and diffusivity) to provide evidence that the hippocampus is mediating this reactivation phenomenon. The study was run in a sample of older adults aged 55-87, taking advantage of the high amount of hippocampal variability present in aging. We replicated the functional reactivation of STS during the preview period, specific to scenes previously paired with faces. Crucially, we also found that this phenomenon is correlated with structural hippocampus integrity. Both STS reactivation and hippocampal structure predicted subsequent recognition performance. These data support the theory that relational memory is sustained by an interaction between hippocampal and cortical sensory processing regions, and that these functions may be at the basis of episodic memory changes in normal aging.

  5. Failure prediction of thin beryllium sheets used in spacecraft structures

    NASA Astrophysics Data System (ADS)

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

    1991-12-01

    The primary objective of this study is to develop a method for prediction of failure of thin beryllium sheets that undergo complex states of stress. Major components of the research include experimental evaluation of strength parameters for cross-rolled beryllium sheet, application of the Tsai-Wu failure criterion to plate bending problems, development of a high order failure criterion, application of the new criterion to a variety of structures, and incorporation of both failure criteria into a finite element code. A Tsai-Wu failure model for SR-200 sheet material is developed from available tensile data, experiments carried out by NASA on two circular plates, and compression and off-axis experiments performed in this study. The failure surface obtained from the resulting criterion forms an ellipsoid. By supplementing experimental data used in the the two-dimensional criterion and modifying previously suggested failure criteria, a multi-dimensional failure surface is proposed for thin beryllium structures. The new criterion for orthotropic material is represented by a failure surface in six-dimensional stress space. In order to determine coefficients of the governing equation, a number of uniaxial, biaxial, and triaxial experiments are required. Details of these experiments and a complementary ultrasonic investigation are described in detail. Finally, validity of the criterion and newly determined mechanical properties is established through experiments on structures composed of SR200 sheet material. These experiments include a plate-plug arrangement under a complex state of stress and a series of plates with an out-of-plane central point load. Both criteria have been incorporated into a general purpose finite element analysis code. Numerical simulation incrementally applied loads to a structural component that is being designed and checks each nodal point in the model for exceedance of a failure criterion. If stresses at all locations do not exceed the failure

  6. Failure prediction of thin beryllium sheets used in spacecraft structures

    NASA Technical Reports Server (NTRS)

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

    1991-01-01

    The primary objective of this study is to develop a method for prediction of failure of thin beryllium sheets that undergo complex states of stress. Major components of the research include experimental evaluation of strength parameters for cross-rolled beryllium sheet, application of the Tsai-Wu failure criterion to plate bending problems, development of a high order failure criterion, application of the new criterion to a variety of structures, and incorporation of both failure criteria into a finite element code. A Tsai-Wu failure model for SR-200 sheet material is developed from available tensile data, experiments carried out by NASA on two circular plates, and compression and off-axis experiments performed in this study. The failure surface obtained from the resulting criterion forms an ellipsoid. By supplementing experimental data used in the the two-dimensional criterion and modifying previously suggested failure criteria, a multi-dimensional failure surface is proposed for thin beryllium structures. The new criterion for orthotropic material is represented by a failure surface in six-dimensional stress space. In order to determine coefficients of the governing equation, a number of uniaxial, biaxial, and triaxial experiments are required. Details of these experiments and a complementary ultrasonic investigation are described in detail. Finally, validity of the criterion and newly determined mechanical properties is established through experiments on structures composed of SR200 sheet material. These experiments include a plate-plug arrangement under a complex state of stress and a series of plates with an out-of-plane central point load. Both criteria have been incorporated into a general purpose finite element analysis code. Numerical simulation incrementally applied loads to a structural component that is being designed and checks each nodal point in the model for exceedance of a failure criterion. If stresses at all locations do not exceed the failure

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

    NASA Astrophysics Data System (ADS)

    Talha, Mohammad; Ashokkumar, Chimpalthradi R.

    2014-05-01

    To explore the applications of functionally graded materials (FGMs) in dynamic structures, structural kinematics based health monitoring technique becomes an important problem. Depending upon the displacements in three dimensions, the health of the material to withstand dynamic loads is inferred in this paper, which is based on the net compressive and tensile displacements that each structural degree of freedom takes. These net displacements at each finite element node predicts damage zones of the FGM where the material is likely to fail due to a vibration response which is categorized according to loading condition. The damage zone prediction of a dynamically active FGMs plate have been accomplished using Reddy's higher-order theory. The constituent material properties are assumed to vary in the thickness direction according to the power-law behavior. The proposed C0 finite element model (FEM) is applied to get net tensile and compressive displacement distributions across the structures. A plate made of Aluminum/Ziconia is considered to illustrate the concept of structural kinematics-based health monitoring aspects of FGMs.

  8. Prediction of Silicon-Based Layered Structures for Optoelectronic Applications

    NASA Astrophysics Data System (ADS)

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

    2015-03-01

    A method based on the particle swarm optimization (PSO) algorithm is presented to design quasi-two-dimensional (Q2D) materials. With this development, various single-layer and bi-layer materials in C, Si, Ge, Sn, and Pb were predicted. A new Si bi-layer structure is found to have a much-favored energy than the previously widely accepted configuration. Both single-layer and bi-layer 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 Si8H2andSi6H2 possessing quasi-direct band gaps of 0.75 eV 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.

  9. Detecting and representing predictable structure during auditory scene analysis

    PubMed Central

    Sohoglu, Ediz; Chait, Maria

    2016-01-01

    We use psychophysics and MEG to test how sensitivity to input statistics facilitates auditory-scene-analysis (ASA). Human subjects listened to ‘scenes’ comprised of concurrent tone-pip streams (sources). On occasional trials a new source appeared partway. Listeners were more accurate and quicker to detect source appearance in scenes comprised of temporally-regular (REG), rather than random (RAND), sources. MEG in passive listeners and those actively detecting appearance events revealed increased sustained activity in auditory and parietal cortex in REG relative to RAND scenes, emerging ~400 ms of scene-onset. Over and above this, appearance in REG scenes was associated with increased responses relative to RAND scenes. The effect of temporal structure on appearance-evoked responses was delayed when listeners were focused on the scenes relative to when listening passively, consistent with the notion that attention reduces ‘surprise’. Overall, the results implicate a mechanism that tracks predictability of multiple concurrent sources to facilitate active and passive ASA. DOI: http://dx.doi.org/10.7554/eLife.19113.001 PMID:27602577

  10. Prediction of a structural transition in the hard disk fluid.

    PubMed

    Piasecki, Jarosław; Szymczak, Piotr; Kozak, John J

    2010-10-28

    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 η(∗)∼0.718, a value that is in substantial agreement with the packing fraction, η∼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)]. Next, we show that the same method of analysis predicts that the exponential damping of oscillations in the hard sphere fluid becomes impossible when λ=4nπσ(3)[1+H(1)]≥34.81, where H(1) is the contact value of the correlation function, n is the number density, and σ is the sphere diameter in exact agreement with the condition, λ≥34.8, which is 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.

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

    NASA Astrophysics Data System (ADS)

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

    2013-10-01

    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.

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

    SciTech Connect

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

    2013-10-15

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

  13. STRUCTURE-BASED PREDICTIVE MODEL FOR COAL CHAR COMBUSTION

    SciTech Connect

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

    1998-06-04

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

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

    PubMed

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

    2007-04-13

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

  15. New insight into the structure of thiolated gold clusters: a structural prediction of the Au187(SR)68 cluster.

    PubMed

    Tlahuice-Flores, A

    2015-02-28

    The structure of the thiolated Au187 cluster has been elucidated by density functional theory calculations. The structural model comprises a Marks-decahedral Au153 core protected with 34 monomer motifs. The predicted structure accomplished in this study is in good agreement with the experimental X-ray diffraction pattern. It is noteworthy that the used methodology represents an advance in the prediction of the molecular structure of thiolated gold clusters constituted by hundreds of gold atoms.

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

    ERIC Educational Resources Information Center

    Ellington, Roni; Wachira, James; Nkwanta, Asamoah

    2010-01-01

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

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

    PubMed

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

    2015-12-01

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

  18. Can the structure of motor variability predict learning rate?

    PubMed

    Barbado Murillo, David; Caballero Sánchez, Carla; Moreside, Janice; Vera-García, Francisco J; Moreno, Francisco J

    2017-03-01

    Recent studies show that motor variability is actively regulated as an exploration tool to promote learning in reward-based tasks. However, its role in learning processes during error-based tasks, when a reduction of the motor variability is required to achieve good performance, is still unclear. In this study, we hypothesized that error-based learning not only depends on exploration but also on the individuals' ability to measure and predict the motor error. Previous studies identified a less auto-correlated motor variability as a higher ability to perform motion adjustments. Two experiments investigated the relationship between motor learning and variability, analyzing the long-range autocorrelation of the center of pressure fluctuations through the α score of a Detrended Fluctuation Analysis in balance tasks. In Experiment 1, we assessed the relationship between variability and learning rate using a standing balance task. Based on the results of this experiment, and to maximize learning, we performed a second experiment with a more difficult sitting balance task and increased practice. The learning rate of the 2 groups with similar balance performances but different α scores was compared. Individuals with a lower α score showed a higher learning rate. Because the α scores reveal how the motor output changes over time, instead of the magnitude of those changes, the higher learning rate is mainly linked to the higher error sensitivity rather than the exploration strategies. The results of this study highlight the relevance of the structure of output motor variability as a predictor of learning rate in error-based tasks. (PsycINFO Database Record

  19. Theoretical prediction of electronic structures of fully π-conjugated zinc oligoporphyrins with curved surface structures

    NASA Astrophysics Data System (ADS)

    Yamaguchi, Yoichi

    2004-05-01

    A theoretical prediction of the electronic structures of fully π-conjugated zinc oligoporphyrins with curved surface, ring, tube, and ball-shaped structures was conducted as the objective for the future development of triply meso-meso-, β-β-, and β-β-linked planar zinc oligoporphyrins. The excitation energies and oscillator strengths for the optimal ring and ball structures were calculated using the time-dependent density functional theory (DFT). Although there is an extremely small energy difference of <0.1 eV between the highest occupied molecular orbital (HOMO) and the lowest unoccupied molecular orbital (LUMO) of the ring structure relative to the same-sized triply linked planar one, the Q and B bands of the former are smaller redshifted excitation energies and intensified oscillator strengths than those of the latter due to the structurally shortened effective π-conjugated lengths for the electron transition. It is expected that the ball structure becomes an excellent electron acceptor and shows the highly redshifted Q' band in the near-IR region relative to the monomer. The minimum value of the HOMO-LUMO energy gaps of the infinite-length ring structures was estimated using periodic boundary conditions within the DFT, resulting in the metallic characters of both the tube structures with and without the spiral triply linked porphyrin array. The relation between the diameters and strain energies of the tube and ball structures was also examined. The present fused zinc porphyrins may become more colorful materials with new optelectronic properties including artificial photosynthesis than the carbon nanotubes and fullerenes when the axial coordinations of the central metal of porphyrins are functionally used.

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

    NASA Astrophysics Data System (ADS)

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

    2016-03-01

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

  1. Comparative modeling: the state of the art and protein drug target structure prediction.

    PubMed

    Liu, Tianyun; Tang, Grace W; Capriotti, Emidio

    2011-07-01

    The goal of computational protein structure prediction is to provide three-dimensional (3D) structures with resolution comparable to experimental results. Comparative modeling, which predicts the 3D structure of a protein based on its sequence similarity to homologous structures, is the most accurate computational method for structure prediction. In the last two decades, significant progress has been made on comparative modeling methods. Using the large number of protein structures deposited in the Protein Data Bank (~65,000), automatic prediction pipelines are generating a tremendous number of models (~1.9 million) for sequences whose structures have not been experimentally determined. Accurate models are suitable for a wide range of applications, such as prediction of protein binding sites, prediction of the effect of protein mutations, and structure-guided virtual screening. In particular, comparative modeling has enabled structure-based drug design against protein targets with unknown structures. In this review, we describe the theoretical basis of comparative modeling, the available automatic methods and databases, and the algorithms to evaluate the accuracy of predicted structures. Finally, we discuss relevant applications in the prediction of important drug target proteins, focusing on the G protein-coupled receptor (GPCR) and protein kinase families.

  2. p53 regulates cyclophosphamide teratogenesis by controlling caspases 3, 8, 9 activation and NF-kappaB DNA binding.

    PubMed

    Pekar, Olga; Molotski, Nataly; Savion, Shoshana; Fein, Amos; Toder, Vladimir; Torchinsky, Arkady

    2007-08-01

    The tumor suppressor protein p53 regulates the sensitivity of embryos to such human teratogens as ionizing radiation, diabetes, and cytostatics. Yet, the molecular mechanisms whereby it fulfills this function remain undefined. We used p53 heterozygous (p53(+/-)) female mice mated with p53(+/-) males and then exposed to cyclophosphamide (CP) to test whether caspases 3, 8, and 9 and the transcription factor nuclear factor (NF)-kappaB may serve as p53 targets. Mice were exposed to CP on day 12 of pregnancy and killed on days 15 and 18 of pregnancy to evaluate CP-induced teratogenic effect. The brain and limbs of embryos harvested 24 h after CP treatment were used to evaluate NF-kappaB (p65) DNA-binding activity by an ELISA-based method, the activity of the caspases by appropriate colorimetric kits, apoptosis, and cell proliferation by TUNEL, and 5'-bromo-2'-deoxyuridine incorporation respectively. We observed that the activation of caspases 3, 8, and 9 and the suppression of NF-kappaB DNA binding following CP-induced teratogenic insult took place only in teratologically sensitive organs of p53(+/+) but not p53(-/-) embryos. CP-induced apoptosis and suppression of cell proliferation were also more intensive in the former, and they exhibited a higher incidence of structural anomalies, such as open eyes, digit, limb, and tail anomalies. The analysis of the correlations between the p53 embryonic genotype, the activity of the tested molecules, and the CP-induced dysmorphic events at the cellular and organ level suggests caspases 3, 8, and 9 and NF-kappaB as components of p53-targeting mechanisms in embryos exposed to the teratogen.

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

    NASA Astrophysics Data System (ADS)

    Davidson, Noel E.; Ma, Yimin

    2012-07-01

    In part 1 of this study, an assessment of commonly used surface pressure profiles to represent TC structures was made. Using the Australian tropical cyclone model, the profiles are tested in case studies of high-resolution prediction of track, structure and intensity. We demonstrate that: (1) track forecasts are mostly insensitive to the imposed structure; (2) in some cases [here Katrina (2005)], specification of vortex structure can have a large impact on prediction of structure and intensity; (3) the forecast model mostly preserves the characteristics of the initial structure and so correct structure at t = 0 is a requirement for improved structure forecasting; and (4) skilful prediction of intensity does not guarantee skilful prediction of structure. It is shown that for Ivan (2004) the initial structure from each profile is preserved during the simulations, and that markedly different structures can have similar intensities. Evidence presented suggests that different initial profiles can sometimes change the timing of intensification. Thus, correct initial vortex structure is an essential ingredient for more accurate intensity and structure prediction.

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

    PubMed

    Kim, H S; Kim, T S; Choi, Y H; Park, S H

    2000-08-01

    In this article, we present a feedback-structured adaptive rational function filter based on a recursive modified Gram-Schmidt algorithm and apply it to the prediction of an EEG signal that has nonlinear and nonstationary characteristics. For the evaluation of the prediction performance, the proposed filter is compared with other methods, where a single-step prediction and a multi-step prediction are considered for a short-term prediction, and the prediction performance is assessed in normalized mean square error. The experimental results show that the proposed filter shows better performance than other methods considered for the short-term prediction of EEG signals.

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

    PubMed

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

    2010-10-01

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

  6. Aircraft Structural Mass Property Prediction Using Conceptual-Level Structural Analysis

    NASA Technical Reports Server (NTRS)

    Sexstone, Matthew G.

    1998-01-01

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

  7. Aircraft Structural Mass Property Prediction Using Conceptual-Level Structural Analysis

    NASA Technical Reports Server (NTRS)

    Sexstone, Matthew G.

    1998-01-01

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

  8. Structural Dynamic Analyses And Test Predictions For Spacecraft Structures With Non-Linearities

    NASA Astrophysics Data System (ADS)

    Vergniaud, Jean-Baptiste; Soula, Laurent; Newerla, Alfred

    2012-07-01

    The overall objective of the mechanical development and verification process is to ensure that the spacecraft structure is able to sustain the mechanical environments encountered during launch. In general the spacecraft structures are a-priori assumed to behave linear, i.e. the responses to a static load or dynamic excitation, respectively, will increase or decrease proportionally to the amplitude of the load or excitation induced. However, past experiences have shown that various non-linearities might exist in spacecraft structures and the consequences of their dynamic effects can significantly affect the development and verification process. Current processes are mainly adapted to linear spacecraft structure behaviour. No clear rules exist for dealing with major structure non-linearities. They are handled outside the process by individual analysis and margin policy, and analyses after tests to justify the CLA coverage. Non-linearities can primarily affect the current spacecraft development and verification process on two aspects. Prediction of flights loads by launcher/satellite coupled loads analyses (CLA): only linear satellite models are delivered for performing CLA and no well-established rules exist how to properly linearize a model when non- linearities are present. The potential impact of the linearization on the results of the CLA has not yet been properly analyzed. There are thus difficulties to assess that CLA results will cover actual flight levels. Management of satellite verification tests: the CLA results generated with a linear satellite FEM are assumed flight representative. If the internal non- linearities are present in the tested satellite then there might be difficulties to determine which input level must be passed to cover satellite internal loads. The non-linear behaviour can also disturb the shaker control, putting the satellite at risk by potentially imposing too high levels. This paper presents the results of a test campaign performed in

  9. CASP11--An Evaluation of a Modular BCL::Fold-Based Protein Structure Prediction Pipeline.

    PubMed

    Fischer, Axel W; Heinze, Sten; Putnam, Daniel K; Li, Bian; Pino, James C; Xia, Yan; Lopez, Carlos F; Meiler, Jens

    2016-01-01

    In silico prediction of a protein's tertiary structure remains an unsolved problem. The community-wide Critical Assessment of Protein Structure Prediction (CASP) experiment provides a double-blind study to evaluate improvements in protein structure prediction algorithms. We developed a protein structure prediction pipeline employing a three-stage approach, consisting of low-resolution topology search, high-resolution refinement, and molecular dynamics simulation to predict the tertiary structure of proteins from the primary structure alone or including distance restraints either from predicted residue-residue contacts, nuclear magnetic resonance (NMR) nuclear overhauser effect (NOE) experiments, or mass spectroscopy (MS) cross-linking (XL) data. The protein structure prediction pipeline was evaluated in the CASP11 experiment on twenty regular protein targets as well as thirty-three 'assisted' protein targets, which also had distance restraints available. Although the low-resolution topology search module was able to sample models with a global distance test total score (GDT_TS) value greater than 30% for twelve out of twenty proteins, frequently it was not possible to select the most accurate models for refinement, resulting in a general decay of model quality over the course of the prediction pipeline. In this study, we provide a detailed overall analysis, study one target protein in more detail as it travels through the protein structure prediction pipeline, and evaluate the impact of limited experimental data.

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

    EPA Science Inventory

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

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

    EPA Science Inventory

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

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

  12. Information theory provides a comprehensive framework for the evaluation of protein structure predictions

    PubMed Central

    Swanson, Rosemarie; Vannucci, Marina; Tsai, Jerry W.

    2008-01-01

    Protein structure prediction has a number of important ad hoc similarity measures for evaluating predictions, but would benefit from a measure that is able to provide a common framework for a broad range of comparisons. Here we show that a mutual information-like measure can provide a comprehensive framework for evaluating protein structure prediction of all types. We discuss the concept of information, its application to secondary structure, and the obstacle to applying it to 3D structure. Based on insights from the secondary structure case, we present an approach to work around the 3D difficulties, and develop a method to measure the mutual information provided by a 3D structure prediction. We integrate the evaluation of all types of protein structure prediction into a single frame work, and compare the amount of information provided by various prediction methods, including secondary structure prediction. Within this broadened framework, the idea that structure is better preserved than sequence during evolution is evaluated quantitatively for the globin family. A nearly perfect sequence match in the globin family corresponds to about 300 bits of information, whereas a nearly perfect structural match for the same two proteins corresponds to about 2500 bits of information, where bits of information describes the probability of obtaining a match of similar closeness by chance. Mutual information provides both a theoretical basis for evaluating structure similarity and an explanatory surround for existing similarity measures. PMID:18704942

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

    PubMed

    Dombroski, Matt; Fischhoff, Baruch; Fischbeck, Paul

    2006-12-01

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

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

    PubMed

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

    2007-01-01

    We describe predictions made using the Rosetta structure prediction methodology for both template-based modeling and free modeling categories in the Seventh Critical Assessment of Techniques for Protein Structure Prediction. For the first time, aggressive sampling and all-atom refinement could be carried out for the majority of targets, an advance enabled by the Rosetta@home distributed computing network. Template-based modeling predictions using an iterative refinement algorithm improved over the best existing templates for the majority of proteins with less than 200 residues. Free modeling methods gave near-atomic accuracy predictions for several targets under 100 residues from all secondary structure classes. These results indicate that refinement with an all-atom energy function, although computationally expensive, is a powerful method for obtaining accurate structure predictions.

  15. Perspective: Role of structure prediction in materials discovery and design

    NASA Astrophysics Data System (ADS)

    Needs, Richard J.; Pickard, Chris J.

    2016-05-01

    Materials informatics owes much to bioinformatics and the Materials Genome Initiative has been inspired by the Human Genome Project. But there is more to bioinformatics than genomes, and the same is true for materials informatics. Here we describe the rapidly expanding role of searching for structures of materials using first-principles electronic-structure methods. Structure searching has played an important part in unraveling structures of dense hydrogen and in identifying the record-high-temperature superconducting component in hydrogen sulfide at high pressures. We suggest that first-principles structure searching has already demonstrated its ability to determine structures of a wide range of materials and that it will play a central and increasing part in materials discovery and design.

  16. RNA-Puzzles Round III: 3D RNA structure prediction of five riboswitches and one ribozyme.

    PubMed

    Miao, Zhichao; Adamiak, Ryszard W; Antczak, Maciej; Batey, Robert T; Becka, Alexander J; Biesiada, Marcin; Boniecki, Michał J; Bujnicki, Janusz; Chen, Shi-Jie; Cheng, Clarence Yu; Chou, Fang-Chieh; Ferré-D'Amaré, Adrian R; Das, Rhiju; Dawson, Wayne K; Feng, Ding; Dokholyan, Nikolay V; Dunin-Horkawicz, Stanisław; Geniesse, Caleb; Kappel, Kalli; Kladwang, Wipapat; Krokhotin, Andrey; Łach, Grzegorz E; Major, François; Mann, Thomas H; Magnus, Marcin; Pachulska-Wieczorek, Katarzyna; Patel, Dinshaw J; Piccirilli, Joseph A; Popenda, Mariusz; Purzycka, Katarzyna J; Ren, Aiming; Rice, Greggory M; Santalucia, John; Sarzynska, Joanna; Szachniuk, Marta; Tandon, Arpit; Trausch, Jeremiah J; Tian, Siqi; Wang, Jian; Weeks, Kevin M; Williams, Benfeard; Xiao, Yi; Xu, Xiaojun; Zhang, Dong; Zok, Tomasz; Westhof, Eric

    2017-01-30

    RNA-Puzzles is a collective experiment in blind 3D RNA structure prediction. We report here a third round of RNA-Puzzles. Five puzzles, 4, 8, 12, 13, 14, all structures of riboswitch aptamers and puzzle 7, a ribozyme structure, are included in this round of the experiment. The riboswitch structures include biological binding sites for small molecules (S-adenosyl methionine, cyclic diadenosine monophosphate, 5-amino 4-imidazole carboxamide riboside 5'-triphosphate, glutamine) and proteins (YbxF) and one set describes large conformational changes between ligand-free and ligand-bound states; the Varkud satellite ribozyme is the most recently solved structure of a known large ribozyme. All the puzzles have established biological functions and require structural understanding to appreciate their molecular mechanisms. Through the use of fast-track experimental data, including multidimensional chemical mapping, and accurate prediction of RNA secondary structure, a large portion of the contacts in 3D have been predicted correctly leading to similar topologies for the top ranking predictions. Template-based and homology-derived predictions could predict structures to particularly high accuracies. However, achieving biological insights from de novo prediction of RNA 3D structures still depends on the size and complexity of the RNA. Blind computational predictions of RNA structures already appear to provide useful structural information in many cases. Similar to the previous RNA-Puzzles Round II experiment, the prediction of non-Watson-Crick interactions and the observed high atomic clash scores reveal notable need for algorithm of improvement. All prediction models and assessment results are available at http://ahsoka.u-strasbg.fr/rnapuzzles/.

  17. Probabilistic predictions of penetrating injury to anatomic structures.

    PubMed Central

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

    1997-01-01

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

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

    PubMed Central

    Laing, Christian; Schlick, Tamar

    2011-01-01

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

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

    PubMed

    Tolmachov, Oleg E

    2012-05-01

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

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

    NASA Astrophysics Data System (ADS)

    Cronin, Mark T. D.

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

  1. Finite Element Based HWB Centerbody Structural Optimization and Weight Prediction

    NASA Technical Reports Server (NTRS)

    Gern, Frank H.

    2012-01-01

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

  2. Inhibition of NF-kappaB DNA binding by nitric oxide.

    PubMed Central

    Matthews, J R; Botting, C H; Panico, M; Morris, H R; Hay, R T

    1996-01-01

    It has been suggested that the NF-kappaB transcription factor family may mediate expression of the gene encoding the cytokine-inducible form of nitric oxide synthase (iNOS). To establish if nitric oxide (NO) could in turn affect activity of NF-kappaB, the ability of NO-donor compounds to influence NF-kappaB DNA binding activity in vitro was investigated. NO-donor compounds sodium nitroprusside (SNP) and S-nitroso-N-acetylpenicillamine (SNAP) both inhibited the DNA binding activity of recombinant NF-kappaB p50 and p65 homodimers and of p50-p65 heterodimers. Inhibition of NF-kappaB p50 DNA binding by NO-donor compounds involved modification of the conserved redox-sensitive C62 residue, as a C62S p50 mutant was significantly more resistant to SNP-mediated inactivation. Non-reducing SDS-polyacrylamide gel electrophoresis demonstrated that SNP could inhibit p50 DNA binding by mechanisms other than the formation of intersubunit disulphide bonds involving p50 residue C62. Electrospray ionization mass spectrometry of a synthetic NF-kappaB p5O peptide containing the C62 residue suggested that NO gas can modify C62 by S-nitrosylation. This study indicates that NO-donors can directly inhibit the DNA binding activity of NF-kappaB family proteins, suggesting that cellular NO provides another control mechanism for modulating the expression of NF-kappaB-responsive genes. PMID:8710491

  3. Statistical potential for assessment and prediction of protein structures

    PubMed Central

    Shen, Min-yi; Sali, Andrej

    2006-01-01

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

  4. Principles for Predicting RNA Secondary Structure Design Difficulty.

    PubMed

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

    2016-02-27

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

  5. Principles for predicting RNA secondary structure design difficulty

    PubMed Central

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

    2015-01-01

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

  6. Climate and species richness predict the phylogenetic structure of African mammal communities.

    PubMed

    Kamilar, Jason M; Beaudrot, Lydia; Reed, Kaye E

    2015-01-01

    We have little knowledge of how climatic variation (and by proxy, habitat variation) influences the phylogenetic structure of tropical communities. Here, we quantified the phylogenetic structure of mammal communities in Africa to investigate how community structure varies with respect to climate and species richness variation across the continent. In addition, we investigated how phylogenetic patterns vary across carnivores, primates, and ungulates. We predicted that climate would differentially affect the structure of communities from different clades due to between-clade biological variation. We examined 203 communities using two metrics, the net relatedness (NRI) and nearest taxon (NTI) indices. We used simultaneous autoregressive models to predict community phylogenetic structure from climate variables and species richness. We found that most individual communities exhibited a phylogenetic structure consistent with a null model, but both climate and species richness significantly predicted variation in community phylogenetic metrics. Using NTI, species rich communities were composed of more distantly related taxa for all mammal communities, as well as for communities of carnivorans or ungulates. Temperature seasonality predicted the phylogenetic structure of mammal, carnivoran, and ungulate communities, and annual rainfall predicted primate community structure. Additional climate variables related to temperature and rainfall also predicted the phylogenetic structure of ungulate communities. We suggest that both past interspecific competition and habitat filtering have shaped variation in tropical mammal communities. The significant effect of climatic factors on community structure has important implications for the diversity of mammal communities given current models of future climate change.

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

    PubMed

    Roy, Ambrish; Kucukural, Alper; Zhang, Yang

    2010-04-01

    The iterative threading assembly refinement (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 (3D) 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 online server systems for the state-of-the-art protein structure and function predictions. The server is available at http://zhanglab.ccmb.med.umich.edu/I-TASSER.

  8. A Method for WD40 Repeat Detection and Secondary Structure Prediction

    PubMed Central

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

    2013-01-01

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

  9. Prediction of gene expression in embryonic structures of Drosophila melanogaster.

    PubMed

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

    2007-07-01

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

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

    PubMed

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

    2013-01-01

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

  11. Prediction of structural features and application to outer membrane protein identification

    PubMed Central

    Yan, Renxiang; Wang, Xiaofeng; Huang, Lanqing; Yan, Feidi; Xue, Xiaoyu; Cai, Weiwen

    2015-01-01

    Protein three-dimensional (3D) structures provide insightful information in many fields of biology. One-dimensional properties derived from 3D structures such as secondary structure, residue solvent accessibility, residue depth and backbone torsion angles are helpful to protein function prediction, fold recognition and ab initio folding. Here, we predict various structural features with the assistance of neural network learning. Based on an independent test dataset, protein secondary structure prediction generates an overall Q3 accuracy of ~80%. Meanwhile, the prediction of relative solvent accessibility obtains the highest mean absolute error of 0.164, and prediction of residue depth achieves the lowest mean absolute error of 0.062. We further improve the outer membrane protein identification by including the predicted structural features in a scoring function using a simple profile-to-profile alignment. The results demonstrate that the accuracy of outer membrane protein identification can be improved by ~3% at a 1% false positive level when structural features are incorporated. Finally, our methods are available as two convenient and easy-to-use programs. One is PSSM-2-Features for predicting secondary structure, relative solvent accessibility, residue depth and backbone torsion angles, the other is PPA-OMP for identifying outer membrane proteins from proteomes. PMID:26104144

  12. Prediction of structural features and application to outer membrane protein identification

    NASA Astrophysics Data System (ADS)

    Yan, Renxiang; Wang, Xiaofeng; Huang, Lanqing; Yan, Feidi; Xue, Xiaoyu; Cai, Weiwen

    2015-06-01

    Protein three-dimensional (3D) structures provide insightful information in many fields of biology. One-dimensional properties derived from 3D structures such as secondary structure, residue solvent accessibility, residue depth and backbone torsion angles are helpful to protein function prediction, fold recognition and ab initio folding. Here, we predict various structural features with the assistance of neural network learning. Based on an independent test dataset, protein secondary structure prediction generates an overall Q3 accuracy of ~80%. Meanwhile, the prediction of relative solvent accessibility obtains the highest mean absolute error of 0.164, and prediction of residue depth achieves the lowest mean absolute error of 0.062. We further improve the outer membrane protein identification by including the predicted structural features in a scoring function using a simple profile-to-profile alignment. The results demonstrate that the accuracy of outer membrane protein identification can be improved by ~3% at a 1% false positive level when structural features are incorporated. Finally, our methods are available as two convenient and easy-to-use programs. One is PSSM-2-Features for predicting secondary structure, relative solvent accessibility, residue depth and backbone torsion angles, the other is PPA-OMP for identifying outer membrane proteins from proteomes.

  13. Structural loads prediction in force-limited vibration testing

    NASA Technical Reports Server (NTRS)

    Chang, K. Y.

    2002-01-01

    The purpose of this paper is to study whether the force limiting has accomplished the notching requirement to limit the equipment structural response in low frequency vibration tests to something less than the design load.

  14. Harmonic Structure Predicts the Enjoyment of Uplifting Trance Music.

    PubMed

    Agres, Kat; Herremans, Dorien; Bigo, Louis; Conklin, Darrell

    2016-01-01

    An empirical investigation of how local harmonic structures (e.g., chord progressions) contribute to the experience and enjoyment of uplifting trance (UT) music is presented. The connection between rhythmic and percussive elements and resulting trance-like states has been highlighted by musicologists, but no research, to our knowledge, has explored whether repeated harmonic elements influence affective responses in listeners of trance music. Two alternative hypotheses are discussed, the first highlighting the direct relationship between repetition/complexity and enjoyment, and the second based on the theoretical inverted-U relationship described by the Wundt curve. We investigate the connection between harmonic structure and subjective enjoyment through interdisciplinary behavioral and computational methods: First we discuss an experiment in which listeners provided enjoyment ratings for computer-generated UT anthems with varying levels of harmonic repetition and complexity. The anthems were generated using a statistical model trained on a corpus of 100 uplifting trance anthems created for this purpose, and harmonic structure was constrained by imposing particular repetition structures (semiotic patterns defining the order of chords in the sequence) on a professional UT music production template. Second, the relationship between harmonic structure and enjoyment is further explored using two computational approaches, one based on average Information Content, and another that measures average tonal tension between chords. The results of the listening experiment indicate that harmonic repetition does in fact contribute to the enjoyment of uplifting trance music. More compelling evidence was found for the second hypothesis discussed above, however some maximally repetitive structures were also preferred. Both computational models provide evidence for a Wundt-type relationship between complexity and enjoyment. By systematically manipulating the structure of chord

  15. Harmonic Structure Predicts the Enjoyment of Uplifting Trance Music

    PubMed Central

    Agres, Kat; Herremans, Dorien; Bigo, Louis; Conklin, Darrell

    2017-01-01

    An empirical investigation of how local harmonic structures (e.g., chord progressions) contribute to the experience and enjoyment of uplifting trance (UT) music is presented. The connection between rhythmic and percussive elements and resulting trance-like states has been highlighted by musicologists, but no research, to our knowledge, has explored whether repeated harmonic elements influence affective responses in listeners of trance music. Two alternative hypotheses are discussed, the first highlighting the direct relationship between repetition/complexity and enjoyment, and the second based on the theoretical inverted-U relationship described by the Wundt curve. We investigate the connection between harmonic structure and subjective enjoyment through interdisciplinary behavioral and computational methods: First we discuss an experiment in which listeners provided enjoyment ratings for computer-generated UT anthems with varying levels of harmonic repetition and complexity. The anthems were generated using a statistical model trained on a corpus of 100 uplifting trance anthems created for this purpose, and harmonic structure was constrained by imposing particular repetition structures (semiotic patterns defining the order of chords in the sequence) on a professional UT music production template. Second, the relationship between harmonic structure and enjoyment is further explored using two computational approaches, one based on average Information Content, and another that measures average tonal tension between chords. The results of the listening experiment indicate that harmonic repetition does in fact contribute to the enjoyment of uplifting trance music. More compelling evidence was found for the second hypothesis discussed above, however some maximally repetitive structures were also preferred. Both computational models provide evidence for a Wundt-type relationship between complexity and enjoyment. By systematically manipulating the structure of chord

  16. Predictive Dynamic Stimulation of Structures with Non-Smooth Nonlinearities

    DTIC Science & Technology

    2005-06-30

    unlimited. 󈧑. SUPPLEMENTARY NOTES 14. ABSTRACT The objective of this work has been to develop model reduction methods for nonlinear structural dynamics...applications. The methods are intended for structures containing components for which large finite element models are necessary tomodel nonlinear...have been two parallel approaches taken in this work. The first model reduction method under development is similar in spirit to the classical Guyan

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

    PubMed

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

    2015-01-10

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

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

    PubMed

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

    2015-06-01

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

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

    PubMed Central

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

    2006-01-01

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

  20. Secondary Structure Predictions for Long RNA Sequences Based on Inversion Excursions and MapReduce.

    PubMed

    Yehdego, Daniel T; Zhang, Boyu; Kodimala, Vikram K R; Johnson, Kyle L; Taufer, Michela; Leung, Ming-Ying

    2013-05-01

    Secondary structures of ribonucleic acid (RNA) molecules play important roles in many biological processes including gene expression and regulation. Experimental observations and computing limitations suggest that we can approach the secondary structure prediction problem for long RNA sequences by segmenting them into shorter chunks, predicting the secondary structures of each chunk individually using existing prediction programs, and then assembling the results to give the structure of the original sequence. The selection of cutting points is a crucial component of the segmenting step. Noting that stem-loops and pseudoknots always contain an inversion, i.e., a stretch of nucleotides followed closely by its inverse complementary sequence, we developed two cutting methods for segmenting long RNA sequences based on inversion excursions: the centered and optimized method. Each step of searching for inversions, chunking, and predictions can be performed in parallel. In this paper we use a MapReduce framework, i.e., Hadoop, to extensively explore meaningful inversion stem lengths and gap sizes for the segmentation and identify correlations between chunking methods and prediction accuracy. We show that for a set of long RNA sequences in the RFAM database, whose secondary structures are known to contain pseudoknots, our approach predicts secondary structures more accurately than methods that do not segment the sequence, when the latter predictions are possible computationally. We also show that, as sequences exceed certain lengths, some programs cannot computationally predict pseudoknots while our chunking methods can. Overall, our predicted structures still retain the accuracy level of the original prediction programs when compared with known experimental secondary structure.

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

    NASA Technical Reports Server (NTRS)

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

    1992-01-01

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

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

    NASA Astrophysics Data System (ADS)

    Suzuki, Tomonori; Miyazaki, Satoru

    2011-01-01

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

  3. Observation selection bias in contact prediction and its implications for structural bioinformatics

    PubMed Central

    Orlando, G.; Raimondi, D.; Vranken, W. F.

    2016-01-01

    Next Generation Sequencing is dramatically increasing the number of known protein sequences, with related experimentally determined protein structures lagging behind. Structural bioinformatics is attempting to close this gap by developing approaches that predict structure-level characteristics for uncharacterized protein sequences, with most of the developed methods relying heavily on evolutionary information collected from homologous sequences. Here we show that there is a substantial observational selection bias in this approach: the predictions are validated on proteins with known structures from the PDB, but exactly for those proteins significantly more homologs are available compared to less studied sequences randomly extracted from Uniprot. Structural bioinformatics methods that were developed this way are thus likely to have over-estimated performances; we demonstrate this for two contact prediction methods, where performances drop up to 60% when taking into account a more realistic amount of evolutionary information. We provide a bias-free dataset for the validation for contact prediction methods called NOUMENON. PMID:27857150

  4. Accurate Prediction of One-Dimensional Protein Structure Features Using SPINE-X.

    PubMed

    Faraggi, Eshel; Kloczkowski, Andrzej

    2017-01-01

    Accurate prediction of protein secondary structure and other one-dimensional structure features is essential for accurate sequence alignment, three-dimensional structure modeling, and function prediction. SPINE-X is a software package to predict secondary structure as well as accessible surface area and dihedral angles ϕ and ψ. For secondary structure SPINE-X achieves an accuracy of between 81 and 84 % depending on the dataset and choice of tests. The Pearson correlation coefficient for accessible surface area prediction is 0.75 and the mean absolute error from the ϕ and ψ dihedral angles are 20(∘) and 33(∘), respectively. The source code and a Linux executables for SPINE-X are available from Research and Information Systems at http://mamiris.com .

  5. Predicting High Explosive Detonation Velocities from Their Composition and Structure

    DTIC Science & Technology

    1978-09-01

    for a gamut of ideal explosives. The explosives ranged from nitroaromatics, cyclic and linear nitramines, nitrate esters and nitro-nitrato...structure is postulated for a gamut of explosives. Since detonation velocity, DQ, is density dependent, the linear regression plot. Figure 1, of the

  6. Structure Building Predicts Grades in College Psychology and Biology

    ERIC Educational Resources Information Center

    Arnold, Kathleen M.; Daniel, David B.; Jensen, Jamie L.; McDaniel, Mark A.; Marsh, Elizabeth J.

    2016-01-01

    Knowing what skills underlie college success can allow students, teachers, and universities to identify and to help at-risk students. One skill that may underlie success across a variety of subject areas is structure building, the ability to create mental representations of narratives (Gernsbacher, Varner, & Faust, 1990). We tested if…

  7. Can Computationally Designed Protein Sequences Improve Secondary Structure Prediction?

    DTIC Science & Technology

    2011-01-01

    PROGRAM ELEMENT NUMBER 6. AUTHOR(S) 5d. PROJECT NUMBER 5e. TASK NUMBER 5f. WORK UNIT NUMBER 7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES) U.S...reported in the literature. Methods In this work, the Astral SCOP 1.75 (Murzin et al., 1995; Hubbard et al., 1999) structural domain database filtered

  8. Can Computationally Designed Protein Sequences Improve Secondary Structure Prediction?

    DTIC Science & Technology

    2011-01-01

    ELEMENT NUMBER 6. AUTHOR(S) 5d. PROJECT NUMBER 5e. TASK NUMBER 5f. WORK UNIT NUMBER 7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES) U.S. Army...1997) using a significantly larger database of known structures than previously reported in the literature. Methods In this work, the Astral SCOP 1.75

  9. Social Structure Predicts Genital Morphology in African Mole-Rats

    PubMed Central

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

    2009-01-01

    Background African mole-rats (Bathyergidae, Rodentia) exhibit a wide range of social structures, from solitary to eusocial. We previously found a lack of sex differences in the external genitalia and morphology of the perineal muscles associated with the phallus in the eusocial naked mole-rat. This was quite surprising, as the external genitalia and perineal muscles are sexually dimorphic in all other mammals examined. We hypothesized that the lack of sex differences in naked mole-rats might be related to their unusual social structure. Methodology/Principal Findings We compared the genitalia and perineal muscles in three African mole-rat species: the naked mole-rat, the solitary silvery mole-rat, and the Damaraland mole-rat, a species considered to be eusocial, but with less reproductive skew than naked mole-rats. Our findings support a relationship between social structure, mating system, and sexual differentiation. Naked mole-rats lack sex differences in genitalia and perineal morphology, silvery mole-rats exhibit sex differences, and Damaraland mole-rats are intermediate. Conclusions/Significance The lack of sex differences in naked mole-rats is not an attribute of all African mole-rats, but appears to have evolved in relation to their unusual social structure and reproductive biology. PMID:19829697

  10. Functional classification of protein 3D structures from predicted local interaction sites.

    PubMed

    Parasuram, Ramya; Lee, Joslynn S; Yin, Pengcheng; Somarowthu, Srinivas; Ondrechen, Mary Jo

    2010-12-01

    A new approach to the functional classification of protein 3D structures is described with application to some examples from structural genomics. This approach is based on functional site prediction with THEMATICS and POOL. THEMATICS employs calculated electrostatic potentials of the query structure. POOL is a machine learning method that utilizes THEMATICS features and has been shown to predict accurate, precise, highly localized interaction sites. Extension to the functional classification of structural genomics proteins is now described. Predicted functionally important residues are structurally aligned with those of proteins with previously characterized biochemical functions. A 3D structure match at the predicted local functional site then serves as a more reliable predictor of biochemical function than an overall structure match. Annotation is confirmed for a structural genomics protein with the ribulose phosphate binding barrel (RPBB) fold. A putative glucoamylase from Bacteroides fragilis (PDB ID 3eu8) is shown to be in fact probably not a glucoamylase. Finally a structural genomics protein from Streptomyces coelicolor annotated as an enoyl-CoA hydratase (PDB ID 3g64) is shown to be misannotated. Its predicted active site does not match the well-characterized enoyl-CoA hydratases of similar structure but rather bears closer resemblance to those of a dehalogenase with similar fold.

  11. Practical theories for service life prediction of critical aerospace structural components

    NASA Technical Reports Server (NTRS)

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

    1992-01-01

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

  12. PARTS: probabilistic alignment for RNA joinT secondary structure prediction.

    PubMed

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

    2008-04-01

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

  13. A Historical Perspective and Overview of Protein Structure Prediction

    NASA Astrophysics Data System (ADS)

    Wooley, John C.; Ye, Yuzhen

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

  14. Prediction of Local Quality of Protein Structure Models Considering Spatial Neighbors in Graphical Models

    PubMed Central

    Shin, Woong-Hee; Kang, Xuejiao; Zhang, Jian; Kihara, Daisuke

    2017-01-01

    Protein tertiary structure prediction methods have matured in recent years. However, some proteins defy accurate prediction due to factors such as inadequate template structures. While existing model quality assessment methods predict global model quality relatively well, there is substantial room for improvement in local quality assessment, i.e. assessment of the error at each residue position in a model. Local quality is a very important information for practical applications of structure models such as interpreting/designing site-directed mutagenesis of proteins. We have developed a novel local quality assessment method for protein tertiary structure models. The method, named Graph-based Model Quality assessment method (GMQ), explicitly considers the predicted quality of spatially neighboring residues using a graph representation of a query protein structure model. GMQ uses conditional random field as its core of the algorithm, and performs a binary prediction of the quality of each residue in a model, indicating if a residue position is likely to be within an error cutoff or not. The accuracy of GMQ was improved by considering larger graphs to include quality information of more surrounding residues. Moreover, we found that using different edge weights in graphs reflecting different secondary structures further improves the accuracy. GMQ showed competitive performance on a benchmark for quality assessment of structure models from the Critical Assessment of Techniques for Protein Structure Prediction (CASP). PMID:28074879

  15. Predicting Protein Secondary Structure Using Consensus Data Mining (CDM) Based on Empirical Statistics and Evolutionary Information.

    PubMed

    Kandoi, Gaurav; Leelananda, Sumudu P; Jernigan, Robert L; Sen, Taner Z

    2017-01-01

    Predicting the secondary structure of a protein from its sequence still remains a challenging problem. The prediction accuracies remain around 80 %, and for very diverse methods. Using evolutionary information and machine learning algorithms in particular has had the most impact. In this chapter, we will first define secondary structures, then we will review the Consensus Data Mining (CDM) technique based on the robust GOR algorithm and Fragment Database Mining (FDM) approach. GOR V is an empirical method utilizing a sliding window approach to model the secondary structural elements of a protein by making use of generalized evolutionary information. FDM uses data mining from experimental structure fragments, and is able to successfully predict the secondary structure of a protein by combining experimentally determined structural fragments based on sequence similarities of the fragments. The CDM method combines predictions from GOR V and FDM in a hierarchical manner to produce consensus predictions for secondary structure. In other words, if sequence fragment are not available, then it uses GOR V to make the secondary structure prediction. The online server of CDM is available at http://gor.bb.iastate.edu/cdm/ .

  16. Prediction of Local Quality of Protein Structure Models Considering Spatial Neighbors in Graphical Models.

    PubMed

    Shin, Woong-Hee; Kang, Xuejiao; Zhang, Jian; Kihara, Daisuke

    2017-01-11

    Protein tertiary structure prediction methods have matured in recent years. However, some proteins defy accurate prediction due to factors such as inadequate template structures. While existing model quality assessment methods predict global model quality relatively well, there is substantial room for improvement in local quality assessment, i.e. assessment of the error at each residue position in a model. Local quality is a very important information for practical applications of structure models such as interpreting/designing site-directed mutagenesis of proteins. We have developed a novel local quality assessment method for protein tertiary structure models. The method, named Graph-based Model Quality assessment method (GMQ), explicitly considers the predicted quality of spatially neighboring residues using a graph representation of a query protein structure model. GMQ uses conditional random field as its core of the algorithm, and performs a binary prediction of the quality of each residue in a model, indicating if a residue position is likely to be within an error cutoff or not. The accuracy of GMQ was improved by considering larger graphs to include quality information of more surrounding residues. Moreover, we found that using different edge weights in graphs reflecting different secondary structures further improves the accuracy. GMQ showed competitive performance on a benchmark for quality assessment of structure models from the Critical Assessment of Techniques for Protein Structure Prediction (CASP).

  17. The structure of evaporating and combusting sprays: Measurements and predictions

    NASA Technical Reports Server (NTRS)

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

    1984-01-01

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

  18. The structure of evaporating and combusting sprays: Measurements and predictions

    NASA Technical Reports Server (NTRS)

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

    1982-01-01

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

  19. Structural Damage Prediction and Analysis for Hypervelocity Impacts: Handbook

    NASA Technical Reports Server (NTRS)

    Elfer, N. C.

    1996-01-01

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

  20. An efficient genetic algorithm for structure prediction at the nanoscale.

    PubMed

    Lazauskas, Tomas; Sokol, Alexey A; Woodley, Scott M

    2017-03-17

    We have developed and implemented a new global optimization technique based on a Lamarckian genetic algorithm with the focus on structure diversity. The key process in the efficient search on a given complex energy landscape proves to be the removal of duplicates that is achieved using a topological analysis of candidate structures. The careful geometrical prescreening of newly formed structures and the introduction of new mutation move classes improve the rate of success further. The power of the developed technique, implemented in the Knowledge Led Master Code, or KLMC, is demonstrated by its ability to locate and explore a challenging double funnel landscape of a Lennard-Jones 38 atom system (LJ38). We apply the redeveloped KLMC to investigate three chemically different systems: ionic semiconductor (ZnO)1-32, metallic Ni13 and covalently bonded C60. All four systems have been systematically explored on the energy landscape defined using interatomic potentials. The new developments allowed us to successfully locate the double funnels of LJ38, find new local and global minima for ZnO clusters, extensively explore the Ni13 and C60 (the buckminsterfullerene, or buckyball) potential energy surfaces.

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

    PubMed Central

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

    2009-01-01

    Accurate free energy estimation is essential for RNA structure prediction. The widely used Turner's energy model works well for nested structures. For pseudoknotted RNAs, however, there is no effective rule for estimation of loop entropy and free energy. In this work we present a new free energy estimation method, termed the pseudoknot predictor in three-dimensional space (pk3D), which goes beyond Turner's model. Our approach treats nested and pseudoknotted structures alike in one unifying physical framework, regardless of how complex the RNA structures are. We first test the ability of pk3D in selecting native structures from a large number of decoys for a set of 43 pseudoknotted RNA molecules, with lengths ranging from 23 to 113. We find that pk3D performs slightly better than the Dirks and Pierce extension of Turner's rule. We then test pk3D for blind secondary structure prediction, and find that pk3D gives the best sensitivity and comparable positive predictive value (related to specificity) in predicting pseudoknotted RNA secondary structures, when compared with other methods. A unique strength of pk3D is that it also generates spatial arrangement of structural elements of the RNA molecule. Comparison of three-dimensional structures predicted by pk3D with the native structure measured by nuclear magnetic resonance or X-ray experiments shows that the predicted spatial arrangement of stems and loops is often similar to that found in the native structure. These close-to-native structures can be used as starting points for further refinement to derive accurate three-dimensional structures of RNA molecules, including those with pseudoknots. PMID:19864433

  2. Structure Based Predictive Model for Coal Char Combustion

    SciTech Connect

    Robert Hurt; Joseph Calo; Robert Essenhigh; Christopher Hadad

    2000-12-30

    This unique collaborative project has taken a very fundamental look at the origin of structure, and combustion reactivity of coal chars. It was a combined experimental and theoretical effort involving three universities and collaborators from universities outside the U.S. and from U.S. National Laboratories and contract research companies. The project goal was to improve our understanding of char structure and behavior by examining the fundamental chemistry of its polyaromatic building blocks. The project team investigated the elementary oxidative attack on polyaromatic systems, and coupled with a study of the assembly processes that convert these polyaromatic clusters to mature carbon materials (or chars). We believe that the work done in this project has defined a powerful new science-based approach to the understanding of char behavior. The work on aromatic oxidation pathways made extensive use of computational chemistry, and was led by Professor Christopher Hadad in the Department of Chemistry at Ohio State University. Laboratory experiments on char structure, properties, and combustion reactivity were carried out at both OSU and Brown, led by Principle Investigators Joseph Calo, Robert Essenhigh, and Robert Hurt. Modeling activities were divided into two parts: first unique models of crystal structure development were formulated by the team at Brown (PI'S Hurt and Calo) with input from Boston University and significant collaboration with Dr. Alan Kerstein at Sandia and with Dr. Zhong-Ying chen at SAIC. Secondly, new combustion models were developed and tested, led by Professor Essenhigh at OSU, Dieter Foertsch (a collaborator at the University of Stuttgart), and Professor Hurt at Brown. One product of this work is the CBK8 model of carbon burnout, which has already found practical use in CFD codes and in other numerical models of pulverized fuel combustion processes, such as EPRI's NOxLOI Predictor. The remainder of the report consists of detailed technical

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

    PubMed

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

    2011-03-01

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

  4. PreSSAPro: a software for the prediction of secondary structure by amino acid properties.

    PubMed

    Costantini, Susan; Colonna, Giovanni; Facchiano, Angelo M

    2007-10-01

    PreSSAPro is a software, available to the scientific community as a free web service designed to provide predictions of secondary structures starting from the amino acid sequence of a given protein. Predictions are based on our recently published work on the amino acid propensities for secondary structures in either large but not homogeneous protein data sets, as well as in smaller but homogeneous data sets corresponding to protein structural classes, i.e. all-alpha, all-beta, or alpha-beta proteins. Predictions result improved by the use of propensities evaluated for the right protein class. PreSSAPro predicts the secondary structure according to the right protein class, if known, or gives a multiple prediction with reference to the different structural classes. The comparison of these predictions represents a novel tool to evaluate what sequence regions can assume different secondary structures depending on the structural class assignment, in the perspective of identifying proteins able to fold in different conformations. The service is available at the URL http://bioinformatica.isa.cnr.it/PRESSAPRO/.

  5. Prediction of the Fundamental Period of Infilled RC Frame Structures Using Artificial Neural Networks.

    PubMed

    Asteris, Panagiotis G; Tsaris, Athanasios K; Cavaleri, Liborio; Repapis, Constantinos C; Papalou, Angeliki; Di Trapani, Fabio; Karypidis, Dimitrios F

    2016-01-01

    The fundamental period is one of the most critical parameters for the seismic design of structures. There are several literature approaches for its estimation which often conflict with each other, making their use questionable. Furthermore, the majority of these approaches do not take into account the presence of infill walls into the structure despite the fact that infill walls increase the stiffness and mass of structure leading to significant changes in the fundamental period. In the present paper, artificial neural networks (ANNs) are used to predict the fundamental period of infilled reinforced concrete (RC) structures. For the training and the validation of the ANN, a large data set is used based on a detailed investigation of the parameters that affect the fundamental period of RC structures. The comparison of the predicted values with analytical ones indicates the potential of using ANNs for the prediction of the fundamental period of infilled RC frame structures taking into account the crucial parameters that influence its value.

  6. Genotyping Oral Commensal Bacteria to Predict Social Contact and Structure

    PubMed Central

    Wallace, Amelia D.; Riley, Lee W.

    2016-01-01

    Social network structure is a fundamental determinant of human health, from infectious to chronic diseases. However, quantitative and unbiased approaches to measuring social network structure are lacking. We hypothesized that genetic relatedness of oral commensal bacteria could be used to infer social contact between humans, just as genetic relatedness of pathogens can be used to determine transmission chains of pathogens. We used a traditional, questionnaire survey-based method to characterize the contact network of the School of Public Health at a large research university. We then collected saliva from a subset of individuals to analyze their oral microflora using a modified deep sequencing multilocus sequence typing (MLST) procedure. We examined micro-evolutionary changes in the S. viridans group to uncover transmission patterns reflecting social network structure. We amplified seven housekeeping gene loci from the Streptococcus viridans group, a group of ubiquitous commensal bacteria, and sequenced the PCR products using next-generation sequencing. By comparing the generated S. viridans reads between pairs of individuals, we reconstructed the social network of the sampled individuals and compared it to the network derived from the questionnaire survey-based method. The genetic relatedness significantly (p-value < 0.001) correlated with social distance in the questionnaire-based network, and the reconstructed network closely matched the network derived from the questionnaire survey-based method. Oral commensal bacterial are thus likely transmitted through routine physical contact or shared environment. Their genetic relatedness can be used to represent a combination of social contact and shared physical space, therefore reconstructing networks of contact. This study provides the first step in developing a method to measure direct social contact based on commensal organism genotyping, potentially capable of unmasking hidden social networks that contribute to

  7. STRUCTURE BASED PREDICTIVE MODEL FOR COAL CHAR COMBUSTION

    SciTech Connect

    Robert Hurt; Joseph Calo; Robert Essenhigh; Christopher Hadad

    2001-06-15

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

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

    PubMed Central

    Haselhuhn, Michael P.; Wong, Elaine M.

    2012-01-01

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

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

    PubMed

    Haselhuhn, Michael P; Wong, Elaine M

    2012-02-07

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

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

    NASA Technical Reports Server (NTRS)

    Ko, William L.; Chen, Tony

    2006-01-01

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

  11. A Consensus Data Mining secondary structure prediction by combining GOR V and Fragment Database Mining.

    PubMed

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

    2006-11-01

    The major aim of tertiary structure prediction is to obtain protein models with the highest possible accuracy. Fold recognition, homology modeling, and de novo prediction methods typically use predicted secondary structures as input, and all of these methods may significantly benefit from more accurate secondary structure predictions. Although there are many different secondary structure prediction methods available in the literature, their cross-validated prediction accuracy is generally <80%. In order to increase the prediction accuracy, we developed a novel hybrid algorithm called Consensus Data Mining (CDM) that combines our two previous successful methods: (1) Fragment Database Mining (FDM), which exploits the Protein Data Bank structures, and (2) GOR V, which is based on information theory, Bayesian statistics, and multiple sequence alignments (MSA). In CDM, the target sequence is dissected into smaller fragments that are compared with fragments obtained from related sequences in the PDB. For fragments with a sequence identity above a certain sequence identity threshold, the FDM method is applied for the prediction. The remainder of the fragments are predicted by GOR V. The results of the CDM are provided as a function of the upper sequence identities of aligned fragments and the sequence identity threshold. We observe that the value 50% is the optimum sequence identity threshold, and that the accuracy of the CDM method measured by Q(3) ranges from 67.5% to 93.2%, depending on the availability of known structural fragments with sufficiently high sequence identity. As the Protein Data Bank grows, it is anticipated that this consensus method will improve because it will rely more upon the structural fragments.

  12. A new expertness index for assessment of secondary structure prediction engines.

    PubMed

    Kazemian, M; Moshiri, B; Nikbakht, H; Lucas, C

    2007-02-01

    Improvement of prediction accuracy of the protein secondary structure is essential for further developments of the whole field of protein research. In this paper, the expertness of protein secondary structure prediction engines has been studied in three levels and a new criterion has been introduced in the third level. This criterion could be considered as an extension of the previous ones based on amino acid index. Using this new criterion, the expertness of some high score secondary structure prediction engines has been reanalyzed and some hidden facts have been discovered. The results of this new assessment demonstrated that a noticeable harmony has been existed among each amino acid prediction behavior in all engines. This harmony has also been seen between single global propensity and prediction accuracy of amino acid types in each secondary structure class. Moreover, it is shown that Proline and Glycine amino acids have been predicted with less accuracy in alpha helices and beta strands. In addition, regardless of different approaches used in prediction engines, beta strands have been predicted with less accuracy.

  13. Link prediction based on hyperbolic mapping with community structure for complex networks

    NASA Astrophysics Data System (ADS)

    Wang, Zuxi; Wu, Yao; Li, Qingguang; Jin, Fengdong; Xiong, Wei

    2016-05-01

    Link prediction is becoming a concerned topic in the complex network field in recent years. However, the existing link prediction methods are unsatisfactory for processing topological information and have high time complexity. This paper presents a novel method of Link Prediction with Community Structure (LPCS) based on hyperbolic mapping. Different from the existing link prediction methods, to utilize global structure information of the network, LPCS deals with the network from an overall perspective. LPCS takes full advantage of the community structure and its hierarchical organization to map networks into hyperbolic space, and obtains the hyperbolic coordinates which depict the global structure information of the network, then uses hyperbolic distance to describe the similarity between the nodes, finally predicts missing links according to the degree of the similarity between unconnected node pairs. The combination of the hyperbolic geometry framework and the community structure makes LPCS perform well in predicting missing links, and the time complexity of LPCS is linear, which makes LPCS can be applied to handle large scale networks in acceptable time. LPCS outperforms many state-of-the-art link prediction methods in the networks obeying power-law degree distribution.

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

    NASA Astrophysics Data System (ADS)

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

    2009-04-01

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

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

    NASA Technical Reports Server (NTRS)

    Schonberg, William P.

    1993-01-01

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

  16. PSRna: Prediction of small RNA secondary structures based on reverse complementary folding method.

    PubMed

    Li, Jin; Xu, Chengzhen; Wang, Lei; Liang, Hong; Feng, Weixing; Cai, Zhongxi; Wang, Ying; Cong, Wang; Liu, Yunlong

    2016-08-01

    Prediction of RNA secondary structures is an important problem in computational biology and bioinformatics, since RNA secondary structures are fundamental for functional analysis of RNA molecules. However, small RNA secondary structures are scarce and few algorithms have been specifically designed for predicting the secondary structures of small RNAs. Here we propose an algorithm named "PSRna" for predicting small-RNA secondary structures using reverse complementary folding and characteristic hairpin loops of small RNAs. Unlike traditional algorithms that usually generate multi-branch loops and 5[Formula: see text] end self-folding, PSRna first estimated the maximum number of base pairs of RNA secondary structures based on the dynamic programming algorithm and a path matrix is constructed at the same time. Second, the backtracking paths are extracted from the path matrix based on backtracking algorithm, and each backtracking path represents a secondary structure. To improve accuracy, the predicted RNA secondary structures are filtered based on their free energy, where only the secondary structure with the minimum free energy was identified as the candidate secondary structure. Our experiments on real data show that the proposed algorithm is superior to two popular methods, RNAfold and RNAstructure, in terms of sensitivity, specificity and Matthews correlation coefficient (MCC).

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

    NASA Technical Reports Server (NTRS)

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

    1990-01-01

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

  18. STRUCTURE-BASED PREDICTIVE MODEL FOR COAL CHAR COMBUSTION

    SciTech Connect

    Robert H. Hurt; Eric M. Suuberg

    2000-05-03

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

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

    NASA Technical Reports Server (NTRS)

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

    1978-01-01

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

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

    PubMed

    Churkin, Alexander; Weinbrand, Lina; Barash, Danny

    2015-01-01

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

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

    PubMed Central

    Dowell, Robin D; Eddy, Sean R

    2004-01-01

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

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

    PubMed Central

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

    2014-01-01

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

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

    PubMed

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

    2012-02-01

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

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

    NASA Technical Reports Server (NTRS)

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

    1994-01-01

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

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

    PubMed Central

    2013-01-01

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

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

    PubMed

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

    2009-12-24

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

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

    PubMed Central

    Sükösd, Zsuzsanna; Andersen, Ebbe S.; Seemann, Stefan E.; Jensen, Mads Krogh; Hansen, Mathias; Gorodkin, Jan; Kjems, Jørgen

    2015-01-01

    A distance constrained secondary structural model of the ≈10 kb RNA genome of the HIV-1 has been predicted but higher-order structures, involving long distance interactions, are currently unknown. We present the first global RNA secondary structure model for the HIV-1 genome, which integrates both comparative structure analysis and information from experimental data in a full-length prediction without distance constraints. Besides recovering known structural elements, we predict several novel structural elements that are conserved in HIV-1 evolution. Our results also indicate that the structure of the HIV-1 genome is highly variable in most regions, with a limited number of stable and conserved RNA secondary structures. Most interesting, a set of long distance interactions form a core organizing structure (COS) that organize the genome into three major structural domains. Despite overlapping protein-coding regions the COS is supported by a particular high frequency of compensatory base changes, suggesting functional importance for this element. This new structural element potentially organizes the whole genome into three major domains protruding from a conserved core structure with potential roles in replication and evolution for the virus. PMID:26476446

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

    PubMed

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

    2015-12-02

    A distance constrained secondary structural model of the ≈10 kb RNA genome of the HIV-1 has been predicted but higher-order structures, involving long distance interactions, are currently unknown. We present the first global RNA secondary structure model for the HIV-1 genome, which integrates both comparative structure analysis and information from experimental data in a full-length prediction without distance constraints. Besides recovering known structural elements, we predict several novel structural elements that are conserved in HIV-1 evolution. Our results also indicate that the structure of the HIV-1 genome is highly variable in most regions, with a limited number of stable and conserved RNA secondary structures. Most interesting, a set of long distance interactions form a core organizing structure (COS) that organize the genome into three major structural domains. Despite overlapping protein-coding regions the COS is supported by a particular high frequency of compensatory base changes, suggesting functional importance for this element. This new structural element potentially organizes the whole genome into three major domains protruding from a conserved core structure with potential roles in replication and evolution for the virus.

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

    PubMed Central

    King, R D; Srinivasan, A

    1996-01-01

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

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

    PubMed Central

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

    2013-01-01

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

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

    PubMed

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

    2013-01-01

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

  12. All-atom 3D structure prediction of transmembrane β-barrel proteins from sequences

    PubMed Central

    Hayat, Sikander; Sander, Chris; Marks, Debora S.

    2015-01-01

    Transmembrane β-barrels (TMBs) carry out major functions in substrate transport and protein biogenesis but experimental determination of their 3D structure is challenging. Encouraged by successful de novo 3D structure prediction of globular and α-helical membrane proteins from sequence alignments alone, we developed an approach to predict the 3D structure of TMBs. The approach combines the maximum-entropy evolutionary coupling method for predicting residue contacts (EVfold) with a machine-learning approach (boctopus2) for predicting β-strands in the barrel. In a blinded test for 19 TMB proteins of known structure that have a sufficient number of diverse homologous sequences available, this combined method (EVfold_bb) predicts hydrogen-bonded residue pairs between adjacent β-strands at an accuracy of ∼70%. This accuracy is sufficient for the generation of all-atom 3D models. In the transmembrane barrel region, the average 3D structure accuracy [template-modeling (TM) score] of top-ranked models is 0.54 (ranging from 0.36 to 0.85), with a higher (44%) number of residue pairs in correct strand–strand registration than in earlier methods (18%). Although the nonbarrel regions are predicted less accurately overall, the evolutionary couplings identify some highly constrained loop residues and, for FecA protein, the barrel including the structure of a plug domain can be accurately modeled (TM score = 0.68). Lower prediction accuracy tends to be associated with insufficient sequence information and we therefore expect increasing numbers of β-barrel families to become accessible to accurate 3D structure prediction as the number of available sequences increases. PMID:25858953

  13. All-atom 3D structure prediction of transmembrane β-barrel proteins from sequences.

    PubMed

    Hayat, Sikander; Sander, Chris; Marks, Debora S; Elofsson, Arne

    2015-04-28

    Transmembrane β-barrels (TMBs) carry out major functions in substrate transport and protein biogenesis but experimental determination of their 3D structure is challenging. Encouraged by successful de novo 3D structure prediction of globular and α-helical membrane proteins from sequence alignments alone, we developed an approach to predict the 3D structure of TMBs. The approach combines the maximum-entropy evolutionary coupling method for predicting residue contacts (EVfold) with a machine-learning approach (boctopus2) for predicting β-strands in the barrel. In a blinded test for 19 TMB proteins of known structure that have a sufficient number of diverse homologous sequences available, this combined method (EVfold_bb) predicts hydrogen-bonded residue pairs between adjacent β-strands at an accuracy of ∼70%. This accuracy is sufficient for the generation of all-atom 3D models. In the transmembrane barrel region, the average 3D structure accuracy [template-modeling (TM) score] of top-ranked models is 0.54 (ranging from 0.36 to 0.85), with a higher (44%) number of residue pairs in correct strand-strand registration than in earlier methods (18%). Although the nonbarrel regions are predicted less accurately overall, the evolutionary couplings identify some highly constrained loop residues and, for FecA protein, the barrel including the structure of a plug domain can be accurately modeled (TM score = 0.68). Lower prediction accuracy tends to be associated with insufficient sequence information and we therefore expect increasing numbers of β-barrel families to become accessible to accurate 3D structure prediction as the number of available sequences increases.

  14. Sexual selection predicts brain structure in dragon lizards.

    PubMed

    Hoops, D; Ullmann, J F P; Janke, A L; Vidal-Garcia, M; Stait-Gardner, T; Dwihapsari, Y; Merkling, T; Price, W S; Endler, J A; Whiting, M J; Keogh, J S

    2017-02-01

    Phenotypic traits such as ornaments and armaments are generally shaped by sexual selection, which often favours larger and more elaborate males compared to females. But can sexual selection also influence the brain? Previous studies in vertebrates report contradictory results with no consistent pattern between variation in brain structure and the strength of sexual selection. We hypothesize that sexual selection will act in a consistent way on two vertebrate brain regions that directly regulate sexual behaviour: the medial preoptic nucleus (MPON) and the ventromedial hypothalamic nucleus (VMN). The MPON regulates male reproductive behaviour whereas the VMN regulates female reproductive behaviour and is also involved in male aggression. To test our hypothesis, we used high-resolution magnetic resonance imaging combined with traditional histology of brains in 14 dragon lizard species of the genus Ctenophorus that vary in the strength of precopulatory sexual selection. Males belonging to species that experience greater sexual selection had a larger MPON and a smaller VMN. Conversely, females did not show any patterns of variation in these brain regions. As the volumes of both these regions also correlated with brain volume (BV) in our models, we tested whether they show the same pattern of evolution in response to changes in BV and found that the do. Therefore, we show that the primary brain nuclei underlying reproductive behaviour in vertebrates can evolve in a mosaic fashion, differently between males and females, likely in response to sexual selection, and that these same regions are simultaneously evolving in concert in relation to overall brain size.

  15. Structural Damage Prediction and Analysis for Hypervelocity Impact

    NASA Technical Reports Server (NTRS)

    Elfer, Norman

    1995-01-01

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

  16. Rotor Airloads Prediction Using Loose Aerodynamic Structural Coupling

    NASA Technical Reports Server (NTRS)

    Potsdam, Mark; Yeo, Hyeonsoo; Johnson, Wayne

    2004-01-01

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

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

    NASA Technical Reports Server (NTRS)

    Balmes, Etienne

    1993-01-01

    An original complete methodology for the construction of predictive models of damped structural vibrations is introduced. A consistent definition of normal and complex modes is given which leads to an original method to accurately identify non-proportionally damped normal mode models. A new method to create predictive hybrid experimental/analytical models of damped structures is introduced, and the ability of hybrid models to predict the response to system configuration changes is discussed. Finally a critical review of the overall methodology is made by application to the case of the MIT/SERC interferometer testbed.

  18. FTIR spectroscopy and sequence prediction: Structure of human α2-macroglobulin

    NASA Astrophysics Data System (ADS)

    Dukor, Rina K.; Liebman, Michael N.; Yuan, Anna I.; Feinman, Richard D.

    1998-06-01

    The structure of a plasma proteinase inhibitor α2-Macroglobulin (α2m) is determined by FTIR spectroscopy and a number of sequence-structure prediction algorithms. In addition, α2M dimers and complexes with methylamine and trypsin are examined. Our FTIR results estimate a helix content of 5-15% and a β-sheet content of 28-36%. None of the sequence prediction algorithms used in this study predicted values close to experimental data. Considerable differences in the FTIR spectra of α2M dimer are observed and somewhat smaller changes are seen upon reaction of α2M with methylamine and dithiodipyridine (DTP).

  19. Aromatic claw: A new fold with high aromatic content that evades structural prediction.

    PubMed

    Sachleben, Joseph R; Adhikari, Aashish N; Gawlak, Grzegorz; Hoey, Robert J; Liu, Gaohua; Joachimiak, Andrzej; Montelione, Gaetano T; Sosnick, Tobin R; Koide, Shohei

    2017-02-01

    We determined the NMR structure of a highly aromatic (13%) protein of unknown function, Aq1974 from Aquifex aeolicus (PDB ID: 5SYQ). The unusual sequence of this protein has a tryptophan content five times the normal (six tryptophan residues of 114 or 5.2% while the average tryptophan content is 1.0%) with the tryptophans occurring in a WXW motif. It has no detectable sequence homology with known protein structures. Although its NMR spectrum suggested that the protein was rich in β-sheet, upon resonance assignment and solution structure determination, the protein was found to be primarily α-helical with a small two-stranded β-sheet with a novel fold that we have termed an Aromatic Claw. As this fold was previously unknown and the sequence unique, we submitted the sequence to CASP10 as a target for blind structural prediction. At the end of the competition, the sequence was classified a hard template based model; the structural relationship between the template and the experimental structure was small and the predictions all failed to predict the structure. CSRosetta was found to predict the secondary structure and its packing; however, it was found that there was little correlation between CSRosetta score and the RMSD between the CSRosetta structure and the NMR determined one. This work demonstrates that even in relatively small proteins, we do not yet have the capacity to accurately predict the fold for all primary sequences. The experimental discovery of new folds helps guide the improvement of structural prediction methods.

  20. Improved prediction of RNA tertiary structure with insights into native state dynamics.

    PubMed

    Bida, John Paul; Maher, L James

    2012-03-01

    The importance of RNA tertiary structure is evident from the growing number of published high resolution NMR and X-ray crystallographic structures of RNA molecules. These structures provide insights into function and create a knowledge base that is leveraged by programs such as Assemble, ModeRNA, RNABuilder, NAST, FARNA, Mc-Sym, RNA2D3D, and iFoldRNA for tertiary structure prediction and design. While these methods sample native-like RNA structures during simulations, all struggle to capture the native RNA conformation after scoring. We propose RSIM, an improved RNA fragment assembly method that preserves RNA global secondary structure while sampling conformations. This approach enhances the quality of predicted RNA tertiary structure, provides insights into the native state dynamics, and generates a powerful visualization of the RNA conformational space. RSIM is available for download from http://www.github.com/jpbida/rsim.

  1. Prediction of Protein–Protein Interaction Sites in Sequences and 3D Structures by Random Forests

    PubMed Central

    Šikić, Mile; Tomić, Sanja; Vlahoviček, Kristian

    2009-01-01

    Identifying interaction sites in proteins provides important clues to the function of a protein and is becoming increasingly relevant in topics such as systems biology and drug discovery. Although there are numerous papers on the prediction of interaction sites using information derived from structure, there are only a few case reports on the prediction of interaction residues based solely on protein sequence. Here, a sliding window approach is combined with the Random Forests method to predict protein interaction sites using (i) a combination of sequence- and structure-derived parameters and (ii) sequence information alone. For sequence-based prediction we achieved a precision of 84% with a 26% recall and an F-measure of 40%. When combined with structural information, the prediction performance increases to a precision of 76% and a recall of 38% with an F-measure of 51%. We also present an attempt to rationalize the sliding window size and demonstrate that a nine-residue window is the most suitable for predictor construction. Finally, we demonstrate the applicability of our prediction methods by modeling the Ras–Raf complex using predicted interaction sites as target binding interfaces. Our results suggest that it is possible to predict protein interaction sites with quite a high accuracy using only sequence information. PMID:19180183

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

    PubMed Central

    He, Yi; Mozolewska, Magdalena A.; Krupa, Paweł; Sieradzan, Adam K.; Wirecki, Tomasz K.; Liwo, Adam; Kachlishvili, Khatuna; Rackovsky, Shalom; Jagieła, Dawid; Ślusarz, Rafał; Czaplewski, Cezary R.; Ołdziej, Stanisław; Scheraga, Harold A.

    2013-01-01

    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

  3. Protein Folding and Structure Prediction from the Ground Up: The Atomistic Associative Memory, Water Mediated, Structure and Energy Model.

    PubMed

    Chen, Mingchen; Lin, Xingcheng; Zheng, Weihua; Onuchic, José N; Wolynes, Peter G

    2016-08-25

    The associative memory, water mediated, structure and energy model (AWSEM) is a coarse-grained force field with transferable tertiary interactions that incorporates local in sequence energetic biases using bioinformatically derived structural information about peptide fragments with locally similar sequences that we call memories. The memory information from the protein data bank (PDB) database guides proper protein folding. The structural information about available sequences in the database varies in quality and can sometimes lead to frustrated free energy landscapes locally. One way out of this difficulty is to construct the input fragment memory information from all-atom simulations of portions of the complete polypeptide chain. In this paper, we investigate this approach first put forward by Kwac and Wolynes in a more complete way by studying the structure prediction capabilities of this approach for six α-helical proteins. This scheme which we call the atomistic associative memory, water mediated, structure and energy model (AAWSEM) amounts to an ab initio protein structure prediction method that starts from the ground up without using bioinformatic input. The free energy profiles from AAWSEM show that atomistic fragment memories are sufficient to guide the correct folding when tertiary forces are included. AAWSEM combines the efficiency of coarse-grained simulations on the full protein level with the local structural accuracy achievable from all-atom simulations of only parts of a large protein. The results suggest that a hybrid use of atomistic fragment memory and database memory in structural predictions may well be optimal for many practical applications.

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

    PubMed

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

    2015-12-01

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

  5. Theoretical predictions of structures in dispersions containing charged colloidal particles and non-adsorbing polymers.

    PubMed

    Xie, Fei; Turesson, Martin; Woodward, Clifford E; van Gruijthuijsen, Kitty; Stradner, Anna; Forsman, Jan

    2016-04-28

    We develop a theoretical model to describe structural effects on a specific system of charged colloidal polystyrene particles, upon the addition of non-adsorbing PEG polymers. This system has previously been investigated experimentally, by scattering methods, so we are able to quantitatively compare predicted structure factors with corresponding experimental data. Our aim is to construct a model that is coarse-grained enough to be computationally manageable, yet detailed enough to capture the important physics. To this end, we utilize classical polymer density functional theory, wherein all possible polymer configurations are accounted for, subject to a mean-field Boltzmann weight. We make efforts to counteract drawbacks with this mean-field approach, resulting in structural predictions that agree very well with computationally more demanding simulations. Electrostatic interactions are handled at the fully non-linear Poisson-Boltzmann level, and we demonstrate that a linearization leads to less accurate predictions. The particle charge is an experimentally unknown parameter. We define the surface charge such that the experimental and theoretical gel point at equal polymer concentration coincide. Assuming a fixed surface charge for a certain salt concentration, we find very good agreements between measured and predicted structure factors across a wide range of polymer concentrations. We also present predictions for other structural quantities, such as radial distribution functions, and cluster size distributions. Finally, we demonstrate that our model predicts the occurrence of equilibrium clusters at high polymer concentrations, but low particle volume fractions and salt levels.

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

    PubMed

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

    2016-05-01

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

  7. Qualitative and quantitative structure-activity relationship modelling for predicting blood-brain barrier permeability of structurally diverse chemicals.

    PubMed

    Gupta, S; Basant, N; Singh, K P

    2015-01-01

    In this study, structure-activity relationship (SAR) models have been established for qualitative and quantitative prediction of the blood-brain barrier (BBB) permeability of chemicals. The structural diversity of the chemicals and nonlinear structure in the data were tested. The predictive and generalization ability of the developed SAR models were tested through internal and external validation procedures. In complete data, the QSAR models rendered ternary classification accuracy of >98.15%, while the quantitative SAR models yielded correlation (r(2)) of >0.926 between the measured and the predicted BBB permeability values with the mean squared error (MSE) <0.045. The proposed models were also applied to an external new in vitro data and yielded classification accuracy of >82.7% and r(2) > 0.905 (MSE < 0.019). The sensitivity analysis revealed that topological polar surface area (TPSA) has the highest effect in qualitative and quantitative models for predicting the BBB permeability of chemicals. Moreover, these models showed predictive performance superior to those reported earlier in the literature. This demonstrates the appropriateness of the developed SAR models to reliably predict the BBB permeability of new chemicals, which can be used for initial screening of the molecules in the drug development process.

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

    ERIC Educational Resources Information Center

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

    2011-01-01

    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…

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

    PubMed

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

    2015-01-01

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

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

    PubMed Central

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

    2015-01-01

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

  11. Using crystal structure prediction to rationalize the hydration propensities of substituted adamantane hydrochloride salts.

    PubMed

    Mohamed, Sharmarke; Karothu, Durga Prasad; Naumov, Panče

    2016-08-01

    The crystal energy landscapes of the salts of two rigid pharmaceutically active molecules reveal that the experimental structure of amantadine hydrochloride is the most stable structure with the majority of low-energy structures adopting a chain hydrogen-bond motif and packings that do not have solvent accessible voids. By contrast, memantine hydrochloride which differs in the substitution of two methyl groups on the adamantane ring has a crystal energy landscape where all structures within 10 kJ mol(-1) of the global minimum have solvent-accessible voids ranging from 3 to 14% of the unit-cell volume including the lattice energy minimum that was calculated after removing water from the hydrated memantine hydrochloride salt structure. The success in using crystal structure prediction (CSP) to rationalize the different hydration propensities of these substituted adamantane hydrochloride salts allowed us to extend the model to predict under blind test conditions the experimental crystal structures of the previously uncharacterized 1-(methylamino)adamantane base and its corresponding hydrochloride salt. Although the crystal structure of 1-(methylamino)adamantane was correctly predicted as the second ranked structure on the static lattice energy landscape, the crystallization of a Z' = 3 structure of 1-(methylamino)adamantane hydrochloride reveals the limits of applying CSP when the contents of the crystallographic asymmetric unit are unknown.

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

    PubMed Central

    Bon, Michaël; Orland, Henri

    2011-01-01

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

  13. MetaTox: Web Application for Predicting Structure and Toxicity of Xenobiotics' Metabolites.

    PubMed

    Rudik, Anastasia V; Bezhentsev, Vladislav M; Dmitriev, Alexander V; Druzhilovskiy, Dmitry S; Lagunin, Alexey A; Filimonov, Dmitry A; Poroikov, Vladimir V

    2017-04-03

    A new freely available web-application MetaTox ( http://www.way2drug.com/mg ) for prediction of xenobiotic's metabolism and calculation toxicity of metabolites based on the structural formula of chemicals has been developed. MetaTox predicts metabolites, which are formed by nine classes of reactions (aliphatic and aromatic hydroxylation, N- and O-glucuronidation, N-, S- and C-oxidation, and N- and O-dealkylation). The calculation of probability for generated metabolites is based on analyses of "structure-biotransformation reactions" and "structure-modified atoms" relationships using a Bayesian approach. Prediction of LD50 values is performed by GUSAR software for the parent compound and each of the generated metabolites using quantitative structure-activity relationahip (QSAR) models created for acute rat toxicity with the intravenous type of administration.

  14. Manual for the prediction of blast and fragment loadings on structures

    SciTech Connect

    Not Available

    1980-11-01

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

  15. Ab initio structure prediction of the antibody hypervariable H3 loop.

    PubMed

    Zhu, Kai; Day, Tyler

    2013-06-01

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

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

    PubMed Central

    Xu, Zhenjiang; Mathews, David H.

    2011-01-01

    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

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

    PubMed

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

    2014-08-01

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

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

    PubMed Central

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

    2015-01-01

    A full understanding of RNA-mediated biology would require the knowledge of three-dimensional (3D) structures, structural flexibility, and stability of RNAs. To predict RNA 3D structures and stability, we have previously proposed a three-bead coarse-grained predictive model with implicit salt/solvent potentials. In this study, we further develop the model by improving the implicit-salt electrostatic potential and including a sequence-dependent coaxial stacking potential to enable the model to simulate RNA 3D structure folding in divalent/monovalent ion solutions. The model presented here can predict 3D structures of RNA hairpins with bulges/internal loops (<77 nucleotides) from their sequences at the corresponding experimental ion conditions with an overall improved accuracy compared to the experimental data; the model also makes reliable predictions for the flexibility of RNA hairpins with bulge loops of different lengths at several divalent/monovalent ion conditions. In addition, the model successfully predicts the stability of RNA hairpins with various loops/stems in divalent/monovalent ion solutions. PMID:26682822

  19. Computational prediction of riboswitch tertiary structures including pseudoknots by RAGTOP: a hierarchical graph sampling approach.

    PubMed

    Kim, Namhee; Zahran, Mai; Schlick, Tamar

    2015-01-01

    The modular organization of RNA structure has been exploited in various computational and theoretical approaches to identify RNA tertiary (3D) motifs and assemble RNA structures. Riboswitches exemplify this modularity in terms of both structural and functional adaptability of RNA components. Here, we extend our computational approach based on tree graph sampling to the prediction of riboswitch topologies by defining additional edges to mimick pseudoknots. Starting from a secondary (2D) structure, we construct an initial graph deduced from predicted junction topologies by our data-mining algorithm RNAJAG trained on known RNAs; we sample these graphs in 3D space guided by knowledge-based statistical potentials derived from bending and torsion measures of internal loops as well as radii of gyration for known RNAs. We present graph sampling results for 10 representative riboswitches, 6 of them with pseudoknots, and compare our predictions to solved structures based on global and local RMSD measures. Our results indicate that the helical arrangements in riboswitches can be approximated using our combination of modified 3D tree graph representations for pseudoknots, junction prediction, graph moves, and scoring functions. Future challenges in the field of riboswitch prediction and design are also discussed.

  20. The Ordered Network Structure and Prediction Summary for M≥7 Earthquakes in Xinjiang Region of China

    NASA Astrophysics Data System (ADS)

    Men, Ke-Pei; Zhao, Kai

    2014-12-01

    M ≥7 earthquakes have showed an obvious commensurability and orderliness in Xinjiang of China and its adjacent region since 1800. The main orderly values are 30 a × k (k = 1,2,3), 11 ~ 12 a, 41 ~ 43 a, 18 ~ 19 a, and 5 ~ 6 a. In the guidance of the information forecasting theory of Wen-Bo Weng, based on previous research results, combining ordered network structure analysis with complex network technology, we focus on the prediction summary of M ≥ 7 earthquakes by using the ordered network structure, and add new information to further optimize network, hence construct the 2D- and 3D-ordered network structure of M ≥ 7 earthquakes. In this paper, the network structure revealed fully the regularity of seismic activity of M ≥ 7 earthquakes in the study region during the past 210 years. Based on this, the Karakorum M7.1 earthquake in 1996, the M7.9 earthquake on the frontier of Russia, Mongol, and China in 2003, and two Yutian M7.3 earthquakes in 2008 and 2014 were predicted successfully. At the same time, a new prediction opinion is presented that the future two M ≥ 7 earthquakes will probably occur around 2019 - 2020 and 2025 - 2026 in this region. The results show that large earthquake occurred in defined region can be predicted. The method of ordered network structure analysis produces satisfactory results for the mid-and-long term prediction of M ≥ 7 earthquakes.

  1. Distributions in Protein Conformation Space: Implications for Structure Prediction and Entropy

    PubMed Central

    Sullivan, David C.; Kuntz, Irwin D.

    2004-01-01

    By considering how polymer structures are distributed in conformation space, we show that it is possible to quantify the difficulty of structural prediction and to provide a measure of progress for prediction calculations. The critical issue is the probability that a conformation is found within a specified distance of another conformer. We address this question by constructing a cumulative distribution function (CDF) for the average probability from observations about its limiting behavior at small displacements and numerical simulations of polyalanine chains. We can use the CDF to estimate the likelihood that a structure prediction is better than random chance. For example, the chance of randomly predicting the native backbone structure of a 150-amino-acid protein to low resolution, say within 6 Å, is 10−14. A high-resolution structural prediction, say to 2 Å, is immensely more difficult (10−57). With additional assumptions, the CDF yields the conformational entropy of protein folding from native-state coordinate variance. Or, using values of the conformational entropy change on folding, we can estimate the native state's conformational span. For example, for a 150-mer protein, equilibrium α-carbon displacements in the native ensemble would be 0.3–0.5 Å based on TΔS of 1.42 kcal/(mol residue). PMID:15240450

  2. Structure-templated predictions of novel protein interactions from sequence information.

    PubMed

    Betel, Doron; Breitkreuz, Kevin E; Isserlin, Ruth; Dewar-Darch, Danielle; Tyers, Mike; Hogue, Christopher W V

    2007-09-01

    The multitude of functions performed in the cell are largely controlled by a set of carefully orchestrated protein interactions often facilitated by specific binding of conserved domains in the interacting proteins. Interacting domains commonly exhibit distinct binding specificity to short and conserved recognition peptides called binding profiles. Although many conserved domains are known in nature, only a few have well-characterized binding profiles. Here, we describe a novel predictive method known as domain-motif interactions from structural topology (D-MIST) for elucidating the binding profiles of interacting domains. A set of domains and their corresponding binding profiles were derived from extant protein structures and protein interaction data and then used to predict novel protein interactions in yeast. A number of the predicted interactions were verified experimentally, including new interactions of the mitotic exit network, RNA polymerases, nucleotide metabolism enzymes, and the chaperone complex. These results demonstrate that new protein interactions can be predicted exclusively from sequence information.

  3. Data mining approaches to high-throughput crystal structure and compound prediction.

    PubMed

    Hautier, Geoffroy

    2014-01-01

    Predicting unknown inorganic compounds and their crystal structure is a critical step of high-throughput computational materials design and discovery. One way to achieve efficient compound prediction is to use data mining or machine learning methods. In this chapter we present a few algorithms for data mining compound prediction and their applications to different materials discovery problems. In particular, the patterns or correlations governing phase stability for experimental or computational inorganic compound databases are statistically learned and used to build probabilistic or regression models to identify novel compounds and their crystal structures. The stability of those compound candidates is then assessed using ab initio techniques. Finally, we report a few cases where data mining driven computational predictions were experimentally confirmed through inorganic synthesis.

  4. Hepatitis B DNA vaccine-polycation nano-complexes enhancing immune response by percutaneous administration with microneedle.

    PubMed

    Yin, Dongfeng; Liang, Wenqing; Xing, Shuxing; Gao, Zhixiang; Zhang, Wei; Guo, Zhili; Gao, Shen

    2013-01-01

    Percutaneous immune method is becoming an attractive alternative for DNA vaccine as a lot of antigen presenting cells are existed in the viable epidermis. However, due to the barrier function of stratum corneum, it would be hard for DNA vaccine to reach the viable epidermis of the skin. In order to deliver the DNA vaccine successfully cross the stratum corneum, pentagram silicon microneedle array was prepared in this study, and fluorescently labeled nanoparticle was taken as the model to observe the situation inside the skin processed by microneedle. Via microneedle nanoparticles could enter the skin through the micro-channel (diameter about 20-30 µm) and its amount is greatly larger than that enter though the hair follicle of intact skin. A new type of gene vector Pluronic P123-modified polyethyleneimine (P123-PEI) was synthesized by high molecular weight polyethylenimine and Pluronic P123 with the molar ratio of 1 : 1 to take the advantage of P123-PEI as low cytotoxicity and high transfection efficiency. Mice were immunized percutaneously with Hepatitis B DNA vaccine/P123-PEI nano-complexes by microneedle. The humoral and cellular immunity generated in percutaneously immunized mice through microneedle array by Hepatitis B DNA vaccine/P123-PEI nano-complex was significantly higher than that of DNA vaccine intramuscular administration.

  5. Predicting RNA 3D structure using a coarse-grain helix-centered model.

    PubMed

    Kerpedjiev, Peter; Höner Zu Siederdissen, Christian; Hofacker, Ivo L

    2015-06-01

    A 3D model of RNA structure can provide information about its function and regulation that is not possible with just the sequence or secondary structure. Current models suffer from low accuracy and long running times and either neglect or presume knowledge of the long-range interactions which stabilize the tertiary structure. Our coarse-grained, helix-based, tertiary structure model operates with only a few degrees of freedom compared with all-atom models while preserving the ability to sample tertiary structures given a secondary structure. It strikes a balance between the precision of an all-atom tertiary structure model and the simplicity and effectiveness of a secondary structure representation. It provides a simplified tool for exploring global arrangements of helices and loops within RNA structures. We provide an example of a novel energy function relying only on the positions of stems and loops. We show that coupling our model to this energy function produces predictions as good as or better than the current state of the art tools. We propose that given the wide range of conformational space that needs to be explored, a coarse-grain approach can explore more conformations in less iterations than an all-atom model coupled to a fine-grain energy function. Finally, we emphasize the overarching theme of providing an ensemble of predicted structures, something which our tool excels at, rather than providing a handful of the lowest energy structures.

  6. Decisive role of structure in food microbial colonization and implications for predictive microbiology.

    PubMed

    Noriega, E; Laca, A; Díaz, M

    2010-05-01

    Predictive models must consider the significant effect of the physical structure of the food on the magnitude and type of microbial growth. Before such models are developed, a thorough characterization of the food structure is mandatory because this information will determine the modeling approach. In this work, several physical structures common in poultry products were classified and described. Chicken breast skin and flesh and minced breasts were examined by scanning electron microscopy and compared with a meat-based model food. Such systems were surface or internally inoculated with Listeria innocua and incubated at 25 degrees C for 24 h. Different structures, including several substructures, found in the studied systems affected microbial distribution and growth. Based on these experimental findings, the most suitable type of model for each physical structure was determined. This information provides further clarification for predictive microbiology models.

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

    SciTech Connect

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

    2013-11-19

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

  8. Prediction of protein-protein interactions: unifying evolution and structure at protein interfaces.

    PubMed

    Tuncbag, Nurcan; Gursoy, Attila; Keskin, Ozlem

    2011-06-01

    The vast majority of the chores in the living cell involve protein-protein interactions. Providing details of protein interactions at the residue level and incorporating them into protein interaction networks are crucial toward the elucidation of a dynamic picture of cells. Despite the rapid increase in the number of structurally known protein complexes, we are still far away from a complete network. Given experimental limitations, computational modeling of protein interactions is a prerequisite to proceed on the way to complete structural networks. In this work, we focus on the question 'how do proteins interact?' rather than 'which proteins interact?' and we review structure-based protein-protein interaction prediction approaches. As a sample approach for modeling protein interactions, PRISM is detailed which combines structural similarity and evolutionary conservation in protein interfaces to infer structures of complexes in the protein interaction network. This will ultimately help us to understand the role of protein interfaces in predicting bound conformations.

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

    SciTech Connect

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

    1999-08-01

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

  10. Reducing model structural uncertainty in predictions for ungauged basins via Bayesian approach.

    NASA Astrophysics Data System (ADS)

    Prieto, Cristina; Le Vine, Nataliya; Vitolo, Claudia; García, Eduardo; Medina, Raúl

    2016-04-01

    A catchment is a complex system where a multitude of interrelated energy, water and vegetation processes occur at different temporal and spatial scales. A rainfall-runoff model is a simplified representation of the system, and serves as a hypothesis about an inner catchment working. In predictions for ungauged basins, a common practice is to use a pre-selected assumed-to-be-perfect model structure to represent all catchments under analysis. However, it is unlikely that the same model structure is appropriate for diverse catchments due to the 'uniqueness of the place'. At the same time, there is no obvious justification to select a single model structure as a suitable description of the system. The contribution of this research is a move forward in the 'one size fits all' problem for predicting flows in ungauged basins. We present a statistical methodology, which allows regionalization that considers the information given by different hydrological model structures. First, the information to be regionalised is compactly represented via Principal Component Analysis. Second, the most significant principal components are regionalised using non-linear regionalisation method based on Random Forests. Third, a regionalisation error structure is derived based on the gauged catchments to be used in the Bayesian condition of the rainfall-runoff structures and their parameters. The methodological developments are demonstrated for predicting flows in ungauged basins of Northern Spain; and the results show that the methodology allows improving the flow prediction.

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

    PubMed

    Sevy, Alexander M; Meiler, Jens

    2014-12-01

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

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

    PubMed Central

    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, José 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

    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 Iβ (PKGIβ) 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 (β-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

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

    NASA Astrophysics Data System (ADS)

    Xu, Tianfang; Valocchi, Albert J.

    2015-11-01

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

  14. Integration of QUARK and I-TASSER for Ab Initio Protein Structure Prediction in CASP11.

    PubMed

    Zhang, Wenxuan; Yang, Jianyi; He, Baoji; Walker, Sara Elizabeth; Zhang, Hongjiu; Govindarajoo, Brandon; Virtanen, Jouko; Xue, Zhidong; Shen, Hong-Bin; Zhang, Yang

    2016-09-01

    We tested two pipelines developed for template-free protein structure prediction in the CASP11 experiment. First, the QUARK pipeline constructs structure models by reassembling fragments of continuously distributed lengths excised from unrelated proteins. Five free-modeling (FM) targets have the model successfully constructed by QUARK with a TM-score above 0.4, including the first model of T0837-D1, which has a TM-score = 0.736 and RMSD = 2.9 Å to the native. Detailed analysis showed that the success is partly attributed to the high-resolution contact map prediction derived from fragment-based distance-profiles, which are mainly located between regular secondary structure elements and loops/turns and help guide the orientation of secondary structure assembly. In the Zhang-Server pipeline, weakly scoring threading templates are re-ordered by the structural similarity to the ab initio folding models, which are then reassembled by I-TASSER based structure assembly simulations; 60% more domains with length up to 204 residues, compared to the QUARK pipeline, were successfully modeled by the I-TASSER pipeline with a TM-score above 0.4. The robustness of the I-TASSER pipeline can stem from the composite fragment-assembly simulations that combine structures from both ab initio folding and threading template refinements. Despite the promising cases, challenges still exist in long-range beta-strand folding, domain parsing, and the uncertainty of secondary structure prediction; the latter of which was found to affect nearly all aspects of FM structure predictions, from fragment identification, target classification, structure assembly, to final model selection. Significant efforts are needed to solve these problems before real progress on FM could be made. Proteins 2016; 84(Suppl 1):76-86. © 2015 Wiley Periodicals, Inc.

  15. Prediction of vibration characteristics in beam structure using sub-scale modeling with experimental validation

    NASA Astrophysics Data System (ADS)

    Zai, Behzad Ahmed; Sami, Saad; Khan, M. Amir; Ahmad, Furqan; Park, Myung Kyun

    2015-09-01

    Geometric or sub-scale modeling techniques are used for the evaluation of large and complex dynamic structures to ensure accurate reproduction of load path and thus leading to true dynamic characteristics of such structures. The sub-scale modeling technique is very effective in the prediction of vibration characteristics of original large structure when the experimental testing is not feasible due to the absence of a large testing facility. Previous researches were more focused on free and harmonic vibration case with little or no consideration for readily encountered random vibration. A sub-scale modeling technique is proposed for estimating the vibration characteristics of any large scale structure such as Launch vehicles, Mega structures, etc., under various vibration load cases by utilizing precise scaled-down model of that dynamic structure. In order to establish an analytical correlation between the original structure and its scaled models, different scale models of isotropic cantilever beam are selected and analyzed under various vibration conditions( i.e. free, harmonic and random) using finite element package ANSYS. The developed correlations are also validated through experimental testing. The prediction made from the vibratory response of the scaled-down beam through the established sets of correlation are found similar to the response measured from the testing of original beam structure. The established correlations are equally applicable in the prediction of dynamic characteristics of any complex structure through its scaled-down models. This paper presents modified sub-scale modeling technique that enables accurate prediction of vibration characteristics of large and complex structure under not only sinusoidal but also for random vibrations.

  16. Taking Materials Design Into The Space Of Polymorphs: Structure Predictions And Realizability

    NASA Astrophysics Data System (ADS)

    Stevanovic, Vladan

    The phenomenon of polymorphism exemplifies the significance of structural degrees of freedom in determining physical properties of solids. Classic case is elemental carbon with markedly different mechanical, optical and electronic properties between its graphite and diamond forms. To harness the richness of this phenomenon and extend rational materials design into the space of polymorphs, there is a need for developing approaches that are capable of exploring systematically and efficiently the potential energy surface, and (desirably) assist in experimental realization of different structures. While the former presents a common place in the field of structure predictions, less attention is given to the latter. Namely, available experimental data indicate that the energy above the ground state alone is insufficient to quantify the realizability of different structures. For example, MgO crystallizes exclusively as the rocksalt despite the predicted existence of a number of low-energy structures. Similarly, ZnO is realized in the wurtzite, zincblende and a relatively high-energy rocksalt structure, again, apparently disregarding a number of theoretically predicted low-energy structures. In this talk I will present recent attempts to tackle these issues focused on partially ionic systems. The structure prediction part is carried out by performing local DFT relaxations on a large set of random supperlattices (RSLs) with atoms distributed randomly over different planes in a way that favors cation-anion coordination. Second, application of the RSL sampling to a range of binary ionic systems such as MgO, ZnO, SnO2 and other, reveals that the frequency of occurrence of a given structure offers an estimate of the volume of configuration space occupied by the corresponding local minimum, which is shown to be connected to the realizability of different structures. This work is part of the Center for Next Generation of Materials by Design, a US DOE funded Energy Frontier

  17. SVM-PB-Pred: SVM based protein block prediction method using sequence profiles and secondary structures.

    PubMed

    Suresh, V; Parthasarathy, S

    2014-01-01

    We developed a support vector machine based web server called SVM-PB-Pred, to predict the Protein Block for any given amino acid sequence. The input features of SVM-PB-Pred include i) sequence profiles (PSSM) and ii) actual secondary structures (SS) from DSSP method or predicted secondary structures from NPS@ and GOR4 methods. There were three combined input features PSSM+SS(DSSP), PSSM+SS(NPS@) and PSSM+SS(GOR4) used to test and train the SVM models. Similarly, four datasets RS90, DB433, LI1264 and SP1577 were used to develop the SVM models. These four SVM models developed were tested using three different benchmarking tests namely; (i) self consistency, (ii) seven fold cross validation test and (iii) independent case test. The maximum possible prediction accuracy of ~70% was observed in self consistency test for the SVM models of both LI1264 and SP1577 datasets, where PSSM+SS(DSSP) input features was used to test. The prediction accuracies were reduced to ~53% for PSSM+SS(NPS@) and ~43% for PSSM+SS(GOR4) in independent case test, for the SVM models of above two same datasets. Using our method, it is possible to predict the protein block letters for any query protein sequence with ~53% accuracy, when the SP1577 dataset and predicted secondary structure from NPS@ server were used. The SVM-PB-Pred server can be freely accessed through http://bioinfo.bdu.ac.in/~svmpbpred.

  18. A Deep Learning Network Approach to ab initio Protein Secondary Structure Prediction.

    PubMed

    Spencer, Matt; Eickholt, Jesse; Jianlin Cheng

    2015-01-01

    Ab initio protein secondary structure (SS) predictions are utilized to generate tertiary structure predictions, which are increasingly demanded due to the rapid discovery of proteins. Although recent developments have slightly exceeded previous methods of SS prediction, accuracy has stagnated around 80 percent and many wonder if prediction cannot be advanced beyond this ceiling. Disciplines that have traditionally employed neural networks are experimenting with novel deep learning techniques in attempts to stimulate progress. Since neural networks have historically played an important role in SS prediction, we wanted to determine whether deep learning could contribute to the advancement of this field as well. We developed an SS predictor that makes use of the position-specific scoring matrix generated by PSI-BLAST and deep learning network architectures, which we call DNSS. Graphical processing units and CUDA software optimize the deep network architecture and efficiently train the deep networks. Optimal parameters for the training process were determined, and a workflow comprising three separately trained deep networks was constructed in order to make refined predictions. This deep learning network approach was used to predict SS for a fully independent test dataset of 198 proteins, achieving a Q3 accuracy of 80.7 percent and a Sov accuracy of 74.2 percent.

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

    PubMed

    Becker, Julien; Maes, Francis; Wehenkel, Louis

    2013-01-01

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

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

    PubMed Central

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

    2011-01-01

    The three-dimensional (3-D) structure prediction of proteins, given their amino acid sequence, is addressed using the first principles–based approach ASTRO-FOLD 2.0. The key features presented are: (1) Secondary structure prediction using a novel optimization-based consensus approach, (2) β-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

  1. Towards Practical Carbonation Prediction and Modelling for Service Life Design of Reinforced Concrete Structures

    NASA Astrophysics Data System (ADS)

    Ekolu, O. S.

    2015-11-01

    Amongst the scientific community, the interest in durability of concrete structures has been high for quite a long time of over 40 years. Of the various causes of degradation of concrete structures, corrosion is the most widespread durability problem and carbonation is one of the two causes of steel reinforcement corrosion. While much scientific understanding has been gained from the numerous carbonation studies undertaken over the past years, it is still presently not possible to accurately predict carbonation and apply it in design of structures. This underscores the complex nature of the mechanisms as influenced by several interactive factors. Based on critical literature and some experience of the author, it is found that there still exist major challenges in establishing a mathematical constitutive relation for realistic carbonation prediction. While most current models employ permeability /diffusion as the main model property, analysis shows that the most practical material property would be compressive strength, which has a low coefficient of variation of 20% compared to 30 to 50% for permeability. This important characteristic of compressive strength, combined with its merit of simplicity and data availability at all stages of a structure's life, promote its potential use in modelling over permeability. By using compressive strength in carbonation prediction, the need for accelerated testing and permeability measurement can be avoided. This paper attempts to examine the issues associated with carbonation prediction, which could underlie the current lack of a sound established prediction method. Suggestions are then made for possible employment of different or alternative approaches.

  2. Development of a Novel Approach for Fatigue Life Prediction of Structural Materials

    DTIC Science & Technology

    2008-12-01

    304L Stainless Steels," Int J Fatigue 24, pp. 1063-1070. [29] Nemat-Nasser, S., Guo, W., and Kihl, D. P., 2001, " Thermomechanical Response of AL6XN...Final Report DEVELOPMENT OF A NOVEL APPROACH FOR FATIGUE LIFE PREDICTION OF STRUCTURAL MATERIALS (N00014-05-1-0777) Yanyao Jiang Department of...SUBTITLE DEVELOPMENT OF A NOVEL APPROACH FOR FATIGUE LIFE PREDICTION OF STRUCTURAL MATERIALS 5a. CONTRACT NUMBER N00014-05-1-0777 5b. GRANT NUMBER

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

    DOEpatents

    Agarwal, Pratul Kumar

    2011-07-19

    The present invention provides a method utilizing primary amino acid sequence of a protein, energy minimization, molecular dynamics and protein vibrational modes to predict three-dimensional structure of a protein. The present invention also determines possible intermediates in the protein folding pathway. The present invention has important applications to the design of novel drugs as well as protein engineering. The present invention predicts the three-dimensional structure of a protein independent of size of the protein, overcoming a significant limitation in the prior art.

  4. From structure prediction to genomic screens for novel non-coding RNAs.

    PubMed

    Gorodkin, Jan; Hofacker, Ivo L

    2011-08-01

    Non-coding RNAs (ncRNAs) are receiving more and more attention not only as an abundant class of genes, but also as regulatory structural elements (some located in mRNAs). A key feature of RNA function is its structure. Computational methods were developed early for folding and prediction of RNA structure with the aim of assisting in functional analysis. With the discovery of more and more ncRNAs, it has become clear that a large fraction of these are highly structured. Interestingly, a large part of the structure is comprised of regular Watson-Crick and GU wobble base pairs. This and the increased amount of available genomes have made it possible to employ structure-based methods for genomic screens. The field has moved from folding prediction of single sequences to computational screens for ncRNAs in genomic sequence using the RNA structure as the main characteristic feature. Whereas early methods focused on energy-directed folding of single sequences, comparative analysis based on structure preserving changes of base pairs has been efficient in improving accuracy, and today this constitutes a key component in genomic screens. Here, we cover the basic principles of RNA folding and touch upon some of the concepts in current methods that have been applied in genomic screens for de novo RNA structures in searches for novel ncRNA genes and regulatory RNA structure on mRNAs. We discuss the strengths and weaknesses of the different strategies and how they can complement each other.

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

    SciTech Connect

    Caputo, Riccarda; Tekin, Adem

    2011-07-15

    Crystal structure prediction from first principles is still one of the most challenging and interesting issue in condensed matter science. we explored the potential energy surface of NaBH{sub 4} by a combined ab-initio approach, based on global structure optimizations and quantum chemistry. In particular, we used simulated annealing (SA) and density functional theory (DFT) calculations. The methodology enabled the identification of several local minima, of which the global minimum corresponded to the tetragonal ground-state structure (P4{sub 2}/nmc), and the prediction of higher energy stable structures, among them a monoclinic (Pm) one was identified to be 22.75 kJ/mol above the ground-state at T=298 K. In between, orthorhombic and cubic structures were recovered, in particular those with Pnma and F4-bar 3m symmetries. - Graphical abstract: The total electron energy difference of the calculated stable structures. Here, the tetragonal (IT 137) and the monoclinic (IT 6) symmetry groups corresponded to the lowest and the highest energy structures, respectively. Highlights: > Potential energy surface of NaBH{sub 4} is investigated. > This is done a combination of global structure optimizations based on simulated annealing and density functional calculations. > We successfully reproduced experimentally found tetragonal and orthorhombic structures of NaBH{sub 4}. > Furthermore, we found a new stable high energy structure.

  6. ORION: a web server for protein fold recognition and structure prediction using evolutionary hybrid profiles

    PubMed Central

    Ghouzam, Yassine; Postic, Guillaume; Guerin, Pierre-Edouard; de Brevern, Alexandre G.; Gelly, Jean-Christophe

    2016-01-01

    Protein structure prediction based on comparative modeling is the most efficient way to produce structural models when it can be performed. ORION is a dedicated webserver based on a new strategy that performs this task. The identification by ORION of suitable templates is performed using an original profile-profile approach that combines sequence and structure evolution information. Structure evolution information is encoded into profiles using structural features, such as solvent accessibility and local conformation —with Protein Blocks—, which give an accurate description of the local protein structure. ORION has recently been improved, increasing by 5% the quality of its results. The ORION web server accepts a single protein sequence as input and searches homologous protein structures within minutes. Various databases such as PDB, SCOP and HOMSTRAD can be mined to find an appropriate structural template. For the modeling step, a protein 3D structure can be directly obtained from the selected template by MODELLER and displayed with global and local quality model estimation measures. The sequence and the predicted structure of 4 examples from the CAMEO server and a recent CASP11 target from the ‘Hard’ category (T0818-D1) are shown as pertinent examples. Our web server is accessible at http://www.dsimb.inserm.fr/ORION/. PMID:27319297

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

    PubMed

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

    2010-12-08

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

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

    SciTech Connect

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

    2000-08-01

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

  9. Analysis and Design of Fuselage Structures Including Residual Strength Prediction Methodology

    NASA Technical Reports Server (NTRS)

    Knight, Norman F.

    1998-01-01

    The goal of this research project is to develop and assess methodologies for the design and analysis of fuselage structures accounting for residual strength. Two primary objectives are included in this research activity: development of structural analysis methodology for predicting residual strength of fuselage shell-type structures; and the development of accurate, efficient analysis, design and optimization tool for fuselage shell structures. Assessment of these tools for robustness, efficient, and usage in a fuselage shell design environment will be integrated with these two primary research objectives.

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

    PubMed

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

    2012-12-14

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

  11. Prediction and analysis of the modular structure of cytochrome P450 monooxygenases

    PubMed Central

    2010-01-01

    Background Cytochrome P450 monooxygenases (CYPs) form a vast and diverse family of highly variable sequences. They catalyze a wide variety of oxidative reactions and are therefore of great relevance in drug development and biotechnological applications. Despite their differences in sequence and substrate specificity, the structures of CYPs are highly similar. Although being in research focus for years, factors mediating selectivity and activity remain vague. Description This systematic comparison of CYPs based on the Cytochrome P450 Engineering Database (CYPED) involved sequence and structure analysis of more than 8000 sequences. 31 structures have been applied to generate a reliable structure-based HMM profile in order to predict structurally conserved regions. Therefore, it was possible to automatically transfer these modules on CYP sequences without any secondary structure information, to analyze substrate interacting residues and to compare interaction sites with redox partners. Conclusions Functionally relevant structural sites of CYPs were predicted. Regions involved in substrate binding were analyzed in all sequences among the CYPED. For all CYPs that require a reductase, two reductase interaction sites were identified and classified according to their length. The newly gained insights promise an improvement of engineered enzyme properties for potential biotechnological application. The annotated sequences are accessible on the current version of the CYPED. The prediction tool can be applied to any CYP sequence via the web interface at http://www.cyped.uni-stuttgart.de/cgi-bin/strpred/dosecpred.pl. PMID:20950472

  12. Protein Folding and Structure Prediction from the Ground Up: The Atomistic Associative Memory, Water Mediated, Structure and Energy Model

    PubMed Central

    Chen, Mingchen; Lin, Xingcheng; Zheng, Weihua; Onuchic, José N.; Wolynes, Peter G.

    2016-01-01

    The associative memory, water mediated, structure and energy model (AWSEM) is a coarse-grained force field with transferable tertiary interactions that incorporates local in sequence energetic biases using bioinformatically derived structural information about peptide fragments with locally similar sequence that we call memories. The memory information from the protein data bank (PDB) database guides proper protein folding. The structural information about available sequences in the database varies in quality and can sometimes lead to frustrated free energy landscapes locally. One way out of this difficulty is to construct the input fragment memory information from all-atom simulations of portions of the complete polypeptide chain. In this paper, we investigate this approach first put forward by Kwac and Wolynes in a more complete way by studying the structure prediction capabilities of this approach for six alpha-helical proteins. This scheme which we call the atomistic associative memory, water mediated, structure and energy model (AAWSEM) amounts to an ab initio protein structure prediction method that starts from the ground-up without using bioinformatic input. The free energy profiles from AAWSEM show that atomistic fragment memories are sufficient to guide the correct folding when tertiary forces are included. AAWSEM combines the efficiency of coarse-grained simulations on the full protein level with the local structural accuracy achievable from all-atom simulations of only parts of a large protein. The results suggest that a hybrid use of atomistic fragment memory and database memory in structural predictions may well be optimal for many practical applications. PMID:27148634

  13. Novel proteases from the genome of the carnivorous plant Drosera capensis: Structural prediction and comparative analysis.

    PubMed

    Butts, Carter T; Bierma, Jan C; Martin, Rachel W

    2016-10-01

    In his 1875 monograph on insectivorous plants, Darwin described the feeding reactions of Drosera flypaper traps and predicted that their secretions contained a "ferment" similar to mammalian pepsin, an aspartic protease. Here we report a high-quality draft genome sequence for the cape sundew, Drosera capensis, the first genome of a carnivorous plant from order Caryophyllales, which also includes the Venus flytrap (Dionaea) and the tropical pitcher plants (Nepenthes). This species was selected in part for its hardiness and ease of cultivation, making it an excellent model organism for further investigations of plant carnivory. Analysis of predicted protein sequences yields genes encoding proteases homologous to those found in other plants, some of which display sequence and structural features that suggest novel functionalities. Because the sequence similarity to proteins of known structure is in most cases too low for traditional homology modeling, 3D structures of representative proteases are predicted using comparative modeling with all-atom refinement. Although the overall folds and active residues for these proteins are conserved, we find structural and sequence differences consistent with a diversity of substrate recognition patterns. Finally, we predict differences in substrate specificities using in silico experiments, providing targets for structure/function studies of novel enzymes with biological and technological significance. Proteins 2016; 84:1517-1533. © 2016 Wiley Periodicals, Inc.

  14. Molecular Phylogeny and Predicted 3D Structure of Plant beta-D-N-Acetylhexosaminidase

    PubMed Central

    Hossain, Md. Anowar

    2014-01-01

    beta-D-N-Acetylhexosaminidase, a family 20 glycosyl hydrolase, catalyzes the removal of β-1,4-linked N-acetylhexosamine residues from oligosaccharides and their conjugates. We constructed phylogenetic tree of β-hexosaminidases to analyze the evolutionary history and predicted functions of plant hexosaminidases. Phylogenetic analysis reveals the complex history of evolution of plant β-hexosaminidase that can be described by gene duplication events. The 3D structure of tomato β-hexosaminidase (β-Hex-Sl) was predicted by homology modeling using 1now as a template. Structural conformity studies of the best fit model showed that more than 98% of the residues lie inside the favoured and allowed regions where only 0.9% lie in the unfavourable region. Predicted 3D structure contains 531 amino acids residues with glycosyl hydrolase20b domain-I and glycosyl hydrolase20 superfamily domain-II including the (β/α)8 barrel in the central part. The α and β contents of the modeled structure were found to be 33.3% and 12.2%, respectively. Eleven amino acids were found to be involved in ligand-binding site; Asp(330) and Glu(331) could play important roles in enzyme-catalyzed reactions. The predicted model provides a structural framework that can act as a guide to develop a hypothesis for β-Hex-Sl mutagenesis experiments for exploring the functions of this class of enzymes in plant kingdom. PMID:25165734

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

    PubMed

    Hossain, Md Anowar; Roslan, Hairul Azman

    2014-01-01

    beta-D-N-Acetylhexosaminidase, a family 20 glycosyl hydrolase, catalyzes the removal of β-1,4-linked N-acetylhexosamine residues from oligosaccharides and their conjugates. We constructed phylogenetic tree of β-hexosaminidases to analyze the evolutionary history and predicted functions of plant hexosaminidases. Phylogenetic analysis reveals the complex history of evolution of plant β-hexosaminidase that can be described by gene duplication events. The 3D structure of tomato β-hexosaminidase (β-Hex-Sl) was predicted by homology modeling using 1now as a template. Structural conformity studies of the best fit model showed that more than 98% of the residues lie inside the favoured and allowed regions where only 0.9% lie in the unfavourable region. Predicted 3D structure contains 531 amino acids residues with glycosyl hydrolase20b domain-I and glycosyl hydrolase20 superfamily domain-II including the (β/α)8 barrel in the central part. The α and β contents of the modeled structure were found to be 33.3% and 12.2%, respectively. Eleven amino acids were found to be involved in ligand-binding site; Asp(330) and Glu(331) could play important roles in enzyme-catalyzed reactions. The predicted model provides a structural framework that can act as a guide to develop a hypothesis for β-Hex-Sl mutagenesis experiments for exploring the functions of this class of enzymes in plant kingdom.

  16. Structure prediction for CASP8 with all-atom refinement using Rosetta.

    PubMed

    Raman, Srivatsan; Vernon, Robert; Thompson, James; Tyka, Michael; Sadreyev, Ruslan; Pei, Jimin; Kim, David; Kellogg, Elizabeth; DiMaio, Frank; Lange, Oliver; Kinch, Lisa; Sheffler, Will; Kim, Bong-Hyun; Das, Rhiju; Grishin, Nick V; Baker, David

    2009-01-01

    We describe predictions made using the Rosetta structure prediction methodology for the Eighth Critical Assessment of Techniques for Protein Structure Prediction. Aggressive sampling and all-atom refinement were carried out for nearly all targets. A combination of alignment methodologies was used to generate starting models from a range of templates, and the models were then subjected to Rosetta all atom refinement. For the 64 domains with readily identified templates, the best submitted model was better than the best alignment to the best template in the Protein Data Bank for 24 cases, and improved over the best starting model for 43 cases. For 13 targets where only very distant sequence relationships to proteins of known structure were detected, models were generated using the Rosetta de novo structure prediction methodology followed by all-atom refinement; in several cases the submitted models were better than those based on the available templates. Of the 12 refinement challenges, the best submitted model improved on the starting model in seven cases. These improvements over the starting template-based models and refinement tests demonstrate the power of Rosetta structure refinement in improving model accuracy.

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

    PubMed

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

    2014-11-15

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

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

    PubMed Central

    Kosciolek, Tomasz; Jones, David T.

    2014-01-01

    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

  19. De novo structure prediction of globular proteins aided by sequence variation-derived contacts.

    PubMed

    Kosciolek, Tomasz; Jones, David T

    2014-01-01

    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.

  20. Structural prediction and analysis of VIH-related peptides from selected crustacean species.

    PubMed

    Nagaraju, Ganji Purna Chandra; Kumari, Nunna Siva; Prasad, Ganji Lakshmi Vara; Rajitha, Balney; Meenu, Madan; Rao, Manam Sreenivasa; Naik, Bannoth Reddya

    2009-08-17

    The tentative elucidation of the 3D-structure of vitellogenesis inhibiting hormone (VIH) peptides is conversely underprivileged by difficulties in gaining enough peptide or protein, diffracting crystals, and numerous extra technical aspects. As a result, no structural information is available for VIH peptide sequences registered in the Genbank. In this situation, it is not surprising that predictive methods have achieved great interest. Here, in this study the molt-inhibiting hormone (MIH) of the kuruma prawn (Marsupenaeus japonicus) is used, to predict the structure of four VIHrelated peptides in the crustacean species. The high similarity of the 3D-structures and the calculated physiochemical characteristics of these peptides suggest a common fold for the entire family.

  1. A Non-parametric Bayesian Approach for Predicting RNA Secondary Structures

    NASA Astrophysics Data System (ADS)

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

    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.

  2. Antibody modeling using the prediction of immunoglobulin structure (PIGS) web server [corrected].

    PubMed

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

    2014-12-01

    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.

  3. Hidden Markov models that use predicted local structure for fold recognition: alphabets of backbone geometry.

    PubMed

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

    2003-06-01

    An important problem in computational biology is predicting the structure of the large number of putative proteins discovered by genome sequencing projects. Fold-recognition methods attempt to solve the problem by relating the target proteins to known structures, searching for template proteins homologous to the target. Remote homologs that may have significant structural similarity are often not detectable by sequence similarities alone. To address this, we incorporated predicted local structure, a generalization of secondary structure, into two-track profile hidden Markov models (HMMs). We did not rely on a simple helix-strand-coil definition of secondary structure, but experimented with a variety of local structure descriptions, following a principled protocol to establish which descriptions are most useful for improving fold recognition and alignment quality. On a test set of 1298 nonhomologous proteins, HMMs incorporating a 3-letter STRIDE alphabet improved fold recognition accuracy by 15% over amino-acid-only HMMs and 23% over PSI-BLAST, measured by ROC-65 numbers. We compared two-track HMMs to amino-acid-only HMMs on a difficult alignment test set of 200 protein pairs (structurally similar with 3-24% sequence identity). HMMs with a 6-letter STRIDE secondary track improved alignment quality by 62%, relative to DALI structural alignments, while HMMs with an STR track (an expanded DSSP alphabet that subdivides strands into six states) improved by 40% relative to CE.

  4. High Cycle Fatigue Prediction for Mistuned Bladed Disks with Fully Coupled Fluid-Structural Interaction

    DTIC Science & Technology

    2006-06-01

    vibration and flutter boundary of 2D NACA 64A010 transonic airfoil, 3D plate wing structural response. The predicted results agree well with benchmark...CFD code coupled with a structural integrator based on the convolution integral to obtain the flutter boundary for a NACA 64A010 airfoil[10]. Alonso and...validation case of the scheme for moving grid system, the forced pitching NACA 64A010 airfoil is calculated. For this transonic airfoil, the Reynolds

  5. Advancing viral RNA structure prediction: measuring the thermodynamics of pyrimidine-rich internal loops.

    PubMed

    Phan, Andy; Mailey, Katherine; Sakai, Jessica; Gu, Xiaobo; Schroeder, Susan J

    2017-02-17

    Accurate thermodynamic parameters improve RNA structure predictions and thus accelerate understanding of RNA function and the identification of RNA drug binding sites. Many viral RNA structures, such as internal ribosome entry sites, have internal loops and bulges that are potential drug target sites. Current models used to predict internal loops are biased towards small, symmetric purine loops, and thus poorly predict asymmetric, pyrimidine-rich loops with more than 6 nucleotides that occur frequently in viral RNA. This paper presents new thermodynamic data for 40 pyrimidine loops, many of which can form UU or protonated CC base pairs. Protonated cytosine and uracil base pairs stabilize asymmetric internal loops. Accurate prediction rules are presented that account for all thermodynamic measurements of RNA asymmetric internal loops. New loop initiation terms for loops with more than 6 nucleotides are presented that do not follow previous assumptions that increasing asymmetry destabilizes loops. Since the last 2004 update, 126 new loops with asymmetry or sizes greater than 2x2 have been measured (Mathews 2004). These new measurements significantly deepen and diversify the thermodynamic database for RNA. These results will help better predict internal loops that are larger, pyrimidine-rich, and occur within viral structures such as internal ribosome entry sites.

  6. Loop-length-dependent SVM prediction of domain linkers for high-throughput structural proteomics.

    PubMed

    Ebina, Teppei; Toh, Hiroyuki; Kuroda, Yutaka

    2009-01-01

    The prediction of structural domains in novel protein sequences is becoming of practical importance. One important area of application is the development of computer-aided techniques for identifying, at a low cost, novel protein domain targets for large-scale functional and structural proteomics. Here, we report a loop-length-dependent support vector machine (SVM) prediction of domain linkers, which are loops separating two structural domains. (DLP-SVM is freely available at: http://www.tuat.ac.jp/ approximately domserv/cgi-bin/DLP-SVM.cgi.) We constructed three loop-length-dependent SVM predictors of domain linkers (SVM-All, SVM-Long and SVM-Short), and also built SVM-Joint, which combines the results of SVM-Short and SVM-Long into a single consolidated prediction. The performances of SVM-Joint were, in most aspects, the highest, with a sensitivity of 59.7% and a specificity of 43.6%, which indicated that the specificity and the sensitivity were improved by over 2 and 3% respectively, when loop-length-dependent characteristics were taken into account. Furthermore, the sensitivity and specificity of SVM-Joint were, respectively, 37.6 and 17.4% higher than those of a random guess, and also superior to those of previously reported domain linker predictors. These results indicate that SVMs can be used to predict domain linkers, and that loop-length-dependent characteristics are useful for improving SVM prediction performances.

  7. Multiscale model for predicting shear zone structure and permeability in deforming rock

    NASA Astrophysics Data System (ADS)

    Cleary, Paul W.; Pereira, Gerald G.; Lemiale, Vincent; Piane, Claudio Delle; Clennell, M. Ben

    2016-04-01

    A novel multiscale model is proposed for the evolution of faults in rocks, which predicts their internal properties and permeability as strain increases. The macroscale model, based on smoothed particle hydrodynamics (SPH), predicts system scale deformation by a pressure-dependent elastoplastic representation of the rock and shear zone. Being a continuum method, SPH contains no intrinsic information on the grain scale structure or behaviour of the shear zone, so a series of discrete element method microscale shear cell models are embedded into the macroscale model at specific locations. In the example used here, the overall geometry and kinematics of a direct shear test on a block of intact rock is simulated. Deformation is imposed by a macroscale model where stresses and displacement rates are applied at the shear cell walls in contact with the rock. Since the microscale models within the macroscale block of deforming rock now include representations of the grains, the structure of the shear zone, the evolution of the size and shape distribution of these grains, and the dilatancy of the shear zone can all be predicted. The microscale dilatancy can be used to vary the macroscale model dilatancy both spatially and temporally to give a full two-way coupling between the spatial scales. The ability of this model to predict shear zone structure then allows the prediction of the shear zone permeability using the Lattice-Boltzmann method.

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

    PubMed

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

    2013-10-01

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

  9. Predicting community structure in snakes on Eastern Nearctic islands using ecological neutral theory and phylogenetic methods.

    PubMed

    Burbrink, Frank T; McKelvy, Alexander D; Pyron, R Alexander; Myers, Edward A

    2015-11-22

    Predicting species presence and richness on islands is important for understanding the origins of communities and how likely it is that species will disperse and resist extinction. The equilibrium theory of island biogeography (ETIB) and, as a simple model of sampling abundances, the unified neutral theory of biodiversity (UNTB), predict that in situations where mainland to island migration is high, species-abundance relationships explain the presence of taxa on islands. Thus, more abundant mainland species should have a higher probability of occurring on adjacent islands. In contrast to UNTB, if certain groups have traits that permit them to disperse to islands better than other taxa, then phylogeny may be more predictive of which taxa will occur on islands. Taking surveys of 54 island snake communities in the Eastern Nearctic along with mainland communities that have abundance data for each species, we use phylogenetic assembly methods and UNTB estimates to predict island communities. Species richness is predicted by island area, whereas turnover from the mainland to island communities is random with respect to phylogeny. Community structure appears to be ecologically neutral and abundance on the mainland is the best predictor of presence on islands. With regard to young and proximate islands, where allopatric or cladogenetic speciation is not a factor, we find that simple neutral models following UNTB and ETIB predict the structure of island communities.

  10. Striatal structure and function predict individual biases in learning to avoid pain

    PubMed Central

    Eldar, Eran; Hauser, Tobias U.; Dayan, Peter; Dolan, Raymond J.

    2016-01-01

    Pain is an elemental inducer of avoidance. Here, we demonstrate that people differ in how they learn to avoid pain, with some individuals refraining from actions that resulted in painful outcomes, whereas others favor actions that helped prevent pain. These individual biases were best explained by differences in learning from outcome prediction errors and were associated with distinct forms of striatal responses to painful outcomes. Specifically, striatal responses to pain were modulated in a manner consistent with an aversive prediction error in individuals who learned predominantly from pain, whereas in individuals who learned predominantly from success in preventing pain, modulation was consistent with an appetitive prediction error. In contrast, striatal responses to success in preventing pain were consistent with an appetitive prediction error in both groups. Furthermore, variation in striatal structure, encompassing the region where pain prediction errors were expressed, predicted participants’ predominant mode of learning, suggesting the observed learning biases may reflect stable individual traits. These results reveal functional and structural neural components underlying individual differences in avoidance learning, which may be important contributors to psychiatric disorders involving pathological harm avoidance behavior. PMID:27071092

  11. Predicting community structure in snakes on Eastern Nearctic islands using ecological neutral theory and phylogenetic methods

    PubMed Central

    Burbrink, Frank T.; McKelvy, Alexander D.; Pyron, R. Alexander; Myers, Edward A.

    2015-01-01

    Predicting species presence and richness on islands is important for understanding the origins of communities and how likely it is that species will disperse and resist extinction. The equilibrium theory of island biogeography (ETIB) and, as a simple model of sampling abundances, the unified neutral theory of biodiversity (UNTB), predict that in situations where mainland to island migration is high, species-abundance relationships explain the presence of taxa on islands. Thus, more abundant mainland species should have a higher probability of occurring on adjacent islands. In contrast to UNTB, if certain groups have traits that permit them to disperse to islands better than other taxa, then phylogeny may be more predictive of which taxa will occur on islands. Taking surveys of 54 island snake communities in the Eastern Nearctic along with mainland communities that have abundance data for each species, we use phylogenetic assembly methods and UNTB estimates to predict island communities. Species richness is predicted by island area, whereas turnover from the mainland to island communities is random with respect to phylogeny. Community structure appears to be ecologically neutral and abundance on the mainland is the best predictor of presence on islands. With regard to young and proximate islands, where allopatric or cladogenetic speciation is not a factor, we find that simple neutral models following UNTB and ETIB predict the structure of island communities. PMID:26609083

  12. Prediction of the Fundamental Period of Infilled RC Frame Structures Using Artificial Neural Networks

    PubMed Central

    Asteris, Panagiotis G.; Tsaris, Athanasios K.; Cavaleri, Liborio; Repapis, Constantinos C.; Papalou, Angeliki; Di Trapani, Fabio; Karypidis, Dimitrios F.

    2016-01-01

    The fundamental period is one of the most critical parameters for the seismic design of structures. There are several literature approaches for its estimation which often conflict with each other, making their use questionable. Furthermore, the majority of these approaches do not take into account the presence of infill walls into the structure despite the fact that infill walls increase the stiffness and mass of structure leading to significant changes in the fundamental period. In the present paper, artificial neural networks (ANNs) are used to predict the fundamental period of infilled reinforced concrete (RC) structures. For the training and the validation of the ANN, a large data set is used based on a detailed investigation of the parameters that affect the fundamental period of RC structures. The comparison of the predicted values with analytical ones indicates the potential of using ANNs for the prediction of the fundamental period of infilled RC frame structures taking into account the crucial parameters that influence its value. PMID:27066069

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

    SciTech Connect

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

    2014-01-28

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

  14. Integrated prediction of protein folding and unfolding rates from only size and structural class.

    PubMed

    De Sancho, David; Muñoz, Victor

    2011-10-14

    Protein stability, folding and unfolding rates are all determined by the multidimensional folding free energy surface, which in turn is dictated by factors such as size, structure, and amino-acid sequence. Work over the last 15 years has highlighted the role of size and 3D structure in determining folding rates, resulting in many procedures for their prediction. In contrast, unfolding rates are thought to depend on sequence specifics and be much more difficult to predict. Here we introduce a minimalist physics-based model that computes one-dimensional folding free energy surfaces using the number of aminoacids (N) and the structural class (α-helical, all-β, or α-β) as only protein-specific input. In this model N sets the overall cost in conformational entropy and the net stabilization energy, whereas the structural class defines the partitioning of the stabilization energy between local and non-local interactions. To test its predictive power, we calibrated the model empirically and implemented it into an algorithm for the PREdiction of Folding and Unfolding Rates (PREFUR). We found that PREFUR predicts the absolute folding and unfolding rates of an experimental database of 52 proteins with accuracies of ±0.7 and ±1.4 orders of magnitude, respectively (relative to experimental spans of 6 and 8 orders of magnitude). Such prediction uncertainty for proteins vastly varying in size and structure is only two-fold larger than the differences in folding (±0.34) and unfolding rates (±0.7) caused by single-point mutations. Moreover, PREFUR predicts protein stability with an accuracy of ±6.3 kJ mol(-1), relative to the 5 kJ mol(-1) average perturbation induced by single-point mutations. The remarkable performance of our simplistic model demonstrates that size and structural class are the major determinants of the folding landscapes of natural proteins, whereas sequence variability only provides the final 10-20% tuning. PREFUR is thus a powerful bioinformatic tool

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

    NASA Astrophysics Data System (ADS)

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

    2006-12-01

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

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

    PubMed

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

    2014-08-04

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

  17. Structural maturation and brain activity predict future working memory capacity during childhood development.

    PubMed

    Ullman, Henrik; Almeida, Rita; Klingberg, Torkel

    2014-01-29

    Human working memory capacity develops during childhood and is a strong predictor of future academic performance, in particular, achievements in mathematics and reading. Predicting working memory development is important for the early identification of children at risk for poor cognitive and academic development. Here we show that structural and functional magnetic resonance imaging data explain variance in children's working memory capacity 2 years later, which was unique variance in addition to that predicted using cognitive tests. While current working memory capacity correlated with frontoparietal cortical activity, the future capacity could be inferred from structure and activity in basal ganglia and thalamus. This gives a novel insight into the neural mechanisms of childhood development and supports the idea that neuroimaging can have a unique role in predicting children's cognitive development.

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

    NASA Astrophysics Data System (ADS)

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

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

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

    PubMed Central

    Brylinski, Michal

    2009-01-01

    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

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

    NASA Technical Reports Server (NTRS)

    Ko, William L.; Fleischer, Van Tran

    2012-01-01

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

  1. RaptorX-Property: a web server for protein structure property prediction

    PubMed Central

    Wang, Sheng; Li, Wei; Liu, Shiwang; Xu, Jinbo

    2016-01-01

    RaptorX Property (http://raptorx2.uchicago.edu/StructurePropertyPred/predict/) is a web server predicting structure property of a protein sequence without using any templates. It outperforms other servers, especially for proteins without close homologs in PDB or with very sparse sequence profile (i.e. carries little evolutionary information). This server employs a powerful in-house deep learning model DeepCNF (Deep Convolutional Neural Fields) to predict secondary structure (SS), solvent accessibility (ACC) and disorder regions (DISO). DeepCNF not only models complex sequence–structure relationship by a deep hierarchical architecture, but also interdependency between adjacent property labels. Our experimental results show that, tested on CASP10, CASP11 and the other benchmarks, this server can obtain ∼84% Q3 accuracy for 3-state SS, ∼72% Q8 accuracy for 8-state SS, ∼66% Q3 accuracy for 3-state solvent accessibility, and ∼0.89 area under the ROC curve (AUC) for disorder prediction. PMID:27112573

  2. Confirmation of a de novo structure prediction for an atomically precise monolayer-coated silver nanoparticle

    PubMed Central

    Conn, Brian E.; Atnagulov, Aydar; Yoon, Bokwon; Barnett, Robert N.; Landman, Uzi; Bigioni, Terry P.

    2016-01-01

    Fathoming the principles underpinning the structures of monolayer-coated molecular metal nanoparticles remains an enduring challenge. Notwithstanding recent x-ray determinations, coveted veritable de novo structural predictions are scarce. Building on recent syntheses and de novo structure predictions of M3AuxAg17−x(TBBT)12, where M is a countercation, x = 0 or 1, and TBBT is 4-tert-butylbenzenethiol, we report an x-ray–determined structure that authenticates an a priori prediction and, in conjunction with first-principles theoretical analysis, lends force to the underlying forecasting methodology. The predicted and verified Ag(SR)3 monomer, together with the recently discovered Ag2(SR)5 dimer and Ag3(SR)6 trimer, establishes a family of unique mount motifs for silver thiolate nanoparticles, expanding knowledge beyond the earlier-known Au-S staples in thiol-capped gold nanoclusters. These findings demonstrate key principles underlying ligand-shell anchoring to the metal core, as well as unique T-like benzene dimer and cyclic benzene trimer ligand bundling configurations, opening vistas for rational design of metal and alloy nanoparticles. PMID:28138537

  3. RaptorX-Property: a web server for protein structure property prediction.

    PubMed

    Wang, Sheng; Li, Wei; Liu, Shiwang; Xu, Jinbo

    2016-07-08

    RaptorX Property (http://raptorx2.uchicago.edu/StructurePropertyPred/predict/) is a web server predicting structure property of a protein sequence without using any templates. It outperforms other servers, especially for proteins without close homologs in PDB or with very sparse sequence profile (i.e. carries little evolutionary information). This server employs a powerful in-house deep learning model DeepCNF (Deep Convolutional Neural Fields) to predict secondary structure (SS), solvent accessibility (ACC) and disorder regions (DISO). DeepCNF not only models complex sequence-structure relationship by a deep hierarchical architecture, but also interdependency between adjacent property labels. Our experimental results show that, tested on CASP10, CASP11 and the other benchmarks, this server can obtain ∼84% Q3 accuracy for 3-state SS, ∼72% Q8 accuracy for 8-state SS, ∼66% Q3 accuracy for 3-state solvent accessibility, and ∼0.89 area under the ROC curve (AUC) for disorder prediction.

  4. Combining structure and sequence information allows automated prediction of substrate specificities within enzyme families.

    PubMed

    Röttig, Marc; Rausch, Christian; Kohlbacher, Oliver

    2010-01-08

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

  5. Predicting hepatotoxicity using ToxCast in vitro bioactivity and chemical structure

    EPA Science Inventory

    Background: The U.S. EPA ToxCastTM program is screening thousands of environmental chemicals for bioactivity using hundreds of high-throughput in vitro assays to build predictive models of toxicity. We represented chemicals based on bioactivity and chemical structure descriptors ...

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

    PubMed

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

    2014-10-01

    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.

  7. 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,...

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

    ERIC Educational Resources Information Center

    Nathan, Lawrence C.

    1985-01-01

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

  9. Evolutionary Trace Annotation Server: automated enzyme function prediction in protein structures using 3D templates

    PubMed Central

    Matthew Ward, R.; Venner, Eric; Daines, Bryce; Murray, Stephen; Erdin, Serkan; Kristensen, David M.; Lichtarge, Olivier

    2009-01-01

    Summary:The Evolutionary Trace Annotation (ETA) Server predicts enzymatic activity. ETA starts with a structure of unknown function, such as those from structural genomics, and with no prior knowledge of its mechanism uses the phylogenetic Evolutionary Trace (ET) method to extract key functional residues and propose a function-associated 3D motif, called a 3D template. ETA then searches previously annotated structures for geometric template matches that suggest molecular and thus functional mimicry. In order to maximize the predictive value of these matches, ETA next applies distinctive specificity filters—evolutionary similarity, function plurality and match reciprocity. In large scale controls on enzymes, prediction coverage is 43% but the positive predictive value rises to 92%, thus minimizing false annotations. Users may modify any search parameter, including the template. ETA thus expands the ET suite for protein structure annotation, and can contribute to the annotation efforts of metaservers. Availability:The ETA Server is a web application available at http://mammoth.bcm.tmc.edu/eta/. Contact: lichtarge@bcm.edu PMID:19307237

  10. Body Vigilance in Nonclinical and Anxiety Disorder Samples: Structure, Correlates, and Prediction of Health Concerns

    ERIC Educational Resources Information Center

    Olatunji, Bunmi O.; Deacon, Brett J.; Abramowitz, Jonathan S.; Valentiner, David P.

    2007-01-01

    The Body Vigilance Scale (BVS) is a measure developed to assess one's conscious attendance to internal cues. The present report investigated the structure, correlates, and predictive utility of the BVS in nonclinical (N=442) and anxiety (N=135) disorder samples. The findings of Study 1 suggest that the BVS is 1-dimensional in a nonclinical sample,…

  11. Predicting the axial structure of the proton using a quark-diquark model

    NASA Astrophysics Data System (ADS)

    Oxholm, Trevor M.; Hobbs, Timothy J.; Miller, Gerald A.

    2017-01-01

    The form factors of the proton typically measured via elastic electroweak scattering have long been a rich testing ground for models of nucleon structure. We explore the ability of a model based in a quark/spectator diquark picture to describe form factors in the electromagnetic sector, as well as to predict the form of the nucleon's axial current. Making use of a realistic spin decomposition and phenomenological vertex factors, we choose model parameters so as to fit experimental data on the electric and magnetic Sachs form factors, as well as the low-momentum isovector axial form factor. With the model we then predict the pseudoscalar form factor and extend axial form factor predictions to higher momenta, comparing our predictions to those of other frameworks and techniques. DOE Grant DE-FG02-97ER-41014, NSF REU program.

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

    PubMed

    Bywater, Robert P

    2016-01-01

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

  13. Predictability of helminth parasite host range using information on geography, host traits and parasite community structure.

    PubMed

    Dallas, Tad; Park, Andrew W; Drake, John M

    2017-02-01

    Host-parasite associations are complex interactions dependent on aspects of hosts (e.g. traits, phylogeny or coevolutionary history), parasites (e.g. traits and parasite interactions) and geography (e.g. latitude). Predicting the permissive host set or the subset of the host community that a parasite can infect is a central goal of parasite ecology. Here we develop models that accurately predict the permissive host set of 562 helminth parasites in five different parasite taxonomic groups. We developed predictive models using host traits, host taxonomy, geographic covariates, and parasite community composition, finding that models trained on parasite community variables were more accurate than any other covariate group, even though parasite community covariates only captured a quarter of the variance in parasite community composition. This suggests that it is possible to predict the permissive host set for a given parasite, and that parasite community structure is an important predictor, potentially because parasite communities are interacting non-random assemblages.

  14. Crystal structure prediction and isostructurality of three small organic halogen compounds.

    PubMed

    Asmadi, Aldi; Kendrick, John; Leusen, Frank J J

    2010-08-14

    A theoretical investigation of the packing stabilities of three small organic halogen compounds is presented based on a crystal structure prediction (CSP) study. Each compound has four identical halogen atoms (fluorine, chlorine, and bromine) and a four-membered ring consisting of carbon and sulfur atoms arranged alternately. Two halogen atoms are attached to each carbon and two oxygen atoms are attached to each sulfur forming SO(2) functional groups. The crystal structures of these compounds have been determined experimentally and show distinct packing arrangements. Utilising the computational approaches implemented in the GRACE software package, each compound is subjected to a full CSP study using a force field specific for each molecule (called the tailor-made force field or TMFF) and a dispersion corrected solid-state density functional method (or DFT(d) method). Energetically feasible crystal structures are generated in all 230 space groups restricted to a single molecule in the crystallographic asymmetric unit (Z' = 1) using the TMFF of each molecule. Next, a selection of structures with low TMFF lattice energies are further refined with the DFT(d) method. The CSP results show that the experimental crystal structures of the molecules containing fluorine and chlorine are well described energetically and geometrically by their TMFFs and the DFT(d) method. Both approaches locate their experimental lattices as the most stable structures. For the molecule containing bromine, a crystal structure corresponding to the force field optimised experimental structure is located as the second structure in the list of force field predicted structures, ranked by calculated lattice energy. Despite the structural similarity of the predicted and experimental structures, close examination of the DFT(d) optimisation results of the experimental structure reveals a slightly lower energy structure than that found by the CSP simulations. Furthermore, minimisation of the force field

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

    PubMed

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

    2011-04-01

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

  16. Beyond the Twilight Zone: automated prediction of structural properties of proteins by recursive neural networks and remote homology information.

    PubMed

    Mooney, Catherine; Pollastri, Gianluca

    2009-10-01

    The prediction of 1D structural properties of proteins is an important step toward the prediction of protein structure and function, not only in the ab initio case but also when homology information to known structures is available. Despite this the vast majority of 1D predictors do not incorporate homology information into the prediction process. We develop a novel structural alignment method, SAMD, which we use to build alignments of putative remote homologues that we compress into templates of structural frequency profiles. We use these templates as additional input to ensembles of recursive neural networks, which we specialise for the prediction of query sequences that show only remote homology to any Protein Data Bank structure. We predict four 1D structural properties - secondary structure, relative solvent accessibility, backbone structural motifs, and contact density. Secondary structure prediction accuracy, tested by five-fold cross-validation on a large set of proteins allowing less than 25% sequence identity between training and test set and query sequences and templates, exceeds 82%, outperforming its ab initio counterpart, other state-of-the-art secondary structure predictors (Jpred 3 and PSIPRED) and two other systems based on PSI-BLAST and COMPASS templates. We show that structural information from homologues improves prediction accuracy well beyond the Twilight Zone of sequence similarity, even below 5% sequence identity, for all four structural properties. Significant improvement over the extraction of structural information directly from PDB templates suggests that the combination of sequence and template information is more informative than templates alone.

  17. Functional insights from structural predictions: analysis of the Escherichia coli genome.

    PubMed Central

    Rychlewski, L.; Zhang, B.; Godzik, A.

    1999-01-01

    Fold assignments for proteins from the Escherichia coli genome are carried out using BASIC, a profile-profile alignment algorithm, recently tested on fold recognition benchmarks and on the Mycoplasma genitalium genome and PSI BLAST, the newest generation of the de facto standard in homology search algorithms. The fold assignments are followed by automated modeling and the resulting three-dimensional models are analyzed for possible function prediction. Close to 30% of the proteins encoded in the E. coli genome can be recognized as homologous to a protein family with known structure. Most of these homologies (23% of the entire genome) can be recognized both by PSI BLAST and BASIC algorithms, but the latter recognizes an additional 260 homologies. Previous estimates suggested that only 10-15% of E. coli proteins can be characterized this way. This dramatic increase in the number of recognized homologies between E. coli proteins and structurally characterized protein families is partly due to the rapid increase of the database of known protein structures, but mostly it is due to the significant improvement in prediction algorithms. Knowing protein structure adds a new dimension to our understanding of its function and the predictions presented here can be used to predict function for uncharacterized proteins. Several examples, analyzed in more detail in this paper, include the DPS protein protecting DNA from oxidative damage (predicted to be homologous to ferritin with iron ion acting as a reducing agent) and the ahpC/tsa family of proteins, which provides resistance to various oxidating agents (predicted to be homologous to glutathione peroxidase). PMID:10091664

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

    PubMed

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

    2016-01-15

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

  19. Managing uncertainty in metabolic network structure and improving predictions using EnsembleFBA.

    PubMed

    Biggs, Matthew B; Papin, Jason A

    2017-03-06

    Genome-scale metabolic network reconstructions (GENREs) are repositories of knowledge about the metabolic processes that occur in an organism. GENREs have been used to discover and interpret metabolic functions, and to engineer novel network structures. A major barrier preventing more widespread use of GENREs, particularly to study non-model organisms, is the extensive time required to produce a high-quality GENRE. Many automated approaches have been developed which reduce this time requirement, but automatically-reconstructed draft GENREs still require curation before useful predictions can be made. We present a novel approach to the analysis of GENREs which improves the predictive capabilities of draft GENREs by representing many alternative network structures, all equally consistent with available data, and generating predictions from this ensemble. This ensemble approach is compatible with many reconstruction methods. We refer to this new approach as Ensemble Flux Balance Analysis (EnsembleFBA). We validate EnsembleFBA by predicting growth and gene essentiality in the model organism Pseudomonas aeruginosa UCBPP-PA14. We demonstrate how EnsembleFBA can be included in a systems biology workflow by predicting essential genes in six Streptococcus species and mapping the essential genes to small molecule ligands from DrugBank. We found that some metabolic subsystems contributed disproportionately to the set of predicted essential reactions in a way that was unique to each Streptococcus species, leading to species-specific outcomes from small molecule interactions. Through our analyses of P. aeruginosa and six Streptococci, we show that ensembles increase the quality of predictions without drastically increasing reconstruction time, thus making GENRE approaches more practical for applications which require predictions for many non-model organisms. All of our functions and accompanying example code are available in an open online repository.

  20. Managing uncertainty in metabolic network structure and improving predictions using EnsembleFBA

    PubMed Central

    2017-01-01

    Genome-scale metabolic network reconstructions (GENREs) are repositories of knowledge about the metabolic processes that occur in an organism. GENREs have been used to discover and interpret metabolic functions, and to engineer novel network structures. A major barrier preventing more widespread use of GENREs, particularly to study non-model organisms, is the extensive time required to produce a high-quality GENRE. Many automated approaches have been developed which reduce this time requirement, but automatically-reconstructed draft GENREs still require curation before useful predictions can be made. We present a novel approach to the analysis of GENREs which improves the predictive capabilities of draft GENREs by representing many alternative network structures, all equally consistent with available data, and generating predictions from this ensemble. This ensemble approach is compatible with many reconstruction methods. We refer to this new approach as Ensemble Flux Balance Analysis (EnsembleFBA). We validate EnsembleFBA by predicting growth and gene essentiality in the model organism Pseudomonas aeruginosa UCBPP-PA14. We demonstrate how EnsembleFBA can be included in a systems biology workflow by predicting essential genes in six Streptococcus species and mapping the essential genes to small molecule ligands from DrugBank. We found that some metabolic subsystems contributed disproportionately to the set of predicted essential reactions in a way that was unique to each Streptococcus species, leading to species-specific outcomes from small molecule interactions. Through our analyses of P. aeruginosa and six Streptococci, we show that ensembles increase the quality of predictions without drastically increasing reconstruction time, thus making GENRE approaches more practical for applications which require predictions for many non-model organisms. All of our functions and accompanying example code are available in an open online repository. PMID:28263984

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

    PubMed

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

    2014-01-01

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

  2. Predicting the structure of the light-harvesting complex II of Rhodospirillum molischianum.

    PubMed Central

    Hu, X.; Xu, D.; Hamer, K.; Schulten, K.; Koepke, J.; Michel, H.

    1995-01-01

    We attempted to predict through computer modeling the structure of the light-harvesting complex II (LH-II) of Rhodospirillum molischianum, before the impending publication of the structure of a homologous protein solved by means of X-ray diffraction. The protein studied is an integral membrane protein of 16 independent polypeptides, 8 alpha-apoproteins and 8 beta-apoproteins, which aggregate and bind to 24 bacteriochlorophyll-a's and 12 lycopenes. Available diffraction data of a crystal of the protein, which could not be phased due to a lack of heavy metal derivatives, served to test the predicted structure, guiding the search. In order to determine the secondary structure, hydropathy analysis was performed to identify the putative transmembrane segments and multiple sequence alignment propensity analyses were used to pinpoint the exact sites of the 20-residue-long transmembrane segment and the 4-residue-long terminal sequence at both ends, which were independently verified and improved by homology modeling. A consensus assignment for the secondary structure was derived from a combination of all the prediction methods used. Three-dimensional structures for the alpha- and the beta-apoprotein were built by comparative modeling. The resulting tertiary structures are combined, using X-PLOR, into an alpha beta dimer pair with bacteriochlorophyll-a's attached under constraints provided by site-directed mutagenesis and spectral data. The alpha beta dimer pairs were then aggregated into a quaternary structure through further molecular dynamics simulations and energy minimization. The structure of LH-II so determined is an octamer of alpha beta heterodimers forming a ring with a diameter of 70 A. PMID:8528066

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

    NASA Astrophysics Data System (ADS)

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

    2014-03-01

    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/μm have been determined. Also, the corresponding site-hit probabilities have been calculated.

  4. Charge Transfer States of Aqueous B-DNA at Energies Above the Bright ^1π&*circ; Exciton States

    NASA Astrophysics Data System (ADS)

    Lange, Adrian; Herbert, John

    2009-03-01

    Charge transfer states have been proposed to explain experimentally observed long-lived excited state dynamics in aqueous DNA oligomersootnotetextCrespo-Hern'andez, C. E.; Cohen, B.;Kohler, B. Nature 2005, 436, 1141.. Due to the large number of atoms, tractably describing such excited states in DNA systems with ab initio theory is limited to TD-DFT. However, standard TD-DFT exchange-correlation functionals significantly underestimate CT excitation energies owing to incorrect asymptotic behavior. To circumvent this error, we instead apply recently developed and optimized long-range corrected TD-DFT functionals to better assess the low lying CT and exciton states of DNA oligomers. We show that long-range corrected TD-DFT yields results comparable to correlated wave function models, placing CT states of aqueous B-DNA at energies above the optically bright ^1π&*circ; exciton states, contrary to TD-DFT results which find CT states below the exciton states.

  5. Histone Acetylation Induced Transformation of B-DNA to Z-DNA in Cells Probed through FT-IR Spectroscopy.

    PubMed

    Zhang, Fengqiu; Huang, Qing; Yan, Jingwen; Chen, Zhu

    2016-04-19

    A nucleosome is made up of DNA and histones, and acetylation of histones perturbs the interaction of DNA and histones and thus affects the chromatin conformation and function. However, whether or how acetylation induces DNA conformation changes is still elusive. In this work, we applied FT-IR spectroscopy to monitor the DNA signals in cells as the histone acetylation was regulated by trichostatin A (TSA), a reversible inhibitor to histone deacetylases (HDACs). Our results unambiguously demonstrate the significant transformation of B-DNA to Z-DNA upon histone acetylation in the TSA treated HeLa cells. This is the first report providing the explicit experimental evidence for such a B-Z transformation of DNA in the epigenetic states of cells.

  6. A Fully Bayesian Approach to Improved Calibration and Prediction of Groundwater Models With Structure Error

    NASA Astrophysics Data System (ADS)

    Xu, T.; Valocchi, A. J.

    2014-12-01

    Effective water resource management typically relies on numerical models to analyse groundwater flow and solute transport processes. These models are usually subject to model structure error due to simplification and/or misrepresentation of the real system. As a result, the model outputs may systematically deviate from measurements, thus violating a key assumption for traditional regression-based calibration and uncertainty analysis. On the other hand, model structure error induced bias can be described statistically in an inductive, data-driven way based on historical model-to-measurement misfit. We adopt a fully Bayesian approach that integrates a Gaussian process error model to account for model structure error to the calibration, prediction and uncertainty analysis of groundwater models. The posterior distributions of parameters of the groundwater model and the Gaussian process error model are jointly inferred using DREAM, an efficient Markov chain Monte Carlo sampler. We test the usefulness of the fully Bayesian approach towards a synthetic case study of surface-ground water interaction under changing pumping conditions. We first illustrate through this example that traditional least squares regression without accounting for model structure error yields biased parameter estimates due to parameter compensation as well as biased predictions. In contrast, the Bayesian approach gives less biased parameter estimates. Moreover, the integration of a Gaussian process error model significantly reduces predictive bias and leads to prediction intervals that are more consistent with observations. The results highlight the importance of explicit treatment of model structure error especially in circumstances where subsequent decision-making and risk analysis require accurate prediction and uncertainty quantification. In addition, the data-driven error modelling approach is capable of extracting more information from observation data than using a groundwater model alone.

  7. GalaxyHomomer: a web server for protein homo-oligomer structure prediction from a monomer sequence or structure.

    PubMed

    Baek, Minkyung; Park, Taeyong; Heo, Lim; Park, Chiwook; Seok, Chaok

    2017-04-06

    Homo-oligomerization of proteins is abundant in nature, and is often intimately related with the physiological functions of proteins, such as in metabolism, signal transduction or immunity. Information on the homo-oligomer structure is therefore important to obtain a molecular-level understanding of protein functions and their regulation. Currently available web servers predict protein homo-oligomer structures either by template-based modeling using homo-oligomer templates selected from the protein structure database or by ab initio docking of monomer structures resolved by experiment or predicted by computation. The GalaxyHomomer server, freely accessible at http://galaxy.seoklab.org/homomer, carries out template-based modeling, ab initio docking or both depending on the availability of proper oligomer templates. It also incorporates recently developed model refinement methods that can consistently improve model quality. Moreover, the server provides additional options that can be chosen by the user depending on the availability of information on the monomer structure, oligomeric state and locations of unreliable/flexible loops or termini. The performance of the server was better than or comparable to that of other available methods when tested on benchmark sets and in a recent CASP performed in a blind fashion.

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

    PubMed Central

    Moriguchi, I; Hirano, H; Hirono, S

    1996-01-01

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

  9. Staple Fitness: A Concept to Understand and Predict the Structures of Thiolated Gold Nanoclusters

    SciTech Connect

    Jiang, Deen

    2011-01-01

    A profound connection has been found between the structures of thiolated gold clusters and the combinatorial problem of pairing up dots on a surface. The bridge is the concept of staple fitness: the fittest combination corresponds to the experimental structure. This connection has been demonstrated for both Au{sub 25}(SR){sub 18} and Au{sub 38}(SR){sub 24} (-SR being a thiolate group) and applied to predict a promising structure for the recently synthesized Au{sub 19}(SR){sub 13}.

  10. Ab initio crystal structure prediction of magnesium (poly)sulfides and calculation of their NMR parameters.

    PubMed

    Mali, Gregor

    2017-03-01

    Ab initio prediction of sensible crystal structures can be regarded as a crucial task in the quickly-developing methodology of NMR crystallography. In this contribution, an evolutionary algorithm was used for the prediction of magnesium (poly)sulfide crystal structures with various compositions. The employed approach successfully identified all three experimentally detected forms of MgS, i.e. the stable rocksalt form and the metastable wurtzite and zincblende forms. Among magnesium polysulfides with a higher content of sulfur, the most probable structure with the lowest formation energy was found to be MgS2, exhibiting a modified rocksalt structure, in which S(2-) anions were replaced by S2(2-) dianions. Magnesium polysulfides with even larger fractions of sulfur were not predicted to be stable. For the lowest-energy structures, (25)Mg quadrupolar coupling constants and chemical shift parameters were calculated using the density functional theory approach. The calculated NMR parameters could be well rationalized by the symmetries of the local magnesium environments, by the coordination of magnesium cations and by the nature of the surrounding anions. In the future, these parameters could serve as a reference for the experimentally determined (25)Mg NMR parameters of magnesium sulfide species.

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

    PubMed Central

    Lorenzo, J. Ramiro; Alonso, Leonardo G.; Sánchez, Ignacio E.

    2015-01-01

    Asparagine residues in proteins undergo spontaneous deamidation, a post-translational modification that may act as a molecular clock for the regulation of protein function and turnover. Asparagine deamidation is modulated by protein local sequence, secondary structure and hydrogen bonding. We present NGOME, an algorithm able to predict non-enzymatic deamidation of internal asparagine residues in proteins in the absence of structural data, using sequence-based predictions of secondary structure and intrinsic disorder. Compared to previous algorithms, NGOME does not require three-dimensional structures yet yields better predictions than available sequence-only methods. Four case studies of specific proteins show how NGOME may help the user identify deamidation-prone asparagine residues, often related to protein gain of function, protein degradation or protein misfolding in pathological processes. A fifth case study applies NGOME at a proteomic scale and unveils a correlation between asparagine deamidation and protein degradation in yeast. NGOME is freely available as a webserver at the National EMBnet node Argentina, URL: http://www.embnet.qb.fcen.uba.ar/ in the subpage “Protein and nucleic acid structure and sequence analysis”. PMID:26674530

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

    PubMed

    Lorenzo, J Ramiro; Alonso, Leonardo G; Sánchez, Ignacio E

    2015-01-01

    Asparagine residues in proteins undergo spontaneous deamidation, a post-translational modification that may act as a molecular clock for the regulation of protein function and turnover. Asparagine deamidation is modulated by protein local sequence, secondary structure and hydrogen bonding. We present NGOME, an algorithm able to predict non-enzymatic deamidation of internal asparagine residues in proteins in the absence of structural data, using sequence-based predictions of secondary structure and intrinsic disorder. Compared to previous algorithms, NGOME does not require three-dimensional structures yet yields better predictions than available sequence-only methods. Four case studies of specific proteins show how NGOME may help the user identify deamidation-prone asparagine residues, often related to protein gain of function, protein degradation or protein misfolding in pathological processes. A fifth case study applies NGOME at a proteomic scale and unveils a correlation between asparagine deamidation and protein degradation in yeast. NGOME is freely available as a webserver at the National EMBnet node Argentina, URL: http://www.embnet.qb.fcen.uba.ar/ in the subpage "Protein and nucleic acid structure and sequence analysis".

  13. Predicting protein ligand binding sites by combining evolutionary sequence conservation and 3D structure.

    PubMed

    Capra, John A; Laskowski, Roman A; Thornton, Janet M; Singh, Mona; Funkhouser, Thomas A

    2009-12-01

    Identifying a protein's functional sites is an important step towards characterizing its molecular function. Numerous structure- and sequence-based methods have been developed for this problem. Here we introduce ConCavity, a small molecule binding site prediction algorithm that integrates evolutionary sequence conservation estimates with structure-based methods for identifying protein surface cavities. In large-scale testing on a diverse set of single- and multi-chain protein structures, we show that ConCavity substantially outperforms existing methods for identifying both 3D ligand binding pockets and individual ligand binding residues. As part of our testing, we perform one of the first direct comparisons of conservation-based and structure-based methods. We find that the two approaches provide largely complementary information, which can be combined to improve upon either approach alone. We also demonstrate that ConCavity has state-of-the-art performance in predicting catalytic sites and drug binding pockets. Overall, the algorithms and analysis presented here significantly improve our ability to identify ligand binding sites and further advance our understanding of the relationship between evolutionary sequence conservation and structural and functional attributes of proteins. Data, source code, and prediction visualizations are available on the ConCavity web site (http://compbio.cs.princeton.edu/concavity/).

  14. RNA secondary structure prediction by using discrete mathematics: an interdisciplinary research experience for undergraduate students.

    PubMed

    Ellington, Roni; Wachira, James; Nkwanta, Asamoah

    2010-01-01

    The focus of this Research Experience for Undergraduates (REU) project was on RNA secondary structure prediction by using a lattice walk approach. The lattice walk approach is a combinatorial and computational biology method used to enumerate possible secondary structures and predict RNA secondary structure from RNA sequences. The method uses discrete mathematical techniques and identifies specified base pairs as parameters. The goal of the REU was to introduce upper-level undergraduate students to the principles and challenges of interdisciplinary research in molecular biology and discrete mathematics. At the beginning of the project, students from the biology and mathematics departments of a mid-sized university received instruction on the role of secondary structure in the function of eukaryotic RNAs and RNA viruses, RNA related to combinatorics, and the National Center for Biotechnology Information resources. The student research projects focused on RNA secondary structure prediction on a regulatory region of the yellow fever virus RNA genome and on an untranslated region of an mRNA of a gene associated with the neurological disorder epilepsy. At the end of the project, the REU students gave poster and oral presentations, and they submitted written final project reports to the program director. The outcome of the REU was that the students gained transferable knowledge and skills in bioinformatics and an awareness of the applications of discrete mathematics to biological research problems.

  15. Function prediction from networks of local evolutionary similarity in protein structure

    PubMed Central

    2013-01-01

    Background Annotating protein function with both high accuracy and sensitivity remains a major challenge in structural genomics. One proven computational strategy has been to group a few key functional amino acids into templates and search for these templates in other protein structures, so as to transfer function when a match is found. To this end, we previously developed Evolutionary Trace Annotation (ETA) and showed that diffusing known annotations over a network of template matches on a structural genomic scale improved predictions of function. In order to further increase sensitivity, we now let each protein contribute multiple templates rather than just one, and also let the template size vary. Results Retrospective benchmarks in 605 Structural Genomics enzymes showed that multiple templates increased sensitivity by up to 14% when combined with single template predictions even as they maintained the accuracy over 91%. Diffusing function globally on networks of single and multiple template matches marginally increased the area under the ROC curve over 0.97, but in a subset of proteins that could not be annotated by ETA, the network approach recovered annotations for the most confident 20-23 of 91 cases with 100% accuracy. Conclusions We improve the accuracy and sensitivity of predictions by using multiple templates per protein structure when constructing networks of ETA matches and diffusing annotations. PMID:23514548

  16. Fully Blind Docking at the Atomic Level for Protein-Peptide Complex Structure Prediction.

    PubMed

    Yan, Chengfei; Xu, Xianjin; Zou, Xiaoqin

    2016-10-04

    Protein-peptide interactions play an important role in many cellular processes. In silico prediction of protein-peptide complex structure is highly desirable for mechanistic investigation of these processes and for therapeutic design. However, predicting all-atom structures of protein-peptide complexes without any knowledge about the peptide binding site and the bound peptide conformation remains a big challenge. Here, we present a docking-based method for predicting protein-peptide complex structures, referred to as MDockPeP, which starts with the peptide sequence and globally docks the all-atom, flexible peptide onto the protein structure. MDockPeP was tested on the peptiDB benchmarking database using both bound and unbound protein structures. The results show that MDockPeP successfully generated near-native peptide binding modes in 95.0% of the bound docking cases and in 92.2% of the unbound docking cases. The performance is significantly better than other existing docking methods. MDockPeP is computationally efficient and suitable for large-scale applications.

  17. Structure and dynamics of glass formers: Predictability at large length scales

    NASA Astrophysics Data System (ADS)

    Berthier, Ludovic; Jack, Robert L.

    2007-10-01

    Dynamic heterogeneity in glass formers has been related to their static structure using the concept of dynamic propensity. We reexamine this relationship by analyzing dynamical fluctuations in two atomistic glass formers and two theoretical models. We introduce quantitative statistical indicators which show that the dynamics of individual particles cannot be predicted on the basis of the propensity or by any structural indicator. However, the spatial structure of the propensity field does have predictive power for the spatial correlations associated with dynamic heterogeneity. Our results suggest that the quest for a connection between the static and dynamic properties of glass formers at the particle level is in vain, but they demonstrate that such a connection does exist on larger length scales.

  18. [Eco-fitness of county-level agricultural leading industry structure: assessment and development prediction].

    PubMed

    Wang, Liang; Zhu, Li-Qun; Zhang, Si-Wei; Zhang, Pei-Qi; Xu, Min-Lun; Bian, Xin-Min

    2012-11-01

    Based on the 'three critical points' theory of eco-fitness, and by using dynamic weighting and fitting methods, an assessment system for the eco-fitness of county-level agricultural leading industry structure was constructed, and, taking Zhangqiu of Shandong Province, East China as a case, the eco-fitness of county-level agricultural leading industry structure was assessed and predicted. Due to the limited agro-ecological resources, the comprehensive eco-fitness index of four kinds of agricultural leading industry in Zhangqiu presented an upward trend from 2005 to 2010, but a downward trend from 2011 to 2015. The eco-fitness indices of oil crops and fruits would be negative in 2015. The applied research in Zhangqiu confirmed the validity of the assessment system constructed for the eco-fitness of county-level agriculture leading industry structure and the rationality of the prediction model.

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

    PubMed

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

    2014-07-21

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

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

    PubMed

    Zhang, Xiao-Long; Lin, Xiao-Li

    2010-09-01

    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.

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

    PubMed

    Gibbs, B M

    2013-05-01

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

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

    PubMed Central

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

    2007-01-01

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

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

    PubMed Central

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

    2010-01-01

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

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

    NASA Technical Reports Server (NTRS)

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

    2007-01-01

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

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

    PubMed

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

    2015-02-01

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

  6. Predicting IQ change from brain structure: A cross-validation study

    PubMed Central

    Price, C.J.; Ramsden, S.; Hope, T.M.H.; Friston, K.J.; Seghier, M.L.

    2013-01-01

    Procedures that can predict cognitive abilities from brain imaging data are potentially relevant to educational assessments and studies of functional anatomy in the developing brain. Our aim in this work was to quantify the degree to which IQ change in the teenage years could be predicted from structural brain changes. Two well-known k-fold cross-validation analyses were applied to data acquired from 33 healthy teenagers – each tested at Time 1 and Time 2 with a 3.5 year interval. One approach, a Leave-One-Out procedure, predicted IQ change for each subject on the basis of structural change in a brain region that was identified from all other subjects (i.e., independent data). This approach predicted 53% of verbal IQ change and 14% of performance IQ change. The other approach used half the sample, to identify regions for predicting IQ change in the other half (i.e., a Split half approach); however – unlike the Leave-One-Out procedure – regions identified using half the sample were not significant. We discuss how these out-of-sample estimates compare to in-sample estimates; and draw some recommendations for k-fold cross-validation procedures when dealing with small datasets that are typical in the neuroimaging literature. PMID:23567505

  7. Predicting IQ change from brain structure: a cross-validation study.

    PubMed

    Price, C J; Ramsden, S; Hope, T M H; Friston, K J; Seghier, M L

    2013-07-01

    Procedures that can predict cognitive abilities from brain imaging data are potentially relevant to educational assessments and studies of functional anatomy in the developing brain. Our aim in this work was to quantify the degree to which IQ change in the teenage years could be predicted from structural brain changes. Two well-known k-fold cross-validation analyses were applied to data acquired from 33 healthy teenagers - each tested at Time 1 and Time 2 with a 3.5 year interval. One approach, a Leave-One-Out procedure, predicted IQ change for each subject on the basis of structural change in a brain region that was identified from all other subjects (i.e., independent data). This approach predicted 53% of verbal IQ change and 14% of performance IQ change. The other approach used half the sample, to identify regions for predicting IQ change in the other half (i.e., a Split half approach); however--unlike the Leave-One-Out procedure--regions identified using half the sample were not significant. We discuss how these out-of-sample estimates compare to in-sample estimates; and draw some recommendations for k-fold cross-validation procedures when dealing with small datasets that are typical in the neuroimaging literature.

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

    PubMed Central

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

    2011-01-01

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

  9. Prediction and Design of Materials from Crystal Structures to Nanocrystal Morphology and Assembly

    NASA Astrophysics Data System (ADS)

    Hennig, Richard

    2012-02-01

    Predictions of structure formation by computational methods have the potential to accelerate materials discovery and design. Here we present two computational approaches for the prediction of crystal structures and the morphology of nanoparticles. Many materials properties are controlled by composition and crystal structure. We show that evolutionary algorithms coupled to ab-initio relaxations can accurately predict the crystal structure and composition of compounds without any prior information about the system. We will discuss results for various systems including the prediction of unexpected quasi-1D and 2D electronic structures in Li-Be compounds under pressure [1] and of the crystal structure of the superconducting high-pressure phase of Eu [2]. The self-assembly of nanocrystals into mesoscale superlattices provides a path to the design of materials with tunable electronic, physical and chemical properties for various applications. The self-assembly is controlled by the nanocrystal shape and by ligand-mediated interactions between them. To understand this, it is necessary to know the effect of the ligands on the surface energies (which tune the nanocrystal shape), as well as the relative coverage of the different facets (which control the interactions). Density functional calculations for the binding energy of oleic acid-based ligands on PbSe nanocrystals determine the surface energies as a function of ligand coverage. The Wulff construction predicts the thermodynamic equilibrium shape of the PbSe nanocrystals as a function of the ligand coverage. We show that the different ligand binding energies on the 100 and 111 facets results in different ligand coverages on the facets and predict a transition in the equilibrium shape from octahedral to cubic when increasing the ligand concentration during synthesis. Our results furthermore suggest that the experimentally observed transformation of the nanocrystal superlattice structure from fcc to bcc is caused by the

  10. Network Structure, Metadata, and the Prediction of Missing Nodes and Annotations

    NASA Astrophysics Data System (ADS)

    Hric, Darko; Peixoto, Tiago P.; Fortunato, Santo

    2016-07-01

    The empirical validation of community detection methods is often based on available annotations on the nodes that serve as putative indicators of the large-scale network structure. Most often, the suitability of the annotations as topological descriptors itself is not assessed, and without this it is not possible to ultimately distinguish between actual shortcomings of the community detection algorithms, on one hand, and the incompleteness, inaccuracy, or structured nature of the data annotations themselves, on the other. In this work, we present a principled method to access both aspects simultaneously. We construct a joint generative model for the data and metadata, and a nonparametric Bayesian framework to infer its parameters from annotated data sets. We assess the quality of the metadata not according to their direct alignment with the network communities, but rather in their capacity to predict the placement of edges in the network. We also show how this feature can be used to predict the connections to missing nodes when only the metadata are available, as well as predicting missing metadata. By investigating a wide range of data sets, we show that while there are seldom exact agreements between metadata tokens and the inferred data groups, the metadata are often informative of the network structure nevertheless, and can improve the prediction of missing nodes. This shows that the method uncovers meaningful patterns in both the data and metadata, without requiring or expecting a perfect agreement between the two.

  11. Predicting US Infants' and Toddlers' TV/Video Viewing Rates: Mothers' Cognitions and Structural Life Circumstances

    PubMed Central

    Vaala, Sarah E.; Hornik, Robert C.

    2014-01-01

    There has been rising international concern over media use with children under two. As little is known about the factors associated with more or less viewing among very young children, this study examines maternal factors predictive of TV/video viewing rates among American infants and toddlers. Guided by the Integrative Model of Behavioral Prediction, this survey study examines relationships between children's rates of TV/video viewing and their mothers' structural life circumstances (e.g., number of children in the home; mother's screen use), and cognitions (e.g., attitudes; norms). Results suggest that mothers' structural circumstances and cognitions respectively contribute independent explanatory power to the prediction of children's TV/video viewing. Influence of structural circumstances is partially mediated through cognitions. Mothers' attitudes as well as their own TV/video viewing behavior were particularly predictive of children's viewing. Implications of these findings for international efforts to understand and reduce infant/toddler TV/video exposure are discussed. PMID:25489335

  12. Prediction of atomic structure of Pt-based bimetallic nanoalloys by using genetic algorithm

    NASA Astrophysics Data System (ADS)

    Oh, Jung Soo; Nam, Ho-Seok; Choi, Jung-Hae; Lee, Seung-Cheol

    2013-05-01

    The atom-arrangements in Pt-based bimetallic nanoalloys were predicted by the combined use of genetic algorithm (GA) and molecular dynamics (MD) simulations. The nanoparticles of these nanoalloys were assumed to be a 3.5 nm-diameter truncated octahedron with Pt and noble metals of fixed composition ratio of 1:1. For the GA, a Python code, which concurrently linked with the MD method that uses the embedded atom method inter-atomic potentials, was developed for the prediction of the atom arrangements in these bimetallic nanoalloys. Successfully, the GA calculation predicted the core-shell structures for both Pt-Ag and Pt-Au nanoalloy, but an onion-like multilayered core-shell structure for Pt-Cu nanoalloy. The structural characteristics in the bimetallic nanoalloy were mainly due to the differences in the surface energy and cohesive energy between Pt and the other alloying metal elements and their miscibility gap and so on. Briefly, the prediction performance was analyzed to show the superior searching ability of GA.

  13. The effect of prediction error correlation on optimal sensor placement in structural dynamics

    NASA Astrophysics Data System (ADS)

    Papadimitriou, Costas; Lombaert, Geert

    2012-04-01

    The problem of estimating the optimal sensor locations for parameter estimation in structural dynamics is re-visited. The effect of spatially correlated prediction errors on the optimal sensor placement is investigated. The information entropy is used as a performance measure of the sensor configuration. The optimal sensor location is formulated as an optimization problem involving discrete-valued variables, which is solved using computationally efficient sequential sensor placement algorithms. Asymptotic estimates for the information entropy are used to develop useful properties that provide insight into the dependence of the information entropy on the number and location of sensors. A theoretical analysis shows that the spatial correlation length of the prediction errors controls the minimum distance between the sensors and should be taken into account when designing optimal sensor locations with potential sensor distances up to the order of the characteristic length of the dynamic problem considered. Implementation issues for modal identification and structural-related model parameter estimation are addressed. Theoretical and computational developments are illustrated by designing the optimal sensor configurations for a continuous beam model, a discrete chain-like stiffness-mass model and a finite element model of a footbridge in Wetteren (Belgium). Results point out the crucial effect the spatial correlation of the prediction errors have on the design of optimal sensor locations for structural dynamics applications, revealing simultaneously potential inadequacies of spatially uncorrelated prediction errors models.

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

    PubMed

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

    2013-09-01

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

  15. Predicting total organic halide formation from drinking water chlorination using quantitative structure-property relationships.

    PubMed

    Luilo, G B; Cabaniss, S E

    2011-10-01

    Chlorinating water which contains dissolved organic matter (DOM) produces disinfection byproducts, the majority of unknown structure. Hence, the total organic halide (TOX) measurement is used as a surrogate for toxic disinfection byproducts. This work derives a robust quantitative structure-property relationship (QSPR) for predicting the TOX formation potential of model compounds. Literature data for 49 compounds were used to train the QSPR in moles of chlorine per mole of compound (Cp) (mol-Cl/mol-Cp). The resulting QSPR has four descriptors, calibration [Formula: see text] of 0.72 and standard deviation of estimation of 0.43 mol-Cl/mol-Cp. Internal and external validation indicate that the QSPR has good predictive power and low bias (‰<‰1%). Applying this QSPR to predict TOX formation by DOM surrogates - tannic acid, two model fulvic acids and two agent-based model assemblages - gave a predicted TOX range of 136-184 µg-Cl/mg-C, consistent with experimental data for DOM, which ranged from 78 to 192 µg-Cl/mg-C. However, the limited structural variation in the training data may limit QSPR applicability; studies of more sulfur-containing compounds, heterocyclic compounds and high molecular weight compounds could lead to a more widely applicable QSPR.

  16. QuaBingo: A Prediction System for Protein Quaternary Structure Attributes Using Block Composition

    PubMed Central

    Tung, Chi-Hua; Chen, Chi-Wei; Guo, Ren-Chao; Ng, Hui-Fuang

    2016-01-01

    Background. Quaternary structures of proteins are closely relevant to gene regulation, signal transduction, and many other biological functions of proteins. In the current study, a new method based on protein-conserved motif composition in block format for feature extraction is proposed, which is termed block composition. Results. The protein quaternary assembly states prediction system which combines blocks with functional domain composition, called QuaBingo, is constructed by three layers of classifiers that can categorize quaternary structural attributes of monomer, homooligomer, and heterooligomer. The building of the first layer classifier uses support vector machines (SVM) based on blocks and functional domains of proteins, and the second layer SVM was utilized to process the outputs of the first layer. Finally, the result is determined by the Random Forest of the third layer. We compared the effectiveness of the combination of block composition, functional domain composition, and pseudoamino acid composition of the model. In the 11 kinds of functional protein families, QuaBingo is 23% of Matthews Correlation Coefficient (MCC) higher than the existing prediction system. The results also revealed the biological characterization of the top five block compositions. Conclusions. QuaBingo provides better predictive ability for predicting the quaternary structural attributes of proteins. PMID:27610389

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

    PubMed Central

    2014-01-01

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

  18. Evaluation of a sophisticated SCFG design for RNA secondary structure prediction.

    PubMed

    Nebel, Markus E; Scheid, Anika

    2011-12-01

    Predicting secondary structures of RNA molecules is one of the fundamental problems of and thus a challenging task in computational structural biology. Over the past decades, mainly two different approaches have been considered to compute predictions of RNA secondary structures from a single sequence: the first one relies on physics-based and the other on probabilistic RNA models. Particularly, the free energy minimization (MFE) approach is usually considered the most popular and successful method. Moreover, based on the paradigm-shifting work by McCaskill which proposes the computation of partition functions (PFs) and base pair probabilities based on thermodynamics, several extended partition function algorithms, statistical sampling methods and clustering techniques have been invented over the last years. However, the accuracy of the corresponding algorithms is limited by the quality of underlying physics-based models, which include a vast number of thermodynamic parameters and are still incomplete. The competing probabilistic approach is based on stochastic context-free grammars (SCFGs) or corresponding generalizations, like conditional log-linear models (CLLMs). These methods abstract from free energies and instead try to learn about the structural behavior of the molecules by learning (a manageable number of) probabilistic parameters from trusted RNA structure databases. In this work, we introduce and evaluate a sophisticated SCFG design that mirrors state-of-the-art physics-based RNA structure prediction procedures by distinguishing between all features of RNA that imply different energy rules. This SCFG actually serves as the foundation for a statistical sampling algorithm for RNA secondary structures of a single sequence that represents a probabilistic counterpart to the sampling extension of the PF approach. Furthermore, some new ways to derive meaningful structure predictions from generated sample sets are presented. They are used to compare the

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

    PubMed

    Fleischer, Everly B; Janda, Kenneth C

    2013-05-16

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

  20. Hazard of pharmaceuticals for aquatic environment: Prioritization by structural approaches and prediction of ecotoxicity.

    PubMed

    Sangion, Alessandro; Gramatica, Paola

    2016-10-01

    Active Pharmaceutical Ingredients (APIs) are recognized as Contaminants of Emerging Concern (CEC) since they are detected in the environment in increasing amount, mainly in aquatic compartment, where they may be hazardous for wildlife. The huge lack of experimental data for a large number of end-points requires tools able to quickly highlight the potentially most hazardous and toxic pharmaceuticals, focusing experiments on the prioritized compounds. In silico tools, like QSAR (Quantitative Structure-Activity Relationship) models based on structural molecular descriptors, can predict missing data for toxic end-points necessary to prioritize existing, or even not yet synthesized chemicals for their potential hazard. In the present study, new externally validated QSAR models, specific to predict acute toxicity of APIs in key organisms of the three main aquatic trophic levels, i.e. algae, Daphnia and two species of fish, were developed using the QSARINS software. These Multiple Linear regressions - Ordinary Least Squares (MLR-OLS) models are based on theoretical molecular descriptors calculated by free PaDEL-Descriptor software and selected by Genetic Algorithm. The models are statistically robust, externally predictive and characterized by a wide structural applicability domain. They were applied to predict acute toxicity for a large set of APIs without experimental data. Then predictions were processed by Principal Component Analysis (PCA) and a trend, driven by the combination of toxicities for all the studied organisms, was highlighted. This trend, named Aquatic Toxicity Index (ATI), allowed the raking of pharmaceuticals according to their potential toxicity upon the whole aquatic environment. Finally a QSAR model for the prediction of this Aquatic Toxicity Index (ATI) was proposed to be applicable in QSARINS for the screening of existing APIs for their potential hazard and the a priori chemical design of not environmentally hazardous APIs.

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

    PubMed

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

    2013-09-01

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

  2. Neural signature of hierarchically structured expectations predicts clustering and transfer of rule sets in reinforcement learning.

    PubMed

    Collins, Anne Gabrielle Eva; Frank, Michael Joshua

    2016-07-01

    Often the world is structured such that distinct sensory contexts signify the same abstract rule set. Learning from feedback thus informs us not only about the value of stimulus-action associations but also about which rule set applies. Hierarchical clustering models suggest that learners discover structure in the environment, clustering distinct sensory events into a single latent rule set. Such structure enables a learner to transfer any newly acquired information to other contexts linked to the same rule set, and facilitates re-use of learned knowledge in novel contexts. Here, we show that humans exhibit this transfer, generalization and clustering during learning. Trial-by-trial model-based analysis of EEG signals revealed that subjects' reward expectations incorporated this hierarchical structure; these structured neural signals were predictive of behavioral transfer and clustering. These results further our understanding of how humans learn and generalize flexibly by building abstract, behaviorally relevant representations of the complex, high-dimensional sensory environment.

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

    PubMed Central

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

    2010-01-01

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

  4. Improved PEP-FOLD Approach for Peptide and Miniprotein Structure Prediction.

    PubMed

    Shen, Yimin; Maupetit, Julien; Derreumaux, Philippe; Tufféry, Pierre

    2014-10-14

    Peptides and mini proteins have many biological and biomedical implications, which motivates the development of accurate methods, suitable for large-scale experiments, to predict their experimental or native conformations solely from sequences. In this study, we report PEP-FOLD2, an improved coarse grained approach for peptide de novo structure prediction and compare it with PEP-FOLD1 and the state-of-the-art Rosetta program. Using a benchmark of 56 structurally diverse peptides with 25-52 amino acids and a total of 600 simulations for each system, PEP-FOLD2 generates higher quality models than PEP-FOLD1, and PEP-FOLD2 and Rosetta generate near-native or native models for 95% and 88% of the targets, respectively. In the situation where we do not have any experimental structures at hand, PEP-FOLD2 and Rosetta return a near-native or native conformation among the top five best scored models for 80% and 75% of the targets, respectively. While the PEP-FOLD2 prediction rate is better than the ROSETTA prediction rate by 5%, this improvement is non-negligible because PEP-FOLD2 explores a larger conformational space than ROSETTA and consists of a single coarse-grained phase. Our results indicate that if the coarse-grained PEP-FOLD2 method is approaching maturity, we are not at the end of the game of mini-protein structure prediction, but this opens new perspectives for large-scale in silico experiments.

  5. Ranking of hair dye substances according to predicted sensitization potency: quantitative structure-activity relationships.

    PubMed

    Søsted, H; Basketter, D A; Estrada, E; Johansen, J D; Patlewicz, G Y

    2004-01-01

    Allergic contact dermatitis following the use of hair dyes is well known. Many chemicals are used in hair dyes and it is unlikely that all cases of hair dye allergy can be diagnosed by means of patch testing with p-phenylenediamine (PPD). The objectives of this study are to identify all hair dye substances registered in Europe and to provide their tonnage data. The sensitization potential of each substance was then estimated by using a quantitative structure-activity relationship (QSAR) model and the substances were ranked according to their predicted potency. A cluster analysis was performed in order to help select a number of chemically diverse hair dye substances that could be used in subsequent clinical work. Various information sources, including the Inventory of Cosmetics Ingredients, new regulations on cosmetics, data on total use and ChemId (the Chemical Search Input website provided by the National Library of Medicine), were used in order to identify the names and structures of the hair dyes. A QSAR model, developed with the help of experimental local lymph node assay data and topological sub-structural molecular descriptors (TOPS-MODE), was used in order to predict the likely sensitization potential. Predictions for sensitization potential were made for the 229 substances that could be identified by means of a chemical structure, the majority of these hair dyes (75%) being predicted to be strong/moderate sensitizers. Only 22% were predicted to be weak sensitizers and 3% were predicted to be extremely weak or non-sensitizing. Eight of the most widely used hair dye substances were predicted to be strong/moderate sensitizers, including PPD - which is the most commonly used hair dye allergy marker in patch testing. A cluster analysis by using TOPS-MODE descriptors as inputs helped us group the hair dye substances according to their chemical similarity. This would facilitate the selection of potential substances for clinical patch testing. A patch-test series

  6. SPOT-Seq-RNA: predicting protein-RNA complex structure and RNA-binding function by fold recognition and binding affinity prediction.

    PubMed

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

    2014-01-01

    RNA-binding proteins (RBPs) play key roles in RNA metabolism and post-transcriptional regulation. Computational methods have been developed separately for prediction of RBPs and RNA-binding residues by machine-learning techniques and prediction of protein-RNA complex structures by rigid or semiflexible structure-to-structure docking. Here, we describe a template-based technique called SPOT-Seq-RNA that integrates prediction of RBPs, RNA-binding residues, and protein-RNA complex structures into a single package. This integration is achieved by combining template-based structure-prediction software, SPARKS X, with binding affinity prediction software, DRNA. This tool yields reasonable sensitivity (46 %) and high precision (84 %) for an independent test set of 215 RBPs and 5,766 non-RBPs. SPOT-Seq-RNA is computationally efficient for genome-scale prediction of RBPs and protein-RNA complex structures. Its application to human genome study has revealed a similar sensitivity and ability to uncover hundreds of novel RBPs beyond simple homology. The online server and downloadable version of SPOT-Seq-RNA are available at http://sparks-lab.org/server/SPOT-Seq-RNA/.

  7. Structure classification and melting temperature prediction in octet AB solids via machine learning

    NASA Astrophysics Data System (ADS)

    Pilania, G.; Gubernatis, J. E.; Lookman, T.

    2015-06-01

    Machine learning methods are being increasingly used in condensed matter physics and materials science to classify crystals structures and predict material properties. However, the reliability of these methods for a given problem, especially when large data sets are unavailable, has not been well studied. By addressing the tasks of classifying crystal structure and predicting melting temperatures of the octet subset of AB solids, we performed such a study and found potential problems with using machine learning methods on relatively small data sets. At the same time, however, we can reaffirm the potential power of such methods for these tasks. In particular, we uncovered an important new material feature, the excess Born effective charge, that significantly increased the accuracy of the predictions for the classification problem we defined. This discovery leads us to propose a new scale for the degree of ionicity and covalency in these solids. More specifically, we partitioned the crystal structures of a set of 75 octet solids into those that are ionic and covalent bonded and thus performed a binary classification task. We found that using the standard indices (rσ,rπ) , suggested by St. John and Bloch several decades ago, enabled an average success in classification of 92 % . Using just rσ and the excess Born effective charge Δ ZA of the A atom enabled an average success of 97 % , but we also found relatively large variations about these averages that were dependent on how certain machine learning methods were used and for which a standard deviation was not a proper measure of the degree of confidence we can place in either average. Instead, we calculated and report with 95 % confidence that the traditional classification pair predicts an accuracy in the interval [89 %,95 %] and the accuracy of the new pair lies in the interval [96 %,99 %] . For melting temperature predictions, the size of our data set was 46. We estimate the root-mean-squared error of our

  8. Curved Displacement Transfer Functions for Geometric Nonlinear Large Deformation Structure Shape Predictions

    NASA Technical Reports Server (NTRS)

    Ko, William L.; Fleischer, Van Tran; Lung, Shun-Fat

    2017-01-01

    For shape predictions of structures under large geometrically nonlinear deformations, Curved Displacement Transfer Functions were formulated based on a curved displacement, traced by a material point from the undeformed position to deformed position. The embedded beam (depth-wise cross section of a structure along a surface strain-sensing line) was discretized into multiple small domains, with domain junctures matching the strain-sensing stations. Thus, the surface strain distribution could be described with a piecewise linear or a piecewise nonlinear function. The discretization approach enabled piecewise integrations of the embedded-beam curvature equations to yield the Curved Displacement Transfer Functions, expressed in terms of embedded beam geometrical parameters and surface strains. By entering the surface strain data into the Displacement Transfer Functions, deflections along each embedded beam can be calculated at multiple points for mapping the overall structural deformed shapes. Finite-element linear and nonlinear analyses of a tapered cantilever tubular beam were performed to generate linear and nonlinear surface strains and the associated deflections to be used for validation. The shape prediction accuracies were then determined by comparing the theoretical deflections with the finiteelement- generated deflections. The results show that the newly developed Curved Displacement Transfer Functions are very accurate for shape predictions of structures under large geometrically nonlinear deformations.

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

    PubMed

    Liu, Taigang; Qin, Yufang; Wang, Yongjie; Wang, Chunhua

    2015-12-24

    The prior knowledge of protein structural class may offer useful clues on understanding its functionality as well as its tertiary structure. Though various significant efforts have been made to find a fast and effective computational approach to address this problem, it is still a challenging topic in the field of bioinformatics. The position-specific score matrix (PSSM) profile has been shown to provide a useful source of information for improving the prediction performance of protein structural class. However, this information has not been adequately explored. To this end, in this study, we present a feature extraction technique which is based on gapped-dipeptides composition computed directly from PSSM. Then, a careful feature selection technique is performed based on support vector machine-recursive feature elimination (SVM-RFE). These optimal features are selected to construct a final predictor. The results of jackknife tests on four working datasets show that our method obtains satisfactory prediction accuracies by extracting features solely based on PSSM and could serve as a very promising tool to predict protein structural class.

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

    NASA Astrophysics Data System (ADS)

    Fridman, Aleksei M.

    2007-02-01

    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.

  11. VfoldCPX Server: Predicting RNA-RNA Complex Structure and Stability

    PubMed Central

    Xu, Xiaojun; Chen, Shi-Jie

    2016-01-01

    RNA-RNA interactions are essential for genomic RNA dimerization, mRNA splicing, and many RNA-related gene expression and regulation processes. The prediction of the structure and folding stability of RNA-RNA complexes is a problem of significant biological importance and receives substantial interest in the biological community. The VfoldCPX server provides a new web interface to predict the two-dimensional (2D) structures of RNA-RNA complexes from the nucleotide sequences. The VfoldCPX server has several novel advantages including the ability to treat RNAs with tertiary contacts (crossing base pairs) such as loop-loop kissing interactions and the use of physical loop entropy parameters. Based on a partition function-based algorithm, the server enables prediction for structure with and without tertiary contacts. Furthermore, the server outputs a set of energetically stable structures, ranked by their stabilities. The results allow users to gain extensive physical insights into RNA-RNA interactions and their roles in RNA function. The web server is freely accessible at “http://rna.physics.missouri.edu/vfoldCPX”. PMID:27657918

  12. Automated antibody structure prediction using Accelrys tools: results and best practices.

    PubMed

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

    2014-08-01

    We de