Science.gov

Sample records for predicting b-dna structure

  1. A DFT study of 2-aminopurine-containing dinucleotides: prediction of stacked conformations with B-DNA structure.

    PubMed

    Smith, Darren A; Holroyd, Leo F; van Mourik, Tanja; Jones, Anita C

    2016-05-25

    The fluorescence properties of dinucleotides incorporating 2-aminopurine (2AP) suggest that the simplest oligonucleotides adopt conformations similar to those found in duplex DNA. However, there is a lack of structural data for these systems. We report a density functional theory (DFT) study of the structures of 2AP-containing dinucleotides (deoxydinucleoside monophosphates), including full geometry optimisation of the sugar-phosphate backbone. Our DFT calculations employ the M06-2X functional for reliable treatment of dispersion interactions and include implicit aqueous solvation. Dinucleotides with 2AP in the 5'-position and each of the natural bases in the 3'-position are examined, together with the analogous 5'-adenine-containing systems. Computed structures are compared in detail with typical B-DNA base-step parameters, backbone torsional angles and sugar pucker, derived from crystallographic data. We find that 2AP-containing dinucleotides adopt structures that closely conform to B-DNA in all characteristic parameters. The structures of 2AP-containing dinucleotides closely resemble those of their adenine-containing counterparts, demonstrating the fidelity of 2AP as a mimic of the natural base. As a first step towards exploring the conformational heterogeneity of dinucleotides, we also characterise an imperfectly stacked conformation and one in which the bases are completely unstacked. PMID:27186599

  2. Associations between intronic non-B DNA structures and exon skipping

    PubMed Central

    Tsai, Zing Tsung-Yeh; Chu, Wen-Yi; Cheng, Jen-Hao; Tsai, Huai-Kuang

    2014-01-01

    Non-B DNA structures are abundant in the genome and are often associated with critical biological processes, including gene regulation, chromosome rearrangement and genome stabilization. In particular, G-quadruplex (G4) may affect alternative splicing based on its ability to impede the activity of RNA polymerase II. However, the specific role of non-B DNA structures in splicing regulation still awaits investigation. Here, we provide a genome-wide and cross-species investigation of the associations between five non-B DNA structures and exon skipping. Our results indicate a statistically significant correlation of each examined non-B DNA structures with exon skipping in both human and mouse. We further show that the contributions of non-B DNA structures to exon skipping are influenced by the occurring region. These correlations and contributions are also significantly different in human and mouse. Finally, we detailed the effects of G4 by showing that occurring on the template strand and the length of G-run, which is highly related to the stability of a G4 structure, are significantly correlated with exon skipping activity. We thus show that, in addition to the well-known effects of RNA and protein structure, the relative positional arrangement of intronic non-B DNA structures may also impact exon skipping. PMID:24153112

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

    PubMed Central

    Sharma, Sudha

    2011-01-01

    In addition to the canonical B-form structure first described by Watson and Crick, DNA can adopt a number of alternative structures. These non-B-form DNA secondary structures form spontaneously on tracts of repeat sequences that are abundant in genomes. In addition, structured forms of DNA with intrastrand pairing may arise on single-stranded DNA produced transiently during various cellular processes. Such secondary structures have a range of biological functions but also induce genetic instability. Increasing evidence suggests that genomic instabilities induced by non-B DNA secondary structures result in predisposition to diseases. Secondary DNA structures also represent a new class of molecular targets for DNA-interactive compounds that might be useful for targeting telomeres and transcriptional control. The equilibrium between the duplex DNA and formation of multistranded non-B-form structures is partly dependent upon the helicases that unwind (resolve) these alternate DNA structures. With special focus on tetraplex, triplex, and cruciform, this paper summarizes the incidence of non-B DNA structures and their association with genomic instability and emphasizes the roles of RecQ-like DNA helicases in genome maintenance by resolution of DNA secondary structures. In future, RecQ helicases are anticipated to be additional molecular targets for cancer chemotherapeutics. PMID:21977309

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

    PubMed Central

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

    1997-01-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2014-08-01

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

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

    PubMed

    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

  8. Singlet Oxygen Attack on Guanine: Reactivity and Structural Signature within the B-DNA Helix.

    PubMed

    Dumont, Elise; Grüber, Raymond; Bignon, Emmanuelle; Morell, Christophe; Aranda, Juan; Ravanat, Jean-Luc; Tuñón, Iñaki

    2016-08-22

    Oxidatively generated DNA lesions are numerous and versatile, and have been the subject of intensive research since the discovery of 8-oxoguanine in 1984. Even for this prototypical lesion, the precise mechanism of formation remains elusive due to the inherent difficulties in characterizing high-energy intermediates. We have probed the stability of the guanine endoperoxide in B-DNA as a key intermediate and determined a unique activation free energy of around 6 kcal mol(-1) for the formation of the first C-O covalent bond upon the attack of singlet molecular oxygen ((1) O2 ) on the central guanine of a solvated 13 base-pair poly(dG-dC), described by means of quantum mechanics/molecular mechanics (QM/MM) simulations. The B-helix remains stable upon oxidation in spite of the bulky character of the guanine endoperoxide. Our modeling study has revealed the nature of the versatile (1) O2 attack in terms of free energy and shows a sensitivity to electrostatics and solvation as it involves a charge-separated intermediate. PMID:27440482

  9. Structure and mechanism of the UvrA-UvrB DNA damage sensor.

    PubMed

    Pakotiprapha, Danaya; Samuels, Martin; Shen, Koning; Hu, Johnny H; Jeruzalmi, David

    2012-03-01

    Nucleotide excision repair (NER) is used by all organisms to eliminate DNA lesions. We determined the structure of the Geobacillus stearothermophilus UvrA-UvrB complex, the damage-sensor in bacterial NER and a new structure of UvrA. We observe that the DNA binding surface of UvrA, previously found in an open shape that binds damaged DNA, also exists in a closed groove shape compatible with native DNA only. The sensor contains two UvrB molecules that flank the UvrA dimer along the predicted path for DNA, ~80 Å from the lesion. We show that the conserved signature domain II of UvrA mediates a nexus of contacts among UvrA, UvrB and DNA. Further, in our new structure of UvrA, this domain adopts an altered conformation while an adjacent nucleotide binding site is vacant. Our findings raise unanticipated questions about NER and also suggest a revised picture of its early stages. PMID:22307053

  10. Structure and mechanism of the UvrA-UvrB DNA damage sensor

    SciTech Connect

    Pakotiprapha, Danaya; Samuels, Martin; Shen, Koning; Hu, Johnny H; Jeruzalmi, David

    2012-04-17

    Nucleotide excision repair (NER) is used by all organisms to eliminate DNA lesions. We determined the structure of the Geobacillus stearothermophilus UvrA-UvrB complex, the damage-sensor in bacterial NER and a new structure of UvrA. We observe that the DNA binding surface of UvrA, previously found in an open shape that binds damaged DNA, also exists in a closed groove shape compatible with native DNA only. The sensor contains two UvrB molecules that flank the UvrA dimer along the predicted path for DNA, ~80 Å from the lesion. We show that the conserved signature domain II of UvrA mediates a nexus of contacts among UvrA, UvrB and DNA. Further, in our new structure of UvrA, this domain adopts an altered conformation while an adjacent nucleotide binding site is vacant. Our findings raise unanticipated questions about NER and also suggest a revised picture of its early stages.

  11. Double-strand break formation by the RAG complex at the bcl-2 major breakpoint region and at other non-B DNA structures in vitro.

    PubMed

    Raghavan, Sathees C; Swanson, Patrick C; Ma, Yunmei; Lieber, Michael R

    2005-07-01

    The most common chromosomal translocation in cancer, t(14;18) at the 150-bp bcl-2 major breakpoint region (Mbr), occurs in follicular lymphomas. The bcl-2 Mbr assumes a non-B DNA conformation, thus explaining its distinctive fragility. This non-B DNA structure is a target of the RAG complex in vivo, but not because of its primary sequence. Here we report that the RAG complex generates at least two independent nicks that lead to double-strand breaks in vitro, and this requires the non-B DNA structure at the bcl-2 Mbr. A 3-bp mutation is capable of abolishing the non-B structure formation and the double-strand breaks. The observations on the bcl-2 Mbr reflect more general properties of the RAG complex, which can bind and nick at duplex-single-strand transitions of other non-B DNA structures, resulting in double-strand breaks in vitro. Hence, the present study reveals novel insight into a third mechanism of action of RAGs on DNA, besides the standard heptamer/nonamer-mediated cleavage in V(D)J recombination and the in vitro transposase activity. PMID:15988007

  12. A non-B-DNA structure at the Bcl-2 major breakpoint region is cleaved by the RAG complex.

    PubMed

    Raghavan, Sathees C; Swanson, Patrick C; Wu, Xiantuo; Hsieh, Chih-Lin; Lieber, Michael R

    2004-03-01

    The causes of spontaneous chromosomal translocations in somatic cells of biological organisms are largely unknown, although double-strand DNA breaks are required in all proposed mechanisms. The most common chromosomal abnormality in human cancer is the reciprocal translocation between chromosomes 14 and 18 (t(14;18)), which occurs in follicular lymphomas. The break at the immunoglobulin heavy-chain locus on chromosome 14 is an interruption of the normal V(D)J recombination process. But the breakage on chromosome 18, at the Bcl-2 gene, occurs within a confined 150-base-pair region (the major breakpoint region or Mbr) for reasons that have remained enigmatic. We have reproduced key features of the translocation process on an episome that propagates in human cells. The RAG complex--which is the normal enzyme for DNA cleavage at V, D or J segments--nicks the Bcl-2 Mbr in vitro and in vivo in a manner that reflects the pattern of the chromosomal translocations; however, the Mbr is not a V(D)J recombination signal. Rather the Bcl-2 Mbr assumes a non-B-form DNA structure within the chromosomes of human cells at 20-30% of alleles. Purified DNA assuming this structure contains stable regions of single-strandedness, which correspond well to the translocation regions in patients. Hence, a stable non-B-DNA structure in the human genome appears to be the basis for the fragility of the Bcl-2 Mbr, and the RAG complex is able to cleave this structure. PMID:14999286

  13. 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.

  14. Molecular stripping in the NF-κB/IκB/DNA genetic regulatory network

    PubMed Central

    Potoyan, Davit A.; Zheng, Weihua; Komives, Elizabeth A.; Wolynes, Peter G.

    2016-01-01

    Genetic switches based on the NF-κB/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 NF-κB/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 NF-κB and its various binary and ternary complexes with DNA and the inhibitor IκB. These studies show that the function of the NF-κB/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 that 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 free energy onto various reaction coordinates reveal the structural details of the stripping pathways. PMID:26699500

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

    PubMed Central

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

    2012-01-01

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

  16. Toolbox for Protein Structure Prediction.

    PubMed

    Roche, Daniel Barry; McGuffin, Liam James

    2016-01-01

    Protein tertiary structure prediction algorithms aim to predict, from amino acid sequence, the tertiary structure of a protein. In silico protein structure prediction methods have become extremely important, as in vitro-based structural elucidation is unable to keep pace with the current growth of sequence databases due to high-throughput next-generation sequencing, which has exacerbated the gaps in our knowledge between sequences and structures.Here we briefly discuss protein tertiary structure prediction, the biennial competition for the Critical Assessment of Techniques for Protein Structure Prediction (CASP) and its role in shaping the field. We also discuss, in detail, our cutting-edge web-server method IntFOLD2-TS for tertiary structure prediction. Furthermore, we provide a step-by-step guide on using the IntFOLD2-TS web server, along with some real world examples, where the IntFOLD server can and has been used to improve protein tertiary structure prediction and aid in functional elucidation. PMID:26519323

  17. De Novo Protein Structure Prediction

    NASA Astrophysics Data System (ADS)

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

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

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

    PubMed Central

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

    1997-01-01

    The divergent synthesis of bDNA structures is described. This new type of branched DNA contains one unique oligonucleotide, the primary sequence, covalently attached through a comb-like branching network to many identical copies of a different oligonucleotide, the secondary sequence. The bDNA comb molecules were assembled on a solid support using parameters optimized for bDNA synthesis. The chemistry was used to synthesize bDNA comb molecules containing 15 secondary sequences. The bDNA comb molecules were elaborated by enzymatic ligation into branched amplification multimers, large bDNA molecules (a total of 1068 nt) containing an average of 36 repeated DNA oligomer sequences, each capable of hybridizing specifically to an alkaline phosphatase-labeled oligonucleotide. The bDNA comb molecules were characterized by electrophoretic methods and by controlled cleavage at periodate-cleavable moieties incorporated during synthesis. The branched amplification multimers have been used as signal amplifiers in nucleic acid quantification assays for detection of viral infection. It is possible to detect as few as 50 molecules with bDNA technology. PMID:9365266

  19. TRITIUM RESERVOIR STRUCTURAL PERFORMANCE PREDICTION

    SciTech Connect

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

    2005-11-10

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

  20. Protein structural domains: definition and prediction.

    PubMed

    Ezkurdia, Iakes; Tress, Michael L

    2011-11-01

    Recognition and prediction of structural domains in proteins is an important part of structure and function prediction. This unit lists the range of tools available for domain prediction, and describes sequence and structural analysis tools that complement domain prediction methods. Also detailed are the basic domain prediction steps, along with suggested strategies for different protein sequences and potential pitfalls in domain boundary prediction. The difficult problem of domain orientation prediction is also discussed. All the resources necessary for domain boundary prediction are accessible via publicly available Web servers and databases and do not require computational expertise. PMID:22045561

  1. De novo design of protein mimics of B-DNA.

    PubMed

    Yüksel, Deniz; Bianco, Piero R; Kumar, Krishna

    2016-01-01

    Structural mimicry of DNA is utilized in nature as a strategy to evade molecular defences mounted by host organisms. One such example is the protein Ocr - the first translation product to be expressed as the bacteriophage T7 infects E. coli. The structure of Ocr reveals an intricate and deliberate arrangement of negative charges that endows it with the ability to mimic ∼24 base pair stretches of B-DNA. This uncanny resemblance to DNA enables Ocr to compete in binding the type I restriction modification (R/M) system, and neutralizes the threat of hydrolytic cleavage of viral genomic material. Here, we report the de novo design and biophysical characterization of DNA mimicking peptides, and describe the inhibitory action of the designed helical bundles on a type I R/M enzyme, EcoR124I. This work validates the use of charge patterning as a design principle for creation of protein mimics of DNA, and serves as a starting point for development of therapeutic peptide inhibitors against human pathogens that employ molecular camouflage as part of their invasion stratagem. PMID:26568416

  2. 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

  3. Local backbone structure prediction of proteins.

    PubMed

    de Brevern, Alexandre G; Benros, Cristina; Gautier, Romain; Valadié, Héléne; Hazout, Serge; Etchebest, Catherine

    2004-01-01

    A statistical analysis of the PDB structures has led us to define a new set of small 3D structural prototypes called Protein Blocks (PBs). This structural alphabet includes 16 PBs, each one is defined by the (phi, psi) dihedral angles of 5 consecutive residues. The amino acid distributions observed in sequence windows encompassing these PBs are used to predict by a Bayesian approach the local 3D structure of proteins from the sole knowledge of their sequences. LocPred is a software which allows the users to submit a protein sequence and performs a prediction in terms of PBs. The prediction results are given both textually and graphically. PMID:15724288

  4. 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

  5. Structure Prediction of Membrane Proteins

    NASA Astrophysics Data System (ADS)

    Hu, Xiche

    Membrane proteins play a central role in many cellular and physiological processes. It is estimated that integral membrane proteins make up about 20-30% of the proteome (Krogh et al., 2001b; Stevens and Arkin, 2000; von Heijne, 1999). They are essential mediators of material and information transfer across cell membranes. Their functions include active and passive transport of molecules into and out of cells and organelles; transduction of energy among various forms (light, electrical, and chemical energy); as well as reception and transduction of chemical and electrical signals across membranes (Avdonin, 2005; Bockaert et al., 2002; Pahl, 1999; Rehling et al., 2004; Stack et al., 1995). Identifying these transmembrane (TM) proteins and deciphering their molecular mechanisms, then, is of great importance, particularly as applied to biomedicine. Membrane proteins are the targets of a large number of pharmacologically and toxicologically active substances, and are directly involved in their uptake, metabolism, and clearance (Bettler et al., 1998; Cohen, 2002; Heusser and Jardieu, 1997; Tibes et al., 2005; Xu et al., 2005). Despite the importance of membrane proteins, the knowledge of their high-resolution structures and mechanisms of action has lagged far behind in comparison to that of water-soluble proteins: less than 1% of all three-dimensional structures deposited in the Protein Data Bank are of membrane proteins. This unfortunate disparity stems from difficulties in overexpression and the crystallization of membrane proteins (Grisshammer and Tate, 1995; Michel, 1991).

  6. Geometric prediction structure for multiview video coding

    NASA Astrophysics Data System (ADS)

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

    2010-02-01

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

  7. Interface Structure Prediction from First-Principles

    SciTech Connect

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

    2014-05-08

    Information about the atomic structures at solid–solid interfaces is crucial for understanding and predicting the performance of materials. Due to the complexity of the interfaces, it is very challenging to resolve their atomic structures using either experimental techniques or computer simulations. In this paper, we present an efficient first-principles computational method for interface structure prediction based on an adaptive genetic algorithm. This approach significantly reduces the computational cost, while retaining the accuracy of first-principles prediction. The method is applied to the investigation of both stoichiometric and nonstoichiometric SrTiO3 Σ3(112)[1¯10] grain boundaries with unit cell containing up to 200 atoms. Several novel low-energy structures are discovered, which provide fresh insights into the structure and stability of the grain boundaries.

  8. A structural alphabet for local protein structures: improved prediction methods.

    PubMed

    Etchebest, Catherine; Benros, Cristina; Hazout, Serge; de Brevern, Alexandre G

    2005-06-01

    Three-dimensional protein structures can be described with a library of 3D fragments that define a structural alphabet. We have previously proposed such an alphabet, composed of 16 patterns of five consecutive amino acids, called Protein Blocks (PBs). These PBs have been used to describe protein backbones and to predict local structures from protein sequences. The Q16 prediction rate reaches 40.7% with an optimization procedure. This article examines two aspects of PBs. First, we determine the effect of the enlargement of databanks on their definition. The results show that the geometrical features of the different PBs are preserved (local RMSD value equal to 0.41 A on average) and sequence-structure specificities reinforced when databanks are enlarged. Second, we improve the methods for optimizing PB predictions from sequences, revisiting the optimization procedure and exploring different local prediction strategies. Use of a statistical optimization procedure for the sequence-local structure relation improves prediction accuracy by 8% (Q16 = 48.7%). Better recognition of repetitive structures occurs without losing the prediction efficiency of the other local folds. Adding secondary structure prediction improved the accuracy of Q16 by only 1%. An entropy index (Neq), strongly related to the RMSD value of the difference between predicted PBs and true local structures, is proposed to estimate prediction quality. The Neq is linearly correlated with the Q16 prediction rate distributions, computed for a large set of proteins. An "expected" prediction rate QE16 is deduced with a mean error of 5%. PMID:15822101

  9. 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. PMID:26441052

  10. 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

  11. Predicting protein dynamics from structural ensembles

    NASA Astrophysics Data System (ADS)

    Copperman, J.; Guenza, M. G.

    2015-12-01

    The biological properties of proteins are uniquely determined by their structure and dynamics. A protein in solution populates a structural ensemble of metastable configurations around the global fold. From overall rotation to local fluctuations, the dynamics of proteins can cover several orders of magnitude in time scales. We propose a simulation-free coarse-grained approach which utilizes knowledge of the important metastable folded states of the protein to predict the protein dynamics. This approach is based upon the Langevin Equation for Protein Dynamics (LE4PD), a Langevin formalism in the coordinates of the protein backbone. The linear modes of this Langevin formalism organize the fluctuations of the protein, so that more extended dynamical cooperativity relates to increasing energy barriers to mode diffusion. The accuracy of the LE4PD is verified by analyzing the predicted dynamics across a set of seven different proteins for which both relaxation data and NMR solution structures are available. Using experimental NMR conformers as the input structural ensembles, LE4PD predicts quantitatively accurate results, with correlation coefficient ρ = 0.93 to NMR backbone relaxation measurements for the seven proteins. The NMR solution structure derived ensemble and predicted dynamical relaxation is compared with molecular dynamics simulation-derived structural ensembles and LE4PD predictions and is consistent in the time scale of the simulations. The use of the experimental NMR conformers frees the approach from computationally demanding simulations.

  12. Predicting structure in nonsymmetric sparse matrix factorizations

    SciTech Connect

    Gilbert, J.R.; Ng, E.

    1991-12-31

    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. Predicting structure in nonsymmetric sparse matrix factorizations

    SciTech Connect

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

    1991-01-01

    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.

  14. 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.

  15. Predicting Odor Perceptual Similarity from Odor Structure

    PubMed Central

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

    2013-01-01

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

  16. 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.

  17. Protein Structure Prediction with Evolutionary Algorithms

    SciTech Connect

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

    1999-02-08

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

  18. Multipass Membrane Protein Structure Prediction Using Rosetta

    PubMed Central

    Yarov-Yarovoy, Vladimir; Schonbrun, Jack; Baker, David

    2006-01-01

    We describe the adaptation of the Rosetta de novo structure prediction method for prediction of helical transmembrane protein structures. The membrane environment is modeled by embedding the protein chain into a model membrane represented by parallel planes defining hydrophobic, interface, and polar membrane layers for each energy evaluation. The optimal embedding is determined by maximizing the exposure of surface hydrophobic residues within the membrane and minimizing hydrophobic exposure outside of the membrane. Protein conformations are built up using the Rosetta fragment assembly method and evaluated using a new membrane-specific version of the Rosetta low-resolution energy function in which residue–residue and residue–environment interactions are functions of the membrane layer in addition to amino acid identity, distance, and density. We find that lower energy and more native-like structures are achieved by sequential addition of helices to a growing chain, which may mimic some aspects of helical protein biogenesis after translocation, rather than folding the whole chain simultaneously as in the Rosetta soluble protein prediction method. In tests on 12 membrane proteins for which the structure is known, between 51 and 145 residues were predicted with root-mean-square deviation <4Å from the native structure. PMID:16372357

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

    PubMed Central

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

    2014-01-01

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

  20. 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.

  1. Structure based prediction of protein folding intermediates.

    PubMed

    Xie, D; Freire, E

    1994-09-01

    The complete unfolding of a protein involves the disruption of non-covalent intramolecular interactions within the protein and the subsequent hydration of the backbone and amino acid side-chains. The magnitude of the thermodynamic parameters associated with this process is known accurately for a growing number of globular proteins for which high-resolution structures are also available. The existence of this database of structural and thermodynamic information has facilitated the development of statistical procedures aimed at quantifying the relationships existing between protein structure and the thermodynamic parameters of folding/unfolding. Under some conditions proteins do not unfold completely, giving rise to states (commonly known as molten globules) in which the molecule retains some secondary structure and remains in a compact configuration after denaturation. This phenomenon is reflected in the thermodynamics of the process. Depending on the nature of the residual structure that exists after denaturation, the observed enthalpy, entropy and heat capacity changes will deviate in a particular and predictable way from the values expected for complete unfolding. For several proteins, these deviations have been shown to exhibit similar characteristics, suggesting that their equilibrium folding intermediates exhibit some common structural features. Employing empirically derived structure-energetic relationships, it is possible to identify in the native structure of the protein those regions with the higher probability of being structured in equilibrium partly folded states. In this work, a thermodynamic search algorithm aimed at identifying the structural determinants of the molten globule state has been applied to six globular proteins; alpha-lactalbumin, barnase, IIIGlc, interleukin-1 beta, phage T4 lysozyme and phage 434 repressor. Remarkably, the structural features of the predicted equilibrium intermediates coincide to a large extent with the known

  2. RNA secondary structure prediction using soft computing.

    PubMed

    Ray, Shubhra Sankar; Pal, Sankar K

    2013-01-01

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

  3. Fractal structure enables temporal prediction in music.

    PubMed

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

    2014-10-01

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

  4. A Role for Non-B DNA Forming Sequences in Mediating Microlesions Causing Human Inherited Disease.

    PubMed

    Kamat, Mihir Anant; Bacolla, Albino; Cooper, David N; Chuzhanova, Nadia

    2016-01-01

    Missense/nonsense mutations and microdeletions/microinsertions (<21 bp) represent ∼ 76% of all mutations causing human inherited disease, and their occurrence has been associated with sequence motifs (direct, inverted, and mirror repeats; G-quartets) capable of adopting non-B DNA structures. We found that a significant proportion (∼ 21%) of both microdeletions and microinsertions occur within direct repeats, and are explicable by slipped misalignment. A novel mutational mechanism, DNA triplex formation followed by DNA repair, may explain ∼ 5% of microdeletions and microinsertions at mirror repeats. Further, G-quartets, direct, and inverted repeats also appear to play a prominent role in mediating missense mutations, whereas only direct and inverted repeats mediate nonsense mutations. We suggest a mutational mechanism involving slipped strand mispairing, slipped structure formation, and DNA repair, to explain ∼ 15% of missense and ∼ 12% of nonsense mutations yielding perfect direct repeats from imperfect repeats, or the extension of existing direct repeats. Similar proportions of missense and nonsense mutations were explicable by hairpin/loop formation and DNA repair, yielding perfect inverted repeats from imperfect repeats. We also propose a model for single base-pair substitution based on one-electron oxidation reactions at G-quadruplex DNA. Overall, the proposed mechanisms provide support for a role for non-B DNA structures in human gene mutagenesis. PMID:26466920

  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. 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.

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

    SciTech Connect

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

    2014-12-14

    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.

  8. Structural load prediction methods for space payloads

    NASA Technical Reports Server (NTRS)

    Wada, B. K.

    1982-01-01

    The state of the art in structural loads prediction procedures for spacecraft is summarized. Three categories of prediction techniques delineated by cost, complexity, comprehensiveness, accuracy, and applications are outlined. The lowest cost method has been used for earth resources, communications, and weather satellites, the medium cost method for sun-synchronous orbits and the large space telescope, and the most expensive for planetary missions, comet rendezvous, and out-of-ecliptic orbits, all assuming Shuttle launch. The lowest cost method involves a mass-acceleration curve. A shock spectra technique predicts a least upper bound for loads. A recovered transient method analyzes the interface acceleration of two connected launch vehicles. The most accurate method devised thus far is a transient analysis of the total launch vehicle/payload dynamic system.

  9. A Structured Approach to Sediment Transport Prediction

    NASA Astrophysics Data System (ADS)

    Wilcock, Peter

    2013-04-01

    There are two types of sediment transport problem. One, flow competence, concerns the conditions that initiate motion of grains on the bed surface. The other, transport capacity, concerns the rate at which sediment is transported and involves sediment found locally on the bed as well as sediment delivered from upstream. The two problems can be linked by the critical stress for incipient motion. A model for critical stress is used directly to predict flow competence. The Ashida/Parker similarity hypothesis provides a useful approximation of transport rates and incorporates local sediment effects entirely via the reference stress, a surrogate for critical stress. Although critical stress is key to both predictions, its application is quite different. The difficult problem of wash load - sizes found in transport in quantities much larger than would be predicted by their presence in the bed - makes the distinction clear and challenges any attempt to predict transport rate from a competence-like approach based on hydraulics and bed material alone. The Shields Diagram and a hiding function provide models for critical stress for uni-size and mixed-size sediment. In addition to grain size - both absolute and relative - other factors alter the critical stress of bed material. These include the proportion of fine-grained material, the aging or freshening of bed material via biologically mediated processes, and the development of bed structure at flows close to the critical stress. Although these factors directly influence the prediction of competent flows, their effect on transport rate is less clear. As flow increases, to what extent does bed strengthening through structuring and other mechanisms persist in dampening transport rate? The answer involves the condition of partial transport in which some grains in a size fraction are active and others remain inactive. Tracing of grains in the flume and field provide guidance on the domain of partial transport and thus on the

  10. The intrinsic mechanics of B-DNA in solution characterized by NMR.

    PubMed

    Imeddourene, Akli Ben; Xu, Xiaoqian; Zargarian, Loussiné; Oguey, Christophe; Foloppe, Nicolas; Mauffret, Olivier; Hartmann, Brigitte

    2016-04-20

    Experimental characterization of the structural couplings in free B-DNA in solution has been elusive, because of subtle effects that are challenging to tackle. Here, the exploitation of the NMR measurements collected on four dodecamers containing a substantial set of dinucleotide sequences provides new, consistent correlations revealing the DNA intrinsic mechanics. The difference between two successive residual dipolar couplings (ΔRDCs) involving C6/8-H6/8, C3'-H3' and C4'-H4' vectors are correlated to the(31)P chemical shifts (δP), which reflect the populations of the BI and BII backbone states. The δPs are also correlated to the internucleotide distances (Dinter) involving H6/8, H2' and H2″ protons. Calculations of NMR quantities on high resolution X-ray structures and controlled models of DNA enable to interpret these couplings: the studied ΔRDCs depend mostly on roll, while Dinterare mainly sensitive to twist or slide. Overall, these relations demonstrate how δP measurements inform on key inter base parameters, in addition to probe the BI↔BII backbone equilibrium, and shed new light into coordinated motions of phosphate groups and bases in free B-DNA in solution. Inspection of the 5' and 3' ends of the dodecamers also supplies new information on the fraying events, otherwise neglected. PMID:26883628

  11. The intrinsic mechanics of B-DNA in solution characterized by NMR

    PubMed Central

    Imeddourene, Akli Ben; Xu, Xiaoqian; Zargarian, Loussiné; Oguey, Christophe; Foloppe, Nicolas; Mauffret, Olivier; Hartmann, Brigitte

    2016-01-01

    Experimental characterization of the structural couplings in free B-DNA in solution has been elusive, because of subtle effects that are challenging to tackle. Here, the exploitation of the NMR measurements collected on four dodecamers containing a substantial set of dinucleotide sequences provides new, consistent correlations revealing the DNA intrinsic mechanics. The difference between two successive residual dipolar couplings (ΔRDCs) involving C6/8-H6/8, C3′-H3′ and C4′-H4′ vectors are correlated to the 31P chemical shifts (δP), which reflect the populations of the BI and BII backbone states. The δPs are also correlated to the internucleotide distances (Dinter) involving H6/8, H2′ and H2″ protons. Calculations of NMR quantities on high resolution X-ray structures and controlled models of DNA enable to interpret these couplings: the studied ΔRDCs depend mostly on roll, while Dinter are mainly sensitive to twist or slide. Overall, these relations demonstrate how δP measurements inform on key inter base parameters, in addition to probe the BI↔BII backbone equilibrium, and shed new light into coordinated motions of phosphate groups and bases in free B-DNA in solution. Inspection of the 5′ and 3′ ends of the dodecamers also supplies new information on the fraying events, otherwise neglected. PMID:26883628

  12. Local sequential minimization of double stranded B-DNA using Monte Carlo annealing.

    PubMed

    Sfyrakis, Konstantinos; Provata, Astero; Povey, David C; Howlin, Brendan J

    2004-06-01

    A software algorithm has been developed to investigate the folding process in B-DNA structures in vacuum under a simple and accurate force field. This algorithm models linear double stranded B-DNA sequences based on a local, sequential minimization procedure. The original B-DNA structures were modeled using initial nucleotide structures taken from the Brookhaven database. The models contain information at the atomic level allowing one to investigate as accurately as possible the structure and characteristics of the resulting DNA structures. A variety of DNA sequences and sizes were investigated containing coding and non-coding, random and real, homogeneous or heterogeneous sequences in the range of 2 to 40 base pairs. The force field contains terms such as angle bend, Lennard-Jones, electrostatic interactions and hydrogen bonding which are set up using the Dreiding II force field and defined to account for the helical parameters such as twist, tilt and rise. A close comparison was made between this local minimization algorithm and a global one (previously published) in order to find out advantages and disadvantages of the different methods. From the comparison, this algorithm gives better and faster results than the previous method, allowing one to minimize larger DNA segments. DNA segments with a length of 40 bases need approximately 4 h, while 2.5 weeks are needed with the previous method. After each minimization the angles between phosphate-oxygen-carbon A1, the oxygen-phosphate-oxygen A2 and the average helical twists were calculated. From the generated fragments it was found that the bond angles are A1=150 degrees +/-2 degrees and A2=130 degrees +/-10 degrees, while the helical twist is 36.6 degrees +/-2 degrees in the A strand and A1=150 degrees +/-6 degrees and A2=130+/-6 degrees with helical twist 39.6 degrees +/-2 degrees in the B strand for the DNA segment with the same sequence as the Dickerson dodecamer. PMID:15042433

  13. Evolutionary Structure Prediction of Stoichiometric Compounds

    NASA Astrophysics Data System (ADS)

    Zhu, Qiang; Oganov, Artem

    2014-03-01

    In general, for a given ionic compound AmBn\\ at ambient pressure condition, its stoichiometry reflects the valence state ratio between per chemical specie (i.e., the charges for each anion and cation). However, compounds under high pressure exhibit significantly behavior, compared to those analogs at ambient condition. Here we developed a method to solve the crystal structure prediction problem based on the evolutionary algorithms, which can predict both the stable compounds and their crystal structures at arbitrary P,T-conditions, given just the set of chemical elements. By applying this method to a wide range of binary ionic systems (Na-Cl, Mg-O, Xe-O, Cs-F, etc), we discovered a lot of compounds with brand new stoichimetries which can become thermodynamically stable. Further electronic structure analysis on these novel compounds indicates that several factors can contribute to this extraordinary phenomenon: (1) polyatomic anions; (2) free electron localization; (3) emergence of new valence states; (4) metallization. In particular, part of the results have been confirmed by experiment, which warrants that this approach can play a crucial role in new materials design under extreme pressure conditions. This work is funded by DARPA (Grants No. W31P4Q1210008 and W31P4Q1310005), NSF (EAR-1114313 and DMR-1231586).

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

    PubMed

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

    2014-02-01

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

  15. 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.

  16. On lattice protein structure prediction revisited.

    PubMed

    Dotu, Ivan; Cebrián, Manuel; Van Hentenryck, Pascal; Clote, Peter

    2011-01-01

    Protein structure prediction is regarded as a highly challenging problem both for the biology and for the computational communities. In recent years, many approaches have been developed, moving to increasingly complex lattice models and off-lattice models. This paper presents a Large Neighborhood Search (LNS) to find the native state for the Hydrophobic-Polar (HP) model on the Face-Centered Cubic (FCC) lattice or, in other words, a self-avoiding walk on the FCC lattice having a maximum number of H-H contacts. The algorithm starts with a tabu-search algorithm, whose solution is then improved by a combination of constraint programming and LNS. The flexible framework of this hybrid algorithm allows an adaptation to the Miyazawa-Jernigan contact potential, in place of the HP model, thus suggesting its potential for tertiary structure prediction. Benchmarking statistics are given for our method against the hydrophobic core threading program HPstruct, an exact method which can be viewed as complementary to our method. PMID:21358007

  17. Phylogenetic Approaches to Natural Product Structure Prediction

    PubMed Central

    Ziemert, Nadine; Jensen, Paul R.

    2015-01-01

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

  18. Accurate Prediction of Docked Protein Structure Similarity.

    PubMed

    Akbal-Delibas, Bahar; Pomplun, Marc; Haspel, Nurit

    2015-09-01

    One of the major challenges for protein-protein docking methods is to accurately discriminate nativelike structures. The protein docking community agrees on the existence of a relationship between various favorable intermolecular interactions (e.g. Van der Waals, electrostatic, desolvation forces, etc.) and the similarity of a conformation to its native structure. Different docking algorithms often formulate this relationship as a weighted sum of selected terms and calibrate their weights against specific training data to evaluate and rank candidate structures. However, the exact form of this relationship is unknown and the accuracy of such methods is impaired by the pervasiveness of false positives. Unlike the conventional scoring functions, we propose a novel machine learning approach that not only ranks the candidate structures relative to each other but also indicates how similar each candidate is to the native conformation. We trained the AccuRMSD neural network with an extensive dataset using the back-propagation learning algorithm. Our method achieved predicting RMSDs of unbound docked complexes with 0.4Å error margin. PMID:26335807

  19. Modeling intercalated PAH metabolites: Explanation for the stereochemical and shape selectivity of B-DNA for bay-region carcinogens

    SciTech Connect

    Szentpaly, L.V.; Shamovsky, I.L.

    1996-12-31

    The equilibrium structures of 22 intercalation complexes of different metabolites of polycyclic aromatic hydrocarbons (PAH) with the dG{sub 2}{lg_bullet}dC{sub 2} dinucleotide are obtained by AMBER and FLEX molecular modeling. The triol carbocations of highly potent carcinogens are stereochemically compatible with the dinucleotide and B-DNA. Their intercalation complexes are found (1) to be stabilized by two hydrogen bonds between DH groups of the triol cation and the N(3) atoms of the adjacent guanine residues, (2) to be {open_quotes}preorganized{close_quotes} for covalent bonding to the N(2) amino group of quanine, (3) to display only minor conformational changes with respect to the uncomplexed dinucleotide in B-DNA. A new explanation for the stereochemical and shape selectivity in the initiation of cancer by PAHa is presented. The molecular mechanics study is sugmented by HF/6-31G{sup I} calculations on the conformations of phenanthrene triol carbocation.

  20. Structure prediction of magnetosome-associated proteins.

    PubMed

    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

  1. 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.

  2. 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. PMID:22868650

  3. 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.

  4. 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. PMID:27484371

  5. 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

  6. 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. PMID:26900058

  7. 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.

  8. RNA-SSPT: RNA Secondary Structure Prediction Tools.

    PubMed

    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

  9. RNAComposer and RNA 3D structure prediction for nanotechnology.

    PubMed

    Biesiada, Marcin; Pachulska-Wieczorek, Katarzyna; Adamiak, Ryszard W; Purzycka, Katarzyna J

    2016-07-01

    RNAs adopt specific, stable tertiary architectures to perform their activities. Knowledge of RNA tertiary structure is fundamental to understand RNA functions beginning with transcription and ending with turnover. Contrary to advanced RNA secondary structure prediction algorithms, which allow good accuracy when experimental data are integrated into the prediction, tertiary structure prediction of large RNAs still remains a significant challenge. However, the field of RNA tertiary structure prediction is rapidly developing and new computational methods based on different strategies are emerging. RNAComposer is a user-friendly and freely available server for 3D structure prediction of RNA up to 500 nucleotide residues. RNAComposer employs fully automated fragment assembly based on RNA secondary structure specified by the user. Importantly, this method allows incorporation of distance restraints derived from the experimental data to strengthen the 3D predictions. The potential and limitations of RNAComposer are discussed and an application to RNA design for nanotechnology is presented. PMID:27016145

  10. Protein structure prediction and analysis using the Robetta server

    PubMed Central

    Kim, David E.; Chivian, Dylan; Baker, David

    2004-01-01

    The Robetta server (http://robetta.bakerlab.org) provides automated tools for protein structure prediction and analysis. For structure prediction, sequences submitted to the server are parsed into putative domains and structural models are generated using either comparative modeling or de novo structure prediction methods. If a confident match to a protein of known structure is found using BLAST, PSI-BLAST, FFAS03 or 3D-Jury, it is used as a template for comparative modeling. If no match is found, structure predictions are made using the de novo Rosetta fragment insertion method. Experimental nuclear magnetic resonance (NMR) constraints data can also be submitted with a query sequence for RosettaNMR de novo structure determination. Other current capabilities include the prediction of the effects of mutations on protein–protein interactions using computational interface alanine scanning. The Rosetta protein design and protein–protein docking methodologies will soon be available through the server as well. PMID:15215442

  11. Protein short loop prediction in terms of a structural alphabet.

    PubMed

    Tyagi, Manoj; Bornot, Aurélie; Offmann, Bernard; de Brevern, Alexandre G

    2009-08-01

    Loops connect regular secondary structures. In many instances, they are known to play crucial biological roles. To bypass the limitation of secondary structure description, we previously defined a structural alphabet composed of 16 structural prototypes, called Protein Blocks (PBs). It leads to an accurate description of every region of 3D protein backbones and has been used in local structure prediction. In the present study, we used our structural alphabet to predict the loops connecting two repetitive structures. Thus, we showed interest to take into account the flanking regions, leading to prediction rate improvement up to 19.8%, but we also underline the sensitivity of such an approach. This research can be used to propose different structures for the loops and to probe and sample their flexibility. It is a useful tool for ab initio loop prediction and leads to insights into flexible docking approach. PMID:19625218

  12. Structural coding versus free-energy predictive coding.

    PubMed

    van der Helm, Peter A

    2016-06-01

    Focusing on visual perceptual organization, this article contrasts the free-energy (FE) version of predictive coding (a recent Bayesian approach) to structural coding (a long-standing representational approach). Both use free-energy minimization as metaphor for processing in the brain, but their formal elaborations of this metaphor are fundamentally different. FE predictive coding formalizes it by minimization of prediction errors, whereas structural coding formalizes it by minimization of the descriptive complexity of predictions. Here, both sides are evaluated. A conclusion regarding competence is that FE predictive coding uses a powerful modeling technique, but that structural coding has more explanatory power. A conclusion regarding performance is that FE predictive coding-though more detailed in its account of neurophysiological data-provides a less compelling cognitive architecture than that of structural coding, which, for instance, supplies formal support for the computationally powerful role it attributes to neuronal synchronization. PMID:26407895

  13. SAM-T08, HMM-based protein structure prediction

    PubMed Central

    Karplus, Kevin

    2009-01-01

    The SAM-T08 web server is a protein structure prediction server that provides several useful intermediate results in addition to the final predicted 3D structure: three multiple sequence alignments of putative homologs using different iterated search procedures, prediction of local structure features including various backbone and burial properties, calibrated E-values for the significance of template searches of PDB and residue–residue contact predictions. The server has been validated as part of the CASP8 assessment of structure prediction as having good performance across all classes of predictions. The SAM-T08 server is available at http://compbio.soe.ucsc.edu/SAM_T08/T08-query.html PMID:19483096

  14. Effects of Complementary DNA and Salt on the Thermoresponsiveness of Poly(N-isopropylacrylamide)-b-DNA.

    PubMed

    Fujita, Masahiro; Hiramine, Hayato; Pan, Pengju; Hikima, Takaaki; Maeda, Mizuo

    2016-02-01

    The thermoresponsive structural transition of poly(N-isopropylacrylamide) (PNIPAAm)-b-DNA copolymers was explored. Molecular assembly of the block copolymers was facilitated by adding salt, and this assembly was not nucleated by the association between DNA strands but by the coil-globule transition of PNIPAAm blocks. Below the lower critical solution temperature (LCST) of PNIPAAm, the copolymer solution remained transparent even at high salt concentrations, regardless of whether DNA was hybridized with its complementary partner to form a double-strand (or single-strand) structure. At the LCST, the hybridized copolymer assembled in spherical nanoparticles, surrounded by double-stranded DNA; subsequently, the non-cross-linking aggregation occurred, while the nanoparticles were dispersed if the salt concentration was low or DNA blocks were unhybridized. When the DNA duplex was denatured to a single-stranded state by heating, the aggregated nanoparticles redispersed owing to the recovery of the steric repulsion of the DNA strands. The changes in the steric and electrostatic effects by hybridization and the addition of salt did not result in any specific attraction between DNA strands but merely decreased the repulsive interactions. The van der Waals attraction between the nanoparticles overcame such repulsive interactions so that the non-cross-linking aggregation of the micellar particles was mediated. PMID:26750407

  15. Prediction of binary hard-sphere crystal structures.

    PubMed

    Filion, Laura; Dijkstra, Marjolein

    2009-04-01

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

  16. 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…

  17. Defining and predicting structurally conserved regions in protein superfamilies

    PubMed Central

    Huang, Ivan K.; Grishin, Nick V.

    2013-01-01

    Motivation: The structures of homologous proteins are generally better conserved than their sequences. This phenomenon is demonstrated by the prevalence of structurally conserved regions (SCRs) even in highly divergent protein families. Defining SCRs requires the comparison of two or more homologous structures and is affected by their availability and divergence, and our ability to deduce structurally equivalent positions among them. In the absence of multiple homologous structures, it is necessary to predict SCRs of a protein using information from only a set of homologous sequences and (if available) a single structure. Accurate SCR predictions can benefit homology modelling and sequence alignment. Results: Using pairwise DaliLite alignments among a set of homologous structures, we devised a simple measure of structural conservation, termed structural conservation index (SCI). SCI was used to distinguish SCRs from non-SCRs. A database of SCRs was compiled from 386 SCOP superfamilies containing 6489 protein domains. Artificial neural networks were then trained to predict SCRs with various features deduced from a single structure and homologous sequences. Assessment of the predictions via a 5-fold cross-validation method revealed that predictions based on features derived from a single structure perform similarly to ones based on homologous sequences, while combining sequence and structural features was optimal in terms of accuracy (0.755) and Matthews correlation coefficient (0.476). These results suggest that even without information from multiple structures, it is still possible to effectively predict SCRs for a protein. Finally, inspection of the structures with the worst predictions pinpoints difficulties in SCR definitions. Availability: The SCR database and the prediction server can be found at http://prodata.swmed.edu/SCR. Contact: 91huangi@gmail.com or grishin@chop.swmed.edu Supplementary information: Supplementary data are available at Bioinformatics

  18. RBO Aleph: leveraging novel information sources for protein structure prediction

    PubMed Central

    Mabrouk, Mahmoud; Putz, Ines; Werner, Tim; Schneider, Michael; Neeb, Moritz; Bartels, Philipp; Brock, Oliver

    2015-01-01

    RBO Aleph is a novel protein structure prediction web server for template-based modeling, protein contact prediction and ab initio structure prediction. The server has a strong emphasis on modeling difficult protein targets for which templates cannot be detected. RBO Aleph's unique features are (i) the use of combined evolutionary and physicochemical information to perform residue–residue contact prediction and (ii) leveraging this contact information effectively in conformational space search. RBO Aleph emerged as one of the leading approaches to ab initio protein structure prediction and contact prediction during the most recent Critical Assessment of Protein Structure Prediction experiment (CASP11, 2014). In addition to RBO Aleph's main focus on ab initio modeling, the server also provides state-of-the-art template-based modeling services. Based on template availability, RBO Aleph switches automatically between template-based modeling and ab initio prediction based on the target protein sequence, facilitating use especially for non-expert users. The RBO Aleph web server offers a range of tools for visualization and data analysis, such as the visualization of predicted models, predicted contacts and the estimated prediction error along the model's backbone. The server is accessible at http://compbio.robotics.tu-berlin.de/rbo_aleph/. PMID:25897112

  19. RBO Aleph: leveraging novel information sources for protein structure prediction.

    PubMed

    Mabrouk, Mahmoud; Putz, Ines; Werner, Tim; Schneider, Michael; Neeb, Moritz; Bartels, Philipp; Brock, Oliver

    2015-07-01

    RBO Aleph is a novel protein structure prediction web server for template-based modeling, protein contact prediction and ab initio structure prediction. The server has a strong emphasis on modeling difficult protein targets for which templates cannot be detected. RBO Aleph's unique features are (i) the use of combined evolutionary and physicochemical information to perform residue-residue contact prediction and (ii) leveraging this contact information effectively in conformational space search. RBO Aleph emerged as one of the leading approaches to ab initio protein structure prediction and contact prediction during the most recent Critical Assessment of Protein Structure Prediction experiment (CASP11, 2014). In addition to RBO Aleph's main focus on ab initio modeling, the server also provides state-of-the-art template-based modeling services. Based on template availability, RBO Aleph switches automatically between template-based modeling and ab initio prediction based on the target protein sequence, facilitating use especially for non-expert users. The RBO Aleph web server offers a range of tools for visualization and data analysis, such as the visualization of predicted models, predicted contacts and the estimated prediction error along the model's backbone. The server is accessible at http://compbio.robotics.tu-berlin.de/rbo_aleph/. PMID:25897112

  20. 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

  1. Genome-wide Membrane Protein Structure Prediction

    PubMed Central

    Piccoli, Stefano; Suku, Eda; Garonzi, Marianna; Giorgetti, Alejandro

    2013-01-01

    Transmembrane proteins allow cells to extensively communicate with the external world in a very accurate and specific way. They form principal nodes in several signaling pathways and attract large interest in therapeutic intervention, as the majority pharmaceutical compounds target membrane proteins. Thus, according to the current genome annotation methods, a detailed structural/functional characterization at the protein level of each of the elements codified in the genome is also required. The extreme difficulty in obtaining high-resolution three-dimensional structures, calls for computational approaches. Here we review to which extent the efforts made in the last few years, combining the structural characterization of membrane proteins with protein bioinformatics techniques, could help describing membrane proteins at a genome-wide scale. In particular we analyze the use of comparative modeling techniques as a way of overcoming the lack of high-resolution three-dimensional structures in the human membrane proteome. PMID:24403851

  2. 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.

  3. A physical approach to protein structure prediction: CASP4 results

    SciTech Connect

    Crivelli, Silvia; Eskow, Elizabeth; Bader, Brett; Lamberti, Vincent; Byrd, Richard; Schnabel, Robert; Head-Gordon, Teresa

    2001-02-27

    We describe our global optimization method called Stochastic Perturbation with Soft Constraints (SPSC), which uses information from known proteins to predict secondary structure, but not in the tertiary structure predictions or in generating the terms of the physics-based energy function. Our approach is also characterized by the use of an all atom energy function that includes a novel hydrophobic solvation function derived from experiments that shows promising ability for energy discrimination against misfolded structures. We present the results obtained using our SPSC method and energy function for blind prediction in the 4th Critical Assessment of Techniques for Protein Structure Prediction (CASP4) competition, and show that our approach is more effective on targets for which less information from known proteins is available. In fact our SPSC method produced the best prediction for one of the most difficult targets of the competition, a new fold protein of 240 amino acids.

  4. Quantifying variances in comparative RNA secondary structure prediction

    PubMed Central

    2013-01-01

    Background With the advancement of next-generation sequencing and transcriptomics technologies, regulatory effects involving RNA, in particular RNA structural changes are being detected. These results often rely on RNA secondary structure predictions. However, current approaches to RNA secondary structure modelling produce predictions with a high variance in predictive accuracy, and we have little quantifiable knowledge about the reasons for these variances. Results In this paper we explore a number of factors which can contribute to poor RNA secondary structure prediction quality. We establish a quantified relationship between alignment quality and loss of accuracy. Furthermore, we define two new measures to quantify uncertainty in alignment-based structure predictions. One of the measures improves on the “reliability score” reported by PPfold, and considers alignment uncertainty as well as base-pair probabilities. The other measure considers the information entropy for SCFGs over a space of input alignments. Conclusions Our predictive accuracy improves on the PPfold reliability score. We can successfully characterize many of the underlying reasons for and variances in poor prediction. However, there is still variability unaccounted for, which we therefore suggest comes from the RNA secondary structure predictive model itself. PMID:23634662

  5. 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.

  6. Protein Structure and Function Prediction Using I-TASSER

    PubMed Central

    Yang, Jianyi; Zhang, Yang

    2016-01-01

    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. PMID:26678386

  7. PredyFlexy: flexibility and local structure prediction from sequence

    PubMed Central

    de Brevern, Alexandre G.; Bornot, Aurélie; Craveur, Pierrick; Etchebest, Catherine; Gelly, Jean-Christophe

    2012-01-01

    Protein structures are necessary for understanding protein function at a molecular level. Dynamics and flexibility of protein structures are also key elements of protein function. So, we have proposed to look at protein flexibility using novel methods: (i) using a structural alphabet and (ii) combining classical X-ray B-factor data and molecular dynamics simulations. First, we established a library composed of structural prototypes (LSPs) to describe protein structure by a limited set of recurring local structures. We developed a prediction method that proposes structural candidates in terms of LSPs and predict protein flexibility along a given sequence. Second, we examine flexibility according to two different descriptors: X-ray B-factors considered as good indicators of flexibility and the root mean square fluctuations, based on molecular dynamics simulations. We then define three flexibility classes and propose a method based on the LSP prediction method for predicting flexibility along the sequence. This method does not resort to sophisticate learning of flexibility but predicts flexibility from average flexibility of predicted local structures. The method is implemented in PredyFlexy web server. Results are similar to those obtained with the most recent, cutting-edge methods based on direct learning of flexibility data conducted with sophisticated algorithms. PredyFlexy can be accessed at http://www.dsimb.inserm.fr/dsimb_tools/predyflexy/. PMID:22689641

  8. 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…

  9. Structural class prediction of protein using novel feature extraction method from chaos game representation of predicted secondary structure.

    PubMed

    Zhang, Lichao; Kong, Liang; Han, Xiaodong; Lv, Jinfeng

    2016-07-01

    Protein structural class prediction plays an important role in protein structure and function analysis, drug design and many other biological applications. Extracting good representation from protein sequence is fundamental for this prediction task. In recent years, although several secondary structure based feature extraction strategies have been specially proposed for low-similarity protein sequences, the prediction accuracy still remains limited. To explore the potential of secondary structure information, this study proposed a novel feature extraction method from the chaos game representation of predicted secondary structure to mainly capture sequence order information and secondary structure segments distribution information in a given protein sequence. Several kinds of prediction accuracies obtained by the jackknife test are reported on three widely used low-similarity benchmark datasets (25PDB, 1189 and 640). Compared with the state-of-the-art prediction methods, the proposed method achieves the highest overall accuracies on all the three datasets. The experimental results confirm that the proposed feature extraction method is effective for accurate prediction of protein structural class. Moreover, it is anticipated that the proposed method could be extended to other graphical representations of protein sequence and be helpful in future research. PMID:27084358

  10. Computational methods in sequence and structure prediction

    NASA Astrophysics Data System (ADS)

    Lang, Caiyi

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

  11. 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

  12. WeFold: a coopetition for protein structure prediction.

    PubMed

    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

    2014-09-01

    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 13 labs. During the collaboration, the laboratories were simultaneously competing with each other. Here, we present the first attempt at "coopetition" in scientific research applied to the protein structure prediction and refinement problems. The coopetition was possible by allowing the participating labs to contribute different components of their protein structure prediction pipelines and create new hybrid pipelines that they tested during CASP10. This manuscript describes both successes and areas needing improvement as identified throughout the first WeFold experiment and discusses the efforts that are underway to advance this initiative. A footprint of all contributions and structures are publicly accessible at http://www.wefold.org. PMID:24677212

  13. PREDICTING MODES OF TOXIC ACTION FROM CHEMICAL STRUCTURE: AN OVERVIEW

    EPA Science Inventory

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

  14. 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.

  15. JPred4: a protein secondary structure prediction server.

    PubMed

    Drozdetskiy, Alexey; Cole, Christian; Procter, James; Barton, Geoffrey J

    2015-07-01

    JPred4 (http://www.compbio.dundee.ac.uk/jpred4) is the latest version of the popular JPred protein secondary structure prediction server which provides predictions by the JNet algorithm, one of the most accurate methods for secondary structure prediction. In addition to protein secondary structure, JPred also makes predictions of solvent accessibility and coiled-coil regions. The JPred service runs up to 94 000 jobs per month and has carried out over 1.5 million predictions in total for users in 179 countries. The JPred4 web server has been re-implemented in the Bootstrap framework and JavaScript to improve its design, usability and accessibility from mobile devices. JPred4 features higher accuracy, with a blind three-state (α-helix, β-strand and coil) secondary structure prediction accuracy of 82.0% while solvent accessibility prediction accuracy has been raised to 90% for residues <5% accessible. Reporting of results is enhanced both on the website and through the optional email summaries and batch submission results. Predictions are now presented in SVG format with options to view full multiple sequence alignments with and without gaps and insertions. Finally, the help-pages have been updated and tool-tips added as well as step-by-step tutorials. PMID:25883141

  16. JPred4: a protein secondary structure prediction server

    PubMed Central

    Drozdetskiy, Alexey; Cole, Christian; Procter, James; Barton, Geoffrey J.

    2015-01-01

    JPred4 (http://www.compbio.dundee.ac.uk/jpred4) is the latest version of the popular JPred protein secondary structure prediction server which provides predictions by the JNet algorithm, one of the most accurate methods for secondary structure prediction. In addition to protein secondary structure, JPred also makes predictions of solvent accessibility and coiled-coil regions. The JPred service runs up to 94 000 jobs per month and has carried out over 1.5 million predictions in total for users in 179 countries. The JPred4 web server has been re-implemented in the Bootstrap framework and JavaScript to improve its design, usability and accessibility from mobile devices. JPred4 features higher accuracy, with a blind three-state (α-helix, β-strand and coil) secondary structure prediction accuracy of 82.0% while solvent accessibility prediction accuracy has been raised to 90% for residues <5% accessible. Reporting of results is enhanced both on the website and through the optional email summaries and batch submission results. Predictions are now presented in SVG format with options to view full multiple sequence alignments with and without gaps and insertions. Finally, the help-pages have been updated and tool-tips added as well as step-by-step tutorials. PMID:25883141

  17. Nucleosome structure incorporated histone acetylation site prediction in arabidopsis thaliana

    PubMed Central

    2010-01-01

    Abstract Background Acetylation is a crucial post-translational modification for histones, and plays a key role in gene expression regulation. Due to limited data and lack of a clear acetylation consensus sequence, a few researches have focused on prediction of lysine acetylation sites. Several systematic prediction studies have been conducted for human and yeast, but less for Arabidopsis thaliana. Results Concerning the insufficient observation on acetylation site, we analyzed contributions of the peptide-alignment-based distance definition and 3D structure factors in acetylation prediction. We found that traditional structure contributes little to acetylation site prediction. Identified acetylation sites of histones in Arabidopsis thaliana are conserved and cross predictable with that of human by peptide based methods. However, the predicted specificity is overestimated, because of the existence of non-observed acetylable site. Here, by performing a complete exploration on the factors that affect the acetylability of lysines in histones, we focused on the relative position of lysine at nucleosome level, and defined a new structure feature to promote the performance in predicting the acetylability of all the histone lysines in A. thaliana. Conclusion We found a new spacial correlated acetylation factor, and defined a ε-N spacial location based feature, which contains five core spacial ellipsoid wired areas. By incorporating the new feature, the performance of predicting the acetylability of all the histone lysines in A. Thaliana was promoted, in which the previous mispredicted acetylable lysines were corrected by comparing to the peptide-based prediction. PMID:21047388

  18. A predictive structural model for bulk metallic glasses.

    PubMed

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

    2015-01-01

    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. PMID:26370667

  19. A predictive structural model for bulk metallic glasses

    PubMed Central

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

    2015-01-01

    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. PMID:26370667

  20. OPTIMIZATION BIAS IN ENERGY-BASED STRUCTURE PREDICTION

    PubMed Central

    Petrella, Robert J.

    2014-01-01

    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. PMID:25552783

  1. Gogny HFB prediction of nuclear structure properties

    SciTech Connect

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

    2011-10-28

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

  2. Predicting RNA secondary structures from sequence and probing data.

    PubMed

    Lorenz, Ronny; Wolfinger, Michael T; Tanzer, Andrea; Hofacker, Ivo L

    2016-07-01

    RNA secondary structures have proven essential for understanding the regulatory functions performed by RNA such as microRNAs, bacterial small RNAs, or riboswitches. This success is in part due to the availability of efficient computational methods for predicting RNA secondary structures. Recent advances focus on dealing with the inherent uncertainty of prediction by considering the ensemble of possible structures rather than the single most stable one. Moreover, the advent of high-throughput structural probing has spurred the development of computational methods that incorporate such experimental data as auxiliary information. PMID:27064083

  3. 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.

  4. SAbPred: a structure-based antibody prediction server.

    PubMed

    Dunbar, James; Krawczyk, Konrad; Leem, Jinwoo; Marks, Claire; Nowak, Jaroslaw; Regep, Cristian; Georges, Guy; Kelm, Sebastian; Popovic, Bojana; Deane, Charlotte M

    2016-07-01

    SAbPred is a server that makes predictions of the properties of antibodies focusing on their structures. Antibody informatics tools can help improve our understanding of immune responses to disease and aid in the design and engineering of therapeutic molecules. SAbPred is a single platform containing multiple applications which can: number and align sequences; automatically generate antibody variable fragment homology models; annotate such models with estimated accuracy alongside sequence and structural properties including potential developability issues; predict paratope residues; and predict epitope patches on protein antigens. The server is available at http://opig.stats.ox.ac.uk/webapps/sabpred. PMID:27131379

  5. 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.

  6. 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

  7. Structure Prediction of RNA Loops with a Probabilistic Approach.

    PubMed

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

    2016-08-01

    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

  8. Prediction of residual strength of impact damaged aerospace composite structures

    SciTech Connect

    Garg, A.C.

    1993-12-31

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

  9. Text Prediction on Structured Data Entry in Healthcare

    PubMed Central

    Hua, L.; Wang, S.; Gong, Y.

    2014-01-01

    Summary Background Structured data entry pervades computerized patient safety event reporting systems and serves as a key component in collecting patient-related information in electronic health records. Clinicians would spend more time being with patients and arrive at a high probability of proper diagnosis and treatment, if data entry can be completed efficiently and effectively. Historically it has been proven text prediction holds potential for human performance regarding data entry in a variety of research areas. Objective This study aimed at examining a function of text prediction proposed for increasing efficiency and data quality in structured data entry. Methods We employed a two-group randomized design with fifty-two nurses in this usability study. Each participant was assigned the task of reporting patient falls by answering multiple choice questions either with or without the text prediction function. t-test statistics and linear regression model were applied to analyzing the results of the two groups. Results While both groups of participants exhibited a good capacity of accomplishing the assigned task, the results were an overall 13.0% time reduction and 3.9% increase of response accuracy for the group utilizing the prediction function. Conclusion As a primary attempt investigating the effectiveness of text prediction in healthcare, study findings validated the necessity of text prediction to structured date entry, and laid the ground for further research improving the effectiveness of text prediction in clinical settings. PMID:24734137

  10. 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. PMID:26718864

  11. Protein structure prediction enhanced with evolutionary diversity : SPEED.

    SciTech Connect

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

    2010-03-01

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

  12. 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…

  13. Contingency Table Browser - prediction of early stage protein structure.

    PubMed

    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

  14. 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.

  15. 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

  16. Predicting crystal structures ab initio: group 14 nitrides and phosphides.

    PubMed

    Hart, Judy N; Allan, Neil L; Claeyssens, Frederik

    2010-08-14

    Crystal structures are predicted for a range of group 14 nitrides and phosphides with 1 : 1 stoichiometry, following our method of starting from the known structures for a range of binary compounds and looking for trends in the preferred local bonding environments in the optimised structures. We have previously applied this method to predict the structures of carbon nitride and phosphorus carbide. Here, we use a similar approach to predict the structures of silicon and germanium nitrides and phosphides with 1 : 1 stoichiometry. We find that the local bonding environments in the preferred structures for the nitrides are the same as those for the 3 : 4 stoichiometry. For the phosphides, we have found several possible structures with similar energies. Structures containing hypervalent phosphorus must be considered as these are often low in energy, particularly for GeP; these have not been included in previous work. The greater tendency to form hypervalent phosphorus in GeP than SiP can be rationalised by considering the bond enthalpies for the two compositions. PMID:20603659

  17. Confidence-Guided Local Structure Prediction with HHfrag

    PubMed Central

    Kalev, Ivan; Habeck, Michael

    2013-01-01

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

  18. 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. PMID:25343279

  19. 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

  20. 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. PMID:23971032

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

    PubMed Central

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

    2014-01-01

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

  2. Servers for sequence–structure relationship analysis and prediction

    PubMed Central

    Dosztányi, Zsuzsanna; Magyar, Csaba; Tusnády, Gábor E.; Cserző, Miklós; Fiser, András; Simon, István

    2003-01-01

    We describe several algorithms and public servers that were developed to analyze and predict various features of protein structures. These servers provide information about the covalent state of cysteine (CYSREDOX), as well as about residues involved in non-covalent cross links that play an important role in the structural stability of proteins (SCIDE and SCPRED). We also discuss methods and servers developed to identify helical transmembrane proteins from large databases and rough genomic data, including two of the most popular transmembrane prediction methods, DAS and HMMTOP. Several biologically interesting applications of these servers are also presented. The servers are available through http://www.enzim.hu/servers.html. PMID:12824327

  3. Servers for sequence-structure relationship analysis and prediction.

    PubMed

    Dosztányi, Zsuzsanna; Magyar, Csaba; Tusnády, Gábor E; Cserzo, Miklós; Fiser, András; Simon, István

    2003-07-01

    We describe several algorithms and public servers that were developed to analyze and predict various features of protein structures. These servers provide information about the covalent state of cysteine (CYSREDOX), as well as about residues involved in non-covalent cross links that play an important role in the structural stability of proteins (SCIDE and SCPRED). We also discuss methods and servers developed to identify helical transmembrane proteins from large databases and rough genomic data, including two of the most popular transmembrane prediction methods, DAS and HMMTOP. Several biologically interesting applications of these servers are also presented. The servers are available through http://www.enzim.hu/servers.html. PMID:12824327

  4. Adaptive modelling of structured molecular representations for toxicity prediction

    NASA Astrophysics Data System (ADS)

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

    2012-12-01

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

  5. A novel fold recognition method using composite predicted secondary structures.

    PubMed

    An, Yuling; Friesner, Richard A

    2002-08-01

    In this work, we introduce a new method for fold recognition using composite secondary structures assembled from different secondary structure prediction servers for a given target sequence. An automatic, complete, and robust way of finding all possible combinations of predicted secondary structure segments (SSS) for the target sequence and clustering them into a few flexible clusters, each containing patterns with the same number of SSS, is developed. This program then takes two steps in choosing plausible homologues: (i) a SSS-based alignment excludes impossible templates whose SSS patterns are very different from any of those of the target; (ii) a residue-based alignment selects good structural templates based on sequence similarity and secondary structure similarity between the target and only those templates left in the first stage. The secondary structure of each residue in the target is selected from one of the predictions to find the best match with the template. Truncation is applied to a target where different predictions vary. In most cases, a target is also divided into N-terminal and C-terminal fragments, each of which is used as a separate subsequence. Our program was tested on the fold recognition targets from CASP3 with known PDB codes and some available targets from CASP4. The results are compared with a structural homologue list for each target produced by the CE program (Shindyalov and Bourne, Protein Eng 1998;11:739-747). The program successfully locates homologues with high Z-score and low root-mean-score deviation within the top 30-50 predictions in the overwhelming majority of cases. PMID:12112702

  6. PCI-SS: MISO dynamic nonlinear protein secondary structure prediction

    PubMed Central

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

    2009-01-01

    Background Since the function of a protein is largely dictated by its three dimensional configuration, determining a protein's structure is of fundamental importance to biology. Here we report on a novel approach to determining the one dimensional secondary structure of proteins (distinguishing α-helices, β-strands, and non-regular structures) from primary sequence data which makes use of Parallel Cascade Identification (PCI), a powerful technique from the field of nonlinear system identification. Results Using PSI-BLAST divergent evolutionary profiles as input data, dynamic nonlinear systems are built through a black-box approach to model the process of protein folding. Genetic algorithms (GAs) are applied in order to optimize the architectural parameters of the PCI models. The three-state prediction problem is broken down into a combination of three binary sub-problems and protein structure classifiers are built using 2 layers of PCI classifiers. Careful construction of the optimization, training, and test datasets ensures that no homology exists between any training and testing data. A detailed comparison between PCI and 9 contemporary methods is provided over a set of 125 new protein chains guaranteed to be dissimilar to all training data. Unlike other secondary structure prediction methods, here a web service is developed to provide both human- and machine-readable interfaces to PCI-based protein secondary structure prediction. This server, called PCI-SS, is available at . In addition to a dynamic PHP-generated web interface for humans, a Simple Object Access Protocol (SOAP) interface is added to permit invocation of the PCI-SS service remotely. This machine-readable interface facilitates incorporation of PCI-SS into multi-faceted systems biology analysis pipelines requiring protein secondary structure information, and greatly simplifies high-throughput analyses. XML is used to represent the input protein sequence data and also to encode the resulting

  7. 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.

  8. Prediction of protein folding rates from simplified secondary structure alphabet.

    PubMed

    Huang, Jitao T; Wang, Titi; Huang, Shanran R; Li, Xin

    2015-10-21

    Protein folding is a very complicated and highly cooperative dynamic process. However, the folding kinetics is likely to depend more on a few key structural features. Here we find that secondary structures can determine folding rates of only large, multi-state folding proteins and fails to predict those for small, two-state proteins. The importance of secondary structures for protein folding is ordered as: extended β strand > α helix > bend > turn > undefined secondary structure>310 helix > isolated β strand > π helix. Only the first three secondary structures, extended β strand, α helix and bend, can achieve a good correlation with folding rates. This suggests that the rate-limiting step of protein folding would depend upon the formation of regular secondary structures and the buckling of chain. The reduced secondary structure alphabet provides a simplified description for the machine learning applications in protein design. PMID:26247139

  9. 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. PMID:27251549

  10. Predicting electrical measurements by applying scatterometry to complex spacer structures

    NASA Astrophysics Data System (ADS)

    Sendelbach, Matthew; Ayala, Javier; Herrera, Pedro

    2007-03-01

    The comparison of scatterometry measurements of complex spacer structures to electrical test measurements is discussed. Details of the NFET and PFET structures are presented, along with a summary of the scatterometry models used to represent the structures. Before comparison data are shown, a methodology and set of metrics are presented that assist in the analysis and interpretation of comparison data. The methodology, called Prediction Analysis, has its roots in TMU analysis, where both measurements are subject to error. But in Prediction Analysis, an "apples-to-apples" comparison of the measurements is not the goal, and the measurements may be reported in different units. The goal of Prediction Analysis is to analyze the components of error in a correlation and use this analysis to predict a measurement based on the knowledge of another measurement, such that the predicted measurement is bounded. This method is used in this work to determine how well scatterometry measurements of certain parameters correlate to electrical measurements of gate resistance, gate Lpoly, and transistor current Ion. Clear correlations are demonstrated, and physical explanations that explain these correlations are presented. Due to the correlations, the scatterometry measurements can be used as a predictor of electrical performance significantly before the electrical test occurs. Because of this, scatterometry can be a reliable measurement technique for improving spacer controls and reducing the mean time to detect (MTTD) some profile abnormalities.

  11. Prediction of reactive hazards based on molecular structure.

    PubMed

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

    2003-03-17

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

  12. 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.

  13. 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.

  14. 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.

  15. 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.

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

    PubMed

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

    2014-10-20

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

  17. Predicting PDZ domain mediated protein interactions from structure

    PubMed Central

    2013-01-01

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

  18. Automatic Prediction of Facial Trait Judgments: Appearance vs. Structural Models

    PubMed Central

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

    2011-01-01

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

  19. Ab Initio Prediction of Transcription Factor Targets Using Structural Knowledge

    PubMed Central

    Kaplan, Tommy; Friedman, Nir; Margalit, Hanah

    2005-01-01

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

  20. A dynamic programming algorithm for RNA structure prediction including pseudoknots.

    PubMed

    Rivas, E; Eddy, S R

    1999-02-01

    We describe a dynamic programming algorithm for predicting optimal RNA secondary structure, including pseudoknots. The algorithm has a worst case complexity of O(N6) in time and O(N4) in storage. The description of the algorithm is complex, which led us to adopt a useful graphical representation (Feynman diagrams) borrowed from quantum field theory. We present an implementation of the algorithm that generates the optimal minimum energy structure for a single RNA sequence, using standard RNA folding thermodynamic parameters augmented by a few parameters describing the thermodynamic stability of pseudoknots. We demonstrate the properties of the algorithm by using it to predict structures for several small pseudoknotted and non-pseudoknotted RNAs. Although the time and memory demands of the algorithm are steep, we believe this is the first algorithm to be able to fold optimal (minimum energy) pseudoknotted RNAs with the accepted RNA thermodynamic model. PMID:9925784

  1. Predicting inclusion behaviour and framework structures in organic crystals.

    PubMed

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

    2009-12-01

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

  2. Human papillomavirus type 6b DNA required for initiation but not maintenance of transformation of C127 mouse cells.

    PubMed Central

    Morgan, D; Pecoraro, G; Rosenberg, I; Defendi, V

    1990-01-01

    We describe the transformation of C127 mouse fibroblasts with human papillomavirus type 6b (HPV-6b) DNA, which is associated primarily with benign tumors of the human genital tract. The major transformed phenotype of the HPV-6b-transfected cells lines, which had been G418 selected, pooled, and maintained without subsequent selection, was tumorigenicity in nude mice. We found that, unlike that reported for other HPVs or papovaviruses, the transformed phenotype was expressed after a delay, in which the cells had undergone extensive culture passages (about 20 passages or 100 generations). Interestingly, the HPV-6b DNA had become reduced or nondetectable in copy number in the cells by the time the transformed phenotype was expressed and in most of the tumors induced by the cells in nude mice, indicating that high levels of HPV-6b DNA were not required for maintenance of the transformed phenotype. Clonal cell lines gave similar results. When continued G418 selection was used to maintain high-copy-number HPV-6b DNA, the cells were tumorigenic, indicating that high levels of HPV-6b DNA did not suppress tumorigenesis. These studies suggest that HPV-6b DNA initiates transformation of C127 cells but is dispensable for expression or maintenance of the transformed phenotype. Transformation by HPV-6b DNA in vitro may provide insights into the HPV type-specific association with benign versus malignant lesions in vivo and may elucidate some of the oncogenic processes involved in tumor progression. Images PMID:2154622

  3. Protein Secondary Structure Prediction Using Deep Convolutional Neural Fields.

    PubMed

    Wang, Sheng; Peng, Jian; Ma, Jianzhu; Xu, Jinbo

    2016-01-01

    Protein secondary structure (SS) prediction is important for studying protein structure and function. When only the sequence (profile) information is used as input feature, currently the best predictors can obtain ~80% Q3 accuracy, which has not been improved in the past decade. Here we present DeepCNF (Deep Convolutional Neural Fields) for protein SS prediction. DeepCNF is a Deep Learning extension of Conditional Neural Fields (CNF), which is an integration of Conditional Random Fields (CRF) and shallow neural networks. DeepCNF can model not only complex sequence-structure relationship by a deep hierarchical architecture, but also interdependency between adjacent SS labels, so it is much more powerful than CNF. Experimental results show that DeepCNF can obtain ~84% Q3 accuracy, ~85% SOV score, and ~72% Q8 accuracy, respectively, on the CASP and CAMEO test proteins, greatly outperforming currently popular predictors. As a general framework, DeepCNF can be used to predict other protein structure properties such as contact number, disorder regions, and solvent accessibility. PMID:26752681

  4. Protein Secondary Structure Prediction Using Deep Convolutional Neural Fields

    PubMed Central

    Wang, Sheng; Peng, Jian; Ma, Jianzhu; Xu, Jinbo

    2016-01-01

    Protein secondary structure (SS) prediction is important for studying protein structure and function. When only the sequence (profile) information is used as input feature, currently the best predictors can obtain ~80% Q3 accuracy, which has not been improved in the past decade. Here we present DeepCNF (Deep Convolutional Neural Fields) for protein SS prediction. DeepCNF is a Deep Learning extension of Conditional Neural Fields (CNF), which is an integration of Conditional Random Fields (CRF) and shallow neural networks. DeepCNF can model not only complex sequence-structure relationship by a deep hierarchical architecture, but also interdependency between adjacent SS labels, so it is much more powerful than CNF. Experimental results show that DeepCNF can obtain ~84% Q3 accuracy, ~85% SOV score, and ~72% Q8 accuracy, respectively, on the CASP and CAMEO test proteins, greatly outperforming currently popular predictors. As a general framework, DeepCNF can be used to predict other protein structure properties such as contact number, disorder regions, and solvent accessibility. PMID:26752681

  5. Protein Secondary Structure Prediction Using Deep Convolutional Neural Fields

    NASA Astrophysics Data System (ADS)

    Wang, Sheng; Peng, Jian; Ma, Jianzhu; Xu, Jinbo

    2016-01-01

    Protein secondary structure (SS) prediction is important for studying protein structure and function. When only the sequence (profile) information is used as input feature, currently the best predictors can obtain ~80% Q3 accuracy, which has not been improved in the past decade. Here we present DeepCNF (Deep Convolutional Neural Fields) for protein SS prediction. DeepCNF is a Deep Learning extension of Conditional Neural Fields (CNF), which is an integration of Conditional Random Fields (CRF) and shallow neural networks. DeepCNF can model not only complex sequence-structure relationship by a deep hierarchical architecture, but also interdependency between adjacent SS labels, so it is much more powerful than CNF. Experimental results show that DeepCNF can obtain ~84% Q3 accuracy, ~85% SOV score, and ~72% Q8 accuracy, respectively, on the CASP and CAMEO test proteins, greatly outperforming currently popular predictors. As a general framework, DeepCNF can be used to predict other protein structure properties such as contact number, disorder regions, and solvent accessibility.

  6. 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

  7. Prediction of the structure of symmetrical protein assemblies

    PubMed Central

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

    2007-01-01

    Biological supramolecular systems are commonly built up by the self-assembly of identical protein subunits to produce symmetrical oligomers with cyclical, icosahedral, or helical symmetry that play roles in processes ranging from allosteric control and molecular transport to motor action. The large size of these systems often makes them difficult to structurally characterize using experimental techniques. We have developed a computational protocol to predict the structure of symmetrical protein assemblies based on the structure of a single subunit. The method carries out simultaneous optimization of backbone, side chain, and rigid-body degrees of freedom, while restricting the search space to symmetrical conformations. Using this protocol, we can reconstruct, starting from the structure of a single subunit, the structure of cyclic oligomers and the icosahedral virus capsid of satellite panicum virus using a rigid backbone approximation. We predict the oligomeric state of EscJ from the type III secretion system both in its proposed cyclical and crystallized helical form. Finally, we show that the method can recapitulate the structure of an amyloid-like fibril formed by the peptide NNQQNY from the yeast prion protein Sup35 starting from the amino acid sequence alone and searching the complete space of backbone, side chain, and rigid-body degrees of freedom. PMID:17978193

  8. Structure based activity prediction of HIV-1 reverse transcriptase inhibitors.

    PubMed

    de Jonge, Marc R; Koymans, Lucien M H; Vinkers, H Maarten; Daeyaert, Frits F D; Heeres, Jan; Lewi, Paul J; Janssen, Paul A J

    2005-03-24

    We have developed a fast and robust computational method for prediction of antiviral activity in automated de novo design of HIV-1 reverse transcriptase inhibitors. This is a structure-based approach that uses a linear relation between activity and interaction energy with discrete orientation sampling and with localized interaction energy terms. The localization allows for the analysis of mutations of the protein target and for the separation of inhibition and a specific binding to the enzyme. We apply the method to the prediction of pIC(50) of HIV-1 reverse transcriptase inhibitors. The model predicts the activity of an arbitrary compound with a q(2) of 0.681 and an average absolute error of 0.66 log value, and it is fast enough to be used in high-throughput computational applications. PMID:15771460

  9. Predicting loop–helix tertiary structural contacts in RNA pseudoknots

    PubMed Central

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

    2010-01-01

    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 (ΔH) of −14 kcal/mol and a contact entropy (ΔS) of −38 cal/mol/K for a protonated C+•(G–C) base triple at pH 7.0, and (ΔH = −7 kcal/mol, ΔS = −19 cal/mol/K) for an unprotonated base triple. Tests of the model for a series of pseudoknots show good theory–experiment agreement. Based on the extracted energy parameters for the tertiary structural contacts, the model enables predictions for the structure, stability, and folding pathways for RNA pseudoknots with known or postulated loop–stem tertiary contacts from the nucleotide sequence alone. PMID:20100813

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

    NASA Astrophysics Data System (ADS)

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

    2016-08-01

    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.

  11. 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.

  12. A Hybrid Loss for Multiclass and Structured Prediction.

    PubMed

    Shi, Qinfeng; Reid, Mark; Caetano, Tiberio; van den Hengel, Anton; Wang, Zhenhua

    2015-01-01

    We propose a novel hybrid loss for multiclass and structured prediction problems that is a convex combination of a log loss for Conditional Random Fields (CRFs) and a multiclass hinge loss for Support Vector Machines (SVMs). We provide a sufficient condition for when the hybrid loss is Fisher consistent for classification. This condition depends on a measure of dominance between labels-specifically, the gap between the probabilities of the best label and the second best label. We also prove Fisher consistency is necessary for parametric consistency when learning models such as CRFs. We demonstrate empirically that the hybrid loss typically performs least as well as-and often better than-both of its constituent losses on a variety of tasks, such as human action recognition. In doing so we also provide an empirical comparison of the efficacy of probabilistic and margin based approaches to multiclass and structured prediction. PMID:26353204

  13. Structure-Based Prediction of Protein-Folding Transition Paths.

    PubMed

    Jacobs, William M; Shakhnovich, Eugene I

    2016-09-01

    We propose a general theory to describe the distribution of protein-folding transition paths. We show that transition paths follow a predictable sequence of high-free-energy transient states that are separated by free-energy barriers. Each transient state corresponds to the assembly of one or more discrete, cooperative units, which are determined directly from the native structure. We show that the transition state on a folding pathway is reached when a small number of critical contacts are formed between a specific set of substructures, after which folding proceeds downhill in free energy. This approach suggests a natural resolution for distinguishing parallel folding pathways and provides a simple means to predict the rate-limiting step in a folding reaction. Our theory identifies a common folding mechanism for proteins with diverse native structures and establishes general principles for the self-assembly of polymers with specific interactions. PMID:27602721

  14. 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. PMID:27563967

  15. 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. PMID:23982106

  16. Prediction of protein structural classes and subcellular locations.

    PubMed

    Chou, K C

    2000-09-01

    The structural class and subcellular location are the two important features of proteins that are closely related to their biological functions. With the rapid increase in new protein sequences entering into data banks, it is highly desirable to develop a fast and accurate method for predicting the attributes of these features for them. This can expedite the functionality determination of new proteins and the process of prioritizing genes and proteins identified by genomics efforts as potential molecular targets for drug design. Various prediction methods have been developed during the last two decades. This review is devoted to presenting a systematic introduction and comparison of the existing methods in respect to the prediction algorithm and classification scheme. The attention is focused on the state-of-the-art, which is featured by the covarient-discriminant algorithm developed very recently, as well as some new classification schemes for protein structural classes and subcellular locations. Particularly, addressed are also the physical chemistry foundation of the existing prediction methods, and the essence why the covariant-discriminant algorithm is so powerful. PMID:12369916

  17. 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. PMID:25260851

  18. Predicting olfactory receptor neuron responses from odorant structure

    PubMed Central

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

    2007-01-01

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

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

    PubMed

    Gonnelli, Giulia; Rooman, Marianne; Dehouck, Yves

    2012-10-31

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

  20. A tool for the prediction of structures of complex sugars.

    PubMed

    Xia, Junchao; Margulis, Claudio

    2008-12-01

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

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

    PubMed Central

    Wu, Sitao; Szilagyi, Andras; Zhang, Yang

    2011-01-01

    Summary Although residue-residue contact maps dictate the topology of proteins, sequence-based ab initio contact predictions have been found little use in actual structure prediction due to the low accuracy. We developed a composite set of nine SVM-based contact predictors which are used in I-TASSER simulation in combination with sparse template contact restraints. When testing the strategy on 273 non-homologous targets, remarkable improvements of I-TASSER models were observed for both easy and hard targets, with P-value by student s t-test below 0.00001 and 0.001, respectively. In several cases, TM-score increases by >30%, which essentially converts “non-foldable” targets into “foldable” ones. In CASP9, I-TASSER employed ab initio contact predictions, and generated models for 26 FM targets with a GDT-score 16% and 44% higher than the second and third best servers from other groups, respectively. These findings demonstrate a new avenue to improve the accuracy of protein structure prediction especially for free-modeling targets. PMID:21827953

  2. 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.

  3. 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. PMID:25069136

  4. Protein secondary structure prediction using logic-based machine learning.

    PubMed

    Muggleton, S; King, R D; Sternberg, M J

    1992-10-01

    Many attempts have been made to solve the problem of predicting protein secondary structure from the primary sequence but the best performance results are still disappointing. In this paper, the use of a machine learning algorithm which allows relational descriptions is shown to lead to improved performance. The Inductive Logic Programming computer program, Golem, was applied to learning secondary structure prediction rules for alpha/alpha domain type proteins. The input to the program consisted of 12 non-homologous proteins (1612 residues) of known structure, together with a background knowledge describing the chemical and physical properties of the residues. Golem learned a small set of rules that predict which residues are part of the alpha-helices--based on their positional relationships and chemical and physical properties. The rules were tested on four independent non-homologous proteins (416 residues) giving an accuracy of 81% (+/- 2%). This is an improvement, on identical data, over the previously reported result of 73% by King and Sternberg (1990, J. Mol. Biol., 216, 441-457) using the machine learning program PROMIS, and of 72% using the standard Garnier-Osguthorpe-Robson method. The best previously reported result in the literature for the alpha/alpha domain type is 76%, achieved using a neural net approach. Machine learning also has the advantage over neural network and statistical methods in producing more understandable results. PMID:1480619

  5. 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

  6. 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

  7. Translocation and deletion breakpoints in cancer genomes are associated with potential non-B DNA-forming sequences.

    PubMed

    Bacolla, Albino; Tainer, John A; Vasquez, Karen M; Cooper, David N

    2016-07-01

    Gross chromosomal rearrangements (including translocations, deletions, insertions and duplications) are a hallmark of cancer genomes and often create oncogenic fusion genes. An obligate step in the generation of such gross rearrangements is the formation of DNA double-strand breaks (DSBs). Since the genomic distribution of rearrangement breakpoints is non-random, intrinsic cellular factors may predispose certain genomic regions to breakage. Notably, certain DNA sequences with the potential to fold into secondary structures [potential non-B DNA structures (PONDS); e.g. triplexes, quadruplexes, hairpin/cruciforms, Z-DNA and single-stranded looped-out structures with implications in DNA replication and transcription] can stimulate the formation of DNA DSBs. Here, we tested the postulate that these DNA sequences might be found at, or in close proximity to, rearrangement breakpoints. By analyzing the distribution of PONDS-forming sequences within ±500 bases of 19 947 translocation and 46 365 sequence-characterized deletion breakpoints in cancer genomes, we find significant association between PONDS-forming repeats and cancer breakpoints. Specifically, (AT)n, (GAA)n and (GAAA)n constitute the most frequent repeats at translocation breakpoints, whereas A-tracts occur preferentially at deletion breakpoints. Translocation breakpoints near PONDS-forming repeats also recur in different individuals and patient tumor samples. Hence, PONDS-forming sequences represent an intrinsic risk factor for genomic rearrangements in cancer genomes. PMID:27084947

  8. Translocation and deletion breakpoints in cancer genomes are associated with potential non-B DNA-forming sequences

    PubMed Central

    Bacolla, Albino; Tainer, John A.; Vasquez, Karen M.; Cooper, David N.

    2016-01-01

    Gross chromosomal rearrangements (including translocations, deletions, insertions and duplications) are a hallmark of cancer genomes and often create oncogenic fusion genes. An obligate step in the generation of such gross rearrangements is the formation of DNA double-strand breaks (DSBs). Since the genomic distribution of rearrangement breakpoints is non-random, intrinsic cellular factors may predispose certain genomic regions to breakage. Notably, certain DNA sequences with the potential to fold into secondary structures [potential non-B DNA structures (PONDS); e.g. triplexes, quadruplexes, hairpin/cruciforms, Z-DNA and single-stranded looped-out structures with implications in DNA replication and transcription] can stimulate the formation of DNA DSBs. Here, we tested the postulate that these DNA sequences might be found at, or in close proximity to, rearrangement breakpoints. By analyzing the distribution of PONDS-forming sequences within ±500 bases of 19 947 translocation and 46 365 sequence-characterized deletion breakpoints in cancer genomes, we find significant association between PONDS-forming repeats and cancer breakpoints. Specifically, (AT)n, (GAA)n and (GAAA)n constitute the most frequent repeats at translocation breakpoints, whereas A-tracts occur preferentially at deletion breakpoints. Translocation breakpoints near PONDS-forming repeats also recur in different individuals and patient tumor samples. Hence, PONDS-forming sequences represent an intrinsic risk factor for genomic rearrangements in cancer genomes. PMID:27084947

  9. Predicting the stability of large structured food webs.

    PubMed

    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

  10. 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

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

    PubMed

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

    2016-07-01

    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

  12. 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

  13. 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

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

    PubMed

    Palopoli, Nicolas; Monzon, Alexander Miguel; 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

  15. PREDICTING RNA STRUCTURE BY MULTIPLE TEMPLATE HOMOLOGY MODELING

    PubMed Central

    FLORES, SAMUEL C.; WAN, YAQI; RUSSELL, RICK; ALTMAN, RUSS B.

    2010-01-01

    Despite the importance of 3D structure to understand the myriad functions of RNAs in cells, most RNA molecules remain out of reach of crystallographic and NMR methods. However, certain structural information such as base pairing and some tertiary contacts can be determined readily for many RNAs by bioinformatics or relatively low cost experiments. Further, because RNA structure is highly modular, it is possible to deduce local 3D structure from the solved structures of evolutionarily related RNAs or even unrelated RNAs that share the same module. RNABuilder is a software package that generates model RNA structures by treating the kinematics and forces at separate, multiple levels of resolution. Kinematically, bonds in bases, certain stretches of residues, and some entire molecules are rigid while other bonds remain flexible. Forces act on the rigid bases and selected individual atoms. Here we use RNABuilder to predict the structure of the 200-nucleotide Azoarcus group I intron by homology modeling against fragments of the distantly-related Twort and Tetrahymena group I introns and by incorporating base pairing forces where necessary. In the absence of any information from the solved Azoarcus intron crystal structure, the model accurately depicts the global topology, secondary and tertiary connections, and gives an overall RMSD value of 4.6 Å relative to the crystal structure. The accuracy of the model is even higher in the intron core (RMSD = 3.5 Å), whereas deviations are modestly larger for peripheral regions that differ more substantially between the different introns. These results lay the groundwork for using this approach for larger and more diverse group I introns, as well for still larger RNAs and RNA-protein complexes such as group II introns and the ribosomal subunits. PMID:19908374

  16. FOURIER ANALYSIS OF EXTENDED FINE STRUCTURE WITH AUTOREGRESSIVE PREDICTION

    SciTech Connect

    Barton, J.; Shirley, D.A.

    1985-01-01

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

  17. A protein structural class prediction method based on novel features.

    PubMed

    Zhang, Lichao; Zhao, Xiqiang; Kong, Liang

    2013-09-01

    In this study, a 12-dimensional feature vector is constructed to reflect the general contents and spatial arrangements of the secondary structural elements of a given protein sequence. Among the 12 features, 6 novel features are specially designed to improve the prediction accuracies for α/β and α + β classes based on the distributions of α-helices and β-strands and the characteristics of parallel β-sheets and anti-parallel β-sheets. To evaluate our method, the jackknife cross-validating test is employed on two widely-used datasets, 25PDB and 1189 datasets with sequence similarity lower than 40% and 25%, respectively. The performance of our method outperforms the recently reported methods in most cases, and the 6 newly-designed features have significant positive effect to the prediction accuracies, especially for α/β and α + β classes. PMID:23770446

  18. Symmetry-adapted digital modeling II. The double-helix B-DNA.

    PubMed

    Janner, A

    2016-05-01

    The positions of phosphorus in B-DNA have the remarkable property of occurring (in axial projection) at well defined points in the three-dimensional space of a projected five-dimensional decagonal lattice, subdividing according to the golden mean ratio τ:1:τ [with τ = (1+\\sqrt {5})/2] the edges of an enclosing decagon. The corresponding planar integral indices n1, n2, n3, n4 (which are lattice point coordinates) are extended to include the axial index n5 as well, defined for each P position of the double helix with respect to the single decagonal lattice ΛP(aP, cP) with aP = 2.222 Å and cP = 0.676 Å. A finer decagonal lattice Λ(a, c), with a = aP/6 and c = cP, together with a selection of lattice points for each nucleotide with a given indexed P position (so as to define a discrete set in three dimensions) permits the indexing of the atomic positions of the B-DNA d(AGTCAGTCAG) derived by M. J. P. van Dongen. This is done for both DNA strands and the single lattice Λ. Considered first is the sugar-phosphate subsystem, and then each nucleobase guanine, adenine, cytosine and thymine. One gets in this way a digital modeling of d(AGTCAGTCAG) in a one-to-one correspondence between atomic and indexed positions and a maximal deviation of about 0.6 Å (for the value of the lattice parameters given above). It is shown how to get a digital modeling of the B-DNA double helix for any given code. Finally, a short discussion indicates how this procedure can be extended to derive coarse-grained B-DNA models. An example is given with a reduction factor of about 2 in the number of atomic positions. A few remarks about the wider interest of this investigation and possible future developments conclude the paper. PMID:27126108

  19. Protein-protein interface prediction based on hexagon structure similarity.

    PubMed

    Guo, Fei; Ding, Yijie; Li, Shuai Cheng; Shen, Chao; Wang, Lusheng

    2016-08-01

    Studies on protein-protein interaction are important in proteome research. How to build more effective models based on sequence information, structure information and physicochemical characteristics, is the key technology in protein-protein interface prediction. In this paper, we study the protein-protein interface prediction problem. We propose a novel method for identifying residues on interfaces from an input protein with both sequence and 3D structure information, based on hexagon structure similarity. Experiments show that our method achieves better results than some state-of-the-art methods for identifying protein-protein interface. Comparing to existing methods, our approach improves F-measure value by at least 0.03. On a common dataset consisting of 41 complexes, our method has overall precision and recall values of 63% and 57%. On Benchmark v4.0, our method has overall precision and recall values of 55% and 56%. On CAPRI targets, our method has overall precision and recall values of 52% and 55%. PMID:26936323

  20. Cortical structure predicts success in performing musical transformation judgments.

    PubMed

    Foster, Nicholas E V; Zatorre, Robert J

    2010-10-15

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

  1. Tailor-made force fields for crystal-structure prediction.

    PubMed

    Neumann, Marcus A

    2008-08-14

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

  2. Structure prediction and analysis of neuraminidase sequence variants.

    PubMed

    Thayer, Kelly M

    2016-07-01

    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 highly mutable influenza virus protein neuraminidase, which is the key target in the development of therapeutics. In light of recent pandemics, understanding how mutations confer drug resistance, which translates at the molecular level to understanding how different sequence variants differ, constitutes an area of great interest because of the ramifications in public health. This lab targets upper level undergraduate biochemistry students, and aims to introduce tools to be used to explore protein folding and protein visualization in the context of the neuraminidase case study. Students proceed to critically evaluate the folded models by comparison with crystallographic structures. When validity is established, they fold a neuraminidase sequence for which a structure is not available. Through structural alignment and visual inspection of the 150 loop, students gain molecular insight into two possible conformations of the protein, which are actively being studied. Folding the third chosen sequence mimics a true research environment in allowing students to generate a structure from a sequence for which a structure was not previously available, and to assess whether their particular variant has an open or closed loop. From this vantage, they are then challenged to speculate about the connection between loop conformation and drug susceptibility. © 2016 by The International Union of Biochemistry and Molecular Biology, 44(4):361-376, 2016. PMID:26900942

  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. The sequential structure of brain activation predicts skill.

    PubMed

    Anderson, John R; Bothell, Daniel; Fincham, Jon M; Moon, Jungaa

    2016-01-29

    In an fMRI study, participants were trained to play a complex video game. They were scanned early and then again after substantial practice. While better players showed greater activation in one region (right dorsal striatum) their relative skill was better diagnosed by considering the sequential structure of whole brain activation. Using a cognitive model that played this game, we extracted a characterization of the mental states that are involved in playing a game and the statistical structure of the transitions among these states. There was a strong correspondence between this measure of sequential structure and the skill of different players. Using multi-voxel pattern analysis, it was possible to recognize, with relatively high accuracy, the cognitive states participants were in during particular scans. We used the sequential structure of these activation-recognized states to predict the skill of individual players. These findings indicate that important features about information-processing strategies can be identified from a model-based analysis of the sequential structure of brain activation. PMID:26707716

  5. 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.

  6. 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.

  7. 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.

  8. 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.

  9. Predicted novel hydrogen hydrate structures under pressure from first principles

    NASA Astrophysics Data System (ADS)

    Qian, Guangrui; Lyakhov, Andriy; Zhu, Qiang; Oganov, Artem; Dong, Xiao

    2014-03-01

    Gas hydrates are systems of prime importance. In particular, hydrogen hydrates are potential materials of icy satellites and comets, and may be used for hydrogen storage. We explore the H2O-H2 system at pressures in the range 0 ~ 100 GPa with ab initio variable-composition evolutionary simulations. According to our calculation and previous experiments, the H2O-H2 system undergoes a series of transformations with pressure, and adopts the known open-network clathrate structures (sII, C0), dense ``filled ice'' structures (C1, C2) and two novel hydrogen hydrate phases. One of these structures is based on the hexagonal ice framework and has the same H2O:H2 ratio (2:1) as the C0 phase at low pressures and similar enthalpy (we name this phase Ih-C0). The other newly predicted hydrate phase has a 1:2 H2O:H2 ratio and structure based on cubic ice. This phase (which we name C3) is predicted to be thermodynamically stable above 38 GPa when including van der Waals interactions and zero-point vibrational energy. This is the hydrogen-richest hydrate and this phase has the highest gravimetric densities (18 wt.%) of extractable hydrogen among all known materials. We thank the DARPA (Grants No. W31P4Q1310005 and No. W31P4Q1210008), National Science Founda- tion (EAR-1114313, DMR-1231586), AFOSR (FA9550- 13-C-0037), DOE (DE-AC02-98CH10886), CRDF Global (UKE2-7034-KV-11) for financial support. We thank Purdue University Teragrid for providing computational resources and technical support for this work (Charge No.: TG-DMR110058).

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

    NASA Astrophysics Data System (ADS)

    Kasahara, Naoto

    Elevated temperature structural design codes pay attention to strain concentration at structural discontinuities due to creep and plasticity, since it causes an increase in creep-fatigue damage of materials. One of the difficulties in predicting strain concentration is its dependence on the magnitude of loading, the constitutive equations, and the duration of loading. In this study, the author investigated the fundamental mechanism of strain concentration and its main factors. The results revealed that strain concentration is caused by strain redistribution between elastic and inelastic regions, which can be quantified by the characteristics of structural compliance. The characteristics of structural compliance are controlled by elastic region in structures and are insensitive to constitutive equations. It means that inelastic analysis can be easily applied to obtain compliance characteristics. By utilizing this fact, a simplified inelastic analysis method was proposed based on the characteristics of compliance change for the prediction of strain concentration.

  11. Unbiased charge oscillations in B-DNA: monomer polymers and dimer polymers.

    PubMed

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

    2015-09-01

    We call monomer a B-DNA base pair and examine, analytically and numerically, electron or hole oscillations in monomer and dimer polymers, i.e., periodic sequences with repetition unit made of one or two monomers. We employ a tight-binding (TB) approach at the base-pair level to readily determine the spatiotemporal evolution of a single extra carrier along a N base-pair B-DNA segment. We study highest occupied molecular orbital and lowest unoccupied molecular orbital eigenspectra as well as the mean over time probabilities to find the carrier at a particular monomer. We use the pure mean transfer rate k to evaluate the easiness of charge transfer. The inverse decay length β for exponential fits k(d), where d is the charge transfer distance, and the exponent η for power-law fits k(N) are computed; generally power-law fits are better. We illustrate that increasing the number of different parameters involved in the TB description, the fall of k(d) or k(N) becomes steeper and show the range covered by β and η. Finally, for both the time-independent and the time-dependent problems, we analyze the palindromicity and the degree of eigenspectrum dependence of the probabilities to find the carrier at a particular monomer. PMID:26465516

  12. Unbiased charge oscillations in B-DNA: Monomer polymers and dimer polymers

    NASA Astrophysics Data System (ADS)

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

    2015-09-01

    We call monomer a B-DNA base pair and examine, analytically and numerically, electron or hole oscillations in monomer and dimer polymers, i.e., periodic sequences with repetition unit made of one or two monomers. We employ a tight-binding (TB) approach at the base-pair level to readily determine the spatiotemporal evolution of a single extra carrier along a N base-pair B-DNA segment. We study highest occupied molecular orbital and lowest unoccupied molecular orbital eigenspectra as well as the mean over time probabilities to find the carrier at a particular monomer. We use the pure mean transfer rate k to evaluate the easiness of charge transfer. The inverse decay length β for exponential fits k (d ) , where d is the charge transfer distance, and the exponent η for power-law fits k (N ) are computed; generally power-law fits are better. We illustrate that increasing the number of different parameters involved in the TB description, the fall of k (d ) or k (N ) becomes steeper and show the range covered by β and η . Finally, for both the time-independent and the time-dependent problems, we analyze the palindromicity and the degree of eigenspectrum dependence of the probabilities to find the carrier at a particular monomer.

  13. 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.

  14. Predicting protein structural class with AdaBoost Learner.

    PubMed

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

    2006-01-01

    The structural class is an important feature in characterizing the overall topological folding type of a protein or the domains therein. Prediction of protein structural classification has attracted the attention and efforts from many investigators. In this paper a novel predictor, the AdaBoost Learner, was introduced to deal with this problem. The essence of the AdaBoost Learner is that a combination of many 'weak' learning algorithms, each performing just slightly better than a random guessing algorithm, will generate a 'strong' learning algorithm. Demonstration thru jackknife cross-validation on two working datasets constructed by previous investigators indicated that AdaBoost outperformed other predictors such as SVM (support vector machine), a powerful algorithm widely used in biological literatures. It has not escaped our notice that AdaBoost may hold a high potential for improving the quality in predicting the other protein features as well, such as subcellular location and receptor type, among many others. Or at the very least, it will play a complementary role to many of the existing algorithms in this regard. PMID:16800803

  15. Predicting the bifurcation structure of localized snaking patterns

    NASA Astrophysics Data System (ADS)

    Makrides, Elizabeth; Sandstede, Björn

    2014-02-01

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

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

    PubMed

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

    2010-06-01

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

  17. 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

  18. 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

  19. 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.

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

    PubMed

    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

  1. 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

  2. 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.

  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. PMID:26246559

  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. 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

  7. 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.

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

    PubMed

    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. PMID:16241480

  9. 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.

  10. 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

  11. 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.

  12. Applications of tree-structured regression for regional precipitation prediction

    NASA Astrophysics Data System (ADS)

    Li, Xiangshang

    2000-11-01

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

  13. Automatic measurement of voice onset time using discriminative structured prediction.

    PubMed

    Sonderegger, Morgan; Keshet, Joseph

    2012-12-01

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

  14. An RNA secondary structure prediction method based on minimum and suboptimal free energy structures.

    PubMed

    Fu, Haoyue; Yang, Lianping; Zhang, Xiangde

    2015-09-01

    The function of an RNA-molecule is mainly determined by its tertiary structures. And its secondary structure is an important determinant of its tertiary structure. The comparative methods usually give better results than the single-sequence methods. Based on minimum and suboptimal free energy structures, the paper presents a novel method for predicting conserved secondary structure of a group of related RNAs. In the method, the information from the known RNA structures is used as training data in a SVM (Support Vector Machine) classifier. Our method has been tested on the benchmark dataset given by Puton et al. The results show that the average sensitivity of our method is higher than that of other comparative methods such as CentroidAlifold, MXScrana, RNAalifold, and TurboFold. PMID:26100179

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

    NASA Astrophysics Data System (ADS)

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

    2004-04-01

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

  16. Application of cytochrome b DNA sequences for the authentication of endangered snake species.

    PubMed

    Wong, Ka-Lok; Wang, Jun; But, Paul Pui-Hay; Shaw, Pang-Chui

    2004-01-01

    In order to enforce the conservation program and curbing the illegal trading and consumption of endangered snake species, the value of cytochrome b sequence in the authentication of snake species was evaluated. As an illustration, DNA was extracted, selected cytochrome b DNA sequences amplified and sequenced from six snakes commonly consumed in Hong Kong. Cataloging with sequences available in public, a cytochrome b database containing 90 species of snakes was constructed. In this database, sequence homology between snakes ranged from 70.68 to 95.11%. On the other hand, intraspecific variation of three tested snakes was 0-0.98%. Using the database, we were able to determine the identity of six meat samples confiscated by the Agriculture, Fisheries and Conservation Department, HKSAR. PMID:14687773

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

    PubMed Central

    2013-01-01

    Background Accurate secondary structure prediction provides important information to undefirstafinding the tertiary structures and thus the functions of ncRNAs. However, the accuracy of the native structure derivation of ncRNAs is still not satisfactory, especially on sequences containing pseudoknots. It is recently shown that using the abstract shapes, which retain adjacency and nesting of structural features but disregard the length details of helix and loop regions, can improve the performance of structure prediction. In this work, we use SVM-based feature selection to derive the consensus abstract shape of homologous ncRNAs and apply the predicted shape to structure prediction including pseudoknots. Results Our approach was applied to predict shapes and secondary structures on hundreds of ncRNA data sets with and without psuedoknots. The experimental results show that we can achieve 18% higher accuracy in shape prediction than the state-of-the-art consensus shape prediction tools. Using predicted shapes in structure prediction allows us to achieve approximate 29% higher sensitivity and 10% higher positive predictive value than other pseudoknot prediction tools. Conclusions Extensive analysis of RNA properties based on SVM allows us to identify important properties of sequences and structures related to their shapes. The combination of mass data analysis and SVM-based feature selection makes our approach a promising method for shape and structure prediction. The implemented tools, Knot Shape and Knot Structure are open source software and can be downloaded at: http://www.cse.msu.edu/~achawana/KnotShape. PMID:23369147

  18. 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

  19. Accurate prediction of protein secondary structure and solvent accessibility by consensus combiners of sequence and structure information

    PubMed Central

    Pollastri, Gianluca; Martin, Alberto JM; Mooney, Catherine; Vullo, Alessandro

    2007-01-01

    Background Structural properties of proteins such as secondary structure and solvent accessibility contribute to three-dimensional structure prediction, not only in the ab initio case but also when homology information to known structures is available. Structural properties are also routinely used in protein analysis even when homology is available, largely because homology modelling is lower throughput than, say, secondary structure prediction. Nonetheless, predictors of secondary structure and solvent accessibility are virtually always ab initio. Results Here we develop high-throughput machine learning systems for the prediction of protein secondary structure and solvent accessibility that exploit homology to proteins of known structure, where available, in the form of simple structural frequency profiles extracted from sets of PDB templates. We compare these systems to their state-of-the-art ab initio counterparts, and with a number of baselines in which secondary structures and solvent accessibilities are extracted directly from the templates. We show that structural information from templates greatly improves secondary structure and solvent accessibility prediction quality, and that, on average, the systems significantly enrich the information contained in the templates. For sequence similarity exceeding 30%, secondary structure prediction quality is approximately 90%, close to its theoretical maximum, and 2-class solvent accessibility roughly 85%. Gains are robust with respect to template selection noise, and significant for marginal sequence similarity and for short alignments, supporting the claim that these improved predictions may prove beneficial beyond the case in which clear homology is available. Conclusion The predictive system are publicly available at the address . PMID:17570843

  20. 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.

  1. Composite failure prediction of π-joint structures under bending

    NASA Astrophysics Data System (ADS)

    Huang, Hong-mei; Yuan, Shen-fang

    2012-03-01

    In this article, the composite -joint is investigated under bending loads. The "L" preform is the critical component regarding composite -joint failure. The study is presented in the failure detection of a carbon fiber composite -joint structure under bending loads using fiber Bragg grating (FBG) sensor. Firstly, based on the general finite element method (FEM) software, the 3-D finite element (FE) model of composite -joint is established, and the failure process and every lamina failure load of composite -joint are investigated by maximum stress criteria. Then, strain distributions along the length of FBG are extracted, and the reflection spectra of FBG are calculated according to the strain distribution. Finally, to verify the numerical results, a test scheme is performed and the experimental spectra of FBG are recorded. The experimental results indicate that the failure sequence and the corresponding critical loads of failure are consistent with the numerical predictions, and the computational error of failure load is less than 6.4%. Furthermore, it also verifies the feasibility of the damage detection system.

  2. 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.

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

    PubMed

    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. PMID:27602577

  4. 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

  5. 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

  6. Identification of a New Motif in Family B DNA Polymerases by Mutational Analyses of the Bacteriophage T4 DNA Polymerase

    PubMed Central

    Li, Vincent; Hogg, Matthew; Reha-Krantz, Linda J.

    2011-01-01

    Structure-based protein sequence alignments of family B DNA polymerases revealed a conserved motif that is formed from interacting residues between loops from the N-terminal and palm domains and between the N-terminal loop and a conserved proline residue. The importance of the motif for function of the bacteriophage T4 DNA polymerase was revealed by suppressor analysis. T4 DNA polymerases that form weak replicating complexes cannot replicate DNA when the dGTP pool is reduced. The conditional lethality provides the means to identify amino acid substitutions that restore replication activity under low dGTP conditions by either correcting the defect produced by the first amino acid substitution or by generally increasing the stability of polymerase complexes; the second type are global suppressors that can effectively counter the reduced stability caused by a variety of amino acid substitutions. Some amino acid substitutions that increase the stability of polymerase complexes produce a new phenotype - sensitivity to the antiviral drug phosphonoacetic acid. Amino acid substitutions that confer decreased ability to replicate DNA under low dGTP conditions or drug sensitivity were identified in the new motif, which suggests that the motif functions in regulating the stability of polymerase complexes. Additional suppressor analyses revealed an apparent network of interactions that link the new motif to the fingers domain and to two patches of conserved residues that bind DNA. The collection of mutant T4 DNA polymerases provides a foundation for future biochemical studies to determine how DNA polymerases remain stably associated with DNA while waiting for the next available dNTP, how DNA polymerases translocate, and the biochemical basis for sensitivity to antiviral drugs. PMID:20493878

  7. STRUCTURE-BASED PREDICTIVE MODEL FOR COAL CHAR COMBUSTION

    SciTech Connect

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

    1998-09-11

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

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

    PubMed Central

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

    2011-01-01

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

  9. Accurate prediction of protein structural classes by incorporating predicted secondary structure information into the general form of Chou's pseudo amino acid composition.

    PubMed

    Kong, Liang; Zhang, Lichao; Lv, Jinfeng

    2014-03-01

    Extracting good representation from protein sequence is fundamental for protein structural classes prediction tasks. In this paper, we propose a novel and powerful method to predict protein structural classes based on the predicted secondary structure information. At the feature extraction stage, a 13-dimensional feature vector is extracted to characterize general contents and spatial arrangements of the secondary structural elements of a given protein sequence. Specially, four segment-level features are designed to elevate discriminative ability for proteins from the α/β and α+β classes. After the features are extracted, a multi-class non-linear support vector machine classifier is used to implement protein structural classes prediction. We report extensive experiments comparing the proposed method to the state-of-the-art in protein structural classes prediction on three widely used low-similarity benchmark datasets: FC699, 1189 and 640. Our method achieves competitive performance on prediction accuracies, especially for the overall prediction accuracies which have exceeded the best reported results on all of the three datasets. PMID:24316044

  10. AWSEM-MD: Protein Structure Prediction Using Coarse-grained Physical Potentials and Bioinformatically Based Local Structure Biasing

    PubMed Central

    Davtyan, Aram; Schafer, Nicholas P.; Zheng, Weihua; Clementi, Cecilia; Wolynes, Peter G.; Papoian, Garegin A.

    2012-01-01

    The Associative memory, Water mediated, Structure and Energy Model (AWSEM) is a coarse-grained protein force field. AWSEM contains physically motivated terms, such as hydrogen bonding, as well as a bioinformatically based local structure biasing term, which efficiently takes into account many-body effects that are modulated by the local sequence. When combined with appropriate local or global alignments to choose memories, AWSEM can be used to perform de novo protein structure prediction. Herein we present structure prediction results for a particular choice of local sequence alignment method based on short residue sequences called fragments. We demonstrate the model’s structure prediction capabilities for three levels of global homology between the target sequence and those proteins used for local structure biasing, all of which assume that the structure of the target sequence is not known. When there are no homologs in the database of structures used for local structure biasing, AWSEM calculations produce structural predictions that are somewhat improved compared with prior works using related approaches. The inclusion of a small number of structures from homologous sequences improves structure prediction only marginally but when the fragment search is restricted to only homologous sequences, AWSEM can perform high resolution structure prediction and can be used for kinetics and dynamics studies. PMID:22545654

  11. 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. PMID:26465238

  12. 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…

  13. 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.

  14. Energy-based RNA consensus secondary structure prediction in multiple sequence alignments.

    PubMed

    Washietl, Stefan; Bernhart, Stephan H; Kellis, Manolis

    2014-01-01

    Many biologically important RNA structures are conserved in evolution leading to characteristic mutational patterns. RNAalifold is a widely used program to predict consensus secondary structures in multiple alignments by combining evolutionary information with traditional energy-based RNA folding algorithms. Here we describe the theory and applications of the RNAalifold algorithm. Consensus secondary structure prediction not only leads to significantly more accurate structure models, but it also allows to study structural conservation of functional RNAs. PMID:24639158

  15. A permutation based simulated annealing algorithm to predict pseudoknotted RNA secondary structures.

    PubMed

    Tsang, Herbert H; Wiese, Kay C

    2015-01-01

    Pseudoknots are RNA tertiary structures which perform essential biological functions. This paper discusses SARNA-Predict-pk, a RNA pseudoknotted secondary structure prediction algorithm based on Simulated Annealing (SA). The research presented here extends previous work of SARNA-Predict and further examines the effect of the new algorithm to include prediction of RNA secondary structure with pseudoknots. An evaluation of the performance of SARNA-Predict-pk in terms of prediction accuracy is made via comparison with several state-of-the-art prediction algorithms using 20 individual known structures from seven RNA classes. We measured the sensitivity and specificity of nine prediction algorithms. Three of these are dynamic programming algorithms: Pseudoknot (pknotsRE), NUPACK, and pknotsRG-mfe. One is using the statistical clustering approach: Sfold and the other five are heuristic algorithms: SARNA-Predict-pk, ILM, STAR, IPknot and HotKnots algorithms. The results presented in this paper demonstrate that SARNA-Predict-pk can out-perform other state-of-the-art algorithms in terms of prediction accuracy. This supports the use of the proposed method on pseudoknotted RNA secondary structure prediction of other known structures. PMID:26558299

  16. 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.

  17. A survey of machine learning methods for secondary and supersecondary protein structure prediction.

    PubMed

    Ho, Hui Kian; Zhang, Lei; Ramamohanarao, Kotagiri; Martin, Shawn

    2013-01-01

    In this chapter we provide a survey of protein secondary and supersecondary structure prediction using methods from machine learning. Our focus is on machine learning methods applicable to β-hairpin and β-sheet prediction, but we also discuss methods for more general supersecondary structure prediction. We provide background on the secondary and supersecondary structures that we discuss, the features used to describe them, and the basic theory behind the machine learning methods used. We survey the machine learning methods available for secondary and supersecondary structure prediction and compare them where possible. PMID:22987348

  18. 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

  19. 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.

  20. 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.

  1. 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.

  2. 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

  3. 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. PMID:27046050

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

    PubMed Central

    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. PMID:27046050

  5. 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...

  6. 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...

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

    EPA Science Inventory

    Consideration of Reaction Intermediates in Structure- Activity Relationships: A Key to Understanding and Prediction

    A structure-activity relationship (SAR) represents an empirical means for generalizing chemical information relative to biological activity, and is frequent...

  8. Automated Detection of Eruptive Structures for Solar Eruption Prediction

    NASA Astrophysics Data System (ADS)

    Georgoulis, Manolis K.

    2012-07-01

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

  9. Predicting Gene Structures from Multiple RT-PCR Tests

    NASA Astrophysics Data System (ADS)

    Kováč, Jakub; Vinař, Tomáš; Brejová, Broňa

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

  10. 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.

  11. 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

  12. Probabilistic predictions of penetrating injury to anatomic structures.

    PubMed

    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. PMID:9357718

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

    PubMed

    Semnani, Abolfazl; Keshavarz, Mohammad Hossein

    2010-06-15

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

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

    PubMed Central

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

    2014-01-01

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

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

    PubMed Central

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

    2011-01-01

    We calculate the water and ion spatial distributions around charged oligonucleotides using a renormalized three-dimensional reference interaction site theory coupled with the HNC closure. Our goal is to understand the balance between inter-DNA strand forces and solvation forces as a function of oligonucleotide length in the short strand limit. The DNA is considered in aqueous electrolyte solutions of 1 M KCl, 0.1 M KCl or 0.1 M NaCl. The current theoretical results are compared to MD simulations and experiments. It is found that the IE theory replicates the MD and the experimental results for the base-specific hydration patterns in both the major and minor grooves. We are also able to discern characteristic structural pattern differences between Na+ and K+ ions. When compared to Poisson-Boltzmann methods the IE theory, like simulation, predicts a richly structured ion environment which is better described as multi-layer rather than double-layer. PMID:21190358

  16. 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

  17. Predicting total clearance in humans from chemical structure.

    PubMed

    Yu, Melvin J

    2010-07-26

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

  18. Observed & Predicted Debris Disks Structures Beyond the Reach of Kepler

    NASA Astrophysics Data System (ADS)

    Stark, Christopher C.

    2014-06-01

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

  19. 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.

  20. The impact of population structure on genomic prediction in stratified populations.

    PubMed

    Guo, Zhigang; Tucker, Dominic M; Basten, Christopher J; Gandhi, Harish; Ersoz, Elhan; Guo, Baohong; Xu, Zhanyou; Wang, Daolong; Gay, Gilles

    2014-03-01

    Impacts of population structure on the evaluation of genomic heritability and prediction were investigated and quantified using high-density markers in diverse panels in rice and maize. Population structure is an important factor affecting estimation of genomic heritability and assessment of genomic prediction in stratified populations. In this study, our first objective was to assess effects of population structure on estimations of genomic heritability using the diversity panels in rice and maize. Results indicate population structure explained 33 and 7.5% of genomic heritability for rice and maize, respectively, depending on traits, with the remaining heritability explained by within-subpopulation variation. Estimates of within-subpopulation heritability were higher than that derived from quantitative trait loci identified in genome-wide association studies, suggesting 65% improvement in genetic gains. The second objective was to evaluate effects of population structure on genomic prediction using cross-validation experiments. When population structure exists in both training and validation sets, correcting for population structure led to a significant decrease in accuracy with genomic prediction. In contrast, when prediction was limited to a specific subpopulation, population structure showed little effect on accuracy and within-subpopulation genetic variance dominated predictions. Finally, effects of genomic heritability on genomic prediction were investigated. Accuracies with genomic prediction increased with genomic heritability in both training and validation sets, with the former showing a slightly greater impact. In summary, our results suggest that the population structure contribution to genomic prediction varies based on prediction strategies, and is also affected by the genetic architectures of traits and populations. In practical breeding, these conclusions may be helpful to better understand and utilize the different genetic resources in genomic

  1. 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. PMID:26902426

  2. 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. PMID:22793686

  3. Climate and Species Richness Predict the Phylogenetic Structure of African Mammal Communities

    PubMed Central

    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. PMID:25875361

  4. 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.

  5. 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/. PMID:25883046

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

    PubMed Central

    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

    2015-01-01

    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/. PMID:25883046

  7. Multidrug resistance ABC transporter structure predictions by homology modeling approaches.

    PubMed

    Honorat, Mylène; Falson, Pierre; Terreux, Raphael; Di Pietro, Attilio; Dumontet, Charles; Payen, Léa

    2011-03-01

    Human multidrug resistance ABC transporters are ubiquitous membrane proteins responsible for the efflux of multiple, endogenous or exogenous, compounds out of the cells, and therefore they are involved in multi-drug resistance phenotype (MDR). They thus deeply impact the pharmacokinetic parameters and toxicity properties of drugs. A great pressure to develop inhibitors of these pumps is carried out, by either ligand-based drug design or (more ideally) structure-based drug design. In that goal, many biochemical studies have been carried out to characterize their transport functions, and many efforts have been spent to get high-resolution structures. Currently, beside the 3D-structures of bacterial ABC transporters Sav1866 and MsbA, only the mouse ABCB1 complete structure has been published at high-resolution, illustrating the tremendous difficulty in getting such information, taking into account that the human genome accounts for 48 ABC transporters encoding genes. Homology modeling is consequently a reasonable approach to overcome this obstacle. The present review describes, in the first part, the different approaches which have been published to set up human ABC pump 3D-homology models allowing the localization of binding sites for drug candidates, and the identification of critical residues therein. In a second part, the review proposes a more accurate strategy and practical keys to use such biological tools for initiating structure-based drug design. PMID:21470105

  8. HYPLOSP: a knowledge-based approach to protein local structure prediction.

    PubMed

    Chen, Ching-Tai; Lin, Hsin-Nan; Sung, Ting-Yi; Hsu, Wen-Lian

    2006-12-01

    Local structure prediction can facilitate ab initio structure prediction, protein threading, and remote homology detection. However, the accuracy of existing methods is limited. In this paper, we propose a knowledge-based prediction method that assigns a measure called the local match rate to each position of an amino acid sequence to estimate the confidence of our method. Empirically, the accuracy of the method correlates positively with the local match rate; therefore, we employ it to predict the local structures of positions with a high local match rate. For positions with a low local match rate, we propose a neural network prediction method. To better utilize the knowledge-based and neural network methods, we design a hybrid prediction method, HYPLOSP (HYbrid method to Protein LOcal Structure Prediction) that combines both methods. To evaluate the performance of the proposed methods, we first perform cross-validation experiments by applying our knowledge-based method, a neural network method, and HYPLOSP to a large dataset of 3,925 protein chains. We test our methods extensively on three different structural alphabets and evaluate their performance by two widely used criteria, Maximum Deviation of backbone torsion Angle (MDA) and Q(N), which is similar to Q(3) in secondary structure prediction. We then compare HYPLOSP with three previous studies using a dataset of 56 new protein chains. HYPLOSP shows promising results in terms of MDA and Q(N) accuracy and demonstrates its alphabet-independent capability. PMID:17245815

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

    NASA Astrophysics Data System (ADS)

    Sebastijanovic, Nebojsa

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

  10. Building a Better Fragment Library for De Novo Protein Structure Prediction

    PubMed Central

    de Oliveira, Saulo H. P.; Shi, Jiye; Deane, Charlotte M.

    2015-01-01

    Fragment-based approaches are the current standard for de novo protein structure prediction. These approaches rely on accurate and reliable fragment libraries to generate good structural models. In this work, we describe a novel method for structure fragment library generation and its application in fragment-based de novo protein structure prediction. The importance of correct testing procedures in assessing the quality of fragment libraries is demonstrated. In particular, the exclusion of homologs to the target from the libraries to correctly simulate a de novo protein structure prediction scenario, something which surprisingly is not always done. We demonstrate that fragments presenting different predominant predicted secondary structures should be treated differently during the fragment library generation step and that exhaustive and random search strategies should both be used. This information was used to develop a novel method, Flib. On a validation set of 41 structurally diverse proteins, Flib libraries presents both a higher precision and coverage than two of the state-of-the-art methods, NNMake and HHFrag. Flib also achieves better precision and coverage on the set of 275 protein domains used in the two previous experiments of the the Critical Assessment of Structure Prediction (CASP9 and CASP10). We compared Flib libraries against NNMake libraries in a structure prediction context. Of the 13 cases in which a correct answer was generated, Flib models were more accurate than NNMake models for 10. “Flib is available for download at: http://www.stats.ox.ac.uk/research/proteins/resources”. PMID:25901595

  11. An adaptive genetic algorithm for crystal structure prediction

    SciTech Connect

    Wu, Shunqing; Ji, Min; Wang, Cai-Zhuang; Nguyen, Manh Cuong; Zhao, Xin; Umemoto, K.; Wentzcovitch, R. M.; Ho, Kai-Ming

    2013-12-18

    We present a genetic algorithm (GA) for structural search that combines the speed of structure exploration by classical potentials with the accuracy of density functional theory (DFT) calculations in an adaptive and iterative way. This strategy increases the efficiency of the DFT-based GA by several orders of magnitude. This gain allows a considerable increase in the size and complexity of systems that can be studied by first principles. The performance of the method is illustrated by successful structure identifications of complex binary and ternary intermetallic compounds with 36 and 54 atoms per cell, respectively. The discovery of a multi-TPa Mg-silicate phase with unit cell containing up to 56 atoms is also reported. Such a phase is likely to be an essential component of terrestrial exoplanetary mantles.

  12. The unexpected structure of the designed protein Octarellin V.1 forms a challenge for protein structure prediction tools.

    PubMed

    Figueroa, Maximiliano; Sleutel, Mike; Vandevenne, Marylene; Parvizi, Gregory; Attout, Sophie; Jacquin, Olivier; Vandenameele, Julie; Fischer, Axel W; Damblon, Christian; Goormaghtigh, Erik; Valerio-Lepiniec, Marie; Urvoas, Agathe; Durand, Dominique; Pardon, Els; Steyaert, Jan; Minard, Philippe; Maes, Dominique; Meiler, Jens; Matagne, André; Martial, Joseph A; Van de Weerdt, Cécile

    2016-07-01

    Despite impressive successes in protein design, designing a well-folded protein of more 100 amino acids de novo remains a formidable challenge. Exploiting the promising biophysical features of the artificial protein Octarellin V, we improved this protein by directed evolution, thus creating a more stable and soluble protein: Octarellin V.1. Next, we obtained crystals of Octarellin V.1 in complex with crystallization chaperons and determined the tertiary structure. The experimental structure of Octarellin V.1 differs from its in silico design: the (αβα) sandwich architecture bears some resemblance to a Rossman-like fold instead of the intended TIM-barrel fold. This surprising result gave us a unique and attractive opportunity to test the state of the art in protein structure prediction, using this artificial protein free of any natural selection. We tested 13 automated webservers for protein structure prediction and found none of them to predict the actual structure. More than 50% of them predicted a TIM-barrel fold, i.e. the structure we set out to design more than 10years ago. In addition, local software runs that are human operated can sample a structure similar to the experimental one but fail in selecting it, suggesting that the scoring and ranking functions should be improved. We propose that artificial proteins could be used as tools to test the accuracy of protein structure prediction algorithms, because their lack of evolutionary pressure and unique sequences features. PMID:27181418

  13. Molecular docking studies of phytochemicals from Phyllanthus niruri against Hepatitis B DNA Polymerase

    PubMed Central

    Mohan, Mekha; James, Priyanka; Valsalan, Ravisankar; Nazeem, Puthiyaveetil Abdulla

    2015-01-01

    Hepatitis B virus (HBV) infection is the leading cause for liver disorders and can lead to hepatocellular carcinoma, cirrhosis and liver damage which in turn can cause death of patients. HBV DNA Polymerase is essential for HBV replication in the host and hence is used as one of the most potent pharmacological target for the inhibition of HBV. Chronic hepatitis B is currently treated with nucleotide analogues that suppress viral reverse transcriptase activity and most of them are reported to have viral resistance. Therefore, it is of interest to model HBV DNA polymerase to dock known phytochemicals. The present study focuses on homology modeling and molecular docking analysis of phytocompounds from the traditional antidote Phyllanthus niruri and other nucleoside analogues against HBV DNA Polymerase using the software Discovery studio 4.0. 3D structure of HBV DNA Polymerase was predicted based on previously reported alignment. Docking studies revealed that a few phytochemicals from Phyllanthus niruri had good interactions with HBV DNA Polymerase. These compounds had acceptable binding properties for further in vitro validation. Thus the study puts forth experimental validation for traditional antidote and these phytocompounds could be further promoted as potential lead molecule. PMID:26527851

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

    PubMed Central

    Seeliger, Daniel; de Groot, Bert L.

    2010-01-01

    Biological function of proteins is frequently associated with the formation of complexes with small-molecule ligands. Experimental structure determination of such complexes at atomic resolution, however, can be time-consuming and costly. Computational methods for structure prediction of protein/ligand complexes, particularly docking, are as yet restricted by their limited consideration of receptor flexibility, rendering them not applicable for predicting protein/ligand complexes if large conformational changes of the receptor upon ligand binding are involved. Accurate receptor models in the ligand-bound state (holo structures), however, are a prerequisite for successful structure-based drug design. Hence, if only an unbound (apo) structure is available distinct from the ligand-bound conformation, structure-based drug design is severely limited. We present a method to predict the structure of protein/ligand complexes based solely on the apo structure, the ligand and the radius of gyration of the holo structure. The method is applied to ten cases in which proteins undergo structural rearrangements of up to 7.1 Å backbone RMSD upon ligand binding. In all cases, receptor models within 1.6 Å backbone RMSD to the target were predicted and close-to-native ligand binding poses were obtained for 8 of 10 cases in the top-ranked complex models. A protocol is presented that is expected to enable structure modeling of protein/ligand complexes and structure-based drug design for cases where crystal structures of ligand-bound conformations are not available. PMID:20066034

  15. LOCUSTRA: accurate prediction of local protein structure using a two-layer support vector machine approach.

    PubMed

    Zimmermann, Olav; Hansmann, Ulrich H E

    2008-09-01

    Constraint generation for 3d structure prediction and structure-based database searches benefit from fine-grained prediction of local structure. In this work, we present LOCUSTRA, a novel scheme for the multiclass prediction of local structure that uses two layers of support vector machines (SVM). Using a 16-letter structural alphabet from de Brevern et al. (Proteins: Struct., Funct., Bioinf. 2000, 41, 271-287), we assess its prediction ability for an independent test set of 222 proteins and compare our method to three-class secondary structure prediction and direct prediction of dihedral angles. The prediction accuracy is Q16=61.0% for the 16 classes of the structural alphabet and Q3=79.2% for a simple mapping to the three secondary classes helix, sheet, and coil. We achieve a mean phi(psi) error of 24.74 degrees (38.35 degrees) and a median RMSDA (root-mean-square deviation of the (dihedral) angles) per protein chain of 52.1 degrees. These results compare favorably with related approaches. The LOCUSTRA web server is freely available to researchers at http://www.fz-juelich.de/nic/cbb/service/service.php. PMID:18763837

  16. 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.

  17. 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

  18. A Structural Equation Model for Predicting Business Student Performance

    ERIC Educational Resources Information Center

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

    2008-01-01

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

  19. 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…

  20. 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."

  1. Ab initio NMR Confirmed Evolutionary Structure Prediction for Organic Molecular Crystals

    NASA Astrophysics Data System (ADS)

    Pham, Cong-Huy; Kucukbenli, Emine; de Gironcoli, Stefano

    2015-03-01

    Ab initio crystal structure prediction of even small organic compounds is extremely challenging due to polymorphism, molecular flexibility and difficulties in addressing the dispersion interaction from first principles. We recently implemented vdW-aware density functionals and demonstrated their success in energy ordering of aminoacid crystals. In this work we combine this development with the evolutionary structure prediction method to study cholesterol polymorphs. Cholesterol crystals have paramount importance in various diseases, from cancer to atherosclerosis. The structure of some polymorphs (e.g. ChM, ChAl, ChAh) have already been resolved while some others, which display distinct NMR spectra and are involved in disease formation, are yet to be determined. Here we thoroughly assess the applicability of evolutionary structure prediction to address such real world problems. We validate the newly predicted structures with ab initio NMR chemical shift data using secondary referencing for an improved comparison with experiments.

  2. RNAsoft: a suite of RNA secondary structure prediction and design software tools

    PubMed Central

    Andronescu, Mirela; Aguirre-Hernández, Rosalía; Condon, Anne; Hoos, Holger H.

    2003-01-01

    DNA and RNA strands are employed in novel ways in the construction of nanostructures, as molecular tags in libraries of polymers and in therapeutics. New software tools for prediction and design of molecular structure will be needed in these applications. The RNAsoft suite of programs provides tools for predicting the secondary structure of a pair of DNA or RNA molecules, testing that combinatorial tag sets of DNA and RNA molecules have no unwanted secondary structure and designing RNA strands that fold to a given input secondary structure. The tools are based on standard thermodynamic models of RNA secondary structure formation. RNAsoft can be found online at http://www.RNAsoft.ca. PMID:12824338

  3. Using the Rosetta algorithm and selected inter-residue distances to predict protein structure

    NASA Astrophysics Data System (ADS)

    Crecca, Christina; Roitberg, Adrian E.

    The Rosetta algorithm has had much success in protein structure prediction as demonstrated in the recent Critical Assessment of Protein Structure Prediction (CASP) experiments. For many proteins, Rosetta generates several low root mean square deviation (RMSD) decoy structures but finding the best structure among the decoys can be difficult. Experimental data can be used to aid in the discrimination process. Our protein structure prediction method involves three steps: using the Rosetta algorithm to generate decoys, measuring inter-residue distances, and comparing the measured distances with those calculated in each decoy. Decoys with similar three-dimensional structure will also have several similar inter-residue distances. To develop our search protocol, we determined the optimal number of decoys to generate as well as the minimum number of distance constraints needed to distinguish between the low and high RMSD structures. To test our method, we simulate experimental data by measuring alpha-carbon distances from the experimentally determined structures of our target proteins. We have employed the Rosetta algorithm to generate decoy sets of different sizes for four target proteins. Our predicted structures ranged in Calpha RMSD from 2.4 to 4.6 Å compared with the experimental structures. Using only twenty-five distance constraints, reliable predictions were made.

  4. 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.

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

    NASA Astrophysics Data System (ADS)

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

    1984-07-01

    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.

  6. 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.

  7. 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.

  8. Predicting Homogeneous Pilus Structure from Monomeric Data and Sparse Constraints.

    PubMed

    Xiao, Ke; Shu, Chuanjun; Yan, Qin; Sun, Xiao

    2015-01-01

    Type IV pili (T4P) and T2SS (Type II Secretion System) pseudopili are filaments extending beyond microbial surfaces, comprising homologous subunits called "pilins." In this paper, we presented a new approach to predict pseudo atomic models of pili combining ambiguous symmetric constraints with sparse distance information obtained from experiments and based neither on electronic microscope (EM) maps nor on accurate a priori symmetric details. The approach was validated by the reconstruction of the gonococcal (GC) pilus from Neisseria gonorrhoeae, the type IVb toxin-coregulated pilus (TCP) from Vibrio cholerae, and pseudopilus of the pullulanase T2SS (the PulG pilus) from Klebsiella oxytoca. In addition, analyses of computational errors showed that subunits should be treated cautiously, as they are slightly flexible and not strictly rigid bodies. A global sampling in a wider range was also implemented and implied that a pilus might have more than one but fewer than many possible intact conformations. PMID:26064954

  9. Computational Predictions of Structures of Multichromosomes of Budding Yeast

    PubMed Central

    Gürsoy, Gamze; Xu, Yun; Liang, Jie

    2016-01-01

    Knowledge of the global architecture of the cell nucleus and the spatial organization of genome is critical for understanding gene expression and nuclear function. Single-cell imaging techniques provide a wealth of information on the spatial organization of chromosomes. Computational tools for modelling chromosome structure have broad implications in studying the effect of cell nucleus on higher-order genome organization. Here we describe a multichromosome constrained self-avoiding chromatin model for studying ensembles of genome structural models of budding yeast nucleus. We successfully generated a large number of model genomes of yeast with appropriate chromatin fiber diameter, persistence length, and excluded volume under spatial confinement. By incorporating details of the constraints from single-cell imaging studies, our method can model the budding yeast genome realistically. The model developed here provides a general computational framework for studying the overall architecture of budding yeast genome. PMID:25570855

  10. 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.

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

    PubMed Central

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

    2001-01-01

    A function annotation method using the sequence-to-structure-to-function paradigm is applied to the identification of all disulfide oxidoreductases in the Saccharomyces cerevisiae genome. The method identifies 27 sequences as potential disulfide oxidoreductases. All previously known thioredoxins, glutaredoxins, and disulfide isomerases are correctly identified. Three of the 27 predictions are probable false-positives. Three novel predictions, which subsequently have been experimentally validated, are presented. Two additional novel predictions suggest a disulfide oxidoreductase regulatory mechanism for two subunits (OST3 and OST6) of the yeast oligosaccharyltransferase complex. Based on homology, this prediction can be extended to a potential tumor suppressor gene, N33, in humans, whose biochemical function was not previously known. Attempts to obtain a folded, active N33 construct to test the prediction were unsuccessful. The results show that structure prediction coupled with biochemically relevant structural motifs is a powerful method for the function annotation of genome sequences and can provide more detailed, robust predictions than function prediction methods that rely on sequence comparison alone. PMID:11316881

  12. GTfold: Enabling parallel RNA secondary structure prediction on multi-core desktops

    PubMed Central

    2012-01-01

    Background Accurate and efficient RNA secondary structure prediction remains an important open problem in computational molecular biology. Historically, advances in computing technology have enabled faster and more accurate RNA secondary structure predictions. Previous parallelized prediction programs achieved significant improvements in runtime, but their implementations were not portable from niche high-performance computers or easily accessible to most RNA researchers. With the increasing prevalence of multi-core desktop machines, a new parallel prediction program is needed to take full advantage of today’s computing technology. Findings We present here the first implementation of RNA secondary structure prediction by thermodynamic optimization for modern multi-core computers. We show that GTfold predicts secondary structure in less time than UNAfold and RNAfold, without sacrificing accuracy, on machines with four or more cores. Conclusions GTfold supports advances in RNA structural biology by reducing the timescales for secondary structure prediction. The difference will be particularly valuable to researchers working with lengthy RNA sequences, such as RNA viral genomes. PMID:22747589

  13. 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

  14. In silico predicted structural and functional robustness of piscine steroidogenesis.

    PubMed

    Hala, D; Huggett, D B

    2014-03-21

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

  15. Failure/leakage predictions of concrete structures containing cracks

    SciTech Connect

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

    1984-06-01

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

  16. Fibpredictor: a computational method for rapid prediction of amyloid fibril structures.

    PubMed

    Tabatabaei Ghomi, Hamed; Topp, Elizabeth M; Lill, Markus A

    2016-09-01

    Amyloid fibrils are important in diseases such as Alzheimer's disease and Parkinson's disease, and are also a common instability in peptide and protein drug products. Despite their importance, experimental structures of amyloid fibrils in atomistic detail are rare. To address this limitation, we have developed a novel, rapid computational method to predict amyloid fibril structures (Fibpredictor). The method combines β-sheet model building, β-sheet replication, and symmetry operations with side-chain prediction and statistical scoring functions. When applied to nine amyloid fibrils with experimentally determined structures, the method predicted the correct structures of amyloid fibrils and enriched those among the top-ranked structures. These models can be used as the initial heuristic structures for more complicated computational studies. Fibpredictor is available at http://nanohub.org/resources/fibpredictor . PMID:27502172

  17. Predicting Homogeneous Pilus Structure from Monomeric Data and Sparse Constraints

    PubMed Central

    Xiao, Ke; Shu, Chuanjun; Yan, Qin; Sun, Xiao

    2015-01-01

    Type IV pili (T4P) and T2SS (Type II Secretion System) pseudopili are filaments extending beyond microbial surfaces, comprising homologous subunits called “pilins.” In this paper, we presented a new approach to predict pseudo atomic models of pili combining ambiguous symmetric constraints with sparse distance information obtained from experiments and based neither on electronic microscope (EM) maps nor on accurate a priori symmetric details. The approach was validated by the reconstruction of the gonococcal (GC) pilus from Neisseria gonorrhoeae, the type IVb toxin-coregulated pilus (TCP) from Vibrio cholerae, and pseudopilus of the pullulanase T2SS (the PulG pilus) from Klebsiella oxytoca. In addition, analyses of computational errors showed that subunits should be treated cautiously, as they are slightly flexible and not strictly rigid bodies. A global sampling in a wider range was also implemented and implied that a pilus might have more than one but fewer than many possible intact conformations. PMID:26064954

  18. 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

  19. Correction: Substituent interference on supramolecular assembly in urea gelators: synthesis, structure prediction and NMR.

    PubMed

    Piana, Francesca; Case, David H; Ramalhete, Susana M; Pileio, Giuseppe; Facciotti, Marco; Day, Graeme M; Khimyak, Yaroslav Z; Angulo, Jesús; Brown, Richard C D; Gale, Philip A

    2016-06-28

    Correction for 'Substituent interference on supramolecular assembly in urea gelators: synthesis, structure prediction and NMR' by Francesca Piana et al., Soft Matter, 2016, 12, 4034-4043. PMID:27254024

  20. 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.

  1. A scoring function based on solvation thermodynamics for protein structure prediction

    PubMed Central

    Du, Shiqiao; Harano, Yuichi; Kinoshita, Masahiro; Sakurai, Minoru

    2012-01-01

    We predict protein structure using our recently developed free energy function for describing protein stability, which is focused on solvation thermodynamics. The function is combined with the current most reliable sampling methods, i.e., fragment assembly (FA) and comparative modeling (CM). The prediction is tested using 11 small proteins for which high-resolution crystal structures are available. For 8 of these proteins, sequence similarities are found in the database, and the prediction is performed with CM. Fairly accurate models with average Cα root mean square deviation (RMSD) ∼ 2.0 Å are successfully obtained for all cases. For the rest of the target proteins, we perform the prediction following FA protocols. For 2 cases, we obtain predicted models with an RMSD ∼ 3.0 Å as the best-scored structures. For the other case, the RMSD remains larger than 7 Å. For all the 11 target proteins, our scoring function identifies the experimentally determined native structure as the best structure. Starting from the predicted structure, replica exchange molecular dynamics is performed to further refine the structures. However, we are unable to improve its RMSD toward the experimental structure. The exhaustive sampling by coarse-grained normal mode analysis around the native structures reveals that our function has a linear correlation with RMSDs < 3.0 Å. These results suggest that the function is quite reliable for the protein structure prediction while the sampling method remains one of the major limiting factors in it. The aspects through which the methodology could further be improved are discussed.

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

    SciTech Connect

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

    2004-10-01

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

  3. Combining Evolutionary Information and an Iterative Sampling Strategy for Accurate Protein Structure Prediction

    PubMed Central

    Braun, Tatjana; Koehler Leman, Julia; Lange, Oliver F.

    2015-01-01

    Recent work has shown that the accuracy of ab initio structure prediction can be significantly improved by integrating evolutionary information in form of intra-protein residue-residue contacts. Following this seminal result, much effort is put into the improvement of contact predictions. However, there is also a substantial need to develop structure prediction protocols tailored to the type of restraints gained by contact predictions. Here, we present a structure prediction protocol that combines evolutionary information with the resolution-adapted structural recombination approach of Rosetta, called RASREC. Compared to the classic Rosetta ab initio protocol, RASREC achieves improved sampling, better convergence and higher robustness against incorrect distance restraints, making it the ideal sampling strategy for the stated problem. To demonstrate the accuracy of our protocol, we tested the approach on a diverse set of 28 globular proteins. Our method is able to converge for 26 out of the 28 targets and improves the average TM-score of the entire benchmark set from 0.55 to 0.72 when compared to the top ranked models obtained by the EVFold web server using identical contact predictions. Using a smaller benchmark, we furthermore show that the prediction accuracy of our method is only slightly reduced when the contact prediction accuracy is comparatively low. This observation is of special interest for protein sequences that only have a limited number of homologs. PMID:26713437

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

    ERIC Educational Resources Information Center

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

    2000-01-01

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

  5. 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.

  6. 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.

  7. Web applet for predicting structure and thermodynamics of complex fluids

    NASA Astrophysics Data System (ADS)

    Popp, Theodore R.; Hollingshead, Kyle B.; Truskett, Thomas M.

    2015-03-01

    Based on a recently introduced analytical strategy [Hollingshead et al., J. Chem. Phys. 139, 161102 (2013)], we present a web applet that can quickly and semi-quantitatively estimate the equilibrium radial distribution function and related thermodynamic properties of a fluid from knowledge of its pair interaction. We describe the applet's features and present two (of many possible) examples of how it can be used to illustrate concepts of interest for introductory statistical mechanics courses: the transition from ideal gas-like behavior to correlated-liquid behavior with increasing density, and the tradeoff between dominant length scales with changing temperature in a system with ramp-shaped repulsions. The latter type of interaction qualitatively captures distinctive thermodynamic properties of liquid water, because its energetic bias toward locally open structures mimics that of water's hydrogen-bond network.

  8. 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.

  9. Handling context-sensitivity in protein structures using graph theory: bona fide prediction.

    PubMed

    Samudrala, R; Moult, J

    1997-01-01

    We constructed five comparative models in a blind manner for the second meeting on the Critical Assessment of protein Structure Prediction methods (CASP2). The method used is based on a novel graph-theoretic clique-finding approach, and attempts to address the problem of interconnected structural changes in the comparative modeling of protein structures. We discuss briefly how the method is used for protein structure prediction, and detail how it performs in the blind tests. We find that compared to CASP1, significant improvements in building insertions and deletions and sidechain conformations have been achieved. PMID:9485494

  10. 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.

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

    PubMed Central

    Masica, David L.; Gray, Jeffrey J.

    2009-01-01

    Abstract We have developed a multiscale structure prediction technique to study solution- and adsorbed-state ensembles of biomineralization proteins. The algorithm employs a Metropolis Monte Carlo-plus-minimization strategy that varies all torsional and rigid-body protein degrees of freedom. We applied the technique to fold statherin, starting from a fully extended peptide chain in solution, in the presence of hydroxyapatite (HAp) (001), (010), and (100) monoclinic crystals. Blind (unbiased) predictions capture experimentally observed macroscopic and high-resolution structural features and show minimal statherin structural change upon adsorption. The dominant structural difference between solution and adsorbed states is an experimentally observed folding event in statherin's helical binding domain. Whereas predicted statherin conformers vary slightly at three different HAp crystal faces, geometric and chemical similarities of the surfaces allow structurally promiscuous binding. Finally, we compare blind predictions with those obtained from simulation biased to satisfy all previously published solid-state NMR (ssNMR) distance and angle measurements (acquired from HAp-adsorbed statherin). Atomic clashes in these structures suggest a plausible, alternative interpretation of some ssNMR measurements as intermolecular rather than intramolecular. This work demonstrates that a combination of ssNMR and structure prediction could effectively determine high-resolution protein structures at biomineral interfaces. PMID:19383454

  12. Influence of assignment on the prediction of transmembrane helices in protein structures.

    PubMed

    Pylouster, Jean; Bornot, Aurélie; Etchebest, Catherine; de Brevern, Alexandre G

    2010-11-01

    α-Helical transmembrane proteins (TMPα) are composed of a series of helices embedded in the lipid bilayer. Due to technical difficulties, few 3D structures are available. Therefore, the design of structural models of TMPα is of major interest. We study the secondary structures of TMPα by analyzing the influence of secondary structures assignment methods (SSAMs). For this purpose, a published and updated benchmark databank of TMPα is used and several SSAMs (9) are evaluated. The analysis of the results points to significant differences in SSA depending on the methods used. Pairwise comparisons between SSAMs led to more than 10% of disagreement. Helical regions corresponding to transmembrane zones are often correctly characterized. The study of the sequence-structure relationship shows very limited differences with regard to the structural disagreement. Secondary structure prediction based on Bayes' rule and using only a single sequence give correct prediction rates ranging from 78 to 81%. A structural alphabet approach gives a slightly better prediction, i.e., only 2% less than the best equivalent approach, whereas the prediction rate with a very different assignment bypasses 86%. This last result highlights the importance of the correct assignment choice to evaluate the prediction assessment. PMID:20349322

  13. Prediction of membrane protein structures with complex topologies using limited constraints

    PubMed Central

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

    2009-01-01

    Reliable structure-prediction methods for membrane proteins are important because the experimental determination of high-resolution membrane protein structures remains very difficult, especially for eukaryotic proteins. However, membrane proteins are typically longer than 200 aa and represent a formidable challenge for structure prediction. We have developed a method for predicting the structures of large membrane proteins by constraining helix–helix packing arrangements at particular positions predicted from sequence or identified by experiments. We tested the method on 12 membrane proteins of diverse topologies and functions with lengths ranging between 190 and 300 residues. Enforcing a single constraint during the folding simulations enriched the population of near-native models for 9 proteins. In 4 of the cases in which the constraint was predicted from the sequence, 1 of the 5 lowest energy models was superimposable within 4 Å on the native structure. Near-native structures could also be selected for heme-binding and pore-forming domains from simulations in which pairs of conserved histidine-chelating hemes and one experimentally determined salt bridge were constrained, respectively. These results suggest that models within 4 Å of the native structure can be achieved for complex membrane proteins if even limited information on residue-residue interactions can be obtained from protein structure databases or experiments. PMID:19190187

  14. 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). PMID:27045556

  15. AIDA: ab initio domain assembly for automated multi-domain protein structure prediction and domain–domain interaction prediction

    PubMed Central

    Xu, Dong; Jaroszewski, Lukasz; Li, Zhanwen; Godzik, Adam

    2015-01-01

    Motivation: Most proteins consist of multiple domains, independent structural and evolutionary units that are often reshuffled in genomic rearrangements to form new protein architectures. Template-based modeling methods can often detect homologous templates for individual domains, but templates that could be used to model the entire query protein are often not available. Results: We have developed a fast docking algorithm ab initio domain assembly (AIDA) for assembling multi-domain protein structures, guided by the ab initio folding potential. This approach can be extended to discontinuous domains (i.e. domains with ‘inserted’ domains). When tested on experimentally solved structures of multi-domain proteins, the relative domain positions were accurately found among top 5000 models in 86% of cases. AIDA server can use domain assignments provided by the user or predict them from the provided sequence. The latter approach is particularly useful for automated protein structure prediction servers. The blind test consisting of 95 CASP10 targets shows that domain boundaries could be successfully determined for 97% of targets. Availability and implementation: The AIDA package as well as the benchmark sets used here are available for download at http://ffas.burnham.org/AIDA/. Contact: adam@sanfordburnham.org Supplementary information: Supplementary data are available at Bioinformatics online. PMID:25701568

  16. 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.

  17. 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

  18. Target highlights in CASP9: Experimental target structures for the critical assessment of techniques for protein structure prediction.

    PubMed

    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; Seetharaman, Jayaraman; 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, that is, 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 article, 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 fiber protein gp37 from bacteriophage T4, the cyclic GMP-dependent protein kinase Iβ dimerization/docking domain, the ectodomain of the JTB (jumping translocation breakpoint) transmembrane receptor, Autotaxin in complex with an inhibitor, the DNA-binding J-binding protein 1 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 core-membrane linker phycobiliprotein ApcE from Synechocystis, the heat shock protein 90 activators PFC0360w and PFC0270w from Plasmodium falciparum, and 2-oxo-3-deoxygalactonate kinase from Klebsiella pneumoniae. PMID:22020785

  19. STRUCTURE-BASED PREDICTIVE MODEL FOR COAL CHAR COMBUSTION

    SciTech Connect

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

    1999-01-13

    Significant progress continued to be made during the past reporting quarter on both major technical tasks. During the reporting period at OSU, computational investigations were conducted of addition vs. abstraction reactions of H, O(3 P), and OH with monocyclic aromatic hydrocarbons. The potential energy surface for more than 80 unique reactions of H, O ( 3 P), and OH with aromatic hydrocarbons were determined at the B3LYP/6-31G(d) level of theory. The calculated transition state barriers and reaction free energies indicate that the addition channel is preferred at 298K, but that the abstraction channel becomes dominant at high temperatures. The thermodynamic preference for reactivity with aromatic hydrocarbons increases in the order O(3 P) < H < OH. Abstraction from six-membered aromatic rings is more facile than abstraction from five-membered aromatic rings. However, addition to five-membered rings is thermodynamically more favorable than addition to six-membered rings. The free energies for the abstraction and addition reactions of H, O, and OH with aromatic hydrocarbons and the characteristics of the respective transition states can be used to calculate the reaction rate constants for these important combustion reactions. Experimental work at Brown University on the effect of reaction on the structural evolution of different chars (i.e., phenolic resin char and chars produced from three different coals) have been investigated in a TGA/TPD-MS system. It has been found that samples of different age of these chars appeared to lose their "memory" concerning their initial structures at high burn-offs. During the reporting period, thermal desorption experiments of selected samples were conducted. These spectra show that the population of low temperature oxygen surface complexes, which are primarily responsible for reactivity, are more similar for the high burn-off than for the low burn-off samples of different ages; i.e., the population of active sites are more

  20. 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.

  1. 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

  2. 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

    2015-12-01

    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

  3. 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. PMID:19950907

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

    SciTech Connect

    King, R.D.; Srinivasan, A.

    1996-10-01

    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. 29 refs., 2 figs., 4 tabs.

  5. 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

  6. 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.

  7. 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.

  8. Direct-Coupling Analysis of nucleotide coevolution facilitates RNA secondary and tertiary structure prediction

    PubMed Central

    De Leonardis, Eleonora; Lutz, Benjamin; Ratz, Sebastian; Cocco, Simona; Monasson, Rémi; Schug, Alexander; Weigt, Martin

    2015-01-01

    Despite the biological importance of non-coding RNA, their structural characterization remains challenging. Making use of the rapidly growing sequence databases, we analyze nucleotide coevolution across homologous sequences via Direct-Coupling Analysis to detect nucleotide-nucleotide contacts. For a representative set of riboswitches, we show that the results of Direct-Coupling Analysis in combination with a generalized Nussinov algorithm systematically improve the results of RNA secondary structure prediction beyond traditional covariance approaches based on mutual information. Even more importantly, we show that the results of Direct-Coupling Analysis are enriched in tertiary structure contacts. By integrating these predictions into molecular modeling tools, systematically improved tertiary structure predictions can be obtained, as compared to using secondary structure information alone. PMID:26420827

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

    NASA Astrophysics Data System (ADS)

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

    2014-12-01

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

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

    PubMed

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

    2014-01-01

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

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

    PubMed Central

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

    2014-01-01

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

  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. 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.

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

    NASA Technical Reports Server (NTRS)

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

    1977-01-01

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

  15. 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

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

    PubMed

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

    2011-10-01

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

  17. Assessing a novel approach for predicting local 3D protein structures from sequence.

    PubMed

    Benros, Cristina; de Brevern, Alexandre G; Etchebest, Catherine; Hazout, Serge

    2006-03-01

    We developed a novel approach for predicting local protein structure from sequence. It relies on the Hybrid Protein Model (HPM), an unsupervised clustering method we previously developed. This model learns three-dimensional protein fragments encoded into a structural alphabet of 16 protein blocks (PBs). Here, we focused on 11-residue fragments encoded as a series of seven PBs and used HPM to cluster them according to their local similarities. We thus built a library of 120 overlapping prototypes (mean fragments from each cluster), with good three-dimensional local approximation, i.e., a mean accuracy of 1.61 A Calpha root-mean-square distance. Our prediction method is intended to optimize the exploitation of the sequence-structure relations deduced from this library of long protein fragments. This was achieved by setting up a system of 120 experts, each defined by logistic regression to optimize the discrimination from sequence of a given prototype relative to the others. For a target sequence window, the experts computed probabilities of sequence-structure compatibility for the prototypes and ranked them, proposing the top scorers as structural candidates. Predictions were defined as successful when a prototype <2.5 A from the true local structure was found among those proposed. Our strategy yielded a prediction rate of 51.2% for an average of 4.2 candidates per sequence window. We also proposed a confidence index to estimate prediction quality. Our approach predicts from sequence alone and will thus provide valuable information for proteins without structural homologs. Candidates will also contribute to global structure prediction by fragment assembly. PMID:16385557

  18. 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. PMID:26910956

  19. Bayesian probabilistic approach for predicting backbone structures in terms of protein blocks.

    PubMed

    de Brevern, A G; Etchebest, C; Hazout, S

    2000-11-15

    By using an unsupervised cluster analyzer, we have identified a local structural alphabet composed of 16 folding patterns of five consecutive C(alpha) ("protein blocks"). The dependence that exists between successive blocks is explicitly taken into account. A Bayesian approach based on the relation protein block-amino acid propensity is used for prediction and leads to a success rate close to 35%. Sharing sequence windows associated with certain blocks into "sequence families" improves the prediction accuracy by 6%. This prediction accuracy exceeds 75% when keeping the first four predicted protein blocks at each site of the protein. In addition, two different strategies are proposed: the first one defines the number of protein blocks in each site needed for respecting a user-fixed prediction accuracy, and alternatively, the second one defines the different protein sites to be predicted with a user-fixed number of blocks and a chosen accuracy. This last strategy applied to the ubiquitin conjugating enzyme (alpha/beta protein) shows that 91% of the sites may be predicted with a prediction accuracy larger than 77% considering only three blocks per site. The prediction strategies proposed improve our knowledge about sequence-structure dependence and should be very useful in ab initio protein modelling. PMID:11025540

  20. Challenging the state-of-the-art in protein structure prediction: Highlights of experimental target structures for the 10th Critical Assessment of Techniques for Protein Structure Prediction Experiment CASP10

    PubMed Central

    Kryshtafovych, Andriy; Moult, John; Bales, Patrick; Bazan, J. Fernando; Biasini, Marco; Burgin, Alex; Chen, Chen; Cochran, Frank V.; Craig, Timothy K.; Das, Rhiju; Fass, Deborah; Garcia-Doval, Carmela; Herzberg, Osnat; Lorimer, Donald; Luecke, Hartmut; Ma, Xiaolei; Nelson, Daniel C.; van Raaij, Mark J.; Rohwer, Forest; Segall, Anca; Seguritan, Victor; Zeth, Kornelius; Schwede, Torsten

    2014-01-01

    For the last two decades, CASP has assessed the state of the art in techniques for protein structure prediction and identified areas which required further development. CASP would not have been possible without the prediction targets provided by the experimental structural biology community. In the latest experiment, CASP10, over 100 structures were suggested as prediction targets, some of which appeared to be extraordinarily difficult for modeling. In this paper, authors of some of the most challenging targets discuss which specific scientific question motivated the experimental structure determination of the target protein, which structural features were especially interesting from a structural or functional perspective, and to what extent these features were correctly reproduced in the predictions submitted to CASP10. Specifically, the following targets will be presented: the acid-gated urea channel, a difficult to predict trans-membrane protein from the important human pathogen Helicobacter pylori; the structure of human interleukin IL-34, a recently discovered helical cytokine; the structure of a functionally uncharacterized enzyme OrfY from Thermoproteus tenax formed by a gene duplication and a novel fold; an ORFan domain of mimivirus sulfhydryl oxidase R596; the fibre protein gp17 from bacteriophage T7; the Bacteriophage CBA-120 tailspike protein; a virus coat protein from metagenomic samples of the marine environment; and finally an unprecedented class of structure prediction targets based on engineered disulfide-rich small proteins. PMID:24318984

  1. Recent improvements in prediction of protein structure by global optimization of a potential energy function

    PubMed Central

    Pillardy, Jarosław; Czaplewski, Cezary; Liwo, Adam; Lee, Jooyoung; Ripoll, Daniel R.; Kaźmierkiewicz, Rajmund; Ołdziej, Stanisław; Wedemeyer, William J.; Gibson, Kenneth D.; Arnautova, Yelena A.; Saunders, Jeff; Ye, Yuan-Jie; Scheraga, Harold A.

    2001-01-01

    Recent improvements of a hierarchical ab initio or de novo approach for predicting both α and β structures of proteins are described. The united-residue energy function used in this procedure includes multibody interactions from a cumulant expansion of the free energy of polypeptide chains, with their relative weights determined by Z-score optimization. The critical initial stage of the hierarchical procedure involves a search of conformational space by the conformational space annealing (CSA) method, followed by optimization of an all-atom model. The procedure was assessed in a recent blind test of protein structure prediction (CASP4). The resulting lowest-energy structures of the target proteins (ranging in size from 70 to 244 residues) agreed with the experimental structures in many respects. The entire experimental structure of a cyclic α-helical protein of 70 residues was predicted to within 4.3 Å α-carbon (Cα) rms deviation (rmsd) whereas, for other α-helical proteins, fragments of roughly 60 residues were predicted to within 6.0 Å Cα rmsd. Whereas β structures can now be predicted with the new procedure, the success rate for α/β- and β-proteins is lower than that for α-proteins at present. For the β portions of α/β structures, the Cα rmsd's are less than 6.0 Å for contiguous fragments of 30–40 residues; for one target, three fragments (of length 10, 23, and 28 residues, respectively) formed a compact part of the tertiary structure with a Cα rmsd less than 6.0 Å. Overall, these results constitute an important step toward the ab initio prediction of protein structure solely from the amino acid sequence. PMID:11226239

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

    NASA Astrophysics Data System (ADS)

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

    2010-03-01

    Since demonstrating that a protein's sequence encodes its structure, the prediction of structure from sequence remains an outstanding problem that impacts numerous scientific disciplines including many genome projects. By iteratively fixing secondary structure assignments of residues during Monte Carlo simulations of folding, our coarse grained model without information concerning homology or explicit side chains outperforms current homology-based secondary structure prediction methods for many proteins. The computationally rapid algorithm using only single residue (phi, psi) dihedral angle moves also generates tertiary structures of comparable accuracy to existing all-atom methods for many small proteins, particularly ones with low homology. Hence, given appropriate search strategies and scoring functions, reduced representations can be used for accurately predicting secondary structure as well as providing three-dimensional structures, thereby increasing the size of proteins approachable by homology-free methods and the accuracy of template methods whose accuracy depends on the quality of the input secondary structure. Inclusion of information from evolutionarily related sequences enhances the statistics and the accuracy of the predictions.

  3. Transmembrane structure predictions with hydropathy index/charge two-dimensional trajectories of stochastic dynamical systems.

    PubMed

    Kaburagi, Takashi; Muramatsu, Daigo; Matsumoto, Takashi

    2007-06-01

    A novel algorithm is proposed for predicting transmembrane protein secondary structure from two-dimensional vector trajectories consisting of a hydropathy index and formal charge of a test amino acid sequence using stochastic dynamical system models. Two prediction problems are discussed. One is the prediction of transmembrane region counts; another is that of transmembrane regions, i.e. predicting whether or not each amino acid belongs to a transmembrane region. The prediction accuracies, using a collection of well-characterized transmembrane protein sequences and benchmarking sequences, suggest that the proposed algorithm performs reasonably well. An experiment was performed with a glutamate transporter homologue from Pyrococcus horikoshii. The predicted transmembrane regions of the five human glutamate transporter sequences and observations based on the computed likelihood are reported. PMID:17688311

  4. A multilayer evaluation approach for protein structure prediction and model quality assessment.

    PubMed

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

    2011-01-01

    Protein tertiary structures are essential for studying functions of proteins at molecular level. An indispensable approach for protein structure solution is computational prediction. Most protein structure prediction methods generate candidate models first and select the best candidates by model quality assessment (QA). In many cases, good models can be produced, but the QA tools fail to select the best ones from the candidate model pool. Because of incomplete understanding of protein folding, each QA method only reflects partial facets of a structure model and thus has limited discerning power with no one consistently outperforming others. In this article, we developed a set of new QA methods, including two QA methods for evaluating target/template alignments, a molecular dynamics (MD)-based QA method, and three consensus QA methods with selected references to reveal new facets of protein structures complementary to the existing methods. Moreover, the underlying relationship among different QA methods were analyzed and then integrated into a multilayer evaluation approach to guide the model generation and model selection in prediction. All methods are integrated and implemented into an innovative and improved prediction system hereafter referred to as MUFOLD. In CASP8 and CASP9, MUFOLD has demonstrated the proof of the principles in terms of both QA discerning power and structure prediction accuracy. PMID:21997706

  5. Improving protein fold recognition and structural class prediction accuracies using physicochemical properties of amino acids.

    PubMed

    Raicar, Gaurav; Saini, Harsh; Dehzangi, Abdollah; Lal, Sunil; Sharma, Alok

    2016-08-01

    Predicting the three-dimensional (3-D) structure of a protein is an important task in the field of bioinformatics and biological sciences. However, directly predicting the 3-D structure from the primary structure is hard to achieve. Therefore, predicting the fold or structural class of a protein sequence is generally used as an intermediate step in determining the protein's 3-D structure. For protein fold recognition (PFR) and structural class prediction (SCP), two steps are required - feature extraction step and classification step. Feature extraction techniques generally utilize syntactical-based information, evolutionary-based information and physicochemical-based information to extract features. In this study, we explore the importance of utilizing the physicochemical properties of amino acids for improving PFR and SCP accuracies. For this, we propose a Forward Consecutive Search (FCS) scheme which aims to strategically select physicochemical attributes that will supplement the existing feature extraction techniques for PFR and SCP. An exhaustive search is conducted on all the existing 544 physicochemical attributes using the proposed FCS scheme and a subset of physicochemical attributes is identified. Features extracted from these selected attributes are then combined with existing syntactical-based and evolutionary-based features, to show an improvement in the recognition and prediction performance on benchmark datasets. PMID:27164998

  6. Structure Prediction of the Second Extracellular Loop in G-Protein-Coupled Receptors

    PubMed Central

    Kmiecik, Sebastian; Jamroz, Michal; Kolinski, Michal

    2014-01-01

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

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

    PubMed Central

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

    1997-01-01

    A rapid and easy-to-use method of predicting the conductance of an ion channel from its three-dimensional structure is presented. The method combines the pore dimensions of the channel as measured in the HOLE program with an Ohmic model of conductance. An empirically based correction factor is then applied. The method yielded good results for six experimental channel structures (none of which were included in the training set) with predictions accurate to within an average factor of 1.62 to the true values. The predictive r2 was equal to 0.90, which is indicative of a good predictive ability. The procedure is used to validate model structures of alamethicin and phospholamban. Two genuine predictions for the conductance of channels with known structure but without reported conductances are given. A modification of the procedure that calculates the expected results for the effect of the addition of nonelectrolyte polymers on conductance is set out. Results for a cholera toxin B-subunit crystal structure agree well with the measured values. The difficulty in interpreting such studies is discussed, with the conclusion that measurements on channels of known structure are required. Images FIGURE 1 FIGURE 3 FIGURE 4 FIGURE 6 FIGURE 10 PMID:9138559

  8. 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

  9. 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. PMID:27484376

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

    NASA Technical Reports Server (NTRS)

    Miller, J. M.

    1980-01-01

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

  11. 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.

  12. Structural protein descriptors in 1-dimension and their sequence-based predictions.

    PubMed

    Kurgan, Lukasz; Disfani, Fatemeh Miri

    2011-09-01

    The last few decades observed an increasing interest in development and application of 1-dimensional (1D) descriptors of protein structure. These descriptors project 3D structural features onto 1D strings of residue-wise structural assignments. They cover a wide-range of structural aspects including conformation of the backbone, burying depth/solvent exposure and flexibility of residues, and inter-chain residue-residue contacts. We perform first-of-its-kind comprehensive comparative review of the existing 1D structural descriptors. We define, review and categorize ten structural descriptors and we also describe, summarize and contrast over eighty computational models that are used to predict these descriptors from the protein sequences. We show that the majority of the recent sequence-based predictors utilize machine learning models, with the most popular being neural networks, support vector machines, hidden Markov models, and support vector and linear regressions. These methods provide high-throughput predictions and most of them are accessible to a non-expert user via web servers and/or stand-alone software packages. We empirically evaluate several recent sequence-based predictors of secondary structure, disorder, and solvent accessibility descriptors using a benchmark set based on CASP8 targets. Our analysis shows that the secondary structure can be predicted with over 80% accuracy and segment overlap (SOV), disorder with over 0.9 AUC, 0.6 Matthews Correlation Coefficient (MCC), and 75% SOV, and relative solvent accessibility with PCC of 0.7 and MCC of 0.6 (0.86 when homology is used). We demonstrate that the secondary structure predicted from sequence without the use of homology modeling is as good as the structure extracted from the 3D folds predicted by top-performing template-based methods. PMID:21787299

  13. conSSert: Consensus SVM Model for Accurate Prediction of Ordered Secondary Structure.

    PubMed

    Kieslich, Chris A; Smadbeck, James; Khoury, George A; Floudas, Christodoulos A

    2016-03-28

    Accurate prediction of protein secondary structure remains a crucial step in most approaches to the protein-folding problem, yet the prediction of ordered secondary structure, specifically beta-strands, remains a challenge. We developed a consensus secondary structure prediction method, conSSert, which is based on support vector machines (SVM) and provides exceptional accuracy for the prediction of beta-strands with QE accuracy of over 0.82 and a Q2-EH of 0.86. conSSert uses as input probabilities for the three types of secondary structure (helix, strand, and coil) that are predicted by four top performing methods: PSSpred, PSIPRED, SPINE-X, and RAPTOR. conSSert was trained/tested using 4261 protein chains from PDBSelect25, and 8632 chains from PISCES. Further validation was performed using targets from CASP9, CASP10, and CASP11. Our data suggest that poor performance in strand prediction is likely a result of training bias and not solely due to the nonlocal nature of beta-sheet contacts. conSSert is freely available for noncommercial use as a webservice: http://ares.tamu.edu/conSSert/ . PMID:26928531

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

    PubMed

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

    2011-12-01

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

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

    PubMed

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

    2011-07-28

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

  16. Prediction and structural uncertainty analyses of artificial neural networks using hierarchical Bayesian model averaging

    NASA Astrophysics Data System (ADS)

    Chitsazan, Nima; Nadiri, Ata Allah; Tsai, Frank T.-C.

    2015-09-01

    This study adopts a hierarchical Bayesian model averaging (HBMA) method to analyze prediction uncertainty resulted from uncertain components in artificial neural networks (ANNs). The HBMA is an ensemble method for prediction and is used to segregate the sources of model structure uncertainty in ANNs and investigate their variance contributions to total prediction variance. Specific sources of uncertainty considered in ANNs include the uncertainty in neural network weights and biases (model parameters), uncertainty of selecting an activation function for the hidden layer, and uncertainty of selecting a number of hidden layer nodes (model structure). Prediction uncertainties due to uncertain inputs and ANN model parameters are represented by within-model variance. Prediction uncertainties due to uncertain activation function and uncertain number of nodes for the hidden layer are represented by between-model variance. The method is demonstrated through a study that employs ANNs to predict fluoride concentration in the aquifers of the Maku area, Azarbaijan, Iran. The results show that uncertain inputs and ANN model parameters produces the most prediction variance, followed by prediction variances from uncertain number of hidden layer nodes and uncertain activation function.

  17. A non-B DNA can replace heptamer of V(D)J recombination when present along with a nonamer: implications in chromosomal translocations and cancer.

    PubMed

    Nishana, Mayilaadumveettil; Raghavan, Sathees C

    2012-11-15

    The RAG (recombination-activating gene) complex is responsible for the generation of antigen receptor diversity by acting as a sequence-specific nuclease. Recent studies have shown that it also acts as a structure-specific nuclease. However, little is known about the factors regulating this activity at the genomic level. We show in the present study that the proximity of a V(D)J nonamer to heteroduplex DNA significantly increases RAG cleavage and binding efficiencies at physiological concentrations of MgCl(2). The position of the nonamer with respect to heteroduplex DNA was important, but not orientation. A spacer length of 18 bp between the nonamer and mismatch was optimal for RAG-mediated DNA cleavage. Mutations to the sequence of the nonamer and deletion of the nonamer-binding domain of RAG1 reinforced the role of the nonamer in the enhancement in RAG cleavage. Interestingly, partial mutation of the nonamer did not significantly reduce RAG cleavage on heteroduplex DNA, suggesting that even cryptic nonamers were sufficient to enhance RAG cleavage. More importantly, we show that the fragile region involved in chromosomal translocations associated with BCL2 (B-cell lymphoma 2) can be cleaved by RAGs following a nonamer-dependent mechanism. Hence our results from the present study suggest that a non-B DNA can replace the heptamer of RSS (recombination signal sequence) when present adjacent to nonamers, explaining the generation of certain chromosomal translocations in lymphoid malignancies. PMID:22891626

  18. 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. PMID:25726463

  19. Structural link prediction based on ant colony approach in social networks

    NASA Astrophysics Data System (ADS)

    Sherkat, Ehsan; Rahgozar, Maseud; Asadpour, Masoud

    2015-02-01

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

  20. Structure-Templated Predictions of Novel Protein Interactions from Sequence Information

    PubMed Central

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

    2007-01-01

    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. PMID:17892321

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

    PubMed Central

    Kerpedjiev, Peter; Höner zu Siederdissen, Christian; Hofacker, Ivo L.

    2015-01-01

    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. PMID:25904133

  2. 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.

  3. Numerical criteria for the evaluation of ab initio predictions of protein structure.

    PubMed

    Zemla, A; Venclovas, C; Reinhardt, A; Fidelis, K; Hubbard, T J

    1997-01-01

    As part of the CASP2 protein structure prediction experiment, a set of numerical criteria were defined for the evaluation of "ab initio" predictions. The evaluation package comprises a series of electronic submission formats, a submission validator, evaluation software, and a series of scripts to summarize the results for the CASP2 meeting and for presentation via the World Wide Web (WWW). The evaluation package is accessible for use on new predictions via WWW so that results can be compared to those submitted to CASP2. With further input from the community, the evaluation criteria are expected to evolve into a comprehensive set of measures capturing the overall quality of a prediction as well as critical detail essential for further development of prediction methods. We discuss present measures, limitations of the current criteria, and possible improvements. PMID:9485506

  4. 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.

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

    PubMed

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

    2009-10-30

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

  6. 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.

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

    PubMed

    Gerek, Omer N; Cetin, A Enis

    2006-01-01

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

  8. Quantitative structure property relationship modeling of excipient properties for prediction of formulation characteristics.

    PubMed

    Gaikwad, Vinod L; Bhatia, Neela M; Desai, Sujit A; Bhatia, Manish S

    2016-10-20

    Quantitative structure property relationship (QSPR) is used to relate the excipient descriptors with the formulation properties. A QSPR model is developed by regression analysis of selected descriptors contributing towards the targeted formulation properties. Developed QSPR model is validated by the true external method where it showed good accuracy and precision in predicting the formulation composition as experimental t90% (61.35min) is observed very close to predicted t90% (67.37min). Hence, QSPR approach saves resources by predicting drug release from an unformulated formulation; avoiding repetitive trials in the development of a new formulation and/or optimization of existing one. PMID:27474604

  9. A Deep Learning Network Approach to ab initio Protein Secondary Structure Prediction

    PubMed Central

    Spencer, Matt; Eickholt, Jesse; Cheng, Jianlin

    2014-01-01

    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% 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 data set of 198 proteins, achieving a Q3 accuracy of 80.7% and a Sov accuracy of 74.2%. PMID:25750595

  10. 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. PMID:25750595

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

    PubMed Central

    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 on the benchmark dataset SPX, which corresponds to improvement over the state of the art. A web-application is

  12. 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

  13. 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.

  14. An automatic method for CASP9 free modeling structure prediction assessment

    PubMed Central

    Cong, Qian; Kinch, Lisa N.; Pei, Jimin; Shi, Shuoyong; Grishin, Vyacheslav N.; Li, Wenlin; Grishin, Nick V.

    2011-01-01

    Motivation: Manual inspection has been applied to and is well accepted for assessing critical assessment of protein structure prediction (CASP) free modeling (FM) category predictions over the years. Such manual assessment requires expertise and significant time investment, yet has the problems of being subjective and unable to differentiate models of similar quality. It is beneficial to incorporate the ideas behind manual inspection to an automatic score system, which could provide objective and reproducible assessment of structure models. Results: Inspired by our experience in CASP9 FM category assessment, we developed an automatic superimposition independent method named Quality Control Score (QCS) for structure prediction assessment. QCS captures both global and local structural features, with emphasis on global topology. We applied this method to all FM targets from CASP9, and overall the results showed the best agreement with Manual Inspection Scores among automatic prediction assessment methods previously applied in CASPs, such as Global Distance Test Total Score (GDT_TS) and Contact Score (CS). As one of the important components to guide our assessment of CASP9 FM category predictions, this method correlates well with other scoring methods and yet is able to reveal good-quality models that are missed by GDT_TS. Availability: The script for QCS calculation is available at http://prodata.swmed.edu/QCS/. Contact: grishin@chop.swmed.edu Supplementary Information: Supplementary data are available at Bioinformatics online. PMID:21994223

  15. Structure-aided prediction of mammalian transcription factor complexes in conserved non-coding elements.

    PubMed

    Guturu, Harendra; Doxey, Andrew C; Wenger, Aaron M; Bejerano, Gill

    2013-12-19

    Mapping the DNA-binding preferences of transcription factor (TF) complexes is critical for deciphering the functions of cis-regulatory elements. Here, we developed a computational method that compares co-occurring motif spacings in conserved versus unconserved regions of the human genome to detect evolutionarily constrained binding sites of rigid TF complexes. Structural data were used to estimate TF complex physical plausibility, explore overlapping motif arrangements seldom tackled by non-structure-aware methods, and generate and analyse three-dimensional models of the predicted complexes bound to DNA. Using this approach, we predicted 422 physically realistic TF complex motifs at 18% false discovery rate, the majority of which (326, 77%) contain some sequence overlap between binding sites. The set of mostly novel complexes is enriched in known composite motifs, predictive of binding site configurations in TF-TF-DNA crystal structures, and supported by ChIP-seq datasets. Structural modelling revealed three cooperativity mechanisms: direct protein-protein interactions, potentially indirect interactions and 'through-DNA' interactions. Indeed, 38% of the predicted complexes were found to contain four or more bases in which TF pairs appear to synergize through overlapping binding to the same DNA base pairs in opposite grooves or strands. Our TF complex and associated binding site predictions are available as a web resource at http://bejerano.stanford.edu/complex. PMID:24218641

  16. Ab initio prediction of protein structure with both all-atom and simplified force fields

    NASA Astrophysics Data System (ADS)

    Scheraga, Harold

    2004-03-01

    Using only a physics-based ab initio method, and both all-atom (ECEPP/3) and simplified united-residue (UNRES) force fields, global optimization of both potential functions with Monte Carlo-plus-Minimization (MCM) and Conformational Space Annealing (CSA), respectively, provides predicted structures of proteins without use of knowledge-based information. The all-atom approach has been applied to the 46-residue protein A, and the UNRES approach has been applied to larger CASP targets. The predicted structures will be described.

  17. 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.

  18. 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.

  19. 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

  20. ORION: a web server for protein fold recognition and structure prediction using evolutionary hybrid profiles.

    PubMed

    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

  1. 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.

  2. 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.

  3. Protein Structure Prediction using a Docking-based Hierarchical Folding scheme

    PubMed Central

    Kifer, Ilona; Nussinov, Ruth; Wolfson, Haim J.

    2011-01-01

    The pathways by which proteins fold into their specific native structure is still an unsolved mystery. Currently many methods for protein structure prediction are available, most of them tackle the problem by relying on the vast amounts of data collected from known protein structures. These methods are often not concerned with the route the protein follows to reach its final fold. This work is based on the premise that proteins fold in a hierarchical manner. We present FOBIA, an automated method for predicting a protein structure. FOBIA consists of two main stages: the first finds matches between parts of the target sequence and independently-folding structural units using profile-profile comparison. The second assembles these units into a 3D structure by searching and ranking their possible orientations towards each other using a docking-based approach. We have previously reported an application of an initial version of this strategy to homology based targets. Since then we have considerably enhanced our method’s abilities to allow it to address the more difficult template-based target category. This allows us to now apply FOBIA to the Template-Based targets of CASP8 and to show that it is both very efficient and promising. Our method can provide an alternative for Template-Based structure prediction, and in particular, the docking-based ranking technique presented here can be incorporated into any profile-profile comparison based method. PMID:21445943

  4. Protein structure prediction using a docking-based hierarchical folding scheme.

    PubMed

    Kifer, Ilona; Nussinov, Ruth; Wolfson, Haim J

    2011-06-01

    The pathways by which proteins fold into their specific native structure are still an unsolved mystery. Currently, many methods for protein structure prediction are available, and most of them tackle the problem by relying on the vast amounts of data collected from known protein structures. These methods are often not concerned with the route the protein follows to reach its final fold. This work is based on the premise that proteins fold in a hierarchical manner. We present FOBIA, an automated method for predicting a protein structure. FOBIA consists of two main stages: the first finds matches between parts of the target sequence and independently folding structural units using profile-profile comparison. The second assembles these units into a 3D structure by searching and ranking their possible orientations toward each other using a docking-based approach. We have previously reported an application of an initial version of this strategy to homology based targets. Since then we have considerably enhanced our method's abilities to allow it to address the more difficult template-based target category. This allows us to now apply FOBIA to the template-based targets of CASP8 and to show that it is both very efficient and promising. Our method can provide an alternative for template-based structure prediction, and in particular, the docking-basedranking technique presented here can be incorporated into any profile-profile comparison based method. PMID:21445943

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

    PubMed Central

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

    2010-01-01

    Summary Protein-biomineral interactions are paramount to materials production in biology, including the mineral phase of hard tissue. Unfortunately, the structure of biomineral-associated proteins cannot be determined by X-ray crystallography or solution NMR. Here we report a method for determining the structure of biomineral-associated proteins. The method combines solid-state NMR (ssNMR) and ssNMR-biased computational structure prediction. In addition, the algorithm is able to identify lattice geometries most compatible with ssNMR constraints, representing a quantitative, novel method for investigating crystal-face binding specificity. We use this new method to determine most of the structure of human salivary statherin interacting with the mineral phase of tooth enamel. Computation and experiment converge on an ensemble of related structures and identify preferential binding at three crystal surfaces. The work represents a significant advance toward determining structure of biomineral-adsorbed protein using experimentally biased structure prediction. This method is generally applicable to proteins that can be chemically synthesized. PMID:21134646

  6. 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

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

    PubMed Central

    Rivas, Elena

    2013-01-01

    Any method for RNA secondary structure prediction is determined by four ingredients. The architecture is the choice of features implemented by the model (such as stacked basepairs, loop length distributions, etc.). The architecture determines the number of parameters in the model. The scoring scheme is the nature of those parameters (whether thermodynamic, probabilistic, or weights). The parameterization stands for the specific values assigned to the parameters. These three ingredients are referred to as “the model.” The fourth ingredient is the folding algorithms used to predict plausible secondary structures given the model and the sequence of a structural RNA. Here, I make several unifying observations drawn from looking at more than 40 years of methods for RNA secondary structure prediction in the light of this classification. As a final observation, there seems to be a performance ceiling that affects all methods with complex architectures, a ceiling that impacts all scoring schemes with remarkable similarity. This suggests that modeling RNA secondary structure by using intrinsic sequence-based plausible “foldability” will require the incorporation of other forms of information in order to constrain the folding space and to improve prediction accuracy. This could give an advantage to probabilistic scoring systems since a probabilistic framework is a natural platform to incorporate different sources of information into one single inference problem. PMID:23695796

  8. 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

  9. 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. PMID:27148634

  10. ENTPRISE: An Algorithm for Predicting Human Disease-Associated Amino Acid Substitutions from Sequence Entropy and Predicted Protein Structures

    PubMed Central

    Zhou, Hongyi; Gao, Mu; Skolnick, Jeffrey

    2016-01-01

    The advance of next-generation sequencing technologies has made exome sequencing rapid and relatively inexpensive. A major application of exome sequencing is the identification of genetic variations likely to cause Mendelian diseases. This requires processing large amounts of sequence information and therefore computational approaches that can accurately and efficiently identify the subset of disease-associated variations are needed. The accuracy and high false positive rates of existing computational tools leave much room for improvement. Here, we develop a boosted tree regression machine-learning approach to predict human disease-associated amino acid variations by utilizing a comprehensive combination of protein sequence and structure features. On comparing our method, ENTPRISE, to the state-of-the-art methods SIFT, PolyPhen-2, MUTATIONASSESSOR, MUTATIONTASTER, FATHMM, ENTPRISE exhibits significant improvement. In particular, on a testing dataset consisting of only proteins with balanced disease-associated and neutral variations defined as having the ratio of neutral/disease-associated variations between 0.3 and 3, the Mathews Correlation Coefficient by ENTPRISE is 0.493 as compared to 0.432 by PPH2-HumVar, 0.406 by SIFT, 0.403 by MUTATIONASSESSOR, 0.402 by PPH2-HumDiv, 0.305 by MUTATIONTASTER, and 0.181 by FATHMM. ENTPRISE is then applied to nucleic acid binding proteins in the human proteome. Disease-associated predictions are shown to be highly correlated with the number of protein-protein interactions. Both these predictions and the ENTPRISE server are freely available for academic users as a web service at http://cssb.biology.gatech.edu/entprise/. PMID:26982818

  11. 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. PMID:26950026

  12. A parallel strategy for predicting the secondary structure of polycistronic microRNAs.

    PubMed

    Han, Dianwei; Tang, Guiliang; Zhang, Jun

    2013-01-01

    The biogenesis of a functional microRNA is largely dependent on the secondary structure of the microRNA precursor (pre-miRNA). Recently, it has been shown that microRNAs are present in the genome as the form of polycistronic transcriptional units in plants and animals. It will be important to design efficient computational methods to predict such structures for microRNA discovery and its applications in gene silencing. In this paper, we propose a parallel algorithm based on the master-slave architecture to predict the secondary structure from an input sequence. We conducted some experiments to verify the effectiveness of our parallel algorithm. The experimental results show that our algorithm is able to produce the optimal secondary structure of polycistronic microRNAs. PMID:23467060

  13. Structural predictions based on the compositions of cathodic materials by first-principles calculations

    NASA Astrophysics Data System (ADS)

    Li, Yang; Lian, Fang; Chen, Ning; Hao, Zhen-jia; Chou, Kuo-chih

    2015-05-01

    A first-principles method is applied to comparatively study the stability of lithium metal oxides with layered or spinel structures to predict the most energetically favorable structure for different compositions. The binding and reaction energies of the real or virtual layered LiMO2 and spinel LiM2O4 (M = Sc-Cu, Y-Ag, Mg-Sr, and Al-In) are calculated. The effect of element M on the structural stability, especially in the case of multiple-cation compounds, is discussed herein. The calculation results indicate that the phase stability depends on both the binding and reaction energies. The oxidation state of element M also plays a role in determining the dominant structure, i.e., layered or spinel phase. Moreover, calculation-based theoretical predictions of the phase stability of the doped materials agree with the previously reported experimental data.

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

    NASA Astrophysics Data System (ADS)

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

    2012-07-01

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

  15. 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. PMID:12784210

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

    PubMed

    Mäki-Marttunen, Tuomo; Aćimović, Jugoslava; Ruohonen, Keijo; Linne, Marja-Leena

    2013-01-01

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

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

    PubMed Central

    Mäki-Marttunen, Tuomo; Aćimović, Jugoslava; Ruohonen, Keijo; Linne, Marja-Leena

    2013-01-01

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

  18. PiDNA: Predicting protein-DNA interactions with structural models.

    PubMed

    Lin, Chih-Kang; Chen, Chien-Yu

    2013-07-01

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

  19. The expert system for toxicity prediction of chemicals based on structure-activity relationship.

    PubMed Central

    Nakadate, M; Hayashi, M; Sofuni, T; Kamata, E; Aida, Y; Osada, T; Ishibe, T; Sakamura, Y; Ishidate, M

    1991-01-01

    The prediction systems of chemical toxicity has been developed by means of structure-activity relationship based on the computerized fact database (BL-DB). Numbers and ratio of elements, side chains, bonding, position, and microenvironment of side chains were used as structural factors of the chemical for the prediction. Such information was obtained from the BL-DB database by Wiswesser line-formula chemical notation. In the present study, the Salmonella/microsome assay was chosen as indicative of the target toxicity of chemicals. A set of chemicals specified with mutagenicity data was retrieved, and necessary information was extracted and transferred to the working file. Rules of the relations between characteristics of chemical structure and the assay result are extracted as parameters for rules by experts on the rearranged data set. These were analyzed statistically by the discriminant analysis and the prediction with the rules were evaluated by the elimination method. Eight kinds of rules to predict Salmonella/microsome assay were constructed, and currently results of the assay on aliphatic and heterocyclic compounds can be predicted as accurately as +90%. PMID:1820282

  20. 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.

  1. 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

  2. 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. PMID:26609083

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

    SciTech Connect

    Wang, Hui; Department of Physics and Engineering Physics, University of Saskatchewan, Saskatoon, Saskatchewan S7N 5E2 ; LeBlanc, K. A.; Gao, Bo; Yao, Yansun; Canadian Light Source, Saskatoon, Saskatchewan S7N 0X4

    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.

  4. 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

  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. PMID:27066069

  6. 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

    2014-08-01

    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. PMID:24961789

  7. 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.

  8. DEVELOPMENT OF QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIPS FOR PREDICTING BIODEGRADATION KINETICS

    EPA Science Inventory

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

  9. 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. PMID:25231198

  10. Aircraft interior noise prediction using a structural-acoustic analogy in NASTRAN modal synthesis

    NASA Technical Reports Server (NTRS)

    Grosveld, Ferdinand W.; Sullivan, Brenda M.; Marulo, Francesco

    1988-01-01

    The noise induced inside a cylindrical fuselage model by shaker excitation is investigated theoretically and experimentally. The NASTRAN modal-synthesis program is used in the theoretical analysis, and the predictions are compared with experimental measurements in extensive graphs. Good general agreement is obtained, but the need for further refinements to account for acoustic-cavity damping and structural-acoustic interaction is indicated.

  11. 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 ...

  12. 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,...

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

    PubMed Central

    Röttig, Marc; Rausch, Christian; Kohlbacher, Oliver

    2010-01-01

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

  14. 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)

  15. Relative packing groups in template-based structure prediction: cooperative effects of true positive constraints.

    PubMed

    Day, Ryan; Qu, Xiaotao; Swanson, Rosemarie; Bohannan, Zach; Bliss, Robert; Tsai, Jerry

    2011-01-01

    Most current template-based structure prediction methods concentrate on finding the correct backbone conformation and then packing sidechains within that backbone. Our packing-based method derives distance constraints from conserved relative packing groups (RPGs). In our refinement approach, the RPGs provide a level of resolution that restrains global topology while allowing conformational sampling. In this study, we test our template-based structure prediction method using 51 prediction units from CASP7 experiments. RPG-based constraints are able to substantially improve approximately two-thirds of starting templates. Upon deeper investigation, we find that true positive spatial constraints, especially those non-local in sequence, derived from the RPGs were important to building nearer native models. Surprisingly, the fraction of incorrect or false positive constraints does not strongly influence the quality of the final candidate. This result indicates that our RPG-based true positive constraints sample the self-consistent, cooperative interactions of the native structure. The lack of such reinforcing cooperativity explains the weaker effect of false positive constraints. Generally, these findings are encouraging indications that RPGs will improve template-based structure prediction. PMID:21210729

  16. Incorporating chemical modification constraints into a dynamic programming algorithm for prediction of RNA secondary structure

    PubMed Central

    Mathews, David H.; Disney, Matthew D.; Childs, Jessica L.; Schroeder, Susan J.; Zuker, Michael; Turner, Douglas H.

    2004-01-01

    A dynamic programming algorithm for prediction of RNA secondary structure has been revised to accommodate folding constraints determined by chemical modification and to include free energy increments for coaxial stacking of helices when they are either adjacent or separated by a single mismatch. Furthermore, free energy parameters are revised to account for recent experimental results for terminal mismatches and hairpin, bulge, internal, and multibranch loops. To demonstrate the applicability of this method, in vivo modification was performed on 5S rRNA in both Escherichia coli and Candida albicans with 1-cyclohexyl-3-(2-morpholinoethyl) carbodiimide metho-p-toluene sulfonate, dimethyl sulfate, and kethoxal. The percentage of known base pairs in the predicted structure increased from 26.3% to 86.8% for the E. coli sequence by using modification constraints. For C. albicans, the accuracy remained 87.5% both with and without modification data. On average, for these sequences and a set of 14 sequences with known secondary structure and chemical modification data taken from the literature, accuracy improves from 67% to 76%. This enhancement primarily reflects improvement for three sequences that are predicted with <40% accuracy on the basis of energetics alone. For these sequences, inclusion of chemical modification constraints improves the average accuracy from 28% to 78%. For the 11 sequences with <6% pseudoknotted base pairs, structures predicted with constraints from chemical modification contain on average 84% of known canonical base pairs. PMID:15123812

  17. Two-scale hadronic structure and elastic pp scattering: Predicted and measured

    NASA Astrophysics Data System (ADS)

    Kopeliovich, B. Z.; Potashnikova, I. K.; Povh, B.

    2012-09-01

    We update the comparison with an experiment of the dynamical model of high-energy hadron interactions based on the two-scale structure of hadrons. All predictions made over a decade ago are confirmed with a high precision by the TOTEM experiment at LHC.

  18. Prediction of trabecular bone principal structural orientation using quantitative ultrasound scanning

    PubMed Central

    Lin, Liangjun; Cheng, Jiqi; Lin, Wei; Qin, Yi-Xian

    2016-01-01

    Bone has the ability to adapt its structure in response to the mechanical environment as defined as Wolff’s Law. The alignment of trabecular structure is intended to adapt to the particular mechanical milieu applied to it. Due to the absence of normal mechanical loading, it will be extremely important to assess the anisotropic deterioration of bone during the extreme conditions, i.e., long term space mission and disease orientated disuse, to predict risk of fractures. The propagation of ultrasound wave in trabecular bone is substantially influenced by the anisotropy of the trabecular structure. Previous studies have shown that both ultrasound velocity and amplitude is dependent on the incident angle of the ultrasound signal into the bone sample. In this work, seven bovine trabecular bone balls were used for rotational ultrasound measurement around three anatomical axes to elucidate the ability of ultrasound to identify trabecular orientation. Both ultrasound attenuation (ATT) and fast wave velocity (UV) were used to calculate the principal orientation of the trabecular bone. By comparing to the mean intercept length (MIL) tensor obtained from μCT, the angle difference of the prediction by UV was 4.45°, while it resulted in 11.67° angle difference between direction predicted by μCT and the prediction by ATT. This result demonstrates the ability of ultrasound as a non-invasive measurement tool for the principal structural orientation of the trabecular bone. PMID:22560370

  19. Weighted Structural Regression: A Broad Class of Adaptive Methods for Improving Linear Prediction.

    ERIC Educational Resources Information Center

    Pruzek, Robert M.; Lepak, Greg M.

    1992-01-01

    Adaptive forms of weighted structural regression are developed and discussed. Bootstrapping studies indicate that the new methods have potential to recover known population regression weights and predict criterion score values routinely better than do ordinary least squares methods. The new methods are scale free and simple to compute. (SLD)

  20. 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

  1. 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.

  2. RaptorX-Property: a web server for protein structure property prediction.

    PubMed

    Wang, Sheng; Li, Wei; Liu, Shiwang; Xu, Jinbo

    2016-07-01

    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

  3. Comparison of Algorithms for Prediction of Protein Structural Features from Evolutionary Data

    PubMed Central

    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. PMID:26963911

  4. Prediction model of atmospheric refractive index structure parameter in coastal area

    NASA Astrophysics Data System (ADS)

    Wang, Hongxing; Li, Bifeng; Wu, Xiaojun; Liu, Chuanhui; Hu, Zhihui; Xu, Pengfei

    2015-09-01

    In this paper, we focus on the prediction of atmospheric refractive index structure parameter (?) in coastal area using the routine meteorological parameters. Based on the micrometeorology, macrometeorology and Monin-Obukhov similarity theory, three modified prediction models of ? are presented in combination with the long-term observation data of ? and meteorological parameters in coastal city, respectively. For different weather, the applicable cases of three ? prediction models are comparatively analysed and their applicable effects are comprehensively evaluated. The results indicate that the modified micrometeorology model of ? shows better applicability for overcast sky, the offshore macrometeorology model of ? displays better predictability for sunny day and the offshore Thiermann model provides better availability for overcast sky, cloudy day, overcast to sunny or sunny to overcast day.

  5. Interaction of the IκBα C-terminal PEST sequence with NF-κB: Insights into the Inhibition of NF-κB DNA binding by IκBα

    PubMed Central

    Sue, Shih-Che; Dyson, H. Jane

    2009-01-01

    The transcription factor NF-κB (p50/p65) binds either a κB DNA element or its inhibitor protein, IκBα, but these two binding events are mutually exclusive. The reason for this exclusivity is not obvious from the available crystal structure data. The C-terminal PEST-like sequence of IκBα appears to be involved in the process, but it is located in both of the published X-ray structures of the IκBα/NF-κB complex at a significant distance away from the DNA contact loop in the NF-κB DNA-binding domain. We have used nuclear magnetic resonance spectroscopy and differential isotopic labeling to probe the interactions between the p50/p65 NF-κB heterodimer and IκBα in solution. Our measurements are able to resolve a local structural discrepancy between the two crystal structures, and we confirm that the primary interaction of the IκBα PEST domain is with the DNA-binding domain of the p65 subunit. Mutagenesis of key arginine residues in the DNA contact sequence results in the loss of specific interaction of the PEST sequence with the p65 subdomain. We conclude that the local structure of the IκBα/NF-κB complex in the region of the PEST sequence is consistent with a direct interaction of this acidic sequence with the basic DNA contact sequence in p65, thus reducing the affinity of NF-κB for DNA by a competitive mechanism that is still to be elucidated fully. PMID:19327364

  6. 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

  7. Validation of a fluid-structure interaction numerical model for predicting flow transients in arteries.

    PubMed

    Kanyanta, V; Ivankovic, A; Karac, A

    2009-08-01

    Fluid-structure interaction (FSI) numerical models are now widely used in predicting blood flow transients. This is because of the importance of the interaction between the flowing blood and the deforming arterial wall to blood flow behaviour. Unfortunately, most of these FSI models lack rigorous validation and, thus, cannot guarantee the accuracy of their predictions. This paper presents the comprehensive validation of a two-way coupled FSI numerical model, developed to predict flow transients in compliant conduits such as arteries. The model is validated using analytical solutions and experiments conducted on polyurethane mock artery. Flow parameters such as pressure and axial stress (and precursor) wave speeds, wall deformations and oscillating frequency, fluid velocity and Poisson coupling effects, were used as the basis of this validation. Results show very good comparison between numerical predictions, analytical solutions and experimental data. The agreement between the three approaches is generally over 95%. The model also shows accurate prediction of Poisson coupling effects in unsteady flows through flexible pipes, which up to this stage have only being predicted analytically. Therefore, this numerical model can accurately predict flow transients in compliant vessels such as arteries. PMID:19482285

  8. 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

  9. 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.

  10. 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

  11. Prediction of the three-dimensional structure of human interleukin-7 by homology modeling.

    PubMed

    Kroemer, R T; Doughty, S W; Robinson, A J; Richards, W G

    1996-06-01

    The three-dimensional structure of human interleukin (IL)-7 has been predicted based on homology to human IL-2, IL-4, granulocyte-macrophage colony stimulating factor and growth hormone. The model has a topology common to other cytokines and displays a unique disulfide pattern. Knowledge of the tertiary structure of IL-7 has implications for analysis of key binding regions, suggestions for mutagenesis experiments and design of (ant)agonists. In this context, the model is discussed and compared with other cytokine structures. PMID:8862549

  12. 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}.

  13. Development and application of vibroacoustic structural data banks in predicting vibration design and test criteria for rocket vehicle structures

    NASA Technical Reports Server (NTRS)

    Bandgren, H. J.; Smith, W. C.

    1973-01-01

    A method of predicting broadband random vibration criteria for components on space vehicles is presented. Large amounts of vibration and acoustic data obtained from flights and static firing tests of space vehicle were formulated into vibroacoustic data banks for structural categories of ring frame, skin stringer, and honeycomb. The vibration spectra with their associated acoustic spectra are normalized to a reference acoustic spectrum. The individual normalized spectra are grouped according to definite structural characteristics and statistically analyzed to form the vibroacoustic data banks described in this report. These data banks represent the reference vibration criteria available for determining the new vehicle vibration criteria.

  14. Ligand-Target Prediction by Structural Network Biology Using nAnnoLyze

    PubMed Central

    Martínez-Jiménez, Francisco; Marti-Renom, Marc A.

    2015-01-01

    Target identification is essential for drug design, drug-drug interaction prediction, dosage adjustment and side effect anticipation. Specifically, the knowledge of structural details is essential for understanding the mode of action of a compound on a target protein. Here, we present nAnnoLyze, a method for target identification that relies on the hypothesis that structurally similar binding sites bind similar ligands. nAnnoLyze integrates structural information into a bipartite network of interactions and similarities to predict structurally detailed compound-protein interactions at proteome scale. The method was benchmarked on a dataset of 6,282 pairs of known interacting ligand-target pairs reaching a 0.96 of area under the Receiver Operating Characteristic curve (AUC) when using the drug names as an input feature for the classifier, and a 0.70 of AUC for “anonymous” compounds or compounds not present in the training set. nAnnoLyze resulted in higher accuracies than its predecessor, AnnoLyze. We applied the method to predict interactions for all the compounds in the DrugBank database with each human protein structure and provide examples of target identification for known drugs against human diseases. The accuracy and applicability of our method to any compound indicate that a comparative docking approach such as nAnnoLyze enables large-scale annotation and analysis of compound–protein interactions and thus may benefit drug development. PMID:25816344

  15. sDFIRE: Sequence-specific statistical energy function for protein structure prediction by decoy selections.

    PubMed

    Hoque, Md Tamjidul; Yang, Yuedong; Mishra, Avdesh; Zhou, Yaoqi

    2016-05-01

    An important unsolved problem in molecular and structural biology is the protein folding and structure prediction problem. One major bottleneck for solving this is the lack of an accurate energy to discriminate near-native conformations against other possible conformations. Here we have developed sDFIRE energy function, which is an optimized linear combination of DFIRE (the Distance-scaled Finite Ideal gas Reference state based Energy), the orientation dependent (polar-polar and polar-nonpolar) statistical potentials, and the matching scores between predicted and model structural properties including predicted main-chain torsion angles and solvent accessible surface area. The weights for these scoring terms are optimized by three widely used decoy sets consisting of a total of 134 proteins. Independent tests on CASP8 and CASP9 decoy sets indicate that sDFIRE outperforms other state-of-the-art energy functions in selecting near native structures and in the Pearson's correlation coefficient between the energy score and structural accuracy of the model (measured by TM-score). © 2016 Wiley Periodicals, Inc. PMID:26849026

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

    PubMed Central

    Ellington, Roni; Wachira, James

    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. PMID:20810968

  17. 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

  18. Microbes as Engines of Ecosystem Function: When Does Community Structure Enhance Predictions of Ecosystem Processes?

    PubMed

    Graham, Emily B; Knelman, Joseph E; Schindlbacher, Andreas; Siciliano, Steven; Breulmann, Marc; Yannarell, Anthony; Beman, J M; Abell, Guy; Philippot, Laurent; Prosser, James; Foulquier, Arnaud; Yuste, Jorge C; Glanville, Helen C; Jones, Davey L; Angel, Roey; Salminen, Janne; Newton, Ryan J; Bürgmann, Helmut; Ingram, Lachlan J; Hamer, Ute; Siljanen, Henri M P; Peltoniemi, Krista; Potthast, Karin; Bañeras, Lluís; Hartmann, Martin; Banerjee, Samiran; Yu, Ri-Qing; Nogaro, Geraldine; Richter, Andreas; Koranda, Marianne; Castle, Sarah C; Goberna, Marta; Song, Bongkeun; Chatterjee, Amitava; Nunes, Olga C; Lopes, Ana R; Cao, Yiping; Kaisermann, Aurore; Hallin, Sara; Strickland, Michael S; Garcia-Pausas, Jordi; Barba, Josep; Kang, Hojeong; Isobe, Kazuo; Papaspyrou, Sokratis; Pastorelli, Roberta; Lagomarsino, Alessandra; Lindström, Eva S; Basiliko, Nathan; Nemergut, Diana R

    2016-01-01

    Microorganisms are vital in mediating the earth's biogeochemical cycles; yet, despite our rapidly increasing ability to explore complex environmental microbial communities, the relationship between microbial community structure and ecosystem processes remains poorly understood. Here, we address a fundamental and unanswered question in microbial ecology: 'When do we need to understand microbial community structure to accurately predict function?' We present a statistical analysis investigating the value of environmental data and microbial community structure independently and in combination for explaining rates of carbon and nitrogen cycling processes within 82 global datasets. Environmental variables were the strongest predictors of process rates but left 44% of variation unexplained on average, suggesting the potential for microbial data to increase model accuracy. Although only 29% of our datasets were significantly improved by adding information on microbial community structure, we observed improvement in models of processes mediated by narrow phylogenetic guilds via functional gene data, and conversely, improvement in models of facultative microbial processes via community diversity metrics. Our results also suggest that microbial diversity can strengthen predictions of respiration rates beyond microbial biomass parameters, as 53% of models were improved by incorporating both sets of predictors compared to 35% by microbial biomass alone. Our analysis represents the first comprehensive analysis of research examining links between microbial community structure and ecosystem function. Taken together, our results indicate that a greater understanding of microbial communities informed by ecological principles may enhance our ability to predict ecosystem process rates relative to assessments based on environmental variables and microbial physiology. PMID:26941732

  19. An integrative structure-based framework for predicting biological effects mediated by antipeptide antibodies.

    PubMed

    Caoili, Salvador Eugenio C

    2015-12-01

    A general framework is presented for predicting quantitative biological effects mediated by antipeptide antibodies, primarily on the basis of antigen structure (possibly featuring intrinsic disorder) analyzed to estimate epitope-paratope binding affinities, which in turn is considered within the context of dose-response relationships as regards antibody concentration. This is illustrated mainly using an approach based on protein structural energetics, whereby expected amounts of solvent-accessible surface area buried upon epitope-paratope binding are related to the corresponding binding affinity, which is estimated from putative B-cell epitope structure with implicit treatment of paratope structure, for antipeptide antibodies either reacting with peptides or cross-reacting with cognate protein antigens. Key methods described are implemented in SAPPHIRE/SUITE (Structural-energetic Analysis Program for Predicting Humoral Immune Response Epitopes/SAPPHIRE User Interface Tool Ensemble; publicly accessible via http://freeshell.de/~badong/suite.htm). Representative results thus obtained are compared with published experimental data on binding affinities and quantitative biological effects, with special attention to loss of paratope sidechain conformational entropy (neglected in previous analyses) and in light of key in-vivo constraints on antigen-antibody binding affinity and antibody-mediated effects. Implications for further refinement of B-cell epitope prediction methods are discussed as regards envisioned biomedical applications including the development of prophylactic and therapeutic antibodies, peptide-based vaccines and immunodiagnostics. PMID:26410103

  20. 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. PMID:20810968

  1. Accurate Structure Prediction and Conformational Analysis of Cyclic Peptides with Residue-Specific Force Fields.

    PubMed

    Geng, Hao; Jiang, Fan; Wu, Yun-Dong

    2016-05-19

    Cyclic peptides (CPs) are promising candidates for drugs, chemical biology tools, and self-assembling nanomaterials. However, the development of reliable and accurate computational methods for their structure prediction has been challenging. Here, 20 all-trans CPs of 5-12 residues selected from Cambridge Structure Database have been simulated using replica-exchange molecular dynamics with four different force fields. Our recently developed residue-specific force fields RSFF1 and RSFF2 can correctly identify the crystal-like conformations of more than half CPs as the most populated conformation. The RSFF2 performs the best, which consistently predicts the crystal structures of 17 out of 20 CPs with rmsd < 1.1 Å. We also compared the backbone (ϕ, ψ) sampling of residues in CPs with those in short linear peptides and in globular proteins. In general, unlike linear peptides, CPs have local conformational free energies and entropies quite similar to globular proteins. PMID:27128113

  2. 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/. PMID:24607742

  3. Measuring and Predicting the Internal Structure of Semiconductor Nanocrystals through Raman Spectroscopy.

    PubMed

    Mukherjee, Prabuddha; Lim, Sung Jun; Wrobel, Tomasz P; Bhargava, Rohit; Smith, Andrew M

    2016-08-31

    Nanocrystals composed of mixed chemical domains have diverse properties that are driving their integration in next-generation electronics, light sources, and biosensors. However, the precise spatial distribution of elements within these particles is difficult to measure and control, yet profoundly impacts their quality and performance. Here we synthesized a unique series of 42 different quantum dot nanocrystals, composed of two chemical domains (CdS:CdSe), arranged in 7 alloy and (core)shell structural classes. Chemometric analyses of far-field Raman spectra accurately classified their internal structures from their vibrational signatures. These classifications provide direct insight into the elemental arrangement of the alloy as well as an independent prediction of fluorescence quantum yield. This nondestructive, rapid approach can be broadly applied to greatly enhance our capacity to measure, predict and monitor multicomponent nanomaterials for precise tuning of their structures and properties. PMID:27472011

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

    PubMed

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

    2002-01-01

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

  5. Neural-network design applied to protein-secondary-structure predictions

    SciTech Connect

    Yu, R.C.; Head-Gordon, T.

    1995-04-01

    The success of neural networks is often limited by a sparse database of training examples, deficient neural-network architectures, and nonglobal optimization of the network variables. The convolution of these three problems has curtailed the application of network models to protein-structure predictions, where homology modeling or information theory approaches are considered better controlled alternatives. This paper introduces our broad objective of disentangling the three degrading features of neural networks cited above, beginning with improved designs of network architectures used in the prediction of protein secondary structure. This work demonstrates that network architecture design considerations greatly improve generalization and more efficiently extract complex sequence-structure relationships from the existing database, as compared to arbitrary architectures with the same size input window.

  6. 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

  7. 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

  8. 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.

  9. Structure based function prediction of proteins using fragment library frequency vectors

    PubMed Central

    Yadav, Akshay; Jayaraman, Valadi Krishnamoorthy

    2012-01-01

    The function of the protein is primarily dictated by its structure. Therefore it is far more logical to find the functional clues of the protein in its overall 3-dimensional fold or its global structure. In this paper, we have developed a novel Support Vector Machines (SVM) based prediction model for functional classification and prediction of proteins using features extracted from its global structure based on fragment libraries. Fragment libraries have been previously used for abintio modelling of proteins and protein structure comparisons. The query protein structure is broken down into a collection of short contiguous backbone fragments and this collection is discretized using a library of fragments. The input feature vector is frequency vector that counts the number of each library fragment in the collection of fragments by all-to-all fragment comparisons. SVM models were trained and optimised for obtaining the best 10-fold Cross validation accuracy for classification. As an example, this method was applied for prediction and classification of Cell Adhesion molecules (CAMs). Thirty-four different fragment libraries with sizes ranging from 4 to 400 and fragment lengths ranging from 4 to 12 were used for obtaining the best prediction model. The best 10-fold CV accuracy of 95.25% was obtained for library of 400 fragments of length 10. An accuracy of 87.5% was obtained on an unseen test dataset consisting of 20 CAMs and 20 NonCAMs. This shows that protein structure can be accurately and uniquely described using 400 representative fragments of length 10. PMID:23144557

  10. 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

  11. 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

  12. 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. PMID:23824509

  13. 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

  14. Predicting the auto-ignition temperatures of organic compounds from molecular structure using support vector machine.

    PubMed

    Pan, Yong; Jiang, Juncheng; Wang, Rui; Cao, Hongyin; Cui, Yi

    2009-05-30

    A quantitative structure-property relationship (QSPR) study is suggested for the prediction of auto-ignition temperatures (AIT) of organic compounds. Various kinds of molecular descriptors were calculated to represent the molecular structures of compounds, such as topological, charge, and geometric descriptors. The variable selection method of genetic algorithm (GA) was employed to select optimal subset of descriptors that have significant contribution to the overall AIT property from the large pool of calculated descriptors. The novel modeling method of support vector machine (SVM) was then employed to model the possible quantitative relationship existed between these selected descriptors and AIT property. The resulted model showed high prediction ability with the average absolute error being 28.88 degrees C, and the root mean square error being 36.86 for the prediction set, which are within the range of the experimental error of AIT measurements. The proposed method can be successfully used to predict the auto-ignition temperatures of organic compounds with only nine pre-selected theoretical descriptors which can be calculated directly from molecular structure alone. PMID:18952371

  15. QuaBingo: A Prediction System for Protein Quaternary Structure Attributes Using Block Composition.

    PubMed

    Tung, Chi-Hua; Chen, Chi-Wei; Guo, Ren-Chao; Ng, Hui-Fuang; Chu, Yen-Wei

    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

  16. 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

  17. 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. PMID:23600658

  18. Predictive grain yield models based on canopy structure and structural plasticity

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Structural dimensions, digitally measured on stems and leaves of soybean plants during the first six reproductive growth stages (R1-R6), were used to assess the impact of five management strategies including cropping systems (conventional (C) vs. organic, (O)), tillage (conventional moldboard (C) vs...

  19. 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. PMID:23764236

  20. 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. PMID:27082659

  1. BetaSCPWeb: side-chain prediction for protein structures using Voronoi diagrams and geometry prioritization.

    PubMed

    Ryu, Joonghyun; Lee, Mokwon; Cha, Jehyun; Laskowski, Roman A; Ryu, Seong Eon; Kim, Deok-Soo

    2016-07-01

    Many applications, such as protein design, homology modeling, flexible docking, etc. require the prediction of a protein's optimal side-chain conformations from just its amino acid sequence and backbone structure. Side-chain prediction (SCP) is an NP-hard energy minimization problem. Here, we present BetaSCPWeb which efficiently computes a conformation close to optimal using a geometry-prioritization method based on the Voronoi diagram of spherical atoms. Its outputs are visual, textual and PDB file format. The web server is free and open to all users at http://voronoi.hanyang.ac.kr/betascpweb with no login requirement. PMID:27151195

  2. BetaSCPWeb: side-chain prediction for protein structures using Voronoi diagrams and geometry prioritization

    PubMed Central

    Ryu, Joonghyun; Lee, Mokwon; Cha, Jehyun; Laskowski, Roman A.; Ryu, Seong Eon; Kim, Deok-Soo

    2016-01-01

    Many applications, such as protein design, homology modeling, flexible docking, etc. require the prediction of a protein's optimal side-chain conformations from just its amino acid sequence and backbone structure. Side-chain prediction (SCP) is an NP-hard energy minimization problem. Here, we present BetaSCPWeb which efficiently computes a conformation close to optimal using a geometry-prioritization method based on the Voronoi diagram of spherical atoms. Its outputs are visual, textual and PDB file format. The web server is free and open to all users at http://voronoi.hanyang.ac.kr/betascpweb with no login requirement. PMID:27151195

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

    NASA Technical Reports Server (NTRS)

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

    1994-01-01

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

  4. 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

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

    PubMed Central

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

    2014-01-01

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

  6. A fast method for large-scale de novo peptide and miniprotein structure prediction.

    PubMed

    Maupetit, Julien; Derreumaux, Philippe; Tufféry, Pierre

    2010-03-01

    Although peptides have many biological and biomedical implications, an accurate method predicting their equilibrium structural ensembles from amino acid sequences and suitable for large-scale experiments is still missing. We introduce a new approach-PEP-FOLD-to the de novo prediction of peptides and miniproteins. It first predicts, in the terms of a Hidden Markov Model-derived structural alphabet, a limited number of local conformations at each position of the structure. It then performs their assembly using a greedy procedure driven by a coarse-grained energy score. On a benchmark of 52 peptides with 9-23 amino acids, PEP-FOLD generates lowest-energy conformations within 2.8 and 2.3 A Calpha root-mean-square deviation from the full nuclear magnetic resonance structures (NMR) and the NMR rigid cores, respectively, outperforming previous approaches. For 13 miniproteins with 27-49 amino acids, PEP-FOLD reaches an accuracy of 3.6 and 4.6 A Calpha root-mean-square deviation for the most-native and lowest-energy conformations, using the nonflexible regions identified by NMR. PEP-FOLD simulations are fast-a few minutes only-opening therefore, the door to in silico large-scale rational design of new bioactive peptides and miniproteins. PMID:19569182

  7. Structure-based activity prediction for an enzyme of unknown function

    PubMed Central

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

    2008-01-01

    With many genomes sequenced, a pressing challenge in biology is predicting the function of the proteins that the genes encode. When proteins are unrelated to others of known activity, bioinformatics inference for function becomes problematic. It would thus be useful to interrogate protein structures for function directly. Here, we predict the function of an enzyme of unknown activity, Tm0936 from Thermotoga maritima, by docking high-energy intermediate forms of thousands of candidate metabolites. The docking hit list was dominated by adenine analogues, which appeared to undergo C6-deamination. Four of these, including 5-methylthioadenosine and S-adenosylhomocysteine (SAH), were tested as substrates, and three had substantial catalytic rate constants (105 M−1s−1). The X-ray crystal structure of the complex between Tm0936 and the product resulting from the deamination of SAH, S-inosylhomocysteine, was determined, and it corresponded closely to the predicted structure. The deaminated products can be further metabolized by T. maritima in a previously uncharacterized SAH degradation pathway. Structure-based docking with high-energy forms of potential substrates may be a useful tool to annotate enzymes for function. PMID:17603473

  8. Predicting protein structural classes based on complex networks and recurrence analysis.

    PubMed

    Olyaee, Mohammad H; Yaghoubi, Ali; Yaghoobi, Mahdi

    2016-09-01

    Protein sequences are divided into four structural classes. The determination of class is a challenging and beneficial task in the bioinformatics field. Several methods have been proposed to this end, but most utilize too many features and produce unsuitable results. In the present, features are extracted based on the predicted secondary structures. At first, predicted secondary structure sequences are mapped into two time series by the chaos game representation. Then, a recurrence matrix is calculated from each of the time series. The recurrence matrix is identified with the adjacency matrix of a complex network and measures are applied for the characterization of complex networks to these recurrence matrixes. For a given protein sequence, a total of 24 characteristic features can be calculated and these are fed into Fisher's discriminated analysis algorithm for classification. To examine the proposed method, two widely used low similarity benchmark datasets design and test its performance. A comparison with the results of existing methods shows that the current study's approach provides a satisfactory performance for protein structural class prediction. PMID:27320678

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

    PubMed Central

    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

  10. An open source multistep model to predict mutagenicity from statistical analysis and relevant structural alerts

    PubMed Central

    2010-01-01

    Background Mutagenicity is the capability of a substance to cause genetic mutations. This property is of high public concern because it has a close relationship with carcinogenicity and potentially with reproductive toxicity. Experimentally, mutagenicity can be assessed by the Ames test on Salmonella with an estimated experimental reproducibility of 85%; this intrinsic limitation of the in vitro test, along with the need for faster and cheaper alternatives, opens the road to other types of assessment methods, such as in silico structure-activity prediction models. A widely used method checks for the presence of known structural alerts for mutagenicity. However the presence of such alerts alone is not a definitive method to prove the mutagenicity of a compound towards Salmonella, since other parts of the molecule can influence and potentially change the classification. Hence statistically based methods will be proposed, with the final objective to obtain a cascade of modeling steps with custom-made properties, such as the reduction of false negatives. Results A cascade model has been developed and validated on a large public set of molecular structures and their associated Salmonella mutagenicity outcome. The first step consists in the derivation of a statistical model and mutagenicity prediction, followed by further checks for specific structural alerts in the "safe" subset of the prediction outcome space. In terms of accuracy (i.e., overall correct predictions of both negative and positives), the obtained model approached the 85% reproducibility of the experimental mutagenicity Ames test. Conclusions The model and the documentation for regulatory purposes are freely available on the CAESAR website. The input is simply a file of molecular structures and the output is the classification result. PMID:20678181

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

    PubMed Central

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

    2011-01-01

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

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

    PubMed Central

    Sun, Xueguang; Zhulin, Igor; Wartell, Roger M.

    2002-01-01

    Hfq, a bacterial RNA-binding protein, was recently shown to contain the Sm1 motif, a characteristic of Sm and LSm proteins that function in RNA processing events in archaea and eukaryotes. In this report, comparative structural modeling was used to predict a three-dimensional structure of the Hfq core sequence. The predicted structure aligns with most major features of the Methanobacterium thermoautotrophicum LSm protein structure. Conserved residues in Hfq are positioned at the same structural locations responsible for subunit assembly and RNA interaction in Sm proteins. A highly conserved portion of Hfq assumes a structural fold similar to the Sm2 motif of Sm proteins. The evolution of the Hfq protein was explored by conducting a BLAST search of microbial genomes followed by phylogenetic analysis. Approximately half of the 140 complete or nearly complete genomes examined contain at least one gene coding for Hfq. The presence or absence of Hfq closely followed major bacterial clades. It is absent from high-level clades and present in the ancient Thermotogales-Aquificales clade and all proteobacteria except for those that have undergone major reduction in genome size. Residues at three positions in Hfq form signatures for the beta/gamma proteobacteria, alpha proteobacteria and low GC Gram-positive bacteria groups. PMID:12202750

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

    PubMed Central

    Vasquez, Karen M.; Wang, Guliang

    2013-01-01

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

  14. Blade-Vortex Interaction (BVI) Noise and Airload Prediction Using Loose Aerodynamic/Structural Coupling

    NASA Technical Reports Server (NTRS)

    Sim, B. W.; Lim, J. W.

    2007-01-01

    Predictions of blade-vortex interaction (BVI) noise, using blade airloads obtained from a coupled aerodynamic and structural methodology, are presented. This methodology uses an iterative, loosely-coupled trim strategy to cycle information between the OVERFLOW-2 (CFD) and CAMRAD-II (CSD) codes. Results are compared to the HART-II baseline, minimum noise and minimum vibration conditions. It is shown that this CFD/CSD state-of-the-art approach is able to capture blade airload and noise radiation characteristics associated with BVI. With the exception of the HART-II minimum noise condition, predicted advancing and retreating side BVI for the baseline and minimum vibration conditions agrees favorably with measured data. Although the BVI airloads and noise amplitudes are generally under-predicted, this CFD/CSD methodology provides an overall noteworthy improvement over the lifting line aerodynamics and free-wake models typically used in CSD comprehensive analysis codes.

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

    NASA Technical Reports Server (NTRS)

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

    1990-01-01

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

  16. Predictive modeling of multicellular structure formation by using Cellular Particle Dynamics simulations

    NASA Astrophysics Data System (ADS)

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

    2014-03-01

    Cellular Particle Dynamics (CPD) is an effective computational method for describing and predicting the time evolution of biomechanical relaxation processes of multicellular systems. A typical example is the fusion of spheroidal bioink particles during post bioprinting structure formation. In CPD cells are modeled as an ensemble of cellular particles (CPs) that interact via short-range contact interactions, characterized by an attractive (adhesive interaction) and a repulsive (excluded volume interaction) component. The time evolution of the spatial conformation of the multicellular system is determined by following the trajectories of all CPs through integration of their equations of motion. CPD was successfully applied to describe and predict the fusion of 3D tissue construct involving identical spherical aggregates. Here, we demonstrate that CPD can also predict tissue formation involving uneven spherical aggregates whose volumes decrease during the fusion process. Work supported by NSF [PHY-0957914]. Computer time provided by the University of Missouri Bioinformatics Consortium.

  17. The effect of ligand-based tautomer and protomer prediction on structure-based virtual screening.

    PubMed

    Kalliokoski, Tuomo; Salo, Heikki S; Lahtela-Kakkonen, Maija; Poso, Antti

    2009-12-01

    As tautomerism and ionization may significantly change the interaction possibilities between a ligand and a target protein, these phenomena could have an effect on structure-based virtual screening. Tautomeric- and protonation-state enumeration ensures that the state with optimal interaction possibilities is included in the screening process, as the predicted state may not always be the optimal binder. However, there is very little information published if tautomer and protomer enumeration actually improves the enrichment of active molecules compared to the alternative of using a predicted form of each molecule. In this study, a retrospective virtual screening was performed using AutoDock on 19 drug targets with a publicly available data set. It is proposed that tautomer and protomer prediction can significantly save computing resources and can yield similar results to enumeration. PMID:19928753

  18. Predictive value of tender joints compared to synovitis for structural damage in rheumatoid arthritis

    PubMed Central

    Cheung, Peter P; Mari, Karine; Devauchelle-Pensec, Valérie; Jousse-Joulin, Sandrine; D'Agostino, Maria Antonietta; Chalès, Gérard; Gaudin, Philippe; Mariette, Xavier; Saraux, Alain; Dougados, Maxime

    2016-01-01

    Objective To evaluate the predictive value of tender joints compared to synovitis for structural damage in rheumatoid arthritis (RA). Methods A post hoc analysis was performed on a prospective 2-year study of 59 patients with active RA starting on antitumour necrosis factor (TNF). Tenderness and synovitis was assessed clinically at baseline, followed by blinded ultrasound assessment (B-mode and power Doppler ultrasound (PDUS)) on the hands and feet (2 wrists, 10 metacarpophalangeal, 10 proximal interphalangeal and 10 metatarsophalangeal (MTP) joints). Radiographs of these joints were performed at baseline and at 2 years. The risk of radiographic progression with respect to the presence of baseline tenderness or synovitis, as well as its persistence (after 4 months of anti-TNF), was estimated by OR (95% CI). Results Baseline tender joints were the least predictive for radiographic progression (OR=1.53 (95% CI 1.02 to 2.29) p<0.04), when compared to synovitis (clinical OR=2.08 (95% CI 1.39 to 3.11) p<0.001 or PDUS OR=1.80 (95% CI 1.20 to 2.71) p=0.005, respectively). Tender joints with the presence of synovitis were predictive of radiographic progression (OR=1.89 (95% CI 1.25 to 2.85) p=0.002), especially seen in the MTP joints. Non-tender joints with no synovitis were negatively predictive (OR=0.57 (95% CI 0.39 to 0.82) p=0.003). Persistence of tender joints was negatively predictive (OR=0.38 (95% CI 0.18 to 0.78) p=0.009) while persistence of synovitis was predictive (OR=2.41 (95% CI 1.24 to 4.67) p=0.01) of radiographic progression. Conclusions Synovitis is better than tenderness to predict for subsequent structural progression. However, coexistence of tenderness and synovitis at the level of an individual joint is predictive of structural damage, particularly in the MTP joints. Trial registration number NCT00444691. PMID:27042336

  19. Profiles and Majority Voting-Based Ensemble Method for Protein Secondary Structure Prediction

    PubMed Central

    Bouziane, Hafida; Messabih, Belhadri; Chouarfia, Abdallah

    2011-01-01

    Machine learning techniques have been widely applied to solve the problem of predicting protein secondary structure from the amino acid sequence. They have gained substantial success in this research area. Many methods have been used including k-Nearest Neighbors (k-NNs), Hidden Markov Models (HMMs), Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs), which have attracted attention recently. Today, the main goal remains to improve the prediction quality of the secondary structure elements. The prediction accuracy has been continuously improved over the years, especially by using hybrid or ensemble methods and incorporating evolutionary information in the form of profiles extracted from alignments of multiple homologous sequences. In this paper, we investigate how best to combine k-NNs, ANNs and Multi-class SVMs (M-SVMs) to improve secondary structure prediction of globular proteins. An ensemble method which combines the outputs of two feed-forward ANNs, k-NN and three M-SVM classifiers has been applied. Ensemble members are combined using two variants of majority voting rule. An heuristic based filter has also been applied to refine the prediction. To investigate how much improvement the general ensemble method can give rather than the individual classifiers that make up the ensemble, we have experimented with the proposed system on the two widely used benchmark datasets RS126 and CB513 using cross-validation tests by including PSI-BLAST position-specific scoring matrix (PSSM) profiles as inputs. The experimental results reveal that the proposed system yields significant performance gains when compared with the best individual classifier. PMID:22058650

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

    PubMed Central

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

    2014-01-01

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

  1. Towards universal structure-based prediction of class II MHC epitopes for diverse allotypes.

    PubMed

    Bordner, Andrew J

    2010-01-01

    The binding of peptide fragments of antigens to class II MHC proteins is a crucial step in initiating a helper T cell immune response. The discovery of these peptide epitopes is important for understanding the normal immune response and its misregulation in autoimmunity and allergies and also for vaccine design. In spite of their biomedical importance, the high diversity of class II MHC proteins combined with the large number of possible peptide sequences make comprehensive experimental determination of epitopes for all MHC allotypes infeasible. Computational methods can address this need by predicting epitopes for a particular MHC allotype. We present a structure-based method for predicting class II epitopes that combines molecular mechanics docking of a fully flexible peptide into the MHC binding cleft followed by binding affinity prediction using a machine learning classifier trained on interaction energy components calculated from the docking solution. Although the primary advantage of structure-based prediction methods over the commonly employed sequence-based methods is their applicability to essentially any MHC allotype, this has not yet been convincingly demonstrated. In order to test the transferability of the prediction method to different MHC proteins, we trained the scoring method on binding data for DRB1*0101 and used it to make predictions for multiple MHC allotypes with distinct peptide binding specificities including representatives from the other human class II MHC loci, HLA-DP and HLA-DQ, as well as for two murine allotypes. The results showed that the prediction method was able to achieve significant discrimination between epitope and non-epitope peptides for all MHC allotypes examined, based on AUC values in the range 0.632-0.821. We also discuss how accounting for peptide binding in multiple registers to class II MHC largely explains the systematically worse performance of prediction methods for class II MHC compared with those for class I MHC

  2. Geological and geomorphological study of the Amudariya syneclise (middle Asia) for petroleum-bearing structure prediction

    SciTech Connect

    Smirnova, I.

    1995-08-01

    The integrated analysis of geophysical, geological, geochemical, geomorphological and remotely sensed data was carried out using the computerized technology at two test sites of Amudaria syneclise: eastern part of the Charjou step and the area of gas field Gasly (Bukharskaya step). At the Charjou step the geological modelling of petroleum-bearing structures (anticlines, reefs) as well as different horizons of sedimentary cover was conducted. In the models we used the morphological parameters of structures from the depth of petroliferous units up to the surface by drilling and seismic data, gravity and magnetic data, geochemical characteristics of soils, characteristics of relief, landscape elements distribution, spectral characteristics extracted from remotely sensed data and others. The using in the models of the landscape data is based on the theory that landscape components, their distribution and changes are connected with deep geological structures due to neotectonic movements, alterations of the rocks covering petroleum pools and seeping fluids. The modelling of known structures allows to reveal the types of their expression in relief and to predict the location of these structures and their parameters (amplitude, size) on week investigated areas. The results of structural horizons modelling allow to compose the schemes of tectonic and petroleum zonation and to predict the spreading of rest formation on Charjou step. The results of retrospective multispectral satellite and field data processing have permitted to reveal the secondary gas pool formed at the depth near 200 meters after failure on exploration well. At the test site Gasly geomorphological investigations using retrospective aerial and satellite images were carried out for the study of geological consequences of large gas field exploitation in connection with two destructive earthquakes. We obtain the data connected with recent tectonic movement which may be used for prediction of the earthquakes.

  3. Structure prediction and evolution of a halo-acid dehalogenase of Burkholderia mallei

    PubMed Central

    Rai, Alok R; Singh, Raghvendra Pratap; Srivastava, Alok Kumar; Dubey, Ramesh Chandra

    2012-01-01

    Environmental pollutants containing halogenated organic compounds e.g. haloacid, can cause a plethora of health problems. The structural and functional analyses of the gene responsible of their degradation are an important aspect for environmental studies and are important to human well-being. It has been shown that some haloacids are toxic and mutagenic. Microorganisms capable of degrading these haloacids can be found in the natural environment. One of these, a soil-borne Burkholderia mallei posses the ability to grow on monobromoacetate (MBA). This bacterium produces a haloacid dehalogenase that allows the cell to grow on MBA, a highly toxic and mutagenic environmental pollutant. For the structural and functional analysis, a 346 amino