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1

Predicting B-DNA structure from sequence  

SciTech Connect

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

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

1995-12-31

2

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

SciTech Connect

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

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

2012-10-23

3

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

NASA Technical Reports Server (NTRS)

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

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

1986-01-01

4

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

PubMed

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

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

2013-01-01

5

B-DNA structure and stability: the role of hydrogen bonding, ?-? stacking interactions, twist-angle, and solvation.  

PubMed

We have computationally investigated the structure and stability of B-DNA. To this end, we have analyzed the bonding in a series of 47 stacks consisting of two base pairs, in which the base pairs cover the full range of natural Watson-Crick pairs, mismatched pairs, and artificial DNA base pairs. Our analyses provide detailed insight into the role and relative importance of the various types of interactions, such as, hydrogen bonding, ?-? stacking interactions, and solvation/desolvation. Furthermore, we have analyzed the functionality of the twist-angle on the stability of the structure. Interestingly, we can show that all stacked base pairs benefit from a stabilization by 6 to 12 kcal mol(-1) if stacked base pairs are twisted from 0° to 36°, that is, if they are mutually rotated from a congruent superposition to the mutually twisted stacking configuration that occurs in B-DNA. This holds especially for stacked AT pairs but also for other stacked base pairs, including GC. The electronic mechanism behind this preference for a twisted arrangement depends on the base pairs involved. We also show that so-called "diagonal interactions" (or cross terms) in the stacked base pairs are crucial for understanding the stability of B-DNA, in particular, in GC-rich sequences. PMID:24871817

Poater, Jordi; Swart, Marcel; Bickelhaupt, F Matthias; Fonseca Guerra, Célia

2014-07-14

6

Crystal structure of the B-DNA hexamer d(CTCGAG): model for an A-to-B transition.  

PubMed Central

The crystal structure of the B-DNA hexamer d(CTCGAG) has been solved at 1.9 A resolution by iterative single isomorphous replacement, using the brominated derivative d(CG5BrCGAG), and refined to an R-factor of 18.6% for 120 nonhydrogen nucleic acid atoms and 32 water molecules. Although the central four base pairs form a typical B-form helix, several parameters suggest a transition to an A-like conformation at the termini. Based on this observation, a B-to-A transition was modeled, maintaining efficient base stacking across the junction. The wide minor groove (approximately 6.9 A) is reminiscent of that in the side-by-side double drug-DNA complexes and hosts a double spine of hydration. The global helix axes of the pseudo-continuous helices are at an acute angle of 60 degrees. The pseudocontinuous stacking is reinforced by the minor groove water structure extending between the two duplexes. The crossover point of two pairs of stacked duplexes is at the stacking junction, unlike that observed in the B-DNA decamers and dodecamers. This arrangement may have implications for the structure of a four-way DNA junction. The duplexes are arranged around a large (approximately 20 A diameter) channel centered on a 6(2) screw axis. PMID:8744323

Wahl, M C; Rao, S T; Sundaralingam, M

1996-01-01

7

Structure and mechanism of the UvrA?UvrB DNA damage sensor  

SciTech Connect

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

Pakotiprapha, Danaya; Samuels, Martin; Shen, Koning; Hu, Johnny H.; Jeruzalmi, David (Harvard)

2012-04-17

8

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

PubMed Central

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

Northam, Matthew R.; Moore, Elizabeth A.; Mertz, Tony M.; Binz, Sara K.; Stith, Carrie M.; Stepchenkova, Elena I.; Wendt, Kathern L.; Burgers, Peter M. J.; Shcherbakova, Polina V.

2014-01-01

9

Crystal structure and prediction.  

PubMed

The notion of structure is central to the subject of chemistry. This review traces the development of the idea of crystal structure since the time when a crystal structure could be determined from a three-dimensional diffraction pattern and assesses the feasibility of computationally predicting an unknown crystal structure of a given molecule. Crystal structure prediction is of considerable fundamental and applied importance, and its successful execution is by no means a solved problem. The ease of crystal structure determination today has resulted in the availability of large numbers of crystal structures of higher-energy polymorphs and pseudopolymorphs. These structural libraries lead to the concept of a crystal structure landscape. A crystal structure of a compound may accordingly be taken as a data point in such a landscape. PMID:25422850

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

2015-04-01

10

Protein Structure Prediction Center  

NSDL National Science Digital Library

Our goal is to help advance the methods of identifying protein structure from sequence. The Center has been organized to provide the means of objective testing of these methods via the process of blind prediction. In addition to support of the CASP meetings our goal is to promote an objective evaluation of prediction methods on a continuing basis.

11

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

PubMed Central

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

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

2012-01-01

12

Protein Structure Prediction and Structural Genomics  

NASA Astrophysics Data System (ADS)

Genome sequencing projects are producing linear amino acid sequences, but full understanding of the biological role of these proteins will require knowledge of their structure and function. Although experimental structure determination methods are providing high-resolution structure information about a subset of the proteins, computational structure prediction methods will provide valuable information for the large fraction of sequences whose structures will not be determined experimentally. The first class of protein structure prediction methods, including threading and comparative modeling, rely on detectable similarity spanning most of the modeled sequence and at least one known structure. The second class of methods, de novo or ab initio methods, predict the structure from sequence alone, without relying on similarity at the fold level between the modeled sequence and any of the known structures. In this Viewpoint, we begin by describing the essential features of the methods, the accuracy of the models, and their application to the prediction and understanding of protein function, both for single proteins and on the scale of whole genomes. We then discuss the important role that protein structure prediction methods play in the growing worldwide effort in structural genomics.

Baker, David; Sali, Andrej

2001-10-01

13

Protein structure Predictive methods  

E-print Network

;3 Principles of Protein Structure GFCHIKAYTRLIMVG... Anabaena7120 Anacystisnidulans Condruscrispus MODELING 0 (100)2 (50) 1 (80) Ca RMSD Ã? (% EQV) % SEQUENCE IDENTITY 20 50 100 Anabaena 7120 Anacystis

Sjölander, Kimmen

14

Protein Structure Prediction and Structural Genomics  

NSDL National Science Digital Library

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

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

2001-10-05

15

Protein structure Predictive methods and  

E-print Network

& Rhodopsin http://www.mhl.soton.ac.uk/public/research/projects/current/rhodopsin/index.html #12;10 Flavodoxin classification #12;15 Structural Classification of Proteins (SCOP) and the Astral datasets #12;16 Evolution inclusion of structure prediction and analysis Drosomycin (Drosophila) #12;17 SCOP and ASTRAL · SCOP

Sjölander, Kimmen

16

De Novo Protein Structure Prediction  

NASA Astrophysics Data System (ADS)

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

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

17

The (gt)n(ga)m containing intron 2 of HLA-DRB alleles binds a zinc-dependent protein and forms non B-DNA structures.  

PubMed

We studied protein binding and structural features of perfect and imperfect composite (gt)n(ga)m blocks from different HLA-DRB1 alleles in their original genomic and artificial environments. The major retarded protein/DNA complex of the genomic (gt)n(ga)m fragments comprises a zinc-dependent protein present in nuclear extracts from different cell types. The protein binding is characterized by moderate affinities independent of the polymorphic form of the physiological microsatellite allele. The binding affinity depends on the 5' and 3' adjacent single copy parts. DNase I footprinting of genome-derived fragments revealed that the 5' adjacent sequence and the (gt)n repeat are preferentially protected on the (gt)n(ga)m strand. Comparing three alleles, a regular pattern of footprints was not detectable in the (gt)n part, indicating that the zinc-dependent protein recognizes structural rather than sequence-specific features in this region. Chemical probing resulted in a pattern characteristic for Z-DNA in the (gt)n tract of the fragments. However, EMSA experiments using the Z-DNA specific monoclonal antibody mABZ-22 did not prove the presence of Z-DNA. As demonstrated by chemical modifications of the different (ga)m targets, only one of three (gt)n(ga)m fragments formed intramolecular triplexes of the type H-y3 and H-y5. DNase I footprinting revealed only weak protection, if any, in the homopurine tract. Rather, the (tc)m strands are hypersensitive for DNase I. This is probably due to structural conversions into intramolecular *H-triplexes after binding of HIZP. PMID:9889299

Mäueler, W; Bassili, G; Arnold, R; Renkawitz, R; Epplen, J T

1999-01-01

18

TRITIUM RESERVOIR STRUCTURAL PERFORMANCE PREDICTION  

SciTech Connect

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

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

2005-11-10

19

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

PubMed Central

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

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

1997-01-01

20

Bayesian Nonparametric Methods for Protein Structure Prediction  

E-print Network

ABSTRACT Bayesian Nonparametric Methods for Protein Structure Prediction. (August 2010) Kristin Patricia Lennox, B.S., Texas A&M University; M.S., Texas A&M University Chair of Advisory Committee: Dr. David B. Dahl The protein structure prediction... problem consists of determining a protein?s three-dimensional structure from the underlying sequence of amino acids. A standard approach for predicting such structures is to conduct a stochastic search of conformation space in an attempt to find a...

Lennox, Kristin Patricia

2011-10-21

21

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

Microsoft Academic Search

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

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

2011-01-01

22

Protein structure Predictive methods and  

E-print Network

." Baxevanis & Ouellette (Ch. 9, p.224, Wishart) Principles of Protein Structure GFCHIKAYTRLIMVG... Anabaena (% EQV) % SEQUENCE IDENTITY 20 50 100 Anabaena 7120 Anacystis nidulans Condrus crispus Desulfovibrio

Sjölander, Kimmen

23

Computational Prediction of RNA Tertiary Structure  

NASA Astrophysics Data System (ADS)

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

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

2012-02-01

24

Predicting pseudoknotted structures across two RNA sequences  

PubMed Central

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

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

2012-01-01

25

Protein Structure Prediction Jayanthi Sourirajan  

E-print Network

polymer made up of one of the 20 different amino acids. They perform a wide variety of functions of protein ­bio-molecular interactions, study of evolutionary relationship between proteins or protein were aligned according to their ability to form or break a secondary structure. They were classified

26

Short Specialist Review Gene structure prediction  

E-print Network

as well as genome-survey sequencing is in progress for dozens of plant species, all of which appearShort Specialist Review Gene structure prediction in plant genomes Volker Brendel Iowa State prediction in vertebrates. The second reason is pragmatic. Expressed Sequence Tag (EST) sequencing and whole-genome

Brendel, Volker

27

Prediction of protein tertiary structures using MUFOLD  

PubMed Central

There have been steady improvements in protein structure prediction during the past two decades. However, current methods are still far from consistently predicting structural models accurately with computing power accessible to common users. To address this challenge, we developed MUFOLD, a hybrid method of using whole and partial template information along with new computational techniques for protein tertiary structure prediction. MUFOLD covers both template-based and ab initio predictions using the same framework and aims to achieve high accuracy and fast computing. Two major novel contributions of MUFOLD are graph-based model generation and molecular dynamics ranking (MDR). By formulating prediction as a graph realization problem, we apply an efficient optimization approach of Multidimensional Scaling (MDS) to speed up the prediction dramatically. In addition, under this framework, we enhance the predictions consistently by iteratively using the information from generated models. MDR, in contrast to widely used static scoring functions, exploits dynamics properties of structures to evaluate their qualities, which can often identify best structures from a pool more effectively. PMID:22130979

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

2015-01-01

28

Data Mining for Protein Secondary Structure Prediction  

Microsoft Academic Search

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

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

29

Data Mining for Protein Secondary Structure Prediction  

Microsoft Academic Search

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

Haitao Cheng; Taner Sen; Robert Jernigan; Andrzej Kloczkowski

30

Interface Structure Prediction from First-Principles  

SciTech Connect

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

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

2014-05-08

31

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

PubMed

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

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

2011-04-01

32

Characteristics and prediction of RNA structure.  

PubMed

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

Li, Hengwu; Zhu, Daming; Zhang, Caiming; Han, Huijian; Crandall, Keith A

2014-01-01

33

Characteristics and Prediction of RNA Structure  

PubMed Central

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

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

2014-01-01

34

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

PubMed Central

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

Das, Swapan Kumar; Chowdhury, Shantanu

2009-01-01

35

Predicting structure in nonsymmetric sparse matrix factorizations  

SciTech Connect

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

Gilbert, J.R. (Xerox Palo Alto Research Center, CA (United States)); Ng, E.G. (Oak Ridge National Lab., TN (United States))

1992-10-01

36

Protein Structure Prediction by Protein Threading  

Microsoft Academic Search

\\u000a The seminal work of Bowie, Lüthy, and Eisenberg (Bowie et al., 1991) on “the inverse protein folding problem” laid the foundation of protein structure prediction by protein threading. By using\\u000a simple measures for fitness of different amino acid types to local structural environments defined in terms of solvent accessibility\\u000a and protein secondary structure, the authors derived a simple and yet

Ying Xu; Zhijie Liu; Liming Cai; Dong Xu

2007-01-01

37

Improving RNA secondary structure prediction with structure mapping data.  

PubMed

Methods to probe RNA secondary structure, such as small molecule modifying agents, secondary structure-specific nucleases, inline probing, and SHAPE chemistry, are widely used to study the structure of functional RNA. Computational secondary structure prediction programs can incorporate probing data to predict structure with high accuracy. In this chapter, an overview of current methods for probing RNA secondary structure is provided, including modern high-throughput methods. Methods for guiding secondary structure prediction algorithms using these data are explained, and best practices for using these data are provided. This chapter concludes by listing a number of open questions about how to best use probing data, and what these data can provide. PMID:25726462

Sloma, Michael F; Mathews, David H

2015-01-01

38

MPGAfold in dengue secondary structure prediction.  

PubMed

This chapter presents the computational prediction of the secondary structures within the 5' and 3' untranslated regions of the dengue virus serotype 2 (DENV2), with the focus on the conformational prediction of the two dumbbell-like structures, 5' DB and 3' DB, found in the core region of the 3' untranslated region of DENV2. For secondary structure prediction purposes we used a 719 nt-long subgenomic RNA construct from DENV2, which we refer to as the minigenome. The construct combines the 5'-most 226 nt from the 5' UTR and a fragment of the capsid coding region with the last 42 nt from the non-structural protein NS5 coding region and the 451 nt of the 3' UTR. This minigenome has been shown to contain the elements needed for translation, as well as negative strand RNA synthesis. We present the Massively Parallel Genetic Algorithm MPGAfold, a non-deterministic algorithm, that was used to predict the secondary structures of the DENV2 719 nt long minigenome construct, as well as our computational workbench called StructureLab that was used to interactively explore the solution spaces produced by MPGAfold. The MPGAfold algorithm is first introduced at the conceptual level. Then specific parameters guiding its performance are discussed and illustrated with a representative selection of the results from the study. Plots of the solution spaces generated by MPGAfold illustrate the algorithm, while selected secondary structures focus on variable formation of the dumbbell structures and other identified structural motifs. They also serve as illustrations of some of the capabilities of the StructureLab workbench. Results of the computational structure determination calculations are discussed and compared to the experimental data. PMID:24696339

Kasprzak, Wojciech K; Shapiro, Bruce A

2014-01-01

39

Predicting polymeric crystal structures by evolutionary algorithms  

NASA Astrophysics Data System (ADS)

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

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

2014-10-01

40

Protein Structure Prediction with Evolutionary Algorithms  

SciTech Connect

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

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

1999-02-08

41

Protein Structure Prediction by Protein Threading  

NASA Astrophysics Data System (ADS)

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

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

42

Predicting Polymeric Crystal Structures by Evolutionary Algorithms  

E-print Network

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

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

2014-06-05

43

Ko Displacement Theory for Structural Shape Predictions  

NASA Technical Reports Server (NTRS)

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

Ko, William L.

2010-01-01

44

Predicting protein structure using hidden Markov models  

E-print Network

Predicting protein structure using hidden Markov models Kevin Karplusy Kimmen Sjolanderz Christian Santa Cruz, CA 95064 USA abstract We discuss how methods based on hidden Markov models performed in the fold recogni- tion section of the CASP2 experiment. Hidden Markov models were built for a set of about

Karplus, Kevin

45

Predicting Protein Structure Using Hidden Markov Models  

E-print Network

Predicting Protein Structure Using Hidden Markov Models Kevin Karplus,1,* Kimmen Sjo¨lander,2 Markov models performed in the fold-recognition section of the CASP2 ex- periment. Hidden Markov models of California, Santa Cruz, California 3EBI, United Kingdom ABSTRACT We discuss how methods based on hidden

Sjölander, Kimmen

46

Secondary Structure Prediction of Proposed RNAi  

E-print Network

, as they function to recruit translational machinery to the mRNA, are potential targets for RNAi. In addition, #12Secondary Structure Prediction of Proposed RNAi Targets: Can Current Energy Minimization Algorithms-dimensional shape of a protein can provide essential information in the determination of its function, the activity

47

CALYPSO: A method for crystal structure prediction  

NASA Astrophysics Data System (ADS)

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

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

2012-10-01

48

RNA secondary structure prediction using soft computing.  

PubMed

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

Ray, Shubhra Sankar; Pal, Sankar K

2013-01-01

49

Gene structure prediction by linguistic methods  

SciTech Connect

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

Dong, S.; Searls, D.B. [Univ. of Pennsylvania School of Medicine, Philadelphia, PA (United States)] [Univ. of Pennsylvania School of Medicine, Philadelphia, PA (United States)

1994-10-01

50

A dynamic programming algorithm for RNA structure prediction including pseudoknots  

E-print Network

A dynamic programming algorithm for RNA structure prediction including pseudoknots Elena Rivas describe a dynamic programming algorithm for predicting opti­ mal RNA secondary structure, including thermodynamic model. Running title RNA pseudoknot prediction by dynamic programming. Keywords RNA, secondary

Eddy, Sean

51

Scatter search algorithm for protein structure prediction.  

PubMed

In this paper, we present a Scatter Search (SS) algorithm for predicting 3D structures of proteins based on torsion angles representation. Given the protein's sequence of Amino Acids (AAs), our algorithm produces a 3D structure that aims to minimise the energy function associated with the structure. SS is an evolutionary approach that is based on a population of candidate solutions. These candidates undergo evolutionary operations that combine search intensification and diversification over a number of iterations. We evaluate our algorithm on three proteins taken from a Protein Data Bank (PDB). The results show that our algorithm is able to produce 3D structures with good sub-optimal energy values. Also, the Root Mean Square Deviations (RMSD) of these structures from the reference proteins are promising within limits imposed by the assumptions made. PMID:19778866

Mansour, Nashat; Kehyayan, Christine; Khachfe, Hassan

2009-01-01

52

A Dynamic Programming Algorithm for RNA Structure Prediction Including Pseudoknots  

E-print Network

A Dynamic Programming Algorithm for RNA Structure Prediction Including Pseudoknots Elena Rivas a dynamic programming algorithm for predicting optimal RNA secondary structure, including pseudoknots), is an ef®cient dynamic programming algorithm for identifying the globally minimal energy structure

Eddy, Sean

53

Multithreaded parsing for predicting RNA secondary structures.  

PubMed

Many computational approaches have been developed for modelling and analysing the RNA secondary structure. These approaches are based on diverse methods such as grammars, dynamic programming, matching and evolutionary algorithms. This paper proposes a new parsing algorithm for the prediction of RNA secondary structures. The proposed algorithm is based on the shift-reduce LR parsing algorithm for programming languages. It has two main contributions: it extends the LR parsing algorithm by using a Stochastic Context-Free Grammar (SCFG) instead of Context-Free Grammar (CFG) for parsing RNA secondary structures; it extends the LR parsing algorithm by using a multithreaded approach to handle the LR parsing conflicts resulting from the use of ambiguous grammars. PMID:21354966

Al-Mulhem, Muhammed S

2010-01-01

54

PROTEIN STRUCTURE PREDICTION II Jeffrey Skolnick1 ,2  

E-print Network

protein tertiary structure prediction and its ability to predict NMR quality structures in 1 is to provide a complete library of solved protein structures so that an arbitrary sequence is within modeling distance of an already known structure[13] . Then, the protein folding problem, viz. the prediction

Zhang, Yang

55

Structure of allergens and structure based epitope predictions?  

PubMed Central

The structure determination of major allergens is a prerequisite for analyzing surface exposed areas of the allergen and for mapping conformational epitopes. These may be determined by experimental methods including crystallographic and NMR-based approaches or predicted by computational methods. In this review we summarize the existing structural information on allergens and their classification in protein fold families. The currently available allergen-antibody complexes are described and the experimentally obtained epitopes compared. Furthermore we discuss established methods for linear and conformational epitope mapping, putting special emphasis on a recently developed approach, which uses the structural similarity of proteins in combination with the experimental cross-reactivity data for epitope prediction. PMID:23891546

Dall’Antonia, Fabio; Pavkov-Keller, Tea; Zangger, Klaus; Keller, Walter

2014-01-01

56

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

PubMed Central

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

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

2014-01-01

57

Predicting road accidents: Structural time series approach  

NASA Astrophysics Data System (ADS)

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.

Junus, Noor Wahida Md; Ismail, Mohd Tahir

2014-07-01

58

Advances in Rosetta protein structure prediction on massively parallel systems  

Microsoft Academic Search

One of the key challenges in computational biology is prediction of three-dimensional protein structures from amino-acid sequences. For most proteins, the ''native state'' lies at the bottom of a free- energy landscape. Protein structure prediction involves varying the degrees of freedom of the protein in a constrained manner until it approaches its native state. In the Rosetta protein structure prediction

Srivatsan Raman; Bin Qian; David Baker; Ross C. Walker

2008-01-01

59

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

NASA Astrophysics Data System (ADS)

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

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

2014-12-01

60

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

PubMed

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

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

2014-12-14

61

Structure prediction of magnetosome-associated proteins  

PubMed Central

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

Nudelman, Hila; Zarivach, Raz

2014-01-01

62

Structure prediction of magnetosome-associated proteins.  

PubMed

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

Nudelman, Hila; Zarivach, Raz

2014-01-01

63

Prediction of ionic structure in hydrocarbon flames  

SciTech Connect

The objective of this research is to model the appearance and behavior of combustion-generated ions in hydrocarbon flames. An understanding of ionic phenomena is important to the development of advanced combustion technology including electrical control of flame structure and suppression of soot formation. Computer models were developed to evaluate the formation and behavior of ions in acetylene flames. The results of computations are compared to experimental data of other researchers. Several important qualitative features were successfully modeled. Peak ion concentrations of 10/sup 9/ to 10/sup 11/ cm/sup -3/ are consistent with experimental measurements. The ratio of large ions to small ions increases sharply as the flame is made richer. The build-up and decay rates of ions observed experimentally are predicted by the model.

Eraslan, A.N.

1987-01-01

64

Structure Prediction for Multicomponent Materials Using Biminima  

NASA Astrophysics Data System (ADS)

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

Schebarchov, D.; Wales, D. J.

2014-10-01

65

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

NASA Astrophysics Data System (ADS)

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

Lucas, Amand A.

2008-05-01

66

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

PubMed Central

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

Mrinal, Nirotpal; Tomar, Archana; Nagaraju, Javaregowda

2011-01-01

67

Predicting Peptide Structures Using NMR Data and Deterministic  

E-print Network

Predicting Peptide Structures Using NMR Data and Deterministic Global Optimization J. L. KLEPEIS,1 energy structures of peptides modeled by full atom force Correspondence to: C. A. Floudas; e-8651 / 99 / 131354-17 #12;PREDICTING PEPTIDE STRUCTURES fields. Finally, the approach is applied

Neumaier, Arnold

68

RNA-SSPT: RNA Secondary Structure Prediction Tools.  

PubMed

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

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

2013-01-01

69

Statistical energy analysis response prediction methods for structural systems  

NASA Technical Reports Server (NTRS)

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.

Davis, R. F.

1979-01-01

70

Evolutionary Crystal Structure Prediction and Novel High-Pressure Phases  

Microsoft Academic Search

\\u000a Prediction of stable crystal structures at given pressure-temperature conditions, based only on the knowledge of the chemical\\u000a composition, is a central problem of condensed matter physics. This extremely challenging problem is often termed “crystal\\u000a structure prediction problem”, and recently developed evolutionary algorithm USPEX (Universal Structure Predictor: Evolutionary\\u000a Xtallography) made an important progress in solving it, enabling efficient and reliable prediction

Artem R. Oganov; Yanming Ma; Andriy O. Lyakhov; Mario Valle; Carlo Gatti

2010-01-01

71

Deleterious mutation prediction in the secondary structure of RNAs  

PubMed Central

Methods for computationally predicting deleterious mutations have recently been investigated for proteins, mainly by probabilistic estimations in the context of genomic research for identifying single nucleotide polymorphisms that can potentially affect protein function. It has been demonstrated that in cases where a few homologs are available, ab initio predicted structures modeled by the Rosetta method can become useful for including structural information to improve the deleterious mutation prediction methods for proteins. In the field of RNAs where very few homologs are available at present, this analogy can serve as a precursor to investigate a deleterious mutation prediction approach that is based on RNA secondary structure. When attempting to develop models for the prediction of deleterious mutations in RNAs, useful structural information is available from folding algorithms that predict the secondary structure of RNAs, based on energy minimization. Detecting mutations with desired structural effects among all possible point mutations may then be valuable for the prediction of deleterious mutations that can be tested experimentally. Here, a method is introduced for the prediction of deleterious mutations in the secondary structure of RNAs. The mutation prediction method, based on subdivision of the initial structure into smaller substructures and construction of eigenvalue tables, is independent of the folding algorithms but relies on their success to predict the folding of small RNA structures. Application of this method to predict mutations that may cause structural rearrangements, thereby disrupting stable motifs, is given for prokaryotic transcription termination in the thiamin pyrophosphate and S-adenosyl-methionine induced riboswitches. Ribo switches are mRNA structures that have recently been found to regulate transcription termination or translation initiation in bacteria by conformation rearrangement in response to direct metabolite binding. Predicting deleterious mutations on riboswitches may succeed to systematically intervene in bacterial genetic control. PMID:14602917

Barash, Danny

2003-01-01

72

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

PubMed

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

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

2004-06-01

73

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

SciTech Connect

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.

Szentpaly, L.V.; Shamovsky, I.L. [Univ. of the West Indies, Kingston (Jamaica)

1996-12-31

74

Mining Residue Contacts in Proteins Using Local Structure Predictions  

E-print Network

offers a new paradigm to protein structure prediction by employing data mining methods like clusteringMining Residue Contacts in Proteins Using Local Structure Predictions Mohammed J. Zaki , Shan Jin or not a segment of the protein would tend to fold independently of the rest 1 #12;of the molecule. Cross

Bystroff, Chris

75

Mining Residue Contacts in Proteins Using Local Structure Predictions  

E-print Network

offers a new paradigm to protein structure prediction by employing data mining methods like clusteringMining Residue Contacts in Proteins Using Local Structure Predictions Mohammed J. Zaki + , Shan Jin or not a segment of the protein would tend to fold independently of the rest 1 #12; of the molecule. Cross

Bystroff, Chris

76

Predicting equilibrium structures in freezing processes Dieter Gottwald  

E-print Network

the equilibrium structures of the solid phases are chosen from a preselected set of candidates, genetic algorithmsPredicting equilibrium structures in freezing processes Dieter Gottwald Center for Computational 23 May 2005 We propose genetic algorithms as a new tool that is able to predict all possible solid

Likos, Christos N.

77

A SOFTWARE PIPELINE FOR PROTEIN STRUCTURE PREDICTION Michael S. Lee  

E-print Network

a software suite to predict protein structures from sequence through the integration of multiple non and vaccine design as well as many areas of basic biological research. In this work, initial assessments of the software are made. Most importantly, these tests include evaluation of the quality of predicted structural

78

A novel protein structural classes prediction method based on predicted secondary structure.  

PubMed

Knowledge of structural classes plays an important role in understanding protein folding patterns. In this paper, features based on the predicted secondary structure sequence and the corresponding E-H sequence are extracted. Then, an 11-dimensional feature vector is selected based on a wrapper feature selection algorithm and a support vector machine (SVM). Among the 11 selected features, 4 novel features are newly designed to model the differences between ?/? class and ? + ? class, and other 7 rational features are proposed by previous researchers. To examine the performance of our method, a total of 5 datasets are used to design and test the proposed method. The results show that competitive prediction accuracies can be achieved by the proposed method compared to existing methods (SCPRED, RKS-PPSC and MODAS), and 4 new features are demonstrated essential to differentiate ?/? and ? + ? classes. Standalone version of the proposed method is written in JAVA language and it can be downloaded from http://web.xidian.edu.cn/slzhang/paper.html. PMID:22353242

Ding, Shuyan; Zhang, Shengli; Li, Yang; Wang, Tianming

2012-05-01

79

RNAstructure: software for RNA secondary structure prediction and analysis  

PubMed Central

Background To understand an RNA sequence's mechanism of action, the structure must be known. Furthermore, target RNA structure is an important consideration in the design of small interfering RNAs and antisense DNA oligonucleotides. RNA secondary structure prediction, using thermodynamics, can be used to develop hypotheses about the structure of an RNA sequence. Results RNAstructure is a software package for RNA secondary structure prediction and analysis. It uses thermodynamics and utilizes the most recent set of nearest neighbor parameters from the Turner group. It includes methods for secondary structure prediction (using several algorithms), prediction of base pair probabilities, bimolecular structure prediction, and prediction of a structure common to two sequences. This contribution describes new extensions to the package, including a library of C++ classes for incorporation into other programs, a user-friendly graphical user interface written in JAVA, and new Unix-style text interfaces. The original graphical user interface for Microsoft Windows is still maintained. Conclusion The extensions to RNAstructure serve to make RNA secondary structure prediction user-friendly. The package is available for download from the Mathews lab homepage at http://rna.urmc.rochester.edu/RNAstructure.html. PMID:20230624

2010-01-01

80

Improving the accuracy of protein secondary structure prediction using structural alignment  

Microsoft Academic Search

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

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

2006-01-01

81

Neural network definitions of highly predictable protein secondary structure classes  

SciTech Connect

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

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

1994-02-01

82

Predicting Career Advancement with Structural Equation Modelling  

ERIC Educational Resources Information Center

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

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

2012-01-01

83

Genome-wide Membrane Protein Structure Prediction  

PubMed Central

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

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

2013-01-01

84

Genome-wide Membrane Protein Structure Prediction.  

PubMed

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

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

2013-08-01

85

Data mining for structure type prediction  

E-print Network

Determining the stable structure types of an alloy is critical to determining many properties of that material. This can be done through experiment or computation. Both methods can be expensive and time consuming. Computational ...

Tibbetts, Kevin (Kevin Joseph)

2004-01-01

86

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

PubMed Central

The sequence-dependent structural variability and conformational dynamics of DNA play pivotal roles in many biological milieus, such as in the site-specific binding of transcription factors to target regulatory elements. To better understand DNA structure, function, and dynamics in general, and protein···DNA recognition in the ‘?B’ family of genetic regulatory elements in particular, we performed molecular dynamics simulations of a 20-bp DNA encompassing a cognate ?B site recognized by the proto-oncogenic ‘c-Rel’ subfamily of NF-?B transcription factors. Simulations of the ?B DNA in explicit water were extended to microsecond duration, providing a broad, atomically detailed glimpse into the structural and dynamical behavior of double helical DNA over many timescales. Of particular note, novel (and structurally plausible) conformations of DNA developed only at the long times sampled in this simulation—including a peculiar state arising at ?0.7 ?s and characterized by cross-strand intercalative stacking of nucleotides within a longitudinally sheared base pair, followed (at ?1 ?s) by spontaneous base flipping of a neighboring thymine within the A-rich duplex. Results and predictions from the microsecond-scale simulation include implications for a dynamical NF-?B recognition motif, and are amenable to testing and further exploration via specific experimental approaches that are suggested herein. PMID:18653524

Mura, Cameron; McCammon, J. Andrew

2008-01-01

87

An Atomic Environment Potential for use in Protein Structure Prediction  

E-print Network

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

Summa, Christopher M.

88

Protein structure prediction using sparse dipolar coupling data  

Microsoft Academic Search

Residual dipolar coupling (RDC) represents one of the most exciting emerging NMR techniques for protein structure studies. However, solving a protein structure using RDC data alone is still a highly challenging problem. We report here a com- puter program, RDC-PROSPECT, for protein struc- ture prediction based on a structural homolog or analog of the target protein in the Protein Data

Youxing Qu; Jun-tao Guo; Victor Olman; Ying Xu

2004-01-01

89

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

PubMed Central

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

Skolnick, Jeffrey; Zhou, Hongyi; Gao, Mu

2013-01-01

90

Prediction of ionic structure in hydrocarbon flames  

Microsoft Academic Search

The objective of this research is to model the appearance and behavior of combustion-generated ions in hydrocarbon flames. An understanding of ionic phenomena is important to the development of advanced combustion technology including electrical control of flame structure and suppression of soot formation. Computer models were developed to evaluate the formation and behavior of ions in acetylene flames. The results

Eraslan

1987-01-01

91

Predicting protein structure using hidden Markov models  

Microsoft Academic Search

We discuss how methods based on hidden Markov models performed in the fold-recognition sectionof the CASP2 experiment. Hidden Markov models were built for a representative set of just over onethousand structures from the Protein Data Bank (pdb). Each CASP2 target sequence was scored againstthis library of hmms. In addition, an hmm was built for each of the target sequences, and

Kevin Karplus; Kimmen Sjölander; Christian Barrett; Melissa Cline; David Haussler; Richard Hughey; Liisa Holm; Chris Sander

1997-01-01

92

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

E-print Network

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

Paris-Sud XI, Université de

93

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...

94

A machine learning approach to crystal structure prediction  

E-print Network

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

Fischer, Christopher Carl

2007-01-01

95

Elite Values Versus Organizational Structure in Predicting Innovation  

ERIC Educational Resources Information Center

Compares the predictive power of the concept of elite values with leader values; member values; and the three structural variables of complexity, centralization, and formalization in predicting innovative organizational performance. Elite values proved to be slightly better predictors than do either leader values or complexity. (Authors)

Hage, Jerald; Dewar, Robert

1973-01-01

96

Prediction of protein secondary structure by the hidden Markov model.  

PubMed

The purpose of this paper is to introduce a new method for analyzing the amino acid sequences of proteins using the hidden Markov model (HMM), which is a type of stochastic model. Secondary structures such as helix, sheet and turn are learned by HMMs, and these HMMs are applied to new sequences whose structures are unknown. The output probabilities from the HMMs are used to predict the secondary structures of the sequences. The authors tested this prediction system on approximately 100 sequences from a public database (Brookhaven PDB). Although the implementation is 'without grammar' (no rule for the appearance patterns of secondary structure) the result was reasonable. PMID:8481815

Asai, K; Hayamizu, S; Handa, K

1993-04-01

97

OPTIMIZATION BIAS IN ENERGY-BASED STRUCTURE PREDICTION  

PubMed Central

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

Petrella, Robert J.

2014-01-01

98

High-speed prediction of crystal structures for organic molecules  

NASA Astrophysics Data System (ADS)

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

Obata, Shigeaki; Goto, Hitoshi

2015-02-01

99

Methods for evaluating the predictive accuracy of structural dynamic models  

NASA Technical Reports Server (NTRS)

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.

Hasselman, Timothy K.; Chrostowski, Jon D.

1991-01-01

100

Improving structure-based function prediction using molecular dynamics  

PubMed Central

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

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

2009-01-01

101

Computational methods in sequence and structure prediction  

NASA Astrophysics Data System (ADS)

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

Lang, Caiyi

102

A neural network structure for prediction of chemical agent fate  

NASA Astrophysics Data System (ADS)

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

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

2014-05-01

103

Assessment of template based protein structure predictions in CASP10  

PubMed Central

Template Based Modeling (TBM) is a major component of the Critical Assessment of Protein Structure Prediction (CASP). In CASP10, some 41,740 predicted models submitted by 150 predictor groups were assessed as TBM predictions. The accuracy of protein structure prediction was assessed by geometric comparison with experimental X-ray crystal and NMR structures using a composite score that included both global alignment metrics and distance-matrix based metrics. These included GDT-HA and GDC-all global alignment scores, and the superimposition-independent LDDT distance-matrix based score. In addition, a superimposition-independent RPF metric, similar to that described previously for comparing protein models against experimental NMR data, was used for comparing predicted protein structure models against experimental protein structures. In order to score well on all four of these metrics, models must feature accurate predictions of both backbone and side-chain conformations. Performance rankings were determined independently for server and the combined server plus human-curated predictor groups. Final rankings were made using pair-wise head-to-head Student’s t-test analysis of raw metric scores among the top 25 performing groups in each category. PMID:24323734

Huang, Yuanpeng J.; Mao, Binchen; Aramini, James M.; Montelione, Gaetano T

2014-01-01

104

Predicting crystal structure by merging data mining with quantum mechanics  

E-print Network

ARTICLES Predicting crystal structure by merging data mining with quantum mechanics CHRISTOPHER C@mit.edu Published online: 9 July 2006; doi:10.1038/nmat1691 Modern methods of quantum mechanics have proved with quantum mechanics if an algorithm to direct the search through the large space of possible structures

Ceder, Gerbrand

105

Analytical Predictions of the Air Gap Response of Floating Structures  

Microsoft Academic Search

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

Lance Manuel; Bert Sweetman; Steven R. Winterstein

2001-01-01

106

Prediction of Protein Structural Classes by Modified Mahalanobis Discriminant Algorithm  

Microsoft Academic Search

We first discuss quantitative rules for determining the protein structural classes based on their secondary structures. Then we propose a modification of the least Mahalanobis distance method for prediction of protein classes. It is a generalization of a quadratic discriminant function to the case of degenerate covariance matrices. The resubstitution tests and leave-one-out tests are carried out to compare several

Wei-min Liu; Kuo-Chen Chou

1998-01-01

107

Predicting chemical activities from structures by attributed molecular graph classification  

Microsoft Academic Search

Designing Quantitative Structure-Activity Relationship (QSAR) models has been a recurrent research interest for biologists and computer scientists. An example is to predict the toxicity of chemical compounds using their structural properties as features represented by graphs. A popular method to classify these graphs is to exploit classifiers such as support vector machines (SVMs) and graph kernels to incorporate the sequential,

Qian Xu; Derek Hao Hu; Hong Xue; Qiang Yang

2010-01-01

108

Crystal structure prediction via particle-swarm optimization  

NASA Astrophysics Data System (ADS)

We have developed a method for crystal structure prediction from “scratch” through particle-swarm optimization (PSO) algorithm within the evolutionary scheme. PSO technique is different with the genetic algorithm and has apparently avoided the use of evolution operators (e.g., crossover and mutation). The approach is based on an efficient global minimization of free-energy surfaces merging total-energy calculations via PSO technique and requires only chemical compositions for a given compound to predict stable or metastable structures at given external conditions (e.g., pressure). A particularly devised geometrical structure parameter which allows the elimination of similar structures during structure evolution was implemented to enhance the structure search efficiency. The application of designed variable unit-cell size technique has greatly reduced the computational cost. Moreover, the symmetry constraint imposed in the structure generation enables the realization of diverse structures, leads to significantly reduced search space and optimization variables, and thus fastens the global structure convergence. The PSO algorithm has been successfully applied to the prediction of many known systems (e.g., elemental, binary, and ternary compounds) with various chemical-bonding environments (e.g., metallic, ionic, and covalent bonding). The high success rate demonstrates the reliability of this methodology and illustrates the promise of PSO as a major technique on crystal structure determination.

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

2010-09-01

109

Brain structure predicts risk for obesity ?  

PubMed Central

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

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

2014-01-01

110

Protein structure prediction enhanced with evolutionary diversity: SPEED  

PubMed Central

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

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

2010-01-01

111

Protein structure prediction enhanced with evolutionary diversity : SPEED.  

SciTech Connect

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

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

2010-03-01

112

Bayesian Model of Protein Primary Sequence for Secondary Structure Prediction  

PubMed Central

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

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

2014-01-01

113

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

PubMed Central

Background Knowledge of structural class is used by numerous methods for identification of structural/functional characteristics of proteins and could be used for the detection of remote homologues, particularly for chains that share twilight-zone similarity. In contrast to existing sequence-based structural class predictors, which target four major classes and which are designed for high identity sequences, we predict seven classes from sequences that share twilight-zone identity with the training sequences. Results The proposed MODular Approach to Structural class prediction (MODAS) method is unique as it allows for selection of any subset of the classes. MODAS is also the first to utilize a novel, custom-built feature-based sequence representation that combines evolutionary profiles and predicted secondary structure. The features quantify information relevant to the definition of the classes including conservation of residues and arrangement and number of helix/strand segments. Our comprehensive design considers 8 feature selection methods and 4 classifiers to develop Support Vector Machine-based classifiers that are tailored for each of the seven classes. Tests on 5 twilight-zone and 1 high-similarity benchmark datasets and comparison with over two dozens of modern competing predictors show that MODAS provides the best overall accuracy that ranges between 80% and 96.7% (83.5% for the twilight-zone datasets), depending on the dataset. This translates into 19% and 8% error rate reduction when compared against the best performing competing method on two largest datasets. The proposed predictor provides accurate predictions at 58% accuracy for membrane proteins class, which is not considered by majority of existing methods, in spite that this class accounts for only 2% of the data. Our predictive model is analyzed to demonstrate how and why the input features are associated with the corresponding classes. Conclusions The improved predictions stem from the novel features that express collocation of the secondary structure segments in the protein sequence and that combine evolutionary and secondary structure information. Our work demonstrates that conservation and arrangement of the secondary structure segments predicted along the protein chain can successfully predict structural classes which are defined based on the spatial arrangement of the secondary structures. A web server is available at http://biomine.ece.ualberta.ca/MODAS/. PMID:20003388

2009-01-01

114

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

PubMed

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

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

2015-01-01

115

MUFOLD: A new solution for protein 3D structure prediction  

PubMed Central

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

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

2010-01-01

116

A life prediction model for laminated composite structural components  

NASA Technical Reports Server (NTRS)

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

Allen, David H.

1990-01-01

117

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

PubMed Central

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

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

118

A new protein structure representation for efficient protein function prediction.  

PubMed

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

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

2014-12-01

119

Adaptive modelling of structured molecular representations for toxicity prediction  

NASA Astrophysics Data System (ADS)

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

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

2012-12-01

120

Sizing Structures and Predicting Weight of a Spacecraft  

NASA Technical Reports Server (NTRS)

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

Cerro, Jeffrey; Shore, C. P.

2006-01-01

121

PDB-UF: database of predicted enzymatic functions for unannotated protein structures from structural genomics  

Microsoft Academic Search

Background: The number of protein structures from structural genomics centers dramatically increases in the Protein Data Bank (PDB). Many of these structures are functionally unannotated because they have no sequence similarity to proteins of known function. However, it is possible to successfully infer function using only structural similarity. Results: Here we present the PDB-UF database, a web-accessible collection of predictions

Marcin Von Grotthuss; Dariusz Plewczynski; Krzysztof Ginalski; Leszek Rychlewski; Eugene I. Shakhnovich

2006-01-01

122

Improving the accuracy of protein secondary structure prediction using structural alignment  

PubMed Central

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

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

2006-01-01

123

Improved Chou-Fasman method for protein secondary structure prediction  

PubMed Central

Background Protein secondary structure prediction is a fundamental and important component in the analytical study of protein structure and functions. The prediction technique has been developed for several decades. The Chou-Fasman algorithm, one of the earliest methods, has been successfully applied to the prediction. However, this method has its limitations due to low accuracy, unreliable parameters, and over prediction. Thanks to the recent development in protein folding type-specific structure propensities and wavelet transformation, the shortcomings in Chou-Fasman method are able to be overcome. Results We improved Chou-Fasman method in three aspects. (a) Replace the nucleation regions with extreme values of coefficients calculated by the continuous wavelet transform. (b) Substitute the original secondary structure conformational parameters with folding type-specific secondary structure propensities. (c) Modify Chou-Fasman rules. The CB396 data set was tested by using improved Chou-Fasman method and three indices: Q3, Qpre, SOV were used to measure this method. We compared the indices with those obtained from the original Chou-Fasman method and other four popular methods. The results showed that our improved Chou-Fasman method performs better than the original one in all indices, about 10–18% improvement. It is also comparable to other currently popular methods considering all the indices. Conclusion Our method has greatly improved Chou-Fasman method. It is able to predict protein secondary structure as good as current popular methods. By locating nucleation regions with refined wavelet transform technology and by calculating propensity factors with larger size data set, it is likely to get a better result. PMID:17217506

Chen, Hang; Gu, Fei; Huang, Zhengge

2006-01-01

124

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

PubMed Central

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

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

2015-01-01

125

Predicting protein structures with a multiplayer online game  

E-print Network

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

Baker, David

126

Structural Damage Prediction and Analysis for Hypervelocity Impact: Consulting  

NASA Technical Reports Server (NTRS)

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

1995-01-01

127

Process for predicting structural performance of mechanical systems  

DOEpatents

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

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

1998-01-01

128

Classification, representation and prediction of crystal structures of ionic compounds  

Microsoft Academic Search

The aim of this paper is to show that with the aid of a qualitative model of ionic bonding, including polarizability, many crystal structures, mainly of halides and chalcogenides, can be explained or even predicted. Polarization of O2- and F- ions may be neglected unless these ions have very small, or small and highly charged cation neighbours. The polarizability of

E. W. Gorter

1970-01-01

129

Predicting protein structure using hidden Markov models Kevin Karplusy  

E-print Network

Predicting protein structure using hidden Markov models Kevin Karplusy Computer Engineering U, Supplement 1, 1997 Abstract We discuss how methods based on hidden Markov models performed in the fold-recognition section of the CASP2 experiment. Hidden Markov models were built for a representative set of just over one

Karplus, Kevin

130

Process for predicting structural performance of mechanical systems  

DOEpatents

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

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

1998-05-19

131

Predicting PDZ domain mediated protein interactions from structure  

PubMed Central

Background PDZ domains are structural protein domains that recognize simple linear amino acid motifs, often at protein C-termini, and mediate protein-protein interactions (PPIs) in important biological processes, such as ion channel regulation, cell polarity and neural development. PDZ domain-peptide interaction predictors have been developed based on domain and peptide sequence information. Since domain structure is known to influence binding specificity, we hypothesized that structural information could be used to predict new interactions compared to sequence-based predictors. Results We developed a novel computational predictor of PDZ domain and C-terminal peptide interactions using a support vector machine trained with PDZ domain structure and peptide sequence information. Performance was estimated using extensive cross validation testing. We used the structure-based predictor to scan the human proteome for ligands of 218 PDZ domains and show that the predictions correspond to known PDZ domain-peptide interactions and PPIs in curated databases. The structure-based predictor is complementary to the sequence-based predictor, finding unique known and novel PPIs, and is less dependent on training–testing domain sequence similarity. We used a functional enrichment analysis of our hits to create a predicted map of PDZ domain biology. This map highlights PDZ domain involvement in diverse biological processes, some only found by the structure-based predictor. Based on this analysis, we predict novel PDZ domain involvement in xenobiotic metabolism and suggest new interactions for other processes including wound healing and Wnt signalling. Conclusions We built a structure-based predictor of PDZ domain-peptide interactions, which can be used to scan C-terminal proteomes for PDZ interactions. We also show that the structure-based predictor finds many known PDZ mediated PPIs in human that were not found by our previous sequence-based predictor and is less dependent on training–testing domain sequence similarity. Using both predictors, we defined a functional map of human PDZ domain biology and predict novel PDZ domain function. Users may access our structure-based and previous sequence-based predictors at http://webservice.baderlab.org/domains/POW. PMID:23336252

2013-01-01

132

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

PubMed Central

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

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

2011-01-01

133

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

PubMed

A long standing problem in structural bioinformatics is to determine the three-dimensional (3-D) structure of a protein when only a sequence of amino acid residues is given. Many computational methodologies and algorithms have been proposed as a solution to the 3-D Protein Structure Prediction (3-D-PSP) problem. These methods can be divided in four main classes: (a) first principle methods without database information; (b) first principle methods with database information; (c) fold recognition and threading methods; and (d) comparative modeling methods and sequence alignment strategies. Deterministic computational techniques, optimization techniques, data mining and machine learning approaches are typically used in the construction of computational solutions for the PSP problem. Our main goal with this work is to review the methods and computational strategies that are currently used in 3-D protein prediction. PMID:25462334

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

2014-10-12

134

Solvent structure improves docking prediction in lectin-carbohydrate complexes.  

PubMed

Recognition and complex formation between proteins and carbohydrates is a key issue in many important biological processes. Determination of the three-dimensional structure of such complexes is thus most relevant, but particularly challenging because of their usually low binding affinity. In silico docking methods have a long-standing tradition in predicting protein-ligand complexes, and allow a potentially fast exploration of a number of possible protein-carbohydrate complex structures. However, determining which of these predicted complexes represents the correct structure is not always straightforward. In this work, we present a modification of the scoring function provided by AutoDock4, a widely used docking software, on the basis of analysis of the solvent structure adjacent to the protein surface, as derived from molecular dynamics simulations, that allows the definition and characterization of regions with higher water occupancy than the bulk solvent, called water sites. They mimic the interaction held between the carbohydrate -OH groups and the protein. We used this information for an improved docking method in relation to its capacity to correctly predict the protein-carbohydrate complexes for a number of tested proteins, whose ligands range in size from mono- to tetrasaccharide. Our results show that the presented method significantly improves the docking predictions. The resulting solvent-structure-biased docking protocol, therefore, appears as a powerful tool for the design and optimization of development of glycomimetic drugs, while providing new insights into protein-carbohydrate interactions. Moreover, the achieved improvement also underscores the relevance of the solvent structure to the protein carbohydrate recognition process. PMID:23089616

Gauto, Diego F; Petruk, Ariel A; Modenutti, Carlos P; Blanco, Juan I; Di Lella, Santiago; Martí, Marcelo A

2013-02-01

135

Protein 8-class secondary structure prediction using conditional neural fields  

PubMed Central

Compared with the protein 3-class secondary structure (SS) prediction, the 8-class prediction gains less attention and is also much more challenging, especially for proteins with few sequence homologs. This paper presents a new probabilistic method for 8-class SS prediction using conditional neural fields (CNFs), a recently invented probabilistic graphical model. This CNF method not only models the complex relationship between sequence features and SS, but also exploits the interdependency among SS types of adjacent residues. In addition to sequence profiles, our method also makes use of non-evolutionary information for SS prediction. Tested on the CB513 and RS126 data sets, our method achieves Q8 accuracy of 64.9 and 64.7%, respectively, which are much better than the SSpro8 web server (51.0 and 48.0%, respectively). Our method can also be used to predict other structure properties (e.g. solvent accessibility) of a protein or the SS of RNA. PMID:21805636

Wang, Zhiyong; Zhao, Feng; Peng, Jian; Xu, Jinbo

2012-01-01

136

Pharmacokinetic parameter prediction from drug structure using artificial neural networks.  

PubMed

Simple methods for determining the human pharmacokinetics of known and unknown drug-like compounds is a much sought-after goal in the pharmaceutical industry. The current study made use of artificial neural networks (ANNs) for the prediction of clearances, fraction bound to plasma proteins, and volume of distribution of a series of structurally diverse compounds. A number of theoretical descriptors were generated from the drug structures and both automated and manual pruning were used to derive optimal subsets of descriptors for quantitative structure-pharmacokinetic relationship models. Models were trained on one set of compounds and validated with another. Absolute predicted ability was evaluated using a further independent test set of compounds. Correlations for test compounds ranged from 0.855 to 0.992. Predicted values agreed closely with experimental values for total clearance, renal clearance, and volume of distribution, while predictions for protein binding were encouraging. The combination of descriptor generation, ANNs, and the speed and success of this technique compared with conventional methods shows strong potential for use in pharmaceutical product development. PMID:14726136

Turner, Joseph V; Maddalena, Desmond J; Cutler, David J

2004-02-11

137

A Structure-Based Model for Predicting Serum Albumin Binding  

PubMed Central

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

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

2014-01-01

138

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

PubMed

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

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

2012-02-01

139

Virality Prediction and Community Structure in Social Networks  

PubMed Central

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

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

2013-01-01

140

Predicting Earthquake Response of Civil Structures from Ambient Noise  

NASA Astrophysics Data System (ADS)

Increased monitoring of civil structures for response to earthquake motions is fundamental for reducing seismic hazard. Seismic monitoring is difficult because typically only a few useful, intermediate to large earthquakes occur per decade near instrumented structures. Here we demonstrate that the impulse response function (IRF) of a multi-story building can be generated from ambient noise. Estimated shear-wave velocity, attenuation values, and resonance frequencies from the IRFs agree with previous estimates for the instrumented UCLA Factor building. The accuracy of the approach is demonstrated by predicting the Factor building’s response to an M4.2 earthquake. The methodology described here allows for rapid non-invasive determination of structural parameters from the IRFs within days and could be used as a new tool for stateof- health monitoring of civil structures (buildings, bridges, etc.) before and/or after major earthquakes.

Prieto, G.; Lawrence, J. F.; Chung, A. I.; Kohler, M. D.

2009-12-01

141

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

E-print Network

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

Jaroslaw Piasecki; Piotr Szymczak; John J. Kozak

2011-08-15

142

Fragment-HMM: a new approach to protein structure prediction.  

PubMed

We designed a simple position-specific hidden Markov model to predict protein structure. Our new framework naturally repeats itself to converge to a final target, conglomerating fragment assembly, clustering, target selection, refinement, and consensus, all in one process. Our initial implementation of this theory converges to within 6 A of the native structures for 100% of decoys on all six standard benchmark proteins used in ROSETTA (discussed by Simons and colleagues in a recent paper), which achieved only 14%-94% for the same data. The qualities of the best decoys and the final decoys our theory converges to are also notably better. PMID:18723665

Li, Shuai Cheng; Bu, Dongbo; Xu, Jinbo; Li, Ming

2008-11-01

143

Effects of scale in predicting global structural response  

NASA Technical Reports Server (NTRS)

Analytical techniques for scale-up effects were reviewed. The advantages and limitations of applying the principles of similitude to composite structures is summarized and illustrated by simple examples. An analytical procedure was formulated to design scale models of an axially compressed composite cylinder. A building-block approach was outlined where each structural detail is analyzed independently and the probable failure sequence of a selected component is predicted, taking into account load redistribution subsequent to first element failure. Details of this building-block approach are under development.

Deo, R. B.; Kan, H. P.

1991-01-01

144

A dynamic programming algorithm for RNA structure prediction including pseudoknots  

Microsoft Academic Search

We describe a dynamic programming algorithm for predicting optimal RNA secondary structure, including pseudoknots. The algorithm has a worst case complexity of ${\\\\cal O}(N^6)$ in time and ${\\\\cal O}(N^4)$ 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

Elena Rivas; Sean R. Eddy

1998-01-01

145

Structure and stoichiometry prediction of surfaces reacting with multicomponent gases.  

PubMed

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

Herrmann, Philipp; Heimel, Georg

2015-01-14

146

An Intriguing Controversy over Protein Structural Class Prediction  

Microsoft Academic Search

A recent report by Bahar et al. [(1997), Proteins29, 172–185] indicates that the coupling effects among different amino acid components as originally formulated by K. C. Chou [(1995), Proteins21, 319–344] are important for improving the prediction of protein structural classes. These authors have further proposed a compact lattice model to illuminate the physical insight contained in the component-coupled algorithm. However,

Guo-Ping Zhou

1998-01-01

147

A tool for the prediction of structures of complex sugars.  

PubMed

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

Xia, Junchao; Margulis, Claudio

2008-12-01

148

Factors influencing protein tyrosine nitration – structure-based predictive models  

PubMed Central

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

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

2010-01-01

149

Improved hybrid optimization algorithm for 3D protein structure prediction.  

PubMed

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

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

2014-07-01

150

How Good Are Simplified Models for Protein Structure Prediction?  

PubMed Central

Protein structure prediction (PSP) has been one of the most challenging problems in computational biology for several decades. The challenge is largely due to the complexity of the all-atomic details and the unknown nature of the energy function. Researchers have therefore used simplified energy models that consider interaction potentials only between the amino acid monomers in contact on discrete lattices. The restricted nature of the lattices and the energy models poses a twofold concern regarding the assessment of the models. Can a native or a very close structure be obtained when structures are mapped to lattices? Can the contact based energy models on discrete lattices guide the search towards the native structures? In this paper, we use the protein chain lattice fitting (PCLF) problem to address the first concern; we developed a constraint-based local search algorithm for the PCLF problem for cubic and face-centered cubic lattices and found very close lattice fits for the native structures. For the second concern, we use a number of techniques to sample the conformation space and find correlations between energy functions and root mean square deviation (RMSD) distance of the lattice-based structures with the native structures. Our analysis reveals weakness of several contact based energy models used that are popular in PSP. PMID:24876837

Newton, M. A. Hakim; Rashid, Mahmood A.; Pham, Duc Nghia; Sattar, Abdul

2014-01-01

151

EVO—Evolutionary algorithm for crystal structure prediction  

NASA Astrophysics Data System (ADS)

We present EVO—an evolution strategy designed for crystal structure search and prediction. The concept and main features of biological evolution such as creation of diversity and survival of the fittest have been transferred to crystal structure prediction. EVO successfully demonstrates its applicability to find crystal structures of the elements of the 3rd main group with their different spacegroups. For this we used the number of atoms in the conventional cell and multiples of it. Running EVO with different numbers of carbon atoms per unit cell yields graphite as the lowest energy structure as well as a diamond-like structure, both in one run. Our implementation also supports the search for 2D structures and was able to find a boron sheet with structural features so far not considered in literature. Program summaryProgram title: EVO Catalogue identifier: AEOZ_v1_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEOZ_v1_0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: GNU General Public License version 3 No. of lines in distributed program, including test data, etc.: 23488 No. of bytes in distributed program, including test data, etc.: 1830122 Distribution format: tar.gz Programming language: Python. Computer: No limitations known. Operating system: Linux. RAM: Negligible compared to the requirements of the electronic structure programs used Classification: 7.8. External routines: Quantum ESPRESSO (http://www.quantum-espresso.org/), GULP (https://projects.ivec.org/gulp/) Nature of problem: Crystal structure search is a global optimisation problem in 3N+3 dimensions where N is the number of atoms in the unit cell. The high dimensional search space is accompanied by an unknown energy landscape. Solution method: Evolutionary algorithms transfer the main features of biological evolution to use them in global searches. The combination of the "survival of the fittest" (deterministic) and the randomised choice of the parents and normally distributed mutation steps (non-deterministic) provides a thorough search. Restrictions: The algorithm is in principle only restricted by a huge search space and simultaneously increasing calculation time (memory, etc.), which is not a problem for our piece of code but for the used electronic structure programs. Running time: The simplest provided case runs serially and takes 30 minutes to one hour. All other calculations run for significantly longer time depending on the parameters like the number and sort of atoms and the electronic structure program in use as well as the level of parallelism included.

Bahmann, Silvia; Kortus, Jens

2013-06-01

152

Predicted folding of beta-structure in myelin basic protein.  

PubMed

Predictions of myelin basic protein secondary structure have not previously considered a major role for beta-structure in the organization of the native molecule because optical rotatory dispersion and circular dichroism studies have provided little, if any, evidence for beta-structure, and because a polycationic protein is generally considered to resist folding into a compact structure. However, the Chou-Fasman, Lim, and Robson algorithms identify a total of five beta-strands in the amino acid sequence. Four of these hydrophobic amino acid sequences (37-45, 87-95, 110-118, and 150-158) could form a hairpin intermediate that initiates folding of a Greek-key-type beta-structure. A second fold on the more hydrophobic side, with the addition of a strand from the N-terminus (residues 13-21), would complete the five-stranded antiparallel beta-sheet. A unique strand alignment can be predicted by phasing the hydrophobic residues. The unusual triproline sequence of myelin basic protein (100-102) is enclosed in the 14-residue hairpin loop. If these prolines are in the trans conformation, models show that a reverse turn could occur at residues 102-105 (Pro-Ser-Gln-Gly). Algorithms do not agree on the prediction of alpha-helices, but each of the two large loops could accommodate an alpha-helix. Myelin basic protein is known to be phosphorylated in vivo on as many as five Ser/Thr residues. Phosphorylation might alter the dynamics of folding if the nascent polypeptide were phosphorylated in the cytoplasm. In particular, phosphorylation of Thr-99 could neutralize cationic residues Lys-106 and Arg-108 within the hairpin loop. In addition, the methylation of Arg-108 might stabilize the hairpin loop structure through hydrophobic interaction with the side chain of Pro-97. The cationic side chains of arginine and lysine residues located on the faces of the beta-sheet (Arg-43, Arg-114, Lys-13, Lys-92, Lys-153, and Lys-156) could provide sites for interaction with phospholipids and other anionic structures on the surface of the myelin lipid bilayer. PMID:6204015

Stoner, G L

1984-08-01

153

Integrating Chemical Footprinting Data into RNA Secondary Structure Prediction  

PubMed Central

Chemical and enzymatic footprinting experiments, such as shape (selective 2?-hydroxyl acylation analyzed by primer extension), yield important information about RNA secondary structure. Indeed, since the -hydroxyl is reactive at flexible (loop) regions, but unreactive at base-paired regions, shape yields quantitative data about which RNA nucleotides are base-paired. Recently, low error rates in secondary structure prediction have been reported for three RNAs of moderate size, by including base stacking pseudo-energy terms derived from shape data into the computation of minimum free energy secondary structure. Here, we describe a novel method, RNAsc (RNA soft constraints), which includes pseudo-energy terms for each nucleotide position, rather than only for base stacking positions. We prove that RNAsc is self-consistent, in the sense that the nucleotide-specific probabilities of being unpaired in the low energy Boltzmann ensemble always become more closely correlated with the input shape data after application of RNAsc. From this mathematical perspective, the secondary structure predicted by RNAsc should be ‘correct’, in as much as the shape data is ‘correct’. We benchmark RNAsc against the previously mentioned method for eight RNAs, for which both shape data and native structures are known, to find the same accuracy in 7 out of 8 cases, and an improvement of 25% in one case. Furthermore, we present what appears to be the first direct comparison of shape data and in-line probing data, by comparing yeast asp-tRNA shape data from the literature with data from in-line probing experiments we have recently performed. With respect to several criteria, we find that shape data appear to be more robust than in-line probing data, at least in the case of asp-tRNA. PMID:23091593

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

2012-01-01

154

Reconstruction and stability of secondary structure elements in the context of protein structure prediction.  

PubMed

Efficient and accurate reconstruction of secondary structure elements in the context of protein structure prediction is the major focus of this work. We present a novel approach capable of reconstructing alpha-helices and beta-sheets in atomic detail. The method is based on Metropolis Monte Carlo simulations in a force field of empirical potentials that are designed to stabilize secondary structure elements in room-temperature simulations. Particular attention is paid to lateral side-chain interactions in beta-sheets and between the turns of alpha-helices, as well as backbone hydrogen bonding. The force constants are optimized using contrastive divergence, a novel machine learning technique, from a data set of known structures. Using this approach, we demonstrate the applicability of the framework to the problem of reconstructing the overall protein fold for a number of commonly studied small proteins, based on only predicted secondary structure and contact map. For protein G and chymotrypsin inhibitor 2, we are able to reconstruct the secondary structure elements in atomic detail and the overall protein folds with a root mean-square deviation of <10 A. For cold-shock protein and the SH3 domain, we accurately reproduce the secondary structure elements and the topology of the 5-stranded beta-sheets, but not the barrel structure. The importance of high-quality secondary structure and contact map prediction is discussed. PMID:19486664

Podtelezhnikov, Alexei A; Wild, David L

2009-06-01

155

Gene Function Prediction Based on the Gene Ontology Hierarchical Structure  

PubMed Central

The information of the Gene Ontology annotation is helpful in the explanation of life science phenomena, and can provide great support for the research of the biomedical field. The use of the Gene Ontology is gradually affecting the way people store and understand bioinformatic data. To facilitate the prediction of gene functions with the aid of text mining methods and existing resources, we transform it into a multi-label top-down classification problem and develop a method that uses the hierarchical relationships in the Gene Ontology structure to relieve the quantitative imbalance of positive and negative training samples. Meanwhile the method enhances the discriminating ability of classifiers by retaining and highlighting the key training samples. Additionally, the top-down classifier based on a tree structure takes the relationship of target classes into consideration and thus solves the incompatibility between the classification results and the Gene Ontology structure. Our experiment on the Gene Ontology annotation corpus achieves an F-value performance of 50.7% (precision: 52.7% recall: 48.9%). The experimental results demonstrate that when the size of training set is small, it can be expanded via topological propagation of associated documents between the parent and child nodes in the tree structure. The top-down classification model applies to the set of texts in an ontology structure or with a hierarchical relationship. PMID:25192339

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

2014-01-01

156

Failure prediction of thin beryllium sheets used in spacecraft structures  

NASA Technical Reports Server (NTRS)

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

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

1991-01-01

157

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

ERIC Educational Resources Information Center

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

Lucas, Amand A.

2008-01-01

158

Methods for evaluating the predictive accuracy of structural dynamic models  

NASA Technical Reports Server (NTRS)

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

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

1990-01-01

159

Predicting fracture in micron-scale polycrystalline silicon MEMS structures.  

SciTech Connect

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

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

2010-09-01

160

Factors Influencing Progressive Failure Analysis Predictions for Laminated Composite Structure  

NASA Technical Reports Server (NTRS)

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

Knight, Norman F., Jr.

2008-01-01

161

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

NASA Astrophysics Data System (ADS)

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

Kasahara, Naoto

162

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

PubMed Central

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

2014-01-01

163

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

NASA Astrophysics Data System (ADS)

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

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

2013-12-01

164

Predicting the bifurcation structure of localized snaking patterns  

NASA Astrophysics Data System (ADS)

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

Makrides, Elizabeth; Sandstede, Björn

2014-02-01

165

Structure-Based Predictive model for Coal Char Combustion.  

SciTech Connect

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

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

1997-09-24

166

Protein structure prediction with local adjust tabu search algorithm  

PubMed Central

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

2014-01-01

167

How evolutionary crystal structure prediction works--and why.  

PubMed

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

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

2011-03-15

168

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

E-print Network

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

Das, Rhiju

169

Why Can't We Predict RNA Structure At Atomic Resolution?  

E-print Network

will hasten the development of atomic accuracy methods for modeling RNA structures without extensiveChapter 4 Why Can't We Predict RNA Structure At Atomic Resolution? Parin Sripakdeevong, Kyle the performance of current structure prediction algorithms. 4.1 RNA as a Model System Predicting the three

Das, Rhiju

170

Artificial Neural Networks and Hidden Markov Models for Predicting the Protein Structures: The Secondary Structure  

E-print Network

1 Artificial Neural Networks and Hidden Markov Models for Predicting the Protein Structures advice on the development of this project #12;2 Artificial Neural Networks and Hidden Markov Models learning methods: artificial neural networks (ANN) and hidden Markov models (HMM) (Rost 2002; Karplus et al

171

Application of protein structure alignments to iterated hidden Markov model protocols for structure prediction  

Microsoft Academic Search

BACKGROUND: One of the most powerful methods for the prediction of protein structure from sequence information alone is the iterative construction of profile-type models. Because profiles are built from sequence alignments, the sequences included in the alignment and the method used to align them will be important to the sensitivity of the resulting profile. The inclusion of highly diverse sequences

Eric D. Scheeff; Philip E. Bourne

2006-01-01

172

Lifetime Reliability Prediction of Ceramic Structures Under Transient Thermomechanical Loads  

NASA Technical Reports Server (NTRS)

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

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

2005-01-01

173

Simple neural substrate predicts complex rhythmic structure in duetting birds  

NASA Astrophysics Data System (ADS)

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

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

2005-09-01

174

Prediction of silicon-based layered structures for optoelectronic applications.  

PubMed

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

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

2014-11-12

175

Structural Acoustic Prediction and Interior Noise Control Technology  

NASA Technical Reports Server (NTRS)

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

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

2001-01-01

176

Optimizing Non-Decomposable Loss Functions in Structured Prediction  

PubMed Central

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

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

2012-01-01

177

PUDGE: a flexible, interactive server for protein structure prediction.  

PubMed

The construction of a homology model for a protein can involve a number of decisions requiring the integration of different sources of information and the application of different modeling tools depending on the particular problem. Functional information can be especially important in guiding the modeling process, but such information is not generally integrated into modeling pipelines. Pudge is a flexible, interactive protein structure prediction server, which is designed with these issues in mind. By dividing the modeling into five stages (template selection, alignment, model building, model refinement and model evaluation) and providing various tools to visualize, analyze and compare the results at each stage, we enable a flexible modeling strategy that can be tailored to the needs of a given problem. Pudge is freely available at http://wiki.c2b2.columbia.edu/honiglab_public/index.php/Software:PUDGE. PMID:20525783

Norel, Raquel; Petrey, Donald; Honig, Barry

2010-07-01

178

Engineering Property Prediction Tools for Tailored Polymer Composite Structures  

SciTech Connect

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

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

2009-12-23

179

High Precision Prediction of Functional Sites in Protein Structures  

PubMed Central

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

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

2014-01-01

180

An On/Off Lattice Approach to Protein Structure Prediction from Contact Maps  

E-print Network

. An important unsolved problem in structural bioinformat- ics is that of protein structure prediction (PSP consequence of their three-dimensional structure. Approaches to solve the PSP use protein models that range Introduction Protein structure prediction (PSP) is the problem of inferring the tertiary struc- ture

Passerini, Andrea

181

Structure-Based Predictive model for Coal Char Combustion.  

SciTech Connect

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

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

1997-06-25

182

A composite score for predicting errors in protein structure models  

PubMed Central

Reliable prediction of model accuracy is an important unsolved problem in protein structure modeling. To address this problem, we studied 24 individual assessment scores, including physics-based energy functions, statistical potentials, and machine learning–based scoring functions. Individual scores were also used to construct ?85,000 composite scoring functions using support vector machine (SVM) regression. The scores were tested for their abilities to identify the most native-like models from a set of 6000 comparative models of 20 representative protein structures. Each of the 20 targets was modeled using a template of <30% sequence identity, corresponding to challenging comparative modeling cases. The best SVM score outperformed all individual scores by decreasing the average RMSD difference between the model identified as the best of the set and the model with the lowest RMSD (?RMSD) from 0.63 Å to 0.45 Å, while having a higher Pearson correlation coefficient to RMSD (r = 0.87) than any other tested score. The most accurate score is based on a combination of the DOPE non-hydrogen atom statistical potential; surface, contact, and combined statistical potentials from MODPIPE; and two PSIPRED/DSSP scores. It was implemented in the SVMod program, which can now be applied to select the final model in various modeling problems, including fold assignment, target–template alignment, and loop modeling. PMID:16751606

Eramian, David; Shen, Min-yi; Devos, Damien; Melo, Francisco; Sali, Andrej; Marti-Renom, Marc A.

2006-01-01

183

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

PubMed Central

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

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

2014-01-01

184

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

E-print Network

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

de Gispert, Adrià

185

Failure prediction of thin beryllium sheets used in spacecraft structures  

NASA Technical Reports Server (NTRS)

The primary objective of this study is to develop a method for prediction of failure of thin beryllium sheets that undergo complex states of stress. Major components of the research include experimental evaluation of strength parameters for cross-rolled beryllium sheet, application of the Tsai-Wu failure criterion to plate bending problems, development of a high order failure criterion, application of the new criterion to a variety of structures, and incorporation of both failure criteria into a finite element code. A Tsai-Wu failure model for SR-200 sheet material is developed from available tensile data, experiments carried out by NASA on two circular plates, and compression and off-axis experiments performed in this study. The failure surface obtained from the resulting criterion forms an ellipsoid. By supplementing experimental data used in the the two-dimensional criterion and modifying previously suggested failure criteria, a multi-dimensional failure surface is proposed for thin beryllium structures. The new criterion for orthotropic material is represented by a failure surface in six-dimensional stress space. In order to determine coefficients of the governing equation, a number of uniaxial, biaxial, and triaxial experiments are required. Details of these experiments and a complementary ultrasonic investigation are described in detail. Finally, validity of the criterion and newly determined mechanical properties is established through experiments on structures composed of SR200 sheet material. These experiments include a plate-plug arrangement under a complex state of stress and a series of plates with an out-of-plane central point load. Both criteria have been incorporated into a general purpose finite element analysis code. Numerical simulation incrementally applied loads to a structural component that is being designed and checks each nodal point in the model for exceedance of a failure criterion. If stresses at all locations do not exceed the failure criterion, the load is increased and the process is repeated. Failure results for the plate-plug and clamped plate tests are accurate to within 2 percent.

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

1991-01-01

186

An evolutionary method for learning HMM structure: prediction of protein secondary structure  

PubMed Central

Background The prediction of the secondary structure of proteins is one of the most studied problems in bioinformatics. Despite their success in many problems of biological sequence analysis, Hidden Markov Models (HMMs) have not been used much for this problem, as the complexity of the task makes manual design of HMMs difficult. Therefore, we have developed a method for evolving the structure of HMMs automatically, using Genetic Algorithms (GAs). Results In the GA procedure, populations of HMMs are assembled from biologically meaningful building blocks. Mutation and crossover operators were designed to explore the space of such Block-HMMs. After each step of the GA, the standard HMM estimation algorithm (the Baum-Welch algorithm) was used to update model parameters. The final HMM captures several features of protein sequence and structure, with its own HMM grammar. In contrast to neural network based predictors, the evolved HMM also calculates the probabilities associated with the predictions. We carefully examined the performance of the HMM based predictor, both under the multiple- and single-sequence condition. Conclusion We have shown that the proposed evolutionary method can automatically design the topology of HMMs. The method reads the grammar of protein sequences and converts it into the grammar of an HMM. It improved previously suggested evolutionary methods and increased the prediction quality. Especially, it shows good performance under the single-sequence condition and provides probabilistic information on the prediction result. The protein secondary structure predictor using HMMs (P.S.HMM) is on-line available http://www.binf.ku.dk/~won/pshmm.htm. It runs under the single-sequence condition. PMID:17888163

Won, Kyoung-Jae; Hamelryck, Thomas; Prügel-Bennett, Adam; Krogh, Anders

2007-01-01

187

Structural failure prediction of quasi-brittle structures: Modeling and simulation  

Microsoft Academic Search

In order to avoid the loss of well-posedness in the post-localization range, some continuum damage theories introduce higher order gradients of the damage variable in the constitutive model. This paper discuss the possibility of structural failure prediction of quasi-brittle materials through a special kind of gradient-enhanced damage theory in which the material is considered to possess a substructure or microstructure.

Heraldo da Costa-Mattos; Stella Maris Pires-Domingues; Fernando Alves Rochinha

2009-01-01

188

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

NASA Astrophysics Data System (ADS)

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.

Talha, Mohammad; Ashokkumar, Chimpalthradi R.

2014-05-01

189

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

E-print Network

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

Joshi, T.

190

Prediction of a Structural Transition in the Hard Disk Fluid  

E-print Network

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

Jaroslaw Piasecki; Piotr Szymczak; John J. Kozak

2010-09-16

191

Predictive modeling of pedestal structure in KSTAR using EPED model  

NASA Astrophysics Data System (ADS)

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

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

2013-10-01

192

Predictive modeling of pedestal structure in KSTAR using EPED model  

SciTech Connect

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

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

2013-10-15

193

An Excel spreadsheet computer program combining algorithms for prediction of protein structural characteristics.  

PubMed

A program running on personal computers (either Apple Macintosh or PC, using Excel worksheets) for the prediction of some protein structural characteristics is reported. The program runs according to the Chou and Fasman algorithm, with some modifications, for secondary structure prediction. The program also incorporates several complementary analyses for secondary structure prediction to help the user in the decision-making process: rules for amino acid preferences in the N-cap and C-cap of alpha-helices; prediction of the protein structural class and search of sequential motifs related to secondary structure. Additional algorithms performed by the program are: prediction of domain boundaries, prediction of loops, prediction of the state of cysteines (reduced or in disulfide bridge), hydropathy profiles according to Kyte and Doolittle, Hoop and Woods, and flexibility plot according to Karplus and Schulz. PMID:7828064

Clotet, J; Cedano, J; Querol, E

1994-09-01

194

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

E-print Network

HAAD: A Quick Algorithm for Accurate Prediction of Hydrogen Atoms in Protein Structures Yunqi Li diffraction structures decreased by 26% and 11%, respectively. Furthermore, hydrogen atoms placed by HAAD have Algorithm for Accurate Prediction of Hydrogen Atoms in Protein Structures. PLoS ONE 4(8): e6701. doi:10

Zhang, Yang

195

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

E-print Network

). The problem of finding the MFE structure given a certain sequence has been conceptually solved provided pursued. There is convincing evidence showing that, as in NMR protein structure prediction, the secondaryTT2NE: a novel algorithm to predict RNA secondary structures with pseudoknots Michae¨ l Bon

Paris-Sud XI, Université de

196

Predicting the secondary structure of globular proteins using neural network models  

Microsoft Academic Search

We present a new method for predicting the secondary structure of globular proteins based on non-linear neural network models. Network models learn from existing protein structures how to predict the secondary structure of local sequences of amino acids. The average success rate of our method on a testing set of proteins non-homologous with the corresponding training set was 643% on

Ning Qian; Terrence J. Sejnowski

1988-01-01

197

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

ERIC Educational Resources Information Center

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

Ellington, Roni; Wachira, James; Nkwanta, Asamoah

2010-01-01

198

STRUCTURE-BASED PREDICTIVE MODEL FOR COAL CHAR COMBUSTION  

SciTech Connect

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

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

1998-06-04

199

STRUCTURE-BASED PREDICTIVE MODEL FOR COAL CHAR COMBUSTION  

SciTech Connect

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

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

1998-09-11

200

Spatial structure and potential predictability of summer precipitation in Ethiopia  

NASA Astrophysics Data System (ADS)

Variations in sea surface temperature (SST) and atmospheric circulation on both regional and global scales substantially influence interannual variability of precipitation in Ethiopia and the surrounding countries. Previous studies have revealed links between ENSO and summer rainfall in East Africa. As this region has been frequently affected by severe droughts during the last few decades, most recently in 2011, improving understanding of these influences is crucial for developing prediction methods for seasonal precipitation variability. More than half of the Ethiopian precipitation occurs during the Kiremt season (JJAS), which is therefore closely related to drought events. In the northwestern part the Kiremt rains are most prominent whereas the Belg precipitation (FMAM) is important for the southeastern part. We here objectively define homogenous rainfall regions in East Africa and analyse links between the rainfall in these regions with global SST. PCA of the gridded GPCP dataset (1979-2010), which includes station records and satellite data, reveals a dipole structure with two precipitation regimes divided geographically by the Ethiopian Rift Valley. We will show the response of precipitation in these regions to changes in Pacific SST, using the HadSST2 dataset. First results of concurrent relationships between Ethiopian precipitation (for the total over the whole country and for the northwestern part) and SST are consistent with an ENSO signal with positive correlation in the north- and southwestern Pacific, as well as negative correlation in the central eastern Pacific. Further investigations will also include lagged correlations. These findings corroborate the results of previous studies but extend them by using cross-validated principal component multiple linear regression (PC-MLR) models to estimate NW-, SE- and total Ethiopian rainfall from Pacific SST. It has already been shown by Eden et al. (see Poster in Session CL3.3/NP5.4, EGU2012-10302) that spring variability of an individual precipitation record from Addis Ababa can be partly estimated from Pacific SST. Considering our findings in seasonal prediction models may improve drought forecasting across East Africa.

Wild, S.; Eden, J. M.; Widmann, M.; Leckebusch, G. C.

2012-04-01

201

Prediction of three social cognitive-motivational structure types.  

PubMed

Previously, using interviews from Baumrind's longitudinal study, three cognitive-motivational structures (CMSs) were predicted in 68 adolescents from caregiving settings and from the CMS types of their mothers, based on the mothers' interviews elicited six years earlier. CMS theory proposes that during Piaget's Concrete Operational Period care-receiving influences the child's adoption of a social cognitive style, which corresponds to one of Piaget's stages of cognitive development. One who is classified as an Operational experiences the caregiving setting as tuned to the child's long-term interests, becomes focused on function and control of function and grasps the distinctions between and gradations of social attributes. One classified as future Intuitive experiences the caregiving as insufficient or unreliable and becomes focused on getting and having, and assesses social situations based on current striking dimensions. A person classified as being future Symbolic experiences the caregiving as out of tune with the self or the world, becomes focused on identity and emotional closeness, and may define self or object by a single attribute. This previous study did not distinguish between the influence of caregiving (including mothers' CMS) on the formation of adolescent CMS type and the possible constancy of CMS type from ages 9 to 15 years. The current study was designed to distinguish between these two possibilities, using data from 67 of the same mothers. Mothers' interviews were purged of descriptions of her child's behavior. Another interview was composed of the purged descriptions of child behavior. This was also done for interviews held when the child was 4 and 15 as well as at 9. From interviews with descriptions of child behavior purged, mother's CMS type at the child's age of 4 and 9 yr. agreed with her adolescent's previously assigned CMS type (p<.05), and caregiving setting at 9 years predicted the adolescent's CMS type (p<.05). From interviews composed of descriptions of only the child's behavior, adolescent CMS type agreed with previously assigned adolescent CMS type (p<.01). Findings were consonant with the idea that CMS type formation is influenced at about Age 9 and sufficiently established to be recognized at Age 15. PMID:11783566

Malerstein, A J; Ahern, M M; Pulos, S

2001-10-01

202

Theoretical prediction of electronic structures of fully ?-conjugated zinc oligoporphyrins with curved surface structures  

NASA Astrophysics Data System (ADS)

A theoretical prediction of the electronic structures of fully ?-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-, ?-?-, and ?-?-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 ?-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.

Yamaguchi, Yoichi

2004-05-01

203

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

NASA Astrophysics Data System (ADS)

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

Davidson, Noel E.; Ma, Yimin

2012-07-01

204

Nucleosome structure incorporated histone acetylation site prediction in arabidopsis thaliana  

Microsoft Academic Search

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

Chen Zhao; Hui Liu; Jiang Li; Youping Deng; Tieliu Shi

2010-01-01

205

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

E-print Network

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

Mag Washietl, Stefan

206

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...

207

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...

208

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...

209

Automated functional classification of experimental and predicted protein structures  

Microsoft Academic Search

Background: Proteins that are similar in sequence or structure may perform different functions in nature. In such cases, function cannot be inferred from sequence or structural similarity. Results: We analyzed experimental structures belonging to the Structural Classification of Proteins (SCOP) database and showed that about half of them belong to multi-functional fold families for which protein similarity alone is not

Kai Wang; Ram Samudrala

2006-01-01

210

An electrostatic model of B-DNA for its stability against unwinding.  

PubMed

In single molecule experiments Smith et al. (Science 271 (1996) 795) have unwound the B-DNA helix by stretching it in an aqueous salt solution. They found that the stretching force required for the transition decreases significantly with lowering of the salt concentration. We show that the observed salt effect is consistent with a uniformly charged cylinder model of DNA surrounded by a Poisson-Boltzmann ionic atmosphere. We also derive a simple connection between the sharpness of the center part of the transition and its cooperativity in terms of an average block size of base pairs that unwinds or rewinds. PMID:10048891

Stigter, D

1998-12-14

211

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

E-print Network

studies of individuals at risk of AD. In this study we combine (i) a novel assessment. Optimizing such MRI-based biomarkers for detection and prediction of AD as being valuable tools when designing therapeutic studies of individuals at risk

Boyer, Edmond

212

Aircraft Structural Mass Property Prediction Using Conceptual-Level Structural Analysis  

NASA Technical Reports Server (NTRS)

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

Sexstone, Matthew G.

1998-01-01

213

Aircraft Structural Mass Property Prediction Using Conceptual-Level Structural Analysis  

NASA Technical Reports Server (NTRS)

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

Sexstone, Matthew G.

1998-01-01

214

The Predictive Validity of the Structured Assessment of Violence Risk in Youth in Secondary Educational Settings  

Microsoft Academic Search

Current developments in violence risk assessment warrant consideration for use within educational settings. Using a structured professional judgment (SPJ) model, the present study investigated the predictive validity of the Structured Assessment of Violence in Youth (SAVRY) within educational settings. The predictive accuracy of the SAVRY scales was assessed using a retrospective file review to gather data on 87 adolescents ranging

Mark R. McGowan; Robert A. Horn; Ramona N. Mellott

2011-01-01

215

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

E-print Network

Video Article A Protocol for Computer-Based Protein Structure and Function Prediction Ambrish Roy1,2, Dong Xu1, Jonathan Poisson1, Yang Zhang1,2 Correspondence to: Yang Zhang at zhng@umich.edu URL: http://www.jove.com/details.php., Zhang, Y. A Protocol for Computer-Based Protein Structure and Function Prediction. J. Vis. Exp. (57), e

Zhang, Yang

216

Prediction of genetic structure in eukaryotic DNA using reference point logistic regression and sequence alignment  

Microsoft Academic Search

Motivation: Current software tools are moderately ef- fective in predicting genetic structure (exons, introns, intergenic regions, and complete genes) from raw DNA sequence data. Improvements in accuracy and speed are needed to deal with the increasing volume of data from large scale sequencing projects. Results: We present a two-stage computer program to predict genetic structure in eukaryotic DNA. The first

Peter M. Hooper; Haiyan Zhang; David S. Wishart

2000-01-01

217

Tertiary Structure Predictions on a Comprehensive Benchmark of Medium to Large Size Proteins  

E-print Network

or equal to 300 residues that have corresponding multiple NMR structures in the Protein Data Bank, 20% of the models generated by TASSER are closer to the NMR structure centroid than the farthest individual NMR The protein structure prediction problem, that is, deducing the tertiary structure of a protein from its

Zhang, Yang

218

Theoretical and experimental predictions of the hydroelastic response of a very large floating structure in waves  

Microsoft Academic Search

A prediction method for the hydroelastic behavior of a very large box-shaped flexible structure in regular waves is proposed. The structure considered is representative of such structures as a floating international airport and thus the horizontal dimensions are expected to be as large as several kilometers in both length and width. In the analysis, the structure is divided into a

Hiroshi Kagemoto; Masataka Fujino; Motohiko Murai

1998-01-01

219

A Contact-assisted Approach to Protein Structure Prediction and Its Assessment in CASP10  

E-print Network

A Contact-assisted Approach to Protein Structure Prediction and Its Assessment in CASP10 Badri residue- residue contact map and then construct a full 3D structure from the contact-map. Instead of building a structure purely from contacts information, here we describe a contact- assisted structure

Cheng, Jianlin Jack

220

Vfold: A Web Server for RNA Structure and Folding Thermodynamics Prediction  

PubMed Central

Background The ever increasing discovery of non-coding RNAs leads to unprecedented demand for the accurate modeling of RNA folding, including the predictions of two-dimensional (base pair) and three-dimensional all-atom structures and folding stabilities. Accurate modeling of RNA structure and stability has far-reaching impact on our understanding of RNA functions in human health and our ability to design RNA-based therapeutic strategies. Results The Vfold server offers a web interface to predict (a) RNA two-dimensional structure from the nucleotide sequence, (b) three-dimensional structure from the two-dimensional structure and the sequence, and (c) folding thermodynamics (heat capacity melting curve) from the sequence. To predict the two-dimensional structure (base pairs), the server generates an ensemble of structures, including loop structures with the different intra-loop mismatches, and evaluates the free energies using the experimental parameters for the base stacks and the loop entropy parameters given by a coarse-grained RNA folding model (the Vfold model) for the loops. To predict the three-dimensional structure, the server assembles the motif scaffolds using structure templates extracted from the known PDB structures and refines the structure using all-atom energy minimization. Conclusions The Vfold-based web server provides a user friendly tool for the prediction of RNA structure and stability. The web server and the source codes are freely accessible for public use at “http://rna.physics.missouri.edu”. PMID:25215508

Xu, Xiaojun; Zhao, Peinan; Chen, Shi-Jie

2014-01-01

221

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

PubMed

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 prediction. PMID:24452438

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

2014-03-01

222

Automated Detection of Eruptive Structures for Solar Eruption Prediction  

NASA Astrophysics Data System (ADS)

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

Georgoulis, Manolis K.

2012-07-01

223

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

PubMed

We offer a general approach to predicting public compliance with emergency recommendations. It begins with a formal risk assessment of an anticipated emergency, whose parameters include factors potentially affecting and affected by behavior, as identified by social science research. Standard procedures are used to elicit scientific experts' judgments regarding these behaviors and dependencies, in the context of an emergency scenario. Their judgments are used to refine the model and scenario, enabling local emergency coordinators to predict the behavior of citizens in their area. The approach is illustrated with a case study involving a radiological dispersion device (RDD) exploded in downtown Pittsburgh, PA. Both groups of experts (national and local) predicted approximately 80-90% compliance with an order to evacuate workplaces and 60-70% compliance with an order to shelter in place at home. They predicted 10% lower compliance for people asked to shelter at the office or to evacuate their homes. They predicted 10% lower compliance should the media be skeptical, rather than supportive. They also identified preparatory policies that could improve public compliance by 20-30%. We consider the implications of these results for improving emergency risk assessment models and for anticipating and improving preparedness for disasters, using Hurricane Katrina as a further case in point. PMID:17184405

Dombroski, Matt; Fischhoff, Baruch; Fischbeck, Paul

2006-12-01

224

TOPITS: Threading one-dimensional predictions into three-dimensional structures  

SciTech Connect

Homology modelling, currently, is the only theoretical tool which can successfully predict protein 3D structure. As 3D structure is conserved in sequence families, homology modelling allows to predict 3D structure for 20% of SWISSPROT. 20% of the proteins in PDB are remote homologues to another PDB protein. Threading techniques attempt to predict such remote homologues based on sequence information. Here, a new threading method is presented. First, for a list of PDB proteins, 3D structure was projected onto ID strings of secondary structure and relative solvent accessibility. Men, secondary structure and accessibility were predicted by neural network systems (PHD). Finally, the predicted and observed ID strings were aligned by dynamic programming. The resulting alignment was used to detect remote 3D homologues. Four results stand out. Firstly, even for an optimal prediction (assignment based on known structure), only about half the hits that ranked above a given threshold were correctly identified as remote homologues; only about 25% of the first bits were correct. Secondly, real predictions (PHD) were not much worse: about 20% of the first hits were correct. Thirdly, a simple filtering procedure improved prediction performance to about 30% correct first hits. The correct hit ranked among the first three for more than 23 out of 46 cases. Fourthly, the combination of the ID threading and sequence alignments markedly improved the performance of the threading method TOPITS for some selected cases.

Rost, B. [EMBL, Heidelberg (Germany)

1995-12-31

225

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

PubMed Central

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

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

2015-01-01

226

Predicting Gene Structures from Multiple RT-PCR Tests  

NASA Astrophysics Data System (ADS)

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

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

227

Knowledge base and neural network approach for protein secondary structure prediction.  

PubMed

Protein structure prediction is of great relevance given the abundant genomic and proteomic data generated by the genome sequencing projects. Protein secondary structure prediction is addressed as a sub task in determining the protein tertiary structure and function. In this paper, a novel algorithm, KB-PROSSP-NN, which is a combination of knowledge base and modeling of the exceptions in the knowledge base using neural networks for protein secondary structure prediction (PSSP), is proposed. The knowledge base is derived from a proteomic sequence-structure database and consists of the statistics of association between the 5-residue words and corresponding secondary structure. The predicted results obtained using knowledge base are refined with a Backpropogation neural network algorithm. Neural net models the exceptions of the knowledge base. The Q3 accuracy of 90% and 82% is achieved on the RS126 and CB396 test sets respectively which suggest improvement over existing state of art methods. PMID:25128736

Patel, Maulika S; Mazumdar, Himanshu S

2014-11-21

228

Probabilistic predictions of penetrating injury to anatomic structures.  

PubMed Central

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

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

1997-01-01

229

Electronic polarization stabilizes tertiary structure prediction of HP-36.  

PubMed

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

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

2014-04-01

230

Multivariable predictive control for vibrating structures: An application  

Microsoft Academic Search

This paper proposes the use of Model Predictive Control (MPC) to control a fast mechanical system. In particular an MPC strategy is applied to a laboratory flexible arm to perform a fast positioning of the end-effector with limited oscillations during the maneuver. The on-line implementation of a fast MPC is obtained with an ad hoc platform based on C++ and

L. Bossi; C. Rottenbacher; G. Mimmi; L. Magni

2011-01-01

231

OPTIMAL CHARACTERIZATION OF STRUCTURE FOR PREDICTION OF PROPERTIES  

EPA Science Inventory

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

232

Predicting Emergency Evacuation and Sheltering Behavior: A Structured Analytical Approach  

Microsoft Academic Search

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

Matt Dombroski; Baruch Fischhoff; Paul Fischbeck

2006-01-01

233

Prediction of Harmful Human Health Effects of Chemicals from Structure  

NASA Astrophysics Data System (ADS)

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

Cronin, Mark T. D.

234

Evaluating the accuracy of SHAPE-directed RNA secondary structure predictions  

PubMed Central

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

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

2013-01-01

235

Predicting Earthquake Response of Civil Structures from Ambient Noise  

Microsoft Academic Search

Increased monitoring of civil structures for response to earthquake motions is fundamental for reducing seismic hazard. Seismic monitoring is difficult because typically only a few useful, intermediate to large earthquakes occur per decade near instrumented structures. Here we demonstrate that the impulse response function (IRF) of a multi-story building can be generated from ambient noise. Estimated shear-wave velocity, attenuation values,

G. Prieto; J. F. Lawrence; A. I. Chung; M. D. Kohler

2009-01-01

236

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

PubMed Central

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

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

2014-01-01

237

Protein-specific prediction of mRNA binding using RNA sequences, binding motifs and predicted secondary structures  

PubMed Central

Background RNA-binding proteins interact with specific RNA molecules to regulate important cellular processes. It is therefore necessary to identify the RNA interaction partners in order to understand the precise functions of such proteins. Protein-RNA interactions are typically characterized using in vivo and in vitro experiments but these may not detect all binding partners. Therefore, computational methods that capture the protein-dependent nature of such binding interactions could help to predict potential binding partners in silico. Results We have developed three methods to predict whether an RNA can interact with a particular RNA-binding protein using support vector machines and different features based on the sequence (the Oli method), the motif score (the OliMo method) and the secondary structure (the OliMoSS method). We applied these approaches to different experimentally-derived datasets and compared the predictions with RNAcontext and RPISeq. Oli outperformed OliMoSS and RPISeq, confirming our protein-specific predictions and suggesting that tetranucleotide frequencies are appropriate discriminative features. Oli and RNAcontext were the most competitive methods in terms of the area under curve. A precision-recall curve analysis achieved higher precision values for Oli. On a second experimental dataset including real negative binding information, Oli outperformed RNAcontext with a precision of 0.73 vs. 0.59. Conclusions Our experiments showed that features based on primary sequence information are sufficiently discriminating to predict specific RNA-protein interactions. Sequence motifs and secondary structure information were not necessary to improve these predictions. Finally we confirmed that protein-specific experimental data concerning RNA-protein interactions are valuable sources of information that can be used for the efficient training of models for in silico predictions. The scripts are available upon request to the corresponding author. PMID:24780077

2014-01-01

238

Finite Element Based HWB Centerbody Structural Optimization and Weight Prediction  

NASA Technical Reports Server (NTRS)

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

Gern, Frank H.

2012-01-01

239

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

NASA Technical Reports Server (NTRS)

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

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

1992-01-01

240

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

PubMed Central

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

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

2013-01-01

241

Link Prediction based on Structural Properties of Online Social Networks  

Microsoft Academic Search

Question-Answering Bulletin Boards (QABB), such as Yahoo! Answers and Windows Live QnA, are gaining popularity recently. Questions\\u000a are submitted on QABB and let somebody in the internet answer them. Communications on QABB connect users, and the overall\\u000a connections can be regarded as a social network. If the evolution of social networks can be predicted, it is quite useful\\u000a for encouraging

Tsuyoshi Murata; Sakiko Moriyasu

2008-01-01

242

Computational prediction of coiled-coil interaction structure specificity  

E-print Network

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

Gutwin, Karl N. (Karl Nickolai)

2009-01-01

243

Structure prediction methods (2D and 3D)  

E-print Network

in bioinformatics today actually are concerned with structure analysis." "The origins of bioinformatics actually lie domains ­ Some domain detection tools make use of this pattern, looking for covariation between positions

Sjölander, Kimmen

244

Metabolic pathway predictions for metabolomics: a molecular structure matching approach.  

PubMed

Metabolic pathways are composed of a series of chemical reactions occurring within a cell. In each pathway, enzymes catalyze the conversion of substrates into structurally similar products. Thus, structural similarity provides a potential means for mapping newly identified biochemical compounds to known metabolic pathways. In this paper, we present TrackSM, a cheminformatics tool designed to associate a chemical compound to a known metabolic pathway based on molecular structure matching techniques. Validation experiments show that TrackSM is capable of associating 93% of tested structures to their correct KEGG pathway class and 88% to their correct individual KEGG pathway. This suggests that TrackSM may be a valuable tool to aid in associating previously unknown small molecules to known biochemical pathways and improve our ability to link metabolomics, proteomic, and genomic data sets. TrackSM is freely available at http://metabolomics.pharm.uconn.edu/?q=Software.html . PMID:25668446

Hamdalla, Mai A; Rajasekaran, Sanguthevar; Grant, David F; M?ndoiu, Ion I

2015-03-23

245

Practical theories for service life prediction of critical aerospace structural components  

NASA Technical Reports Server (NTRS)

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

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

1992-01-01

246

ANALYTICAL PREDICTIONS OF THE AIR GAP RESPONSE OF FLOATING STRUCTURES Lance Manuel1  

E-print Network

ANALYTICAL PREDICTIONS OF THE AIR GAP RESPONSE OF FLOATING STRUCTURES Lance Manuel1 , Bert Sweetman interest. While air gap modeling is of interest both for fixed and floating structures, it is particularly because the heave, pitch, and roll motions of the floating structure are generally coupled. Moreover

Manuel, Lance

247

The protein structure prediction problem could be solved using the current PDB library  

E-print Network

The protein structure prediction problem could be solved using the current PDB library Yang Zhang of the structures in the current Protein Data Bank (PDB) library for use in full-length model construction-size proteins that cover the PDB at the level of 35% sequence identity and identify templates by structure

Zhang, Yang

248

Available online at www.sciencedirect.com Progress and challenges in protein structure prediction  

E-print Network

Available online at www.sciencedirect.com Progress and challenges in protein structure prediction Yang Zhang Depending on whether similar structures are found in the PDB library, the protein structure and conformational search. Address Center for Bioinformatics and Department of Molecular Biosciences, University

Zhang, Yang

249

Understanding predictive uncertainty in hydrologic modeling: The challenge of identifying input and structural errors  

Microsoft Academic Search

Meaningful quantification of data and structural uncertainties in conceptual rainfall-runoff modeling is a major scientific and engineering challenge. This paper focuses on the total predictive uncertainty and its decomposition into input and structural components under different inference scenarios. Several Bayesian inference schemes are investigated, differing in the treatment of rainfall and structural uncertainties, and in the precision of the priors

Benjamin Renard; Dmitri Kavetski; George Kuczera; Mark Thyer; Stewart W. Franks

2010-01-01

250

Magnetic study of the electronic states of B -DNA and M -DNA doped with metal ions  

NASA Astrophysics Data System (ADS)

The magnetic properties of the pristine and metal ion doped deoxyribonucleic acid (DNA) of salmon are investigated with electron paramagnetic resonance (EPR), superconducting quantum interference device and energy dispersive x-ray flourescence spectroscopy. Purified salmon DNA gives intrinsically no EPR signal, which is consistent with DNA being a semiconductor, but not with DNA having metallic or superconducting properties as reported previously. Several kinds of divalent ions (Zn, Mn, Ca, …) are used as dopants, resulting in no substantial EPR signal except in the case of Mn. This leads to the conclusion that a metal ion counterbalances two phosphate anions instead of Na counterions in B -DNA, which contradicts the metallic behavior reported previously [A. Rakitin , Phys. Rev. Lett. 86, 3670 (2001)].

Mizoguchi, Kenji; Tanaka, Shunsuke; Ogawa, Tasuku; Shiobara, Naofumi; Sakamoto, Hirokazu

2005-07-01

251

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

PubMed Central

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

2012-01-01

252

An adaptive genetic algorithm for crystal structure prediction  

SciTech Connect

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.

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

2013-10-31

253

An adaptive genetic algorithm for crystal structure prediction.  

PubMed

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. PMID:24351274

Wu, S Q; Ji, M; Wang, C Z; Nguyen, M C; Zhao, X; Umemoto, K; Wentzcovitch, R M; Ho, K M

2014-01-22

254

RNA secondary structure prediction from sequence alignments using a network of k-nearest neighbor classifiers  

PubMed Central

We present a machine learning method (a hierarchical network of k-nearest neighbor classifiers) that uses an RNA sequence alignment in order to predict a consensus RNA secondary structure. The input to the network is the mutual information, the fraction of complementary nucleotides, and a novel consensus RNAfold secondary structure prediction of a pair of alignment columns and its nearest neighbors. Given this input, the network computes a prediction as to whether a particular pair of alignment columns corresponds to a base pair. By using a comprehensive test set of 49 RFAM alignments, the program KNetFold achieves an average Matthews correlation coefficient of 0.81. This is a significant improvement compared with the secondary structure prediction methods PFOLD and RNAalifold. By using the example of archaeal RNase P, we show that the program can also predict pseudoknot interactions. PMID:16495232

BINDEWALD, ECKART; SHAPIRO, BRUCE A.

2006-01-01

255

Predicting RNA secondary structure by the comparative approach: how to select the homologous sequences  

PubMed Central

Background The secondary structure of an RNA must be known before the relationship between its structure and function can be determined. One way to predict the secondary structure of an RNA is to identify covarying residues that maintain the pairings (Watson-Crick, Wobble and non-canonical pairings). This "comparative approach" consists of identifying mutations from homologous sequence alignments. The sequences must covary enough for compensatory mutations to be revealed, but comparison is difficult if they are too different. Thus the choice of homologous sequences is critical. While many possible combinations of homologous sequences may be used for prediction, only a few will give good structure predictions. This can be due to poor quality alignment in stems or to the variability of certain sequences. This problem of sequence selection is currently unsolved. Results This paper describes an algorithm, SSCA, which measures the suitability of sequences for the comparative approach. It is based on evolutionary models with structure constraints, particularly those on sequence variations and stem alignment. We propose three models, based on different constraints on sequence alignments. We show the results of the SSCA algorithm for predicting the secondary structure of several RNAs. SSCA enabled us to choose sets of homologous sequences that gave better predictions than arbitrarily chosen sets of homologous sequences. Conclusion SSCA is an algorithm for selecting combinations of RNA homologous sequences suitable for secondary structure predictions with the comparative approach. PMID:18045491

Engelen, Stéfan; Tahi, Fariza

2007-01-01

256

Prediction for Nonabelian Fine Structure Constants from Multicriticality  

E-print Network

In developing a model for predicting the nonabelian gauge coupling constants we argue for the phenomenological validity of a ``principle of multiple point criticality''. This is supplemented with the assumption of an ``(grand) anti-unified'' gauge group $SMG^{N_{gen.}}\\sim U(1)^{N_{gen.}}\\times SU(2)^{N_{gen.}}\\times SU(3)^{N_{gen.}}$ that, at the Planck scale, breaks down to the diagonal subgroup. Here $N_{gen}$ is the number of generations which is assumed to be 3. According to this ``multiple point criticality principle'', the Planck scale experimental couplings correspond to multiple point couplings of the bulk phase transition of a lattice gauge theory (with gauge group $SMG^{N_{gen.}}$). Predictions from this principle agree with running nonabelian couplings (after an extrapolation to the Planck scale using the assumption of a ``desert'') to an accuracy of 7\\%. As an explanation for the existence of the multiple point, a speculative model using a glassy lattice gauge theory is presented.

D. L. Bennett H. B. Nielsen

1993-11-19

257

BRIEF REPORTS Structural Resemblance to Emotional Expressions Predicts Evaluation of  

E-print Network

the hypothesis that these inferences are driven in part by structural resemblance to emotional expressions valence resemble happiness, faces that are perceived to have negative valence resemble disgust and fear this hypothesis, emotion recognition systems, which typically extract accurate information about a person

Todorov, Alex

258

Acoustic fatigue life prediction for non-linear structures  

NASA Technical Reports Server (NTRS)

Using an approach based on a time domain analysis, a method of equivalent linearization is applied for an estimation of the strain response of complex nonlinear structures having nearly arbitrary complexity. Fatigue lives estimated for a nonlinear beam with random excitation using the approximate method were compared with results obtained using a conventional numerical simulation, yielding nearly identical results.

Sun, J. Q.; Miles, R. N.

1991-01-01

259

A Structural Equation Model for Predicting Business Student Performance  

ERIC Educational Resources Information Center

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

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

2008-01-01

260

Brain Structure Predicts the Learning of Foreign Speech Sounds  

E-print Network

). The acoustic parameters that critically distin- guish these phonemes are in the first 30--50 ms of the sounds rapidly changing information. In previous work, we showed that nonnative listeners who are faster of structural difference in this region is modulated by the proficiency attained and the age at acquisition

Pallier, Christophe

261

IMPORTANCE OF SECONDARY STRUCTURE ELEMENTS FOR PREDICTION OF GO ANNOTATIONS  

E-print Network

Bank (PDB) [22] association file, which contains only the assignments for the proteins present in the PDB database. To be able to fetch sequence and structure information from PDB, we used the PDB. To remove sequence homologs, PDB's scheme is applied. PDB provides several clusterings of proteins generated

Cataltepe, Zehra

262

Predictive modelling of structure evolution in sandbox experiments  

Microsoft Academic Search

In this paper a computational approach is presented that is able to forward model complex structural evolution with multiple intersecting faults that exhibit large relative movement. The approach adopts the Lagrangian method, complemented by robust and efficient automated adaptive meshing techniques, a constitutive model based on critical state concepts and global energy dissipation regularized by inclusion of fracture energy in

A. J. L. Crook; S. M. Willson; J. G. Yu; D. R. J. Owen

2006-01-01

263

Prediction of Complete Gene Structures in Human Genomic DNA  

Microsoft Academic Search

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

Christopher B. Burge; Samuel Karlin

1997-01-01

264

Memoir: template-based structure prediction for membrane proteins  

PubMed Central

Membrane proteins are estimated to be the targets of 50% of drugs that are currently in development, yet we have few membrane protein crystal structures. As a result, for a membrane protein of interest, the much-needed structural information usually comes from a homology model. Current homology modelling software is optimized for globular proteins, and ignores the constraints that the membrane is known to place on protein structure. Our Memoir server produces homology models using alignment and coordinate generation software that has been designed specifically for transmembrane proteins. Memoir is easy to use, with the only inputs being a structural template and the sequence that is to be modelled. We provide a video tutorial and a guide to assessing model quality. Supporting data aid manual refinement of the models. These data include a set of alternative conformations for each modelled loop, and a multiple sequence alignment that incorporates the query and template. Memoir works with both ?-helical and ?-barrel types of membrane proteins and is freely available at http://opig.stats.ox.ac.uk/webapps/memoir. PMID:23640332

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

2013-01-01

265

Probabilistic Approaches to Predicting the Secondary Structure of Proteins  

Microsoft Academic Search

Today's increasingly unaffordable medical treatment forces genomic research to have far-reaching consequences. Most members of the public do not realize that the genetic sequence does not only encode information about hereditary make-up, but that it also contains the necessary blueprints for the structural formation of essential proteins. As the central dogma of molecular biology declares, DNA is transcribed into RNA,

Roopal Sampat

266

Advanced inpainting-based macroblock prediction with regularized structure propagation in video compression  

Microsoft Academic Search

In this paper, we propose an optimized inpainting-based macroblock (MB) prediction mode (IP-mode) in the state-of-the-art H.264\\/AVC video compression engine, and investigate a natural extension of structured sparsity over the ordered Belief Propagation (BP) inference in inpainting-based prediction. The IP-mode is regularized by a global spatio-temporal consistency between the predicted content and the co-located known texture, and could be adopted

Yang Xu; Hongkai Xiong

2010-01-01

267

Multivariable neural network predictive control based on variable structure objective optimization  

Microsoft Academic Search

A special single neural network predictive control algorithm aimed at practical application of specific large time-delay nonlinear system is presented. This algorithm is based on a variable structure objective optimization controller to decouple the multiple control variables of the system. Consequently, the multiple single neural network predictive controller with a multi-step-ahead differential predictive cost objective function can realize the multivariable

Yang Peng; Li Lina; Liu Pinjie; Zhang Yan

2008-01-01

268

A Historical Perspective and Overview of Protein Structure Prediction  

NASA Astrophysics Data System (ADS)

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

Wooley, John C.; Ye, Yuzhen

269

Real-time nondestructive evaluation of airframe structures for health monitoring and residual life prediction  

Microsoft Academic Search

The paper presents real-time non-destructive evaluation (NDE) of fatigue crack damage using ultrasonic sensing. The damage sensing system is suitable for health monitoring and residual life prediction in both aging and new aircraft structures

Eric Keller; Asok Ray

2001-01-01

270

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,...

271

Neural Network Prediction of Reduced Ion Mobility of Amino Acid Based on Molecular Structure  

NASA Astrophysics Data System (ADS)

We present a new input feature mapping technique which is based on Riemannian metric tensor to enhance the neural network learning capability for predicting the reduced ion mobility based on the molecular structure for NASA remote applications.

Duong, T. A.; Liu, D.; Kanik, I.

2006-03-01

272

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

NASA Technical Reports Server (NTRS)

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

Ko, William L.; Chen, Tony

2006-01-01

273

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

E-print Network

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

Cheng, Jianlin Jack

274

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

NASA Technical Reports Server (NTRS)

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.

Schonberg, William P.

1993-01-01

275

Prediction of structural response to large earthquakes by using recordings from smaller earthquakes  

USGS Publications Warehouse

The feasibility of predicting structural response to large earthquakes by using recorded responses from collocated smaller earthquakes is investigated. Records from large earthquakes can be approximated as linear combinations of records from smaller earthquakes. Two methods are introduced to predict structural response to a large earthquake by using the recorded response to a smaller earthquake. The accuracy of the methods are tested by applying them to data from a highway overpass.

Safak, Erdal

1994-01-01

276

GeneSeqer add PlantGDB: gene structure prediction in plant genomes  

Microsoft Academic Search

The GeneSeqer@PlantGDB Web server (http:\\/\\/ www.plantgdb.org\\/cgi-bin\\/GeneSeqer.cgi) provides a gene structure prediction tool tailored for applica- tions to plant genomic sequences. Predictions are based on spliced alignment with source-native ESTs and full-length cDNAs or non-native probes derived from putative homologous genes. The tool is illustrated with applications to refinement of current gene structure annotation and de novo annotation of draft genomic

Shannon D. Schlueter; Qunfeng Dong; Volker Brendel

2003-01-01

277

The structure of evaporating and combusting sprays: Measurements and predictions  

NASA Technical Reports Server (NTRS)

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

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

1984-01-01

278

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

NASA Technical Reports Server (NTRS)

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

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

1978-01-01

279

Structural Damage Prediction and Analysis for Hypervelocity Impacts: Handbook  

NASA Technical Reports Server (NTRS)

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

Elfer, N. C.

1996-01-01

280

Social Structure Predicts Genital Morphology in African Mole-Rats  

Microsoft Academic Search

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

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

2009-01-01

281

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

PubMed

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

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

2014-06-01

282

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

PubMed

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

Churkin, Alexander; Weinbrand, Lina; Barash, Danny

2015-01-01

283

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

PubMed Central

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

Dowell, Robin D; Eddy, Sean R

2004-01-01

284

Structure Based Predictive Model for Coal Char Combustion  

SciTech Connect

This unique collaborative project has taken a very fundamental look at the origin of structure, and combustion reactivity of coal chars. It was a combined experimental and theoretical effort involving three universities and collaborators from universities outside the U.S. and from U.S. National Laboratories and contract research companies. The project goal was to improve our understanding of char structure and behavior by examining the fundamental chemistry of its polyaromatic building blocks. The project team investigated the elementary oxidative attack on polyaromatic systems, and coupled with a study of the assembly processes that convert these polyaromatic clusters to mature carbon materials (or chars). We believe that the work done in this project has defined a powerful new science-based approach to the understanding of char behavior. The work on aromatic oxidation pathways made extensive use of computational chemistry, and was led by Professor Christopher Hadad in the Department of Chemistry at Ohio State University. Laboratory experiments on char structure, properties, and combustion reactivity were carried out at both OSU and Brown, led by Principle Investigators Joseph Calo, Robert Essenhigh, and Robert Hurt. Modeling activities were divided into two parts: first unique models of crystal structure development were formulated by the team at Brown (PI'S Hurt and Calo) with input from Boston University and significant collaboration with Dr. Alan Kerstein at Sandia and with Dr. Zhong-Ying chen at SAIC. Secondly, new combustion models were developed and tested, led by Professor Essenhigh at OSU, Dieter Foertsch (a collaborator at the University of Stuttgart), and Professor Hurt at Brown. One product of this work is the CBK8 model of carbon burnout, which has already found practical use in CFD codes and in other numerical models of pulverized fuel combustion processes, such as EPRI's NOxLOI Predictor. The remainder of the report consists of detailed technical discussion organized into chapters whose organization is dictated by the nature of the research performed. Chapter 2 is entitled 'Experimental Work on Char Structure, Properties, and Reactivity', and focuses on fundamental structural studies at Brown using both phenollformaldehyde resin chars as model carbons and real coal chars. This work includes the first known in site high resolution TEM studies of carbonization processes, and some intriguing work on 'memory loss', a form of interaction between annealing and oxidation phenomena in chars. Chapter 3 entitled 'Computational Chemistry of Aromatic Oxidation Pathways' presents in detail the OSU work targeted at understanding the elementary molecular pathways of aromatic oxidation. Chapter 4 describes the 'Mesoscale Structural Models', using a combination of thermodynamic (equilibrium) approaches based on liquid crystal theory and kinetic simulations accounting for the effects of limited layer mobility in many fossil fuel derived carbons containing cross-linking agents. Chapter 5 entitled 'Combustion Modeling' presents work on extinction in the late stages of combustion and the development and features of the CBK8 model.

Robert Hurt; Joseph Calo; Robert Essenhigh; Christopher Hadad

2000-12-30

285

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

NASA Technical Reports Server (NTRS)

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

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

1994-01-01

286

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

PubMed Central

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

2013-01-01

287

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

PubMed Central

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

King, R D; Srinivasan, A

1996-01-01

288

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

PubMed Central

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

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

2014-01-01

289

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

NASA Astrophysics Data System (ADS)

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

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

2014-12-01

290

Using Neural Networks to Predict Secondary Structure by Integration of Amino Acid Conformational Preference and Multiple Sequence Alignment  

E-print Network

Using Neural Networks to Predict Secondary Structure by Integration of Amino Acid Conformational a protein sequence with amino acids {S1, S 2 ...S n}, the Secondary Structure Prediction (SSP) problem). The prediction of the protein structure from amino acid sequence is a key step to understand the relationship

Hefei Institute of Intelligent Machines

291

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

PubMed

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 I? 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 (?-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

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

292

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

NASA Technical Reports Server (NTRS)

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

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

1977-01-01

293

Finite element prediction of wave motion in structural waveguides  

NASA Astrophysics Data System (ADS)

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

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

2005-05-01

294

Finite element prediction of wave motion in structural waveguides.  

PubMed

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

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

2005-05-01

295

Variability in anger intensity profiles: structure and predictive basis.  

PubMed

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

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

2015-01-01

296

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

PubMed Central

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

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

2013-01-01

297

Prediction  

NSDL National Science Digital Library

Students must be guided to state not only what they think will happen, but also a reason or explanation for what will happen based upon their prior knowledge. Therefore, the predictions students write should activate prior knowledge, relate to their focus

Michael P. Klentschy

2008-04-01

298

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

PubMed

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

Tolmachov, Oleg E

2012-05-01

299

Web applet for predicting structure and thermodynamics of complex fluids  

NASA Astrophysics Data System (ADS)

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.

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

2015-03-01

300

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

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

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

301

STRUCTURE-BASED PREDICTIVE MODEL FOR COAL CHAR COMBUSTION  

SciTech Connect

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

Robert H. Hurt; Eric M. Suuberg

2000-05-03

302

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

E-print Network

in Reference 3, available on the Sandia web site www.sandia.gov/Renewable_Energy/Wind_Energy/. DELAMINATION1 PREDICTION OF DELAM INATION IN WIND TURBINE BLADE STRUCTURAL DETAILS John F. Mandell, Douglas S materials structures such as wind turbine blades. Design methodologies to prevent such failures have

303

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

Microsoft Academic Search

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

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

1999-01-01

304

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

NASA Astrophysics Data System (ADS)

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.

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

2010-03-01

305

Evaluation and Improvement of Multiple Sequence Methods for Protein Secondary Structure Prediction  

E-print Network

Evaluation and Improvement of Multiple Sequence Methods for Protein Secondary Structure Prediction over PHD, which was the best single method evaluated. Segment Overlap Ac- curacy (SOV) is 75 is developed and used to evaluate the perfor- mance of the protein secondary structure predic- tion algorithms

Barton, Geoffrey J.

306

HYPROSP: a hybrid protein secondary structure prediction algorithm--a knowledge-based approach  

Microsoft Academic Search

We develop a knowledge-based approach (called PROSP) for protein secondary struc- ture prediction. The knowledge base contains small peptide fragments together with their secondary structural information. A quantitative measure M, called match rate, is defined to measure the amount of structural information that a target protein can ex- tract from the knowledge base. Our experimental results show that proteins with

Kuen-Pin Wu; Hsin-Nan Lin; Jia-Ming Chang; Ting-Yi Sung; Wen-Lian Hsu

2004-01-01

307

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

E-print Network

to confirm our structure's predicted negative refraction, using the interfaces of the photonic and calculations; the horn antenna was orientated so that the incident waves make an angle of 45 with the normal of the ­M interface. Our structure exhibits the maximum angular range of negative refraction at an operating

Jones, James Holland

308

Comparative modelling: an essential methodology for protein structure prediction in the post-genomic era  

Microsoft Academic Search

The gap between the number of protein sequences and protein structures is increasing rapidly, exacerbated by the completion of numerous genome projects now flooding into public databases. To fill this gap, comparative protein modelling is widely considered the most accurate technique for predicting the three-dimensional shape of proteins. High-throughput, automatic protein modelling should considerably increase our access to protein structures

Bruno Contreras-Moreira; Paul W Fitzjohn; Paul A Bates

309

A geometric knowledge-based coarse-grained scoring potential for structure prediction evaluation  

E-print Network

to a better performing potential or scoring function [4]. Although fast and accurate accessible surface areaA geometric knowledge-based coarse-grained scoring potential for structure prediction evaluation S´ebastien Loriot1, Fr´ed´eric Cazals1, Michael Levitt2, Julie Bernauer1,2 1 Algorithms, Biology, Structure project

Paris-Sud XI, Université de

310

A multi-layer evaluation approach for protein structure prediction and model quality assessment  

PubMed Central

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 paper, we developed a set of new QA methods, including two QA methods for 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 multi-layer 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

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

2011-01-01

311

Biochemical functional predictions for protein structures of unknown or uncertain function  

PubMed Central

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.

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

2015-01-01

312

STRUCTURE-BASED PREDICTIVE MODEL FOR COAL CHAR COMBUSTION  

SciTech Connect

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 similar for the ?younger? and ?older? chars at high burn-offs. Progress continued on experimental work at OSU. Another furnace run was conducted with a Pittsburgh seam coal. Temperature profiles were obtained, as well as char samples from three sampling ports. Nonisothermal TGA reactivities were also obtained for these samples. Work is continuing on final ?fine-tuning? of the gas analysis section.

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

1999-01-13

313

Assessment and refinement of eukaryotic gene structure prediction with gene-structure-aware multiple protein sequence alignment  

PubMed Central

Background Accurate computational identification of eukaryotic gene organization is a long-standing problem. Despite the fundamental importance of precise annotation of genes encoded in newly sequenced genomes, the accuracy of predicted gene structures has not been critically evaluated, mostly due to the scarcity of proper assessment methods. Results We present a gene-structure-aware multiple sequence alignment method for gene prediction using amino acid sequences translated from homologous genes from many genomes. The approach provides rich information concerning the reliability of each predicted gene structure. We have also devised an iterative method that attempts to improve the structures of suspiciously predicted genes based on a spliced alignment algorithm using consensus sequences or reliable homologs as templates. Application of our methods to cytochrome P450 and ribosomal proteins from 47 plant genomes indicated that 50?~?60 % of the annotated gene structures are likely to contain some defects. Whereas more than half of the defect-containing genes may be intrinsically broken, i.e. they are pseudogenes or gene fragments, located in unfinished sequencing areas, or corresponding to non-productive isoforms, the defects found in a majority of the remaining gene candidates can be remedied by our iterative refinement method. Conclusions Refinement of eukaryotic gene structures mediated by gene-structure-aware multiple protein sequence alignment is a useful strategy to dramatically improve the overall prediction quality of a set of homologous genes. Our method will be applicable to various families of protein-coding genes if their domain structures are evolutionarily stable. It is also feasible to apply our method to gene families from all kingdoms of life, not just plants. PMID:24927652

2014-01-01

314

Inference and updating of probabilistic structural life prediction models  

NASA Astrophysics Data System (ADS)

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

Cross, Richard J.

315

Rotor Airloads Prediction Using Loose Aerodynamic Structural Coupling  

NASA Technical Reports Server (NTRS)

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

Potsdam, Mark; Yeo, Hyeonsoo; Johnson, Wayne

2004-01-01

316

Prediction and constancy of cognitive-motivational structures in mothers and their adolescents.  

PubMed

Three clinically-derived, cognitive-motivational structures were predicted in 68 adolescents from their caregiving situations as revealed in their mothers' interviews, elicited six years earlier. Basic to each structure is a motivational concern and its related social cognitive style, a style which corresponds to a Piagetian cognitive stage: concrete operational, intuitive or symbolic. Because these structure types parse a non-clinical population, current views of health and accordingly goals of treatment may need modification. PMID:7736804

Malerstein, A J; Ahern, M M; Pulos, S; Arasteh, J D

1995-01-01

317

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

PubMed Central

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

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

2011-01-01

318

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

PubMed Central

We present a continuous benchmarking approach for the assessment of RNA secondary structure prediction methods implemented in the CompaRNA web server. As of 3 October 2012, the performance of 28 single-sequence and 13 comparative methods has been evaluated on RNA sequences/structures released weekly by the Protein Data Bank. We also provide a static benchmark generated on RNA 2D structures derived from the RNAstrand database. Benchmarks on both data sets offer insight into the relative performance of RNA secondary structure prediction methods on RNAs of different size and with respect to different types of structure. According to our tests, on the average, the most accurate predictions obtained by a comparative approach are generated by CentroidAlifold, MXScarna, RNAalifold and TurboFold. On the average, the most accurate predictions obtained by single-sequence analyses are generated by CentroidFold, ContextFold and IPknot. The best comparative methods typically outperform the best single-sequence methods if an alignment of homologous RNA sequences is available. This article presents the results of our benchmarks as of 3 October 2012, whereas the rankings presented online are continuously updated. We will gladly include new prediction methods and new measures of accuracy in the new editions of CompaRNA benchmarks. PMID:23435231

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

2013-01-01

319

Constructing Templates for Protein Structure Prediction by Simulation of Protein Folding Pathways  

PubMed Central

How a one-dimensional protein sequence folds into a specific 3D structure remains a difficult challenge in structural biology. Many computational methods have been developed in an attempt to predict the tertiary structure of the protein; most of these employ approaches that are based on the accumulated knowledge of solved protein structures. Here we introduce a novel and fully automated approach for predicting the 3-dimensional structure of a protein that is based on the well accepted notion that protein folding is a hierarchical process. Our algorithm follows the hierarchical model by employing two stages: the first aims to find a match between the sequences of short independently-folding structural entities and parts of the target sequence and assigns the respective structures. The second assembles these local structural parts into a complete 3D structure, allowing for long-range interactions between them. We present the results of applying our method to a subset of the targets from CASP6 and CASP7. Our results indicate that for targets with a significant sequence similarity to known structures we are often able to provide predictions that are better than those achieved by two leading servers, and that the most significant improvements in comparison with these methods occur in regions of a gapped structural alignment between the native structure and the closest available structural template. We conclude that in addition to performing well for targets with known homologous structures, our method shows great promise for addressing the more general category of comparative modeling targets, which is our next goal. PMID:18433063

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

2009-01-01

320

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

PubMed Central

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

2014-01-01

321

Structural link prediction based on ant colony approach in social networks  

NASA Astrophysics Data System (ADS)

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

Sherkat, Ehsan; Rahgozar, Maseud; Asadpour, Masoud

2015-02-01

322

Predicting the Structure of the Solar Corona During the December 4, 2002 Total Solar Eclipse  

NASA Astrophysics Data System (ADS)

We describe the application of a three-dimensional magnetohydrodynamic (MHD) model to the prediction of the structure of the corona during the total solar eclipse that is expected to occur on 4 December 2002. The calculation uses the observed photospheric radial magnetic field as a boundary condition. This model makes it possible to determine the large-scale structure of the magnetic field in the corona, as well as the distribution of the solar wind velocity, plasma density, and temperature. We will use magnetic fields observed on the solar disk prior to eclipse day to predict what the corona will look like during the eclipse. The estimated coronal density and temperature will be used to predict the plane-of-sky polarization brightness and emission of UV radiation prior to the eclipse. The prediction will be posted on our web site (http://haven.saic.com) prior to the eclipse.

Mikic, Z.; Linker, J. A.; Lionello, R.; Riley, P.

2002-12-01

323

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

NASA Technical Reports Server (NTRS)

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

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

1996-01-01

324

MAPs: a database of modular antibody parts for predicting tertiary structures and designing affinity matured antibodies  

PubMed Central

Background The de novo design of a novel protein with a particular function remains a formidable challenge with only isolated and hard-to-repeat successes to date. Due to their many structurally conserved features, antibodies are a family of proteins amenable to predictable rational design. Design algorithms must consider the structural diversity of possible naturally occurring antibodies. The human immune system samples this design space (2 1012) by randomly combining variable, diversity, and joining genes in a process known as V-(D)-J recombination. Description By analyzing structural features found in affinity matured antibodies, a database of Modular Antibody Parts (MAPs) analogous to the variable, diversity, and joining genes has been constructed for the prediction of antibody tertiary structures. The database contains 929 parts constructed from an analysis of 1168 human, humanized, chimeric, and mouse antibody structures and encompasses all currently observed structural diversity of antibodies. Conclusions The generation of 260 antibody structures shows that the MAPs database can be used to reliably predict antibody tertiary structures with an average all-atom RMSD of 1.9 Å. Using the broadly neutralizing anti-influenza antibody CH65 and anti-HIV antibody 4E10 as examples, promising starting antibodies for affinity maturation are identified and amino acid changes are traced as antibody affinity maturation occurs. PMID:23718826

2013-01-01

325

RPI-Pred: predicting ncRNA-protein interaction using sequence and structural information  

PubMed Central

RNA-protein complexes are essential in mediating important fundamental cellular processes, such as transport and localization. In particular, ncRNA-protein interactions play an important role in post-transcriptional gene regulation like mRNA localization, mRNA stabilization, poly-adenylation, splicing and translation. The experimental methods to solve RNA-protein interaction prediction problem remain expensive and time-consuming. Here, we present the RPI-Pred (RNA-protein interaction predictor), a new support-vector machine-based method, to predict protein-RNA interaction pairs, based on both the sequences and structures. The results show that RPI-Pred can correctly predict RNA-protein interaction pairs with ?94% prediction accuracy when using sequence and experimentally determined protein and RNA structures, and with ?83% when using sequences and predicted protein and RNA structures. Further, our proposed method RPI-Pred was superior to other existing ones by predicting more experimentally validated ncRNA-protein interaction pairs from different organisms. Motivated by the improved performance of RPI-Pred, we further applied our method for reliable construction of ncRNA-protein interaction networks. The RPI-Pred is publicly available at: http://ctsb.is.wfubmc.edu/projects/rpi-pred. PMID:25609700

Suresh, V.; Liu, Liang; Adjeroh, Donald; Zhou, Xiaobo

2015-01-01

326

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

PubMed

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

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

2014-07-01

327

RPI-Pred: predicting ncRNA-protein interaction using sequence and structural information.  

PubMed

RNA-protein complexes are essential in mediating important fundamental cellular processes, such as transport and localization. In particular, ncRNA-protein interactions play an important role in post-transcriptional gene regulation like mRNA localization, mRNA stabilization, poly-adenylation, splicing and translation. The experimental methods to solve RNA-protein interaction prediction problem remain expensive and time-consuming. Here, we present the RPI-Pred (RNA-protein interaction predictor), a new support-vector machine-based method, to predict protein-RNA interaction pairs, based on both the sequences and structures. The results show that RPI-Pred can correctly predict RNA-protein interaction pairs with ?94% prediction accuracy when using sequence and experimentally determined protein and RNA structures, and with ?83% when using sequences and predicted protein and RNA structures. Further, our proposed method RPI-Pred was superior to other existing ones by predicting more experimentally validated ncRNA-protein interaction pairs from different organisms. Motivated by the improved performance of RPI-Pred, we further applied our method for reliable construction of ncRNA-protein interaction networks. The RPI-Pred is publicly available at: http://ctsb.is.wfubmc.edu/projects/rpi-pred. PMID:25609700

Suresh, V; Liu, Liang; Adjeroh, Donald; Zhou, Xiaobo

2015-02-18

328

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

PubMed Central

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%.

Spencer, Matt; Eickholt, Jesse; Cheng, Jianlin

2014-01-01

329

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

PubMed Central

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

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

2013-01-01

330

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

PubMed Central

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

Kryshtafovych, Andriy; Moult, John; Bartual, Sergio G.; Bazan, J. Fernando; Berman, Helen; Casteel, Darren E.; Christodoulou, Evangelos; Everett, John K.; Hausmann, Jens; Heidebrecht, Tatjana; Hills, Tanya; Hui, Raymond; Hunt, John F.; Jayaraman, Seetharaman; Joachimiak, Andrzej; Kennedy, Michael A.; Kim, Choel; Lingel, Andreas; Michalska, Karolina; Montelione, Gaetano T.; Otero, 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

331

Fast computational methods for predicting protein structure from primary amino acid sequence  

DOEpatents

The present invention provides a method utilizing primary amino acid sequence of a protein, energy minimization, molecular dynamics and protein vibrational modes to predict three-dimensional structure of a protein. The present invention also determines possible intermediates in the protein folding pathway. The present invention has important applications to the design of novel drugs as well as protein engineering. The present invention predicts the three-dimensional structure of a protein independent of size of the protein, overcoming a significant limitation in the prior art.

Agarwal, Pratul Kumar (Knoxville, TN)

2011-07-19

332

Analysis and Design of Fuselage Structures Including Residual Strength Prediction Methodology  

NASA Technical Reports Server (NTRS)

The goal of this research project is to develop and assess methodologies for the design and analysis of fuselage structures accounting for residual strength. Two primary objectives are included in this research activity: development of structural analysis methodology for predicting residual strength of fuselage shell-type structures; and the development of accurate, efficient analysis, design and optimization tool for fuselage shell structures. Assessment of these tools for robustness, efficient, and usage in a fuselage shell design environment will be integrated with these two primary research objectives.

Knight, Norman F.

1998-01-01

333

Patterned defect structures predicted for graphene are observed on single-layer silica films.  

PubMed

Topological defects in two-dimensional materials such as graphene are considered as a tool for tailoring their physical properties. Here, we studied defect structures on a single-layer silica (silicatene) supported on Ru(0001) using a low energy electron diffraction, scanning tunneling microscopy, infrared reflection-absorption spectroscopy, and photoelectron spectroscopy. The results revealed easy formation of periodic defect structures, which were previously predicted for graphene on a theoretical ground, yet experimentally unrealized. The structural similarities between single-layer materials (graphene, silicene, silicatene) open a new playground for deeper understanding and tailoring structural, electronic, and chemical properties of the truly two-dimensional systems. PMID:23937399

Yang, Bing; Boscoboinik, Jorge Anibal; Yu, Xin; Shaikhutdinov, Shamil; Freund, Hans-Joachim

2013-09-11

334

Small-molecule 3D Structure Prediction Using Open Crystallography Data  

PubMed Central

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

Sadowski, Peter; Baldi, Pierre

2014-01-01

335

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

NASA Astrophysics Data System (ADS)

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

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

2012-12-01

336

Protein Structure Prediction using a Docking-based Hierarchical Folding scheme  

PubMed Central

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

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

2011-01-01

337

Molecular Phylogeny and Predicted 3D Structure of Plant beta-D-N-Acetylhexosaminidase  

PubMed Central

beta-D-N-Acetylhexosaminidase, a family 20 glycosyl hydrolase, catalyzes the removal of ?-1,4-linked N-acetylhexosamine residues from oligosaccharides and their conjugates. We constructed phylogenetic tree of ?-hexosaminidases to analyze the evolutionary history and predicted functions of plant hexosaminidases. Phylogenetic analysis reveals the complex history of evolution of plant ?-hexosaminidase that can be described by gene duplication events. The 3D structure of tomato ?-hexosaminidase (?-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 (?/?)8 barrel in the central part. The ? and ? 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 ?-Hex-Sl mutagenesis experiments for exploring the functions of this class of enzymes in plant kingdom. PMID:25165734

Hossain, Md. Anowar

2014-01-01

338

A Non-parametric Bayesian Approach for Predicting RNA Secondary Structures  

NASA Astrophysics Data System (ADS)

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

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

339

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

PubMed Central

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

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

2010-01-01

340

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

PubMed Central

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

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

2011-01-01

341

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

PubMed Central

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

Kosciolek, Tomasz; Jones, David T.

2014-01-01

342

Prediction of the conformation and geometry of loops in globular proteins: testing ArchDB, a structural classification of loops.  

PubMed

In protein structure prediction, a central problem is defining the structure of a loop connecting 2 secondary structures. This problem frequently occurs in homology modeling, fold recognition, and in several strategies in ab initio structure prediction. In our previous work, we developed a classification database of structural motifs, ArchDB. The database contains 12,665 clustered loops in 451 structural classes with information about phi-psi angles in the loops and 1492 structural subclasses with the relative locations of the bracing secondary structures. Here we evaluate the extent to which sequence information in the loop database can be used to predict loop structure. Two sequence profiles were used, a HMM profile and a PSSM derived from PSI-BLAST. A jack-knife test was made removing homologous loops using SCOP superfamily definition and predicting afterwards against recalculated profiles that only take into account the sequence information. Two scenarios were considered: (1) prediction of structural class with application in comparative modeling and (2) prediction of structural subclass with application in fold recognition and ab initio. For the first scenario, structural class prediction was made directly over loops with X-ray secondary structure assignment, and if we consider the top 20 classes out of 451 possible classes, the best accuracy of prediction is 78.5%. In the second scenario, structural subclass prediction was made over loops using PSI-PRED (Jones, J Mol Biol 1999;292:195-202) secondary structure prediction to define loop boundaries, and if we take into account the top 20 subclasses out of 1492, the best accuracy is 46.7%. Accuracy of loop prediction was also evaluated by means of RMSD calculations. PMID:16021623

Fernandez-Fuentes, Narcis; Querol, Enrique; Aviles, Francesc X; Sternberg, Michael J E; Oliva, Baldomero

2005-09-01

343

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

SciTech Connect

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

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

2014-01-28

344

Critical assessment of methods of protein structure prediction (CASP) — round x  

PubMed Central

This article is an introduction to the special issue of the journal PROTEINS, dedicated to the tenth Critical Assessment of Structure Prediction (CASP) experiment to assess the state of the art in protein structure modeling. The article describes the conduct of the experiment, the categories of prediction included, and outlines the evaluation and assessment procedures. The 10 CASP experiments span almost 20 years of progress in the field of protein structure modeling, and there have been enormous advances in methods and model accuracy in that period. Notable in this round is the first sustained improvement of models with refinement methods, using molecular dynamics. For the first time, we tested the ability of modeling methods to make use of sparse experimental three-dimensional contact information, such as may be obtained from new experimental techniques, with encouraging results. On the other hand, new contact prediction methods, though holding considerable promise, have yet to make an impact in CASP testing. The nature of CASP targets has been changing in recent CASPs, reflecting shifts in experimental structural biology, with more irregular structures, more multi-domain and multi-subunit structures, and less standard versions of known folds. When allowance is made for these factors, we continue to see steady progress in the overall accuracy of models, particularly resulting from improvement of non-template regions. PMID:24344053

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

2015-01-01

345

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

NASA Astrophysics Data System (ADS)

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

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

346

Synthesis of a specified, silica molecular sieve by using computationally predicted organic structure-directing agents.  

PubMed

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

Schmidt, Joel E; Deem, Michael W; Davis, Mark E

2014-08-01

347

Rational Design of Temperature-Sensitive Alleles Using Computational Structure Prediction  

PubMed Central

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

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

2011-01-01

348

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

Microsoft Academic Search

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

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

1997-01-01

349

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

Microsoft Academic Search

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

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

2000-01-01

350

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

PubMed Central

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

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

2004-01-01

351

Extreme response prediction for nonlinear floating offshore structures by Monte Carlo simulation  

Microsoft Academic Search

The paper describes a method for the prediction of extreme response statistics of floating offshore structures subjected to random seas by Monte Carlo simulation. The particular case of the horizontal surge motions of a tension leg platform is considered, taking into account both the first order, wave frequency and the second order, slow-drift motions. The advantage of the Monte Carlo

A. Naess; O. Gaidai; P. S. Teigen

2007-01-01

352

FPGA accelerator for protein secondary structure prediction based on the GOR algorithm  

Microsoft Academic Search

BACKGROUND: Protein is an important molecule that performs a wide range of functions in biological systems. Recently, the protein folding attracts much more attention since the function of protein can be generally derived from its molecular structure. The GOR algorithm is one of the most successful computational methods and has been widely used as an efficient analysis tool to predict

Fei Xia; Yong Dou; Guoqing Lei; Yusong Tan

2011-01-01

353

Body Vigilance in Nonclinical and Anxiety Disorder Samples: Structure, Correlates, and Prediction of Health Concerns  

ERIC Educational Resources Information Center

The Body Vigilance Scale (BVS) is a measure developed to assess one's conscious attendance to internal cues. The present report investigated the structure, correlates, and predictive utility of the BVS in nonclinical (N=442) and anxiety (N=135) disorder samples. The findings of Study 1 suggest that the BVS is 1-dimensional in a nonclinical sample,…

Olatunji, Bunmi O.; Deacon, Brett J.; Abramowitz, Jonathan S.; Valentiner, David P.

2007-01-01

354

PROTEIN SECONDARY STRUCTURE PREDICTION BASED ON THE AMINO ACIDS CONFORMATIONAL CLASSIFICATION AND NEURAL NETWORK TECHNIQUE  

E-print Network

PROTEIN SECONDARY STRUCTURE PREDICTION BASED ON THE AMINO ACIDS CONFORMATIONAL CLASSIFICATION from the Protein Data Bank (PDB), we group the 20 different amino acids into f (Former), b (Breaker, -sheets and Coil), which reflect the in- trinsic preference of that amino acid for a given type of sec

Hefei Institute of Intelligent Machines

355

Prediction of the structure of the martian upper atmosphere for the Mars Reconnaissance Orbiter (MRO) mission  

Microsoft Academic Search

The Mars Reconnaissance Orbiter (MRO) spacecraft will undergo aerobraking from April through September 2006 in order to achieve the desired mapping orbit. This aerobraking process requires knowledge of the structure of the Mars upper atmosphere, which is provided through General Circulation Model predictions summarized in this paper.

Stephen W. Bougher; James R. Murphy; Jared M. Bell; Richard W. Zurek

2006-01-01

356

Human microRNA prediction through a probabilistic co-learning model of sequence and structure  

E-print Network

Human microRNA prediction through a probabilistic co-learning model of sequence and structure Jin been identified through experimental complementary DNA cloning methods and computational efforts-validation with 136 referenced human data- sets, the efficiency of the classification shows 73% sensitivity and 96

357

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

Microsoft Academic Search

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

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

2005-01-01

358

Evolved Matrix Operations for Post-Processing Protein Secondary Structure Predictions  

E-print Network

of proteins is a hard problem, so many have opted instead to predict the secondary structural state (usually helix, strand or coil) of each amino acid residue. This should be an easier task, but it now seems probabilities produced by the popular, state-of-the-art neural network-based PSIPRED by David Jones. We show

MacCallum, Bob

359

Predicting the Present with Bayesian Structural Time Series Steven L. Scott  

E-print Network

Predicting the Present with Bayesian Structural Time Series Steven L. Scott Hal Varian June 28, and Lopes (2012) for others. Choi and Varian (2009, 2012) demonstrated that Google search data could be used. Scott is Senior Economic Analyst, and Hal Varian is Chief Economist at Google, Inc. The authors thank

Varian, Hal R.

360

Appears in the SIGGRAPH 2012 Proceedings. Structure-aware Synthesis for Predictive Woven Fabric Appearance  

E-print Network

Appears in the SIGGRAPH 2012 Proceedings. Structure-aware Synthesis for Predictive Woven Fabric: We synthesize volumetric appearance models of fabrics with complex designs using a small set of exemplars: (a) density information of exemplars obtained using micro CT imaging; (b) fabric designs

Bala, Kavita

361

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

E-print Network

Realizing Predicted Crystal Structures at Extreme Conditions: The Low-Temperature and High-Pressure was crystallized from ethanol. Different polymorphs were obtained at high pressure by compression of the liquids, at high-pressure both compounds form zigzag chains disposed about 21 screw-axes, behavior more

de Gispert, Adrià

362

Structural Analysis, Failure Prediction, and Cost Analysis of Alternative Material for Composite Wind Turbine Blades  

Microsoft Academic Search

In wind turbines, blades are critical design members because performance depends on blade material, shape, twist angle, etc. The first part of this paper deals with structural analysis and fatigue behavior of the present blade material, and aerofoil section (NACA4412). The second part of the paper concentrates on failure prediction of blade material using modified failure criteria. The last part

J. Selwin Rajadurai; G. Thanigaiyarasu

2009-01-01

363

Shaping up the protein folding funnel by local interaction: Lesson from a structure prediction study  

E-print Network

Shaping up the protein folding funnel by local interaction: Lesson from a structure prediction study George Chikenji* , Yoshimi Fujitsuka , and Shoji Takada*§¶ *Department of Chemistry, Faculty is the fragment assembly (FA) method. Here, we address why the FA method is so successful and its lesson

Takada, Shoji

364

AB Initio Protein Tertiary Structure Prediction: Comparative-Genetic Algorithm with Graph Theoretical Methods  

Microsoft Academic Search

During the period from September 1, 1998 until September 1, 2000 I was awarded a Sloan\\/DOE postdoctoral fellowship to work in collaboration with Professor John Moult at the Center for Advanced Research in Biotechnology (CARB). Our research project, ''Ab Initio Protein Tertiary Structure Prediction and a Comparative Genetic algorithm'', yielded promising initial results. In short, the project is designed to

Gregurick

2001-01-01

365

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

PubMed Central

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

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

2014-01-01

366

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

NASA Technical Reports Server (NTRS)

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.

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

1988-01-01

367

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

Microsoft Academic Search

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

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

2001-01-01

368

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

ERIC Educational Resources Information Center

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

Nathan, Lawrence C.

1985-01-01

369

Gene Structure Prediction by Spliced Alignment of Genomic DNA with Protein Sequences: Increased  

E-print Network

Gene Structure Prediction by Spliced Alignment of Genomic DNA with Protein Sequences: Increased, the cDNA sequences will come from independently sequenced cDNA libraries, and assignment of a cDNA in the genomic DNA (Usuka et al., 2000). Simul- taneous scoring for sequence similarity and intrinsic quality

Brendel, Volker

370

proteinsSTRUCTURE O FUNCTION O BIOINFORMATICS Predicting nucleic acid binding interfaces  

E-print Network

proteinsSTRUCTURE O FUNCTION O BIOINFORMATICS Predicting nucleic acid binding interfaces from and Yael Mandel-Gutfreund1 * 1 Faculty of Biology, Technion ­ Israel Institute of Technology, Haifa, Israel Shazman contributed equally to this work. *Correspondence to: Yael Mandel-Gutfreund, Faculty of Biology

Zhang, Yang

371

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

ERIC Educational Resources Information Center

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)

Pruzek, Robert M.; Lepak, Greg M.

1992-01-01

372

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

E-print Network

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

Moorcroft, Paul R.

373

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

NASA Technical Reports Server (NTRS)

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

Ko, William L.; Fleischer, Van Tran

2012-01-01

374

Plant Genotypic Diversity Predicts Community Structure and Governs an Ecosystem Process  

Microsoft Academic Search

Theory predicts, and recent empirical studies have shown, that the diversity of plant species determines the diversity of associated herbivores and mediates ecosystem processes, such as aboveground net primary productivity (ANPP). However, an often-overlooked component of plant diversity, namely population genotypic diversity, may also have wide-ranging effects on community structure and ecosystem processes. We showed experimentally that increasing population genotypic

Gregory M. Crutsinger; Michael D. Collins; James A. Fordyce; Zachariah Gompert; Chris C. Nice; Nathan J. Sanders

2006-01-01

375

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

E-print Network

receptor (GLP1R) is a G protein-coupled receptor (GPCR) involved in insulin synthesis and regulation; therePredicted structure of agonist-bound glucagon-like peptide 1 receptor, a class B G protein the MembStruk method for scanning TM bundle conformations. We used protein­protein docking methods

Goddard III, William A.

376

X-ray crystallographic validation of structure predictions used in computational design for protein stabilization.  

PubMed

Protein engineering aimed at enhancing enzyme stability is increasingly supported by computational methods for calculation of mutant folding energies and for the design of disulfide bonds. To examine the accuracy of mutant structure predictions underlying these computational methods, crystal structures of thermostable limonene epoxide hydrolase variants obtained by computational library design were determined. Four different predicted effects indeed contributed to the obtained stabilization: (i) enhanced interactions between a flexible loop close to the N-terminus and the rest of the protein; (ii) improved interactions at the dimer interface; (iii) removal of unsatisfied hydrogen bonding groups; and (iv) introduction of additional positively charged groups at the surface. The structures of an eightfold and an elevenfold mutant showed that most mutations introduced the intended stabilizing interactions, and side-chain conformations were correctly predicted for 72-88% of the point mutations. However, mutations that introduced a disulfide bond in a flexible region had a larger influence on the backbone conformation than predicted. The enzyme active sites were unaltered, in agreement with the observed preservation of catalytic activities. The structures also revealed how a c-Myc tag, which was introduced for facile detection and purification, can reduce access to the active site and thereby lower the catalytic activity. Finally, sequence analysis showed that comprehensive mutant energy calculations discovered stabilizing mutations that are not proposed by the consensus or B-FIT methods. Proteins 2015; 83:940-951. © 2015 Wiley Periodicals, Inc. PMID:25739581

Floor, Robert J; Wijma, Hein J; Jekel, Peter A; Terwisscha van Scheltinga, Anke C; Dijkstra, Bauke W; Janssen, Dick B

2015-05-01

377

Protein Secondary Structure Prediction Based on Position-specific Scoring Matrices  

Microsoft Academic Search

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

David T. Jones

1999-01-01

378

STRUCTURAL-LEXICAL PREDICTABILITY OF MATERIALS WHICH PREDICTOR HAS PREVIOUSLY PRODUCED OR READ.  

ERIC Educational Resources Information Center

THE CLOZE PROCEDURE WAS USED TO INVESTIGATE THE PREDICTABILITY OF LANGUAGE MATERIALS AND TO EXAMINE THE RELATIONSHIP OF THE WRITTEN PRODUCTION OF LANGUAGE AND READING TO STRUCTURAL AND LEXICAL CONSTRUCTS. FIFTY-SIX SOPHOMORES RANDOMLY SELECTED FROM 152 STUDENTS ENROLLED IN INTRODUCTORY PSYCHOLOGY COURSES AT CAMPBELL COLLEGE WERE RANDOMLY ASSIGNED…

BICKLEY, A. C.; WEAVER, WENDELL W.

379

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

E-print Network

FLUTTER PREDICTION OF A TRANSONIC FAN WITH TRAVELLING WAVE USING FULLY COUPLED FLUID/structure interac- tion (FSI) to investigate the flutter mechanism of a modern transonic fan rotor with a forward, resulting in flutter. The flutter of the transonic fan observed in this study occurs at the 1st mode before

Zha, Gecheng

380

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

PubMed

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

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

2014-12-16

381

Structural characterization of extracellular ribonuclease of Bacillus polymyxa: amino acid sequence determination and spatial structure prediction.  

PubMed

The primary structure of extracellular Bacillus polymyxa ribonuclease (RNase Bpo) was established by mass spectroscopy analysis and automatic Edman degradation of the individual peptides obtained from protein digestion with Glu-specific protease V8. RNase Bpo consists of 111 amino acid residues, with a relative molecular weight of 12 607. RNase Bpo is a close structural homolog of RNases of B. amyloliquefaciens (RNase Ba) and B. intermedius (RNase Bi), the similarity of their primary structures being 68%. Molecular modelling of the structure of the complex of RNase Bpo with substrate analog d(CGAC) was performed and a spatial model based on the known crystal structure of RNase Ba complex with the corresponding nucleotide was constructed using the methods of interactive computer graphics and energy minimization. The differences in the primary and tertiary structures of the enzymes were analyzed in order to understand the substrate specificity of Bacillus RNases. PMID:8772184

Lebedev, A A; Shlyapnikov, S V; Pustobaev, V N; Kirpichnikov, M P; Dementiev, A A

1996-08-26

382

Predicted structures for kappa opioid g-protein coupled receptor bound to selective agonists.  

PubMed

Human kappa opioid receptor (?-OR), a G protein-coupled receptor (GPCR), has been identified as a drug target for treatment of such human disorders as pain perception, neuroendocrine physiology, affective behavior, and cognition. In order to find more selective and active agonists, one would like to do structure based drug design. Indeed, there is an X-ray structure for an antagonist bound to ?-OR, but structures for activated GPCRs are quite different from those for the inactive GPCRs. Here we predict the ensemble of 24 low-energy structures of human kappa opioid receptor (?-OR), obtained by application of the GEnSeMBLE (GPCR Ensemble of Structures in Membrane Bilayer Environment) complete sampling method, which evaluates 13 trillion combinations of tilt and rotation angles for ?-OR to select the best 24. To validate these structures, we used the DarwinDock complete sampling method to predict the binding sites for five known agonists (ethylketocyclazocine, bremazocine, pentazocine, nalorphine, and morphine) bound to all 24 ?-OR conformations. We find that some agonists bind selectively to receptor conformations that lack the salt bridge between transmembrane domains 3 and 6 as expected for active conformations. These 3D structures for ?-OR provide a structural basis for understanding ligand binding and activation of ?-OR, which should be useful for guiding subtype specific drug design. PMID:25642595

Li, Quanjie; Kim, Soo-Kyung; Goddard, William A; Chen, Guangju; Tan, Hongwei

2015-03-23

383

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

PubMed

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

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

2013-08-26

384

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

PubMed Central

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

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

2010-01-01

385

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

PubMed Central

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

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

2014-01-01

386

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

NASA Astrophysics Data System (ADS)

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

Ashwill, Thomas D.; Veers, Paul S.

387

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

PubMed Central

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

Moriguchi, I; Hirano, H; Hirono, S

1996-01-01

388

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

SciTech Connect

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. 12 refs., 4 tabs.

Moriguchi, Ikuo; Hirono, Shuichi [Kitasato Univ., Tokyo (Japan); Hirano, Hiroyuki [Zeria Pharmaceutical Co., Ltd., Tokyo (Japan)

1996-10-01

389

Staple Fitness: A Concept to Understand and Predict the Structures of Thiolated Gold Nanoclusters  

SciTech Connect

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}.

Jiang, Deen [ORNL

2011-01-01

390

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

PubMed Central

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

Ellington, Roni; Wachira, James

2010-01-01

391

Ligand-Target Prediction by Structural Network Biology Using nAnnoLyze  

PubMed Central

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

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

2015-01-01

392

Prediction of protein tertiary structure from sequences using a very large back-propagation neural network  

SciTech Connect

We have implemented large scale back-propagation neural networks on a 544 node Connection Machine, CM-5, using the C language in MIMD mode. The program running on 512 processors performs backpropagation learning at 0.53 Gflops, which provides 76 million connection updates per second. We have applied the network to the prediction of protein tertiary structure from sequence information alone. A neural network with one hidden layer and 40 million connections is trained to learn the relationship between sequence and tertiary structure. The trained network yields predicted structures of some proteins on which it has not been trained given only their sequences. Presentation of the Fourier transform of the sequences accentuates periodicity in the sequence and yields good generalization with greatly increased training efficiency. Training simulations with a large, heterologous set of protein structures (111 proteins from CM-5 time) to solutions with under 2% RMS residual error within the training set (random responses give an RMS error of about 20%). Presentation of 15 sequences of related proteins in a testing set of 24 proteins yields predicted structures with less than 8% RMS residual error, indicating good apparent generalization.

Liu, X.; Wilcox, G.L. [Univ. of Minnesota, Minneapolis, MN (United States)

1993-12-31

393

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

NASA Technical Reports Server (NTRS)

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

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

2007-01-01

394

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

PubMed

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

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

2015-02-01

395

Hidden Markov models that use predicted secondary structures for fold recognition.  

PubMed

There are many proteins that share the same fold but have no clear sequence similarity. To predict the structure of these proteins, so called "protein fold recognition methods" have been developed. During the last few years, improvements of protein fold recognition methods have been achieved through the use of predicted secondary structures (Rice and Eisenberg, J Mol Biol 1997;267:1026-1038), as well as by using multiple sequence alignments in the form of hidden Markov models (HMM) (Karplus et al., Proteins Suppl 1997;1:134-139). To test the performance of different fold recognition methods, we have developed a rigorous benchmark where representatives for all proteins of known structure are matched against each other. Using this benchmark, we have compared the performance of automatically-created hidden Markov models with standard-sequence-search methods. Further, we combine the use of predicted secondary structures and multiple sequence alignments into a combined method that performs better than methods that do not use this combination of information. Using only single sequences, the correct fold of a protein was detected for 10% of the test cases in our benchmark. Including multiple sequence information increased this number to 16%, and when predicted secondary structure information was included as well, the fold was correctly identified in 20% of the cases. Moreover, if the correct secondary structure was used, 27% of the proteins could be correctly matched to a fold. For comparison, blast2, fasta, and ssearch identifies the fold correctly in 13-17% of the cases. Thus, standard pairwise sequence search methods perform almost as well as hidden Markov models in our benchmark. This is probably because the automatically-created multiple sequence alignments used in this study do not contain enough diversity and because the current generation of hidden Markov models do not perform very well when built from a few sequences. PMID:10373007

Hargbo, J; Elofsson, A

1999-07-01

396

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

NASA Technical Reports Server (NTRS)

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.

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

1973-01-01

397

Clinical prediction from structural brain MRI scans: a large-scale empirical study.  

PubMed

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

Sabuncu, Mert R; Konukoglu, Ender

2015-01-01

398

Prediction and Design of Materials from Crystal Structures to Nanocrystal Morphology and Assembly  

NASA Astrophysics Data System (ADS)

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 preferential detachment of ligands from particular facets, leading to anisotropic ligand coverage [3]. [4pt] [1] J. Feng, R. G. Hennig, N. W. Ashcroft and Roald Hoffmann. Nature 451, 445 (2008). [0pt] [2] W. Bi, Y. Meng, R. S. Kumar, A. L. Cornelius, W. W. Tipton, R. G. Hennig, Y. Zhang, C. Chen, and J. S. Schilling. Phys. Rev. B 83, 104106 (2011). [0pt] [3] J. J. Choi, C. R. Bealing, K. Bian, K. J. Hughes, W. Zhang, D.-M. Smilgies, R. G. Hennig, James R. Engstrom, and Tobias Hanrath. J. Am. Chem. Soc. 133, 3131 (2011).

Hennig, Richard

2012-02-01

399

Predicting US Infants' and Toddlers' TV/Video Viewing Rates: Mothers' Cognitions and Structural Life Circumstances  

PubMed Central

There has been rising international concern over media use with children under two. As little is known about the factors associated with more or less viewing among very young children, this study examines maternal factors predictive of TV/video viewing rates among American infants and toddlers. Guided by the Integrative Model of Behavioral Prediction, this survey study examines relationships between children's rates of TV/video viewing and their mothers' structural life circumstances (e.g., number of children in the home; mother's screen use), and cognitions (e.g., attitudes; norms). Results suggest that mothers' structural circumstances and cognitions respectively contribute independent explanatory power to the prediction of children's TV/video viewing. Influence of structural circumstances is partially mediated through cognitions. Mothers' attitudes as well as their own TV/video viewing behavior were particularly predictive of children's viewing. Implications of these findings for international efforts to understand and reduce infant/toddler TV/video exposure are discussed. PMID:25489335

Vaala, Sarah E.; Hornik, Robert C.

2014-01-01

400

Prediction of contact angle for pharmaceutical solids from their molecular structure.  

PubMed

Three methods for modeling and predicting water contact angle for a heterogeneous series of pharmaceuticals using computed molecular descriptors and statistical analysis were developed. A number of theoretical molecular descriptors that were related to the structure and physicochemical properties were computed for compounds (n=34) whose experimental water contact angle was known. Thereafter, the descriptors were subjected to partial least squares projections to latent structures analysis. Three multivariate models were derived that allowed theoretical prediction of water contact angle for structurally heterogeneous materials. The R2 and Q2 values of the models ranged from 0.57 to 0.80 and 0.42 to 0.66, respectively. The models had moderate predictive ability and provided useful information about the molecular and physicochemical properties that affect material water contact angle. Increases in the bulkiness and hydrophobic molecular surface area of a molecule increased material water contact angle, whereas the greater presence of hydrophilic surfaces, which are not capable for hydrogen bonding, decrease materials water contact angle. Water contact angle can be predicted well for pharmaceutical solids using theoretical molecular descriptors that reflect the interaction potential of crystal/particle surfaces with water. PMID:15682381

Suihko, Eero; Forbes, Robert T; Korhonen, Ossi; Ketolainen, Jarkko; Paronen, Petteri; Gynther, Jukka; Poso, Antti

2005-04-01

401

Hybrid scaled structural dynamic models and their use in damping prediction  

NASA Technical Reports Server (NTRS)

Analytical and experimental techniques for the prediction and ground verification of the damped structural dynamics of space structures are developed. The options available for similarity-scaled model testing, including replica and multiple scale approaches, are reviewed. For the case when the distortion of potentially dissipative or nonlinear joints, which would be required in multiple-scale modeling, is impractical, a new type of modeling is introduced, which uses a hybrid of joints at replica scale and connecting elements at a modified multiple scale. The model design requirements for replica, multiple-scale, and hybrid models are developed, and the expected scaling of nonlinear dissipation in joints is derived. A damping prediction scheme is developed that relies on a finite element model of the undamped structure and measurements of the individual joint properties to predict the modal damping of the truss attributable to the joints. A hybrid-scaled model of a segment of the Space Station was built and dynamically tested. The predicted and measured truss damping compared favorably.

Crawley, Edward F.; Sigler, Jonathan L.; Van Schoor, Marthinus C.; Gronet, Marc J.

1990-01-01

402

SAHG, a comprehensive database of predicted structures of all human proteins.  

PubMed

Most proteins from higher organisms are known to be multi-domain proteins and contain substantial numbers of intrinsically disordered (ID) regions. To analyse such protein sequences, those from human for instance, we developed a special protein-structure-prediction pipeline and accumulated the products in the Structure Atlas of Human Genome (SAHG) database at http://bird.cbrc.jp/sahg. With the pipeline, human proteins were examined by local alignment methods (BLAST, PSI-BLAST and Smith-Waterman profile-profile alignment), global-local alignment methods (FORTE) and prediction tools for ID regions (POODLE-S) and homology modeling (MODELLER). Conformational changes of protein models upon ligand-binding were predicted by simultaneous modeling using templates of apo and holo forms. When there were no suitable templates for holo forms and the apo models were accurate, we prepared holo models using prediction methods for ligand-binding (eF-seek) and conformational change (the elastic network model and the linear response theory). Models are displayed as animated images. As of July 2010, SAHG contains 42,581 protein-domain models in approximately 24,900 unique human protein sequences from the RefSeq database. Annotation of models with functional information and links to other databases such as EzCatDB, InterPro or HPRD are also provided to facilitate understanding the protein structure-function relationships. PMID:21051360

Motono, Chie; Nakata, Junichi; Koike, Ryotaro; Shimizu, Kana; Shirota, Matsuyuki; Amemiya, Takayuki; Tomii, Kentaro; Nagano, Nozomi; Sakaya, Naofumi; Misoo, Kiyotaka; Sato, Miwa; Kidera, Akinori; Hiroaki, Hidekazu; Shirai, Tsuyoshi; Kinoshita, Kengo; Noguchi, Tamotsu; Ota, Motonori

2011-01-01

403

Multi-class support vector machines for protein secondary structure prediction.  

PubMed

The solution of binary classification problems using the Support Vector Machine (SVM) method has been well developed. Though multi-class classification is typically solved by combining several binary classifiers, recently, several multi-class methods that consider all classes at once have been proposed. However, these methods require resolving a much larger optimization problem and are applicable to small datasets. Three methods based on binary classifications: one-against-all (OAA), one-against-one (OAO), and directed acyclic graph (DAG), and two approaches for multi-class problem by solving one single optimization problem, are implemented to predict protein secondary structure. Our experiments indicate that multi-class SVM methods are more suitable for protein secondary structure (PSS) prediction than the other methods, including binary SVMs, because their capacity to solve an optimization problem in one step. Furthermore, in this paper, we argue that it is feasible to extend the prediction accuracy by adding a second-stage multi-class SVM to capture the contextual information among secondary structural elements and thereby further improving the accuracies. We demonstrate that two-stage SVMs perform better than single-stage SVM techniques for PSS prediction using two datasets and report a maximum accuracy of 79.5%. PMID:15706536

Nguyen, Minh N; Rajapakse, Jagath C

2003-01-01

404

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

PubMed

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

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

2009-02-11

405

Structure-based prediction reveals capping motifs that inhibit ?-helix aggregation  

PubMed Central

The parallel ?-helix is a geometrically regular fold commonly found in the proteomes of bacteria, viruses, fungi, archaea, and some vertebrates. ?-helix structure has been observed in monomeric units of some aggregated amyloid fibers. In contrast, soluble ?-helices, both right- and left-handed, are usually “capped” on each end by one or more secondary structures. Here, an in-depth classification of the diverse range of ?-helix cap structures reveals subtle commonalities in structural components and in interactions with the ?-helix core. Based on these uncovered commonalities, a toolkit of automated predictors was developed for the two distinct types of cap structures. In vitro deletion of the toolkit-predicted C-terminal cap from the pertactin ?-helix resulted in increased aggregation and the formation of soluble oligomeric species. These results suggest that ?-helix cap motifs can prevent specific, ?-sheet-mediated oligomeric interactions, similar to those observed in amyloid formation. PMID:21685332

Bryan, Allen W.; Starner-Kreinbrink, Jennifer L.; Hosur, Raghavendra; Clark, Patricia L.; Berger, Bonnie

2011-01-01

406

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

PubMed

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

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

2013-09-01

407

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

PubMed Central

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

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

2014-01-01

408

PredUs: a web server for predicting protein interfaces using structural neighbors.  

PubMed

We describe PredUs, an interactive web server for the prediction of protein-protein interfaces. Potential interfacial residues for a query protein are identified by 'mapping' contacts from known interfaces of the query protein's structural neighbors to surface residues of the query. We calculate a score for each residue to be interfacial with a support vector machine. Results can be visualized in a molecular viewer and a number of interactive features allow users to tailor a prediction to a particular hypothesis. The PredUs server is available at: http://wiki.c2b2.columbia.edu/honiglab_public/index.php/Software:PredUs. PMID:21609948

Zhang, Qiangfeng Cliff; Deng, Lei; Fisher, Markus; Guan, Jihong; Honig, Barry; Petrey, Donald

2011-07-01

409

RNAdigest: A Web-Based Tool for the Analysis and Prediction of Structure - Specific RNAse Digestion Results  

PubMed Central

Despite recent developments in analyzing RNA secondary structures, relatively few RNA structures have been determined. To date, many investigators have relied on the traditional method of using structure-specific RNAse enzymes to probe RNA secondary structures. However, if these data were combined with novel computational approaches, investigators would have an informative and valuable tool for RNA structural analysis. To this end, we created the web server “RNAdigest.” RNAdigest uses mfold RNA structural models in order to predict the results of RNAse digestion experiments. Furthermore, RNAdigest also utilizes both RNA sequence and the experimental digestion patterns to formulate the constraints for predicting secondary structures of the RNA. Thus, RNAdigest allows for the structural interpretation of RNAse digestion experiments. Overall, RNAdigest simplifies RNAse digestion result analyses while allowing for the identification of unique fragments. These unique fragments can then be used for testing predicted mfold structures and for designing structural-specific DNA/RNA probes. PMID:24801507

Madanecki, Piotr; Nozell, Susan; Ochocka, Renata; Collawn, James F.; Bartoszewski, Rafal

2014-01-01

410

Correlation of pp data with predictions of improved six-quark structure models  

NASA Astrophysics Data System (ADS)

Recent experimental data indicate a structure in ??L corresponding to a pp mass of 2.7 GeV/c2, as earlier predicted for a six-quark 1S0 state by an R-matrix treatment of the cloudy-bag-model quark degrees of freedom interior to a coupled-isobar-channel system. The 1S0 model is improved to agree with 2? production data at 800 MeV laboratory energy. The resulting 1S0 partial wave and recently improved models of the background partial waves as well as older versions of the phase parameters predict experimental observables in the resonance region. The predicted width and inelasticity are consistent with the data. Detailed energy and angular dependence of the model are in agreement with ??L, CLL, and CNN data in the resonance energy region. More data on these observables are needed to confirm the structure and its characteristics. Measurable aspects of the structure in other observables are displayed. Another six-quark resonance structure, in the 1D2 state, is described.

González, P.; Lafrance, P.; Lomon, E. L.

1987-04-01

411

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

NASA Astrophysics Data System (ADS)

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

Fridman, Aleksei M.

2007-02-01

412

A comparative assessment and analysis of 20 representative sequence alignment methods for protein structure prediction  

PubMed Central

Protein sequence alignment is essential for template-based protein structure prediction and function annotation. We collect 20 sequence alignment algorithms, 10 published and 10 newly developed, which cover all representative sequence- and profile-based alignment approaches. These algorithms are benchmarked on 538 non-redundant proteins for protein fold-recognition on a uniform template library. Results demonstrate dominant advantage of profile-profile based methods, which generate models with average TM-score 26.5% higher than sequence-profile methods and 49.8% higher than sequence-sequence alignment methods. There is no obvious difference in results between methods with profiles generated from PSI-BLAST PSSM matrix and hidden Markov models. Accuracy of profile-profile alignments can be further improved by 9.6% or 21.4% when predicted or native structure features are incorporated. Nevertheless, TM-scores from profile-profile methods including experimental structural features are still 37.1% lower than that from TM-align, demonstrating that the fold-recognition problem cannot be solved solely by improving accuracy of structure feature predictions. PMID:24018415

Yan, Renxiang; Xu, Dong; Yang, Jianyi; Walker, Sara; Zhang, Yang

2013-01-01

413

Structure-based prediction of protein-protein interactions on a genome-wide scale  

PubMed Central

The genome-wide identification of pairs of interacting proteins is an important step in the elucidation of cell regulatory mechanisms1,2. Much of our current knowledge derives from high-throughput techniques such as yeast two hybrid and affinity purification3, as well as from manual curation of experiments on individual systems4. A variety of computational approaches based, for example, on sequence homology, gene co-expression, and phylogenetic profiles have also been developed for the genome-wide inference of protein-protein interactions (PPIs)5,6. Yet, comparative studies suggest that the development of accurate and complete repertoires of PPIs is still in its early stages7–9. Here we show that three-dimensional structural information can be used to predict PPIs with an accuracy and coverage that are superior to predictions based on non-structural evidence. Moreover, an algorithm, PrePPI, that combines structural information with other functional clues is comparable in accuracy to high-throughput experiments, yielding over 30,000 high confidence interactions for yeast and over 300,000 for human. Experimental tests of a number of predictions demonstrate the ability of the PrePPI algorithm to identify unexpected PPIs of significant biological interest. The surprising effectiveness of three-dimensional structural information can be attributed to the use of homology models combined with the exploitation of both close and remote geometric relationships between proteins. PMID:23023127

Zhang, Qiangfeng Cliff; Petrey, Donald; Deng, Lei; Qiang, Li; Shi, Yu; Thu, Chan Aye; Bisikirska, Brygida; Lefebvre, Celine; Accili, Domenico; Hunter, Tony; Maniatis, Tom; Califano, Andrea; Honig, Barry

2012-01-01

414

Three Dimensional Structure Prediction of Fatty Acid Binding Site on Human Transmembrane Receptor CD36.  

PubMed

CD36 is an integral membrane protein which is thought to have a hairpin-like structure with alpha-helices at the C and N terminals projecting through the membrane as well as a larger extracellular loop. This receptor interacts with a number of ligands including oxidized low density lipoprotein and long chain fatty acids (LCFAs). It is also implicated in lipid metabolism and heart diseases. It is therefore important to determine the 3D structure of the CD36 site involved in lipid binding. In this study, we predict the 3D structure of the fatty acid (FA) binding site [127-279 aa] of the CD36 receptor based on homology modeling with X-ray structure of Human Muscle Fatty Acid Binding Protein (PDB code: 1HMT). Qualitative and quantitative analysis of the resulting model suggests that this model was reliable and stable, taking in consideration over 97.8% of the residues in the most favored regions as well as the significant overall quality factor. Protein analysis, which relied on the secondary structure prediction of the target sequence and the comparison of 1HMT and CD36 [127-279 aa] secondary structures, led to the determination of the amino acid sequence consensus. These results also led to the identification of the functional sites on CD36 and revealed the presence of residues which may play a major role during ligand-protein interactions. PMID:24348024

Tarhda, Zineb; Semlali, Oussama; Kettani, Anas; Moussa, Ahmed; Abumrad, Nada A; Ibrahimi, Azeddine

2013-01-01

415

Three Dimensional Structure Prediction of Fatty Acid Binding Site on Human Transmembrane Receptor CD36  

PubMed Central

CD36 is an integral membrane protein which is thought to have a hairpin-like structure with alpha-helices at the C and N terminals projecting through the membrane as well as a larger extracellular loop. This receptor interacts with a number of ligands including oxidized low density lipoprotein and long chain fatty acids (LCFAs). It is also implicated in lipid metabolism and heart diseases. It is therefore important to determine the 3D structure of the CD36 site involved in lipid binding. In this study, we predict the 3D structure of the fatty acid (FA) binding site [127–279 aa] of the CD36 receptor based on homology modeling with X-ray structure of Human Muscle Fatty Acid Binding Protein (PDB code: 1HMT). Qualitative and quantitative analysis of the resulting model suggests that this model was reliable and stable, taking in consideration over 97.8% of the residues in the most favored regions as well as the significant overall quality factor. Protein analysis, which relied on the secondary structure prediction of the target sequence and the comparison of 1HMT and CD36 [127–279 aa] secondary structures, led to the determination of the amino acid sequence consensus. These results also led to the identification of the functional sites on CD36 and revealed the presence of residues which may play a major role during ligand-protein interactions. PMID:24348024

Tarhda, Zineb; Semlali, Oussama; Kettani, Anas; Moussa, Ahmed; Abumrad, Nada A.; Ibrahimi, Azeddine

2013-01-01

416

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

PubMed Central

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

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

2002-01-01

417

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

PubMed Central

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

2010-01-01

418

Computational Prediction of Conformational B-Cell Epitopes from Antigen Primary Structures by Ensemble Learning  

PubMed Central

Motivation The conformational B-cell epitopes are the specific sites on the antigens that have immune functions. The identification of conformational B-cell epitopes is of great importance to immunologists for facilitating the design of peptide-based vaccines. As an attempt to narrow the search for experimental validation, various computational models have been developed for the epitope prediction by using antigen structures. However, the application of these models is undermined by the limited number of available antigen structures. In contrast to the most of available structure-based methods, we here attempt to accurately predict conformational B-cell epitopes from antigen sequences. Methods In this paper, we explore various sequence-derived features, which have been observed to be associated with the location of epitopes or ever used in the similar tasks. These features are evaluated and ranked by their discriminative performance on the benchmark datasets. From the perspective of information science, the combination of various features can usually lead to better results than the individual features. In order to build the robust model, we adopt the ensemble learning approach to incorporate various features, and develop the ensemble model to predict conformational epitopes from antigen sequences. Results Evaluated by the leave-one-out cross validation, the proposed method gives out the mean AUC scores of 0.687 and 0.651 on two datasets respectively compiled from the bound structures and unbound structures. When compared with publicly available servers by using the independent dataset, our method yields better or comparable performance. The results demonstrate the proposed method is useful for the sequence-based conformational epitope prediction. Availability The web server and datasets are freely available at http://bcell.whu.edu.cn. PMID:22927994

Zhang, Wen; Niu, Yanqing; Xiong, Yi; Zhao, Meng; Yu, Rongwei; Liu, Juan

2012-01-01

419

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

PubMed Central

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

2010-01-01

420

Optimized distance-dependent atom-pair-based potential DOOP for protein structure prediction.  

PubMed

The DOcking decoy-based Optimized Potential (DOOP) energy function for protein structure prediction is based on empirical distance-dependent atom-pair interactions. To optimize the atom-pair interactions, native protein structures are decomposed into polypeptide chain segments that correspond to structural motives involving complete secondary structure elements. They constitute near native ligand-receptor systems (or just pairs). Thus, a total of 8609 ligand-receptor systems were prepared from 954 selected proteins. For each of these hypothetical ligand-receptor systems, 1000 evenly sampled docking decoys with 0-10 Å interface root-mean-square-deviation (iRMSD) were generated with a method used before for protein-protein docking. A neural network-based optimization method was applied to derive the optimized energy parameters using these decoys so that the energy function mimics the funnel-like energy landscape for the interaction between these hypothetical ligand-receptor systems. Thus, our method hierarchically models the overall funnel-like energy landscape of native protein structures. The resulting energy function was tested on several commonly used decoy sets for native protein structure recognition and compared with other statistical potentials. In combination with a torsion potential term which describes the local conformational preference, the atom-pair-based potential outperforms other reported statistical energy functions in correct ranking of native protein structures for a variety of decoy sets. This is especially the case for the most challenging ROSETTA decoy set, although it does not take into account side chain orientation-dependence explicitly. The DOOP energy function for protein structure prediction, the underlying database of protein structures with hypothetical ligand-receptor systems and their decoys are freely available at http://agknapp.chemie.fu-berlin.de/doop/. Proteins 2015; 83:881-890. © 2015 Wiley Periodicals, Inc. PMID:25693513

Chae, Myong-Ho; Krull, Florian; Knapp, Ernst-Walter

2015-05-01

421

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

PubMed

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

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

2009-12-01

422

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

NASA Technical Reports Server (NTRS)

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

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

1990-01-01