Science.gov

Sample records for structure based predictive

  1. Collective prediction based on community structure

    NASA Astrophysics Data System (ADS)

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

    2017-01-01

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

  2. Structure-Based Predictions of Activity Cliffs

    PubMed Central

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

    2015-01-01

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

  3. Improving structure-based function prediction using molecular dynamics

    PubMed Central

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

    2009-01-01

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

  4. OPTIMIZATION BIAS IN ENERGY-BASED STRUCTURE PREDICTION.

    PubMed

    Petrella, Robert J

    2013-12-01

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

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

    PubMed Central

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

    2009-01-01

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

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

    PubMed Central

    Dukka, B KC

    2013-01-01

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

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

    PubMed

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

    2009-03-01

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

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

    PubMed

    Mariano, Rachelle; Wuchty, Stefan

    2017-03-16

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

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

    PubMed

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

    2008-07-01

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

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

    PubMed

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

    2014-01-01

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

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

    PubMed Central

    Liu, Bin

    2016-01-01

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

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

    PubMed

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

    2014-11-12

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

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

    PubMed

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

    2017-03-16

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

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

    NASA Astrophysics Data System (ADS)

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

    2015-03-01

    A method based on the particle swarm optimization (PSO) algorithm is presented to design quasi-two-dimensional (Q2D) materials. With this development, various single-layer and bi-layer materials in C, Si, Ge, Sn, and Pb were predicted. A new Si bi-layer structure is found to have a much-favored energy than the previously widely accepted configuration. Both single-layer and bi-layer Si materials have small band gaps, limiting their usages in optoelectronic applications. Hydrogenation has therefore been used to tune the electronic and optical properties of Si layers. We discover two hydrogenated materials of layered Si8H2andSi6H2 possessing quasi-direct band gaps of 0.75 eV and 1.59 eV, respectively. Their potential applications for light emitting diode and photovoltaics are proposed and discussed. Our study opened up the possibility of hydrogenated Si layered materials as next-generation optoelectronic devices.

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

    NASA Technical Reports Server (NTRS)

    Gern, Frank H.

    2012-01-01

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

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

    SciTech Connect

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

    1997-09-24

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

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

    PubMed

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

    2015-01-01

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

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

    PubMed

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

    2007-04-13

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

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

    NASA Astrophysics Data System (ADS)

    Talha, Mohammad; Ashokkumar, Chimpalthradi R.

    2014-05-01

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

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

    SciTech Connect

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

    1997-06-25

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

  1. STRUCTURE BASED PREDICTIVE MODEL FOR COAL CHAR COMBUSTION

    SciTech Connect

    Robert Hurt; Joseph Calo; Robert Essenhigh; Christopher Hadad

    2001-06-15

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

  2. STRUCTURE-BASED PREDICTIVE MODEL FOR COAL CHAR COMBUSTION

    SciTech Connect

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

    1998-06-04

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

  3. Structure Based Predictive Model for Coal Char Combustion

    SciTech Connect

    Robert Hurt; Joseph Calo; Robert Essenhigh; Christopher Hadad

    2000-12-30

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

  4. STRUCTURE-BASED PREDICTIVE MODEL FOR COAL CHAR COMBUSTION

    SciTech Connect

    Robert H. Hurt; Eric M. Suuberg

    2000-05-03

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

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

    PubMed

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

    2016-08-01

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

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

    PubMed

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

    2017-01-01

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

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

    PubMed

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

    2013-05-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2016-05-01

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

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

    PubMed Central

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

    2014-01-01

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

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

    PubMed

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

    2015-01-10

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

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

    PubMed

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

    2014-08-01

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

  12. Prediction of protein secondary structure using probability based features and a hybrid system.

    PubMed

    Ghanty, Pradip; Pal, Nikhil R; Mudi, Rajani K

    2013-10-01

    In this paper, we propose some co-occurrence probability-based features for prediction of protein secondary structure. The features are extracted using occurrence/nonoccurrence of secondary structures in the protein sequences. We explore two types of features: position-specific (based on position of amino acid on fragments of protein sequences) as well as position-independent (independent of amino acid position on fragments of protein sequences). We use a hybrid system, NEUROSVM, consisting of neural networks and support vector machines for classification of secondary structures. We propose two schemes NSVMps and NSVM for protein secondary structure prediction. The NSVMps uses position-specific probability-based features and NEUROSVM classifier whereas NSVM uses the same classifier with position-independent probability-based features. The proposed method falls in the single-sequence category of methods because it does not use any sequence profile information such as position specific scoring matrices (PSSM) derived from PSI-BLAST. Two widely used datasets RS126 and CB513 are used in the experiments. The results obtained using the proposed features and NEUROSVM classifier are better than most of the existing single-sequence prediction methods. Most importantly, the results using NSVMps that are obtained using lower dimensional features, are comparable to those by other existing methods. The NSVMps and NSVM are finally tested on target proteins of the critical assessment of protein structure prediction experiment-9 (CASP9). A larger dataset is used to compare the performance of the proposed methods with that of two recent single-sequence prediction methods. We also investigate the impact of presence of different amino acid residues (in protein sequences) that are responsible for the formation of different secondary structures.

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

    PubMed Central

    Brylinski, Michal

    2009-01-01

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

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

    PubMed

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

    2012-12-14

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

  15. Structure- and Sequence-Based Function Prediction for Non-Homologous Proteins

    PubMed Central

    Sael, Lee; Chitale, Meghana; Kihara, Daisuke

    2012-01-01

    The structural genomics projects have been accumulating an increasing number of protein structures, many of which remain functionally unknown. In parallel effort to experimental methods, computational methods are expected to make a significant contribution for functional elucidation of such proteins. However, conventional computational methods that transfer functions from homologous proteins do not help much for these uncharacterized protein structures because they do not have apparent structural or sequence similarity with the known proteins. Here, we briefly review two avenues of computational function prediction methods, i.e. structure-based methods and sequence-based methods. The focus is on our recently developments of local structure-based methods and sequence-based methods, which can effectively extract function information from distantly related proteins. Two structure-based methods, Pocket-Surfer and Patch-Surfer, identify similar known ligand binding sites for pocket regions in a query protein without using global protein fold similarity information. Two sequence-based methods, PFP and ESG, make use of weakly similar sequences that are conventionally discarded in homology based function annotation. Combined together with experimental methods we hope that computational methods will make leading contribution in functional elucidation of the protein structures. PMID:22270458

  16. SVM-PB-Pred: SVM based protein block prediction method using sequence profiles and secondary structures.

    PubMed

    Suresh, V; Parthasarathy, S

    2014-01-01

    We developed a support vector machine based web server called SVM-PB-Pred, to predict the Protein Block for any given amino acid sequence. The input features of SVM-PB-Pred include i) sequence profiles (PSSM) and ii) actual secondary structures (SS) from DSSP method or predicted secondary structures from NPS@ and GOR4 methods. There were three combined input features PSSM+SS(DSSP), PSSM+SS(NPS@) and PSSM+SS(GOR4) used to test and train the SVM models. Similarly, four datasets RS90, DB433, LI1264 and SP1577 were used to develop the SVM models. These four SVM models developed were tested using three different benchmarking tests namely; (i) self consistency, (ii) seven fold cross validation test and (iii) independent case test. The maximum possible prediction accuracy of ~70% was observed in self consistency test for the SVM models of both LI1264 and SP1577 datasets, where PSSM+SS(DSSP) input features was used to test. The prediction accuracies were reduced to ~53% for PSSM+SS(NPS@) and ~43% for PSSM+SS(GOR4) in independent case test, for the SVM models of above two same datasets. Using our method, it is possible to predict the protein block letters for any query protein sequence with ~53% accuracy, when the SP1577 dataset and predicted secondary structure from NPS@ server were used. The SVM-PB-Pred server can be freely accessed through http://bioinfo.bdu.ac.in/~svmpbpred.

  17. A protein structural classes prediction method based on PSI-BLAST profile.

    PubMed

    Ding, Shuyan; Yan, Shoujiang; Qi, Shuhua; Li, Yan; Yao, Yuhua

    2014-07-21

    Knowledge of protein structural classes plays an important role in understanding protein folding patterns. Prediction of protein structural class based solely on sequence data remains to be a challenging problem. In this study, we extract the long-range correlation information and linear correlation information from position-specific score matrix (PSSM). A total of 3600 features are extracted, then, 278 features are selected by a filter feature selection method based on 1189 dataset. To verify the performance of our method (named by LCC-PSSM), jackknife tests are performed on three widely used low similarity benchmark datasets. Comparison of our results with the existing methods shows that our method provides the favorable performance for protein structural class prediction. Stand-alone version of the proposed method (LCC-PSSM) is written in MATLAB language and it can be downloaded from http://bioinfo.zstu.edu.cn/LCC-PSSM/.

  18. Prediction of Protein Structural Class Based on Gapped-Dipeptides and a Recursive Feature Selection Approach.

    PubMed

    Liu, Taigang; Qin, Yufang; Wang, Yongjie; Wang, Chunhua

    2015-12-24

    The prior knowledge of protein structural class may offer useful clues on understanding its functionality as well as its tertiary structure. Though various significant efforts have been made to find a fast and effective computational approach to address this problem, it is still a challenging topic in the field of bioinformatics. The position-specific score matrix (PSSM) profile has been shown to provide a useful source of information for improving the prediction performance of protein structural class. However, this information has not been adequately explored. To this end, in this study, we present a feature extraction technique which is based on gapped-dipeptides composition computed directly from PSSM. Then, a careful feature selection technique is performed based on support vector machine-recursive feature elimination (SVM-RFE). These optimal features are selected to construct a final predictor. The results of jackknife tests on four working datasets show that our method obtains satisfactory prediction accuracies by extracting features solely based on PSSM and could serve as a very promising tool to predict protein structural class.

  19. Optimized distance-dependent atom-pair-based potential DOOP for protein structure prediction.

    PubMed

    Chae, Myong-Ho; Krull, Florian; Knapp, Ernst-Walter

    2015-05-01

    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/.

  20. An Energy Based Fatigue Life Prediction Framework for In-Service Structural Components

    SciTech Connect

    H. Ozaltun; M. H.H. Shen; T. George; C. Cross

    2011-06-01

    An energy based fatigue life prediction framework has been developed for calculation of remaining fatigue life of in service gas turbine materials. The purpose of the life prediction framework is to account aging effect caused by cyclic loadings on fatigue strength of gas turbine engines structural components which are usually designed for very long life. Previous studies indicate the total strain energy dissipated during a monotonic fracture process and a cyclic process is a material property that can be determined by measuring the area underneath the monotonic true stress-strain curve and the sum of the area within each hysteresis loop in the cyclic process, respectively. The energy-based fatigue life prediction framework consists of the following entities: (1) development of a testing procedure to achieve plastic energy dissipation per life cycle and (2) incorporation of an energy-based fatigue life calculation scheme to determine the remaining fatigue life of in-service gas turbine materials. The accuracy of the remaining fatigue life prediction method was verified by comparison between model approximation and experimental results of Aluminum 6061-T6. The comparison shows promising agreement, thus validating the capability of the framework to produce accurate fatigue life prediction.

  1. Protein subcellular localization prediction based on compartment-specific features and structure conservation

    PubMed Central

    Su, Emily Chia-Yu; Chiu, Hua-Sheng; Lo, Allan; Hwang, Jenn-Kang; Sung, Ting-Yi; Hsu, Wen-Lian

    2007-01-01

    Background Protein subcellular localization is crucial for genome annotation, protein function prediction, and drug discovery. Determination of subcellular localization using experimental approaches is time-consuming; thus, computational approaches become highly desirable. Extensive studies of localization prediction have led to the development of several methods including composition-based and homology-based methods. However, their performance might be significantly degraded if homologous sequences are not detected. Moreover, methods that integrate various features could suffer from the problem of low coverage in high-throughput proteomic analyses due to the lack of information to characterize unknown proteins. Results We propose a hybrid prediction method for Gram-negative bacteria that combines a one-versus-one support vector machines (SVM) model and a structural homology approach. The SVM model comprises a number of binary classifiers, in which biological features derived from Gram-negative bacteria translocation pathways are incorporated. In the structural homology approach, we employ secondary structure alignment for structural similarity comparison and assign the known localization of the top-ranked protein as the predicted localization of a query protein. The hybrid method achieves overall accuracy of 93.7% and 93.2% using ten-fold cross-validation on the benchmark data sets. In the assessment of the evaluation data sets, our method also attains accurate prediction accuracy of 84.0%, especially when testing on sequences with a low level of homology to the training data. A three-way data split procedure is also incorporated to prevent overestimation of the predictive performance. In addition, we show that the prediction accuracy should be approximately 85% for non-redundant data sets of sequence identity less than 30%. Conclusion Our results demonstrate that biological features derived from Gram-negative bacteria translocation pathways yield a significant

  2. Genetic programming based quantitative structure-retention relationships for the prediction of Kovats retention indices.

    PubMed

    Goel, Purva; Bapat, Sanket; Vyas, Renu; Tambe, Amruta; Tambe, Sanjeev S

    2015-11-13

    The development of quantitative structure-retention relationships (QSRR) aims at constructing an appropriate linear/nonlinear model for the prediction of the retention behavior (such as Kovats retention index) of a solute on a chromatographic column. Commonly, multi-linear regression and artificial neural networks are used in the QSRR development in the gas chromatography (GC). In this study, an artificial intelligence based data-driven modeling formalism, namely genetic programming (GP), has been introduced for the development of quantitative structure based models predicting Kovats retention indices (KRI). The novelty of the GP formalism is that given an example dataset, it searches and optimizes both the form (structure) and the parameters of an appropriate linear/nonlinear data-fitting model. Thus, it is not necessary to pre-specify the form of the data-fitting model in the GP-based modeling. These models are also less complex, simple to understand, and easy to deploy. The effectiveness of GP in constructing QSRRs has been demonstrated by developing models predicting KRIs of light hydrocarbons (case study-I) and adamantane derivatives (case study-II). In each case study, two-, three- and four-descriptor models have been developed using the KRI data available in the literature. The results of these studies clearly indicate that the GP-based models possess an excellent KRI prediction accuracy and generalization capability. Specifically, the best performing four-descriptor models in both the case studies have yielded high (>0.9) values of the coefficient of determination (R(2)) and low values of root mean squared error (RMSE) and mean absolute percent error (MAPE) for training, test and validation set data. The characteristic feature of this study is that it introduces a practical and an effective GP-based method for developing QSRRs in gas chromatography that can be gainfully utilized for developing other types of data-driven models in chromatography science.

  3. Machine-learning scoring functions to improve structure-based binding affinity prediction and virtual screening.

    PubMed

    Ain, Qurrat Ul; Aleksandrova, Antoniya; Roessler, Florian D; Ballester, Pedro J

    2015-01-01

    Docking tools to predict whether and how a small molecule binds to a target can be applied if a structural model of such target is available. The reliability of docking depends, however, on the accuracy of the adopted scoring function (SF). Despite intense research over the years, improving the accuracy of SFs for structure-based binding affinity prediction or virtual screening has proven to be a challenging task for any class of method. New SFs based on modern machine-learning regression models, which do not impose a predetermined functional form and thus are able to exploit effectively much larger amounts of experimental data, have recently been introduced. These machine-learning SFs have been shown to outperform a wide range of classical SFs at both binding affinity prediction and virtual screening. The emerging picture from these studies is that the classical approach of using linear regression with a small number of expert-selected structural features can be strongly improved by a machine-learning approach based on nonlinear regression allied with comprehensive data-driven feature selection. Furthermore, the performance of classical SFs does not grow with larger training datasets and hence this performance gap is expected to widen as more training data becomes available in the future. Other topics covered in this review include predicting the reliability of a SF on a particular target class, generating synthetic data to improve predictive performance and modeling guidelines for SF development. WIREs Comput Mol Sci 2015, 5:405-424. doi: 10.1002/wcms.1225 For further resources related to this article, please visit the WIREs website.

  4. Prediction of atomic structure of Pt-based bimetallic nanoalloys by using genetic algorithm

    NASA Astrophysics Data System (ADS)

    Oh, Jung Soo; Nam, Ho-Seok; Choi, Jung-Hae; Lee, Seung-Cheol

    2013-05-01

    The atom-arrangements in Pt-based bimetallic nanoalloys were predicted by the combined use of genetic algorithm (GA) and molecular dynamics (MD) simulations. The nanoparticles of these nanoalloys were assumed to be a 3.5 nm-diameter truncated octahedron with Pt and noble metals of fixed composition ratio of 1:1. For the GA, a Python code, which concurrently linked with the MD method that uses the embedded atom method inter-atomic potentials, was developed for the prediction of the atom arrangements in these bimetallic nanoalloys. Successfully, the GA calculation predicted the core-shell structures for both Pt-Ag and Pt-Au nanoalloy, but an onion-like multilayered core-shell structure for Pt-Cu nanoalloy. The structural characteristics in the bimetallic nanoalloy were mainly due to the differences in the surface energy and cohesive energy between Pt and the other alloying metal elements and their miscibility gap and so on. Briefly, the prediction performance was analyzed to show the superior searching ability of GA.

  5. Fast reconstruction and prediction of frozen flow turbulence based on structured Kalman filtering.

    PubMed

    Fraanje, Rufus; Rice, Justin; Verhaegen, Michel; Doelman, Niek

    2010-11-01

    Efficient and optimal prediction of frozen flow turbulence using the complete observation history of the wavefront sensor is an important issue in adaptive optics for large ground-based telescopes. At least for the sake of error budgeting and algorithm performance, the evaluation of an accurate estimate of the optimal performance of a particular adaptive optics configuration is important. However, due to the large number of grid points, high sampling rates, and the non-rationality of the turbulence power spectral density, the computational complexity of the optimal predictor is huge. This paper shows how a structure in the frozen flow propagation can be exploited to obtain a state-space innovation model with a particular sparsity structure. This sparsity structure enables one to efficiently compute a structured Kalman filter. By simulation it is shown that the performance can be improved and the computational complexity can be reduced in comparison with auto-regressive predictors of low order.

  6. Highly Accurate Structure-Based Prediction of HIV-1 Coreceptor Usage Suggests Intermolecular Interactions Driving Tropism.

    PubMed

    Kieslich, Chris A; Tamamis, Phanourios; Guzman, Yannis A; Onel, Melis; Floudas, Christodoulos A

    2016-01-01

    HIV-1 entry into host cells is mediated by interactions between the V3-loop of viral glycoprotein gp120 and chemokine receptor CCR5 or CXCR4, collectively known as HIV-1 coreceptors. Accurate genotypic prediction of coreceptor usage is of significant clinical interest and determination of the factors driving tropism has been the focus of extensive study. We have developed a method based on nonlinear support vector machines to elucidate the interacting residue pairs driving coreceptor usage and provide highly accurate coreceptor usage predictions. Our models utilize centroid-centroid interaction energies from computationally derived structures of the V3-loop:coreceptor complexes as primary features, while additional features based on established rules regarding V3-loop sequences are also investigated. We tested our method on 2455 V3-loop sequences of various lengths and subtypes, and produce a median area under the receiver operator curve of 0.977 based on 500 runs of 10-fold cross validation. Our study is the first to elucidate a small set of specific interacting residue pairs between the V3-loop and coreceptors capable of predicting coreceptor usage with high accuracy across major HIV-1 subtypes. The developed method has been implemented as a web tool named CRUSH, CoReceptor USage prediction for HIV-1, which is available at http://ares.tamu.edu/CRUSH/.

  7. Structure Based Thermostability Prediction Models for Protein Single Point Mutations with Machine Learning Tools.

    PubMed

    Jia, Lei; Yarlagadda, Ramya; Reed, Charles C

    2015-01-01

    Thermostability issue of protein point mutations is a common occurrence in protein engineering. An application which predicts the thermostability of mutants can be helpful for guiding decision making process in protein design via mutagenesis. An in silico point mutation scanning method is frequently used to find "hot spots" in proteins for focused mutagenesis. ProTherm (http://gibk26.bio.kyutech.ac.jp/jouhou/Protherm/protherm.html) is a public database that consists of thousands of protein mutants' experimentally measured thermostability. Two data sets based on two differently measured thermostability properties of protein single point mutations, namely the unfolding free energy change (ddG) and melting temperature change (dTm) were obtained from this database. Folding free energy change calculation from Rosetta, structural information of the point mutations as well as amino acid physical properties were obtained for building thermostability prediction models with informatics modeling tools. Five supervised machine learning methods (support vector machine, random forests, artificial neural network, naïve Bayes classifier, K nearest neighbor) and partial least squares regression are used for building the prediction models. Binary and ternary classifications as well as regression models were built and evaluated. Data set redundancy and balancing, the reverse mutations technique, feature selection, and comparison to other published methods were discussed. Rosetta calculated folding free energy change ranked as the most influential features in all prediction models. Other descriptors also made significant contributions to increasing the accuracy of the prediction models.

  8. Disulfide Connectivity Prediction Based on Modelled Protein 3D Structural Information and Random Forest Regression.

    PubMed

    Yu, Dong-Jun; Li, Yang; Hu, Jun; Yang, Xibei; Yang, Jing-Yu; Shen, Hong-Bin

    2015-01-01

    Disulfide connectivity is an important protein structural characteristic. Accurately predicting disulfide connectivity solely from protein sequence helps to improve the intrinsic understanding of protein structure and function, especially in the post-genome era where large volume of sequenced proteins without being functional annotated is quickly accumulated. In this study, a new feature extracted from the predicted protein 3D structural information is proposed and integrated with traditional features to form discriminative features. Based on the extracted features, a random forest regression model is performed to predict protein disulfide connectivity. We compare the proposed method with popular existing predictors by performing both cross-validation and independent validation tests on benchmark datasets. The experimental results demonstrate the superiority of the proposed method over existing predictors. We believe the superiority of the proposed method benefits from both the good discriminative capability of the newly developed features and the powerful modelling capability of the random forest. The web server implementation, called TargetDisulfide, and the benchmark datasets are freely available at: http://csbio.njust.edu.cn/bioinf/TargetDisulfide for academic use.

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

    PubMed

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

    2014-12-01

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

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

    PubMed

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

    2016-01-01

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

  11. Crystal structure and prediction.

    PubMed

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

    2015-04-01

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

  12. Crystal Structure and Prediction

    NASA Astrophysics Data System (ADS)

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

    2015-04-01

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

  13. Molecular Simulation-Based Structural Prediction of Protein Complexes in Mass Spectrometry: The Human Insulin Dimer

    PubMed Central

    Li, Jinyu; Rossetti, Giulia; Dreyer, Jens; Raugei, Simone; Ippoliti, Emiliano; Lüscher, Bernhard; Carloni, Paolo

    2014-01-01

    Protein electrospray ionization (ESI) mass spectrometry (MS)-based techniques are widely used to provide insight into structural proteomics under the assumption that non-covalent protein complexes being transferred into the gas phase preserve basically the same intermolecular interactions as in solution. Here we investigate the applicability of this assumption by extending our previous structural prediction protocol for single proteins in ESI-MS to protein complexes. We apply our protocol to the human insulin dimer (hIns2) as a test case. Our calculations reproduce the main charge and the collision cross section (CCS) measured in ESI-MS experiments. Molecular dynamics simulations for 0.075 ms show that the complex maximizes intermolecular non-bonded interactions relative to the structure in water, without affecting the cross section. The overall gas-phase structure of hIns2 does exhibit differences with the one in aqueous solution, not inferable from a comparison with calculated CCS. Hence, care should be exerted when interpreting ESI-MS proteomics data based solely on NMR and/or X-ray structural information. PMID:25210764

  14. Predicting adsorption of aromatic compounds by carbon nanotubes based on quantitative structure property relationship principles

    NASA Astrophysics Data System (ADS)

    Rahimi-Nasrabadi, Mehdi; Akhoondi, Reza; Pourmortazavi, Seied Mahdi; Ahmadi, Farhad

    2015-11-01

    Quantitative structure property relationship (QSPR) models were developed to predict the adsorption of aromatic compounds by carbon nanotubes (CNTs). Five descriptors chosen by combining self-organizing map and stepwise multiple linear regression (MLR) techniques were used to connect the structure of the studied chemicals with their adsorption descriptor (K∞) using linear and nonlinear modeling techniques. Correlation coefficient (R2) of 0.99 and root-mean square error (RMSE) of 0.29 for multilayered perceptron neural network (MLP-NN) model are signs of the superiority of the developed nonlinear model over MLR model with R2 of 0.93 and RMSE of 0.36. The results of cross-validation test showed the reliability of MLP-NN to predict the K∞ values for the aromatic contaminants. Molar volume and hydrogen bond accepting ability were found to be the factors much influencing the adsorption of the compounds. The developed QSPR, as a neural network based model, could be used to predict the adsorption of organic compounds by CNTs.

  15. Prediction of molecular properties including symmetry from quantum-based molecular structural formulas, VIF.

    PubMed

    Alia, Joseph D; Vlaisavljevich, Bess; Abbot, Matthew; Warneke, Hallie; Mastin, Tyson

    2008-10-09

    Structurally covariant valency interaction formulas, VIF, gain chemical significance by comparison with resonance structures and natural bond orbital, NBO, bonding schemes and at the same time allow for additional prediction such as symmetry of ring systems and destabilization of electron pairs with respect to reference energy of -1/2 Eh. Comparisons are based on three chemical interpretations of Sinanoğlu's theory of structural covariance: (1) sets of structurally covariant quantum structural formulas, VIF, are interpreted as the same quantum operator represented in linearly related basis frames; (2) structurally covariant VIF pictures are interpreted as sets of molecular species with similar energy; and (3) the same VIF picture can be interpreted as different quantum operators, one-electron density or Hamiltonian; for example. According to these three interpretations, bond pair, lone pair, and free radical electrons understood in terms of a localized orbital representation are recognized as having energies above, below, or equal to a predetermined reference, frequently-1/2 Eh. The probable position of electron pairs and radical electrons is predicted. The selectivity of concerted ring closures in allyl anion and cation is described. Symmetries of conjugated ring systems are predicted according to their numbers of pi-electrons and spin-multiplicity. The pi-distortivity of benzene is predicted.The 3c/2e- H-bridging bonds in diborane are derived in a natural way according to the notion that the bridging bonds will have delocalizing interactions between them consistent with results of the NBO method. Key chemical bonding motifs are described using VIF. These include 2c/1e-, 2c/2e-, 2c/3e-, 3c/2e-, 3c/3e-,3c/4e-, 4n antiaromatic, and 4n+2 aromatic bonding systems. Some common organic functional groups are represented as VIF pictures and because these pictures can be interpreted simultaneously as one-electron density and Hamiltonian operators, the valence shell

  16. A Novel Wavelet-Based Approach for Predicting Nucleosome Positions Using DNA Structural Information.

    PubMed

    Gan, Yanglan; Zou, Guobing; Guan, Jihong; Xu, Guangwei

    2014-01-01

    Nucleosomes are basic elements of chromatin structure. The positioning of nucleosomes along a genome is very important to dictate eukaryotic DNA compaction and access. Current computational methods have focused on the analysis of nucleosome occupancy and the positioning of well-positioned nucleosomes. However, fuzzy nucleosomes require more complex configurations and are more difficult to predict their positions. We analyzed the positioning of well-positioned and fuzzy nucleosomes from a novel structural perspective, and proposed WaveNuc, a computational approach for inferring their positions based on continuous wavelet transformation. The comparative analysis demonstrates that these two kinds of nucleosomes exhibit different propeller twist structural characteristics. Well-positioned nucleosomes tend to locate at sharp peaks of the propeller twist profile, whereas fuzzy nucleosomes correspond to broader peaks. The sharpness of these peaks shows that the propeller twist profile may contain nucleosome positioning information. Exploiting this knowledge, we applied WaveNuc to detect the two different kinds of peaks of the propeller twist profile along the genome. We compared the performance of our method with existing methods on real data sets. The results show that the proposed method can accurately resolve complex configurations of fuzzy nucleosomes, which leads to better performance of nucleosome positioning prediction on the whole genome.

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

    NASA Technical Reports Server (NTRS)

    Brinson, Hal F.

    1991-01-01

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

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

    NASA Technical Reports Server (NTRS)

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

    1990-01-01

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

  19. Coupled agent-based and finite-element models for predicting scar structure following myocardial infarction.

    PubMed

    Rouillard, Andrew D; Holmes, Jeffrey W

    2014-08-01

    Following myocardial infarction, damaged muscle is gradually replaced by collagenous scar tissue. The structural and mechanical properties of the scar are critical determinants of heart function, as well as the risk of serious post-infarction complications such as infarct rupture, infarct expansion, and progression to dilated heart failure. A number of therapeutic approaches currently under development aim to alter infarct mechanics in order to reduce complications, such as implantation of mechanical restraint devices, polymer injection, and peri-infarct pacing. Because mechanical stimuli regulate scar remodeling, the long-term consequences of therapies that alter infarct mechanics must be carefully considered. Computational models have the potential to greatly improve our ability to understand and predict how such therapies alter heart structure, mechanics, and function over time. Toward this end, we developed a straightforward method for coupling an agent-based model of scar formation to a finite-element model of tissue mechanics, creating a multi-scale model that captures the dynamic interplay between mechanical loading, scar deformation, and scar material properties. The agent-based component of the coupled model predicts how fibroblasts integrate local chemical, structural, and mechanical cues as they deposit and remodel collagen, while the finite-element component predicts local mechanics at any time point given the current collagen fiber structure and applied loads. We used the coupled model to explore the balance between increasing stiffness due to collagen deposition and increasing wall stress due to infarct thinning and left ventricular dilation during the normal time course of healing in myocardial infarcts, as well as the negative feedback between strain anisotropy and the structural anisotropy it promotes in healing scar. The coupled model reproduced the observed evolution of both collagen fiber structure and regional deformation following coronary

  20. Structured prediction models for RNN based sequence labeling in clinical text

    PubMed Central

    Jagannatha, Abhyuday N; Yu, Hong

    2016-01-01

    Sequence labeling is a widely used method for named entity recognition and information extraction from unstructured natural language data. In clinical domain one major application of sequence labeling involves extraction of medical entities such as medication, indication, and side-effects from Electronic Health Record narratives. Sequence labeling in this domain, presents its own set of challenges and objectives. In this work we experimented with various CRF based structured learning models with Recurrent Neural Networks. We extend the previously studied LSTM-CRF models with explicit modeling of pairwise potentials. We also propose an approximate version of skip-chain CRF inference with RNN potentials. We use these methodologies1 for structured prediction in order to improve the exact phrase detection of various medical entities. PMID:28004040

  1. Structure-based constitutive model can accurately predict planar biaxial properties of aortic wall tissue.

    PubMed

    Polzer, S; Gasser, T C; Novak, K; Man, V; Tichy, M; Skacel, P; Bursa, J

    2015-03-01

    Structure-based constitutive models might help in exploring mechanisms by which arterial wall histology is linked to wall mechanics. This study aims to validate a recently proposed structure-based constitutive model. Specifically, the model's ability to predict mechanical biaxial response of porcine aortic tissue with predefined collagen structure was tested. Histological slices from porcine thoracic aorta wall (n=9) were automatically processed to quantify the collagen fiber organization, and mechanical testing identified the non-linear properties of the wall samples (n=18) over a wide range of biaxial stretches. Histological and mechanical experimental data were used to identify the model parameters of a recently proposed multi-scale constitutive description for arterial layers. The model predictive capability was tested with respect to interpolation and extrapolation. Collagen in the media was predominantly aligned in circumferential direction (planar von Mises distribution with concentration parameter bM=1.03 ± 0.23), and its coherence decreased gradually from the luminal to the abluminal tissue layers (inner media, b=1.54 ± 0.40; outer media, b=0.72 ± 0.20). In contrast, the collagen in the adventitia was aligned almost isotropically (bA=0.27 ± 0.11), and no features, such as families of coherent fibers, were identified. The applied constitutive model captured the aorta biaxial properties accurately (coefficient of determination R(2)=0.95 ± 0.03) over the entire range of biaxial deformations and with physically meaningful model parameters. Good predictive properties, well outside the parameter identification space, were observed (R(2)=0.92 ± 0.04). Multi-scale constitutive models equipped with realistic micro-histological data can predict macroscopic non-linear aorta wall properties. Collagen largely defines already low strain properties of media, which explains the origin of wall anisotropy seen at this strain level. The structure and mechanical

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

    PubMed Central

    2014-01-01

    Background Human Immunodeficiency Virus 1 enters host cells through interaction of its V3 loop (which is part of the gp120 protein) with the host cell receptor CD4 and one of two co-receptors, namely CCR5 or CXCR4. Entry inhibitors binding the CCR5 co-receptor can prevent viral entry. As these drugs are only available for CCR5-using viruses, accurate prediction of this so-called co-receptor tropism is important in order to ensure an effective personalized therapy. With the development of next-generation sequencing technologies, it is now possible to sequence representative subpopulations of the viral quasispecies. Results Here we present T-CUP 2.0, a model for predicting co-receptor tropism. Based on our recently published T-CUP model, we developed a more accurate and even faster solution. Similarly to its predecessor, T-CUP 2.0 models co-receptor tropism using information of the electrostatic potential and hydrophobicity of V3-loops. However, extracting this information from a simplified structural vacuum-model leads to more accurate and faster predictions. The area-under-the-ROC-curve (AUC) achieved with T-CUP 2.0 on the training set is 0.968±0.005 in a leave-one-patient-out cross-validation. When applied to an independent dataset, T-CUP 2.0 has an improved prediction accuracy of around 3% when compared to the original T-CUP. Conclusions We found that it is possible to model co-receptor tropism in HIV-1 based on a simplified structure-based model of the V3 loop. In this way, genotypic prediction of co-receptor tropism is very accurate, fast and can be applied to large datasets derived from next-generation sequencing technologies. The reduced complexity of the electrostatic modeling makes T-CUP 2.0 independent from third-party software, making it easy to install and use. PMID:25120583

  3. Structure-based function prediction of the expanding mollusk tyrosinase family

    NASA Astrophysics Data System (ADS)

    Huang, Ronglian; Li, Li; Zhang, Guofan

    2017-01-01

    Tyrosinase (Ty) is a common enzyme found in many different animal groups. In our previous study, genome sequencing revealed that the Ty family is expanded in the Pacific oyster (Crassostrea gigas). Here, we examine the larger number of Ty family members in the Pacific oyster by high-level structure prediction to obtain more information about their function and evolution, especially the unknown role in biomineralization. We verified 12 Ty gene sequences from Crassostrea gigas genome and Pinctada fucata martensii transcriptome. By using phylogenetic analysis of these Tys with functionally known Tys from other molluscan species, eight subgroups were identified (CgTy_s1, CgTy_s2, MolTy_s1, MolTy-s2, MolTy-s3, PinTy-s1, PinTy-s2 and PviTy). Structural data and surface pockets of the dinuclear copper center in the eight subgroups of molluscan Ty were obtained using the latest versions of prediction online servers. Structural comparison with other Ty proteins from the protein databank revealed functionally important residues (HA1, HA2, HA3, HB1, HB2, HB3, Z1-Z9) and their location within these protein structures. The structural and chemical features of these pockets which may related to the substrate binding showed considerable variability among mollusks, which undoubtedly defines Ty substrate binding. Finally, we discuss the potential driving forces of Ty family evolution in mollusks. Based on these observations, we conclude that the Ty family has rapidly evolved as a consequence of substrate adaptation in mollusks.

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

    NASA Astrophysics Data System (ADS)

    Suzuki, Tomonori; Miyazaki, Satoru

    2011-01-01

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

  5. Prediction of retention in hydrophilic interaction liquid chromatography using solute molecular descriptors based on chemical structures.

    PubMed

    Taraji, Maryam; Haddad, Paul R; Amos, Ruth I J; Talebi, Mohammad; Szucs, Roman; Dolan, John W; Pohl, Christopher A

    2017-02-24

    Quantitative structure-retention relationship (QSRR) models are developed to predict the retention times of analytes on five hydrophilic interaction liquid chromatography (HILIC) stationary phases (bare silica, amine, amide, diol and zwitterionic), with a view to selecting the most suitable stationary phase(s) for the separation of these analytes. The study was conducted using six β-adrenergic agonists as target analytes. Molecular descriptors were calculated based only on chemical structures optimized using density functional theory. A genetic algorithm (GA) was then used to select the most relevant molecular descriptors and these were used to build a retention model for each stationary phase using partial least squares (PLS) regression. This model was then used to predict the retention of the test set of target analytes. This process created an optimized descriptor set which enhanced the reliability of the developed QSRR models. Finally, the QSRR models developed in the work were utilized to provide some insight into the separation mechanisms operating in the HILIC mode. Three performance criteria - mean absolute error (MAE), root mean square error of prediction scaled to retention time (RMSEP), and the number of selected descriptors, were used to evaluate the developed models when applied to an external test set of six β-adrenergic agonists and showed highly predictive abilities. MAE values ranged from 13 to 25s on four of the stationary phases, with a somewhat higher error (50s) being observed for the zwitterionic phase. RMSEP values of 4.88-11.12% were recorded. Validation was performed through Y-randomization and chemical domain applicability, from which it was evident that the developed optimized GA-PLS models were robust. The high levels of accuracy, reliability and applicability of the models were to a large extent due to the optimization of the GA descriptor set and the presence of relevant structural and geometric molecular descriptors, together with

  6. APL: An angle probability list to improve knowledge-based metaheuristics for the three-dimensional protein structure prediction.

    PubMed

    Borguesan, Bruno; Barbachan e Silva, Mariel; Grisci, Bruno; Inostroza-Ponta, Mario; Dorn, Márcio

    2015-12-01

    Tertiary protein structure prediction is one of the most challenging problems in structural bioinformatics. Despite the advances in algorithm development and computational strategies, predicting the folded structure of a protein only from its amino acid sequence remains as an unsolved problem. We present a new computational approach to predict the native-like three-dimensional structure of proteins. Conformational preferences of amino acid residues and secondary structure information were obtained from protein templates stored in the Protein Data Bank and represented as an Angle Probability List. Two knowledge-based prediction methods based on Genetic Algorithms and Particle Swarm Optimization were developed using this information. The proposed method has been tested with twenty-six case studies selected to validate our approach with different classes of proteins and folding patterns. Stereochemical and structural analysis were performed for each predicted three-dimensional structure. Results achieved suggest that the Angle Probability List can improve the effectiveness of metaheuristics used to predicted the three-dimensional structure of protein molecules by reducing its conformational search space.

  7. Direct data-based model predictive control with applications to structures, robotic swarms, and aircraft

    NASA Astrophysics Data System (ADS)

    Barlow, Jonathan S.

    A direct method to design data-based model predictive controllers is presented. The design method uses system identification techniques to identify model predictive controller gains directly from a set of excitation input and disturbance corrupted output. The design is direct in that the controller gains can be designed directly from input and disturbance corrupted output data without an intermediate identification step. The direct design is simpler than previous two-step designs and reduces computation time for the design of the controller. The direct design also enables an adaptive implementation capable of identifying controller gains online. The direct data-based controllers can be used for vibration suppression, disturbance rejection, tracking and is applied to structures, robot swarms and aircraft. For the cases of vibration suppression and disturbance rejection, the data-based controller has the advantage that any disturbances present in the design data are automatically rejected without needing to know the details of the disturbances. For the case of robot swarms, extensions are made for formation control and obstacle avoidance, and the controller can be implemented as a decentralized controller in real time and in parallel on individual vehicles with communication limited to past input and past output data. A formulation for improving the robustness of the controller to parametric variations is also developed. Finally, the adaptive implementation is shown to be useful for the control of linear time-varying systems and has been successfully implemented to control a linear time-varying model of a Cruise Efficient Short Take-Off and Landing (CESTOL) type aircraft.

  8. Structure-based approach to the prediction of disulfide bonds in proteins.

    PubMed

    Salam, Noeris K; Adzhigirey, Matvey; Sherman, Woody; Pearlman, David A

    2014-10-01

    Protein engineering remains an area of growing importance in pharmaceutical and biotechnology research. Stabilization of a folded protein conformation is a frequent goal in projects that deal with affinity optimization, enzyme design, protein construct design, and reducing the size of functional proteins. Indeed, it can be desirable to assess and improve protein stability in order to avoid liabilities such as aggregation, degradation, and immunogenic response that may arise during development. One way to stabilize a protein is through the introduction of disulfide bonds. Here, we describe a method to predict pairs of protein residues that can be mutated to form a disulfide bond. We combine a physics-based approach that incorporates implicit solvent molecular mechanics with a knowledge-based approach. We first assign relative weights to the terms that comprise our scoring function using a genetic algorithm applied to a set of 75 wild-type structures that each contains a disulfide bond. The method is then tested on a separate set of 13 engineered proteins comprising 15 artificial stabilizing disulfides introduced via site-directed mutagenesis. We find that the native disulfide in the wild-type proteins is scored well, on average (within the top 6% of the reasonable pairs of residues that could form a disulfide bond) while 6 out of the 15 artificial stabilizing disulfides scored within the top 13% of ranked predictions. Overall, this suggests that the physics-based approach presented here can be useful for triaging possible pairs of mutations for disulfide bond formation to improve protein stability.

  9. Ligand and Structure-based Methodologies for the Prediction of the Activity of G Protein-Coupled Receptor Ligands

    PubMed Central

    Costanzi, Stefano; Tikhonova, Irina G.; Harden, T. Kendall; Jacobson, Kenneth A.

    2008-01-01

    Summary Accurate in silico models for the quantitative prediction of the activity of G protein-coupled receptor (GPCR) ligands would greatly facilitate the process of drug discovery and development. Several methodologies have been developed based on the properties of the ligands, the direct study of the receptor-ligand interactions, or a combination of both approaches. Ligand-based three-dimensional quantitative structure-activity relationships (3D-QSAR) techniques, not requiring knowledge of the receptor structure, have been historically the first to be applied to the prediction of the activity of GPCR ligands. They are generally endowed with robustness and good ranking ability; however they are highly dependent on training sets. Structure-based techniques generally do not provide the level of accuracy necessary to yield meaningful rankings when applied to GPCR homology models. However, they are essentially independent from training sets and have a sufficient level of accuracy to allow an effective discrimination between binders and nonbinders, thus qualifying as viable lead discovery tools. The combination of ligand and structure-based methodologies in the form of receptor-based 3D-QSAR and ligand and structure-based consensus models results in robust and accurate quantitative predictions. The contribution of the structure-based component to these combined approaches is expected to become more substantial and effective in the future, as more sophisticated scoring functions are developed and more detailed structural information on GPCRs is gathered. PMID:18483766

  10. Ligand and structure-based methodologies for the prediction of the activity of G protein-coupled receptor ligands

    NASA Astrophysics Data System (ADS)

    Costanzi, Stefano; Tikhonova, Irina G.; Harden, T. Kendall; Jacobson, Kenneth A.

    2009-11-01

    Accurate in silico models for the quantitative prediction of the activity of G protein-coupled receptor (GPCR) ligands would greatly facilitate the process of drug discovery and development. Several methodologies have been developed based on the properties of the ligands, the direct study of the receptor-ligand interactions, or a combination of both approaches. Ligand-based three-dimensional quantitative structure-activity relationships (3D-QSAR) techniques, not requiring knowledge of the receptor structure, have been historically the first to be applied to the prediction of the activity of GPCR ligands. They are generally endowed with robustness and good ranking ability; however they are highly dependent on training sets. Structure-based techniques generally do not provide the level of accuracy necessary to yield meaningful rankings when applied to GPCR homology models. However, they are essentially independent from training sets and have a sufficient level of accuracy to allow an effective discrimination between binders and nonbinders, thus qualifying as viable lead discovery tools. The combination of ligand and structure-based methodologies in the form of receptor-based 3D-QSAR and ligand and structure-based consensus models results in robust and accurate quantitative predictions. The contribution of the structure-based component to these combined approaches is expected to become more substantial and effective in the future, as more sophisticated scoring functions are developed and more detailed structural information on GPCRs is gathered.

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

    SciTech Connect

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

    2000-08-01

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

  12. Prediction of Protein Structural Classes for Low-Similarity Sequences Based on Consensus Sequence and Segmented PSSM.

    PubMed

    Liang, Yunyun; Liu, Sanyang; Zhang, Shengli

    2015-01-01

    Prediction of protein structural classes for low-similarity sequences is useful for understanding fold patterns, regulation, functions, and interactions of proteins. It is well known that feature extraction is significant to prediction of protein structural class and it mainly uses protein primary sequence, predicted secondary structure sequence, and position-specific scoring matrix (PSSM). Currently, prediction solely based on the PSSM has played a key role in improving the prediction accuracy. In this paper, we propose a novel method called CSP-SegPseP-SegACP by fusing consensus sequence (CS), segmented PsePSSM, and segmented autocovariance transformation (ACT) based on PSSM. Three widely used low-similarity datasets (1189, 25PDB, and 640) are adopted in this paper. Then a 700-dimensional (700D) feature vector is constructed and the dimension is decreased to 224D by using principal component analysis (PCA). To verify the performance of our method, rigorous jackknife cross-validation tests are performed on 1189, 25PDB, and 640 datasets. Comparison of our results with the existing PSSM-based methods demonstrates that our method achieves the favorable and competitive performance. This will offer an important complementary to other PSSM-based methods for prediction of protein structural classes for low-similarity sequences.

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

    NASA Astrophysics Data System (ADS)

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

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

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

    ERIC Educational Resources Information Center

    Nathan, Lawrence C.

    1985-01-01

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

  15. Local structure based method for prediction of the biochemical function of proteins: Applications to glycoside hydrolases.

    PubMed

    Parasuram, Ramya; Mills, Caitlyn L; Wang, Zhouxi; Somasundaram, Saroja; Beuning, Penny J; Ondrechen, Mary Jo

    2016-01-15

    Thousands of protein structures of unknown or uncertain function have been reported as a result of high-throughput structure determination techniques developed by Structural Genomics (SG) projects. However, many of the putative functional assignments of these SG proteins in the Protein Data Bank (PDB) are incorrect. While high-throughput biochemical screening techniques have provided valuable functional information for limited sets of SG proteins, the biochemical functions for most SG proteins are still unknown or uncertain. Therefore, computational methods for the reliable prediction of protein function from structure can add tremendous value to the existing SG data. In this article, we show how computational methods may be used to predict the function of SG proteins, using examples from the six-hairpin glycosidase (6-HG) and the concanavalin A-like lectin/glucanase (CAL/G) superfamilies. Using a set of predicted functional residues, obtained from computed electrostatic and chemical properties for each protein structure, it is shown that these superfamilies may be sorted into functional families according to biochemical function. Within these superfamilies, a total of 18 SG proteins were analyzed according to their predicted, local functional sites: 13 from the 6-HG superfamily, five from the CAL/G superfamily. Within the 6-HG superfamily, an uncharacterized protein BACOVA_03626 from Bacteroides ovatus (PDB 3ON6) and a hypothetical protein BT3781 from Bacteroides thetaiotaomicron (PDB 2P0V) are shown to have very strong active site matches with exo-α-1,6-mannosidases, thus likely possessing this function. Also in this superfamily, it is shown that protein BH0842, a putative glycoside hydrolase from Bacillus halodurans (PDB 2RDY), has a predicted active site that matches well with a known α-L-galactosidase. In the CAL/G superfamily, an uncharacterized glycosyl hydrolase family 16 protein from Mycobacterium smegmatis (PDB 3RQ0) is shown to have local structural

  16. Predictive Methods for Dense Polymer Networks: Combating Bias with Bio-Based Structures

    DTIC Science & Technology

    2016-03-16

    Briefing Charts 3. DATES COVERED (From - To) 24 February 2016 – 18 March 2016 4. TITLE AND SUBTITLE Predictive methods for dense polymer networks...PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES) 8. PERFORMING ORGANIZATION REPORT NO. Air Force Research Laboratory (AFMC) AFRL/RQRP 10 E. Saturn...unlimited. PA Clearance 16152 Integrity  Service  Excellence Predictive methods for dense polymer networks: Combating bias with bio-based

  17. Fine-grained parallelism accelerating for RNA secondary structure prediction with pseudoknots based on FPGA.

    PubMed

    Xia, Fei; Jin, Guoqing

    2014-06-01

    PKNOTS is a most famous benchmark program and has been widely used to predict RNA secondary structure including pseudoknots. It adopts the standard four-dimensional (4D) dynamic programming (DP) method and is the basis of many variants and improved algorithms. Unfortunately, the O(N(6)) computing requirements and complicated data dependency greatly limits the usefulness of PKNOTS package with the explosion in gene database size. In this paper, we present a fine-grained parallel PKNOTS package and prototype system for accelerating RNA folding application based on FPGA chip. We adopted a series of storage optimization strategies to resolve the "Memory Wall" problem. We aggressively exploit parallel computing strategies to improve computational efficiency. We also propose several methods that collectively reduce the storage requirements for FPGA on-chip memory. To the best of our knowledge, our design is the first FPGA implementation for accelerating 4D DP problem for RNA folding application including pseudoknots. The experimental results show a factor of more than 50x average speedup over the PKNOTS-1.08 software running on a PC platform with Intel Core2 Q9400 Quad CPU for input RNA sequences. However, the power consumption of our FPGA accelerator is only about 50% of the general-purpose micro-processors.

  18. Guided macro-mutation in a graded energy based genetic algorithm for protein structure prediction.

    PubMed

    Rashid, Mahmood A; Iqbal, Sumaiya; Khatib, Firas; Hoque, Md Tamjidul; Sattar, Abdul

    2016-04-01

    Protein structure prediction is considered as one of the most challenging and computationally intractable combinatorial problem. Thus, the efficient modeling of convoluted search space, the clever use of energy functions, and more importantly, the use of effective sampling algorithms become crucial to address this problem. For protein structure modeling, an off-lattice model provides limited scopes to exercise and evaluate the algorithmic developments due to its astronomically large set of data-points. In contrast, an on-lattice model widens the scopes and permits studying the relatively larger proteins because of its finite set of data-points. In this work, we took the full advantage of an on-lattice model by using a face-centered-cube lattice that has the highest packing density with the maximum degree of freedom. We proposed a graded energy-strategically mixes the Miyazawa-Jernigan (MJ) energy with the hydrophobic-polar (HP) energy-based genetic algorithm (GA) for conformational search. In our application, we introduced a 2 × 2 HP energy guided macro-mutation operator within the GA to explore the best possible local changes exhaustively. Conversely, the 20 × 20 MJ energy model-the ultimate objective function of our GA that needs to be minimized-considers the impacts amongst the 20 different amino acids and allow searching the globally acceptable conformations. On a set of benchmark proteins, our proposed approach outperformed state-of-the-art approaches in terms of the free energy levels and the root-mean-square deviations.

  19. Evaluation of machine learning algorithms for treatment outcome prediction in patients with epilepsy based on structural connectome data.

    PubMed

    Munsell, Brent C; Wee, Chong-Yaw; Keller, Simon S; Weber, Bernd; Elger, Christian; da Silva, Laura Angelica Tomaz; Nesland, Travis; Styner, Martin; Shen, Dinggang; Bonilha, Leonardo

    2015-09-01

    The objective of this study is to evaluate machine learning algorithms aimed at predicting surgical treatment outcomes in groups of patients with temporal lobe epilepsy (TLE) using only the structural brain connectome. Specifically, the brain connectome is reconstructed using white matter fiber tracts from presurgical diffusion tensor imaging. To achieve our objective, a two-stage connectome-based prediction framework is developed that gradually selects a small number of abnormal network connections that contribute to the surgical treatment outcome, and in each stage a linear kernel operation is used to further improve the accuracy of the learned classifier. Using a 10-fold cross validation strategy, the first stage in the connectome-based framework is able to separate patients with TLE from normal controls with 80% accuracy, and second stage in the connectome-based framework is able to correctly predict the surgical treatment outcome of patients with TLE with 70% accuracy. Compared to existing state-of-the-art methods that use VBM data, the proposed two-stage connectome-based prediction framework is a suitable alternative with comparable prediction performance. Our results additionally show that machine learning algorithms that exclusively use structural connectome data can predict treatment outcomes in epilepsy with similar accuracy compared with "expert-based" clinical decision. In summary, using the unprecedented information provided in the brain connectome, machine learning algorithms may uncover pathological changes in brain network organization and improve outcome forecasting in the context of epilepsy.

  20. Damage Prediction and Estimation in Structural Mechanics Based on Data Mining

    SciTech Connect

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

    2001-07-23

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

  1. Evaluation of machine learning algorithms for treatment outcome prediction in patients with epilepsy based on structural connectome data

    PubMed Central

    Munsell, Brent C.; Wee, Chong-Yaw; Keller, Simon S.; Weber, Bernd; Elger, Christian; da Silva, Laura Angelica Tomaz; Nesland, Travis; Styner, Martin; Shen, Dinggang; Bonilha, Leonardo

    2015-01-01

    The objective of this study is to evaluate machine learning algorithms aimed at predicting surgical treatment outcomes in groups of patients with temporal lobe epilepsy (TLE) using only the structural brain connectome. Specifically, the brain connectome is reconstructed using white matter fiber tracts from presurgical diffusion tensor imaging. To achieve our objective, a two-stage connectome-based prediction framework is developed that gradually selects a small number of abnormal network connections that contribute to the surgical treatment outcome, and in each stage a linear kernel operation is used to further improve the accuracy of the learned classifier. Using a 10-fold cross validation strategy, the first stage in the connectome-based framework is able to separate patients with TLE from normal controls with 80% accuracy, and second stage in the connectome-based framework is able to correctly predict the surgical treatment outcome of patients with TLE with 70% accuracy. Compared to existing state-of-the-art methods that use VBM data, the proposed two-stage connectome-based prediction framework is a suitable alternative with comparable prediction performance. Our results additionally show that machine learning algorithms that exclusively use structural connectome data can predict treatment outcomes in epilepsy with similar accuracy compared with “expert-based” clinical decision. In summary, using the unprecedented information provided in the brain connectome, machine learning algorithms may uncover pathological changes in brain network organization and improve outcome forecasting in the context of epilepsy. PMID:26054876

  2. Structure-based prediction of free energy changes of binding of PTP1B inhibitors

    NASA Astrophysics Data System (ADS)

    Wang, Jing; Ling Chan, Shek; Ramnarayan, Kal

    2003-08-01

    The goals were (1) to understand the driving forces in the binding of small molecule inhibitors to the active site of PTP1B and (2) to develop a molecular mechanics-based empirical free energy function for compound potency prediction. A set of compounds with known activities was docked onto the active site. The related energy components and molecular surface areas were calculated. The bridging water molecules were identified and their contributions were considered. Linear relationships were explored between the above terms and the binding free energies of compounds derived based on experimental inhibition constants. We found that minimally three terms are required to give rise to a good correlation (0.86) with predictive power in five-group cross-validation test (q2 = 0.70). The dominant terms are the electrostatic energy and non-electrostatic energy stemming from the intra- and intermolecular interactions of solutes and from those of bridging water molecules in complexes.

  3. hβ2R-Gαs complex: prediction versus crystal structure--how valuable are predictions based on molecular modeling studies?

    PubMed

    Straßer, Andrea; Wittmann, Hans-Joachim

    2012-07-01

    In 2010, we predicted two models for the hβ(2)R-Gα(s) complex by combining the technique of homology modeling with a potential energy surface scan, since a complete crystal structure of the hβ(2)R-Gα(s) complex was not available. The crystal structure of opsin co-crystallized with part of the C-terminus of Gα (3DQB) was used as a template to model the hβ(2)R, whereas the crystal structure of Gα (1AZT) was used as a template to model Gα(s). Utilizing a potential energy surface scan between hβ(2)R and Gα(s), a six-dimensional potential energy surface was obtained. Two significant minimum regions were located on this surface, and each was associated with a distinct hβ(2)R-Gα(s) complex, namely model I and model II [Straßer A, Wittmann H-J (2010) J Mol Model 16:1307-1318]. The crystal structure of the hβ(2)R-Gα(s)βγ complex has recently been published. Thus, the aim of the current study was, on the one hand, to compare our predicted structures with the true crystal structure, and on the other to discuss the question: how valuable are predictions based on molecular modeling studies?

  4. Rigorous assessment and integration of the sequence and structure based features to predict hot spots

    PubMed Central

    2011-01-01

    Background Systematic mutagenesis studies have shown that only a few interface residues termed hot spots contribute significantly to the binding free energy of protein-protein interactions. Therefore, hot spots prediction becomes increasingly important for well understanding the essence of proteins interactions and helping narrow down the search space for drug design. Currently many computational methods have been developed by proposing different features. However comparative assessment of these features and furthermore effective and accurate methods are still in pressing need. Results In this study, we first comprehensively collect the features to discriminate hot spots and non-hot spots and analyze their distributions. We find that hot spots have lower relASA and larger relative change in ASA, suggesting hot spots tend to be protected from bulk solvent. In addition, hot spots have more contacts including hydrogen bonds, salt bridges, and atomic contacts, which favor complexes formation. Interestingly, we find that conservation score and sequence entropy are not significantly different between hot spots and non-hot spots in Ab+ dataset (all complexes). While in Ab- dataset (antigen-antibody complexes are excluded), there are significant differences in two features between hot pots and non-hot spots. Secondly, we explore the predictive ability for each feature and the combinations of features by support vector machines (SVMs). The results indicate that sequence-based feature outperforms other combinations of features with reasonable accuracy, with a precision of 0.69, a recall of 0.68, an F1 score of 0.68, and an AUC of 0.68 on independent test set. Compared with other machine learning methods and two energy-based approaches, our approach achieves the best performance. Moreover, we demonstrate the applicability of our method to predict hot spots of two protein complexes. Conclusion Experimental results show that support vector machine classifiers are quite

  5. Decomposing cerebral blood flow MRI into functional and structural components: A non-local approach based on prediction

    PubMed Central

    Kandel, Benjamin M.; Wang, Danny JJ; Detre, John A.; Gee, James C.; Avants, Brian B.

    2014-01-01

    We present RIPMMARC (Rotation Invariant Patch-based Multi-Modality Analysis aRChitecture), a flexible and widely applicable method for extracting information unique to a given modality from a multi-modal data set. We use RIPMMARC to improve interpretation of arterial spin labeling (ASL) perfusion images by removing the component of perfusion that is predicted by the underlying anatomy. Using patch-based, rotation invariant descriptors derived from the anatomical image, we learn a predictive relationship between local neuroanatomical structure and the corresponding perfusion image. This relation allows us to produce an image of perfusion that would be predicted given only the underlying anatomy and a residual image that represents perfusion information that cannot be predicted by anatomical features. Our learned structural features are significantly better at predicting brain perfusion than tissue probability maps, which are the input to standard partial volume correction techniques. Studies in test-retest data show that both the anatomically predicted and residual perfusion signal are highly replicable for a given subject. In a pediatric population, both the raw perfusion and structurally predicted images are tightly linked to age throughout adolescence throughout the brain. Interestingly, the residual perfusion also shows a strong correlation with age in select regions including the hippocampi (corr= 0.38, p-value < 10−6), precuneus (corr= −0.44, p < 10−5), and combined default mode network regions (corr= −0.45, p < 10−8) that is independent of global anatomy-perfusion trends. This finding suggests that there is a regionally heterogeneous pattern of functional specialization that is distinct from that of cortical structural development. PMID:25449745

  6. Decomposing cerebral blood flow MRI into functional and structural components: a non-local approach based on prediction.

    PubMed

    Kandel, Benjamin M; Wang, Danny J J; Detre, John A; Gee, James C; Avants, Brian B

    2015-01-15

    We present RIPMMARC (Rotation Invariant Patch-based Multi-Modality Analysis aRChitecture), a flexible and widely applicable method for extracting information unique to a given modality from a multi-modal data set. We use RIPMMARC to improve the interpretation of arterial spin labeling (ASL) perfusion images by removing the component of perfusion that is predicted by the underlying anatomy. Using patch-based, rotation invariant descriptors derived from the anatomical image, we learn a predictive relationship between local neuroanatomical structure and the corresponding perfusion image. This relation allows us to produce an image of perfusion that would be predicted given only the underlying anatomy and a residual image that represents perfusion information that cannot be predicted by anatomical features. Our learned structural features are significantly better at predicting brain perfusion than tissue probability maps, which are the input to standard partial volume correction techniques. Studies in test-retest data show that both the anatomically predicted and residual perfusion signals are highly replicable for a given subject. In a pediatric population, both the raw perfusion and structurally predicted images are tightly linked to age throughout adolescence throughout the brain. Interestingly, the residual perfusion also shows a strong correlation with age in selected regions including the hippocampi (corr = 0.38, p-value <10(-6)), precuneus (corr = -0.44, p < 10(-5)), and combined default mode network regions (corr = -0.45, p < 10(-8)) that is independent of global anatomy-perfusion trends. This finding suggests that there is a regionally heterogeneous pattern of functional specialization that is distinct from that of cortical structural development.

  7. A Structure Based Model for the Prediction of Phospholipidosis Induction Potential of Small Molecules

    PubMed Central

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

    2012-01-01

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

  8. Predicting the equilibrium protein folding pathway: structure-based analysis of staphylococcal nuclease.

    PubMed

    Hilser, V J; Freire, E

    1997-02-01

    The equilibrium folding pathway of staphylococcal nuclease (SNase) has been approximated using a statistical thermodynamic formalism that utilizes the high-resolution structure of the native state as a template to generate a large ensemble of partially folded states. Close to 400,000 different states ranging from the native to the completely unfolded states were included in the analysis. The probability of each state was estimated using an empirical structural parametrization of the folding energetics. It is shown that this formalism predicts accurately the stability of the protein, the cooperativity of the folding/unfolding transition observed by differential scanning calorimetry (DSC) or urea denaturation and the thermodynamic parameters for unfolding. More importantly, this formalism provides a quantitative account of the experimental hydrogen exchange protection factors measured under native conditions for SNase. These results suggest that the computer-generated distribution of states approximates well the ensemble of conformations existing in solution. Furthermore, this formalism represents the first model capable of quantitatively predicting within a unified framework the probability distribution of states seen under native conditions and its change upon unfolding.

  9. DFT-based prediction of fission product sorption on carbon structures under O2 ingress conditions

    NASA Astrophysics Data System (ADS)

    Londono-Hurtado, Alejandro; Szlufarska, Izabela; Morgan, Dane

    2013-06-01

    An isotherm based model for the prediction of Cs sorption on the carbon components of a High Temperature Reactor (HTR) under O2 ingress conditions is presented. Isotherms are derived from a thermodynamic model based on binding energies calculated using Density Functional Theory (DFT). The DFT derived isotherms are compared with isotherms obtained from experimental calculations and sources of discrepancies are discussed. A DFT only model and a second model combining DFT and experimental calculations are used to predict fission product inventories in a HTR vessel during O2 ingress conditions. Results suggest that the carbon type (i.e. graphitic vs. amorphous) plays a central role on fission product sorption and release. During normal reactor conditions (T around 1400 K, low P) graphitic carbon will absorb a small percentage of a monolayer of Cs, while amorphous carbon will be approximately saturated at an entire monolayer of Cs. Results also indicate that, for the case of O2 ingress to the reactor's vessel, the Cs will form Cs2O. In the case of graphitic carbon, the Cs2O will bind more weakly than Cs, leading to Cs release in the form of Cs2O during O ingress. However, the weak binding of Cs to graphite means that only small release is expected. In the case of amorphous carbon, Cs2O binds almost as strongly Cs, and so no significant change in Cs absorbed to the amorphous carbon is predicted, although the form of the absorbed Cs is predicted to be Cs2O. For the case of low release conditions, consistent with modern TRISO fuels, the core will adsorb the entire Cs inventory at normal operating temperatures. However, for high Cs release conditions, consistent with older TRISO fuels, the surface sites on the core will be saturated and most of the Cs will remain in gas form or plate out on other surfaces.

  10. Physics-based protein-structure prediction using a hierarchical protocol based on the UNRES force field: Assessment in two blind tests

    PubMed Central

    Ołdziej, S.; Czaplewski, C.; Liwo, A.; Chinchio, M.; Nanias, M.; Vila, J. A.; Khalili, M.; Arnautova, Y. A.; Jagielska, A.; Makowski, M.; Schafroth, H. D.; Kaźmierkiewicz, R.; Ripoll, D. R.; Pillardy, J.; Saunders, J. A.; Kang, Y. K.; Gibson, K. D.; Scheraga, H. A.

    2005-01-01

    Recent improvements in the protein-structure prediction method developed in our laboratory, based on the thermodynamic hypothesis, are described. The conformational space is searched extensively at the united-residue level by using our physics-based UNRES energy function and the conformational space annealing method of global optimization. The lowest-energy coarse-grained structures are then converted to an all-atom representation and energy-minimized with the ECEPP/3 force field. The procedure was assessed in two recent blind tests of protein-structure prediction. During the first blind test, we predicted large fragments of α and α+β proteins [60–70 residues with Cα rms deviation (rmsd) <6 Å]. However, for α+β proteins, significant topological errors occurred despite low rmsd values. In the second exercise, we predicted whole structures of five proteins (two α and three α+β, with sizes of 53–235 residues) with remarkably good accuracy. In particular, for the genomic target TM0487 (a 102-residue α+β protein from Thermotoga maritima), we predicted the complete, topologically correct structure with 7.3-Å Cα rmsd. So far this protein is the largest α+β protein predicted based solely on the amino acid sequence and a physics-based potential-energy function and search procedure. For target T0198, a phosphate transport system regulator PhoU from T. maritima (a 235-residue mainly α-helical protein), we predicted the topology of the whole six-helix bundle correctly within 8 Å rmsd, except the 32 C-terminal residues, most of which form a β-hairpin. These and other examples described in this work demonstrate significant progress in physics-based protein-structure prediction. PMID:15894609

  11. Prediction of Infant MRI Appearance and Anatomical Structure Evolution using Sparse Patch-based Metamorphosis Learning Framework

    PubMed Central

    Rekik, Islem; Li, Gang; Wu, Guorong; Lin, Weili; Shen, Dinggang

    2016-01-01

    Magnetic resonance imaging (MRI) of pediatric brain provides invaluable information for early normal and abnormal brain development. Longitudinal neuroimaging has spanned various research works on examining infant brain development patterns. However, studies on predicting postnatal brain image evolution remain scarce, which is very challenging due to the dynamic tissue contrast change and even inversion in postnatal brains. In this paper, we unprecedentedly propose a dual image intensity and anatomical structure (label) prediction framework that nicely links the geodesic image metamorphosis model with sparse patch-based image representation, thereby defining spatiotemporal metamorphic patches encoding both image photometric and geometric deformation. In the training stage, we learn the 4D metamorphosis trajectories for each training subject. In the prediction stage, we define various strategies to sparsely represent each patch in the testing image using the training metamorphosis patches; as we progressively increment the richness of the patch (from appearance-based to multimodal kinetic patches). We used the proposed framework to predict 6, 9 and 12-month brain MR image intensity and structure (white and gray matter maps) from 3 months in 10 infants. Our seminal work showed promising preliminary prediction results for the spatiotemporally complex, drastically changing brain images.

  12. Ab Initio Based 2D Continuum Mechanics - Sensitivity Prediction for Contact Resonance Atomic Force Microscopy Based Structure Fingerprints

    NASA Astrophysics Data System (ADS)

    Tu, Qing; Lange, Björn; Lopes, J. Marcelo J.; Zauscher, Stefan; Blum, Volker

    Contact resonance AFM is demonstrated as a powerful tool for mapping differences in the mechanical properties of 2D materials and heterostructures, permitting to resolve surface and subsurface structural differences of different domains. Measured contact resonance frequencies are related to the contact stiffness of the combined tip-sample system. Based on first principles predicted elastic properties and a continuum approach to model the mechanical impedance, we find contact stiffness ratios between different domains of few-layer graphene on 3C-SiC(111) in excellent agreement with experiment. We next demonstrate that the approach is able to quantitatively resolve differences between other 2D materials domains, e.g., for h-BN, MoS2 and MoO3 on graphene on SiC. We show that the combined effect of several materials parameters, especially the in-plane elastic properties and the layer thickness, determines the contact stiffness, therefore boosting the sensitivity even if the out-of-plane elastic properties are similar.

  13. On potential energy models for EA-based ab initio protein structure prediction.

    PubMed

    Mijajlovic, Milan; Biggs, Mark J; Djurdjevic, Dusan P

    2010-01-01

    Ab initio protein structure prediction involves determination of the three-dimensional (3D) conformation of proteins on the basis of their amino acid sequence, a potential energy (PE) model that captures the physics of the interatomic interactions, and a method to search for and identify the global minimum in the PE (or free energy) surface such as an evolutionary algorithm (EA). Many PE models have been proposed over the past three decades and more. There is currently no understanding of how the behavior of an EA is affected by the PE model used. The study reported here shows that the EA behavior can be profoundly affected: the EA performance obtained when using the ECEPP PE model is significantly worse than that obtained when using the Amber, OPLS, and CVFF PE models, and the optimal EA control parameter values for the ECEPP model also differ significantly from those associated with the other models.

  14. Physical scoring function based on AMBER force field and Poisson-Boltzmann implicit solvent for protein structure prediction.

    PubMed

    Hsieh, Meng-Juei; Luo, Ray

    2004-08-15

    A well-behaved physics-based all-atom scoring function for protein structure prediction is analyzed with several widely used all-atom decoy sets. The scoring function, termed AMBER/Poisson-Boltzmann (PB), is based on a refined AMBER force field for intramolecular interactions and an efficient PB model for solvation interactions. Testing on the chosen decoy sets shows that the scoring function, which is designed to consider detailed chemical environments, is able to consistently discriminate all 62 native crystal structures after considering the heteroatom groups, disulfide bonds, and crystal packing effects that are not included in the decoy structures. When NMR structures are considered in the testing, the scoring function is able to discriminate 8 out of 10 targets. In the more challenging test of selecting near-native structures, the scoring function also performs very well: for the majority of the targets studied, the scoring function is able to select decoys that are close to the corresponding native structures as evaluated by ranking numbers and backbone Calpha root mean square deviations. Various important components of the scoring function are also studied to understand their discriminative contributions toward the rankings of native and near-native structures. It is found that neither the nonpolar solvation energy as modeled by the surface area model nor a higher protein dielectric constant improves its discriminative power. The terms remaining to be improved are related to 1-4 interactions. The most troublesome term is found to be the large and highly fluctuating 1-4 electrostatics term, not the dihedral-angle term. These data support ongoing efforts in the community to develop protein structure prediction methods with physics-based potentials that are competitive with knowledge-based potentials.

  15. 3D structure prediction of human β1-adrenergic receptor via threading-based homology modeling for implications in structure-based drug designing.

    PubMed

    Ul-Haq, Zaheer; Saeed, Maria; Halim, Sobia Ahsan; Khan, Waqasuddin

    2015-01-01

    Dilated cardiomyopathy is a disease of left ventricular dysfunction accompanied by impairment of the β1-adrenergic receptor (β1-AR) signal cascade. The disturbed β1-AR function may be based on an elevated sympathetic tone observed in patients with heart failure. Prolonged adrenergic stimulation may induce metabolic and electrophysiological disturbances in the myocardium, resulting in tachyarrhythmia that leads to the development of heart failure in human and sudden death. Hence, β1-AR is considered as a promising drug target but attempts to develop effective and specific drug against this tempting pharmaceutical target is slowed down due to the lack of 3D structure of Homo sapiens β1-AR (hsβADR1). This study encompasses elucidation of 3D structural and physicochemical properties of hsβADR1 via threading-based homology modeling. Furthermore, the docking performance of several docking programs including Surflex-Dock, FRED, and GOLD were validated by re-docking and cross-docking experiments. GOLD and Surflex-Dock performed best in re-docking and cross docking experiments, respectively. Consequently, Surflex-Dock was used to predict the binding modes of four hsβADR1 agonists. This study provides clear understanding of hsβADR1 structure and its binding mechanism, thus help in providing the remedial solutions of cardiovascular, effective treatment of asthma and other diseases caused by malfunctioning of the target protein.

  16. Predicting Surgery Targets in Temporal Lobe Epilepsy through Structural Connectome Based Simulations

    PubMed Central

    Hutchings, Frances; Han, Cheol E.; Keller, Simon S.; Weber, Bernd; Taylor, Peter N.; Kaiser, Marcus

    2015-01-01

    Temporal lobe epilepsy (TLE) is a prevalent neurological disorder resulting in disruptive seizures. In the case of drug resistant epilepsy resective surgery is often considered. This is a procedure hampered by unpredictable success rates, with many patients continuing to have seizures even after surgery. In this study we apply a computational model of epilepsy to patient specific structural connectivity derived from diffusion tensor imaging (DTI) of 22 individuals with left TLE and 39 healthy controls. We validate the model by examining patient-control differences in simulated seizure onset time and network location. We then investigate the potential of the model for surgery prediction by performing in silico surgical resections, removing nodes from patient networks and comparing seizure likelihood post-surgery to pre-surgery simulations. We find that, first, patients tend to transit from non-epileptic to epileptic states more often than controls in the model. Second, regions in the left hemisphere (particularly within temporal and subcortical regions) that are known to be involved in TLE are the most frequent starting points for seizures in patients in the model. In addition, our analysis also implicates regions in the contralateral and frontal locations which may play a role in seizure spreading or surgery resistance. Finally, the model predicts that patient-specific surgery (resection areas chosen on an individual, model-prompted, basis and not following a predefined procedure) may lead to better outcomes than the currently used routine clinical procedure. Taken together this work provides a first step towards patient specific computational modelling of epilepsy surgery in order to inform treatment strategies in individuals. PMID:26657566

  17. An Analysis of the Revere, Quincy and Stamford Structure Data Bases for Predicting Building Material Distribution.

    DTIC Science & Technology

    1985-05-01

    AD-A±57 58 AN ANALYSIS OF THE REVERE QUINCY AND STANFORD STRUCTURE i/I DATA BASES FOR PR.. (U) COLD REGIONS RESEARCH AND ENGINEERING LAO HANOVER NH C...suggest a rejection of the null hypothesis that the variables are statistically independent ( Walpole and Myers 1978). This does not imply a cause-and...0.1180 0.0296 0.0077 Stamford *hypothesis of equality of means ( Walpole and Myers 1978). By separating * building surface area by building type

  18. Structure-Based Prediction of Unstable Regions in Proteins: Applications to Protein Misfolding Diseases

    NASA Astrophysics Data System (ADS)

    Guest, Will; Cashman, Neil; Plotkin, Steven

    2009-03-01

    Protein misfolding is a necessary step in the pathogenesis of many diseases, including Creutzfeldt-Jakob disease (CJD) and familial amyotrophic lateral sclerosis (fALS). Identifying unstable structural elements in their causative proteins elucidates the early events of misfolding and presents targets for inhibition of the disease process. An algorithm was developed to calculate the Gibbs free energy of unfolding for all sequence-contiguous regions of a protein using three methods to parameterize energy changes: a modified G=o model, changes in solvent-accessible surface area, and solution of the Poisson-Boltzmann equation. The entropic effects of disulfide bonds and post-translational modifications are treated analytically. It incorporates a novel method for finding local dielectric constants inside a protein to accurately handle charge effects. We have predicted the unstable parts of prion protein and superoxide dismutase 1, the proteins involved in CJD and fALS respectively, and have used these regions as epitopes to prepare antibodies that are specific to the misfolded conformation and show promise as therapeutic agents.

  19. Predicting healthy older adult's brain age based on structural connectivity networks using artificial neural networks.

    PubMed

    Lin, Lan; Jin, Cong; Fu, Zhenrong; Zhang, Baiwen; Bin, Guangyu; Wu, Shuicai

    2016-03-01

    Brain ageing is followed by changes of the connectivity of white matter (WM) and changes of the grey matter (GM) concentration. Neurodegenerative disease is more vulnerable to an accelerated brain ageing, which is associated with prospective cognitive decline and disease severity. Accurate detection of accelerated ageing based on brain network analysis has a great potential for early interventions designed to hinder atypical brain changes. To capture the brain ageing, we proposed a novel computational approach for modeling the 112 normal older subjects (aged 50-79 years) brain age by connectivity analyses of networks of the brain. Our proposed method applied principal component analysis (PCA) to reduce the redundancy in network topological parameters. Back propagation artificial neural network (BPANN) improved by hybrid genetic algorithm (GA) and Levenberg-Marquardt (LM) algorithm is established to model the relation among principal components (PCs) and brain age. The predicted brain age is strongly correlated with chronological age (r=0.8). The model has mean absolute error (MAE) of 4.29 years. Therefore, we believe the method can provide a possible way to quantitatively describe the typical and atypical network organization of human brain and serve as a biomarker for presymptomatic detection of neurodegenerative diseases in the future.

  20. Defect Prediction and Control for Ultra-high-strength Steel Complex Structure in Hot Forming Based on FEM

    NASA Astrophysics Data System (ADS)

    Shang, Xin; Zhou, Jie; Zhuo, Fang; Luo, Yan; Li, Yang

    2015-06-01

    Cracking is the main defect in ultra-high-strength steel (UHSS) forming products. In order to avoid cracking, either adjusting process parameters or changing die's design is usually applied. However, under the condition of forming parts with unreasonable structure design, it makes little difference through the traditional methods of modifying process parameters. In this paper, true stress-strain curves under different strain rates and temperatures are obtained via the hot tensile tests. Then, the material constitutive model of UHSS is introduced into software CAE; this step is used to analyze and predict defects of UHSS hot forming complex structural parts based on FEM. In addition, simulation results of changed structure (open end) are compared with original structure (closed end). The results have shown that both maximum reduction ratio and stress in all directions are sharply reduced, i.e., the forming quality is improved significantly after changing the end structure. Finally, the prediction and control methods of forming defects are verified to be feasible in actual production.

  1. Quantitative vapor-phase IR intensities and DFT computations to predict absolute IR spectra based on molecular structure: I. Alkanes

    NASA Astrophysics Data System (ADS)

    Williams, Stephen D.; Johnson, Timothy J.; Sharpe, Steven W.; Yavelak, Veronica; Oates, R. P.; Brauer, Carolyn S.

    2013-11-01

    Recently recorded quantitative IR spectra of a variety of gas-phase alkanes are shown to have integrated intensities in both the C3H stretching and C3H bending regions that depend linearly on the molecular size, i.e. the number of C3H bonds. This result is well predicted from CH4 to C15H32 by density functional theory (DFT) computations of IR spectra using Becke's three parameter functional (B3LYP/6-31+G(d,p)). Using the experimental data, a simple model predicting the absolute IR band intensities of alkanes based only on structural formula is proposed: For the C3H stretching band envelope centered near 2930 cm-1 this is given by (km/mol) CH_str=(34±1)×CH-(41±23) where CH is number of C3H bonds in the alkane. The linearity is explained in terms of coordinated motion of methylene groups rather than the summed intensities of autonomous -CH2-units. The effect of alkyl chain length on the intensity of a C3H bending mode is explored and interpreted in terms of conformer distribution. The relative intensity contribution of a methyl mode compared to the total C3H stretch intensity is shown to be linear in the number of methyl groups in the alkane, and can be used to predict quantitative spectra a priori based on structure alone.

  2. Quantitative Vapor-phase IR Intensities and DFT Computations to Predict Absolute IR Spectra based on Molecular Structure: I. Alkanes

    SciTech Connect

    Williams, Stephen D.; Johnson, Timothy J.; Sharpe, Steven W.; Yavelak, Veronica; Oats, R. P.; Brauer, Carolyn S.

    2013-11-13

    Recently recorded quantitative IR spectra of a variety of gas-phase alkanes are shown to have integrated intensities in both the C-H stretching and C-H bending regions that depend linearly on the molecular size, i.e. the number of C-H bonds. This result is well predicted from CH4 to C15H32 by DFT computations of IR spectra at the B3LYP/6-31+G(d,p) level of DFT theory. A simple model predicting the absolute IR band intensities of alkanes based only on structural formula is proposed: For the C-H stretching band near 2930 cm-1 this is given by (in km/mol): CH¬_str = (34±3)*CH – (41±60) where CH is number of C-H bonds in the alkane. The linearity is explained in terms of coordinated motion of methylene groups rather than the summed intensities of autonomous -CH2- units. The effect of alkyl chain length on the intensity of a C-H bending mode is explored and interpreted in terms of conformer distribution. The relative intensity contribution of a methyl mode compared to the total C-H stretch intensity is shown to be linear in the number of terminal methyl groups in the alkane, and can be used to predict quantitative spectra a priori based on structure alone.

  3. Multiscale modeling of interwoven Kevlar fibers based on random walk to predict yarn structural response

    NASA Astrophysics Data System (ADS)

    Recchia, Stephen

    Kevlar is the most common high-end plastic filament yarn used in body armor, tire reinforcement, and wear resistant applications. Kevlar is a trade name for an aramid fiber. These are fibers in which the chain molecules are highly oriented along the fiber axis, so the strength of the chemical bond can be exploited. The bulk material is extruded into filaments that are bound together into yarn, which may be chorded with other materials as in car tires, woven into a fabric, or layered in an epoxy to make composite panels. The high tensile strength to low weight ratio makes this material ideal for designs that decrease weight and inertia, such as automobile tires, body panels, and body armor. For designs that use Kevlar, increasing the strength, or tenacity, to weight ratio would improve performance or reduce cost of all products that are based on this material. This thesis computationally and experimentally investigates the tenacity and stiffness of Kevlar yarns with varying twist ratios. The test boundary conditions were replicated with a geometrically accurate finite element model, resulting in a customized code that can reproduce tortuous filaments in a yarn was developed. The solid model geometry capturing filament tortuosity was implemented through a random walk method of axial geometry creation. A finite element analysis successfully recreated the yarn strength and stiffness dependency observed during the tests. The physics applied in the finite element model was reproduced in an analytical equation that was able to predict the failure strength and strain dependency of twist ratio. The analytical solution can be employed to optimize yarn design for high strength applications.

  4. Impact of the subtle differences in MMP-12 structure on Glide-based molecular docking for pose prediction of inhibitors

    NASA Astrophysics Data System (ADS)

    Zhang, Huan; Wang, Yajing; Xu, Feng

    2014-11-01

    Human MMP-12 is involved in many aspects of disease pathology. Substantial efforts have been made to develop MMP-12 inhibitors. However, the mechanism of some MMP-12 inhibitors is still unclear. Recently, the method of molecular modeling was used to explore the mechanism, but selecting the best candidate among the wealth of MMP-12 structures poses a challenge. In this study, we attempted to identify several criteria to predict the most appropriate MMP-12 PDB ID for enzyme-ligand interaction studies based on cross-docking by Glide. Furthermore, the parameters from PDB files such as R-free, resolution, B factor, and the molecular volume of the ligand in the complex can provide useful clues for choosing a suitable approximate initial model for pose prediction for MMP-12 inhibitors. This work might also provide a useful reference for other drug targets.

  5. Impact of computational structure-based predictive toxicology in drug discovery.

    PubMed

    Mohan, Chethampadi Gopi

    2011-06-01

    Computational tools for predicting toxicity have been envisioned to have the potential to broadly impact up on the attrition rate of compounds in pre-clinical drug discovery and development. An integrated approach of computer-assisted, predictive, and physico-chemical properties of a compound, along with its in vitro and in vivo analysis, needs to be routinely exercised in the lead identification and lead optimization processes. Starting with a good lead can save a lot of money and it can significantly reduce the entire drug discovery process. The journey towards triple R's- reduce, replace and refine, further proves to be successful in predicting adverse drug reactions in patients (or animals) enrolled in clinical trials. However, the impact of predictive toxicity analysis was modest and relatively narrow in scope, due to the limited domain knowledge in this field. It is important to note that advances within medical science and newer approaches in drug development will require predictive toxicology applications to be viable. The field of computational toxicology has been heading in a direction more relevant to human diseases by reducing the adverse drug reactions. Therefore, efforts must be directed to integrating these tools relevant to the goal of preventing undesired toxicity in pre-clinical trials followed by different phases of clinical trials.

  6. Protein Structure Prediction with Visuospatial Analogy

    NASA Astrophysics Data System (ADS)

    Davies, Jim; Glasgow, Janice; Kuo, Tony

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

  7. Structure-based prediction of subtype-selectivity of Histamine H3 receptor selective antagonists in clinical trials

    PubMed Central

    Kim, Soo-Kyung; Fristrup, Peter; Abrol, Ravinder; Goddard, William A.

    2011-01-01

    Histamine receptors (HRs) are excellent drug targets for the treatment of diseases such as schizophrenia, psychosis, depression, migraine, allergies, asthma ulcers, and hypertension. Among them, the human H3 Histamine receptor (hH3HR) antagonists have been proposed for specific therapeutic applications, including treatment of Alzheimer's disease, attention deficit hyperactivity disorder (ADHD), epilepsy, and obesity.1 However, many of these drug candidates cause undesired side effects through the cross-reactivity with other histamine receptor subtypes. In order to develop improved selectivity and activity for such treatments it would be useful to have the three dimensional structures for all four HRs. We report here the predicted structures of four HR subtypes (H1, H2, H3, and H4) using the GEnSeMBLE (GPCR Ensemble of Structures in Membrane BiLayer Environment) Monte Carlo protocol.2 sampling ~ 35 million combinations of helix packings to predict the 10 most stable packings for each of the four subtypes. Then we used these best 10 protein structures with the DarwinDock Monte Carlo protocol to sample ~ 50,000*20 poses to predict the optimum ligand-protein structures for various agonists and antagonists. We find that E2065.46 contributes most in binding H3 selective agonists (5, 6, 7) in agreement with experimental mutation studies. We also find that conserved E5.46/ S5.43 in both of hH3HR and hH4HR are involved in H3/ H4 subtype selectivity. In addition, we find that M3786.55 in hH3HR provides additional hydrophobic interactions different from hH4HR (the corresponding amino acid of T3236.55 in hH4HR) to provide additional subtype bias. From these studies we developed a pharmacophore model based on our predictions for known hH3HR selective antagonists in clinical study [ABT-239 1, GSK-189,254 2, PF-3654746 3, and BF2.649 (Tiprolisant) 4] that suggests critical selectivity directing elements are: the basic proton interacting with D1143.32, the spacer, the aromatic

  8. Real-time prediction of atmospheric Lagrangian coherent structures based on forecast data: An application and error analysis

    NASA Astrophysics Data System (ADS)

    BozorgMagham, Amir E.; Ross, Shane D.; Schmale, David G.

    2013-09-01

    The language of Lagrangian coherent structures (LCSs) provides a new means for studying transport and mixing of passive particles advected by an atmospheric flow field. Recent observations suggest that LCSs govern the large-scale atmospheric motion of airborne microorganisms, paving the way for more efficient models and management strategies for the spread of infectious diseases affecting plants, domestic animals, and humans. In addition, having reliable predictions of the timing of hyperbolic LCSs may contribute to improved aerobiological sampling of microorganisms with unmanned aerial vehicles and LCS-based early warning systems. Chaotic atmospheric dynamics lead to unavoidable forecasting errors in the wind velocity field, which compounds errors in LCS forecasting. In this study, we reveal the cumulative effects of errors of (short-term) wind field forecasts on the finite-time Lyapunov exponent (FTLE) fields and the associated LCSs when realistic forecast plans impose certain limits on the forecasting parameters. Objectives of this paper are to (a) quantify the accuracy of prediction of FTLE-LCS features and (b) determine the sensitivity of such predictions to forecasting parameters. Results indicate that forecasts of attracting LCSs exhibit less divergence from the archive-based LCSs than the repelling features. This result is important since attracting LCSs are the backbone of long-lived features in moving fluids. We also show under what circumstances one can trust the forecast results if one merely wants to know if an LCS passed over a region and does not need to precisely know the passage time.

  9. Finite element prediction of seismic response modification of monumental structures utilizing base isolation

    NASA Astrophysics Data System (ADS)

    Spanos, Konstantinos; Anifantis, Nikolaos; Kakavas, Panayiotis

    2015-05-01

    The analysis of the mechanical behavior of ancient structures is an essential engineering task concerning the preservation of architectural heritage. As many monuments of classical antiquity are located in regions of earthquake activity, the safety assessment of these structures, as well as the selection of possible restoration interventions, requires numerical models capable of correctly representing their seismic response. The work presented herein was part of a research project in which a better understanding of the dynamics of classical column-architrave structures was sought by means of numerical techniques. In this paper, the seismic behavior of ancient monumental structures with multi-drum classical columns is investigated. In particular, the column-architrave classical structure under strong ground excitations was represented by a finite element method. This approach simulates the individual rock blocks as distinct rigid blocks interconnected with slidelines and incorporates seismic isolation dampers under the basement of the structure. Sliding and rocking motions of individual stone blocks and drums are modeled utilizing non-linear frictional contact conditions. The seismic isolation is modeled through the application of pad bearings under the basement of the structure. These pads are interpreted by appropriate rubber and steel layers. Time domain analyses were performed, considering the geometric and material non-linear behavior at the joints and the characteristics of pad bearings. The deformation and failure modes of drum columns subject to seismic excitations of various types and intensities were analyzed. The adverse influence of drum imperfections on structural safety was also examined.

  10. Interplay of I-TASSER and QUARK for template-based and ab initio protein structure prediction in CASP10.

    PubMed

    Zhang, Yang

    2014-02-01

    We develop and test a new pipeline in CASP10 to predict protein structures based on an interplay of I-TASSER and QUARK for both free-modeling (FM) and template-based modeling (TBM) targets. The most noteworthy observation is that sorting through the threading template pool using the QUARK-based ab initio models as probes allows the detection of distant-homology templates which might be ignored by the traditional sequence profile-based threading alignment algorithms. Further template assembly refinement by I-TASSER resulted in successful folding of two medium-sized FM targets with >150 residues. For TBM, the multiple threading alignments from LOMETS are, for the first time, incorporated into the ab initio QUARK simulations, which were further refined by I-TASSER assembly refinement. Compared with the traditional threading assembly refinement procedures, the inclusion of the threading-constrained ab initio folding models can consistently improve the quality of the full-length models as assessed by the GDT-HA and hydrogen-bonding scores. Despite the success, significant challenges still exist in domain boundary prediction and consistent folding of medium-size proteins (especially beta-proteins) for nonhomologous targets. Further developments of sensitive fold-recognition and ab initio folding methods are critical for solving these problems.

  11. Static compressive strength prediction of open-hole structure based on non-linear shear behavior and micro-mechanics

    NASA Astrophysics Data System (ADS)

    Li, Wangnan; Cai, Hongneng; Li, Chao

    2014-11-01

    This paper deals with the characterization of the strength of the constituents of carbon fiber reinforced plastic laminate (CFRP), and a prediction of the static compressive strength of open-hole structure of polymer composites. The approach combined with non-linear analysis in macro-level and a linear elastic micromechanical failure analysis in microlevel (non-linear MMF) is proposed to improve the prediction accuracy. A face-centered cubic micromechanics model is constructed to analyze the stresses in fiber and matrix in microlevel. Non-interactive failure criteria are proposed to characterize the strength of fiber and matrix. The non-linear shear behavior of the laminate is studied experimentally, and a novel approach of cubic spline interpolation is used to capture significant non-linear shear behavior of laminate. The user-defined material subroutine UMAT for the non-linear share behavior is developed and combined in the mechanics analysis in the macro-level using the Abaqus Python codes. The failure mechanism and static strength of open-hole compressive (OHC) structure of polymer composites is studied based on non-linear MMF. The UTS50/E51 CFRP is used to demonstrate the application of theory of non-linear MMF.

  12. An efficient fragment-based approach for predicting the ground-state energies and structures of large molecules.

    PubMed

    Li, Shuhua; Li, Wei; Fang, Tao

    2005-05-18

    An efficient fragment-based approach for predicting the ground-state energies and structures of large molecules at the Hartree-Fock (HF) and post-HF levels is described. The physical foundation of this approach is attributed to the "quantum locality" of the electron correlation energy and the HF total energy, which is revealed by a new energy decomposition analysis of the HF total energy proposed in this work. This approach is based on the molecular fractionation with conjugated caps (MFCC) scheme (Zhang, D. W.; Zhang, J. Z. H. J. Chem. Phys. 2003, 119, 3599), by which a macromolecule is partitioned into various capped fragments and conjugated caps formed by two adjacent caps. We find that the MFCC scheme, if corrected by the interaction between non-neighboring fragments, can be used to predict the total energy of large molecules only from energy calculations on a series of small subsystems. The approach, named as energy-corrected MFCC (EC-MFCC), computationally achieves linear scaling with the molecular size. Our test calculations on a broad range of medium- and large molecules demonstrate that this approach is able to reproduce the conventional HF and second-order Moller-Plesset perturbation theory (MP2) energies within a few millihartree in most cases. With the EC-MFCC optimization algorithm described in this work, we have obtained the optimized structures of long oligomers of trans-polyacetylene and BN nanotubes with up to about 400 atoms, which are beyond the reach of traditional computational methods. In addition, the EC-MFCC approach is also applied to estimate the heats of formation for a series of organic compounds. This approach provides an appealing approach alternative to the traditional additivity rules based on either bond or group contributions for the estimation of thermochemical properties.

  13. Annotation inconsistencies beyond sequence similarity-based function prediction - phylogeny and genome structure.

    PubMed

    Promponas, Vasilis J; Iliopoulos, Ioannis; Ouzounis, Christos A

    2015-01-01

    The function annotation process in computational biology has increasingly shifted from the traditional characterization of individual biochemical roles of protein molecules to the system-wide detection of entire metabolic pathways and genomic structures. The so-called genome-aware methods broaden misannotation inconsistencies in genome sequences beyond protein function assignments, encompassing phylogenetic anomalies and artifactual genomic regions. We outline three categories of error propagation in databases by providing striking examples - at various levels of appreciation by the community from traditional to emerging, thus raising awareness for future solutions.

  14. Computational Prediction of RNA Tertiary Structure

    NASA Astrophysics Data System (ADS)

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

    2012-02-01

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

  15. "Adapted Linear Interaction Energy": A Structure-Based LIE Parametrization for Fast Prediction of Protein-Ligand Affinities.

    PubMed

    Linder, Mats; Ranganathan, Anirudh; Brinck, Tore

    2013-02-12

    We present a structure-based parametrization of the Linear Interaction Energy (LIE) method and show that it allows for the prediction of absolute protein-ligand binding energies. We call the new model "Adapted" LIE (ALIE) because the α and β coefficients are defined by system-dependent descriptors and do therefore not require any empirical γ term. The best formulation attains a mean average deviation of 1.8 kcal/mol for a diverse test set and depends on only one fitted parameter. It is robust with respect to additional fitting and cross-validation. We compare this new approach with standard LIE by Åqvist and co-workers and the LIE + γSASA model (initially suggested by Jorgensen and co-workers) against in-house and external data sets and discuss their applicabilities.

  16. Predicting RNA structure: advances and limitations.

    PubMed

    Hofacker, Ivo L; Lorenz, Ronny

    2014-01-01

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

  17. Predicting pseudoknotted structures across two RNA sequences

    PubMed Central

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

    2012-01-01

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

  18. Water-Regulated Self-Assembly Structure Transformation and Gelation Behavior Prediction Based on a Hydrazide Derivative.

    PubMed

    Li, Yajie; Ran, Xia; Li, Qiuyue; Gao, Qiongqiong; Guo, Lijun

    2016-08-05

    Herein, we report the water-regulated supramolecular self-assembly structure transformation and the predictability of the gelation ability based on an azobenzene derivative bearing a hydrazide group, namely, N-(3,4,5-tributoxyphenyl)-N'-4-[(4-hydroxyphenyl)azophenyl] benzohydrazide (BNB-t4). The regulation effects are demonstrated in the morphological transformation from spherical to lamellar particles then back to spherical in different solvent ratios of n-propanol/water. The self-assembly behavior of BNB-t4 was characterized by minimum gelation concentration, microstructure, thermal, and mechanical stabilities. From the spectroscopy studies, it is suggested that gel formation of BNB-t4 is mainly driven by intermolecular hydrogen bonding, accompanied with the contribution from π-π stacking as well as hydrophobic interactions. The successfully established correlation between the self-assembly behavior and solubility parameters yields a facile way to predict the gelation performance of other molecules in other single or mixed solvents.

  19. An allometry-based approach for understanding forest structure, predicting tree-size distribution and assessing the degree of disturbance

    PubMed Central

    Anfodillo, Tommaso; Carrer, Marco; Simini, Filippo; Popa, Ionel; Banavar, Jayanth R.; Maritan, Amos

    2013-01-01

    Tree-size distribution is one of the most investigated subjects in plant population biology. The forestry literature reports that tree-size distribution trajectories vary across different stands and/or species, whereas the metabolic scaling theory suggests that the tree number scales universally as −2 power of diameter. Here, we propose a simple functional scaling model in which these two opposing results are reconciled. Basic principles related to crown shape, energy optimization and the finite-size scaling approach were used to define a set of relationships based on a single parameter that allows us to predict the slope of the tree-size distributions in a steady-state condition. We tested the model predictions on four temperate mountain forests. Plots (4 ha each, fully mapped) were selected with different degrees of human disturbance (semi-natural stands versus formerly managed). Results showed that the size distribution range successfully fitted by the model is related to the degree of forest disturbance: in semi-natural forests the range is wide, whereas in formerly managed forests, the agreement with the model is confined to a very restricted range. We argue that simple allometric relationships, at an individual level, shape the structure of the whole forest community. PMID:23193128

  20. An allometry-based approach for understanding forest structure, predicting tree-size distribution and assessing the degree of disturbance.

    PubMed

    Anfodillo, Tommaso; Carrer, Marco; Simini, Filippo; Popa, Ionel; Banavar, Jayanth R; Maritan, Amos

    2013-01-22

    Tree-size distribution is one of the most investigated subjects in plant population biology. The forestry literature reports that tree-size distribution trajectories vary across different stands and/or species, whereas the metabolic scaling theory suggests that the tree number scales universally as -2 power of diameter. Here, we propose a simple functional scaling model in which these two opposing results are reconciled. Basic principles related to crown shape, energy optimization and the finite-size scaling approach were used to define a set of relationships based on a single parameter that allows us to predict the slope of the tree-size distributions in a steady-state condition. We tested the model predictions on four temperate mountain forests. Plots (4 ha each, fully mapped) were selected with different degrees of human disturbance (semi-natural stands versus formerly managed). Results showed that the size distribution range successfully fitted by the model is related to the degree of forest disturbance: in semi-natural forests the range is wide, whereas in formerly managed forests, the agreement with the model is confined to a very restricted range. We argue that simple allometric relationships, at an individual level, shape the structure of the whole forest community.

  1. Predicting crystal structures of organic compounds.

    PubMed

    Price, Sarah L

    2014-04-07

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

  2. Direct prediction of profiles of sequences compatible to a protein structure by neural networks with fragment-based local and energy-based nonlocal profiles

    PubMed Central

    Li, Zhixiu; Yang, Yuedong; Faraggi, Eshel; Zhan, Jian; Zhou, Yaoqi

    2014-01-01

    Locating sequences compatible to a protein structural fold is the well-known inverse protein-folding problem. While significant progress has been made, the success rate of protein design remains low. As a result, a library of designed sequences or profile of sequences is currently employed for guiding experimental screening or directed evolution. Sequence profiles can be computationally predicted by iterative mutations of a random sequence to produce energy-optimized sequences, or by combining sequences of structurally similar fragments in a template library. The latter approach is computationally more efficient but yields less accurate profiles than the former because of lacking tertiary structural information. Here we present a method called SPIN that predicts Sequence Profiles by Integrated Neural network based on fragment-derived sequence profiles and structure-derived energy profiles. SPIN improves over the fragment-derived profile by 6.7% (from 23.6% to 30.3%) in sequence identity between predicted and wild-type sequences. The method also reduces the number of residues in low complex regions by 15.7% and has a significant better balance of hydrophilic and hydrophobic residues at protein surfaces. The accuracy of sequence profiles obtained is comparable to those generated from the protein design program RosettaDesign 3.5. This highly efficient method for predicting sequence profiles from structures will be useful as a single-body scoring term for improving scoring functions used in protein design and fold recognition. It also complements protein design programs in guiding experimental design of the sequence library for screening and directed evolution of designed sequences. The SPIN server is available at http://sparks-lab.org. PMID:24898915

  3. Vertical Chlorophyll Canopy Structure Affects the Remote Sensing Based Predictability of LAI, Chlorophyll and Leaf Nitrogen in Agricultural Fields

    NASA Astrophysics Data System (ADS)

    Boegh, E.; Houborg, R.; Bienkowski, J.; Braban, C. F.; Dalgaard, T.; van Dijk, N.; Dragosits, U.; Holmes, E.; Magliulo, V.; Schelde, K.; Di Tommasi, P.; Vitale, L.; Theobald, M.; Cellier, P.; Sutton, M.

    2012-12-01

    Leaf nitrogen and leaf surface area influence the exchange of gases between terrestrial ecosystems and the atmosphere, and they play a significant role in the global cycles of carbon, nitrogen and water. Remote sensing can be used to estimate leaf area index (LAI), chlorophyll content (CHL) and leaf nitrogen (N), but methods are often developed using plot-scale data and not verified over extended regions characterized by variations in environmental boundary conditions (soil, atmosphere) and canopy structures. Estimation of N can be indirect due to its association with CHL, however N is also included in pigments such as carotenoids and anthocyanin which have different spectral signatures than CHL. Photosynthesis optimization theory suggests that plants will distribute their N resources in proportion to the light gradient within the canopy. Such vertical variation in CHL and N complicates the evaluation of remote sensing-based methods. Typically remote sensing studies measure CHL of the upper leaf, which is then multiplied by the green LAI to represent canopy chlorophyll content, or random sampling is used. In this study, field measurements and high spatial resolution (10-20 m) remote sensing images acquired from the HRG and HRVIR sensors aboard the SPOT satellites were used to assess the predictability of LAI, CHL and N in five European agricultural landscapes located in Denmark, Scotland (United Kingdom), Poland, The Netherlands and Italy . All satellite images were atmospherically using the 6SV1 model with atmospheric inputs estimated by MODIS and AIRS data. Five spectral vegetation indices (SVIs) were calculated (the Normalized Difference Vegetation index, the Simple Ratio, the Enhanced Vegetation Index-2, the Green Normalized Difference Vegetation Index, and the green Chlorophyll Index), and an image-based inverse canopy radiative transfer modelling system, REGFLEC (REGularized canopy reFLECtance) was applied to each of the five European landscapes. While the

  4. Predictive Models Based on Support Vector Machines: Whole-Brain versus Regional Analysis of Structural MRI in the Alzheimer's Disease.

    PubMed

    Retico, Alessandra; Bosco, Paolo; Cerello, Piergiorgio; Fiorina, Elisa; Chincarini, Andrea; Fantacci, Maria Evelina

    2015-01-01

    Decision-making systems trained on structural magnetic resonance imaging data of subjects affected by the Alzheimer's disease (AD) and healthy controls (CTRL) are becoming widespread prognostic tools for subjects with mild cognitive impairment (MCI). This study compares the performances of three classification methods based on support vector machines (SVMs), using as initial sets of brain voxels (ie, features): (1) the segmented grey matter (GM); (2) regions of interest (ROIs) by voxel-wise t-test filtering; (3) parceled ROIs, according to prior knowledge. The recursive feature elimination (RFE) is applied in all cases to investigate whether feature reduction improves the classification accuracy. We analyzed more than 600 AD Neuroimaging Initiative (ADNI) subjects, training the SVMs on the AD/CTRL dataset, and evaluating them on a trial MCI dataset. The classification performance, evaluated as the area under the receiver operating characteristic curve (AUC), reaches AUC = (88.9 ± .5)% in 20-fold cross-validation on the AD/CTRL dataset, when the GM is classified as a whole. The highest discrimination accuracy between MCI converters and nonconverters is achieved when the SVM-RFE is applied to the whole GM: with AUC reaching (70.7 ± .9)%, it outperforms both ROI-based approaches in predicting the AD conversion.

  5. Improved Predictions of Secondary Structures for RNA

    NASA Astrophysics Data System (ADS)

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

    1989-10-01

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

  6. Genome3D: a UK collaborative project to annotate genomic sequences with predicted 3D structures based on SCOP and CATH domains

    PubMed Central

    Lewis, Tony E.; Sillitoe, Ian; Andreeva, Antonina; Blundell, Tom L.; Buchan, Daniel W.A.; Chothia, Cyrus; Cuff, Alison; Dana, Jose M.; Filippis, Ioannis; Gough, Julian; Hunter, Sarah; Jones, David T.; Kelley, Lawrence A.; Kleywegt, Gerard J.; Minneci, Federico; Mitchell, Alex; Murzin, Alexey G.; Ochoa-Montaño, Bernardo; Rackham, Owen J. L.; Smith, James; Sternberg, Michael J. E.; Velankar, Sameer; Yeats, Corin; Orengo, Christine

    2013-01-01

    Genome3D, available at http://www.genome3d.eu, is a new collaborative project that integrates UK-based structural resources to provide a unique perspective on sequence–structure–function relationships. Leading structure prediction resources (DomSerf, FUGUE, Gene3D, pDomTHREADER, Phyre and SUPERFAMILY) provide annotations for UniProt sequences to indicate the locations of structural domains (structural annotations) and their 3D structures (structural models). Structural annotations and 3D model predictions are currently available for three model genomes (Homo sapiens, E. coli and baker’s yeast), and the project will extend to other genomes in the near future. As these resources exploit different strategies for predicting structures, the main aim of Genome3D is to enable comparisons between all the resources so that biologists can see where predictions agree and are therefore more trusted. Furthermore, as these methods differ in whether they build their predictions using CATH or SCOP, Genome3D also contains the first official mapping between these two databases. This has identified pairs of similar superfamilies from the two resources at various degrees of consensus (532 bronze pairs, 527 silver pairs and 370 gold pairs). PMID:23203986

  7. Characteristics and Prediction of RNA Structure

    PubMed Central

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

    2014-01-01

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

  8. Prediction of Human Blood: Air Partition Coefficient: A Comparison of Structure-Based and Property-Based Methods

    DTIC Science & Technology

    2007-11-02

    models developed using experimental properties, including saline;air partition coefficient (longP saline;air) and olive oil ;air partition coefficient...logP olive oil ;air), as independent variables, indicating that the structure-property correlations are comparable to the property-property

  9. Particle-swarm structure prediction on clusters

    NASA Astrophysics Data System (ADS)

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

    2012-08-01

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

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

    PubMed

    Miao, Zhichao; Westhof, Eric

    2017-03-30

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

  11. RFDT: A Rotation Forest-based Predictor for Predicting Drug-Target Interactions using Drug Structure and Protein Sequence Information.

    PubMed

    Wang, Lei; You, Zhu-Hong; Chen, Xing; Yan, Xin; Liu, Gang; Zhang, Wei

    2016-11-14

    Identification of interaction between drugs and target proteins plays an important role in discovering new drug candidates. However, through the experimental method to identify the drug-target interactions remain to be extremely time-consuming, expensive and challenging even nowadays. Therefore, it is urgent to develop new computational methods to predict potential drug-target interactions (DTI). In this article, a novel computational model is developed for predicting potential drug-target interactions under the theory that each drug-target interaction pair can be represented by the structural properties from drugs and evolutionary information derived from proteins. Specifically, the protein sequences are encoded as Position-Specific Scoring Matrix (PSSM) descriptor which contains information of biological evolutionary and the drug molecules are encoded as fingerprint feature vector which represents the existence of certain functional groups or fragments. Four benchmark datasets involving enzymes, ion channels, GPCRs and nuclear receptors, are independently used for establishing predictive models with Rotation Forest (RF) model. The proposed method achieved the prediction accuracy of 91.3%, 89.1%, 84.1% and 71.1% for four datasets respectively. In order to make our method more persuasive, we compared our classifier with the state-of-the-art Support Vector Machine (SVM) classifier. We also compared the proposed method with other excellent methods. Experimental results demonstrate that the proposed method is effective in the prediction of DTI, and can provide assistance for new drug research and development.

  12. SHAPE-Directed RNA Secondary Structure Prediction

    PubMed Central

    Low, Justin T.; Weeks, Kevin M.

    2010-01-01

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

  13. Prediction of P53 Mutants (Multiple Sites) Transcriptional Activity Based on Structural (2D&3D) Properties

    PubMed Central

    Geetha Ramani, R.; Jacob, Shomona Gracia

    2013-01-01

    Prediction of secondary site mutations that reinstate mutated p53 to normalcy has been the focus of intense research in the recent past owing to the fact that p53 mutants have been implicated in more than half of all human cancers and restoration of p53 causes tumor regression. However laboratory investigations are more often laborious and resource intensive but computational techniques could well surmount these drawbacks. In view of this, we formulated a novel approach utilizing computational techniques to predict the transcriptional activity of multiple site (one-site to five-site) p53 mutants. The optimal MCC obtained by the proposed approach on prediction of one-site, two-site, three-site, four-site and five-site mutants were 0.775,0.341,0.784,0.916 and 0.655 respectively, the highest reported thus far in literature. We have also demonstrated that 2D and 3D features generate higher prediction accuracy of p53 activity and our findings revealed the optimal results for prediction of p53 status, reported till date. We believe detection of the secondary site mutations that suppress tumor growth may facilitate better understanding of the relationship between p53 structure and function and further knowledge on the molecular mechanisms and biological activity of p53, a targeted source for cancer therapy. We expect that our prediction methods and reported results may provide useful insights on p53 functional mechanisms and generate more avenues for utilizing computational techniques in biological data analysis. PMID:23468845

  14. Structure-based activity prediction of CYP21A2 stability variants: A survey of available gene variations

    PubMed Central

    Bruque, Carlos D.; Delea, Marisol; Fernández, Cecilia S.; Orza, Juan V.; Taboas, Melisa; Buzzalino, Noemí; Espeche, Lucía D.; Solari, Andrea; Luccerini, Verónica; Alba, Liliana; Nadra, Alejandro D.; Dain, Liliana

    2016-01-01

    Congenital adrenal hyperplasia due to 21-hydroxylase deficiency accounts for 90–95% of CAH cases. In this work we performed an extensive survey of mutations and SNPs modifying the coding sequence of the CYP21A2 gene. Using bioinformatic tools and two plausible CYP21A2 structures as templates, we initially classified all known mutants (n = 343) according to their putative functional impacts, which were either reported in the literature or inferred from structural models. We then performed a detailed analysis on the subset of mutations believed to exclusively impact protein stability. For those mutants, the predicted stability was calculated and correlated with the variant’s expected activity. A high concordance was obtained when comparing our predictions with available in vitro residual activities and/or the patient’s phenotype. The predicted stability and derived activity of all reported mutations and SNPs lacking functional assays (n = 108) were assessed. As expected, most of the SNPs (52/76) showed no biological implications. Moreover, this approach was applied to evaluate the putative synergy that could emerge when two mutations occurred in cis. In addition, we propose a putative pathogenic effect of five novel mutations, p.L107Q, p.L122R, p.R132H, p.P335L and p.H466fs, found in 21-hydroxylase deficient patients of our cohort. PMID:27966633

  15. Structure-based activity prediction of CYP21A2 stability variants: A survey of available gene variations.

    PubMed

    Bruque, Carlos D; Delea, Marisol; Fernández, Cecilia S; Orza, Juan V; Taboas, Melisa; Buzzalino, Noemí; Espeche, Lucía D; Solari, Andrea; Luccerini, Verónica; Alba, Liliana; Nadra, Alejandro D; Dain, Liliana

    2016-12-14

    Congenital adrenal hyperplasia due to 21-hydroxylase deficiency accounts for 90-95% of CAH cases. In this work we performed an extensive survey of mutations and SNPs modifying the coding sequence of the CYP21A2 gene. Using bioinformatic tools and two plausible CYP21A2 structures as templates, we initially classified all known mutants (n = 343) according to their putative functional impacts, which were either reported in the literature or inferred from structural models. We then performed a detailed analysis on the subset of mutations believed to exclusively impact protein stability. For those mutants, the predicted stability was calculated and correlated with the variant's expected activity. A high concordance was obtained when comparing our predictions with available in vitro residual activities and/or the patient's phenotype. The predicted stability and derived activity of all reported mutations and SNPs lacking functional assays (n = 108) were assessed. As expected, most of the SNPs (52/76) showed no biological implications. Moreover, this approach was applied to evaluate the putative synergy that could emerge when two mutations occurred in cis. In addition, we propose a putative pathogenic effect of five novel mutations, p.L107Q, p.L122R, p.R132H, p.P335L and p.H466fs, found in 21-hydroxylase deficient patients of our cohort.

  16. A universal computational model for predicting antigenic variants of influenza A virus based on conserved antigenic structures

    PubMed Central

    Peng, Yousong; Wang, Dayan; Wang, Jianhong; Li, Kenli; Tan, Zhongyang; Shu, Yuelong; Jiang, Taijiao

    2017-01-01

    Rapid determination of the antigenicity of influenza A virus could help identify the antigenic variants in time. Currently, there is a lack of computational models for predicting antigenic variants of some common hemagglutinin (HA) subtypes of influenza A viruses. By means of sequence analysis, we demonstrate here that multiple HA subtypes of influenza A virus undergo similar mutation patterns of HA1 protein (the immunogenic part of HA). Further analysis on the antigenic variation of influenza A virus H1N1, H3N2 and H5N1 showed that the amino acid residues’ contribution to antigenic variation highly differed in these subtypes, while the regional bands, defined based on their distance to the top of HA1, played conserved roles in antigenic variation of these subtypes. Moreover, the computational models for predicting antigenic variants based on regional bands performed much better in the testing HA subtype than those did based on amino acid residues. Therefore, a universal computational model, named PREDAV-FluA, was built based on the regional bands to predict the antigenic variants for all HA subtypes of influenza A viruses. The model achieved an accuracy of 0.77 when tested with avian influenza H9N2 viruses. It may help for rapid identification of antigenic variants in influenza surveillance. PMID:28165025

  17. Structure-Based Prediction of Drug Distribution Across the Headgroup and Core Strata of a Phospholipid Bilayer Using Surrogate Phases

    PubMed Central

    2015-01-01

    locations for 27 compounds. The resulting structure-based prediction system for intrabilayer distribution will facilitate more realistic modeling of passive transport and drug interactions with those integral membrane proteins, which have the binding sites located in the bilayer, such as some enzymes, influx and efflux transporters, and receptors. If only overall bilayer accumulation is of interest, the 1-octanol/W P values suffice to model the studied set. PMID:25179490

  18. Ko Displacement Theory for Structural Shape Predictions

    NASA Technical Reports Server (NTRS)

    Ko, William L.

    2010-01-01

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

  19. Gene structure prediction by linguistic methods

    SciTech Connect

    Dong, S.; Searls, D.B.

    1994-10-01

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

  20. Input-based structure-specific proficiency predicts the neural mechanism of adult L2 syntactic processing.

    PubMed

    Deng, Taiping; Zhou, Huixia; Bi, Hong-Yan; Chen, Baoguo

    2015-06-12

    This study used Event-Related Potentials (ERPs) to explore the role of input-based structure-specific proficiency in L2 syntactic processing, using English subject-verb agreement structures as the stimuli. A pre-test/trainings/post-test paradigm of experimental and control groups was employed, and Chinese speakers who learned English as a second language (L2) participated in the experiment. At pre-test, no ERP component related to the subject-verb agreement structures violations was observed in either group. At training session, the experimental group learned the subject-verb agreement structures, while the control group learned other syntactic structures. After two continuously intensive input trainings, at post-test, a significant P600 component related to the subject-verb agreement structures violations was elicited in the experimental group, but not in the control group. These findings suggest that input training improves structure-specific proficiency, which is reflected in the neural mechanism of L2 syntactic processing.

  1. Predictive models of biohydrogen and biomethane production based on the compositional and structural features of lignocellulosic materials.

    PubMed

    Monlau, Florian; Sambusiti, Cecilia; Barakat, Abdellatif; Guo, Xin Mei; Latrille, Eric; Trably, Eric; Steyer, Jean-Philippe; Carrere, Hélène

    2012-11-06

    In an integrated biorefinery concept, biological hydrogen and methane production from lignocellulosic substrates appears to be one of the most promising alternatives to produce energy from renewable sources. However, lignocellulosic substrates present compositional and structural features that can limit their conversion into biohydrogen and methane. In this study, biohydrogen and methane potentials of 20 lignocellulosic residues were evaluated. Compositional (lignin, cellulose, hemicelluloses, total uronic acids, proteins, and soluble sugars) as well as structural features (crystallinity) were determined for each substrate. Two predictive partial least square (PLS) models were built to determine which compositional and structural parameters affected biohydrogen or methane production from lignocellulosic substrates, among proteins, total uronic acids, soluble sugars, crystalline cellulose, amorphous holocelluloses, and lignin. Only soluble sugars had a significant positive effect on biohydrogen production. Besides, methane potentials correlated negatively to the lignin contents and, to a lower extent, crystalline cellulose showed also a negative impact, whereas soluble sugars, proteins, and amorphous hemicelluloses showed a positive impact. These findings will help to develop further pretreatment strategies for enhancing both biohydrogen and methane production.

  2. In silico exploratory study using structure-activity relationship models and metabolic information for prediction of mutagenicity based on the Ames test and rodent micronucleus assay.

    PubMed

    Kamath, P; Raitano, G; Fernández, A; Rallo, R; Benfenati, E

    2015-12-01

    The mutagenic potential of chemicals is a cause of growing concern, due to the possible impact on human health. In this paper we have developed a knowledge-based approach, combining information from structure-activity relationship (SAR) and metabolic triggers generated from the metabolic fate of chemicals in biological systems for prediction of mutagenicity in vitro based on the Ames test and in vivo based on the rodent micronucleus assay. In the first part of the work, a model was developed, which comprises newly generated SAR rules and a set of metabolic triggers. These SAR rules and metabolic triggers were further externally validated to predict mutagenicity in vitro, with metabolic triggers being used only to predict mutagenicity of chemicals, which were predicted unknown, by SARpy. Hence, this model has a higher accuracy than the SAR model, with an accuracy of 89% for the training set and 75% for the external validation set. Subsequently, the results of the second part of this work enlist a set of metabolic triggers for prediction of mutagenicity in vivo, based on the rodent micronucleus assay. Finally, the results of the third part enlist a list of metabolic triggers to find similarities and differences in the mutagenic response of chemicals in vitro and in vivo.

  3. Predicting structured metadata from unstructured metadata

    PubMed Central

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

    2016-01-01

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

  4. Multithreaded parsing for predicting RNA secondary structures.

    PubMed

    Al-Mulhem, Muhammed S

    2010-01-01

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

  5. Practical lessons from protein structure prediction

    PubMed Central

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

    2005-01-01

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

  6. A New Structure-Activity Relationship (SAR) Model for Predicting Drug-Induced Liver Injury, Based on Statistical and Expert-Based Structural Alerts

    PubMed Central

    Pizzo, Fabiola; Lombardo, Anna; Manganaro, Alberto; Benfenati, Emilio

    2016-01-01

    The prompt identification of chemical molecules with potential effects on liver may help in drug discovery and in raising the levels of protection for human health. Besides in vitro approaches, computational methods in toxicology are drawing attention. We built a structure-activity relationship (SAR) model for evaluating hepatotoxicity. After compiling a data set of 950 compounds using data from the literature, we randomly split it into training (80%) and test sets (20%). We also compiled an external validation set (101 compounds) for evaluating the performance of the model. To extract structural alerts (SAs) related to hepatotoxicity and non-hepatotoxicity we used SARpy, a statistical application that automatically identifies and extracts chemical fragments related to a specific activity. We also applied the chemical grouping approach for manually identifying other SAs. We calculated accuracy, specificity, sensitivity and Matthews correlation coefficient (MCC) on the training, test and external validation sets. Considering the complexity of the endpoint, the model performed well. In the training, test and external validation sets the accuracy was respectively 81, 63, and 68%, specificity 89, 33, and 33%, sensitivity 93, 88, and 80% and MCC 0.63, 0.27, and 0.13. Since it is preferable to overestimate hepatotoxicity rather than not to recognize unsafe compounds, the model's architecture followed a conservative approach. As it was built using human data, it might be applied without any need for extrapolation from other species. This model will be freely available in the VEGA platform. PMID:27920722

  7. Prediction of binding affinity and efficacy of thyroid hormone receptor ligands using QSAR and structure based modeling methods

    PubMed Central

    Politi, Regina; Rusyn, Ivan; Tropsha, Alexander

    2016-01-01

    The thyroid hormone receptor (THR) is an important member of the nuclear receptor family that can be activated by endocrine disrupting chemicals (EDC). Quantitative Structure-Activity Relationship (QSAR) models have been developed to facilitate the prioritization of THR-mediated EDC for the experimental validation. The largest database of binding affinities available at the time of the study for ligand binding domain (LBD) of THRβ was assembled to generate both continuous and classification QSAR models with an external accuracy of R2=0.55 and CCR=0.76, respectively. In addition, for the first time a QSAR model was developed to predict binding affinities of antagonists inhibiting the interaction of coactivators with the AF-2 domain of THRβ (R2=0.70). Furthermore, molecular docking studies were performed for a set of THRβ ligands (57 agonists and 15 antagonists of LBD, 210 antagonists of the AF-2 domain, supplemented by putative decoys/non-binders) using several THRβ structures retrieved from the Protein Data Bank. We found that two agonist-bound THRβ conformations could effectively discriminate their corresponding ligands from presumed non-binders. Moreover, one of the agonist conformations could discriminate agonists from antagonists. Finally, we have conducted virtual screening of a chemical library compiled by the EPA as part of the Tox21 program to identify potential THRβ-mediated EDCs using both QSAR models and docking. We concluded that the library is unlikely to have any EDC that would bind to the THRβ. Models developed in this study can be employed either to identify environmental chemicals interacting with the THR or, conversely, to eliminate the THR-mediated mechanism of action for chemicals of concern. PMID:25058446

  8. Predicting organic food consumption: A meta-analytic structural equation model based on the theory of planned behavior.

    PubMed

    Scalco, Andrea; Noventa, Stefano; Sartori, Riccardo; Ceschi, Andrea

    2017-05-01

    During the last decade, the purchase of organic food within a sustainable consumption context has gained momentum. Consequently, the amount of research in the field has increased, leading in some cases to discrepancies regarding both methods and results. The present review examines those works that applied the theory of planned behavior (TPB; Ajzen, 1991) as a theoretical framework in order to understand and predict consumers' motivation to buy organic food. A meta-analysis has been conducted to assess the strength of the relationships between attitude, subjective norms, perceived behavioral control, and intention, as well as between intention and behavior. Results confirm the major role played by individual attitude in shaping buying intention, followed by subjective norms and perceived behavioral control. Intention-behavior shows a large effect size, few studies however explicitly reported such an association. Furthermore, starting from a pooled correlation matrix, a meta-analytic structural equation model has been applied to jointly evaluate the strength of the relationships among the factors of the original model. Results suggest the robustness of the TPB model. In addition, mediation analysis indicates a potential direct effect from subjective norms to individual attitude in the present context. Finally, some issues regarding methodological aspects of the application of the TPB within the context of organic food are discussed for further research developments.

  9. Mechanics based model for predicting structure-induced rolling resistance (SRR) of the tire-pavement system

    NASA Astrophysics Data System (ADS)

    Shakiba, Maryam; Ozer, Hasan; Ziyadi, Mojtaba; Al-Qadi, Imad L.

    2016-11-01

    The structure-induced rolling resistance of pavements, and its impact on vehicle fuel consumption, is investigated in this study. The structural response of pavement causes additional rolling resistance and fuel consumption of vehicles through deformation of pavement and various dissipation mechanisms associated with inelastic material properties and damping. Accurate and computationally efficient models are required to capture these mechanisms and obtain realistic estimates of changes in vehicle fuel consumption. Two mechanistic-based approaches are currently used to calculate vehicle fuel consumption as related to structural rolling resistance: dissipation-induced and deflection-induced methods. The deflection-induced approach is adopted in this study, and realistic representation of pavement-vehicle interactions (PVIs) is incorporated. In addition to considering viscoelastic behavior of asphalt concrete layers, the realistic representation of PVIs in this study includes non-uniform three-dimensional tire contact stresses and dynamic analysis in pavement simulations. The effects of analysis type, tire contact stresses, pavement viscoelastic properties, pavement damping coefficients, vehicle speed, and pavement temperature are then investigated.

  10. Web-based toolkits for topology prediction of transmembrane helical proteins, fold recognition, structure and binding scoring, folding-kinetics analysis and comparative analysis of domain combinations.

    PubMed

    Zhou, Hongyi; Zhang, Chi; Liu, Song; Zhou, Yaoqi

    2005-07-01

    We have developed the following web servers for protein structural modeling and analysis at http://theory.med.buffalo.edu: THUMBUP, UMDHMM(TMHP) and TUPS, predictors of transmembrane helical protein topology based on a mean-burial-propensity scale of amino acid residues (THUMBUP), hidden Markov model (UMDHMM(TMHP)) and their combinations (TUPS); SPARKS 2.0 and SP3, two profile-profile alignment methods, that match input query sequence(s) to structural templates by integrating sequence profile with knowledge-based structural score (SPARKS 2.0) and structure-derived profile (SP3); DFIRE, a knowledge-based potential for scoring free energy of monomers (DMONOMER), loop conformations (DLOOP), mutant stability (DMUTANT) and binding affinity of protein-protein/peptide/DNA complexes (DCOMPLEX & DDNA); TCD, a program for protein-folding rate and transition-state analysis of small globular proteins; and DOGMA, a web-server that allows comparative analysis of domain combinations between plant and other 55 organisms. These servers provide tools for prediction and/or analysis of proteins on the secondary structure, tertiary structure and interaction levels, respectively.

  11. Prediction of binding affinity and efficacy of thyroid hormone receptor ligands using QSAR and structure-based modeling methods

    SciTech Connect

    Politi, Regina; Rusyn, Ivan; Tropsha, Alexander

    2014-10-01

    The thyroid hormone receptor (THR) is an important member of the nuclear receptor family that can be activated by endocrine disrupting chemicals (EDC). Quantitative Structure–Activity Relationship (QSAR) models have been developed to facilitate the prioritization of THR-mediated EDC for the experimental validation. The largest database of binding affinities available at the time of the study for ligand binding domain (LBD) of THRβ was assembled to generate both continuous and classification QSAR models with an external accuracy of R{sup 2} = 0.55 and CCR = 0.76, respectively. In addition, for the first time a QSAR model was developed to predict binding affinities of antagonists inhibiting the interaction of coactivators with the AF-2 domain of THRβ (R{sup 2} = 0.70). Furthermore, molecular docking studies were performed for a set of THRβ ligands (57 agonists and 15 antagonists of LBD, 210 antagonists of the AF-2 domain, supplemented by putative decoys/non-binders) using several THRβ structures retrieved from the Protein Data Bank. We found that two agonist-bound THRβ conformations could effectively discriminate their corresponding ligands from presumed non-binders. Moreover, one of the agonist conformations could discriminate agonists from antagonists. Finally, we have conducted virtual screening of a chemical library compiled by the EPA as part of the Tox21 program to identify potential THRβ-mediated EDCs using both QSAR models and docking. We concluded that the library is unlikely to have any EDC that would bind to the THRβ. Models developed in this study can be employed either to identify environmental chemicals interacting with the THR or, conversely, to eliminate the THR-mediated mechanism of action for chemicals of concern. - Highlights: • This is the largest curated dataset for ligand binding domain (LBD) of the THRβ. • We report the first QSAR model for antagonists of AF-2 domain of THRβ. • A combination of QSAR and docking enables

  12. Structure prediction of LDLR-HNP1 complex based on docking enhanced by LDLR binding 3D motif.

    PubMed

    Esmaielbeiki, Reyhaneh; Naughton, Declan P; Nebel, Jean-Christophe

    2012-04-01

    Human antimicrobial peptides (AMPs), including defensins, have come under intense scrutiny owing to their key multiple roles as antimicrobial agents. Not only do they display direct action on microbes, but also recently they have been shown to interact with the immune system to increase antimicrobial activity. Unfortunately, since mechanisms involved in the binding of AMPs to mammalian cells are largely unknown, their potential as novel anti-infective agents cannot be exploited yet. Following the reported interaction of Human Neutrophil Peptide 1 dimer (HNP1) with a low density lipoprotein receptor (LDLR), a computational study was conducted to discover their putative mode of interaction. State-of-the-art docking software produced a set of LDLR-HNP1 complex 3D models. Creation of a 3D motif capturing atomic interactions of the LDLR binding interface allowed selection of the most plausible configurations. Eventually, only two models were in agreement with the literature. Binding energy estimations revealed that only one of them is particularly stable, but also interaction with LDLR weakens significantly bonds within the HNP1 dimer. This may be significant since it suggests a mechanism for internalisation of HNP1 in mammalian cells. In addition to a novel approach for complex structure prediction, this study proposes a 3D model of the LDLR-HNP1 complex which highlights the key residues which are involved in the interactions. The putative identification of the receptor binding mechanism should inform the future design of synthetic HNPs to afford maximum internalisation, which could lead to novel anti-infective drugs.

  13. RNA-SSPT: RNA Secondary Structure Prediction Tools

    PubMed Central

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

    2013-01-01

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

  14. Topolology-symmetry law of structure of natural titanosilicate micas and related heterophyllosilicates based on the extended OD theory: Structure prediction

    NASA Astrophysics Data System (ADS)

    Belokoneva, E. L.; Topnikova, A. P.; Aksenov, S. M.

    2015-01-01

    A topology-symmetry analysis of the structures in the family of titanosilicate micas and related heterophyllosilicates based on the extended OD theory reveals their kinship with the family of rhodezite, delhayelite, and other minerals that had been analyzed earlier by distinguishing sheets common for all the structures. Like in the family studied earlier, the structural variety of a more complex titanosilicate family is determined by different local symmetries of sheets. Sheets consist of central O layers of edge-sharing octahedra and H layers formed by tetrahedra connected into diortho groups and Ti(Nb,Fe) semioctahedra (octahedra). Three patterns of connection of O and H layers correspond to sheet symmetry P2/ m, P21/ m, and . Various symmetry modes of sheet connection in the structures are analyzed. Hypothetical structures, including structures with a higher degree of disorder, which can be found in nature or obtained by crystal synthesis, are deduced. Factors responsible for structural variety, including the existence of two main sheet varieties (with P2/ m and P21/ m symmetry) are considered a consequence of the difference in the chemism of the mineral formation medium.

  15. Predicting missing links via structural similarity

    NASA Astrophysics Data System (ADS)

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

    2015-04-01

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

  16. Predicting Protein Structure Using Parallel Genetic Algorithms.

    DTIC Science & Technology

    1994-12-01

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

  17. Retention prediction of low molecular weight anions in ion chromatography based on quantitative structure-retention relationships applied to the linear solvent strength model.

    PubMed

    Park, Soo Hyun; Haddad, Paul R; Talebi, Mohammad; Tyteca, Eva; Amos, Ruth I J; Szucs, Roman; Dolan, John W; Pohl, Christopher A

    2017-02-24

    Quantitative Structure-Retention Relationships (QSRRs) represent a popular technique to predict the retention times of analytes, based on molecular descriptors encoding the chemical structures of the analytes. The linear solvent strength (LSS) model relating the retention factor, k to the eluent concentration (log k=a-blog [eluent]), is a well-known and accurate retention model in ion chromatography (IC). In this work, QSRRs for inorganic and small organic anions were used to predict the regression parameters a and b in the LSS model (and hence retention times) for these analytes under a wide range of eluent conditions, based solely on their chemical structures. This approach was performed on retention data of inorganic and small organic anions from the "Virtual Column" software (Thermo Fisher Scientific). These retention data were recalibrated via a "porting" methodology on three columns (AS20, AS19, and AS11HC), prior to the QSRR modeling. This provided retention data more applicable on recently produced columns which may exhibit changes of column behavior due to batch-to-batch variability. Molecular descriptors for the analytes were calculated with Dragon software using the geometry-optimized molecular structures, employing the AM1 semi-empirical method. An optimal subset of molecular descriptors was then selected using an evolutionary algorithm (EA). Finally, the QSRR models were generated by multiple linear regression (MLR). As a result, six QSRR models with good predictive performance were successfully derived for a- and b-values on three columns (R(2)>0.98 and RMSE<0.11). External validation showed the possibility of using the developed QSRR models as predictive tools in IC (Qext(F3)(2)>0.7 and RMSEP<0.4). Moreover, it was demonstrated that the obtained QSRR models for the a- and b-values can predict the retention times for new analytes with good accuracy and predictability (R(2) of 0.98, RMSE of 0.89min, Qext(F3)(2) of 0.96 and RMSEP of 1.18min).

  18. Vascular endothelial growth factor receptor-2 (VEGFR-2) inhibitors: development and validation of predictive 3-D QSAR models through extensive ligand- and structure-based approaches

    NASA Astrophysics Data System (ADS)

    Ragno, Rino; Ballante, Flavio; Pirolli, Adele; Wickersham, Richard B.; Patsilinakos, Alexandros; Hesse, Stéphanie; Perspicace, Enrico; Kirsch, Gilbert

    2015-08-01

    Vascular endothelial growth factor receptor-2, (VEGFR-2), is a key element in angiogenesis, the process by which new blood vessels are formed, and is thus an important pharmaceutical target. Here, 3-D quantitative structure-activity relationship (3-D QSAR) were used to build a quantitative screening and pharmacophore model of the VEGFR-2 receptors for design of inhibitors with improved activities. Most of available experimental data information has been used as training set to derive optimized and fully cross-validated eight mono-probe and a multi-probe quantitative models. Notable is the use of 262 molecules, aligned following both structure-based and ligand-based protocols, as external test set confirming the 3-D QSAR models' predictive capability and their usefulness in design new VEGFR-2 inhibitors. From a survey on literature, this is the first generation of a wide-ranging computational medicinal chemistry application on VEGFR2 inhibitors.

  19. Structural network efficiency predicts conversion to dementia

    PubMed Central

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

    2016-01-01

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

  20. Toward structure prediction of cyclic peptides.

    PubMed

    Yu, Hongtao; Lin, Yu-Shan

    2015-02-14

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

  1. Predicting road accidents: Structural time series approach

    NASA Astrophysics Data System (ADS)

    Junus, Noor Wahida Md; Ismail, Mohd Tahir

    2014-07-01

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

  2. Structure-based methods for predicting target mutation-induced drug resistance and rational drug design to overcome the problem.

    PubMed

    Hao, Ge-Fei; Yang, Guang-Fu; Zhan, Chang-Guo

    2012-10-01

    Drug resistance has become one of the biggest challenges in drug discovery and/or development and has attracted great research interests worldwide. During the past decade, computational strategies have been developed to predict target mutation-induced drug resistance. Meanwhile, various molecular design strategies, including targeting protein backbone, targeting highly conserved residues and dual/multiple targeting, have been used to design novel inhibitors for combating the drug resistance. In this article we review recent advances in development of computational methods for target mutation-induced drug resistance prediction and strategies for rational design of novel inhibitors that could be effective against the possible drug-resistant mutants of the target.

  3. Energy-directed RNA structure prediction.

    PubMed

    Hofacker, Ivo L

    2014-01-01

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

  4. Structure prediction of the EcoRV DNA methyltransferase based on mutant profiling, secondary structure analysis, comparison with known structures of methyltransferases and isolation of catalytically inactive single mutants.

    PubMed

    Jeltsch, A; Sobotta, T; Pingoud, A

    1996-05-01

    The EcoRV DNA methyltransferase (M.EcoRV) is an alpha-adenine methyltransferase. We have used two different programs to predict the secondary structure of M.EcoRV. The resulting consensus prediction was tested by a mutant profiling analysis. 29 neutral mutations of M.EcoRV were generated by five cycles of random mutagenesis and selection for active variants to increase the reliability of the prediction and to get a secondary structure prediction for some ambiguously predicted regions. The predicted consensus secondary structure elements could be aligned to the common topology of the structures of the catalytic domains of M.HhaI and M.TaqI. In a complementary approach we have isolated nine catalytically inactive single mutants. Five of these mutants contain an amino acid exchange within the catalytic domain of M.EcoRV (Val2-Ala, Lys81Arg, Cys192Arg, Asp193Gly, Trp231Arg). The Trp231Arg mutant binds DNA similarly to wild-type M.EcoRV, but is catalytically inactive. Hence this mutant behaves like a bona fide active site mutant. According to the structure prediction, Trp231 is located in a loop at the putative active site of M.EcoRV. The other inactive mutants were insoluble. They contain amino acid exchanges within the conserved amino acid motifs X, III or IV in M.EcoRV confirming the importance of these regions.

  5. Prediction of Protein Structure Using Surface Accessibility Data

    PubMed Central

    Hartlmüller, Christoph; Göbl, Christoph

    2016-01-01

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

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

    PubMed Central

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

    2005-01-01

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

  7. Comparative analysis of QSAR models for predicting pK(a) of organic oxygen acids and nitrogen bases from molecular structure.

    PubMed

    Yu, Haiying; Kühne, Ralph; Ebert, Ralf-Uwe; Schüürmann, Gerrit

    2010-11-22

    For 1143 organic compounds comprising 580 oxygen acids and 563 nitrogen bases that cover more than 17 orders of experimental pK(a) (from -5.00 to 12.23), the pK(a) prediction performances of ACD, SPARC, and two calibrations of a semiempirical quantum chemical (QC) AM1 approach have been analyzed. The overall root-mean-square errors (rms) for the acids are 0.41, 0.58 (0.42 without ortho-substituted phenols with intramolecular H-bonding), and 0.55 and for the bases are 0.65, 0.70, 1.17, and 1.27 for ACD, SPARC, and both QC methods, respectively. Method-specific performances are discussed in detail for six acid subsets (phenols and aromatic and aliphatic carboxylic acids with different substitution patterns) and nine base subsets (anilines, primary, secondary and tertiary amines, meta/para-substituted and ortho-substituted pyridines, pyrimidines, imidazoles, and quinolines). The results demonstrate an overall better performance for acids than for bases but also a substantial variation across subsets. For the overall best-performing ACD, rms ranges from 0.12 to 1.11 and 0.40 to 1.21 pK(a) units for the acid and base subsets, respectively. With regard to the squared correlation coefficient r², the results are 0.86 to 0.96 (acids) and 0.79 to 0.95 (bases) for ACD, 0.77 to 0.95 (acids) and 0.85 to 0.97 (bases) for SPARC, and 0.64 to 0.87 (acids) and 0.43 to 0.83 (bases) for the QC methods, respectively. Attention is paid to structural and method-specific causes for observed pitfalls. The significant subset dependence of the prediction performances suggests a consensus modeling approach.

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

    PubMed

    Yang, Jianyi; Zhang, Yang

    2015-12-17

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

  9. Predictive Models of Primary Tropical Forest Structure from Geomorphometric Variables Based on SRTM in the Tapajós Region, Brazilian Amazon

    PubMed Central

    Bispo, Polyanna da Conceição; dos Santos, João Roberto; Valeriano, Márcio de Morisson; Graça, Paulo Maurício Lima de Alencastro; Balzter, Heiko; França, Helena; Bispo, Pitágoras da Conceição

    2016-01-01

    Surveying primary tropical forest over large regions is challenging. Indirect methods of relating terrain information or other external spatial datasets to forest biophysical parameters can provide forest structural maps at large scales but the inherent uncertainties need to be evaluated fully. The goal of the present study was to evaluate relief characteristics, measured through geomorphometric variables, as predictors of forest structural characteristics such as average tree basal area (BA) and height (H) and average percentage canopy openness (CO). Our hypothesis is that geomorphometric variables are good predictors of the structure of primary tropical forest, even in areas, with low altitude variation. The study was performed at the Tapajós National Forest, located in the Western State of Pará, Brazil. Forty-three plots were sampled. Predictive models for BA, H and CO were parameterized based on geomorphometric variables using multiple linear regression. Validation of the models with nine independent sample plots revealed a Root Mean Square Error (RMSE) of 3.73 m2/ha (20%) for BA, 1.70 m (12%) for H, and 1.78% (21%) for CO. The coefficient of determination between observed and predicted values were r2 = 0.32 for CO, r2 = 0.26 for H and r2 = 0.52 for BA. The models obtained were able to adequately estimate BA and CO. In summary, it can be concluded that relief variables are good predictors of vegetation structure and enable the creation of forest structure maps in primary tropical rainforest with an acceptable uncertainty. PMID:27089013

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

    PubMed

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

    2017-01-01

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

  11. Critical Features of Fragment Libraries for Protein Structure Prediction

    PubMed Central

    dos Santos, Karina Baptista

    2017-01-01

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

  12. Enhanced multi-view prediction structure

    NASA Astrophysics Data System (ADS)

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

    2016-11-01

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

  13. Development of a Support Vector Machine-Based System to Predict Whether a Compound Is a Substrate of a Given Drug Transporter Using Its Chemical Structure.

    PubMed

    Ose, Atsushi; Toshimoto, Kota; Ikeda, Kazushi; Maeda, Kazuya; Yoshida, Shuya; Yamashita, Fumiyoshi; Hashida, Mitsuru; Ishida, Takashi; Akiyama, Yutaka; Sugiyama, Yuichi

    2016-07-01

    The aim of this study was to develop an in silico prediction system to assess which of 7 categories of drug transporters (organic anion transporting polypeptide [OATP] 1B1/1B3, multidrug resistance-associated protein [MRP] 2/3/4, organic anion transporter [OAT] 1, OAT3, organic cation transporter [OCT] 1/2/multidrug and toxin extrusion [MATE] 1/2-K, multidrug resistance protein 1 [MDR1], and breast cancer resistance protein [BCRP]) can recognize compounds as substrates using its chemical structure alone. We compiled an internal data set consisting of 260 compounds that are substrates for at least 1 of the 7 categories of drug transporters. Four physicochemical parameters (charge, molecular weight, lipophilicity, and plasma unbound fraction) of each compound were used as the basic descriptors. Furthermore, a greedy algorithm was used to select 3 additional physicochemical descriptors from 731 available descriptors. In addition, transporter nonsubstrates tend not to be in the public domain; we, thus, tried to compile an expert-curated data set of putative nonsubstrates for each transporter using personal opinions of 11 researchers in the field of drug transporters. The best prediction was finally achieved by a support vector machine based on 4 basic and 3 additional descriptors. The model correctly judged that 364 of 412 compounds (internal data set) and 111 of 136 compounds (external data set) were substrates, indicating that this model performs well enough to predict the specificity of transporter substrates.

  14. Predicting complex mineral structures using genetic algorithms.

    PubMed

    Mohn, Chris E; Kob, Walter

    2015-10-28

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

  15. Predicting structure in nonsymmetric sparse matrix factorizations

    SciTech Connect

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

    1992-10-01

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

  16. Predicting polymeric crystal structures by evolutionary algorithms

    NASA Astrophysics Data System (ADS)

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

    2014-10-01

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

  17. Crystal structure prediction of rigid molecules.

    PubMed

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

    2016-08-01

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

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

    PubMed

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

    2014-12-01

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

  19. WeFold: A Coopetition for Protein Structure Prediction

    PubMed Central

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

    2014-01-01

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

  20. Status of research aimed at predicting structural integrity

    SciTech Connect

    Reuter, W.G.

    1997-12-31

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

  1. Reduced ceria nanofilms from structure prediction.

    PubMed

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

    2015-03-14

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

  2. Near-edge band structures and band gaps of Cu-based semiconductors predicted by the modified Becke-Johnson potential plus an on-site Coulomb U

    SciTech Connect

    Zhang, Yubo; Zhang, Jiawei; Wang, Youwei; Gao, Weiwei; Abtew, Tesfaye A.; Zhang, Peihong E-mail: wqzhang@mail.sic.ac.cn; Zhang, Wenqing E-mail: wqzhang@mail.sic.ac.cn

    2013-11-14

    Diamond-like Cu-based multinary semiconductors are a rich family of materials that hold promise in a wide range of applications. Unfortunately, accurate theoretical understanding of the electronic properties of these materials is hindered by the involvement of Cu d electrons. Density functional theory (DFT) based calculations using the local density approximation or generalized gradient approximation often give qualitative wrong electronic properties of these materials, especially for narrow-gap systems. The modified Becke-Johnson (mBJ) method has been shown to be a promising alternative to more elaborate theory such as the GW approximation for fast materials screening and predictions. However, straightforward applications of the mBJ method to these materials still encounter significant difficulties because of the insufficient treatment of the localized d electrons. We show that combining the promise of mBJ potential and the spirit of the well-established DFT + U method leads to a much improved description of the electronic structures, including the most challenging narrow-gap systems. A survey of the band gaps of about 20 Cu-based semiconductors calculated using the mBJ + U method shows that the results agree with reliable values to within ±0.2 eV.

  3. Cascaded multiple classifiers for secondary structure prediction.

    PubMed Central

    Ouali, M.; King, R. D.

    2000-01-01

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

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

    PubMed

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

    2009-11-11

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

  5. Three-dimensional quantitative structure-activity relationship analysis for human pregnane X receptor for the prediction of CYP3A4 induction in human hepatocytes: structure-based comparative molecular field analysis.

    PubMed

    Handa, Koichi; Nakagome, Izumi; Yamaotsu, Noriyuki; Gouda, Hiroaki; Hirono, Shuichi

    2015-01-01

    The pregnane X receptor [PXR (NR1I2)] induces the expression of xenobiotic metabolic genes and transporter genes. In this study, we aimed to establish a computational method for quantifying the enzyme-inducing potencies of different compounds via their ability to activate PXR, for the application in drug discovery and development. To achieve this purpose, we developed a three-dimensional quantitative structure-activity relationship (3D-QSAR) model using comparative molecular field analysis (CoMFA) for predicting enzyme-inducing potencies, based on computer-ligand docking to multiple PXR protein structures sampled from the trajectory of a molecular dynamics simulation. Molecular mechanics-generalized born/surface area scores representing the ligand-protein-binding free energies were calculated for each ligand. As a result, the predicted enzyme-inducing potencies for compounds generated by the CoMFA model were in good agreement with the experimental values. Finally, we concluded that this 3D-QSAR model has the potential to predict the enzyme-inducing potencies of novel compounds with high precision and therefore has valuable applications in the early stages of the drug discovery process.

  6. Towards cheminformatics-based estimation of drug therapeutic index: Predicting the protective index of anticonvulsants using a new quantitative structure-index relationship approach.

    PubMed

    Chen, Shangying; Zhang, Peng; Liu, Xin; Qin, Chu; Tao, Lin; Zhang, Cheng; Yang, Sheng Yong; Chen, Yu Zong; Chui, Wai Keung

    2016-06-01

    The overall efficacy and safety profile of a new drug is partially evaluated by the therapeutic index in clinical studies and by the protective index (PI) in preclinical studies. In-silico predictive methods may facilitate the assessment of these indicators. Although QSAR and QSTR models can be used for predicting PI, their predictive capability has not been evaluated. To test this capability, we developed QSAR and QSTR models for predicting the activity and toxicity of anticonvulsants at accuracy levels above the literature-reported threshold (LT) of good QSAR models as tested by both the internal 5-fold cross validation and external validation method. These models showed significantly compromised PI predictive capability due to the cumulative errors of the QSAR and QSTR models. Therefore, in this investigation a new quantitative structure-index relationship (QSIR) model was devised and it showed improved PI predictive capability that superseded the LT of good QSAR models. The QSAR, QSTR and QSIR models were developed using support vector regression (SVR) method with the parameters optimized by using the greedy search method. The molecular descriptors relevant to the prediction of anticonvulsant activities, toxicities and PIs were analyzed by a recursive feature elimination method. The selected molecular descriptors are primarily associated with the drug-like, pharmacological and toxicological features and those used in the published anticonvulsant QSAR and QSTR models. This study suggested that QSIR is useful for estimating the therapeutic index of drug candidates.

  7. Structure Prediction of RNA Loops with a Probabilistic Approach

    PubMed Central

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

    2016-01-01

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

  8. Reduced ceria nanofilms from structure prediction

    NASA Astrophysics Data System (ADS)

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

    2015-02-01

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

  9. 3D protein structure prediction using Imperialist Competitive algorithm and half sphere exposure prediction.

    PubMed

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

    2016-02-21

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

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

    PubMed

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

    2016-08-01

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

  11. Ab initio prediction of the solution structures and populations of a cyclic pentapeptide in DMSO based on an implicit solvation model.

    PubMed

    Baysal, C; Meirovitch, H

    2000-04-15

    Using a recently developed statistical mechanics methodology, the solution structures and populations of the cyclic pentapeptide cyclo(D-Pro(1)-Ala(2)-Ala(3)-Ala(4)-Ala(5)) in DMSO are obtained ab initio, i.e., without using experimental restraints. An important ingredient of this methodology is a novel optimization of implicit solvation parameters, which in our previous publication [Baysal, C.; Meirovitch, H. J Am Chem Soc 1998, 120, 800-812] has been applied to a cyclic hexapeptide in DMSO. The molecule has been described by the simplified energy function E(tot) = E(GRO) + summation operator(k) sigma(k)A(k), where E(GRO) is the GROMOS force-field energy, sigma(k) and A(k) are the atomic solvation parameter (ASP) and the solvent accessible surface area of atom k. This methodology, which relies on an extensive conformational search, Monte Carlo simulations, and free energy calculations, is applied here with E(tot) based on the ASPs derived in our previous work, and for comparison also with E(GRO) alone. For both models, entropy effects are found to be significant. For E(tot), the theoretical values of proton-proton distances and (3)J coupling constants agree very well with the NMR results [Mierke, D. F.; Kurz, M.; Kessler, H. J Am Chem Soc 1994, 116, 1042-1049], while the results for E(GRO) are significantly worse. This suggests that our ASPs might be transferrable to other cyclic peptides in DMSO as well, making our methodology a reliable tool for an ab initio structure prediction; obviously, if necessary, parts of this methodology can also be incorporated in a best-fit analysis where experimental restraints are used.

  12. Toward the Prediction of Organic Hydrate Crystal Structures.

    PubMed

    Hulme, Ashley T; Price, Sarah L

    2007-07-01

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

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

    PubMed Central

    Kalinowska, Barbara; Krzykalski, Artur; Roterman, Irena

    2015-01-01

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

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

    PubMed

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

    2010-04-01

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

  15. Environmental toxicological fate prediction of diverse organic chemicals based on steady-state compartmental chemical mass ratio using quantitative structure-fate relationship (QSFR) models.

    PubMed

    Pramanik, Subrata; Roy, Kunal

    2013-07-01

    Four quantitative prediction models for steady-state compartmental chemical mass concentrations (Wn,g) were obtained from structural information, physiochemical properties, degradation rate and transport coefficients of 455 diverse organic chemicals using chemometric tools in a quantitative structure-fate relationship (QSFR) study. The mass ratio assessment of environmentally prevalent organic chemicals may be helpful to predict their toxicological fate in the ecosystems. Four sets of mass ratios [(1) log(Wair) from water emissions (water to air compartment), (2) log(Wair) from air emissions (within different zones of the air compartment), (3) log(Wwater) from water emissions (within different zones of the water compartment) and (4) log(Wwater) from air emissions (air to water compartment)] have been used. The developed models using genetic function approximation followed by multiple linear regression (GFA-MLR) and subsequent partial least squares (PLS) treatment identify only four descriptors for log(Wair) from water emission, six descriptors for log(Wair) from air emission, five descriptors for log(Wwater) from water emission and seven descriptors for log(Wwater) from air emission for predicting efficiently a large number of test set chemicals (ntest=182). The conclusive models suggest that descriptors such as partition coefficients (Kaw, Kow and Ksw), degradation parameters (Ksoil,Kwater and Kair), vapor pressure (Pv), diffusivity (Dwater), spatial descriptors (Jurs-WNSA-1, Jurs-WNSA-2, Jurs-WPSA-3, Jurs-FNSA-3 and Density), thermodynamic descriptors (MolRef and AlogP98), electrotopological state indices (S_dsN, S_ssNH and S_dsCH) are important for predicting the chemical mass ratios. The developed models may be applicable in toxicological fate prediction of diverse chemicals in the ecosystems.

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

    PubMed

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

    2014-01-01

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

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

    PubMed

    Zhang, Xiuwen; Zunger, Alex; Trimarchi, Giancarlo

    2010-11-21

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

  18. Improving predictions of protein-protein interfaces by combining amino acid-specific classifiers based on structural and physicochemical descriptors with their weighted neighbor averages.

    PubMed

    de Moraes, Fábio R; Neshich, Izabella A P; Mazoni, Ivan; Yano, Inácio H; Pereira, José G C; Salim, José A; Jardine, José G; Neshich, Goran

    2014-01-01

    Protein-protein interactions are involved in nearly all regulatory processes in the cell and are considered one of the most important issues in molecular biology and pharmaceutical sciences but are still not fully understood. Structural and computational biology contributed greatly to the elucidation of the mechanism of protein interactions. In this paper, we present a collection of the physicochemical and structural characteristics that distinguish interface-forming residues (IFR) from free surface residues (FSR). We formulated a linear discriminative analysis (LDA) classifier to assess whether chosen descriptors from the BlueStar STING database (http://www.cbi.cnptia.embrapa.br/SMS/) are suitable for such a task. Receiver operating characteristic (ROC) analysis indicates that the particular physicochemical and structural descriptors used for building the linear classifier perform much better than a random classifier and in fact, successfully outperform some of the previously published procedures, whose performance indicators were recently compared by other research groups. The results presented here show that the selected set of descriptors can be utilized to predict IFRs, even when homologue proteins are missing (particularly important for orphan proteins where no homologue is available for comparative analysis/indication) or, when certain conformational changes accompany interface formation. The development of amino acid type specific classifiers is shown to increase IFR classification performance. Also, we found that the addition of an amino acid conservation attribute did not improve the classification prediction. This result indicates that the increase in predictive power associated with amino acid conservation is exhausted by adequate use of an extensive list of independent physicochemical and structural parameters that, by themselves, fully describe the nano-environment at protein-protein interfaces. The IFR classifier developed in this study is now

  19. Improving Predictions of Protein-Protein Interfaces by Combining Amino Acid-Specific Classifiers Based on Structural and Physicochemical Descriptors with Their Weighted Neighbor Averages

    PubMed Central

    de Moraes, Fábio R.; Neshich, Izabella A. P.; Mazoni, Ivan; Yano, Inácio H.; Pereira, José G. C.; Salim, José A.; Jardine, José G.; Neshich, Goran

    2014-01-01

    Protein-protein interactions are involved in nearly all regulatory processes in the cell and are considered one of the most important issues in molecular biology and pharmaceutical sciences but are still not fully understood. Structural and computational biology contributed greatly to the elucidation of the mechanism of protein interactions. In this paper, we present a collection of the physicochemical and structural characteristics that distinguish interface-forming residues (IFR) from free surface residues (FSR). We formulated a linear discriminative analysis (LDA) classifier to assess whether chosen descriptors from the BlueStar STING database (http://www.cbi.cnptia.embrapa.br/SMS/) are suitable for such a task. Receiver operating characteristic (ROC) analysis indicates that the particular physicochemical and structural descriptors used for building the linear classifier perform much better than a random classifier and in fact, successfully outperform some of the previously published procedures, whose performance indicators were recently compared by other research groups. The results presented here show that the selected set of descriptors can be utilized to predict IFRs, even when homologue proteins are missing (particularly important for orphan proteins where no homologue is available for comparative analysis/indication) or, when certain conformational changes accompany interface formation. The development of amino acid type specific classifiers is shown to increase IFR classification performance. Also, we found that the addition of an amino acid conservation attribute did not improve the classification prediction. This result indicates that the increase in predictive power associated with amino acid conservation is exhausted by adequate use of an extensive list of independent physicochemical and structural parameters that, by themselves, fully describe the nano-environment at protein-protein interfaces. The IFR classifier developed in this study is now

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

    PubMed

    Springer, Timothy A

    2002-12-01

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

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

    PubMed

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

    2010-03-01

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

  2. Evaluation, analysis and prediction of geologic structures

    NASA Astrophysics Data System (ADS)

    Woodward, Nicholas B.

    2012-08-01

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

  3. A system structure for predictive relations in penetration mechanics

    NASA Astrophysics Data System (ADS)

    Korjack, Thomas A.

    1992-02-01

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

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

    PubMed

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

    2009-07-01

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

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

    PubMed Central

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

    2016-01-01

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

  6. Optimizing nondecomposable loss functions in structured prediction.

    PubMed

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

    2013-04-01

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

  7. Quantitative structure-activity relationship models for predicting drug-induced liver injury based on FDA-approved drug labeling annotation and using a large collection of drugs.

    PubMed

    Chen, Minjun; Hong, Huixiao; Fang, Hong; Kelly, Reagan; Zhou, Guangxu; Borlak, Jürgen; Tong, Weida

    2013-11-01

    Drug-induced liver injury (DILI) is one of the leading causes of the termination of drug development programs. Consequently, identifying the risk of DILI in humans for drug candidates during the early stages of the development process would greatly reduce the drug attrition rate in the pharmaceutical industry but would require the implementation of new research and development strategies. In this regard, several in silico models have been proposed as alternative means in prioritizing drug candidates. Because the accuracy and utility of a predictive model rests largely on how to annotate the potential of a drug to cause DILI in a reliable and consistent way, the Food and Drug Administration-approved drug labeling was given prominence. Out of 387 drugs annotated, 197 drugs were used to develop a quantitative structure-activity relationship (QSAR) model and the model was subsequently challenged by the left of drugs serving as an external validation set with an overall prediction accuracy of 68.9%. The performance of the model was further assessed by the use of 2 additional independent validation sets, and the 3 validation data sets have a total of 483 unique drugs. We observed that the QSAR model's performance varied for drugs with different therapeutic uses; however, it achieved a better estimated accuracy (73.6%) as well as negative predictive value (77.0%) when focusing only on these therapeutic categories with high prediction confidence. Thus, the model's applicability domain was defined. Taken collectively, the developed QSAR model has the potential utility to prioritize compound's risk for DILI in humans, particularly for the high-confidence therapeutic subgroups like analgesics, antibacterial agents, and antihistamines.

  8. Predictive modeling of neuroanatomic structures for brain atrophy detection

    NASA Astrophysics Data System (ADS)

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

    2010-03-01

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

  9. Structure prediction of magnetosome-associated proteins

    PubMed Central

    Nudelman, Hila; Zarivach, Raz

    2014-01-01

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

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

    PubMed Central

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

    2008-01-01

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

  11. THE FUTURE OF COMPUTER-BASED TOXICITY PREDICTION: MECHANISM-BASED MODELS VS. INFORMATION MINING APPROACHES

    EPA Science Inventory


    The Future of Computer-Based Toxicity Prediction:
    Mechanism-Based
    Models vs. Information Mining Approaches

    When we speak of computer-based toxicity prediction, we are generally referring to a broad array of approaches which rely primarily upon chemical structure ...

  12. A wave based method to predict the absorption, reflection and transmission coefficient of two-dimensional rigid frame porous structures with periodic inclusions

    SciTech Connect

    Deckers, Elke; Claeys, Claus; Atak, Onur; Groby, Jean-Philippe; Dazel, Olivier; Desmet, Wim

    2016-05-01

    This paper presents an extension to the Wave Based Method to predict the absorption, reflection and transmission coefficients of a porous material with an embedded periodic set of inclusions. The porous unit cell is described using the Multi-Level methodology and by embedding Bloch–Floquet periodicity conditions in the weighted residual scheme. The dynamic pressure field in the semi-infinite acoustic domains is approximated using a novel wave function set that fulfils the Helmholtz equation, the Bloch–Floquet periodicity conditions and the Sommerfeld radiation condition. The method is meshless and computationally efficient, which makes it well suited for optimisation studies.

  13. A wave based method to predict the absorption, reflection and transmission coefficient of two-dimensional rigid frame porous structures with periodic inclusions

    NASA Astrophysics Data System (ADS)

    Deckers, Elke; Claeys, Claus; Atak, Onur; Groby, Jean-Philippe; Dazel, Olivier; Desmet, Wim

    2016-05-01

    This paper presents an extension to the Wave Based Method to predict the absorption, reflection and transmission coefficients of a porous material with an embedded periodic set of inclusions. The porous unit cell is described using the Multi-Level methodology and by embedding Bloch-Floquet periodicity conditions in the weighted residual scheme. The dynamic pressure field in the semi-infinite acoustic domains is approximated using a novel wave function set that fulfils the Helmholtz equation, the Bloch-Floquet periodicity conditions and the Sommerfeld radiation condition. The method is meshless and computationally efficient, which makes it well suited for optimisation studies.

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

    PubMed Central

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

    1990-01-01

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

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

    DTIC Science & Technology

    2009-04-01

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

  16. Understanding the Molecular Determinant of Reversible Human Monoamine Oxidase B Inhibitors Containing 2H-chromen-2-One Core: Structure-Based and Ligand-Based Derived 3-D QSAR Predictive Models.

    PubMed

    Mladenovic, Milan; Patsilinakos, Alexandros; Pirolli, Adele; Sabatino, Manuela; Ragno, Rino

    2017-03-14

    Monoamine oxidase B (MAO B) catalyzes the oxidative deamination of aryalkylamines neurotransmitters with concomitant reduction of oxygen to hydrogen peroxide. Consequently, the enzyme's malfunction can induce oxidative damage to mitochondrial DNA and mediates development of Parkinson's disease. Thus, MAO B emerges as a promising target for developing pharmaceuticals potentially useful to treat this vicious neurodegenerative condition. Aiming to contribute to the development of drugs with the reversible mechanism of MAO B inhibition only, herein, an extended in silico-in vitro procedure for the selection of novel MAO B inhibitors is demonstrated, including: (1) definition of optimized and validated structure-based (SB) 3-D QSAR models derived from available co-crystallized inhibitor-MAO B complexes; (2) elaboration of structure-activity relationships (SAR) features for either irreversible or reversible MAO B inhibitors to characterize and improve coumarin-based inhibitor activity (Protein Data Bank ID: 2V61) as the most potent reversible lead compound; (3) definition of structure-based (SB) and ligand-based (LB) alignment rules assessments by which virtually any untested potential MAO B inhibitor might be evaluated; (4) predictive ability validation of the best 3-D QSAR model through SB/LB modeling of four coumarin-based external test sets (267 compounds); (5) design and SB/LB alignment of novel coumarin-based scaffolds experimentally validated through synthesis and biological evaluation in vitro. Due to the wide range of molecular diversity within the 3-D QSARs training set and derived features, the selected N probe-derived 3-D QSAR model proves to be a valuable tool for virtual screening (VS) of novel MAO B inhibitors and a platform for design, synthesis and evaluation of novel active structures. Accordingly, six highly active and selective MAO B inhibitors (picomolar to low nanomolar range of activity) were disclosed as a result of rational SB/LB 3-D QSAR design

  17. Factor Structure of Self-Regulation in Preschoolers: Testing Models of a Field-Based Assessment for Predicting Early School Readiness

    PubMed Central

    Denham, Susanne A; Warren-Khot, Heather K.; Bassett, Hideko Hamada; Wyatt, Todd; Perna, Alyssa

    2011-01-01

    The importance of early self-regulatory skill has seen increased focus in the applied research literature, given the implications of these skills for early school success. A three-factor latent structure of self-regulation consisting of compliance, cool executive control, and hot executive control, was tested against alternative models, and retained as best fitting. Tests of model equivalence indicated the model held invariant across Head Start and private child care samples. Partial invariance was supported for age and gender. In the validity model, because of substantial amount of shared variance among latent factors, we included a second-order factor explaining the two types of executive control. Higher-Order Executive Control positively predicted teacher report of learning behaviors and social competence in the classroom. These findings are discussed in light of their practical and theoretical significance. PMID:22104321

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

    PubMed

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

    2015-01-01

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

  19. 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...

  20. Entropy-based link prediction in weighted networks

    NASA Astrophysics Data System (ADS)

    Xu, Zhongqi; Pu, Cunlai; Ramiz Sharafat, Rajput; Li, Lunbo; Yang, Jian

    2017-01-01

    Information entropy has been proved to be an effective tool to quantify the structural importance of complex networks. In the previous work (Xu et al, 2016 \\cite{xu2016}), we measure the contribution of a path in link prediction with information entropy. In this paper, we further quantify the contribution of a path with both path entropy and path weight, and propose a weighted prediction index based on the contributions of paths, namely Weighted Path Entropy (WPE), to improve the prediction accuracy in weighted networks. Empirical experiments on six weighted real-world networks show that WPE achieves higher prediction accuracy than three typical weighted indices.

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

    PubMed Central

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

    2016-01-01

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

  2. Generalized Pattern Search Algorithm for Peptide Structure Prediction

    PubMed Central

    Nicosia, Giuseppe; Stracquadanio, Giovanni

    2008-01-01

    Finding the near-native structure of a protein is one of the most important open problems in structural biology and biological physics. The problem becomes dramatically more difficult when a given protein has no regular secondary structure or it does not show a fold similar to structures already known. This situation occurs frequently when we need to predict the tertiary structure of small molecules, called peptides. In this research work, we propose a new ab initio algorithm, the generalized pattern search algorithm, based on the well-known class of Search-and-Poll algorithms. We performed an extensive set of simulations over a well-known set of 44 peptides to investigate the robustness and reliability of the proposed algorithm, and we compared the peptide conformation with a state-of-the-art algorithm for peptide structure prediction known as PEPstr. In particular, we tested the algorithm on the instances proposed by the originators of PEPstr, to validate the proposed algorithm; the experimental results confirm that the generalized pattern search algorithm outperforms PEPstr by 21.17% in terms of average root mean-square deviation, RMSD Cα. PMID:18487293

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

    PubMed

    Dearden, John C; Schüürmann, Gerrit

    2003-08-01

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

  4. Synthesis, characterization, crystal structure and predicting the second-order optical nonlinearity of a new dicobalt(III) complex with Schiff base ligand

    NASA Astrophysics Data System (ADS)

    Zarei, Seyed Amir; Piltan, Mohammad; Hassanzadeh, Keyumars; Akhtari, Keivan; Cinčić, Dominik

    2015-03-01

    The synthesis and characterization of dicobalt(III) complex [Co2L2(OMe)2] of the tetradentate Schiff base ligand N,N‧-bis(2-hydroxybenzylidene)-2,2-dimethyl-1,3-propanediamine (H2L) is reported. The crystal structure of the complex has been determined that exhibited the pseudo-octahedral geometry around both cobalt(III) ions. In the complexation process, H2L acts as two negatively charged tetradentate ligand, L2-, and methoxy group plays as bridging ligand. The geometry structure of the complex is optimized by density functional theory (DFT) using B3LYP/6-311G(d,p). The calculated geometric parameters are in good agreement with the corresponding experimental data. Second-Order Nonlinear Optical (NLO) property of the complex is evaluated by DFT/B3LYP/6-311G(d,p) on the base of the optimized structure that shows the enhancement relative to the calculated value of H2L. The calculated NLO value of the complex is much greater than the corresponding value of urea.

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

    SciTech Connect

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

    2010-09-01

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

  6. Statistical energy analysis response prediction methods for structural systems

    NASA Technical Reports Server (NTRS)

    Davis, R. F.

    1979-01-01

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

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

    PubMed

    Whisstock, James C; Lesk, Arthur M

    2003-08-01

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

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

    PubMed

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

    2010-01-01

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

  9. Prediction of interface structures and energies via virtual screening

    PubMed Central

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

    2016-01-01

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

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

    PubMed

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

    2016-11-01

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

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

    NASA Technical Reports Server (NTRS)

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

    1999-01-01

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

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

    PubMed

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

    2014-07-01

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

  13. Predicting aqueous solubility of environmentally relevant compounds from molecular features: a simple but highly effective four-dimensional model based on Project to Latent Structures.

    PubMed

    Xiao, Feng; Gulliver, John S; Simcik, Matt F

    2013-09-15

    The aqueous solubility (log S) of xenobiotic chemicals has been identified as a key characteristic in determining their bioaccessibility/bioavailability and their fate and transport in aquatic environments. We here explore and evaluate the use of a state-of-the-art data analysis technique (Project to Latent Structures, PLS) to estimate log S of environmentally relevant chemicals. A large number (n = 624) of molecular descriptors was computed for over 1400 organic chemicals, and then refined by a feature selection technique. Candidate predictor descriptors were fitted to data by means of PLS, which was optimized by an internal leave-one-out cross-validation technique and validated by an external data set. The final (best) PLS model with only four variables (AlogP, X1sol, Mv, and E) exhibited noteworthy stability and good predictive power. It was able to explain 91% of the data (n = 1400) variance with an average absolute error of 0.5 log units through the solubilities span over 12 orders of magnitude. The newly proposed model is transparent, easily portable from one user to another, and robust enough to accurately estimate log S of a wide range of emerging contaminants.

  14. Brain structure predicts risk for obesity ☆

    PubMed Central

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

    2014-01-01

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

  15. A comprehensive comparison of comparative RNA structure prediction approaches

    PubMed Central

    Gardner, Paul P; Giegerich, Robert

    2004-01-01

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

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

    PubMed Central

    Parisi, Gustavo; Fornasari, Maria Silvina

    2016-01-01

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

  17. Link prediction based on local community properties

    NASA Astrophysics Data System (ADS)

    Yang, Xu-Hua; Zhang, Hai-Feng; Ling, Fei; Cheng, Zhi; Weng, Guo-Qing; Huang, Yu-Jiao

    2016-09-01

    The link prediction algorithm is one of the key technologies to reveal the inherent rule of network evolution. This paper proposes a novel link prediction algorithm based on the properties of the local community, which is composed of the common neighbor nodes of any two nodes in the network and the links between these nodes. By referring to the node degree and the condition of assortativity or disassortativity in a network, we comprehensively consider the effect of the shortest path and edge clustering coefficient within the local community on node similarity. We numerically show the proposed method provide good link prediction results.

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

    PubMed

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

    2011-07-01

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

  19. On the significance of an RNA tertiary structure prediction

    PubMed Central

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

    2010-01-01

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

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

    NASA Technical Reports Server (NTRS)

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

    1995-01-01

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

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

    PubMed

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

    2006-08-01

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

  2. Prediction of common folding structures of homologous RNAs.

    PubMed Central

    Han, K; Kim, H J

    1993-01-01

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

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

    PubMed

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

    2000-08-01

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

  4. Ichthyophonus parasite phylogeny based on ITS rDNA structure prediction and alignment identifies six clades, with a single dominant marine type

    USGS Publications Warehouse

    Gregg, Jacob; Thompson, Rachel L.; Purcell, Maureen; Friedman, Carolyn S.; Hershberger, Paul

    2016-01-01

    Despite their widespread, global impact in both wild and cultured fishes, little is known of the diversity, transmission patterns, and phylogeography of parasites generally identified as Ichthyophonus. This study constructed a phylogeny based on the structural alignment of internal transcribed spacer (ITS) rDNA sequences to compare Ichthyophonus isolates from fish hosts in the Atlantic and Pacific oceans, and several rivers and aquaculture sites in North America, Europe, and Japan. Structure of the Ichthyophonus ITS1–5.8S–ITS2 transcript exhibited several homologies with other eukaryotes, and 6 distinct clades were identified within Ichthyophonus. A single clade contained a majority (71 of 98) of parasite isolations. This ubiquitous Ichthyophonus type occurred in 13 marine and anadromous hosts and was associated with epizootics in Atlantic herring, Chinook salmon, and American shad. A second clade contained all isolates from aquaculture, despite great geographic separation of the freshwater hosts. Each of the 4 remaining clades contained isolates from single host species. This study is the first to evaluate the genetic relationships among Ichthyophonus species across a significant portion of their host and geographic range. Additionally, parasite infection prevalence is reported in 16 fish species.

  5. Weight prediction in complex networks based on neighbor set

    NASA Astrophysics Data System (ADS)

    Zhu, Boyao; Xia, Yongxiang; Zhang, Xue-Jun

    2016-12-01

    Link weights are essential to network functionality, so weight prediction is important for understanding weighted networks given incomplete real-world data. In this work, we develop a novel method for weight prediction based on the local network structure, namely, the set of neighbors of each node. The performance of this method is validated in two cases. In the first case, some links are missing altogether along with their weights, while in the second case all links are known and weight information is missing for some links. Empirical experiments on real-world networks indicate that our method can provide accurate predictions of link weights in both cases.

  6. Weight prediction in complex networks based on neighbor set

    PubMed Central

    Zhu, Boyao; Xia, Yongxiang; Zhang, Xue-Jun

    2016-01-01

    Link weights are essential to network functionality, so weight prediction is important for understanding weighted networks given incomplete real-world data. In this work, we develop a novel method for weight prediction based on the local network structure, namely, the set of neighbors of each node. The performance of this method is validated in two cases. In the first case, some links are missing altogether along with their weights, while in the second case all links are known and weight information is missing for some links. Empirical experiments on real-world networks indicate that our method can provide accurate predictions of link weights in both cases. PMID:27905497

  7. Protein Structure Prediction Using String Kernels

    DTIC Science & Technology

    2006-03-03

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

  8. Predicting Career Advancement with Structural Equation Modelling

    ERIC Educational Resources Information Center

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

    2012-01-01

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

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

    PubMed

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

    2007-01-01

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

  10. A Software Pipeline for Protein Structure Prediction

    DTIC Science & Technology

    2006-11-01

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

  11. Optimization-based Dynamic Human Lifting Prediction

    DTIC Science & Technology

    2008-06-01

    Anith Mathai, Steve Beck,Timothy Marler , Jingzhou Yang, Jasbir S. Arora, Karim Abdel-Malek Virtual Soldier Research Program, Center for Computer Aided...Rahmatalla, S., Kim, J., Marler , T., Beck, S., Yang, J., busek, J., Arora, J.S., and Abdel-Malek, K. Optimization-based dynamic human walking prediction

  12. Predicting Learned Helplessness Based on Personality

    ERIC Educational Resources Information Center

    Maadikhah, Elham; Erfani, Nasrollah

    2014-01-01

    Learned helplessness as a negative motivational state can latently underlie repeated failures and create negative feelings toward the education as well as depression in students and other members of a society. The purpose of this paper is to predict learned helplessness based on students' personality traits. The research is a predictive…

  13. Fatigue Prediction for Composite Materials and Structures

    DTIC Science & Technology

    2005-10-01

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

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

    SciTech Connect

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

    1994-02-01

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

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

    NASA Technical Reports Server (NTRS)

    Knight, Norman F., Jr.

    2008-01-01

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

  16. Protein structure prediction from sequence variation

    PubMed Central

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

    2015-01-01

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

  17. Prediction of rigid silica based insulation conductivity

    NASA Technical Reports Server (NTRS)

    Williams, Stanley D.; Curry, Donald M.

    1993-01-01

    A method is presented for predicting the thermal conductivity of low density, silica based fibrous insulators. It is shown that the method can be used to extend data values to the upper material temperature limits from those obtained from the test data. It is demonstrated that once the conductivity is accurately determined by the analytical model the conductivity for other atmospheres can be predicted. The method is similar to that presented by previous investigators, but differs significantly in the contribution due to gas and internal radiation.

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

    NASA Astrophysics Data System (ADS)

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

    1999-05-01

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

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

    PubMed

    Roy, Ambrish; Kucukural, Alper; Zhang, Yang

    2010-04-01

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

  20. Plasma Stabilization Based on Model Predictive Control

    NASA Astrophysics Data System (ADS)

    Sotnikova, Margarita

    The nonlinear model predictive control algorithms for plasma current and shape stabilization are proposed. Such algorithms are quite suitable for the situations when the plant to be controlled has essentially nonlinear dynamics. Besides that, predictive model based control algorithms allow to take into account a lot of requirements and constraints involved both on the controlled and manipulated variables. The significant drawback of the algorithms is that they require a lot of time to compute control input at each sampling instant. In this paper the model predictive control algorithms are demonstrated by the example of plasma vertical stabilization for ITER-FEAT tokamak. The tuning of parameters of algorithms is performed in order to decrease computational load.

  1. A Software Pipeline for Protein Structure Prediction

    DTIC Science & Technology

    2006-11-01

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

  2. Epitope prediction based on random peptide library screening: benchmark dataset and prediction tools evaluation.

    PubMed

    Sun, Pingping; Chen, Wenhan; Huang, Yanxin; Wang, Hongyan; Ma, Zhiqiang; Lv, Yinghua

    2011-06-16

    Epitope prediction based on random peptide library screening has become a focus as a promising method in immunoinformatics research. Some novel software and web-based servers have been proposed in recent years and have succeeded in given test cases. However, since the number of available mimotopes with the relevant structure of template-target complex is limited, a systematic evaluation of these methods is still absent. In this study, a new benchmark dataset was defined. Using this benchmark dataset and a representative dataset, five examples of the most popular epitope prediction software products which are based on random peptide library screening have been evaluated. Using the benchmark dataset, in no method did performance exceed a 0.42 precision and 0.37 sensitivity, and the MCC scores suggest that the epitope prediction results of these software programs are greater than random prediction about 0.09-0.13; while using the representative dataset, most of the values of these performance measures are slightly improved, but the overall performance is still not satisfactory. Many test cases in the benchmark dataset cannot be applied to these pieces of software due to software limitations. Moreover chances are that these software products are overfitted to the small dataset and will fail in other cases. Therefore finding the correlation between mimotopes and genuine epitope residues is still far from resolved and much larger dataset for mimotope-based epitope prediction is desirable.

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

    NASA Technical Reports Server (NTRS)

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

    1992-01-01

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

  4. Highway traffic noise prediction based on GIS

    NASA Astrophysics Data System (ADS)

    Zhao, Jianghua; Qin, Qiming

    2014-05-01

    Before building a new road, we need to predict the traffic noise generated by vehicles. Traditional traffic noise prediction methods are based on certain locations and they are not only time-consuming, high cost, but also cannot be visualized. Geographical Information System (GIS) can not only solve the problem of manual data processing, but also can get noise values at any point. The paper selected a road segment from Wenxi to Heyang. According to the geographical overview of the study area and the comparison between several models, we combine the JTG B03-2006 model and the HJ2.4-2009 model to predict the traffic noise depending on the circumstances. Finally, we interpolate the noise values at each prediction point and then generate contours of noise. By overlaying the village data on the noise contour layer, we can get the thematic maps. The use of GIS for road traffic noise prediction greatly facilitates the decision-makers because of GIS spatial analysis function and visualization capabilities. We can clearly see the districts where noise are excessive, and thus it becomes convenient to optimize the road line and take noise reduction measures such as installing sound barriers and relocating villages and so on.

  5. Saliency-based gaze prediction based on head direction.

    PubMed

    Nakashima, Ryoichi; Fang, Yu; Hatori, Yasuhiro; Hiratani, Akinori; Matsumiya, Kazumichi; Kuriki, Ichiro; Shioiri, Satoshi

    2015-12-01

    Despite decades of attempts to create a model for predicting gaze locations by using saliency maps, a highly accurate gaze prediction model for general conditions has yet to be devised. In this study, we propose a gaze prediction method based on head direction that can improve the accuracy of any model. We used a probability distribution of eye position based on head direction (static eye-head coordination) and added this information to a model of saliency-based visual attention. Using empirical data on eye and head directions while observers were viewing natural scenes, we estimated a probability distribution of eye position. We then combined the relationship between eye position and head direction with visual saliency to predict gaze locations. The model showed that information on head direction improved the prediction accuracy. Further, there was no difference in the gaze prediction accuracy between the two models using information on head direction with and without eye-head coordination. Therefore, information on head direction is useful for predicting gaze location when it is available. Furthermore, this gaze prediction model can be applied relatively easily to many daily situations such as during walking.

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

    PubMed Central

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

    2008-01-01

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

  7. Structure Prediction and Analysis of Neuraminidase Sequence Variants

    ERIC Educational Resources Information Center

    Thayer, Kelly M.

    2016-01-01

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

  8. Propagating uncertainties in statistical model based shape prediction

    NASA Astrophysics Data System (ADS)

    Syrkina, Ekaterina; Blanc, Rémi; Székely, Gàbor

    2011-03-01

    This paper addresses the question of accuracy assessment and confidence regions estimation in statistical model based shape prediction. Shape prediction consists in estimating the shape of an organ based on a partial observation, due e.g. to a limited field of view or poorly contrasted images, and generally requires a statistical model. However, such predictions can be impaired by several sources of uncertainty, in particular the presence of noise in the observation, limited correlations between the predictors and the shape to predict, as well as limitations of the statistical shape model - in particular the number of training samples. We propose a framework which takes these into account and derives confidence regions around the predicted shape. Our method relies on the construction of two separate statistical shape models, for the predictors and for the unseen parts, and exploits the correlations between them assuming a joint Gaussian distribution. Limitations of the models are taken into account by jointly optimizing the prediction and minimizing the shape reconstruction error through cross-validation. An application to the prediction of the shape of the proximal part of the human tibia given the shape of the distal femur is proposed, as well as the evaluation of the reliability of the estimated confidence regions, using a database of 184 samples. Potential applications are reconstructive surgery, e.g. to assess whether an implant fits in a range of acceptable shapes, or functional neurosurgery when the target's position is not directly visible and needs to be inferred from nearby visible structures.

  9. A Predictive Structural Model of the Primate Connectome

    PubMed Central

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

    2017-01-01

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

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

    PubMed Central

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

    2006-01-01

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

  11. A predictive structural model for bulk metallic glasses.

    PubMed

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

    2015-09-15

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

  12. Prediction Model for Impulsive Noise on Structures

    DTIC Science & Technology

    2012-09-01

    construction usually have an interior wall finish of: a) gypsum wallboard (also called plasterboard or drywall), b) plaster or c) wood paneling... Gypsum Plaster , Wall Board 11,67 0.04 NA For simply-supported beams vibrating in their fundamental mode, the value of KS is needed for...Dev of log10(f0) for wood panel interior to be average for wood walls with plaster or gypsum board interior. (8) L(w) based on estimated standard

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

    PubMed

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

    2008-04-01

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

  14. The OASE project: Object-based Analysis and Seamless prediction

    NASA Astrophysics Data System (ADS)

    Troemel, Silke; Wapler, Kathrin; Bick, Theresa; Diederich, Malte; Deneke, Hartwig; Horvath, Akos; Senf, Fabian; Simmer, Clemens; Simon, Juergen

    2013-04-01

    The research group on Object-based Analysis and SEamless prediction (OASE) is part of the Hans Ertel Centre for Weather Research (HErZ). The group consists of scientists at the Meteorological Institute, University of Bonn, the Leibniz-Institute for Tropospheric Research in Leipzig and the German Weather Service. OASE addresses seamless prediction of convective events from nowcasting to daily predictions by combining radar/satellite compositing and tracking with high-resolution model-based ensemble generation and prediction. While observation-based nowcasting provides good results for lead times between 0-1 hours, numerical weather prediction addresses lead times between 3-21 hours. Especially the discontinuity between 1-3 hours needs to be addressed. Therefore a central goal of the project is a near real-time high-resolved unprecedented data base. A radar and satellite remote sensing-driven 3D observation-microphysics composite covering Germany, currently under development, contains gridded observations and estimated microphysical quantities. Observations and microphysics are intertwined via forward operators and estimated inverse relations, which also provide uncertainties for model ensemble initialisations. The lifetime evolution of dynamics and microphysics in (severe) convective storms is analysed based on 3D scale-space tracking. An object-based analysis condenses the information contained in the dynamic 3D distributions of observables and related microphysics into descriptors, which will allow identifying governing processes leading to the formation and evolution of severe weather events. The object-based approach efficiently characterises and quantifies the process structure and life cycles of severe weather events, and facilitates nowcasting and the generation and initialisation of model prediction ensembles. Observation-based nowcasting will exploit the dual-composite based 3D feature detection and tracking to generate a set of predictions (observation-based

  15. Characterization of domain-peptide interaction interface: prediction of SH3 domain-mediated protein-protein interaction network in yeast by generic structure-based models.

    PubMed

    Hou, Tingjun; Li, Nan; Li, Youyong; Wang, Wei

    2012-05-04

    Determination of the binding specificity of SH3 domain, a peptide recognition module (PRM), is important to understand their biological functions and reconstruct the SH3-mediated protein-protein interaction network. In the present study, the SH3-peptide interactions for both class I and II SH3 domains were characterized by the intermolecular residue-residue interaction network. We developed generic MIEC-SVM models to infer SH3 domain-peptide recognition specificity that achieved satisfactory prediction accuracy. By investigating the domain-peptide recognition mechanisms at the residue level, we found that the class-I and class-II binding peptides have different binding modes even though they occupy the same binding site of SH3. Furthermore, we predicted the potential binding partners of SH3 domains in the yeast proteome and constructed the SH3-mediated protein-protein interaction network. Comparison with the experimentally determined interactions confirmed the effectiveness of our approach. This study showed that our sophisticated computational approach not only provides a powerful platform to decipher protein recognition code at the molecular level but also allows identification of peptide-mediated protein interactions at a proteomic scale. We believe that such an approach is general to be applicable to other domain-peptide interactions.

  16. Characterization of Domain–Peptide Interaction Interface: Prediction of SH3 Domain-Mediated Protein–Protein Interaction Network in Yeast by Generic Structure-Based Models

    PubMed Central

    Hou, Tingjun; Li, Nan; Li, Youyong; Wang, Wei

    2012-01-01

    Determination of the binding specificity of SH3 domain, a peptide recognition module (PRM), is important to understand their biological functions and reconstruct the SH3-mediated protein–protein interaction network. In the present study, the SH3-peptide interactions for both class I and II SH3 domains were characterized by the intermolecular residue–residue interaction network. We developed generic MIEC-SVM models to infer SH3 domain-peptide recognition specificity that achieved satisfactory prediction accuracy. By investigating the domain–peptide recognition mechanisms at the residue level, we found that the class-I and class-II binding peptides have different binding modes even though they occupy the same binding site of SH3. Furthermore, we predicted the potential binding partners of SH3 domains in the yeast proteome and constructed the SH3-mediated protein–protein interaction network. Comparison with the experimentally determined interactions confirmed the effectiveness of our approach. This study showed that our sophisticated computational approach not only provides a powerful platform to decipher protein recognition code at the molecular level but also allows identification of peptide-mediated protein interactions at a proteomic scale. We believe that such an approach is general to be applicable to other domain–peptide interactions. PMID:22468754

  17. Factor Structure of Self-Regulation in Preschoolers: Testing Models of a Field-Based Assessment for Predicting Early School Readiness

    ERIC Educational Resources Information Center

    Denham, Susanne A.; Warren-Khot, Heather K.; Bassett, Hideko Hamada; Wyatt, Todd; Perna, Alyssa

    2012-01-01

    The importance of early self-regulatory skill has seen increased focus in the applied research literature given the implications of these skills for early school success. A three-factor latent structure of self-regulation consisting of compliance, cool executive control, and hot executive control was tested against alternative models and retained…

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

    NASA Technical Reports Server (NTRS)

    Hasselman, Timothy K.; Chrostowski, Jon D.

    1991-01-01

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

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

    NASA Astrophysics Data System (ADS)

    Obata, Shigeaki; Goto, Hitoshi

    2015-02-01

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

  20. Ceramide structure predicts tumor ganglioside immunosuppressive activity.

    PubMed Central

    Ladisch, S; Li, R; Olson, E

    1994-01-01

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

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

    PubMed

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

    2017-01-30

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

  2. Development of Predictive Equations Based on Pavement Condition Index Data

    DTIC Science & Technology

    1992-03-01

    solutions to eliminate existing problems. A Spavement management system not only evaluates the present condition of a pavement but predicts its future...structural performance [1]. Condition rating data collected periodically will track the performance of a pavement. Most airports presently utilize...three primary objectives of rating a pavement based on the PCI method: (1) Determine present condition of the pavement in terms of the apparent

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

    NASA Astrophysics Data System (ADS)

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

    2016-08-01

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

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

    ERIC Educational Resources Information Center

    Cheng, Wei; Almor, Amit

    2017-01-01

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

  5. Model-Based Predictive Control of Turbulent Channel Flow

    NASA Astrophysics Data System (ADS)

    Kellogg, Steven M.; Collis, S. Scott

    1999-11-01

    In recent simulations of optimal turbulence control, the time horizon over which the control is determined matches the time horizon over which the flow is advanced. A popular workhorse of the controls community, Model-Based Predictive Control (MBPC), suggests using longer predictive horizons than advancement windows. Including additional time information in the optimization may generate improved controls. When the advancement horizon is smaller than the predictive horizon, part of the optimization and resulting control are discarded. Although this inherent inefficiency may be justified by improved control predictions, it has hampered prior investigations of MBPC for turbulent flow due to the expense associated with optimal control based on Direct Numerical Simulation. The current approach overcomes this by using our optimal control formulation based on Large Eddy Simulation. This presentation summarizes the results of optimal control simulations for turbulent channel flow using various ratios of advancement and predictive horizons. These results provide clues as to the roles of foresight, control history, cost functional, and turbulence structures for optimal control of wall-bounded turbulence.

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

    PubMed Central

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

    2013-01-01

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

  7. Statistical Seasonal Sea Surface based Prediction Model

    NASA Astrophysics Data System (ADS)

    Suarez, Roberto; Rodriguez-Fonseca, Belen; Diouf, Ibrahima

    2014-05-01

    The interannual variability of the sea surface temperature (SST) plays a key role in the strongly seasonal rainfall regime on the West African region. The predictability of the seasonal cycle of rainfall is a field widely discussed by the scientific community, with results that fail to be satisfactory due to the difficulty of dynamical models to reproduce the behavior of the Inter Tropical Convergence Zone (ITCZ). To tackle this problem, a statistical model based on oceanic predictors has been developed at the Universidad Complutense of Madrid (UCM) with the aim to complement and enhance the predictability of the West African Monsoon (WAM) as an alternative to the coupled models. The model, called S4CAST (SST-based Statistical Seasonal Forecast) is based on discriminant analysis techniques, specifically the Maximum Covariance Analysis (MCA) and Canonical Correlation Analysis (CCA). Beyond the application of the model to the prediciton of rainfall in West Africa, its use extends to a range of different oceanic, atmospheric and helth related parameters influenced by the temperature of the sea surface as a defining factor of variability.

  8. Quasi-Steady-State Analysis based on Structural Modules and Timed Petri Net Predict System’s Dynamics: The Life Cycle of the Insulin Receptor

    PubMed Central

    Scheidel, Jennifer; Lindauer, Klaus; Ackermann, Jörg; Koch, Ina

    2015-01-01

    The insulin-dependent activation and recycling of the insulin receptor play an essential role in the regulation of the energy metabolism, leading to a special interest for pharmaceutical applications. Thus, the recycling of the insulin receptor has been intensively investigated, experimentally as well as theoretically. We developed a time-resolved, discrete model to describe stochastic dynamics and study the approximation of non-linear dynamics in the context of timed Petri nets. Additionally, using a graph-theoretical approach, we analyzed the structure of the regulatory system and demonstrated the close interrelation of structural network properties with the kinetic behavior. The transition invariants decomposed the model into overlapping subnetworks of various sizes, which represent basic functional modules. Moreover, we computed the quasi-steady states of these subnetworks and demonstrated that they are fundamental to understand the dynamic behavior of the system. The Petri net approach confirms the experimental results of insulin-stimulated degradation of the insulin receptor, which represents a common feature of insulin-resistant, hyperinsulinaemic states. PMID:26694479

  9. Quasi-Steady-State Analysis based on Structural Modules and Timed Petri Net Predict System's Dynamics: The Life Cycle of the Insulin Receptor.

    PubMed

    Scheidel, Jennifer; Lindauer, Klaus; Ackermann, Jörg; Koch, Ina

    2015-12-17

    The insulin-dependent activation and recycling of the insulin receptor play an essential role in the regulation of the energy metabolism, leading to a special interest for pharmaceutical applications. Thus, the recycling of the insulin receptor has been intensively investigated, experimentally as well as theoretically. We developed a time-resolved, discrete model to describe stochastic dynamics and study the approximation of non-linear dynamics in the context of timed Petri nets. Additionally, using a graph-theoretical approach, we analyzed the structure of the regulatory system and demonstrated the close interrelation of structural network properties with the kinetic behavior. The transition invariants decomposed the model into overlapping subnetworks of various sizes, which represent basic functional modules. Moreover, we computed the quasi-steady states of these subnetworks and demonstrated that they are fundamental to understand the dynamic behavior of the system. The Petri net approach confirms the experimental results of insulin-stimulated degradation of the insulin receptor, which represents a common feature of insulin-resistant, hyperinsulinaemic states.

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

    PubMed Central

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

    2015-01-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2015-06-01

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

  12. Anti-HIV-1 Activity Prediction of Novel Gp41 Inhibitors Using Structure-Based Virtual Screening and Molecular Dynamics Simulation.

    PubMed

    Sepehri, Saghi; Saghaie, Lotfollah; Fassihi, Afshin

    2017-03-01

    The fusion of viral and host cell membranes is mediated using gp41 subunit of the human immunodeficiency virus type 1 (HIV-1) envelope glycoprotein. As the HIV-1 enters the host cells, the two helical regions (HR1 and HR2) in the ectodomain of gp41 form a six-helix bundle, which carries the target and viral cell membranes to close proximity. Steps of this process serve as attractive targets for developing HIV-1 fusion inhibitors. Identification of some novel HIV fusion inhibitors with the goal of blocking the formation of the six-helix bundle was accomplished by computer-aided drug design techniques. A virtual screening strategy was employed to recognize small molecules presumably able to bind the gp41 at the internal interface of the NHR helices at the core native viral six-helix. This study was carried out in two stages. In the first stage, a library of more than seven thousand compounds was collected from ZINC, PubChem and BindingDB databases and protein data bank. Key contacts of known inhibitors with gp41 binding site residues were considered as the collecting criteria. In the second stage series of filtering processes were performed on this library in subsequent steps to find the potential gp41 inhibitors. The filtering criteria included pharmacokinetic and ADMET properties as well as in silico anti-HIV-1 prediction. Molecular docking simulation was carried out to identify interactions of the filtered molecules with the key residues in the gp41 binding site. Finally, molecular dynamics simulation indicates the superior inhibitory ability of three selected compounds over the known gp41inhibitor, NB-64.

  13. Physically based prediction of earthquake induced landsliding

    NASA Astrophysics Data System (ADS)

    Marc, Odin; Meunier, Patrick; Hovius, Niels; Gorum, Tolga; Uchida, Taro

    2015-04-01

    Earthquakes are an important trigger of landslides and can contribute significantly to sedimentary or organic matter fluxes. We present a new physically based expression for the prediction of total area and volume of populations of earthquake-induced landslides. This model implements essential seismic processes, linking key parameters such as ground acceleration, fault size, earthquake source depth and seismic moment. To assess the model we have compiled and normalized a database of landslide inventories for 40 earthquakes. We have found that low landscape steepness systematically leads to overprediction of the total area and volume of landslides. When this effect is accounted for, the model is able to predict within a factor of 2 the landslide areas and associated volumes for about two thirds of the cases in our databases. This is a significant improvement on a previously published empirical expression based only on earthquake moment, even though the prediction of total landslide area is more difficult than that of volume because it is affected by additional parameters such as the depth and continuity of soil cover. Some outliers in terms of observed landslide intensity are likely to be associated with exceptional rock mass properties in the epicentral area. Others may be related to seismic source complexities ignored by the model. However, most cases in our catalogue seem to be relatively unaffected by these two effects despite the variety of lithologies and tectonic settings they cover. This makes the model suitable for integration into landscape evolution models, and application to the assessment of secondary hazards and risks associated with earthquakes.

  14. Structure of allergens and structure based epitope predictions☆

    PubMed Central

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

    2014-01-01

    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

  15. Distributed Prognostics based on Structural Model Decomposition

    NASA Technical Reports Server (NTRS)

    Daigle, Matthew J.; Bregon, Anibal; Roychoudhury, I.

    2014-01-01

    Within systems health management, prognostics focuses on predicting the remaining useful life of a system. In the model-based prognostics paradigm, physics-based models are constructed that describe the operation of a system and how it fails. Such approaches consist of an estimation phase, in which the health state of the system is first identified, and a prediction phase, in which the health state is projected forward in time to determine the end of life. Centralized solutions to these problems are often computationally expensive, do not scale well as the size of the system grows, and introduce a single point of failure. In this paper, we propose a novel distributed model-based prognostics scheme that formally describes how to decompose both the estimation and prediction problems into independent local subproblems whose solutions may be easily composed into a global solution. The decomposition of the prognostics problem is achieved through structural decomposition of the underlying models. The decomposition algorithm creates from the global system model a set of local submodels suitable for prognostics. Independent local estimation and prediction problems are formed based on these local submodels, resulting in a scalable distributed prognostics approach that allows the local subproblems to be solved in parallel, thus offering increases in computational efficiency. Using a centrifugal pump as a case study, we perform a number of simulation-based experiments to demonstrate the distributed approach, compare the performance with a centralized approach, and establish its scalability. Index Terms-model-based prognostics, distributed prognostics, structural model decomposition ABBREVIATIONS

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

    PubMed

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

    2017-01-23

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

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

    PubMed

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

    2011-03-01

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

  18. A life prediction model for laminated composite structural components

    NASA Technical Reports Server (NTRS)

    Allen, David H.

    1990-01-01

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

  19. Prediction of grain structures in various solidification processes

    SciTech Connect

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

    1996-03-01

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

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

    NASA Astrophysics Data System (ADS)

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

    1998-05-01

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

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

    PubMed

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

    2017-03-01

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

  2. Genetic variability of Yersinia pestis isolates as predicted by PCR-based IS100 genotyping and analysis of structural genes encoding glycerol-3-phosphate dehydrogenase (glpD).

    PubMed

    Motin, Vladimir L; Georgescu, Anca M; Elliott, Jeffrey M; Hu, Ping; Worsham, Patricia L; Ott, Linda L; Slezak, Tomas R; Sokhansanj, Bahrad A; Regala, Warren M; Brubaker, Robert R; Garcia, Emilio

    2002-02-01

    A PCR-based genotyping system that detects divergence of IS100 locations within the Yersinia pestis genome was used to characterize a large collection of isolates of different biovars and geographical origins. Using sequences derived from the glycerol-negative biovar orientalis strain CO92, a set of 27 locus-specific primers was designed to amplify fragments between the end of IS100 and its neighboring gene. Geographically diverse members of the orientalis biovar formed a homogeneous group with identical genotype with the exception of strains isolated in Indochina. In contrast, strains belonging to the glycerol-positive biovar antiqua showed a variety of fingerprinting profiles. Moreover, strains of the biovar medievalis (also glycerol positive) clustered together with the antiqua isolates originated from Southeast Asia, suggesting their close phylogenetic relationships. Interestingly, a Manchurian biovar antiqua strain Nicholisk 51 displayed a genotyping pattern typical of biovar orientalis isolates. Analysis of the glycerol pathway in Y. pestis suggested that a 93-bp deletion within the glpD gene encoding aerobic glycerol-3-phosphate dehydrogenase might account for the glycerol-negative phenotype of the orientalis biovar. The glpD gene of strain Nicholisk 51 did not possess this deletion, although it contained two nucleotide substitutions characteristic of the glpD version found exclusively in biovar orientalis strains. To account for this close relationship between biovar orientalis strains and the antiqua Nicholisk 51 isolate, we postulate that the latter represents a variant of this biovar with restored ability to ferment glycerol. The fact that such a genetic lesion might be repaired as part of the natural evolutionary process suggests the existence of genetic exchange between different Yersinia strains in nature. The relevance of this observation on the emergence of epidemic Y. pestis strains is discussed.

  3. Genetic Variability of Yersinia pestis Isolates as Predicted by PCR-Based IS100 Genotyping and Analysis of Structural Genes Encoding Glycerol-3-Phosphate Dehydrogenase (glpD)

    PubMed Central

    Motin, Vladimir L.; Georgescu, Anca M.; Elliott, Jeffrey M.; Hu, Ping; Worsham, Patricia L.; Ott, Linda L.; Slezak, Tomas R.; Sokhansanj, Bahrad A.; Regala, Warren M.; Brubaker, Robert R.; Garcia, Emilio

    2002-01-01

    A PCR-based genotyping system that detects divergence of IS100 locations within the Yersinia pestis genome was used to characterize a large collection of isolates of different biovars and geographical origins. Using sequences derived from the glycerol-negative biovar orientalis strain CO92, a set of 27 locus-specific primers was designed to amplify fragments between the end of IS100 and its neighboring gene. Geographically diverse members of the orientalis biovar formed a homogeneous group with identical genotype with the exception of strains isolated in Indochina. In contrast, strains belonging to the glycerol-positive biovar antiqua showed a variety of fingerprinting profiles. Moreover, strains of the biovar medievalis (also glycerol positive) clustered together with the antiqua isolates originated from Southeast Asia, suggesting their close phylogenetic relationships. Interestingly, a Manchurian biovar antiqua strain Nicholisk 51 displayed a genotyping pattern typical of biovar orientalis isolates. Analysis of the glycerol pathway in Y. pestis suggested that a 93-bp deletion within the glpD gene encoding aerobic glycerol-3-phosphate dehydrogenase might account for the glycerol-negative phenotype of the orientalis biovar. The glpD gene of strain Nicholisk 51 did not possess this deletion, although it contained two nucleotide substitutions characteristic of the glpD version found exclusively in biovar orientalis strains. To account for this close relationship between biovar orientalis strains and the antiqua Nicholisk 51 isolate, we postulate that the latter represents a variant of this biovar with restored ability to ferment glycerol. The fact that such a genetic lesion might be repaired as part of the natural evolutionary process suggests the existence of genetic exchange between different Yersinia strains in nature. The relevance of this observation on the emergence of epidemic Y. pestis strains is discussed. PMID:11807062

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

    PubMed

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

    2010-12-01

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

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

    PubMed

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

    2013-01-01

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

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

    PubMed Central

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

    2013-01-01

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

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

    PubMed Central

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

    2017-01-01

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

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

    PubMed

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

    2017-01-11

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

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

    PubMed

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

    2012-11-09

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

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

    PubMed

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

    2005-12-01

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

  11. Fast and accurate automatic structure prediction with HHpred.

    PubMed

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

    2009-01-01

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

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

    PubMed

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

    2016-01-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2009-04-01

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

  14. Sizing Structures and Predicting Weight of a Spacecraft

    NASA Technical Reports Server (NTRS)

    Cerro, Jeffrey; Shore, C. P.

    2006-01-01

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

  15. TOPPER: topology prediction of transmembrane protein based on evidential reasoning.

    PubMed

    Deng, Xinyang; Liu, Qi; Hu, Yong; Deng, Yong

    2013-01-01

    The topology prediction of transmembrane protein is a hot research field in bioinformatics and molecular biology. It is a typical pattern recognition problem. Various prediction algorithms are developed to predict the transmembrane protein topology since the experimental techniques have been restricted by many stringent conditions. Usually, these individual prediction algorithms depend on various principles such as the hydrophobicity or charges of residues. In this paper, an evidential topology prediction method for transmembrane protein is proposed based on evidential reasoning, which is called TOPPER (topology prediction of transmembrane protein based on evidential reasoning). In the proposed method, the prediction results of multiple individual prediction algorithms can be transformed into BPAs (basic probability assignments) according to the confusion matrix. Then, the final prediction result can be obtained by the combination of each individual prediction base on Dempster's rule of combination. The experimental results show that the proposed method is superior to the individual prediction algorithms, which illustrates the effectiveness of the proposed method.

  16. Electrorheological Material Based Smart Structures

    DTIC Science & Technology

    2007-11-02

    based in-situ structural vibration monitoring, and real-time neural network based vibration control. In order to facilitate the basic science...structural vibration model was developed and tested with corresponding experimentation. Novel fiber-optic sensors and neural network controllers were also

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

    DTIC Science & Technology

    2009-07-01

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

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

    PubMed Central

    Xu, Zhenjiang; Mathews, David H.

    2011-01-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2016-03-01

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

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

    PubMed

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

    2015-01-01

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

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

    PubMed Central

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

    2015-01-01

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

  2. Temperature-based bioclimatic parameters can predict nematode metabolic footprints.

    PubMed

    Bhusal, Daya Ram; Tsiafouli, Maria A; Sgardelis, Stefanos P

    2015-09-01

    Nematode metabolic footprints (MFs) refer to the lifetime amount of metabolized carbon per individual, indicating a connection to soil food web functions and eventually to processes supporting ecosystem services. Estimating and managing these at a convenient scale requires information upscaling from the soil sample to the landscape level. We explore the feasibility of predicting nematode MFs from temperature-based bioclimatic parameters across a landscape. We assume that temperature effects are reflected in MFs, since temperature variations determine life processes ranging from enzyme activities to community structure. We use microclimate data recorded for 1 year from sites differing by orientation, altitude and vegetation cover. At the same sites we estimate MFs for each nematode trophic group. Our models show that bioclimatic parameters, specifically those accounting for temporal variations in temperature and extremities, predict most of the variation in nematode MFs. Higher fungivorous and lower bacterivorous nematode MFs are predicted for sites with high seasonality and low isothermality (sites of low vegetation, mostly at low altitudes), indicating differences in the relative contribution of the corresponding food web channels to the metabolism of carbon across the landscape. Higher plant-parasitic MFs were predicted for sites with high seasonality. The fitted models provide realistic predictions of unknown cases within the range of the predictor's values, allowing for the interpolation of MFs within the sampled region. We conclude that upscaling of the bioindication potential of nematode communities is feasible and can provide new perspectives not only in the field of soil ecology but other research areas as well.

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

    PubMed

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

    2007-10-01

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

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

    PubMed

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

    2017-04-03

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

  5. Process for predicting structural performance of mechanical systems

    DOEpatents

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

    1998-05-19

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

  6. Process for predicting structural performance of mechanical systems

    DOEpatents

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

    1998-01-01

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

  7. A Web-Accessible Protein Structure Prediction Pipeline

    DTIC Science & Technology

    2009-06-01

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

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

    NASA Technical Reports Server (NTRS)

    1995-01-01

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

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

    PubMed

    Randhawa, Avleen; Wakeling, James M

    2015-10-07

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

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

    PubMed

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

    2012-02-01

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

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

    PubMed Central

    Šikić, Mile; Tomić, Sanja; Vlahoviček, Kristian

    2009-01-01

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

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

    PubMed Central

    King, R D; Srinivasan, A

    1996-01-01

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

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

    PubMed

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

    2007-02-01

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

  14. RNA Secondary Structure Prediction Using High-throughput SHAPE

    PubMed Central

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

    2013-01-01

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

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

    PubMed

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

    2014-10-12

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

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

    PubMed

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

    2006-11-01

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

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

    PubMed

    Bida, John Paul; Maher, L James

    2012-03-01

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

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

    EPA Pesticide Factsheets

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

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

    PubMed

    Churkin, Alexander; Weinbrand, Lina; Barash, Danny

    2015-01-01

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

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

    PubMed Central

    Dowell, Robin D; Eddy, Sean R

    2004-01-01

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

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

    PubMed

    Kim, Namhee; Zahran, Mai; Schlick, Tamar

    2015-01-01

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

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

    PubMed Central

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

    2015-01-01

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

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

    PubMed

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

    2015-12-02

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

  4. Prediction of Mortality Based on Facial Characteristics

    PubMed Central

    Delorme, Arnaud; Pierce, Alan; Michel, Leena; Radin, Dean

    2016-01-01

    Recent studies have shown that characteristics of the face contain a wealth of information about health, age and chronic clinical conditions. Such studies involve objective measurement of facial features correlated with historical health information. But some individuals also claim to be adept at gauging mortality based on a glance at a person’s photograph. To test this claim, we invited 12 such individuals to see if they could determine if a person was alive or dead based solely on a brief examination of facial photographs. All photos used in the experiment were transformed into a uniform gray scale and then counterbalanced across eight categories: gender, age, gaze direction, glasses, head position, smile, hair color, and image resolution. Participants examined 404 photographs displayed on a computer monitor, one photo at a time, each shown for a maximum of 8 s. Half of the individuals in the photos were deceased, and half were alive at the time the experiment was conducted. Participants were asked to press a button if they thought the person in a photo was living or deceased. Overall mean accuracy on this task was 53.8%, where 50% was expected by chance (p < 0.004, two-tail). Statistically significant accuracy was independently obtained in 5 of the 12 participants. We also collected 32-channel electrophysiological recordings and observed a robust difference between images of deceased individuals correctly vs. incorrectly classified in the early event related potential (ERP) at 100 ms post-stimulus onset. Our results support claims of individuals who report that some as-yet unknown features of the face predict mortality. The results are also compatible with claims about clairvoyance warrants further investigation. PMID:27242466

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

    PubMed

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

    2015-12-01

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

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

    NASA Technical Reports Server (NTRS)

    Sexstone, Matthew G.

    1998-01-01

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

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

    NASA Technical Reports Server (NTRS)

    Sexstone, Matthew G.

    1998-01-01

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

  8. Predicting protein structures with a multiplayer online game.

    PubMed

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

    2010-08-05

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

  9. 3D-Fun: predicting enzyme function from structure.

    PubMed

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

    2008-07-01

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

  10. From structure prediction to genomic screens for novel non-coding RNAs.

    PubMed

    Gorodkin, Jan; Hofacker, Ivo L

    2011-08-01

    Non-coding RNAs (ncRNAs) are receiving more and more attention not only as an abundant class of genes, but also as regulatory structural elements (some located in mRNAs). A key feature of RNA function is its structure. Computational methods were developed early for folding and prediction of RNA structure with the aim of assisting in functional analysis. With the discovery of more and more ncRNAs, it has become clear that a large fraction of these are highly structured. Interestingly, a large part of the structure is comprised of regular Watson-Crick and GU wobble base pairs. This and the increased amount of available genomes have made it possible to employ structure-based methods for genomic screens. The field has moved from folding prediction of single sequences to computational screens for ncRNAs in genomic sequence using the RNA structure as the main characteristic feature. Whereas early methods focused on energy-directed folding of single sequences, comparative analysis based on structure preserving changes of base pairs has been efficient in improving accuracy, and today this constitutes a key component in genomic screens. Here, we cover the basic principles of RNA folding and touch upon some of the concepts in current methods that have been applied in genomic screens for de novo RNA structures in searches for novel ncRNA genes and regulatory RNA structure on mRNAs. We discuss the strengths and weaknesses of the different strategies and how they can complement each other.

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

    PubMed

    Noriega, E; Laca, A; Díaz, M

    2010-05-01

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

  12. Hyperbolic metamaterials based on Bragg polariton structures

    NASA Astrophysics Data System (ADS)

    Sedov, E. S.; Charukhchyan, M. V.; Arakelyan, S. M.; Alodzhants, A. P.; Lee, R.-K.; Kavokin, A. V.

    2016-07-01

    A new hyperbolic metamaterial based on a modified semiconductor Bragg mirror structure with embedded periodically arranged quantum wells is proposed. It is shown that exciton polaritons in this material feature hyperbolic dispersion in the vicinity of the second photonic band gap. Exciton-photon interaction brings about resonant nonlinearity leading to the emergence of nontrivial topological polaritonic states. The formation of spatially localized breather-type structures (oscillons) representing kink-shaped solutions of the effective Ginzburg-Landau-Higgs equation slightly oscillating along one spatial direction is predicted.

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

    PubMed Central

    Bon, Michaël; Orland, Henri

    2011-01-01

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

  14. Virality Prediction and Community Structure in Social Networks

    NASA Astrophysics Data System (ADS)

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

    2013-08-01

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

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

    PubMed

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

    2016-08-12

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

  16. Virality prediction and community structure in social networks.

    PubMed

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

    2013-01-01

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

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

    PubMed

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

    2016-05-01

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

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

    PubMed

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

    2017-02-01

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

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

    NASA Astrophysics Data System (ADS)

    Men, Ke-Pei; Zhao, Kai

    2014-12-01

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

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

    PubMed

    Groom, Colin R; Reilly, Anthony M

    2014-08-01

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

  1. Beyond the Twilight Zone: automated prediction of structural properties of proteins by recursive neural networks and remote homology information.

    PubMed

    Mooney, Catherine; Pollastri, Gianluca

    2009-10-01

    The prediction of 1D structural properties of proteins is an important step toward the prediction of protein structure and function, not only in the ab initio case but also when homology information to known structures is available. Despite this the vast majority of 1D predictors do not incorporate homology information into the prediction process. We develop a novel structural alignment method, SAMD, which we use to build alignments of putative remote homologues that we compress into templates of structural frequency profiles. We use these templates as additional input to ensembles of recursive neural networks, which we specialise for the prediction of query sequences that show only remote homology to any Protein Data Bank structure. We predict four 1D structural properties - secondary structure, relative solvent accessibility, backbone structural motifs, and contact density. Secondary structure prediction accuracy, tested by five-fold cross-validation on a large set of proteins allowing less than 25% sequence identity between training and test set and query sequences and templates, exceeds 82%, outperforming its ab initio counterpart, other state-of-the-art secondary structure predictors (Jpred 3 and PSIPRED) and two other systems based on PSI-BLAST and COMPASS templates. We show that structural information from homologues improves prediction accuracy well beyond the Twilight Zone of sequence similarity, even below 5% sequence identity, for all four structural properties. Significant improvement over the extraction of structural information directly from PDB templates suggests that the combination of sequence and template information is more informative than templates alone.

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

    PubMed Central

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

    2011-01-01

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

  3. Gene essentiality prediction based on fractal features and machine learning.

    PubMed

    Yu, Yongming; Yang, Licai; Liu, Zhiping; Zhu, Chuansheng

    2017-02-28

    Essential genes are required for the viability of an organism. Accurate and rapid identification of new essential genes is of substantial theoretical interest to synthetic biology and has practical applications in biomedicine. Fractals provide facilitated access to genetic structure analysis on a different scale. In this study, machine learning-based methods using solely fractal features are presented and the problem of predicting essential genes in bacterial genomes is evaluated. Six fractal features were investigated to learn the parameters of five supervised classification methods for the binary classification task. The optimal parameters of these classifiers are determined via grid-based searching technique. All the currently available identified genes from the database of essential genes were utilized to build the classifiers. The fractal features were proven to be more robust and powerful in the prediction performance. In a statistical sense, the ELM method shows superiority in predicting the essential genes. Non-parameter tests of the average AUC and ACC showed that the fractal feature is much better than other five compared features sets. Our approach is promising and convenient to identify new bacterial essential genes.

  4. Predicting mortality based on body composition analysis.

    PubMed Central

    Tellado, J M; Garcia-Sabrido, J L; Hanley, J A; Shizgal, H M; Christou, N V

    1989-01-01

    The role of the Nae/Ke ratio (the ratio of exchangeable sodium to exchangeable potassium) was examined as a nutritional marker in surgical patients in relation to anthropometrical and biochemical indexes by its ability to identify patients at risk for mortality after hospitalization. In 73 patients with sepsis and malnutrition (Training Group, Madrid) the following were determined: percentage of recent weight loss, triceps skin fold, midarm muscle circumference, serum albumin, serum transferrin, delayed hypersensitivity skin test response, total lymphocytes, and Nae/Ke ratio by multiple isotope dilution. The predictive power of Nae/Ke ratio was so strong (F = 105.1; p less than 0.00001) that it displaced anthropometric, biochemical, and immunologic variables from the linear equation derived from stepwise discriminant analysis using hospital mortality as the dependent variable. A theoretical curve of expected deaths was developed, based on an equation obtained by logistic regression analysis: Pr/death/ = 1/(1 + e[11.8-5.2 Nae/Ke]). Pre- and post-test probabilities on that curve allowed us to determine two cut-off values, Nae/Ke ratios of 1.5 and 2.5, which were markers for nonrisk and mortality, respectively. The model was tested in a heterogeneous data base of surgical patients (n = 417) in another hospital (Validation Group, Montreal). For patients exhibiting an abnormal Nae/Ke ratio (greater than 1.2) and a greater than 10% of probability of death, 54 deaths were expected and 53 observed (X2 = 1.8 NS). Two tests confirmed the basic agreement between the model and its performance, a G statistic of -0.704 and the area beneath the "receiver-operating-characteristic" (ROC) curve (Az = 0.904 + 0.0516 for the Madrid group vs. Az = 0.915 + 0.0349 for the Montreal group, NS). It was concluded from this analysis that, compared with the usual anthropometric measurements, the Nae/Ke ratio, if available, is the best method for identifying malnourished patients at risk of

  5. Predictive Model of Graphene Based Polymer Nanocomposites: Electrical Performance

    NASA Astrophysics Data System (ADS)

    Manta, Asimina; Gresil, Matthieu; Soutis, Constantinos

    2016-11-01

    In this computational work, a new simulation tool on the graphene/polymer nanocomposites electrical response is developed based on the finite element method (FEM). This approach is built on the multi-scale multi-physics format, consisting of a unit cell and a representative volume element (RVE). The FE methodology is proven to be a reliable and flexible tool on the simulation of the electrical response without inducing the complexity of raw programming codes, while it is able to model any geometry, thus the response of any component. This characteristic is supported by its ability in preliminary stage to predict accurately the percolation threshold of experimental material structures and its sensitivity on the effect of different manufacturing methodologies. Especially, the percolation threshold of two material structures of the same constituents (PVDF/Graphene) prepared with different methods was predicted highlighting the effect of the material preparation on the filler distribution, percolation probability and percolation threshold. The assumption of the random filler distribution was proven to be efficient on modelling material structures obtained by solution methods, while the through-the -thickness normal particle distribution was more appropriate for nanocomposites constructed by film hot-pressing. Moreover, the parametrical analysis examine the effect of each parameter on the variables of the percolation law. These graphs could be used as a preliminary design tool for more effective material system manufacturing.

  6. Predictive Model of Graphene Based Polymer Nanocomposites: Electrical Performance

    NASA Astrophysics Data System (ADS)

    Manta, Asimina; Gresil, Matthieu; Soutis, Constantinos

    2017-04-01

    In this computational work, a new simulation tool on the graphene/polymer nanocomposites electrical response is developed based on the finite element method (FEM). This approach is built on the multi-scale multi-physics format, consisting of a unit cell and a representative volume element (RVE). The FE methodology is proven to be a reliable and flexible tool on the simulation of the electrical response without inducing the complexity of raw programming codes, while it is able to model any geometry, thus the response of any component. This characteristic is supported by its ability in preliminary stage to predict accurately the percolation threshold of experimental material structures and its sensitivity on the effect of different manufacturing methodologies. Especially, the percolation threshold of two material structures of the same constituents (PVDF/Graphene) prepared with different methods was predicted highlighting the effect of the material preparation on the filler distribution, percolation probability and percolation threshold. The assumption of the random filler distribution was proven to be efficient on modelling material structures obtained by solution methods, while the through-the -thickness normal particle distribution was more appropriate for nanocomposites constructed by film hot-pressing. Moreover, the parametrical analysis examine the effect of each parameter on the variables of the percolation law. These graphs could be used as a preliminary design tool for more effective material system manufacturing.

  7. Plasma-based accelerator structures

    SciTech Connect

    Schroeder, Carl B.

    1999-12-01

    Plasma-based accelerators have the ability to sustain extremely large accelerating gradients, with possible high-energy physics applications. This dissertation further develops the theory of plasma-based accelerators by addressing three topics: the performance of a hollow plasma channel as an accelerating structure, the generation of ultrashort electron bunches, and the propagation of laser pulses is underdense plasmas.

  8. SHM-Based Probabilistic Fatigue Life Prediction for Bridges Based on FE Model Updating.

    PubMed

    Lee, Young-Joo; Cho, Soojin

    2016-03-02

    Fatigue life prediction for a bridge should be based on the current condition of the bridge, and various sources of uncertainty, such as material properties, anticipated vehicle loads and environmental conditions, make the prediction very challenging. This paper presents a new approach for probabilistic fatigue life prediction for bridges using finite element (FE) model updating based on structural health monitoring (SHM) data. Recently, various types of SHM systems have been used to monitor and evaluate the long-term structural performance of bridges. For example, SHM data can be used to estimate the degradation of an in-service bridge, which makes it possible to update the initial FE model. The proposed method consists of three steps: (1) identifying the modal properties of a bridge, such as mode shapes and natural frequencies, based on the ambient vibration under passing vehicles; (2) updating the structural parameters of an initial FE model using the identified modal properties; and (3) predicting the probabilistic fatigue life using the updated FE model. The proposed method is demonstrated by application to a numerical model of a bridge, and the impact of FE model updating on the bridge fatigue life is discussed.

  9. SHM-Based Probabilistic Fatigue Life Prediction for Bridges Based on FE Model Updating

    PubMed Central

    Lee, Young-Joo; Cho, Soojin

    2016-01-01

    Fatigue life prediction for a bridge should be based on the current condition of the bridge, and various sources of uncertainty, such as material properties, anticipated vehicle loads and environmental conditions, make the prediction very challenging. This paper presents a new approach for probabilistic fatigue life prediction for bridges using finite element (FE) model updating based on structural health monitoring (SHM) data. Recently, various types of SHM systems have been used to monitor and evaluate the long-term structural performance of bridges. For example, SHM data can be used to estimate the degradation of an in-service bridge, which makes it possible to update the initial FE model. The proposed method consists of three steps: (1) identifying the modal properties of a bridge, such as mode shapes and natural frequencies, based on the ambient vibration under passing vehicles; (2) updating the structural parameters of an initial FE model using the identified modal properties; and (3) predicting the probabilistic fatigue life using the updated FE model. The proposed method is demonstrated by application to a numerical model of a bridge, and the impact of FE model updating on the bridge fatigue life is discussed. PMID:26950125

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

    PubMed

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

    2016-08-01

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

  11. Prediction-based dynamic load-sharing heuristics

    NASA Technical Reports Server (NTRS)

    Goswami, Kumar K.; Devarakonda, Murthy; Iyer, Ravishankar K.

    1993-01-01

    The authors present dynamic load-sharing heuristics that use predicted resource requirements of processes to manage workloads in a distributed system. A previously developed statistical pattern-recognition method is employed for resource prediction. While nonprediction-based heuristics depend on a rapidly changing system status, the new heuristics depend on slowly changing program resource usage patterns. Furthermore, prediction-based heuristics can be more effective since they use future requirements rather than just the current system state. Four prediction-based heuristics, two centralized and two distributed, are presented. Using trace driven simulations, they are compared against random scheduling and two effective nonprediction based heuristics. Results show that the prediction-based centralized heuristics achieve up to 30 percent better response times than the nonprediction centralized heuristic, and that the prediction-based distributed heuristics achieve up to 50 percent improvements relative to their nonprediction counterpart.

  12. Reducing model structural uncertainty in predictions for ungauged basins via Bayesian approach.

    NASA Astrophysics Data System (ADS)

    Prieto, Cristina; Le Vine, Nataliya; Vitolo, Claudia; García, Eduardo; Medina, Raúl

    2016-04-01

    A catchment is a complex system where a multitude of interrelated energy, water and vegetation processes occur at different temporal and spatial scales. A rainfall-runoff model is a simplified representation of the system, and serves as a hypothesis about an inner catchment working. In predictions for ungauged basins, a common practice is to use a pre-selected assumed-to-be-perfect model structure to represent all catchments under analysis. However, it is unlikely that the same model structure is appropriate for diverse catchments due to the 'uniqueness of the place'. At the same time, there is no obvious justification to select a single model structure as a suitable description of the system. The contribution of this research is a move forward in the 'one size fits all' problem for predicting flows in ungauged basins. We present a statistical methodology, which allows regionalization that considers the information given by different hydrological model structures. First, the information to be regionalised is compactly represented via Principal Component Analysis. Second, the most significant principal components are regionalised using non-linear regionalisation method based on Random Forests. Third, a regionalisation error structure is derived based on the gauged catchments to be used in the Bayesian condition of the rainfall-runoff structures and their parameters. The methodological developments are demonstrated for predicting flows in ungauged basins of Northern Spain; and the results show that the methodology allows improving the flow prediction.

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

    PubMed Central

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

    2016-01-01

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

  14. Predicting hepatotoxicity using ToxCast in vitro bioactivity and chemical structure

    EPA Science Inventory

    Background: The U.S. EPA ToxCastTM program is screening thousands of environmental chemicals for bioactivity using hundreds of high-throughput in vitro assays to build predictive models of toxicity. We represented chemicals based on bioactivity and chemical structure descriptors ...

  15. A Non-parametric Bayesian Approach for Predicting RNA Secondary Structures

    NASA Astrophysics Data System (ADS)

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

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

  16. EVO—Evolutionary algorithm for crystal structure prediction

    NASA Astrophysics Data System (ADS)

    Bahmann, Silvia; Kortus, Jens

    2013-06-01

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

  17. Predicting the stability of large structured food webs.

    PubMed

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

    2015-07-22

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

  18. Predicting the stability of large structured food webs

    PubMed Central

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

    2015-01-01

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

  19. High Cycle Fatigue Prediction for Mistuned Bladed Disks with Fully Coupled Fluid-Structural Interaction

    DTIC Science & Technology

    2006-06-01

    vibration and flutter boundary of 2D NACA 64A010 transonic airfoil, 3D plate wing structural response. The predicted results agree well with benchmark...CFD code coupled with a structural integrator based on the convolution integral to obtain the flutter boundary for a NACA 64A010 airfoil[10]. Alonso and...validation case of the scheme for moving grid system, the forced pitching NACA 64A010 airfoil is calculated. For this transonic airfoil, the Reynolds

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

    SciTech Connect

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

    2013-11-19

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

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

    PubMed

    Tuncbag, Nurcan; Gursoy, Attila; Keskin, Ozlem

    2011-06-01

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

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

    PubMed

    Chunhua Shen; Guosheng Lin; van den Hengel, Anton

    2014-10-01

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

  3. Ab-initio crystal structure prediction. A case study: NaBH{sub 4}

    SciTech Connect

    Caputo, Riccarda; Tekin, Adem

    2011-07-15

    Crystal structure prediction from first principles is still one of the most challenging and interesting issue in condensed matter science. we explored the potential energy surface of NaBH{sub 4} by a combined ab-initio approach, based on global structure optimizations and quantum chemistry. In particular, we used simulated annealing (SA) and density functional theory (DFT) calculations. The methodology enabled the identification of several local minima, of which the global minimum corresponded to the tetragonal ground-state structure (P4{sub 2}/nmc), and the prediction of higher energy stable structures, among them a monoclinic (Pm) one was identified to be 22.75 kJ/mol above the ground-state at T=298 K. In between, orthorhombic and cubic structures were recovered, in particular those with Pnma and F4-bar 3m symmetries. - Graphical abstract: The total electron energy difference of the calculated stable structures. Here, the tetragonal (IT 137) and the monoclinic (IT 6) symmetry groups corresponded to the lowest and the highest energy structures, respectively. Highlights: > Potential energy surface of NaBH{sub 4} is investigated. > This is done a combination of global structure optimizations based on simulated annealing and density functional calculations. > We successfully reproduced experimentally found tetragonal and orthorhombic structures of NaBH{sub 4}. > Furthermore, we found a new stable high energy structure.

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

    PubMed Central

    Ghouzam, Yassine; Postic, Guillaume; Guerin, Pierre-Edouard; de Brevern, Alexandre G.; Gelly, Jean-Christophe

    2016-01-01

    Protein structure prediction based on comparative modeling is the most efficient way to produce structural models when it can be performed. ORION is a dedicated webserver based on a new strategy that performs this task. The identification by ORION of suitable templates is performed using an original profile-profile approach that combines sequence and structure evolution information. Structure evolution information is encoded into profiles using structural features, such as solvent accessibility and local conformation —with Protein Blocks—, which give an accurate description of the local protein structure. ORION has recently been improved, increasing by 5% the quality of its results. The ORION web server accepts a single protein sequence as input and searches homologous protein structures within minutes. Various databases such as PDB, SCOP and HOMSTRAD can be mined to find an appropriate structural template. For the modeling step, a protein 3D structure can be directly obtained from the selected template by MODELLER and displayed with global and local quality model estimation measures. The sequence and the predicted structure of 4 examples from the CAMEO server and a recent CASP11 target from the ‘Hard’ category (T0818-D1) are shown as pertinent examples. Our web server is accessible at http://www.dsimb.inserm.fr/ORION/. PMID:27319297

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

    PubMed Central

    Sullivan, David C.; Kuntz, Irwin D.

    2004-01-01

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

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

    PubMed

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

    2014-11-15

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

  7. Integration of QUARK and I-TASSER for Ab Initio Protein Structure Prediction in CASP11.

    PubMed

    Zhang, Wenxuan; Yang, Jianyi; He, Baoji; Walker, Sara Elizabeth; Zhang, Hongjiu; Govindarajoo, Brandon; Virtanen, Jouko; Xue, Zhidong; Shen, Hong-Bin; Zhang, Yang

    2016-09-01

    We tested two pipelines developed for template-free protein structure prediction in the CASP11 experiment. First, the QUARK pipeline constructs structure models by reassembling fragments of continuously distributed lengths excised from unrelated proteins. Five free-modeling (FM) targets have the model successfully constructed by QUARK with a TM-score above 0.4, including the first model of T0837-D1, which has a TM-score = 0.736 and RMSD = 2.9 Å to the native. Detailed analysis showed that the success is partly attributed to the high-resolution contact map prediction derived from fragment-based distance-profiles, which are mainly located between regular secondary structure elements and loops/turns and help guide the orientation of secondary structure assembly. In the Zhang-Server pipeline, weakly scoring threading templates are re-ordered by the structural similarity to the ab initio folding models, which are then reassembled by I-TASSER based structure assembly simulations; 60% more domains with length up to 204 residues, compared to the QUARK pipeline, were successfully modeled by the I-TASSER pipeline with a TM-score above 0.4. The robustness of the I-TASSER pipeline can stem from the composite fragment-assembly simulations that combine structures from both ab initio folding and threading template refinements. Despite the promising cases, challenges still exist in long-range beta-strand folding, domain parsing, and the uncertainty of secondary structure prediction; the latter of which was found to affect nearly all aspects of FM structure predictions, from fragment identification, target classification, structure assembly, to final model selection. Significant efforts are needed to solve these problems before real progress on FM could be made. Proteins 2016; 84(Suppl 1):76-86. © 2015 Wiley Periodicals, Inc.

  8. Predicting protein ligand binding sites by combining evolutionary sequence conservation and 3D structure.

    PubMed

    Capra, John A; Laskowski, Roman A; Thornton, Janet M; Singh, Mona; Funkhouser, Thomas A

    2009-12-01

    Identifying a protein's functional sites is an important step towards characterizing its molecular function. Numerous structure- and sequence-based methods have been developed for this problem. Here we introduce ConCavity, a small molecule binding site prediction algorithm that integrates evolutionary sequence conservation estimates with structure-based methods for identifying protein surface cavities. In large-scale testing on a diverse set of single- and multi-chain protein structures, we show that ConCavity substantially outperforms existing methods for identifying both 3D ligand binding pockets and individual ligand binding residues. As part of our testing, we perform one of the first direct comparisons of conservation-based and structure-based methods. We find that the two approaches provide largely complementary information, which can be combined to improve upon either approach alone. We also demonstrate that ConCavity has state-of-the-art performance in predicting catalytic sites and drug binding pockets. Overall, the algorithms and analysis presented here significantly improve our ability to identify ligand binding sites and further advance our understanding of the relationship between evolutionary sequence conservation and structural and functional attributes of proteins. Data, source code, and prediction visualizations are available on the ConCavity web site (http://compbio.cs.princeton.edu/concavity/).

  9. Prediction and analysis of the modular structure of cytochrome P450 monooxygenases

    PubMed Central

    2010-01-01

    Background Cytochrome P450 monooxygenases (CYPs) form a vast and diverse family of highly variable sequences. They catalyze a wide variety of oxidative reactions and are therefore of great relevance in drug development and biotechnological applications. Despite their differences in sequence and substrate specificity, the structures of CYPs are highly similar. Although being in research focus for years, factors mediating selectivity and activity remain vague. Description This systematic comparison of CYPs based on the Cytochrome P450 Engineering Database (CYPED) involved sequence and structure analysis of more than 8000 sequences. 31 structures have been applied to generate a reliable structure-based HMM profile in order to predict structurally conserved regions. Therefore, it was possible to automatically transfer these modules on CYP sequences without any secondary structure information, to analyze substrate interacting residues and to compare interaction sites with redox partners. Conclusions Functionally relevant structural sites of CYPs were predicted. Regions involved in substrate binding were analyzed in all sequences among the CYPED. For all CYPs that require a reductase, two reductase interaction sites were identified and classified according to their length. The newly gained insights promise an improvement of engineered enzyme properties for potential biotechnological application. The annotated sequences are accessible on the current version of the CYPED. The prediction tool can be applied to any CYP sequence via the web interface at http://www.cyped.uni-stuttgart.de/cgi-bin/strpred/dosecpred.pl. PMID:20950472

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

    PubMed

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

    2013-10-01

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

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

    PubMed

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

    1994-07-01

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

  12. Structure prediction for CASP8 with all-atom refinement using Rosetta.

    PubMed

    Raman, Srivatsan; Vernon, Robert; Thompson, James; Tyka, Michael; Sadreyev, Ruslan; Pei, Jimin; Kim, David; Kellogg, Elizabeth; DiMaio, Frank; Lange, Oliver; Kinch, Lisa; Sheffler, Will; Kim, Bong-Hyun; Das, Rhiju; Grishin, Nick V; Baker, David

    2009-01-01

    We describe predictions made using the Rosetta structure prediction methodology for the Eighth Critical Assessment of Techniques for Protein Structure Prediction. Aggressive sampling and all-atom refinement were carried out for nearly all targets. A combination of alignment methodologies was used to generate starting models from a range of templates, and the models were then subjected to Rosetta all atom refinement. For the 64 domains with readily identified templates, the best submitted model was better than the best alignment to the best template in the Protein Data Bank for 24 cases, and improved over the best starting model for 43 cases. For 13 targets where only very distant sequence relationships to proteins of known structure were detected, models were generated using the Rosetta de novo structure prediction methodology followed by all-atom refinement; in several cases the submitted models were better than those based on the available templates. Of the 12 refinement challenges, the best submitted model improved on the starting model in seven cases. These improvements over the starting template-based models and refinement tests demonstrate the power of Rosetta structure refinement in improving model accuracy.

  13. A Particle Swarm Optimization-Based Approach with Local Search for Predicting Protein Folding.

    PubMed

    Yang, Cheng-Hong; Lin, Yu-Shiun; Chuang, Li-Yeh; Chang, Hsueh-Wei

    2017-03-13

    The hydrophobic-polar (HP) model is commonly used for predicting protein folding structures and hydrophobic interactions. This study developed a particle swarm optimization (PSO)-based algorithm combined with local search algorithms; specifically, the high exploration PSO (HEPSO) algorithm (which can execute global search processes) was combined with three local search algorithms (hill-climbing algorithm, greedy algorithm, and Tabu table), yielding the proposed HE-L-PSO algorithm. By using 20 known protein structures, we evaluated the performance of the HE-L-PSO algorithm in predicting protein folding in the HP model. The proposed HE-L-PSO algorithm exhibited favorable performance in predicting both short and long amino acid sequences with high reproducibility and stability, compared with seven reported algorithms. The HE-L-PSO algorithm yielded optimal solutions for all predicted protein folding structures. All HE-L-PSO-predicted protein folding structures possessed a hydrophobic core that is similar to normal protein folding.

  14. Link prediction based on temporal similarity metrics using continuous action set learning automata

    NASA Astrophysics Data System (ADS)

    Moradabadi, Behnaz; Meybodi, Mohammad Reza

    2016-10-01

    Link prediction is a social network research area that tries to predict future links using network structure. The main approaches in this area are based on predicting future links using network structure at a specific period, without considering the links behavior through different periods. For example, a common traditional approach in link prediction calculates a chosen similarity metric for each non-connected link and outputs the links with higher similarity scores as the prediction result. In this paper, we propose a new link prediction method based on temporal similarity metrics and Continuous Action set Learning Automata (CALA). The proposed method takes advantage of using different similarity metrics as well as different time periods. In the proposed algorithm, we try to model the link prediction problem as a noisy optimization problem and use a team of CALAs to solve the noisy optimization problem. CALA is a reinforcement based optimization tool which tries to learn the optimal behavior from the environment feedbacks. To determine the importance of different periods and similarity metrics on the prediction result, we define a coefficient for each of different periods and similarity metrics and use a CALA for each coefficient. Each CALA tries to learn the true value of the corresponding coefficient. Final link prediction is obtained from a combination of different similarity metrics in different times based on the obtained coefficients. The link prediction results reported here show satisfactory of the proposed method for some social network data sets.

  15. Feature Selection for Neural Network Based Stock Prediction

    NASA Astrophysics Data System (ADS)

    Sugunnasil, Prompong; Somhom, Samerkae

    We propose a new methodology of feature selection for stock movement prediction. The methodology is based upon finding those features which minimize the correlation relation function. We first produce all the combination of feature and evaluate each of them by using our evaluate function. We search through the generated set with hill climbing approach. The self-organizing map based stock prediction model is utilized as the prediction method. We conduct the experiment on data sets of the Microsoft Corporation, General Electric Co. and Ford Motor Co. The results show that our feature selection method can improve the efficiency of the neural network based stock prediction.

  16. BCL::contact-low confidence fold recognition hits boost protein contact prediction and de novo structure determination.

    PubMed

    Karakaş, Mert; Woetzel, Nils; Meiler, Jens

    2010-02-01

    Knowledge of all residue-residue contacts within a protein allows determination of the protein fold. Accurate prediction of even a subset of long-range contacts (contacts between amino acids far apart in sequence) can be instrumental for determining tertiary structure. Here we present BCL::Contact, a novel contact prediction method that utilizes artificial neural networks (ANNs) and specializes in the prediction of medium to long-range contacts. BCL::Contact comes in two modes: sequence-based and structure-based. The sequence-based mode uses only sequence information and has individual ANNs specialized for helix-helix, helix-strand, strand-helix, strand-strand, and sheet-sheet contacts. The structure-based mode combines results from 32-fold recognition methods with sequence information to a consensus prediction. The two methods were presented in the 6(th) and 7(th) Critical Assessment of Techniques for Protein Structure Prediction (CASP) experiments. The present work focuses on elucidating the impact of fold recognition results onto contact prediction via a direct comparison of both methods on a joined benchmark set of proteins. The sequence-based mode predicted contacts with 42% accuracy (7% false positive rate), while the structure-based mode achieved 45% accuracy (2% false positive rate). Predictions by both modes of BCL::Contact were supplied as input to the protein tertiary structure prediction program Rosetta for a benchmark of 17 proteins with no close sequence homologs in the protein data bank (PDB). Rosetta created higher accuracy models, signified by an improvement of 1.3 A on average root mean square deviation (RMSD), when driven by the predicted contacts. Further, filtering Rosetta models by agreement with the predicted contacts enriches for native-like fold topologies.

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

    NASA Technical Reports Server (NTRS)

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

    1991-01-01

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

  18. Offspring social network structure predicts fitness in families.

    PubMed

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

    2012-12-22

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

  19. Offspring social network structure predicts fitness in families

    PubMed Central

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

    2012-01-01

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

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

    PubMed

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

    2015-06-01

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

  1. Antibodies: Computer-Aided Prediction of Structure and Design of Function.

    PubMed

    Sevy, Alexander M; Meiler, Jens

    2014-12-01

    With the advent of high-throughput sequencing, and the increased availability of experimental structures of antibodies and antibody-antigen complexes, comes the improvement of computational approaches to predict the structure and design the function of antibodies and antibody-antigen complexes. While antibodies pose formidable challenges for protein structure prediction and design due to their large size and highly flexible loops in the complementarity-determining regions, they also offer exciting opportunities: the central importance of antibodies for human health results in a wealth of structural and sequence information that-as a knowledge base-can drive the modeling algorithms by limiting the conformational and sequence search space to likely regions of success. Further, efficient experimental platforms exist to test predicted antibody structure or designed antibody function, thereby leading to an iterative feedback loop between computation and experiment. We briefly review the history of computer-aided prediction of structure and design of function in the antibody field before we focus on recent methodological developments and the most exciting application examples.

  2. SPOT-Seq-RNA: predicting protein-RNA complex structure and RNA-binding function by fold recognition and binding affinity prediction.

    PubMed

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

    2014-01-01

    RNA-binding proteins (RBPs) play key roles in RNA metabolism and post-transcriptional regulation. Computational methods have been developed separately for prediction of RBPs and RNA-binding residues by machine-learning techniques and prediction of protein-RNA complex structures by rigid or semiflexible structure-to-structure docking. Here, we describe a template-based technique called SPOT-Seq-RNA that integrates prediction of RBPs, RNA-binding residues, and protein-RNA complex structures into a single package. This integration is achieved by combining template-based structure-prediction software, SPARKS X, with binding affinity prediction software, DRNA. This tool yields reasonable sensitivity (46 %) and high precision (84 %) for an independent test set of 215 RBPs and 5,766 non-RBPs. SPOT-Seq-RNA is computationally efficient for genome-scale prediction of RBPs and protein-RNA complex structures. Its application to human genome study has revealed a similar sensitivity and ability to uncover hundreds of novel RBPs beyond simple homology. The online server and downloadable version of SPOT-Seq-RNA are available at http://sparks-lab.org/server/SPOT-Seq-RNA/.

  3. Predicting the structures of complexes between phosphoinositide 3-kinase (PI3K) and romidepsin-related compounds for the drug design of PI3K/histone deacetylase dual inhibitors using computational docking and the ligand-based drug design approach.

    PubMed

    Oda, Akifumi; Saijo, Ken; Ishioka, Chikashi; Narita, Koichi; Katoh, Tadashi; Watanabe, Yurie; Fukuyoshi, Shuichi; Takahashi, Ohgi

    2014-11-01

    Predictions of the three-dimensional (3D) structures of the complexes between phosphoinositide 3-kinase (PI3K) and two inhibitors were conducted using computational docking and the ligand-based drug design approach. The obtained structures were refined by structural optimizations and molecular dynamics (MD) simulations. The ligands were located deep inside the ligand binding pocket of the p110α subunit of PI3K, and the hydrogen bond formations and hydrophobic effects of the surrounding amino acids were predicted. Although rough structures were obtained for the PI3K-inhibitor complexes before the MD simulations, the refinement of the structures by these simulations clarified the hydrogen bonding patterns of the complexes.

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

    NASA Technical Reports Server (NTRS)

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

    1990-01-01

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

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

    SciTech Connect

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

    2003-04-11

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

  6. Universal structural parameter to quantitatively predict metallic glass properties

    NASA Astrophysics Data System (ADS)

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

    2016-12-01

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

  7. Universal structural parameter to quantitatively predict metallic glass properties

    PubMed Central

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

    2016-01-01

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

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

    DTIC Science & Technology

    2008-12-01

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

  9. Advancing viral RNA structure prediction: measuring the thermodynamics of pyrimidine-rich internal loops.

    PubMed

    Phan, Andy; Mailey, Katherine; Sakai, Jessica; Gu, Xiaobo; Schroeder, Susan J

    2017-02-17

    Accurate thermodynamic parameters improve RNA structure predictions and thus accelerate understanding of RNA function and the identification of RNA drug binding sites. Many viral RNA structures, such as internal ribosome entry sites, have internal loops and bulges that are potential drug target sites. Current models used to predict internal loops are biased towards small, symmetric purine loops, and thus poorly predict asymmetric, pyrimidine-rich loops with more than 6 nucleotides that occur frequently in viral RNA. This paper presents new thermodynamic data for 40 pyrimidine loops, many of which can form UU or protonated CC base pairs. Protonated cytosine and uracil base pairs stabilize asymmetric internal loops. Accurate prediction rules are presented that account for all thermodynamic measurements of RNA asymmetric internal loops. New loop initiation terms for loops with more than 6 nucleotides are presented that do not follow previous assumptions that increasing asymmetry destabilizes loops. Since the last 2004 update, 126 new loops with asymmetry or sizes greater than 2x2 have been measured (Mathews 2004). These new measurements significantly deepen and diversify the thermodynamic database for RNA. These results will help better predict internal loops that are larger, pyrimidine-rich, and occur within viral structures such as internal ribosome entry sites.

  10. Predicting Monoamine Oxidase Inhibitory Activity through Ligand-Based Models

    PubMed Central

    Vilar, Santiago; Ferino, Giulio; Quezada, Elias; Santana, Lourdes; Friedman, Carol

    2013-01-01

    The evolution of bio- and cheminformatics associated with the development of specialized software and increasing computer power has produced a great interest in theoretical in silico methods applied in drug rational design. These techniques apply the concept that “similar molecules have similar biological properties” that has been exploited in Medicinal Chemistry for years to design new molecules with desirable pharmacological profiles. Ligand-based methods are not dependent on receptor structural data and take into account two and three-dimensional molecular properties to assess similarity of new compounds in regards to the set of molecules with the biological property under study. Depending on the complexity of the calculation, there are different types of ligand-based methods, such as QSAR (Quantitative Structure-Activity Relationship) with 2D and 3D descriptors, CoMFA (Comparative Molecular Field Analysis) or pharmacophoric approaches. This work provides a description of a series of ligand-based models applied in the prediction of the inhibitory activity of monoamine oxidase (MAO) enzymes. The controlled regulation of the enzymes’ function through the use of MAO inhibitors is used as a treatment in many psychiatric and neurological disorders, such as depression, anxiety, Alzheimer’s and Parkinson’s disease. For this reason, multiple scaffolds, such as substituted coumarins, indolylmethylamine or pyridazine derivatives were synthesized and assayed toward MAO-A and MAO-B inhibition. Our intention is to focus on the description of ligand-based models to provide new insights in the relationship between the MAO inhibitory activity and the molecular structure of the different inhibitors, and further study enzyme selectivity and possible mechanisms of action. PMID:23231398

  11. Towards Practical Carbonation Prediction and Modelling for Service Life Design of Reinforced Concrete Structures

    NASA Astrophysics Data System (ADS)

    Ekolu, O. S.

    2015-11-01

    Amongst the scientific community, the interest in durability of concrete structures has been high for quite a long time of over 40 years. Of the various causes of degradation of concrete structures, corrosion is the most widespread durability problem and carbonation is one of the two causes of steel reinforcement corrosion. While much scientific understanding has been gained from the numerous carbonation studies undertaken over the past years, it is still presently not possible to accurately predict carbonation and apply it in design of structures. This underscores the complex nature of the mechanisms as influenced by several interactive factors. Based on critical literature and some experience of the author, it is found that there still exist major challenges in establishing a mathematical constitutive relation for realistic carbonation prediction. While most current models employ permeability /diffusion as the main model property, analysis shows that the most practical material property would be compressive strength, which has a low coefficient of variation of 20% compared to 30 to 50% for permeability. This important characteristic of compressive strength, combined with its merit of simplicity and data availability at all stages of a structure's life, promote its potential use in modelling over permeability. By using compressive strength in carbonation prediction, the need for accelerated testing and permeability measurement can be avoided. This paper attempts to examine the issues associated with carbonation prediction, which could underlie the current lack of a sound established prediction method. Suggestions are then made for possible employment of different or alternative approaches.

  12. Antibody modeling using the prediction of immunoglobulin structure (PIGS) web server [corrected].

    PubMed

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

    2014-12-01

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

  13. A Bayesian approach to improved calibration and prediction of groundwater models with structural error

    NASA Astrophysics Data System (ADS)

    Xu, Tianfang; Valocchi, Albert J.

    2015-11-01

    Numerical groundwater flow and solute transport models are usually subject to model structural error due to simplification and/or misrepresentation of the real system, which raises questions regarding the suitability of conventional least squares regression-based (LSR) calibration. We present a new framework that explicitly describes the model structural error statistically in an inductive, data-driven way. We adopt a fully Bayesian approach that integrates Gaussian process error models into the calibration, prediction, and uncertainty analysis of groundwater flow models. We test the usefulness of the fully Bayesian approach with a synthetic case study of the impact of pumping on surface-ground water interaction. We illustrate through this example that the Bayesian parameter posterior distributions differ significantly from parameters estimated by conventional LSR, which does not account for model structural error. For the latter method, parameter compensation for model structural error leads to biased, overconfident prediction under changing pumping condition. In contrast, integrating Gaussian process error models significantly reduces predictive bias and leads to prediction intervals that are more consistent with validation data. Finally, we carry out a generalized LSR recalibration step to assimilate the Bayesian prediction while preserving mass conservation and other physical constraints, using a full error covariance matrix obtained from Bayesian results. It is found that the recalibrated model achieved lower predictive bias compared to the model calibrated using conventional LSR. The results highlight the importance of explicit treatment of model structural error especially in circumstances where subsequent decision-making and risk analysis require accurate prediction and uncertainty quantification.

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

    PubMed

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

    2015-07-01

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

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

    PubMed Central

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

    2015-01-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2015-04-01

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

  17. Sequence-Based Prediction of Type III Secreted Proteins

    PubMed Central

    Arnold, Roland; Brandmaier, Stefan; Kleine, Frederick; Tischler, Patrick; Heinz, Eva; Behrens, Sebastian; Niinikoski, Antti; Mewes, Hans-Werner; Horn, Matthias; Rattei, Thomas

    2009-01-01

    The type III secretion system (TTSS) is a key mechanism for host cell interaction used by a variety of bacterial pathogens and symbionts of plants and animals including humans. The TTSS represents a molecular syringe with which the bacteria deliver effector proteins directly into the host cell cytosol. Despite the importance of the TTSS for bacterial pathogenesis, recognition and targeting of type III secreted proteins has up until now been poorly understood. Several hypotheses are discussed, including an mRNA-based signal, a chaperon-mediated process, or an N-terminal signal peptide. In this study, we systematically analyzed the amino acid composition and secondary structure of N-termini of 100 experimentally verified effector proteins. Based on this, we developed a machine-learning approach for the prediction of TTSS effector proteins, taking into account N-terminal sequence features such as frequencies of amino acids, short peptides, or residues with certain physico-chemical properties. The resulting computational model revealed a strong type III secretion signal in the N-terminus that can be used to detect effectors with sensitivity of ∼71% and selectivity of ∼85%. This signal seems to be taxonomically universal and conserved among animal pathogens and plant symbionts, since we could successfully detect effector proteins if the respective group was excluded from training. The application of our prediction approach to 739 complete bacterial and archaeal genome sequences resulted in the identification of between 0% and 12% putative TTSS effector proteins. Comparison of effector proteins with orthologs that are not secreted by the TTSS showed no clear pattern of signal acquisition by fusion, suggesting convergent evolutionary processes shaping the type III secretion signal. The newly developed program EffectiveT3 (http://www.chlamydiaedb.org) is the first universal in silico prediction program for the identification of novel TTSS effectors. Our findings will

  18. Protein structure prediction with local adjust tabu search algorithm

    PubMed Central

    2014-01-01

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

  19. Evaluation of a sophisticated SCFG design for RNA secondary structure prediction.

    PubMed

    Nebel, Markus E; Scheid, Anika

    2011-12-01

    Predicting secondary structures of RNA molecules is one of the fundamental problems of and thus a challenging task in computational structural biology. Over the past decades, mainly two different approaches have been considered to compute predictions of RNA secondary structures from a single sequence: the first one relies on physics-based and the other on probabilistic RNA models. Particularly, the free energy minimization (MFE) approach is usually considered the most popular and successful method. Moreover, based on the paradigm-shifting work by McCaskill which proposes the computation of partition functions (PFs) and base pair probabilities based on thermodynamics, several extended partition function algorithms, statistical sampling methods and clustering techniques have been invented over the last years. However, the accuracy of the corresponding algorithms is limited by the quality of underlying physics-based models, which include a vast number of thermodynamic parameters and are still incomplete. The competing probabilistic approach is based on stochastic context-free grammars (SCFGs) or corresponding generalizations, like conditional log-linear models (CLLMs). These methods abstract from free energies and instead try to learn about the structural behavior of the molecules by learning (a manageable number of) probabilistic parameters from trusted RNA structure databases. In this work, we introduce and evaluate a sophisticated SCFG design that mirrors state-of-the-art physics-based RNA structure prediction procedures by distinguishing between all features of RNA that imply different energy rules. This SCFG actually serves as the foundation for a statistical sampling algorithm for RNA secondary structures of a single sequence that represents a probabilistic counterpart to the sampling extension of the PF approach. Furthermore, some new ways to derive meaningful structure predictions from generated sample sets are presented. They are used to compare the

  20. Structure and Properties of Carbon Based Nanocomposite Films

    DTIC Science & Technology

    2004-03-18

    Research Institute for Technical Physics and Materials Science of the Hungarian Academy of Sciences Structure and Properties of Carbon Based...MAR 2004 2. REPORT TYPE N/A 3. DATES COVERED - 4. TITLE AND SUBTITLE Structure and Properties of Carbon Based Nanocomposite Films 5a...INTRODUCTION The theoretically predicted superhard β-C3N4 has not yet been experimentally realized, however, the different CNx structures and their

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

    PubMed

    Becker, Julien; Maes, Francis; Wehenkel, Louis

    2013-01-01

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

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

    PubMed Central

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

    2010-01-01

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

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

    PubMed

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

    2011-03-15

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

  4. A windows based mechanistic subsidence prediction model for longwall mining

    SciTech Connect

    Begley, R.; Beheler, P.; Khair, A.W.

    1996-12-31

    The previously developed Mechanistic Subsidence Prediction Model (MSPM) has been incorporated into the graphical interface environment of MS Windows. MSPM has the unique capability of predicting maximum subsidence, angle of draw and the subsidence profile of a longwall panel at various locations for both the transverse and longitudinal orientations. The resultant enhanced model can be operated by individuals with little knowledge of subsidence prediction theories or little computer programming experience. In addition, predictions of subsidence can be made in a matter of seconds without the need to develop input data files or use the keyboard in some cases. The predictions are based upon the following input parameters: panel width, mining height, overburden depth, rock quality designation, and percent hard rock in the immediate roof, main roof and the entire overburden. The recently developed enhanced model has the capability to compare predictions in a graphical format for one half of the predicted subsidence profile based upon changes in input parameters easily and instantly on the same screen. In addition another screen can be obtained from a pull down menu where the operator can compare predictions for the entire subsidence profiles. This paper presents the background of the subsidence prediction model and the methodology of the enhanced model development. The paper also presents comparisons of subsidence predictions for several different sets of input parameters in addition to comparisons of the subsidence predictions with actual field data.

  5. Statistical potential for assessment and prediction of protein structures

    PubMed Central

    Shen, Min-yi; Sali, Andrej

    2006-01-01

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

  6. Principles for Predicting RNA Secondary Structure Design Difficulty.

    PubMed

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

    2016-02-27

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

  7. Principles for predicting RNA secondary structure design difficulty

    PubMed Central

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

    2015-01-01

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

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

    PubMed

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

    2016-01-01

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

  9. Harmonic Structure Predicts the Enjoyment of Uplifting Trance Music

    PubMed Central

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

    2017-01-01

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

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

    PubMed Central

    Moriguchi, I; Hirano, H; Hirono, S

    1996-01-01

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

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

    PubMed Central

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

    2011-01-01

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

  12. Predicting the axial structure of the proton using a quark-diquark model

    NASA Astrophysics Data System (ADS)

    Oxholm, Trevor M.; Hobbs, Timothy J.; Miller, Gerald A.

    2017-01-01

    The form factors of the proton typically measured via elastic electroweak scattering have long been a rich testing ground for models of nucleon structure. We explore the ability of a model based in a quark/spectator diquark picture to describe form factors in the electromagnetic sector, as well as to predict the form of the nucleon's axial current. Making use of a realistic spin decomposition and phenomenological vertex factors, we choose model parameters so as to fit experimental data on the electric and magnetic Sachs form factors, as well as the low-momentum isovector axial form factor. With the model we then predict the pseudoscalar form factor and extend axial form factor predictions to higher momenta, comparing our predictions to those of other frameworks and techniques. DOE Grant DE-FG02-97ER-41014, NSF REU program.

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

    PubMed

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

    2007-07-01

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

  14. Transformation of BCC and B2 High Temperature Phases to HCP and Orthorhombic Structures in the Ti-Al-Nb System. Part I: Microstructural Predictions Based on a Subgroup Relation Between Phases

    PubMed Central

    Bendersky, L. A.; Roytburd, A.; Boettinger, W. J.

    1993-01-01

    Possible paths for the constant composition coherent transformation of BCC or B2 high temperature phases to low temperature HCP or Orthorhombic phases in the Ti-Al-Nb system are analyzed using a sequence of ciystallographic structural relationships developed from subgroup symmetry relations. Symmetry elements lost in each step of the sequence determine the possibilities for variants of the low symmetry phase and domains that can be present in the microstructure. The orientation of interdomain interfaces is determined by requiring the existence of a strain-free interface between the domains. Polydomain structures are also determined that minimize elastic energy. Microstructural predictions are made for comparison to experimental results given by Benderslcy and Boettinger [J. Res. Natl. Inst. Stand. Technol. 98, 585 (1993)]. PMID:28053487

  15. Prediction of residential radon exposure of the whole Swiss population: comparison of model-based predictions with measurement-based predictions.

    PubMed

    Hauri, D D; Huss, A; Zimmermann, F; Kuehni, C E; Röösli, M

    2013-10-01

    Radon plays an important role for human exposure to natural sources of ionizing radiation. The aim of this article is to compare two approaches to estimate mean radon exposure in the Swiss population: model-based predictions at individual level and measurement-based predictions based on measurements aggregated at municipality level. A nationwide model was used to predict radon levels in each household and for each individual based on the corresponding tectonic unit, building age, building type, soil texture, degree of urbanization, and floor. Measurement-based predictions were carried out within a health impact assessment on residential radon and lung cancer. Mean measured radon levels were corrected for the average floor distribution and weighted with population size of each municipality. Model-based predictions yielded a mean radon exposure of the Swiss population of 84.1 Bq/m(3) . Measurement-based predictions yielded an average exposure of 78 Bq/m(3) . This study demonstrates that the model- and the measurement-based predictions provided similar results. The advantage of the measurement-based approach is its simplicity, which is sufficient for assessing exposure distribution in a population. The model-based approach allows predicting radon levels at specific sites, which is needed in an epidemiological study, and the results do not depend on how the measurement sites have been selected.

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

    PubMed Central

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

    2015-01-01

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

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

    PubMed

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

    2015-08-22

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

  18. VfoldCPX Server: Predicting RNA-RNA Complex Structure and Stability

    PubMed Central

    Xu, Xiaojun; Chen, Shi-Jie

    2016-01-01

    RNA-RNA interactions are essential for genomic RNA dimerization, mRNA splicing, and many RNA-related gene expression and regulation processes. The prediction of the structure and folding stability of RNA-RNA complexes is a problem of significant biological importance and receives substantial interest in the biological community. The VfoldCPX server provides a new web interface to predict the two-dimensional (2D) structures of RNA-RNA complexes from the nucleotide sequences. The VfoldCPX server has several novel advantages including the ability to treat RNAs with tertiary contacts (crossing base pairs) such as loop-loop kissing interactions and the use of physical loop entropy parameters. Based on a partition function-based algorithm, the server enables prediction for structure with and without tertiary contacts. Furthermore, the server outputs a set of energetically stable structures, ranked by their stabilities. The results allow users to gain extensive physical insights into RNA-RNA interactions and their roles in RNA function. The web server is freely accessible at “http://rna.physics.missouri.edu/vfoldCPX”. PMID:27657918

  19. Prediction of spectral acceleration response ordinates based on PGA attenuation

    USGS Publications Warehouse

    Graizer, V.; Kalkan, E.

    2009-01-01

    Developed herein is a new peak ground acceleration (PGA)-based predictive model for 5% damped pseudospectral acceleration (SA) ordinates of free-field horizontal component of ground motion from shallow-crustal earthquakes. The predictive model of ground motion spectral shape (i.e., normalized spectrum) is generated as a continuous function of few parameters. The proposed model eliminates the classical exhausted matrix of estimator coefficients, and provides significant ease in its implementation. It is structured on the Next Generation Attenuation (NGA) database with a number of additions from recent Californian events including 2003 San Simeon and 2004 Parkfield earthquakes. A unique feature of the model is its new functional form explicitly integrating PGA as a scaling factor. The spectral shape model is parameterized within an approximation function using moment magnitude, closest distance to the fault (fault distance) and VS30 (average shear-wave velocity in the upper 30 m) as independent variables. Mean values of its estimator coefficients were computed by fitting an approximation function to spectral shape of each record using robust nonlinear optimization. Proposed spectral shape model is independent of the PGA attenuation, allowing utilization of various PGA attenuation relations to estimate the response spectrum of earthquake recordings.

  20. Evaluation and Prediction of Water Resources Based on AHP

    NASA Astrophysics Data System (ADS)

    Li, Shuai; Sun, Anqi

    2017-01-01

    Nowadays, the shortage of water resources is a threat to us. In order to solve the problem of water resources restricted by varieties of factors, this paper establishes a water resources evaluation index model (WREI), which adopts the fuzzy comprehensive evaluation (FCE) based on analytic hierarchy process (AHP) algorithm. After considering influencing factors of water resources, we ignore secondary factors and then hierarchical approach the main factors according to the class, set up a three-layer structure. The top floor is for WREI. Using analytic hierarchy process (AHP) to determine weight first, and then use fuzzy judgment to judge target, so the comprehensive use of the two algorithms reduce the subjective influence of AHP and overcome the disadvantages of multi-level evaluation. To prove the model, we choose India as a target region. On the basis of water resources evaluation index model, we use Matlab and combine grey prediction with linear prediction to discuss the ability to provide clean water in India and the trend of India’s water resources changing in the next 15 years. The model with theoretical support and practical significance will be of great help to provide reliable data support and reference for us to get plans to improve water quality.

  1. A nonlinear regression model-based predictive control algorithm.

    PubMed

    Dubay, R; Abu-Ayyad, M; Hernandez, J M

    2009-04-01

    This paper presents a unique approach for designing a nonlinear regression model-based predictive controller (NRPC) for single-input-single-output (SISO) and multi-input-multi-output (MIMO) processes that are common in industrial applications. The innovation of this strategy is that the controller structure allows nonlinear open-loop modeling to be conducted while closed-loop control is executed every sampling instant. Consequently, the system matrix is regenerated every sampling instant using a continuous function providing a more accurate prediction of the plant. Computer simulations are carried out on nonlinear plants, demonstrating that the new approach is easily implemented and provides tight control. Also, the proposed algorithm is implemented on two real time SISO applications; a DC motor, a plastic injection molding machine and a nonlinear MIMO thermal system comprising three temperature zones to be controlled with interacting effects. The experimental closed-loop responses of the proposed algorithm were compared to a multi-model dynamic matrix controller (MPC) with improved results for various set point trajectories. Good disturbance rejection was attained, resulting in improved tracking of multi-set point profiles in comparison to multi-model MPC.

  2. Multiscale model for predicting shear zone structure and permeability in deforming rock

    NASA Astrophysics Data System (ADS)

    Cleary, Paul W.; Pereira, Gerald G.; Lemiale, Vincent; Piane, Claudio Delle; Clennell, M. Ben

    2016-04-01

    A novel multiscale model is proposed for the evolution of faults in rocks, which predicts their internal properties and permeability as strain increases. The macroscale model, based on smoothed particle hydrodynamics (SPH), predicts system scale deformation by a pressure-dependent elastoplastic representation of the rock and shear zone. Being a continuum method, SPH contains no intrinsic information on the grain scale structure or behaviour of the shear zone, so a series of discrete element method microscale shear cell models are embedded into the macroscale model at specific locations. In the example used here, the overall geometry and kinematics of a direct shear test on a block of intact rock is simulated. Deformation is imposed by a macroscale model where stresses and displacement rates are applied at the shear cell walls in contact with the rock. Since the microscale models within the macroscale block of deforming rock now include representations of the grains, the structure of the shear zone, the evolution of the size and shape distribution of these grains, and the dilatancy of the shear zone can all be predicted. The microscale dilatancy can be used to vary the macroscale model dilatancy both spatially and temporally to give a full two-way coupling between the spatial scales. The ability of this model to predict shear zone structure then allows the prediction of the shear zone permeability using the Lattice-Boltzmann method.

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

    PubMed Central

    Kosciolek, Tomasz; Jones, David T.

    2014-01-01

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

  4. De novo structure prediction of globular proteins aided by sequence variation-derived contacts.

    PubMed

    Kosciolek, Tomasz; Jones, David T

    2014-01-01

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

  5. A Fully Bayesian Approach to Improved Calibration and Prediction of Groundwater Models With Structure Error

    NASA Astrophysics Data System (ADS)

    Xu, T.; Valocchi, A. J.

    2014-12-01

    Effective water resource management typically relies on numerical models to analyse groundwater flow and solute transport processes. These models are usually subject to model structure error due to simplification and/or misrepresentation of the real system. As a result, the model outputs may systematically deviate from measurements, thus violating a key assumption for traditional regression-based calibration and uncertainty analysis. On the other hand, model structure error induced bias can be described statistically in an inductive, data-driven way based on historical model-to-measurement misfit. We adopt a fully Bayesian approach that integrates a Gaussian process error model to account for model structure error to the calibration, prediction and uncertainty analysis of groundwater models. The posterior distributions of parameters of the groundwater model and the Gaussian process error model are jointly inferred using DREAM, an efficient Markov chain Monte Carlo sampler. We test the usefulness of the fully Bayesian approach towards a synthetic case study of surface-ground water interaction under changing pumping conditions. We first illustrate through this example that traditional least squares regression without accounting for model structure error yields biased parameter estimates due to parameter compensation as well as biased predictions. In contrast, the Bayesian approach gives less biased parameter estimates. Moreover, the integration of a Gaussian process error model significantly reduces predictive bias and leads to prediction intervals that are more consistent with observations. The results highlight the importance of explicit treatment of model structure error especially in circumstances where subsequent decision-making and risk analysis require accurate prediction and uncertainty quantification. In addition, the data-driven error modelling approach is capable of extracting more information from observation data than using a groundwater model alone.

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

    NASA Technical Reports Server (NTRS)

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

    2005-01-01

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

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

    PubMed Central

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

    2016-01-01

    The fundamental period is one of the most critical parameters for the seismic design of structures. There are several literature approaches for its estimation which often conflict with each other, making their use questionable. Furthermore, the majority of these approaches do not take into account the presence of infill walls into the structure despite the fact that infill walls increase the stiffness and mass of structure leading to significant changes in the fundamental period. In the present paper, artificial neural networks (ANNs) are used to predict the fundamental period of infilled reinforced concrete (RC) structures. For the training and the validation of the ANN, a large data set is used based on a detailed investigation of the parameters that affect the fundamental period of RC structures. The comparison of the predicted values with analytical ones indicates the potential of using ANNs for the prediction of the fundamental period of infilled RC frame structures taking into account the crucial parameters that influence its value. PMID:27066069

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

    SciTech Connect

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

    2014-01-28

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

  9. Structural Acoustic Prediction and Interior Noise Control Technology

    NASA Technical Reports Server (NTRS)

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

    2001-01-01

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

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

    PubMed

    Sohoglu, Ediz; Chait, Maria

    2016-09-07

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

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

    NASA Astrophysics Data System (ADS)

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

    2005-09-01

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

  12. Optimizing Non-Decomposable Loss Functions in Structured Prediction

    PubMed Central

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

    2012-01-01

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

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

    PubMed

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

    2011-05-01

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

  14. Engineering Property Prediction Tools for Tailored Polymer Composite Structures

    SciTech Connect

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

    2009-12-23

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

  15. High Precision Prediction of Functional Sites in Protein Structures

    PubMed Central

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

    2014-01-01

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

  16. Predictive Multiscale Modeling of Nanocellulose Based Materials and Systems

    NASA Astrophysics Data System (ADS)

    Kovalenko, Andriy

    2014-08-01

    Cellulose Nanocrysals (CNC) is a renewable biodegradable biopolymer with outstanding mechanical properties made from highly abundant natural source, and therefore is very attractive as reinforcing additive to replace petroleum-based plastics in biocomposite materials, foams, and gels. Large-scale applications of CNC are currently limited due to its low solubility in non-polar organic solvents used in existing polymerization technologies. The solvation properties of CNC can be improved by chemical modification of its surface. Development of effective surface modifications has been rather slow because extensive chemical modifications destabilize the hydrogen bonding network of cellulose and deteriorate the mechanical properties of CNC. We employ predictive multiscale theory, modeling, and simulation to gain a fundamental insight into the effect of CNC surface modifications on hydrogen bonding, CNC crystallinity, solvation thermodynamics, and CNC compatibilization with the existing polymerization technologies, so as to rationally design green nanomaterials with improved solubility in non-polar solvents, controlled liquid crystal ordering and optimized extrusion properties. An essential part of this multiscale modeling approach is the statistical- mechanical 3D-RISM-KH molecular theory of solvation, coupled with quantum mechanics, molecular mechanics, and multistep molecular dynamics simulation. The 3D-RISM-KH theory provides predictive modeling of both polar and non-polar solvents, solvent mixtures, and electrolyte solutions in a wide range of concentrations and thermodynamic states. It properly accounts for effective interactions in solution such as steric effects, hydrophobicity and hydrophilicity, hydrogen bonding, salt bridges, buffer, co-solvent, and successfully predicts solvation effects and processes in bulk liquids, solvation layers at solid surface, and in pockets and other inner spaces of macromolecules and supramolecular assemblies. This methodology

  17. Using intron position conservation for homology-based gene prediction

    PubMed Central

    Keilwagen, Jens; Wenk, Michael; Erickson, Jessica L.; Schattat, Martin H.; Grau, Jan; Hartung, Frank

    2016-01-01

    Annotation of protein-coding genes is very important in bioinformatics and biology and has a decisive influence on many downstream analyses. Homology-based gene prediction programs allow for transferring knowledge about protein-coding genes from an annotated organism to an organism of interest. Here, we present a homology-based gene prediction program called GeMoMa. GeMoMa utilizes the conservation of intron positions within genes to predict related genes in other organisms. We assess the performance of GeMoMa and compare it with state-of-the-art competitors on plant and animal genomes using an extended best reciprocal hit approach. We find that GeMoMa often makes more precise predictions than its competitors yielding a substantially increased number of correct transcripts. Subsequently, we exemplarily validate GeMoMa predictions using Sanger sequencing. Finally, we use RNA-seq data to compare the predictions of homology-based gene prediction programs, and find again that GeMoMa performs well. Hence, we conclude that exploiting intron position conservation improves homology-based gene prediction, and we make GeMoMa freely available as command-line tool and Galaxy integration. PMID:26893356

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

    PubMed Central

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

    2016-01